CN114529034A - Intelligent scheduling method for scenic spot pleasure boats based on real-time passenger flow volume - Google Patents

Intelligent scheduling method for scenic spot pleasure boats based on real-time passenger flow volume Download PDF

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CN114529034A
CN114529034A CN202111627912.8A CN202111627912A CN114529034A CN 114529034 A CN114529034 A CN 114529034A CN 202111627912 A CN202111627912 A CN 202111627912A CN 114529034 A CN114529034 A CN 114529034A
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station
scheduling
pleasure
scenic spot
passenger flow
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CN114529034B (en
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廖明
叶冬梅
俞凯杰
姚强
王宇帆
魏鹏
王凯迪
李振翔
杨振凯
严杨
潘丽娟
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Zhejiang Toprs Geographic Information Technology Co ltd
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    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06312—Adjustment or analysis of established resource schedule, e.g. resource or task levelling, or dynamic rescheduling
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q10/00—Administration; Management
    • G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
    • G06Q10/063—Operations research, analysis or management
    • G06Q10/0631—Resource planning, allocation, distributing or scheduling for enterprises or organisations
    • G06Q10/06315—Needs-based resource requirements planning or analysis
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
    • G06Q50/00—Systems or methods specially adapted for specific business sectors, e.g. utilities or tourism
    • G06Q50/10—Services
    • G06Q50/14—Travel agencies
    • G06Q50/40—
    • G—PHYSICS
    • G08—SIGNALLING
    • G08G—TRAFFIC CONTROL SYSTEMS
    • G08G3/00—Traffic control systems for marine craft
    • H—ELECTRICITY
    • H04—ELECTRIC COMMUNICATION TECHNIQUE
    • H04W—WIRELESS COMMUNICATION NETWORKS
    • H04W4/00—Services specially adapted for wireless communication networks; Facilities therefor
    • H04W4/02—Services making use of location information
    • H04W4/021—Services related to particular areas, e.g. point of interest [POI] services, venue services or geofences

Abstract

The invention discloses a scenic spot cruise ship intelligent scheduling method based on real-time passenger flow, which comprises the following steps: data collection is carried out firstly; then determining the instantaneous bearing capacity of the station; then calculating the future passenger flow trend; finally, determining a scheduling station; distributing according to the degree that each station exceeds different instant bearing capacity, and calculating the optimal distance of all the dispatching pleasure boats and dispatching stations to obtain the optimal driving scheme; the problem of in the past can only when visitor's quantity has surpassed website bearing pressure and then arrange the dispatch, lead to the stream of people to evacuate untimely, visitor's latency is long is solved.

Description

Intelligent scheduling method for scenic spot pleasure boats based on real-time passenger flow volume
Technical Field
The invention relates to the field of pleasure boat management and control, in particular to a scenic spot pleasure boat intelligent scheduling method based on real-time passenger flow.
Background
With the rapid development of Chinese economy in recent years, the temperature of the tourism industry is continuously increased, the number of scenic spots developed nationwide and the number of tourists are continuously increased, higher requirements are provided for bearing and balancing capacity of the scenic spots, and particularly, the problems of ticket buying queuing, failure in vehicle and ship scheduling, overlong waiting time and the like caused by the sudden increase of the number of tourists in the scenic spots during holidays can occur. The reasons for such problems are that many tourists exist in scenic spots, the environment is complex, and managers cannot acquire and master the conditions and information of the tourists in the scenic spots in real time and cannot timely guarantee that vehicles and ships are dispatched to the scene. The patent provides a scenic spot pleasure boat intelligent scheduling method based on real-time passenger flow, improves scenic spot pleasure boat operation efficiency, reduce cost, promotes the experience of playing of visitor.
The existing scheme for scheduling the sightseeing boats in the scenic spot is that managers mostly check information of the scenic spot sites through a monitoring system and then initiate scheduling tasks to the managers through interphones according to experience.
In the prior art, a supervision system is mainly used, an administrator can see real-time running conditions of a cruise ship and real-time passenger flow information of a station, but effective analysis is carried out on real-time passenger flow in future time, so that the problem of slow scheduling of the cruise ship is caused.
For example, chinese patent application No.: CN202010525181.5 discloses a cruise ship number scheduling management platform and method, the platform includes: the quantity analysis equipment is used for receiving the first acquisition quantity and the second acquisition quantity and sending a pleasure boat conveying instruction when a quotient obtained by dividing the second acquisition quantity by the first acquisition quantity is less than or equal to a preset quotient; the wireless scheduling interface is connected with the quantity analysis equipment and is used for wirelessly sending the pleasure boat delivery instruction or the pleasure boat delivery instruction to a server of a remote pleasure boat scheduling center; the wireless dispatching interface is also used for synchronously sending the number of the pleasure boats to a server of a remote pleasure boat dispatching center when sending the pleasure boat conveying instruction; the invention has complex process and slow cruise ship scheduling.
Disclosure of Invention
The invention mainly solves the problem of slow cruise ship scheduling in the prior art, and provides a method for intelligently scheduling a cruise ship in advance according to a station passenger flow pressure threshold value.
The technical scheme adopted by the invention for solving the technical problems is as follows:
the invention provides a scenic spot cruise ship intelligent scheduling method based on real-time passenger flow, which comprises the following steps of:
s1: collecting data;
s2: determining the instantaneous bearing capacity of the station; definition i is a number determined by the station of the pleasure ships in the scenic spot according to the docking sequence, the number of the pleasure ships in the station i is n, definition j is a number of the pleasure ships in the station, and the passenger carrying capacity of each pleasure ship is cjThe area of the station is AiThe number of serviceable people per unit area is the greatest
And when the large density is gamma, the maximum number of the instantaneously-served pleasure boats at the station i is as follows:
Figure BDA0003440406310000021
station i service area maximum number of waitable people: wi＝γAi；
According to different states of the pleasure boat, the instantaneous bearing capacity z of the stationiThe method is divided into two parts:
a. when the station has an idle pleasure boat, the instantaneous bearing capacity of the station i is defined as the maximum number of persons that the idle pleasure boat can serve;
b. when the station pleasure boat runs, the instantaneous bearing capacity of the station i defines the number of people waiting in the service area of the station i to be multiplied by a coefficient alpha; from the data collected in step S1, the magnitude of the instantaneous station capacity, which is the primary determinant of cruise ship scheduling, can be determined.
S3: calculating the future passenger flow trend;
s4: a scheduling site is determined.
And distributing according to the degree that each station exceeds different instant bearing capacity, and calculating the optimal distance of all the dispatching pleasure boats and dispatching stations to obtain the optimal driving scheme.
Preferably, the step S1 further includes the steps of:
a: collecting the position of a station where a pleasure boat is parked and the size of a station service area;
b: collecting the number of pleasure boats at each station and the passenger carrying capacity of the pleasure boats;
c: a feasible network between the various sites is determined.
Setting a stop and determining the actual situation of a serving area, and fully considering the riding requirements of tourists; the number of pleasure boats at each station is reasonably distributed at each station according to historical experience; the cruise ship may select a travel route in these feasible networks after receiving the dispatch.
Preferably, the step S3 further includes generating a set of data of the number of guests changing in time series according to the guest flow data returned in real time, and calculating the number of guests in the future time by using linear regression.
Arranging a geographic fence in a certain distance of a pleasure boat stationNumber m of real-time stops serving the area of the stopiThe total number of the tourists returned according to the interface data of the camera of the station in the effective service area of the station i.
Preferably, the linear regression formula is:
y＝bx+α+ε，∈～N(0，，σ2)，
Figure BDA0003440406310000031
wherein
Figure BDA0003440406310000032
Represents the sum of the squares of y; r is a correlation coefficient representing the proportion of the variation that is interpreted by the regression line; syy(1-r2) Or a variation that cannot be explained by regression, i.e., SSE;
Figure BDA0003440406310000033
wherein y isiIs the actual value, YiIs a predicted value calculated according to a linear equation.
Preferably, the step S4 further includes determining that the scheduling station: according to the prediction model, calculating and obtaining the guest flow v of each station at a certain future timeiAnd recording the station pressure value at the moment as follows:
Figure BDA0003440406310000034
when mu isiWhen the number is more than 1, the station needs to be scheduled by the pleasure boat; when the pressure values of a plurality of stations are more than 1, according to muiSorting the values, preferentially scheduling the stations with larger pressure values, and then recalculating the pressure values of the stations.
Preferably, the step S4 further includes determining that the cruise ship is to be dispatched: according to the tourist flow prediction model, the distance between the time needing to be scheduled of the station to be scheduled and the current time is tiAnalyzing the distance from each scheduling pleasure boat to a site needing scheduling, recording the set S of the sites needing scheduling as {1,2,3.. n }, the set of the positions of the scheduling pleasure boats as {1,2,3.. m }, and n<m, the scheme of the shared pleasure boat scheduling comprises the following steps:
Figure BDA0003440406310000035
preferably, the cruise scheduling scheme further includes the following scheme: b isvFor the cruise ship V's driving matrix, if the cruise ship will drive from station i to station j (i and j are any two cruise ship service stations in the scenic spot), then
Figure BDA0003440406310000036
LijAn optimal path distance matrix from a station i to a station j; the sum of the distances for this scheme is:
Figure BDA0003440406310000037
preferably, the step S4 obtains an optimal path for the cruise ship scheduling after calculating the sum of the paths of each solution; when the pleasure boat scheduling scheme is selected, the stations are distributed according to the degree that each station exceeds different instant bearing capacity, and optimal distance calculation is carried out on all scheduled pleasure boats and the scheduled stations to obtain the optimal driving scheme. And continues to execute step S1 after the scheduling is completed until the positive pressure of the passenger flow of each station is 1, and the scheduling is ended.
The invention has the advantages that:
(1) by utilizing the linear regression method, the real-time traffic data of the scenic spot sites in a period of time in the future can be predicted, and the problems that people flow cannot be evacuated timely and the waiting time of tourists is long because the number of the tourists exceeds the bearing pressure of the sites and then the dispatching is arranged in the past are solved.
(2) When the pleasure boat scheduling scheme is selected, the stations are distributed according to the degree that each station exceeds different instant bearing capacity, and optimal distance calculation is carried out on all scheduled pleasure boats and the scheduled stations to obtain the optimal driving scheme.
Drawings
FIG. 1 is a schematic flow chart of a scheduling method of the present invention;
fig. 2 is a bottom view of the pleasure boat running calculation of the invention.
Detailed Description
The technical solutions of the present invention are further described below by way of examples in conjunction with the accompanying drawings, and it should be understood that the preferred examples described herein are only for illustrating and explaining the present invention, and do not limit the present invention.
Example (b): the invention provides a scenic spot cruise ship intelligent scheduling method based on real-time passenger flow, which comprises the following steps as shown in figure 1:
s1: collecting data; collecting the position of a station where a pleasure boat is parked and the size of a station service area; setting a stop station and determining the actual situation of a serving area, fully considering the riding demand of tourists, enabling the stop station to be close to the position with more people stream in a scenic spot as much as possible, and considering other factors such as safety, convenience, comfort and the like by colleagues; collecting the number of pleasure boats at each station and the passenger carrying capacity of the pleasure boats; the number of pleasure boats at the stations is reasonably distributed at each station according to historical experience, so that the scheduling cost is reduced as much as possible; determining a feasible network among all the sites; the cruise ship may select a travel route in these feasible networks after receiving the dispatch.
S2: determining the instantaneous bearing capacity of the station; definition i is a number determined by the station of the pleasure ships in the scenic spot according to the docking sequence, the number of the pleasure ships in the station i is n, definition j is a number of the pleasure ships in the station, and the passenger carrying capacity of each pleasure ship is cjThe area of the station is AiThe number of serviceable people per unit area is the greatest
If the large density is gamma, the maximum number of the instantaneously-served pleasure boats at the station i is as follows:
Figure BDA0003440406310000041
station i service area maximum number of waitable people: wi＝γAi；
According to different states of the pleasure boat, the instantaneous bearing capacity z of the stationiThe method is divided into two parts:
a. when the station has an idle pleasure boat, the instantaneous bearing capacity of the station i is defined as the maximum number of persons that the idle pleasure boat can serve;
b. when the station pleasure boat runs, the instantaneous bearing capacity of the station i defines the number of people waiting in the service area of the station i to be multiplied by a coefficient alpha; from the data collected in step S1, the magnitude of the instantaneous station capacity, which is the primary determinant of cruise ship scheduling, can be determined.
S3: calculating the future passenger flow trend; the number m of people at the station is real-timeiThe number of the tourists is the total number of the tourists returned by the effective service area of the station i according to the data of the camera interface of the station; includes generating a set of data of number of guests varying in time series from the data of the amount of guests returned in real time, as shown in fig. 2, and then calculating the number of guests in the future time using linear regression.
The linear regression formula is:
y＝bx+α+ε，∈～N(0，，σ2)，
Figure BDA0003440406310000051
wherein
Figure BDA0003440406310000052
Represents the sum of the squares of y; r is a correlation coefficient representing the proportion of the variation that is interpreted by the regression line; syy(1-r2) Or a variation that cannot be explained by regression, i.e., SSE;
Figure BDA0003440406310000053
wherein y isiIs the actual value, YiIs a predicted value calculated according to a linear equation. By utilizing the linear regression method, the real-time traffic data of the scenic spot sites in a period of time in the future can be predicted, and the problems that people flow cannot be evacuated timely and the waiting time of tourists is long because the number of the tourists exceeds the bearing pressure of the sites and then the dispatching is arranged in the past are solved.
S4: determining a scheduling station; further comprising determining a scheduling site: according to the prediction model, calculating to obtain the station tour of a certain future momentVolume of passenger viAnd recording the station pressure value at the moment as follows:
Figure BDA0003440406310000054
when mu isiWhen the number is more than 1, the station needs to be scheduled by the pleasure boat; when the pressure values of a plurality of stations are more than 1, according to muiSorting the values, preferentially scheduling the stations with larger pressure values, and then recalculating the pressure values of the stations.
Step S4 further includes determining a dispatch cruise: according to the tourist flow prediction model, the distance between the time needing to be scheduled of the station to be scheduled and the current time is tiAnalyzing the distance from each scheduling pleasure boat to a site needing scheduling, recording the set S of the sites needing scheduling as {1,2,3.. n }, the set of the positions of the scheduling pleasure boats as {1,2,3.. m }, and n<m, the scheme of the shared pleasure-boat scheduling comprises the following steps:
Figure BDA0003440406310000055
the scheme for scheduling the pleasure boat also comprises the following scheme: b isvFor the cruise ship V's driving matrix, if the cruise ship will drive from station i to station j (i and j are any two cruise ship service stations in the scenic spot), then
Figure BDA0003440406310000061
LijAn optimal path distance matrix from a station i to a station j; the sum of the distances for this scheme is:
Figure BDA0003440406310000062
step S4, after the path sum of each scheme is calculated, the optimal path of the cruise ship scheduling is obtained; and continues to execute step S1 after the scheduling is completed until the positive pressure of the passenger flow of each station is 1, and the scheduling is ended. When the pleasure boat scheduling scheme is selected, the stations are distributed according to the degree that each station exceeds different instant bearing capacity, and optimal distance calculation is carried out on all scheduled pleasure boats and the scheduled stations to obtain the optimal driving scheme.
Finally, it should be noted that: although the present invention has been described in detail with reference to the foregoing embodiments, it will be apparent to those skilled in the art that changes may be made in the embodiments and/or equivalents thereof without departing from the spirit and scope of the invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims (8)

1. A scenic spot pleasure boat intelligent scheduling method based on real-time passenger flow is characterized by comprising the following steps:
s1: collecting data;
s2: determining the instantaneous bearing capacity of the station; definition i is a number determined by the station of the pleasure ships in the scenic spot according to the docking sequence, the number of the pleasure ships in the station i is n, definition j is a number of the pleasure ships in the station, and the passenger carrying capacity of each pleasure ship is cjThe area of the station is AiThe number of serviceable people per unit area is the greatest
And when the large density is gamma, the maximum number of the instantaneously-served pleasure boats at the station i is as follows:
Figure FDA0003440406300000011
station i service area maximum number of waitable people: wi＝γAi；
According to different states of the pleasure boat, the instantaneous bearing capacity z of the stationiThe method is divided into two parts:
a. when the station has an empty pleasure boat, the instantaneous bearing capacity of the station i is defined as the maximum number of persons served by the empty pleasure boat;
b. when the station pleasure boat runs, the instantaneous bearing capacity of the station i defines the number of people waiting in the service area of the station i to be multiplied by a coefficient alpha;
s3: calculating the future passenger flow trend;
s4: a scheduling site is determined.
2. The method for intelligent scheduling of scenic spot cruise ships according to claim 1, wherein said step S1 further comprises the steps of:
a: collecting the position of a station where a pleasure boat is parked and the size of a station service area;
b: collecting the number of pleasure boats at each station and the passenger carrying capacity of the pleasure boats;
c: a feasible network between the various sites is determined.
3. The method as claimed in claim 1, wherein the step S3 further includes generating a set of data of number of visitors varying with time sequence according to the real-time returned data of passenger flow, and calculating the number of visitors in future time by linear regression.
4. The intelligent scheduling method for scenic spot cruise ships based on real-time passenger flow according to claim 3, wherein the linear regression formula is as follows:
y＝bx+α+ε，∈～N(0，，σ2)，
Figure FDA0003440406300000021
wherein
Figure FDA0003440406300000022
Represents the sum of the squares of y; r is a correlation coefficient representing the proportion of the variation that is interpreted by the regression line; syy(1-r2) Or a variation that cannot be explained by regression, i.e., SSE;
Figure FDA0003440406300000023
wherein y isiIs the actual value, YiIs a predicted value calculated according to a linear equation.
5. The real-time passenger flow based intelligent regulation system of scenic spot cruise ship according to claim 1The method, wherein the step S4 further includes determining that the scheduling station: according to the prediction model, calculating and obtaining the guest flow v of each station at a certain future timeiAnd recording the station pressure value at the moment as follows:
Figure FDA0003440406300000024
when mu isiWhen the number is more than 1, the station needs to be scheduled by the pleasure boat; when the pressure values of a plurality of stations are more than 1, according to muiSorting the values, preferentially scheduling the stations with larger pressure values, and then recalculating the pressure values of the stations.
6. The method for intelligent scheduling of scenic spot pleasure boats based on real-time passenger flow according to claim 1 or 5, wherein said step S4 further comprises determining that the scheduling pleasure boat: according to the tourist flow prediction model, the distance between the time needing to be scheduled of the station to be scheduled and the current time is tiAnalyzing the distance from each scheduling pleasure boat to a site needing scheduling, recording the set S of the sites needing scheduling as {1,2,3.. n }, the set of the positions of the scheduling pleasure boats as {1,2,3.. m }, and n<m, the scheme of the shared pleasure boat scheduling comprises the following steps:
Figure FDA0003440406300000025
7. the intelligent scheduling method of scenic spot pleasure boats based on real-time passenger flow of claim 6, wherein the scheduling scheme of the pleasure boats further comprises the following scheme: b isvFor the cruise ship V's driving matrix, if the cruise ship will drive from station i to station j (i and j are any two cruise ship service stations in the scenic spot), then
Figure FDA0003440406300000026
LijAn optimal path distance matrix from a station i to a station j; the sum of the distances for this scheme is:
Figure FDA0003440406300000027
8. the intelligent scheduling method for scenic spot pleasure boats based on real-time passenger flow of claim 1, wherein said step S4 is to obtain the optimal path for scheduling the pleasure boats after calculating the sum of the paths of each solution; and continues to execute step S1 after the scheduling is completed until the positive pressure of the passenger flow of each station is 1, and the scheduling is ended.
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