CN111815937B - Control method and device for dispatching of super-parking vehicles, storage medium and electronic equipment - Google Patents
Control method and device for dispatching of super-parking vehicles, storage medium and electronic equipment Download PDFInfo
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- CN111815937B CN111815937B CN201911155432.9A CN201911155432A CN111815937B CN 111815937 B CN111815937 B CN 111815937B CN 201911155432 A CN201911155432 A CN 201911155432A CN 111815937 B CN111815937 B CN 111815937B
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- G08—SIGNALLING
- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/20—Monitoring the location of vehicles belonging to a group, e.g. fleet of vehicles, countable or determined number of vehicles
- G08G1/202—Dispatching vehicles on the basis of a location, e.g. taxi dispatching
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- G08G—TRAFFIC CONTROL SYSTEMS
- G08G1/00—Traffic control systems for road vehicles
- G08G1/123—Traffic control systems for road vehicles indicating the position of vehicles, e.g. scheduled vehicles; Managing passenger vehicles circulating according to a fixed timetable, e.g. buses, trains, trams
Abstract
The embodiment of the disclosure discloses a control method, a control device, a storage medium and electronic equipment for dispatching an overstocked vehicle, wherein the control method comprises the following steps: acquiring the number of overtaking parking vehicles in each parking lot station in the current time period; determining a number of overstock vehicles for each of the parking lot stations in a next time period; determining a preferred assigned vehicle in each of the parking stations based on characteristics of the parking stations. The vehicle sharing method and the vehicle sharing system have the advantages that the advantages of the operation strategy of the shared vehicle, the use data of the shared vehicle, technical support and the algorithm are combined in many aspects, the problems that the overtime parking quantity of the shared vehicle is large and the shared vehicle cannot be taken away by a user in a short time are effectively solved, the parking pressure of a shared vehicle parking station is reduced, the experience of taking and returning the vehicle by the user is improved, and the scheduling cost of the shared vehicle is reduced.
Description
Technical Field
The present disclosure relates to the field of vehicle scheduling technologies, and in particular, to a control method and apparatus for scheduling an extra-parked vehicle, a storage medium, and an electronic device.
Background
In many cities, sharing automobiles is not a novelty. Similar to a shared bicycle, the driving pleasure of 'stop and go on' can be experienced by paying a deposit and completing identity authentication. With the expansion of the shared automobile market, how to improve user experience and realize standardized operations are challenging the wisdom of the shared automobile operators. The shared automobile further meets the requirement pain of users, on one hand, under the background that road and parking space resources become scarce resources in cities, the shared automobile can provide convenience for people to go out without increasing the quantity of purchased vehicles; on the other hand, with the continuous rising of the cost of purchasing and maintaining the cars in cities, the shared cars meet the travel requirement of people that people only need to buy but not maintain, and the cost is not high. Shared cars also face many challenges, however, in situations where credit systems are not fully covered, shared cars face operational maintenance and regulatory vacuum zones. The 'difficult parking, expensive parking and serious over-parking phenomenon' is a pain point generally faced by car owners, all the existing shared cars need to get and return at a designated station, the demand of some parking lots is large, especially at a peak time point, the supply of parking lots is not in accordance with the demand, so that the users often have over-parking behaviors, the over-parking can bring a series of adverse consequences, for example, the users need to pay over-parking cost, the next user is difficult to find the car, the cooperative parking station has many complaints, even offline dispatchers are needed to dispatch the car, and the greater dispatching cost can be brought. Therefore, how to meet the requirement of a user on convenient travel and ensure the high efficiency of the operation of the shared automobile is a problem to be solved urgently.
At present, partial cooperative parking stations and free parking stations for sharing automobiles have strong requirements in certain time periods, the number of parked vehicles exceeds the number of cooperative fixed parking spaces, the overtaking phenomenon is serious, and the following problems are caused: (1) the user experience is poor, the user needs to pay off the line after the cooperative station is over stopped, and the phenomena of payment refusing and complaint of the user frequently occur; (2) influence the cooperative relationship with the parking lot station, the problem of overtime parking leads to more complaints of the cooperative parking lot station, partial free parking lots station is stopped, and even partial users park vehicles to the entrance of the parking lot station or roadside after overtime parking, which influences the social order; (3) the super-parking vehicles in the parking lot station continuously generate parking fee, and a dispatcher is required to transfer the vehicles away, so that larger dispatching cost is generated. Although the conventional methods such as collecting the overdue parking fee for the user, prompting the user to reduce the overdue parking behavior, or guiding the user to park at other parking stations with parking spaces and scheduling vehicles to the overdue parking stations through offline operators solve the problem of the overdue parking vehicles, although the methods can reduce the overdue parking phenomenon to a certain extent or timely relieve the problem when the overdue parking is serious, the problems of poor user experience, high scheduling cost and the like are brought.
Disclosure of Invention
In view of this, the embodiments of the present disclosure provide a control method and apparatus for scheduling an ultra-stopped vehicle, a storage medium, and an electronic device, so as to solve the problems in the prior art that the existing method for solving the ultra-stopped vehicle is inefficient, poor in user experience, and high in scheduling cost.
In one aspect, an embodiment of the present disclosure provides a control method for dispatching a superstored vehicle, including the following steps: acquiring the number of overtaking parking vehicles in each parking lot station in the current time period; determining a number of overstock vehicles for each of the parking lot stations in a next time period; determining a preferred assigned vehicle in each of the parking stations based on characteristics of the parking stations.
In some embodiments, said determining the number of overstroke vehicles for each of said parking stations in the next time period comprises: determining the number of incoming vehicles and the number of outgoing vehicles of each parking lot station in the next time period through a time series model; determining a number of overstock vehicles for each of the parking lot stations in a next time period based on the number of incoming vehicles and the number of outgoing vehicles.
In some embodiments, said determining a preferred vehicle in each of said parking stations based on characteristics of said parking stations further comprises: determining a type of reward for the preferentially-assigned vehicle, and assigning a corresponding type of reward to the preferentially-assigned vehicle.
In some embodiments, said determining a reward type for said prioritized vehicle, said assigning said prioritized vehicle based on said reward type comprises: determining a probability of use of each of the prioritized vehicles for setting a different type of reward; setting a first type award to a first prioritized vehicle for a next time period when the usage probability of the first prioritized vehicle for the first type award is greater than a preset probability.
On the other hand, the disclosed embodiment provides a control device for dispatching a superstored vehicle, which comprises the following devices: an acquisition module for acquiring the number of overstock vehicles in each parking lot station within a current time period; a determination module for determining a number of overstock vehicles for each of the parking stations in a next time period; a setup module that sets up a preferentially assigned vehicle in each of the parking stations based on characteristics of the parking stations.
In some embodiments, the determining module comprises: a first determination unit for determining the number of incoming vehicles and the number of outgoing vehicles for each of the parking lot stations in a next time period through a time series model; a second determination unit for determining the number of overstock vehicles for each of the parking stations in a next time period based on the number of incoming vehicles and the number of outgoing vehicles.
In some embodiments, further comprising: and the allocation module is used for determining the type of the reward aiming at the preferentially allocated vehicle and allocating the corresponding type of reward to the preferentially allocated vehicle.
In some embodiments, the assignment module comprises: a third determination unit for determining a probability of use of each of the preferentially assigned vehicles for setting different types of awards; a setting unit configured to set a first-type bonus to a first-prioritized vehicle for a next time period when the usage probability of the first-prioritized vehicle for the first-type bonus is greater than a preset probability.
In another aspect, an embodiment of the present disclosure provides a storage medium storing a computer program, where the computer program is executed by a processor to implement the steps of the method in any one of the above technical solutions.
In another aspect, an embodiment of the present disclosure provides an electronic device, which at least includes a memory and a processor, where the memory stores a computer program, and the processor implements the steps of the method in any one of the above technical solutions when executing the computer program on the memory.
The method comprises the steps of obtaining the number of the overtime vehicles in the current time period and determining the number of the overtime vehicles in the next time period aiming at the parking lot station; therefore, preferentially-distributed vehicles are determined based on the characteristics of the parking lot stations, the reward types aiming at the preferentially-distributed vehicles can be further determined, and corresponding types of rewards are distributed to the preferentially-distributed vehicles, so that the problems that the number of the shared vehicles exceeding parking is large and the shared vehicles cannot be taken away by users in a short time can be effectively solved, the parking pressure of the shared vehicle parking lot stations is reduced, the experience of the users in getting and returning the vehicles is improved, and the scheduling cost of the shared vehicles is reduced.
Drawings
In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings needed to be used in the description of the embodiments or the prior art will be briefly introduced below, it is obvious that the drawings in the following description are only some embodiments described in the present disclosure, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a schematic structural view of a shared vehicle parking station to which the present disclosure relates;
fig. 2 is a flowchart of a control method according to a first embodiment of the disclosure;
fig. 3 is a flowchart of a control method according to a first embodiment of the disclosure;
fig. 4 is a flowchart of a control method according to a second embodiment of the disclosure;
fig. 5 is a flowchart of a control method according to a second embodiment of the disclosure;
fig. 6 is a block diagram of a control device according to a third embodiment of the present disclosure;
fig. 7 is a block diagram of a control device according to a fourth embodiment of the present disclosure;
fig. 8 is a block diagram of an electronic device according to a sixth embodiment of the present disclosure.
Reference numerals:
10-an acquisition module; 20-a determination module; 30-setting a module; 40-distribution module.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present disclosure more clear, the technical solutions of the embodiments of the present disclosure will be described below clearly and completely with reference to the accompanying drawings of the embodiments of the present disclosure. It is to be understood that the described embodiments are only a few embodiments of the present disclosure, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the described embodiments of the disclosure without any inventive step, are within the scope of protection of the disclosure.
Unless otherwise defined, technical or scientific terms used herein shall have the ordinary meaning as understood by one of ordinary skill in the art to which this disclosure belongs. The use of "first," "second," and similar terms in this disclosure is not intended to indicate any order, quantity, or importance, but rather is used to distinguish one element from another. The word "comprising" or "comprises", and the like, means that the element or item listed before the word covers the element or item listed after the word and its equivalents, but does not exclude other elements or items. The terms "connected" or "coupled" and the like are not restricted to physical or mechanical connections, but may include electrical connections, whether direct or indirect. "upper", "lower", "left", "right", and the like are used merely to indicate relative positional relationships, and when the absolute position of the object being described is changed, the relative positional relationships may also be changed accordingly.
To maintain the following description of the embodiments of the present disclosure clear and concise, a detailed description of known functions and known components have been omitted from the present disclosure.
The first embodiment of the disclosure relates to a control method for scheduling overstocked vehicles, which is mainly used for scheduling the overstocked vehicles to reduce the number and proportion of the overstocked vehicles so as to improve the mobility and the operation efficiency of the vehicles. In the following description of the present embodiment, the overdue parking is represented by an overdue parking, and it should be noted that the vehicle refers to a shared vehicle that can be rented by a user, the shared vehicle needs to be parked in a shared vehicle parking station in an unused state, the shared vehicle parking station is used for operation and parking of the shared vehicle, and the user can take and return vehicles at the shared vehicle parking station.
As shown in fig. 1, the shared vehicle parking lot includes a lot management module and a user car picking module in management, where the lot management module is used as a basis for scheduling control of shared vehicles, and is used to schedule and manage the shared vehicle parking lot and the parked shared vehicles therein, and further includes a newly added lot sub-module, a lot editing sub-module, a lot stop cooperation sub-module, and an attribute configuration sub-module. The basic information of the shared vehicle parking stations can be set and adjusted in the attribute configuration submodule, wherein the basic information comprises position information of the parking stations, parking space amount information, charging rules or standards of the shared vehicle for ordinary parking or overdue parking and the like, and therefore, corresponding station basic information units, station charging rule units and station overbite strategy units can be set in the attribute configuration module.
The user vehicle taking module comprises a first reduction overtaking stopping submodule and a second reduction overtaking stopping submodule, wherein the first reduction overtaking stopping submodule adjusts or interferes the behavior of a user renting the vehicle by giving rewards on the basis of a control strategy for dynamically adjusting the reward degree, and the second reduction overtaking stopping submodule reduces the number of the overtaking stopping vehicles in the parking lot station on the basis of a work order scheduling strategy for real-time decision making.
Considering that the parking spaces for accommodating the shared vehicles in each shared vehicle parking station are limited, some situations may result in too long time for the parked vehicles or the number of the parked vehicles exceeds the specified number of parking spaces, i.e. the shared vehicle overstroke problem in the parking station related to the present embodiment, the vehicle overstroke means that in the designated parking station, the number of the shared vehicles already parked exceeds the specified maximum number of parking spaces in the parking station.
In this embodiment, the shared vehicle parking lot station reduces the number of overstocked vehicles based on a control strategy that dynamically adjusts the extent of awards through operation of a first reduce overstock sub-module in the user pick-up module. For the first reduce overstock submodule, a control method for reducing the number of overstock vehicles thereof is shown in fig. 2, and includes the steps of:
s101, acquiring the number of the overtaking parking vehicles in each parking lot station in the current time period.
As described above, vehicle overstock in a shared vehicle parking station refers to the number of shared vehicles that have been parked in a given shared vehicle parking station exceeding a maximum allowed number of parked vehicles specified in that parking station, where the maximum allowed number of parked vehicles may be the number of shared vehicles that can accommodate the greatest parking in a given parking area of the given shared vehicle parking station, or the number of vehicles allowed to park the greatest determined between the owner of the vehicle and the parking station manager.
In this step, the number of the super-parked vehicles at each parking lot station in the current time period may be obtained in a manner easily known by those skilled in the art, for example, whether a shared vehicle is parked in each parking lot station may be detected by a parking lot parking detection device arranged in each parking lot in the current time period, or may be manually counted, where the time period may be specifically set according to requirements of vehicle management and scheduling, for example, 12 hours, 24 hours, 48 hours, and the like. If it is desired to improve the efficiency of use of the vehicles or to accelerate the movement of the vehicles in the area where the parking lot is located, the time period may be set to be shorter, so that the parking condition of the shared vehicle in the shared vehicle parking lot can be obtained as soon as possible, so as to facilitate the dispatch as soon as possible.
S102, determining the number of the overtaking parking vehicles of each parking lot station in the next time period.
When the user needs to use the shared vehicle, the user can operate on an APP of a handheld terminal of a mobile phone to rent the shared vehicle parked in a shared vehicle parking station, the user can pick up the vehicle from the parking station, and the vehicle is returned to the parking station after the use. Therefore, the shared vehicles flow between the users and the shared vehicle parking stations in real time, so that the shared vehicle parking stations may have overstocked vehicles in the current time period, and in the case that the vehicles flow continuously in real time, the users rent a large number of vehicles in the parking stations, resulting in that the parking stations may not have redundant overstocked vehicles in the next time period, so that the parking stations are not required to schedule or process the overstocked vehicles in a reward manner, and if the users rent a small number of vehicles in the parking stations, the parking stations may still have the overstocked vehicles in the next time period, the redundant overstocked vehicles need to be processed, so that the number of the overstocked vehicles in the next time period of the shared vehicle parking stations needs to be reasonably estimated or determined.
In this step, determining the number of overstroked vehicles in the next time period for each of the parking stations may be performed as shown in fig. 3 in the following manner:
s201, determining the number of inflow vehicles and the number of outflow vehicles of each parking lot station in the next time period through a time series model.
Specifically, the time series model is a model completed through machine learning and training, and the learning and training process is performed based on characteristic parameters, wherein the characteristic parameters in the time series model include at least one of a maximum allowable parking number of the parking lot station, a rental car task completion amount in a previous time period, a rental car task completion amount in a current time period, an active vehicle number in a certain distance range from the parking lot station, a bus station number in a certain distance range from the parking lot station, a subway station number in a certain distance range from the parking lot station, a commercial activity index of the location of the parking lot station, a date and time, a maximum allowable parking number, and the like. Determining the number of incoming vehicles and the number of outgoing vehicles for each of the parking stations in the next time period via a time series model can help to obtain the vehicle flow situation for each parking station.
S202, determining the number of the overstock vehicles of each parking lot station in the next time period based on the number of the inflow vehicles and the number of the outflow vehicles.
After obtaining the number of incoming vehicles and the number of outgoing vehicles in the next time period through the above steps, the number of overstock vehicles in the next time period for each of the parking lot stations may be determined based on the logic of vehicle flow, which may be performed by the following formula:
the number of overstock vehicles in the next time period is equal to the number of parked vehicles at the current moment + the number of incoming vehicles in the next time period-the number of outgoing vehicles in the next time period-the maximum allowable parking number. The number of parked vehicles at each parking lot station at the current time can be obtained by a method easily known by a person skilled in the art, for example, whether shared vehicles are parked in each parking lot of each parking lot station can be detected by a parking lot parking detection device arranged on each parking lot in the current time period, or certainly, a manual counting method can be used. The maximum allowable number of parked vehicles may be the number of shared vehicles that can be accommodated in the designated parking area of the designated shared vehicle parking station, or the maximum allowable number of parked vehicles determined between the owner of the vehicle and the parking station manager.
S103, determining the vehicles which are preferentially distributed in each parking station based on the characteristics of the parking stations.
In this step, the vehicles to be preferentially allocated are determined in each of the parking stations based on the characteristics of the shared vehicle parking station or the type of the parking station, that is, in step S102, in the case where there are overstocked vehicles in the shared vehicle parking station within the next time period, the vehicles to be preferentially allocated in each parking lot are determined according to the different characteristics or types of the parking stations, where the preferentially allocated vehicles to be determined include the number of vehicles and the specific vehicles.
Further, the characteristics of the shared vehicle parking station or the type of parking station referred to in this step may be determined from a number of factors, so as to determine the vehicles in each of said parking stations that are preferentially allocated based on the characteristics of the shared vehicle parking station or the type of parking station, for example, the consideration may be from the factors of improving the efficiency of transferring the user to pick up the shared vehicle or improving the mobility of the shared vehicle, the characteristics or types of the shared vehicle parking stations are determined in a fee-based manner in which the shared vehicles are parked in the parking stations, for example a parking station with the first characteristic may be a parking station in a time-unlimited manner for a fixed parking space, i.e. a fixed fee is charged only for the parking spaces in the parking station in which the shared vehicle is parked regardless of the parking time of the shared vehicle at the parking station, in such a parking station with the first feature, N shared vehicles can be randomly determined as preferentially assigned vehicles; the parking station having the second feature may be a parking station that charges a fee according to the parking time of the shared vehicle in the parking station, that is, different fees are charged according to the actual parking time of the shared vehicle in the parking station, further, such a parking station may charge fees according to different standards in accordance with different parking times or time-divided stages, the longer the parking time is, the higher the fee is charged, the higher the peak parking is than the fee charged for overnight parking, in such a parking station having the second feature, the parking time or parking fee of the shared vehicle in the parking station may be ranked from high to low, and the M vehicles with the longest parking time or the highest parking fee are selected as the vehicles preferentially allocated in the parking station; of course, there may be other features or types of parking stations, and the embodiment is not limited herein.
This is disclosed through obtain in the current time cycle and confirm the number of the super stop vehicle in the next time cycle to parking lot station, can be with the operation strategy of shared vehicle, the use data of shared vehicle, the many-sided advantage set of technical support and algorithm, effectively solve the super stop quantity of shared vehicle more and can't be taken away the problem by the user in the short time, alleviateed the parking pressure of shared vehicle parking lot station, promote the experience that the user got the car and still accomplished, reduce the scheduling cost of shared vehicle.
In a second embodiment of the present disclosure, a method for scheduling an ultra-stop vehicle is provided, which includes steps S101-S103, and after the step S103 is executed and a vehicle to be preferentially allocated is determined in each of the parking lot stations based on the characteristics of the parking lot stations, as shown in fig. 4, the method further includes the following steps:
s104, determining the reward types of the vehicles which are preferentially distributed, and distributing the corresponding type of rewards to the vehicles which are preferentially distributed.
Further, in order that the preferentially assigned vehicle in each parking lot station determined by step S103 can be selected and rented by the user through the APP as soon as possible, feedback may be made with respect to the determined preferentially assigned vehicle to mobilize the aggressiveness of the user' S selection of the preferentially assigned vehicle. This feedback may be embodied in the present embodiment as a reward for the user selecting a vehicle to be preferentially allocated. By giving different vehicles different types of rewards among the prioritized vehicles based on the situation of the different vehicles, the prioritized vehicles can be expedited to be selected and rented by the user, further reducing the number of overstocked vehicles. As shown in fig. 5, this is performed in the following manner:
s301, determining the use probability of each preferentially-distributed vehicle for different types of rewards.
In this step, it is first necessary to determine the type of bonus to be awarded to the vehicle assigned with priority, and here, the bonus is described in the form of a red pack as bonus, assuming that bonus types of X1, X2, X3 …, Xn denominations are awarded to the red pack of the vehicle assigned with priority, and X1< X2< X3< … < Xn, the probability p (Xi) of each preferentially assigned vehicle for use by a red-covered user of denomination Xi can be predicted or determined by the XGboost decision tree model, when the XGBoost decision tree model is used for prediction or determination, historical data, used by users, of shared vehicles with the red pack awards of the Xi are selected as positive samples, historical data, not used by the users, of the shared vehicles with the red pack awards of the Xi are selected as negative samples, two-classification calculation is carried out, and finally the use probability of each red pack denomination award to each vehicle preferentially distributed is determined.
S302, when the using probability of the first preferentially distributed vehicle to the first type reward is larger than the preset probability, the first type reward is set to the first preferentially distributed vehicle in the next time period.
In this step, when each preferentially-allocated vehicle is used with a probability p (Xi) > Ps of red pack of the denomination Xi, which is a parameter threshold, it is considered that the preferentially-allocated vehicle is predetermined and a bonus corresponding to the Xi-denomination red pack is set in the next time period.
Further, if it is expected that the preferentially assigned vehicles will be awarded at the lowest cost, min (Xi) is selected as the red pack award denomination of each preferentially assigned vehicle in the case where all the preferentially assigned vehicles are used by the user for the red pack of the denomination Xi at all the probabilities P (Xi) > Ps, for example, if there are 5 types of the red pack denominations for awarding set for the preferentially assigned vehicles, 5 types of 5 elements, 8 elements, 10 elements, 15 elements, 20 elements, the probabilities P of each preferentially assigned vehicle being used for awarding the red packs of the 5 denominations being set are predicted or determined, respectively, all the red pack denominations satisfying P > Ps are selected, for example, the probabilities of 10 elements, 15 elements, and 20 elements of the red packs being set on each preferentially assigned vehicle being used by the user are all greater than the preset probability, the minimum value among the denominations is selected, that is the red pack of the 10 element denomination being selected to be set on each of the preferentially assigned vehicles, in this way, the overall cost for the reward can be minimized while ensuring that the overstocked vehicle is quickly selected and rented by the user.
In addition, considering that the control strategy based on the first reduction excess stop submodule can dynamically adjust the reward degree, specifically, the control methods of the first embodiment and the second embodiment are used for providing rewards to regulate or intervene the behavior of the user for renting vehicles, and of course, the work order scheduling strategy based on real-time decision by the second reduction excess stop submodule can be further used for reducing the number of excess stop vehicles in the parking lot stations.
The method comprises the steps of obtaining the number of the overtime vehicles in the current time period and determining the number of the overtime vehicles in the next time period aiming at the parking lot station; therefore, preferentially-distributed vehicles are determined based on the characteristics of the parking lot stations, the reward types aiming at the preferentially-distributed vehicles can be further determined, and corresponding types of rewards are distributed to the preferentially-distributed vehicles, so that the problems that the number of the shared vehicles exceeding parking is large and the shared vehicles cannot be taken away by users in a short time can be effectively solved, the parking pressure of the shared vehicle parking lot stations is reduced, the experience of the users in getting and returning the vehicles is improved, and the scheduling cost of the shared vehicles is reduced.
A third embodiment of the present disclosure provides a control device for dispatching a superstored vehicle, which is shown in fig. 6 and includes an obtaining module 10, a determining module 20, and a setting module 30, which are coupled to each other, wherein:
an acquisition module 10 for acquiring the number of overstock vehicles in each parking lot station for the current time period.
Vehicle overstock in a shared vehicle parking station refers to the fact that in a designated shared vehicle parking station, the number of shared vehicles already parked exceeds the maximum allowable parking number specified in the parking station, wherein the maximum allowable parking number may be the number of shared vehicles that can be parked at most in a designated parking area of the designated shared vehicle parking station, or the number of vehicles that are allowed to be parked at most as determined between the owner of the vehicle and the manager of the parking station.
The obtaining of the number of super-parked vehicles at each parking lot station in the current time period through the obtaining module 10 can be implemented in a manner easily known by those skilled in the art, for example, whether a shared vehicle is parked in each parking lot station can be detected through a parking lot parking detection device arranged on each parking lot in the current time period, and of course, a manual counting manner can also be adopted, wherein the time period here can be specifically set according to requirements of vehicle management and scheduling, for example, 12 hours, 24 hours, 48 hours, and the like. If it is desired to improve the efficiency of use of the vehicles or to accelerate the movement of the vehicles in the area where the parking lot is located, the time period may be set to be shorter, so that the parking condition of the shared vehicle in the shared vehicle parking lot can be obtained as soon as possible, so as to facilitate the dispatch as soon as possible.
A determination module 20 for determining a number of overstock vehicles for each of the parking stations in a next time period.
When the user needs to use the shared vehicle, the user can operate on an APP of a handheld terminal of a mobile phone to rent the shared vehicle parked in a shared vehicle parking station, the user can pick up the vehicle from the parking station, and the vehicle is returned to the parking station after the use. Therefore, the shared vehicles flow between the users and the shared vehicle parking stations in real time, so that the shared vehicle parking stations may have overstocked vehicles in the current time period, and in the case that the vehicles flow continuously in real time, the users rent a large number of vehicles in the parking stations, resulting in that the parking stations may not have redundant overstocked vehicles in the next time period, so that the parking stations are not required to schedule or process the overstocked vehicles in a reward manner, and if the users rent a small number of vehicles in the parking stations, the parking stations may still have the overstocked vehicles in the next time period, the redundant overstocked vehicles need to be processed, so that the number of the overstocked vehicles in the next time period of the shared vehicle parking stations needs to be reasonably estimated or determined.
The determination module 20 may include the following components:
a first determination unit for determining the number of incoming vehicles and the number of outgoing vehicles for each of the parking lot stations in a next time period through a time series model.
Specifically, the time series model is a model completed through machine learning and training, and the learning and training process is performed based on characteristic parameters, wherein the characteristic parameters in the time series model include at least one of a maximum allowable parking number of the parking lot station, a rental car task completion amount in a previous time period, a rental car task completion amount in a current time period, an active vehicle number in a certain distance range from the parking lot station, a bus station number in a certain distance range from the parking lot station, a subway station number in a certain distance range from the parking lot station, a commercial activity index of the location of the parking lot station, a date and time, a maximum allowable parking number, and the like. Determining the number of incoming vehicles and the number of outgoing vehicles for each of the parking stations in the next time period via a time series model can help to obtain the vehicle flow situation for each parking station.
A second determination unit for determining the number of overstock vehicles for each of the parking stations in a next time period based on the number of incoming vehicles and the number of outgoing vehicles.
After determining the number of incoming vehicles and the number of outgoing vehicles in the next time period by the first determination unit, the number of overstock vehicles in the next time period for each of the parking lot stations may be determined based on the logic of vehicle flow, which may be done by the following formula:
the number of overstock vehicles in the next time period is equal to the number of parked vehicles at the current moment + the number of incoming vehicles in the next time period-the number of outgoing vehicles in the next time period-the maximum allowable parking number. The number of parked vehicles at each parking lot station at the current time can be obtained by a method easily known by a person skilled in the art, for example, whether shared vehicles are parked in each parking lot of each parking lot station can be detected by a parking lot parking detection device arranged on each parking lot in the current time period, or certainly, a manual counting method can be used. The maximum allowable number of parked vehicles may be the number of shared vehicles that can be accommodated in the designated parking area of the designated shared vehicle parking station, or the maximum allowable number of parked vehicles determined between the owner of the vehicle and the parking station manager.
A setup module 30 for determining a preferred vehicle among each of the parking stations based on characteristics of the parking stations.
Preferentially allocated vehicles are determined in each of the parking stations on the basis of the characteristics of the shared vehicle parking station or the type of the parking station, that is, in the case that it is determined on the basis of the determination module that there are overstocked vehicles within the next time period of the shared vehicle parking station, the vehicles to be preferentially allocated in each parking lot are determined according to the different characteristics or types of the parking stations, wherein the preferentially allocated vehicles to be determined include the number of vehicles and the specific vehicles.
Further, the characteristics of the shared vehicle parking station or the type of parking station may be determined from a number of factors, so as to determine the vehicles in each of said parking stations that are preferentially allocated based on the characteristics of the shared vehicle parking station or the type of parking station, for example, the consideration may be from the factors of improving the efficiency of transferring the user to pick up the shared vehicle or improving the mobility of the shared vehicle, the characteristics or types of the shared vehicle parking stations are determined in a fee-based manner in which the shared vehicles are parked in the parking stations, for example a parking station with the first characteristic may be a parking station in a time-unlimited manner for a fixed parking space, i.e. a fixed fee is charged only for the parking spaces in the parking station in which the shared vehicle is parked regardless of the parking time of the shared vehicle at the parking station, in such a parking station with the first feature, N shared vehicles can be randomly determined as preferentially assigned vehicles; the parking station having the second feature may be a parking station that charges a fee according to the parking time of the shared vehicle in the parking station, that is, different fees are charged according to the actual parking time of the shared vehicle in the parking station, further, such a parking station may charge fees according to different standards in accordance with different parking times or time-divided stages, the longer the parking time is, the higher the fee is charged, the higher the peak parking is than the fee charged for overnight parking, in such a parking station having the second feature, the parking time or parking fee of the shared vehicle in the parking station may be ranked from high to low, and the M vehicles with the longest parking time or the highest parking fee are selected as the vehicles preferentially allocated in the parking station; of course, there may be other features or types of parking stations, and the embodiment is not limited herein.
The method comprises the steps of obtaining the number of the overtime vehicles in the current time period and determining the number of the overtime vehicles in the next time period aiming at the parking lot station; therefore, preferentially-distributed vehicles are determined based on the characteristics of the parking lot stations, the reward types aiming at the preferentially-distributed vehicles can be further determined, and corresponding types of rewards are distributed to the preferentially-distributed vehicles, so that the problems that the number of the shared vehicles exceeding parking is large and the shared vehicles cannot be taken away by users in a short time can be effectively solved, the parking pressure of the shared vehicle parking lot stations is reduced, the experience of the users in getting and returning the vehicles is improved, and the scheduling cost of the shared vehicles is reduced.
In a fourth embodiment of the present disclosure, a control device method for dispatching a superstored vehicle is also provided, which includes an obtaining module 10, a determining module 20 and a setting module 30, and as further shown in fig. 7, includes the following modules:
an assigning module 40 for determining a type of reward for the preferentially assigned vehicle, assigning a corresponding type of reward to the preferentially assigned vehicle.
Further, in order that the preferentially assigned vehicles in each parking lot station determined by the setup module 30 can be selected and rented by the user through the APP as soon as possible, feedback can be made with respect to the determined preferentially assigned vehicles to mobilize the aggressiveness with which the user selects the preferentially assigned vehicle. This feedback may be embodied in the present embodiment as a reward for the user selecting a vehicle to be preferentially allocated. By giving different vehicles different types of rewards among the prioritized vehicles based on the situation of the different vehicles, the prioritized vehicles can be expedited to be selected and rented by the user, further reducing the number of overstocked vehicles. The specific implementation comprises the following parts:
a third determination unit for determining a probability of use of each of the prioritized vehicles for a different type of reward.
By the third determination unit first needing to determine the type of bonus to be awarded for the preferentially allocated vehicle, here, the bonus is explained in the form of a red pack as a bonus, and it is assumed that bonus types of X1, X2, X3 …, Xn denominations are given to the red pack of the preferentially allocated vehicle, and X1< X2< X3< … < Xn, the probability p (Xi) of each preferentially assigned vehicle for use by a red-covered user of denomination Xi can be predicted or determined by the XGboost decision tree model, when the XGBoost decision tree model is used for prediction or determination, historical data, used by users, of shared vehicles with the red pack awards of the Xi are selected as positive samples, historical data, not used by the users, of the shared vehicles with the red pack awards of the Xi are selected as negative samples, two-classification calculation is carried out, and finally the use probability of each red pack denomination award to each vehicle preferentially distributed is determined.
The setting unit is used for setting the first type reward to the first preferentially-distributed vehicle in the next time period when the using probability of the first preferentially-distributed vehicle for the first type reward is larger than the preset probability.
By the setting unit, when each of the priority-assigned vehicles is used for the red pack of the denomination Xi with a probability p (Xi) > Ps, which is a parameter threshold, the priority-assigned vehicle is considered to be predetermined in the next time period and the award corresponding to the Xi denomination red pack is set.
Further, if it is expected that the preferentially assigned vehicles will be awarded at the lowest cost, min (Xi) is selected as the red pack award denomination of each preferentially assigned vehicle in the case where all the preferentially assigned vehicles are used by the user for the red pack of the denomination Xi at all the probabilities P (Xi) > Ps, for example, if there are 5 types of the red pack denominations for awarding set for the preferentially assigned vehicles, 5 types of 5 elements, 8 elements, 10 elements, 15 elements, 20 elements, the probabilities P of each preferentially assigned vehicle being used for awarding the red packs of the 5 denominations being set are predicted or determined, respectively, all the red pack denominations satisfying P > Ps are selected, for example, the probabilities of 10 elements, 15 elements, and 20 elements of the red packs being set on each preferentially assigned vehicle being used by the user are all greater than the preset probability, the minimum value among the denominations is selected, that is the red pack of the 10 element denomination being selected to be set on each of the preferentially assigned vehicles, in this way, the overall cost for the reward can be minimized while ensuring that the overstocked vehicle is quickly selected and rented by the user.
The method comprises the steps of obtaining the number of the overtime vehicles in the current time period and determining the number of the overtime vehicles in the next time period aiming at the parking lot station; therefore, preferentially-distributed vehicles are determined based on the characteristics of the parking lot stations, the reward types aiming at the preferentially-distributed vehicles can be further determined, and corresponding types of rewards are distributed to the preferentially-distributed vehicles, so that the problems that the number of the shared vehicles exceeding parking is large and the shared vehicles cannot be taken away by users in a short time can be effectively solved, the parking pressure of the shared vehicle parking lot stations is reduced, the experience of the users in getting and returning the vehicles is improved, and the scheduling cost of the shared vehicles is reduced.
A fifth embodiment of the present disclosure provides a storage medium, which is a computer-readable medium storing a computer program, which when executed by a processor implements the method provided in any embodiment of the present disclosure, including the following steps S11 to S13:
s11, acquiring the number of the overtaking parking vehicles in each parking lot station in the current time period;
s12, determining the number of overtaking vehicles of each parking lot station in the next time period;
s13, determining a vehicle to be preferentially assigned in each of the parking stations based on the characteristics of the parking stations.
The computer program, when executed by the processor, determines the number of overstroked vehicles for each of said parking stations in a next time period, is specifically executed by the processor to perform the steps of: determining the number of incoming vehicles and the number of outgoing vehicles of each parking lot station in the next time period through a time series model; determining a number of overstock vehicles for each of the parking lot stations in a next time period based on the number of incoming vehicles and the number of outgoing vehicles.
The computer program is executed by the processor to determine a preferred vehicle for allocation in each of said parking stations based on characteristics of said parking stations and is further executed by the processor to: determining a type of reward for the preferentially-assigned vehicle, and assigning a corresponding type of reward to the preferentially-assigned vehicle.
The computer program is executed by the processor to determine the type of the reward for the preferentially-assigned vehicle, and when the preferentially-assigned vehicle is assigned with the corresponding type of the reward, the computer program is further executed by the processor to: determining a probability of use of each of the prioritized vehicles for setting a different type of reward; setting a first type award to a first prioritized vehicle for a next time period when the usage probability of the first prioritized vehicle for the first type award is greater than a preset probability.
The method comprises the steps of obtaining the number of the overtime vehicles in the current time period and determining the number of the overtime vehicles in the next time period aiming at the parking lot station; therefore, preferentially-distributed vehicles are determined based on the characteristics of the parking lot stations, the reward types aiming at the preferentially-distributed vehicles can be further determined, and corresponding types of rewards are distributed to the preferentially-distributed vehicles, so that the problems that the number of the shared vehicles exceeding parking is large and the shared vehicles cannot be taken away by users in a short time can be effectively solved, the parking pressure of the shared vehicle parking lot stations is reduced, the experience of the users in getting and returning the vehicles is improved, and the scheduling cost of the shared vehicles is reduced.
A sixth embodiment of the present disclosure provides an electronic device, a schematic structural diagram of the electronic device may be as shown in fig. 8, where the electronic device includes at least a memory 901 and a processor 902, the memory 901 stores a computer program, and the processor 902, when executing the computer program on the memory 901, implements the method provided in any embodiment of the present disclosure. Illustratively, the electronic device computer program steps are as follows S21-S23:
s21, acquiring the number of the overtaking parking vehicles in each parking lot station in the current time period;
s22, determining the number of overtaking vehicles of each parking lot station in the next time period;
s23, determining a vehicle to be preferentially assigned in each of the parking stations based on the characteristics of the parking stations.
The processor, in executing the computer program stored on the memory, in determining the number of overstroked vehicles for each of the parking stations in the next time period, further executes the computer program of: determining the number of incoming vehicles and the number of outgoing vehicles of each parking lot station in the next time period through a time series model; determining a number of overstock vehicles for each of the parking lot stations in a next time period based on the number of incoming vehicles and the number of outgoing vehicles.
The processor, after executing the computer program stored on the memory to determine a preferred vehicle among each of the parking stations based on the characteristics of the parking stations, further executes the computer program of: determining a type of reward for the preferentially-assigned vehicle, and assigning a corresponding type of reward to the preferentially-assigned vehicle.
The processor, in executing the computer program stored on the memory to determine a type of reward for the preferentially-assigned vehicle, to assign a corresponding type of reward to the preferentially-assigned vehicle, further executes: determining a probability of use of each of the prioritized vehicles for setting a different type of reward; setting a first type award to a first prioritized vehicle for a next time period when the usage probability of the first prioritized vehicle for the first type award is greater than a preset probability.
The method comprises the steps of obtaining the number of the overtime vehicles in the current time period and determining the number of the overtime vehicles in the next time period aiming at the parking lot station; therefore, preferentially-distributed vehicles are determined based on the characteristics of the parking lot stations, the reward types aiming at the preferentially-distributed vehicles can be further determined, and corresponding types of rewards are distributed to the preferentially-distributed vehicles, so that the problems that the number of the shared vehicles exceeding parking is large and the shared vehicles cannot be taken away by users in a short time can be effectively solved, the parking pressure of the shared vehicle parking lot stations is reduced, the experience of the users in getting and returning the vehicles is improved, and the scheduling cost of the shared vehicles is reduced.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may interconnect with any form or medium of digital data communication (e.g., a communications network). Examples of communication networks include a Local Area Network (LAN), a Wide Area Network (WAN), the internet (e.g., the internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The storage medium may be included in the electronic device; or may exist separately without being assembled into the electronic device.
The storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: acquiring at least two internet protocol addresses; sending a node evaluation request comprising at least two internet protocol addresses to node evaluation equipment, wherein the node evaluation equipment selects the internet protocol addresses from the at least two internet protocol addresses and returns the internet protocol addresses; receiving an internet protocol address returned by the node evaluation equipment; wherein the obtained internet protocol address indicates an edge node in the content distribution network.
Alternatively, the storage medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: receiving a node evaluation request comprising at least two internet protocol addresses; selecting an internet protocol address from at least two internet protocol addresses; returning the selected internet protocol address; wherein the received internet protocol address indicates an edge node in the content distribution network.
Computer program code for carrying out operations for the present disclosure may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, Smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
It should be noted that the storage media described above in this disclosure can be computer readable signal media or computer readable storage media or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present disclosure, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In contrast, in the present disclosure, a computer readable signal medium may comprise a propagated data signal with computer readable program code embodied therein, either in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may also be any storage medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a storage medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The units described in the embodiments of the present disclosure may be implemented by software or hardware. Where the name of an element does not in some cases constitute a limitation on the element itself.
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), Application Specific Integrated Circuits (ASICs), Application Specific Standard Products (ASSPs), systems on a chip (SOCs), Complex Programmable Logic Devices (CPLDs), and the like.
In the context of this disclosure, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The foregoing description is only exemplary of the preferred embodiments of the disclosure and is illustrative of the principles of the technology employed. It will be appreciated by those skilled in the art that the scope of the disclosure herein is not limited to the particular combination of features described above, but also encompasses other embodiments in which any combination of the features described above or their equivalents does not depart from the spirit of the disclosure. For example, the above features and (but not limited to) the features disclosed in this disclosure having similar functions are replaced with each other to form the technical solution.
Further, while operations are depicted in a particular order, this should not be understood as requiring that such operations be performed in the particular order shown or in sequential order. Under certain circumstances, multitasking and parallel processing may be advantageous. Likewise, while several specific implementation details are included in the above discussion, these should not be construed as limitations on the scope of the disclosure. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are disclosed as example forms of implementing the claims.
While the present disclosure has been described in detail with reference to the embodiments, the present disclosure is not limited to the specific embodiments, and those skilled in the art can make various modifications and alterations based on the concept of the present disclosure, and the modifications and alterations should fall within the scope of the present disclosure as claimed.
Claims (8)
1. A control method for super stop vehicle dispatch, comprising the steps of:
acquiring the number of overtaking parking vehicles in each parking lot station in the current time period, wherein the overtaking parking vehicles refer to shared vehicles exceeding the maximum allowable parking number specified in the parking lot station; the parking stations include shared vehicle parking stations;
determining a number of overstock vehicles for each of the parking lot stations in a next time period; said determining the number of overstock vehicles for each of said parking stations in a next time period comprises: determining the number of incoming vehicles and the number of outgoing vehicles of each parking lot station in the next time period through a time series model; determining a number of overstock vehicles for each of the parking lot stations in a next time period based on the number of incoming vehicles and the number of outgoing vehicles; the time series model is a model completed through machine learning and training, and the learning and training processes are carried out based on characteristic parameters;
determining a preferred assigned vehicle in each of the parking stations based on characteristics of the parking stations.
2. The control method of claim 1, further comprising, after determining a prioritized vehicle in each of the parking stations based on the characteristics of the parking stations:
determining a type of reward for the preferentially-assigned vehicle, and assigning a corresponding type of reward to the preferentially-assigned vehicle.
3. The control method according to claim 2, wherein the determining a bonus type for the preferentially assigned vehicle, the assigning the preferentially assigned vehicle based on the bonus type includes:
determining a probability of use of each of the prioritized vehicles for setting a different type of reward;
setting a first type award to a first prioritized vehicle for a next time period when the usage probability of the first prioritized vehicle for the first type award is greater than a preset probability.
4. A control device for the dispatch of an extra stop vehicle, characterized by comprising the following means:
an obtaining module, configured to obtain the number of overstock vehicles in each parking lot station in a current time period, where the overstock vehicles are shared vehicles that exceed a maximum allowable parking number specified in the parking lot station; the parking stations include shared vehicle parking stations;
a determination module for determining a number of overstock vehicles for each of the parking stations in a next time period; the determining module comprises: a first determination unit for determining the number of incoming vehicles and the number of outgoing vehicles for each of the parking lot stations in a next time period through a time series model; a second determination unit for determining the number of overstock vehicles per the parking lot station in a next time period based on the number of incoming vehicles and the number of outgoing vehicles; the time series model is a model completed through machine learning and training, and the learning and training processes are carried out based on characteristic parameters;
a setup module that sets up a preferentially assigned vehicle in each of the parking stations based on characteristics of the parking stations.
5. The control device according to claim 4, characterized by further comprising:
and the allocation module is used for determining the type of the reward aiming at the preferentially allocated vehicle and allocating the corresponding type of reward to the preferentially allocated vehicle.
6. The control device of claim 5, wherein the assignment module comprises:
a third determination unit for determining a probability of use of each of the preferentially assigned vehicles for setting different types of awards;
a setting unit configured to set a first-type bonus to a first-prioritized vehicle for a next time period when the usage probability of the first-prioritized vehicle for the first-type bonus is greater than a preset probability.
7. A storage medium storing a computer program, characterized in that the computer program realizes the steps of the method of any one of claims 1 to 3 when executed by a processor.
8. An electronic device comprising at least a memory, a processor, the memory having a computer program stored thereon, characterized in that the processor realizes the steps of the method of any of claims 1 to 3 when executing the computer program on the memory.
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