Oil consumption monitoring method, monitoring device and electronic device

Abstract

The present application provides a method, a monitoring device and an electronic device for monitoring oil consumption. The method comprises: obtaining engine parameters, inputting the engine parameters into a first data-driven model, obtaining the oil consumption output by the first data-driven model, and obtaining predicted oil consumption; obtaining the pressure difference parameter of the particle filter, inputting the pressure difference parameter into a second data-driven model, obtaining the result output by the second data-driven model, and obtaining the ash load; calculating the oil consumption corresponding to the ash load, obtaining the measured oil consumption, calculating the difference between the predicted oil consumption and the measured oil consumption, obtaining the oil consumption difference, and outputting an abnormal warning signal when the oil consumption difference is greater than a preset threshold. Through the present application, the problem that the prior art cannot monitor the oil consumption in real time is solved, and the purpose of real-time monitoring of the oil consumption of the vehicle is achieved.

Description

Technical Field

[0001] The present application relates to the field of vehicle oil monitoring, and in particular, to an oil consumption monitoring method, a monitoring device, a computer-readable storage medium, and an electronic device. Background Art

[0002] The vehicle's oil consumption is related to parameters such as the engine's speed and torque, and the oil will produce ash particles during the combustion process. Ash particles are solid particles in the engine exhaust intercepted by the particulate filter (DPF) and cannot be oxidized and converted into gas. They mainly come from additives in the oil. The oil consumption can be indirectly calculated from the proportion of additive ash in the oil.

[0003] The oil consumption of a vehicle is an important parameter for monitoring vehicle performance. During the operation phase of the vehicle engine bench test, the oil consumption under the current engine parameters can only be obtained through testing, but real-time monitoring and early warning of the oil consumption cannot be performed.

[0004] Therefore, a method for real-time monitoring and early warning of engine oil consumption is needed. Summary of the invention

[0005] The main purpose of the present application is to provide a method for monitoring oil consumption, a monitoring device, a computer-readable storage medium and an electronic device, so as to at least solve the problem in the prior art that the oil consumption cannot be monitored in real time.

[0006] In order to achieve the above-mentioned purpose, according to one aspect of the present application, a method for monitoring oil consumption is provided, comprising: obtaining engine parameters, inputting the engine parameters into a first data-driven model, obtaining the oil consumption output by the first data-driven model, and obtaining predicted oil consumption, wherein the engine parameters include at least engine speed, engine torque and oil temperature, and the first data-driven model is used to predict and output the oil consumption based on the engine parameters; obtaining a pressure difference parameter of a particulate filter, inputting the pressure difference parameter into a second data-driven model, and obtaining a result output by the second data-driven model. The result is used to obtain the ash loading, wherein the pressure difference parameter is the pressure difference between the inlet and outlet ends of the particle filter, and the ash loading is the ash content in the particle filter. The second data-driven model is used to predict and output the ash loading according to the pressure difference parameter; calculate the oil consumption corresponding to the ash loading to obtain the measured oil consumption, calculate the difference between the predicted oil consumption and the measured oil consumption to obtain the oil consumption difference, and output an abnormal warning signal when the oil consumption difference is greater than a preset threshold, wherein the abnormal warning signal indicates that there is an abnormality in the oil consumption.

[0007] Optionally, before obtaining the engine parameters, it also includes: establishing the first data-driven model and obtaining a first training data group, wherein the first training data group includes multiple groups of first training data, each group of the first training data includes a historical engine parameter and a historical oil consumption corresponding to the historical engine parameter; using the multiple groups of historical engine parameters as input parameters of the first data-driven model, and using the historical oil consumption corresponding to each group of historical engine parameters as output parameters of the first data-driven model, to train the first data-driven model.

[0008] Optionally, before obtaining the pressure difference parameter of the particulate filter, it also includes: establishing the second data-driven model and obtaining a second training data group, wherein the second training data group includes multiple groups of second training data, each group of the second training data includes a historical pressure difference parameter and a historical ash loading corresponding to the historical pressure difference parameter; using the multiple historical pressure difference parameters as input parameters of the second data-driven model, and using the historical ash loadings corresponding to the multiple historical pressure difference parameters as output parameters of the second data-driven model, to train the second data-driven model.

[0009] Optionally, before inputting the engine parameters into the first data-driven model and inputting the pressure difference parameters into the second data-driven model, it also includes: performing data preprocessing on the engine parameters and the pressure difference parameters, wherein the data preprocessing step includes at least data fusion and data cleaning.

[0010] Optionally, the above method also includes: when the oil consumption difference is less than or equal to the preset threshold, adding the engine parameters and the corresponding predicted oil consumption to the first training data group, wherein the first training data group includes multiple groups of first training data, each group of the first training data includes a historical engine parameter and a historical oil consumption corresponding to the historical engine parameter; using the pressure difference parameter as the input parameter of the second data-driven model, and using the ash load as the output parameter of the second data-driven model, to train the second data-driven model.

[0011] Optionally, the above method also includes: when the oil consumption difference is less than or equal to the preset threshold, adding the pressure difference parameter and the corresponding ash loading to a second training data group, wherein the second training data group includes multiple groups of second training data, each group of the second training data includes a historical pressure difference parameter and a historical ash loading corresponding to the historical pressure difference parameter.

[0012] Optionally, the method further includes: after calculating the measured oil consumption corresponding to the ash load, outputting the predicted oil consumption and the measured oil consumption and displaying them on a display screen.

[0013] According to another aspect of the present application, a device for monitoring oil consumption is provided, comprising: a first acquisition unit, used to acquire engine parameters, input the engine parameters into a first data-driven model, acquire the oil consumption output by the first data-driven model, and obtain predicted oil consumption, wherein the engine parameters include at least engine speed, engine torque and oil temperature, and the first data-driven model is used to predict and output the oil consumption based on the engine parameters; a second acquisition unit, used to acquire a pressure difference parameter of a particulate filter, input the pressure difference parameter into a second data-driven model, and acquire the output of the second data-driven model As a result, the ash loading is obtained, wherein the pressure difference parameter is the pressure difference between the inlet and outlet ends of the particulate filter, the ash loading is the ash content in the particulate filter, and the second data-driven model is used to predict and output the ash loading according to the pressure difference parameter; the output unit is used to calculate the oil consumption corresponding to the ash loading to obtain the measured oil consumption, calculate the difference between the predicted oil consumption and the measured oil consumption to obtain the oil consumption difference, and output an abnormal warning signal when the oil consumption difference is greater than a preset threshold, wherein the abnormal warning signal indicates that there is an abnormality in the oil consumption.

[0014] According to another aspect of the present application, a computer-readable storage medium is provided, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute any one of the monitoring methods described above.

[0015] According to another aspect of the present application, an electronic device is provided, including a memory and a processor, wherein the memory stores a computer program, and the processor is configured to execute any one of the above-mentioned monitoring methods through the computer program.

[0016] By applying the technical solution of the present application, a first data-driven model and a second data-driven model are pre-established, and engine parameters are first obtained, and the engine parameters are input into the pre-established first data-driven model to obtain the oil consumption output by the first data-driven model to obtain the predicted oil consumption, and then the pressure difference parameters are obtained, and the pressure difference parameters are input into the second data-driven model to obtain the result output by the second data-driven model to obtain the ash loading, and the measured oil consumption is calculated according to the ash loading, and the difference between the predicted oil consumption and the measured oil consumption is calculated to obtain the oil consumption difference, and the oil consumption difference is compared to see whether it is less than a preset threshold value. When the oil consumption difference is less than the preset threshold value, it indicates that the difference between the predicted oil consumption and the actual oil consumption is within the range of the preset threshold value, and the oil consumption is normal. When the oil consumption difference is greater than the preset threshold value, it indicates that the difference between the predicted oil consumption and the actual oil consumption is outside the range of the preset threshold value, and the oil consumption is abnormal, and an abnormal warning signal is output. Compared with the prior art, which can only obtain the oil consumption under specific speed and torque conditions through bench testing, but cannot monitor the oil consumption in real time, the present application can obtain the predicted oil consumption and the measured oil consumption in real time, and determine whether there is an abnormality in the oil consumption based on the difference in oil consumption. Therefore, it can solve the problem that the prior art cannot monitor the oil consumption in real time, and achieve the purpose of real-time monitoring of the vehicle's oil consumption. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] The drawings constituting part of the present application are used to provide a further understanding of the present application. The exemplary embodiments and descriptions of the present application are used to explain the present application and do not constitute an improper limitation on the present application. In the drawings:

[0018] Figure 1 A hardware structure block diagram of a mobile terminal for executing a method for monitoring engine oil consumption provided in an embodiment of the present application is shown;

[0019] Figure 2 A schematic flow chart of a method for monitoring engine oil consumption provided in an embodiment of the present application is shown;

[0020] Figure 3 A schematic flow chart of a specific method for monitoring engine oil consumption provided in an embodiment of the present application is shown;

[0021] Figure 4 A schematic diagram of a data-driven model in a specific method for monitoring engine oil consumption provided in an embodiment of the present application is shown;

[0022] Figure 5 A structural block diagram of an oil consumption monitoring device provided in an embodiment of the present application is shown.

[0023] The above drawings include the following reference numerals:

[0024] 102, processor; 104, memory; 106, transmission device; 108, input and output devices. DETAILED DESCRIPTION

[0025] It should be noted that, in the absence of conflict, the embodiments and features in the embodiments of the present application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0026] In order to enable those skilled in the art to better understand the solution of the present application, the technical solution in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by ordinary technicians in this field without creative work should fall within the scope of protection of the present application.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of the present application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequential order. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present application described here. In addition, the terms "including" and "having" and any of their variations are intended to cover non-exclusive inclusions, for example, a process, method, system, product or device comprising a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0028] For the convenience of description, some nouns or terms involved in the embodiments of the present application are explained below:

[0029] Data-driven model: A method that trains mathematical models based on sensor-collected data and predicts engine performance.

[0030] Ash: A solid particle in the engine exhaust intercepted by the DPF that cannot be oxidized and converted into gas. It mainly comes from the additives in the engine oil. The relationship between engine oil consumption and ash generation can be calculated from the proportion of additive ash in the engine oil.

[0031] DPF differential pressure: The pressure difference between the inlet and outlet of the DPF is mainly affected by the number of particles intercepted in the DPF.

[0032] Data fusion analysis method: Data fusion is a fusion level close to the original engine data, which 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.

[0033] Self-learning: The data-driven model automatically calibrates the model parameters through test data or sensor data.

[0034] As introduced in the background technology, the prior art is unable to monitor the oil consumption in real time. To solve the problem of being unable to monitor the oil consumption in real time, the embodiments of the present application provide an oil consumption monitoring method, a monitoring device, a computer-readable storage medium and an electronic device.

[0035] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0036] The method embodiments provided in the embodiments of the present application can be executed in a mobile terminal, a computer terminal or a similar computing device. Taking running on a mobile terminal as an example, Figure 1 FIG. 1 is a hardware structure block diagram of a mobile terminal of a method for monitoring oil consumption according to an embodiment of the present invention. Figure 1 As shown, the mobile terminal may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data, wherein the mobile terminal may also include a transmission device 106 and an input / output device 108 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is for illustration only and does not limit the structure of the mobile terminal. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations shown.

[0037] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as the computer program corresponding to the oil consumption monitoring method in the embodiment of the present invention. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, the above method is implemented. The memory 104 may include a high-speed random access memory, and may also include a 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 a memory remotely arranged relative to the processor 102, and these remote memories can be connected to the mobile terminal via a network. Examples of the above-mentioned network include but are not limited to the Internet, an intranet, a local area network, a mobile communication network, and a combination thereof. The transmission device 106 is used to receive or send data via a network. The above-mentioned specific examples of the network 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, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 may be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0038] In this embodiment, a method for monitoring oil consumption running on a mobile terminal, a computer terminal or a similar computing device is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0039] Figure 2 FIG. 1 is a flow chart of a method for monitoring engine oil consumption according to an embodiment of the present application. Figure 2 As shown, the method comprises the following steps:

[0040] Step S201, obtaining engine parameters, inputting the engine parameters into a first data-driven model, obtaining the oil consumption output by the first data-driven model, and obtaining predicted oil consumption, wherein the engine parameters at least include engine speed, engine torque and oil temperature, and the first data-driven model is used to predict and output the oil consumption according to the engine parameters;

[0041] Specifically, during the operation of the vehicle's engine, the engine has its corresponding oil consumption under different speeds and torques. It is easy to think that the greater the engine speed and torque, the greater the oil consumption. The above-mentioned oil consumption monitoring method is a data-driven oil consumption monitoring method. First, multiple sets of engine parameters and corresponding oil consumption are obtained through multiple bench tests, a first data-driven model is established, and the first data-driven model is trained by giving the engine parameters and the corresponding oil consumption. After the above-mentioned model training is completed, the parameters of the current engine can be input into the first data training model during the process of being put into use. The trained first data-driven model predicts the oil consumption based on the current engine parameters, outputs the prediction results, and obtains the predicted oil consumption.

[0042] Step S202, obtaining a pressure difference parameter of the particle filter, inputting the pressure difference parameter into a second data-driven model, obtaining a result output by the second data-driven model, and obtaining an ash loading, wherein the pressure difference parameter is a pressure difference between an inlet end and an outlet end of the particle filter, the ash loading is an ash content in the particle filter, and the second data-driven model is used to predict and output the ash loading according to the pressure difference parameter;

[0043] Specifically, during the combustion process of engine oil, a solid particle will be generated, which cannot be oxidized and converted into gas. It mainly comes from the additives in the engine oil, which is called ash. There is also a corresponding relationship between the engine oil consumption and the ash content. The greater the engine oil consumption, the greater the ash content. The ash content is called the ash load. The engine is usually equipped with a particulate filter (DPF) for filtering the above ash content. The DPF has an inlet and an outlet, and there is a pressure difference between the inlet and the outlet. Therefore, the second data-driven model is established and trained through the pressure difference parameter of the DPF and the ash load corresponding to the pressure difference parameter. After the model training is completed, the current pressure difference parameter is obtained during the use process, and the current rated pressure difference parameter is input into the second data-driven model to predict the ash load corresponding to the current pressure difference parameter. The engine oil consumption can be further calculated from the ash load, that is, the engine oil consumption is measured.

[0044] Step S203, calculate the oil consumption corresponding to the above-mentioned ash load to obtain the measured oil consumption, calculate the difference between the above-mentioned predicted oil consumption and the above-mentioned measured oil consumption to obtain the oil consumption difference, and when the above-mentioned oil consumption difference is greater than a preset threshold, output an abnormal warning signal, wherein the above-mentioned abnormal warning signal indicates that there is an abnormality in the oil consumption.

[0045] Specifically, the first data-driven model predicts the oil consumption based on the engine parameters, and the second data-driven model predicts the ash loading based on the DPF pressure difference parameters, and calculates the measured oil consumption through the ash loading, that is, the oil consumption is indirectly obtained based on the emission particles during oil consumption. Under normal circumstances, the oil consumption calculated by the above two models should be equal or within a certain error range. Therefore, the present application presets a threshold value, calculates the difference between the above predicted oil consumption and the measured consumption, and determines whether the difference is within the preset threshold range. The fact that the above difference is not within the preset threshold range indicates that the oil consumption predicted by one of the models is abnormal, which further indicates that the difference between the oil that the engine should consume at a specific speed and the actual consumed oil is large, and there may be engine abnormalities, etc., and an abnormal warning signal is output to investigate the cause.

[0046] Through this embodiment, a first data-driven model and a second data-driven model can be pre-established, and the engine parameters are first obtained, and the engine parameters are input into the pre-established first data-driven model to obtain the oil consumption output by the first data-driven model to obtain the predicted oil consumption, and then the pressure difference parameters are obtained, and the pressure difference parameters are input into the second data-driven model to obtain the result output by the second data-driven model to obtain the ash loading, and the measured oil consumption is calculated according to the ash loading, and the difference between the predicted oil consumption and the measured oil consumption is calculated to obtain the oil consumption difference, and the oil consumption difference is compared to see whether it is less than a preset threshold value. When the oil consumption difference is less than the preset threshold value, it indicates that the difference between the predicted oil consumption and the actual oil consumption is within the range of the preset threshold value, and the oil consumption is normal. When the oil consumption difference is greater than the preset threshold value, it indicates that the difference between the predicted oil consumption and the actual oil consumption is outside the range of the preset threshold value, and the oil consumption is abnormal, and an abnormal warning signal is output. Compared with the prior art, which can only obtain the oil consumption under specific speed and torque conditions through bench testing, but cannot monitor the oil consumption in real time, the present application can obtain the predicted oil consumption and the measured oil consumption in real time, and determine whether there is an abnormality in the oil consumption based on the difference in oil consumption. Therefore, it can solve the problem that the prior art cannot monitor the oil consumption in real time, and achieve the purpose of real-time monitoring of the vehicle's oil consumption.

[0047] In the specific implementation process, the above method further includes the following steps before step S201: establishing the above first data-driven model and obtaining the first training data group, wherein the above first training data group includes multiple groups of first training data, each group of the above first training data includes a historical engine parameter and a historical oil consumption corresponding to the above historical engine parameter; using the multiple groups of the above historical engine parameters as input parameters of the above first data-driven model, using the above historical oil consumption corresponding to each group of the above historical engine parameters as output parameters of the above first data-driven model, and training the above first data-driven model. The method uses the obtained historical engine parameters and historical oil consumption as the training data group of the first data-driven model, so that the first data-driven model can accurately predict the predicted oil consumption according to the engine parameters.

[0048] Specifically, the engine parameters include design-related parameters of the engine and key components, such as: cylinder diameter, stroke, design explosion pressure, rated speed, maximum torque speed, rated power, and physical and chemical properties of the piston and cylinder liner surface. The cylinder pressure, oil temperature, speed, torque and other parameters of the engine operating conditions are obtained through relevant sensors, and then the real-time oil consumption of the engine under each set of engine parameters is obtained through a real-time oil consumption acquisition device. Each set of engine parameters and the corresponding real-time oil consumption are used as the first training data group to train the first data-driven model. The first data-driven model can be a neural network model. The neural network model can specifically include an input layer, a hidden layer and an output layer. It should be noted that this application does not specifically limit the representation form of the above-mentioned first data-driven model.

[0049] In order to enable the second data-driven model to accurately predict the measured oil consumption based on the pressure difference parameter, the present application also includes the following steps before step S201: establishing the above-mentioned second data-driven model and obtaining a second training data group, wherein the above-mentioned second training data group includes multiple groups of second training data, and each group of the above-mentioned second training data includes a historical pressure difference parameter and a historical ash loading corresponding to the above-mentioned historical pressure difference parameter; using the multiple above-mentioned historical pressure difference parameters as input parameters of the above-mentioned second data-driven model, and using the historical ash loadings corresponding to the multiple above-mentioned historical pressure difference parameters as output parameters of the above-mentioned second data-driven model, to train the above-mentioned second data-driven model.

[0050] Specifically, the DPF pressure difference parameter is obtained by a pressure difference sensor, and the ash load in the DPF is obtained by weighing method. Each DPF pressure difference parameter and the corresponding ash load are used as the second training data group to train the second data-driven model. The second data-driven model can also be a neural network model. The neural network model can specifically include an input layer, a hidden layer and an output layer. It should be noted that the present application does not impose specific restrictions on the representation form of the above-mentioned second data-driven model.

[0051] The above steps S201 and S202 also include the following steps: preprocessing the above engine parameters and the above pressure difference parameters, wherein the above data preprocessing step at least includes data fusion and data cleaning. Before inputting the first training data group into the first data-driven model and the second training data group into the second data-driven model for training, the method first preprocesses the first training data group and the second training data group, so as to ensure the accuracy of the trained model.

[0052] In some optional embodiments, the above method is provided with a data integration module, and the engine parameters, oil consumption corresponding to the engine parameters, pressure difference parameters and ash loading corresponding to the pressure difference parameters collected by the sensor are imported into the data integration module to integrate and analyze the data, such as data fusion, data cleaning and data filtering.

[0053] In order to further improve the prediction accuracy of the first data-driven model, in some embodiments, the method can also be implemented by the following steps: when the oil consumption difference is less than or equal to the preset threshold, the engine parameters and the corresponding predicted oil consumption are added to the first training data group, wherein the first training data group includes multiple groups of first training data, each group of the first training data includes a historical engine parameter and the historical oil consumption corresponding to the historical engine parameter; the pressure difference parameter is used as the input parameter of the second data-driven model, and the ash load is used as the output parameter of the second data-driven model to train the second data-driven model. In this way, the accurate predicted historical data can be used as the data in the training data group to train the first data-driven model, thereby improving the prediction accuracy of the model.

[0054] Specifically, in some optional implementations, an automatic learning module is provided in the data-driven model for automatic learning of the data-driven model to continuously improve the prediction accuracy of the model. The engine parameters and oil consumption at this time are added to the automatic learning module of the data-driven model to realize the self-learning function of the data-driven model. The automatic learning module generates new data based on the real-time engine parameters to perform rolling training on the data to improve the accuracy of the calculation of the first data-driven model.

[0055] In some embodiments, the method can also be implemented by the following steps: when the oil consumption difference is less than or equal to the preset threshold, the pressure difference parameter and the corresponding ash load are added to the second training data group, wherein the second training data group includes multiple groups of second training data, each group of the second training data includes a historical pressure difference parameter and a historical ash load corresponding to the historical pressure difference parameter. The method uses the historical data with accurate prediction as the data in the training data group to train the second data-driven model, thereby improving the prediction accuracy of the second data-driven model.

[0056] Specifically, the DPF pressure difference parameters and ash content at this time are added to the automatic learning module of the data-driven model to realize the self-learning function of the data-driven model. The automatic learning module generates new data based on real-time engine parameters to perform rolling training on the data to improve the accuracy of the calculation of the second data-driven model.

[0057] In order to visualize the monitoring results of the oil consumption, in some embodiments, the method further includes the following steps: outputting the predicted oil consumption and the measured oil consumption and displaying them on a display screen.

[0058] Specifically, after predictions are made through two data-driven models, the two sets of prediction results are input into the central control display screen for display. The staff can understand the oil consumption in real time based on the results of the central control display screen, and further investigate the cause of the abnormality when an abnormal warning signal is output to ensure the safety of the vehicle engine.

[0059] In order to enable those skilled in the art to more clearly understand the technical solution of the present application, the implementation process of the method for monitoring oil consumption of the present application will be described in detail below in combination with specific embodiments.

[0060] This embodiment relates to a specific method for monitoring engine oil consumption, such as Figure 3 to Figure 4 As shown, the following steps are included:

[0061] Step S1: Figure 3 A flow chart of a specific method for monitoring engine oil consumption in this application is shown as follows: Figure 3 As shown, engine data is collected to obtain engine parameters and DPF pressure difference parameters, wherein the engine parameters include engine piston, cylinder liner parameters, cylinder pressure curve, speed, torque, etc.;

[0062] Step S2: input the above engine parameters and DPF pressure difference parameters into a data integration module to pre-process the data, wherein the pre-processing steps include data cleaning, data fusion, etc.;

[0063] Step S3: Input the preprocessed engine parameters into the first data-driven model (data-driven model 1), and input the preprocessed DPF pressure difference parameters into the second data-driven model (data-driven model 2). Figure 4 As shown, it includes an input layer, a hidden layer and an output layer, the parameters of the input layer are represented by X0, X1, ..., Xn, V0, V1, ..., Vn represent the weight parameters corresponding to the above input parameters, the parameters of the hidden layer are represented by b0, b1, ..., bn, W0, W1, ..., Wn represent the weight parameters corresponding to the parameters of the above hidden layer, and the parameters of the output layer are represented by Y0, Y1, ...Yn;

[0064] Step S4: obtaining the cumulative value of the time series oil consumption model (predicted oil consumption) output by the first data-driven model (data-driven model 1), obtaining the current DPF ash accumulation (ash load) output by the second data-driven model (data-driven model 2), and calculating the oil consumption model measurement value (measured oil consumption) according to the ash content in the oil;

[0065] Step S5: calculating the difference between the predicted oil consumption and the measured oil consumption, and outputting an oil consumption abnormality alarm (abnormal warning signal) when the difference exceeds a deviation threshold (preset threshold);

[0066] Step S6: When the above-mentioned difference does not exceed the deviation threshold (preset threshold), the engine parameters, the corresponding predicted oil consumption, the pressure difference parameters, and the corresponding measured oil consumption are input into the data integration module, and after preprocessing, they are used for training the data-driven model to improve the prediction accuracy of the data-driven model.

[0067] The embodiment of the present application also provides a monitoring device for oil consumption. It should be noted that the monitoring device for oil consumption in the embodiment of the present application can be used to execute the monitoring method for oil consumption provided in the embodiment of the present application. The device is used to implement the above-mentioned embodiments and preferred implementation modes, and the descriptions that have been made will not be repeated. As used below, the term "module" can implement a combination of software and / or hardware for a predetermined function. Although the devices described in the following embodiments are preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0068] The following is an introduction to the oil consumption monitoring device provided in the embodiment of the present application.

[0069] Figure 5 Schematic diagram of a device for monitoring engine oil consumption according to an embodiment of the present application. Figure 5 As shown, the device comprises:

[0070] A first acquisition unit 10 is used to acquire engine parameters, input the above engine parameters into a first data-driven model, acquire the oil consumption output by the above first data-driven model, and obtain the predicted oil consumption, wherein the above engine parameters at least include engine speed, engine torque and oil temperature, and the above first data-driven model is used to predict and output the above oil consumption according to the above engine parameters;

[0071] Specifically, during the operation of the vehicle's engine, the engine has its corresponding oil consumption under different speeds and torques. It is easy to think that the greater the engine speed and torque, the greater the oil consumption. The above-mentioned oil consumption monitoring method is a data-driven oil consumption monitoring method. First, multiple sets of engine parameters and corresponding oil consumption are obtained through multiple bench tests, a first data-driven model is established, and the first data-driven model is trained by giving the engine parameters and the corresponding oil consumption. After the above-mentioned model training is completed, the parameters of the current engine can be input into the first data training model during the process of being put into use. The trained first data-driven model predicts the oil consumption based on the current engine parameters, outputs the prediction results, and obtains the predicted oil consumption.

[0072] A second acquisition unit 20 is used to acquire a pressure difference parameter of the particle filter, input the pressure difference parameter into a second data-driven model, acquire a result output by the second data-driven model, and obtain an ash load, wherein the pressure difference parameter is a pressure difference between an inlet end and an outlet end of the particle filter, and the ash load is an ash content in the particle filter, and the second data-driven model is used to predict and output the ash load according to the pressure difference parameter;

[0073] Specifically, during the combustion process of engine oil, a solid particle will be generated, which cannot be oxidized and converted into gas. It mainly comes from the additives in the engine oil, which is called ash. There is also a corresponding relationship between the engine oil consumption and the ash content. The greater the engine oil consumption, the greater the ash content. The ash content is called the ash load. The engine is usually equipped with a particulate filter (DPF) for filtering the above ash content. The DPF has an inlet and an outlet, and there is a pressure difference between the inlet and the outlet. Therefore, the second data-driven model is established and trained through the pressure difference parameter of the DPF and the ash load corresponding to the pressure difference parameter. After the model training is completed, the current pressure difference parameter is obtained during the use process, and the current rated pressure difference parameter is input into the second data-driven model to predict the ash load corresponding to the current pressure difference parameter. The engine oil consumption can be further calculated from the ash load, that is, the engine oil consumption is measured.

[0074] The output unit 30 is used to calculate the oil consumption corresponding to the above-mentioned ash load to obtain the measured oil consumption, calculate the difference between the above-mentioned predicted oil consumption and the above-mentioned measured oil consumption to obtain the oil consumption difference, and output an abnormal warning signal when the above-mentioned oil consumption difference is greater than a preset threshold, wherein the above-mentioned abnormal warning signal indicates that there is an abnormality in the oil consumption.

[0075] Specifically, the first data-driven model predicts the oil consumption based on the engine parameters, and the second data-driven model predicts the ash loading based on the DPF pressure difference parameters, and calculates the measured oil consumption through the ash loading, that is, the oil consumption is indirectly obtained based on the emission particles during oil consumption. Under normal circumstances, the oil consumption calculated by the above two models should be equal or within a certain error range. Therefore, the present application presets a threshold value, calculates the difference between the above predicted oil consumption and the measured consumption, and determines whether the difference is within the preset threshold range. The fact that the above difference is not within the preset threshold range indicates that the oil consumption predicted by one of the models is abnormal, which further indicates that the difference between the oil that the engine should consume at a specific speed and the actual consumed oil is large, and there may be engine abnormalities, etc., and an abnormal warning signal is output to investigate the cause.

[0076] Through this embodiment, a first data-driven model and a second data-driven model are pre-established, and the engine parameters are first obtained, and the engine parameters are input into the pre-established first data-driven model to obtain the oil consumption output by the first data-driven model to obtain the predicted oil consumption, and then the pressure difference parameters are obtained, and the pressure difference parameters are input into the second data-driven model to obtain the result output by the second data-driven model to obtain the ash loading, and the measured oil consumption is calculated according to the ash loading, and the difference between the predicted oil consumption and the measured oil consumption is calculated to obtain the oil consumption difference, and the oil consumption difference is compared to see whether it is less than a preset threshold value. When the oil consumption difference is less than the preset threshold value, it indicates that the difference between the predicted oil consumption and the actual oil consumption is within the range of the preset threshold value, and the oil consumption is normal. When the oil consumption difference is greater than the preset threshold value, it indicates that the difference between the predicted oil consumption and the actual oil consumption is outside the range of the preset threshold value, and the oil consumption is abnormal, and an abnormal warning signal is output. Compared with the prior art, which can only obtain the oil consumption under specific speed and torque conditions through bench testing, but cannot monitor the oil consumption in real time, the present application can obtain the predicted oil consumption and the measured oil consumption in real time, and determine whether there is an abnormality in the oil consumption based on the difference in oil consumption. Therefore, it can solve the problem that the prior art cannot monitor the oil consumption in real time, and achieve the purpose of real-time monitoring of the vehicle's oil consumption.

[0077] As an optional solution, in the specific implementation process, the first acquisition unit includes an acquisition module and a training module, wherein the acquisition module is used to establish the above-mentioned first data-driven model and acquire the first training data group, wherein the above-mentioned first training data group includes multiple groups of first training data, each group of the above-mentioned first training data includes a historical engine parameter and a historical oil consumption corresponding to the above-mentioned historical engine parameter; the training module is used to use the multiple groups of the above-mentioned historical engine parameters as input parameters of the above-mentioned first data-driven model, and use the above-mentioned historical oil consumption corresponding to each group of the above-mentioned historical engine parameters as output parameters of the above-mentioned first data-driven model to train the above-mentioned first data-driven model. The method uses the acquired historical engine parameters and historical oil consumption as the training data group of the first data-driven model, so that the first data-driven model can accurately predict the predicted oil consumption according to the engine parameters.

[0078] Specifically, the engine parameters include design-related parameters of the engine and key components, such as: cylinder diameter, stroke, design explosion pressure, rated speed, maximum torque speed, rated power, and physical and chemical properties of the piston and cylinder liner surface. The cylinder pressure, oil temperature, speed, torque and other parameters of the engine operating conditions are obtained through relevant sensors, and then the real-time oil consumption of the engine under each set of engine parameters is obtained through a real-time oil consumption acquisition device. Each set of engine parameters and the corresponding real-time oil consumption are used as the first training data group to train the first data-driven model. The first data-driven model can be a neural network model. The neural network model can specifically include an input layer, a hidden layer and an output layer. It should be noted that this application does not specifically limit the representation form of the above-mentioned first data-driven model.

[0079] In order to enable the second data-driven model to accurately predict the measured oil consumption based on the pressure difference parameter, the second acquisition unit includes an acquisition module and a training module, wherein the acquisition module is used to establish the above-mentioned second data-driven model and acquire the second training data group, wherein the above-mentioned second training data group includes multiple groups of second training data, each group of the above-mentioned second training data includes a historical pressure difference parameter and a historical ash loading corresponding to the above-mentioned historical pressure difference parameter; the training module is used to use the above-mentioned multiple historical pressure difference parameters as input parameters of the above-mentioned second data-driven model, and use the historical ash loadings corresponding to the above-mentioned multiple historical pressure difference parameters as output parameters of the above-mentioned second data-driven model to train the above-mentioned second data-driven model.

[0080] Specifically, the DPF pressure difference parameter is obtained by a pressure difference sensor, and the ash load in the DPF is obtained by weighing method. Each DPF pressure difference parameter and the corresponding ash load are used as the second training data group to train the second data-driven model. The second data-driven model can also be a neural network model. The neural network model can specifically include an input layer, a hidden layer and an output layer. It should be noted that the present application does not impose specific restrictions on the representation form of the above-mentioned second data-driven model.

[0081] The device also includes an execution unit for performing data preprocessing on the engine parameters and the pressure difference parameters, wherein the data preprocessing step includes at least data fusion and data cleaning. The method first preprocesses the first training data group and the second training data group before inputting the first training data group into the first data-driven model and the second training data group into the second data-driven model for training, so as to ensure the accuracy of the trained model.

[0082] In some optional embodiments, the above method is provided with a data integration module, and the engine parameters, oil consumption corresponding to the engine parameters, pressure difference parameters and ash loading corresponding to the pressure difference parameters collected by the sensor are imported into the data integration module to integrate and analyze the data, such as data fusion, data cleaning and data filtering.

[0083] In order to further improve the prediction accuracy of the first data-driven model, in some embodiments, the method further includes a first adding unit, which is used to add the engine parameters and the corresponding predicted oil consumption to the first training data group when the oil consumption difference is less than or equal to the preset threshold, wherein the first training data group includes multiple groups of first training data, each group of the first training data includes a historical engine parameter and the historical oil consumption corresponding to the historical engine parameter; the pressure difference parameter is used as the input parameter of the second data-driven model, and the ash load is used as the output parameter of the second data-driven model to train the second data-driven model. In this way, the accurate predicted historical data can be used as the data in the training data group to train the first data-driven model, thereby improving the prediction accuracy of the model.

[0084] Specifically, in some optional implementations, an automatic learning module is provided in the data-driven model for automatic learning of the data-driven model to continuously improve the prediction accuracy of the model. The engine parameters and oil consumption at this time are added to the automatic learning module of the data-driven model to realize the self-learning function of the data-driven model. The automatic learning module generates new data based on the real-time engine parameters to perform rolling training on the data to improve the accuracy of the calculation of the first data-driven model.

[0085] In some embodiments, the device further includes a second adding unit, which is used to add the pressure difference parameter and the corresponding ash load to the second training data group when the oil consumption difference is less than or equal to the preset threshold, wherein the second training data group includes multiple groups of second training data, each group of second training data includes a historical pressure difference parameter and a historical ash load corresponding to the historical pressure difference parameter. The method uses the accurately predicted historical data as the data in the training data group to train the second data-driven model, thereby improving the prediction accuracy of the second data-driven model.

[0086] Specifically, the DPF pressure difference parameters and ash content at this time are added to the automatic learning module of the data-driven model to realize the self-learning function of the data-driven model. The automatic learning module generates new data based on real-time engine parameters to perform rolling training on the data to improve the accuracy of the calculation of the second data-driven model.

[0087] In order to visualize the monitoring results of the oil consumption, in some embodiments, the above-mentioned device also includes an output unit for outputting the above-mentioned predicted oil consumption and the above-mentioned measured oil consumption and displaying them on a display screen.

[0088] Specifically, after predictions are made through two data-driven models, the two sets of prediction results are input into the central control display screen for display. The staff can understand the oil consumption in real time based on the results of the central control display screen, and further investigate the cause of the abnormality when an abnormal warning signal is output to ensure the safety of the vehicle engine.

[0089] The above-mentioned monitoring device for engine oil consumption includes a processor and a memory. The above-mentioned first acquisition unit, the second acquisition unit and the output unit are all stored in the memory as program units, and the processor executes the above-mentioned program units stored in the memory to realize corresponding functions. The above-mentioned modules are all located in the same processor; or, the above-mentioned modules are located in different processors in any combination.

[0090] The processor includes a kernel, which retrieves the corresponding program unit from the memory. One or more kernels can be set, and the oil consumption of the vehicle can be monitored in real time by adjusting the kernel parameters.

[0091] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM, and the memory includes at least one memory chip.

[0092] An embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the method for monitoring the oil consumption.

[0093] Specifically, the method for monitoring engine oil consumption includes:

[0094] Step S201, obtaining engine parameters, inputting the engine parameters into a first data-driven model, obtaining the oil consumption output by the first data-driven model, and obtaining predicted oil consumption, wherein the engine parameters at least include engine speed, engine torque and oil temperature, and the first data-driven model is used to predict and output the oil consumption according to the engine parameters;

[0095] Specifically, during the operation of the vehicle's engine, the engine has its corresponding oil consumption under different speeds and torques. It is easy to think that the greater the engine speed and torque, the greater the oil consumption. The above-mentioned oil consumption monitoring method is a data-driven oil consumption monitoring method. First, multiple sets of engine parameters and corresponding oil consumption are obtained through multiple bench tests, a first data-driven model is established, and the first data-driven model is trained by giving the engine parameters and the corresponding oil consumption. After the above-mentioned model training is completed, the parameters of the current engine can be input into the first data training model during the process of being put into use. The trained first data-driven model predicts the oil consumption based on the current engine parameters, outputs the prediction results, and obtains the predicted oil consumption.

[0096] Step S202, obtaining a pressure difference parameter of the particle filter, inputting the pressure difference parameter into a second data-driven model, obtaining a result output by the second data-driven model, and obtaining an ash loading, wherein the pressure difference parameter is a pressure difference between an inlet end and an outlet end of the particle filter, the ash loading is an ash content in the particle filter, and the second data-driven model is used to predict and output the ash loading according to the pressure difference parameter;

[0097] Specifically, during the combustion process of engine oil, a solid particle will be generated, which cannot be oxidized and converted into gas. It mainly comes from the additives in the engine oil, which is called ash. There is also a corresponding relationship between the engine oil consumption and the ash content. The greater the engine oil consumption, the greater the ash content. The ash content is called the ash load. The engine is usually equipped with a particulate filter (DPF) for filtering the above ash content. The DPF has an inlet and an outlet, and there is a pressure difference between the inlet and the outlet. Therefore, the second data-driven model is established and trained through the pressure difference parameter of the DPF and the ash load corresponding to the pressure difference parameter. After the model training is completed, the current pressure difference parameter is obtained during the use process, and the current rated pressure difference parameter is input into the second data-driven model to predict the ash load corresponding to the current pressure difference parameter. The engine oil consumption can be further calculated from the ash load, that is, the engine oil consumption is measured.

[0098] Step S203, calculate the oil consumption corresponding to the above-mentioned ash load to obtain the measured oil consumption, calculate the difference between the above-mentioned predicted oil consumption and the above-mentioned measured oil consumption to obtain the oil consumption difference, and when the above-mentioned oil consumption difference is greater than a preset threshold, output an abnormal warning signal, wherein the above-mentioned abnormal warning signal indicates that there is an abnormality in the oil consumption.

[0099] Specifically, the first data-driven model predicts the oil consumption based on the engine parameters, and the second data-driven model predicts the ash loading based on the DPF pressure difference parameters, and calculates the measured oil consumption through the ash loading, that is, the oil consumption is indirectly obtained based on the emission particles during oil consumption. Under normal circumstances, the oil consumption calculated by the above two models should be equal or within a certain error range. Therefore, the present application presets a threshold value, calculates the difference between the above predicted oil consumption and the measured consumption, and determines whether the difference is within the preset threshold range. The fact that the above difference is not within the preset threshold range indicates that the oil consumption predicted by one of the models is abnormal, which further indicates that the difference between the oil that the engine should consume at a specific speed and the actual consumed oil is large, and there may be engine abnormalities, etc., and an abnormal warning signal is output to investigate the cause.

[0100] Optionally, before obtaining the engine parameters, it also includes: establishing the above-mentioned first data-driven model and obtaining the first training data group, wherein the above-mentioned first training data group includes multiple groups of first training data, and each group of the above-mentioned first training data includes a historical engine parameter and a historical oil consumption corresponding to the above-mentioned historical engine parameter; using the multiple groups of the above-mentioned historical engine parameters as input parameters of the above-mentioned first data-driven model, and using the above-mentioned historical oil consumption corresponding to each group of the above-mentioned historical engine parameters as the output parameter of the above-mentioned first data-driven model, to train the above-mentioned first data-driven model.

[0101] Optionally, before obtaining the pressure difference parameter of the particulate filter, it also includes: establishing the above-mentioned second data-driven model and obtaining a second training data group, wherein the above-mentioned second training data group includes multiple groups of second training data, and each group of the above-mentioned second training data includes a historical pressure difference parameter and a historical ash loading corresponding to the above-mentioned historical pressure difference parameter; using the multiple above-mentioned historical pressure difference parameters as input parameters of the above-mentioned second data-driven model, and using the historical ash loadings corresponding to the multiple above-mentioned historical pressure difference parameters as output parameters of the above-mentioned second data-driven model, to train the above-mentioned second data-driven model.

[0102] Optionally, before inputting the above-mentioned engine parameters into the first data-driven model and inputting the above-mentioned pressure difference parameters into the second data-driven model, it also includes: performing data preprocessing on the above-mentioned engine parameters and the above-mentioned pressure difference parameters, wherein the above-mentioned data preprocessing step at least includes data fusion and data cleaning.

[0103] Optionally, the method further includes: when the oil consumption difference is less than or equal to the preset threshold, adding the engine parameters and the corresponding predicted oil consumption to the first training data group, wherein the first training data group includes multiple groups of first training data, each group of the first training data includes a historical engine parameter and a historical oil consumption corresponding to the historical engine parameter; using the pressure difference parameter as the input parameter of the second data-driven model, and using the ash load as the output parameter of the second data-driven model, to train the second data-driven model.

[0104] Optionally, the method further includes: when the oil consumption difference is less than or equal to the preset threshold, adding the pressure difference parameter and the corresponding ash load to a second training data group, wherein the second training data group includes multiple groups of second training data, and each group of the second training data includes a historical pressure difference parameter and a historical ash load corresponding to the historical pressure difference parameter.

[0105] Optionally, the method further includes: after calculating the measured oil consumption corresponding to the ash load, outputting the predicted oil consumption and the measured oil consumption and displaying them on a display screen.

[0106] An embodiment of the present invention provides a processor, and the processor is used to run a program, wherein the method for monitoring engine oil consumption is executed when the program is running.

[0107] Specifically, the method for monitoring engine oil consumption includes:

[0108] Step S201, obtaining engine parameters, inputting the engine parameters into a first data-driven model, obtaining the oil consumption output by the first data-driven model, and obtaining predicted oil consumption, wherein the engine parameters at least include engine speed, engine torque and oil temperature, and the first data-driven model is used to predict and output the oil consumption according to the engine parameters;

[0109] Specifically, during the operation of the vehicle's engine, the engine has its corresponding oil consumption under different speeds and torques. It is easy to think that the greater the engine speed and torque, the greater the oil consumption. The above-mentioned oil consumption monitoring method is a data-driven oil consumption monitoring method. First, multiple sets of engine parameters and corresponding oil consumption are obtained through multiple bench tests, a first data-driven model is established, and the first data-driven model is trained by giving the engine parameters and the corresponding oil consumption. After the above-mentioned model training is completed, the parameters of the current engine can be input into the first data training model during the process of being put into use. The trained first data-driven model predicts the oil consumption based on the current engine parameters, outputs the prediction results, and obtains the predicted oil consumption.

[0110] Step S202, obtaining a pressure difference parameter of the particle filter, inputting the pressure difference parameter into a second data-driven model, obtaining a result output by the second data-driven model, and obtaining an ash loading, wherein the pressure difference parameter is a pressure difference between an inlet end and an outlet end of the particle filter, the ash loading is an ash content in the particle filter, and the second data-driven model is used to predict and output the ash loading according to the pressure difference parameter;

[0111] Specifically, during the combustion process of engine oil, a solid particle will be generated, which cannot be oxidized and converted into gas. It mainly comes from the additives in the engine oil, which is called ash. There is also a corresponding relationship between the engine oil consumption and the ash content. The greater the engine oil consumption, the greater the ash content. The ash content is called the ash load. The engine is usually equipped with a particulate filter (DPF) for filtering the above ash content. The DPF has an inlet and an outlet, and there is a pressure difference between the inlet and the outlet. Therefore, the second data-driven model is established and trained through the pressure difference parameter of the DPF and the ash load corresponding to the pressure difference parameter. After the model training is completed, the current pressure difference parameter is obtained during the use process, and the current rated pressure difference parameter is input into the second data-driven model to predict the ash load corresponding to the current pressure difference parameter. The engine oil consumption can be further calculated from the ash load, that is, the engine oil consumption is measured.

[0112] Step S203, calculate the oil consumption corresponding to the above-mentioned ash load to obtain the measured oil consumption, calculate the difference between the above-mentioned predicted oil consumption and the above-mentioned measured oil consumption to obtain the oil consumption difference, and when the above-mentioned oil consumption difference is greater than a preset threshold, output an abnormal warning signal, wherein the above-mentioned abnormal warning signal indicates that there is an abnormality in the oil consumption.

[0113] Specifically, the first data-driven model predicts the oil consumption based on the engine parameters, and the second data-driven model predicts the ash loading based on the DPF pressure difference parameters, and calculates the measured oil consumption through the ash loading, that is, the oil consumption is indirectly obtained based on the emission particles during oil consumption. Under normal circumstances, the oil consumption calculated by the above two models should be equal or within a certain error range. Therefore, the present application presets a threshold value, calculates the difference between the above predicted oil consumption and the measured consumption, and determines whether the difference is within the preset threshold range. The fact that the above difference is not within the preset threshold range indicates that the oil consumption predicted by one of the models is abnormal, which further indicates that the difference between the oil that the engine should consume at a specific speed and the actual consumed oil is large, and there may be engine abnormalities, etc., and an abnormal warning signal is output to investigate the cause.

[0114] Optionally, before obtaining the engine parameters, it also includes: establishing the above-mentioned first data-driven model and obtaining the first training data group, wherein the above-mentioned first training data group includes multiple groups of first training data, and each group of the above-mentioned first training data includes a historical engine parameter and a historical oil consumption corresponding to the above-mentioned historical engine parameter; using the multiple groups of the above-mentioned historical engine parameters as input parameters of the above-mentioned first data-driven model, and using the above-mentioned historical oil consumption corresponding to each group of the above-mentioned historical engine parameters as the output parameter of the above-mentioned first data-driven model, to train the above-mentioned first data-driven model.

[0115] Optionally, before obtaining the pressure difference parameter of the particulate filter, it also includes: establishing the above-mentioned second data-driven model and obtaining a second training data group, wherein the above-mentioned second training data group includes multiple groups of second training data, and each group of the above-mentioned second training data includes a historical pressure difference parameter and a historical ash loading corresponding to the above-mentioned historical pressure difference parameter; using the multiple above-mentioned historical pressure difference parameters as input parameters of the above-mentioned second data-driven model, and using the historical ash loadings corresponding to the multiple above-mentioned historical pressure difference parameters as output parameters of the above-mentioned second data-driven model, to train the above-mentioned second data-driven model.

[0116] Optionally, before inputting the above-mentioned engine parameters into the first data-driven model and inputting the above-mentioned pressure difference parameters into the second data-driven model, it also includes: performing data preprocessing on the above-mentioned engine parameters and the above-mentioned pressure difference parameters, wherein the above-mentioned data preprocessing step at least includes data fusion and data cleaning.

[0117] Optionally, the method further includes: when the oil consumption difference is less than or equal to the preset threshold, adding the engine parameters and the corresponding predicted oil consumption to the first training data group, wherein the first training data group includes multiple groups of first training data, each group of the first training data includes a historical engine parameter and a historical oil consumption corresponding to the historical engine parameter; using the pressure difference parameter as the input parameter of the second data-driven model, and using the ash load as the output parameter of the second data-driven model, to train the second data-driven model.

[0118] Optionally, the method further includes: when the oil consumption difference is less than or equal to the preset threshold, adding the pressure difference parameter and the corresponding ash load to a second training data group, wherein the second training data group includes multiple groups of second training data, and each group of the second training data includes a historical pressure difference parameter and a historical ash load corresponding to the historical pressure difference parameter.

[0119] Optionally, the method further includes: after calculating the measured oil consumption corresponding to the ash load, outputting the predicted oil consumption and the measured oil consumption and displaying them on a display screen.

[0120] An embodiment of the present invention provides a device, the device including a processor, a memory, and a program stored in the memory and executable on the processor, and when the processor executes the program, at least the following steps are implemented:

[0121] Step S201, obtaining engine parameters, inputting the engine parameters into a first data-driven model, obtaining the oil consumption output by the first data-driven model, and obtaining predicted oil consumption, wherein the engine parameters at least include engine speed, engine torque and oil temperature, and the first data-driven model is used to predict and output the oil consumption according to the engine parameters;

[0122] Specifically, during the operation of the vehicle's engine, the engine has its corresponding oil consumption under different speeds and torques. It is easy to think that the greater the engine speed and torque, the greater the oil consumption. The above-mentioned oil consumption monitoring method is a data-driven oil consumption monitoring method. First, multiple sets of engine parameters and corresponding oil consumption are obtained through multiple bench tests, a first data-driven model is established, and the first data-driven model is trained by giving the engine parameters and the corresponding oil consumption. After the above-mentioned model training is completed, the parameters of the current engine can be input into the first data training model during the process of being put into use. The trained first data-driven model predicts the oil consumption based on the current engine parameters, outputs the prediction results, and obtains the predicted oil consumption.

[0123] Step S202, obtaining a pressure difference parameter of the particle filter, inputting the pressure difference parameter into a second data-driven model, obtaining a result output by the second data-driven model, and obtaining an ash loading, wherein the pressure difference parameter is a pressure difference between an inlet end and an outlet end of the particle filter, the ash loading is an ash content in the particle filter, and the second data-driven model is used to predict and output the ash loading according to the pressure difference parameter;

[0124] Specifically, during the combustion process of engine oil, a solid particle will be generated, which cannot be oxidized and converted into gas. It mainly comes from the additives in the engine oil, which is called ash. There is also a corresponding relationship between the engine oil consumption and the ash content. The greater the engine oil consumption, the greater the ash content. The ash content is called the ash load. The engine is usually equipped with a particulate filter (DPF) for filtering the above ash content. The DPF has an inlet and an outlet, and there is a pressure difference between the inlet and the outlet. Therefore, the second data-driven model is established and trained through the pressure difference parameter of the DPF and the ash load corresponding to the pressure difference parameter. After the model training is completed, the current pressure difference parameter is obtained during the use process, and the current rated pressure difference parameter is input into the second data-driven model to predict the ash load corresponding to the current pressure difference parameter. The engine oil consumption can be further calculated from the ash load, that is, the engine oil consumption is measured.

[0125] Step S203, calculate the oil consumption corresponding to the above-mentioned ash load to obtain the measured oil consumption, calculate the difference between the above-mentioned predicted oil consumption and the above-mentioned measured oil consumption to obtain the oil consumption difference, and when the above-mentioned oil consumption difference is greater than a preset threshold, output an abnormal warning signal, wherein the above-mentioned abnormal warning signal indicates that there is an abnormality in the oil consumption.

[0126] Specifically, the first data-driven model predicts the oil consumption based on the engine parameters, and the second data-driven model predicts the ash loading based on the DPF pressure difference parameters, and calculates the measured oil consumption through the ash loading, that is, the oil consumption is indirectly obtained based on the emission particles during oil consumption. Under normal circumstances, the oil consumption calculated by the above two models should be equal or within a certain error range. Therefore, the present application presets a threshold value, calculates the difference between the above predicted oil consumption and the measured consumption, and determines whether the difference is within the preset threshold range. The fact that the above difference is not within the preset threshold range indicates that the oil consumption predicted by one of the models is abnormal, which further indicates that the difference between the oil that the engine should consume at a specific speed and the actual consumed oil is large, and there may be engine abnormalities, etc., and an abnormal warning signal is output to investigate the cause.

[0127] The devices in this article can be servers, PCs, PADs, mobile phones, etc.

[0128] Optionally, before obtaining the engine parameters, it also includes: establishing the above-mentioned first data-driven model and obtaining the first training data group, wherein the above-mentioned first training data group includes multiple groups of first training data, and each group of the above-mentioned first training data includes a historical engine parameter and a historical oil consumption corresponding to the above-mentioned historical engine parameter; using the multiple groups of the above-mentioned historical engine parameters as input parameters of the above-mentioned first data-driven model, and using the above-mentioned historical oil consumption corresponding to each group of the above-mentioned historical engine parameters as the output parameter of the above-mentioned first data-driven model, to train the above-mentioned first data-driven model.

[0129] Optionally, before obtaining the pressure difference parameter of the particulate filter, it also includes: establishing the above-mentioned second data-driven model and obtaining a second training data group, wherein the above-mentioned second training data group includes multiple groups of second training data, and each group of the above-mentioned second training data includes a historical pressure difference parameter and a historical ash loading corresponding to the above-mentioned historical pressure difference parameter; using the multiple above-mentioned historical pressure difference parameters as input parameters of the above-mentioned second data-driven model, and using the historical ash loadings corresponding to the multiple above-mentioned historical pressure difference parameters as output parameters of the above-mentioned second data-driven model, to train the above-mentioned second data-driven model.

[0130] Optionally, before inputting the above-mentioned engine parameters into the first data-driven model and inputting the above-mentioned pressure difference parameters into the second data-driven model, it also includes: performing data preprocessing on the above-mentioned engine parameters and the above-mentioned pressure difference parameters, wherein the above-mentioned data preprocessing step at least includes data fusion and data cleaning.

[0131] Optionally, the method further includes: when the oil consumption difference is less than or equal to the preset threshold, adding the engine parameters and the corresponding predicted oil consumption to the first training data group, wherein the first training data group includes multiple groups of first training data, each group of the first training data includes a historical engine parameter and a historical oil consumption corresponding to the historical engine parameter; using the pressure difference parameter as the input parameter of the second data-driven model, and using the ash load as the output parameter of the second data-driven model, to train the second data-driven model.

[0132] Optionally, the method further includes: when the oil consumption difference is less than or equal to the preset threshold, adding the pressure difference parameter and the corresponding ash load to a second training data group, wherein the second training data group includes multiple groups of second training data, and each group of the second training data includes a historical pressure difference parameter and a historical ash load corresponding to the historical pressure difference parameter.

[0133] Optionally, the method further includes: after calculating the measured oil consumption corresponding to the ash load, outputting the predicted oil consumption and the measured oil consumption and displaying them on a display screen.

[0134] The present application also provides a computer program product, which, when executed on a data processing device, is suitable for executing a program for initializing at least the following method steps:

[0135] Step S201, obtaining engine parameters, inputting the engine parameters into a first data-driven model, obtaining the oil consumption output by the first data-driven model, and obtaining predicted oil consumption, wherein the engine parameters at least include engine speed, engine torque and oil temperature, and the first data-driven model is used to predict and output the oil consumption according to the engine parameters;

[0136] Specifically, during the operation of the vehicle's engine, the engine has its corresponding oil consumption under different speeds and torques. It is easy to think that the greater the engine speed and torque, the greater the oil consumption. The above-mentioned oil consumption monitoring method is a data-driven oil consumption monitoring method. First, multiple sets of engine parameters and corresponding oil consumption are obtained through multiple bench tests, a first data-driven model is established, and the first data-driven model is trained by giving the engine parameters and the corresponding oil consumption. After the above-mentioned model training is completed, the parameters of the current engine can be input into the first data training model during the process of being put into use. The trained first data-driven model predicts the oil consumption based on the current engine parameters, outputs the prediction results, and obtains the predicted oil consumption.

[0137] Step S202, obtaining a pressure difference parameter of the particle filter, inputting the pressure difference parameter into a second data-driven model, obtaining a result output by the second data-driven model, and obtaining an ash loading, wherein the pressure difference parameter is a pressure difference between an inlet end and an outlet end of the particle filter, the ash loading is an ash content in the particle filter, and the second data-driven model is used to predict and output the ash loading according to the pressure difference parameter;

[0138] Specifically, during the combustion process of engine oil, a solid particle will be generated, which cannot be oxidized and converted into gas. It mainly comes from the additives in the engine oil, which is called ash. There is also a corresponding relationship between the engine oil consumption and the ash content. The greater the engine oil consumption, the greater the ash content. The ash content is called the ash load. The engine is usually equipped with a particulate filter (DPF) for filtering the above ash content. The DPF has an inlet and an outlet, and there is a pressure difference between the inlet and the outlet. Therefore, the second data-driven model is established and trained through the pressure difference parameter of the DPF and the ash load corresponding to the pressure difference parameter. After the model training is completed, the current pressure difference parameter is obtained during the use process, and the current rated pressure difference parameter is input into the second data-driven model to predict the ash load corresponding to the current pressure difference parameter. The engine oil consumption can be further calculated from the ash load, that is, the engine oil consumption is measured.

[0139] Step S203, calculate the oil consumption corresponding to the above-mentioned ash load to obtain the measured oil consumption, calculate the difference between the above-mentioned predicted oil consumption and the above-mentioned measured oil consumption to obtain the oil consumption difference, and when the above-mentioned oil consumption difference is greater than a preset threshold, output an abnormal warning signal, wherein the above-mentioned abnormal warning signal indicates that there is an abnormality in the oil consumption.

[0140] Specifically, the first data-driven model predicts the oil consumption based on the engine parameters, and the second data-driven model predicts the ash loading based on the DPF pressure difference parameters, and calculates the measured oil consumption through the ash loading, that is, the oil consumption is indirectly obtained based on the emission particles during oil consumption. Under normal circumstances, the oil consumption calculated by the above two models should be equal or within a certain error range. Therefore, the present application presets a threshold value, calculates the difference between the above predicted oil consumption and the measured consumption, and determines whether the difference is within the preset threshold range. The fact that the above difference is not within the preset threshold range indicates that the oil consumption predicted by one of the models is abnormal, which further indicates that the difference between the oil that the engine should consume at a specific speed and the actual consumed oil is large, and there may be engine abnormalities, etc., and an abnormal warning signal is output to investigate the cause.

[0141] Optionally, before obtaining the engine parameters, it also includes: establishing the above-mentioned first data-driven model and obtaining the first training data group, wherein the above-mentioned first training data group includes multiple groups of first training data, and each group of the above-mentioned first training data includes a historical engine parameter and a historical oil consumption corresponding to the above-mentioned historical engine parameter; using the multiple groups of the above-mentioned historical engine parameters as input parameters of the above-mentioned first data-driven model, and using the above-mentioned historical oil consumption corresponding to each group of the above-mentioned historical engine parameters as the output parameter of the above-mentioned first data-driven model, to train the above-mentioned first data-driven model.

[0142] Optionally, before obtaining the pressure difference parameter of the particulate filter, it also includes: establishing the above-mentioned second data-driven model and obtaining a second training data group, wherein the above-mentioned second training data group includes multiple groups of second training data, and each group of the above-mentioned second training data includes a historical pressure difference parameter and a historical ash loading corresponding to the above-mentioned historical pressure difference parameter; using the multiple above-mentioned historical pressure difference parameters as input parameters of the above-mentioned second data-driven model, and using the historical ash loadings corresponding to the multiple above-mentioned historical pressure difference parameters as output parameters of the above-mentioned second data-driven model, to train the above-mentioned second data-driven model.

[0143] Optionally, before inputting the above-mentioned engine parameters into the first data-driven model and inputting the above-mentioned pressure difference parameters into the second data-driven model, it also includes: performing data preprocessing on the above-mentioned engine parameters and the above-mentioned pressure difference parameters, wherein the above-mentioned data preprocessing step at least includes data fusion and data cleaning.

[0144] Optionally, the method further includes: when the oil consumption difference is less than or equal to the preset threshold, adding the engine parameters and the corresponding predicted oil consumption to the first training data group, wherein the first training data group includes multiple groups of first training data, each group of the first training data includes a historical engine parameter and a historical oil consumption corresponding to the historical engine parameter; using the pressure difference parameter as the input parameter of the second data-driven model, and using the ash load as the output parameter of the second data-driven model, to train the second data-driven model.

[0145] Optionally, the method further includes: when the oil consumption difference is less than or equal to the preset threshold, adding the pressure difference parameter and the corresponding ash load to a second training data group, wherein the second training data group includes multiple groups of second training data, and each group of the second training data includes a historical pressure difference parameter and a historical ash load corresponding to the historical pressure difference parameter.

[0146] Optionally, the method further includes: after calculating the measured oil consumption corresponding to the ash load, outputting the predicted oil consumption and the measured oil consumption and displaying them on a display screen.

[0147] Obviously, those skilled in the art should understand that the above modules or steps of the present invention can be implemented by a general computing device, they can be concentrated on a single computing device, or distributed on a network composed of multiple computing devices, they can be implemented by a program code executable by a computing device, so that they can be stored in a storage device and executed by the computing device, and in some cases, the steps shown or described can be executed in a different order than here, or they can be made into individual integrated circuit modules, or multiple modules or steps therein can be made into a single integrated circuit module for implementation. Thus, the present invention is not limited to any specific combination of hardware and software.

[0148] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0149] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0150] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0151] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process in the computer or other programmable device. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0152] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0153] The memory may include non-permanent memory in a computer-readable medium, random access memory (RAM) and / or non-volatile memory in the form of read-only memory (ROM) or flash RAM. The memory is an example of a computer-readable medium.

[0154] Computer readable media include permanent and non-permanent, removable and non-removable media that can be implemented by any method or technology to store information. Information can be computer readable instructions, data structures, program modules or other data. Examples of computer storage media 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 disk read-only memory (CD-ROM), digital versatile disk (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices or any other non-transmission media that can be used to store information that can be accessed by a computing device. As defined herein, computer readable media does not include temporary computer readable media (transitory media), such as modulated data signals and carrier waves.

[0155] It should also be noted that the terms "include", "comprises" or any other variations thereof are intended to cover non-exclusive inclusion, so that a process, method, commodity or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, commodity or device. In the absence of more restrictions, the elements defined by the sentence "comprises a ..." do not exclude the existence of other identical elements in the process, method, commodity or device including the elements.

[0156] From the above description, it can be seen that the above embodiments of the present application achieve the following technical effects:

[0157] 1) In the oil consumption monitoring method of the present application, a first data-driven model and a second data-driven model are pre-established, and the engine parameters are first obtained, and the engine parameters are input into the pre-established first data-driven model to obtain the oil consumption output by the first data-driven model to obtain the predicted oil consumption, and then the pressure difference parameters are obtained, and the pressure difference parameters are input into the second data-driven model to obtain the result output by the second data-driven model to obtain the ash loading, and the measured oil consumption is calculated according to the ash loading, and the difference between the predicted oil consumption and the measured oil consumption is calculated to obtain the oil consumption difference, and the oil consumption difference is compared to see whether it is less than a preset threshold value. When the oil consumption difference is less than the preset threshold value, it indicates that the difference between the predicted oil consumption and the actual oil consumption is within the range of the preset threshold value, and the oil consumption is normal. When the oil consumption difference is greater than the preset threshold value, it indicates that the difference between the predicted oil consumption and the actual oil consumption is outside the range of the preset threshold value, and the oil consumption is abnormal, and an abnormal warning signal is output. Compared with the prior art, which can only obtain the oil consumption under specific speed and torque conditions through bench testing, but cannot monitor the oil consumption in real time, the present application can obtain the predicted oil consumption and the measured oil consumption in real time, and determine whether there is an abnormality in the oil consumption based on the difference in oil consumption. Therefore, it can solve the problem that the prior art cannot monitor the oil consumption in real time, and achieve the purpose of real-time monitoring of the vehicle's oil consumption.

[0158] 2) In the oil consumption monitoring device of the present application, a first data-driven model and a second data-driven model are established, and the engine parameters are first obtained, and the engine parameters are input into the pre-established first data-driven model, and the oil consumption output by the first data-driven model is obtained to obtain the predicted oil consumption, and the pressure difference parameters are obtained, and the pressure difference parameters are input into the second data-driven model, and the result output by the second data-driven model is obtained to obtain the ash loading, and the measured oil consumption is calculated according to the ash loading, and the difference between the above-mentioned predicted oil consumption and the measured oil consumption is calculated to obtain the oil consumption difference, and the above-mentioned oil consumption difference is compared to see whether it is less than a preset threshold value. When the above-mentioned oil consumption difference is less than the preset threshold value, it indicates that the difference between the predicted oil consumption and the actual oil consumption is within the range of the preset threshold value, and the oil consumption is normal. When the above-mentioned oil consumption difference is greater than the preset threshold value, it indicates that the difference between the predicted oil consumption and the actual oil consumption is outside the range of the preset threshold value, and the oil consumption is abnormal, and an abnormal warning signal is output. Compared with the prior art, which can only obtain the oil consumption under specific speed and torque conditions through bench testing and cannot monitor the oil consumption in real time, the present application can obtain the predicted oil consumption and the measured oil consumption in real time, and determine whether there is an abnormality in the oil consumption based on the difference in oil consumption. Therefore, it can solve the problem that the prior art cannot monitor the oil consumption in real time and achieve the purpose of real-time monitoring of the vehicle's oil consumption.

[0159] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A method for monitoring engine oil consumption, characterized in that: include: Acquire engine parameters, input the engine parameters into a first data-driven model, acquire the oil consumption output by the first data-driven model, and obtain predicted oil consumption, wherein the engine parameters include at least engine speed, engine torque, and oil temperature, and the first data-driven model is used to predict and output the oil consumption according to the engine parameters; Obtaining a pressure difference parameter of the particle filter, inputting the pressure difference parameter into a second data-driven model, obtaining a result output by the second data-driven model, and obtaining an ash loading, wherein the pressure difference parameter is a pressure difference between an inlet end and an outlet end of the particle filter, the ash loading is an ash content in the particle filter, and the second data-driven model is used to predict and output the ash loading according to the pressure difference parameter; Calculate the oil consumption corresponding to the ash load to obtain the measured oil consumption, calculate the difference between the predicted oil consumption and the measured oil consumption to obtain the oil consumption difference, and output an abnormal warning signal when the oil consumption difference is greater than a preset threshold, wherein the abnormal warning signal indicates that there is an abnormality in the oil consumption.

2. The monitoring method according to claim 1, characterized in that: Before obtaining engine parameters, it also includes: Establishing the first data-driven model and acquiring a first training data group, wherein the first training data group includes multiple groups of first training data, and each group of the first training data includes a historical engine parameter and a historical oil consumption corresponding to the historical engine parameter; The first data-driven model is trained by using multiple groups of historical engine parameters as input parameters of the first data-driven model and using the historical oil consumption corresponding to each group of historical engine parameters as an output parameter of the first data-driven model.

3. The monitoring method according to claim 1, characterized in that: Before obtaining the differential pressure parameters of the particle filter, it also includes: Establishing the second data-driven model and acquiring a second training data group, wherein the second training data group includes multiple groups of second training data, and each group of the second training data includes a historical pressure difference parameter and a historical ash loading corresponding to the historical pressure difference parameter; The second data-driven model is trained by using the plurality of historical pressure difference parameters as input parameters of the second data-driven model and the historical ash loads corresponding to the plurality of historical pressure difference parameters as output parameters of the second data-driven model.

4. The monitoring method according to claim 1, characterized in that: Before inputting the engine parameter into the first data-driven model and inputting the pressure difference parameter into the second data-driven model, the method further includes: The engine parameters and the pressure difference parameters are subjected to data preprocessing, wherein the data preprocessing step at least includes data fusion and data cleaning.

5. The monitoring method according to claim 1, characterized in that: Also includes: When the oil consumption difference is less than or equal to the preset threshold, the engine parameter and the corresponding predicted oil consumption are added to a first training data group, wherein the first training data group includes multiple groups of first training data, each group of the first training data includes a historical engine parameter and a historical oil consumption corresponding to the historical engine parameter; The pressure difference parameter is used as an input parameter of the second data-driven model, and the ash load is used as an output parameter of the second data-driven model to train the second data-driven model.

6. The monitoring method according to claim 1, characterized in that: Also includes: When the oil consumption difference is less than or equal to the preset threshold, the pressure difference parameter and the corresponding ash loading are added to the second training data group, wherein the second training data group includes multiple groups of second training data, each group of the second training data includes a historical pressure difference parameter and a historical ash loading corresponding to the historical pressure difference parameter.

7. The monitoring method according to claim 1, characterized in that: After calculating the measured oil consumption corresponding to the ash loading, the method further comprises: The predicted oil consumption and the measured oil consumption are output and displayed on a display screen.

8. A device for monitoring engine oil consumption, characterized in that: include: a first acquisition unit, configured to acquire engine parameters, input the engine parameters into a first data-driven model, acquire the oil consumption output by the first data-driven model, and obtain predicted oil consumption, wherein the engine parameters at least include engine speed, engine torque, and oil temperature, and the first data-driven model is configured to predict and output the oil consumption according to the engine parameters; a second acquisition unit, configured to acquire a pressure difference parameter of the particle filter, input the pressure difference parameter into a second data-driven model, acquire a result output by the second data-driven model, and obtain an ash loading, wherein the pressure difference parameter is a pressure difference between an inlet end and an outlet end of the particle filter, the ash loading is an ash content in the particle filter, and the second data-driven model is configured to predict and output the ash loading according to the pressure difference parameter; The output unit is used to calculate the oil consumption corresponding to the ash load to obtain the measured oil consumption, calculate the difference between the predicted oil consumption and the measured oil consumption to obtain the oil consumption difference, and output an abnormal warning signal when the oil consumption difference is greater than a preset threshold, wherein the abnormal warning signal indicates that there is an abnormality in the oil consumption.

9. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a stored program, wherein when the program is executed, the device where the computer-readable storage medium is located is controlled to execute the monitoring method according to any one of claims 1 to 7.

10. An electronic device comprising a memory and a processor, characterized in that: A computer program is stored in the memory, and the processor is configured to execute the monitoring method according to any one of claims 1 to 7 through the computer program.

Images