Intelligent scenic area operation management system and method based on cloud computing

Abstract

The invention discloses an intelligent scenic region operation management system and method based on cloud computing, and relates to the technical field of intelligent scenic region operation, and the system comprises a scenic region operation server construction module, a to-be-analyzed scenic region lease optimization point analysis module, an effective scenic region lease optimization point analysis module, an early warning critical value analysis module, and an intelligent regulation and control module. The scenic spot operation server construction module is used for constructing an intelligent integrated scenic spot operation server; the to-be-analyzed scenic spot rental optimization point analysis module is used for extracting tourist route data recorded by the rental events and analyzing a to-be-analyzed scenic spot rental optimization point corresponding to each rental event; the effective scenic spot rental optimization point analysis module is used for determining effective scenic spot rental optimization points based on log data, and the early warning critical value analysis module is used for analyzing driving speed early warning critical values of each type of return paths corresponding to different personnel flows; and the intelligent regulation and control module is used for matching tourists to carry out intelligent regulation and control on the speed per hour of the shared traffic equipment in the corresponding leasing event scene.

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 background technology of intelligent scenic spot operation mainly relies on the development of modern information technology, especially the widespread application of technologies such as the Internet of Things, big data, artificial intelligence, virtual reality and augmented reality, and 5G communication. For example, the Internet of Things technology is the cornerstone of the construction of smart scenic spots; by deploying various sensors and smart devices in the scenic area, real-time monitoring of the environment, traffic flow, and traffic can be achieved. For example, the temperature sensor can monitor the ambient temperature and provide tourists with a comfortable tour experience; the crowd counter can count the number of tourists in real time to help the scenic spot control the flow of people and manage safety. In addition, the Internet of Things technology can also realize the remote control and management of equipment and improve the operational efficiency of the scenic spot; and the equipment placed in the scenic spot can also be managed and controlled through the Internet of Things, such as shared electric vehicles and other transportation equipment rented in the scenic spot. These equipment are often arranged at the entrance of the scenic spot, but for some tourists, they may be tired in the middle of the tour and only intend to choose to rent shared transportation equipment, so there will be a situation where there is no rental point in the scenic spot or the rental point is unreasonable. At the same time, when tourists go to the rental point and return to play, there may be a situation where the vehicle is driving too fast, because at this time, there is a high probability of repeated play for the return path, so it is also particularly important to monitor the speed of the vehicle. 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 purpose, the present invention provides the following technical solution: an intelligent scenic spot operation management method based on cloud computing, the management method comprising the following specific steps:

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

[0006] Step S2: extracting the rental events of shared transportation equipment stored in the scenic area operation service end, where the rental event refers to an event in which there is a time interval between the time when the tourist enters the scenic area and the time when the shared transportation equipment rental is started; the time interval refers to the time length added by the tourist due to the spatial distance; the time interval is greater than the preset time length threshold; extracting the tourist route data recorded in the rental event to analyze 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 communication area, extract the user end log data of the historical records, determine the effective scenic spot rental optimization point based on the log data, and generate a set of effective scenic spot rental optimization points;

[0008] Step S4: extracting the return path in the rental event corresponding to the effective scenic spot rental optimization point, obtaining the driving data of the shared traffic equipment recorded in 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, matching tourists with corresponding rental event scenarios to perform intelligent control of the speed of shared transportation equipment.

[0010] Further, the optimization points of scenic spot leasing to be analyzed corresponding to each leasing event include the following specific steps:

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

[0012] Step S22: Plot the tourist's tour path in each rental event in the coordinate system to generate a path coordinate graph, and 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 space 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 the 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 alternative 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 situation 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 the preset error range corresponding to each scenic spot rental optimization point to be analyzed is taken as the target monitoring area. The log data based on the user end of all tourists in the target monitoring area is extracted, and the user corresponding to the log data containing the shared transportation equipment rental keyword in both the engine search record and the browsing record is marked as the target user;

[0017] Step S32: The monitoring cycle is intercepted as a number of monitoring periods on average, 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, and the demand response index G of each target monitoring area in each monitoring period is calculated, 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, G0=(1 / k)∑G, 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 and optimize the needs from the perspective of tourists' actual communication data; because when there is a demand for leasing, tourists often use their mobile phones to obtain relevant information to determine their leasing intentions.

[0020] Further, 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, and the driving data includes driving speed and braking data; extract all driving data recorded on 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 tourists' braking. C1 represents the total number of braking times recorded on the corresponding return path in each rental event; Cmax represents the maximum number of braking times recorded in all rental events on the same return path; a1 and a2 represent corresponding reference coefficients;

[0022] The greater the speed fluctuation index, the greater the intensity and frequency of the tourists' braking on the return path; the greater the safety risk factor for other tourists on the road and themselves;

[0023] Step S42: sorting the vehicle speed fluctuation indexes Q corresponding to the same return path for all rental events, selecting the maximum value Qmax and the minimum value Qmin of the vehicle speed fluctuation index in the same return path; extracting 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 When the tourist flow rate of the return path of the monitored scenic spot belongs to the tourist flow rate range F2, the corresponding average driving speed recorded when the tourist flow rate belongs to the tourist flow rate range F1 is output as the driving speed warning critical value;

[0025] like Extract the maximum driving speed recorded in Qmin when the tourist flow range F2 is taken as the critical value of driving speed warning in this flow range; extract the maximum value Qmax and minimum value Q1min of the vehicle 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 may have already visited the attractions on the path, so the sightseeing speed will increase after using shared transportation equipment. Therefore, the safety analysis of the driving speed on this section is particularly important, which can effectively reduce the risk factor of tourists' sightseeing in this scenario.

[0028] Further, 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 turned on within the time interval, the scenic spot operation service end responds to the monitoring device, 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 composed of the effective scenic spot rental optimization point and the shared transportation equipment rental point;

[0030] Match the tourist flow range corresponding to the real-time tourist flow in each path, output the critical value of driving speed warning under the corresponding tourist flow range to 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 management system based on cloud computing, comprising 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 end construction module is used to build an intelligent and integrated scenic area operation service end;

[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 of the optimized points of scenic spot leasing 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 plane map of the scenic spot stored in the scenic spot operation server, and establish a two-dimensional rectangular coordinate system with the scenic spot entrance as the coordinate far point;

[0040] The local space distance calculation unit is used to calculate the local space distance of the rental event by taking 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] Further, 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 take the communication area within the preset error range corresponding to each scenic spot 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] Further, 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 the 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 indexes on the same return path, and use the tourist flow range corresponding to the maximum and minimum values ​​as the corresponding range;

[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 the tourist data collected by the scenic spot service end based on cloud computing, determines that there may be rental points where shared transportation equipment needs to be set up, and can analyze the optimization direction of the scenic spot operation management in the later stage based on big data with the help of cloud computing for the optimization points of rental in the scenic spot to be analyzed, thereby improving the feasibility of operation management; and based on the optimization points of rental in the scenic spot to be analyzed, the actual communication data of tourists is accurately analyzed to realize further analysis of 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 The present invention is a schematic diagram of the structure of an intelligent scenic spot operation and management system based on cloud computing. DETAILED DESCRIPTION

[0055] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. 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 creative work are within the scope of protection of the present invention.

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

[0057] Step S1: Building an intelligent integrated scenic spot operation service end. Users scan the QR code to log in to the service end to establish interactive connections 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 in the scenic spot, such as sharing a sightseeing bike in the scenic spot and renting a bike by scanning a QR code.

[0058] Step S2: extracting the rental events of shared transportation equipment stored in the scenic area operation service end, where the rental event refers to an event in which there is a time interval between the time when the tourist enters the scenic area and the time when the shared transportation equipment rental is started; the time interval refers to the time length added by the tourist due to the spatial distance; the time interval is greater than the preset time length threshold; extracting the tourist route data recorded in the rental event to analyze the rental optimization points of the scenic area to be analyzed corresponding to each rental event;

[0059] Because shared transportation equipment is generally placed at the entrance of a scenic spot, the interval between entering the scenic spot and when tourists start the shared transportation equipment will be much shorter than the above time interval; therefore, the maximum value of the interval time can be selected as the preset time threshold; the time of entering the scenic spot refers to the time when tourists enter the scenic spot by logging into the scenic spot operation server to verify 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 communication area, extract the user end log data of the historical records, determine the effective scenic spot rental optimization point based on the log data, and generate a set of effective scenic spot rental optimization points;

[0061] Step S4: extracting the return path in the rental event corresponding to the effective scenic spot rental optimization point, obtaining the driving data of the shared traffic equipment recorded in 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, matching tourists with corresponding rental event scenarios to perform intelligent control of the speed of shared transportation equipment.

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

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

[0065] Step S22: Plot the tourist's tour path in each rental event in the coordinate system to generate a path coordinate graph, and 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 space 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 the 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 alternative 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 situation 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 the preset error range corresponding to each scenic spot rental optimization point to be analyzed is taken as the target monitoring area. The log data based on the user end of all tourists in the target monitoring area is extracted, and the user corresponding to the log data containing the shared transportation equipment rental keyword in both the engine search record and the browsing record is marked as the target user;

[0070] Step S32: The monitoring cycle is intercepted as a number of monitoring periods on average, 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, and the demand response index G of each target monitoring area in each monitoring period is calculated, 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, G0=(1 / k)∑G, 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 and optimize the needs from the perspective of tourists' actual communication data; because when there is a demand for leasing, tourists often use their 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, and the driving data includes driving speed and braking data; extract all driving data recorded on 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 tourists' braking. C1 represents the total number of braking times recorded on the corresponding return path in each rental event; Cmax represents the maximum number of braking times recorded in all rental events on the same return path; a1 and a2 represent corresponding reference coefficients;

[0075] The greater the speed fluctuation index, the greater the intensity and frequency of the tourists' braking on the return path; the greater the safety risk factor for other tourists on the road and themselves;

[0076] Step S42: sorting the vehicle speed fluctuation indexes Q corresponding to the same return path for all rental events, selecting the maximum value Qmax and the minimum value Qmin of the vehicle speed fluctuation index in the same return path; extracting 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 When the tourist flow rate of the return path of the monitored scenic spot belongs to the tourist flow rate range F2, the corresponding average driving speed recorded when the tourist flow rate belongs to the tourist flow rate range F1 is output as the driving speed warning critical value;

[0078] When the two ranges do not intersect, it means that there is a clear difference in the speed fluctuation index under different passenger flows, so the party with a 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], which means that the speed fluctuation index and passenger flow data in the corresponding range are linearly related, and the two do not overlap;

[0079] like Extract the maximum driving speed recorded in Qmin when the tourist flow range F2 is taken as the critical value of driving speed warning in this flow range; extract the maximum value Qmax and minimum value Q1min of the vehicle 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 example: It shows that although there is a distinction between vehicle speed fluctuation indexes, there will still be a situation where the vehicle speed fluctuation index is small under a large range of passenger flow, and a situation where the vehicle speed fluctuation index is large under a small range of passenger flow. When the range of passenger flow is small, the driving speed in the rental event corresponding to the minimum value of the vehicle speed fluctuation index can be selected as the warning value, which ensures safety within the corresponding flow range and reduces the tediousness of data analysis for other situations; when the flow of passengers is large, 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, which can improve 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 may have already visited the attractions on the path, so the sightseeing speed will increase after using shared transportation equipment. Therefore, the safety analysis of the driving speed on this section is particularly important, which can effectively reduce the risk factor of tourists' sightseeing 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 turned on within the time interval, the scenic spot operation service end responds to the monitoring device, 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 composed of the effective scenic spot rental optimization point and the shared transportation equipment rental point;

[0086] Match the tourist flow range corresponding to the real-time tourist flow in each path, output the critical value of driving speed warning under the corresponding tourist flow range to 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 management system based on cloud computing, comprising 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 end construction module is used to build an intelligent and integrated scenic area operation service end;

[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 of the optimized points of scenic spot leasing 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 plane map of the scenic spot stored in the scenic spot operation server, and establish a two-dimensional rectangular coordinate system with the scenic spot entrance as the coordinate far point;

[0096] The local space distance calculation unit is used to calculate the local space distance of the rental event by taking 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 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;

[0099] The target monitoring area determination unit is used to take the communication area within the preset error range corresponding to each scenic spot 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 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;

[0104] The vehicle speed fluctuation index calculation unit is used to extract all the 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 indexes on the same return path, and use the tourist flow range corresponding to the maximum and minimum values ​​as the corresponding range;

[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 invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. An intelligent scenic spot operation and management method based on cloud computing, characterized by: The management method comprises the following specific steps: Step S1: Building an intelligent integrated scenic spot operation service end, where users scan a 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 a user end, a device end, and a service system end; Step S2: extracting the rental events of shared transportation equipment stored in the scenic area operation service end, wherein the rental event refers to an event in which there is a time interval between the time when the tourist enters the scenic area and the time when the shared transportation equipment rental is started; the time interval refers to the time length added by the tourist due to the spatial distance; the time interval is greater than a preset time length threshold; extracting the tourist route data recorded in the rental event to analyze the rental optimization points 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 communication area, extract the user end log data of the historical records, determine the effective scenic spot rental optimization point based on the log data, and generate a set of effective scenic spot rental optimization points; Step S4: extracting the return path in the rental event corresponding to the effective scenic spot rental optimization point, obtaining the driving data of the shared traffic equipment recorded in 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, matching tourists with corresponding rental event scenarios to perform intelligent control of the speed of shared transportation equipment.

2. According to the cloud computing-based intelligent scenic spot operation and management method of claim 1, it is characterized by: The analysis of the optimization points of scenic spot leasing to be analyzed corresponding to each leasing event includes the following specific steps: Step S21: The tourist route data refers to the tourist route generated on the user side based on the location record after the tourist logs in to the scenic spot operation server; extracting the two-dimensional plane map of the scenic spot stored in the scenic spot operation server, establishing a two-dimensional rectangular coordinate system with the scenic spot entrance as the coordinate far point, and marking the rental point of the shared transportation equipment in the coordinate system; The scenario analyzed in this application is that the rental point only exists at the entrance and is set close to the entrance of the scenic spot; Step S22: Plot the tourist's tour path in each rental event in the coordinate system to generate a path coordinate graph, and 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 space 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. According to the cloud computing-based intelligent scenic spot operation and management method of claim 1, it is characterized by: The step S3 comprises the following specific steps: Step S31: the log data includes engine search records and browsing records, and the communication area within the preset error range corresponding to each scenic spot rental optimization point to be analyzed is taken as the target monitoring area, and the log data based on the user end of all tourists in the target monitoring area is extracted, and the user corresponding to the log data containing the shared transportation equipment rental keyword in both the engine search record and the browsing record is marked as the target user; Step S32: The monitoring cycle is intercepted as a number of monitoring periods on average, 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, and the demand response index G of each target monitoring area in each monitoring period is calculated, 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, G0=(1 / k)∑G, k represents the total number of divided monitoring periods; 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.

4. The intelligent scenic spot operation and management method based on cloud computing according to claim 1 is characterized by: The step S4 comprises 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 on 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), wherein V1 represents the instantaneous speed before braking, V2 represents the instantaneous speed after braking, C1 represents the total number of braking times recorded on the corresponding return path in each rental event; Cmax represents the maximum number of braking times recorded in all rental events on the same return path; a1 and a2 represent corresponding reference coefficients; Step S42: sorting the vehicle speed fluctuation indexes Q corresponding to the same return path for all rental events, selecting the maximum value Qmax and the minimum value Qmin of the vehicle speed fluctuation index in the same return path; extracting 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 When the tourist flow rate of the return path of the monitored scenic spot belongs to the tourist flow rate range F2, the corresponding average driving speed recorded when the tourist flow rate belongs to the tourist flow rate range F1 is output as the driving speed warning critical value; like Extract the maximum driving speed recorded in Qmin when the tourist flow range F2 is taken as the critical value of driving speed warning in this flow range; extract the maximum value Qmax and minimum value Q1min of the vehicle 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.

5. The intelligent scenic spot operation and management method based on cloud computing according to claim 1 is characterized by: The step S5 comprises 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, 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 composed of the effective scenic spot rental optimization point and the shared transportation equipment rental point; Match the tourist flow range corresponding to the real-time tourist flow in each path, output the critical value of driving speed warning under the corresponding tourist flow range to intelligently control the speed of shared transportation equipment; The intelligent control means that when tourists turn on the shared transportation equipment, the system limits the maximum driving speed of the transportation equipment to the driving speed warning critical value.

6. An intelligent scenic spot operation and management system based on cloud computing, such as the intelligent scenic spot operation and management method based on cloud computing according to any one of claims 1 to 5, characterized in that: It includes 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, a warning critical value analysis module and an intelligent control module; The scenic area operation service end construction module is used to construct an intelligent and integrated scenic area operation service end; The to-be-analyzed scenic spot rental optimization point analysis module is used to extract the tourist route data recorded in the rental event to 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 personnel flow corresponding to each type of return path; The intelligent control module is used to match tourists to intelligently control the speed of shared transportation equipment in corresponding rental event scenarios.

7. The intelligent scenic spot operation and management system based on cloud computing according to claim 6 is characterized by: 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 spot stored in the scenic spot operation service end, and establish a two-dimensional rectangular coordinate system with the scenic spot entrance as the coordinate far point; 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 diagram 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.

8. The intelligent scenic spot operation and management system based on cloud computing according to claim 6 is characterized by: 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 take the communication area within the preset error range corresponding to each scenic spot 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 whose engine search records and browsing records both contain the shared transportation equipment rental keyword 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.

9. The intelligent scenic spot operation and management system based on cloud computing according to claim 1 is characterized by: 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 the 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 indexes on the same return path, and use the tourist flow range corresponding to the maximum and minimum values ​​as the corresponding range; 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.

Images