Driving mode automatic selection method and device based on big data, equipment and medium

Abstract

The invention discloses a driving mode automatic selection method and device based on big data, equipment and a medium, and relates to the technical field of vehicle intelligent control, and the method comprises the steps: collecting the driving information of the same vehicle type in a preset number of time periods, dividing the time periods into a plurality of time periods, and obtaining a driving information set corresponding to each time period; calculating the vehicle speed corresponding to each target position on the target driving route in the current driving information set, and obtaining the vehicle speed information of the target position on the target driving route corresponding to each time period; calculating the average speed and the average acceleration of the target driving route corresponding to each time period; and realizing automatic selection of the driving mode of the current vehicle based on the average speed and the average acceleration of the target driving route corresponding to each time period obtained through calculation, the time period of the current time and the SOC of the current vehicle on the target driving route. According to the invention, automatic switching of the vehicle driving mode based on the current driving scene can be realized.

Description

Technical Field

[0001] The present application relates to the field of vehicle intelligent control technology, and specifically to a method, device, equipment and medium for automatic selection of driving modes based on big data. Background Art

[0002] With the development of intelligent connected car technology, the functions of intelligent connected cars are becoming more and more abundant. Cars can meet many driving needs, such as setting sports mode, energy-saving mode and other driving modes on the same car. In actual applications, the vehicle driving mode can be switched based on the current road conditions or routes to adapt to the current driving scene, thereby improving the user's driving experience.

[0003] Currently, the driving mode is generally switched actively, that is, the driver manually selects the vehicle's driving mode during the vehicle startup or driving phase, and needs to switch manually every time after getting in the vehicle, which increases the complexity of the entire vehicle use. Therefore, how to realize the automatic switching of vehicle driving modes based on the current driving scenario has become an urgent problem to be solved. Summary of the invention

[0004] The present application provides a method, device, equipment and medium for automatic selection of driving modes based on big data, which can realize automatic switching of vehicle driving modes based on the current driving scenario.

[0005] In a first aspect, an embodiment of the present application provides a method for automatically selecting a driving mode based on big data, and the method for automatically selecting a driving mode based on big data includes:

[0006] Collecting driving information of the same vehicle type within a preset number of time periods, and dividing the time period into multiple time periods to obtain a driving information set corresponding to each time period, wherein the driving information set is driving information of all vehicles passing through the target driving route within the current time period under each time period;

[0007] Based on the speed of each vehicle at each position on the target driving route in the driving information set, the speed corresponding to each target position on the target driving route in the current driving information set is calculated, and the speed information of the target position on the target driving route corresponding to each time period is obtained;

[0008] Based on the vehicle speed information of the target position on the target driving route corresponding to each time period, the average speed and average acceleration of the target driving route corresponding to each time period are calculated;

[0009] Based on the calculated average speed and average acceleration of the target driving route corresponding to each time period, as well as the SOC of the current vehicle in the current time period and on the target driving route, the driving mode of the current vehicle is automatically selected.

[0010] In combination with the first aspect, in one implementation,

[0011] The driving information includes latitude and longitude information, time information and vehicle speed information;

[0012] The time period is a day, wherein the day is evenly divided to obtain a plurality of time periods;

[0013] The driving information set is driving information of all vehicles passing through the target driving route within the current time period of consecutive days for the collected driving information.

[0014] In combination with the first aspect, in one implementation,

[0015] The driving information is collected in real time, and after the driving information collection of the latest time period is completed, the driving information of the first time period collected among the preset number of time periods is deleted to obtain the latest driving information and the driving information set corresponding to each time period;

[0016] Based on the latest set of driving information corresponding to each time period, the real-time update of the vehicle speed information of the target position in the target driving route corresponding to each time period, as well as the real-time update of the average speed and average acceleration of the target driving route corresponding to each time period are realized.

[0017] In combination with the first aspect, in one implementation, the calculating the speed corresponding to each target position on the target driving route in the current driving information set based on the speed of each vehicle at each position on the target driving route in the driving information set, and obtaining the speed information of the target position on the target driving route corresponding to each time period specifically includes:

[0018] Obtain the speed of each vehicle at each longitude and latitude of the target driving route in the driving information set corresponding to the current time period, perform normal distribution analysis on the obtained speed, and the expected value of the normal distribution is the speed corresponding to each longitude and latitude of the target driving route;

[0019] A position point is selected as the target position at each preset distance on the target driving route, and the vehicle speed at each longitude and latitude of the target driving route is obtained based on the normal distribution. The speed of the target position is obtained and used as the speed of each target position on the target driving route corresponding to the current time period, and the speed information of the target position on the target driving route corresponding to each time period is obtained.

[0020] In combination with the first aspect, in one implementation, based on the vehicle speed information of the target position on the target driving route corresponding to each time period, the average speed and average acceleration of the target driving route corresponding to each time period are calculated, wherein the calculation of the average speed and average acceleration of the target driving route corresponding to the current time period specifically includes:

[0021] Obtain the vehicle speed information of the target position on the target driving route corresponding to the current time period, calculate the average speed of all target positions, and obtain the average speed of the target driving route corresponding to the current time period;

[0022] Based on the vehicle speed at each target location, determine the acceleration segment on the target driving route, and calculate the speed change rate of each acceleration segment;

[0023] Based on the speed change rate of all acceleration sections, the average calculation is performed to obtain the average acceleration of the target driving route corresponding to the current time period.

[0024] In combination with the first aspect, in one implementation,

[0025] The average speed of the target driving route corresponding to the current time period is calculated as follows:

[0026] V 平均 =(V1+V2+…V n ) / n

[0027] Among them, V 平均 represents the average speed, V n represents the vehicle speed at the nth target location, and n represents the total number of target locations on the target driving route;

[0028] For the speed change rate of each acceleration segment of the target driving route corresponding to the current time period, the speed change rate of the current acceleration segment is calculated as follows:

[0029] ΔV=(V x -V y ) / i

[0030] Among them, ΔV represents the speed change rate of the current acceleration section, V x Indicates the vehicle speed at the end target position of the current acceleration segment, V y represents the vehicle speed at the starting target position of the current acceleration segment, i represents the average time taken by all vehicles in the driving information set corresponding to the current time period to travel from the starting target position of the current acceleration segment to the ending target position of the current acceleration segment;

[0031] The average acceleration of the target driving route corresponding to the current time period is calculated as follows:

[0032] ΔV 平均 =(ΔV1+ΔV2+…ΔV k ) / k

[0033] Where, ΔV 平均 represents the average acceleration, ΔV krepresents the speed change rate of the kth acceleration segment on the target driving route corresponding to the current time period, and k represents the total number of acceleration segments on the target driving route corresponding to the current time period.

[0034] In combination with the first aspect, in one implementation, the automatic selection of the driving mode of the current vehicle is achieved based on the calculated average speed and average acceleration of the target driving route corresponding to each time period, and the SOC of the current vehicle in the time period at the current time and on the target driving route, specifically including:

[0035] Based on the time period of the current time, the average speed and average acceleration of the target driving route corresponding to the time period are obtained, and according to the SOC of the current vehicle on the target driving route, and the obtained average speed and average acceleration, a judgment is made:

[0036] If the SOC is less than the preset SOC value, the current vehicle driving mode is set to eco mode;

[0037] If the SOC is greater than the preset SOC value, then:

[0038] -When the average speed is less than a first preset average speed, setting the driving mode of the current vehicle to an eco mode;

[0039] - when the average speed is greater than a first preset average speed, the average speed is less than a second preset average speed, and the average acceleration is less than a preset average acceleration, setting the current driving mode of the vehicle to a standard mode;

[0040] - when the average speed is greater than a first preset average speed, the average speed is less than a second preset average speed, and the average acceleration is greater than a preset average acceleration, setting the current driving mode of the vehicle to a sport mode;

[0041] -When the average speed is greater than a second preset average speed, setting the driving mode of the current vehicle to the sport mode.

[0042] In a second aspect, an embodiment of the present application provides a driving mode automatic selection device based on big data, and the driving mode automatic selection device based on big data includes:

[0043] A collection module, which is used to collect driving information of the same vehicle type within a preset number of time periods, and divide the time period into multiple time periods to obtain a driving information set corresponding to each time period, wherein the driving information set is the driving information of all vehicles passing through the target driving route within the current time period under each time period;

[0044] The first calculation module is used to calculate the speed of each target position on the target driving route in the current driving information set based on the speed of each vehicle at each position on the target driving route in the driving information set, and obtain the speed information of the target position on the target driving route corresponding to each time period;

[0045] A second calculation module is used to calculate the average speed and average acceleration of the target driving route corresponding to each time period based on the vehicle speed information of the target position on the target driving route corresponding to each time period;

[0046] The selection module is used to automatically select the driving mode of the current vehicle based on the calculated average speed and average acceleration of the target driving route corresponding to each time period, as well as the SOC of the current vehicle in the time period at the current time and on the target driving route.

[0047] In a third aspect, an embodiment of the present application provides an automatic driving mode selection device based on big data, wherein the automatic driving mode selection device based on big data comprises a processor, a memory, and an automatic driving mode selection program based on big data stored in the memory and executable by the processor, wherein when the automatic driving mode selection program based on big data is executed by the processor, the steps of the automatic driving mode selection method based on big data described above are implemented.

[0048] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium, on which is stored a driving mode automatic selection program based on big data, wherein when the driving mode automatic selection program based on big data is executed by a processor, the steps of the above-mentioned driving mode automatic selection method based on big data are implemented.

[0049] The beneficial effects brought by the technical solution provided in the embodiments of the present application include:

[0050] Through big data analysis, the speed distribution of the same vehicle model on the target driving route in a certain time period is counted. Based on the vehicle speed information of the target position on the target driving route corresponding to each time period, the average speed and average acceleration of the target driving route corresponding to each time period are calculated. Then, based on the current time period and the SOC of the current vehicle on the target driving route, the driving mode of the current vehicle is automatically selected. The influence of time is taken into account when processing the speed distribution on the target driving route. When automatically selecting the driving mode, it can be dynamically selected according to the driving characteristics on the target driving route at the current moment, thereby increasing the driving mileage and effectively improving the user's driving experience. BRIEF DESCRIPTION OF THE DRAWINGS

[0051] Figure 1 This is a flow chart of the automatic driving mode selection method based on big data in this application;

[0052] Figure 2 This is a functional module diagram of the driving mode automatic selection device based on big data in this application;

[0053] Figure 3 This is a schematic diagram of the hardware structure of the device for automatically selecting driving modes based on big data in this application. DETAILED DESCRIPTION

[0054] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of this application.

[0055] In order to make the objectives, technical solutions and advantages of the present application clearer, the implementation methods of the present application will be further described in detail below with reference to the accompanying drawings.

[0056] On the first aspect, an embodiment of the present application provides a method for automatic selection of driving modes based on big data, which obtains the distribution of speed on a target driving route in a certain time period as the longitude and latitude of the route through big data, analyzes driving characteristics such as the average speed and speed change rate on the target driving route according to the specific speed distribution on the target driving route, and then automatically selects the driving mode according to the driving characteristics, thereby realizing automatic selection and switching of the vehicle driving mode based on the driving scenario.

[0057] In one embodiment, referring to Figure 1 , Figure 1 This is a flow chart of the automatic driving mode selection method based on big data in this application. Figure 1 As shown, the automatic driving mode selection method based on big data includes:

[0058] S1: collecting driving information of the same vehicle type within a preset number of time periods, and dividing the time period into multiple time periods to obtain a driving information set corresponding to each time period, wherein the driving information set is driving information of all vehicles passing through the target driving route within the current time period under each time period;

[0059] S2: Based on the speed of each vehicle at each position on the target driving route in the driving information set, the speed corresponding to each target position on the target driving route in the current driving information set is calculated, and the speed information of the target position on the target driving route corresponding to each time period is obtained;

[0060] S3: Based on the vehicle speed information of the target position on the target driving route corresponding to each time period, the average speed and average acceleration of the target driving route corresponding to each time period are calculated;

[0061] S4: Automatically select the driving mode of the current vehicle based on the calculated average speed and average acceleration of the target driving route corresponding to each time period, as well as the SOC of the current vehicle in the current time period and on the target driving route.

[0062] In actual applications, the collection of driving information can be based on the cloud big data system and the vehicle data collection module installed on the vehicle to collect the driving information of all vehicles in use whose models need to be automatically selected for driving mode, and the collected driving information is cached in the cloud big data system. Driving information includes latitude and longitude information, time information and vehicle speed information.

[0063] In this application, the time period is a day, wherein the day is divided equally to obtain multiple time periods; the driving information set is the driving information of all vehicles passing through the target driving route in the current time period of consecutive days for the collected driving information. In a possible implementation, the preset number of time periods is 7 days, that is, the driving information within 7 days is collected and cached to the cloud. The day is divided into intervals of 0.5 hours, and a day contains a total of 24 hours, so 48 time periods can be divided, and each time period corresponds to a driving information set. For example, the target driving route is from location A to location B, and the vehicle model that needs to be automatically selected for the driving mode is model M. For the 8:00-8:30 time period, its driving information set is the driving information of all vehicles in model M passing from location A to location B during the 8:00-8:30 time period of each day in the last 7 days; for the 8:30-9:30 time period, its driving information set is the driving information of all vehicles in model M passing from location A to location B during the 8:30-9:30 time period of each day in the last 7 days.

[0064] Furthermore, in one embodiment, based on the speed of each vehicle at each position on the target driving route in the driving information set, the speed corresponding to each target position on the target driving route in the current driving information set is calculated, and the speed information of the target position on the target driving route corresponding to each time period is obtained, specifically including:

[0065] S201: Obtain the speed of each vehicle at each longitude and latitude of the target driving route in the driving information set corresponding to the current time period, and perform normal distribution analysis on the obtained speeds. The expected value of the normal distribution is the speed corresponding to each longitude and latitude of the target driving route.

[0066] For example, for the time period from 8:00 to 8:30, the vehicles in the corresponding driving information set include vehicle a, vehicle b, and vehicle c. Based on the speeds of vehicle a, vehicle b, and vehicle c at each longitude and latitude of the target driving route, a normal distribution analysis is performed to obtain the corresponding speeds at each longitude and latitude of the target driving route.

[0067] S202: Select a position point as the target position at every preset distance on the target driving route, obtain the vehicle speed at each longitude and latitude of the target driving route based on the normal distribution, obtain the vehicle speed of the target position and use it as the vehicle speed of each target position on the target driving route corresponding to the current time period, and obtain the vehicle speed information of the target position on the target driving route corresponding to each time period.

[0068] For example, for the vehicle speed information of the target position on the target driving route corresponding to the time period of 8:00-8:30, after obtaining the vehicle speed corresponding to each longitude and latitude of the target driving route in the time period of 8:00-8:30, the longitude and latitude points are mapped to the map, and a point is taken as the target position every 1 meter of the road route on the map. According to the vehicle speed corresponding to each longitude and latitude of the target driving route obtained previously, the vehicle speed corresponding to each target position is obtained (for example, the longitude and latitude of the target position x is y, and the vehicle speed at the longitude and latitude y is D, then the vehicle speed corresponding to the target position x is D), thereby obtaining the vehicle speed of each target position on the target driving route in the time period of 8:00-8:30; the vehicle speed of each target position on the target driving route in other time periods is obtained in this way.

[0069] Furthermore, in one embodiment, based on the vehicle speed information of the target position on the target driving route corresponding to each time period, the average speed and average acceleration of the target driving route corresponding to each time period are calculated, wherein the calculation of the average speed and average acceleration of the target driving route corresponding to the current time period specifically includes:

[0070] S301: Obtain vehicle speed information of target positions on the target driving route corresponding to the current time period, calculate the average of the vehicle speeds of all target positions, and obtain the average speed of the target driving route corresponding to the current time period;

[0071] S302: determining the acceleration section on the target driving route based on the vehicle speed at each target position, and calculating the speed change rate of each acceleration section;

[0072] S303: Perform mean calculation based on the speed change rates of all acceleration segments to obtain the average acceleration of the target driving route corresponding to the current time period.

[0073] Specifically, when the current vehicle that needs to automatically select the driving mode is powered on for the first time on the day, the communication module receives the speed of each target position on the target driving route in the time period obtained after the cloud big data system processes the driving information, and saves it in the vehicle memory, while overwriting the data of the previous day. The vehicle processor extracts the speed of each target position on the target driving route in the corresponding time period from the vehicle memory according to the longitude and latitude of the current vehicle's driving route and the driving time. If the driving time exceeds the statistical interval of a single time period, the vehicle processor reads the speed information of the next time period; if the driving time crosses 0 o'clock, the vehicle processor cyclically reads the speed information of the first time period.

[0074] For example, if the current driving time is 8:10, then read the speed of each target position on the target driving route in the time period of 8:00-8:30, and the speed of each target position is a counting point. Take the average of the speeds of each target position to obtain the average speed of the target driving route in the time period of 8:00-8:30. For the judgment of the acceleration section on the target driving route, if the speed of target position 1 is 10km / h, the speed of target position 2 is 15km / h, and the speed of target position 3 is 20km / h, then the speed of target position 3 is 15km / h, then the speed of target position 1 to target position 3 is an acceleration section, then based on the difference between the speed of target position 3 and the speed of target position 1, and the average time taken by all vehicles in the corresponding driving information set in the time period of 8:00-8:30 to travel from target position 1 to target position 3, the speed change rate of the acceleration section is obtained. For the average acceleration of the target driving route corresponding to the time period of 8:00-8:30, the number of acceleration segments of the target driving route corresponding to the time period of 8:00-8:30 and the speed change rate of each acceleration segment are obtained, so as to calculate the average acceleration of the target driving route corresponding to the time period of 8:00-8:30. Finally, the calculated average speed and average acceleration are written into the vehicle memory.

[0075] In this application, the average speed of the target driving route corresponding to the current time period is calculated as follows:

[0076] V 平均 =(V1+V2+…V n ) / n

[0077] Among them, V 平均 represents the average speed, V n represents the vehicle speed at the nth target location, and n represents the total number of target locations on the target driving route;

[0078] For the speed change rate of each acceleration segment of the target driving route corresponding to the current time period, the speed change rate of the current acceleration segment is calculated as follows:

[0079] ΔV=(Vx -V y ) / i

[0080] Among them, ΔV represents the speed change rate of the current acceleration section, V x Indicates the vehicle speed at the end target position of the current acceleration segment, V y represents the vehicle speed at the starting target position of the current acceleration segment, i represents the average time taken by all vehicles in the driving information set corresponding to the current time period to travel from the starting target position of the current acceleration segment to the ending target position of the current acceleration segment;

[0081] The average acceleration of the target driving route corresponding to the current time period is calculated as follows:

[0082] ΔV 平均 =(ΔV1+ΔV2+…ΔV k ) / k

[0083] Where, ΔV 平均 represents the average acceleration, ΔV k represents the speed change rate of the kth acceleration segment on the target driving route corresponding to the current time period, and k represents the total number of acceleration segments on the target driving route corresponding to the current time period.

[0084] Furthermore, in one embodiment, based on the calculated average speed and average acceleration of the target driving route corresponding to each time period, and the SOC of the current vehicle in the time period and on the target driving route at the current time, the automatic selection of the driving mode of the current vehicle is realized, specifically including:

[0085] Based on the time period of the current time, the average speed and average acceleration of the target driving route corresponding to the time period are obtained, and according to the SOC (State of Charge) of the current vehicle on the target driving route, as well as the obtained average speed and average acceleration, a judgment is made:

[0086] If the SOC is less than the preset SOC value, the current vehicle driving mode is set to eco mode (economic mode);

[0087] If the SOC is greater than the preset SOC value, then:

[0088] -When the average speed is less than a first preset average speed, setting the driving mode of the current vehicle to an eco mode;

[0089] - when the average speed is greater than a first preset average speed, the average speed is less than a second preset average speed, and the average acceleration is less than a preset average acceleration, setting the current driving mode of the vehicle to a standard mode;

[0090] - when the average speed is greater than a first preset average speed, the average speed is less than a second preset average speed, and the average acceleration is greater than a preset average acceleration, setting the current driving mode of the vehicle to a sport mode;

[0091] -When the average speed is greater than a second preset average speed, setting the driving mode of the current vehicle to the sport mode.

[0092] It should be noted that the values ​​of the preset SOC value, the first preset average speed, the second preset average speed, and the preset average acceleration are determined according to actual conditions.

[0093] Furthermore, the driving information is collected in real time, and after the collection of the driving information of the latest time period is completed, the driving information of the first time period collected among the preset number of time periods is deleted to obtain the latest driving information and the driving information set corresponding to each time period; based on the obtained latest driving information set corresponding to each time period, the real-time update of the vehicle speed information of the target position in the target driving route corresponding to each time period is realized, as well as the real-time update of the average speed and average acceleration of the target driving route corresponding to each time period.

[0094] The automatic driving mode selection method based on big data in the embodiment of the present application analyzes and counts the speed distribution of the same vehicle model on the target driving route in a certain time period through big data, and calculates the average speed and average acceleration of the target driving route corresponding to each time period based on the vehicle speed information of the target position on the target driving route corresponding to each time period. Then, based on the current time period and the SOC of the current vehicle on the target driving route, the automatic driving mode selection of the current vehicle is realized, the influence of time is taken into account when processing the speed distribution on the target driving route, and the driving mode can be dynamically selected according to the driving characteristics on the target driving route at the current moment when the driving mode is automatically selected, thereby improving the driving mileage and effectively improving the user's driving experience.

[0095] On the second aspect, an embodiment of the present application also provides an automatic driving mode selection device based on big data.

[0096] In one embodiment, referring to Figure 2 , Figure 2 This is a functional module diagram of the driving mode automatic selection device based on big data in this application. Figure 2 As shown, the automatic driving mode selection device based on big data includes: a collection module, a first calculation module, a second calculation module, and a selection module.

[0097] The acquisition module is used to collect driving information of the same vehicle model within a preset number of time periods, and divide the time period into multiple time periods to obtain a driving information set corresponding to each time period, wherein the driving information set is the driving information of all vehicles passing through the target driving route within the current time period under each time period; the first calculation module is used to calculate the speed corresponding to each target position on the target driving route in the current driving information set based on the speed of each vehicle at each position on the target driving route in the driving information set, and obtain the speed information of the target position on the target driving route corresponding to each time period; the second calculation module is used to calculate the average speed and average acceleration of the target driving route corresponding to each time period based on the speed information of the target position on the target driving route corresponding to each time period; the selection module is used to realize automatic selection of the driving mode of the current vehicle based on the calculated average speed and average acceleration of the target driving route corresponding to each time period, as well as the SOC of the current vehicle in the time period and on the target driving route at the current time.

[0098] On the third aspect, an embodiment of the present application provides an automatic driving mode selection device based on big data. The automatic driving mode selection device based on big data can be a personal computer (PC), a laptop computer, a server, or other device with data processing capabilities.

[0099] Reference Figure 3 , Figure 3 The hardware structure diagram of the automatic driving mode selection device based on big data involved in the embodiment of the present application is shown in FIG. In the embodiment of the present application, the automatic driving mode selection device based on big data may include a processor, a memory, a communication interface, and a communication bus.

[0100] The communication bus may be of any type and is used to interconnect the processor, the memory, and the communication interface.

[0101] The communication interface includes an input / output (I / O) interface, a physical interface, and a logical interface, etc., which are used to interconnect the devices inside the automatic selection device for driving mode based on big data, and an interface for interconnecting the automatic selection device for driving mode based on big data with other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, an optical fiber interface, an ATM interface, etc.; the user device can be a display, a keyboard, etc.

[0102] The memory can be various types of storage media, such as random access memory (RAM), read-only memory (ROM), non-volatile RAM (NVRAM), flash memory, optical storage, hard disk, programmable ROM (PROM), erasable PROM (EPROM), electrically erasable PROM (EEPROM), etc.

[0103] The processor may be a general-purpose processor, which may call the automatic driving mode selection program based on big data stored in the memory and execute the automatic driving mode selection method based on big data provided in the embodiment of the present application. For example, the general-purpose processor may be a central processing unit (CPU). The method executed when the automatic driving mode selection program based on big data is called may refer to the various embodiments of the automatic driving mode selection method based on big data of the present application, which will not be repeated here.

[0104] Those skilled in the art will understand that Figure 3 The hardware structure shown in the figure does not constitute a limitation on the present application, and may include more or less components than shown in the figure, or combine certain components, or arrange the components differently.

[0105] In a fourth aspect, an embodiment of the present application also provides a computer-readable storage medium.

[0106] The computer-readable storage medium of the present application stores an automatic driving mode selection program based on big data, wherein when the automatic driving mode selection program based on big data is executed by a processor, the steps of the automatic driving mode selection method based on big data as described above are implemented.

[0107] Among them, the method implemented when the automatic driving mode selection program based on big data is executed can refer to the various embodiments of the automatic driving mode selection method based on big data of the present application, and will not be repeated here.

[0108] The terms "including" and "having" and any variations thereof in the specification and claims of this application and the above-mentioned drawings are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device that includes a series of steps or units is not limited to the listed steps or units, but optionally includes steps or units that are not listed, or optionally includes other steps or units inherent to these processes, methods, products or devices. The terms "first", "second" and "third" are used to distinguish different objects, etc., and do not represent a sequence, nor do they limit "first", "second" and "third" to different types.

[0109] In the description of the embodiments of the present application, "exemplary", "for example" or "for example" are used to indicate examples, illustrations or descriptions. Any embodiment or design described as "exemplary", "for example" or "for example" in the embodiments of the present application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplary", "for example" or "for example" is intended to present related concepts in a specific way.

[0110] In the description of the embodiments of the present application, unless otherwise specified, “ / ” means or, for example, A / B can mean A or B; the “and / or” in the text is merely a description of the association relationship of associated objects, indicating that three relationships may exist, for example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, in the description of the embodiments of the present application, “multiple” refers to two or more than two.

[0111] In some processes described in the embodiments of the present application, multiple operations or steps that appear in a specific order are included, but it should be understood that these operations or steps may not be executed in the order in which they appear in the embodiments of the present application or in parallel, and the sequence number of the operation is only used to distinguish the different operations, and the sequence number itself does not represent any execution order. In addition, these processes may include more or fewer operations, and these operations or steps may be executed in sequence or in parallel, and these operations or steps may be combined.

[0112] Through the description of the above implementation methods, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be implemented by means of software plus a necessary general hardware platform, and of course by hardware, but in many cases the former is a better implementation method. Based on such an understanding, the technical solution of the present application is essentially or the part that contributes to the prior art can be embodied in the form of a software product, which is stored in a storage medium (such as ROM / RAM, disk, CD) as described above, and includes a number of instructions for a terminal device to execute the methods described in each embodiment of the present application.

[0113] The above are only preferred embodiments of the present application, and are not intended to limit the patent scope of the present application. Any equivalent structure or equivalent process transformation made using the contents of the present application specification and drawings, or directly or indirectly applied in other related technical fields, are also included in the patent protection scope of the present application.

Claims

1. A driving mode automatic selection method based on big data, characterized in that: The method for automatically selecting a driving mode based on big data includes: Collecting driving information of the same vehicle type within a preset number of time periods, and dividing the time period into multiple time periods to obtain a driving information set corresponding to each time period, wherein the driving information set is driving information of all vehicles passing through the target driving route within the current time period under each time period; Based on the speed of each vehicle at each position on the target driving route in the driving information set, the speed corresponding to each target position on the target driving route in the current driving information set is calculated, and the speed information of the target position on the target driving route corresponding to each time period is obtained; Based on the vehicle speed information of the target position on the target driving route corresponding to each time period, the average speed and average acceleration of the target driving route corresponding to each time period are calculated; Based on the calculated average speed and average acceleration of the target driving route corresponding to each time period, as well as the SOC of the current vehicle in the current time period and on the target driving route, the driving mode of the current vehicle is automatically selected.

2. The method for automatic driving mode selection based on big data according to claim 1, characterized in that: The driving information includes latitude and longitude information, time information and vehicle speed information; The time period is a day, wherein the day is evenly divided to obtain a plurality of time periods; The driving information set is driving information of all vehicles passing through the target driving route within the current time period of consecutive days for the collected driving information.

3. The method for automatic driving mode selection based on big data as claimed in claim 2, characterized in that: The driving information is collected in real time, and after the driving information collection of the latest time period is completed, the driving information of the first time period collected among the preset number of time periods is deleted to obtain the latest driving information and the driving information set corresponding to each time period; Based on the latest set of driving information corresponding to each time period, the real-time update of the vehicle speed information of the target position in the target driving route corresponding to each time period, as well as the real-time update of the average speed and average acceleration of the target driving route corresponding to each time period are realized.

4. The method for automatic driving mode selection based on big data as claimed in claim 1, characterized in that: The method of calculating the speed of each vehicle at each position on the target driving route based on the driving information set is to obtain the speed of each target position on the target driving route in the current driving information set, and obtain the speed information of the target position on the target driving route corresponding to each time period, specifically including: Obtain the speed of each vehicle at each longitude and latitude of the target driving route in the driving information set corresponding to the current time period, perform normal distribution analysis on the obtained speed, and the expected value of the normal distribution is the speed corresponding to each longitude and latitude of the target driving route; A position point is selected as the target position at each preset distance on the target driving route, and the vehicle speed at each longitude and latitude of the target driving route is obtained based on the normal distribution. The speed of the target position is obtained and used as the speed of each target position on the target driving route corresponding to the current time period, and the speed information of the target position on the target driving route corresponding to each time period is obtained.

5. The method for automatic driving mode selection based on big data as claimed in claim 1, characterized in that: The average speed and average acceleration of the target driving route corresponding to each time period are calculated based on the vehicle speed information of the target position on the target driving route corresponding to each time period, wherein the calculation of the average speed and average acceleration of the target driving route corresponding to the current time period specifically includes: Obtain the vehicle speed information of the target position on the target driving route corresponding to the current time period, calculate the average speed of all target positions, and obtain the average speed of the target driving route corresponding to the current time period; Based on the vehicle speed at each target location, determine the acceleration segment on the target driving route, and calculate the speed change rate of each acceleration segment; Based on the speed change rate of all acceleration sections, the average calculation is performed to obtain the average acceleration of the target driving route corresponding to the current time period.

6. The method for automatic driving mode selection based on big data as claimed in claim 5, characterized in that: The average speed of the target driving route corresponding to the current time period is calculated as follows: V 平均 =(V1+V2+…V n ) / n Among them, V 平均 represents the average speed, V n represents the vehicle speed at the nth target location, and n represents the total number of target locations on the target driving route; For the speed change rate of each acceleration segment of the target driving route corresponding to the current time period, the speed change rate of the current acceleration segment is calculated as follows: ΔV=(V x -V y ) / i Among them, ΔV represents the speed change rate of the current acceleration section, V x Indicates the vehicle speed at the end target position of the current acceleration segment, V y represents the vehicle speed at the starting target position of the current acceleration segment, i represents the average time taken by all vehicles in the driving information set corresponding to the current time period to travel from the starting target position of the current acceleration segment to the ending target position of the current acceleration segment; The average acceleration of the target driving route corresponding to the current time period is calculated as follows: ΔV 平均 =(ΔV1+ΔV2+…ΔV k ) / k Where, ΔV 平均 represents the average acceleration, ΔV k represents the speed change rate of the kth acceleration segment on the target driving route corresponding to the current time period, and k represents the total number of acceleration segments on the target driving route corresponding to the current time period.

7. The method for automatic driving mode selection based on big data as claimed in claim 1, characterized in that: The automatic selection of the driving mode of the current vehicle is achieved based on the calculated average speed and average acceleration of the target driving route corresponding to each time period, and the SOC of the current vehicle in the time period and on the target driving route at the current time, specifically including: Based on the time period of the current time, the average speed and average acceleration of the target driving route corresponding to the time period are obtained, and according to the SOC of the current vehicle on the target driving route, and the obtained average speed and average acceleration, a judgment is made: If the SOC is less than the preset SOC value, the current vehicle driving mode is set to eco mode; If the SOC is greater than the preset SOC value, then: -When the average speed is less than a first preset average speed, setting the driving mode of the current vehicle to an eco mode; - when the average speed is greater than a first preset average speed, the average speed is less than a second preset average speed, and the average acceleration is less than a preset average acceleration, setting the current driving mode of the vehicle to a standard mode; - when the average speed is greater than a first preset average speed, the average speed is less than a second preset average speed, and the average acceleration is greater than a preset average acceleration, setting the current driving mode of the vehicle to a sport mode; -When the average speed is greater than a second preset average speed, setting the driving mode of the current vehicle to the sport mode.

8. A driving mode automatic selection device based on big data, characterized in that: The driving mode automatic selection device based on big data includes: A collection module, which is used to collect driving information of the same vehicle type within a preset number of time periods, and divide the time period into multiple time periods to obtain a driving information set corresponding to each time period, wherein the driving information set is the driving information of all vehicles passing through the target driving route within the current time period under each time period; The first calculation module is used to calculate the speed of each target position on the target driving route in the current driving information set based on the speed of each vehicle at each position on the target driving route in the driving information set, and obtain the speed information of the target position on the target driving route corresponding to each time period; The second calculation module is used to calculate the average speed and average acceleration of the target driving route corresponding to each time period based on the vehicle speed information of the target position on the target driving route corresponding to each time period; The selection module is used to automatically select the driving mode of the current vehicle based on the calculated average speed and average acceleration of the target driving route corresponding to each time period, as well as the SOC of the current vehicle in the time period at the current time and on the target driving route.

9. A driving mode automatic selection device based on big data, characterized in that: The big data-based driving mode automatic selection device includes a processor, a memory, and a big data-based driving mode automatic selection program stored in the memory and executable by the processor, wherein when the big data-based driving mode automatic selection program is executed by the processor, the steps of the big data-based driving mode automatic selection method as described in any one of claims 1 to 7 are implemented.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores an automatic driving mode selection program based on big data, wherein when the automatic driving mode selection program based on big data is executed by a processor, the steps of the automatic driving mode selection method based on big data as described in any one of claims 1 to 7 are implemented.

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