An intelligent scenic spot operation management system and method based on cloud computing

Abstract

The present invention discloses an intelligent scenic spot operation management system and method based on cloud computing, which relates to the field of intelligent scenic spot operation technology, including a scenic spot operation service end construction module, an analysis module for scenic spot rental optimization points to be analyzed, an analysis module for effective scenic spot rental optimization points, an early warning critical value analysis module and an intelligent control module; the scenic spot operation service end construction module is used to construct an intelligent and integrated scenic spot operation service end; the analysis module for scenic spot rental optimization points to be analyzed is used to extract tourist route data recorded in rental events and analyze the scenic spot rental optimization points to be analyzed corresponding to each rental event; the analysis module for effective scenic spot rental optimization points is used to determine effective scenic spot rental optimization points based on log data; the early warning critical value analysis module is used to analyze the driving speed early warning critical value of each type of return path corresponding to different personnel flow rates; the intelligent control module is used to match tourists to perform intelligent control of the speed of shared transportation equipment in corresponding rental event scenarios.

Description

Technical Field

[0001] The present invention relates to the field of intelligent scenic spot operation technology, and in particular to an intelligent scenic spot operation management system and method based on cloud computing. Background Art

[0002] The technology behind smart scenic area operations relies primarily on the development of modern information technology, particularly the widespread application of technologies such as the Internet of Things (IoT), big data, artificial intelligence (AI), virtual and augmented reality (VR), and 5G communications. IoT technology, for example, is the cornerstone of smart scenic area construction. By deploying various sensors and smart devices within a scenic area, real-time monitoring of the environment, crowd flow, and traffic can be achieved. For example, temperature sensors can monitor ambient temperature to provide a comfortable experience for visitors, while crowd counters can count visitors in real time, helping scenic areas manage crowd flow and safety. IoT technology also enables remote control and management of equipment, improving the efficiency of scenic area operations. Furthermore, IoT data management and control of equipment within scenic areas can be achieved through IoT. For example, shared electric vehicles, such as those rented within scenic areas, are often located at the entrance. However, some tourists may only choose to rent shared vehicles when they feel tired mid-sighted. Consequently, there may be a lack of rental points within the scenic area, or the number of rental points may be inappropriate. Furthermore, vehicles may travel too fast when traveling to and from rental points, as visitors may repeat their journeys on the return route. Therefore, monitoring vehicle speed is particularly important. Summary of the Invention

[0003] The purpose of the present invention is to provide an intelligent scenic spot operation management system and method based on cloud computing to solve the problems raised in the prior art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a cloud computing-based intelligent scenic spot operation and management method, the management method comprising the following specific steps:

[0005] Step S1: Build an intelligent and integrated scenic spot operation service end. Users scan the QR code to log in to the service end to establish an interactive connection with the scenic spot. The scenic spot operation service end includes the user end, the device end, and the service system end.

[0006] Step S2: Extracting rental events of shared transportation equipment stored in the scenic area operation server. A rental event refers to an event with a time interval between the time a tourist enters the scenic area and the time the shared transportation equipment rental is started. The time interval refers to the time added due to the spatial distance between the tourist and the time interval is greater than a preset time threshold. Extracting tourist route data recorded in the rental event records analyzes the rental optimization points of the scenic area to be analyzed corresponding to each rental event.

[0007] Step S3: Traverse all rental events to construct a set of all scenic spot rental optimization points to be analyzed. For each scenic spot rental optimization point to be analyzed in the set, the communication area within the preset error range is corresponding to the corresponding communication area. The user end log data of the historical records is extracted, and the valid scenic spot rental optimization points are determined based on the log data, and a valid scenic spot rental optimization point set is generated.

[0008] Step S4: extracting the return path from the rental event corresponding to the effective scenic spot rental optimization point, obtaining the driving data of the shared transportation equipment recording the return path, and analyzing the driving speed warning critical value corresponding to each type of return path under different personnel flow based on the driving data;

[0009] Step S5: Based on the driving speed warning critical value, the speed of shared transportation equipment is intelligently controlled according to the tourist's rental event scenario.

[0010] Furthermore, the optimization points of scenic spot leasing corresponding to each leasing event are analyzed, including the following specific steps:

[0011] Step S21: Tourist route data refers to the location record that is generated after the tourist logs in to the scenic area operation server and the tourist's tour path generated on the user side; extract the two-dimensional plan view of the scenic area stored in the scenic area operation server, establish a two-dimensional rectangular coordinate system with the scenic area entrance as the coordinate origin, and mark the rental points of shared transportation equipment in the coordinate system; the scenario analyzed is that the rental point only exists at the entrance and is set close to the scenic area entrance;

[0012] Step S22: Plot the tourist's tour path in each rental event in the coordinate system to generate a path coordinate graph. Calculate the local spatial distance L of the rental event with the coordinate origin as the starting point and any coordinate point corresponding to the tour path in the path coordinate graph as the end point. L = [(x2-x1)+(y2-y1)] 1 / 2 , traverse all coordinate points of the tour path corresponding to the rental event to generate a distance set containing all local spatial distances;

[0013] Step S23: Extract the maximum value Lmax of the local spatial distance in the distance set and the spatial distance H between the rental point and the coordinate origin, calculate the displacement difference L1 of the corresponding rental event, L1=Lmax-H; set the displacement difference L0, and extract the coordinate point corresponding to the maximum value Lmax of the local spatial distance in the tour path when L1≥L0 as the rental optimization point of the scenic spot to be analyzed.

[0014] The rental optimization points of the scenic spot to be analyzed refer to the turning points in the tour route for tourists based on their rental intentions. Because the setting of rental points is limited to the entrance of the scenic spot, the intention of some tourists who do not make a plan or tourists who enter the scenic spot for the first time to use the means of transportation will not be very obvious, so it is easy for them to have the desire to rent due to their own physical reasons in the middle of the tour; thus, there is a possibility of turning back or changing the route; therefore, the rental optimization points of the scenic spot to be analyzed can be based on big data with the help of cloud computing to analyze the optimization direction of the later scenic spot operation and management, and improve the feasibility of operation and management.

[0015] Furthermore, step S3 includes the following specific steps:

[0016] Step S31: The log data includes engine search records and browsing records. The communication area within a preset error range corresponding to each scenic spot rental optimization point to be analyzed is used as the target monitoring area. The user-side log data of all tourists in the target monitoring area is extracted, and the users whose engine search records and browsing records contain the shared transportation equipment rental keyword are marked as target users.

[0017] Step S32: The monitoring cycle is averaged to several monitoring periods, and the maximum value M1 of the flow of people in the target monitoring area in each monitoring period and the number N1 marked as target users in the monitoring period are extracted. The demand response index G of each target monitoring area in each monitoring period is calculated, where G=N1 / M1; the demand response index corresponding to all monitoring periods is extracted and the average demand response index G0 corresponding to the target monitoring area is calculated, where G0=(1 / k)∑G, where k represents the total number of divided monitoring periods.

[0018] Step S33: Set the demand response index threshold G1, extract the target monitoring area corresponding to G0>G1 as the effective scenic spot rental optimization point; and calculate the average demand response index of the communication areas corresponding to all tour routes except the target monitoring area recorded by the scenic spot operation service end. When G0>G1 exists, extract the center point of the corresponding communication area as the effective scenic spot rental optimization point.

[0019] Analyzing the effective optimization points for scenic spot leasing is to accurately identify optimization needs from the perspective of tourists' actual communication data; because when there is a demand for leasing, tourists often use mobile phones to obtain relevant information to determine their leasing intentions.

[0020] Furthermore, step S4 includes the following:

[0021] Step S41: Generate a return path with the shared transportation equipment rental point as the starting point and the effective scenic spot rental optimization point as the end point. The driving data includes driving speed and braking data. Extract all driving data recorded for the same return path and calculate the vehicle speed fluctuation index Q corresponding to the same return path, Q=a1×{(1 / C1)∑[(V1-V2) / V1]}+a2×(C1 / Cmax), where V1 represents the instantaneous speed before braking, V2 represents the instantaneous speed after braking, and the speed change reflects the intensity of the tourist's braking. C1 represents the total number of braking times recorded for the corresponding return path in each rental event; Cmax represents the maximum number of braking times recorded in all rental events for the same return path; a1 and a2 represent corresponding reference coefficients.

[0022] The larger the speed fluctuation index, the greater the intensity and frequency of the tourists' braking on the return path, which in turn poses a greater safety risk to other tourists on the road and themselves.

[0023] Step S42: Sort the vehicle speed fluctuation indexes Q corresponding to all rental events on the same return path, select the maximum value Qmax and the minimum value Qmin of the vehicle speed fluctuation index on the same return path; extract the tourist flow range F1 recorded for the return path corresponding to Qmax and the tourist flow range F2 recorded for the return path corresponding to Qmin;

[0024] Step S43: If F1∩F2=∅, then when the tourist flow on the return path of the monitored scenic spot falls within the tourist flow range F2, the corresponding average driving speed recorded when the tourist flow falls within the tourist flow range F1 is output as the driving speed warning critical value;

[0025] If F1∩F2≠∅, extract the maximum driving speed recorded in Qmin when the tourist flow range F2 is used as the driving speed warning critical value under this flow range; extract the maximum value Qmax and minimum value Q1min of the speed fluctuation index corresponding to all rental events in the tourist flow range F1, and mark and analyze the minimum driving speed V in the rental event corresponding to the maximum value Qmax. b And analyze the minimum value Q1min corresponding to the maximum driving speed V in the rental event c ;

[0026] Step S44: When V b >V c When V c As the critical value of the driving speed warning corresponding to the tourist flow range F2; when V b ≤V c When V b As the critical value of driving speed warning corresponding to the tourist flow range F2.

[0027] The speed analysis on the return path is because during the return process, tourists often have already visited the attractions along the path, so the sightseeing speed will increase after using shared transportation equipment. Therefore, the safety analysis of the driving speed on this section of road is particularly important, which can effectively reduce the risk factor of tourists visiting in this scenario.

[0028] Furthermore, step S5 includes the following:

[0029] Monitor the real-time paths of real-time tourists after they arrive at the effective scenic spot rental optimization point. When there is a real-time path that is the same as the historical record return path and the shared transportation equipment rental is started within the time interval, the scenic spot operation service end responds to the monitoring device and extracts the real-time tourist flow of the real-time return path recorded by the monitoring device and the real-time tourist flow of all paths formed by the effective scenic spot rental optimization point and the shared transportation equipment rental point;

[0030] Match the real-time tourist flow on each route to the tourist flow range, output the speed warning threshold under the corresponding tourist flow range, and intelligently control the speed of shared transportation equipment;

[0031] Intelligent control means that when tourists turn on shared transportation equipment, the system limits the maximum driving speed of the transportation equipment to the driving speed warning critical value.

[0032] An intelligent scenic spot operation and management system based on cloud computing, including a scenic spot operation service end construction module, a scenic spot rental optimization point analysis module to be analyzed, an effective scenic spot rental optimization point analysis module, an early warning critical value analysis module and an intelligent control module;

[0033] The scenic area operation service terminal construction module is used to build an intelligent and integrated scenic area operation service terminal;

[0034] The rental optimization point analysis module of the scenic spot to be analyzed is used to extract the tourist route data recorded in the rental event to analyze the rental optimization point of the scenic spot to be analyzed corresponding to each rental event;

[0035] The effective scenic spot rental optimization point analysis module is used to determine the effective scenic spot rental optimization points based on log data.

[0036] The warning critical value analysis module is used to analyze the driving speed warning critical value under different passenger flow conditions for each type of return path;

[0037] The intelligent control module is used to match tourists with the intelligent control of the speed of shared transportation equipment in corresponding rental event scenarios.

[0038] Furthermore, the analysis module for the optimization point analysis of the scenic spot rental to be analyzed includes a coordinate system establishment unit, a local space distance calculation unit, and a displacement difference calculation unit;

[0039] The coordinate system establishment unit is used to extract the two-dimensional plan of the scenic area stored in the scenic area operation server and establish a two-dimensional rectangular coordinate system with the entrance of the scenic area as the coordinate origin;

[0040] The local space distance calculation unit is used to calculate the local space distance of the rental event with the coordinate origin as the starting point and any coordinate point of the corresponding tour path in the path coordinate graph as the end point;

[0041] The displacement difference calculation unit is used to extract the maximum value of the local spatial distance in the distance set and the spatial distance between the rental point and the coordinate origin, and calculate the displacement difference of the corresponding rental event; and when the displacement difference is greater than or equal to the threshold, the coordinate point corresponding to the maximum value of the local spatial distance in the tour path is the rental optimization point of the scenic spot to be analyzed.

[0042] Furthermore, the effective scenic spot rental optimization point analysis module includes a target monitoring area determination unit, a target user determination unit, a demand response index calculation unit and an effective scenic spot rental optimization point determination unit;

[0043] The target monitoring area determination unit is used to set the communication area within the preset error range corresponding to each scenic area rental optimization point to be analyzed as the target monitoring area.

[0044] The target user determination unit is used to mark the user corresponding to the log data whose engine search records and browsing records both contain the shared transportation equipment rental keyword as the target user;

[0045] The demand response index calculation unit is used to calculate the demand response index of each target monitoring area in each monitoring period;

[0046] The effective scenic spot rental optimization point determination unit is used to set the demand response index threshold, and extract the target monitoring area corresponding to the average demand response index greater than the demand response index threshold as the effective scenic spot rental optimization point.

[0047] Furthermore, the warning critical value analysis module includes a vehicle speed fluctuation index calculation unit, a tourist flow range determination unit and a warning critical value output unit;

[0048] The vehicle speed fluctuation index calculation unit is used to extract all driving data recorded on the same return path and calculate the vehicle speed fluctuation index corresponding to the same return path;

[0049] The tourist flow range determination unit is used to sort the vehicle speed fluctuation indexes corresponding to all rental events on the same return path, select the maximum and minimum values ​​of the vehicle speed fluctuation index on the same return path, and use the tourist flow ranges corresponding to the maximum and minimum values ​​as the corresponding ranges;

[0050] The warning critical value output unit is used to output the driving speed warning critical value based on the analysis of the tourist flow range determination unit.

[0051] Compared with the prior art, the present invention has the following beneficial effects:

[0052] 1. The present invention analyzes tourist data collected by scenic area service terminals based on cloud computing to determine possible rental points where shared transportation equipment may be needed. The optimization points for rental in the scenic area to be analyzed can be analyzed based on big data and cloud computing to determine the optimization direction for the subsequent operation and management of the scenic area, thereby improving the feasibility of operation and management. Furthermore, based on the optimization points for rental in the scenic area to be analyzed, the actual communication data of tourists can be accurately analyzed to further identify optimization needs.

[0053] 2. After determining the optimization point requirements, analyze the driving speed of traffic equipment, and intelligently control the speed limit based on the flow of people in different road scenarios while ensuring safety; improve the efficiency, comprehensiveness and intelligent monitoring of scenic area safety. BRIEF DESCRIPTION OF THE DRAWINGS

[0054] Figure 1 This is a structural diagram of an intelligent scenic spot operation and management system based on cloud computing in the present invention. DETAILED DESCRIPTION

[0055] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0056] Example: Figure 1 As shown, the present invention provides a cloud computing-based intelligent scenic spot operation management system and method technical solution, a cloud computing-based intelligent scenic spot operation management method, the management method includes the following specific steps:

[0057] Step S1: Build an intelligent and integrated scenic spot operation service end. Users scan the QR code to log in to the service end and establish interactive connection with the scenic spot. The scenic spot operation service end includes the user end, the device end, and the service system end. The scenic spot operation service end means that tourists can use mobile devices such as mobile phones as user ends to interact with various intelligent scenes set up in the scenic spot, such as shared bicycle sightseeing vehicles in the scenic spot and rent them by scanning the QR code;

[0058] Step S2: Extracting rental events of shared transportation equipment stored in the scenic area operation server. A rental event refers to an event with a time interval between the time a tourist enters the scenic area and the time the shared transportation equipment rental is started. The time interval refers to the time added due to the spatial distance between the tourist and the time interval is greater than a preset time threshold. Extracting tourist route data recorded in the rental event records analyzes the rental optimization points of the scenic area to be analyzed corresponding to each rental event.

[0059] Because shared transportation devices are generally placed at the entrance of a scenic spot, the time between entering the scenic spot and when a tourist activates the shared transportation device is much shorter than the above time interval; therefore, the maximum value of the interval can be selected as the preset time threshold; the time of entering the scenic spot refers to the time when a tourist enters the scenic spot after logging into the scenic spot operation server and verifying the gate;

[0060] Step S3: Traverse all rental events to construct a set of all scenic spot rental optimization points to be analyzed. For each scenic spot rental optimization point to be analyzed in the set, the communication area within the preset error range is corresponding to the corresponding communication area. The user end log data of the historical records is extracted, and the valid scenic spot rental optimization points are determined based on the log data, and a valid scenic spot rental optimization point set is generated.

[0061] Step S4: extracting the return path from the rental event corresponding to the effective scenic spot rental optimization point, obtaining the driving data of the shared transportation equipment recording the return path, and analyzing the driving speed warning critical value corresponding to each type of return path under different personnel flow based on the driving data;

[0062] Step S5: Based on the driving speed warning critical value, the speed of shared transportation equipment is intelligently controlled according to the tourist's rental event scenario.

[0063] Analyze the optimization points of the scenic spot rental corresponding to each rental event, including the following specific steps:

[0064] Step S21: Tourist route data refers to the location record that is generated after the tourist logs in to the scenic area operation server and the tourist's tour path generated on the user side; extract the two-dimensional plan view of the scenic area stored in the scenic area operation server, establish a two-dimensional rectangular coordinate system with the scenic area entrance as the coordinate origin, and mark the rental points of shared transportation equipment in the coordinate system; the scenario analyzed is that the rental point only exists at the entrance and is set close to the scenic area entrance;

[0065] Step S22: Plot the tourist's tour path in each rental event in the coordinate system to generate a path coordinate graph. Calculate the local spatial distance L of the rental event with the coordinate origin as the starting point and any coordinate point corresponding to the tour path in the path coordinate graph as the end point. L = [(x2-x1)+(y2-y1)] 1 / 2 , traverse all coordinate points of the tour path corresponding to the rental event to generate a distance set containing all local spatial distances;

[0066] Step S23: Extract the maximum value Lmax of the local spatial distance in the distance set and the spatial distance H between the rental point and the coordinate origin, calculate the displacement difference L1 of the corresponding rental event, L1=Lmax-H; set the displacement difference L0, and extract the coordinate point corresponding to the maximum value Lmax of the local spatial distance in the tour path when L1≥L0 as the rental optimization point of the scenic spot to be analyzed.

[0067] The rental optimization points of the scenic spot to be analyzed refer to the turning points in the tour route for tourists based on their rental intentions. Because the setting of rental points is limited to the entrance of the scenic spot, the intention of some tourists who do not make a plan or tourists who enter the scenic spot for the first time to use the means of transportation will not be very obvious, so it is easy for them to have the desire to rent due to their own physical reasons in the middle of the tour; thus, there is a possibility of turning back or changing the route; therefore, the rental optimization points of the scenic spot to be analyzed can be based on big data with the help of cloud computing to analyze the optimization direction of the later scenic spot operation and management, and improve the feasibility of operation and management.

[0068] Step S3 includes the following specific steps:

[0069] Step S31: The log data includes engine search records and browsing records. The communication area within a preset error range corresponding to each scenic spot rental optimization point to be analyzed is used as the target monitoring area. The user-side log data of all tourists in the target monitoring area is extracted, and the users whose engine search records and browsing records contain the shared transportation equipment rental keyword are marked as target users.

[0070] Step S32: The monitoring cycle is averaged to several monitoring periods, and the maximum value M1 of the flow of people in the target monitoring area in each monitoring period and the number N1 marked as target users in the monitoring period are extracted. The demand response index G of each target monitoring area in each monitoring period is calculated, where G=N1 / M1; the demand response index corresponding to all monitoring periods is extracted and the average demand response index G0 corresponding to the target monitoring area is calculated, where G0=(1 / k)∑G, where k represents the total number of divided monitoring periods.

[0071] Step S33: Set the demand response index threshold G1, extract the target monitoring area corresponding to G0>G1 as the effective scenic spot rental optimization point; and calculate the average demand response index of the communication areas corresponding to all tour routes except the target monitoring area recorded by the scenic spot operation service end. When G0>G1 exists, extract the center point of the corresponding communication area as the effective scenic spot rental optimization point.

[0072] Analyzing the effective optimization points for scenic spot leasing is to accurately identify optimization needs from the perspective of tourists' actual communication data; because when there is a demand for leasing, tourists often use mobile phones to obtain relevant information to determine their leasing intentions.

[0073] Step S4 includes the following:

[0074] Step S41: Generate a return path with the shared transportation equipment rental point as the starting point and the effective scenic spot rental optimization point as the end point. The driving data includes driving speed and braking data. Extract all driving data recorded for the same return path and calculate the vehicle speed fluctuation index Q corresponding to the same return path, Q=a1×{(1 / C1)∑[(V1-V2) / V1]}+a2×(C1 / Cmax), where V1 represents the instantaneous speed before braking, V2 represents the instantaneous speed after braking, and the speed change reflects the intensity of the tourist's braking. C1 represents the total number of braking times recorded for the corresponding return path in each rental event; Cmax represents the maximum number of braking times recorded in all rental events for the same return path; a1 and a2 represent corresponding reference coefficients.

[0075] The larger the speed fluctuation index, the greater the intensity and frequency of the tourists' braking on the return path, which in turn poses a greater safety risk to other tourists on the road and themselves.

[0076] Step S42: Sort the vehicle speed fluctuation indexes Q corresponding to all rental events on the same return path, select the maximum value Qmax and the minimum value Qmin of the vehicle speed fluctuation index on the same return path; extract the tourist flow range F1 recorded for the return path corresponding to Qmax and the tourist flow range F2 recorded for the return path corresponding to Qmin;

[0077] Step S43: If F1∩F2=∅, then when the tourist flow on the return path of the monitored scenic spot falls within the tourist flow range F2, the corresponding average driving speed recorded when the tourist flow falls within the tourist flow range F1 is output as the driving speed warning critical value;

[0078] When the two ranges do not overlap, it indicates that there is a clear difference in the vehicle speed fluctuation index under different passenger flows, so the vehicle with the larger speed fluctuation index needs to be reminded to control the speed. For example, the tourist flow range corresponding to Qmax is [1500, 2000], and the tourist flow range corresponding to Qmin is [500, 800]. This shows that the vehicle speed fluctuation index and passenger flow data within the corresponding ranges are linearly related, and the two do not overlap.

[0079] If F1∩F2≠∅, extract the maximum driving speed recorded in Qmin when the tourist flow range F2 is used as the driving speed warning critical value under this flow range; extract the maximum value Qmax and minimum value Q1min of the speed fluctuation index corresponding to all rental events in the tourist flow range F1, and mark and analyze the minimum driving speed V in the rental event corresponding to the maximum value Qmax. b And analyze the minimum value Q1min corresponding to the maximum driving speed V in the rental event c ;

[0080] Step S44: When V b >V c When V c As the critical value of the driving speed warning corresponding to the tourist flow range F2; when V b ≤V c When V b As the critical value of driving speed warning corresponding to the tourist flow range F2.

[0081] As shown in the embodiment: when F1∩F2≠∅, it means that although there is a distinction between vehicle speed fluctuation indexes, there may still be cases where the vehicle speed fluctuation index is small under a large range of pedestrian flow, and cases where the vehicle speed fluctuation index is large under a small range of pedestrian flow. For the case of a small range of pedestrian flow, the driving speed in the rental event corresponding to the minimum value of the calculated vehicle speed fluctuation index can be selected as the warning value, thereby ensuring safety within the corresponding flow range and reducing the tediousness of data analysis for other situations; and in the case of a large range of pedestrian flow, the driving speed in the event where there is no high vehicle speed fluctuation index in this scenario can be recorded as the warning value, thereby improving safety more effectively and realistically.

[0082] For example, when the tourist flow range corresponding to Qmax is [1500,2000], there is Q1min, and Q1min corresponds to the maximum driving speed V c , at this time V c <V b This shows that when the speed fluctuation index is small under the condition of large passenger flow, it is because tourists effectively control the speed during driving, so this speed can be used as a reference standard under the current passenger flow.

[0083] The speed analysis on the return path is because during the return process, tourists often have already visited the attractions along the path, so the sightseeing speed will increase after using shared transportation equipment. Therefore, the safety analysis of the driving speed on this section of road is particularly important, which can effectively reduce the risk factor of tourists visiting in this scenario.

[0084] Step S5 includes the following:

[0085] Monitor the real-time paths of real-time tourists after they arrive at the effective scenic spot rental optimization point. When there is a real-time path that is the same as the historical record return path and the shared transportation equipment rental is started within the time interval, the scenic spot operation service end responds to the monitoring device and extracts the real-time tourist flow of the real-time return path recorded by the monitoring device and the real-time tourist flow of all paths formed by the effective scenic spot rental optimization point and the shared transportation equipment rental point;

[0086] Match the real-time tourist flow on each route to the tourist flow range, output the speed warning threshold under the corresponding tourist flow range, and intelligently control the speed of shared transportation equipment;

[0087] Intelligent control means that when tourists turn on shared transportation equipment, the system limits the maximum driving speed of the transportation equipment to the driving speed warning critical value.

[0088] An intelligent scenic spot operation and management system based on cloud computing, including a scenic spot operation service end construction module, a scenic spot rental optimization point analysis module to be analyzed, an effective scenic spot rental optimization point analysis module, an early warning critical value analysis module and an intelligent control module;

[0089] The scenic area operation service terminal construction module is used to build an intelligent and integrated scenic area operation service terminal;

[0090] The rental optimization point analysis module of the scenic spot to be analyzed is used to extract the tourist route data recorded in the rental event to analyze the rental optimization point of the scenic spot to be analyzed corresponding to each rental event;

[0091] The effective scenic spot rental optimization point analysis module is used to determine the effective scenic spot rental optimization points based on log data.

[0092] The warning critical value analysis module is used to analyze the driving speed warning critical value under different passenger flow conditions for each type of return path;

[0093] The intelligent control module is used to match tourists with the intelligent control of the speed of shared transportation equipment in corresponding rental event scenarios.

[0094] The analysis module for optimizing the rental points of the scenic spot to be analyzed includes a coordinate system establishment unit, a local space distance calculation unit, and a displacement difference calculation unit;

[0095] The coordinate system establishment unit is used to extract the two-dimensional plan of the scenic area stored in the scenic area operation server and establish a two-dimensional rectangular coordinate system with the entrance of the scenic area as the coordinate origin;

[0096] The local space distance calculation unit is used to calculate the local space distance of the rental event with the coordinate origin as the starting point and any coordinate point of the corresponding tour path in the path coordinate graph as the end point;

[0097] The displacement difference calculation unit is used to extract the maximum value of the local spatial distance in the distance set and the spatial distance between the rental point and the coordinate origin, and calculate the displacement difference of the corresponding rental event; and when the displacement difference is greater than or equal to the threshold, the coordinate point corresponding to the maximum value of the local spatial distance in the tour path is the rental optimization point of the scenic spot to be analyzed.

[0098] The effective scenic spot leasing optimization point analysis module includes a target monitoring area determination unit, a target user determination unit, a demand response index calculation unit and an effective scenic spot leasing optimization point determination unit;

[0099] The target monitoring area determination unit is used to set the communication area within the preset error range corresponding to each scenic area rental optimization point to be analyzed as the target monitoring area.

[0100] The target user determination unit is used to mark the user corresponding to the log data whose engine search records and browsing records both contain the shared transportation equipment rental keyword as the target user;

[0101] The demand response index calculation unit is used to calculate the demand response index of each target monitoring area in each monitoring period;

[0102] The effective scenic spot rental optimization point determination unit is used to set the demand response index threshold, and extract the target monitoring area corresponding to the average demand response index greater than the demand response index threshold as the effective scenic spot rental optimization point.

[0103] The early warning critical value analysis module includes a vehicle speed fluctuation index calculation unit, a tourist flow range determination unit and an early warning critical value output unit;

[0104] The vehicle speed fluctuation index calculation unit is used to extract all driving data recorded on the same return path and calculate the vehicle speed fluctuation index corresponding to the same return path;

[0105] The tourist flow range determination unit is used to sort the vehicle speed fluctuation indexes corresponding to all rental events on the same return path, select the maximum and minimum values ​​of the vehicle speed fluctuation index on the same return path, and use the tourist flow ranges corresponding to the maximum and minimum values ​​as the corresponding ranges;

[0106] The warning critical value output unit is used to output the driving speed warning critical value based on the analysis of the tourist flow range determination unit.

[0107] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above and that the invention can be embodied in other specific forms without departing from the spirit or essential characteristics of the invention. Therefore, the embodiments should be considered in all respects as illustrative and non-restrictive, and the scope of the invention is defined by the appended claims, not the foregoing description, and all variations within the meaning and range of equivalents of the claims are intended to be included therein. Any reference sign in a claim should not be construed as limiting the claim to which it relates.

Claims

1. A cloud computing-based intelligent scenic spot operation and management method, characterized by: The management method comprises the following specific steps: Step S1: Build an intelligent and integrated scenic spot operation service terminal. Users scan the QR code to log in to the service terminal to establish interactive connection with the scenic spot. The scenic spot operation service terminal includes a user terminal, a device terminal, and a service system terminal. Step S2: Extracting rental events of shared transportation equipment stored in the scenic area operation server. The rental event refers to an event with a time interval between the time when a tourist enters the scenic area and the time when the shared transportation equipment rental is started; the time interval refers to the time added due to the spatial distance generated by the tourist; the time interval is greater than a preset time threshold; extracting the tourist route data recorded in the rental event and analyzing the rental optimization point of the scenic area to be analyzed corresponding to each rental event; Step S3: Traverse all rental events to construct a set of all scenic spot rental optimization points to be analyzed. For each scenic spot rental optimization point to be analyzed in the set, the communication area within the preset error range is corresponding to the corresponding communication area. The user end log data of the historical records is extracted, and the valid scenic spot rental optimization points are determined based on the log data, and a valid scenic spot rental optimization point set is generated. The step S3 includes the following specific steps: Step S31: The log data includes engine search records and browsing records. The communication area within a preset error range corresponding to each scenic spot rental optimization point to be analyzed is used as the target monitoring area. The user-side log data of all tourists in the target monitoring area is extracted, and the users corresponding to the log data containing the shared transportation equipment rental keyword in both the engine search records and browsing records are marked as target users. Step S32: The monitoring cycle is averaged to several monitoring periods, and the maximum value M1 of the flow of people in the target monitoring area in each monitoring period and the number N1 marked as target users in the monitoring period are extracted. The demand response index G of each target monitoring area in each monitoring period is calculated, where G=N1 / M1; the demand response index corresponding to all monitoring periods is extracted and the average demand response index G0 corresponding to the target monitoring area is calculated, where G0=(1 / k)∑G, where k represents the total number of divided monitoring periods. Step S33: Setting a demand response index threshold G1, extracting the target monitoring area corresponding to G0>G1 as the effective scenic spot rental optimization point; and calculating the average demand response index of the communication areas corresponding to all tour routes excluding the target monitoring area recorded by the scenic spot operation service end. When G0>G1 exists, extracting the center point of the corresponding communication area as the effective scenic spot rental optimization point; Step S4: extracting the return path from the rental event corresponding to the effective scenic spot rental optimization point, obtaining the driving data of the shared transportation equipment recording the return path, and analyzing the driving speed warning critical value corresponding to each type of return path under different personnel flow based on the driving data; Step S5: Based on the driving speed warning critical value, the speed of shared transportation equipment is intelligently controlled according to the tourist's rental event scenario.

2. The cloud computing-based intelligent scenic spot operation and management method according to claim 1, characterized in that: The analysis of the optimization points of the scenic spot rental to be analyzed corresponding to each rental event includes the following specific steps: Step S21: The tourist route data refers to the location record started after the tourist logs in to the scenic area operation server and the tourist's tour path generated on the user side; extracting the two-dimensional plan view of the scenic area stored in the scenic area operation server, establishing a two-dimensional rectangular coordinate system with the scenic area entrance as the coordinate origin, and marking the rental points of shared transportation equipment in the coordinate system; the scenario analyzed is that the rental point only exists at the entrance and is set close to the scenic area entrance; Step S22: Plot the tourist's tour path in each rental event in the coordinate system to generate a path coordinate graph. Calculate the local spatial distance L of the rental event with the coordinate origin as the starting point and any coordinate point corresponding to the tour path in the path coordinate graph as the end point. L = [(x2-x1)+(y2-y1)] 1 / 2 , traverse all coordinate points of the tour path corresponding to the rental event to generate a distance set containing all local spatial distances; Step S23: extract the maximum value Lmax of the local spatial distance in the distance set and the spatial distance H between the rental point and the coordinate origin, and calculate the displacement difference L1 corresponding to the rental event, L1=Lmax-H; Set the displacement difference L0, and extract the coordinate point corresponding to the maximum local spatial distance Lmax in the tour path when L1≥L0 as the rental optimization point of the scenic spot to be analyzed.

3. The cloud computing-based intelligent scenic spot operation and management method according to claim 1, characterized in that: The step S4 includes the following: Step S41: Generate a return path with the shared transportation equipment rental point as the starting point and the effective scenic spot rental optimization point as the end point, wherein the driving data includes driving speed and braking data; extract all driving data recorded for the same return path, and calculate the vehicle speed fluctuation index Q corresponding to the same return path, Q=a1×{(1 / C1)∑[(V1-V2) / V1]}+a2×(C1 / Cmax), where V1 represents the instantaneous speed before braking, V2 represents the instantaneous speed after braking, C1 represents the total number of braking times recorded for the corresponding return path in each rental event; Cmax represents the maximum number of braking times recorded in all rental events for the same return path; a1 and a2 represent corresponding reference coefficients; Step S42: Sort the vehicle speed fluctuation indexes Q corresponding to all rental events on the same return path, select the maximum value Qmax and the minimum value Qmin of the vehicle speed fluctuation index on the same return path; extract the tourist flow range F1 recorded for the return path corresponding to Qmax and the tourist flow range F2 recorded for the return path corresponding to Qmin; Step S43: If F1∩F2=∅, then when the tourist flow on the return path of the monitored scenic spot falls within the tourist flow range F2, the corresponding average driving speed recorded when the tourist flow falls within the tourist flow range F1 is output as the driving speed warning critical value; If F1∩F2≠∅, extract the maximum driving speed recorded in Qmin when the tourist flow range F2 is used as the driving speed warning critical value under this flow range; extract the maximum value Qmax and minimum value Q1min of the speed fluctuation index corresponding to all rental events in the tourist flow range F1, and mark and analyze the minimum driving speed V in the rental event corresponding to the maximum value Qmax. b And analyze the minimum value Q1min corresponding to the maximum driving speed V in the rental event c ; Step S44: When V b >V c When V c As the critical value of the driving speed warning corresponding to the tourist flow range F2; when V b ≤V c When V b As the critical value of driving speed warning corresponding to the tourist flow range F2.

4. The cloud computing-based intelligent scenic spot operation and management method according to claim 1, characterized in that: The step S5 includes the following: Monitor the real-time paths of real-time tourists after arriving at the effective scenic spot rental optimization point. When there is a real-time path that is the same as the historical record return path and the shared transportation equipment rental is started within the time interval, the scenic spot operation service end responds to the monitoring device and extracts the real-time tourist flow of the real-time return path recorded by the monitoring device and the real-time tourist flow of all paths formed by the effective scenic spot rental optimization point and the shared transportation equipment rental point; Match the real-time tourist flow on each route to the tourist flow range, output the speed warning threshold under the corresponding tourist flow range, and intelligently control the speed of shared transportation equipment; The intelligent control means that when tourists turn on shared transportation equipment, the system limits the maximum driving speed of the transportation equipment to the driving speed warning critical value.

5. A cloud computing-based intelligent scenic spot operation and management system, such as the cloud computing-based intelligent scenic spot operation and management method according to any one of claims 1 to 4, characterized in that: It includes the scenic area operation service end construction module, the scenic area rental optimization point analysis module to be analyzed, the effective scenic area rental optimization point analysis module, the early warning critical value analysis module and the intelligent control module; The scenic area operation service terminal construction module is used to build an intelligent and integrated scenic area operation service terminal; The to-be-analyzed scenic spot rental optimization point analysis module is used to extract tourist route data recorded in rental events and analyze the to-be-analyzed scenic spot rental optimization point corresponding to each rental event; The effective scenic spot rental optimization point analysis module is used to determine the effective scenic spot rental optimization point based on log data, The warning critical value analysis module is used to analyze the driving speed warning critical value under different passenger flow conditions corresponding to each type of return path; The intelligent control module is used to match tourists with the intelligent control of the speed of shared transportation equipment in corresponding rental event scenarios.

6. The intelligent scenic spot operation and management system based on cloud computing according to claim 5, characterized in that: The scenic spot rental optimization point analysis module to be analyzed includes a coordinate system establishment unit, a local space distance calculation unit, and a displacement difference calculation unit; The coordinate system establishment unit is used to extract the two-dimensional plan view of the scenic area stored in the scenic area operation server, and establish a two-dimensional rectangular coordinate system with the entrance of the scenic area as the coordinate origin; The local space distance calculation unit is used to calculate the local space distance of the rental event with the coordinate origin as the starting point and any coordinate point of the corresponding tour path in the path coordinate graph as the end point; The displacement difference calculation unit is used to extract the maximum value of the local spatial distance in the distance set and the spatial distance between the rental point and the coordinate origin, and calculate the displacement difference of the corresponding rental event; and when the displacement difference is greater than or equal to the threshold, the coordinate point corresponding to the maximum value of the local spatial distance in the tour path is the rental optimization point of the scenic spot to be analyzed.

7. The cloud computing-based intelligent scenic spot operation and management system according to claim 5, characterized in that: The effective scenic spot rental optimization point analysis module includes a target monitoring area determination unit, a target user determination unit, a demand response index calculation unit and an effective scenic spot rental optimization point determination unit; The target monitoring area determination unit is used to set the communication area within the preset error range corresponding to each scenic area rental optimization point to be analyzed as the target monitoring area. The target user determination unit is used to mark the user corresponding to the log data containing the shared transportation equipment rental keyword in both the engine search record and the browsing record as the target user; The demand response index calculation unit is used to calculate the demand response index of each target monitoring area in each monitoring period; The effective scenic spot rental optimization point determination unit is used to set a demand response index threshold, and extract the target monitoring area corresponding to the average demand response index greater than the demand response index threshold as the effective scenic spot rental optimization point.

8. The intelligent scenic spot operation and management system based on cloud computing according to claim 5, characterized in that: The warning critical value analysis module includes a vehicle speed fluctuation index calculation unit, a tourist flow range determination unit and a warning critical value output unit; The vehicle speed fluctuation index calculation unit is used to extract all driving data recorded on the same return path and calculate the vehicle speed fluctuation index corresponding to the same return path; The tourist flow range determination unit is used to sort the vehicle speed fluctuation indexes corresponding to all rental events on the same return path, select the maximum and minimum values ​​of the vehicle speed fluctuation index on the same return path, and use the tourist flow ranges corresponding to the maximum and minimum values ​​as the corresponding ranges; The warning critical value output unit is used to output the driving speed warning critical value based on the analysis of the tourist flow range determination unit.

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