Travel mileage statistical method, device and equipment based on road type and medium

Abstract

The invention relates to the field of vehicles, in particular to a road type-based driving mileage statistical method, device, equipment and medium, the method comprises the following steps: acquiring vehicle-mounted data of a vehicle at each sampling moment in N driving mileages, and for any driving mileage, calculating to obtain a corresponding driving characteristic parameter according to the vehicle-mounted data corresponding to the driving mileage; obtaining a trained clustering model to confirm the road type to which each driving mileage belongs; aiming at any driving mileage, correcting the road type to which the driving mileage belongs to obtain a final road type; according to the final road types of all travelled distances, calculating to obtain the travelled distance ratio of the road type corresponding to the vehicle, and correcting the clustering result of the clustering model to optimize the road type to which each travelled distance belongs so as to avoid the clustering error caused by low training precision of the clustering model and improve the accuracy of the clustering result. Therefore, the counted mileage ratio of each road type is more objective and rigorous.

Description

Technical Field

[0001] The present invention relates to the field of vehicles, and in particular to a method, device, equipment and medium for counting mileage based on road types. Background Art

[0002] When a car is driving on different types of roads, different degrees of load and damage will be incurred. Therefore, in the process of developing vehicle performance, it is necessary to count the proportion of roads driven by each vehicle in order to guide the development of vehicle performance based on the proportion of roads driven. For example, when driving on mountain roads, the proportion of SUV models will be higher than that of sedans.

[0003] At present, the statistics of the road travel ratio of vehicles are generally collected through questionnaire surveys or small sample data collection. Among them, the questionnaire survey is hosted by a professional questionnaire survey agency, which distributes questionnaires to users (online / offline), and the users fill in the questionnaires subjectively. The questionnaire survey agency then collects and counts the mileage ratio of each road type. Small sample data collection is to survey the vehicles of typical users and count the mileage ratio of each road type based on the driving data of the vehicles.

[0004] However, the sample size of the questionnaire survey is too large, the cost is high, and users have different definitions of road types, so it is impossible to accurately count the mileage share of each road type. The amount of data collected by the small sample data is limited, and the confidence level of the statistical results of the mileage share of each road type is relatively low. Therefore, how to improve the statistical results of the mileage share of each road type has become an urgent problem to be solved. Summary of the invention

[0005] In view of this, an embodiment of the present invention provides a method, device, equipment and medium for counting mileage based on road types to solve the problem of poor statistical results of mileage proportions of each road type.

[0006] In a first aspect, an embodiment of the present invention provides a method for counting mileage based on road types, the method comprising:

[0007] Obtaining the vehicle's onboard data at each sampling time in N mileages, and for any mileage, calculating the corresponding driving characteristic parameter according to the onboard data corresponding to the mileage, where N is an integer greater than 1;

[0008] Obtaining a trained clustering model, inputting the driving characteristic parameters of all driving mileages into the trained clustering model for classification, and confirming the road type to which each driving mileage belongs;

[0009] For any driving mileage, detecting whether the driving characteristic parameters of the driving mileage meet the road type correction condition; if it is detected that the driving characteristic parameters of the driving mileage meet the road type correction condition, correcting the road type to which the driving mileage belongs to obtain a final road type;

[0010] According to the final road type of all mileages, the number of each road type is obtained by counting, and according to all mileages and the number of each road type, the mileage ratio of the road type corresponding to the vehicle is calculated.

[0011] In a second aspect, an embodiment of the present invention provides a mileage counting device based on road type, the mileage counting device comprising:

[0012] A data processing module is used to obtain the vehicle data at each sampling time in N driving mileages, and for any driving mileage, calculate the corresponding driving characteristic parameter according to the vehicle data corresponding to the driving mileage, where N is an integer greater than 1;

[0013] A data classification module is used to obtain a trained clustering model, input the driving characteristic parameters of all driving mileages into the trained clustering model for classification, and confirm the road type to which each driving mileage belongs;

[0014] A type correction module is used to detect, for any driving mileage, whether the driving characteristic parameters of the driving mileage meet the road type correction condition, and if it is detected that the driving characteristic parameters of the driving mileage meet the road type correction condition, correct the road type to which the driving mileage belongs to obtain a final road type;

[0015] The proportion statistics module is used to obtain the number of each road type according to the final road type of all mileage, and calculate the mileage proportion of the road type corresponding to the vehicle according to all mileage and the number of each road type.

[0016] In a third aspect, an embodiment of the present invention provides a computer device, comprising a processor, a memory, and a computer program stored in the memory and executable on the processor, wherein the processor implements the mileage statistics method as described in the first aspect when executing the computer program.

[0017] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the mileage statistics method as described in the first aspect is implemented.

[0018] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0019] The present invention obtains on-board data of a vehicle at each sampling time in N mileages, and for any mileage, calculates corresponding driving characteristic parameters according to the on-board data corresponding to the mileage, where N is an integer greater than 1; obtains a trained clustering model, inputs the driving characteristic parameters of all mileages into the trained clustering model for classification, and confirms the road type to which each mileage belongs; for any mileage, detects whether the driving characteristic parameters of the mileage meet a road type correction condition, and if it is detected that the driving characteristic parameters of the mileage meet the road type correction condition, corrects the road type to which the mileage belongs to obtain a final road type; according to the final road types of all mileages, obtains the number of each road type by statistics, and calculates the mileage ratio of the road type corresponding to the vehicle according to all mileages and the number of each road type. Among them, the driving characteristic parameters are analyzed through the on-board data of the vehicle during driving, which are used to characterize the driving status of the vehicle on the road. Then, the driving characteristic parameters of multiple mileages are clustered using the clustering model trained by big data and machine learning. The road type to which each mileage belongs can be confirmed efficiently and accurately, and the clustering results of the clustering model can be corrected based on the road type correction conditions to optimize the road type to which each mileage belongs, so as to avoid clustering errors caused by low training accuracy of the clustering model, thereby making the detection of road types more accurate and the statistical mileage proportion of each road type more objective and rigorous. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0021] Figure 1 This is a schematic diagram of an application environment of a method for counting mileage based on road types provided in the first embodiment of the present invention;

[0022] Figure 2 It is a flowchart of a method for counting mileage based on road types provided in the first embodiment of the present invention;

[0023] Figure 3 It is a structural schematic diagram of a device for counting mileage based on road types provided in the second embodiment of the present invention;

[0024] Figure 4 It is a structural diagram of a computer device provided in Embodiment 3 of the present invention. DETAILED DESCRIPTION

[0025] In the following description, specific details such as specific system structures, technologies, etc. are provided for the purpose of illustration rather than limitation, so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted to prevent unnecessary details from obstructing the description of the present invention.

[0026] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or combinations thereof.

[0027] It should also be understood that the term "and / or" used in the present description and the appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0028] As used in the present specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to detecting", depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is detected" may be interpreted as meaning "upon determination" or "in response to determining" or "upon detection of [described condition or event]" or "in response to detecting [described condition or event]", depending on the context.

[0029] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0030] References to "one embodiment" or "some embodiments" etc. described in the present specification mean that one or more embodiments of the present invention include specific features, structures or characteristics described in conjunction with the embodiment. Therefore, the statements "in one embodiment", "in some embodiments", "in some other embodiments", "in some other embodiments", etc. that appear in different places in this specification do not necessarily refer to the same embodiment, but mean "one or more but not all embodiments", unless otherwise specifically emphasized in other ways. The terms "including", "comprising", "having" and their variations all mean "including but not limited to", unless otherwise specifically emphasized in other ways.

[0031] It should be understood that the order of execution of the steps in the following embodiments does not imply a precedence of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0032] In order to illustrate the technical solution of the present invention, specific embodiments are provided below for illustration.

[0033] A mileage statistics method based on road type provided in the first embodiment of the present invention can be applied to Figure 1 In an application environment, the client communicates with the server. The client includes but is not limited to PDAs, desktop computers, laptops, ultra-mobile personal computers (UMPCs), netbooks, cloud terminal devices, personal digital assistants (PDAs) and other computer devices. The server can be an independent server or a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, content delivery networks (CDNs), and big data and artificial intelligence platforms.

[0034] See also Figure 2 , is a flow chart of a method for counting mileage based on road types provided in the first embodiment of the present invention. The above-mentioned method for counting mileage can be applied to Figure 1 The client in the client, the computer device corresponding to the client connects to the server to obtain the corresponding background data. For example, the vehicle data at each sampling time and the trained clustering model are used for the subsequent statistics of the mileage ratio of the corresponding road type of the vehicle.

[0035] like Figure 2 As shown, the mileage statistics method may include the following steps:

[0036] Step S201, obtaining the vehicle's onboard data at each sampling time in N driving mileages, and for any driving mileage, calculating the corresponding driving characteristic parameters according to the onboard data corresponding to the driving mileage.

[0037] Wherein, N is an integer greater than 1. Mileage refers to a section of road traveled by a vehicle. On-board data refers to data of the vehicle during driving, including but not limited to the vehicle speed signal, which is not limited by the present invention. For example: longitude and latitude, altitude, vehicle speed, steering wheel angle, brake pedal opening, and accelerator pedal opening. Driving characteristic parameters are used to characterize the road type. Therefore, in an embodiment of the present invention, a data acquisition instrument capable of reading the on-board controller area network (CAN) signal is installed on the vehicle, and the sampling frequency is set to be not less than 1 Hz. Based on the sampling frequency, the data acquisition instrument is used to obtain the on-board data of the vehicle at each sampling time in N mileages, and for any mileage, the corresponding driving characteristic parameters are calculated according to the on-board data corresponding to the mileage, which are used to characterize the driving state of the vehicle in each mileage.

[0038] Optionally, the vehicle-mounted data includes vehicle speed, steering wheel angle, altitude, brake pedal opening, and accelerator pedal opening. The corresponding driving characteristic parameters are calculated based on the vehicle-mounted data corresponding to the mileage, including:

[0039] Calculate the maximum speed and average speed of the vehicle during the mileage according to the vehicle speed at each sampling moment;

[0040] According to the steering wheel angle at each sampling moment, a proportion of steering time within a first angle range and a proportion of steering time within a second angle range in the mileage are calculated, wherein the first angle range is larger than the second angle range;

[0041] Calculating the altitude difference in the mileage according to the altitude at each sampling moment;

[0042] According to the brake pedal opening and the accelerator pedal opening at each sampling moment, the number of vehicle starts in the driving mileage is obtained by counting;

[0043] The maximum vehicle speed, the average vehicle speed, the proportion of the turning time within the first angle range, the proportion of the turning time within the second angle range, the altitude difference and the number of vehicle starts constitute driving characteristic parameters.

[0044] The vehicle data in the embodiment of the present invention includes vehicle speed, steering wheel angle, altitude, brake pedal opening and accelerator pedal opening. Therefore, the vehicle speed at each sampling moment in the mileage is compared to obtain the highest vehicle speed in the mileage, and the average of the vehicle speeds at all sampling moments in the mileage is calculated as the average vehicle speed.

[0045] Taking the steering wheel being able to turn two and a half circles as an example, the corresponding angle range of the steering wheel angle is [0°, 900°]. Then, in order from large to small, the angle range of the steering wheel angle is divided into a first angle range, a second angle range and a third angle range, which can be understood as the steering wheel angle being divided into large-angle steering, medium-angle steering and small-angle steering. For example, the angle range of the steering wheel angle is evenly divided into a first angle range (600°, 900°], a second angle range (300°, 600°] and a third angle range [0°, 300°]. The present invention is not limited to this, and it can be divided according to actual needs. Then, according to the steering wheel angle at each sampling moment, the proportion of the steering time when the steering is performed with the steering wheel angle within the first angle range in the mileage, and the proportion of the steering time when the steering is performed with the steering wheel angle within the second angle range in the mileage are calculated.

[0046] According to the altitude at all sampling moments in the mileage, the maximum altitude and the minimum altitude are obtained, and the absolute value of the altitude difference between the maximum altitude and the minimum altitude is calculated as the altitude difference. For any sampling moment, when the vehicle speed at the sampling moment is 0 and the vehicle speed at the next adjacent sampling moment is greater than 0, it is confirmed that the vehicle is in the starting condition at the sampling moment, and then all sampling moments are traversed to confirm whether the vehicle is in the starting condition at each sampling moment, and then the number of sampling moments when the vehicle is in the starting condition is counted as the number of vehicle starts.

[0047] At this point, the embodiment of the present invention uses the maximum vehicle speed, average vehicle speed, the proportion of turning time within the first angle range, the proportion of turning time within the second angle range, the altitude difference and the number of vehicle starts to constitute the driving characteristic parameters in the driving mileage.

[0048] Optionally, the calculating corresponding driving characteristic parameters according to the vehicle-mounted data corresponding to the driving mileage further includes:

[0049] Calculating the number of vehicle operating conditions and the number of turns in the mileage according to the vehicle speed, steering wheel angle, brake pedal opening and accelerator pedal opening at each sampling moment in the mileage;

[0050] The number of vehicle operating conditions includes acceleration times, coasting times, braking times, stopping times, following times and cruising times, and the acceleration times, coasting times, braking times, stopping times, following times and cruising times are obtained according to the vehicle speed, acceleration and deceleration characteristics, the brake pedal opening and the accelerator pedal opening;

[0051] The number of turns includes the number of turns in a first angle range, the number of turns in a second angle range, and the number of turns in a third angle range. According to the steering wheel angle, the number of turns in the first angle range, the number of turns in the second angle range, and the number of turns in the third angle range are obtained, wherein the second angle range is larger than the third angle range.

[0052] In the embodiment of the present invention, the index value of the acceleration and deceleration characteristics at the current moment is obtained according to the vehicle speed at each sampling moment. Specifically, if the vehicle speed at the previous sampling moment is greater than the vehicle speed at the current sampling moment, it is confirmed that the current moment is an acceleration process, and the corresponding index value of the acceleration and deceleration characteristics is set to 1. If the vehicle speed at the next sampling moment is less than the vehicle speed at the current sampling moment, it is confirmed that the current moment is a deceleration process, and the corresponding index value of the acceleration and deceleration characteristics is set to -1; if the vehicle speed at the next sampling moment is equal to the vehicle speed at the current sampling moment, it is confirmed that the current moment is a deceleration process, and the corresponding index value of the acceleration and deceleration characteristics is set to 0. At the same time, the vehicle speed threshold, accelerator pedal opening threshold, and brake pedal opening threshold for judging the vehicle operating state are set. For example, the vehicle speed threshold is 60, the accelerator pedal opening threshold is 5, and the brake pedal opening threshold is 0.4. The present invention does not limit the setting of the threshold.

[0053] Regarding the acquisition of the number of acceleration times, for any sampling moment, when the vehicle speed at the sampling moment is not 0, the index value of the acceleration and deceleration characteristics is greater than or equal to 0, and the accelerator pedal opening is not 0, it is determined that the vehicle is in an accelerating state at the sampling moment, and the corresponding number of acceleration times is increased by 1. Therefore, the number of acceleration times can be obtained based on the vehicle speed, acceleration and deceleration characteristics, and accelerator pedal opening at each sampling moment, that is, all sampling moments are traversed to confirm whether the vehicle is in an accelerating state at each sampling moment, and then the number of sampling moments when the vehicle is in an accelerating state is counted as the number of acceleration times.

[0054] Regarding the acquisition of the number of coasting times, for any sampling moment, when the vehicle speed at the sampling moment is greater than 0, the index value of the acceleration and deceleration characteristics is less than 0, and the accelerator pedal opening is less than or equal to the accelerator pedal opening threshold, it is determined that the vehicle is in a coasting state at the sampling moment, and the corresponding number of coasting times is increased by 1. Therefore, the number of coasting times can be obtained based on the vehicle speed, acceleration and deceleration characteristics, and accelerator pedal opening at each sampling moment, that is, all sampling moments are traversed to confirm whether the vehicle is in a coasting state at each sampling moment, and then the number of sampling moments when the vehicle is in a coasting state is counted as the number of coasting times.

[0055] Regarding the acquisition of the number of braking times, for any sampling moment, when the vehicle speed at the sampling moment is greater than 0, the index value of the acceleration and deceleration characteristics is 0, and the accelerator pedal opening is less than or equal to the accelerator pedal opening threshold, it is determined that the vehicle is in a braking state at the sampling moment, and the corresponding number of braking times is increased by 1. Therefore, the number of braking times can be obtained based on the vehicle speed, acceleration and deceleration characteristics, and accelerator pedal opening at each sampling moment, that is, all sampling moments are traversed to confirm whether the vehicle is in a braking state at each sampling moment, and then the number of sampling moments when the vehicle is in a braking state is counted as the number of braking times.

[0056] Regarding the acquisition of the number of parking times, when the vehicle speed is equal to 0, it is determined that the vehicle is in a parked state, and the corresponding number of parking times increases by 1. Therefore, the number of parking times can be obtained based on the vehicle speed at each sampling moment, that is, all sampling moments are traversed to confirm whether the vehicle is in a parked state at each sampling moment, and then the number of sampling moments when the vehicle is in a parked state is counted as the number of parking times.

[0057] Regarding the acquisition of the number of following vehicle times, when the accelerator pedal opening is greater than the accelerator pedal opening threshold and the brake pedal opening is greater than the brake pedal opening threshold, it is determined that the vehicle is in the following vehicle state, and the corresponding number of following vehicle times increases by 1. Therefore, the number of following vehicle times can be obtained based on the brake pedal opening and the accelerator pedal opening at each sampling moment, that is, all sampling moments are traversed to confirm whether the vehicle is in the following vehicle state at each sampling moment, and then the number of sampling moments when the vehicle is in the following vehicle state is counted as the number of following vehicle times.

[0058] Regarding the acquisition of the number of cruise times, when the vehicle speed is greater than the vehicle speed threshold, the accelerator pedal opening is 0, and the brake pedal opening is less than or equal to the brake pedal opening threshold, it is confirmed that the vehicle is in a cruising state, and the corresponding cruise times are increased by 1. Therefore, the number of cruise times can be obtained based on the vehicle speed, brake pedal opening and accelerator pedal opening at each sampling moment, that is, all sampling moments are traversed to confirm whether the vehicle is in a cruising state at each sampling moment, and then the number of sampling moments when the vehicle is in a cruising state is counted as the number of cruise times.

[0059] A first steering angle threshold and a second steering angle threshold of the steering wheel angle are set, and the first steering angle threshold is less than the second steering angle threshold, for example, the first steering angle threshold is 45 degrees, and the second steering angle threshold is 180 degrees. For any sampling moment, if the absolute value of the steering wheel angle at the sampling moment is less than or equal to the first steering angle threshold, it is confirmed that the steering wheel angle is within the third angle range, that is, a small turn, and the number of turns corresponding to the third angle range is increased by 1; if the absolute value of the steering wheel angle at the sampling moment is greater than the first steering angle threshold and less than or equal to the second steering angle threshold, it is confirmed that the steering wheel angle is within the second angle range, that is, a medium turn, and the number of turns corresponding to the second angle range is increased by 1; if the absolute value of the steering wheel angle at the sampling moment is greater than the second steering angle threshold, it is confirmed that the steering wheel angle is within the first angle range, that is, a large turn, and the number of turns corresponding to the first angle range is increased by 1. Therefore, the number of turns in the first angle range, the number of turns in the second angle range, and the number of turns in the third angle range can be obtained according to the steering wheel angle at each sampling moment.

[0060] Optionally, after calculating the number of vehicle operating conditions and the number of turns in the driving mileage, the method further includes calculating a vehicle operating condition duration ratio and a turn duration ratio;

[0061] The vehicle operating time proportion includes acceleration time proportion, coasting time proportion, braking time proportion, parking time proportion, following vehicle behavior time proportion, and constant cruise time proportion. The acceleration time proportion is obtained according to the acceleration times, the coasting time proportion is obtained according to the coasting times, the braking time proportion is obtained according to the braking times, the parking time proportion is obtained according to the parking times, the following vehicle behavior time proportion is obtained according to the following vehicle times, and the constant cruise time proportion is obtained according to the cruise times;

[0062] The turning time proportion includes the turning time proportion within the first angle range, the turning time proportion within the second angle range and the turning time proportion within the third angle range. According to the number of turns in the first angle range, the turning time proportion within the first angle range is obtained, according to the number of turns in the second angle range, the turning time proportion within the second angle range is obtained, and according to the number of turns in the third angle range, the turning time proportion within the third angle range is obtained.

[0063] In an embodiment of the present invention, the total number of sampling moments of vehicles is obtained, and the acceleration time ratio is calculated according to the ratio between the acceleration number and the total number; the coasting time ratio is calculated according to the ratio between the coasting number and the total number; the braking time ratio is calculated according to the ratio between the braking number and the total number; the parking time ratio is calculated according to the ratio between the parking number and the total number; the following behavior time ratio is calculated according to the ratio between the following number and the total number; and the constant cruising time ratio is calculated according to the ratio between the cruising number and the total number.

[0064] According to the ratio between the number of turns in the first angle range and the total number, the proportion of turning time within the first angle range is calculated; according to the ratio between the number of turns in the second angle range and the total number, the proportion of turning time within the second angle range is calculated; according to the ratio between the number of turns in the third angle range and the total number, the proportion of turning time within the third angle range is calculated.

[0065] Optionally, the vehicle data includes vehicle speed, and before obtaining the vehicle data of the vehicle at each sampling time in the N mileages, the following steps are included:

[0066] Obtaining the vehicle's onboard data at each sampling moment during the driving process, and obtaining the total onboard data of the vehicle within a preset time period;

[0067] According to the vehicle speed in the vehicle data at each sampling moment, the total mileage corresponding to the total vehicle data is obtained, and the total mileage is segmented according to preset distance intervals to obtain all segmented mileages;

[0068] For any segment mileage, detecting whether the segment mileage is less than the mileage corresponding to the preset distance interval, if it is detected that the segment mileage is less than the mileage corresponding to the preset distance interval, filtering out the segment mileage;

[0069] Traverse all segmented mileages and obtain the N driving mileages corresponding to the retained segmented mileages.

[0070] In the embodiment of the present invention, the preset time period may be one week, one month, two months, etc. The vehicle-mounted data at each sampling time during the vehicle driving process is obtained to obtain the vehicle-mounted total data of the vehicle within the preset time period. The vehicle speed in the vehicle-mounted data is integrated to obtain the total mileage corresponding to the vehicle-mounted total data, that is, the distance traveled by the vehicle in the preset time period. Then the total mileage is segmented according to a certain distance (preset distance interval) to obtain multiple segmented mileages. Correspondingly, the vehicle-mounted total data is also segmented into multiple segments of vehicle-mounted sub-data. It is detected whether each segmented mileage is less than the mileage corresponding to the preset distance interval. If it is detected that the segmented mileage is less than the mileage corresponding to the preset distance interval, the corresponding segmented mileage is filtered out to remove the vehicle-mounted data corresponding to the mileage with insufficient mileage, exclude invalid data, and speed up the subsequent calculation. Then the segmented mileage that meets the mileage corresponding to the preset distance interval is retained, so as to obtain N mileages of the vehicle, that is, the effective mileage.

[0071] Optionally, the acquiring of the vehicle-borne data at each sampling moment during the driving process to obtain the total vehicle-borne data of the vehicle within a preset time period includes:

[0072] For the vehicle data at any sampling time, detecting whether the vehicle data at the sampling time exceeds a preset vehicle data range, and if it is detected that the vehicle data exceeds the preset vehicle data range, deleting the vehicle data at the sampling time;

[0073] The vehicle-borne data at each sampling moment within the preset time period is traversed, and the vehicle-borne data at the retained sampling moments constitute the total vehicle-borne data of the vehicle within the preset time period.

[0074] In the embodiment of the present invention, taking the altitude, longitude and latitude, and vehicle speed in the vehicle-mounted data as an example, for the vehicle-mounted data at any sampling time, if the altitude in the vehicle-mounted data is greater than the altitude threshold, it means that the altitude is too high, indicating that the vehicle-mounted data at the sampling time is unreasonable data, and the reason may be noise in the sampling process. Therefore, the vehicle-mounted data at the sampling time is deleted. Similarly, if it is detected that the longitude and latitude in the vehicle-mounted data exceed the preset longitude and latitude range, it means that the longitude and latitude mutation is too large, or if it is detected that the vehicle speed in the vehicle-mounted data exceeds the preset vehicle speed threshold, it means that the vehicle speed is too high, indicating that the vehicle-mounted data at the sampling time is unreasonable data, and the vehicle-mounted data at the sampling time is also deleted. Therefore, by detecting whether the vehicle-mounted data at each sampling time exceeds the preset vehicle-mounted data range, the unreasonable vehicle-mounted data is deleted, so that the retained vehicle-mounted data at the sampling time constitutes the total vehicle-mounted data of the vehicle in the preset time period.

[0075] Step S202, obtaining a trained clustering model, inputting the driving characteristic parameters of all driving mileages into the trained clustering model for classification, and confirming the road type to which each driving mileage belongs.

[0076] Among them, the trained clustering model refers to a clustering model that has been trained and has strong clustering capabilities. When facing new data, it can accurately obtain clustering results. Road types include urban road types, general road types, expressway types, mountain road types, and bad road types. It is worth mentioning that the characteristics of urban road types are: roads within the urban area generally have more traffic lights, and vehicles driving on urban roads generally have more starts and stops, slower speeds, and more large-angle turns; the characteristics of general road types are: roads connecting cities are generally national highways, provincial highways, rural roads, urban expressways, etc., and vehicles driving on general roads generally have more straight-line driving, faster speeds, and slight starts and stops; the characteristics of expressway types are: on sections with speed limits above 80km / h, vehicles driving on expressways have fewer turns, and the average and maximum speeds are relatively high; the characteristics of mountain road types are: winding mountain roads, and vehicles driving on mountain roads generally have large height differences in the Global Positioning System (GPS) and more turns; the characteristics of bad road types are: unpaved roads and damaged roads that may appear in cities, rural areas or mountains, and vehicles driving on bad roads generally have very slow speeds, large vehicle acceleration amplitudes and small frequencies.

[0077] In the embodiment of the present invention, after the driving characteristic parameters of all driving mileages are obtained, the driving characteristic parameters of all driving mileages are input into a trained clustering model for clustering to obtain clustering results, and the road type to which each driving mileage belongs is confirmed according to the clustering results.

[0078] Optionally, the obtaining a trained clustering model includes:

[0079] For any road type, obtaining historical driving characteristic parameters under the road type, and using the road type as a label of the historical driving characteristic parameters;

[0080] The historical driving characteristic parameters are input into the clustering model for classification, and a corresponding classification result is obtained. The clustering model is trained according to the classification result and the label to obtain a trained clustering model.

[0081] In the embodiment of the present invention, a support vector machine can be sampled as a clustering model, and other neural networks can also be used as clustering models, which is not required. The training of the clustering model is specifically as follows: for any road type, the historical driving characteristic parameters of multiple mileages under the road type are obtained, and the road type is used as the label of the historical driving characteristic parameters of each mileage. The labels of all road types and all historical driving characteristic parameters are combined to form a feature matrix, and the elements in the feature matrix are shuffled, and then a certain proportion of elements in the shuffled feature matrix are randomly selected as the training set, and the remaining elements are used as the test set.

[0082] The historical driving characteristic parameters in the training set are input into the clustering model for classification, and the corresponding classification results are obtained. According to the difference between the classification results and the labels of each historical driving characteristic parameter in the training set, the model parameters of the clustering model are adjusted until the difference meets the preset difference conditions, and a trained clustering model is obtained. After the clustering model is trained using the training set, the trained clustering model can be verified using the test set to detect the training accuracy of the clustering model.

[0083] It is worth noting that if the amount of data in the training set is large, principal component analysis can be performed before training the clustering model, and the data in the training set can be reduced in dimension according to the principle of retaining no less than 90% of the information to ensure normal training of the model.

[0084] Step S203, for any driving mileage, detecting whether the driving characteristic parameters of the driving mileage meet the road type correction condition, if it is detected that the driving characteristic parameters of the driving mileage meet the road type correction condition, correcting the road type to which the driving mileage belongs to obtain a final road type.

[0085] Among them, the road type correction condition is used as a benchmark for detecting whether the road type is accurate. The final road type refers to the adjusted road type. Therefore, in the embodiment of the present invention, by detecting whether the driving characteristic parameters of each mileage meet the road type correction condition, when the driving characteristic parameters of the mileage meet the road type correction condition, the road type to which the mileage belongs is adjusted, and the adjusted road type is used as the final road type. When the driving characteristic parameters of the mileage do not meet the road type correction condition, the road type to which the mileage belongs is not adjusted, that is, the road type confirmed according to the clustering model is the final road type, and the classification result of the clustering model is optimized and adjusted, so that the road type to which each mileage belongs is detected more accurately.

[0086] Optionally, the detecting whether the driving characteristic parameter of the driving mileage satisfies a road type correction condition includes:

[0087] Detecting whether the average vehicle speed and / or the maximum vehicle speed in the driving characteristic parameters are greater than a preset vehicle speed threshold, and if it is detected that the average vehicle speed and / or the maximum vehicle speed in the driving characteristic parameters are greater than the preset vehicle speed threshold, confirming that the driving characteristic parameters of the mileage meet the road type correction condition;

[0088] The correcting the road type to which the driving mileage belongs to obtain a final road type includes:

[0089] The road type to which the mileage belongs is modified into a highway type.

[0090] In the embodiment of the present invention, for any driving characteristic parameter of a driving mileage, if the average vehicle speed and / or the maximum vehicle speed in the driving characteristic parameter is greater than a preset vehicle speed threshold, it means that the driving characteristic parameter of the driving mileage satisfies the road type correction condition, and then the road type to which the driving mileage belongs is modified to a highway type. For example, assuming that a driving mileage is determined to belong to a general road type through a clustering model, but the average vehicle speed and / or the maximum vehicle speed in the driving characteristic parameter of the driving mileage are greater than the preset vehicle speed threshold, the general road type of the driving mileage is adjusted to a highway type.

[0091] Optionally, the detecting whether the driving characteristic parameter of the driving mileage satisfies a road type correction condition includes:

[0092] Detecting whether the number of vehicle starts in the driving characteristic parameter is greater than a preset number threshold, and if it is detected that the number of vehicle starts in the driving characteristic parameter is greater than the preset number threshold, confirming that the driving characteristic parameter of the mileage meets the road type correction condition;

[0093] The correcting the road type to which the driving mileage belongs to obtain a final road type includes:

[0094] The road type to which the mileage belongs is modified into an urban road type.

[0095] In the embodiment of the present invention, for any driving characteristic parameter of driving mileage, if the number of vehicle starts in the driving characteristic parameter is greater than a preset number threshold, the road type to which the driving mileage belongs is modified to an urban road type.

[0096] Optionally, the detecting whether the driving characteristic parameter of the driving mileage satisfies a road type correction condition includes:

[0097] Detecting whether the altitude difference in the driving characteristic parameter is greater than a preset altitude difference threshold, and if it is detected that the altitude difference in the driving characteristic parameter is greater than the preset altitude difference threshold, confirming that the driving characteristic parameter of the mileage meets the road type correction condition;

[0098] The correcting the road type to which the driving mileage belongs to obtain a final road type includes:

[0099] The road type to which the mileage belongs is modified into a mountain road type.

[0100] In the embodiment of the present invention, for any driving characteristic parameter of driving mileage, if the altitude difference in the driving characteristic parameter is greater than a preset altitude difference threshold, the road type to which the driving mileage belongs is modified to a mountain road type.

[0101] Optionally, the detecting whether the driving characteristic parameter of the driving mileage satisfies a road type correction condition includes:

[0102] Detecting whether the road type to which the driving mileage belongs is an urban road type. If it is detected that the road type to which the driving mileage belongs is an urban road type, detecting whether the proportion of the turning time within the first angle range and the proportion of the turning time within the second angle range in the driving characteristic parameters are both equal to a preset proportion, and whether the number of vehicle starts is equal to a preset number;

[0103] If it is detected that the proportion of the turning time within the first angle range and the proportion of the turning time within the second angle range in the driving characteristic parameters are both equal to the preset proportions, and the number of vehicle starts is equal to the preset number, it is confirmed that the driving characteristic parameters of the mileage meet the road type correction condition;

[0104] The correcting the road type to which the driving mileage belongs to obtain a final road type includes:

[0105] The urban road type to which the mileage belongs is changed to a general road type.

[0106] In an embodiment of the present invention, for mileage whose road type is an urban road type, if it is detected whether the proportion of turning time within the first angle range and the proportion of turning time within the second angle range in the driving characteristic parameters of the mileage are both equal to the preset proportions, and whether the number of vehicle starts is equal to the preset number, the urban road type to which the mileage belongs is modified to a general road type. Optionally, the preset proportion is 0 and the preset number is 0. Correspondingly, when the large-angle turning, medium-angle turning and the number of vehicle starts in the driving characteristic parameters of any mileage under the urban road type are all 0, the urban road type is modified to a general road type.

[0107] Step S204, based on the final road type of all mileages, the number of each road type is counted, and based on all mileages and the number of each road type, the mileage ratio of the road type corresponding to the vehicle is calculated.

[0108] Among them, the mileage ratio of the road type is used to characterize the road driving situation of the vehicle. Therefore, in the embodiment of the present invention, in the final road type to which all mileages belong, the number involved in each road type is counted, and for any road type, all mileages under the road type are added to obtain the corresponding total mileage. Similarly, the total mileage of all road types is obtained, and a mileage matrix of the road type is formed according to the total mileage of each road type. The mileage matrix is ​​divided by the total mileage of the vehicle in the preset time period to obtain the mileage ratio of each road type of the vehicle in the preset time period.

[0109] The embodiment of the present invention obtains the vehicle-borne data of the vehicle at each sampling time in N mileages, and for any mileage, calculates the corresponding driving characteristic parameter according to the vehicle-borne data corresponding to the mileage, where N is an integer greater than 1; obtains a trained clustering model, inputs the driving characteristic parameters of all mileages into the trained clustering model for classification, and confirms the road type to which each mileage belongs; for any mileage, detects whether the driving characteristic parameters of the mileage meet the road type correction condition, and if it is detected that the driving characteristic parameters of the mileage meet the road type correction condition, corrects the road type to which the mileage belongs to obtain a final road type; according to the final road types of all mileages, obtains the number of each road type by statistics, and calculates the mileage ratio of the road type corresponding to the vehicle according to all mileages and the number of each road type. Among them, the driving characteristic parameters are analyzed through the on-board data of the vehicle during driving, which are used to characterize the driving status of the vehicle on the road. Then, the driving characteristic parameters of multiple mileages are clustered using the clustering model trained by big data and machine learning. The road type to which each mileage belongs can be confirmed efficiently and accurately, and the clustering results of the clustering model can be corrected based on the road type correction conditions to optimize the road type to which each mileage belongs, so as to avoid clustering errors caused by low training accuracy of the clustering model, thereby making the detection of road types more accurate and the statistical mileage proportion of each road type more objective and rigorous.

[0110] Corresponding to the mileage statistics method in the above embodiment, Figure 3 The structural block diagram of a mileage counting device based on road type provided by the second embodiment of the present invention is shown. The mileage counting device is applied to Figure 1 The client in the embodiment of the present invention is connected to the server by a computer device corresponding to the client to obtain the corresponding background data. For example, the vehicle data and the trained clustering model at each sampling time are used to subsequently count the mileage ratio of the road type corresponding to the vehicle. For the convenience of explanation, only the parts related to the embodiments of the present invention are shown.

[0111] See also Figure 3, the mileage counting device comprises:

[0112] The data processing module 31 is used to obtain the vehicle data at each sampling time in N driving mileages, and for any driving mileage, calculate the corresponding driving characteristic parameter according to the vehicle data corresponding to the driving mileage, where N is an integer greater than 1;

[0113] The data classification module 32 is used to obtain a trained clustering model, input the driving characteristic parameters of all driving mileages into the trained clustering model for classification, and confirm the road type to which each driving mileage belongs;

[0114] The type correction module 33 is used to detect, for any driving mileage, whether the driving characteristic parameters of the driving mileage meet the road type correction condition. If it is detected that the driving characteristic parameters of the driving mileage meet the road type correction condition, the road type to which the driving mileage belongs is corrected to obtain a final road type;

[0115] The proportion statistics module 34 is used to obtain the number of each road type according to the final road type of all mileages, and calculate the mileage proportion of the road type corresponding to the vehicle according to all mileages and the number of each road type.

[0116] Optionally, the vehicle-mounted data includes vehicle speed, steering wheel angle, altitude, brake pedal opening, and accelerator pedal opening, and the data processing module 31 includes:

[0117] A speed analysis unit, used to calculate the maximum speed and the average speed of the vehicle in the mileage according to the vehicle speed at each sampling moment;

[0118] A steering analysis unit, configured to calculate a proportion of steering time within a first angle range and a proportion of steering time within a second angle range in the mileage according to a steering wheel angle at each sampling moment;

[0119] An altitude analysis unit, configured to calculate the altitude difference in the mileage according to the altitude at each sampling moment;

[0120] a start statistics unit, for obtaining the number of vehicle starts in the driving mileage according to the brake pedal opening and the accelerator pedal opening at each sampling moment;

[0121] The feature construction unit is used to form driving feature parameters by combining the maximum vehicle speed, the average vehicle speed, the proportion of turning time within the first angle range, the proportion of turning time within the second angle range, the altitude difference and the number of vehicle starts.

[0122] Optionally, the type correction module 33 includes:

[0123] A first detection unit is used to detect whether the average vehicle speed and / or the maximum vehicle speed in the driving characteristic parameters are greater than a preset vehicle speed threshold, and if it is detected that the average vehicle speed and / or the maximum vehicle speed in the driving characteristic parameters are greater than the preset vehicle speed threshold, it is confirmed that the driving characteristic parameters of the mileage meet the road type correction condition;

[0124] The first correction unit is used to modify the road type to which the driving mileage belongs to into a highway type.

[0125] Optionally, the type correction module 33 includes:

[0126] A second detection unit is used to detect whether the number of vehicle starts in the driving characteristic parameter is greater than a preset number threshold, and if it is detected that the number of vehicle starts in the driving characteristic parameter is greater than the preset number threshold, it is confirmed that the driving characteristic parameter of the mileage meets the road type correction condition;

[0127] The second correction unit is used to modify the road type to which the driving mileage belongs to into an urban road type.

[0128] Optionally, the type correction module 33 includes:

[0129] a third detection unit, configured to detect whether the altitude difference in the driving characteristic parameter is greater than a preset altitude difference threshold, and if it is detected that the altitude difference in the driving characteristic parameter is greater than the preset altitude difference threshold, confirming that the driving characteristic parameter of the mileage meets the road type correction condition;

[0130] The third correction unit is used to modify the road type to which the driving mileage belongs to into a mountain road type.

[0131] Optionally, the type correction module 33 includes:

[0132] a fourth detection unit, configured to detect whether the road type to which the driving mileage belongs is an urban road type, and if it is detected that the road type to which the driving mileage belongs is an urban road type, to detect whether the proportion of the turning time within the first angle range and the proportion of the turning time within the second angle range in the driving characteristic parameters are both equal to a preset proportion, and whether the number of vehicle starts is equal to a preset number;

[0133] a detection result determination unit, configured to confirm that the driving characteristic parameter of the mileage meets the road type correction condition if it is detected that the proportion of the turning time within the first angle range and the proportion of the turning time within the second angle range in the driving characteristic parameter are both equal to the preset proportions, and the number of vehicle starts is equal to the preset number;

[0134] The fourth correction unit is used to modify the urban road type to which the mileage belongs to into a general road type.

[0135] Optionally, the data processing module 31 includes: a frequency analysis unit, for calculating the number of vehicle operating conditions and the number of turns in the mileage according to the vehicle speed, steering wheel angle, brake pedal opening and accelerator pedal opening at each sampling moment in the mileage;

[0136] a first statistical unit, for the vehicle operating condition quantity including acceleration times, coasting times, braking times, stopping times, following times and cruising times, and obtaining the acceleration times, coasting times, braking times, stopping times, following times and cruising times according to the vehicle speed, acceleration and deceleration characteristics, the brake pedal opening and the accelerator pedal opening;

[0137] A second statistical unit is used for the number of turns, including the number of turns in a first angle range, the number of turns in a second angle range, and the number of turns in a third angle range. According to the steering wheel angle, the number of turns in the first angle range, the number of turns in the second angle range, and the number of turns in the third angle range are obtained, wherein the second angle range is larger than the third angle range.

[0138] Optionally, the data processing module 31 includes:

[0139] A duration analysis unit is used to calculate the number of vehicle operating conditions and the number of turns in the driving mileage, and further calculate the vehicle operating condition duration ratio and the turn duration ratio;

[0140] The third statistical unit is used for the vehicle working condition duration proportion, including acceleration duration proportion, coasting duration proportion, braking duration proportion, parking duration proportion, following vehicle behavior duration proportion, and constant cruise duration proportion. The acceleration duration proportion is obtained according to the acceleration times, the coasting duration proportion is obtained according to the coasting times, the braking duration proportion is obtained according to the braking times, the parking duration proportion is obtained according to the parking times, the following vehicle behavior duration proportion is obtained according to the following vehicle times, and the constant cruise duration proportion is obtained according to the cruise times;

[0141] The fourth statistical unit is used for the turning time proportion including the turning time proportion within the first angle range, the turning time proportion within the second angle range and the turning time proportion within the third angle range. According to the number of turns in the first angle range, the turning time proportion within the first angle range is obtained; according to the number of turns in the second angle range, the turning time proportion within the second angle range is obtained; according to the number of turns in the third angle range, the turning time proportion within the third angle range is obtained.

[0142] Optionally, the vehicle-mounted data includes vehicle speed, and the mileage statistics device includes:

[0143] A data statistics module is used to obtain the vehicle-borne data of the vehicle at each sampling moment during the driving process before obtaining the vehicle-borne data at each sampling moment in N driving mileages, and obtain the total vehicle-borne data of the vehicle in a preset time period;

[0144] A data segmentation module, used to obtain the total mileage corresponding to the total vehicle data according to the vehicle speed in the vehicle data at each sampling time, and segment the total mileage according to a preset distance interval to obtain all segmented mileages;

[0145] A data screening module is used to detect, for any segment mileage, whether the segment mileage is less than the mileage corresponding to the preset distance interval, and if it is detected that the segment mileage is less than the mileage corresponding to the preset distance interval, filter out the segment mileage;

[0146] The data retention module is used to traverse all segmented mileages and obtain N driving mileages corresponding to the retained segmented mileages.

[0147] Optionally, the data statistics module includes:

[0148] A data deleting unit, for detecting, for the vehicle data at any sampling time, whether the vehicle data at the sampling time exceeds a preset vehicle data range, and deleting the vehicle data at the sampling time if it is detected that the vehicle data exceeds the preset vehicle data range;

[0149] The data traversal unit is used to traverse the vehicle-mounted data at each sampling moment within the preset time period, and use the retained vehicle-mounted data at the sampling moment to form the total vehicle-mounted data of the vehicle within the preset time period.

[0150] Optionally, the data classification module 32 includes:

[0151] A historical data acquisition unit, used for acquiring, for any road type, historical driving characteristic parameters of the road type, and using the road type as a label of the historical driving characteristic parameters;

[0152] The model training unit is used to input the historical driving characteristic parameters into the clustering model for classification, obtain a corresponding classification result, and train the clustering model according to the classification result and the label to obtain a trained clustering model.

[0153] It should be noted that the information interaction, execution process and other contents between the above-mentioned modules and units are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0154] Figure 4 This is a schematic diagram of the structure of a computer device provided in Embodiment 3 of the present invention. Figure 4 As shown, the computer device of this embodiment includes: at least one processor ( Figure 4 Only one is shown in the figure), a memory, and a computer program stored in the memory and executable on at least one processor, wherein when the processor executes the computer program, the steps in any of the above-mentioned mileage statistics method embodiments are implemented.

[0155] The computer device may include, but is not limited to, a processor and a memory. Those skilled in the art will appreciate that Figure 4 This is merely an example of a computer device and does not constitute a limitation on the computer device. The computer device may include more or fewer components than those shown in the figure, or a combination of certain components, or different components. For example, it may also include a network interface, a display screen, and an input device.

[0156] The processor may be a CPU, or other general-purpose processors, a digital signal processor (DSP), an application-specific integrated circuit (ASIC), a field-programmable gate array (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0157] The memory includes a readable storage medium, an internal memory, etc., wherein the internal memory may be the memory of a computer device, and the internal memory provides an environment for the operation of an operating system and computer-readable instructions in the readable storage medium. The readable storage medium may be a hard disk of a computer device, and in other embodiments may also be an external storage device of a computer device, for example, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), etc. equipped on a computer device. Further, the memory may also include both an internal storage unit of a computer device and an external storage device. The memory is used to store an operating system, an application program, a boot loader (BootLoader), data, and other programs, such as program codes of computer programs, etc. The memory may also be used to temporarily store data that has been output or is to be output.

[0158] The technicians in the relevant field can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example. In practical applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device is divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned device can refer to the corresponding process in the above-mentioned method embodiment, which will not be repeated here. If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such understanding, the present invention implements all or part of the processes in the above-mentioned embodiment method, which can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned method embodiment can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may at least include: any entity or device capable of carrying computer program code, recording medium, computer memory, read-only memory (ROM), random access memory (RAM), electric carrier signal, telecommunication signal and software distribution medium. For example, a USB flash drive, a mobile hard disk, a magnetic disk or an optical disk. In some jurisdictions, according to legislation and patent practice, computer-readable media cannot be electric carrier signals and telecommunication signals.

[0159] The present invention implements all or part of the processes in the above-mentioned embodiment method, and can also be completed by a computer program product. When the computer program product runs on a computer device, the computer device can implement the steps in the above-mentioned method embodiment when executing.

[0160] In the above embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0161] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0162] In the embodiments provided by the present invention, it should be understood that the disclosed devices / computer equipment and methods can be implemented in other ways. For example, the device / computer equipment embodiments described above are only schematic, for example, the division of modules or units is only a logical function division, and there may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0163] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0164] The above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A mileage statistics method based on road type, characterized in that: The mileage counting method comprises: Obtaining the vehicle's onboard data at each sampling time in N mileages, and for any mileage, calculating the corresponding driving characteristic parameter according to the onboard data corresponding to the mileage, where N is an integer greater than 1; Obtaining a trained clustering model, inputting the driving characteristic parameters of all driving mileages into the trained clustering model for classification, and confirming the road type to which each driving mileage belongs; For any driving mileage, detecting whether the driving characteristic parameters of the driving mileage meet the road type correction condition; if it is detected that the driving characteristic parameters of the driving mileage meet the road type correction condition, correcting the road type to which the driving mileage belongs to obtain a final road type; According to the final road type of all mileages, the number of each road type is counted, and according to all mileages and the number of each road type, the mileage ratio of the road type corresponding to the vehicle is calculated.

2. The mileage counting method according to claim 1, characterized in that: The vehicle-mounted data includes vehicle speed, steering wheel angle, altitude, brake pedal opening, and accelerator pedal opening. The corresponding driving characteristic parameters are calculated based on the vehicle-mounted data corresponding to the mileage, including: Calculate the maximum speed and average speed of the vehicle during the mileage according to the vehicle speed at each sampling moment; According to the steering wheel angle at each sampling moment, a proportion of the steering time within a first angle range and a proportion of the steering time within a second angle range in the mileage are calculated, wherein the first angle range is greater than the second angle range; Calculating the altitude difference in the mileage according to the altitude at each sampling moment; According to the brake pedal opening and the accelerator pedal opening at each sampling moment, the number of vehicle starts in the driving mileage is obtained by counting; The maximum vehicle speed, the average vehicle speed, the proportion of the turning time within the first angle range, the proportion of the turning time within the second angle range, the altitude difference and the number of vehicle starts constitute driving characteristic parameters.

3. The mileage counting method according to claim 2, characterized in that: The detecting whether the driving characteristic parameter of the driving mileage meets the road type correction condition includes: Detecting whether the average vehicle speed and / or the maximum vehicle speed in the driving characteristic parameters are greater than a preset vehicle speed threshold, and if it is detected that the average vehicle speed and / or the maximum vehicle speed in the driving characteristic parameters are greater than the preset vehicle speed threshold, confirming that the driving characteristic parameters of the mileage meet the road type correction condition; The correcting the road type to which the driving mileage belongs to obtain a final road type includes: The road type to which the mileage belongs is modified into a highway type.

4. The mileage counting method according to claim 2, characterized in that: The detecting whether the driving characteristic parameter of the driving mileage meets the road type correction condition includes: Detecting whether the number of vehicle starts in the driving characteristic parameter is greater than a preset number threshold, and if it is detected that the number of vehicle starts in the driving characteristic parameter is greater than the preset number threshold, confirming that the driving characteristic parameter of the mileage meets the road type correction condition; The correcting the road type to which the driving mileage belongs to obtain a final road type includes: The road type to which the mileage belongs is modified into an urban road type.

5. The mileage counting method according to claim 2, characterized in that: The detecting whether the driving characteristic parameter of the driving mileage meets the road type correction condition includes: Detecting whether the altitude difference in the driving characteristic parameter is greater than a preset altitude difference threshold, and if it is detected that the altitude difference in the driving characteristic parameter is greater than the preset altitude difference threshold, confirming that the driving characteristic parameter of the mileage meets the road type correction condition; The correcting the road type to which the driving mileage belongs to obtain a final road type includes: The road type to which the mileage belongs is modified into a mountain road type.

6. The mileage counting method according to claim 2, characterized in that: The detecting whether the driving characteristic parameter of the driving mileage meets the road type correction condition includes: Detecting whether the road type to which the driving mileage belongs is an urban road type. If it is detected that the road type to which the driving mileage belongs is an urban road type, detecting whether the proportion of the turning time within the first angle range and the proportion of the turning time within the second angle range in the driving characteristic parameters are both equal to a preset proportion, and whether the number of vehicle starts is equal to a preset number; If it is detected that the proportion of the turning time within the first angle range and the proportion of the turning time within the second angle range in the driving characteristic parameters are both equal to the preset proportions, and the number of vehicle starts is equal to the preset number, it is confirmed that the driving characteristic parameters of the mileage meet the road type correction condition; The correcting the road type to which the driving mileage belongs to obtain a final road type includes: The urban road type to which the mileage belongs is changed to a general road type.

7. The mileage counting method according to claim 2, characterized in that: The step of calculating corresponding driving characteristic parameters according to the vehicle-mounted data corresponding to the driving mileage further includes: Calculating the number of vehicle operating conditions and the number of turns in the mileage according to the vehicle speed, steering wheel angle, brake pedal opening and accelerator pedal opening at each sampling moment in the mileage; The number of vehicle operating conditions includes acceleration times, coasting times, braking times, stopping times, following times and cruising times, and the acceleration times, coasting times, braking times, stopping times, following times and cruising times are obtained according to the vehicle speed, acceleration and deceleration characteristics, the brake pedal opening and the accelerator pedal opening; The number of turns includes the number of turns in a first angle range, the number of turns in a second angle range, and the number of turns in a third angle range. According to the steering wheel angle, the number of turns in the first angle range, the number of turns in the second angle range, and the number of turns in the third angle range are obtained, wherein the second angle range is larger than the third angle range.

8. The mileage counting method according to claim 7, characterized in that: After the calculation of the number of vehicle operating conditions and the number of turns in the driving mileage, the calculation also includes obtaining the vehicle operating condition duration ratio and the turn duration ratio; The vehicle operating time proportion includes acceleration time proportion, coasting time proportion, braking time proportion, parking time proportion, following vehicle behavior time proportion, and constant cruise time proportion. The acceleration time proportion is obtained according to the acceleration times, the coasting time proportion is obtained according to the coasting times, the braking time proportion is obtained according to the braking times, the parking time proportion is obtained according to the parking times, the following vehicle behavior time proportion is obtained according to the following vehicle times, and the constant cruise time proportion is obtained according to the cruise times; The turning time proportion includes the turning time proportion within the first angle range, the turning time proportion within the second angle range and the turning time proportion within the third angle range. According to the number of turns in the first angle range, the turning time proportion within the first angle range is obtained, according to the number of turns in the second angle range, the turning time proportion within the second angle range is obtained, and according to the number of turns in the third angle range, the turning time proportion within the third angle range is obtained.

9. The mileage counting method according to claim 1, characterized in that: The vehicle data includes vehicle speed, and before obtaining the vehicle data at each sampling time in the N mileages, the following steps are included: Obtaining the vehicle's onboard data at each sampling moment during the driving process, and obtaining the total onboard data of the vehicle within a preset time period; According to the vehicle speed in the vehicle data at each sampling moment, the total mileage corresponding to the total vehicle data is obtained, and the total mileage is segmented according to preset distance intervals to obtain all segmented mileages; For any segment mileage, detecting whether the segment mileage is less than the mileage corresponding to the preset distance interval, if it is detected that the segment mileage is less than the mileage corresponding to the preset distance interval, filtering out the segment mileage; Traverse all segmented mileages and obtain the N driving mileages corresponding to the retained segmented mileages.

10. The mileage counting method according to claim 9, characterized in that: The step of acquiring the vehicle-borne data at each sampling moment during the driving process to obtain the total vehicle-borne data of the vehicle within a preset time period includes: For the vehicle data at any sampling time, detecting whether the vehicle data at the sampling time exceeds a preset vehicle data range, and if it is detected that the vehicle data exceeds the preset vehicle data range, deleting the vehicle data at the sampling time; The vehicle-borne data at each sampling moment within the preset time period is traversed, and the vehicle-borne data at the retained sampling moments constitute the total vehicle-borne data of the vehicle within the preset time period.

11. The mileage counting method according to any one of claims 1 to 10, characterized in that: The step of obtaining the trained clustering model comprises: For any road type, obtaining historical driving characteristic parameters under the road type, and using the road type as a label of the historical driving characteristic parameters; The historical driving characteristic parameters are input into the clustering model for classification, and a corresponding classification result is obtained. The clustering model is trained according to the classification result and the label to obtain a trained clustering model.

12. A device for counting mileage based on road type, characterized in that: The mileage counting device comprises: A data processing module is used to obtain the vehicle data at each sampling time in N driving mileages, and for any driving mileage, calculate the corresponding driving characteristic parameter according to the vehicle data corresponding to the driving mileage, where N is an integer greater than 1; A data classification module is used to obtain a trained clustering model, input the driving characteristic parameters of all driving mileages into the trained clustering model for classification, and confirm the road type to which each driving mileage belongs; A type correction module is used to detect, for any driving mileage, whether the driving characteristic parameters of the driving mileage meet the road type correction condition, and if it is detected that the driving characteristic parameters of the driving mileage meet the road type correction condition, correct the road type to which the driving mileage belongs to obtain a final road type; The proportion statistics module is used to obtain the number of each road type according to the final road type of all mileage, and calculate the mileage proportion of the road type corresponding to the vehicle according to all mileage and the number of each road type.

13. A computer device, characterized in that: The computer device includes a processor, a memory, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the mileage statistics method according to any one of claims 1 to 11 is implemented.

14. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the mileage statistics method according to any one of claims 1 to 11 is implemented.

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