Method, device and equipment for predicting service life of engine oil, medium and vehicle

Abstract

The invention discloses a method, device and equipment for predicting the service life of engine oil, a medium and a vehicle. The method comprises the following steps: predicting physicochemical property parameters of engine oil according to vehicle working condition data and operation state parameters of the engine oil in a vehicle engine; and predicting the residual life of the engine oil according to the running state parameters and the physicochemical property parameters. The embodiment of the invention can realize accurate prediction of the engine oil life of the engine.

Description

Technical Field

[0001] The present invention relates to the technical field of vehicle maintenance, and in particular to a method, device, equipment, medium and vehicle for predicting engine oil life. Background Art

[0002] The engine oil in the engine has multiple functions such as lubrication, cooling, cleaning, and sealing the engine. As time goes by and the mileage increases, the performance of the engine oil will decline due to aging. Therefore, the engine oil needs to be replaced regularly to complete the maintenance of the engine.

[0003] The engine maintenance plans currently designed by automobile manufacturers mostly use relatively conservative maximum oil change mileage or maximum oil change time to calculate the life percentage. They are unable to provide the optimal maintenance mileage recommendation based on the actual usage of the engine, and are prone to changing the oil too early or too late. The former is likely to cause waste of resources and economic losses, while the latter is likely to affect driving safety. Summary of the Invention

[0004] The present invention provides a method, device, equipment, medium and vehicle for predicting the life of engine oil, so as to achieve accurate prediction of the life of engine oil.

[0005] According to one aspect of the present invention, a method for predicting engine oil life is provided, comprising:

[0006] Predicting physical and chemical property parameters of the engine oil based on vehicle operating condition data and operating state parameters of the engine oil in the vehicle engine;

[0007] The remaining life of the engine oil is predicted based on the operating state parameter and the physical and chemical characteristic parameter.

[0008] According to another aspect of the present invention, there is provided a device for predicting engine oil life, comprising:

[0009] a parameter prediction module, configured to predict the physical and chemical property parameters of the engine oil based on vehicle operating condition data and operating state parameters of the engine oil in the vehicle engine;

[0010] A life prediction module is used to predict the remaining life of the engine oil based on the operating state parameters and the physical and chemical characteristic parameters.

[0011] According to another aspect of the present invention, a computer program product is provided, comprising a computer program, wherein when the computer program is executed by a processor, the computer program implements the method for predicting engine oil life according to any embodiment of the present invention.

[0012] According to another aspect of the present invention, an electronic device is provided, comprising: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores a computer program executable by the at least one processor, and the computer program is executed by the at least one processor so that the at least one processor can execute the method for predicting engine oil life as described in any embodiment of the present invention.

[0013] According to another aspect of the present invention, a computer-readable storage medium is provided, wherein the computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for predicting engine oil life according to any embodiment of the present invention when executed.

[0014] According to another aspect of the present invention, a vehicle is provided, on which a background monitoring system is deployed, and the background monitoring system is used to implement the method for predicting the engine oil life according to any embodiment of the present invention.

[0015] The embodiments of the present invention predict the physical and chemical properties of engine oil based on existing data, and accordingly predict the remaining life of the engine oil. This can provide optimal maintenance time and mileage recommendations based on the customer's actual operating conditions, eliminating the need for the customer to mechanically perform engine maintenance based on the maximum oil change cycle / mileage. This not only saves the customer's expenses, but also ensures the customer's driving safety.

[0016] It should be understood that the content described in this section is not intended to identify the key or important features of the embodiments of the present invention, nor is it intended to limit the scope of the present invention. Other features of the present invention will become readily understood through the following description. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0018] Figure 1 is a flow chart of a method for predicting engine oil life according to one embodiment of the present invention;

[0019] Figure 2A is a flow chart of a method for predicting engine oil life according to another embodiment of the present invention;

[0020] Figure 2B is a schematic diagram of a prediction process provided according to another embodiment of the present invention;

[0021] Figure 2C is a schematic diagram of a prediction effect provided according to another embodiment of the present invention;

[0022] Figure 3 is a schematic structural diagram of a device for predicting engine oil life according to another embodiment of the present invention;

[0023] Figure 4 It is a schematic structural diagram of an electronic device implementing an embodiment of the present invention. DETAILED DESCRIPTION

[0024] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts should fall within the scope of protection of the present invention.

[0025] It should be noted that the terms "first," "second," and the like in the present invention are used to distinguish similar objects and are not necessarily used to describe a specific order or precedence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments of the present invention described herein can be implemented in an order other than those illustrated or described herein. In addition, the terms "including," "having," and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product, or apparatus comprising a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units that are not explicitly listed or that are inherent to these processes, methods, products, or apparatuses.

[0026] Figure 1 This is a flowchart of a method for predicting the life of an engine oil provided by one embodiment of the present invention. This embodiment is applicable to situations where a maintenance plan is formulated using the predicted remaining life of the oil, rather than a fixed maximum oil change mileage or maximum oil change time. This method can be executed by an engine oil life prediction device, which can be implemented in the form of hardware and / or software. The device can be configured in an electronic device with corresponding data processing capabilities, such as a background monitoring system (deployed in a vehicle or an intelligent cloud platform). Figure 1 As shown, the method includes:

[0027] S110 , predicting physical and chemical property parameters of the engine oil based on vehicle operating condition data and operating state parameters of the engine oil in the vehicle engine.

[0028] S120: Predict the remaining life of the engine oil according to the operating state parameter and the physical and chemical characteristic parameter.

[0029] Vehicle operating condition data refers to data collected by the vehicle's electronic control unit (ECU), such as ambient temperature, vehicle mileage, extended-range mileage, vehicle driving mode, battery SOC, extended-range operating time, range extender speed, range extender power, coolant temperature, and oil pressure. Operating status parameters directly reflect the real-time operating status of the engine oil, primarily including oil temperature and level. Physicochemical property parameters are physical or chemical properties used to evaluate engine oil performance, primarily including viscosity, pH value, oxidation-nitration value, initial pH value, fuel dilution rate, and water contamination rate. Remaining life can be expressed as a percentage, such as 60% remaining life; or as a time, such as 9 months remaining life.

[0030] Specifically, the backend monitoring system collects vehicle operating data collected by the electronic control unit during vehicle operation, as well as the operating parameters of the engine oil. Periodically, or when a request for remaining life is received, the system predicts the physical and chemical properties of the engine oil based on this data.

[0031] The engine is pre-tested under different vehicle operating conditions, and engine oil is sampled during the test to obtain physical and chemical property parameters under different operating conditions and operating state parameters. These parameters serve as training data for the physical and chemical property parameter prediction model. A machine learning model with an appropriate model structure is selected and trained using the training data to obtain the physical and chemical property parameter prediction model.

[0032] After training, the physicochemical property parameter prediction model is deployed to the backend monitoring system. When the physicochemical property parameter prediction of engine oil is needed, the system calls the physicochemical property parameter prediction model, inputs vehicle operating data and operating status parameters, and predicts the physicochemical property parameter output of the engine oil.

[0033] Both operating parameters and physical and chemical characteristics can reflect the degree of oil aging in a specific area. For each parameter (operating status / physical and chemical characteristics), the degree of oil aging under that parameter is determined through direct calculation or curve fitting. The remaining life of the oil is determined by comprehensively analyzing the degree of oil aging under various parameters, and a vehicle maintenance plan is formulated based on this remaining life.

[0034] In addition, the lower limit of the engine oil life in each stage is pre-set to determine whether the remaining life is lower than the lower limit of the life in the current stage. If it is not lower, the calculated remaining life is reported and no other processing is performed. If it does not meet the requirements, in addition to reporting the calculated remaining life, it is also necessary to warn the customer of oil abnormalities and remind the customer to go to the after-sales service to check the engine in time. For example, the lower limit of the remaining life of the engine oil in the current stage is 9 months, and the remaining life calculated this time is 6 months. After reminding the customer of the oil abnormality, the customer went to the after-sales service to check the engine and perform maintenance in advance. It was found that the engine oil pan was damaged by bumps and bumps, resulting in abnormal remaining life of the engine oil, which effectively protects the customer's driving safety.

[0035] In addition, after the physical and chemical property parameter prediction model is launched, the analysis results given during after-sales maintenance of the engine oil and the physical and chemical property parameters predicted by the physical and chemical property parameter prediction model are compared, and reinforcement learning training and continuous iteration are carried out on the physical and chemical property parameters to improve the model accuracy.

[0036] According to tests, compared with the previous maximum oil change mileage of 20,000 kilometers for all customers, the present invention has initially achieved an oil change mileage of more than 30,000 kilometers for 80% of customers and more than 35,000 kilometers for 30% of customers, which greatly reduces customers' time and money expenditure.

[0037] The embodiment of the present invention predicts the physical and chemical properties of engine oil based on existing data, and accordingly predicts the remaining life of the engine oil. It can provide optimal maintenance time and mileage recommendations based on the customer's actual usage, eliminating the need for the customer to mechanically perform engine maintenance based on the maximum oil change cycle / mileage. On the one hand, this saves the customer's expenses, and on the other hand, it also ensures the customer's driving safety.

[0038] Figure 2A This is a flow chart of a method for predicting engine oil life provided by another embodiment of the present invention. This embodiment optimizes and improves the operating state of the above embodiment. Figure 2A As shown, the method includes:

[0039] S210 . Based on the vehicle operating condition data, predict the level of the engine oil using an engine oil level prediction model; based on the vehicle operating condition data, predict the temperature of the engine oil using an engine oil temperature prediction model.

[0040] Specifically, an oil temperature mechanism model was established based on the oil-water heat exchange model (using a reverse flow model), the temperature delay model, and the flow delay model. This was then integrated with a data-driven machine learning model to create an oil temperature prediction model, enabling high-precision, real-time prediction of oil temperature. A mechanism model for engine oil water content was established based on condensation, evaporation, and heat and mass transfer. The data-driven machine learning model was used to fit the mechanism model error to create an oil level prediction model, achieving strong generalization and high-precision prediction of oil level.

[0041] Existing solutions often rely on deploying level sensors and temperature sensors to determine the oil level and oil temperature, respectively, resulting in high costs. The present invention integrates a mechanistic physical model with a machine learning model to produce an oil level prediction model and an oil temperature prediction model. Subsequently, by inputting vehicle operating condition data into the oil level prediction model, the oil level in the engine, as predicted by the oil level prediction model, is obtained; by inputting vehicle operating condition data into the oil temperature prediction model, the oil temperature in the engine, as predicted by the oil temperature prediction model, is obtained. This eliminates the need for deploying corresponding sensor hardware on the engine, reducing hardware, production, and maintenance costs.

[0042] S220 , inputting the vehicle operating condition data and the operating state parameters of the engine oil in the vehicle engine into a physical and chemical property parameter prediction model to obtain the physical and chemical property parameters of the engine oil.

[0043] Specifically, when the number of physical and chemical property parameters to be predicted is large, multiple physical and chemical property parameter prediction models can be trained. Each physical and chemical property parameter prediction model focuses on the prediction of one or several physical and chemical property parameters, thereby achieving efficient decomposition of prediction tasks and improving prediction accuracy.

[0044] Based on the above embodiment, the number of the physical and chemical characteristic parameters and the physical and chemical characteristic parameter prediction models are at least two and they correspond one to one. The physical and chemical characteristic parameters of the engine oil are obtained by inputting the vehicle operating condition data and the operating state parameters of the engine oil in the vehicle engine into the physical and chemical characteristic parameter prediction model, including:

[0045] For each physical and chemical characteristic parameter to be predicted, determining a target physical and chemical characteristic parameter prediction model corresponding to the physical and chemical characteristic parameter;

[0046] The vehicle operating condition data and the operating state parameters of the engine oil in the vehicle engine are input into the target physical and chemical characteristic parameter prediction model to obtain the physical and chemical characteristic parameter of the engine oil.

[0047] Specifically, to improve the accuracy of physicochemical property parameter predictions, when the number of physicochemical property parameters is not unique, a corresponding physicochemical property parameter prediction model is trained for each physicochemical property parameter. When predicting physicochemical property parameters, for each physicochemical property parameter to be predicted, a target physicochemical property parameter prediction model corresponding to that physicochemical property parameter is selected from at least two physicochemical property parameter prediction models. Vehicle operating condition data and operating state parameters are then input into the target physicochemical property parameter prediction model to obtain the physicochemical property parameter for that engine oil. This process is repeated for the remaining physicochemical property parameters to obtain the respective physicochemical property parameters for the engine oil.

[0048] In addition, to further improve the prediction effect, the model structure of each physical and chemical characteristic parameter prediction model can be set in a targeted manner to match the physical and chemical characteristic parameter prediction model with the physical and chemical characteristic parameter prediction to achieve the best prediction results. After experimental testing, the model structure of each physical and chemical characteristic parameter prediction model is as follows:

[0049] The prediction model for the physical and chemical property parameters corresponding to engine oil viscosity is a random forest decision tree regression model;

[0050] The prediction model for the physical and chemical property parameters corresponding to the oil oxidation value is a gradient boosting framework model;

[0051] The prediction model for the physical and chemical property parameters corresponding to the nitration value of engine oil is a gradient boosting framework model;

[0052] The prediction model of the physical and chemical property parameters corresponding to the oil acid value is the ridge regression linear model;

[0053] The prediction model of the physical and chemical characteristic parameters corresponding to the base number of the engine oil is the ridge regression linear model;

[0054] The prediction model for the physical and chemical property parameters corresponding to the water content of engine oil is a gradient boosting decision tree model;

[0055] The prediction model of the physical and chemical property parameters corresponding to the oil and fuel dilution rate is a gradient boosting decision tree regression model.

[0056] The model parameters of some model structures are shown in Table 1 below:

[0057] Table 1

[0058]

[0059] It should be noted that the initial pH value is a fixed value. It is used as a reference when predicting the remaining life of the engine oil, but it does not need to be predicted based on vehicle operating data and operating status parameters.

[0060] For example, Figure 2B As shown, vehicle operating condition data is first input into the oil level prediction model and the oil temperature prediction model to obtain the oil temperature and level. The oil temperature, oil level, and vehicle operating condition data are then sequentially input into the prediction models corresponding to the various physical and chemical property parameters to obtain the respective physical and chemical property parameters.

[0061] Based on the above embodiment, optionally, the training process of the physical and chemical property parameter prediction model is as follows:

[0062] Collect vehicle operating condition data, engine oil operating status parameters, and physical and chemical property parameters during the engine test to obtain initial training data;

[0063] Performing feature engineering and time alignment on the initial training data to obtain target training data;

[0064] The machine learning model is trained according to the target training data to obtain a physical and chemical property parameter prediction model.

[0065] Specifically, the engine is tested under different operating conditions, and during the test process, the vehicle operating condition data, the operating status parameters and the physical and chemical property parameters of the engine oil are sampled regularly (for example, once every 12 hours) to obtain the initial training data, and the initial training data is preprocessed (data cleaning, completion and anomaly replacement).

[0066] For the preprocessed data, the cumulative value of the vehicle operating condition data is calculated within each sampling time interval, and its 25%, 50% (median) and 75% quantile values ​​are calculated within the sampling interval to obtain the input operating condition characteristic parameters.

[0067] According to the sampling time, the operating condition characteristic parameters (input), operating state parameters (input) and the corresponding oil physical and chemical characteristic parameters (output) are time-synchronized and aligned to obtain the target training data.

[0068] The target training data is divided into a training set and a test set. The former is used to train the machine learning model, and the latter is used to test the machine learning model. The machine learning model that passes the test is determined as a physical and chemical property parameter prediction model.

[0069] S230: Predict the remaining life of the engine oil according to the operating state parameter and the physical and chemical characteristic parameter.

[0070] In the operating state of the above embodiment, optionally, determining the remaining life of the engine oil according to the operating state parameter and the physical and chemical characteristic parameter includes:

[0071] Determining the aging degree of the operating state parameter and the physical and chemical characteristic parameter based on the preset parameter initial value and parameter failure value;

[0072] The remaining life of the engine oil is predicted by comprehensively considering the aging degree of the operating state parameters and the physical and chemical characteristic parameters.

[0073] Specifically, the direct calculation method requires presetting initial and expiration values ​​for each parameter. For parameters requiring direct calculation to determine aging, the absolute value of the first difference between the current value and the expiration value, as well as the absolute value of the second difference between the initial value and the final value, are calculated. The quotient of the first and second absolute values ​​of the difference is subtracted from 1 to determine the aging degree of the parameter. By combining the aging degrees of each parameter, the remaining life of the engine oil can be determined.

[0074] For example, the direct calculation formula is as follows:

[0075]

[0076] In the operating state of the above embodiment, optionally, determining the remaining life of the engine oil according to the operating state parameter and the physical and chemical characteristic parameter includes:

[0077] Determining the aging degree of the operating state parameter and the physical and chemical characteristic parameter based on a preset engine oil aging curve;

[0078] The remaining life of the engine oil is predicted by comprehensively considering the aging degree of the operating state parameters and the physical and chemical characteristic parameters.

[0079] Specifically, the curve fitting method requires extensive engine durability testing to establish oil aging curves for various parameters from the beginning to the end of the oil's life. For parameters whose aging needs to be determined using the curve fitting method, the current value of the parameter is substituted into the oil aging curve for that parameter. The corresponding position of the parameter on the oil performance aging curve is determined. The aging degree of the parameter is then determined based on the distance from the starting and ending points of the curve. By combining the aging degrees of various parameters, the remaining life of the oil can be determined.

[0080] It should be noted that whether to use the curve fitting method or the direct calculation method depends on the type of parameter. For parameters with linear aging, the aging degree can be determined by the direct calculation method; for parameters with nonlinear aging, the aging degree should be determined by the curve fitting method.

[0081] For example, Figure 2C As shown, the accuracy of the prediction scheme of the present invention is analyzed by taking the prediction of acid value TAN and base number TBN as an example.

[0082] During the engine operation time of 400 hours or more, the oil pH value is predicted and sampled for multiple times to obtain the predicted value at each time point (i.e. Figure 2C The yellow dots in the figure), and the current (Actual) values ​​at each time point (i.e. Figure 2C blue dot in the .

[0083] Comparing the two curves obtained from the fitting points in the graph, it is easy to see that the dividing line is 300 hours. During the first 300 hours, the predicted and actual values ​​of the total pH value of the engine oil are very close, indicating that the prediction model for the physical and chemical properties of the engine oil has a high degree of accuracy. After 300 hours, although the predicted value deviates from the actual value, this deviation is quickly corrected, and the predicted value gradually approaches the actual value, and the accuracy of the prediction results quickly recovers.

[0084] By using the oil level prediction model and the oil temperature prediction model, the embodiment of the present invention eliminates the need to deploy corresponding sensor hardware on the engine, thereby reducing hardware, production, and maintenance costs.

[0085] Figure 3 This is a schematic diagram of the structure of a device for predicting engine oil life provided by another embodiment of the present invention. Figure 3 As shown, the device includes:

[0086] a parameter prediction module 310 for predicting the physical and chemical property parameters of the engine oil based on the vehicle operating condition data and the operating state parameters of the engine oil in the vehicle engine;

[0087] The life prediction module 320 is used to predict the remaining life of the engine oil based on the operating state parameters and the physical and chemical characteristic parameters.

[0088] The engine oil life prediction device provided in the embodiment of the present invention can execute the engine oil life prediction method provided in any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0089] Optionally, the parameter prediction module 310 is specifically used to: input the vehicle operating condition data and the operating state parameters of the engine oil in the vehicle engine into the physical and chemical characteristic parameter prediction model to obtain the physical and chemical characteristic parameters of the engine oil.

[0090] Optionally, the number of the physicochemical characteristic parameters and the physicochemical characteristic parameter prediction models are at least two and are in one-to-one correspondence, and the parameter prediction module 310 includes:

[0091] A model selection unit is used to determine, for each physical and chemical characteristic parameter to be predicted, a target physical and chemical characteristic parameter prediction model corresponding to the physical and chemical characteristic parameter;

[0092] The parameter prediction unit is used to input the vehicle operating condition data and the operating state parameters of the engine oil in the vehicle engine into the target physical and chemical characteristic parameter prediction model to obtain the physical and chemical characteristic parameter of the engine oil.

[0093] Optionally, the training process of the physical and chemical property parameter prediction model is as follows:

[0094] Collect vehicle operating condition data, engine oil operating status parameters, and physical and chemical property parameters during the engine test to obtain initial training data;

[0095] Performing feature engineering and time alignment on the initial training data to obtain target training data;

[0096] The machine learning model is trained according to the target training data to obtain a physical and chemical property parameter prediction model.

[0097] Optionally, the device further includes:

[0098] A liquid level prediction module is used to predict the liquid level of the engine oil based on the vehicle operating condition data through the oil level prediction model;

[0099] The temperature prediction module is used to predict the temperature of the engine oil through the oil temperature prediction model based on the vehicle operating condition data.

[0100] Optionally, the life prediction module 320 is specifically used to: determine the aging degree of the operating state parameters and the physical and chemical characteristic parameters based on a preset engine oil aging curve; and predict the remaining life of the engine oil based on the aging degree of the operating state parameters and the physical and chemical characteristic parameters.

[0101] Optionally, the life prediction module 320 is specifically used to: determine the aging degree of the operating status parameters and the physical and chemical characteristic parameters based on preset parameter initial values ​​and parameter failure values; and predict the remaining life of the engine oil based on the aging degree of the operating status parameters and the physical and chemical characteristic parameters.

[0102] The engine oil life prediction device further described can also execute the engine oil life prediction method provided by any embodiment of the present invention, and has the corresponding functional modules and beneficial effects of the execution method.

[0103] Figure 4 A schematic diagram of the structure of an electronic device 40 that can be used to implement an embodiment of the present invention is shown. The electronic device is intended to represent various forms of digital computers, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processing, cellular phones, smart phones, wearable devices (such as helmets, glasses, watches, etc.) and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the present invention described and / or claimed herein.

[0104] like Figure 4As shown, the electronic device 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42, a random access memory (RAM) 43, etc., which is communicatively connected to the at least one processor 41. The memory stores a computer program that can be executed by the at least one processor, and the processor 41 can perform various appropriate actions and processes according to the computer program stored in the read-only memory (ROM) 42 or the computer program loaded from the storage unit 48 into the random access memory (RAM) 43. Various programs and data required for the operation of the electronic device 40 can also be stored in the RAM 43. The processor 41, ROM 42, and RAM 43 are connected to each other via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0105] Multiple components in the electronic device 40 are connected to the I / O interface 45, including an input unit 46, such as a keyboard, a mouse, etc.; an output unit 47, such as various types of displays, speakers, etc.; a storage unit 48, such as a magnetic disk, an optical disk, etc.; and a communication unit 49, such as a network card, a modem, a wireless communication transceiver, etc. The communication unit 49 allows the electronic device 40 to exchange information / data with other devices via a computer network such as the Internet and / or various telecommunication networks.

[0106] Processor 41 can be any general-purpose and / or specialized processing component with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various specialized artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, digital signal processors (DSPs), and any other suitable processor, controller, microcontroller, etc. Processor 41 executes the various methods and processes described above, such as the method for predicting engine oil life.

[0107] In some embodiments, the engine oil life prediction method can be implemented as a computer program tangibly embodied in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program can be loaded and / or installed on electronic device 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the engine oil life prediction method described above can be performed. Alternatively, in other embodiments, processor 41 can be configured to execute the engine oil life prediction method in any other suitable manner (e.g., via firmware).

[0108] In some embodiments, the engine oil life prediction method can be implemented by a background monitoring system installed on the vehicle. The background monitoring system is deployed locally on the vehicle to timely obtain operating data and other data required for analyzing the remaining oil life, and then locally analyze this data to determine the remaining oil life.

[0109] Various embodiments of the systems and techniques described herein can be implemented in digital electronic circuit systems, integrated circuit systems, field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), system-on-chip systems (SOCs), programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include being implemented in one or more computer programs that are executable and / or interpreted on a programmable system that includes at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0110] Computer programs for implementing the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when the computer program is executed by the processor, the functions / operations specified in the flowcharts and / or block diagrams are implemented. The computer program may be executed entirely on the machine, partially on the machine, as a stand-alone software package, partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0111] In the context of the present invention, computer-readable storage media can be tangible media that can contain or store a computer program for use with an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. Computer-readable storage media can include but are not limited to electronic, magnetic, optical, electromagnetic, infrared or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. Alternatively, computer-readable storage media can be machine-readable signal media. More specific examples of machine-readable storage media can include electrical connections based on one or more lines, portable computer disks, hard disks, random access memories (RAM), read-only memories (ROM), erasable programmable read-only memories (EPROM or flash memory), optical fibers, portable compact disk read-only memories (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0112] To provide interaction with a customer, the systems and techniques described herein can be implemented on an electronic device having: a display device (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the customer; and a keyboard and pointing device (e.g., a mouse or trackball) through which the customer can provide input to the electronic device. Other types of devices can also be used to provide interaction with the customer; for example, the feedback provided to the customer can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and the input from the customer can be received in any form (including acoustic input, voice input, or tactile input).

[0113] The systems and techniques described herein can be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a client computer with a graphical client interface or web browser through which a client can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system can be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), a blockchain network, and the Internet.

[0114] A computing system may include clients and servers. The clients and servers are typically remote from each other and typically interact via a communication network. This client-server relationship arises through computer programs running on the respective computers, creating a client-server relationship. The server may be a cloud server, also known as a cloud computing server or cloud host. This server is a hosting product within the cloud computing service ecosystem that addresses the management difficulties and limited scalability of traditional physical hosting and VPS services.

[0115] It should be understood that the various forms of the processes shown above can be used to reorder, add, or delete steps. For example, the steps described in the present invention can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technical solution of the present invention can be achieved. This is not limited herein.

[0116] The above specific embodiments do not limit the scope of protection of the present invention. Those skilled in the art will appreciate that various modifications, combinations, sub-combinations, and substitutions may be made based on design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of the present invention are intended to be included within the scope of protection of the present invention.

Claims

1. A method for predicting engine oil life, characterized in that: The method comprises: Predicting physical and chemical property parameters of the engine oil based on vehicle operating condition data and operating state parameters of the engine oil in the vehicle engine; The remaining life of the engine oil is predicted based on the operating state parameter and the physical and chemical characteristic parameter.

2. The method according to claim 1, characterized in that The method of predicting the physical and chemical property parameters of the engine oil based on the vehicle operating condition data and the operating state parameters of the engine oil in the vehicle engine includes: The vehicle operating condition data and the operating state parameters of the engine oil in the vehicle engine are input into the physical and chemical characteristic parameter prediction model to obtain the physical and chemical characteristic parameters of the engine oil.

3. The method according to claim 2, characterized in that The number of the physical and chemical characteristic parameters and the physical and chemical characteristic parameter prediction models are at least two and are in one-to-one correspondence. The physical and chemical characteristic parameters of the engine oil are obtained by inputting the vehicle operating condition data and the operating state parameters of the engine oil in the vehicle engine into the physical and chemical characteristic parameter prediction model, including: For each physical and chemical characteristic parameter to be predicted, determining a target physical and chemical characteristic parameter prediction model corresponding to the physical and chemical characteristic parameter; The vehicle operating condition data and the operating state parameters of the engine oil in the vehicle engine are input into the target physical and chemical characteristic parameter prediction model to obtain the physical and chemical characteristic parameter of the engine oil.

4. The method according to claim 3, characterized in that The training process of the physicochemical property parameter prediction model is as follows: Collect vehicle operating condition data, engine oil operating status parameters, and physical and chemical property parameters during the engine test to obtain initial training data; Performing feature engineering and time alignment on the initial training data to obtain target training data; The machine learning model is trained according to the target training data to obtain a physical and chemical property parameter prediction model.

5. The method according to any one of claims 1 to 4, characterized in that The predicting of the remaining life of the engine oil according to the operating state parameter and the physical and chemical characteristic parameter includes: Determining the aging degree of the operating state parameter and the physical and chemical characteristic parameter based on the preset parameter initial value and parameter failure value; The remaining life of the engine oil is predicted by comprehensively considering the aging degree of the operating state parameters and the physical and chemical characteristic parameters.

6. The method according to any one of claims 1 to 4, characterized in that The predicting of the remaining life of the engine oil according to the operating state parameter and the physical and chemical characteristic parameter includes: Determining the aging degree of the operating state parameter and the physical and chemical characteristic parameter based on a preset engine oil aging curve; The remaining life of the engine oil is predicted by comprehensively considering the aging degree of the operating state parameters and the physical and chemical characteristic parameters.

7. The method according to any one of claims 1 to 4, characterized in that The operating state parameters include temperature and liquid level. Before predicting the physical and chemical property parameters of the engine oil based on the vehicle operating condition data and the operating state parameters of the engine oil in the vehicle engine, the method further includes: Based on vehicle operating condition data, the oil level in the engine is predicted using an oil level prediction model; Based on vehicle operating condition data, the temperature of the engine oil is predicted using an oil temperature prediction model.

8. A device for predicting engine oil life, characterized in that: The device comprises: a parameter prediction module, configured to predict the physical and chemical property parameters of the engine oil based on vehicle operating condition data and operating state parameters of the engine oil in the vehicle engine; A life prediction module is used to predict the remaining life of the engine oil based on the operating state parameters and the physical and chemical characteristic parameters.

9. An electronic device, characterized in that: The electronic device comprises: at least one processor; and a memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor. The computer program is executed by the at least one processor to enable the at least one processor to perform the method for predicting engine oil life according to any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a processor to implement the method for predicting the engine oil life according to any one of claims 1 to 7 when executed.

11. A vehicle, characterized in that: A background monitoring system is deployed on the vehicle, and the background monitoring system is used to implement the engine oil life prediction method according to any one of claims 1 to 7.

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