CN115660784A - Operation simulation method and device - Google Patents
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- CN115660784A CN115660784A CN202211386869.5A CN202211386869A CN115660784A CN 115660784 A CN115660784 A CN 115660784A CN 202211386869 A CN202211386869 A CN 202211386869A CN 115660784 A CN115660784 A CN 115660784A
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Abstract
According to the operation simulation method and device, a simulation order of a target date and a simulation driver of a first quantity are obtained; dispatching each simulated order to each simulated driver based on a dispatching rule; obtaining the income of each simulated driver; and determining the simulated operation running water of the target date based on the income of each simulated driver, so that the operation simulation of any day in any city can be realized, and the simulated operation running water of the whole day is produced.
Description
Technical Field
The application relates to the technical field of network taxi appointment, in particular to an operation simulation method and device.
Background
The network car booking company can often carry out various operation activities, for example, in a certain city, the rewarding activity is expected to be established to stimulate the drivers to go on line, thereby bringing the improvement of operation flow. The traditional method is biased to qualitative analysis, for example, according to actual operation conditions, the operation running water of each day is determined, supply and demand analysis is carried out to obtain whether the city lacks drivers, but the relation between the increase of the drivers and the new running water cannot be evaluated, namely the input-output ratio of the operation activities cannot be accurately estimated.
Disclosure of Invention
In view of the above problems in the related art, the present application provides an operation simulation method and apparatus.
The application provides an operation simulation method, which comprises the following steps:
acquiring a simulated order of a target date and a first number of simulated drivers;
dispatching each simulated order to each simulated driver based on a dispatching rule;
obtaining the income of each simulated driver;
and determining the simulated operation water flow of the target date based on the income of each simulated driver.
In some embodiments, the method further comprises:
acquiring real operation running water of the target date;
and determining the newly added operation running water based on the real operation running water and the simulated operation running water.
In some embodiments, the number of real drivers for the target date is a second number; the first number and the second number are different, the method further comprising:
determining a change amount of the driver based on the first amount and the second amount;
establishing a first relation between the change quantity and the newly added operation flow;
and outputting the first relation.
In some embodiments, the method further comprises:
adjusting the first amount;
determining a second relation corresponding to the adjusted first quantity, wherein the second relation is a relation between the change quantity corresponding to the adjusted first quantity and the newly added operation flow;
an optimal number of changes is determined from the respective first numbers based on the first and second relationships.
In some embodiments, dispatching each simulated order to each simulated driver based on a dispatching rule comprises:
sequentially selecting target orders based on the time sequence of the simulated orders;
determining the getting-on position of the target order;
searching whether an idle driver exists within a preset distance from the boarding position;
under the conditions that idle drivers are available at the preset distance and the order is judged not to be cancelled according to a pre-established cancellation rate, the target order is dispatched to the idle driver closest to the boarding position, wherein each idle driver carries out random walk based on a random walk model;
updating the driver state and the order state of an idle driver corresponding to the target order;
all the simulated orders are traversed, and each simulated order is dispatched to an idle driver.
In some embodiments, the method further comprises:
and under the condition that no idle driver and/or the target order are/is cancelled according to the probability judgment of the cancellation rate in the preset distance, discarding the target order.
In some embodiments, the method further comprises:
in the process of simulating the dispatching of the orders, updating an on-line driver, deleting an off-line driver, updating an order driver, and updating a driver in a home or on-road mode at a preset time period.
In some embodiments, the method further comprises:
obtaining order canceling information within a historical preset time length;
a cancellation rate is determined based on the order cancellation information.
In some embodiments, the method further comprises:
acquiring information of random walk of an idle driver within a historical preset time;
determining the online time and coordinate points of each idle driver based on the information about the random walk of the idle driver;
mapping the online time and coordinate points into corresponding time slices and honeycomb grids;
calculating the probability of each driver moving into any honeycomb grid in any time slice;
a random walk model is built based on the probability of each driver moving into any cell grid within any time slice.
An embodiment of the present application provides an operation simulation apparatus, including:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a simulated order of a target date and a simulated driver of a first quantity;
the order dispatching module is used for dispatching each simulation order to each simulation driver based on an order dispatching rule;
the second acquisition module is used for acquiring the income of each simulated driver;
and the first determination module is used for determining the simulated operation running water of the target date based on the income of each simulated driver.
The embodiment of the application provides an operation simulation method and device, wherein a simulation order of a target date and a simulation driver of a first number are obtained; dispatching each simulated order to each simulated driver based on a dispatching rule; obtaining the income of each simulated driver; and determining the simulated operation running water of the target date based on the income of each simulated driver, so that the operation simulation of any day in any city can be realized, and the simulated operation running water of the whole day is produced.
Drawings
The present application will be described in more detail hereinafter on the basis of embodiments and with reference to the accompanying drawings.
Fig. 1 is a schematic flow chart illustrating an implementation of an operation simulation method according to an embodiment of the present application;
fig. 2 is a schematic structural diagram of an operation simulation apparatus according to an embodiment of the present disclosure;
fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
In the drawings, like parts are designated with like reference numerals, and the drawings are not drawn to scale.
Detailed Description
In order to make the objectives, technical solutions and advantages of the present application clearer, the present application will be described in further detail with reference to the attached drawings, the described embodiments should not be considered as limiting the present application, and all other embodiments obtained by a person of ordinary skill in the art without creative efforts shall fall within the protection scope of the present application.
In the following description, reference is made to "some embodiments" which describe a subset of all possible embodiments, but it is understood that "some embodiments" may be the same subset or different subsets of all possible embodiments, and may be combined with each other without conflict.
The following description will be added if a similar description of "first \ second \ third" appears in the application file, and in the following description, the terms "first \ second \ third" merely distinguish similar objects and do not represent a specific ordering for the objects, and it should be understood that "first \ second \ third" may be interchanged under certain circumstances in a specific order or sequence, so that the embodiments of the application described herein can be implemented in an order other than that shown or described herein.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein is for the purpose of describing embodiments of the present application only and is not intended to be limiting of the application.
Based on the problems in the related art, an embodiment of the present application provides an operation simulation method, where the method is applied to an electronic device, and the electronic device may be a mobile phone, a tablet Computer, a notebook Computer, an Ultra-mobile Personal Computer (UMPC), a handheld Computer, a netbook, a server, a network booking platform, and the like. The functions implemented by the operation simulation method provided by the embodiment of the application can be implemented by calling a program code by a processor of the electronic device, wherein the program code can be stored in a computer storage medium. Fig. 1 is a schematic flow chart of an implementation of an operation simulation method provided in an embodiment of the present application, as shown in fig. 1, including:
step S1: a simulated order for a target date and a first number of simulated drivers are obtained.
In the embodiment of the present application, the target date may be any day, and may be a historical time or a future time.
In the embodiment of the application, the simulated order of the target date and the simulated driver of the first number can be obtained through the input of the input device. The input device may be a mouse, keyboard, voice device, etc.
In some embodiments, the order record of the target date can be obtained in the order system through a Structured Query Language (SQL) script, the driver up-and-down record, the driver idle record, and the driver open forward-to-home mode record can be obtained in the driver system through the SQL script, and the like.
In some embodiments, a real order for a target date may be obtained and a simulated order may be generated based on the real order, and a simulated driver may be generated based on historical driver data for the target date. The first number may be configurable.
And S2, dispatching each simulation order to each simulation driver based on the dispatching rule.
In the embodiment of the application, the order dispatching rule is pre-established, and after the simulated orders on the target date and the first number of simulated drivers are obtained, each simulated order can be dispatched to each simulated driver based on the order dispatching rule. In the embodiment of the application, the simulation rule can be constructed in advance, and then the order is sent to each simulated driver to carry out real simulation.
In the embodiment of the present application, step S2 may be implemented by the following steps:
and S21, sequentially selecting target orders based on the time sequence of the simulation orders.
In the embodiment of the application, the simulation order can be analyzed, so that the time sequence is determined. In the embodiment of the application, the principle of ordering and dispatching orders is followed.
And S22, determining the getting-on position of the target order.
In the embodiment of the application, the target order can be analyzed, so that the getting-on position can be determined.
And S23, searching whether an idle driver exists within a preset distance from the boarding position.
In the embodiment of the application, whether an idle driver exists in the preset distance of the parking position can be determined through a geographic system.
In the embodiment of the present application, step S24 is executed when there is an idle driver, and step S26 is executed if there is no idle driver.
In the embodiment of the present application, the preset distance may be configured, and for example, the preset distance may be 3km.
And step S24, under the condition that idle drivers are available in the preset distance and the order is judged not to be cancelled according to the pre-established cancellation rate, sending the target order to the idle driver closest to the boarding position, wherein each idle driver carries out random walk based on a random walk model.
In the embodiment of the application, the cancellation rate and the random walk model are established in advance.
In the embodiment of the application, order canceling information within a preset time length can be acquired, and a canceling rate is determined based on the order canceling information. The preset time period may be 30 days. In this embodiment of the present application, the cancellation rate may include: a pre-dispatch cancellation rate and a post-dispatch cancellation rate.
In the embodiment of the application, the pre-dispatch cancellation is cancelled because the aggregation platform does not dispatch the order or the personal reason of the passenger before being distributed to the driver. And after dispatch cancellation is assigned to the driver, the passenger or the driver cancels due to factors such as too far distance of driving meeting and the like.
The calculation formula of the cancellation rate is as follows:
pre-dispatch cancellation rate: (lower unit-number of answers)/lower unit;
post-dispatch cancellation rate: (number of answers-odd number)/number of answers.
In the embodiment of the application, the information that the idle driver randomly walks within the historical preset time can be obtained; determining the online time and coordinate points of each idle driver based on the information about the random walk of the idle driver; mapping the online time and coordinate points into corresponding time slices and honeycomb grids; calculating the probability of each driver moving into any honeycomb grid in any time slice; a random walk model is built based on the probability of each driver moving into any cell grid within any time slice. In the embodiment of the application, the random walk model is used for simulating the transfer condition of the idle driver in the order dispatching process.
And step S25, updating the driver state and the order state of the idle driver corresponding to the target order.
In the embodiment of the application, after the idle driver is dispatched, the driver state of the idle driver is changed into a non-idle driver, and the dispatch can not be carried out under the condition that the driver is the non-idle driver. After the target order is dispatched, the order status of the target order is changed to dispatched if the dispatch is not available.
And S26, under the condition that no idle driver and/or order are/is cancelled according to the probability judgment of the cancellation rate in the preset distance, discarding the order.
After step S25 and step S26, step S27 is executed.
And S27, traversing all the simulation orders, and dispatching each simulation order to an idle driver.
In the embodiment of the present application, after completing one target order, the steps from step S21 to step S25 or the steps from step S21 to step S26 are continuously adopted to complete the processing of the next target order, so that all the simulation orders are traversed, and each simulation order can be dispatched to an idle driver.
Illustratively, when the dispatching is simulated, the target orders are selected according to time in sequence, and the dispatching is performed before the orders are placed. According to the getting-on coordinates of the target order, all idle drivers within 3km of the order radius are searched from the geographic system, and the order is judged to be cancelled before and after the order is sent, if the idle drivers are within the order sending radius of the order and the order is judged not to be cancelled according to the cancellation rate probability, the order is assigned to the nearest driver, otherwise, the order is discarded, the order sending of the next target order is carried out, and the order state is recorded in the process. And when each simulated order designates a driver, calling a path planning (RP) program to estimate the driving time, and recording the state change of the driver by combining the estimated driving time of the order. And finally, updating the order state and the driver state, and circularly traversing until all orders are finished.
And S3, obtaining the income of each simulated driver.
In the embodiment of the application, the driving time can be estimated based on a calling path planning (RP) program, and the income of each order can be determined by combining the estimated driving time, the distance and the like of the order. And then counting the income of each order completed by each driver so as to determine the income of each simulated driver.
And S4, determining the simulated operation running water of the target date based on the profits of all simulated drivers.
In the embodiment of the application, the profits of all the simulated drivers can be counted, so that the simulated operation running water of the target date can be determined.
The embodiment of the application provides an operation simulation method, which comprises the steps of obtaining a simulation order of a target date and a simulation driver of a first quantity; dispatching each simulated order to each simulated driver based on a dispatching rule; obtaining the income of each simulated driver; and determining the simulated operation running water of the target date based on the income of each simulated driver, so that the operation simulation of any day in any city can be realized, and the simulated operation running water of the whole day is produced.
In some embodiments, after step S4, the method further comprises:
and S5, acquiring the real operation running water of the target date.
In the embodiment of the present application, the target date is a history date.
And S6, determining the newly added operation running water based on the real operation running water and the simulated operation running water.
In the embodiment of the application, the newly increased operation running water can be determined by subtracting the real operation running water from the simulated operation running water, and the newly increased operation running water can be a positive value or a negative value.
In the embodiment of the application, the newly added operation running water is determined to assist and guide future operation decisions based on the real operation running water and the simulated operation running water.
In some embodiments, the number of real drivers for the target date is a second number; the first number and the second number are different, and after step S6, the method further comprises:
step S7, determining the change amount of the driver based on the first amount and the second amount.
In the embodiment of the present application, the number of changes may be a reduced number or an increased number.
And S8, establishing a first relation between the change quantity and the newly added operation running water.
Illustratively, a relationship between the number of drivers and the new operation flow can be established, so that the decision of the future operation can be assisted and guided.
And S9, outputting the first relation.
In some embodiments, after step S9, the method further comprises:
step S10, adjusting the first quantity.
In the embodiment of the present application, the first number may be reduced, or the first number may be increased.
And S11, determining a second relation corresponding to the adjusted first quantity, wherein the second relation is the relation between the change quantity corresponding to the adjusted first quantity and the newly added operation flow.
In the embodiment of the application, the second relationships corresponding to different first numbers can be obtained in the above manner.
Step S12, determining an optimum number of changes from the respective first numbers based on the first relationship and the second relationship.
Illustratively, the optimal input-output ratio is quantitatively analyzed by adding N1, N2.. Nn drivers to the simulation to respectively produce the simulated operation running water of the whole day.
In some embodiments, during the simulated dispatch process, the on-line driver is updated, the off-line driver is deleted, the off-order driver is updated, the home-back or on-road driver is updated at preset time periods.
In the embodiment of the present application, the preset time period may be configured, for example, may be 5 minutes. In some embodiments, it is also desirable to update the free driver random walks.
Based on the foregoing embodiments, the embodiments of the present application provide an operation simulation apparatus, where each module included in the apparatus and each unit included in each module may be implemented by a processor in a computer device; of course, the implementation can also be realized through a specific logic circuit; in the implementation process, the processor may be a Central Processing Unit (CPU), a Microprocessor Unit (MPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), or the like.
An operation simulation apparatus is provided in the embodiment of the present application, fig. 2 is a schematic structural diagram of the operation simulation apparatus provided in the embodiment of the present application, and as shown in fig. 2, an operation simulation apparatus 200 includes:
a first obtaining module 201, configured to obtain a simulated order of a target date and a simulated driver of a first quantity;
a dispatch module 202 for dispatching each simulated order to each simulated driver based on a dispatch rule;
the second acquisition module 203 is used for acquiring the income of each simulated driver;
a first determining module 204, configured to determine the simulated operation flow of the target date based on the revenue of each simulated driver.
In some embodiments, the operation simulation apparatus 200 further includes:
the third acquisition module is used for acquiring the real operation running water of the target date;
and the second determining module is used for determining the newly added operation flow based on the real operation flow and the simulated operation flow.
In some embodiments, the number of real drivers for the target date is a second number; the first number is different from the second number, and the operation simulation apparatus 200 further includes:
a third determination module to determine a change amount of the driver based on the first amount and the second amount;
the establishing module is used for establishing a first relation between the change quantity and the newly added operation flow;
and the output module is used for outputting the first relation.
In some embodiments, the operation simulation apparatus 200 further includes:
an adjustment module for adjusting the first number;
a fourth determining module, configured to determine a second relationship corresponding to the adjusted first quantity, where the second relationship is a relationship between a change quantity corresponding to the adjusted first quantity and the newly added operation flow;
a fifth determining module for determining an optimal number of changes from the respective first numbers based on the first and second relationships.
In some embodiments, a dispatch module, comprising:
the selection unit is used for sequentially selecting the target orders based on the time sequence of the simulation orders;
the first determining unit is used for determining the getting-on position of the target order;
the searching unit is used for searching whether an idle driver exists within a preset distance from the boarding position;
the order dispatching unit is used for dispatching the target order to the idle driver closest to the boarding position under the condition that idle drivers are in the preset distance and the order is judged not to be cancelled according to a pre-established cancellation rate, wherein each idle driver carries out random walk based on a random walk model;
the updating unit is used for updating the driver state of an idle driver corresponding to the target order and the order state;
and the traversing unit is used for traversing all the simulation orders and dispatching each simulation order to an idle driver.
In some embodiments, the order module further comprises:
and the discarding unit is used for discarding the order under the condition that no idle driver and/or the order are/is cancelled according to the probability judgment of the cancellation rate in the preset distance.
In some embodiments, the operation simulation apparatus 200 further includes:
and the updating module is used for updating an on-line driver, deleting an off-line driver, updating an order driver, updating a driver in a home-returning or on-road mode in a preset time period in the process of simulating the order dispatching.
In some embodiments, the operation simulation apparatus 200 further includes:
the fourth acquisition module is used for acquiring order cancellation information within historical preset duration;
a sixth determining module to determine a cancellation rate based on the order cancellation information.
In some embodiments, the operation simulation apparatus 200 further includes:
the fifth acquisition module is used for acquiring information of random walk of an idle driver within a historical preset time length;
the seventh determining module is used for determining the online time and the coordinate point of each idle driver based on the information about the random walk of the idle driver;
a mapping module for mapping the online time and coordinate points into corresponding time slices and honeycomb grids;
the calculating module is used for calculating the probability that each driver moves to any honeycomb grid in any time slice;
and the establishing module is used for establishing a random walk model based on the probability that each driver moves into any honeycomb grid in any time slice.
It should be noted that, in the embodiment of the present application, if the operation simulation method is implemented in the form of a software functional module and is sold or used as a standalone product, the operation simulation method may also be stored in a computer-readable storage medium. Based on such understanding, the technical solutions of the embodiments of the present application may be essentially implemented or portions thereof contributing to the prior art may be embodied in the form of a software product stored in a storage medium, and including several 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 methods described in the embodiments of the present application. And the aforementioned storage medium includes: various media capable of storing program codes, such as a usb disk, a removable hard disk, a Read Only Memory (ROM), a magnetic disk, or an optical disk. Thus, embodiments of the present application are not limited to any specific combination of hardware and software.
Accordingly, embodiments of the present application provide a storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps in the operation simulation method provided in the above embodiments.
The embodiment of the application provides an electronic device; fig. 3 is a schematic structural diagram of an electronic device according to an embodiment of the present application, and as shown in fig. 3, the electronic device 300 includes: a processor 301, at least one communication bus 302, a user interface 303, at least one external communication interface 304, a memory 305. Wherein the communication bus 302 is configured to enable connective communication between these components. The user interface 303 may comprise a display screen, and the external communication interface 304 may comprise a standard wired interface and a wireless interface, among others. The processor 301 is configured to execute the operation simulation method program stored in the memory to implement the steps in the operation simulation method provided in the above-described embodiment.
The above description of the electronic device and storage medium embodiments, similar to the description of the method embodiments above, has similar advantageous effects as the method embodiments. For technical details not disclosed in the embodiments of the computer device and the storage medium of the present application, reference is made to the description of the embodiments of the method of the present application for understanding.
It should be appreciated that reference throughout this specification to "one embodiment" or "an embodiment" means that a particular feature, structure or characteristic described in connection with the embodiment is included in at least one embodiment of the present application. Thus, the appearances of the phrases "in one embodiment" or "in an embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, the particular features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. It should be understood that, in the various embodiments of the present application, the sequence numbers of the above-mentioned processes do not mean the execution sequence, and the execution sequence of each process should be determined by its function and inherent logic, and should not constitute any limitation to the implementation process of the embodiments of the present application. The above-mentioned serial numbers of the embodiments of the present application are merely for description and do not represent the merits of the embodiments.
It should be noted that, in this document, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a … …" does not exclude the presence of another identical element in a process, method, article, or apparatus that comprises the element.
In the several embodiments provided in the present application, it should be understood that the disclosed apparatus and method may be implemented in other ways. The above-described device embodiments are merely illustrative, for example, the division of the unit is only one logical function division, and there may be other division ways in actual implementation, such as: multiple units or components may be combined, or may be integrated into another system, or some features may be omitted, or not implemented. In addition, the coupling, direct coupling or communication connection between the components shown or discussed may be through some interfaces, and the indirect coupling or communication connection between the devices or units may be electrical, mechanical or in other forms.
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; can be located in one place or 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, all functional units in the embodiments of the present application may be integrated into one processing unit, or each unit may be separately regarded as one unit, or two or more units may be integrated into one unit; the integrated unit can be realized in a form of hardware, or in a form of hardware plus a software functional unit.
Those of ordinary skill in the art will understand that: all or part of the steps for realizing the method embodiments can be completed by hardware related to program instructions, the program can be stored in a computer readable storage medium, and the program executes the steps comprising the method embodiments when executed; and the aforementioned storage medium includes: various media that can store program codes, such as a removable Memory device, a Read Only Memory (ROM), a magnetic disk, or an optical disk.
Alternatively, the integrated units described above in the present application may be stored in a computer-readable storage medium if they are implemented in the form of software functional modules and sold or used as independent products. Based on such understanding, the technical solutions of the embodiments of the present application may be embodied in the form of a software product, which is stored in a storage medium and includes instructions for causing a controller to execute all or part of the methods described in the embodiments of the present application. And the aforementioned storage medium includes: a removable storage device, a ROM, a magnetic or optical disk, or other various media that can store program code.
The above description is only for the embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily conceive of changes or substitutions within the technical scope of the present application, and shall be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (10)
1. An operation simulation method, comprising:
acquiring a simulated order of a target date and a first number of simulated drivers;
dispatching each simulated order to each simulated driver based on a dispatching rule;
obtaining the income of each simulated driver;
and determining the simulated operation water flow of the target date based on the income of each simulated driver.
2. The method of claim 1, further comprising:
acquiring real operation running water of the target date;
and determining the newly added operation running water based on the real operation running water and the simulated operation running water.
3. The method of claim 2, wherein the number of real drivers for the target date is a second number; the first number and the second number are different, the method further comprising:
determining a change amount of the driver based on the first amount and the second amount;
establishing a first relation between the change quantity and the newly added operation flow;
and outputting the first relation.
4. The method of claim 3, further comprising:
adjusting the first amount;
determining a second relation corresponding to the adjusted first quantity, wherein the second relation is a relation between the change quantity corresponding to the adjusted first quantity and the newly added operation flow;
an optimal number of changes is determined from the respective first numbers based on the first and second relationships.
5. The method of claim 1, wherein dispatching each simulated order to each simulated driver based on a dispatching rule comprises:
sequentially selecting target orders based on the time sequence of the simulated orders;
determining the getting-on position of the target order;
searching whether an idle driver exists within a preset distance from the boarding position;
under the condition that idle drivers are available in the preset distance and the order is judged not to be cancelled according to the pre-established cancellation rate, the target order is dispatched to the idle driver closest to the boarding position, wherein each idle driver carries out random walk based on a random walk model;
updating the driver state and the order state of an idle driver corresponding to the target order;
and traversing all the simulated orders, and dispatching each simulated order to an idle driver.
6. The method of claim 5, further comprising:
and under the condition that no idle driver and/or the target order are/is cancelled according to the probability judgment of the cancellation rate in the preset distance, discarding the target order.
7. The method of claim 6, further comprising:
in the process of simulating the dispatching of the orders, updating an on-line driver, deleting an off-line driver, updating an order driver, and updating a driver in a home or on-road mode at a preset time period.
8. The method of claim 5, further comprising:
obtaining order canceling information within a historical preset time length;
a cancellation rate is determined based on the order cancellation information.
9. The method of claim 1, further comprising:
acquiring information of random walk of an idle driver within a historical preset time;
determining the online time and coordinate points of each idle driver based on the information about the random walk of the idle driver;
mapping the online time and coordinate points into corresponding time slices and honeycomb grids;
calculating the probability of each driver moving into any honeycomb grid in any time slice;
a random walk model is built based on the probability of each driver moving into any cell grid within any time slice.
10. An operation simulation apparatus, comprising:
the system comprises a first acquisition module, a second acquisition module and a third acquisition module, wherein the first acquisition module is used for acquiring a simulated order of a target date and a simulated driver of a first quantity;
the order dispatching module is used for dispatching each simulation order to each simulation driver based on an order dispatching rule;
the second acquisition module is used for acquiring the income of each simulated driver;
and the first determination module is used for determining the simulated operation running water of the target date based on the income of each simulated driver.
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Citations (8)
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