Intelligent tourist attraction optimization management system and method based on big data

Abstract

The invention discloses an intelligent travel scenic spot optimization management system and method based on big data, and belongs to the technical field of scenic spot optimization management. Obtaining an overall layout of the scenic area and marking a touring route; acquiring affiliated scenic spots of the touring route; dividing a touring route into line segments in sequence based on the affiliated scenic spots; obtaining average passenger flow volume data in the line segment; adding a time label to the average passenger flow data in the line segment, and calculating an experience fluidity index of the line segment based on the average passenger flow data in a single time point; and analyzing the experience fluidity index of the line segment, outputting an early warning, calculating the transmitted passenger flow volume, and carrying out distribution. According to the invention, the experience fluidity index is calculated to balance the load of each line segment; the tourist experience of tourists is improved, congestion and queuing time is reduced, scenic spot resource allocation is optimized, scenic spot operation efficiency is improved, and safety and order are guaranteed.

Description

Technical Field

[0001] The present invention relates to the technical field of scenic area optimization management, and particularly to an intelligent cultural and tourism scenic area optimization management system and method based on big data. Background Technique

[0002] In recent years, with the booming development of technologies such as big data, Internet of Things, and artificial intelligence, scenic area management has gradually transformed towards intelligence and dataization; the intelligent scenic area management method based on big data can realize the prediction and dynamic adjustment of the tourist flow trend by collecting tourist behavior data, environmental data, etc. in real time; the application of these technologies can not only improve the scenic area management efficiency, but also enhance the tourist experience to a certain extent; however, the traditional management method cannot flexibly respond to the fluctuations in tourist flow and the changes in tourist demands within the scenic area. Especially during peak hours, some popular scenic spots within the scenic area may face serious congestion, affecting the tourist experience and the operation safety of the scenic area.

[0003] The existing intelligent cultural and tourism scenic area management technologies still have problems such as uneven data distribution, insufficient flow allocation, and lack of intelligent emergency dispatching; for example, although some existing systems can obtain the tourist flow data within the scenic area through sensors, these systems often cannot calculate and respond to complex tourist flow patterns in real time, resulting in frequent overcrowding in some line segments within the scenic area and being unable to flexibly dispatch tourists to other lines; this limitation makes the mobility of tourists and the optimization space of scenic area management not fully utilized, and the carrying capacity of the scenic area cannot be dynamically adjusted in an intelligent manner. Summary of the Invention

[0004] The purpose of the present invention is to provide an intelligent cultural and tourism scenic area optimization management system and method based on big data to solve the problems raised in the above background technique.

[0005] To solve the above technical problems, the present invention provides the following technical solutions:

[0006] An intelligent cultural and tourism scenic area optimization management method based on big data, the method includes the following steps: Step S1: Obtain the overall layout map of the scenic area and mark out the tour routes; obtain the affiliated scenic spots of the tour routes; Step S2: Based on the affiliated scenic spots, divide the tour routes into line segments in sequence; obtain the average passenger flow data within the line segments; Step S3: Attach time tags to the average passenger flow data within the line segments, and calculate the experience mobility index of the line segments 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, record it as a line segment to be diverted and give an early warning for maintenance; obtain the line segments with an experience mobility index less than the mobility index threshold, calculate the sum of the transferred passenger flows and allocate them.

[0007] As a preferred solution of the intelligent cultural and tourism scenic area optimization management method based on big data according to the present invention, by carrying a remote sensing survey device on a drone, an overall layout map of the scenic area is obtained. The overall layout map includes a tour route, and the tour route is numbered. The i-th tour route is denoted as TR i , and the tour route TR is marked on the overall layout map i .

[0008] Based on the overall layout map, all scenic spot location information of the scenic area is obtained; for each scenic spot, the straight-line distance from the scenic spot to the tour route TR i is obtained. If the straight-line distance from the scenic spot to the tour route TR i is less than or equal to the straight-line distance from the scenic spot to all tour routes except the tour route TR i , then the scenic spot is marked as an affiliated scenic spot of the tour route TR i ; if the straight-line distance from the scenic spot to the tour route TR i is greater than the straight-line distance from the scenic spot to all tour routes except the tour route TR i , then the tour route with the minimum straight-line distance is obtained, and the scenic spot is marked as an affiliated scenic spot of this tour route. If there are multiple tour routes with the minimum straight-line distance, the scenic spot is marked as an affiliated scenic spot of the tour route with the smallest number

[0009] As a preferred solution of the intelligent cultural and tourism scenic area optimization management method based on big data according to the present invention, based on all the affiliated scenic spots on the tour route TR i , a secondary marking is performed on the tour route TR marked on the overall layout map i . The secondary marking means marking all the affiliated scenic spots on the tour route TR i ; based on the marked affiliated scenic spots, the tour route TR i is sequentially divided into J - 1 line segments. Among them, the starting point of the j-th line segment is the j-th affiliated scenic spot, and the end point 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 ) represents the j-th affiliated scenic spot on the tour route TR i , and A j+1 (TR i ) represents the (j + 1)-th affiliated scenic spot on the tour route TR i . J represents the total number of affiliated scenic spots

[0010] On the line segment XD[A j (TRi ) → A j+1 (TR i )] is provided with a laser sensor, and the laser sensor is used to monitor the pedestrian flow data in the line segment to obtain the line segment XD[A j (TR i ) → A j+1 (TR i )] of the average passenger flow data.

[0011] As a preferred solution of the intelligent cultural and tourism scenic area optimization management method based on big data according to the present invention, time tags are added to the average passenger flow data in the line segment XD[A j (TR i ) → A j+1 (TR i )], and the average passenger flow data at time point t is denoted as AP t [A j (TR i ) → A j+1 (TR i )].

[0012] Based on the average passenger flow data AP at time point t t [A j (TR i ) → A j+1 (TR i )], calculate the experience mobility index of the line segment XD[A j (TR i ) → A j+1 (TR i )] at time point t, and the calculation formula is as follows:

[0013]

[0014] Wherein, EMI t [A j (TR i ) → A j+1 (TR i )] represents the experience mobility index of the line segment XD[A j (TR i ) → A j+1 (TR i )] 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 carrying passenger flow.

[0015] It should be noted that the experience mobility index is a key technical means of the present invention, which is a core indicator that can help scenic area management personnel understand the real-time distribution of passenger flow in the scenic area. By monitoring the experience mobility index of each line segment, the management personnel can quickly discover which line segments may be congested.

[0016] As a preferred solution of the intelligent cultural and tourism scenic area optimization management method based on big data described in the present invention, if the line segment XD[A j (TR i )→A j+1 (TR i )] The experience mobility index EMI at time point t t [A j (TR i )→A j+1 (TR i )] is greater than or equal to the mobility index threshold, then the line segment XD[A j (TR i )→A j+1 (TR i )] is recorded as the line segment to be diverted, and a warning is issued to remind the staff to carry out infrastructure maintenance.

[0017] Based on the overall layout diagram, obtain the line segment that is closest to the line segment XD[A j (TR i )→A j+1 (TR i )] and has an experience mobility index less than the mobility index threshold, and perform passenger flow transfer. The calculation formula for the flow transfer is as follows:

[0018]

[0019] Wherein, represents the flow transfer coefficient, represents the preset initial flow transfer coefficient, β represents the preset adjustment factor, EMI t [A k (TR i )→A k+1 (TR i )] represents the line segment that is closest to the line segment XD[A j (TR i )→A j+1 (TR i )] and has an experience mobility index less than the mobility index threshold, and k≠j, F i,j,t represents the passenger flow that needs to be transferred.

[0020] The passenger flow volume F to be transferred i,j,t , from line segment XD[A j (TR i )→A j+1 (TR i )] is transferred to line segment EMI t [A k (TR i )→A k+1 (TR i )].

[0021] It should be noted that the main function of this formula in the present invention is to achieve the dynamic optimal allocation of the passenger flow volume in the scenic area. When there is a crowded passenger flow situation in a certain line segment (i.e., the experience liquidity index is relatively high), by calculating the flow transfer coefficient and the passenger flow volume to be transferred, some tourists can be reasonably guided to other relatively loose line segments; for example, if the passenger flow volume of a line segment of a popular scenic spot is too large, through the calculation of these formulas, it can be determined how many tourists should be guided to the line segments of other adjacent scenic spots to balance the passenger flow volume of each line segment; this way of passenger flow volume allocation has a high degree of flexibility and adaptability, and can be dynamically adjusted according to the real-time passenger flow situation in the scenic area, avoiding the rigidity of the fixed management mode; by reasonably guiding the passenger flow volume, not only can the overall tour experience of tourists be improved and the queuing waiting time be reduced, but also all scenic spots and facilities in the scenic area can be fully utilized, avoiding the situation that some line segment resources are idle while some line segments are overused. At the same time, this dynamic management method helps the scenic area to quickly respond when facing a sudden passenger flow peak and ensures the orderly operation of the scenic area.

[0022] An intelligent cultural and tourism scenic area optimization management system based on big data, this system includes: a data collection and scenic spot association module, an affiliated scenic spot annotation and passenger flow volume monitoring module, an experience liquidity index calculation module, and a passenger flow volume dynamic allocation module.

[0023] The data collection and scenic spot association module: obtains the overall layout map of the scenic area and marks the tour route; obtains the affiliated scenic spots of the tour route.

[0024] The affiliated scenic spot annotation and passenger flow volume monitoring module: based on the affiliated scenic spots, divides the tour route into line segments in sequence; obtains the average passenger flow volume data within the line segments.

[0025] The experience liquidity index calculation module: attaches time tags to the average passenger flow volume data within the line segments, and calculates the experience liquidity index of the line segments based on the average passenger flow volume data within a single time point.

[0026] The passenger flow dynamic allocation module: 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 diverted and is subject to early warning and maintenance; obtain line segments with an experience mobility index less than the mobility index threshold, calculate the sum of the transferred passenger flows and allocate them.

[0027] Further, the data collection and scenic spot association module includes a data collection unit and a scenic spot association unit.

[0028] The data collection unit: By carrying remote sensing survey equipment on a drone, obtain the overall layout map of the scenic area. The overall layout map includes the tour route, number the tour route, and mark the tour route on the overall layout map.

[0029] 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 other tour routes except the tour route, mark the scenic spot 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 other tour routes except the tour route, obtain the tour route with the smallest straight-line distance and mark the scenic spot as an affiliated scenic spot of this tour route. If there are multiple tour routes with the smallest straight-line distance, mark the scenic spot as an affiliated scenic spot of the tour route with the smallest number.

[0030] Further, the affiliated scenic spot annotation and passenger flow monitoring module includes an affiliated scenic spot annotation unit and a passenger flow monitoring unit.

[0031] The affiliated scenic spot annotation unit: Based on all the affiliated scenic spots on the tour route, perform secondary annotation on the marked tour route on the overall layout map. The secondary annotation means marking all the affiliated scenic spots on the tour route; based on the marked affiliated scenic spots, divide the tour route sequence into multiple line segments.

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

[0033] Further, the experience mobility index calculation module includes an experience mobility index calculation unit.

[0034] The experience mobility index calculation unit: Attach a time tag to the average passenger flow data in the line segment; based on the average passenger flow data at a single time point, calculate the experience mobility index of the line segment at time point t.

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

[0036] The warning and maintenance unit: If the experience liquidity index of a line segment at a single time point is greater than or equal to the liquidity index threshold, the line segment is recorded as a line segment to be diverted, and a warning is issued to remind the staff to perform infrastructure maintenance.

[0037] The passenger flow dynamic allocation unit: Based on the overall layout diagram, obtain the line segment that is closest to the line segment and has an experience liquidity index less than the liquidity index threshold, and calculate the passenger flow that needs to be transferred; transfer the passenger flow that needs to be transferred between the line segments.

[0038] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: In an intelligent cultural and tourism scenic area optimization management system and method based on big data provided by the present invention, by accurately obtaining the overall layout diagram and tour route of the scenic area, marking the affiliated scenic spots and dividing the tour route into multiple line segments, and using laser sensors to real-time monitor the passenger flow data of each line segment, a reliable basis is provided for subsequent passenger flow analysis; on this basis, by calculating the experience liquidity index, the congestion degree of each line segment is objectively evaluated, helping the scenic area management personnel to accurately discover possible congestion areas; if the experience liquidity index of a certain line segment is greater than or equal to the liquidity index threshold, the system will automatically issue a diversion warning, timely guide tourists to the line segment with less traffic, and optimize the passenger flow distribution through the dynamic traffic transfer formula, balance the load of each line segment; this method not only improves the tourist experience, reduces congestion and queuing time, but also optimizes the scenic area resource allocation, improves the scenic area operation efficiency, and ensures safety and order. BRIEF DESCRIPTION OF THE DRAWINGS

[0039] The drawings are used to provide a further understanding of the present invention, and constitute a part of the specification, and are used to explain the present invention together with the embodiments of the present invention, and do not constitute a limitation to the present invention.

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

[0041] Figure 2 It is a schematic diagram of the structure of an intelligent cultural and tourism scenic area optimization management system based on big data of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0042] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0043] Please refer to Figure 1 In the first embodiment: A method for optimizing the management of an intelligent cultural and tourism scenic area 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 affiliated scenic spots of the tour route.

[0045] Specifically, by carrying a remote sensing survey device on a drone, obtain the overall layout map of the scenic area. The overall layout map includes the tour route, number the tour route, and denote the i-th tour route as TR i Mark the tour route TR on the overall layout map i .

[0046] Furthermore, 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 TR i . If the straight-line distance from the scenic spot to the tour route TR i is less than or equal to the straight-line distance from the scenic spot to all other tour routes except the tour route TR i , then mark the scenic spot as an affiliated scenic spot of the tour route TR i ; if the straight-line distance from the scenic spot to the tour route TR i is greater than the straight-line distance from the scenic spot to all other tour routes except the tour route TR i , then obtain the tour route with the minimum straight-line distance and mark the scenic spot as an affiliated scenic spot of that tour route. If there are multiple tour routes with the minimum straight-line distance, then mark the scenic spot as an affiliated scenic spot of the tour route with the smallest number.

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

[0048] Specifically, based on all the affiliated scenic spots on the tour route TR i , perform secondary marking on the tour route TR marked on the overall layout map i . The secondary marking means marking all the affiliated scenic spots on the tour route TR i ; based on the marked affiliated scenic spots, divide the tour route TR i sequentially into J - 1 segments. Among them, the starting point of the j-th segment is the j-th affiliated scenic spot, and the ending point is the (j + 1)-th affiliated scenic spot. Denote the j-th segment as XD[A j (TR i )→A j+1 (TR i )], where A j (TR i) represents the j-th subsidiary scenic spot on the tour route TR i and A j+1 (TR i ) represents the j + 1-th subsidiary scenic spot on the tour route TR i , where J represents the total number of subsidiary scenic spots.

[0049] Furthermore, laser sensors are arranged within the line segment XD[A j (TR i ) → A j+1 (TR i )]. The laser sensors are used to monitor the pedestrian flow data within the line segment and obtain the average passenger flow data within the line segment XD[A j (TR i ) → A j+1 (TR i )].

[0050] Step S3: Attach 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 at a single time point.

[0051] Specifically, attach time tags to the average passenger flow data within the line segment XD[A j (TR i ) → A j+1 (TR i )], and denote the average passenger flow data at time point t as AP t [A j (TR i ) → A j+1 (TR i )].

[0052] Furthermore, based on the average passenger flow data AP t [A j (TR i ) → A j+1 (TR i )] at time point t, calculate the experience mobility index of the line segment XD[A j (TR i ) → A j+1 (TR i )] at time point t. The calculation formula is as follows:

[0053]

[0054] where EMI t [A j (TR i ) → A j+1 (TR i )] represents the line segment XD[Aj (TR i ) → A j+1 (TR i )] Experience 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 )]'s maximum passenger-carrying capacity.

[0055] For example, assume that the average passenger flow data AP 1 (TR i1 ) → A 2 (TR 1 )] of the line segment XD[A 1 [A 1 (TR 1 ) → A 2 (TR 1 )] at time point 1 is 100, and the maximum passenger-carrying capacity C 1 (TR 1 ) → A 2 (TR 1 ) of the preset line segment XD[A cap (XD[A 1 (TR 1 ) → A 2 (TR 1 )]) is 98. Then, substituting into the formula, the calculated experience liquidity index EMI 1 [A 1 (TR 1 ) → A 2 (TR 1 )] = 1.02.

[0056] Step S4: If the experience liquidity index of the line segment is greater than or equal to the liquidity index threshold, mark it as a line segment to be diverted and issue a warning for maintenance; obtain the line segments with an experience liquidity index less than the liquidity index threshold, calculate the sum of the transferred passenger flows and allocate them.

[0057] Specifically, if the experience liquidity index EMI j (TR i ) → A j+1 (TR i )] of the line segment XD[A t [A j (TR i ) → A j+1 (TRi ) If it is greater than or equal to the liquidity index threshold, then the line segment XD[A j (TR i )→A j+1 (TR i ) is recorded as the line segment to be diverted, and a warning is issued to remind the staff to perform infrastructure maintenance.

[0058] Furthermore, based on the overall layout diagram, obtain the line segment that is closest to the line segment XD[A j (TR i )→A j+1 (TR i ) and whose experienced liquidity index is less than the liquidity index threshold, and perform passenger flow transfer. The calculation formula for the flow transfer is as follows:

[0059]

[0060]

[0061] Wherein, represents the flow transfer coefficient, represents the preset initial flow transfer coefficient, β represents the preset adjustment factor, EMI t [A k (TR i )→A k+1 (TR i )] represents the line segment that is closest to the line segment XD[A j (TR i )→A j+1 (TR i )] and whose experienced liquidity index is less than the liquidity index threshold, and k≠j, F i,j,t represents the passenger flow to be transferred.

[0062] Transfer the passenger flow F i,j,t , which needs to be transferred, from the line segment XD[A j (TR i )→A j+1 (TR i )] to the line segment EMI t [A k (TR i )→A k+1 (TR i )].

[0063] For example, the liquidity index threshold can usually be set to the value 1. When the experienced liquidity index of a certain line segment approaches or exceeds 1, it means that the passenger flow of this line segment has approached or reached its maximum carrying capacity, and there may be situations such as overcrowding of tourists and a decline in the tour experience. The experienced liquidity index is highly objective and accurate, enabling scenic area managers to get rid of the limitations of subjective judgment and manage in a more scientific way. By real-time monitoring the experienced liquidity index, managers can take effective optimization measures in a timely manner, such as guiding tourists to other less crowded line segments in crowded line segments, or adding temporary facilities when necessary to relieve the passenger flow pressure. This can not only improve the tour comfort of tourists but also ensure the operation safety and order of the scenic area. When the experienced liquidity index EMI t [A j (TR i )→A j+1 (TR i )] is 0, it means that there are no tourists on this line segment at time point t, and the experienced liquidity is extremely poor. When the experienced liquidity index EMI t [A j (TR i )→A j+1 (TR i )] is 1, it means that this line segment has been in a full-load state at time point t, and the experienced liquidity reaches the limit. If the experienced liquidity index EMI t [A j (TR i )→A j+1 (TR i )] is greater than 1, it means that at time point t, the actual passenger flow on this line segment has exceeded the maximum passenger flow that this line segment can carry, which may lead to a sharp decline in the tourist experience, such as overcrowding and excessive queuing time. Moreover, for a line segment where the experienced liquidity index EMI t [A j (TR i )→A j+1 (TR i )] is greater than or equal to 1, on the other hand, it can indicate that the attractions at both ends of this line segment are highly popular and attractive. However, for the attractions, it may lead to increased wear and tear of the infrastructure inside the attractions and there is a risk of failure.

[0064] For example, assume that the preset initial flow transfer coefficient is 0.3, the preset adjustment factor β is 0.5, and EMI 1 [A 2 (TR 1 )→A 3 (TR 1 )] is 0.65, then substituting into the formula to calculate the flow transfer coefficient

[0065] It should be noted that this method can accurately reflect the carrying capacity of each line segment by real-time monitoring the passenger flow of each tourist route in the scenic area and calculating the experience mobility index in combination with time tags; by dynamically adjusting the tourist flow and reasonably diverting it, it avoids the static constraints in the traditional management mode and provides a more flexible and real-time responsive scenic area management solution; especially through the calculation of the experience mobility index and the flow transfer mechanism, the present invention can intelligently adjust the tourist routes of tourists, reduce congestion, improve the overall experience of tourists, and at the same time enhance the safety and efficiency of scenic area management; therefore, the present invention provides a more innovative and practically applicable optimization solution for the existing intelligent scenic area management technology, filling the gap in emergency scheduling and dynamic adjustment of traditional management methods.

[0066] Please refer to Figure 2 , in the second embodiment: Provide an intelligent cultural and tourism scenic area optimization management system based on big data, which includes: a data collection and scenic spot association module, a subsidiary scenic spot annotation and passenger flow monitoring module, an experience mobility index calculation module, and a passenger flow dynamic allocation module.

[0067] The data collection and scenic spot association module: Obtain the overall layout map of the scenic area and mark the tourist routes; obtain the subsidiary scenic spots of the tourist routes.

[0068] The subsidiary scenic spot annotation and passenger flow monitoring module: Based on the subsidiary scenic spots, divide the tourist route into line segments in sequence; obtain the average passenger flow data within the line segments.

[0069] The experience mobility index calculation module: Attach time tags to the average passenger flow data within the line segments, and calculate the experience mobility index of the line segments based on the average passenger flow data at a single time point.

[0070] The passenger flow dynamic allocation module: If the experience mobility index of the line segment is greater than or equal to the mobility index threshold, record it as a line segment to be diverted and give an early warning for maintenance; obtain the line segments with an experience mobility index less than the mobility index threshold, calculate the sum of the transferred passenger flow and allocate it.

[0071] Furthermore, the data collection and scenic spot association module includes a data collection unit and a scenic spot association unit.

[0072] The data collection unit: Obtain the overall layout map of the scenic area by carrying remote sensing survey equipment on an unmanned aerial vehicle. The overall layout map includes tourist routes, number the tourist routes, and mark the tourist routes on the overall layout map.

[0073] 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 other tour routes except the tour route, mark the scenic spot 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 other tour routes except the tour route, obtain the tour route with the minimum straight-line distance, and mark the scenic spot as an affiliated scenic spot of this tour route. If there are multiple tour routes with the minimum straight-line distance, mark the scenic spot as an affiliated scenic spot of the tour route with the smallest number.

[0074] Further, the affiliated scenic spot annotation and passenger flow monitoring module includes an affiliated scenic spot annotation unit and a passenger flow monitoring unit.

[0075] The affiliated scenic spot annotation unit: Based on all the affiliated scenic spots on the tour route, perform secondary annotation on the marked tour route on the overall layout map. The secondary annotation means marking all the affiliated scenic spots on the tour route; based on the marked affiliated scenic spots, divide the tour route sequence into multiple line segments.

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

[0077] Further, the experience mobility index calculation module includes an experience mobility index calculation unit.

[0078] The experience mobility index calculation unit: Attach time tags to the average passenger flow data within the line segments; based on the average passenger flow data at a single time point, calculate the experience mobility index of the line segment at time point t.

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

[0080] The early warning and maintenance unit: If the experience mobility index of the line segment at a single time point is greater than or equal to the mobility index threshold, mark the line segment as a line segment to be diverted, and issue an early warning to remind the staff to perform infrastructure maintenance.

[0081] The passenger flow dynamic allocation unit: Based on the overall layout map, obtain the line segment that is closest to the line segment and has an experience mobility index less than the mobility index threshold, and calculate the passenger flow that needs to be transferred; transfer the passenger flow that needs to be transferred between the line segments.

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

[0083] Finally, it should be noted that the above are only preferred embodiments of the present invention and are not used 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 perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for optimizing the management of intelligent cultural and tourism scenic spots based on big data, characterized in that: The method comprises the following steps: Step S1: Obtain the overall layout of the scenic area and mark the tour route; obtain the affiliated attractions of the tour route; Step S2: based on the affiliated scenic spots, divide the tour route into segments in order; and obtain average passenger flow data within the segments; Step S3: adding a time tag to the average passenger flow data within the line segment, and calculating 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 diverted and an early warning maintenance is issued; obtain the line segment whose experience mobility index is less than the mobility index threshold, calculate the sum of the transmitted passenger flow and distribute it.

2. According to the big data-based intelligent cultural and tourism scenic spot optimization management method of claim 1, it is characterized in that: The specific implementation process of step S1 includes: By carrying remote sensing survey equipment on the drone, the overall layout of the scenic spot is obtained. The overall layout includes tour routes. The tour routes are numbered, and the i-th tour route is recorded as TR i , mark the tour route TR on the overall layout map i ; Based on the overall layout diagram, obtain the location information of all scenic spots in the scenic area; for each scenic spot, obtain the route TR from the scenic spot to the tour route i If the straight-line distance from the scenic spot to the tour route TR i The straight-line distance is less than or equal to the scenic spot to the tour route TR i If the straight-line distance of all tour routes other than , the scenic spot is marked as tour route TR i If the attractions are connected to the tour route TR i The straight-line distance is greater than the distance from the scenic spot to the tour route TR i If the straight-line distance of all tour routes other than the first one is calculated, the tour route with the shortest straight-line distance is obtained, and the scenic spot is marked as an affiliated scenic spot of the tour route. If there are multiple tour routes with the shortest straight-line distance, the scenic spot is marked as an affiliated scenic spot of the tour route with the smallest number.

3. According to the big data-based intelligent cultural and tourism scenic spot optimization management method of claim 2, it is characterized in that: The specific implementation process of step S2 includes: Based on the tour route TR i All the affiliated attractions on the map, the tour route TR marked on the overall layout map i A secondary annotation is performed on the tour route TR i All the subsidiary attractions are marked on the top; based on the marked subsidiary attractions, the tour route TR i The jth line segment is divided into J-1 line segments in sequence, wherein the beginning of the jth line segment is the jth subsidiary scenic spot, and the end is the j+1th subsidiary scenic spot. The jth line segment is recorded as XD[A j (TR i ) → A j+1 (TR i )], where A j (TR i ) indicates tour route TR i The jth subsidiary attraction on j+1 (TR i ) indicates tour route TR i The j+1th subsidiary attraction on , where J represents the total number of subsidiary attractions; In line segment XD[A j (TR i ) → A j+1 (TR i )], and a laser sensor is arranged inside the line segment to monitor the flow of people in the line segment and obtain the line segment XD[A j (TR i ) → A j+1 (TR i )] within the average passenger flow data.

4. According to the big data-based intelligent cultural and tourism scenic spot optimization management method of claim 3, it is characterized in that: The specific implementation process of step S3 includes: For line segment XD[A j (TR i ) → A j+1 (TR i )], and the average passenger flow data at time point t is recorded as AP t [A j (TR i ) → A j+1 (TR i )]; Based on the average passenger flow data AP at time point t t [A j (TR i ) → A j+1 (TR i )], calculate the line segment XD[A j (TR i ) → A j+1 (TR i )] The experience mobility index at time point t is calculated as follows: Among them, EMI t [A j (TR i ) → A j+1 (TR i )] represents the line segment XD[A j (TR i ) → A j+1 (TR i )] Experience mobility 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 )]’s maximum passenger flow capacity.

5. The method for optimizing and managing intelligent cultural and tourism scenic spots based on big data according to claim 4 is characterized in that: The specific implementation process of step S4 includes: If the line segment XD[A j (TR i ) → A j+1 (TR i )] Experienced liquidity index EMI at time point t t [A j (TR i ) → A j+1 (TR i )] is greater than or equal to the liquidity index threshold, then the line segment XD[A j (TR i ) → A j+1 (TR i )] is recorded as a line segment to be diverted, and an early warning is issued to remind staff to carry out infrastructure maintenance; Based on the overall layout diagram, obtain the distance line segment XD[A j (TR i ) → A j+1 (TR i )] is closest to the line segment whose experience mobility index is less than the mobility index threshold, and the passenger flow is transferred. The calculation formula of the flow transfer is as follows: in, represents the flow transfer coefficient, represents the preset initial flow transfer coefficient, 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 )] is the closest segment whose experience mobility index is less than the mobility index threshold, and k≠j, F i,j,t Indicates the passenger flow that needs to be transferred; The passenger flow F that needs to be transferred i,j,t , from the line segment XD[A j (TR i ) → A j+1 (TR i )] is transmitted to the line segment EMI t [A k (TR i ) → A k+1 (TR i )]middle.

6. An intelligent cultural and tourism scenic spot optimization management system based on big data, executing an intelligent cultural and tourism scenic spot optimization management method based on big data as described in any one of claims 1-5, characterized in that: The system includes: a data collection and scenic spot association module, an affiliated scenic spot marking and passenger flow monitoring module, an experience mobility index calculation module and a passenger flow dynamic allocation module; The data collection and scenic spot association module: obtains the overall layout of the scenic spot and marks the tour route; obtains the affiliated scenic spots of the tour route; The subsidiary scenic spot marking and passenger flow monitoring module: based on the subsidiary scenic spots, the tour route is divided into line segments in sequence; and the average passenger flow data within the line segments is obtained; The experience mobility index calculation module is configured to add a time tag 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; The passenger flow dynamic allocation module: if the experience fluidity index of the line segment is greater than or equal to the fluidity index threshold, it is recorded as a line segment to be diverted and an early warning maintenance is issued; the line segment whose experience fluidity index is less than the fluidity index threshold is obtained, the transmitted passenger flow and the total are calculated and allocated.

7. According to claim 6, a smart cultural and tourism scenic spot optimization management system based on big data is characterized by: The data collection and scenic spot association module includes a data collection unit and a scenic spot association unit; The data acquisition unit: obtains an overall layout map of the scenic spot by carrying a remote sensing survey device on the drone, the overall layout map includes a tour route, numbers the tour routes, and marks the tour routes on the overall layout map; The scenic spot association unit: based on the overall layout diagram, 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, and 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 except the tour route, the scenic spot is marked as a subsidiary 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 except the tour route, the tour route with the shortest straight-line distance is obtained, and the scenic spot is marked as a subsidiary scenic spot of the tour route; if there are multiple tour routes with the shortest straight-line distance, the scenic spot is marked as a subsidiary scenic spot of the tour route with the smallest number.

8. According to claim 7, a smart cultural and tourism scenic spot optimization management system based on big data is characterized by: The subsidiary scenic spot marking and passenger flow monitoring module includes a subsidiary scenic spot marking unit and a passenger flow monitoring unit; The subsidiary scenic spot marking unit: based on all the subsidiary scenic spots on the tour route, performs secondary marking on the tour route marked on the overall layout map, wherein the secondary marking indicates marking all the subsidiary scenic spots on the tour route; Based on the annotated subsidiary attractions, the tour route is sequentially divided into multiple line segments; The passenger flow monitoring unit: a laser sensor is arranged in the line segment, and the laser sensor is used to monitor the passenger flow data in the line segment and obtain the average passenger flow data in the line segment.

9. The intelligent cultural and tourism scenic spot optimization management system based on big data according to claim 8 is characterized by: The experience mobility index calculation module includes an experience mobility index calculation unit; The experience mobility index calculation unit: adds a time tag 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.

10. The intelligent cultural and tourism scenic spot optimization management system based on big data according to claim 9, characterized in that: The passenger flow dynamic allocation module includes an early warning and maintenance unit and a passenger flow dynamic allocation unit; The warning and maintenance unit: if the experienced mobility index of a line segment at a single time point is greater than or equal to the mobility index threshold, the line segment is recorded as a line segment to be diverted, and a warning is issued to remind staff to perform infrastructure maintenance; The passenger flow dynamic allocation unit: based on the overall layout diagram, obtains the line segment closest to the line segment and whose experience mobility index is less than the mobility index threshold, and calculates the passenger flow that needs to be transferred; The passenger flow that needs to be transferred is transferred between line segments.

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