CN110852524B

CN110852524B - Method, device and equipment for determining time window and storage medium

Info

Publication number: CN110852524B
Application number: CN201911136272.3A
Authority: CN
Inventors: 侯琛
Original assignee: Tencent Technology Shenzhen Co Ltd
Current assignee: Tencent Technology Shenzhen Co Ltd
Priority date: 2019-11-19
Filing date: 2019-11-19
Publication date: 2023-04-18
Anticipated expiration: 2039-11-19
Also published as: CN110852524A

Abstract

The application discloses a method, a device, equipment and a storage medium for determining a time window, which are applied to the field of intelligent transportation. The method comprises the following steps: acquiring task information and m vehicles in a vehicle-road cooperative system, wherein the task information comprises n tasks and n task processing times t corresponding to the n tasks one by one _n Each task is a task which needs to be completed by a single vehicle, each vehicle executes the single task at the same time, the task processing time is used for representing the time required by the single vehicle to process and complete the single task, and n and m are positive integers; calculating the average task processing time and the maximum time of a single task according to the task information and the m vehicles; the upper and lower bounds of a time window are determined from the average task processing time and the individual task maximum time, the time window being used to estimate the time required for the m vehicles to process n tasks in parallel. The method can accurately determine the time window and improve the working efficiency of the vehicle-road cooperative system.

Description

Method, device and equipment for determining time window and storage medium

Technical Field

The present application relates to the field of intelligent transportation, and in particular, to a method, an apparatus, a device, and a storage medium for determining a time window.

Background

The vehicle-road cooperative system comprises a traffic management platform and a plurality of vehicles controlled by the traffic management platform, and the traffic management platform can distribute a plurality of tasks to the plurality of vehicles. The task can be a road condition sensing task, a signal lamp sensing task, a vehicle distance sensing task, a weather broadcasting task and the like.

One vehicle can only process one task at the same time, and a plurality of vehicles can process a plurality of tasks at the same time; a task can only be processed by one vehicle and is not interruptible. The traffic management platform needs to estimate the completion time of a task before distributing a batch of tasks, namely how long a plurality of vehicles need to process and complete the batch of tasks, and the completion time is a time window. In the related art, the longest processing time is determined as the time window of the batch of tasks, and the longest processing time is the processing time of one of the batch of tasks with the longest processing time, for example, the longest processing time is the time required for processing the one of the batch of tasks that is the most time-consuming to complete.

The method for determining the time window in the related art has a defect in some scenes, and when the number of vehicles is small and a single vehicle needs to process a plurality of tasks, the vehicles cannot process and complete the tasks in the time window, so that the estimation of the time window is inaccurate, and the working efficiency of the vehicle-road cooperative system is reduced.

Disclosure of Invention

The embodiment of the application provides a method, a device, equipment and a storage medium for determining a time window, and can solve the problems that in a mode of determining the time window in the related art, when the number of vehicles is small and a single vehicle needs to process a plurality of tasks, the vehicle cannot process and complete the tasks in the time window, the estimation of the time window is inaccurate, and the working efficiency of a vehicle-road cooperative system is reduced. The technical scheme is as follows:

according to an aspect of the present application, there is provided a method for determining a time window, the method including:

task information and m vehicles in a vehicle-road cooperative system are obtained, wherein the task information comprises n tasks and n task processing times t corresponding to the n tasks one to one _n Each task is a task which needs to be completed by a single vehicle, each vehicle executes the single task at the same time, the task processing time is used for representing the time required by the single vehicle to process and complete the single task, and n and m are positive integers;

calculating the average task processing time and the maximum time of a single task according to the task information and the m vehicles;

and determining an upper limit and a lower limit of a time window according to the average task processing time and the single task maximum time, wherein the time window is used for estimating the time required by the m vehicles to process and complete the n tasks in parallel.

According to another aspect of the present application, there is provided an apparatus for determining a time window, the apparatus comprising:

an obtaining module, configured to obtain task information and m vehicles in a vehicle-road coordination system, where the task information includes n tasks and n task processing times t corresponding to the n tasks one to one _n Each task is a task that needs to be completed by a single vehicle, each vehicle being on the same sideExecuting a single task at one time, wherein the task processing time is used for representing the time required by a single vehicle to process and complete the single task, and n and m are positive integers;

the computing module is used for computing the average task processing time and the single task maximum time according to the task information and the m vehicles;

and the determining module is used for determining an upper limit and a lower limit of a time window according to the average task processing time and the maximum time of the single task, wherein the time window is used for estimating the time required by the m vehicles to process and complete the n tasks in parallel.

According to another aspect of the present application, there is provided a computer device comprising: a processor and a memory, the memory having stored therein at least one instruction, at least one program, set of codes, or set of instructions, which is loaded and executed by the processor to implement the method of determining a time window as described above.

According to another aspect of the application, there is provided a computer readable storage medium having stored therein at least one instruction, at least one program, code set or instruction set, which is loaded and executed by the processor to implement the method of determining a time window as described above.

The beneficial effects brought by the technical scheme provided by the embodiment of the application at least comprise:

according to the information of the two aspects of the tasks and the vehicles in the vehicle-road cooperative system, two parameters of the average task processing time and the maximum processing time of a single task are calculated, and the upper limit and the lower limit of a time window are calculated according to the two parameters, so that the range of the time window is obtained. The upper and lower limits of the time window are finally determined by comprehensively considering the information of the task and the vehicle, so that the traffic management platform can accurately estimate the time for completing the task of the vehicle, and the problems of disordered task allocation of the traffic management platform and reduced working efficiency of a vehicle-road cooperative system caused by unreasonable estimation of the time window are solved.

Drawings

In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings required to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the description below are only some embodiments of the present application, and it is obvious for those skilled in the art to obtain other drawings without creative efforts.

FIG. 1 is a block diagram of a vehicle-to-road coordination system provided in an exemplary embodiment of the present application;

FIG. 2 is a schematic illustration of a vehicle-to-road coordination system provided in another exemplary embodiment of the present application;

FIG. 3 is a schematic illustration of a time window provided by another exemplary embodiment of the present application;

FIG. 4 is a flow chart of a method for determining a time window provided by an exemplary embodiment of the present application;

FIG. 5 is a flow chart of a method for determining a time window provided by another exemplary embodiment of the present application;

FIG. 6 is a flow chart of a method for determining a time window provided by another exemplary embodiment of the present application;

FIG. 7 is a flow chart of a method for determining a time window provided by another exemplary embodiment of the present application;

FIG. 8 is a flow chart of a method for determining a time window as provided by another exemplary embodiment of the present application;

FIG. 9 is a schematic diagram of a time window determination apparatus provided in another exemplary embodiment of the present application;

fig. 10 is a schematic structural diagram of a computer device according to an exemplary embodiment of the present application.

Detailed Description

To make the objects, technical solutions and advantages of the present application more clear, the following detailed description of the embodiments of the present application will be made with reference to the accompanying drawings.

First, terms referred to in the embodiments of the present application will be described:

an Intelligent Transportation System (ITS), also called Intelligent Transportation System (Intelligent Transportation System), is a comprehensive Transportation System that effectively applies advanced scientific technologies (information technology, computer technology, data communication technology, sensor technology, electronic control technology, automatic control theory, operational research, artificial intelligence, etc.) to Transportation, service control and vehicle manufacturing, and strengthens the connection among vehicles, roads and users, thereby forming a comprehensive Transportation System that ensures safety, improves efficiency, improves environment and saves energy.

An Intelligent Vehicle Infrastructure Cooperative System (IVICS), referred to as a Vehicle Infrastructure Cooperative system for short, is a development direction of an Intelligent Transportation System (ITS). The vehicle-road cooperative system adopts the advanced wireless communication, new generation internet and other technologies, implements vehicle-vehicle and vehicle-road dynamic real-time information interaction in all directions, develops vehicle active safety control and road cooperative management on the basis of full-time dynamic traffic information acquisition and fusion, fully realizes effective cooperation of human and vehicle roads, ensures traffic safety, improves traffic efficiency, and thus forms a safe, efficient and environment-friendly road traffic system.

The method provided by the application can be applied to Internet of things systems such as an Internet of vehicles system, an intelligent transportation system, a vehicle road coordination system and a safety auxiliary driving system. The method provided by the application can also be applied to products such as automobile clouds, internet of vehicles, vehicle road coordination, safe auxiliary driving and automatic driving, and particularly to products such as automobile clouds, internet of vehicles, vehicle road coordination, safe auxiliary driving and automatic driving which have time requirements on task processing. For example, the following exemplary embodiments provided herein are applied to a vehicle-road coordination system as an example.

First, a vehicle-road coordination system, a time window, and a method for determining a time window in the related art are described.

FIG. 1 illustrates a vehicle road coordination system. Within the vehicle-to-road coordination system 1000 are management platforms, vehicles, other platforms or devices. The platform, the Vehicle or the equipment in the Vehicle-road cooperative system performs information interaction through a V2X (Vehicle To influencing) service platform.

The management platform is used for controlling other platforms or equipment in the vehicle-road cooperative system to complete tasks. For example, the platform in the vehicle-road cooperation system may be at least one of a server, an application program, and a computer system. Illustratively, the management platform is a control center of the vehicle-road cooperative system, and the management platform has at least one function of managing information in the vehicle-road cooperative system, allocating tasks to vehicles/other platforms/devices, scheduling resources of the vehicle-road cooperative system, and coordinating work of the vehicle-road cooperative system. Illustratively, the management platform may be a traffic management platform 101 as shown in FIG. 1. Illustratively, the traffic management platform has functions of vehicle driving management, vehicle law violation monitoring, signal control and the like. For example, the traffic management platform sends a driving risk assessment command to the vehicle, and the driving risk assessment command is used for instructing the vehicle to complete a task of driving risk assessment. The traffic management platform can also provide system information of the vehicle-road cooperative system for the vehicle, wherein the system information comprises: at least one of a total number of vehicles, a total number of tasks, vehicle performance, and task processing time.

Vehicles are performers of tasks in the vehicle-to-road coordination system. Illustratively, the vehicle-road coordination system provides traffic assistance information for the vehicle to assist in driving the vehicle. Illustratively, the vehicle further includes an onboard device, e.g., as shown in fig. 1, the vehicle 102 includes: a vehicle 1021, a meter 1022, a vehicle navigation, a vehicle radar, a cell phone 1023, a computer, etc. For example, tasks in the vehicle-to-road coordination system require the participation of the vehicle. For example, a safety alarm system in the vehicle performs the task of self-fault detection.

The other platforms or devices are platforms or devices in the vehicle-road coordination system other than the management platform and the vehicle. For example, other platforms or devices may assist the vehicle in completing tasks assigned by the management platform. For example, other platforms or devices may apply for new tasks from the management platform. For example, other platforms or devices may provide vehicle path information to the vehicle path coordination system. For example, the other platforms or devices may be the intelligent travel service platform 103 and/or the roadside intelligent sensing node 104 shown in fig. 1.

The intelligent travel service platform is an application program installed on the terminal, and the terminal can be a mobile phone, a vehicle-mounted navigation device, a tablet, a computer or other computer equipment. Exemplarily, wisdom trip service platform can connect the internet, provides internet information for car road cooperative system, and internet information includes: at least one of weather conditions, map navigation, real-time road conditions, road congestion conditions. For example, the intelligent travel service platform for the vehicle completes the task of providing the vehicle driver with non-instant services, which are services that do not need to be enjoyed immediately, for example, the intelligent travel service platform for the vehicle completes the task of providing the vehicle driver with weather forecast at three points in the afternoon. For example, information interaction between the intelligent travel service platform and the vehicle may not be performed through the V2X service platform, for example, information interaction between the intelligent travel service platform and a mobile phone may be performed through the internet.

And the roadside intelligent sensing node is used for completing the task of the surrounding environment of the area where the sensing system is located by the vehicle. Illustratively, the roadside intelligent sensing node is a device with a sensing function. Illustratively, the roadside intelligent sensing node is an induction device installed at the roadside. For example, the drive test intelligent sensing node is a surveillance camera, a drive test radar, a drive test sensing unit, a pressure sensor, a temperature sensor and the like. Illustratively, the drive test intelligent sensing node can provide road condition information for the vehicle-road cooperative system. The road condition information comprises at least one of signal lamp real-time state information, traffic signs, vehicle distance information, road video information, vehicle pictures, vehicle numbers, vehicle running states and road condition real-time information. For example, the traffic information may be, traffic sign: speed limit signs and indication signs; vehicle number plate: identifying the number plate of the vehicle according to the monitoring camera; vehicle driving state: a certain vehicle runs from east to west at the speed of 1 kilometer per hour; road condition real-time information: the road section is a congested road section due to the fact that vehicles on the road section are too many and the running speed is too slow.

The V2X service platform is connected with different platforms/equipment/vehicles in the vehicle-road coordination system to carry out information interaction. Illustratively, the communication protocols used by the different platforms in the vehicle-to-road coordination system are different. The V2X service platform provides a universal communication protocol for the vehicle-road cooperative system. Illustratively, the V2X service platform is a communication protocol converter, or, the V2X service platform is a communication protocol conversion algorithm. For example, the V2X service platform may be referred to as a V2X protocol or a V2X format, and a platform/device/vehicle in the vehicle-road cooperation system converts information to be transmitted into the V2X protocol or the V2X format, and then transmits the converted information to another platform/device/vehicle.

As an example, as shown in fig. 2, an application scenario of the vehicle-road coordination system is shown, at a certain intersection, there are a vehicle a, a vehicle B, a vehicle C, a monitoring camera D, and a monitoring camera E. The monitoring camera can acquire a real-time picture of the crossroad and identify license plates of a vehicle A, a vehicle B and a vehicle C; identifying the state of the signal lamp; and identifying passers-by F and passers-by G. The radar sensing device arranged on the vehicle can acquire the distances among the vehicles A, B and C. The vehicle and the monitoring camera can transmit information through V2X, and the vehicle and the monitoring camera can upload the information to the management platform or receive the information sent by the management platform through V2X.

Illustratively, platforms, systems, devices and equipment with other functions can be connected in the vehicle-road cooperation system. Illustratively, a platform, a system, a device and equipment capable of performing information interaction with the vehicle-road coordination system belong to the vehicle-road coordination system.

For example, the different platforms in the vehicle-road cooperation system may be software platforms built by using the same or different programming languages. For example, a control program for realizing an intelligent sensing node is written by using a C language, a control program for realizing a traffic management platform by using a C # language, a control program for realizing an intelligent presence service platform is realized by using a Python language, and an android program and a WeChat applet are called by using a Java language to realize a self-checking program of a vehicle-mounted system on a vehicle-mounted computer. Illustratively, there is no dependency between the various programs.

In the vehicle-road cooperative system, the management platform allocates tasks to the vehicles and estimates the time for the vehicles to complete the tasks. For example, the vehicles in the vehicle-to-road coordination system may process the multiple tasks in parallel.

The parallel processing is that a plurality of vehicles cooperatively process a plurality of tasks in parallel. Illustratively, the vehicle parallel processing tasks are: one vehicle can only process one task at the same time, and different vehicles can simultaneously process different tasks; the same task can be processed by only one vehicle and the processing can not be interrupted, and different tasks are mutually independent and have no dependency relationship. For example, when the management platform allocates tasks to vehicles in a parallel processing mode, in order to improve task processing efficiency, it is ensured that no vehicle is in an idle state until all tasks are started to be processed. That is, when the vehicle a completes the task assigned by the management platform, and there is a task that has not been started to be processed in the batch of tasks, the management platform will assign the task to the vehicle a again until no task can be assigned. For example, the management platform assigns tasks by assigning one task to one vehicle and reassigning the next task after the vehicle completes the task. All vehicles are in a state to process the tasks before all tasks are assigned. For example, the management platform assigns tasks in a manner that assigns multiple tasks to a vehicle. Then when vehicle a has completed all of his tasks and vehicle B has tasks that have not begun processing, the management system will assign the tasks that vehicle B has not begun processing to vehicle a processing.

That is, one vehicle can handle multiple tasks, but the vehicle can only begin the next task after the previous task is completed. For example, the task A can only be processed by one vehicle, and one vehicle cannot process the front half part of the task A and the other vehicle cannot process the rear half part of the task A.

For example, at a certain time, there are n tasks in the vehicle-road coordination system and m vehicles capable of executing the tasks, and the management platform needs to allocate n tasks to m vehicles, and at this time, the management platform may predict how long m vehicles will take to complete the n tasks. For example, the management platform may obtain the time required for completing each of the n tasks, i.e., the task processing time. For example, the management platform acquires that it takes ten minutes for the vehicle to finish task a, five minutes for the vehicle to finish task B, and one hour for the vehicle to finish task C.

The time window is the time that the vehicle-road coordination system estimates that a plurality of vehicles need to complete a plurality of tasks. Illustratively, a time window is a period of time within which all tasks are processed. Illustratively, the time window is a period of time beginning when the first vehicle begins processing the first task and ending when the last vehicle completes processing the last task.

Task processing time is the time required for a vehicle to process the task to complete.

Illustratively, as shown in fig. 3, at a certain time, the management platform needs to allocate 8 tasks to the vehicles a, B, and C. The task processing times for the 8 tasks are T1-T8, respectively. At time t1, vehicles a, B, and C start processing tasks 1, 2, and 3, respectively. Vehicle C completes task 3 at time t2 and then starts processing tasks 4, 5, and 7. Vehicle a starts processing task 8 after completing task 1 at time t 3. Finally, the vehicle B completes all tasks at time t6, at which time there are no tasks that have not started processing, and the vehicle B starts entering the idle state. Vehicle C completes all tasks at time t7 and vehicle a completes all tasks at time t8. The time window is from the time the first vehicle starts processing the first task, i.e., time t1, to the time the last vehicle processes the last task, i.e., time t8, at which vehicle a completes task 8.

In the related art, the time window is determined as follows:

firstly, obtaining vehicles in a vehicle-road cooperative system;

secondly, acquiring tasks to be processed by the vehicle-road cooperative system;

thirdly, acquiring the task processing time of each task;

and fourthly, setting the maximum task processing time as a time window.

For example, the vehicle-road coordination system acquires four tasks, and the task processing time of the four tasks is as follows: five minutes, ten minutes, three minutes, fifteen minutes, then the related art processes the longest of the four tasks: fifteen minutes, determined as a time window.

The related art determination method is accurate to set the maximum task processing time as the time window if there are a large number of vehicles, each of which processes only one task. However, if one vehicle needs to process multiple tasks, when the vehicle-road coordination system allocates tasks, the task with the maximum task processing time and another task may be allocated to the same vehicle, and the vehicle must not complete all tasks within the maximum task processing time, i.e. the time window estimation is inaccurate.

Because the management platform needs to plan the work in the vehicle-road cooperative system in advance and coordinate the work of each part in the vehicle-road cooperative system, the accurate time window is very important, and the more accurate the estimation of the time window is, the less the vehicle is idle, and the higher the working efficiency of the vehicle-road cooperative system is.

Therefore, the method for determining the time window can accurately estimate the time window and improve the working efficiency of the vehicle-road cooperative system.

Fig. 4 is a flowchart illustrating a method for determining a time window according to an exemplary embodiment of the present application, which may be applied to the traffic management platform 101 or other platforms in the vehicle-road coordination system shown in fig. 1. The method comprises the following steps:

step 301, acquiring task information and m vehicles in the vehicle-road cooperative system, wherein the task information comprises n tasks and n task processing times t corresponding to the n tasks one by one _n Each task is a task which needs to be completed by a single vehicle, each vehicle executes the single task at the same time, the task processing time is used for representing the time required by the single vehicle to process the single task, and n and m are positive integers.

And the traffic management platform acquires task information and m vehicles in the vehicle-road cooperative system.

For example, the traffic management platform may obtain the character information and the m vehicles in the vehicle-road coordination system from any one of a server, a platform, a database, a computer device, or an apparatus in the vehicle-road coordination system. Illustratively, the traffic management platform obtains the task information and m vehicles in the vehicle-road coordination system from the traffic management platform.

The task information is information of at least one task that needs to be processed at a certain time in the vehicle-road cooperative system. The task information at least comprises: task number, task content, and task processing time. Illustratively, a task number user identifies a certain task. Illustratively, each task corresponds to a task processing time, e.g., each task number corresponds to a task processing time. For example, the traffic management platform acquires n tasks J in the vehicle-road cooperative system ₁ 、J ₂ 、J ₃ ···J _n And n task processing times t corresponding to the n tasks one by one ₁ 、t ₂ 、t ₃ ···t _n 。

The m vehicles are vehicles that can handle the n tasks in the vehicle-road cooperative system. For example, the m vehicles may refer to m vehicles, and may also refer to vehicle-mounted systems, vehicle-mounted radars, and the like on the m vehicles. Illustratively, the traffic management platform acquires m vehicles V in the vehicle-road cooperative system ₁ 、V ₂ 、V ₃ ···V _m 。

Step 303, calculating the average task processing time and the maximum time of a single task according to the task information and the m vehicles.

And the traffic management platform calculates the average task processing time and the maximum time of a single task according to the task information and the m vehicles.

The average task processing time is the shortest time required for m vehicles to process n tasks.

The individual task maximum time is the longest time required for a single vehicle to complete a single task.

The upper and lower bounds of the time window used to estimate the time required for the m vehicles to process in parallel to complete the n tasks are determined from the average task processing time and the single task maximum time, step 304.

The traffic management platform determines an upper and lower bound for the time window based on the average mission processing time and the individual mission maximum time.

The time window is the time required by the traffic management platform to estimate that the m vehicles process and complete the n tasks. By way of example, the value range of the time window can be determined by using the provided time window determination method. Within this range, it is likely that m vehicles will be processing the actual time needed to complete n tasks. For example, the traffic management platform may determine a specific time window according to the value range.

The lower bound of the time window is the minimum of the time window. I.e. the minimum time required for m vehicles to process n tasks.

The upper bound of the time window is the maximum value of the time window. I.e. the longest time required for m vehicles to process n tasks.

In summary, according to the method provided by this embodiment, two parameters, namely, the average task processing time and the maximum processing time of a single task, are calculated according to the information of the tasks and the vehicles in the vehicle-road coordination system, and the upper limit and the lower limit of the time window are calculated according to the two parameters, so as to obtain the range of a time window. The upper and lower limits of the time window are finally determined by comprehensively considering information of two aspects of tasks and vehicles, so that the traffic management platform can accurately predict the time for completing the tasks of the vehicles, and the problems of disordered task allocation of the traffic management platform and reduced working efficiency of a vehicle-road cooperative system caused by unreasonable estimation of the time window are solved.

Exemplary embodiments of calculating an average task processing time, a single task maximum time, an upper bound of a time window, and a lower bound of a time window are also presented.

Fig. 5 is a flowchart illustrating a method for determining a time window according to an exemplary embodiment of the present application, which may be applied to the traffic management platform 101 or other platforms in the vehicle-road coordination system shown in fig. 1. The method comprises the following steps:

301, acquiring task information and m vehicles in the vehicle-road cooperative system, wherein the task information includes n tasks and n tasks corresponding to the n tasks one by one during processingTime t _n Each task is a task which needs to be completed by a single vehicle, each vehicle executes the single task at the same time, the task processing time is used for representing the time required by the single vehicle to process the single task, and n and m are positive integers.

3031, processing time t according to n tasks _n The sum is divided by the number m of vehicles to calculate the average task processing time.

The traffic management platform processes time t according to n tasks _n The sum is divided by the number m of vehicles to calculate the average task processing time.

Illustratively, the traffic management platform acquires n tasks J in the vehicle-road cooperative system ₁ 、J ₂ 、J ₃ ···J _n N task processing times t corresponding to the n tasks one by one ₁ 、t ₂ 、t ₃ ···t _n And m vehicles V ₁ 、V ₂ 、V ₃ ···V _m Then using the formula

And calculating to obtain the average task processing time.

For example, in order to facilitate understanding of the physical meaning of the average task processing time, assuming that a task can be split and processed on a plurality of vehicles, that is, the task processing time of the task can be shared among the plurality of vehicles, the minimum time required for m vehicles to process n tasks is the average task processing time. However, the n tasks are processed in parallel by the m vehicles, that is, the tasks cannot be split, and one task can be completed by only one vehicle, so that the time required for the m vehicles to process the n tasks in parallel is longer than the average task processing time. I.e. the time window must be larger than the average task processing time.

Step 3032, obtaining the maximum task processing time t in the m task processing times _max 。

The traffic management platform acquires the maximum task processing time t in the m task processing times _max 。

The maximum task processing time isAnd the task processing time corresponding to the task with the longest task processing time in the m tasks. I.e. n task processing times t _n Maximum value of (1) _max 。

3033 processing the maximum task time t _max Determined as a single task maximum time.

The traffic management platform processes the maximum task processing time t _max Determined as a single task maximum time.

The already explained time window in step 3031 must be larger than the average task processing time. However, if the average task processing time is less than the maximum time of a single task, that is, the average task processing time is less than the time for a vehicle to process a task, then, if it is obviously unreasonable that the time window still takes the average task processing time, the time window should at least ensure that a vehicle can completely process a task. Thus, there is a single task maximum time, and the time window is at least greater than the single task maximum time.

Step 3041, determine the larger of the average task processing time and the single task maximum time as the lower bound of the time window.

And the traffic management platform determines the larger one of the average task processing time and the maximum time of the single task as the lower limit of the time window.

As can be seen from the explanation of step 3031 and step 3033, the time window is at least greater than the largest of the average task processing time and the individual task maximum time. I.e. the time window is larger than

Step 3042, determine the sum of the average task processing time and the single task maximum time as the upper bound of the time window.

The traffic management platform determines the sum of the average mission processing time and the individual mission maximum time as the upper bound of the time window.

Illustratively, the time window is less than the sum of the average task processing time and the maximum time for a single task. Illustratively, the time window is smallIn that

Illustratively, to facilitate understanding of the upper bound of the time window, assume that the traffic management platform will process the maximum time t _max The corresponding task is placed in the last task for distribution, and then the parallel processing does not allow that vehicles are in an idle state before the task is not distributed and finished, namely when the vehicle A of which the m vehicles process the n-1 tasks first finishes the last task, other vehicles continue to process the last task, at the moment, the time x taken by the vehicle A is less than the average task processing time, because the current m vehicles and n-1 vehicles do not finish processing, and other vehicles are in a task processing state. At this moment, the traffic management platform will allocate the maximum processing time t to the vehicle A _max And (4) corresponding tasks. Due to the maximum processing time t _max The task processing time for the corresponding task is the longest, and the maximum processing time t is obtained when the vehicle A completes processing _max After the corresponding task, other vehicles must finish the last task, namely, the vehicle A finishes the maximum processing time t _max The time of the corresponding task is the final time of the time window. Thus, the time it takes vehicle a to process all of his tasks is time x plus the maximum time for a single task, since time x is a time less than the average task processing time. The maximum value of the time window must not exceed the average task processing time plus the maximum time of a single task.

Illustratively, the above process may be understood with reference to FIG. 3. Illustratively, there are 9 tasks, where the task processing time T9 of task 9 is the longest task processing time, i.e., T9 is a single task maximum time. As shown in fig. 3, vehicle B has completed all of his tasks at time t6, at which time vehicle a is processing task 8, vehicle C is processing task 7, and tasks 1-6 have been completed. The time T2 taken by the vehicle B at this time is always less than the average task processing time (T1 + T2+ T3+ T4+ T5+ T6+ T7+ T8+ T9)/3. In this regard, it can also be seen from fig. 3 that, assuming (T7-T6) = (T8-T7), it can be seen that (T1 + T2+ T3+ T4+ T5+ T6+ T7+ T8)/3 = (T7-T1), while T6 is significantly less than T7. It is apparent that T2 is less than the average task processing time. As the traffic management center does not have any tasks to allocate, the vehicle B is in an idle state, and inevitably, the traffic management center allocates the task 9 to the vehicle B. Since the task processing time of task 9 is the longest, when vehicle B processes completion task 9, vehicle a has necessarily processed completion task 8, and vehicle C has necessarily processed completion task 7. I.e., the time window is from the beginning of t1 to the end of the time at which vehicle B processes completion task 9. I.e. the time window is T2+ T9. Since T2 is less than the average task processing time, T2+ T9 is necessarily less than the average task processing time + T9. I.e. the time window is necessarily smaller than the sum of the average task processing time and the maximum time of a single task.

Illustratively, the upper bound of the time window may also be less than

Referring to the above example shown in fig. 3, it can be seen that the time T2 taken by the vehicle B is necessarily less than (T1 + T2+ T3+ T4+ T5+ T6+ T7+ T8)/3 = (T7-T1), (T1 + T2+ T3+ T4+ T5+ T6+ T7+ T8)/3 is ÷ based on the total weight of the vehicle B>

Thus, the time window is less than +>

In summary, the method provided in this embodiment provides an exemplary embodiment for calculating the average task processing time, the maximum time of a single task, the upper limit of a time window, and the lower limit of the time window, and calculates two parameters, i.e., the average task processing time and the maximum processing time of a single task, according to the information of both the task and the vehicle in the vehicle-road coordination system, and calculates the upper limit and the lower limit of the time window according to the two parameters, thereby obtaining the range of a time window. The upper and lower limits of the time window are finally determined by comprehensively considering the information of the task and the vehicle, so that the traffic management platform can accurately estimate the time for completing the task of the vehicle, and the problems of disordered task allocation of the traffic management platform and reduced working efficiency of a vehicle-road cooperative system caused by unreasonable estimation of the time window are solved.

For example, due to different vehicle performances of different vehicles, the speed of the vehicle processing task is affected by the problems of equipment aging, network delay, weather deterioration and the like, that is, the task processing time used when different vehicles process the same task is different, and the application also provides a method for determining the time window according to the performance coefficient of the vehicle in consideration of the difference of the vehicle performances.

Fig. 6 is a flowchart illustrating a method for determining a time window according to another exemplary embodiment of the present application, which may be applied to the traffic management platform 101 or other platforms in the vehicle-road coordination system shown in fig. 1. The method comprises the following steps:

Step 302, m individual performance coefficients delta corresponding to m vehicles one to one are obtained _m 。

The traffic management platform acquires m individual performance coefficients delta corresponding to m vehicles one by one _m 。

Exemplary, m characteristic coefficients δ _m Are the coefficients of performance that correspond one-to-one to the m vehicles. For example, the coefficient of performance of the vehicle is used to describe the speed at which the vehicle processes the task. Illustratively, the faster the vehicle processes the task, the smaller the coefficient of performance of the vehicle; conversely, the larger the size. Illustratively, the coefficient of performance is determined based on the performance of the vehicle. The vehicle performance includes, but is not limited to, the state of the vehicle-road coordination system sensor, the computing power of the vehicle-mounted computer in the vehicle, the network signal quality of the area where the vehicle-road coordination system is located, whether the vehicle is faulty or not, and the like. Exemplary, coefficient of performance δ and vehicle serviceThe correlation is recorded. For example, the coefficient of performance of the vehicle is equal to 1 minus the failure rate of the vehicle plus the update rate of the vehicle. Wherein, the failure rate of the vehicle and the update rate of the vehicle can be obtained according to the maintenance record of the vehicle.

For example, the coefficient of performance of the vehicle may also be obtained according to a real-time vehicle condition of the vehicle, for example, the coefficient of performance of the vehicle is calculated in real time according to a self-inspection result of the vehicle, the coefficients of performance of all vehicles in a part of a region are updated in real time according to a weather condition, and the coefficient of performance of the vehicle is adjusted in real time according to a network speed condition of the vehicle.

For example, the mode of acquiring the performance coefficient of the vehicle by the traffic management platform may be to acquire the performance coefficient of the vehicle stored in the cloud from a database already stored in the traffic management platform according to the serial number/license plate number of the vehicle, and for example, the mode of acquiring the performance coefficient of the vehicle by the traffic management platform may also be to acquire the current state of the vehicle and calculate the performance coefficient of the vehicle in real time according to the current state of the vehicle. For example, the traffic management platform may obtain the coefficient of performance by directly commanding the vehicle to transmit the coefficient of performance, so as to obtain the coefficient of performance of the vehicle.

Step 3034, obtaining m individual performance coefficients delta _m Maximum coefficient of performance δ in _max 。

The traffic management platform acquires m individual performance coefficients delta _m Maximum coefficient of performance δ in (1) _max 。

Illustratively, the traffic management platform obtains m individual performance coefficients δ _m Maximum value δ in _max . Exemplary, maximum coefficient of performance δ _max The corresponding vehicle is the slowest vehicle to handle the task.

3035, processing time t according to n tasks _n The sum is divided by the number m of vehicles multiplied by a maximum coefficient of performance δ _max And calculating to obtain the average task processing time.

The traffic management platform processes time t according to n tasks _n The sum is divided by the number m of vehicles multiplied by a maximum coefficient of performance δ _max And calculating to obtain the average task processing time.

Exemplary embodiments of the inventionIn order to ensure that all tasks can be processed and completed within the time window determined by the application, the performance coefficient delta corresponding to the vehicle with the worst vehicle performance is obtained _max To calculate the average task processing time. If the vehicle with the worst vehicle performance can process all tasks within the time window, the vehicle with the better vehicle performance can process all tasks within the time window.

3037 processing time t according to maximum task _max Multiplication by the maximum coefficient of performance δ _max And calculating to obtain the maximum time of the single task.

The traffic management platform processes the time t according to the maximum task _max Multiplication by the maximum coefficient of performance δ _max And calculating to obtain the maximum time of the single task.

Illustratively, the traffic management platform calculates the maximum time required for a single vehicle to process a single task, i.e., the maximum task processing time t with the maximum task processing time _max Multiplication by the maximum coefficient of performance delta _max And calculating to obtain the maximum time of the single task.

And step 304, determining an upper limit and a lower limit of a time window according to the average task processing time and the maximum time of a single task, wherein the time window is used for estimating the time required by the m vehicles to process and complete the n tasks in parallel.

For example, the traffic management platform may calculate the upper and lower limits of the time window according to the methods of

steps

3041 and 3042.

In summary, the method provided in this embodiment calculates two parameters, namely, the average task processing time and the maximum processing time of a single task, according to the task processing time, the number of vehicles, and the vehicle performance coefficient in the vehicle-road coordination system, and calculates the upper limit and the lower limit of the time window according to the two parameters, thereby obtaining the range of one time window. The influence of the performance of the vehicle on the task processing time is fully considered, the upper limit and the lower limit of the time window are accurately determined according to the performance coefficient of the vehicle, m vehicles are ensured to be capable of processing n tasks in the time window, the traffic management platform is helped to estimate the task completing time of the vehicle more accurately, and the problems that the task allocation of the traffic management platform is disordered and the working efficiency of a vehicle-road cooperative system is reduced due to unreasonable estimation of the time window are solved.

For example, the time window determination method provided by the application can be used for helping the traffic management platform to estimate the time window, and when the traffic management platform wants to complete the task in a time period or a time point, the traffic management platform can evaluate whether the task can be completed or not and the probability of completion.

Fig. 7 is a flowchart illustrating a method for determining a time window according to an exemplary embodiment of the present application, which may be applied to the traffic management platform 101 or other platforms in the vehicle-road coordination system shown in fig. 1. On the basis of the exemplary embodiment shown in fig. 4, the method further comprises the following steps:

step 305, acquiring a time index, wherein the time index is the preset time required by the parallel processing of the m vehicles to complete the n tasks.

The traffic management platform acquires a time index, wherein the time index is the time required by the preset m vehicles to finish n tasks in parallel processing.

The time index is the time that the traffic management platform expects m vehicles to process n tasks in parallel. Illustratively, the time index may be a period of time from several points to several points, or may be a period of time of several hours.

For example, the time index is a time from 8 am tomorrow to 10 am tomorrow, i.e., two hours.

Step 3061, when the time index is less than the lower bound of the time window, it is determined that m vehicles cannot process in parallel within the time index to complete n tasks.

And when the time index is smaller than the lower limit of the time window, the traffic management platform determines that the m vehicles can not process in parallel within the time index to complete the n tasks.

Illustratively, when the time index is less than the lower bound of the time window, m vehicles may not be able to process n tasks in parallel within the time index.

For example, if the estimated lower bound of the time window is four hours at the upper bound of three hours, two hours of the time index are smaller than the lower bound of the time window by three hours, and the traffic management platform determines that m vehicles cannot complete n tasks within the time index.

For example, after the traffic management platform determines that m vehicles cannot complete n tasks within the time index, the completion degree of the tasks can be estimated. The traffic management platform can estimate the task completion degree according to the upper and lower limits of the time window and the time index. That is, the minimum completion is equal to the length of time of the time index divided by the upper bound of the time window, and the maximum completion is equal to the length of time of the time index divided by the lower bound of the time window.

For example, a minimum completion equal to 2 hours of the time index divided by the upper bound of the time window of 4 hours equals 50%, and a maximum completion equal to 2 hours of the time index divided by the lower bound of the time window of 3 hours equals approximately 66.7%.

Step 3062, when the time index is greater than or equal to the lower limit of the time window and less than the upper limit of the time window, it is determined that there is a probability that m vehicles will process in parallel within the time index to complete n tasks.

And when the time index is greater than or equal to the lower limit of the time window and smaller than the upper limit of the time window, the traffic management platform determines that m vehicles have the probability to perform parallel processing in the time index to complete n tasks.

When the time index is greater than or equal to the lower limit of the time window and less than the upper limit of the time window, i.e., the time index is within the range of the time window, then m vehicles may be able to complete n tasks within the time index.

Step 3063, when the time index is greater than or equal to the upper limit of the time window, it is determined that m vehicles can process in parallel within the time index to complete n tasks.

And when the time index is greater than or equal to the upper limit of the time window, the traffic management platform determines that the m vehicles can process in parallel in the time index to complete n tasks.

When the time index is larger than or equal to the upper limit of the time window, namely the time index is larger than the range of the time window, the m vehicles can certainly complete n tasks in the time index.

And 307, calculating to obtain a probability according to a first difference value and a second difference value, wherein the first difference value is a difference value between the time index and a time window lower limit, the second difference value is a difference value between a time window upper limit and a time window lower limit, and the probability is the probability that m vehicles perform parallel processing in the time index to complete n tasks.

The traffic management platform calculates and obtains the probability according to a first difference value and a second difference value, the first difference value is the difference value between the time index and the lower limit of the time window, the second difference value is the difference value between the upper limit of the time window and the lower limit of the time window, and the probability is the probability that m vehicles finish n tasks in parallel processing in the time index.

Illustratively, when the time index is greater than or equal to the lower bound of the time window and less than the upper bound of the time window, there is a probability that m vehicles will perform parallel processing within the time index to complete n tasks. At this time, the traffic management platform can also estimate the probability according to the upper and lower boundary lines of the time window.

For example, the time index is 2 hours, the lower bound of the time window is 1 hour, the upper bound of the time window is 3 hours, where the time index is within the range of the time window, 1 hour is equal to 1 hour minus the lower bound of 2 hours for the time index, 2 hours is equal to 2 hours minus the lower bound of 3 hours for the time window, and 50% is equal to 1 hour divided by 2 hours for the probability. The traffic management platform determines that m vehicles have a 50% probability of performing n tasks in parallel within the time index.

In conclusion, the feasibility analysis of completing the task in the time index can be performed by using the time window determination method provided by the application, so that the traffic management platform can be helped to more accurately estimate the task completion condition, and the working efficiency of the vehicle-road cooperative system is improved.

For example, the working capacity of a vehicle-road cooperative system can be evaluated by using the time window determination method provided by the application.

Fig. 8 is a flowchart illustrating a method for determining a time window according to an exemplary embodiment of the present application, which may be applied to the traffic management platform 101 or other platforms in the vehicle-road coordination system shown in fig. 1. On the basis of the exemplary embodiment shown in fig. 4, the method further comprises the following steps:

step 308, an estimated time window is determined randomly according to the lower limit of the time window and the upper limit of the time window in one experiment.

And the traffic management platform uniformly and randomly determines an estimated time window in one experiment according to the lower limit of the time window and the upper limit of the time window.

The uniform random determination is to randomly take a numerical value from the upper and lower limit ranges of the time window by the same random number taking algorithm and determine the numerical value as the estimated time window.

For example, the uniform random determination may be: ten values in the range are taken at one tenth of the pitch of the range and then a value is randomly taken from the ten values.

309, when the actual processing time for completing the n tasks by the parallel processing of the m vehicles is less than or equal to the estimated time window, determining that the experimental result is valid; otherwise, determining that the experimental result is invalid.

When the actual processing time for completing n tasks by parallel processing of m vehicles is less than or equal to the estimated time window, the traffic management platform determines that the experimental result is valid; otherwise, determining that the experimental result is invalid.

Then, the m vehicles start actually processing the n tasks, and when the m vehicles process the n tasks, there is an actual processing time taken for the m vehicles to actually process the n vehicles. Comparing the actual combing time with the estimated time window in step 308, if the actual processing time is less than or equal to the estimated time window, processing the m vehicles in the estimated time window to complete n tasks, at this moment, determining that the experimental result of the experiment is valid, if the actual processing time is greater than the estimated time window, not processing the m vehicles in the estimated time window to complete n tasks, at this moment, determining that the experimental result of the experiment is invalid.

For example, if the estimated time window determined in step 306 is 3 hours, and it only takes 2 hours for m vehicles to process n tasks, the result of the experiment is determined to be valid. And if the m vehicles actually take 5 hours to process and complete the n tasks, determining that the experimental result is invalid.

And 310, repeating the steps to obtain a plurality of experimental results.

And the traffic management platform repeats the steps to obtain a plurality of experimental results.

Illustratively, the traffic management platform of the vehicle-road cooperation system repeats the above experiment for a plurality of times to obtain a plurality of experiment results. For example, the plurality of experiments may be different time window upper and lower limits obtained for different tasks and different vehicles. That is, except that the vehicle-road coordination system is the same vehicle-road coordination system, the scenes of experiment implementation are random, and the more the experiment number is, the better. Illustratively, the number of experiments may be three thousand.

For example, ten experiments were performed on a certain vehicle-road coordination system, and the experimental results shown in table one were obtained.

Watch 1

The 4 th experiment result is invalid, and the other experiment results are valid.

And 311, calculating the effective rate of the vehicle-road cooperative system by dividing the effective number of the experimental results by the total number of all the experimental results.

And the traffic management platform calculates the effective rate of the vehicle-road cooperative system according to the effective number of the experimental results in the experimental results divided by the total number of all the experimental results.

Efficiency is a parameter used to describe the operational capability of the vehicle-road coordination system. The effective rate is equal to the number of valid results in the experimental results divided by the total number of all experimental results. For example, the higher the effective rate is, the higher the working capacity of the vehicle-road cooperative system is, whereas the lower the effective rate is, the lower the working capacity of the vehicle-road cooperative system is, and if the effective rate is, it is necessary to check whether the vehicle-road cooperative system is in a problem.

For example, in the experimental results shown in table one, the effective rate equals the number 9 where the experimental results are effective divided by the total number 10 of experimental results equals 90%.

In summary, the time window determination method provided by the application can also be used for evaluating the working capacity of a vehicle-road cooperative system so as to detect the working state of the vehicle-road cooperative system, and when the efficiency is too low, the abnormal working state of the system can be prompted so as to detect the fault. And the work of the vehicle-road cooperative system is supervised, and the work efficiency of the vehicle-road cooperative system is improved.

The following are embodiments of the apparatus of the present application, and for details that are not described in detail in the embodiments of the apparatus, reference may be made to corresponding descriptions in the above method embodiments, and details are not described herein again.

Fig. 9 shows a schematic structural diagram of a device for determining a time window according to an exemplary embodiment of the present application. The apparatus may be implemented as all or part of a server or computer system by software, hardware or a combination of both, the apparatus comprising:

an obtaining module 401, configured to obtain task information and m vehicles in a vehicle-road coordination system, where the task information includes n tasks and n task processing times t corresponding to the n tasks one to one _n Each task is required to be completed by a single vehicle, each vehicle executes the single task at the same time, the task processing time is used for representing the time required by the single vehicle to process and complete the single task, and n and m are positive integers;

a calculation module 403, configured to calculate an average task processing time and a single task maximum time according to the task information and the m vehicles;

a determining module 402, configured to determine an upper bound and a lower bound of a time window according to the average task processing time and the single task maximum time, where the time window is used to estimate a time required for the m vehicles to process the n tasks in parallel.

In an optional embodiment, the determining module 402 is further configured to determine a larger one of the average task processing time and the single task maximum time as a lower bound of the time window.

In an alternative embodiment, the determining module 402 is further configured to determine the sum of the average task processing time and the single task maximum time as an upper bound of a time window.

In an optional embodiment, the calculating module 403 is further configured to process the time t according to the n tasks _n And dividing the sum by the number m of the vehicles to obtain the average task processing time by calculation.

In an optional embodiment, the obtaining module 401 is further configured to obtain a maximum task processing time t of the m task processing times _max ；

The determining module 402 is further configured to process the maximum task processing time t _max Determined as a single task maximum time.

In an optional embodiment, the obtaining module 401 is further configured to obtain m performance coefficients δ corresponding to the m vehicles _m ；

The obtaining module 401 is further configured to obtain the m performance coefficients δ _m Maximum coefficient of performance δ in (1) _max ；

The computing module 403 is further configured to process the time t according to the n tasks _n The sum divided by the number m of vehicles multiplied by the maximum coefficient of performance δ _max And calculating to obtain the average task processing time.

The obtaining module 401 is further configured to obtain the m individual performance coefficients δ _m Maximum coefficient of performance δ in _max ；

The acquisition module 401And is further configured to obtain a maximum task processing time t among the m task processing times _max ；

The computing module 403 is further configured to process the time t according to the maximum task _max Multiplying by the maximum coefficient of performance δ _max And calculating to obtain the maximum time of the single task.

In an alternative embodiment, the coefficient of performance is determined based on the performance of the vehicle, and the coefficient of performance δ is related to a service record for the vehicle.

In an optional embodiment, the obtaining module 401 is further configured to obtain a time index, where the time index is a preset time required for the m vehicles to perform parallel processing on the n tasks;

the determining module 402, further configured to determine that the m vehicles cannot complete the n tasks in parallel within the time indicator when the time indicator is smaller than the lower limit of the time window;

the determining module 402 is further configured to determine that the m vehicles have a probability of completing the n tasks in parallel within the time indicator when the time indicator is greater than or equal to a lower limit of the time window and less than an upper limit of the time window;

the determining module 402, configured to determine that the m vehicles can complete the n tasks in parallel within the time index when the time index is greater than or equal to an upper limit of the time window.

In an optional embodiment, the calculating module 403 is further configured to calculate the probability according to a difference between the time index and the lower time window limit divided by a difference between the upper time window limit and the lower time window limit, where the probability is a probability that the m vehicles complete the n tasks in parallel processing within the time index.

In an alternative embodiment, the determining module 402 is further configured to randomly determine an estimated time window according to the lower limit of the time window and the upper limit of the time window in a single experiment;

the determining module 402 is further configured to determine that the experimental result is valid when the actual processing time for the m vehicles to perform parallel processing on the n tasks is less than or equal to the estimated time window; otherwise, determining that the experiment result is invalid;

the obtaining module 401 is further configured to repeat the above steps to obtain a plurality of experimental results;

the calculating module 403 is further configured to calculate an effective rate of the vehicle-road coordination system according to the number of effective experimental results in the experimental results divided by the total number of all experimental results.

Referring to fig. 10, a block diagram of a computer device 1300 according to an exemplary embodiment of the present application is shown. The computer device 1300 may be a portable mobile terminal, such as: the mobile phone comprises a vehicle navigation device, a smart phone, a tablet computer, an MP3 player (Moving Picture Experts Group Audio Layer III, standard Audio Layer 3 for motion Picture Experts compression), and an MP4 player (Moving Picture Experts Group Audio Layer IV, standard Audio Layer 4 for motion Picture Experts compression). Computer device 1300 may also be referred to by other names such as user equipment, portable terminal, etc.

Generally, computer device 1300 includes: a processor 1301 and a memory 1302.

Processor 1301 may include one or more processing cores, such as a 4-core processor, an 8-core processor, and the like. The processor 1301 may be implemented in at least one of a DSP (Digital Signal Processing), an FPGA (Field-Programmable Gate Array), and a PLA (Programmable Logic Array). The processor 1301 may also include a main processor and a coprocessor, where the main processor is a processor for Processing data in an awake state, and is also referred to as a Central Processing Unit (CPU); a coprocessor is a low power processor for processing data in a standby state. In some embodiments, the processor 1301 may be integrated with a GPU (Graphics Processing Unit), which is responsible for rendering and drawing content required to be displayed on a display screen. In some embodiments, processor 1301 may further include an AI (Artificial Intelligence) processor for processing computational operations related to machine learning.

The memory 1302 may include one or more computer-readable storage media, which may be tangible and non-transitory. The memory 1302 may also include high speed random access memory, as well as non-volatile memory, such as one or more magnetic disk storage devices, flash memory storage devices. In some embodiments, a non-transitory computer readable storage medium in memory 1302 is used to store at least one instruction for execution by processor 1301 to implement the method of determining a time window provided herein.

In some embodiments, computer device 1300 may also optionally include: a peripheral interface 1303 and at least one peripheral. Specifically, the peripheral device includes: at least one of radio frequency circuitry 1304, touch display 1305, camera 1306, audio circuitry 1307, positioning component 1308, and power supply 1309.

Peripheral interface 1303 may be used to connect at least one peripheral associated with I/O (Input/Output) to processor 1301 and memory 1302. In some embodiments, processor 1301, memory 1302, and peripheral interface 1303 are integrated on the same chip or circuit board; in some other embodiments, any one or two of the processor 1301, the memory 1302, and the peripheral device interface 1303 may be implemented on a separate chip or circuit board, which is not limited in this embodiment.

The Radio Frequency circuit 1304 is used to receive and transmit RF (Radio Frequency) signals, also called electromagnetic signals. Radio frequency circuit 1304 communicates with communication networks and other communication devices via electromagnetic signals. The radio frequency circuit 1304 converts an electrical signal into an electromagnetic signal to transmit, or converts a received electromagnetic signal into an electrical signal. Optionally, the radio frequency circuit 1304 includes: an antenna system, an RF transceiver, one or more amplifiers, a tuner, an oscillator, a digital signal processor, a codec chipset, a subscriber identity module card, and so forth. Radio frequency circuit 1304 may communicate with other terminals via at least one wireless communication protocol. The wireless communication protocols include, but are not limited to: the world wide web, metropolitan area networks, intranets, various generations of mobile communication networks (2G, 3G, 4G, and 5G), wireless local area networks, and/or WiFi (Wireless Fidelity) networks. In some embodiments, the radio frequency circuit 1304 may further include NFC (Near Field Communication) related circuits, which are not limited in this application.

The touch display 1305 is used to display a UI (User Interface). The UI may include graphics, text, icons, video, and any combination thereof. The touch display 1305 also has the capability to collect touch signals on or over the surface of the touch display 1305. The touch signal may be input to the processor 1301 as a control signal for processing. The touch display 1305 is used to provide virtual buttons and/or a virtual keyboard, also referred to as soft buttons and/or a soft keyboard. In some embodiments, the touch display 1305 may be one, providing the front panel of the computer device 1300; in other embodiments, the touch display 1305 may be at least two, respectively disposed on different surfaces of the computer device 1300 or in a folded design; in still other embodiments, the touch display 1305 may be a flexible display disposed on a curved surface or on a folded surface of the computer device 1300. Even more, the touch screen 1305 may be arranged in a non-rectangular irregular pattern, i.e., a shaped screen. The touch Display 1305 may be made of LCD (Liquid Crystal Display), OLED (Organic Light-Emitting Diode), or the like.

The camera assembly 1306 is used to capture images or video. Optionally, camera assembly 1306 includes a front camera and a rear camera. Generally, a front camera is used for realizing video call or self-shooting, and a rear camera is used for realizing shooting of pictures or videos. In some embodiments, the number of the rear cameras is at least two, and each of the rear cameras is any one of a main camera, a depth-of-field camera and a wide-angle camera, so that the main camera and the depth-of-field camera are fused to realize a background blurring function, and the main camera and the wide-angle camera are fused to realize panoramic shooting and VR (Virtual Reality) shooting functions. In some embodiments, camera head assembly 1306 may also include a flash. The flash lamp can be a monochrome temperature flash lamp or a bicolor temperature flash lamp. The double-color-temperature flash lamp is a combination of a warm-light flash lamp and a cold-light flash lamp, and can be used for light compensation at different color temperatures.

The audio circuit 1307 is used to provide an audio interface between the user and the computer device 1300. The audio circuit 1307 may include a microphone and a speaker. The microphone is used for collecting sound waves of a user and the environment, converting the sound waves into electric signals, and inputting the electric signals to the processor 1301 for processing, or inputting the electric signals to the radio frequency circuit 1304 for realizing voice communication. The microphones may be multiple and placed at different locations on the computer device 1300 for stereo sound acquisition or noise reduction purposes. The microphone may also be an array microphone or an omni-directional pick-up microphone. The speaker is used to convert electrical signals from the processor 1301 or the radio frequency circuitry 1304 into sound waves. The loudspeaker can be a traditional film loudspeaker or a piezoelectric ceramic loudspeaker. When the speaker is a piezoelectric ceramic speaker, the speaker can be used for purposes such as converting an electric signal into a sound wave audible to a human being, or converting an electric signal into a sound wave inaudible to a human being to measure a distance. In some embodiments, the audio circuit 1307 may also include a headphone jack.

The positioning component 1308 is used to locate the current geographic Location of the computer device 1300 for navigation or LBS (Location Based Service). The Positioning component 1308 can be a Positioning component based on the Global Positioning System (GPS) in the united states, the beidou System in china, or the galileo System in russia.

The power supply 1309 is used to supply power to the various components in the computer device 1300. The power supply 1309 may be alternating current, direct current, disposable or rechargeable batteries. When the power source 1309 comprises a rechargeable battery, the rechargeable battery may be a wired rechargeable battery or a wireless rechargeable battery. The wired rechargeable battery is a battery charged through a wired line, and the wireless rechargeable battery is a battery charged through a wireless coil. The rechargeable battery may also be used to support fast charge technology.

In some embodiments, computer device 1300 also includes one or more sensors 1310. The one or more sensors 1310 include, but are not limited to: acceleration sensor 1311, gyro sensor 1312, pressure sensor 1313, fingerprint sensor 1314, optical sensor 1315, and proximity sensor 1316.

The acceleration sensor 1311 may detect the magnitude of acceleration in three coordinate axes of the coordinate system established with the computer apparatus 1300. For example, the acceleration sensor 1311 may be used to detect components of the gravitational acceleration in three coordinate axes. The processor 1301 may control the touch display screen 1305 to display the user interface in a landscape view or a portrait view according to the gravitational acceleration signal collected by the acceleration sensor 1311. The acceleration sensor 1311 may also be used for acquisition of motion data of a game or a user.

The gyro sensor 1312 may detect a body direction and a rotation angle of the computer device 1300, and the gyro sensor 1312 may cooperate with the acceleration sensor 1311 to collect a 3D motion of the user with respect to the computer device 1300. Processor 1301, based on the data collected by gyroscope sensor 1312, may perform the following functions: motion sensing (such as changing the UI according to a user's tilting operation), image stabilization while shooting, game control, and inertial navigation.

The pressure sensors 1313 may be disposed on the side bezel of the computer device 1300 and/or underneath the touch display 1305. When the pressure sensor 1313 is provided on the side frame of the computer apparatus 1300, a holding signal of the user to the computer apparatus 1300 can be detected, and right-left-hand recognition or shortcut operation can be performed based on the holding signal. When the pressure sensor 1313 is disposed on the lower layer of the touch display 1305, it is possible to control an operability control on the UI interface according to a pressure operation of the user on the touch display 1305. The operability control comprises at least one of a button control, a scroll bar control, an icon control and a menu control.

The fingerprint sensor 1314 is used for collecting the fingerprint of the user to identify the identity of the user according to the collected fingerprint. When the identity of the user is identified as a trusted identity, the processor 1301 authorizes the user to perform relevant sensitive operations, including unlocking a screen, viewing encrypted information, downloading software, paying, changing settings, and the like. The fingerprint sensor 1314 may be disposed on the front, back, or side of the computer device 1300. When a physical key or vendor Logo is provided on the computer device 1300, the fingerprint sensor 1314 may be integrated with the physical key or vendor Logo.

The optical sensor 1315 is used to collect the ambient light intensity. In one embodiment, the processor 1301 can control the display brightness of the touch display screen 1305 according to the intensity of the ambient light collected by the optical sensor 1315. Specifically, when the ambient light intensity is high, the display brightness of the touch display screen 1305 is increased; when the ambient light intensity is low, the display brightness of the touch display panel 1305 is turned down. In another embodiment, the processor 1301 can also dynamically adjust the shooting parameters of the camera assembly 1306 according to the ambient light intensity collected by the optical sensor 1315.

A proximity sensor 1316, also known as a distance sensor, is typically disposed on the front face of the computer device 1300. The proximity sensor 1316 is used to capture the distance between the user and the front face of the computer device 1300. In one embodiment, the touch display 1305 is controlled by the processor 1301 to be used from the bright screen state to the rest screen state when the proximity sensor 1316 detects that the distance between the user and the front face of the computer device 1300 gradually decreases; the touch display 1305 is controlled by the processor 1301 to be used from the rest state to the bright state when the proximity sensor 1316 detects that the distance between the user and the front surface of the computer device 1300 is gradually increasing.

Those skilled in the art will appreciate that the architecture illustrated in FIG. 10 does not constitute a limitation of computer device 1300, and may include more or fewer components than those illustrated, or may combine certain components, or may employ a different arrangement of components.

The present application further provides a terminal, including: a processor and a memory, the memory having stored therein at least one instruction, at least one program, set of codes, or set of instructions, which is loaded and executed by the processor to implement the method for determining a time window provided by the above-described method embodiments.

The present application further provides a computer device, comprising: a processor and a memory, the storage medium having stored therein at least one instruction, at least one program, set of codes, or set of instructions, which is loaded and executed by the processor to implement the method for determining a time window provided by the above-described method embodiments.

The present application further provides a computer-readable storage medium, in which at least one instruction, at least one program, a set of codes, or a set of instructions is stored, and the at least one instruction, the at least one program, the set of codes, or the set of instructions is loaded and executed by a processor to implement the method for determining a time window provided by the above-mentioned method embodiments.

It should be understood that reference herein to "a plurality" means two or more. "and/or" describes the association relationship of the associated object, indicating that there may be three relationships, for example, a and/or B, which may indicate: a exists alone, A and B exist simultaneously, and B exists alone. The character "/" generally indicates that the former and latter associated objects are in an "or" relationship.

It will be understood by those skilled in the art that all or part of the steps for implementing the above embodiments may be implemented by hardware, or may be implemented by a program instructing relevant hardware, where the program may be stored in a computer-readable storage medium, and the above-mentioned storage medium may be a read-only memory, a magnetic disk or an optical disk, etc.

The above description is only exemplary of the present application and should not be taken as limiting, as any modification, equivalent replacement, or improvement made within the spirit and principle of the present application should be included in the protection scope of the present application.

Claims

1. A method for determining a time window, the method comprising:

obtainingTask information and m vehicles in the vehicle-road cooperative system, wherein the task information comprises n tasks and n task processing times t corresponding to the n tasks one by one _n Each task is required to be completed by a single vehicle, each vehicle executes the single task at the same time, the task processing time is used for representing the time required by the single vehicle to process and complete the single task, and n and m are positive integers;

and determining the larger one of the average task processing time and the single task maximum time as the lower limit of a time window, and determining the sum of the average task processing time and the single task maximum time as the upper limit of the time window, wherein the time window is used for estimating the time required by the m vehicles to process and complete the n tasks in parallel.

2. The method of claim 1, wherein said calculating an average task processing time from said task information and said m vehicles comprises:

processing time t according to the n tasks _n And dividing the sum by the number m of the vehicles to calculate the average task processing time.

3. The method of claim 1, wherein said calculating a single mission maximum time from said mission information and said m vehicles comprises:

obtaining the maximum task processing time t in the n task processing times _max ；

Processing the maximum task time t _max And determining the single task maximum time.

4. The method of claim 1, further comprising:

obtaining m individual performance coefficients delta corresponding to the m vehicles one to one _m ；

The calculating an average task processing time according to the task information and the m vehicles comprises:

obtaining the m individual performance coefficients delta _m Maximum coefficient of performance δ in _max ；

According to the n task processing times t _n The sum divided by the number m of vehicles multiplied by the maximum coefficient of performance δ _max And calculating to obtain the average task processing time.

5. The method of claim 1, further comprising:

The calculating a single task maximum time according to the task information and the m vehicles includes:

According to the maximum task processing time t _max Multiplying by said maximum coefficient of performance δ _max And calculating to obtain the maximum time of the single task.

6. The method according to claim 4 or 5, characterized in that the coefficient of performance is determined from the performance of the vehicle, or that the coefficient of performance δ is related to a service record of the vehicle.

7. The method of claim 1, further comprising:

acquiring a time index, wherein the time index is preset time required by the m vehicles to finish the n tasks in parallel processing;

when the time index is smaller than the lower limit of the time window, determining that the m vehicles cannot process in parallel within the time index to complete the n tasks;

when the time index is greater than or equal to the lower limit of the time window and smaller than the upper limit of the time window, determining that the m vehicles have the probability to complete the n tasks in parallel in the time index;

and when the time index is larger than or equal to the upper limit of the time window, determining that the m vehicles can process in parallel within the time index to complete the n tasks.

8. The method of claim 7, wherein determining that the m vehicles have a probability of completing the n tasks in parallel processing within the time indicator after the time indicator is greater than or equal to a lower bound of the time window and less than an upper bound of the time window further comprises:

calculating the probability according to a first difference value and a second difference value, wherein the first difference value is a difference value between the time index and the lower limit of the time window, the second difference value is a difference value between the upper limit of the time window and the lower limit of the time window, and the probability is the probability that the m vehicles can process the n tasks in parallel in the time index.

9. The method of claim 1, further comprising:

uniformly and randomly determining an estimated time window in one experiment according to the lower limit of the time window and the upper limit of the time window;

when the actual processing time for the m vehicles to finish the n tasks in parallel processing is less than or equal to the estimated time window, determining that the experimental result is valid; otherwise, determining that the experiment result is invalid;

repeating the steps to obtain a plurality of experimental results;

and calculating to obtain the effective rate of the vehicle-road cooperative system according to the effective number of the experimental results in the experimental results divided by the total number of all the experimental results.

10. An apparatus for determining a time window, the apparatus comprising:

an obtaining module, configured to obtain task information and m vehicles in a vehicle-road coordination system, where the task information includes n tasks and n task processing times t corresponding to the n tasks one to one _n Each task is required to be completed by a single vehicle, each vehicle executes the single task at the same time, the task processing time is used for representing the time required by the single vehicle to process and complete the single task, and n and m are positive integers;

and the determining module is used for determining the larger one of the average task processing time and the single task maximum time as the lower limit of a time window, and determining the sum of the average task processing time and the single task maximum time as the upper limit of the time window, wherein the time window is used for estimating the time required by the m vehicles to process and complete the n tasks in parallel.

11. A computer device, the computer comprising: a processor and a memory, said memory having stored therein at least one instruction, at least one program, a set of codes, or a set of instructions, which is loaded and executed by said processor to implement the method of determining a time window according to any one of claims 1 to 9.

12. A computer-readable storage medium having stored thereon at least one instruction, which is loaded and executed by a processor, to implement the method for determining a time window according to any one of claims 1 to 9.