CN112801555B - Vehicle dynamic property comprehensive evaluation method based on Internet of vehicles big data - Google Patents
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Abstract
The invention provides a vehicle dynamic comprehensive evaluation method based on Internet of vehicles big data, which comprises a data acquisition stage, a data processing stage, a model training stage and a timing prediction stage; the data acquisition stage comprises: step 1-1) data acquisition to obtain original message data, step 1-2) data analysis to obtain original data, and step 1-3) data storage; the data processing stage comprises: step 2-1) data cleaning, step 2-2) feature construction, and step 2-3) feature extraction; the model training phase comprises: step 3-1) model construction, step 3-2) model training and step 3-3) model result display; the timing prediction stage performs prediction based on the dynamic anomaly detection model generated in the model training stage. The invention has the advantages that: 1) the dynamic abnormal condition of each vehicle can be regularly monitored every day; 2) the defects that the traditional detection method is time-consuming, labor-consuming and not universal are overcome.
Description
Technical Field
The invention relates to a vehicle dynamic property comprehensive evaluation method based on Internet of vehicles big data, and belongs to the field of big data processing analysis and vehicle dynamic property evaluation.
Background
The period of the high-speed growth of the vehicle market in China has passed, the vehicle industry is about to carry out an industrial revolution of an era of quality improvement, efficiency enhancement and big data intelligence, people are also researching various engines, and the dynamic property of the vehicle is an important index for evaluating the performance of the engine; the traditional vehicle dynamic analysis generally adopts a test detection method, tests are carried out on the vehicle dynamic under various scenes, the test result meeting the standard is real and effective, but the traditional vehicle dynamic analysis has the defects of long period and high cost, and is only specific to a single vehicle or a single brand of vehicle and cannot represent the long-distance dynamic of the vehicle; therefore, sampling the vehicle dynamics for periodic testing also becomes a time-consuming and labor-intensive and unavoidable resource overhead.
In recent years, with the rapid development of scientific technology and the appearance of big data, a new change of the vehicle industry is also caused, the generation of big data of the internet of vehicles also provides rich resource environment for people, the data acquisition and data storage industry chain of the internet of vehicles is continuously perfected, but the corresponding vehicle dynamic analysis still cannot meet the current requirements.
The traditional vehicle dynamic property comprehensive evaluation method needs to regularly sample and detect a large number of outgoing vehicles to ensure the accuracy and universality of results, not only consumes time and labor, but also cannot monitor the abnormal situation of the dynamic property of each vehicle, has numerous dynamic property influence factors, is lack of a scientific vehicle dynamic property evaluation and analysis method at present, provides massive resource data by the current big data technology, does not have a corresponding analysis method, and cannot solve the problem of vehicle dynamic property comprehensive evaluation.
Disclosure of Invention
The invention provides a vehicle dynamic property comprehensive evaluation method based on Internet of vehicles big data, and aims to solve the problem that whether the dynamic property of each vehicle is abnormal cannot be monitored by an existing vehicle dynamic property detection method.
The technical solution of the invention is as follows: a vehicle dynamic comprehensive evaluation method based on Internet of vehicles big data comprises a data acquisition stage, a data processing stage, a model training stage and a timing prediction stage; the data acquisition phase comprises: step 1-1) data acquisition to obtain original message data, step 1-2) data analysis to obtain original data, and step 1-3) data storage; the data processing stage comprises: step 2-1) data cleaning, step 2-2) feature construction to form a training set, and step 2-3) feature extraction to form a feature data set; the model training phase comprises: step 3-1) model construction, step 3-2) model training and step 3-3) model result display; the timing prediction phase comprises: step 4-1) model prediction, and step 4-2) prediction data display; the model training stage is the premise of the timing prediction stage, and the timing prediction stage predicts based on a dynamic abnormity detection model generated in the model training stage.
Further, the acquiring of the original message data by the data acquisition specifically includes: collecting original message data through a vehicle terminal; the analyzing the data to obtain the original data specifically comprises: performing field analysis on original message data obtained by data acquisition to generate readable information to obtain original data; the data storage specifically comprises: and storing the original data obtained by data analysis to a distributed file system.
Further, the data cleansing specifically includes: unreasonable data and null data in original data obtained by data analysis are removed; the removing of the unreasonable data specifically comprises the following processes:
1) the method comprises the steps of carrying out interruption marking on the continuity of original data obtained by data analysis, marking two continuous pieces of original data with the time interval larger than 60s aiming at two continuous pieces of original data of each vehicle, regarding the time interval exceeding 60s as interruption, marking a point of time interruption, and screening a continuous operation interval;
2) after the original data are subjected to interrupt marking, the original data of the vehicles with the number of the data in the continuous operation interval being less than 100 are removed; the continuous operation interval data is data between two time interruption points;
3) removing abnormal data: the abnormal data is one or two of original data with vehicle speed more than or equal to 200 km/h and negative engine speed;
4) and according to the data acquisition time, converting the time into days, wherein the second-level original data is converted into day-level original data.
Further, the feature construction specifically includes:
1) carrying out time aggregation on the original data after data cleaning, counting the original data amount of each vehicle per day, and filtering out original data with the data amount of less than 100 vehicles per day;
2) filtering out original data of opening of the torque limit identifier; the limited torque mark is a flag bit field obtained after data analysis;
3) selecting related variables and characteristic variables capable of describing the engine dynamics to form a training set; relevant and characteristic variables that can describe engine dynamics include engine net output torque and percent engine friction torque.
Further, the feature extraction specifically includes: after the features are constructed to form a training set, feature extraction is required to be carried out on the training set to form a feature data set, the feature extraction on the training set comprises feature extraction aiming at the net output torque of an engine and feature extraction aiming at the percentage of the friction torque of the engine, the net output torque of the engine is defined as a positive index, and the percentage of the friction torque of the engine is defined as a negative index; the positive and negative indicators were converted to comparable scores.
Further, the feature extraction for the net output torque of the engine specifically comprises the following steps:
1) screening training set data with the engine rotating speed being more than 600 r/min and the net output torque of the engine being more than 0 to ensure that the working characteristics of the engine are met;
2) counting the maximum value, the minimum value, the average value and the standard deviation of the net output torque of the engine of each vehicle on the day, and recording;
3) and calculating the variation coefficient of the net output torque of the engine of each vehicle, namely dividing the average value of the net output torque of the engine of each vehicle by the standard deviation to obtain the variation coefficient of the forward index of each vehicle, wherein the larger the variation coefficient is, the stronger the dynamic property is.
Further, the feature extraction for the percentage of the friction torque of the engine specifically includes the following steps:
1) screening training set data with the engine rotating speed being more than 600 r/min and the net output torque of the engine being more than 0 to ensure that the working characteristics of the engine are met;
2) using quadratic curves
Fitting the relation between the percentage of the engine friction torque and the engine speed of each vehicle as an engine characteristic curve; wherein,Xis the rotational speed of the engine and,Yfor the percentage of the engine friction torque, a is obtained by curve fitting all the engine speeds and the percentage of the engine friction torque of each vehicle in the training set taken out of the dayB and c;
3) using the quadratic curve obtained by fitting, calculating the percentage of the engine friction torque in a plurality of engine speed intervals respectively, and obtaining the median of each engine speed interval as the quadratic curveXValue of the parameter, obtainingYObtaining the percentage of the friction torque of the engine; the engine speed range comprises 950-1050, 1050-1150, 1150-1250, 1250-1350, 1350-1450, 1450-1550, 1550-1650, 1650-1750, 1750-1850, 1850-1950 and 1950-2050, and the unit of the engine speed is r/min;
4) and respectively calculating the number of data points in each engine speed interval, calculating the ratio of the number of the data points in each engine speed interval to the number of all the data points in all the engine speed intervals, and calculating the ratio of the number of the data points in each engine speed interval to the number of all the data points in all the engine speed intervals to be used as a basis for weighting the percentage of the friction torque of the engines in a plurality of engine speed intervals.
Further, the method for converting the positive index and the negative index into comparable scores specifically comprises calculating a Z-Score standard Score of the positive index and a Z-Score standard Score of the negative index respectively;
and respectively adopting different methods to calculate respective Z-Score standard scores according to the positive indexes and the negative indexes:
1) directly calculating the Z-Score standard Score of the variation coefficient of the forward index as the Z-Score standard Score of the forward index;
2) for negative direction index, firstly calculating a quadratic curve
And then, calibrating the Z-Score of the percentage of the engine friction torque corresponding to the median in each engine speed interval according to the previously calculated point number ratio of the number of data points in each engine speed interval to the number of all data points in all engine speed intervals, and calibrating the percentage of the engine friction torque corresponding to the median in each engine speed intervalAnd weighting the standard Score to obtain the Z-Score standard Score of the negative indicator.
Further, the model building comprises: aiming at a feature data set formed by feature extraction, establishing a dynamic anomaly detection model for the feature data set by using an isolated forest anomaly detection algorithm;
the model training comprises: and putting the characteristic data set into a dynamic abnormity detection model for training and learning.
Further, the model result display visually displays the result of model training; the model prediction is to place the characteristic data set into a trained dynamic abnormity detection model for calculation, and calculate the prediction result of the vehicle dynamic; and the prediction data display visually displays the prediction result of the model.
The invention has the beneficial effects that:
1) the invention combines big data and machine learning technology to complete the comprehensive evaluation of the dynamic property of the vehicle, and can regularly monitor the abnormal situation of the dynamic property of each vehicle every day;
2) the defects that the traditional detection method is time-consuming, labor-consuming and not universal are overcome.
Drawings
FIG. 1 is a flow chart of the overall implementation of the present invention.
FIG. 2 is a data flow diagram of the model training phase of the present invention.
FIG. 3 is a data flow diagram of the timing prediction phase of the present invention.
Detailed Description
The present invention will be described in detail with reference to the following embodiments.
A vehicle dynamic comprehensive evaluation method based on Internet of vehicles big data comprises a data acquisition stage, a data processing stage, a model training stage and a timing prediction stage; the data acquisition phase comprises: step 1-1) data acquisition to obtain original message data, step 1-2) data analysis to obtain original data, and step 1-3) data storage; the data processing stage comprises: step 2-1) data cleaning, step 2-2) feature construction to form a training set, and step 2-3) feature extraction to form a feature data set; the model training phase comprises: step 3-1) model construction, step 3-2) model training and step 3-3) model result display; the timing prediction phase comprises: step 4-1) model prediction, and step 4-2) prediction data display; the model training stage is the premise of the timing prediction stage, and the timing prediction stage predicts based on a dynamic abnormity detection model generated in the model training stage.
The step 1-1) of acquiring data to obtain the original message data specifically comprises: and acquiring original message data through the vehicle terminal.
The step 1-2) of analyzing the data to obtain the original data specifically comprises the following steps: and performing field analysis on the original message data acquired by data acquisition to generate readable information to acquire the original data.
The step 1-3) of data storage specifically comprises: and storing the original data obtained by data analysis to a distributed file system (HDFS) for carrying out a subsequent data processing stage.
The step 2-1) of data cleaning specifically comprises the following steps: unreasonable data and null data in original data obtained by data analysis are removed; the removing of the unreasonable data specifically comprises the following processes:
1) the method comprises the steps of carrying out interruption marking on the continuity of original data obtained by data analysis, marking two continuous original data with a time interval larger than 60s aiming at two continuous original data of each vehicle, regarding the time interval exceeding 60s as interruption, marking a point of time interruption, and screening a continuous operation interval to avoid selective deviation caused by subsequent screening of continuous working conditions;
2) after the original data are subjected to interrupt marking, the original data of the vehicles with the number of the data in the continuous operation interval being less than 100 are removed; the continuous operation interval data is data between two time interruption points; the distribution of original data with fewer data in the continuous operation interval has larger difference, and the training process of the model is interfered;
3) removing abnormal data: the abnormal data is one or two of original data with vehicle speed more than or equal to 200 km/h and negative engine speed;
4) converting time into days according to the data acquisition time (such as 2021-02-1313: 08:15 to 2021-02-13), wherein the original data of the second level is converted into original data of the day level; the method is used for meeting the requirement of time aggregation on the original data during subsequent feature construction by converting the second-level original data into the day-level original data.
The data cleaning is caused by the reasons of personal behavior of a driver, abnormal state of a terminal, network transmission, analysis error and the like, so that the integrity of original data obtained after partial vehicle data are analyzed is poor, and the condition that the original data are discontinuous is very common; therefore, before the feature is constructed, data cleaning needs to be carried out on the original data so as to be used for the subsequent steps; the data after data cleaning can basically meet the requirements of the subsequent steps of feature construction, feature extraction and the like.
The step 2-2) feature construction specifically comprises the following steps: and constructing the original data after data cleaning into a training set of the engine dynamics related characteristics for subsequent characteristic extraction.
The comprehensive evaluation method for the dynamic property of the vehicle mainly aims to extract common variables from equipment of different vehicle types and different transmission characteristics, construct indexes for evaluating the dynamic property, and only select characteristics related to the dynamic property of the engine as key data for constructing a training set in order to avoid selective deviation caused by difference of data distribution.
The specific steps of constructing the raw data after data cleaning into a training set of relevant characteristics of the engine dynamics comprise the following steps:
1) carrying out time aggregation on the original data, counting the original data amount of each vehicle per day, and filtering out original data with the data amount of each vehicle per day being less than 100; because the judgment of the final dynamic abnormity detection model is influenced by a small amount of vehicle data per day, original data with a small amount of vehicle data per day needs to be filtered;
2) filtering out original data of opening of the torque limit identifier; the limited torque mark is a flag bit field obtained after data analysis; torque is a key factor for directly measuring engine power, engine net output torque is closely related to engine rotating speed, actually, gear switching is performed to ensure that the engine net output torque is kept stable as much as possible, and engine limit torque (the engine limit torque means that the torque output of an engine is limited) affects an engine torque curve, so that original data of opening of a limit torque mark needs to be filtered;
3) selecting related variables and characteristic variables capable of describing the engine dynamics to form a training set; relevant and characteristic variables that can describe engine dynamics include engine net output torque and percent engine friction torque; further, the related variables and characteristic variables describing the dynamic property of the engine also comprise variables describing related characteristics of the engine, such as engine speed, engine water temperature, actual torque percentage, actual total torque percentage and the like; preferably, the net engine output torque and the percentage of engine friction torque are selected as the relevant and characteristic variables describing engine dynamics.
The training set is formed by carrying out feature construction on the data after data cleaning, only the features related to the engine dynamic property are reserved in the training set, the problem that evaluation cannot be carried out due to vehicle type difference can be solved, feature statistics is carried out on the extracted related features, and the detection of the dynamic property abnormity detection model by the isolated forest abnormity detection algorithm becomes possible.
The step 2-3) of feature extraction specifically comprises the following steps: after the features are constructed to form a training set, feature extraction needs to be carried out on the training set to form a feature data set, the feature extraction comprises feature extraction aiming at the net output torque of an engine and feature extraction aiming at the percentage of the friction torque of the engine, the net output torque of the engine is defined as a positive index, the percentage of the friction torque of the engine is defined as a negative index, the positive index and the negative index are converted into comparable scores, and therefore an isolated forest anomaly detection algorithm in a subsequent model training stage is easy to learn.
In general, key features for vehicle dynamics evaluation include two-way indicators:
1) the forward direction index is as follows: net engine output torque; in general, when the engine speed fluctuates in a large range, if the net output torque of the engine can be ensured to be stable and has a high value, the dynamic property of the engine is better;
2) negative direction index: percent engine friction torque; as the engine speed increases, the percentage of engine friction torque increases, and the faster the increase, the more torque is lost and the greater the power loss.
Therefore, the key characteristic of the comprehensive evaluation method for the vehicle dynamic property is that the characteristic construction is respectively carried out on the net output torque of the engine and the percentage of the friction torque of the engine, and the index describing the dynamic property difference among different engines is constructed by taking days as dimensions, so that the evaluation on the dynamic property is realized.
The feature extraction for the net output torque of the engine specifically comprises the following steps:
1) screening training set data with the engine rotating speed being more than 600 r/min and the net output torque of the engine being more than 0 to ensure that the working characteristics of the engine are met;
2) counting the maximum value, the minimum value, the average value and the standard deviation of the net output torque of the engine of each vehicle on the day, and recording;
3) and calculating the variation coefficient of the net output torque of the engine of each vehicle, namely dividing the average value of the net output torque of the engine of each vehicle by the standard deviation to obtain the variation coefficient of the forward index of each vehicle, wherein the larger the variation coefficient is, the stronger the dynamic property is.
The feature extraction for the percentage of the friction torque of the engine specifically comprises the following steps:
1) screening training set data with the engine rotating speed being more than 600 r/min and the net output torque of the engine being more than 0 to ensure that the working characteristics of the engine are met;
2) using quadratic curves
Fitting a relationship between percentage of engine friction torque and engine speed for each vehicle as an engine characteristicA curve; wherein,Xis the rotational speed of the engine and,Ytaking out all the engine rotating speeds and the engine friction torque percentages of the same day from each vehicle in the training set, and performing curve fitting to obtain coefficients a, b and c;
3) using the quadratic curve obtained by fitting, respectively calculating the percentage of the engine friction torque in a plurality of engine speed intervals, and obtaining the median (for example, the median in the interval of 950-1050 is 1000) of each engine speed interval as the quadratic curveXValue of the parameter, obtainingYI.e. percentage engine friction torque; the engine speed range comprises 950-1050, 1050-1150, 1150-1250, 1250-1350, 1350-1450, 1450-1550, 1550-1650, 1650-1750, 1750-1850, 1850-1950 and 1950-2050, and the unit of the engine speed is r/min;
4) and respectively calculating the number of data points in each engine speed interval, and calculating the proportion of the data points to the overall data (namely the number of all data points in all engine speed intervals), and calculating the ratio of the number of the data points in each engine speed interval to the number of all data points in all engine speed intervals, wherein the ratio is used as a basis for weighting the percentage of the friction torque of the engine in a plurality of engine speed intervals.
Through the calculation of the positive indexes and the negative indexes, characteristic data for constructing a dynamic abnormity detection model can be obtained; when the model is actually constructed, the data difference between the positive index and the negative index is large, so that direct comparison cannot be carried out, and the positive index and the negative index can be used for model construction only by converting the positive index and the negative index into comparable scores through processing.
The method for converting the positive index and the negative index into comparable scores specifically comprises the steps of calculating the Z-Score standard scores of the positive index and the negative index respectively;
and respectively adopting different methods to calculate respective Z-Score standard scores according to the positive indexes and the negative indexes:
1) directly calculating the Z-Score standard Score of the variation coefficient of the forward index as the Z-Score standard Score of the forward index;
2) for negative direction index, firstly calculating a quadratic curve
And weighting the Z-Score standard Score of the engine friction torque percentage corresponding to the median in each engine speed interval according to the number ratio of the previously calculated data points in each engine speed interval to the data points in all the engine speed intervals to obtain the Z-Score standard Score of the negative indicator.
The formula for calculating the Z-Score standard Score is shown in formula (1), and is the value to be calculated, the mean value, and the standard deviation, and the Z-Score standard Score is:
by calculating the Z-Score standard Score, data which have different dimensions and cannot be directly compared can be effectively converted into standard data for comparison.
The calculation and the construction of a dynamic abnormity detection model are simplified by calculating the Z-Score standard scores of the positive indexes and the negative indexes; the higher the Z-Score standard Score of the positive index is, the better the Z-Score standard Score of the negative index is, and the lower the Z-Score standard Score of the negative index is, the better the Z-Score standard Score of the positive index is, and the model is constructed based on a feature data set (feature extraction is performed on a training set to form the feature data set).
The step 3-1) of model construction comprises the following steps: aiming at a feature data set formed by feature extraction, establishing a dynamic anomaly detection model for the feature data set by using an isolated forest anomaly detection algorithm; and setting the hyper-parameters related to the isolated forest anomaly detection algorithm as default values.
On the basis of the constructed characteristic data set, an isolated Forest anomaly detection algorithm (Isolation Forest) is used for giving an alarm to vehicles with positive indexes and negative indexes obviously deviating from a normal range.
Meanwhile, the isolated forest anomaly detection algorithm does not have a "good-good" evaluation criterion of a single characteristic, so that a vehicle with an abnormal output from the dynamic anomaly detection model may also be a very excellent device (such as a new vehicle), and it is necessary to check in a final data result which quadrant the vehicle determined to be abnormal is located in to determine whether to warn the business side.
The model training result can be constructed by two-dimensional orthogonal coordinate axes, the Z-Score standard Score of the positive index is an X axis, the Z-Score standard Score of the negative index is a Y axis, each vehicle corresponds to a data point on a coordinate system, most of the data points are concentrated, discrete data points are abnormal data points, for example, the abnormal data points are in a first quadrant, the data in the quadrant has higher Z-Score standard Score of the positive index and higher Z-Score standard Score of the negative index, but the fact that the dynamic property of the vehicle has certain problems cannot be implied, and the vehicle is possibly caused by insufficient characteristic data quantity; the real interesting abnormal data point is in the second quadrant, the positive index Z-Score standard Score of the data in the second quadrant is low, the negative index Z-Score standard Score is high, the probability is that the engine dynamics is abnormal, and the business is needed to further investigate.
The step 3-2) of model training comprises: and putting the processed characteristic data set into a dynamic abnormity detection model for training and learning to produce a stable and reliable dynamic abnormity detection model.
Taking the feature data set subjected to feature processing as data for training and learning of the dynamic anomaly detection model, wherein the data comprises a Z-Score standard Score of a positive indicator and a Z-Score standard Score of a negative indicator, and recording related key data; the specific parameters to be recorded in the model training process include:
1) a total amount of data; preferably, the total number of the original data after data cleaning is used as the total data volume;
2) the amount of data available; preferably, the characteristic data lump number is the available data quantity;
3) the Z-Score standard Score of the forward index of each piece of feature data in the feature data set;
4) a Z-Score standard Score of a negative indicator for each feature in the feature data set.
An available dynamic abnormity detection model is generated in a model training stage, timing prediction is carried out by means of the trained dynamic abnormity detection model, vehicles with positive indexes and negative indexes obviously deviating from a normal range are detected, the Z-Score standard Score of the positive indexes is low, abnormal data with high Z-Score standard Score of the negative indexes are combined with engine characteristics and actual services, the probability is that the engine dynamic is abnormal, and the auxiliary services are used for rapidly finding the vehicles with abnormal dynamic.
And 3-3) the model result display is mainly to visually display the result of model training.
The overall data flow from the data processing phase to the model training phase includes several links, and the general flow of data processing is shown in fig. 2.
And 4-1) model prediction, namely, putting the processed characteristic data set into a trained dynamic abnormity detection model for calculation, and calculating a prediction result of the vehicle dynamic property.
And 4-2) displaying the prediction data in the step of visually displaying the result of model prediction.
The overall data flow from the data processing phase to the timing prediction phase includes several links, and the general flow of data processing is shown in fig. 3.
The invention is based on the original data after the vehicle collection and analysis for many days, and selects a proper data set and characteristics by carrying out data cleaning and statistical analysis on a large amount of original data of the vehicle and combining the mechanism knowledge of vehicle operation, wherein the most main key characteristics mainly comprise two indexes: the method comprises the steps that a positive index (net output torque of an engine) and a negative index (percentage of friction torque of the engine) are trained and learned through a dynamic abnormity detection model (Isolation Forest) built based on an isolated Forest abnormity detection algorithm, and a vehicle with a Z-Score standard Score of the positive index and a Z-Score standard Score of the negative index obviously deviating from a normal range is detected; the dynamic anomaly detection model can be trained and iterated for multiple times according to requirements to generate different model versions so as to adapt to model timing prediction; the invention adopts a timing prediction mode, the vehicle original data transmitted in real time cannot ensure the complete cycle period of the vehicle inside and the continuity of the vehicle working condition, the selective deviation caused by the subsequent continuous screening of the continuous working condition is avoided, the prediction result of a model is interfered, and the Z-Score standard Score of a positive index and the Z-Score standard Score of a negative index depend on continuous data calculation, so that the original data needs to be aggregated, the time is converted into day calculation, and the timing prediction mode is used for predicting data of yesterday, one week or one month.
In the timing prediction stage, online deployment of a dynamic abnormity detection model is realized by means of a distributed computing engine, an analyzed characteristic data set is put into the dynamic abnormity detection model for timing prediction, and an auxiliary service quickly finds vehicles with abnormal dynamic performance and visually displays the prediction result; the vehicle dynamic performance monitoring method is an unsupervised learning model, does not have a correct label, needs to combine with engine characteristics and actual services, has low Z-Score standard Score of positive indexes and high abnormal data of Z-Score standard Score of negative indexes, and has the probability that the engine dynamic performance is abnormal and assists services to quickly find vehicles with abnormal dynamic performance; the model training result and the prediction result are visually displayed, the dynamic analysis condition of each vehicle is visually shown, in addition, the abnormal vehicle is pushed to pay attention, and a set of stable vehicle dynamic performance detection system is formed.
The method combines the current internet of vehicles big data and machine learning technology, forms a simple, high-efficiency and light-weight vehicle dynamic detection method, is not limited to single vehicle or single brand vehicle detection, has high cost performance, can meet the requirement of abnormal detection of the dynamic of each vehicle, solves the defects of time consumption, labor consumption and incapability of reaching universality of the traditional detection method, and greatly improves the economic benefit; the method can predict the abnormal dynamic condition of each vehicle at regular time every day, visually display the indexes, and find out vehicles with possible problems in real time, thereby helping vehicle enterprises to reduce loss and better obtain benefits; based on the data of the national standard field, a sensor does not need to be additionally installed, so that the resource overhead is reduced; and the collected vehicle data is reused, so that the resource utilization is maximized, and the economic value and the social value are generated.
Some terms in the present invention are defined as follows:
engine characteristic curve: a relationship between percent engine friction torque and engine speed;
and (3) cycle period: the period from starting (engine starts working) to stopping (engine stops working) of the vehicle, and the idling state is not used as the basis of the splitting cycle period;
unsupervised learning model: the unsupervised learning model is usually used for tasks such as clustering, anomaly detection and the like, and is characterized in that a data set has no label and a class of algorithms for learning according to distance or density characteristics;
score: the statistical Z-Score is mainly used to normalize raw non-comparable scores, and the data is divided by the standard deviation by subtracting the mean value, so that the variables with larger differences in data ranges become comparable.
Claims (9)
1. A vehicle dynamic comprehensive evaluation method based on Internet of vehicles big data is characterized by comprising a data acquisition stage, a data processing stage, a model training stage and a timing prediction stage; the data acquisition phase comprises: step 1-1) data acquisition to obtain original message data, step 1-2) data analysis to obtain original data, and step 1-3) data storage; the data processing stage comprises: step 2-1) data cleaning, step 2-2) feature construction to form a training set, and step 2-3) feature extraction to form a feature data set; the model training phase comprises: step 3-1) model construction, step 3-2) model training and step 3-3) model result display; the timing prediction phase comprises: step 4-1) model prediction, and step 4-2) prediction data display; the model training stage is the premise of a timing prediction stage, and the timing prediction stage predicts based on a dynamic abnormity detection model generated in the model training stage;
the feature construction specifically includes:
1) carrying out time aggregation on the original data after data cleaning, counting the original data amount of each vehicle per day, and filtering out original data with the data amount of less than 100 vehicles per day;
2) filtering out original data of opening of the torque limit identifier; the limited torque mark is a flag bit field obtained after data analysis;
3) selecting related variables and characteristic variables capable of describing the engine dynamics to form a training set; relevant and characteristic variables that can describe engine dynamics include engine net output torque and percent engine friction torque.
2. The vehicle dynamics comprehensive evaluation method based on the internet of vehicles big data according to claim 1, wherein the data acquisition to obtain the original message data specifically comprises: collecting original message data through a vehicle terminal; the analyzing the data to obtain the original data specifically comprises: performing field analysis on original message data obtained by data acquisition to generate readable information to obtain original data; the data storage specifically comprises: and storing the original data obtained by data analysis to a distributed file system.
3. The vehicle dynamics comprehensive evaluation method based on the internet of vehicles big data according to claim 1 or 2, characterized in that the data washing specifically comprises: unreasonable data and null data in original data obtained by data analysis are removed; the removing of the unreasonable data specifically comprises the following processes:
1) the method comprises the steps of carrying out interruption marking on the continuity of original data obtained by data analysis, marking two continuous pieces of original data with the time interval larger than 60s aiming at two continuous pieces of original data of each vehicle, regarding the time interval exceeding 60s as interruption, marking a point of time interruption, and screening a continuous operation interval;
2) after the original data are subjected to interrupt marking, the original data of the vehicles with the number of the data in the continuous operation interval being less than 100 are removed; the continuous operation interval data is data between two time interruption points;
3) removing abnormal data: the abnormal data is one or two of original data with vehicle speed more than or equal to 200 km/h and negative engine speed;
4) and according to the data acquisition time, converting the time into days, wherein the second-level original data is converted into day-level original data.
4. The vehicle dynamics comprehensive evaluation method based on the internet of vehicles big data according to claim 1, characterized in that the feature extraction specifically comprises: after the features are constructed to form a training set, feature extraction is required to be carried out on the training set to form a feature data set, the feature extraction on the training set comprises feature extraction aiming at the net output torque of an engine and feature extraction aiming at the percentage of the friction torque of the engine, the net output torque of the engine is defined as a positive index, and the percentage of the friction torque of the engine is defined as a negative index; the positive and negative indicators were converted to comparable scores.
5. The method for comprehensively evaluating the dynamic property of the vehicle based on the Internet of vehicles big data as claimed in claim 4, wherein the feature extraction for the net output torque of the engine specifically comprises the following steps:
1) screening training set data with the engine rotating speed being more than 600 r/min and the net output torque of the engine being more than 0 to ensure that the working characteristics of the engine are met;
2) counting the maximum value, the minimum value, the average value and the standard deviation of the net output torque of the engine of each vehicle on the day, and recording;
3) and calculating the variation coefficient of the net output torque of the engine of each vehicle, namely dividing the average value of the net output torque of the engine of each vehicle by the standard deviation to obtain the variation coefficient of the forward index of each vehicle, wherein the larger the variation coefficient is, the stronger the dynamic property is.
6. The vehicle dynamics comprehensive evaluation method based on the internet of vehicles big data according to claim 4, wherein the feature extraction for the percentage of the friction torque of the engine specifically comprises the following steps:
1) screening training set data with the engine rotating speed being more than 600 r/min and the net output torque of the engine being more than 0 to ensure that the working characteristics of the engine are met;
2) using quadratic curves
Fitting the relation between the percentage of the engine friction torque and the engine speed of each vehicle as an engine characteristic curve; wherein,Xis the rotational speed of the engine and,Ytaking out all the engine rotating speeds and the engine friction torque percentages of the same day from each vehicle in the training set, and performing curve fitting to obtain coefficients a, b and c;
3) using the quadratic curve obtained by fitting, calculating the percentage of the engine friction torque in a plurality of engine speed intervals respectively, and obtaining the median of each engine speed interval as the quadratic curveXValue of the parameter, obtainingYObtaining the percentage of the friction torque of the engine; the engine speed range comprises 950-1050, 1050-1150, 1150-1250, 1250-1350, 1350-1450, 1450-1550, 1550-1650, 1650-1750, 1750-1850, 1850-1950 and 1950-2050, and the unit of the engine speed is r/min;
4) and respectively calculating the number of data points in each engine speed interval, calculating the ratio of the number of the data points in each engine speed interval to the number of all the data points in all the engine speed intervals, and calculating the ratio of the number of the data points in each engine speed interval to the number of all the data points in all the engine speed intervals to be used as a basis for weighting the percentage of the friction torque of the engines in a plurality of engine speed intervals.
7. The method for comprehensive evaluation of vehicle dynamics based on internet of vehicles big data as claimed in claim 4, wherein the method for converting the positive index and the negative index into comparable scores comprises calculating the Z-Score standard Score of the positive index and the Z-Score standard Score of the negative index respectively;
and respectively adopting different methods to calculate respective Z-Score standard scores according to the positive indexes and the negative indexes:
1) directly calculating the Z-Score standard Score of the variation coefficient of the forward index as the Z-Score standard Score of the forward index;
2) for negative direction index, firstly calculating a quadratic curve
And weighting the Z-Score standard Score of the engine friction torque percentage corresponding to the median in each engine speed interval according to the number ratio of the previously calculated data points in each engine speed interval to the data points in all the engine speed intervals to obtain the Z-Score standard Score of the negative indicator.
8. The vehicle dynamics comprehensive evaluation method based on the internet of vehicles big data according to claim 1 or 2, characterized in that the model construction comprises: aiming at a feature data set formed by feature extraction, establishing a dynamic anomaly detection model for the feature data set by using an isolated forest anomaly detection algorithm;
the model training comprises: and putting the characteristic data set into a dynamic abnormity detection model for training and learning.
9. The vehicle dynamics comprehensive evaluation method based on the internet of vehicles big data according to claim 1 or 2, characterized in that the model result display visually displays the result of model training; the model prediction is to place the characteristic data set into a trained dynamic abnormity detection model for calculation, and calculate the prediction result of the vehicle dynamic; and the prediction data display visually displays the prediction result of the model.
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