CN118469207A - Method and system for scheduling bus transportation tasks based on cloud management - Google Patents
Method and system for scheduling bus transportation tasks based on cloud management Download PDFInfo
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Abstract
The invention discloses a class transportation task scheduling method and system based on cloud management, which belongs to the technical field of general control or regulation systems, wherein a receiving end receives a temporary riding application of staff, screens class vehicles conforming to passenger conditions, builds a traffic jam time neural network prediction model under the influence of weather and builds a traffic jam time neural network prediction model under the influence of traffic flow, and outputting the traffic jam time of each road section at the corresponding time point, calculating the traffic comprehensive jam time of each road section according to the traffic comprehensive jam calculation strategy, predicting the time of the screened class car reaching the destination station, and distributing the dispatching task to the optimal class car, thereby improving the transportation efficiency of the class car and the dispatching accuracy of the class car.
Description
Technical Field
The invention belongs to the technical field of general control or regulation systems, and particularly relates to a class transportation task scheduling method and system based on cloud management.
Background
Enterprise airliner scheduling refers to an enterprise providing airliner services to employees so that employees can arrive at a job site on time or return home from the job site. The scheduling of the regular buses of enterprises relates to various aspects including line planning, vehicle management, shift arrangement, staff information management and the like, and through a scientific and reasonable scheduling scheme, the efficiency and the punctuality of the regular buses can be improved, and meanwhile, the working comfort and the satisfaction of staff are improved.
The current commercial bus dispatching system is more fixed shifts and fixed stations, accords with regular commuting demands of going up and down, but faces flexible bus riding application and can not send out the shortest-time sent buses reaching the stations of the staff in a short time to meet the demands in time. For example, chinese patent with publication number CN115034522a discloses a dynamic scheduling method for commuting buses based on employee departure time and departure station, comprising the following steps: s1: collecting employee riding demand data; s2: determining a list of empty vehicles capable of carrying passengers and capable of being used in a scheduling mode in each shift; s3: determining a staff list of each shift capable of being ridden; s4: optimizing the departure station points of the same class; s5: the position of each site in the site set is adjusted and optimized; s6: adopting an ant colony algorithm to distribute vehicles and plan routes for passengers; s7: and reporting the vehicle return time and scheduling the next shift.
In order to solve the problems, the invention designs a class transportation task scheduling method and system based on cloud management.
Disclosure of Invention
The purpose of the invention is that: the receiving end receives the temporary riding application of staff, screens buses meeting passenger conditions, builds a traffic jam time neural network prediction model under the influence of weather and builds a traffic jam time neural network prediction model under the influence of traffic flow, outputs traffic jam time of each road section of a historical corresponding time point, calculates traffic comprehensive jam time of each road section according to a traffic comprehensive jam calculation strategy, predicts the time of the screened buses reaching a destination station, distributes scheduling tasks to optimal buses, and improves the efficiency of bus transportation and the accuracy of bus scheduling.
In order to achieve the above purpose, the present invention provides the following technical solutions:
A class transportation task scheduling method based on cloud management comprises the following specific steps:
S1, extracting employee information and bus information, and storing the employee information and the bus information in an information storage module, and planning bus departure quantity, departure time and driving routes according to the employee information;
s2, receiving a temporary passenger carrying task issued by the message notification module, acquiring an employee boarding point and a alighting point, and screening out buses which contain the employee boarding point and alighting point and have a vacancy in a route;
S3, acquiring historical weather information and traffic congestion conditions of all road sections under the historical weather, constructing a traffic congestion time neural network prediction model under the weather influence, substituting weather of a historical corresponding time point into the constructed traffic congestion time neural network prediction model under the weather influence, and outputting traffic congestion time of all road sections under the weather influence of the historical corresponding time point, wherein the definition of the congestion time is to subtract the time of passing a certain road section by the time of normally running at a safe speed;
S4, acquiring historical traffic flow information and traffic congestion conditions of all road sections under the historical traffic flow, constructing a traffic congestion time neural network prediction model under the influence of the traffic flow, substituting the traffic flow of the historical corresponding time point into the constructed traffic congestion time neural network prediction model under the influence of the traffic flow, and outputting traffic congestion time of all road sections under the influence of the traffic flow of the historical corresponding time point;
S5, substituting the traffic jam time of each road section under the influence of weather of the output historical corresponding time point and the traffic jam time of each road section under the influence of traffic flow of the historical corresponding time point into a traffic comprehensive jam time calculation formula to calculate the traffic comprehensive jam time of each road section, estimating the time of the screened class car reaching the destination station according to the calculated traffic comprehensive jam time of each road section, and distributing the scheduling task to a class car with the shortest time for reaching the destination station.
Specifically, the step S1 includes the following specific steps:
The staff information comprises staff names, boarding points, alighting points and scheduling information, the bus information comprises the number of checked passengers, the number of real-time passengers, the real-time positioning and the real-time speed of the bus, the staff information and the bus information are extracted and stored in the information storage module, the bus departure time is determined according to the staff scheduling information, the bus driving route is determined according to the site position, and the bus departure quantity is determined according to the number of boarding points and alighting points;
specifically, the step S2 includes the following specific steps:
the staff temporarily applies for taking the buses, the message notification module issues temporary passenger carrying tasks, the boarding points and the alighting points of the staff are obtained, and buses which contain the boarding points and the alighting points of the staff and have gaps in the route are screened out from the buses;
specifically, the step S3 includes the following specific steps:
S31, acquiring historical weather information and traffic congestion conditions of all road sections under historical weather, dividing the data into 2 subsets, wherein the first 80% is a training data set, the second 20% is a test data set, inputting the first 80% training data set into a traffic congestion time neural network prediction model under the weather influence for training to obtain an initial traffic congestion time neural network prediction model under the weather influence, testing the initial traffic congestion time neural network prediction model under the weather influence by using the second 20% test data set, and outputting the traffic congestion time neural network prediction model under the weather influence with the highest accuracy in judging the traffic congestion time, wherein a calculation formula of the traffic congestion time neural network prediction model under the weather influence for judging the traffic congestion time is as follows: Wherein, For the traffic congestion time output by the traffic congestion time neural network model of the ith area under the influence of weather, x i is the traffic congestion time of the ith area under the influence of actual weather, k is the number of crowded areas generated for each road section, and the point multiplication between vectors is carried out;
s32, calculating a traffic jam time neural network prediction model under the influence of weather, wherein the calculating formula is as follows: wherein, w i is the connection weight between each neuron from the hidden layer to the output layer, w 0 is the deviation, the general value is 0, p i is the input data of the i-th area, e 1 is the center of the basis function of the neuron, sigma 1 is the width of the basis function, and I are norms;
S33, substituting weather of the historical corresponding time points into the constructed traffic congestion time neural network prediction model under the weather influence to output traffic congestion time of each road section under the weather influence of the historical corresponding time points;
specifically, the step S4 includes the following specific steps:
S41, acquiring historical traffic flow information and traffic congestion conditions of all road sections under the historical traffic flow, dividing the data into 2 subsets, wherein the first 80% is a training data set, the second 20% is a test data set, inputting the first 80% training data set into a traffic congestion time neural network prediction model under the influence of the traffic flow for training, obtaining an initial traffic congestion time neural network prediction model under the influence of the traffic flow, testing the traffic congestion time neural network prediction model under the influence of the initial traffic flow by using the second 20% test data set, and outputting the traffic congestion time neural network prediction model under the influence of the traffic flow with the minimum traffic congestion time judgment error, wherein a calculation formula of the traffic congestion time neural network prediction model under the influence of the traffic flow for the traffic congestion time judgment error is as follows: Wherein, For the traffic congestion time output by the traffic congestion time neural network model of the jth region under the influence of the traffic flow, y j is the traffic congestion time of the jth region under the influence of the actual traffic flow, and l is the number of crowded regions generated for each road section;
S42, calculating a traffic jam time neural network prediction model under the influence of the traffic flow, wherein the calculating formula is as follows: Wherein t j is the connection weight between each neuron from the hidden layer to the output layer, m 0 is the deviation, the general value is 0, t j is the input data of the j-th area, e 2 is the center of the basis function of the neuron, sigma 2 is the width of the basis function, and I are norms;
s43, substituting the traffic flow of the historical corresponding time point into the constructed traffic congestion time neural network prediction model under the influence of the traffic flow, and outputting the traffic congestion time of each road section under the influence of the traffic flow of the historical corresponding time point;
Specifically, the step S5 includes the following specific steps:
S51, substituting the traffic congestion time of each road section under the influence of weather of the output historical corresponding time point and the traffic congestion time of each road section under the influence of the traffic flow of the historical corresponding time point into a traffic comprehensive congestion time calculation formula to calculate the traffic comprehensive congestion time of each road section, wherein the traffic comprehensive congestion time calculation formula is as follows: Wherein, α 1 is a weather-influencing traffic duty ratio coefficient, α 2 is a traffic-flow-influencing traffic duty ratio coefficient, α 1＞0,α2 is greater than 0 and α 1+α2 =1, and it should be noted that the values of the weather-influencing traffic duty ratio coefficient and the traffic-flow-influencing traffic duty ratio coefficient are as follows: the method comprises the steps of taking 5000 groups of traffic jam time data under the influence of historical weather and traffic jam time data under the influence of traffic flow, selecting 50 traffic practitioners to evaluate the traffic jam time data under the influence of the historical weather and the traffic jam time data under the influence of the traffic flow, selecting 5 groups of weather-influence traffic proportion coefficients and traffic flow-influence traffic proportion coefficients, and selecting a group of weather-influence traffic proportion coefficients and traffic flow-influence traffic proportion coefficients with the highest similarity with the actual traffic congestion;
S52, estimating the time of the screened class vehicles reaching the destination station according to the calculated comprehensive traffic congestion time of each road section, distributing the scheduling task to a class vehicle with the shortest time of reaching the destination station, and receiving staff from the destination station according to vehicle-mounted navigation after the class vehicle receives the scheduling task.
The class transportation task scheduling system based on cloud management is realized based on the class transportation task scheduling method based on cloud management, and comprises an information storage module, a message notification module, a weather influence module, a traffic flow influence module, a traffic comprehensive congestion calculation module and a control module;
Specifically, the information storage module is used for storing employee information and bus information, the message notification module is used for issuing temporary passenger carrying tasks and distributing scheduling tasks, the weather influence module is used for inputting weather of a time point corresponding to history in the constructed traffic congestion time neural network prediction model under the weather influence, and outputting traffic congestion time of each road section under the weather influence of the time point corresponding to the history.
Specifically, the traffic flow influence module is used for inputting traffic flow of a time point corresponding to a history in the constructed traffic congestion time neural network prediction model under the influence of the traffic flow, outputting traffic congestion time of each road section under the influence of the traffic flow of the time point corresponding to the history, and the traffic comprehensive congestion calculation module is used for calculating traffic comprehensive congestion time of each road section according to the traffic congestion time of each road section under the influence of weather and the traffic congestion time of each road section under the influence of the traffic flow and estimating the time of the screened class bus to reach a destination station.
Specifically, the control module is used for controlling the operation of the information storage module, the message notification module, the weather influence module, the traffic flow influence module and the traffic comprehensive congestion calculation module.
An electronic device, comprising: the system comprises a processor and a memory, wherein the memory stores a computer program which can be called by the processor, and the processor executes the class vehicle transportation task scheduling method based on cloud management by calling the computer program stored in the memory.
A computer readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a method for scheduling a mission of a bus based on cloud management as described above.
The beneficial effects of the invention are as follows: the receiving end receives the temporary riding application of staff, screens buses meeting passenger conditions, builds a traffic jam time neural network prediction model under the influence of weather and builds a traffic jam time neural network prediction model under the influence of traffic flow, outputs traffic jam time of each road section at a time point corresponding to history, calculates traffic comprehensive jam time of each road section according to a traffic comprehensive jam calculation strategy, predicts the time of the screened buses reaching a destination station, distributes scheduling tasks to optimal buses, and improves the efficiency of bus transportation and the accuracy of bus scheduling.
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FIG. 1 is a schematic flow chart of a class transportation task scheduling method based on cloud management;
fig. 2 is a schematic diagram of an overall framework of a class transportation task scheduling system based on cloud management.
Detailed Description
The invention will be further illustrated with reference to specific examples. It is to be understood that these examples are illustrative of the present invention and are not intended to limit the scope of the present invention. Further, it is understood that various changes and modifications of the present invention may be made by those skilled in the art after reading the description of the present invention, and that such equivalents are intended to fall within the scope of the invention as defined in the appended claims.
Example 1
Referring to fig. 1, an embodiment of the present invention is provided: a class transportation task scheduling method based on cloud management comprises the following specific steps:
S1, extracting employee information and bus information, and storing the employee information and the bus information in an information storage module, and planning bus departure quantity, departure time and driving routes according to the employee information;
in this embodiment, S1 includes the following specific steps:
The staff information comprises staff names, boarding points, alighting points and scheduling information, the bus information comprises the number of checked passengers, the number of real-time passengers, the real-time positioning and the real-time speed of the bus, the staff information and the bus information are extracted and stored in the information storage module, the bus departure time is determined according to the staff scheduling information, the bus driving route is determined according to the site position, and the bus departure quantity is determined according to the number of boarding points and alighting points.
The method is characterized in that staff information can acquire the name, the boarding point, the alighting point and the scheduling information of staff through equipment such as an intelligent bracelet or an intelligent watch worn by the staff, the equipment generally has a GPS positioning function, the position of the staff can be recorded in real time, and the equipment is connected with an information system of a company so as to acquire travel data of the staff; the information of the buses can be obtained through the vehicle-mounted equipment arranged on the buses, such as the number of authorized passengers, the number of real-time passengers, the real-time positioning, the real-time speed of the buses and the like, and the equipment generally has the functions of GPS positioning, video monitoring, sensors and the like, can monitor the running state and the passenger carrying condition of the buses in real time, and is connected with the information system of a company so as to be convenient for scheduling and management.
S2, receiving a temporary passenger carrying task issued by the message notification module, acquiring an employee boarding point and a alighting point, and screening out buses which contain the employee boarding point and alighting point and have a vacancy in a route;
in this embodiment, S2 includes the following specific steps:
the staff temporarily applies for taking the buses, the message notification module issues temporary passenger carrying tasks, the boarding points and the alighting points of the staff are obtained, and buses with the boarding points and the alighting points of the staff and empty positions are screened out from the buses.
S3, acquiring historical weather information and traffic congestion conditions of all road sections under the historical weather, constructing a traffic congestion time neural network prediction model under the weather influence, substituting weather of a historical corresponding time point into the constructed traffic congestion time neural network prediction model under the weather influence, and outputting traffic congestion time of all road sections under the weather influence of the historical corresponding time point, wherein the definition of the congestion time is to subtract the time of passing a certain road section by the time of normally running at a safe speed;
in this embodiment, S3 includes the following specific steps:
S31, acquiring historical weather information and traffic congestion conditions of all road sections under historical weather, dividing the data into 2 subsets, wherein the first 80% is a training data set, the second 20% is a test data set, inputting the first 80% training data set into a traffic congestion time neural network prediction model under the weather influence for training to obtain an initial traffic congestion time neural network prediction model under the weather influence, testing the initial traffic congestion time neural network prediction model under the weather influence by using the second 20% test data set, and outputting the traffic congestion time neural network prediction model under the weather influence with the highest accuracy in judging the traffic congestion time, wherein a calculation formula of the traffic congestion time neural network prediction model under the weather influence for judging the traffic congestion time is as follows: Wherein, For the traffic congestion time of the ith area under the influence of weather, x i is the traffic congestion time of the ith area under the influence of actual weather, k is the number of crowded areas generated for each road section, and is the dot product among vectors, wherein the division mode of the areas is as follows: acquiring a road section driving road surface needing congestion calculation, and uniformly dividing the driving road surface into a plurality of areas according to the length;
S32, calculating a traffic jam time neural network prediction model under the influence of weather, wherein the calculating formula is as follows: wherein, w i is the connection weight between each neuron from the hidden layer to the output layer, w 0 is the deviation, the general value is 0, p i is the input data of the i-th area, e 1 is the center of the basis function of the neuron, sigma 1 is the width of the basis function, and I are norms;
s33, substituting weather of the historical corresponding time point into the constructed traffic congestion time neural network prediction model under the weather influence to output traffic congestion time of each road section under the weather influence of the historical corresponding time point.
It should be noted that, the obtained historical weather information includes rainfall, snowfall, visibility and the like, rainfall may cause the road surface to be slippery, and increase the risk of vehicle skidding, so as to affect traffic, snow and visibility decrease may obstruct the sight distance of a driver, may cause road closure and increase of traffic accidents, and in low-temperature weather, road surface icing after snowfall may cause danger to the running vehicle, cause traffic accidents to occur, and reduce visibility due to foggy days, haze and air pollution, and increase the risk of traffic accidents;
Taking rainfall information as an example, acquiring historical rainfall and traffic congestion conditions of all road sections under the historical rainfall, dividing the data into 2 subsets, wherein the first 80% is a training data set, the second 20% is a test data set, inputting the first 80% training data set into a traffic congestion time neural network prediction model under the influence of the rainfall for training, obtaining an initial traffic congestion time neural network prediction model under the influence of the rainfall, testing the traffic congestion time neural network prediction model under the influence of the initial rainfall by using the second 20% test data set, and outputting the traffic congestion time neural network prediction model under the influence of the rainfall with the highest accuracy of traffic congestion time judgment.
S4, acquiring historical traffic flow information and traffic congestion conditions of all road sections under the historical traffic flow, constructing a traffic congestion time neural network prediction model under the influence of the traffic flow, substituting the traffic flow of the historical corresponding time point into the constructed traffic congestion time neural network prediction model under the influence of the traffic flow, and outputting traffic congestion time of all road sections under the influence of the traffic flow of the historical corresponding time point;
in this embodiment, S4 includes the following specific steps:
S41, acquiring historical traffic flow information and traffic congestion conditions of all road sections under the historical traffic flow, dividing the data into 2 subsets, wherein the first 80% is a training data set, the second 20% is a test data set, inputting the first 80% training data set into a traffic congestion time neural network prediction model under the influence of the traffic flow for training, obtaining an initial traffic congestion time neural network prediction model under the influence of the traffic flow, testing the traffic congestion time neural network prediction model under the influence of the initial traffic flow by using the second 20% test data set, and outputting the traffic congestion time neural network prediction model under the influence of the traffic flow with the minimum traffic congestion time judgment error, wherein a calculation formula of the traffic congestion time neural network prediction model under the influence of the traffic flow for the traffic congestion time judgment error is as follows: Wherein, For the traffic congestion time output by the traffic congestion time neural network model of the jth region under the influence of the traffic flow, y j is the traffic congestion time of the jth region under the influence of the actual traffic flow, and l is the number of crowded regions generated for each road section;
s42, calculating a traffic jam time neural network prediction model under the influence of the traffic flow, wherein the calculating formula is as follows: Wherein t j is the connection weight between each neuron from the hidden layer to the output layer, m 0 is the deviation, the general value is 0, t j is the input data of the j-th area, e 2 is the center of the basis function of the neuron, sigma 2 is the width of the basis function, and I are norms;
S43, substituting the traffic flow of the historical corresponding time point into the constructed traffic congestion time neural network prediction model under the influence of the traffic flow, and outputting the traffic congestion time of each road section under the influence of the traffic flow of the historical corresponding time point.
It should be noted that the traffic flow has a great influence on traffic congestion, and the more vehicles, the higher the probability of occurrence of traffic accidents; at high traffic flows, the driver will typically drive more cautiously to avoid collisions, which may result in reduced vehicle speeds that reduce the throughput of the road and thereby increase traffic congestion.
S5, substituting the traffic jam time of each road section under the influence of weather of the output historical corresponding time point and the traffic jam time of each road section under the influence of traffic flow of the historical corresponding time point into a traffic comprehensive jam time calculation formula to calculate the traffic comprehensive jam time of each road section, estimating the time of the screened class car reaching the destination station according to the calculated traffic comprehensive jam time of each road section, and distributing the scheduling task to a class car with the shortest time for reaching the destination station.
In this embodiment, S5 includes the following specific steps:
S51, substituting the traffic congestion time of each road section under the influence of weather of the output historical corresponding time point and the traffic congestion time of each road section under the influence of the traffic flow of the historical corresponding time point into a traffic comprehensive congestion time calculation formula to calculate the traffic comprehensive congestion time of each road section, wherein the traffic comprehensive congestion time calculation formula is as follows: Wherein, α 1 is a weather-influencing traffic duty ratio coefficient, α 2 is a traffic-flow-influencing traffic duty ratio coefficient, α 1＞0,α2 is greater than 0 and α 1+α2 =1, and it should be noted that the values of the weather-influencing traffic duty ratio coefficient and the traffic-flow-influencing traffic duty ratio coefficient are as follows: the method comprises the steps of taking 5000 groups of traffic jam time data under the influence of historical weather and traffic jam time data under the influence of traffic flow, selecting 50 traffic practitioners to evaluate the traffic jam time data under the influence of the historical weather and the traffic jam time data under the influence of the traffic flow, selecting 5 groups of weather-influence traffic proportion coefficients and traffic flow-influence traffic proportion coefficients, and selecting a group of weather-influence traffic proportion coefficients and traffic flow-influence traffic proportion coefficients with the highest similarity with the actual traffic congestion;
S52, estimating the time of the screened class vehicles reaching the destination station according to the calculated comprehensive traffic congestion time of each road section, distributing the scheduling task to a class vehicle with the shortest time of reaching the destination station, and receiving staff from the destination station according to vehicle-mounted navigation after the class vehicle receives the scheduling task.
It should be noted that, the accuracy is higher by combining weather and traffic flow to calculate traffic jam time, the traffic jam is a dynamic process and is influenced by a plurality of factors, such as traffic flow, road condition, accident rate and the like, by comprehensively considering two dynamic factors, namely weather and traffic flow, the actual condition of the traffic jam can be more comprehensively reflected, the traffic jam condition can be more conveniently calculated, and a decision basis is provided for traffic management departments.
Example 2
Referring to fig. 2, the cloud management-based class transportation task scheduling system is implemented based on the cloud management-based class transportation task scheduling method, and includes an information storage module, a message notification module, a weather influence module, a traffic flow influence module, a traffic comprehensive congestion calculation module and a control module.
In this embodiment, the information storage module is configured to store employee information and bus information, the message notification module is configured to issue temporary passenger-carrying tasks and allocation scheduling tasks, the weather influence module is configured to input weather of a time point corresponding to a history in the constructed traffic congestion time neural network prediction model under weather influence, and output traffic congestion time of each road section under weather influence of the time point corresponding to the history;
In this embodiment, the traffic flow influence module is configured to input traffic flow of a time point corresponding to a history in the constructed traffic congestion time neural network prediction model under the influence of the traffic flow, output traffic congestion time of each road section under the influence of the traffic flow of the time point corresponding to the history, and the traffic comprehensive congestion calculation module is configured to calculate traffic comprehensive congestion time of each road section according to the traffic congestion time of each road section under the influence of weather and the traffic congestion time of each road section under the influence of the traffic flow, and estimate time of the screened class vehicle to reach the destination station;
In this embodiment, the control module is configured to control operations of the information storage module, the message notification module, the weather effect module, the traffic flow effect module, and the traffic comprehensive congestion calculation module.
Example 3
The present embodiment provides an electronic device including: the system comprises a processor and a memory, wherein the memory stores a computer program which can be called by the processor, and the processor executes the class transportation task scheduling method based on cloud management by calling the computer program stored in the memory.
The electronic device may have a relatively large difference due to different configurations or performances, and may include one or more processors (Central Processing Units, CPU) and one or more memories, where at least one computer program is stored in the memories, and the computer program is loaded and executed by the processors to implement a class transportation task scheduling method based on cloud management provided by the foregoing method embodiment.
Example 4
The present embodiment provides a computer readable storage medium storing instructions that, when executed on a computer, cause the computer to perform a method for scheduling a mission of a bus based on cloud management as described above.
For example, the computer readable storage medium can be Read-Only Memory (ROM), random access Memory (Random Access Memory, RAM), compact disk Read-Only Memory (Compact Disc Read-Only Memory, CD-ROM), magnetic tape, floppy disk, optical data storage device, and the like.
It should be understood that, in various embodiments of the present invention, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present invention.
It should be understood that determining B from a does not mean determining B from a alone, but can also determine B from a and/or other information.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions in accordance with embodiments of the present invention are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a computer network, or other programmable apparatus. The computer instructions may be stored in or transmitted from one computer-readable storage medium to another, for example, by way of wired or/and wireless networks from one website site, computer, server, or data center to another. Computer readable storage media can be any available media that can be accessed by a computer or data storage devices, such as servers, data centers, etc. that contain one or more collections of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present invention.
Claims (10)
1. The method for scheduling the transportation tasks of the buses based on cloud management is characterized by comprising the following specific steps of:
S1, extracting employee information and bus information, storing the employee information and the bus information, and acquiring the bus departure number, departure time and driving route;
s2, receiving a temporary riding application of staff by a receiving end, and screening buses meeting passenger carrying conditions;
s3, constructing a traffic jam time neural network prediction model under the influence of weather, inputting weather of a time point corresponding to the history into the traffic jam time neural network prediction model under the influence of the weather, and outputting traffic jam time of each road section under the influence of the weather of the time point corresponding to the history;
S4, constructing a traffic jam time neural network prediction model under the influence of the traffic flow, inputting the traffic flow of the time point corresponding to the history into the traffic jam time neural network prediction model under the influence of the traffic flow, and outputting the traffic jam time of each road section under the influence of the traffic flow of the time point corresponding to the history;
And S5, calculating traffic comprehensive congestion time of each road section according to a traffic comprehensive congestion calculation strategy, predicting the time of the screened class car reaching a destination station, and distributing a dispatching task to the optimal class car.
2. The method for scheduling the transportation tasks of the buses based on cloud management as claimed in claim 1, wherein said S1 comprises the following specific steps:
the staff information comprises staff names, boarding points, alighting points and scheduling information, and the bus information comprises the number of authorized passengers, the number of real-time passengers, the real-time positioning and the real-time speed of the bus, and the staff information and the bus information are extracted and stored in the information storage module to obtain the bus departure number, departure time and travel route.
3. The method for scheduling the transportation tasks of the buses based on cloud management as claimed in claim 2, wherein said S2 comprises the following specific steps:
the staff temporarily applies for taking the buses, the message notification module issues temporary passenger carrying tasks, the boarding points and the alighting points of the staff are obtained, and buses with the boarding points and the alighting points of the staff and empty positions are screened out from the buses.
4. The method for scheduling the transportation tasks of the buses based on cloud management as claimed in claim 3, wherein said S3 comprises the following specific steps:
S31, acquiring historical weather information and traffic congestion conditions of all road sections under historical weather, dividing the data into 2 subsets, wherein the first 80% is a training data set, the second 20% is a test data set, inputting the first 80% training data set into a traffic congestion time neural network prediction model under the weather influence for training to obtain an initial traffic congestion time neural network prediction model under the weather influence, testing the initial traffic congestion time neural network prediction model under the weather influence by using the second 20% test data set, and outputting the traffic congestion time neural network prediction model under the weather influence with the highest accuracy in judging the traffic congestion time, wherein a calculation formula of the traffic congestion time neural network prediction model under the weather influence for judging the traffic congestion time is as follows:
Wherein, The method comprises the steps that (1) the traffic congestion time of an ith area under the influence of weather is output by a traffic congestion time neural network model, xi is the traffic congestion time of the ith area under the influence of actual weather, k is the number of crowded areas generated for each road section, and the k is the dot product among vectors;
s32, calculating a traffic jam time neural network prediction model under the influence of weather, wherein the calculating formula is as follows: Wherein w i is the connection weight between each neuron from the hidden layer to the output layer, w 0 is the deviation, p i is the input data of the ith region, e 1 is the center of the basis function of the neurons, sigma 1 is the width of the basis function, and is the norm;
s33, substituting weather of the historical corresponding time point into the constructed traffic congestion time neural network prediction model under the weather influence to output traffic congestion time of each road section under the weather influence of the historical corresponding time point.
5. The method for scheduling the transportation tasks of the buses based on cloud management as claimed in claim 3, wherein said S4 comprises the following specific steps:
S41, acquiring historical traffic flow information and traffic congestion conditions of all road sections under the historical traffic flow, dividing the data into 2 subsets, wherein the first 80% is a training data set, the second 20% is a test data set, inputting the first 80% training data set into a traffic congestion time neural network prediction model under the influence of the traffic flow for training, obtaining an initial traffic congestion time neural network prediction model under the influence of the traffic flow, testing the traffic congestion time neural network prediction model under the influence of the initial traffic flow by using the second 20% test data set, and outputting the traffic congestion time neural network prediction model under the influence of the traffic flow with the minimum traffic congestion time judgment error, wherein a calculation formula of the traffic congestion time neural network prediction model under the influence of the traffic flow for the traffic congestion time judgment error is as follows: Wherein, For the traffic congestion time output by the traffic congestion time neural network model of the jth region under the influence of the traffic flow, y j is the traffic congestion time of the jth region under the influence of the actual traffic flow, and l is the number of crowded regions generated for each road section;
S42, calculating a traffic jam time neural network prediction model under the influence of the traffic flow, wherein the calculating formula is as follows: Wherein t j is the connection weight between each neuron from the hidden layer to the output layer, m 0 is the deviation, t j is the input data of the j-th region, e 2 is the center of the basis function of the neurons, sigma 2 is the width of the basis function, and is the norm;
S43, substituting the traffic flow of the historical corresponding time point into the constructed traffic congestion time neural network prediction model under the influence of the traffic flow, and outputting the traffic congestion time of each road section under the influence of the traffic flow of the historical corresponding time point.
6. The method for scheduling the transportation tasks of the buses based on cloud management as claimed in claim 5, wherein said S5 comprises the following specific steps:
S51, substituting the traffic congestion time of each road section under the influence of weather of the output historical corresponding time point and the traffic congestion time of each road section under the influence of the traffic flow of the historical corresponding time point into a traffic comprehensive congestion time calculation formula to calculate the traffic comprehensive congestion time of each road section, wherein the traffic comprehensive congestion time calculation formula is as follows:
Wherein, α 1 is a weather-influencing traffic duty cycle, α 2 is a traffic flow-influencing traffic duty cycle, α 1＞0,α2 > 0 and α 1+α2 =1;
S52, estimating the time of the screened class vehicles reaching the destination station according to the calculated comprehensive traffic congestion time of each road section, distributing the scheduling task to a class vehicle with the shortest time of reaching the destination station, and receiving staff from the destination station according to vehicle-mounted navigation after the class vehicle receives the scheduling task.
7. The bus transportation task scheduling system based on cloud management is realized based on the bus transportation task scheduling method based on cloud management according to any one of claims 1-6, and is characterized by comprising an information storage module, a message notification module, a weather influence module, a traffic flow influence module, a traffic comprehensive congestion calculation module and a control module, wherein the information storage module is used for storing employee information and bus information, the message notification module is used for distributing temporary passenger carrying tasks and distributing scheduling tasks, the weather influence module is used for inputting weather of a historical corresponding time point in a constructed traffic congestion time neural network prediction model under the weather influence, and outputting traffic congestion time of each road section under the weather influence of the historical corresponding time point.
8. The class transportation task scheduling system based on cloud management as claimed in claim 7, wherein the traffic flow influencing module is configured to input traffic flow of a time point corresponding to a history in a traffic congestion time neural network prediction model under the influence of the constructed traffic flow, output traffic congestion time of each road segment under the influence of the traffic flow of the time point corresponding to the history, and the traffic comprehensive congestion calculating module is configured to calculate traffic comprehensive congestion time of each road segment according to the traffic congestion time of each road segment under the influence of weather and the traffic congestion time of each road segment under the influence of the traffic flow, and estimate time of the screened class to arrive at a destination site, and the control module is configured to control operations of the information storage module, the message notifying module, the weather influencing module, the traffic flow influencing module and the traffic comprehensive congestion calculating module.
9. An electronic device, comprising: a memory and a processor, wherein the memory stores a computer program which can be called by the processor, and the processor executes the class transportation task scheduling method based on cloud management according to any one of claims 1-6 by calling the computer program stored in the memory.
10. A computer-readable storage medium, characterized by: instructions stored thereon which, when executed on a computer, cause the computer to perform a method for scheduling mission tasks for a bus based on cloud management as claimed in any one of claims 1 to 6.
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Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105489001A (en) * | 2015-12-11 | 2016-04-13 | 惠州Tcl移动通信有限公司 | Taxi scheduling optimization method and system |
| CN109191849A (en) * | 2018-10-22 | 2019-01-11 | 北京航空航天大学 | A kind of traffic congestion Duration Prediction method based on multi-source data feature extraction |
| CN109215346A (en) * | 2018-10-11 | 2019-01-15 | 平安科技（深圳）有限公司 | A kind of prediction technique, storage medium and the server of traffic transit time |
| CN110857110A (en) * | 2018-08-24 | 2020-03-03 | 比亚迪股份有限公司 | Train scheduling method, scheduling device, scheduling system, terminal equipment and train |
| CN116524720A (en) * | 2023-06-19 | 2023-08-01 | 安徽省通信产业服务有限公司 | 5G technology-based integrated intelligent traffic management control system for Internet of vehicles |
-
2024
- 2024-05-17 CN CN202410617263.0A patent/CN118469207A/en active Pending
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105489001A (en) * | 2015-12-11 | 2016-04-13 | 惠州Tcl移动通信有限公司 | Taxi scheduling optimization method and system |
| CN110857110A (en) * | 2018-08-24 | 2020-03-03 | 比亚迪股份有限公司 | Train scheduling method, scheduling device, scheduling system, terminal equipment and train |
| CN109215346A (en) * | 2018-10-11 | 2019-01-15 | 平安科技（深圳）有限公司 | A kind of prediction technique, storage medium and the server of traffic transit time |
| CN109191849A (en) * | 2018-10-22 | 2019-01-11 | 北京航空航天大学 | A kind of traffic congestion Duration Prediction method based on multi-source data feature extraction |
| CN116524720A (en) * | 2023-06-19 | 2023-08-01 | 安徽省通信产业服务有限公司 | 5G technology-based integrated intelligent traffic management control system for Internet of vehicles |
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