CN116464533A

CN116464533A - Method and device for monitoring engine oil consumption and electronic device

Info

Publication number: CN116464533A
Application number: CN202310417734.9A
Authority: CN
Inventors: 付贵昕; 刘军; 徐鸿; 刘成; 蒋年顺; 朱桂香
Original assignee: Weichai Power Co Ltd
Current assignee: Weichai Power Co Ltd
Priority date: 2023-04-13
Filing date: 2023-04-13
Publication date: 2023-07-21

Abstract

The application provides a method and device for monitoring engine oil consumption and an electronic device. The method comprises the following steps: acquiring engine parameters, inputting the engine parameters into a first data driving model, acquiring the oil consumption output by the first data driving model, and obtaining predicted oil consumption; acquiring differential pressure parameters of the particulate filter, inputting the differential pressure parameters into a second data driving model, and acquiring a result output by the second data driving model to obtain ash content; and calculating the oil consumption corresponding to the ash loading amount to obtain a measured oil consumption, calculating the difference between the predicted oil consumption and the measured oil consumption to obtain an oil consumption difference, and outputting an abnormality warning signal under the condition that the oil consumption difference is larger than a preset threshold. Through this application, solved prior art and can't carry out real-time supervision's problem to the engine oil consumption, reached the purpose that carries out real-time supervision to the engine oil consumption of vehicle.

Description

Method and device for monitoring engine oil consumption and electronic device

Technical Field

The present application relates to the field of oil monitoring for vehicles, and in particular, to a method for monitoring oil consumption, a monitoring device, a computer-readable storage medium, and an electronic device.

Background

The engine oil consumption of the vehicle is related to parameters such as the rotating speed and torque of an engine, ash particles are generated in the combustion process of the engine oil, the ash particles are solid particles in engine exhaust intercepted in a particle filter (DPF), the engine exhaust cannot be oxidized and converted into a gaseous state, the engine oil consumption is mainly derived from an additive in the engine oil, and the engine oil consumption can be calculated indirectly by the ash ratio of the additive of the engine oil.

The oil consumption of a vehicle is an important parameter for monitoring the performance of the vehicle. In the operation stage of the vehicle engine bench test, the engine oil consumption under the condition of the current engine parameters can only be obtained in a test mode, and the engine oil consumption cannot be monitored and early-warned in real time.

Therefore, a method capable of monitoring and early warning the consumption of engine oil in real time is needed.

Disclosure of Invention

The main objective of the present application is to provide a method, a device, a computer readable storage medium and an electronic device for monitoring the consumption of engine oil, so as to at least solve the problem that the consumption of engine oil cannot be monitored in real time in the prior art.

In order to achieve the above object, according to one aspect of the present application, there is provided a method of monitoring oil consumption, comprising: acquiring engine parameters, inputting the engine parameters into a first data driving model, acquiring the oil consumption output by the first data driving model, and obtaining predicted oil consumption, wherein the engine parameters at least comprise engine speed, engine torque and oil temperature, and the first data driving model is used for predicting and outputting the oil consumption according to the engine parameters; acquiring a differential pressure parameter of a particulate filter, inputting the differential pressure parameter into a second data driving model, and acquiring a result output by the second data driving model to obtain ash content, wherein the differential pressure parameter is a pressure difference value between an inlet end and an outlet end of the particulate filter, the ash content is the ash content in the particulate filter, and the second data driving model is used for predicting and outputting the ash content according to the differential pressure parameter; and calculating the oil consumption corresponding to the ash loading amount to obtain a measured oil consumption, calculating a difference between the predicted oil consumption and the measured oil consumption to obtain an oil consumption difference, and outputting an abnormality warning signal under the condition that the oil consumption difference is larger than a preset threshold value, wherein the abnormality warning signal indicates that the oil consumption is abnormal.

Optionally, before acquiring the engine parameter, the method further comprises: establishing the first data driving model, and acquiring a first training data set, wherein the first training data set comprises a plurality of groups of first training data, and each group of first training data comprises a historical engine parameter and a historical engine oil consumption corresponding to the historical engine parameter; and taking a plurality of groups of historical engine parameters as input parameters of the first data driving model, taking the historical engine oil consumption corresponding to each group of historical engine parameters as output parameters of the first data driving model, and training the first data driving model.

Optionally, before acquiring the differential pressure parameter of the particulate filter, the method further comprises: establishing the second data driving model, and acquiring a second training data set, wherein the second training data set comprises a plurality of groups of second training data, and each group of second training data comprises a historical pressure difference parameter and a historical ash content corresponding to the historical pressure difference parameter; and taking the plurality of historical pressure difference parameters as input parameters of the second data driving model, taking the historical ash content corresponding to the plurality of historical pressure difference parameters as output parameters of the second data driving model, and training the second data driving model.

Optionally, before inputting the engine parameter into the first data-driven model and the differential pressure parameter into the second data-driven model, further comprising: and carrying out data preprocessing on the engine parameter and the pressure difference parameter, wherein the data preprocessing step at least comprises data fusion and data cleaning.

Optionally, the method further comprises: adding the engine parameter and the corresponding predicted engine oil consumption to a first training data set when the engine oil consumption difference is less than or equal to the preset threshold, wherein the first training data set comprises a plurality of sets of first training data, and each set of first training data comprises a historical engine parameter and a historical engine oil consumption corresponding to the historical engine parameter; and taking the pressure difference parameter as an input parameter of the second data driving model, taking the ash content as an output parameter of the second data driving model, and training the second data driving model.

Optionally, the method further comprises: and under the condition that the engine oil consumption difference value is smaller than or equal to the preset threshold value, adding the pressure difference parameter and the ash content corresponding to the pressure difference parameter into a second training data set, wherein the second training data set comprises a plurality of groups of second training data, and each group of second training data comprises a historical pressure difference parameter and a historical ash content corresponding to the historical pressure difference parameter.

Optionally, the method further comprises: after calculating the measured oil consumption corresponding to the ash loading, further comprising: and outputting the predicted engine oil consumption and the measured engine oil consumption and displaying the predicted engine oil consumption and the measured engine oil consumption on a display screen.

According to another aspect of the present application, there is provided a monitoring device of oil consumption, including: the first acquisition unit is used for acquiring engine parameters, inputting the engine parameters into a first data driving model, acquiring the oil consumption output by the first data driving model, and obtaining predicted oil consumption, wherein the engine parameters at least comprise engine speed, engine torque and oil temperature, and the first data driving model is used for predicting and outputting the oil consumption according to the engine parameters; the second acquisition unit is used for acquiring a differential pressure parameter of the particulate filter, inputting the differential pressure parameter into a second data driving model, and acquiring a result output by the second data driving model to obtain ash content, wherein the differential pressure parameter is a pressure difference value between an inlet end and an outlet end of the particulate filter, the ash content is the ash content in the particulate filter, and the second data driving model is used for predicting and outputting the ash content according to the differential pressure parameter; and the output unit is used for calculating the oil consumption corresponding to the ash content loading amount to obtain a measured oil consumption, calculating the difference between the predicted oil consumption and the measured oil consumption to obtain an oil consumption difference, and outputting an abnormality warning signal under the condition that the oil consumption difference is larger than a preset threshold value, wherein the abnormality warning signal indicates that the oil consumption is abnormal.

According to still another aspect of the present application, there is provided a computer readable storage medium, where the computer readable storage medium includes a stored program, and when the program runs, controls a device in which the computer readable storage medium is located to execute any one of the above-mentioned monitoring methods.

According to a further aspect of the present application there is provided an electronic device comprising a memory having a computer program stored therein and a processor arranged to perform any one of the above mentioned monitoring methods by means of the computer program.

By means of the technical scheme, a first data driving model and a second data driving model are built in advance, engine parameters are firstly obtained, the engine parameters are input into the first data driving model which is built in advance, the first data driving model is obtained to output oil consumption, predicted oil consumption is obtained, then differential pressure parameters are obtained, the differential pressure parameters are input into the second data driving model, the result output by the second data driving model is obtained, ash loading is obtained, measured oil consumption is obtained according to ash loading calculation, the difference value between the predicted oil consumption and the measured oil consumption is calculated, the difference value of the oil consumption is obtained, whether the difference value of the oil consumption is smaller than a preset threshold value is compared, the difference value of the oil consumption is smaller than the preset threshold value or not is indicated, the difference value of the predicted oil consumption and the actual oil consumption is within the range of the preset threshold value, the oil consumption is normal, the difference value of the predicted oil consumption and the actual oil consumption is outside the range of the preset threshold value when the difference value of the oil consumption is larger than the preset threshold value, the oil consumption is indicated, and an abnormal warning signal is output. Compared with the prior art, the method for acquiring the engine oil consumption under the conditions of specific rotating speed, torque and the like only through bench test, and the method for monitoring the engine oil consumption in real time cannot be used, the method for acquiring the predicted engine oil consumption and the measured engine oil consumption in real time and determining whether the engine oil consumption is abnormal or not according to the engine oil consumption difference value can be used for solving the problem that the prior art cannot monitor the engine oil consumption in real time, and achieving the purpose of monitoring the engine oil consumption of a vehicle in real time.

Drawings

The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this application, illustrate embodiments of the application and together with the description serve to explain the application and do not constitute an undue limitation to the application. In the drawings:

fig. 1 is a block diagram showing a hardware configuration of a mobile terminal for performing a method of monitoring oil consumption according to an embodiment of the present application;

FIG. 2 is a flow chart of a method for monitoring oil consumption according to an embodiment of the present disclosure;

FIG. 3 is a schematic flow chart of a specific method for monitoring oil consumption according to an embodiment of the present application;

FIG. 4 is a schematic diagram of a data driving model in a specific method for monitoring oil consumption according to an embodiment of the present application;

fig. 5 shows a block diagram of a device for monitoring oil consumption according to an embodiment of the present application.

Wherein the above figures include the following reference numerals:

102. a processor; 104. a memory; 106. a transmission device; 108. and an input/output device.

Detailed Description

It should be noted that, in the case of no conflict, the embodiments and features in the embodiments may be combined with each other. The present application will be described in detail below with reference to the accompanying drawings in conjunction with embodiments.

In order to make the present application solution better understood by those skilled in the art, the following description will be made in detail and with reference to the accompanying drawings in the embodiments of the present application, it is apparent that the described embodiments are only some embodiments of the present application, not all embodiments. All other embodiments, which can be made by one of ordinary skill in the art based on the embodiments herein without making any inventive effort, shall fall within the scope of the present application.

It should be noted that the terms "first," "second," and the like in the description and claims of the present application and the above figures are used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate in order to describe the embodiments of the present application described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.

For convenience of description, the following will describe some terms or terms related to the embodiments of the present application:

data driven model: a method for mathematical model training and engine performance prediction based on sensor acquisition data.

Ash content: the solid particles in the engine exhaust intercepted in the DPF can not be oxidized and converted into a gaseous state, and mainly originate from additives in engine oil, and the relation between the consumption of the engine oil and the generated ash can be calculated from the ash content ratio of the additives in the engine oil.

DPF differential pressure: the pressure difference between the inlet and outlet ends of the DPF is mainly affected by the number of particles trapped in the DPF.

The data fusion analysis method comprises the following steps: the data fusion is a fusion hierarchy close to the original engine data, can effectively eliminate redundant information in the data, remove abnormal information and noise, and provide an information basis for the next layer of feature extraction.

Self-learning: the data driving model automatically calibrates model parameters through test data or sensor data.

As described in the background art, in the prior art, the engine oil consumption cannot be monitored in real time, so as to solve the problem that the engine oil consumption cannot be monitored in real time, the embodiments of the present application provide a method, a device, a computer readable storage medium and an electronic device for monitoring the engine oil consumption.

The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention.

The method embodiments provided in the embodiments of the present application may be performed in a mobile terminal, a computer terminal or similar computing device. Taking the mobile terminal as an example, fig. 1 is a block diagram of a hardware structure of the mobile terminal according to a method for monitoring the oil consumption according to an embodiment of the present invention. As shown in fig. 1, a mobile terminal may include one or more (only one is shown in fig. 1) processors 102 (the processor 102 may include, but is not limited to, a microprocessor MCU or a processing device such as a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 for communication functions and an input-output device 108. It will be appreciated by those skilled in the art that the structure shown in fig. 1 is merely illustrative and not limiting of the structure of the mobile terminal described above. For example, the mobile terminal may also include more or fewer components than shown in fig. 1, or have a different configuration than shown in fig. 1.

The memory 104 may be used to store a computer program, for example, a software program of application software and a module, such as a computer program corresponding to a method for monitoring oil consumption in an embodiment of the present invention, and the processor 102 executes the computer program stored in the memory 104 to perform various functional applications and data processing, that is, to implement the above-described method. Memory 104 may include high-speed random access memory, and may also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some examples, the memory 104 may further include memory remotely located relative to the processor 102, which may be connected to the mobile terminal via a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof. The transmission device 106 is used to receive or transmit data via a network. Specific examples of the network described above may include a wireless network provided by a communication provider of the mobile terminal. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, simply referred to as NIC) that can connect to other network devices through a base station to communicate with the internet. In one example, the transmission device 106 may be a Radio Frequency (RF) module, which is configured to communicate with the internet wirelessly.

In the present embodiment, a method of monitoring the amount of oil consumed by operating a mobile terminal, a computer terminal, or a similar computing device is provided, and it is to be noted that the steps shown in the flowchart of the drawings may be performed in a computer system such as a set of computer-executable instructions, and that although a logical order is shown in the flowchart, in some cases, the steps shown or described may be performed in an order different from that shown herein.

Fig. 2 is a flowchart of a method of monitoring oil consumption according to an embodiment of the present application. As shown in fig. 2, the method comprises the steps of:

step S201, obtaining engine parameters, inputting the engine parameters into a first data driving model, obtaining the oil consumption output by the first data driving model, and obtaining predicted oil consumption, wherein the engine parameters at least comprise engine speed, engine torque and oil temperature, and the first data driving model is used for predicting and outputting the oil consumption according to the engine parameters;

specifically, during the running process of the engine of the vehicle, the engine has corresponding engine oil consumption under the conditions of different rotation speeds and torques, and it is easy to think that the larger the rotation speed and the torque of the engine, the more the engine oil consumption is, and the monitoring method of the engine oil consumption is based on the data driving. Firstly, a plurality of groups of engine parameters and corresponding engine oil consumption are obtained through a plurality of bench tests, a first data driving model is established, the engine parameters and the corresponding engine oil consumption are given to train the first data driving model, after the model training is completed, the parameters of the current engine can be input into the first data training model in the process of being put into use, the first data driving model after training predicts the engine oil consumption based on the current engine parameters, and a prediction result is output to obtain the predicted engine oil consumption.

Step S202, acquiring a differential pressure parameter of a particulate filter, inputting the differential pressure parameter into a second data driving model, and acquiring a result output by the second data driving model to obtain an ash content, wherein the differential pressure parameter is a pressure difference value between an inlet end and an outlet end of the particulate filter, the ash content is the ash content in the particulate filter, and the second data driving model is used for predicting and outputting the ash content according to the differential pressure parameter;

specifically, the engine oil generates solid particles in the combustion process, the solid particles cannot be oxidized and converted into a gaseous state, the solid particles are mainly derived from additives in the engine oil, namely ash, the consumption of the engine oil and the content of the ash also have a corresponding relation, and the larger the consumption of the engine oil is, the larger the content of the ash is, and the content of the ash is called ash loading. The engine is usually provided with a particulate filter (DPF) for filtering the ash content, the DPF has an inlet end and an outlet end, and the inlet end and the outlet end of the DPF have differential pressure, so that a second data driving model is built and trained through differential pressure parameters of the DPF and ash loading corresponding to the differential pressure parameters, after model training is completed, in the process of being put into use, the current differential pressure parameters are acquired, the current differential pressure parameters are input into the second data driving model to predict the ash loading corresponding to the current differential pressure parameters, and the oil consumption can be further calculated by the ash loading, namely, the oil consumption is measured.

Step S203, calculating the oil consumption corresponding to the ash load to obtain a measured oil consumption, calculating a difference between the predicted oil consumption and the measured oil consumption to obtain an oil consumption difference, and outputting an abnormality warning signal when the oil consumption difference is greater than a preset threshold, wherein the abnormality warning signal indicates that the oil consumption is abnormal.

Specifically, the first data driving model predicts the oil consumption according to the parameters of the engine, the second data driving model predicts the ash content according to the DPF differential pressure parameters, and calculates the measured oil consumption according to the ash content, that is, indirectly obtains the oil consumption according to the exhaust particles when the oil is consumed, under normal conditions, the oil consumption calculated by the two models should be equal or within a certain error range, therefore, the application presets the threshold value, calculates the difference between the predicted oil consumption and the measured consumption and determines whether the difference is within the preset threshold value range, the difference is not within the preset threshold value range, and further indicates that the oil consumption predicted by one model is abnormal, so that the difference between the engine oil which should be consumed by the engine under the condition of specific rotating speed and the actually consumed engine is larger, the abnormal condition of the engine may exist, etc., and outputs an abnormal warning signal and performs cause investigation.

According to the embodiment, a first data driving model and a second data driving model can be established in advance, engine parameters are firstly acquired, the engine parameters are input into the first data driving model which is established in advance, the engine parameters are acquired to output oil consumption, predicted oil consumption is obtained, then differential pressure parameters are acquired, the differential pressure parameters are input into the second data driving model, the result output by the second data driving model is acquired, ash loading is obtained, measured oil consumption is calculated according to the ash loading, the difference between the predicted oil consumption and the measured oil consumption is calculated, the difference between the oil consumption and the measured oil consumption is obtained, whether the difference between the oil consumption and the measured oil consumption is smaller than a preset threshold is compared, the difference between the predicted oil consumption and the actual oil consumption is within the range of the preset threshold when the difference between the oil consumption and the actual oil consumption is smaller than the preset threshold, the difference between the predicted oil consumption and the actual oil consumption is outside the range of the preset threshold when the difference between the oil consumption and the actual oil consumption is larger than the preset threshold, the oil consumption is abnormal, and an abnormal warning signal is output. Compared with the prior art, the method for acquiring the engine oil consumption under the conditions of specific rotating speed, torque and the like only through bench test, and the method for monitoring the engine oil consumption in real time cannot be used, the method for acquiring the predicted engine oil consumption and the measured engine oil consumption in real time and determining whether the engine oil consumption is abnormal or not according to the engine oil consumption difference value can be used for solving the problem that the prior art cannot monitor the engine oil consumption in real time, and achieving the purpose of monitoring the engine oil consumption of a vehicle in real time.

In a specific implementation process, the method further includes the following steps before step S201: establishing the first data driving model, and acquiring a first training data set, wherein the first training data set comprises a plurality of groups of first training data, and each group of first training data comprises a historical engine parameter and a historical engine oil consumption corresponding to the historical engine parameter; and training the first data driving model by taking a plurality of groups of historical engine parameters as input parameters of the first data driving model and taking the historical engine oil consumption corresponding to each group of historical engine parameters as output parameters of the first data driving model. According to the method, the obtained historical engine parameters and the historical engine oil consumption are used as training data sets of the first data driving model, so that the first data driving model can accurately predict and obtain the predicted engine oil consumption according to the engine parameters.

Specifically, the engine parameters include design related parameters of the engine and key components, such as: cylinder diameter, stroke, design detonation pressure, rated rotation speed, high torque rotation speed, rated power and physical and chemical properties of surfaces of a piston and a cylinder sleeve. Parameters such as cylinder pressure, engine oil temperature, rotating speed and torque of an engine operating condition are obtained through relevant sensors, then real-time engine oil consumption of the engine under the condition of each set of engine parameters is obtained through real-time engine oil consumption acquisition equipment, each set of engine parameters and corresponding real-time engine oil consumption are used as a first training data set to train a first data driving model, the first data driving model can be a neural network model, the neural network model can specifically comprise an input layer, an hidden layer and an output layer, and the application does not specifically limit the representation form of the first data driving model.

In order to enable the second data driving model to accurately predict the measured oil consumption according to the differential pressure parameter, the present application further includes the following steps before step S201: establishing the second data driving model, and acquiring a second training data set, wherein the second training data set comprises a plurality of groups of second training data, and each group of second training data comprises a historical pressure difference parameter and a historical ash content corresponding to the historical pressure difference parameter; and training the second data driving model by taking the plurality of historical pressure difference parameters as input parameters of the second data driving model and taking the historical ash content corresponding to the plurality of historical pressure difference parameters as output parameters of the second data driving model.

Specifically, the differential pressure sensor is used for obtaining the differential pressure parameter of the DPF, the ash content in the DPF is obtained through a weighing method, each differential pressure parameter of the DPF and the corresponding ash content are used as a second training data set to train a second data driving model, the second data driving model can also be a neural network model, and the neural network model specifically can comprise an input layer, an hidden layer and an output layer.

The steps S201 and S202 further include the steps of: and carrying out data preprocessing on the engine parameter and the pressure difference parameter, wherein the data preprocessing step at least comprises data fusion and data cleaning. According to the method, before the first training data set is input into the first data driving model and the second training data set is input into the second data driving model for training, the first training data set and the second training data set are preprocessed, and therefore accuracy of a model obtained through training can be guaranteed.

In some optional embodiments, the method is provided with a data integration module, and the sensor collects the engine parameter, the engine oil consumption corresponding to the engine parameter, the differential pressure parameter and the ash content corresponding to the differential pressure parameter, and the data integration module is used for performing integrated analysis on the data, for example: data fusion, data cleaning, data filtering, and the like.

In order to further improve the prediction accuracy of the first data driven model, in some embodiments, the above method may further be implemented by: adding the engine parameter and the corresponding predicted engine oil consumption to a first training data set when the engine oil consumption difference is less than or equal to the preset threshold, wherein the first training data set comprises a plurality of sets of first training data, and each set of first training data comprises a historical engine parameter and a historical engine oil consumption corresponding to the historical engine parameter; and training the second data driving model by taking the pressure difference parameter as an input parameter of the second data driving model and the ash content as an output parameter of the second data driving model. Therefore, the historical data with accurate prediction can be used as data in a training data set to train the first data driving model, and the prediction accuracy of the model is improved.

Specifically, in some optional embodiments, an automatic learning module is provided in the data-driven model, and is used for automatic learning of the data-driven model, so as to continuously improve the prediction accuracy of the model. And adding the engine parameters and the engine oil consumption to an automatic learning module of the data driving model at the moment to realize the self-learning function of the data driving model. The automatic learning module generates new data according to real-time engine parameters to perform rolling training on the data, and accuracy of calculation of the first data driving model is improved.

In some embodiments, the above method may be further implemented by: and under the condition that the difference value of the engine oil consumption is smaller than or equal to the preset threshold value, adding the differential pressure parameter and the ash content corresponding to the differential pressure parameter into a second training data set, wherein the second training data set comprises a plurality of groups of second training data, and each group of second training data comprises a historical differential pressure parameter and a historical ash content corresponding to the historical differential pressure parameter. According to the method, the historical data with accurate prediction is used as data in the training data set to train the second data driving model, and the prediction accuracy of the second data driving model is improved.

Specifically, the DPF differential pressure parameter and ash content are added into an automatic learning module of the data driving model, so that the self-learning function of the data driving model is realized. And the automatic learning module generates new data according to the real-time engine parameters to perform rolling training on the data, so that the calculation accuracy of the second data driving model is improved.

To visualize the results of monitoring the oil consumption, in some embodiments, the above method further comprises the steps of: and outputting the predicted engine oil consumption and the measured engine oil consumption and displaying the predicted engine oil consumption and the measured engine oil consumption on a display screen.

Specifically, after the prediction is performed through the two data driving models, the two groups of prediction results are input to the central control display screen for display, and a worker can know the consumption of engine oil in real time according to the result of the central control display screen and further check the abnormality reason under the condition of outputting an abnormality warning signal so as to ensure the safety of the vehicle engine.

In order to enable those skilled in the art to more clearly understand the technical solutions of the present application, the implementation process of the method for monitoring the oil consumption of the present application will be described in detail below with reference to specific embodiments.

The embodiment relates to a specific method for monitoring the consumption of engine oil, as shown in fig. 3 to 4, comprising the following steps:

step S1: FIG. 3 is a flow chart of a specific method for monitoring oil consumption, as shown in FIG. 3, for acquiring engine data, and obtaining engine parameters and DPF differential pressure parameters, wherein the engine parameters include engine piston, cylinder liner parameters, cylinder pressure curve, rotational speed, torque, etc.;

step S2: inputting the engine parameters and the DPF differential pressure parameters into a data integration module, and preprocessing data, wherein the preprocessing step comprises data cleaning, data fusion and the like;

step S3: inputting the preprocessed engine parameters into a first data driving model (data driving model 1), inputting the preprocessed DPF differential pressure parameters into a second data driving model (data driving model 2), wherein the data driving model is shown in fig. 4 and comprises an input layer, an hidden layer and an output layer, the parameters of the input layer are represented as X0, X1, … Xn, V0, V1, … and Vn and represent weight parameters corresponding to the input parameters, the parameters of the hidden layer are represented as b0, b1, …, bn, W0, W1, … and Wn and represent weight parameters corresponding to the parameters of the hidden layer, and the parameters of the output layer are represented as Y0, Y1 and … Yn;

Step S4: acquiring a time sequence engine oil consumption model accumulated value (predicted engine oil consumption) output by a first data driving model (data driving model 1), acquiring a current DPF accumulated ash quantity (ash load) output by a second data driving model (data driving model 2), and calculating a computer oil consumption model measured value (measured engine oil consumption) according to the ash ratio in engine oil;

step S5: calculating a difference between the predicted oil consumption and the measured oil consumption, and outputting an oil consumption abnormality warning (abnormality warning signal) when the difference exceeds a deviation threshold (preset threshold);

step S6: under the condition that the difference value does not exceed the deviation threshold value (preset threshold value), the engine parameter, the corresponding predicted engine oil consumption amount, the differential pressure parameter and the corresponding measured engine oil consumption amount are input into the data integration module, and after preprocessing, the data integration module is used for training the data driving model so as to improve the prediction accuracy of the data driving model.

The embodiment of the application also provides a device for monitoring the oil consumption, and the device for monitoring the oil consumption can be used for executing the method for monitoring the oil consumption. The device is used for realizing the above embodiments and preferred embodiments, and is not described in detail. As used below, the term "module" may be a combination of software and/or hardware that implements a predetermined function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.

The following describes a device for monitoring the oil consumption provided in the embodiment of the present application.

Fig. 5 is a schematic view of an oil consumption monitoring device according to an embodiment of the present application. As shown in fig. 5, the apparatus includes:

a first obtaining unit 10, configured to obtain an engine parameter, input the engine parameter into a first data driving model, obtain an oil consumption output by the first data driving model, and obtain a predicted oil consumption, where the engine parameter at least includes an engine speed, an engine torque, and an oil temperature, and the first data driving model is configured to predict and output the oil consumption according to the engine parameter;

A second obtaining unit 20, configured to obtain a differential pressure parameter of the particulate filter, input the differential pressure parameter to a second data driving model, and obtain a result output by the second data driving model, to obtain an ash load, where the differential pressure parameter is a pressure difference between an inlet end and an outlet end of the particulate filter, the ash load is a content of ash in the particulate filter, and the second data driving model is configured to predict and output the ash load according to the differential pressure parameter;

And an output unit 30, configured to calculate an oil consumption corresponding to the ash load, obtain a measured oil consumption, calculate a difference between the predicted oil consumption and the measured oil consumption, obtain an oil consumption difference, and output an abnormality warning signal when the oil consumption difference is greater than a preset threshold, where the abnormality warning signal indicates that the oil consumption is abnormal.

According to the embodiment, a first data driving model and a second data driving model are built in advance, engine parameters are firstly obtained, the engine parameters are input into the first data driving model which is built in advance, the engine parameters are obtained to output oil consumption of the first data driving model, predicted oil consumption is obtained, then differential pressure parameters are obtained, the differential pressure parameters are input into the second data driving model, the result output by the second data driving model is obtained, ash loading is obtained, measured oil consumption is obtained according to ash loading calculation, the difference value between the predicted oil consumption and the measured oil consumption is calculated, the difference value of the oil consumption is obtained, whether the difference value of the oil consumption is smaller than a preset threshold value is compared, the difference value of the oil consumption is smaller than the preset threshold value, the difference value between the predicted oil consumption and the actual oil consumption is within the range of the preset threshold value, the oil consumption is normal, the difference value between the predicted oil consumption and the actual oil consumption is outside the range of the preset threshold value when the difference value of the oil consumption is larger than the preset threshold value, and an abnormal warning signal is output. Compared with the prior art, the method for acquiring the engine oil consumption under the conditions of specific rotating speed, torque and the like only through bench test, and the method for monitoring the engine oil consumption in real time cannot be used, the method for acquiring the predicted engine oil consumption and the measured engine oil consumption in real time and determining whether the engine oil consumption is abnormal or not according to the engine oil consumption difference value can be used for solving the problem that the prior art cannot monitor the engine oil consumption in real time, and achieving the purpose of monitoring the engine oil consumption of a vehicle in real time.

As an optional solution, in a specific implementation process, the first obtaining unit includes an obtaining module and a training module, where the obtaining module is configured to establish the first data driving model and obtain a first training data set, where the first training data set includes multiple sets of first training data, and each set of first training data includes a historical engine parameter and a historical engine oil consumption corresponding to the historical engine parameter; the training module is used for taking a plurality of groups of historical engine parameters as input parameters of the first data driving model, taking the historical engine oil consumption corresponding to each group of historical engine parameters as output parameters of the first data driving model, and training the first data driving model. According to the method, the obtained historical engine parameters and the historical engine oil consumption are used as training data sets of the first data driving model, so that the first data driving model can accurately predict and obtain the predicted engine oil consumption according to the engine parameters.

In order to enable the second data driving model to accurately predict and obtain the measured engine oil consumption according to the differential pressure parameter, the second acquisition unit comprises an acquisition module and a training module, wherein the acquisition module is used for establishing the second data driving model and acquiring a second training data set, wherein the second training data set comprises a plurality of groups of second training data, and each group of second training data comprises a historical differential pressure parameter and a historical ash content corresponding to the historical differential pressure parameter; the training module is used for taking the plurality of historical pressure difference parameters as input parameters of the second data driving model, taking the historical ash content corresponding to the plurality of historical pressure difference parameters as output parameters of the second data driving model, and training the second data driving model.

The device further comprises an execution unit for data preprocessing the engine parameter and the differential pressure parameter, wherein the data preprocessing step at least comprises data fusion and data cleaning. According to the method, before the first training data set is input into the first data driving model and the second training data set is input into the second data driving model for training, the first training data set and the second training data set are preprocessed, and therefore accuracy of a model obtained through training can be guaranteed.

In order to further improve the prediction accuracy of the first data driving model, in some embodiments, the method further includes a first adding unit configured to add the engine parameter and the corresponding predicted engine oil consumption to a first training data set if the engine oil consumption difference is less than or equal to the preset threshold, where the first training data set includes a plurality of sets of first training data, and each set of first training data includes a historical engine parameter and a historical engine oil consumption corresponding to the historical engine parameter; and training the second data driving model by taking the pressure difference parameter as an input parameter of the second data driving model and the ash content as an output parameter of the second data driving model. Therefore, the historical data with accurate prediction can be used as data in a training data set to train the first data driving model, and the prediction accuracy of the model is improved.

In some embodiments, the apparatus further includes a second adding unit configured to add the pressure difference parameter and the ash load corresponding to the pressure difference parameter to a second training data set when the oil consumption difference is less than or equal to the preset threshold, where the second training data set includes a plurality of sets of second training data, and each set of second training data includes a historical pressure difference parameter and a historical ash load corresponding to the historical pressure difference parameter. According to the method, the historical data with accurate prediction is used as data in the training data set to train the second data driving model, and the prediction accuracy of the second data driving model is improved.

In order to visualize the monitoring result of the oil consumption, in some embodiments, the apparatus further includes an output unit for outputting the predicted oil consumption and the measured oil consumption and displaying them on a display screen.

The engine oil consumption monitoring device comprises a processor and a memory, wherein the first acquisition unit, the second acquisition unit, the output unit and the like are stored in the memory as program units, and the processor executes the program units stored in the memory to realize corresponding functions. The modules are all located in the same processor; alternatively, the above modules may be located in different processors in any combination.

The processor includes a kernel, and the kernel fetches the corresponding program unit from the memory. The kernel can be provided with one or more, and the engine oil consumption of the vehicle is monitored in real time by adjusting the kernel parameters.

The memory may include volatile memory, random Access Memory (RAM), and/or nonvolatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM), among other forms in computer readable media, the memory including at least one memory chip.

The embodiment of the invention provides a computer readable storage medium, which comprises a stored program, wherein the program is used for controlling equipment where the computer readable storage medium is located to execute the method for monitoring the engine oil consumption.

Specifically, the method for monitoring the oil consumption includes:

Optionally, before acquiring the engine parameter, the method further comprises: establishing the first data driving model, and acquiring a first training data set, wherein the first training data set comprises a plurality of groups of first training data, and each group of first training data comprises a historical engine parameter and a historical engine oil consumption corresponding to the historical engine parameter; and training the first data driving model by taking a plurality of groups of historical engine parameters as input parameters of the first data driving model and taking the historical engine oil consumption corresponding to each group of historical engine parameters as output parameters of the first data driving model.

Optionally, before acquiring the differential pressure parameter of the particulate filter, the method further comprises: establishing the second data driving model, and acquiring a second training data set, wherein the second training data set comprises a plurality of groups of second training data, and each group of second training data comprises a historical pressure difference parameter and a historical ash content corresponding to the historical pressure difference parameter; and training the second data driving model by taking the plurality of historical pressure difference parameters as input parameters of the second data driving model and taking the historical ash content corresponding to the plurality of historical pressure difference parameters as output parameters of the second data driving model.

Optionally, before inputting the engine parameter into the first data driving model and the differential pressure parameter into the second data driving model, the method further comprises: and carrying out data preprocessing on the engine parameter and the pressure difference parameter, wherein the data preprocessing step at least comprises data fusion and data cleaning.

Optionally, the method further comprises: adding the engine parameter and the corresponding predicted engine oil consumption to a first training data set when the engine oil consumption difference is less than or equal to the preset threshold, wherein the first training data set comprises a plurality of sets of first training data, and each set of first training data comprises a historical engine parameter and a historical engine oil consumption corresponding to the historical engine parameter; and training the second data driving model by taking the pressure difference parameter as an input parameter of the second data driving model and the ash content as an output parameter of the second data driving model.

Optionally, the method further comprises: and under the condition that the difference value of the engine oil consumption is smaller than or equal to the preset threshold value, adding the differential pressure parameter and the ash content corresponding to the differential pressure parameter into a second training data set, wherein the second training data set comprises a plurality of groups of second training data, and each group of second training data comprises a historical differential pressure parameter and a historical ash content corresponding to the historical differential pressure parameter.

Optionally, the method further comprises: after calculating the measured oil consumption corresponding to the ash load, the method further comprises: and outputting the predicted engine oil consumption and the measured engine oil consumption and displaying the predicted engine oil consumption and the measured engine oil consumption on a display screen.

The embodiment of the invention provides a processor, which is used for running a program, wherein the monitoring method of the engine oil consumption is executed when the program runs.

Specifically, the method for monitoring the oil consumption includes:

The embodiment of the invention provides equipment, which comprises a processor, a memory and a program stored in the memory and capable of running on the processor, wherein the processor realizes at least the following steps when executing the program:

The device herein may be a server, PC, PAD, cell phone, etc.

The present application also provides a computer program product adapted to perform a program initialized with at least the following method steps when executed on a data processing device:

It will be appreciated by those skilled in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, they may be implemented in program code executable by computing devices, so that they may be stored in a storage device for execution by computing devices, and in some cases, the steps shown or described may be performed in a different order than that shown or described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.

It will be appreciated by those skilled in the art that embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.

The present application is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems) and computer program products according to embodiments of the application. It will be understood that each flow and/or block of the flowchart illustrations and/or block diagrams, and combinations of flows and/or blocks in the flowchart illustrations and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.

These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.

In one typical configuration, a computing device includes one or more processors (CPUs), input/output interfaces, network interfaces, and memory.

The memory may include volatile memory in a computer-readable medium, random Access Memory (RAM) and/or nonvolatile memory, etc., such as Read Only Memory (ROM) or flash RAM. Memory is an example of a computer-readable medium.

Computer readable media, including both non-transitory and non-transitory, removable and non-removable media, may implement information storage by any method or technology. The information may be computer readable instructions, data structures, modules of a program, or other data. Examples of storage media for a computer include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Discs (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transmission medium, which can be used to store information that can be accessed by a computing device. Computer-readable media, as defined herein, does not include transitory computer-readable media (transmission media), such as modulated data signals and carrier waves.

It should also be noted that 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.

From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

1) In the method for monitoring the oil consumption, a first data driving model and a second data driving model are established in advance, engine parameters are firstly acquired, the engine parameters are input into the first data driving model which is established in advance, the oil consumption is output by the first data driving model, a predicted oil consumption is obtained, then a differential pressure parameter is acquired, the differential pressure parameter is input into the second data driving model, the result output by the second data driving model is obtained, ash loading is obtained, measured oil consumption is obtained according to ash loading calculation, the difference between the predicted oil consumption and the measured oil consumption is calculated, the difference between the oil consumption and the difference is obtained, whether the difference between the oil consumption and the measured oil consumption is smaller than a preset threshold value is compared, the difference between the predicted oil consumption and the actual oil consumption is within the range of the preset threshold value when the difference between the oil consumption and the actual oil consumption is smaller than the preset threshold value, the difference between the predicted oil consumption and the actual oil consumption is outside the range of the preset threshold value when the difference between the oil consumption and the actual oil consumption is larger than the preset threshold value, and an abnormal signal is output. Compared with the prior art, the method for acquiring the engine oil consumption under the conditions of specific rotating speed, torque and the like only through bench test, and the method for monitoring the engine oil consumption in real time cannot be used, the method for acquiring the predicted engine oil consumption and the measured engine oil consumption in real time and determining whether the engine oil consumption is abnormal or not according to the engine oil consumption difference value can be used for solving the problem that the prior art cannot monitor the engine oil consumption in real time, and achieving the purpose of monitoring the engine oil consumption of a vehicle in real time.

2) In the monitoring device for the oil consumption, a first data driving model and a second data driving model are established, engine parameters are firstly acquired, the engine parameters are input into the first data driving model which is established in advance, the oil consumption is output by the first data driving model, the predicted oil consumption is obtained, differential pressure parameters are acquired, the differential pressure parameters are input into the second data driving model, the result output by the second data driving model is obtained, ash loading is obtained, measured oil consumption is obtained according to ash calculation, the difference between the predicted oil consumption and the measured oil consumption is calculated, the difference between the oil consumption and the measured oil consumption is obtained, whether the difference between the oil consumption and the predicted oil consumption is smaller than a preset threshold or not is compared, the difference between the predicted oil consumption and the actual oil consumption is within the range of the preset threshold when the difference between the oil consumption and the actual oil consumption is smaller than the preset threshold, the difference between the predicted oil consumption and the actual oil consumption is outside the range of the preset threshold when the difference between the oil consumption and the actual oil consumption is larger than the preset threshold, the oil consumption is abnormal, and an abnormal warning signal is output. Compared with the prior art, the device which can only acquire the engine oil consumption under the conditions of specific rotating speed, torque and the like in a bench test mode and cannot monitor the engine oil consumption in real time, the device can acquire the predicted engine oil consumption and the measured engine oil consumption in real time and determine whether the engine oil consumption is abnormal or not according to the engine oil consumption difference value, so that the problem that the prior art cannot monitor the engine oil consumption in real time can be solved, and the aim of monitoring the engine oil consumption of a vehicle in real time is fulfilled.

The foregoing description is only of the preferred embodiments of the present application and is not intended to limit the same, but rather, various modifications and variations may be made by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principles of the present application should be included in the protection scope of the present application.

Claims

1. A method for monitoring oil consumption, comprising:

acquiring engine parameters, inputting the engine parameters into a first data driving model, acquiring the oil consumption output by the first data driving model, and obtaining predicted oil consumption, wherein the engine parameters at least comprise engine speed, engine torque and oil temperature, and the first data driving model is used for predicting and outputting the oil consumption according to the engine parameters;

acquiring a differential pressure parameter of a particulate filter, inputting the differential pressure parameter into a second data driving model, and acquiring a result output by the second data driving model to obtain ash content, wherein the differential pressure parameter is a pressure difference value between an inlet end and an outlet end of the particulate filter, the ash content is the ash content in the particulate filter, and the second data driving model is used for predicting and outputting the ash content according to the differential pressure parameter;

And calculating the oil consumption corresponding to the ash loading amount to obtain a measured oil consumption, calculating a difference between the predicted oil consumption and the measured oil consumption to obtain an oil consumption difference, and outputting an abnormality warning signal under the condition that the oil consumption difference is larger than a preset threshold value, wherein the abnormality warning signal indicates that the oil consumption is abnormal.

2. The monitoring method according to claim 1, further comprising, prior to acquiring the engine parameter:

establishing the first data driving model, and acquiring a first training data set, wherein the first training data set comprises a plurality of groups of first training data, and each group of first training data comprises a historical engine parameter and a historical engine oil consumption corresponding to the historical engine parameter;

and taking a plurality of groups of historical engine parameters as input parameters of the first data driving model, taking the historical engine oil consumption corresponding to each group of historical engine parameters as output parameters of the first data driving model, and training the first data driving model.

3. The method of monitoring of claim 1, further comprising, prior to acquiring the differential pressure parameter of the particulate filter:

Establishing the second data driving model, and acquiring a second training data set, wherein the second training data set comprises a plurality of groups of second training data, and each group of second training data comprises a historical pressure difference parameter and a historical ash content corresponding to the historical pressure difference parameter;

and taking the plurality of historical pressure difference parameters as input parameters of the second data driving model, taking the historical ash content corresponding to the plurality of historical pressure difference parameters as output parameters of the second data driving model, and training the second data driving model.

4. The monitoring method of claim 1, further comprising, prior to inputting the engine parameter into a first data-driven model and the differential pressure parameter into a second data-driven model:

and carrying out data preprocessing on the engine parameter and the pressure difference parameter, wherein the data preprocessing step at least comprises data fusion and data cleaning.

5. The method of monitoring according to claim 1, further comprising:

adding the engine parameter and the corresponding predicted engine oil consumption to a first training data set when the engine oil consumption difference is less than or equal to the preset threshold, wherein the first training data set comprises a plurality of sets of first training data, and each set of first training data comprises a historical engine parameter and a historical engine oil consumption corresponding to the historical engine parameter;

And taking the pressure difference parameter as an input parameter of the second data driving model, taking the ash content as an output parameter of the second data driving model, and training the second data driving model.

6. The method of monitoring according to claim 1, further comprising:

and under the condition that the engine oil consumption difference value is smaller than or equal to the preset threshold value, adding the pressure difference parameter and the ash content corresponding to the pressure difference parameter into a second training data set, wherein the second training data set comprises a plurality of groups of second training data, and each group of second training data comprises a historical pressure difference parameter and a historical ash content corresponding to the historical pressure difference parameter.

7. The monitoring method of claim 1, further comprising, after calculating the measured oil consumption corresponding to the ash loading:

and outputting the predicted engine oil consumption and the measured engine oil consumption and displaying the predicted engine oil consumption and the measured engine oil consumption on a display screen.

8. A monitoring device for oil consumption, characterized by comprising:

the first acquisition unit is used for acquiring engine parameters, inputting the engine parameters into a first data driving model, acquiring the oil consumption output by the first data driving model, and obtaining predicted oil consumption, wherein the engine parameters at least comprise engine speed, engine torque and oil temperature, and the first data driving model is used for predicting and outputting the oil consumption according to the engine parameters;

The second acquisition unit is used for acquiring a differential pressure parameter of the particulate filter, inputting the differential pressure parameter into a second data driving model, and acquiring a result output by the second data driving model to obtain ash content, wherein the differential pressure parameter is a pressure difference value between an inlet end and an outlet end of the particulate filter, the ash content is the ash content in the particulate filter, and the second data driving model is used for predicting and outputting the ash content according to the differential pressure parameter;

and the output unit is used for calculating the oil consumption corresponding to the ash content loading amount to obtain a measured oil consumption, calculating the difference between the predicted oil consumption and the measured oil consumption to obtain an oil consumption difference, and outputting an abnormality warning signal under the condition that the oil consumption difference is larger than a preset threshold value, wherein the abnormality warning signal indicates that the oil consumption is abnormal.

9. A computer-readable storage medium, characterized in that the computer-readable storage medium comprises a stored program, wherein the program, when run, controls a device in which the computer-readable storage medium is located to perform the monitoring method of any one of claims 1 to 7.

10. An electronic device comprising a memory and a processor, characterized in that the memory has stored therein a computer program, the processor being arranged to execute the monitoring method according to any of claims 1 to 7 by means of the computer program.