Intelligent scheduling method, device and equipment for vehicles and ships in scenic spot

Abstract

The invention discloses an intelligent scheduling method, device and equipment for vehicles and ships in a scenic spot, and the method comprises the steps: obtaining historical passenger flow data, carrying out the statistical analysis of the historical passenger flow data, and generating historical entrance passenger flow information; acquiring ticket selling data of the day, and predicting according to the ticket selling data of the day and the historical entering passenger flow information of the same period through a preset random forest model to obtain future entering passenger flow information of every N minutes of the next day; generating a departure timetable of the next day according to the future parking passenger flow information and preset scenic region operation parameters; calculating a passenger flow wave crest period according to the future parking passenger flow information, and determining the minimum number of allocated vehicles and the number of standby vehicles according to the passenger flow wave crest period; and generating a target passenger-vehicle shift timetable according to the minimum vehicle distribution number and the standby vehicle number in combination with the departure timetable of the second day. The vehicle and ship operation and use efficiency and the shift arrangement precision can be improved, the touring experience of tourists can be effectively improved, and the overall management level of scenic spots is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent scheduling of vehicles and ships in scenic spots, and in particular to an intelligent scheduling method, device and equipment for vehicles and ships in scenic spots. Background Art

[0002] With the booming tourism industry, scenic area sightseeing vehicles and cruise ships are key means of transportation connecting tourists to various scenic spots in scenic areas. Their operating efficiency directly affects tourists' sightseeing experience and the overall management level of scenic areas. However, traditional scenic spots still face many challenges in scheduling vehicles and ships. First, the scheduling process is highly dependent on manual experience and lacks scientific data support, which makes it difficult for scheduling results to accurately match actual passenger flow demand. Secondly, the passenger flow of scenic spots is affected by various factors such as seasons, weather, and holidays, and fluctuates greatly. Traditional scheduling methods are difficult to flexibly respond to these changes, often resulting in problems of excess or insufficient resources. Finally, due to the lack of an effective forecasting and adjustment mechanism, scenic spots often experience long waiting times for tourists and chaotic vehicle scheduling during peak periods, while vehicles may be idle and resources may be wasted during off-peak periods. Summary of the invention

[0003] In view of this, the purpose of the present invention is to propose an intelligent scheduling method, device and equipment for scenic area vehicles and ships, aiming to solve the problems of low scheduling accuracy and low utilization efficiency of vehicles and ships in scenic areas in the prior art.

[0004] To achieve the above object, the present invention provides an intelligent scheduling method for scenic area vehicles and ships, the method comprising:

[0005] Obtain historical passenger flow data, perform statistical analysis on the historical passenger flow data, and generate historical park entrance passenger flow information;

[0006] Obtain the ticket sales data for the day, and use a preset random forest model to make a prediction based on the ticket sales data for the day and the historical park passenger flow information for the same period to obtain the future park passenger flow information every N minutes on the next day;

[0007] Generate a departure schedule for the next day based on the future park passenger flow information and preset scenic spot operation parameters;

[0008] Calculate the peak period of passenger flow according to the future inbound passenger flow information, and determine the minimum number of allocated vehicles and the number of spare vehicles according to the peak period of passenger flow;

[0009] A target passenger-vehicle schedule is generated according to the minimum number of allocated vehicles and the number of spare vehicles in combination with the departure schedule of the second day.

[0010] Preferably, the statistical analysis of the historical passenger flow data to generate historical park passenger flow information includes:

[0011] The historical passenger flow data is statistically analyzed according to holidays and weather to generate the historical passenger flow information including the time of entry, the number of passengers, the weather conditions of the day, and whether it is a holiday.

[0012] Preferably, the scenic spot operation parameters include the opening and closing times of the scenic spot, the departure interval, the proportion of spare vehicles, and the full load rate of the shifts; the future inbound passenger flow information includes the number of passengers corresponding to different times.

[0013] Preferably, generating a departure schedule for the next day according to the future park passenger flow information and preset scenic spot operation parameters includes:

[0014] S13-1, obtaining the first passenger flow number C at the first moment T before the park opening time and the stranded number V of the previous shift, taking the first passenger flow number C and the stranded number V as the current passenger flow number VC, and when it is determined that the current passenger flow number VC is greater than the average vehicle load X, proceeding to step S13-3, otherwise, proceeding to step S13-2;

[0015] S13-2, when it is determined that the number of stranded persons V is less than 0, proceed to step S13-4, otherwise, generate a departure time information, and proceed to step S13-4, wherein the departure time information includes the departure time and the estimated number of passengers;

[0016] S13-3, calculating the required number of vehicles at the first moment according to the current passenger flow number VC and the average vehicle load X, generating a departure plan corresponding to the first moment according to the departure interval and the required number of vehicles, and updating the stranded number V according to V=VC-X*(VC / X), and proceeding to step S13-4;

[0017] S13-4, updating the first time T according to T=preset time+T, and when it is determined that the updated first time T is less than the park closing time, proceeding to step S13-1, otherwise ending.

[0018] Preferably, the calculating of the passenger flow peak period according to the future park passenger flow information includes:

[0019] The average passenger flow is calculated according to the number of departure intervals and the number of passengers corresponding to the departure intervals, and the passenger flow peak period is determined according to the average passenger flow; wherein,

[0020] Average passenger flow

[0021] In the formula, n represents the number of departure intervals, x i Represents the number of passengers in the i-th departure interval.

[0022] Preferably, the determining of the minimum number of allocated vehicles and the number of spare vehicles according to the passenger flow peak period includes:

[0023] The minimum number of vehicles to be allocated is calculated based on the turnover time during the passenger flow peak period / the departure interval during the passenger flow peak period;

[0024] The number of spare vehicles is calculated based on the minimum number of vehicles*the ratio of spare vehicles; wherein,

[0025] The turnover time during the peak passenger flow period = the time it takes for the vehicle to travel one circle + the average waiting time for tourists at the scenic spot + the rest time;

[0026] The departure interval during the peak passenger flow period = peak passenger flow duration / number of trips = peak passenger flow duration / (peak passenger flow number / average vehicle load);

[0027] The passenger flow peak duration=the end time of the passenger flow peak period-the start time of the passenger flow peak period.

[0028] Preferably, when it is determined that the turnover time of the passenger flow peak period is greater than the passenger flow peak duration, the turnover time of the passenger flow peak period is set equal to the passenger flow peak duration.

[0029] To achieve the above object, the present invention also provides an intelligent scheduling device for scenic area vehicles and ships, the device comprising:

[0030] A statistical unit, used to obtain historical passenger flow data, perform statistical analysis on the historical passenger flow data, and generate historical park passenger flow information;

[0031] A prediction unit is used to obtain the ticket sales data of the day, and to make a prediction based on the ticket sales data of the day and the historical park passenger flow information of the same period through a preset random forest model to obtain the future park passenger flow information every N minutes of the next day;

[0032] A generating unit, configured to generate a departure timetable for the next day according to the future park passenger flow information and preset scenic spot operation parameters;

[0033] A calculation unit, wherein the user calculates the peak period of passenger flow according to the future park passenger flow information, and determines the minimum number of allocated vehicles and the number of spare vehicles according to the peak period of passenger flow;

[0034] The scheduling unit is used to generate a target passenger and vehicle shift schedule according to the minimum number of allocated vehicles and the number of spare vehicles in combination with the departure schedule of the second day.

[0035] In order to achieve the above-mentioned objectives, the present invention also proposes an intelligent scheduling device for scenic area vehicles and ships, comprising a processor, a memory, and a computer program stored in the memory, wherein the computer program is executed by the processor to implement the steps of an intelligent scheduling method for scenic area vehicles and ships as described in the above-mentioned embodiment.

[0036] In order to achieve the above objectives, the present invention also proposes a computer-readable storage medium, on which a computer program is stored. The computer program is executed by a processor to implement the steps of a method for intelligent scheduling of scenic area vehicles and ships as described in the above embodiment.

[0037] In order to achieve the above objectives, the present invention also proposes a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of a method for intelligent scheduling of vehicles and ships in a scenic area as described in the above embodiment.

[0038] Beneficial effects:

[0039] The above scheme obtains historical passenger flow data and conducts statistical analysis, combines the ticket sales data of the day and uses the random forest model to predict future passenger flow into the park, so that the scheduling plan can more accurately match the actual passenger flow demand, reduce the waste of resources or long waiting time of tourists caused by improper scheduling, and can not only improve the efficiency of vehicle and ship operation and the accuracy of vehicle and ship scheduling, but also effectively improve the tourists' sightseeing experience. Through data analysis and prediction, scenic area managers can formulate operation strategies more scientifically, improve management efficiency and service quality, and improve the overall management level of the scenic area, laying a solid foundation for the sustainable development of the scenic area.

[0040] The above scheme dynamically adjusts the scheduling plan according to real-time factors such as weather and holidays to ensure efficient operation even when passenger flow fluctuates greatly. At the same time, the flexibility and adjustability of scheduling are further enhanced through preset scenic area operation parameters such as departure intervals and spare vehicle ratios.

[0041] The above scheme achieves the optimal allocation of vehicle and ship resources by calculating the peak passenger flow period and determining the minimum number of vehicles and spare vehicles accordingly. During the peak passenger flow period, it can ensure that there are enough vehicles to meet the needs of tourists; during the off-peak period, it can reduce idle vehicles, reduce unnecessary personnel, vehicles and ships, and reduce operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0043] Figure 1 A schematic flow chart of an intelligent scheduling method for scenic area vehicles and ships provided in one embodiment of the present invention.

[0044] Figure 2 A schematic diagram of predicted future park visitor flow information provided by an embodiment of the present invention.

[0045] Figure 3 A schematic diagram of a process for generating a departure timetable provided in an embodiment of the present invention.

[0046] Figure 4 The present invention provides a schematic diagram of a departure time table according to an embodiment of the present invention.

[0047] Figure 5 A schematic structural diagram of an intelligent scheduling device for scenic area vehicles and ships provided in one embodiment of the present invention.

[0048] The realization of the purpose, functional features and advantages of the invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0049] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, rather than all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the invention claimed for protection, but merely represents the selected embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0050] In the description of the present invention, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include one or more of the features.

[0051] The present invention is described in detail below with reference to the embodiments.

[0052] Reference Figure 1 The figure is a flow chart of an intelligent scheduling method for scenic area vehicles and ships provided by one embodiment of the present invention.

[0053] In this embodiment, the method includes:

[0054] S11, obtaining historical passenger flow data, performing statistical analysis on the historical passenger flow data, and generating historical park entrance passenger flow information.

[0055] Furthermore, in step S11, the historical passenger flow data is statistically analyzed to generate historical park passenger flow information, including:

[0056] The historical passenger flow data is statistically analyzed according to holidays and weather to generate the historical passenger flow information including the time of entry, the number of passengers, the weather conditions of the day, and whether it is a holiday.

[0057] In this embodiment, historical passenger flow data including the time of entry and the number of passengers are collected, cleaned and statistically analyzed to generate historical passenger flow information every N minutes. The historical passenger flow information includes the time of entry, the number of passengers, the weather conditions of the day, and whether it is a holiday.

[0058] S12, obtaining the ticket sales data for the day, and using a preset random forest model to make a prediction based on the ticket sales data for the day and the historical park passenger flow information for the same period, to obtain the future park passenger flow information every N minutes on the next day.

[0059] In this embodiment, the ticket sales data of the day (such as online ticket sales data and group ticket data before 20:00 on the day) is obtained, and the random forest model is used to make predictions based on the ticket sales data of the day and the historical park passenger flow information of the same period. For example, predictions are made in a dimension of 15 minutes to obtain the future park passenger flow information (including the number of tourists entering the park and the time of entry, etc.) every N minutes (such as 15 minutes) of the next day. The time for tourists to enter the park generally increases gradually with time, and then gradually decreases after reaching the peak of entry, which is in line with the peak distribution. Figure 2 The diagram shows the predicted future park passenger flow information.

[0060] S13, generating a departure schedule for the next day according to the future park passenger flow information and preset scenic spot operation parameters.

[0061] Among them, the scenic spot operation parameters include the opening and closing times of the scenic spot, departure intervals, the proportion of spare vehicles, and the full load rate of the shifts; the future inbound passenger flow information includes the number of passengers corresponding to different times.

[0062] Furthermore, in step S13, the generation of a departure schedule for the next day based on the future park passenger flow information and preset scenic spot operation parameters includes:

[0063] S13-1, obtaining the first passenger flow number C at the first moment T before the park opening time and the stranded number V of the previous shift, taking the first passenger flow number C and the stranded number V as the current passenger flow number VC, and when it is determined that the current passenger flow number VC is greater than the average vehicle load X, proceeding to step S13-3, otherwise, proceeding to step S13-2;

[0064] S13-2, when it is determined that the number of stranded persons V is less than 0, proceed to step S13-4, otherwise, generate a departure time information, and proceed to step S13-4, wherein the departure time information includes the departure time and the estimated number of passengers;

[0065] S13-3, calculating the required number of vehicles at the first moment according to the current passenger flow number VC and the average vehicle load X, generating a departure plan corresponding to the first moment according to the departure interval and the required number of vehicles, and updating the stranded number V according to V=VC-X*(VC / X), and proceeding to step S13-4;

[0066] S13-4, updating the first time T according to T=preset time+T, and when it is determined that the updated first time T is less than the park closing time, proceeding to step S13-1, otherwise ending.

[0067] In this embodiment, the future inbound passenger flow information including the number of tourists entering the park and the time of entry obtained from the above prediction is combined with the preset scenic area operation parameters to generate the departure timetable for sightseeing buses and / or cruise ships for the next day, wherein the preset scenic area operation parameters include the opening and closing time of the scenic area, the proportion of spare vehicles, the departure interval (such as the minimum departure interval of 3 minutes and the maximum departure interval of 15 minutes), the full load rate of the shift, the driver's shift rules, etc. The full load rate of the shift means that the vehicle corresponding to the shift reaches the predetermined full load rate, and the full load rate = the number of people on the vehicle / the number of people allowed to be carried by the vehicle, and the number of people allowed to be carried by the vehicle refers to the number of people allowed to be carried by a specific vehicle; the full load rate of the shift is a dynamic parameter and can be set according to previous driving data, such as the full load rate reaching 80% before the departure shift is allowed to be generated; the driver's shift rules: the first shift of each day is the most important shift, and in order to ensure fairness to the drivers at work, the drivers take turns to work the first shift, such as adopting a 1-day 1-round-robin mode. For a flowchart of the specific generated departure timetable, see Figure 3 As shown. Assuming the average vehicle load is X, the average vehicle load is the average load of all operating vehicles, the number of departures * average vehicle load X>= the predicted number of passengers; the specific steps include:

[0068] Step 1: Get the predicted passenger flow C at the opening time T+15 minutes, add the number of passengers V stranded in the previous shift, and get the total current passenger flow VC=V+C within T+15 minutes; if VC is greater than the average vehicle load X, proceed to step 3, otherwise proceed to step 2;

[0069] Step 2: Determine whether the number of people V stranded in the previous shift is greater than 0; if V is less than 0, proceed to step 4; if V is greater than 0, generate a departure time information according to the maximum departure interval (e.g., if the current distance to the park opening is T minutes, then generate a departure time of T+15 minutes), the departure time information includes the planned departure time and the expected number of passengers, proceed to step 4;

[0070] Step 3: According to the total current passenger flow number VC / vehicle average load X within T+15 minutes, the total number of vehicles required Q is obtained, and the departure interval within 15 minutes = 15 minutes / Q, so as to generate the departure plan within 15 minutes; update the number of stranded people in 15 minutes V = VC-X*(VC / X), and go to step 4;

[0071] Step 4: Update T=T+15. If the updated T is greater than the closing time of the scenic spot, the process ends; otherwise, repeat step 1.

[0072] Finally, all the generated departure time information for each bus is summarized to obtain the departure timetable for the next day. Figure 4 The departure times shown are intended to indicate departure times.

[0073] S14, calculating the peak period of passenger flow according to the future park-entering passenger flow information, and determining the minimum number of allocated vehicles and the number of spare vehicles according to the peak period of passenger flow.

[0074] Furthermore, in step S14, the calculation of the passenger flow peak period according to the future park passenger flow information includes:

[0075] The average passenger flow is calculated according to the number of departure intervals predicted by S12 and the number of passengers corresponding to the departure intervals, and the passenger flow peak period is determined according to the average passenger flow; wherein,

[0076] Average passenger flow

[0077] Where n represents the number of departure intervals (which is determined based on the number of entry trips predicted every N minutes in step S12), x i Represents the number of passengers in the i-th departure interval.

[0078] Furthermore, in step S14, determining the minimum number of allocated vehicles and the number of spare vehicles according to the passenger flow peak period includes:

[0079] The minimum number of vehicles to be allocated is calculated based on the turnover time during the passenger flow peak period / the departure interval during the passenger flow peak period;

[0080] The number of spare vehicles is calculated based on the minimum number of vehicles*the ratio of spare vehicles; wherein,

[0081] The turnover time during the peak passenger flow period = the time it takes for the vehicle to travel one circle + the average waiting time for tourists at the scenic spot + the rest time;

[0082] The departure interval during the peak passenger flow period = peak passenger flow duration / number of trips = peak passenger flow duration / (peak passenger flow number / average vehicle load);

[0083] The passenger flow peak duration=the end time of the passenger flow peak period-the start time of the passenger flow peak period.

[0084] Furthermore, when it is determined that the turnover time during the passenger flow peak period is greater than the passenger flow peak duration, the turnover time during the passenger flow peak period is set equal to the passenger flow peak duration.

[0085] S15, generating a target passenger-vehicle schedule according to the minimum number of allocated vehicles and the number of spare vehicles in combination with the departure schedule of the second day.

[0086] In this embodiment, the visitor entry data is distributed in bands according to time within a day, that is, the visitor entry time of the scenic spot conforms to the band distribution. This embodiment uses the passenger flow data at intervals of 15 minutes to calculate the passenger flow peak period. First, the average passenger flow in each time interval is calculated according to the following formula:

[0087] Average passenger flow (μ):

[0088] In the formula, n represents the number of departure intervals, x i Represents the number of passengers in the i-th departure interval.

[0089] Secondly, according to Figure 2 As shown in the passenger flow distribution diagram (the horizontal axis in the diagram represents time), the ordered clustering algorithm is first used to preliminarily divide the passenger flow number in the predicted future park passenger flow information into passenger flow peaks and troughs, and the starting point (S) and ending point (E) of the passenger flow peak are obtained; secondly, the starting point (S) and ending point (E) of the passenger flow peak are progressively optimized according to the passenger flow peak and the average passenger flow (μ); assuming that there are N passenger flow points in 15 minutes, the optimization logic is as follows:

[0090] 1) First, find the starting point (S) of the optimized passenger flow peak:

[0091] A. Set x = starting point of passenger flow peak (S) - 1, and go to step B;

[0092] B. If x<0, the starting point of the passenger flow peak (S) = x+1, and the search ends;

[0093] If x>=0, go to step C;

[0094] C. If the number of passengers at point x is greater than μ, then this point is the passenger flow peak point, set x = x-1, and re-

[0095] Repeat step B; if the number of passengers at point x is less than μ, proceed to step D;

[0096] D. If (x-1)<0, the starting point of the passenger flow peak (S)=x, and the search ends;

[0097] If the number of passengers at point x-1 is less than μ, the starting point of the passenger flow peak (S) = x, and the search ends;

[0098] If the number of passengers at point x-1 is greater than μ, set x=x-1 and repeat step B;

[0099] 2) Next, find the optimized end point of the passenger flow peak (E):

[0100] A. Set x = the end point of the passenger flow peak (E) + 1, and go to step B;

[0101] B. If x>N, then the end point of the passenger flow peak (E) = x-1, and the search ends;

[0102] If x<=N, go to step C;

[0103] C. If the number of passengers at point x is greater than μ, then this point is the passenger flow peak point, set x = x + 1, and re-

[0104] Repeat step B; if the number of passengers at point x is less than μ, proceed to step D;

[0105] D. If (x+1)>N, the end point of the passenger flow peak (E)=x, and the search ends;

[0106] If the number of passengers at point x+1 is less than μ, the end point of the passenger flow peak (E) = x, and the search ends;

[0107] If the number of passengers at point x+1 is greater than μ, set x=x+1 and repeat step B;

[0108] According to the above method, the starting point and end point of the passenger flow peak are found, and the passenger flow in this interval is defined as the passenger flow peak number, and the time period within the passenger flow peak is defined as the passenger flow peak time period.

[0109] Furthermore, the minimum number of allocated vehicles is obtained according to the following formula, i.e., the minimum number of allocated vehicles = G / S, where G represents the turnover duration during the peak passenger flow period, S represents the departure interval during the peak passenger flow period, G = the duration of a vehicle's one-round trip + the average passenger waiting duration at scenic spots + the rest duration, S = the peak passenger flow duration / the number of trips = the peak passenger flow duration / (the peak passenger flow number / the average vehicle load), and the peak passenger flow duration = the end time of the peak passenger flow period - the start time of the peak passenger flow period; if the peak passenger flow duration < G, then T = the peak passenger flow duration.

[0110] After obtaining the minimum number of allocated vehicles, the number of standby vehicles can be obtained according to the formula: the number of standby vehicles = the minimum number of allocated vehicles * the standby vehicle ratio. According to the requirements of the minimum number of allocated vehicles calculated above, the drivers for the next day's work are selected, and shifts and vehicles are correspondingly assigned to the drivers, and finally a target vehicle and driver shift schedule including drivers, vehicles, and departure timetables is generated.

[0111] Refer to Figure 5 The following is a schematic structural diagram of an intelligent scheduling device for scenic area vehicles and boats provided by an embodiment of the present invention.

[0112] In this embodiment, the device 20 includes:

[0113] A statistical unit 21, configured to obtain historical passenger flow data, perform statistical analysis on the historical passenger flow data, and generate historical park entry passenger flow information;

[0114] A prediction unit 22, configured to obtain the ticket sales data of the current day, and predict through a preset random forest model according to the current day's ticket sales data and the historical park entry passenger flow information in the same period to obtain the future park entry passenger flow information every N minutes for the next day;

[0115] A generation unit 23, configured to generate a departure timetable for the next day according to the future park entry passenger flow information and preset scenic area operation parameters;

[0116] A calculation unit 24, configured to calculate the peak passenger flow period according to the future park entry passenger flow information, and determine the minimum number of allocated vehicles and the number of standby vehicles according to the peak passenger flow period;

[0117] A scheduling unit 25, configured to generate a target vehicle and driver shift schedule according to the minimum number of allocated vehicles and the number of standby vehicles and in combination with the departure timetable for the next day.

[0118] Each unit module of the device 20 can respectively execute the corresponding steps in the above method embodiments, so the unit modules will not be elaborated here. For details, please refer to the descriptions of the above corresponding steps.

[0119] The embodiment of the present invention also provides an intelligent scheduling device for scenic area vehicles and ships, the device includes the intelligent scheduling device for scenic area vehicles and ships as described above, wherein the intelligent scheduling device for scenic area vehicles and ships can adopt Figure 5 The structure of the embodiment can be executed accordingly. Figure 1 The technical solution of the method embodiment shown has similar implementation principles and technical effects. For details, please refer to the relevant records in the above embodiments, which will not be repeated here.

[0120] The device includes: a mobile phone, a digital camera, a tablet computer, or other devices with a camera function, or a device with an image processing function, or a device with an image display function. The device may include components such as a memory, a processor, an input unit, a display unit, and a power supply.

[0121] Among them, the memory can be used to store software programs and modules, and the processor executes various functional applications and data processing by running the software programs and modules stored in the memory. The memory may mainly include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function (such as an image playback function, etc.), etc.; the data storage area may store data created according to the use of the device, etc. In addition, the memory may include a high-speed random access memory, and may also include a non-volatile memory, such as at least one disk storage device, a flash memory device, or other volatile solid-state storage devices. Accordingly, the memory may also include a memory controller to provide the processor and the input unit with access to the memory.

[0122] The input unit can be used to receive input digital or character or image information, and generate keyboard, mouse, joystick, optical or trackball signal input related to user settings and function control. Specifically, the input unit of this embodiment includes not only a camera, but also a touch-sensitive surface (such as a touch display) and other input devices.

[0123] The display unit can be used to display information input by the user or information provided to the user and various graphical user interfaces of the device, which can be composed of graphics, text, icons, videos and any combination thereof. The display unit may include a display panel, and optionally, the display panel may be configured in the form of LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), etc. Further, the touch-sensitive surface may cover the display panel, and when the touch-sensitive surface detects a touch operation on or near it, it is transmitted to the processor to determine the type of touch event, and then the processor provides a corresponding visual output on the display panel according to the type of touch event.

[0124] The embodiment of the present invention further provides a computer-readable storage medium, which may be a computer-readable storage medium included in the memory in the above embodiment; or a computer-readable storage medium that exists independently and is not installed in a device. The computer-readable storage medium stores at least one instruction, which is loaded and executed by a processor to implement Figure 1 The computer readable storage medium may be a read-only memory, a disk or an optical disk, etc.

[0125] The embodiment of the present invention also provides a computer program product, including a computer program / instruction, which is loaded and executed by a processor to implement Figure 1 An intelligent scheduling method for scenic area vehicles and ships is shown.

[0126] It should be noted that each embodiment in this specification is described in a progressive manner, and each embodiment focuses on the differences from other embodiments, and the same or similar parts between the embodiments can be referred to each other. For the device embodiment, equipment embodiment and storage medium embodiment, since they are basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0127] Furthermore, in this document, the terms "comprises," "comprising," or any other variation thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or device that includes a series of elements includes not only those elements, but also other elements not explicitly listed, or elements inherent to such process, method, article, or device. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of additional identical elements in the process, method, article, or device that includes the element.

[0128] The above description shows and describes the preferred embodiments of the present invention. It should be understood that the present invention is not limited to the form disclosed herein, and should not be regarded as excluding other embodiments, but can be used in various other combinations, modifications and environments, and can be modified within the scope of the invention, through the above teachings or the technology or knowledge of the relevant field. The changes and modifications made by those skilled in the art shall not depart from the spirit and scope of the present invention, and shall be within the scope of protection of the claims attached to the present invention.

Claims

1. An intelligent scheduling method for scenic area vehicles and ships, characterized in that: The method comprises: Obtain historical passenger flow data, perform statistical analysis on the historical passenger flow data, and generate historical park entrance passenger flow information; Obtain the ticket sales data for the day, and use a preset random forest model to make a prediction based on the ticket sales data for the day and the historical park passenger flow information for the same period to obtain the future park passenger flow information every N minutes on the next day; Generate a departure schedule for the next day based on the future park passenger flow information and preset scenic spot operation parameters; Calculate the peak period of passenger flow according to the future inbound passenger flow information, and determine the minimum number of allocated vehicles and the number of spare vehicles according to the peak period of passenger flow; A target passenger-vehicle schedule is generated according to the minimum number of allocated vehicles and the number of spare vehicles in combination with the departure schedule of the second day.

2. The intelligent scheduling method for scenic area vehicles and ships according to claim 1 is characterized in that: The statistical analysis of the historical passenger flow data to generate historical park passenger flow information includes: The historical passenger flow data is statistically analyzed according to holidays and weather to generate the historical passenger flow information including the time of entry, the number of passengers, the weather conditions of the day, and whether it is a holiday.

3. The intelligent scheduling method for scenic area vehicles and ships according to claim 1 is characterized in that: The scenic spot operation parameters include the opening and closing times of the scenic spot, the departure interval, the proportion of spare vehicles, and the full load rate of the shifts; the future inbound passenger flow information includes the number of passengers corresponding to different times.

4. The intelligent scheduling method for scenic area vehicles and ships according to claim 3 is characterized in that: The generating of the next day's departure schedule according to the future park passenger flow information and preset scenic spot operation parameters includes: S13-1, obtaining the first passenger flow number C at the first moment T before the park opening time and the stranded number V of the previous shift, taking the first passenger flow number C and the stranded number V as the current passenger flow number VC, and when it is determined that the current passenger flow number VC is greater than the average vehicle load X, proceeding to step S13-3, otherwise, proceeding to step S13-2; S13-2, when it is determined that the number of stranded persons V is less than 0, proceed to step S13-4, otherwise, generate a departure time information, and proceed to step S13-4, wherein the departure time information includes the departure time and the estimated number of passengers; S13-3, calculating the required number of vehicles at the first moment according to the current passenger flow number VC and the average vehicle load X, generating a departure plan corresponding to the first moment according to the departure interval and the required number of vehicles, and updating the stranded number V according to V=VC-X*(VC / X), and proceeding to step S13-4; S13-4, updating the first time T according to T=preset time+T, and when it is determined that the updated first time T is less than the park closing time, proceeding to step S13-1, otherwise ending.

5. The intelligent scheduling method for scenic area vehicles and ships according to claim 1 is characterized in that: The calculating of the passenger flow peak period according to the future park passenger flow information includes: The average passenger flow is calculated according to the number of departure intervals and the number of passengers corresponding to the departure intervals, and the passenger flow peak period is determined according to the average passenger flow; wherein, Average passenger flow In the formula, n represents the number of departure intervals, x i Represents the number of passengers in the i-th departure interval.

6. The intelligent scheduling method for scenic area vehicles and ships according to claim 5 is characterized in that: Determining the minimum number of allocated vehicles and the number of spare vehicles according to the passenger flow peak period includes: The minimum number of vehicles to be allocated is calculated based on the turnover time during the passenger flow peak period / the departure interval during the passenger flow peak period; The number of spare vehicles is calculated based on the minimum number of vehicles*the ratio of spare vehicles; wherein, The turnover time during the peak passenger flow period = the time it takes for the vehicle to travel one circle + the average waiting time for tourists at the scenic spot + the rest time; The departure interval during the peak passenger flow period = peak passenger flow duration / number of trips = peak passenger flow duration / (peak passenger flow number / average vehicle load); The passenger flow peak duration=the end time of the passenger flow peak period-the start time of the passenger flow peak period.

7. The intelligent scheduling method for scenic area vehicles and ships according to claim 6 is characterized in that: When it is determined that the turnover time during the passenger flow peak period is greater than the passenger flow peak duration, the turnover time during the passenger flow peak period is set equal to the passenger flow peak duration.

8. An intelligent scheduling device for scenic area vehicles and ships, characterized in that: The device comprises: A statistical unit, used to obtain historical passenger flow data, perform statistical analysis on the historical passenger flow data, and generate historical park passenger flow information; A prediction unit is used to obtain the ticket sales data of the day, and to make a prediction based on the ticket sales data of the day and the historical park passenger flow information of the same period through a preset random forest model to obtain the future park passenger flow information every N minutes of the next day; A generating unit, configured to generate a departure timetable for the next day according to the future park passenger flow information and preset scenic spot operation parameters; A calculation unit, wherein the user calculates the peak period of passenger flow according to the future park passenger flow information, and determines the minimum number of allocated vehicles and the number of spare vehicles according to the peak period of passenger flow; The scheduling unit is used to generate a target passenger and vehicle shift schedule according to the minimum number of allocated vehicles and the number of spare vehicles in combination with the departure schedule of the second day.

9. An intelligent scheduling device for scenic area vehicles and ships, characterized in that: The method comprises a processor, a memory and a computer program stored in the memory, wherein the computer program is executed by the processor to implement the steps of the intelligent scheduling method for vehicles and ships in a scenic area as described in any one of claims 1 to 7.

10. A computer program product, comprising a computer program / instruction, wherein the computer program / instruction is executed by the processor to implement the steps of the intelligent scheduling method for scenic area vehicles and ships as described in any one of claims 1 to 7.

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