CN109636166A - To the method that the net about vehicle in predetermined area is scheduled, system and storage medium - Google Patents
To the method that the net about vehicle in predetermined area is scheduled, system and storage medium Download PDFInfo
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Abstract
This application discloses a kind of method that the net about vehicle to predetermined area is scheduled, system and storage medium.Wherein this method, comprising: determined from multiple and different regions and supply the insufficient region of insufficient supply in the excess supply region of following predetermined instant net about vehicle excess supply and net about vehicle；Determine the excess supply quantity in excess supply region, wherein excess supply quantity be excess supply region in relative to net about vehicle demand the quantity of extra net about vehicle；Determine the supply gap quantity for supplying insufficient region, wherein supply gap quantity is the quantity supplied in insufficient region relative to net net about vehicle about lacking in vehicle demand；The idle net about Chinese herbaceous peony in excess supply region is determined toward each mileage travelled for supplying insufficient region and travels duration；And preset computation model is utilized, according to identified excess supply quantity, supply gap quantity, mileage travelled and traveling duration, determine the scheduling parameter for dispatching the idle net about vehicle in excess supply region.
Description
Technical Field
The present invention relates to the field of network appointment vehicles, and in particular, to a method, a system and a storage medium for scheduling network appointment vehicles in a predetermined area.
Background
As the income level of people increases, the demand of city residents for using net appointment vehicles (e.g., special vehicles) during traveling is also increasing. For a particular city, the supply and space-time distribution of net appointment cars tend to be relatively stable, and the travel demands of net appointment cars are relatively random with respect to the supply. Due to the mismatch of the supply and the demand of the network appointment vehicle service in the space-time distribution, the network appointment vehicle demand in some areas can not be met in some time periods, and the network appointment vehicle transport capacity supply in some areas in other time periods is excessive. Therefore, for enterprises and organizations providing network car booking service, the distribution of network car booking demands on space and time can be predicted in advance, and the network car booking dispatching system for dispatching network car booking supply in advance according to demand change can improve the efficiency problem of random order taking of drivers and has an important role in balance of supply and demand of urban network car booking travel.
However, no effective solution has been proposed so far for the technical problems that the mismatch of the supply and demand of the network taxi appointment service in the space-time distribution in the prior art as described above results in some areas of network taxi appointment demand being not satisfied in some time periods, and some areas of other time periods have excess supply of network taxi appointment capacity.
Disclosure of Invention
The embodiment of the disclosure provides a method, a system and a storage medium for scheduling network appointment vehicles in a predetermined region, so as to at least solve the technical problem that mismatching of network appointment vehicle service supply and demand on space-time distribution in the prior art generates the situation that network appointment vehicle demand in some regions cannot be met in some time periods, and network appointment vehicle transport capacity supply is excessive in some regions in other time periods.
According to an aspect of the embodiments of the present disclosure, there is provided a method for scheduling a network appointment car in a predetermined area, wherein the predetermined area is divided into a plurality of different areas, the method comprising: determining an oversupply area where the net appointment is oversupplied and an undersupply area where the net appointment is undersupplied at a predetermined time in the future from among the plurality of different areas; determining the surplus supply quantity of the surplus supply area, wherein the surplus supply quantity is the quantity of net appointment vehicles which are redundant relative to the net appointment vehicle requirement in the surplus supply area; determining the number of supply gaps of the insufficient supply area, wherein the number of the supply gaps is the number of net appointment vehicles which are lacked relative to the net appointment requirements in the insufficient supply area; determining the driving mileage and driving time of the idle network appointment vehicle in the surplus supply area to each insufficient supply area; and determining a scheduling parameter for scheduling the idle network car reservation in the surplus supply area according to the determined surplus supply quantity, the supply gap quantity, the driving mileage and the driving time length by using a preset calculation model.
According to another aspect of the embodiments of the present disclosure, there is also provided a storage medium including a stored program, wherein the method of any one of the above is performed by a processor when the program is executed.
According to another aspect of the embodiments of the present disclosure, there is also provided a system for scheduling a network appointment car in a predetermined area, wherein the predetermined area is divided into a plurality of different areas, the system including: the prediction module is used for determining an excess supply area with excess supply and an insufficient supply area with insufficient supply of the net appointment car at a future scheduled time from a plurality of different areas; the first determining module is used for determining the supply surplus quantity of the supply surplus area, wherein the supply surplus quantity is the quantity of the net appointment vehicles which are redundant relative to the net appointment vehicle requirement in the supply surplus area; the second determination module is used for determining the number of the supply gaps of the supply shortage area, wherein the number of the supply gaps is the number of the net appointment vehicles which are lacked relative to the net appointment vehicle requirement in the supply shortage area; the third determining module is used for determining the driving mileage and the driving time of the idle network appointment vehicle in the surplus supply area to each insufficient supply area; and the fourth determining module is used for determining scheduling parameters for scheduling idle network car reservation in the surplus supply area according to the determined surplus supply quantity, supply gap quantity, driving mileage and driving time length by using a preset calculation model.
According to another aspect of the embodiments of the present disclosure, there is also provided an apparatus for scheduling a network appointment car in a predetermined area, wherein the predetermined area is divided into a plurality of different areas, the apparatus including: a processor; and a memory coupled to the processor for providing instructions to the processor for processing the following processing steps: determining an oversupply area where the net appointment is oversupplied and an undersupply area where the net appointment is undersupplied at a predetermined time in the future from among the plurality of different areas; determining the surplus supply quantity of the surplus supply area, wherein the surplus supply quantity is the quantity of net appointment vehicles which are redundant relative to the net appointment vehicle requirement in the surplus supply area; determining the number of supply gaps of the insufficient supply area, wherein the number of the supply gaps is the number of net appointment vehicles which are lacked relative to the net appointment requirements in the insufficient supply area; determining the driving mileage and driving time of the idle network appointment vehicle in the surplus supply area to each insufficient supply area; and determining a scheduling parameter for scheduling the idle network car reservation in the surplus supply area according to the determined surplus supply quantity, the supply gap quantity, the driving mileage and the driving time length by using a preset calculation model.
In the embodiment of the present disclosure, the preset calculation model may be utilized to finally determine the scheduling parameters for scheduling the vacant driver in the surplus-in-supply area according to the number of surplus-in-supply areas, the number of supply gaps of the surplus-in-supply areas, the travel distance from the vacant net of the surplus-in-supply areas to the shortage-in-supply areas, and the travel time length determined by prediction. Therefore, network appointment vehicle supply is dispatched in advance according to the change of the demand, and the efficiency of random order taking of drivers and the balance of supply and demand of urban network appointment vehicle outgoing are improved. The technical problems that the demand of the network car booking service in some areas cannot be met in some time periods due to mismatching of the supply and the demand on space-time distribution, and the situation that the capacity supply of the network car booking is excessive in some other areas in other time periods are solved.
Drawings
The accompanying drawings, which are included to provide a further understanding of the disclosure and are incorporated in and constitute a part of this application, illustrate embodiment(s) of the disclosure and together with the description serve to explain the disclosure and not to limit the disclosure. In the drawings:
fig. 1 is a hardware configuration block diagram of a [ computer terminal (or mobile device) ] for implementing the method according to embodiment 1 of the present disclosure;
fig. 2 is a schematic diagram of a system for scheduling network appointment cars in a predetermined area according to embodiment 1 of the present disclosure;
fig. 3 is a schematic diagram of a predetermined area and a division of the predetermined area into a plurality of regions according to embodiment 1 of the present disclosure;
fig. 4 is a schematic flow chart of a method for scheduling a network appointment of a predetermined area according to a first aspect of embodiment 1 of the present disclosure;
fig. 5 is a specific flowchart of a method for scheduling a network appointment in a predetermined area according to the first aspect of embodiment 1 of the present disclosure;
fig. 6 is a schematic diagram of a system for scheduling network appointment cars in a predetermined area according to embodiment 2 of the present disclosure; and
fig. 7 is a schematic diagram of an apparatus for scheduling network appointment cars in a predetermined area according to embodiment 3 of the present disclosure
Detailed Description
In order to make those skilled in the art better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure. It is to be understood that the described embodiments are merely exemplary of some, and not all, of the present disclosure. All other embodiments, which can be derived by a person skilled in the art from the embodiments disclosed herein without making any creative effort, shall fall within the protection scope of the present disclosure.
It should be noted that the terms "first," "second," and the like in the description and claims of the present disclosure and in the above-described drawings are used for distinguishing between similar elements and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used is interchangeable under appropriate circumstances such that the embodiments of the disclosure described herein are capable of operation in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed, but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
The method provided by the embodiment can be executed in a mobile terminal, a computer terminal or a similar operation device. Fig. 1 shows a hardware configuration block diagram of a computer terminal (or mobile device) for implementing a method for scheduling a network appointment in a predetermined area. As shown in fig. 1, the computer terminal 10 (or mobile device 10) may include one or more (shown as 102a, 102b, … …, 102 n) processors 102 (the processors 102 may include, but are not limited to, a processing device such as a microprocessor MCU or a programmable logic device FPGA, etc.), a memory 104 for storing data, and a transmission module 106 for communication functions. Besides, the method can also comprise the following steps: a display, an input/output interface (I/O interface), a Universal Serial Bus (USB) port (which may be included as one of the ports of the I/O interface), a network interface, a power source, and/or a camera. It will be understood by those skilled in the art that the structure shown in fig. 1 is only an illustration and is not intended to limit the structure of the electronic device. For example, the computer terminal 10 may also include more or fewer components than shown in FIG. 1, or have a different configuration than shown in FIG. 1.
It should be noted that the one or more processors 102 and/or other data processing circuitry described above may be referred to generally herein as "data processing circuitry". The data processing circuitry may be embodied in whole or in part in software, hardware, firmware, or any combination thereof. Further, the data processing circuit may be a single stand-alone processing module, or incorporated in whole or in part into any of the other elements in the computer terminal 10 (or mobile device). As referred to in the disclosed embodiments, the data processing circuit acts as a processor control (e.g., selection of a variable resistance termination path connected to the interface).
The memory 104 may be used to store software programs and modules of application software, such as program instructions/data storage devices corresponding to the method for scheduling a taxi appointment in a predetermined area in the embodiment of the present disclosure, and the processor 102 executes various functional applications and data processing by running the software programs and modules stored in the memory 104, so as to implement the method for scheduling a taxi appointment in a predetermined area of the application program. The memory 104 may include high speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory located remotely from the processor 102, which may be connected to the computer terminal 10 via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The transmission device 106 is used for receiving or transmitting data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the computer terminal 10. In one example, the transmission device 106 includes a Network adapter (NIC) that can be connected to other Network devices through a base station to communicate with the internet. In one example, the transmission device 106 can be a Radio Frequency (RF) module, which is used to communicate with the internet in a wireless manner.
The display may be, for example, a touch screen type Liquid Crystal Display (LCD) that may enable a user to interact with a user interface of the computer terminal 10 (or mobile device).
It should be noted here that in some alternative embodiments, the computer device (or mobile device) shown in fig. 1 described above may include hardware elements (including circuitry), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that fig. 1 is only one example of a particular specific example and is intended to illustrate the types of components that may be present in the computer device (or mobile device) described above.
Fig. 2 is a schematic diagram of a network appointment scheduling system according to the embodiment. Referring to fig. 2, the system includes a server 210 for communicating with a driver 230 of a network appointment 220 through a network, informing the driver 230 of which area in a city has a gap in supply and demand, and guiding the driver 230 to the area to provide a network appointment service. It should be noted that the server 210 in the system is adapted to the above-described hardware structure.
In the operating environment, according to the first aspect of the present embodiment, a method for scheduling a network appointment in a predetermined area is provided, and the method is implemented by the server 210 shown in fig. 2. Therein, referring to FIG. 3, the predetermined area 300 may be divided into a plurality of different areas Z, for example1～Z21. Fig. 4 shows a flow diagram of the method, which, with reference to fig. 4, comprises:
s402: determining an excess supply area where the supply of the net appointment is excessive and a deficient supply area where the supply of the net appointment is deficient at a predetermined time in the future from the plurality of areas;
s404: determining the surplus supply quantity of the surplus supply area, wherein the surplus supply quantity is the quantity of net appointment vehicles which are redundant relative to the net appointment vehicle requirement in the surplus supply area;
s406: determining the number of supply gaps of the insufficient supply area, wherein the number of the supply gaps is the number of net appointment vehicles which are lacked relative to the net appointment requirements in the insufficient supply area;
s408: determining the driving mileage and driving time of the idle network appointment vehicle in the surplus supply area to each insufficient supply area; and
s410: and determining dispatching parameters for dispatching idle drivers in the surplus supply area according to the determined surplus supply quantity, the determined supply gap quantity, the driving mileage and the driving time length by utilizing a preset calculation model.
As described in the background, the supply and spatial-temporal distribution of networked appointments are often relatively stable for a particular region 300 (e.g., a city), and the demand for networked appointments is more random than the supply. Due to the mismatch of the supply and the demand of the network appointment vehicle service in the space-time distribution, the network appointment vehicle demand in some areas can not be met in some time periods, and the network appointment vehicle transport capacity supply in some areas in other time periods is excessive. Therefore, for enterprises and organizations providing network car booking service, the distribution of network car booking demands on space and time can be predicted in advance, and the network car booking dispatching system for dispatching network car booking supply in advance according to demand change can improve the efficiency problem of random order taking of drivers and has an important role in balance of supply and demand of urban network car booking travel.
In view of this, the present embodiment proposes a method for scheduling a network appointment car in the predetermined area 300. Wherein the predetermined area 300 is divided into a plurality of different zones Z1～Z21. Specifically, the S2 algorithm proposed by google, for example, can be used to divide the space of the predetermined area 300 into non-overlapping regions Z of appropriate size1～Z21。
According to the method of the present embodiment, the server 210 first selects a plurality of zones Z1～Z21The method includes determining a supply-excess area where the net appointment car is supplied with excess supply and a supply-deficiency area where the net appointment car is supplied with deficiency supply at a predetermined time T + delta T after the current time T. For example, the server 210 determines the predetermined time T + Δ T, zone Z by prediction1、Z3、Z8And Z15To supply the surplus area. In these areas, the supply of net appointment cars is greater than the demand for net appointment cars. Then, by prediction, the server 210 determines the predetermined time T + Δ T, the zone Z5、Z7And Z11Is an area of insufficient supply, wherein the supply of net appointment vehicles is less than the demand for net appointment vehicles.
Then, the server210 determine the number of over-supplies of the over-supply area and the number of supply notches of the under-supply area. For example, server 210 determines the over-supply area Z1、Z3、Z8And Z15The number of net appointment vehicles is the number of vacant vehicles, which is a portion where the supply amount of net appointment vehicles is increased with respect to the required amount of net appointment vehicles. And, the server 210 also determines the under-supply area Z5、Z7And Z11The number of net appointment vehicles that are missing with respect to the demand of net appointment vehicles.
Then, the server 210 determines the travel distance and the travel time period for the free network appointment in the over-offered area to travel to each under-offered area. For example to determine the zone Z1In the idle network taxi-booking heading to zone Z5The driving distance and the driving time length of the vehicle; determining the region Z1In the idle network taxi-booking heading to zone Z7The driving distance and the driving time length of the vehicle; determining the region Z1In the idle network taxi-booking heading to zone Z11The driving distance and the driving time length of the vehicle; determining the region Z3In the idle network taxi-booking heading to zone Z5The driving distance and the driving time length of the vehicle; determining the region Z3In the idle network taxi-booking heading to zone Z7The driving distance and the driving time length of the vehicle; determining the region Z3In the idle network taxi-booking heading to zone Z11The driving distance and the driving time length of the vehicle; determining the region Z8In the idle network taxi-booking heading to zone Z5The driving distance and the driving time length of the vehicle; determining the region Z8In the idle network taxi-booking heading to zone Z7The driving distance and the driving time length of the vehicle; determining the region Z8In the idle network taxi-booking heading to zone Z11The driving distance and the driving time length of the vehicle; determining the region Z15In the idle network taxi-booking heading to zone Z5The driving distance and the driving time length of the vehicle; determining the region Z15In the idle network taxi-booking heading to zone Z7The driving distance and the driving time length of the vehicle; and determining the zone Z15In the idle network taxi-booking heading to zone Z11The mileage and the running time period.
Finally, the server 210 determines a scheduling parameter for scheduling the free driver in the surplus-supply area according to the determined surplus-supply number, supply gap number, mileage, and travel time period, using a preset calculation model.
Thus, according to the method provided by the present embodiment, the scheduling parameters for scheduling the free driver in the surplus-supply area can be finally determined by predicting the determined number of surplus supplies in the surplus-supply area, the number of supply gaps in the shortage area, the travel distance from the free net in the surplus-supply area to the shortage area, and the travel time period, using a calculation model set in advance. Therefore, network appointment vehicle supply is dispatched in advance according to the change of the demand, and the efficiency of random order taking of drivers and the balance of supply and demand of urban network appointment vehicle outgoing are improved.
Further, as for the method of determining the over-supply area and the under-supply area, the method of determining the number of over-supplies and the number of supply notches, and the method of determining the travel distance and the travel time period for the free net in the over-supply area to approach the respective under-supply areas, the prediction may be performed using a known machine learning model.
For example, the server 210 in the scheduling system divides the predetermined area 300 into a plurality of zones Z1～Z21Then, time can be segmented by taking a specific time length as a unit, characteristics such as order information, driver information, region information and weather information are extracted from each region in each time slice through characteristic engineering processing, and a characteristic space-time matrix is constructed; and (3) predicting the supply and demand state of each region after a plurality of unit time slices by using the characteristic time matrix as data and using a machine learning model.
For example, with respect to zone Z1The form of the characteristic spatio-temporal matrix can be shown in the following table:
the order information features can be selected according to actual conditions. For example, the number of orders, the destination with the largest number of orders, etc. The driver information characteristic may, for example, select a number of drivers. The area information feature may be, for example, a feature such as selecting another area around the area or whether the area is congested. Of course, the selection of the features is not limited thereto.
The machine learning model can then be trained based on the feature spatio-temporal matrix, and future supply and demand can be predicted by the machine learning model.
And as for the driving mileage and the driving time length of the idle network appointment vehicle in the surplus supply area to each insufficient supply area, the driving mileage and the driving time length can be predicted and determined according to historical records, and the predicted time length and the predicted mileage can be obtained by calling a commercial map interface.
Further optionally, the operation of determining a scheduling parameter for scheduling free drivers in the surplus-supply region includes: determining the total number H of idle network appointment cars in the preset area according to the supply surplus quantity of each supply surplus area, and further determining the parameter a of the following scheduling matrix AhnWherein H is more than or equal to 1 and less than or equal to H, and N is more than or equal to 1 and less than or equal to N:
wherein N is the total number of starved areas, ahnFor indicating whether the h-th idle network appointment is dispatched to the n-th insufficient supply area. Wherein a ishnIf 1 is taken, the h-th idle network appointment vehicle is to be dispatched to the nth shortage supply area, and if 0 is taken, the h-th idle network appointment vehicle is not to be dispatched to the nth shortage supply area, namely:
specifically, the server 210 may be configured to provide the respective surplus supply areas Z1、Z3、Z8And Z15The total number of idle network appointments is determined. For example, the region Z of surplus supply1H1, and an excess supply region Z3H2, and an excess supply region Z8Is h3 and is supplied to the surplus region Z15The surplus supply amount of (2) is h 4. The total number H of idle network appointments in the predetermined area is H1+ H2+ H3+ H4.
And, the server 210 further determines the parameter a of the scheduling matrix ahn. For example, suppose zone Z5Corresponding to the 1 st starved area, Z7Corresponding to the 2 nd underfeed region and Z11Corresponding to the 3 rd starved area α31Indicating whether the 3 rd idle net appointment is dispatched to the insufficient supply area Z5；α32Indicating whether the 3 rd idle net appointment is dispatched to the insufficient supply area Z7；α33Indicating whether the 3 rd idle net appointment is dispatched to the insufficient supply area Z7。
Therefore, the method determines which insufficient supply area each idle network appointment vehicle is dispatched to through a 0-1 binary dispatching matrix, so that the method is more intuitive and is convenient for the processing and operation of subsequent programs.
And further, determining a parameter a in the scheduling matrix A according to the determined number of the scheduled supply gaps and the scheduling costhnComprises determining a parameter a in the scheduling matrix A according to the following formulahn：
Wherein,the initial feeding gap number of the nth feeding-insufficient area, namely the initial feeding gap number of the nth feeding-insufficient area at the current time T; u shapenThe number of supply gaps for the scheduled nth starved area, parameter α is a weighting parameter for weighing the number of supply gaps after scheduling and the scheduling cost, dhnDispatching the driving mileage to the nth area with insufficient supply for the h-th idle network appointment; p is a radical ofnObtaining the expected price of the order after the idle network car booking is dispatched to the nth insufficient supply area; o ismThe supply surplus number for the m-th scheduled supply surplus region; Δ T is a time interval of supply and demand forecast for indicating the future scheduled time T + Δ T after the current time T; and thnAnd dispatching the h-th idle network to the nth under-supply area for the driving time.
Specifically, at a specific time point (i.e., the current time T), the server 210 predicts that the supply and demand situation will occur in some areas at a time T + Δ T in the future, and triggers the scheduling mechanism.
For example, the server 210 predicts that M over-supply areas and N under-supply areas will occur at T + Δ T. Wherein the surplus supply region is denoted as DmWhere M is 1, 2, … …, M, and the initial surplus supply amount (i.e., the surplus supply amount at the current time T) of each corresponding surplus supply region isWherein M is 1, 2, … …, M. And wherein the short supply area is marked as DnWherein N is 1, 2, … …, N. The corresponding initial number of feed notches per starved area (i.e., the number of feed notches at the current time T) isWherein N is 1, 2, … …, N. And the average value of the order of each corresponding starved area is pnWherein N is 1, 2, … …, N.
In the over-supply area D when the current time point T is recordedmThe set of network appointments in the idle state is Hm, M is 1, 2, … …, M; the sum of the data of all network appointments in the supply surplus area in the idle state is
Then, the server 210 determines a travel distance matrix D and a travel time period matrix T representing the travel distance required for each free driver in the surplus-supply area to travel to each insufficient-supply area, which are respectively represented as follows:
wherein d ishn，thnRespectively representing the estimated driving distance and the estimated driving time required by the h-th idle network car appointment to the n-th insufficient supply area.
The server 210 then sets the scheduling matrix A described above, and sets the oversupply quantity O of the network appointment in each oversupply area after schedulingmAnd M is 1, …, M, the calculation method is that the initial supply surplus quantity of each area is subtracted by the quantity of net appointment cars called out from the area, namely:
and the server 210 also sets the number of supply notches U for each under-supplied area after schedulingnAnd N is 1, …, N, and the calculation method is that the number of the net appointment cars adjusted to each area is subtracted from the number of the initial supply gaps of each area, namely:
then, the server 210 builds a 0-1 planning model with linear constraint conditions after sequentially building data structures required by the model according to the data building manner. Specifically, the following factors need to be considered:
the scheduling by the server 210 aims to minimize the under-supply area Z at the lowest possible scheduling cost5、Z7And Z11The supply notch. On the one hand, each direction of the short supply region Z5、Z7Or Z11When a network appointment car is dispatched, a supply gap is reduced; on the other hand, each time the scheduling is generated, the scheduling distance and the cost of uncertain bill receiving amount after the user goes to the area are generated.
Thus, the scheme of the present embodiment uses the average amount of notch reduction for the starved areas (e.g., i.e., for each starved area Z)5、Z7And Z11Averaging the gap reductions) represents how much the scheduling process is relieved of supply-demand imbalance. And considering that the larger the supply gap of the starved area, the greater the demand for dispatching capacity thereto, hence the number of initial supply gapsAs weight pair scheduled gap UnA weighted average is performed. The following expression was thus obtained:thus, under the same scheduling cost condition, the capacity is preferentially adjusted to the area with larger supply gap.
In addition, the expected cost of scheduling is related to the scheduling distance and the expected price, where the ratio of the scheduling distance to the expected price is used to measure the cost of scheduling (i.e., the ratio of the scheduling distance to the expected price)) And the network appointment vehicle represents the scheduling distance which is required to be paid after the network appointment vehicle is scheduled to a certain area and the money is paid on average. The smaller the dispatch distance, the expected order price for the target area (e.g., the average value p of orders per starved area may be used)nRepresents) the higher the expected cost of scheduling, a parameter α is set between post-scheduling gap and scheduling cost to adjust the relative importance between the two objectives, collectively serving as the optimization objective of the planning model.
Furthermore, the above optimization objectives may be constrained by a series of constraints:
first, after the drivers in the oversupply area are called out, the remaining drivers should be able to meet the net appointment demand in the area, i.e., the number of vehicles called out in the area should be no greater than the initial oversupply number. That is, the number of surplus supplies in the scheduled surplus supply region cannot be less than 0 (0)m≥0)。
Secondly, to avoid wasting resources, the number of vehicles directed to the area with insufficient supply should not be greater than the initial supply gap of the area. That is, the number of supply notches of the scheduled insufficient supply area cannot be less than 0 (U)n≥0)。
Thirdly, in order to ensure the effectiveness of the supply and demand forecast for the dispatching process, the navigation time length required for each network appointment vehicle generating the dispatching to go from the current position to the target area must be less than the time interval delta T of the supply and demand forecast (namely a)hnthn＜ΔT)；
Finally, for all idle network appointments in the surplus supply area, each network appointment can be dispatched to only one area at most in the same time period (namely,)。
thus, to summarize the above, the server 210 determines the parameter a in the scheduling matrix A using the following formula for describing the scheduling objectives and constraintshnAnd obtaining the most scheduling matrix A.
Therefore, according to the scheduling method of the embodiment, not only the condition of unbalanced supply and demand in different areas in a city is considered, but also the order value in different areas and the driving mileage of a driver to different areas are taken into the model consideration range. Therefore, the vehicle using requirements of different areas are met under the condition that the vehicle using requirements of the network appointment are relatively random, meanwhile, the limited vehicle supply of the network appointment is enabled to generate a larger value as much as possible, and the benefits of residents with the vehicle using requirements of the city and enterprises and institutions providing the vehicle using services of the network appointment are improved.
In addition, fig. 5 shows a specific method flow of the server 210 in the present embodiment for scheduling planning.
First, the server 210 acquires information such as order data, vehicle data, weather data, and regional information of a predetermined area (e.g., inside a city) (S502);
the server 210 then divides the predetermined area 300 into a plurality of different zones Z1～Z21And further, time is divided in units of a certain time length, thereby implementing a space-time division of the predetermined region 300 (S504);
the server 210 then uses, for example, a machine learning model to identify each zone Z of the predetermined area1～Z21Performing supply and demand forecasting (S506);
then, the server 210 receives the request fromMultiple zones Z1～Z21In selecting a region Z in which insufficient supply is predicted to occur5、Z7And Z11And an excess supply region Z in which excess supply is performed1、Z3、Z8And Z15(S508)；
Then, the server 210 determines the insufficient supply area (S512), determines the expected order price of the insufficient supply area (S510), determines the excessive supply area (S514), and determines the number of free network appointments (S516);
and on this basis, the server 210 determines the location of the idle network car booking (S518) and constructs the duration/distance/scheduling matrix of the idle network car booking, i.e., the matrix D and the matrix T (S520);
the server 210 then constructs a scheduling matrix a and solves it using the following equation to arrive at the optimal scheduling matrix a (S522)
Finally, the server 210 sends a notification to the idle network car booking driver according to the obtained scheduling matrix a (S524).
Therefore, the method according to the first aspect of the embodiment solves the technical problems that the mismatch of the supply and the demand of the network car booking service on the space-time distribution in the prior art generates the situation that the network car booking demand of some regions cannot be met in some time periods, and the surplus supply of the network car booking transport capacity exists in some regions in other time periods. Therefore, the vehicle using requirements of different areas are met under the condition that the vehicle using requirements of the network appointment are relatively random, meanwhile, the limited vehicle supply of the network appointment is enabled to generate a larger value as much as possible, and the benefits of residents with the vehicle using requirements of the city and enterprises and institutions providing the vehicle using services of the network appointment are improved.
Further, referring to fig. 1, according to a third aspect of the present embodiment, there is provided a storage medium 104. The storage medium 104 comprises a stored program, wherein the method of any of the above is performed by a processor when the program is run.
It should be noted that, for simplicity of description, the above-mentioned method embodiments are described as a series of acts or combination of acts, but those skilled in the art will recognize that the present invention is not limited by the order of acts, as some steps may occur in other orders or concurrently in accordance with the invention. Further, those skilled in the art should also appreciate that the embodiments described in the specification are preferred embodiments and that the acts and modules referred to are not necessarily required by the invention.
Through the above description of the embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by software plus a necessary general hardware platform, and certainly can also be implemented by hardware, but the former is a better implementation mode in many cases. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which is stored in a storage medium (e.g., ROM/RAM, magnetic disk, optical disk) and includes instructions for enabling a terminal device (e.g., a mobile phone, a computer, a server, or a network device) to execute the method according to the embodiments of the present invention.
Example 2
Fig. 6 shows a system 600 for scheduling network appointments in a predetermined area according to the present embodiment, wherein the system 600 corresponds to the method according to the first aspect of embodiment 1. Referring to fig. 6, the system 600 includes: a prediction module 610 for determining an oversupply area where the net appointment is oversupplied and an undersupplied area where the net appointment is undersupplied at a predetermined time in the future from among a plurality of different areas; a first determining module 620, configured to determine an excess supply quantity of the excess supply area, where the excess supply quantity is a quantity of the net appointment vehicles which are redundant with respect to the net appointment demand in the excess supply area; a second determining module 630 for determining a number of feed gaps for the starved area, wherein the number of feed gaps is a number of net appointments missing in the starved area relative to a net appointment demand; the third determining module 640 is used for determining the driving mileage and the driving time length of the idle network appointment vehicle in the surplus supply area to each insufficient supply area; and a fourth determining module 650, configured to determine, by using a preset calculation model, a scheduling parameter for scheduling the idle network car reservation in the surplus supply area according to the determined surplus supply amount, the supply gap amount, the driving distance, and the driving time.
Optionally, the fourth determining module includes: a determining submodule for determining the total number H of idle network appointment cars in the predetermined area according to the supply surplus quantity of each supply surplus area, and further determining the parameter a of the following scheduling matrix AhnWhere H is 1. ltoreq. H, N is 1. ltoreq. n.ltoreq.N, N being the total number of starved areas:
ahnfor indicating whether the h-th idle network appointment is dispatched to the n-th insufficient supply area, wherein ahnIf 1 is taken, the h-th idle network appointment vehicle is to be dispatched to the n-th insufficient supply area, ahnIf 0 is taken, the h-th idle network appointment vehicle cannot be dispatched to the n-th insufficient supply area, namely:
optionally, the determining sub-module comprises: a first determination unit for determining that the insufficient supply region is in adjustmentThe number of supply gaps after the dispatching and the dispatching cost for dispatching the idle network taxi to the area with insufficient supply; a second determining unit, configured to determine a parameter a in the scheduling matrix a according to the determined number of scheduled supply gaps and the scheduling costhn。
Optionally, the second determining unit includes: a determining subunit, configured to determine a parameter a in the scheduling matrix a according to the following formulahn：
Wherein,the initial feeding gap number of the nth feeding-insufficient area, namely the initial feeding gap number of the nth feeding-insufficient area at the current time T; u shapenThe number of supply gaps for the scheduled nth starved area, parameter α is a weighting parameter for weighing the number of supply gaps after scheduling and the scheduling cost, dhnDispatching the driving mileage to the nth area with insufficient supply for the h-th idle network appointment; p is a radical ofnObtaining the expected price of the order after the idle network car booking is dispatched to the nth insufficient supply area; o ismThe supply surplus number for the m-th scheduled supply surplus region; Δ T is a time interval of supply and demand prediction, and is used to indicate a future scheduled time T + Δ T after the current time T; and thnAnd dispatching the h-th idle network to the nth under-supply area for the driving time.
Thus, the system 600 is provided according to the present embodiment. The scheduling parameters for scheduling the free driver in the surplus supply area can be finally determined by using a preset calculation model according to the surplus supply number of the surplus supply area, the supply gap number of the shortage supply area, the travel distance of the free network reservation of the surplus supply area to the shortage supply area and the travel time length determined by prediction. Therefore, network appointment vehicle supply is dispatched in advance according to the change of the demand, and the efficiency of random order taking of drivers and the balance of supply and demand of urban network appointment vehicle outgoing are improved.
Example 3
Fig. 7 shows an apparatus 700 for scheduling a network appointment in a predetermined area according to the present embodiment, where the apparatus 700 corresponds to the method according to the first aspect of the embodiment 1. Referring to fig. 7, the apparatus 700 includes: a processor 710; and a memory 720, coupled to the processor 710, for providing instructions to the processor 710 to process the following process steps: determining an oversupply area where the net appointment is oversupplied and an undersupply area where the net appointment is undersupplied at a predetermined time in the future from among the plurality of different areas; determining the surplus supply quantity of the surplus supply area, wherein the surplus supply quantity is the quantity of net appointment vehicles which are redundant relative to the net appointment vehicle requirement in the surplus supply area; determining the number of supply gaps of the insufficient supply area, wherein the number of the supply gaps is the number of net appointment vehicles which are lacked relative to the net appointment requirements in the insufficient supply area; determining the driving mileage and driving time of the idle network appointment vehicle in the surplus supply area to each insufficient supply area; and determining a scheduling parameter for scheduling the idle network car reservation in the surplus supply area according to the determined surplus supply quantity, the supply gap quantity, the driving mileage and the driving time length by using a preset calculation model.
Optionally, the operation of determining a scheduling parameter for scheduling free drivers in the surplus supply region includes: determining the total number H of idle network appointment cars in the preset area according to the supply surplus quantity of each supply surplus area, and further determining the parameter a of the following scheduling matrix AhnWhere H is 1. ltoreq. H, N is 1. ltoreq. n.ltoreq.N, N being the total number of starved areas:
ahnfor indicating whether the h-th idle network appointment is dispatched to the n-th insufficient supply area, wherein ahnIf 1 is taken, the h-th idle network appointment vehicle is to be dispatched to the n-th insufficient supply area, ahnIf 0 is taken, the h-th idle network appointment vehicle cannot be dispatched to the n-th insufficient supply area, namely:
optionally, the operation of determining the scheduling matrix a includes: determining the number of supply gaps of the insufficient supply area after dispatching and dispatching cost for dispatching the idle network appointment vehicle to the insufficient supply area; determining a parameter a in a scheduling matrix A according to the determined number of the scheduled supply gaps and the scheduling costhn。
Optionally, determining a parameter a in the scheduling matrix a according to the determined number of scheduled supply gaps and the scheduling costhnComprises determining a parameter a in the scheduling matrix A according to the following formulahn：
Wherein,the initial feeding gap number of the nth feeding-insufficient area, namely the initial feeding gap number of the nth feeding-insufficient area at the current time T; u shapenFor the nth under-supplied area after schedulingThe number of supply gaps, a parameter α which is a weighting parameter for weighing the number of supply gaps after scheduling and the scheduling cost, dhnDispatching the driving mileage to the nth area with insufficient supply for the h-th idle network appointment; p is a radical ofnObtaining the expected price of the order after the idle network car booking is dispatched to the nth insufficient supply area; o ismThe supply surplus number for the m-th scheduled supply surplus region; Δ T is a time interval of supply and demand prediction, and is used to indicate a future scheduled time T + Δ T after the current time T; and thnAnd dispatching the h-th idle network to the nth under-supply area for the driving time.
Thus, the apparatus 700 is provided according to the present embodiment. The scheduling parameters for scheduling the free driver in the surplus supply area can be finally determined by using a preset calculation model according to the surplus supply number of the surplus supply area, the supply gap number of the shortage supply area, the travel distance of the free network reservation of the surplus supply area to the shortage supply area and the travel time length determined by prediction. Therefore, network appointment vehicle supply is dispatched in advance according to the change of the demand, and the efficiency of random order taking of drivers and the balance of supply and demand of urban network appointment vehicle outgoing are improved.
The above-mentioned serial numbers of the embodiments of the present invention are merely for description and do not represent the merits of the embodiments.
In the above embodiments of the present invention, the descriptions of the respective embodiments have respective emphasis, and for parts that are not described in detail in a certain embodiment, reference may be made to related descriptions of other embodiments.
In the embodiments provided in the present application, it should be understood that the disclosed technology can be implemented in other ways. The above-described embodiments of the apparatus are merely illustrative, and for example, the division of the units is only one type of division of logical functions, and there may be other divisions when actually implemented, for example, a plurality of units or components may be combined or may be integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, units or modules, and may be in an electrical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit. The integrated unit can be realized in a form of hardware, and can also be realized in a form of a software functional unit.
The integrated unit, if implemented in the form of a software functional unit and sold or used as a stand-alone product, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present invention may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device) to execute all or part of the steps of the method according to the embodiments of the present invention. And the aforementioned storage medium includes: a U-disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a removable hard disk, a magnetic or optical disk, and other various media capable of storing program codes.
The foregoing is only a preferred embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A method for scheduling network appointments for a predetermined area, wherein the predetermined area is divided into a plurality of different zones, the method comprising:
determining an oversupply area where the net appointment is oversupplied and an undersupply area where the net appointment is undersupplied at a predetermined time in the future from among the plurality of different areas;
determining the supply surplus quantity of the supply surplus area, wherein the supply surplus quantity is the quantity of net appointment vehicles which are redundant relative to the net appointment demand in the supply surplus area;
determining a number of supply gaps of the starved area, wherein the number of supply gaps is the number of net appointment vehicles lacking relative to the net appointment demand in the starved area;
determining the driving mileage and driving time of the idle network appointment vehicle in the surplus supply area to each insufficient supply area; and
and determining scheduling parameters for scheduling idle network appointment vehicles in the surplus supply area according to the determined surplus supply quantity, supply gap quantity, driving mileage and driving time length by using a preset calculation model.
2. The method of claim 1, wherein determining scheduling parameters for scheduling free drivers in the surplus supply area comprises:
determining the total number H of idle network appointment cars of the preset area according to the supply surplus quantity of each supply surplus area, and further determining the parameter a of the following scheduling matrix AhnWhere H is 1. ltoreq. H, N is 1. ltoreq. n.ltoreq.N, N being the total number of starved areas:
ahnfor indicating whether the h-th idle network appointment is dispatched to the n-th insufficient supply area, wherein ahnIf 1 is taken, the h-th idle network appointment vehicle is to be dispatched to the n-th insufficient supply area, ahnIf 0 is taken, the h-th idle network appointment vehicle cannot be dispatched to the n-th insufficient supply area, namely:
3. the method of claim 2, wherein determining the scheduling matrix a comprises:
determining the number of supply gaps of the insufficient supply area after scheduling and scheduling cost for scheduling idle network appointment vehicles to the insufficient supply area;
determining a parameter a in a scheduling matrix A according to the determined number of the scheduled supply gaps and the scheduling costhn。
4. Method according to claim 3, characterized in that the parameter a in the scheduling matrix A is determined based on the determined number of scheduled supply gaps and the scheduling costhnComprises determining a parameter a in the scheduling matrix A according to the following formulahn：
Wherein,the initial feeding gap number of the nth feeding-insufficient area, namely the initial feeding gap number of the nth feeding-insufficient area at the current time T;
Unthe number of supply gaps for the n-th scheduled supply shortage region;
the parameter α is a weighting parameter for weighing the number of supply gaps after scheduling and the scheduling cost;
dhndispatching the driving mileage to the nth area with insufficient supply for the h-th idle network appointment;
pnobtaining the expected price of the order after the idle network car booking is dispatched to the nth insufficient supply area;
Omthe supply surplus number for the m-th scheduled supply surplus region;
Δ T is a time interval of supply and demand forecast for indicating the future scheduled time T + Δ T after the current time T; and
thnand dispatching the h-th idle network to the nth under-supply area for the driving time.
5. A storage medium comprising a stored program, wherein the method of any one of claims 1 to 4 is performed by a processor when the program is run.
6. A system for scheduling network appointments for a predetermined area, wherein the predetermined area is divided into a plurality of different zones, comprising:
the prediction module is used for determining an excess supply area with excess supply and an insufficient supply area with insufficient supply of the net appointment car at a predetermined time in the future from the plurality of different areas;
the first determining module is used for determining the supply surplus quantity of the supply surplus area, wherein the supply surplus quantity is the quantity of the net appointment vehicles which are redundant relative to the net appointment vehicle requirement in the supply surplus area;
a second determining module, configured to determine a number of supply gaps of the insufficient supply area, where the number of supply gaps is a number of net appointment vehicles lacking relative to a net appointment demand in the insufficient supply area;
the third determining module is used for determining the driving mileage and the driving time of the idle network appointment vehicle in the surplus supply area to each insufficient supply area; and
and the fourth determining module is used for determining scheduling parameters for scheduling the idle network car reservation in the surplus supply area according to the determined surplus supply quantity, supply gap quantity, driving mileage and driving time length by using a preset calculation model.
7. The system of claim 6, wherein the fourth determination module comprises:
a determining submodule for determining an idle network contract of the predetermined area according to the supply surplus number of each supply surplus areaThe total number of cars H, and further determines the parameters a of the following scheduling matrix AhnWhere H is 1. ltoreq. H, N is 1. ltoreq. n.ltoreq.N, N being the total number of starved areas:
ahnfor indicating whether the h-th idle network appointment is dispatched to the n-th insufficient supply area, wherein ahnIf 1 is taken, the h-th idle network appointment vehicle is to be dispatched to the n-th insufficient supply area, ahnIf 0 is taken, the h-th idle network appointment vehicle cannot be dispatched to the n-th insufficient supply area, namely:
8. the system of claim 7, wherein the determination submodule comprises:
the first determining unit is used for determining the number of the supply gaps of the insufficient supply area after dispatching and dispatching cost for dispatching the idle network appointment to the insufficient supply area;
a second determining unit, configured to determine a parameter a in the scheduling matrix a according to the determined number of scheduled supply gaps and the scheduling costhn。
9. The system of claim 8, wherein the second determining unit comprises:
a determining subunit, configured to determine a parameter a in the scheduling matrix a according to the following formulahn：
Wherein,the initial feeding gap number of the nth feeding-insufficient area, namely the initial feeding gap number of the nth feeding-insufficient area at the current time T;
Unthe number of supply gaps for the n-th scheduled supply shortage region;
the parameter α is a weighting parameter for weighing the number of supply gaps after scheduling and the scheduling cost;
dhndispatching the driving mileage to the nth area with insufficient supply for the h-th idle network appointment;
pnobtaining the expected price of the order after the idle network car booking is dispatched to the nth insufficient supply area;
Omthe supply surplus number for the m-th scheduled supply surplus region;
Δ T is a time interval of supply and demand forecast for indicating the future scheduled time T + Δ T after the current time T; and
thnand dispatching the h-th idle network to the nth under-supply area for the driving time.
10. An apparatus for scheduling network appointments for a predetermined area, wherein the predetermined area is divided into a plurality of different zones, comprising:
a processor; and
a memory coupled to the processor for providing instructions to the processor for processing the following processing steps:
determining an oversupply area where the net appointment is oversupplied and an undersupply area where the net appointment is undersupplied at a predetermined time in the future from among the plurality of different areas;
determining the supply surplus quantity of the supply surplus area, wherein the supply surplus quantity is the quantity of net appointment vehicles which are redundant relative to the net appointment demand in the supply surplus area;
determining a number of supply gaps of the starved area, wherein the number of supply gaps is the number of net appointment vehicles lacking relative to the net appointment demand in the starved area;
determining the driving mileage and driving time of the idle network appointment vehicle in the surplus supply area to each insufficient supply area; and
and determining scheduling parameters for scheduling idle network appointment vehicles in the surplus supply area according to the determined surplus supply quantity, supply gap quantity, driving mileage and driving time length by using a preset calculation model.
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Application publication date: 20190416 |