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

Abstract

The application discloses a driving mode automatic selection method and device based on big data, equipment and medium, relates to the technical field of vehicle intelligent control, and the method comprises the following steps: collecting driving information of the same vehicle model in a preset number of time periods, dividing the time period into a plurality of time periods, 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, obtaining the target position vehicle speed information on the target driving route corresponding to each time period, calculating the average speed and average acceleration of the target driving route corresponding to each time period, and based on the average speed and average acceleration of the target driving route corresponding to each time period, the time period in which the current time is located and the SOC of the current vehicle located on the target driving route, the driving mode automatic selection of the current vehicle is realized. The application can realize the automatic switching of the vehicle driving mode based on the current driving scene.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of vehicle intelligent control, in particular to a driving mode automatic selection method and device based on big data, equipment and medium. BACKGROUND

[0002] With the development of intelligent and networked automobile technology, the functions of intelligent and networked automobiles are becoming more and more rich, and the automobiles can meet many driving needs, such as setting up driving modes such as sports mode and energy-saving mode on the same vehicle. In actual application, the driving mode of the vehicle can be switched based on the current road condition or route to adapt to the current driving scene, thereby improving the driving experience of the user.

[0003] Currently, the switching of the driving mode is generally in an active switching mode, that is, the driver manually selects the driving mode of the vehicle during the starting stage or the driving stage of the vehicle, and needs to manually switch each time after starting the vehicle, which increases the complexity of the use of the whole vehicle. Therefore, how to realize the automatic switching of the driving mode of the vehicle based on the current driving scene has become a problem to be solved at present. SUMMARY

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

[0005] In a first aspect, the present application provides a driving mode automatic selection method based on big data, which comprises:

[0006] Collecting driving information of the same vehicle model in a preset number of time periods, and dividing the time periods into a plurality of time segments to obtain a driving information set corresponding to each time segment, wherein the driving information set is the driving information of all vehicles passing through a target driving route in the current time segment in each time period;

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

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

[0009] Based on the calculated average speed and average acceleration of the target driving route corresponding to each time segment, and the time segment in which the current time is located and the SOC of the current vehicle located on the target driving route, the driving mode automatic selection of the current vehicle is realized.

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

[0011] The driving information comprises longitude and latitude 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 in a current time period of consecutive days for the collected driving information.

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

[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 earliest collected time period in 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 obtained driving information set corresponding to each time period, real-time updating of vehicle speed information of a target position in the target driving route corresponding to each time period and real-time updating of 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 an implementation manner, the vehicle speed corresponding to each target position on the target driving route in the current driving information set is calculated based on the vehicle speed of each vehicle at each position of the target driving route in the driving information set, to obtain the vehicle speed information of the target position on the target driving route corresponding to each time period, and the calculation specifically comprises:

[0018] The vehicle 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 is obtained, and normal distribution analysis is performed on the obtained vehicle speed, and the expected value of the normal distribution is the vehicle speed corresponding to each longitude and latitude of the target driving route.

[0019] A position point is selected as a target position every interval of a preset distance on the target driving route, and the vehicle speed of the target position is obtained based on the vehicle speed of each longitude and latitude of the target driving route obtained by the normal distribution, to obtain the vehicle speed of each target position on the target driving route corresponding to the current time period, and the vehicle 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 an implementation manner, 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, and the calculation of the average speed and average acceleration of the target driving route corresponding to the current time period specifically comprises:

[0021] Obtaining target position vehicle speed information on the target driving route corresponding to the current time period, performing mean value calculation on the vehicle speeds of all target positions to obtain the average speed of the target driving route corresponding to the current time period;

[0022] Based on the vehicle speed of each target position, an acceleration section on the target driving route is determined, and the speed change rate of each acceleration section is calculated;

[0023] Based on the speed change rate of all acceleration sections, mean value 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 an implementation manner,

[0025] For the average speed of the target driving route corresponding to the current time period, the calculation manner is:

[0026] V 平均 =(V1+V2+…V n ) / n

[0027] Wherein, V 平均 represents the average speed, V n represents the vehicle speed of the nth target position, and n represents the total number of target positions on the target driving route;

[0028] For the speed change rate of each acceleration section of the target driving route corresponding to the current time period, the calculation manner of the speed change rate of the current acceleration section is:

[0029] ΔV=(V x -V y ) / i

[0030] Wherein, ΔV represents the speed change rate of the current acceleration section, V x represents the vehicle speed of the terminal target position of the current acceleration section, V y represents the vehicle speed of the starting target position of the current acceleration section, and i represents the average time taken by all vehicles in the current time period corresponding to the driving information set to travel from the starting target position of the current acceleration section to the terminal target position of the current acceleration section.

[0031] For the average acceleration of the target driving route corresponding to the current time period, the calculation manner is:

[0032] ΔV 平均 =(ΔV1+ΔV2+…ΔV k ) / k

[0033] Wherein, ΔV 平均 represents the average acceleration, and ΔV kdenotes the speed change rate of the kth acceleration section on the target driving route corresponding to the current time period, and k denotes the total number of acceleration sections on the target driving route corresponding to the current time period.

[0034] In combination with the first aspect, in an implementation manner, the average speed and the average acceleration of the target driving route corresponding to each time period are calculated, and the SOC of the current vehicle located on the target driving route and the time period in which the current time is located are used to automatically select the driving mode of the current vehicle, and the automatic selection specifically includes:

[0035] Based on the time period in which the current time is located, the average speed and the average acceleration of the target driving route corresponding to the time period are obtained, and the SOC of the current vehicle located on the target driving route, the average speed and the average acceleration obtained are used to make a judgment.

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

[0037] If the SOC is greater than the preset SOC value, the driving mode of the current vehicle is set to the eco mode.

[0038] When the average speed is less than the first preset average speed, the driving mode of the current vehicle is set to the eco mode.

[0039] When the average speed is greater than the first preset average speed, the average speed is less than the second preset average speed, and the average acceleration is less than the preset average acceleration, the driving mode of the current vehicle is set to the standard mode.

[0040] When the average speed is greater than the first preset average speed, the average speed is less than the second preset average speed, and the average acceleration is greater than the preset average acceleration, the driving mode of the current vehicle is set to the sport mode.

[0041] When the average speed is greater than the second preset average speed, the driving mode of the current vehicle is set 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] The collection module is configured to collect driving information of the same vehicle model in a preset number of time periods, divide the time periods into a plurality of time periods, and 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 in the current time period in each time period.

[0044] a first calculation module configured 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;

[0045] a second calculation module configured to calculate the average speed and the average acceleration of the target driving route in each time period based on the target position speed information on the target driving route in each time period;

[0046] a selection module configured to automatically select the driving mode of the current vehicle based on the average speed and the average acceleration of the target driving route in each time period, the time period in which the current time is located, and the SOC of the current vehicle on the target driving route.

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

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

[0049] The technical scheme provided by the embodiments of the present application has the following beneficial effects:

[0050] The speed distribution of the same vehicle model on the target driving route in a certain time period is analyzed and counted through big data, the average speed and the average acceleration of the target driving route in each time period are calculated based on the target position speed information on the target driving route in each time period, and then the driving mode of the current vehicle is automatically selected based on the time period in which the current time is located and the SOC of the current vehicle on the target driving route. When processing the speed distribution on the target driving route, the influence of time is considered, and when automatically selecting the driving mode, the driving characteristics on the target driving route at the current time can be dynamically selected, which improves the driving range and effectively improves the driving experience of the user. BRIEF DESCRIPTION OF DRAWINGS

[0051] Figure 1 FIG. 1 is a flowchart of the big data-based driving mode automatic selection method of the present application;

[0052] Figure 2 The function module schematic diagram of the driving mode automatic selection device based on big data of the present application;

[0053] Figure 3 The hardware structure schematic diagram of the driving mode automatic selection device based on big data of the present application. DETAILED DESCRIPTION

[0054] In order for those skilled in the art to better understand the scheme of the present application, the technical solutions in the embodiments of the present application will be described clearly and completely below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only some of the embodiments of the present application, not all. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor fall within the scope of protection of the present application.

[0055] In order to make the purpose, technical solutions and advantages of the present application clearer, the embodiments of the present application will be described in further detail below in conjunction with the drawings.

[0056] In a first aspect, the embodiments of the present application provide a driving mode automatic selection method based on big data. The speed distribution of a target driving route with respect to the latitude and longitude of the route is obtained through big data in a certain time period. The driving characteristics such as average speed and speed variation rate of the target driving route are analyzed according to the specific speed distribution of the target driving route. Then, the driving mode is automatically selected according to the driving characteristics, so as to realize the automatic selection and switching of the vehicle driving mode based on the driving scene.

[0057] In an embodiment, the driving mode automatic selection method based on big data comprises the following steps: Figure 1 Figure 1 The flowchart of the driving mode automatic selection method based on big data of the present application is shown in FIG. 1. As shown in FIG. 1, the driving mode automatic selection method based on big data comprises the following steps: Figure 1

[0058] S1: collecting the driving information of the same vehicle model in a preset number of time periods, and dividing the time periods into multiple time periods to obtain the 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 in the current time period in each time period;

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

[0060] ​​S3: based on the target position speed information 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: based on the average speed and average acceleration of the target driving route corresponding to each time period, and the SOC of the current vehicle located on the target driving route and the time period in which the current time is located, the driving mode of the current vehicle is automatically selected.

[0062] In actual application, for the collection of driving information, specifically, based on the cloud big data system and through the vehicle data collection module installed on the vehicle, the driving information of all vehicles in use of the vehicle type which needs to perform driving mode automatic selection is collected, and the collected driving information is cached in the cloud big data system. The driving information includes latitude and longitude information, time information and speed information

[0063] In this application, the time period is day, wherein the day is evenly divided to obtain a plurality of 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. In one possible implementation, the preset number of time periods is 7 days, that is, the driving information in 7 days is collected and cached to the cloud. The day is divided every 0.5 hours, and a total of 24 hours are included in a day, so 48 time periods can be divided, each of which corresponds to a driving information set. For example, the target driving route is from location A to location B, and the vehicle type which needs to perform driving mode automatic selection is vehicle type M. For the 8:00-8:30 time period, the driving information set is the driving information of all vehicles of vehicle type M passing through location A to location B in the 8:00-8:30 time period of each of the last 7 days; for the 8:30-9:30 time period, the driving information set is the driving information of all vehicles of vehicle type M passing through location A to location B in the 8:30-9:30 time period of each of the last 7 days.

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

[0065] S201: obtaining the speed of each vehicle in the driving information set at each latitude and longitude on the target driving route corresponding to the current time period, and performing normal distribution analysis on the obtained speed, wherein the expected value of the normal distribution is the speed corresponding to each latitude and longitude on the target driving route;

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

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

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

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

[0070] S301: Obtain the target position vehicle speed information on the target driving route corresponding to the current time period, perform mean value calculation on 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: Determine the acceleration section on the target driving route based on the vehicle speeds of each target position, and calculate the speed change rate of each acceleration section;

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

[0073] Specifically, the current vehicle requiring automatic selection of driving mode receives the vehicle speed of each target position on the target driving route in the time period obtained by the cloud big data system processing driving information through the communication module when the current vehicle is powered on for the first time on the day, and saves it in the vehicle storage memory, while covering the previous day's data. The vehicle processor extracts the vehicle speed of each target position on the target driving route in the corresponding time period from the vehicle storage memory according to the latitude and longitude of the driving route and the driving time of the current vehicle. If the driving time exceeds the single time period statistical interval, the vehicle processor reads the vehicle speed information of the next time period; if the driving time spans 0 o'clock, the vehicle processor cyclically reads the vehicle speed information of the first time period.

[0074] For example, the current driving time is 8:10, the vehicle speed of each target position on the target driving route in the 8:00-8:30 time period is read, and the vehicle speed of each target position is a counting point. The average speed of the target driving route in the 8:00-8:30 time period is obtained by taking the average of the vehicle speed of each target position. For the judgment of the acceleration section on the target driving route, if the vehicle speed of target position 1 is 10 km / h, the vehicle speed of target position 2 is 15 km / h, the vehicle speed of target position 3 is 20 km / h, and the vehicle speed of target position 3 is 15 km / h, target position 1 to target position 3 is an acceleration section, and the speed change rate of the acceleration section is obtained based on the difference between the vehicle speed of target position 3 and the vehicle speed of target position 1, and the average time taken by all vehicles in the 8:00-8:30 time period from target position 1 to target position 3. For the average acceleration of the target driving route corresponding to the 8:00-8:30 time period, the number of acceleration sections of the target driving route corresponding to the 8:00-8:30 time period and the speed change rate of each acceleration section are obtained, and the average acceleration of the target driving route corresponding to the 8:00-8:30 time period is calculated. Finally, the calculated average speed and average acceleration are written into the vehicle storage 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] Where V 平均 represents the average speed, V n represents the vehicle speed of the nth target position, and n represents the total number of target positions on the target driving route.

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

[0079] ΔV=(Vx -V y ) / i

[0080] wherein, ΔV represents the speed change rate of the current acceleration section, V x represents the vehicle speed at the target position of the end of the current acceleration section, V y represents the vehicle speed at the target position of the start of the current acceleration section, i represents all vehicles corresponding to the driving information set of the current time section, and the average time taken by the vehicles to travel from the target position of the start of the current acceleration section to the target position of the end of the current acceleration section;

[0081] The average acceleration of the target driving route corresponding to the current time section is calculated in the following manner:

[0082] ΔV 平均 = (ΔV1+ ΔV2+ … ΔV k ) / k

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

[0084] Further, in an embodiment, based on the calculated average speed and average acceleration of the target driving route corresponding to each time section, and the SOC of the current vehicle located on the target driving route and the time section in which the current time is located, the driving mode of the current vehicle is automatically selected, specifically including:

[0085] Based on the time section in which the current time is located, the average speed and average acceleration of the target driving route corresponding to the time section are obtained, and based on the SOC of the current vehicle on the target driving route, and the obtained average speed and average acceleration, a judgment is made:

[0086] If the SOC is less than a preset SOC value, the driving mode of the current vehicle is set to an 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, the driving mode of the current vehicle is set to an eco mode;

[0089] -when the average speed is greater than the 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, the driving mode of the current vehicle is set to a standard mode;

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

[0091] - when the average speed is greater than the 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] Further, the driving information is collected in real time, and after the driving information of the latest time period is collected, the driving information of the earliest collected time period in 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 target position speed information in the target driving route corresponding to each time period and the real-time update of the average speed and the average acceleration of the target driving route corresponding to each time period are realized.

[0094] The driving mode automatic selection method based on big data of the embodiment of the application calculates the average speed and the average acceleration of the target driving route corresponding to each time period based on the target position speed information on the target driving route corresponding to each time period by analyzing and counting the speed distribution of the same vehicle model on the target driving route in a time period, and then realizes the automatic selection of the driving mode of the current vehicle based on the time period in which the current time is located and the SOC of the current vehicle on the target driving route. When processing the speed distribution on the target driving route, the influence of time is considered, and when automatically selecting the driving mode, the driving characteristics on the target driving route at the current time can be dynamically selected, which improves the driving mileage and effectively improves the driving experience of the user.

[0095] In a second aspect, the embodiment of the application further provides a driving mode automatic selection device based on big data.

[0096] In an embodiment, the driving mode automatic selection device based on big data comprises a collection module, a first calculation module, a second calculation module, and a selection module. Figure 2 , Figure 2 FIG. 1 is a schematic diagram of the functional modules of the driving mode automatic selection device based on big data of the application. As shown in FIG. 1, the driving mode automatic selection device based on big data comprises a collection module, a first calculation module, a second calculation module, and a selection module. Figure 2

[0097] ​The collection module is configured to collect driving information of the same vehicle model in a preset number of time periods, divide the time periods into a plurality of time segments, and obtain a driving information set corresponding to each time segment, the driving information set being driving information of all vehicles passing through a target driving route in a current time segment in each time period; the first calculation module is configured to calculate a vehicle speed corresponding to each target position on the target driving route in the current driving information set based on the vehicle speed of each vehicle at each position on the target driving route in the driving information set, and obtain target position vehicle speed information on the target driving route corresponding to each time segment; the second calculation module is configured to calculate an average speed and an average acceleration of the target driving route corresponding to each time segment based on the target position vehicle speed information on the target driving route corresponding to each time segment; and the selection module is configured to automatically select a driving mode of a current vehicle based on the average speed and the average acceleration of the target driving route corresponding to each time segment, and the time segment in which the current time is located and the SOC of the current vehicle located on the target driving route.

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

[0099] Reference Figure 3 , Figure 3 FIG. 1 is a schematic diagram of a hardware structure of a big data-based driving mode automatic selection device according to an embodiment of the present application. In the embodiment, the big data-based driving mode automatic selection device can include a processor, a memory, a communication interface, and a communication bus.

[0100] The communication bus can 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, and is used to interconnect devices inside the big data-based driving mode automatic selection device, and is also used to interconnect the big data-based driving mode automatic selection device with other devices (such as other computing devices or user devices). The physical interface can be an Ethernet interface, a fiber interface, an ATM interface, etc. The user device can be a display (Display), a keyboard (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), and the like.

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

[0104] Those skilled in the art can understand that, Figure 3 The hardware structure shown in the figure is not a limitation of the present application, and can include more or less components, or combine certain components, or different component arrangements.

[0105] In a fourth aspect, the embodiments of the present application further provide a computer readable storage medium.

[0106] The computer readable storage medium of the present application stores a big data based driving mode automatic selection program, 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 above are implemented.

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

[0108] The terms "comprise", "comprising", "include", "including", "have" and "having" and any variations thereof in the specification and in the claims are intended to cover both the singular and the plural unless otherwise indicated. For example, a process, method, system, product, or apparatus that comprises a list of steps or elements is not limited to only those steps or elements but can include other steps or elements not expressly listed or inherent to such process, method, system, product, or apparatus. The terms "first", "second", and "third" and the like, are used merely as labels, and are not intended to impose numerical requirements on their objects.

[0109] In the description of the embodiments of the present application, "exemplary", "for example", "e.g." or "for instance" are used on the basis that a person of ordinary skill in the art will be aware that one or more embodiments of the present application can include other embodiments as appropriate, and also on the basis that one of ordinary skill in the art will be aware of many ways for modifying or performing the embodiments of the present application.

[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; "and / or" in the text merely describes the relationship of associated objects, and means that there can be three relationships, for example, A and / or B, which means that there are three cases of A alone, A and B together, and B alone. In addition, in the description of the embodiments of the present application, "plurality" means two or more than two.

[0111] In some of the processes described in the embodiments of the present application, a plurality of operations or steps are presented in a specific order. However, it should be understood that the operations or steps can be performed in an order different than presented, or performed in parallel. The order or sequence of the operations or steps is not to be construed as a requirement or limitation unless explicitly stated. Additionally, the processes can include more or fewer operations or steps than those disclosed herein, and the operations or steps can be performed in an order different than presented or in parallel.

[0112] From the above description of the embodiments, those skilled in the art can clearly understand that the above-mentioned embodiment methods can be realized by means of software and necessary general hardware platforms, of course, they can also be realized by hardware, but in many cases the former is a better embodiment. Based on such understanding, the technical solutions of the present application can be embodied in the form of a software product, which is stored in a storage medium (such as a ROM / RAM, a magnetic disk, or an optical disk) as described above, and includes a plurality of instructions for causing a terminal device to execute the methods described in the various embodiments of the present application.

[0113] The preferred embodiments of the present application have been described above with the illustrated embodiments, and are not intended to limit the scope of patent protection for the present application. Any equivalent structure or equivalent process variations, which directly or indirectly incorporate the contents of the specification and drawings of the present application, are also intended to be included within the scope of patent protection for 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 the driving information of all vehicles passing through the target driving route within the current time period of 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; 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; in, 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 multiple time periods; The driving information set is driving information of all vehicles that pass through the target driving route within the current time period of consecutive days for the collected driving information.

2. The method for automatic driving mode selection based on big data according to claim 1, characterized in that: 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 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 is achieved, as well as the real-time update of the average speed and average acceleration of the target driving route corresponding to each time period.

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

4. The method for automatic driving mode selection based on big data according to 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 at the target position on the target driving route corresponding to each time period. 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 location on the target driving route corresponding to the current time period, calculate the average speed of all target locations, 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 value is calculated to obtain the average acceleration of the target driving route corresponding to the current time period.

5. The method for automatic driving mode selection based on big data according to claim 4, characterized in that: The average speed of the target driving route corresponding to the current time period is calculated as follows: in, represents the average speed, Indicates the The speed of the vehicle at the target location, Indicates 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: in, Indicates the speed change rate of the current acceleration section. Indicates the vehicle speed at the end target position of the current acceleration segment. Indicates the vehicle speed at the starting target position of the current acceleration segment. 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: in, represents the average acceleration, Indicates the number of seconds on the target driving route corresponding to the current time period. The speed change rate of the acceleration section, Indicates the total number of acceleration segments on the target driving route corresponding to the current time period.

6. The method for automatic driving mode selection based on big data according to claim 1, characterized in that: The 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 and the SOC of the current vehicle in the current time period and on the target driving route specifically includes: Based on the time period of the current time, obtain the average speed and average acceleration of the target driving route corresponding to the time period, and make a judgment based on the current vehicle SOC on the target driving route and the obtained average speed and average acceleration: 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 current vehicle driving mode to 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 vehicle driving mode 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 vehicle driving mode to a sport mode; -When the average speed is greater than a second preset average speed, setting the current driving mode of the vehicle to the sport mode.

7. 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 for collecting driving information of the same vehicle type within a preset number of time periods, dividing the time periods into multiple time periods, and obtaining 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 of each time period; a first calculation module configured to calculate, 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, and obtain the speed information of the target position on the target driving route corresponding to each time period; 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; A selection module, which is used to automatically select a driving mode for 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; in, 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 multiple time periods; The driving information set is driving information of all vehicles that pass through the target driving route within the current time period of consecutive days for the collected driving information.

8. A driving mode automatic selection device based on big data, characterized in that: The big data-based automatic driving mode selection device includes a processor, a memory, and a big data-based automatic driving mode selection program stored on the memory and executable by the processor, wherein when the big data-based automatic driving mode selection program is executed by the processor, the steps of the big data-based automatic driving mode selection method as described in any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium, characterized in that The computer-readable storage medium stores 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 driving mode automatic selection method based on big data are implemented as described in any one of claims 1 to 6.

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