CN116307580A - Method and device for scheduling capacity, electronic equipment and storage medium - Google Patents

Method and device for scheduling capacity, electronic equipment and storage medium Download PDF

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CN116307580A
CN116307580A CN202310264969.9A CN202310264969A CN116307580A CN 116307580 A CN116307580 A CN 116307580A CN 202310264969 A CN202310264969 A CN 202310264969A CN 116307580 A CN116307580 A CN 116307580A
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张毅
晏松
杨正
胡坚明
张佐
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Tsinghua University
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Abstract

The invention provides a capacity scheduling method, a capacity scheduling device, electronic equipment and a storage medium. The method comprises the following steps: acquiring first information; the first information characterizes the state of vehicles in the park; acquiring corresponding second information for each site in at least one site; the second information at least comprises information related to passengers in the station; determining corresponding third information based on the second information; the third information characterizes passenger distribution conditions of different target stations; determining fourth information; the fourth information represents constraint conditions used when determining a vehicle driving path, and the fourth information at least comprises information related to vehicles in a park and traffic planning information of the vehicles; and determining a driving path of each vehicle in the at least one vehicle at the station by using a scheduling model based on the third information and the fourth information. The scheme provided by the invention can improve the rationality of the capacity scheduling result, thereby improving the efficiency of transporting passengers by the vehicle and the vehicle utilization rate.

Description

Method and device for scheduling capacity, electronic equipment and storage medium
Technical Field
The invention belongs to the technical field of traffic scheduling, and particularly relates to a traffic scheduling method, a traffic scheduling device, electronic equipment and a storage medium.
Background
In the application scenes such as a tourism park, reasonable scheduling of transport capacity in the park is needed in order to meet the riding requirements of passengers in the park; for example, the release time is divided into a peak time and an off-peak time according to the passenger flow volume, the number of vehicles connected in the peak time is increased, and the vehicle interval time is shortened so as to meet the riding requirements of the passenger flow volume in different time periods.
However, when scheduling is performed using the capacity scheduling method in the related art, the efficiency of the vehicle to transport passengers is low and the vehicle utilization is low.
Disclosure of Invention
The invention provides a capacity scheduling method, a capacity scheduling device, electronic equipment and a storage medium.
The invention adopts the technical scheme that:
the embodiment of the invention provides a capacity scheduling method, which is applied to electronic equipment and comprises the following steps:
acquiring first information; the first information characterizes the state of vehicles in the park;
acquiring corresponding second information for each site in at least one site; the second information at least comprises information related to passengers in the station;
determining corresponding third information based on the second information; the third information characterizes passenger distribution conditions of different target stations;
determining fourth information; the fourth information represents constraint conditions used when determining a vehicle driving path, and the fourth information at least comprises information related to vehicles in a park and traffic planning information of the vehicles;
and determining a driving path of each vehicle in the at least one vehicle at the station by using a scheduling model based on the third information and the fourth information.
In the above scheme, the method further comprises:
updating the target parameters and updating the scheduling model based on the updated target parameters; wherein,,
the target parameter comprises at least one of:
total number of passenger transportation;
vehicle operating mileage;
delivery service time.
In the above solution, the obtaining the first information includes:
acquiring position related information and ticket related information of passengers in a park; determining a destination station for each passenger in the station based on the location-related information and ticket-related information; determining first information based on a destination station for each passenger within the station;
and/or the number of the groups of groups,
acquiring park history tour data of a first time period; the park history tour data at least comprises a history passenger number of a first time period in a website; first information is determined based on the campus historical tour data.
In the above aspect, the determining, based on the third information and the fourth information, a travel path of each vehicle in the at least one vehicle at the station using a scheduling model includes:
and determining the driving path of each vehicle in at least one vehicle in the station by using a scheduling model and a searching algorithm based on the third information and the fourth information.
In the above scheme, the method further comprises:
the first information is updated according to a first preset period and/or the second information is updated according to a second preset period.
In the above aspect, after determining the driving path of each vehicle in at least one vehicle in the station, the method further includes:
for each of the at least one vehicle, determining a location of a next station based on the corresponding travel path;
and determining the running track from the current station to the next station based on the scheduling model and the A-algorithm.
In the above scheme, the optimization objective of the a-algorithm at least includes the shortest distance from the current station to the next station and the shortest travel time from the current station to the next station.
In the above solution, the method may further include:
determining a cost function from the first station to each of the at least one second station for each of the at least one vehicle to obtain at least one cost function;
selecting a second site with the minimum cost function value as the next site of the first site;
a travel path of the vehicle is determined based on the determined next station.
The embodiment of the application also provides a capacity scheduling device, which comprises:
an acquisition unit configured to acquire first information; the first information characterizes the state of vehicles in the park; and acquiring corresponding second information for each site in the at least one site; the second information at least comprises information related to passengers in the station;
a processing unit, configured to determine corresponding third information based on the second information; the third information characterizes passenger distribution conditions of different target stations; determining fourth information; the fourth information represents constraint conditions used when determining a vehicle driving path, and the fourth information at least comprises information related to vehicles in a park and traffic planning information of the vehicles; and determining a travel path of each of the at least one vehicle at the station using a scheduling model based on the third information and the fourth information.
The embodiment of the invention also provides electronic equipment, which comprises: a processor and a memory for storing a computer program capable of running on the processor,
wherein the processor is configured to execute the steps of any of the methods described above when the computer program is run.
The embodiment of the invention also provides a storage medium, on which a computer program is stored, which when executed by a processor, implements the steps of any of the methods described above.
The invention provides a capacity scheduling method, a capacity scheduling device, electronic equipment and a storage medium,
acquiring first information; the first information characterizes the state of vehicles in the park; acquiring corresponding second information for each site in at least one site; the second information at least comprises information related to passengers in the station; determining corresponding third information based on the second information; the third information characterizes passenger distribution conditions of different target stations; determining fourth information; the fourth information represents constraint conditions used when determining a vehicle driving path, and the fourth information at least comprises information related to vehicles in a park and traffic planning information of the vehicles; and determining a driving path of each vehicle in the at least one vehicle at the station by using a scheduling model based on the third information and the fourth information. According to the scheme provided by the invention, the vehicle driving path is determined by utilizing the mathematical model according to the road condition and the travel demands of the passengers, namely, the capacity scheduling is adjusted by combining the real-time road condition and the conveying demands, so that the rationality of the capacity scheduling result can be improved, and the efficiency of transporting the passengers by the vehicle and the vehicle utilization rate are improved; further, as the scheduling is performed based on the overall planning of all vehicles in the station, the comprehensiveness and rationality of the capacity scheduling result are improved, and parameters of multiple dimensions such as vehicle information, traffic planning and the like are considered in the process of determining the scheduling result, so that the balance and effectiveness of the capacity scheduling result can be improved, and the vehicle utilization rate is improved.
Drawings
FIG. 1 is a schematic flow chart of a capacity scheduling method according to an embodiment of the present invention;
FIG. 2 is a schematic diagram showing the comparison of the results of the capacity obtained by the capacity scheduling method and the capacity obtained without the capacity scheduling method in the application example of the embodiment of the present invention;
FIG. 3 is a schematic diagram of a capacity scheduling device according to an embodiment of the present invention;
fig. 4 is a schematic structural diagram of an electronic device according to an embodiment of the invention.
Detailed Description
The invention is further described below with reference to the drawings and examples.
For fixed areas such as tourist parks, the travel time, the travel route and other information of passengers cannot be predicted in advance, certain randomness and uncertainty exist, tidal effects of park traffic can be caused, for example, for parks with event venues, a peak of entrance appears at the beginning time of the event, a peak of exit appears at the ending time of the event, short-time and large-flow traffic demands appear at the peak period, and great pressure is brought to vehicle scheduling and personnel evacuation in the parks.
In view of the above problems, in the related art, a dispatching center responsible for capacity dispatching adjusts a capacity dispatching scheme according to the situation of the event, for example, in 1 hour before the start of the event, adds a connection vehicle destined for the event to be at a venue, and in 1 hour after the end of the event, adds a connection vehicle at a station near the venue where the event is at. However, the capacity scheduling scheme in the related art only considers the traffic tide effect, and the target parameters for formulating the scheduling scheme are single, so that the vehicle conveying efficiency is low and the vehicle utilization rate is low.
Based on the above, in various embodiments of the present invention, according to the road condition and the travel demand of the passengers, the vehicle travel path is determined by using the mathematical model, that is, the capacity scheduling is adjusted by combining the real-time road condition and the transportation demand, so that the rationality of the capacity scheduling result can be improved, and the efficiency of transporting the passengers by the vehicle and the vehicle utilization rate are improved; further, as the scheduling is performed based on the overall planning of all vehicles in the station, the comprehensiveness and rationality of the capacity scheduling result are improved, and parameters of multiple dimensions such as vehicle information, traffic planning and the like are considered in the process of determining the scheduling result, so that the balance and effectiveness of the capacity scheduling result can be improved, and the vehicle utilization rate is improved.
The embodiment of the invention provides a capacity scheduling method which is applied to electronic equipment, and can be particularly applied to servers, personal Computers (PC), vehicle-mounted terminal computers and the like; as shown in fig. 1, the method includes:
step 101: acquiring first information; the first information characterizes the state of vehicles in the park;
step 102: acquiring corresponding second information for each site in at least one site; the second information at least comprises information related to passengers in the station;
step 103: determining corresponding third information based on the second information; the third information characterizes passenger distribution conditions of different target stations;
step 104: determining fourth information; the fourth information represents constraint conditions used when determining a vehicle driving path, and the fourth information at least comprises information related to vehicles in a park and traffic planning information of the vehicles;
step 105: and determining a driving path of each vehicle in the at least one vehicle at the station by using a scheduling model based on the third information and the fourth information.
In actual application, in step 101, the first information may include status information of the vehicles in the campus, and may specifically include information of a location, a working status (whether a delivery task is being executed), a endurance mileage, a passenger number, and the like of the vehicles.
In an embodiment, the acquiring the first information may include:
acquiring position related information and ticket related information of passengers in a park; determining a destination station for each passenger in the station based on the location-related information and ticket-related information; determining first information based on a destination station for each passenger within the station;
and/or the number of the groups of groups,
acquiring park history tour data of a first time period; the park history tour data at least comprises a history passenger number of a first time period in a website; first information is determined based on the campus historical tour data.
In actual application, in step 102, the information related to the passengers in the station in the second information may specifically include real-time number of passengers in the station, target stations (i.e. target positions) of the passengers, and the like; here, the real-time number of passengers in the station may be acquired through a mobile terminal used by the passengers, or may be acquired through a collecting device of the station, and the target station of the passengers may be acquired through the mobile terminal used by the passengers.
Here, in actual application, the first information and the second information are information for calculating the capacity demand, which may also be referred to as capacity demand related information of the station.
In practical application, under different conditions (such as before, during and after a large-scale event), the travel demands of passengers at each station can show different rules, for example, before the event starts, the passengers can intensively reach the event venue from each station to show a state of being concentrated from multiple points to one point, and after the event is finished, the tourists are scattered from the event venue to each station to show a state of being scattered from one point to multiple points; according to the embodiment of the invention, the related information of the capacity demand can be obtained in real time, and the capacity can be adjusted according to the obtained information, so that the dynamic scheduling of the capacity under different application situations can be supported; meanwhile, the situation that the number of passengers to be transported at a certain station suddenly rises can be detected in time, corresponding transportation capacity allocation is rapidly performed, so that the station tourist congestion situation can be rapidly relieved, the effectiveness of a transportation capacity scheduling scheme and the vehicle transportation efficiency are improved, and the user experience can be improved. In practical application, the first information and the second information can be collected according to a preset period, for example, travel demand data of each site is updated with 1min as a frequency and is input as a scheduling model.
Based on this, in an embodiment, the method may further include:
the first information is updated according to a first preset period and/or the second information is updated according to a second preset period.
In practical application, the first preset period and the second preset period may be the same or different, which is not limited in this embodiment of the present application.
In actual application, according to the acquired second information, the passenger distribution conditions of different target sites, namely the transport capacity demands for reaching different target sites, can be determined; on the basis of this, the capacity scheduling scheme, in particular, the travel path of each vehicle in the station can be determined based on the determined capacity demand and the constraint condition, i.e., the fourth information.
In actual application, fourth information is determined, namely, information such as road network, riding station distribution, vehicle energy supplementary station distribution, vehicle basic parameters and the like in the target area is read; specifically, for vehicle related information, a vehicle entity model is established and stored, and information such as the endurance mileage, the passenger carrying quantity and the like of the vehicle is updated in real time according to the actual running condition of the vehicle; for traffic planning information of vehicles, road nodes and road classes of park roads are established, positions of the road nodes, riding stations and energy supplementing stations are determined through longitude and latitude coordinates of a park map, and constraint conditions such as road length, connectivity among the roads and the like are determined; after the vehicle-related information and the traffic planning information of the vehicle are determined, the vehicle driving path is constrained according to the actual road network condition, for example, the driving range of the vehicle cannot exceed the road boundary, and the vehicle track planning needs to be performed according to the actual road network.
It should be noted that, because the information such as the endurance mileage, the position, the passenger capacity, etc. of the vehicle is updated in real time, in order to ensure the accuracy of the capacity scheduling result, the constraint condition needs to be updated; in practical application, the constraint condition may be updated according to a preset period, or may be updated by the dispatching center according to the actual traffic condition, which is not limited in the embodiment of the present application.
In practical application, in order to accurately obtain the optimal capacity scheduling result, a search algorithm may be used to determine a capacity scheduling scheme.
Based on this, in an embodiment, the determining, using a scheduling model, a travel path of each of the at least one vehicle at the station based on the third information and the fourth information includes:
and determining the driving path of each vehicle in at least one vehicle in the station by using a scheduling model and a searching algorithm based on the third information and the fourth information.
In actual application, when determining the driving path of each vehicle, the embodiment of the invention models the vehicle scheduling problem as an optimization problem, namely, the number of tourists, waiting time of passengers and total mileage of the vehicle can be shortest after the vehicle passes through which stations and which passengers are connected in turn, and the whole optimization problem is segmented into sub-optimization problems when each vehicle selects the next station at each time; in determining the travel path of each vehicle, the cost consumed by the vehicle to travel to the possible next station may be quantified by constructing a cost function, thereby selecting the station with the smallest cost as the vehicle next station.
Based on this, in an embodiment, the method may further include:
determining a cost function from the first station to each of the at least one second station for each of the at least one vehicle to obtain at least one cost function;
selecting a second site with the minimum cost function value as the next site of the first site;
a travel path of the vehicle is determined based on the determined next station.
For example, the process of determining the vehicle travel path may include:
step S1: acquiring a set of available vehicles, and recording the set as Bus avail 。
Step S2: for Bus avail Each vehicle in the set sequentially calculates cost functions of each station and stations which can be reached, and selects the station with the minimum cost function as the next station of the corresponding station to obtain the running path of the vehicle; calling a preset path plan, and calculating the time required by the vehicle to complete the current transportation task (and the driving path) by utilizing a path rule;
step S3: judging whether the time required for completing the current transportation task is longer than a preset period, and moving the vehicle out of the Bus when the time required for completing the current transportation task is longer than the preset period avail The method comprises the steps of carrying out a first treatment on the surface of the When Bus avail If the capacity is empty, the capacity scheduling of the period is finished; finally, obtaining the transportation task of each vehicle, namely the sequence of sequentially passing through the stations;
here, real-time updates are made to the vehicle and platform conditions between algorithm run cycles: when a vehicle arrives at a certain station, firstly deleting tourists with the destination of the station from the vehicle, indicating that the tourists get off after arriving at the destination, and completing the transportation task of the tourists by the vehicle; after the next station is determined, starting from the first station where the vehicle runs, arranging the destination station to get on for tourists of the next station in sequence until the number of the passengers borne by the vehicle reaches a certain threshold value or all passengers get on; before the task allocation of the next vehicle in the available vehicle set is carried out, the situation of the requirements of tourists at all stations along the vehicle is updated, the stations of the next vehicle are selected, and the task allocation is carried out according to the new station state.
When the method is applied in real time, when the driving path of the vehicle is determined, the driving paths of multiple vehicles can be planned at the same time, and the multiple vehicles can be planned in sequence.
Based on this, in an embodiment, the method may further include:
simultaneously determining a driving path of multiple vehicles in at least one vehicle;
the travel paths of multiple vehicles in at least one vehicle are sequentially determined.
It should be noted that, in the process of sequentially determining the driving paths of multiple vehicles in at least one vehicle, in order to meet the requirement of the dynamic adjustment scheduling scheme, whether the issued vehicle meets the capacity requirement may be determined according to the capacity requirement, specifically, according to the constraint condition (fourth information) sent by the scheduling center, whether the currently issued vehicle meets the capacity requirement is determined, when the current capacity requirement is met, the next task of the issued vehicle and the subsequent driving record are determined by using the first model, and correspondingly, when the current capacity requirement is not met, the transportation vehicle is increased, and the capacity requirement is recalculated.
In practical application, the cost function of each station is a core of selecting the next station for each vehicle, and the component parts of the cost function specifically may include:
Figure BDA0004132745500000081
an optimized function value related to the running time of the vehicle, expressed as +.>
Figure BDA0004132745500000082
t S The driving time required for the vehicle from the current station to the rest stations is set;
Figure BDA0004132745500000083
an optimized function value related to the driving distance of the vehicle, expressed as +.>
Figure BDA0004132745500000084
d S The driving mileage required from the current station to the rest stations of the vehicle is given;
Figure BDA0004132745500000085
an optimization function associated with the number of site guests, expressed as +.>
Figure BDA0004132745500000086
Figure BDA0004132745500000087
Recording the number of tourists on S platform as P S ；
Figure BDA0004132745500000088
The optimization function value of each site related to the guest target site is expressed as +.>
Figure BDA0004132745500000089
Figure BDA00041327455000000810
Wherein->
Figure BDA00041327455000000811
Representing the proportion of the number of tourists taking S site as a target site to the total number of tourists; suppose that the vehicle has passed through { S }, in turn, during the task allocation process 1 ,S 2 ,…,S k First of all, the k sitesCounting the number of visitors of the k sites and integrating, and marking as +.>
Figure BDA00041327455000000812
The destination distribution of the k site guests is then calculated, and the number of guests destined for site m can be calculated by: />
Figure BDA00041327455000000813
Wherein P is s (target=m) represents the number of guests at s stops, destined for mtase;
Figure BDA00041327455000000814
cost function value related to average waiting time of tourist, expressed as +.>
Figure BDA00041327455000000815
Figure BDA0004132745500000091
Wherein, at a certain moment, N is arranged in a certain station S The average waiting time of tourists is counted>
Figure BDA0004132745500000092
Figure BDA0004132745500000093
A cost function value related to the remaining endurance of the vehicle, expressed as +.>
Figure BDA0004132745500000094
Figure BDA0004132745500000095
Wherein the current remaining range of the vehicle is B l Setting the threshold value as B t 。
The cost function may be expressed by the following formula:
Figure BDA0004132745500000096
wherein R is S The method is a random term, so that the problem of optimization can be prevented from being trapped in local optimization; k (k) t ,k d ,k p ,l α ,k wait ,k B The weight of each cost function value represents the degree of importance to each factor.
In practical application, after the driving path of each vehicle in the station is determined by using the scheduling model, the overall planning scheme from the station to all target stations is obtained, that is, the embodiment of the invention optimizes the capacity scheduling result on the whole and avoids the optimization result from being trapped into local optimum. After the overall planning scheme is obtained, in order to further improve the dispatching efficiency, the most available running track can be determined for each vehicle with a specific dispatching task.
Based on this, in an embodiment, after determining the travel path of each of the at least one vehicle within the station, the method further comprises:
for each of the at least one vehicle, determining a location of a next station based on the corresponding travel path;
and determining the running track from the current station to the next station based on the scheduling model and the A-algorithm.
In actual application, the shortest distance and the shortest time are respectively used as optimization targets, and the running track from the current position to the next station of the vehicle is updated.
Based on this, in an embodiment, the optimization objective of the a algorithm includes at least a shortest distance from the current station to the next station and a shortest travel time from the current station to the next station.
Illustratively, in determining the traveling direction of the vehicle at each intersection, the mileage that the vehicle has travelled and the predicted mileage that the vehicle arrives at the destination site are calculated, roads are selected according to the sum of the two, and inspection and backtracking are performed to determine the shortest path.
In practical application, in order to adapt to the use requirements under different application scenes, the target parameters of the scheduling model can be dynamically adjusted.
Based on this, in an embodiment, the method may further include:
updating the target parameters and updating the scheduling model based on the updated target parameters; wherein,,
the target parameter comprises at least one of:
total number of passenger transportation;
vehicle operating mileage;
delivery service time.
Here, updating the target parameter, that is, the target parameter in the target function, may be understood as updating the weights of different target parameters in the target function, so that the scheduling model may dynamically adjust the capacity scheduling scheme according to different use requirements; for example, in an application scenario in which the passenger experience needs to be improved, the weight of the target parameter, namely the delivery service time, in the target function is increased, so that the passenger delivery service time is reduced, and the passenger experience is improved. In practical application, the target parameter may also be referred to as an optimization index, which is not limited in the embodiment of the present invention, so long as the function thereof can be realized.
The optimal parameter setting and optimizing target parameters of each scene obtained by adopting the capacity scheduling method of the embodiment of the invention, and the scene parameters and the optimal parameter evaluation results are shown in table 1 and table 2:
Figure BDA0004132745500000101
TABLE 1
Figure BDA0004132745500000102
TABLE 2
Based on the scenes of table 1 and table 2, the results of the carrying capacity obtained by adopting the carrying capacity scheduling method according to the embodiment of the present application and the carrying capacity obtained by not adopting the carrying capacity scheduling method are shown in fig. 2, wherein in the histogram of each scene, the left histogram is the carrying capacity when the scheduling algorithm is not used, and the right histogram is the carrying capacity when the scheduling algorithm is used; therefore, the average number of people transported in the park can be effectively improved by adopting the transport capacity scheduling method.
The capacity scheduling method provided by the embodiment of the invention obtains the first information; the first information characterizes the state of vehicles in the park; acquiring corresponding second information for each site in at least one site; the second information at least comprises information related to passengers in the station; determining corresponding third information based on the second information; the third information characterizes passenger distribution conditions of different target stations; determining fourth information; the fourth information represents constraint conditions used when determining a vehicle driving path, and the fourth information at least comprises information related to vehicles in a park and traffic planning information of the vehicles; determining a travel path of each of the at least one vehicle at the station using a scheduling model based on the third information and the fourth information; according to road conditions and passenger travel demands, a mathematical model is utilized to determine a vehicle travel path, namely, the transportation capacity scheduling is adjusted by combining real-time road conditions and transportation demands, so that the rationality of the transportation capacity scheduling result can be improved, and the efficiency of transporting passengers by the vehicle and the vehicle utilization rate are improved; further, as the scheduling is performed based on the overall planning of all vehicles in the station, the comprehensiveness and rationality of the capacity scheduling result are improved, and parameters of multiple dimensions such as vehicle information, traffic planning and the like are considered in the process of determining the scheduling result, so that the balance and effectiveness of the capacity scheduling result can be improved, and the vehicle utilization rate is improved.
In order to implement the capacity scheduling method of the present invention, an embodiment of the present invention further provides a capacity scheduling device, which is disposed on an electronic device, as shown in fig. 3, and the device includes:
an acquiring unit 301, configured to acquire first information; the first information characterizes the state of vehicles in the park; and acquiring corresponding second information for each site in the at least one site; the second information at least comprises information related to passengers in the station;
a processing unit 302, configured to determine corresponding third information based on the second information; the third information characterizes passenger distribution conditions of different target stations; determining fourth information; the fourth information represents constraint conditions used when determining a vehicle driving path, and the fourth information at least comprises information related to vehicles in a park and traffic planning information of the vehicles; and determining a travel path of each of the at least one vehicle at the station using a scheduling model based on the third information and the fourth information.
In an embodiment, the processing unit 302 is further configured to:
updating the target parameters and updating the scheduling model based on the updated target parameters; wherein,,
the target parameter comprises at least one of:
total number of passenger transportation;
vehicle operating mileage;
delivery service time.
In an embodiment, the processing unit 302 is specifically configured to:
acquiring position related information and ticket related information of passengers in a park; determining a destination station for each passenger in the station based on the location-related information and ticket-related information; determining first information based on a destination station for each passenger within the station;
and/or the number of the groups of groups,
acquiring park history tour data of a first time period; the park history tour data at least comprises a history passenger number of a first time period in a website; first information is determined based on the campus historical tour data.
In an embodiment, the processing unit 302 is specifically configured to:
and determining the driving path of each vehicle in at least one vehicle in the station by using a scheduling model and a searching algorithm based on the third information and the fourth information.
In an embodiment, the processing unit 302 may be further configured to:
the first information is updated according to a first preset period and/or the second information is updated according to a second preset period.
In an embodiment, the processing unit 302 may be further configured to, after determining a driving path of each of at least one vehicle in the station:
for each of the at least one vehicle, determining a location of a next station based on the corresponding travel path;
and determining the running track from the current station to the next station based on the scheduling model and the A-algorithm.
In an embodiment, the optimization objective of the a-algorithm includes at least a shortest distance from a current station to a next station and a shortest travel time from the current station to the next station.
In an embodiment, the processing unit 302 is specifically configured to:
determining a cost function from the first station to each of the at least one second station for each of the at least one vehicle to obtain at least one cost function;
selecting a second site with the minimum cost function value as the next site of the first site;
a travel path of the vehicle is determined based on the determined next station.
It should be noted that: in the capacity scheduling device provided in the above embodiment, only the division of each program module is used for illustration, and in practical application, the process allocation may be performed by different program modules according to needs, that is, the internal structure of the device is divided into different program modules, so as to complete all or part of the processes described above. In addition, the capacity scheduling device and the capacity scheduling method provided in the foregoing embodiments belong to the same concept, and specific implementation processes thereof are detailed in the method embodiments and are not described herein again.
Based on the hardware implementation of the program modules, and in order to implement the method according to the embodiment of the present invention, an electronic device is further provided according to the embodiment of the present invention, as shown in fig. 4, the electronic device 400 includes:
the communication interface 401 is capable of performing information interaction with other devices, for example, acquiring first information;
a processor 402, connected to the communication interface 401, for implementing information interaction with other devices, and configured to execute the capacity scheduling method provided by one or more of the above technical solutions when running a computer program;
a memory 403, said computer program being stored on said memory 403.
Specifically, the communication interface 401 is configured to obtain first information; the first information characterizes the state of vehicles in the park; and acquiring corresponding second information for each site in the at least one site; the second information at least comprises information related to passengers in the station;
the processor 402 is configured to determine corresponding third information based on the second information; the third information characterizes passenger distribution conditions of different target stations; determining fourth information; the fourth information represents constraint conditions used when determining a vehicle driving path, and the fourth information at least comprises information related to vehicles in a park and traffic planning information of the vehicles; and determining a travel path of each of the at least one vehicle at the station using a scheduling model based on the third information and the fourth information.
In an embodiment, the processor 402 is further configured to:
updating the target parameters and updating the scheduling model based on the updated target parameters; wherein,,
the target parameter comprises at least one of:
total number of passenger transportation;
vehicle operating mileage;
delivery service time.
In one embodiment, the processor 402 is specifically configured to:
acquiring position related information and ticket related information of passengers in the park by using the communication interface 401; determining a destination station for each passenger in the station based on the location-related information and ticket-related information; determining first information based on a destination station for each passenger within the station;
and/or the number of the groups of groups,
acquiring park history tour data for a first time period using the communication interface 401; the park history tour data at least comprises a history passenger number of a first time period in a website; first information is determined based on the campus historical tour data.
In one embodiment, the processor 402 is specifically configured to:
and determining the driving path of each vehicle in at least one vehicle in the station by using a scheduling model and a searching algorithm based on the third information and the fourth information.
In an embodiment, the processor 402 may be further configured to:
the first information is updated according to a first preset period and/or the second information is updated according to a second preset period.
In one embodiment, the processor 402, after determining the travel path of each of the at least one vehicle within the station, may be further configured to:
for each of the at least one vehicle, determining a location of a next station based on the corresponding travel path;
and determining the running track from the current station to the next station based on the scheduling model and the A-algorithm.
In an embodiment, the optimization objective of the a-algorithm includes at least a shortest distance from a current station to a next station and a shortest travel time from the current station to the next station.
In one embodiment, the processor 402 is specifically configured to:
determining a cost function from the first station to each of the at least one second station for each of the at least one vehicle to obtain at least one cost function;
selecting a second site with the minimum cost function value as the next site of the first site;
a travel path of the vehicle is determined based on the determined next station.
It should be noted that: the specific processing of the processor 402 may be understood with reference to the methods described above.
Of course, in actual practice, the various components in electronic device 400 are coupled together via bus system 404. It is appreciated that the bus system 404 serves to facilitate connected communications between these components. The bus system 404 includes a power bus, a control bus, and a status signal bus in addition to the data bus. But for clarity of illustration the various buses are labeled as bus system 404 in fig. 4.
The memory 403 in embodiments of the present invention is used to store various types of data to support the operation of the electronic device 400. Examples of such data include: any computer program for operating on electronic device 400.
In an exemplary embodiment, the present invention also provides a storage medium, i.e. a computer storage medium, in particular a computer readable storage medium, for example comprising a memory 403 storing a computer program executable by the processor 402 of the electronic device 400 for performing the steps of the trajectory prediction method described above.
It should be noted that the above embodiments are only for illustrating the technical solution of the present invention, and other modifications and equivalents thereof by those skilled in the art should be included in the scope of the claims of the present invention without departing from the spirit and scope of the technical solution of the present invention. In addition, the embodiments of the present invention may be arbitrarily combined without any collision.

Claims (10)

1. A capacity scheduling method, applied to an electronic device, comprising:
acquiring first information; the first information characterizes the state of vehicles in the park;
acquiring corresponding second information for each site in at least one site; the second information at least comprises information related to passengers in the station;
determining corresponding third information based on the second information; the third information characterizes passenger distribution conditions of different target stations;
determining fourth information; the fourth information represents constraint conditions used when determining a vehicle driving path, and the fourth information at least comprises information related to vehicles in a park and traffic planning information of the vehicles;
and determining a driving path of each vehicle in the at least one vehicle at the station by using a scheduling model based on the third information and the fourth information.
2. The method of claim 1, the method further comprising:
updating the target parameters and updating the scheduling model based on the updated target parameters; wherein,,
the target parameter comprises at least one of:
total number of passenger transportation;
vehicle operating mileage;
delivery service time.
3. The method of claim 1, wherein the obtaining the first information comprises:
acquiring position related information and ticket related information of passengers in a park; determining a destination station for each passenger in the station based on the location-related information and ticket-related information; determining first information based on a destination station for each passenger within the station;
and/or the number of the groups of groups,
acquiring park history tour data of a first time period; the park history tour data at least comprises a history passenger number of a first time period in a website; first information is determined based on the campus historical tour data.
4. The method of claim 1, wherein the determining a travel path for each of the at least one vehicle at the station using a scheduling model based on the third information and the fourth information comprises:
and determining the driving path of each vehicle in at least one vehicle in the station by using a scheduling model and a searching algorithm based on the third information and the fourth information.
5. The method according to claim 4, wherein the method further comprises:
the first information is updated according to a first preset period and/or the second information is updated according to a second preset period.
6. The method of claim 4, wherein after determining the travel path of each of the at least one vehicle within the station, the method further comprises:
for each of the at least one vehicle, determining a location of a next station based on the corresponding travel path;
and determining the running track from the current station to the next station based on the scheduling model and the A-algorithm.
7. The method according to claim 6, wherein the optimization objective of the a-algorithm includes at least a shortest distance from a current station to a next station and a shortest travel time from the current station to the next station.
8. The method according to claim 1, wherein the method further comprises:
determining a cost function from the first station to each of the at least one second station for each of the at least one vehicle to obtain at least one cost function;
selecting a second site with the minimum cost function value as the next site of the first site;
a travel path of the vehicle is determined based on the determined next station.
9. An electronic device, comprising: a processor and a memory for storing a computer program capable of running on the processor,
wherein the processor is adapted to perform the steps of the method of any of claims 1 to 7 when the computer program is run.
10. A storage medium having stored thereon a computer program, which when executed by a processor performs the steps of the method according to any of claims 1 to 7.
CN202310264969.9A 2023-03-17 2023-03-17 Method and device for scheduling capacity, electronic equipment and storage medium Pending CN116307580A (en)

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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117689185A (en) * 2024-02-02 2024-03-12 深圳市拓远能源科技有限公司 Equipment data scheduling optimization method based on Internet of things

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117689185A (en) * 2024-02-02 2024-03-12 深圳市拓远能源科技有限公司 Equipment data scheduling optimization method based on Internet of things
CN117689185B (en) * 2024-02-02 2024-05-07 深圳市拓远能源科技有限公司 Equipment data scheduling optimization method based on Internet of things

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