CN114862115B - Method and device for scheduling shared vehicle, electronic equipment and storage medium - Google Patents
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Abstract
The application provides a dispatching method, a device, electronic equipment and a storage medium of a shared vehicle, and relates to the technical field of dispatching of the shared vehicle, wherein the method comprises the steps of obtaining beta distribution corresponding to the riding-out probability of vehicles of each parking fence in a preset time period according to vehicle flow data; acquiring initialized beta parameters of the beta distribution, and updating the initialized beta parameters to obtain predicted beta distribution; randomly sampling based on the predicted beta distribution to obtain a target parking fence, wherein the target parking fence is configured as a parking fence that increases the vehicle tuning amount.
Description
Technical Field
The embodiment of the disclosure relates to the technical field of dispatching of shared vehicles, and more particularly relates to a dispatching method, device, electronic equipment and storage medium of a shared vehicle.
Background
In a shared bicycle service scenario, the number of riding orders by a user in a bicycle launch site must not be greater than the number of supplies of the vehicle, but there may be potential orders where the number of riding orders by the vehicle is equal to the number of supplies, i.e., the demand in the site may be greater than the number of supplies. To be able to explore these potential order demands as much as possible, it is necessary to explore the possible orders by heuristically adding a certain amount of tuning to a partial point location.
The technical scheme for exploring possible orders is to manually select points according to operation conditions or randomly select hot points, then increase daily dispatching amount, and evaluate the speed of scattered vehicles to explore. However, the method for manually selecting the point positions is limited by the cognition of people on operation, the point positions which can be explored are very limited, potential point positions can not be fully explored, and the workload is large. The random point selection mode is too high in randomness, the exploration effect is poor, the cost is wasted, and the income is reduced. And the speed of the vehicle is influenced by various factors, so that the exploring effect is difficult to evaluate accurately, and the scheme is insufficient in sustainability.
Disclosure of Invention
An object of the present disclosure is to provide a new technical solution of a scheduling method, an apparatus, an electronic device, and a storage medium for a shared vehicle.
According to a first aspect, an embodiment of the present application provides a method for scheduling a shared vehicle, including obtaining a beta distribution corresponding to a vehicle riding probability of each parking fence within a preset time period according to vehicle flow data; acquiring initialized beta parameters of the beta distribution, and updating the initialized beta parameters to obtain predicted beta distribution; randomly sampling based on the predicted beta distribution to obtain a target parking fence, wherein the target parking fence is configured as a parking fence that increases the vehicle tuning amount.
Optionally, before acquiring the initialized beta parameters of the beta distribution, the method comprises: acquiring beta parameters of the beta distribution; and preprocessing the beta parameters according to a preset threshold value to obtain initialized beta parameters of the beta distribution.
Optionally, the updating the initialization parameter to obtain a predicted beta distribution includes: acquiring the vehicle outflow and supply quantity in the preset time period; and updating the initialized beta parameters according to a preset attenuation coefficient, the outflow and the supply to obtain the predicted beta distribution.
Optionally, the updating the initialized beta parameter according to a preset attenuation coefficient and the outflow and the supply includes: updating the initialized beta parameters according to a preset attenuation coefficient, the outflow and the supply quantity under the condition that the supply quantity is larger than zero; and updating the initialization beta parameter according to the outflow and the supply amount when the supply amount is equal to zero.
Optionally, the vehicle flow data includes weekday vehicle flow data and holiday vehicle flow data, the obtaining, according to the vehicle flow data, a beta distribution corresponding to a vehicle riding-out probability of each parking fence within a preset time period, further including: obtaining first beta distribution according to the working day vehicle flow data; obtaining second beta distribution according to the holiday vehicle flow data; wherein the first beta distribution is used to obtain a target parking fence corresponding to a weekday and the second beta distribution is used to obtain a target parking fence corresponding to a holiday.
Optionally, in the case that the beta distribution includes a first beta distribution and a second beta distribution, the updating the initialized beta parameter to obtain a predicted beta distribution includes: and updating the initialized beta parameters of the first beta distribution and the second beta distribution respectively to obtain a first predicted beta distribution and a second predicted beta distribution, wherein when one of the first beta distribution and the second beta distribution is updated, the parameters of the other beta distribution are kept unchanged.
Optionally, the randomly sampling based on the predicted beta distribution to obtain a target parking fence includes: for each parking fence, generating a random number by adopting a thompson sampling method within the preset time, and selecting N parking fences with the maximum random number as target parking fences.
In a second aspect, an embodiment of the present application provides a scheduling apparatus for a shared vehicle, the apparatus including: the data processing module is used for obtaining beta distribution corresponding to the riding-out probability of the vehicle of each parking fence in a preset time period according to the vehicle flow data; the parameter updating module is used for acquiring initialized beta parameters of the beta distribution, and updating the initialized beta parameters to obtain predicted beta distribution; and the target determining module is used for randomly sampling based on the predicted beta distribution to obtain a target parking fence, wherein the target parking fence is configured as a parking fence for increasing the vehicle adjustment amount.
In a third aspect, an embodiment of the present application provides an electronic device, including a memory for storing a computer program, and a processor; the processor is configured to execute the computer program to implement the steps of the method for scheduling a shared vehicle according to the first aspect.
In a fourth aspect, an embodiment of the present application provides a readable storage medium having stored thereon a computer program which, when executed by a processor, implements the steps of the method for scheduling a shared vehicle according to any one of the first aspects.
The beneficial effects of the embodiment of the disclosure are that the Beta distribution corresponding to the riding probability of the vehicle in the preset time period of each parking fence is obtained according to the vehicle flow data, and each parking fence can be used as an exploration point to carry out exploratory scheduling on the bicycle fence; and acquiring initialized beta parameters of the beta distribution, updating the initialized beta parameters, timely updating the parameters according to daily vehicle flow data to obtain predicted beta distribution so as to improve exploration precision, randomly sampling based on the predicted beta distribution to explore possible unknown vehicle outflow, obtaining a target parking fence with unknown orders, and exploring the unknown orders by increasing the vehicle dispatching amount of the target parking fence, thereby realizing reasonable dispatching of vehicles of each parking fence.
Other features of the present specification and its advantages will become apparent from the following detailed description of exemplary embodiments thereof, which proceeds with reference to the accompanying drawings.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments of the specification and together with the description, serve to explain the principles of the specification.
FIG. 1 is a schematic diagram of a system architecture that may be used to implement embodiments of the present disclosure.
Fig. 2 is a flow chart of a method for scheduling a shared vehicle according to an embodiment.
Fig. 3 is a block schematic diagram of a scheduler of a shared vehicle according to another embodiment.
Fig. 4 is a block schematic diagram of an electronic device according to one embodiment.
Detailed Description
Various exemplary embodiments of the present disclosure will now be described in detail with reference to the accompanying drawings. It should be noted that: the relative arrangement of the components and steps, numerical expressions and numerical values set forth in these embodiments do not limit the scope of the present invention unless it is specifically stated otherwise.
The following description of at least one exemplary embodiment is merely exemplary in nature and is in no way intended to limit the invention, its application, or uses.
Techniques, methods, and apparatus known to one of ordinary skill in the relevant art may not be discussed in detail, but are intended to be part of the specification where appropriate.
In all examples shown and discussed herein, any specific values should be construed as merely illustrative, and not a limitation. Thus, other examples of exemplary embodiments may have different values.
It should be noted that: like reference numerals and letters denote like items in the following figures, and thus once an item is defined in one figure, no further discussion thereof is necessary in subsequent figures.
Embodiments of the present disclosure relate to an application scenario for vehicle scheduling of shared vehicles.
In a shared bicycle service scenario, the number of riding orders by a user in a bicycle launch site must not be greater than the number of supplies of the vehicle, but there may be potential orders where the number of riding orders by the vehicle is equal to the number of supplies, i.e., the demand in the site may be greater than the number of supplies. To be able to explore these potential order demands as much as possible, it is necessary to explore the possible orders by heuristically adding a certain amount of tuning to a partial point location.
Aiming at the requirements, the point positions can be selected manually according to the operation conditions, or hot point positions are selected randomly, and then daily adjustment quantity is increased, and the speed of the scattered vehicles is estimated to search. However, the method for manually selecting the point positions is limited by the cognition of people on operation, the point positions which can be explored are very limited, potential point positions can not be fully explored, and the workload is large. The random point selection mode is too high in randomness, the exploration effect is poor, the cost is wasted, and the income is reduced. And the speed of the vehicle is influenced by various factors, so that the exploring effect is difficult to evaluate accurately, and the scheme is insufficient in sustainability.
Aiming at the technical problems in the above embodiments, the present disclosure provides a scheduling method, a scheduling device, an electronic device and a storage medium for a shared vehicle. Taking exploration scheduling as a problem of recommending parking fences, selecting exploration points, namely points where the recommended parking fences most likely to be supplied inadequately are located, describing the conventional vehicle flow data by Beta distribution, obtaining a target parking fence based on Topson sampling, and exploring unknown orders by continuously increasing the amount of the exploration in the target parking fence.
< Hardware configuration >
Fig. 1 is a schematic diagram of a system architecture that may be used to implement embodiments of the present disclosure.
As shown in fig. 1, the system includes a server 2000 and an electronic device 1000. The server 2000 performs information interaction with the electronic device 1000 via a network 3000, and the network 3000 may be a wireless network or a wired network.
The server 2000 provides the service points for processing, databases, communication facilities. The server 2000 may be a monolithic server, a distributed server across multiple computers, a computer data center, a cloud server, or a cluster of servers deployed in the cloud, etc. The server may be of various types such as, but not limited to, a web server, news server, mail server, message server, advertisement server, file server, application server, interaction server, database server, or proxy server. In some embodiments, each server may include hardware, software, or embedded logic components or a combination of two or more such components for performing the appropriate functions supported by or implemented by the server.
The server 2000 specific configuration may include, but is not limited to, a processor 2100, a memory 2200, an interface device 2300, a communication device 2400. The processor 2100 is configured to execute a computer program written in an instruction set of an architecture such as x86, arm, RISC, MIPS, SSE, etc. The memory 2200 is, for example, ROM (read only memory), RAM (random access memory), nonvolatile memory such as a hard disk, or the like. The interface device 2300 is, for example, a USB interface, a serial interface, a parallel interface, a network interface, or the like. The communication device 2400 is capable of wired or wireless communication, for example, and may include WiFi communication, bluetooth communication, 2G/3G/4G/5G communication, and the like, for example.
The memory 2200 of the server 2000 is used to store a computer program for controlling the processor 2100 to operate to implement the method according to the embodiments of the present disclosure, which is applied to the embodiments of the present disclosure. The skilled person can design the computer program according to the disclosure of the present disclosure. How the computer program controls the processor to operate is well known in the art and will not be described in detail here.
Those skilled in the art will appreciate that the server 2000 may include other devices in addition to those shown in fig. 1, and is not limited in this regard.
The electronic device 1000 may include, but is not limited to, a processor 1100, a memory 1200, an interface device 1300, a communication device 1400, a display device 1500, an input device 1600, a speaker 1700, a microphone 1800, and the like. The processor 1100 may be a central processing unit CPU, a graphics processor GPU, a microprocessor MCU, etc. for executing a computer program written in an instruction set of an architecture such as x86, arm, RISC, MIPS, SSE, etc. The memory 1200 includes, for example, ROM (read only memory), RAM (random access memory), nonvolatile memory such as a hard disk, and the like. The interface device 1300 includes, for example, a USB interface, a serial interface, a parallel interface, and the like. The communication device 1400 can perform wired communication using an optical fiber or a cable, or perform wireless communication, for example, and specifically can include WiFi communication, bluetooth communication, 2G/3G/4G/5G communication, and the like. The display device 1500 is, for example, a liquid crystal display, a touch display, or the like. The input device 1600 may include, for example, a touch screen, keyboard, somatosensory input, and the like. The speaker 1700 is for outputting audio signals. Microphone 1800 is used to collect audio signals.
The electronic device 1000 may be a smart phone, a portable computer, a desktop computer, a tablet computer, a wearable device, etc., without limitation.
In this embodiment, the electronic device 1000 may be configured to obtain, according to the vehicle flow data, a beta distribution corresponding to a vehicle riding-out probability of each parking fence within a preset time period; acquiring initialized beta parameters of the beta distribution, and updating the initialized beta parameters to obtain predicted beta distribution; and randomly sampling based on the predicted beta distribution to obtain the target parking fence.
The memory 1200 of the electronic device 1000 is used for storing a computer program for controlling the processor 1100 to operate to implement the method according to any of the embodiments of the present disclosure, as applied to the embodiments of the present disclosure. The skilled person can design the computer program according to the disclosure of the present disclosure. How the computer program controls the processor to operate is well known in the art and will not be described in detail here. The electronic device 1000 may be installed with an intelligent operating system (e.g., windows, linux, android, IOS, etc. systems) and application software.
It will be appreciated by those skilled in the art that although a plurality of devices of the electronic device 1000 are shown in fig. 1, the electronic device 1000 of the embodiments of the present disclosure may refer to only some of the devices thereof, e.g., only the processor 1100, the memory 1200, etc.
Various embodiments and examples according to the present invention are described below with reference to the accompanying drawings.
< Method example >
FIG. 2 is a flow diagram of a method of scheduling a shared vehicle according to one embodiment, which may be implemented by the electronic device or server of FIG. 1.
As shown in fig. 2, the dispatching method of the shared vehicle of the present embodiment may include the steps of:
S210, obtaining the beta distribution corresponding to the riding-out probability of the vehicle in the preset time period of each parking fence according to the vehicle flow data.
In this embodiment, the vehicle flow data may include data of a vehicle supply amount, an outflow amount, an inflow amount, a call-in amount, and the like in each parking fence. The preset time period may be set according to practical situations, for example, the preset time period may be one hour. The vehicle ride-out probability refers to a probability that a certain vehicle in the parking enclosure is ridden out of the enclosure within a preset period of time.
If a single car within one hour of a fence is considered to be a Bernoulli distribution, the best way to characterize its riding-out probability from a Bayesian statistical perspective is the Beta distribution, hereinafter referred to as the Beta distribution.
For one hour of one fence, the riding amount and the supply amount for the past N days are selected, the riding amount outflow is summed as a parameter a of the Beta distribution, the supply amount supply is summed, and the riding amount outflow is subtracted to sum as a parameter B of the Beta distribution. The Beta distribution is obtained:
Beta(A，B)＝Beta(∑outflow,∑supply-∑outflow)
the distribution Beta (a, B) represents the probability that a bicycle will be ridden within one hour of a fence.
S220, acquiring initialized beta parameters of the beta distribution, and updating the initialized beta parameters to obtain the predicted beta distribution.
In one example, directly obtaining the Beta parameter from the vehicle flow data may have problems with too large or too small parameters, and if the parameters are too large, the parameters may be updated too slowly, and part of the pens are repeatedly recommended. Too small parameters may cause too large randomness, and the determined actual order quantity of the vehicle of the target parking fence is not large, so that the exploration effect is poor, and the cost is wasted. Therefore, a preprocessing is required before acquiring the initialized Beta parameters of the Beta distribution, and the preprocessing method in this embodiment includes: acquiring beta parameters of beta distribution; preprocessing the beta parameters according to a preset threshold value to obtain initialized beta parameters of the beta distribution.
In one embodiment, preprocessing the beta parameters according to a preset threshold includes preprocessing the oversized parameters. For example, the beta parameters of the beta distribution are a and B, and when either a or B is greater than the preset threshold T max, the beta parameter equal ratio greater than the preset threshold T max is scaled down to T max. Thereby avoiding the influence caused by overlarge parameters.
In one embodiment, preprocessing the beta parameters according to a preset threshold includes preprocessing parameters that are too small. For example, when the beta parameters of the beta distribution are a and B, and when either one of a or B is smaller than a preset threshold T min, a 30% quantile of the vehicle outflow rate of a preset area is obtained according to the vehicle flow data to obtain an initial parameter, where the preset area may be a city, a district or a county, for example, the 30% quantile of the vehicle outflow rate of a city is p, and if the updated initial parameter B '=20, the updated initial parameter a' =20×p/(1-p). In this embodiment, the overall average outflow rate of a preset area is used to replace the too small parameters, so that inaccurate prediction effect caused by too small beta parameters can be avoided.
In this embodiment, updating the initialization parameter to obtain the predicted beta distribution includes: acquiring the vehicle outflow and supply quantity in a preset time period; and updating the initialized beta parameters according to the preset attenuation coefficient, the outflow quantity and the supply quantity to obtain the predicted beta distribution.
The supply amount in this embodiment includes a preliminary supply amount of the vehicle in a preset period of time, an inflow amount into the parking fence in a preset time, an adjustment amount in a preset time, and an adjustment amount in a preset time. It will be appreciated that the maximum value of the supply amount in the preset time period is equal to the sum of the preliminary supply amount of the vehicle, the inflow amount into the parking fence in the preset time period, and the adjustment amount in the preset time period minus the adjustment amount in the preset time period. Since the outflow amount cannot be larger than the supply amount, the supply amount of the vehicle, that is, the maximum outflow amount upper limit value of the vehicle in the preset period. The vehicle outflow amount is an actual vehicle riding amount.
In one example, the more recent the data value is, the more time-efficient the traffic data of the vehicle is. Therefore, when updating the distribution parameters, a certain forgetting capability needs to be given to the parameters. The present embodiment sets the decay factor c for the old parameter during the updating of the parameter, the magnitude of which depends on the half-life of the desired factor decay. For example, assuming the desired half-life is h (units: days), the attenuation coefficient can be calculated from c h = 0.5. For example, if the half-life is 7 days, c= 0.9057.
In this embodiment, it is considered that if there is no vehicle supply for a long period of time at one place, in order to retain effective information as much as possible, the forgetting ability thereof should be reduced. Therefore, this scheme sets a judgment of the supply amount before multiplying by the attenuation coefficient. That is, in the process of updating the initialized beta parameter according to the preset attenuation coefficient, the outflow amount and the supply amount, the supply amount needs to be judged, and when the supply amount is larger than zero, the initialized beta parameter is updated according to the preset attenuation coefficient, the outflow amount and the supply amount; when the supply amount is equal to zero, the initialization beta parameter is updated according to the outflow amount and the supply amount. That is, when the supply amount is greater than 0, the beta parameter is attenuated, and when the supply amount is equal to 0, no attenuation is performed.
In one example, assuming that the vehicle outflow amount at the previous time is a t-1, the vehicle supply amount at the previous time is b t-1, and the Beta distribution in the preset period before updating is Beta (a t-1,Bt-1), the Beta distribution in the preset period after updating is Beta (a t,Bt), and the Beta parameter after updating is:
At＝c·At-1+at-1，
Bt＝c·Bt-1+max(bt-1–at-1)，
where c is the attenuation coefficient and max (b t-1–at-1) is the maximum remaining vehicle in the parking enclosure at the end of the previous time.
According to the method, the attenuation coefficient is introduced in the process of obtaining the predicted beta distribution, so that the timeliness of data can be guaranteed, and the obtained beta distribution is more accurate. It will be appreciated that updating the initialized beta parameters to obtain the predicted beta distribution is continuous, e.g., updating the beta distribution over a preset one-hour period of time each day to obtain the predicted beta distribution on the same day until the preset distribution effect is reached.
S230, randomly sampling based on the predicted beta distribution to obtain the target parking fence.
Wherein the target parking fence is configured as a parking fence that increases the amount of vehicle adjustment. For example, if the fence D is a target fence, 5 vehicles can be deployed into the fence D every day until the beta distribution of the fence D meets the expectations.
In this embodiment, random sampling is performed based on predicted beta distribution to obtain a target parking fence, including: for each parking fence, a Thompson sampling method is adopted to generate a random number in a preset time, and N parking fences with the largest random number are selected as target parking fences.
For example, each fence generates a random number according to Beta distribution every hour, n corresponding fences with the largest random number are selected, and repeated k times, so that nk fences are obtained in total, it is to be noted that the nk fences may have repetition, a target fence is obtained according to the nk fences, d vehicles are scheduled to each target fence, and exploration is performed. After d vehicles are scheduled to each target fence, obtaining a beta distribution corresponding to the vehicle riding-out probability of each parking fence in a preset time period according to the vehicle flow data of the current day; updating the Beta parameters of the Beta distribution to obtain the predicted Beta distribution of the next day; and randomly sampling based on the predicted beta distribution to obtain a target parking fence, and circulating the target parking fence so as to determine the target fence, so that the scheduling of vehicles is assisted, more unknown orders are explored, and the traffic is increased.
In addition, in order to cope with business environments in which the working days and the holidays are different, the present embodiment separately calculates the working days and the holidays, described with two distributions, considering that the amounts of vehicles used in the working days and the holidays are different.
In this embodiment, the vehicle flow data includes weekday vehicle flow data and holiday vehicle flow data, the beta distribution corresponding to the vehicle riding-out probability of each parking fence in a preset time period is obtained according to the vehicle flow data, and the method further includes: obtaining first beta distribution according to the vehicle flow data of the working day; obtaining second beta distribution according to the holiday vehicle flow data; wherein the first beta distribution is used to obtain a target parking fence corresponding to a weekday and the second beta distribution is used to obtain a target parking fence corresponding to a holiday.
For example, the first Beta distribution is Beta1 (A, B), the second Beta distribution is Beta2 (A, B), and the working day is separated from the holiday parameter, so that the prediction result is more accurate.
In this embodiment, when the beta distribution includes the first beta distribution and the second beta distribution, if the initialized beta parameter is updated, a predicted beta distribution is obtained, including: updating initialized beta parameters of the first beta distribution and the second beta distribution respectively to obtain a first predicted beta distribution and a second predicted beta distribution, wherein when one of the first beta distribution and the second beta distribution is updated, the parameters of the other beta distribution are kept unchanged. Therefore, mutual independence of the working day and the holiday is ensured, and the prediction result can be more accurate.
That is, in the case where the beta distribution includes the first beta distribution and the second beta distribution, it is necessary to determine whether the previous day is a working day or a holiday, for example, the day is a working day, and the previous day is also a working day, then the corresponding first beta distribution on the previous day may be selected, the initial beta parameters of the first beta distribution may be updated to obtain the first predicted beta distribution, and if the day is a holiday and the previous day is a working day, the corresponding second beta distribution corresponding to the last holiday may be selected, and the initialized beta parameters of the second beta distribution may be updated.
In one example, the implementation result of this embodiment may be: trial runs in both M and N cities for two weeks, with the predicted time for most target parking pens in both cities occurring at an early peak. All time periods are all over the day on weekends. The most of the positions of the target parking fences in the M city are in the subway entrance or very close to the subway entrance no matter the working day or the holiday. In actual execution, on average, 6M city target parking pens are obtained daily, the adjustment amount of 93 is increased, and additional increase of 1087 turnover orders is brought. On average, 10N city target parking pens per day are obtained, increasing the dispatch volume of 121, bringing additional growth in turn-around orders for 217. Therefore, the embodiment can realize the exploration of the unknown order, so that the vehicle scheduling guidance is performed heuristically, and the vehicle riding order is increased.
According to the vehicle flow data, the Beta distribution corresponding to the vehicle riding-out probability of each parking fence in the preset time period is obtained, and each parking fence can be used as an exploration point to carry out exploratory scheduling on the bicycle fence; and acquiring initialized beta parameters of the beta distribution, updating the initialized beta parameters, timely updating the parameters according to daily vehicle flow data to obtain predicted beta distribution so as to improve exploration precision, randomly sampling based on the predicted beta distribution to explore possible unknown vehicle outflow, obtaining a target parking fence with unknown orders, and exploring the unknown orders by increasing the vehicle dispatching amount of the target parking fence, thereby realizing reasonable dispatching of vehicles of each parking fence.
< Device example >
Fig. 3 is a functional block diagram of a shared-vehicle scheduler 300 according to one embodiment. As shown in fig. 3, the scheduling apparatus of the shared vehicle may include:
The data processing module 301 is configured to obtain, according to the vehicle flow data, a beta distribution corresponding to a probability of the vehicle riding out of each parking fence within a preset time period.
And the parameter updating module 302 is configured to obtain an initialized beta parameter of the beta distribution, and update the initialized beta parameter to obtain a predicted beta distribution.
A target determination module 303 for randomly sampling based on the predicted beta distribution to obtain a target parking fence, wherein the target parking fence is configured as a parking fence that increases the vehicle adjustment amount.
In one embodiment, the data processing module 301 may be configured to obtain beta parameters of the beta distribution; and preprocessing the beta parameters according to a preset threshold value to obtain initialized beta parameters of the beta distribution.
In one embodiment, the parameter updating module 302 may be configured to obtain the vehicle outflow and supply during the preset time period; and updating the initialized beta parameters according to a preset attenuation coefficient, the outflow and the supply to obtain the predicted beta distribution.
In one embodiment, the parameter updating module 302 may be configured to update the initialized beta parameter according to a preset attenuation coefficient and the outflow and the supply when the supply is greater than zero; and when the supply amount is equal to zero, updating the initialization beta parameter according to the outflow amount and the supply amount.
In one embodiment, the data processing module 301 may be configured to obtain a first beta distribution according to the weekday vehicle flow data; obtaining second beta distribution according to the holiday vehicle flow data; wherein the first beta distribution is used to obtain a target parking fence corresponding to a weekday and the second beta distribution is used to obtain a target parking fence corresponding to a holiday.
In one embodiment, the parameter updating module 302 may update the initialized beta parameters of the first beta distribution and the second beta distribution to obtain a first predicted beta distribution and a second predicted beta distribution, where when one of the first beta distribution and the second beta distribution is updated, the parameter of the other beta distribution is kept unchanged.
In one embodiment, the target determination module 303 may be configured to generate a random number for each parking fence during a preset time by using a thompson sampling method, and select N parking fences with the largest random number as the target parking fences.
The apparatus of this embodiment may be the processor 1100 of fig. 1.
According to the vehicle flow data, the Beta distribution corresponding to the vehicle riding-out probability of each parking fence in a preset time period is obtained; and acquiring initialized beta parameters of the beta distribution, updating the initialized beta parameters, and timely updating parameters to obtain predicted beta distribution, and randomly sampling based on the predicted beta distribution to obtain a target parking fence, wherein the vehicle adjustment amount is increased for the target parking fence and used for exploring the possible unknown vehicle outflow amount, so that the exploration of the next position order can be well guided.
< Electronic device embodiment >
Fig. 4 is a functional block diagram of an electronic device 400. As shown in fig. 4, the electronic device 400 includes: the processor 410, the memory 420, and the program or instructions stored on the memory 420 and executable on the processor 410, when executed by the processor, implement the steps of the shared vehicle scheduling method described above.
< Storage Medium embodiment >
The present embodiment provides a computer-readable storage medium having stored therein executable instructions that, when executed by a processor, perform the method described in any of the method embodiments of the present specification.
One or more embodiments of the present description may be a system, method, and/or computer program product. The computer program product may include a computer-readable storage medium having computer-readable program instructions embodied thereon for causing a processor to implement aspects of the present description.
The computer readable storage medium may be a tangible device that can hold and store instructions for use by an instruction execution device. The computer readable storage medium may be, for example, but not limited to, an electronic storage device, a magnetic storage device, an optical storage device, an electromagnetic storage device, a semiconductor storage device, or any suitable combination of the foregoing. More specific examples (a non-exhaustive list) of the computer-readable storage medium would include the following: portable computer disks, hard disks, random Access Memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), static Random Access Memory (SRAM), portable compact disk read-only memory (CD-ROM), digital Versatile Disks (DVD), memory sticks, floppy disks, mechanical coding devices, punch cards or in-groove structures such as punch cards or grooves having instructions stored thereon, and any suitable combination of the foregoing. Computer-readable storage media, as used herein, are not to be construed as transitory signals per se, such as radio waves or other freely propagating electromagnetic waves, electromagnetic waves propagating through waveguides or other transmission media (e.g., optical pulses through fiber optic cables), or electrical signals transmitted through wires.
The computer readable program instructions described herein may be downloaded from a computer readable storage medium to a respective computing/processing device or to an external computer or external storage device over a network, such as the internet, a local area network, a wide area network, and/or a wireless network. The network may include copper transmission cables, fiber optic transmissions, wireless transmissions, routers, firewalls, switches, gateway computers and/or edge servers. The network interface card or network interface in each computing/processing device receives computer readable program instructions from the network and forwards the computer readable program instructions for storage in a computer readable storage medium in the respective computing/processing device.
The computer program instructions for carrying out operations of embodiments of the present description may be assembly instructions, instruction Set Architecture (ISA) instructions, machine-related instructions, microcode, firmware instructions, state setting data, or source or object code written in any combination of one or more programming languages, including an object oriented programming language such as SMALLTALK, C ++ or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The computer readable program instructions may be executed 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 kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computer (for example, through the Internet using an Internet service provider). In some embodiments, aspects of the present description are implemented by personalizing electronic circuitry, such as programmable logic circuitry, field Programmable Gate Arrays (FPGAs), or Programmable Logic Arrays (PLAs), with state information for computer-readable program instructions, which may execute the computer-readable program instructions.
Various aspects of the present description are described herein with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the specification. It will be understood that each block of the flowchart illustrations and/or block diagrams, and combinations of blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer-readable program instructions.
These computer readable program instructions may be provided to a processor of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions/acts specified in the flowchart and/or block diagram block or blocks. These computer readable program instructions may also be stored in a computer readable storage medium that can direct a computer, programmable data processing apparatus, and/or other devices to function in a particular manner, such that the computer readable medium having the instructions stored therein includes an article of manufacture including instructions which implement the function/act specified in the flowchart and/or block diagram block or blocks.
The computer readable program instructions may also be loaded onto a computer, other programmable data processing apparatus, or other devices to cause a series of operational steps to be performed on the computer, other programmable apparatus or other devices to produce a computer implemented process such that the instructions which execute on the computer, other programmable apparatus or other devices implement the functions/acts specified in the flowchart and/or block diagram block or blocks.
The flowcharts 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 description. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of instructions, which comprises one or more executable instructions for implementing the specified logical function(s). 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. It is well known to those skilled in the art that implementation by hardware, implementation by software, and implementation by a combination of software and hardware are all equivalent.
The embodiments of the present specification have been described above, and the above description is illustrative, not exhaustive, and not limited to the embodiments disclosed. Many modifications and variations will be apparent to those of ordinary skill in the art without departing from the scope and spirit of the various embodiments described. The terminology used herein was chosen in order to best explain the principles of the embodiments, the practical application, or the technical improvement in the marketplace, or to enable others of ordinary skill in the art to understand the embodiments disclosed herein. The scope of the application is defined by the appended claims.
Claims (8)
1. A method of scheduling a shared vehicle, the method comprising:
obtaining beta distribution corresponding to the vehicle riding-out probability of each parking fence in a preset time period according to the vehicle flow data;
Acquiring initialized beta parameters of the beta distribution, and updating the initialized beta parameters to obtain predicted beta distribution;
Randomly sampling based on the predicted beta distribution to obtain a target parking fence, wherein the target parking fence is configured as a parking fence for increasing the vehicle adjustment amount;
The updating the initialized beta parameters to obtain the predicted beta distribution includes:
acquiring the vehicle outflow and supply quantity in the preset time period;
updating the initialized beta parameters according to a preset attenuation coefficient, the outflow and the supply to obtain predicted beta distribution;
wherein updating the initialization beta parameters according to the preset attenuation coefficient, the outflow amount and the supply amount comprises:
updating the initialized beta parameters according to a preset attenuation coefficient, the outflow and the supply quantity under the condition that the supply quantity is larger than zero;
and updating the initialization beta parameter according to the outflow and the supply amount when the supply amount is equal to zero.
2. The method of claim 1, wherein prior to obtaining the initialized beta parameters for the beta distribution, the method comprises:
Acquiring beta parameters of the beta distribution;
and preprocessing the beta parameters according to a preset threshold value to obtain initialized beta parameters of the beta distribution.
3. The method of claim 1, wherein the vehicle flow data comprises weekday vehicle flow data and holiday vehicle flow data, the deriving a beta distribution corresponding to a vehicle ride-out probability for each parking fence over a preset period of time from the vehicle flow data, comprising:
obtaining first beta distribution according to the working day vehicle flow data;
Obtaining second beta distribution according to the holiday vehicle flow data;
Wherein the first beta distribution is used to obtain a target parking fence corresponding to a weekday and the second beta distribution is used to obtain a target parking fence corresponding to a holiday.
4. A method according to claim 3, wherein, in case the beta distribution comprises a first beta distribution and a second beta distribution, the updating of the initialized beta parameters results in a predicted beta distribution, comprising:
And updating the initialized beta parameters of the first beta distribution and the second beta distribution respectively to obtain a first predicted beta distribution and a second predicted beta distribution, wherein when one of the first beta distribution and the second beta distribution is updated, the parameters of the other beta distribution are kept unchanged.
5. The method of claim 1, wherein the randomly sampling based on the predicted beta distribution to obtain a target parking fence comprises:
for each parking fence, generating a random number by adopting a thompson sampling method within the preset time, and selecting N parking fences with the maximum random number as target parking fences.
6. A scheduling apparatus for a shared vehicle, the apparatus comprising:
the data processing module is used for obtaining beta distribution corresponding to the riding-out probability of the vehicle of each parking fence in a preset time period according to the vehicle flow data;
The parameter updating module is used for acquiring initialized beta parameters of the beta distribution, and updating the initialized beta parameters to obtain predicted beta distribution;
A target determination module for randomly sampling based on the predicted beta distribution to obtain a target parking fence, wherein the target parking fence is configured as a parking fence for increasing the vehicle adjustment amount;
The updating the initialized beta parameters to obtain the predicted beta distribution includes:
acquiring the vehicle outflow and supply quantity in the preset time period;
updating the initialized beta parameters according to a preset attenuation coefficient, the outflow and the supply to obtain predicted beta distribution;
wherein updating the initialization beta parameters according to the preset attenuation coefficient, the outflow amount and the supply amount comprises:
updating the initialized beta parameters according to a preset attenuation coefficient, the outflow and the supply quantity under the condition that the supply quantity is larger than zero;
and updating the initialization beta parameter according to the outflow and the supply amount when the supply amount is equal to zero.
7. An electronic device comprising a memory and a processor, the memory for storing a computer program; the processor is configured to execute the computer program to implement the method according to any one of claims 1-5.
8. A readable storage medium, characterized in that it has stored thereon a computer program which, when executed by a processor, implements the method according to any of claims 1-5.
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