CN117609329A - Idle speed oil consumption analysis method and device based on big data - Google Patents
Idle speed oil consumption analysis method and device based on big data Download PDFInfo
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Abstract
The application relates to an idling oil consumption analysis method and device based on big data, and relates to the technical field of energy-saving control of an engine, wherein the method comprises the following steps: constructing an engine idle speed oil consumption database aiming at different engine types based on vehicle operation data of the engines of different types in an engine idle speed state and corresponding fuel consumption information; identifying real-time running data of a target vehicle in an engine idle state; and comparing the real-time running data of the vehicle with an engine idle speed oil consumption database corresponding to the engine with the same model or the same displacement of the target vehicle to obtain corresponding fuel consumption prediction information. According to the method, the database is built based on the fuel consumption data of the engines of different types in the idle state, the idle fuel consumption analysis and prediction are carried out according to the real-time data of the vehicle of the target vehicle, the idle fuel consumption condition of the vehicle is efficiently and reliably analyzed and predicted, the vehicle monitoring requirements are met, and the data basis is provided for the energy-saving control of the engine.
Description
Technical Field
The application relates to the technical field of energy-saving control of engines, in particular to an idling oil consumption analysis method and device based on big data.
Background
Engine idle oil consumption analysis has become an important area of automotive engineering and fuel economy research. Analysis of engine idle fuel consumption is helpful for evaluating fuel utilization efficiency of the vehicle in a non-driving state after the engine is started. By finding out the engine with high oil consumption, corresponding optimization measures can be adopted aiming at the engine, so that the fuel consumption is reduced, the energy utilization efficiency is improved, and the aims of energy conservation and emission reduction are fulfilled. The analysis of the idle oil consumption of the engine can also help to evaluate the exhaust emission condition of the engine in an idle state, and the exhaust emission can be reduced, the air quality is improved and the environmental health is protected by reducing the idle oil consumption. The analysis of the idle oil consumption of the engine can also reveal the working condition of the engine in an idle state, such as idle shake, the influence of air conditioner load on the oil consumption and the like. These metrics are important bases for automobile manufacturers and engineers to evaluate engine performance and improve design. By optimizing the engine design and the control system, the working stability of the engine in an idle state can be further improved, and the driving comfort and reliability of the whole vehicle are improved.
However, in the prior art, an instrument measurement method is mostly adopted to perform special idle oil consumption analysis on an operating vehicle, and fuel consumption measurement equipment is required to be additionally arranged, so that the accuracy is high, but the time and the cost are high, and the normal travel of the vehicle is influenced.
Therefore, in order to meet the actual demands, a new idle oil consumption analysis technology is provided.
Disclosure of Invention
The idling oil consumption analysis method and device based on big data are used for establishing a database based on oil consumption data of engines of different types in an idling state, carrying out idling oil consumption analysis and prediction according to real-time vehicle data of a target vehicle, efficiently and reliably carrying out analysis and prediction on idling oil consumption conditions of the vehicle, meeting vehicle monitoring requirements and providing data basis for energy-saving control of the engine.
To achieve the above object, the present application provides the following aspects.
In a first aspect, the present application provides an idle oil consumption analysis method based on big data, the method including the steps of:
constructing an engine idle speed oil consumption database aiming at different engine types based on vehicle operation data of the engines of different types in an engine idle speed state and corresponding fuel consumption information;
identifying real-time running data of a target vehicle in an engine idle state;
and comparing the real-time running data of the vehicle with an engine idle speed oil consumption database corresponding to the engine with the same model or the same displacement of the target vehicle to obtain corresponding fuel consumption prediction information.
Further, before constructing an engine idle speed oil consumption database for different engine types based on vehicle operation data of different engine types in an engine idle speed state and corresponding fuel consumption information, the method further comprises an information preprocessing flow, wherein the information preprocessing flow comprises the following steps:
identifying field missing conditions of the vehicle operation data and the corresponding fuel consumption information;
when the ratio of the data quantity of the missing field to the corresponding total data quantity is not higher than a first preset threshold value, deleting the data;
and when the ratio of the data quantity of the missing field to the corresponding total data quantity is higher than a first preset threshold value, filling the missing field based on the corresponding historical data.
Further, the information preprocessing flow further comprises the following steps:
identifying the vehicle operation data and the corresponding data value of the fuel consumption information, and carrying out data abnormality judgment based on the corresponding abnormality judgment value;
when the abnormal vehicle operation data or the corresponding data of the fuel consumption information is judged, the outlier detection algorithm is utilized to replace the data numerical value of the abnormal vehicle operation data and the corresponding fuel consumption information.
Further, comparing the real-time running data of the vehicle with an engine idle speed oil consumption database corresponding to the engine with the same type as the target vehicle to obtain corresponding fuel consumption prediction information, comprising the following steps:
extracting characteristic information in the real-time running data of the vehicle, and recording the characteristic information as first characteristic information;
extracting characteristic information in an engine idle speed oil consumption database corresponding to the engine with the same model as the target vehicle, and recording the characteristic information as second characteristic information;
comparing the first characteristic information with the second characteristic information, and obtaining corresponding fuel consumption prediction information based on the fuel consumption information of an engine idle speed fuel consumption database corresponding to the engine with the same model or the same displacement of the target vehicle through comparison.
Further, the characteristic information comprises a fan rotating speed average value, a fan rotating speed overrun duty ratio, a water temperature average value, a water temperature overrun duty ratio, an air conditioner on-time duty ratio, a parking regeneration mark, an idle time duty ratio, a long idle speed accumulation time duty ratio, an average idle time and a longest idle time.
Further, the vehicle operation data includes time, vehicle speed, engine speed, output torque, coolant temperature, fan speed, and air conditioner on state fields.
In a second aspect, the present application provides an idle oil consumption analysis device based on big data, the device including:
the database construction module is used for constructing an engine idle speed oil consumption database aiming at different engine types based on vehicle operation data and corresponding fuel consumption information of the engines of different types in an engine idle speed state;
a data collection module for identifying vehicle real-time operation data of a target vehicle in an engine idle state;
and the fuel consumption prediction module is used for comparing the real-time running data of the vehicle with an engine idle speed fuel consumption database corresponding to the engine with the same model or the same displacement of the target vehicle to obtain corresponding fuel consumption prediction information.
Further, the device further comprises:
the information preprocessing module is used for identifying field missing conditions of the vehicle operation data and the corresponding fuel consumption information;
the information preprocessing module is further used for deleting data when the ratio of the data quantity of the missing field to the corresponding total data quantity is not higher than a first preset threshold value;
the information preprocessing module is further configured to fill the missing field based on the corresponding historical data when a ratio of the data amount of the missing field to the corresponding total data amount is higher than a first preset threshold.
Further, the information preprocessing module is further used for identifying the vehicle operation data and the corresponding data value of the fuel consumption information, and carrying out data abnormality judgment based on the corresponding abnormality judgment value;
the information preprocessing module is further used for replacing data numerical values of the abnormal vehicle operation data and the corresponding fuel consumption information by utilizing an outlier detection algorithm when the abnormal vehicle operation data or the corresponding fuel consumption information data are judged.
Further, the fuel consumption prediction module is further used for extracting characteristic information in the real-time running data of the vehicle and recording the characteristic information as first characteristic information;
the fuel consumption prediction module is also used for extracting characteristic information in an engine idle speed fuel consumption database corresponding to the engine with the same model or the same displacement of the target vehicle, and recording the characteristic information as second characteristic information;
the fuel consumption prediction module is further configured to compare the first characteristic information with the second characteristic information, and obtain corresponding fuel consumption prediction information based on the fuel consumption information of an engine idle speed fuel consumption database corresponding to an engine with the same model or the same displacement as the target vehicle through comparison.
The beneficial effects that technical scheme that this application provided brought include:
according to the method, the database is built based on the fuel consumption data of the engines of different types in the idle state, the idle fuel consumption analysis and prediction are carried out according to the real-time data of the vehicle of the target vehicle, the idle fuel consumption condition of the vehicle is efficiently and reliably analyzed and predicted, the vehicle monitoring requirements are met, and the data basis is provided for the energy-saving control of the engine.
Drawings
Term interpretation:
APP: application, mobile phone software;
CAN: controller Area Network, controller area network bus.
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the description of the embodiments will be briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of steps of an idle speed fuel consumption analysis method based on big data provided in an embodiment of the present application;
fig. 2 is a hardware basic schematic diagram of an idle oil consumption analysis method based on big data provided in an embodiment of the present application.
Fig. 3 is a block diagram of an idle oil consumption analysis device based on big data according to an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions of the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art without undue burden from the present disclosure, are within the scope of the present application based on the embodiments herein.
Embodiments of the present application are described in further detail below with reference to the accompanying drawings.
The embodiment of the application provides an idling oil consumption analysis method and device based on big data, which are used for establishing a database based on oil consumption data of engines of different types in an idling state, carrying out idling oil consumption analysis and prediction according to real-time vehicle data of a target vehicle, efficiently and reliably carrying out analysis and prediction on idling oil consumption conditions of the vehicle, meeting vehicle monitoring requirements and providing data basis for energy-saving control of the engine.
In order to achieve the technical effects, the general idea of the application is as follows:
an idle oil consumption analysis method based on big data comprises the following steps:
s1, constructing an engine idle speed oil consumption database aiming at different engine types based on vehicle operation data and corresponding fuel consumption information of different engine types in an engine idle speed state;
s2, identifying real-time running data of the target vehicle in an engine idle state;
and S3, comparing the real-time running data of the vehicle with an engine idle speed oil consumption database corresponding to the same type or same displacement engine of the target vehicle to obtain corresponding fuel consumption prediction information.
Embodiments of the present application are described in further detail below with reference to the accompanying drawings.
In a first aspect, referring to fig. 1 to 2, an embodiment of the present application provides an idle oil consumption analysis method based on big data, the method includes the following steps:
s1, constructing an engine idle speed oil consumption database aiming at different engine types based on vehicle operation data and corresponding fuel consumption information of different engine types in an engine idle speed state;
s2, identifying real-time running data of the target vehicle in an engine idle state;
and S3, comparing the real-time running data of the vehicle with an engine idle speed oil consumption database corresponding to the same type or same displacement engine of the target vehicle to obtain corresponding fuel consumption prediction information.
In the step S1, an engine idle speed oil consumption database for different engine types is constructed;
in step S3, there may be no engine of the same model as the engine of the rain target vehicle in the database, but there may be an engine of the same displacement, so the comparison is made with the engine of the same model or displacement in step S3.
According to the method and the device, the database is built based on the fuel consumption data of the engines of different types in the idle state, the idle fuel consumption analysis and prediction are carried out according to the real-time data of the target vehicle, the idle fuel consumption condition of the vehicle is efficiently and reliably analyzed and predicted, the vehicle monitoring requirements are met, and the data basis is provided for the energy-saving control of the engine.
Further, before constructing an engine idle speed oil consumption database for different engine types based on vehicle operation data of different engine types in an engine idle speed state and corresponding fuel consumption information, the method further comprises an information preprocessing flow, wherein the information preprocessing flow comprises the following steps:
identifying field missing conditions of the vehicle operation data and the corresponding fuel consumption information;
when the ratio of the data quantity of the missing field to the corresponding total data quantity is not higher than a first preset threshold value, deleting the data;
and when the ratio of the data quantity of the missing field to the corresponding total data quantity is higher than a first preset threshold value, filling the missing field based on the corresponding historical data.
Further, the information preprocessing flow further comprises the following steps:
identifying the vehicle operation data and the corresponding data value of the fuel consumption information, and carrying out data abnormality judgment based on the corresponding abnormality judgment value;
when the abnormal vehicle operation data or the corresponding data of the fuel consumption information is judged, the outlier detection algorithm is utilized to replace the data numerical value of the abnormal vehicle operation data and the corresponding fuel consumption information.
Further, comparing the real-time running data of the vehicle with an engine idle speed oil consumption database corresponding to the same type or same displacement engine of the target vehicle to obtain corresponding fuel consumption prediction information, and comprising the following steps:
extracting characteristic information in the real-time running data of the vehicle, and recording the characteristic information as first characteristic information;
extracting characteristic information in an engine idle speed oil consumption database corresponding to the engine with the same model as the target vehicle, and recording the characteristic information as second characteristic information;
comparing the first characteristic information with the second characteristic information, and obtaining corresponding fuel consumption prediction information based on the fuel consumption information of an engine idle speed fuel consumption database corresponding to the engine with the same model or the same displacement of the target vehicle through comparison.
Further, the characteristic information comprises a fan rotating speed average value, a fan rotating speed overrun duty ratio, a water temperature average value, a water temperature overrun duty ratio, an air conditioner on-time duty ratio, a parking regeneration mark, an idle time duty ratio, a long idle speed accumulation time duty ratio, an average idle time and a longest idle time.
Further, the vehicle operating data includes vehicle speed, engine speed, output torque, coolant temperature, and fan speed fields.
Based on the technical scheme of the embodiment of the application, the following is the case when the implementation is carried out:
firstly, the technical scheme can be constructed into an engine idle speed oil consumption analysis system based on big data, as shown in fig. 2 of the attached drawings of the specification, which is a hardware foundation executed by the technical scheme of the embodiment of the application, and the engine idle speed oil consumption analysis system based on big data at least comprises a controller, a vehicle-mounted T-BOX, a cloud platform and a mobile phone APP. The controller and the vehicle-mounted T-BOX are both installed on the vehicle, the cloud platform is a data network platform, and the mobile phone APP is required to be installed on a mobile phone of a driver. The controller collects information such as the speed, the engine rotating speed, the engine output torque, the fuel consumption and the like of the vehicle in real time. The controller packages the collected signals and sends the signals to the vehicle-mounted T-BOX through the CAN bus, the vehicle-mounted T-BOX sends the signals to the cloud platform, the cloud platform analyzes the signals in real time according to the collected signals, compares the collected signals with historical data on the one hand, analyzes idle oil consumption in a period of time, and gives the level of the idle oil consumption and abnormal conditions. And sending the statistical result to the mobile phone APP according to the set period to inform the driver of the idle oil consumption condition of the engine in the driving period, so as to help the driver to know the running state of the engine in a period of time.
The engine idle speed oil consumption analysis system based on big data comprises a controller, a vehicle-mounted T-BOX, a mobile phone and a cloud platform;
the running state (vehicle speed, engine rotating speed, engine output torque and fuel consumption) of the vehicle is acquired in real time through a data acquisition module in the controller to form a data packet;
and the controller transmits the packed CAN frames to the TBox through a CAN bus. The TBox receives and analyzes the data and transmits the data to the cloud platform through the wireless communication module;
the cloud platform continuously collects data, and analyzes and predicts the idle speed condition of an engine of the vehicle and idle speed oil consumption through an idle speed oil consumption analysis algorithm, and provides services such as idle speed oil consumption assessment, vehicle health state monitoring and the like;
and sending the statistical result to the mobile phone through the mobile phone APP according to the set period, so as to inform a driver of the idle oil consumption of the vehicle.
Secondly, according to the technical scheme of the embodiment of the application, an engine idle speed oil consumption database based on big data can be constructed, and the specific conditions are as follows:
and the controller and the TBox system of the vehicle are used for collecting related data of the engine in an idle state in real time, such as vehicle speed, engine rotating speed, load, fuel consumption, cooling liquid temperature, fan rotating speed, air conditioning state and the like, and static information of vehicle types and engine models. And integrating and storing the data through network connection and cloud computing technology to form an engine idle speed oil consumption database.
First, a large-scale dataset field is included in the database, as follows:
firstly, classifying according to engine types, wherein each engine type comprises engine type and vehicle type information.
Secondly, carrying out data partitioning on the acquired information according to the acquisition time, oil consumption, regional environment, vehicle state, engine state, accessory state and other modules; wherein,
the acquisition time information comprises date and time of data acquisition;
the regional environment comprises information such as atmospheric environment, atmospheric pressure, altitude, humidity and the like;
the vehicle state comprises information such as vehicle speed, accelerator opening, gear, throttle opening and the like;
the engine state comprises information such as engine speed, load, coolant temperature, engine oil pressure and the like;
the accessory status includes information such as fan speed, fan engagement status, air conditioning status, PTO status, etc.
According to the classification mode, the real-time data and the historical data are synchronized, and the historical data of a period of time can be traced, so that the long-term trend analysis and comparison of the idle oil consumption of the vehicle are realized, the current oil consumption state is monitored in time, the driving behavior is adjusted, the oil consumption is reduced, and the fuel cost is saved.
The second point is that the database has abnormality detection and early warning functions, and abnormal idle oil consumption conditions can be identified:
the real-time data is compared with the historical data of the database by utilizing a big data analysis technology and a pattern recognition algorithm, and the standard of idle oil consumption is given in the historical data, for example, the standard is used in different idle speed, cooling liquid temperature, fan speed and air conditioner on or off states, namely, various working modes.
Under different working modes, different idle oil consumption standard values exist, and after real-time data are transmitted, mode identification is carried out, and then comparison is carried out with the standard values. The reasons of abnormal oil consumption, such as faults, bad driving habits and the like, are detected, and an alarm is timely given to a user so that the user can take corresponding measures to repair or correct the oil consumption.
Thirdly, the database utilizes big data analysis to conduct intelligent oil consumption analysis and prediction:
based on historical data and related parameters, an idle oil consumption prediction result of the vehicle under different working conditions and environmental conditions is provided by establishing a statistical model and a prediction algorithm. And a reasonable arrangement route and an optimized driving strategy are provided for a driver so as to reduce the oil consumption.
Fourth, the database provides a user-friendly visual interface and interactive functionality:
the user can intuitively check and compare the idle oil consumption conditions under different models, vehicle types, driving behaviors, working conditions and the like through the modes of charts, curves and the like. The user can also select a specific visualization mode and parameters according to own needs and expertise so as to obtain deeper analysis results.
Thirdly, the technical scheme of the embodiment of the application provides a method for analyzing large data of idle oil consumption of an engine.
Data preprocessing: the collected data needs to be preprocessed, including data cleaning, missing value processing, abnormal value elimination and the like, so that the quality and accuracy of the data are ensured;
and the data cleaning is used for eliminating the data outside the given range of the speed, the engine speed, the output torque, the fuel consumption, the cooling liquid temperature and the fan speed.
The principle of processing missing values is to fill or process the missing values by a suitable method to restore the integrity and usability of the data based on the characteristics and intended goals of the data.
The missing values are processed because they may lead to invalid or erroneous analysis results, biased sample analysis, reduced amounts of available data, reduced model stability and robustness, reduced compliance and reliability during data analysis and modeling, thus processing missing values may improve data quality and accuracy, maintain data integrity, improve model effectiveness, and gain more comprehensive analysis and insight. This is one of the indispensable links in the data processing flow.
Deleting the timestamp data corresponding to the missing field when the data quantity of the missing field accounts for less than 5% of the total data quantity of the current day;
deleting the fields except for fuel consumption, engine speed, output torque, cooling liquid temperature and fan speed when the data quantity of the field deletion accounts for more than 5% of the total daily data quantity;
and for the missing data of the fuel consumption, the engine speed, the output torque, the cooling liquid temperature and the fan speed fields, the fields are used as independent variables, and the missing fields are predicted and filled by using a regression model of historical data.
The abnormal values of the speed removal, engine rotating speed, output torque, fuel consumption, cooling liquid temperature and fan rotating speed fields are cut off by adopting abnormal values, and the abnormal values exceeding a certain threshold value are set as the threshold value;
outliers of the vehicle speed, the engine speed, the output torque, the fuel consumption, the coolant temperature and the fan speed fields are aggregated by adopting an outlier detection algorithm based on time slice data of 30 seconds before and after the outlier point, and the outlier is identified and removed and replaced by the mode of the aggregated data point.
Feature selection: and when the key features of idle oil consumption are mined, selecting the features of the collected data. The target characteristics comprise information such as idle oil consumption, idle oil quantity and idle oil quantity ratio. The influence factor features comprise a fan rotating speed average value and a fan rotating speed overrun duty ratio, a water temperature average value and a water temperature overrun duty ratio, an air conditioner on-time and time length duty ratio, a parking regeneration mark, an idle time and idle time length duty ratio, a long idle accumulated time length and long idle accumulated time length duty ratio, an average idle time length and a longest idle time length. The purpose of feature selection is to select the most relevant and important features from a large number of features to improve the accuracy and stability of the model.
First, the selected model types include linear regression models, polynomial regression models, data distribution and nonlinear relationships between the primary consideration target and factors, and evaluation of the model's predictive power and stability of the target variable.
If complex, logistic regression models, support vector regression models, decision tree regression models, etc. may also be selected, and embodiments of the present application are not limited to a particular model type.
Second, the dataset is divided into a training set and a testing set. For example 80% of the data is used to train the model, the remaining 20% is used to test the model;
according to the determined characteristic data, a regression model (including linear regression and polynomial regression) of the target characteristic and the influence factor characteristic can be established; wherein,
idle fuel consumption=f (time, geographical environment, vehicle status, engine status, accessory status),
engine state=f (water temperature average, water temperature overrun duty cycle),
accessory status = f (average fan speed, overrun fan speed ratio, air conditioner on-time ratio),
idle oil amount=f (time, duration, geographical environment, vehicle status, engine status, accessory status),
duration=f (idle duration, long idle cumulative duration, average idle duration, longest idle duration, idle duration duty ratio, long idle cumulative duration duty ratio),
idle oil amount ratio=idle oil amount/accumulated oil amount,
and selecting a proper model according to actual conditions, training, and learning and fitting known data through the model to obtain the capability of predicting idle oil consumption.
And thirdly, performing model training according to the model type and the training set selected in the prior art, and finding the most suitable model parameters by minimizing the sum of squares of residual errors. And predicting by using the trained model, and estimating the idle oil consumption.
In addition, visualization and application can also be realized:
the results after model training can be displayed and applied in a visual mode. The prediction result and the related analysis can be displayed in the forms of a chart, an image, a curve and the like, the idle oil consumption condition of the vehicle is monitored in real time, and the idle oil consumption condition is provided for drivers or vehicle owners to help the drivers to know and adjust driving behaviors, and the fuel economy is improved.
The method for analyzing the large data of the idle oil consumption of the engine can provide the capability of predicting and optimizing the idle oil consumption by collecting large-scale data, feature selection, model establishment and training, visualization and application and the like, and provide guidance for driving behavior improvement and fuel economy.
Fourth, according to the technical scheme of the embodiment of the present application, an optimization and fuel consumption prediction method for an idle fuel consumption model of an engine is provided, and the specific situations are as follows:
after the model is built, data mining and analysis can be performed using the collected data. The method comprises the steps of predicting by using a supervised learning method, and obtaining a predicted idle oil consumption result by carrying out model prediction by inputting characteristic data. At the same time, the model can be evaluated and optimized, such as calculating the Mean Square Error (MSE) of the regression model or the accuracy of the decision tree, etc., to evaluate the performance of the model and make adjustments and improvements.
Model optimization is primarily the use of data in a test set to evaluate the performance and accuracy of the model. The fitting degree of the model can be measured by calculating indexes such as mean square error and the like.
And then carrying out necessary parameter adjustment according to the evaluation result. For example, regularization terms may be added to control the complexity of the model.
And finally, predicting by using the trained model, and estimating the idle oil consumption.
In summary, through optimization of the model, the fitting capacity and generalization capacity of the model can be balanced in the model training process, and overfitting is prevented. It is important to select proper parameters, and tuning and selection are needed according to actual data and problems;
the engine idle oil consumption model is continuously improved and optimized through continuous collection and analysis of a large-scale data set. The method adopts a neural network, a regression model and the like, and combines actual data to train and optimize the precision and the prediction performance of the model. Meanwhile, the model can be automatically updated by using a machine learning algorithm, and a real-time fuel consumption prediction result is provided.
In a second aspect, referring to fig. 3, based on the same inventive concept as the method embodiment, the embodiment of the present application provides an idle oil consumption analysis device based on big data, including:
the database construction module is used for constructing an engine idle speed oil consumption database aiming at different engine types based on vehicle operation data and corresponding fuel consumption information of the engines of different types in an engine idle speed state;
a data collection module for identifying vehicle real-time operation data of a target vehicle in an engine idle state;
and the fuel consumption prediction module is used for comparing the real-time running data of the vehicle with an engine idle speed fuel consumption database corresponding to the engine with the same model or the same displacement of the target vehicle to obtain corresponding fuel consumption prediction information.
According to the method and the device, the database is built based on the fuel consumption data of the engines of different types in the idle state, the idle fuel consumption analysis and prediction are carried out according to the real-time data of the target vehicle, the idle fuel consumption condition of the vehicle is efficiently and reliably analyzed and predicted, the vehicle monitoring requirements are met, and the data basis is provided for the energy-saving control of the engine.
Further, the device further comprises:
the information preprocessing module is used for identifying field missing conditions of the vehicle operation data and the corresponding fuel consumption information;
the information preprocessing module is further used for deleting data when the ratio of the data quantity of the missing field to the corresponding total data quantity is not higher than a first preset threshold value;
the information preprocessing module is further configured to fill the missing field based on the corresponding historical data when a ratio of the data amount of the missing field to the corresponding total data amount is higher than a first preset threshold.
Further, the information preprocessing module is further used for identifying the vehicle operation data and the corresponding data value of the fuel consumption information, and carrying out data abnormality judgment based on the corresponding abnormality judgment value;
the information preprocessing module is further used for replacing data numerical values of the abnormal vehicle operation data and the corresponding fuel consumption information by utilizing an outlier detection algorithm when the abnormal vehicle operation data or the corresponding fuel consumption information data are judged.
Further, the fuel consumption prediction module is further used for extracting characteristic information in the real-time running data of the vehicle and recording the characteristic information as first characteristic information;
the fuel consumption prediction module is also used for extracting characteristic information in an engine idle speed fuel consumption database corresponding to the engine with the same model or the same displacement of the target vehicle, and recording the characteristic information as second characteristic information;
the fuel consumption prediction module is further configured to compare the first characteristic information with the second characteristic information, and obtain corresponding fuel consumption prediction information based on the fuel consumption information of an engine idle speed fuel consumption database corresponding to an engine with the same model or the same displacement as the target vehicle through comparison.
It should be noted that, the idling oil consumption analysis device based on big data provided in the embodiment of the present application has technical problems, technical means and technical effects corresponding to the idling oil consumption analysis device, and is similar to the principle of the idling oil consumption analysis method based on big data from the principle level.
It should be noted that in this application, relational terms such as "contrast" and "second", and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises an element.
The foregoing is merely a specific embodiment of the application to enable one skilled in the art to understand or practice the application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. An idle oil consumption analysis method based on big data is characterized by comprising the following steps:
constructing an engine idle speed oil consumption database aiming at different engine types based on vehicle operation data of the engines of different types in an engine idle speed state and corresponding fuel consumption information;
identifying real-time running data of a target vehicle in an engine idle state;
and comparing the real-time running data of the vehicle with an engine idle speed oil consumption database corresponding to the engine with the same model or the same displacement of the target vehicle to obtain corresponding fuel consumption prediction information.
2. The big data based idle oil consumption analysis method of claim 1, further comprising an information preprocessing procedure before constructing an engine idle oil consumption database for different engine models based on vehicle operation data of different engine models in an engine idle state and corresponding fuel consumption information, wherein the information preprocessing procedure comprises the following steps:
identifying field missing conditions of the vehicle operation data and the corresponding fuel consumption information;
when the ratio of the data quantity of the missing field to the corresponding total data quantity is not higher than a first preset threshold value, deleting the data;
and when the ratio of the data quantity of the missing field to the corresponding total data quantity is higher than a first preset threshold value, filling the missing field based on the corresponding historical data.
3. The big data based idle oil consumption analysis method of claim 2, wherein the information preprocessing flow further comprises the steps of:
identifying the vehicle operation data and the corresponding data value of the fuel consumption information, and carrying out data abnormality judgment based on the corresponding abnormality judgment value;
when the abnormal vehicle operation data or the corresponding data of the fuel consumption information is judged, the outlier detection algorithm is utilized to replace the data numerical value of the abnormal vehicle operation data and the corresponding fuel consumption information.
4. The big data based idle speed fuel consumption analysis method of claim 1, wherein comparing the real-time running data of the vehicle with an engine idle speed fuel consumption database corresponding to the same type of engine of the target vehicle to obtain corresponding fuel consumption prediction information, comprises the following steps:
extracting characteristic information in the real-time running data of the vehicle, and recording the characteristic information as first characteristic information;
extracting characteristic information in an engine idle speed oil consumption database corresponding to the engine with the same model or the same displacement as the target vehicle, and recording the characteristic information as second characteristic information;
comparing the first characteristic information with the second characteristic information, and obtaining corresponding fuel consumption prediction information based on the fuel consumption information of an engine idle speed fuel consumption database corresponding to the engine with the same model or the same displacement of the target vehicle through comparison.
5. The big data based idle fuel consumption analysis method of claim 4, wherein:
the characteristic information comprises a fan rotating speed average value, a fan rotating speed overrun duty ratio, a water temperature average value, a water temperature overrun duty ratio, an air conditioner on-time duty ratio, a parking regeneration mark, an idle time duty ratio, a long idle accumulated time duty ratio, an average idle time and a longest idle time.
6. The big data based idle fuel consumption analysis method of claim 1, wherein:
the vehicle operation data includes time, vehicle speed, engine speed, output torque, coolant temperature, fan speed, air conditioner on state fields.
7. An idle oil consumption analysis device based on big data, the device comprising:
the database construction module is used for constructing an engine idle speed oil consumption database aiming at different engine types based on vehicle operation data and corresponding fuel consumption information of the engines of different types in an engine idle speed state;
a data collection module for identifying vehicle real-time operation data of a target vehicle in an engine idle state;
and the fuel consumption prediction module is used for comparing the real-time running data of the vehicle with an engine idle speed fuel consumption database corresponding to the engine with the same model or the same displacement of the target vehicle to obtain corresponding fuel consumption prediction information.
8. The big data based idle fuel consumption analysis device of claim 7, further comprising:
the information preprocessing module is used for identifying field missing conditions of the vehicle operation data and the corresponding fuel consumption information;
the information preprocessing module is further used for deleting data when the ratio of the data quantity of the missing field to the corresponding total data quantity is not higher than a first preset threshold value;
the information preprocessing module is further configured to fill the missing field based on the corresponding historical data when a ratio of the data amount of the missing field to the corresponding total data amount is higher than a first preset threshold.
9. The big data based idle fuel consumption analysis device of claim 8, wherein:
the information preprocessing module is also used for identifying the vehicle operation data and the data value of the corresponding fuel consumption information and carrying out data abnormality judgment based on the corresponding abnormality judgment value;
the information preprocessing module is further used for replacing data numerical values of the abnormal vehicle operation data and the corresponding fuel consumption information by utilizing an outlier detection algorithm when the abnormal vehicle operation data or the corresponding fuel consumption information data are judged.
10. The big data based idle fuel consumption analysis device of claim 7, wherein:
the fuel consumption prediction module is also used for extracting characteristic information in the real-time running data of the vehicle and recording the characteristic information as first characteristic information;
the fuel consumption prediction module is also used for extracting characteristic information in an engine idle speed fuel consumption database corresponding to the engine with the same model or the same displacement of the target vehicle, and recording the characteristic information as second characteristic information;
the fuel consumption prediction module is further configured to compare the first characteristic information with the second characteristic information, and obtain corresponding fuel consumption prediction information based on the fuel consumption information of an engine idle speed fuel consumption database corresponding to an engine with the same model or the same displacement as the target vehicle through comparison.
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