CN113902198A - Engine oil life prediction method and device, electronic equipment and storage medium - Google Patents
Engine oil life prediction method and device, electronic equipment and storage medium Download PDFInfo
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Abstract
The application relates to an oil life prediction method, an oil life prediction device, an electronic device and a storage medium, wherein the method comprises the following steps: acquiring the traveled mileage of the vehicle and the last time of engine oil replacement; determining total working time of engine oil and standard working time of the engine oil; determining the percentage of the service life of the engine oil according to the total working time of the engine oil and the standard working time of the engine oil; and inputting the service life percentage of the engine oil, the traveled mileage of the vehicle and the last time of engine oil replacement into a pre-trained engine oil prediction model to obtain the remaining maintenance mileage and time of the engine oil. The influence of an engine oil working load coefficient, an engine oil working temperature coefficient, an engine oil dilution coefficient, a pure electric drive running time coefficient, a natural time coefficient, an owner style coefficient and an engine oil service life threshold value is considered in the determination of the total engine oil working time and the standard engine oil working time, the accuracy of engine oil service life prediction is improved, engine oil remaining maintenance mileage and time are obtained in a pre-trained engine oil prediction model, the engine oil service life can be predicted, and the effect of accurate maintenance is achieved.
Description
Technical Field
The present disclosure relates to the field of engine oil life prediction technologies, and in particular, to a method and an apparatus for predicting engine oil life, an electronic device, and a storage medium.
Background
The market share of electric vehicles eating fuel oil vehicles is being increased, but the fuel oil vehicles and the electric vehicles on the street are still continuously present for a long time in the future although the trend of electric driving is not good. The electric vehicle has a plurality of advantages, and the fuel vehicle still has reference space. For example, electric vehicles require little maintenance during use, while fuel-powered vehicles require mileage or regular oil changes. Through relevant research, the current engine oil replacement strategy is too rough, the phenomenon of over-maintenance generally exists, maintenance cost is increased for vehicle owners, fuel vehicle public praise is affected, and resource waste is caused to the society.
Some methods are relevant to oil life prediction, but the effect is limited. For example, in an engine oil life prediction method proposed by an east wind commercial vehicle, an engine oil viscosity sensor is required to be used for monitoring the viscosity of engine oil, and then the engine oil pressure, temperature, water content overrun times and the like are used for correction, most internal combustion engine vehicles are not provided with the engine oil viscosity sensor, and the correlation between the overrun times of engine oil parameters and the engine oil life is not large. For example, the oil change indicator connected to the odometer proposed by the limited industry has been considered to be strongly correlated with the oil life and the mileage of the vehicle, and is used as a basis for the degree of oil life, which is only a rough estimate in the market. Further, as proposed by the united electronic company, the method based on the crank-weighted cumulative number of revolutions and the engine-oil weighted cumulative time of use has some effects, but also has disadvantages.
Disclosure of Invention
Based on how to accurately determine the time for changing the engine oil, which is not mentioned in the prior art, the application provides an engine oil life prediction method, an engine oil life prediction device, electronic equipment and a storage medium.
In a first aspect, an embodiment of the present application provides an engine oil life prediction method, including:
acquiring the traveled mileage of the vehicle and the last time of engine oil replacement;
determining total working time of engine oil and standard working time of the engine oil;
determining the percentage of the service life of the engine oil according to the total working time of the engine oil and the standard working time of the engine oil;
and inputting the service life percentage of the engine oil, the traveled mileage of the vehicle and the last time of engine oil replacement into a pre-trained engine oil prediction model to obtain the remaining maintenance mileage and time of the engine oil.
Further, in the oil life prediction method, determining the total operating time of the oil includes:
determining the basic time of engine oil work;
multiplying and correcting the engine oil working basic time according to the first coefficient to obtain the total working time of the first engine oil; adding and correcting the total working time of the first engine oil according to a second coefficient to obtain the total working time of the engine oil;
wherein the first coefficient includes at least: the engine oil working load coefficient, the engine oil working temperature coefficient and the engine oil dilution coefficient; the second coefficients include at least: and the pure electric drive running time coefficient and the natural time coefficient.
Further, in the oil life prediction method, determining the oil operation base time includes:
acquiring the working state of an engine oil pump; when the engine oil pump is in a working state, summing the engine oil working time to obtain the engine oil working basic time;
and when the working state of the engine oil pump is not obtained, summing the engine oil working time when the rotating speed of the engine is greater than a preset threshold value to obtain the engine oil working basic time.
Further, in the oil life prediction method, determining the standard operating time of the oil includes:
acquiring a style coefficient of a vehicle owner;
determining an engine oil life threshold according to the engine oil variety;
determining the standard engine oil working time according to the product of the style coefficient of the vehicle owner and the engine oil life threshold;
wherein, car owner style coefficient includes at least: conservative coefficient, popular coefficient and economic coefficient.
Further, in the oil life prediction method, determining the percentage of the oil life according to the total operating time of the oil and the standard operating time of the oil includes:
and calculating the ratio of the total working time of the engine oil to the standard working time of the engine oil.
Further, in the oil life prediction method, the training step of the pre-trained oil prediction model is as follows:
obtaining historical engine oil data;
determining the relation between the percentage of the service life of the engine oil and the traveled mileage and the time for replacing the engine oil according to the historical data of the engine oil;
and training the model according to the relation between the percentage of the service life of the engine oil and the traveled mileage and the engine oil replacement time to obtain a pre-trained engine oil prediction model.
Further, the oil life prediction method further includes:
the accuracy of the pre-trained oil prediction model is optimized through machine learning.
In a second aspect, an embodiment of the present invention further provides an engine oil life prediction apparatus, including:
an acquisition module: the engine oil changing device is used for acquiring the traveled mileage of the vehicle and the last engine oil changing time;
a first determination module: the system is used for determining the total working time of the engine oil and the standard working time of the engine oil;
a second determination module: the engine oil service life percentage is determined according to the total engine oil working time and the standard engine oil working time;
an input module and an obtaining module: and the engine oil remaining maintenance mileage and time are obtained by inputting the percentage of the service life of the engine oil, the historical driving mileage of the life cycle of the engine oil and the last time of replacing the engine oil into the engine oil prediction model.
In a third aspect, an embodiment of the present invention further provides an electronic device, including: a processor and a memory;
the processor is used for executing the engine oil life prediction method according to any one of the above items by calling the program or the instructions stored in the memory.
In a fourth aspect, the present invention further provides a computer-readable storage medium, which stores a program or instructions for causing a computer to execute the method for predicting engine oil life as described in any one of the above.
The embodiment of the application has the advantages that: the influence of an engine oil working load coefficient, an engine oil working temperature coefficient, an engine oil dilution coefficient, a pure electric drive running time coefficient, a natural time coefficient, an owner style coefficient and an engine oil life threshold value is considered in the process of determining the total engine oil working time and the standard engine oil working time, so that the accuracy of determining the percentage of the life of the engine oil is improved, a pre-trained engine oil prediction model is obtained through training of a large amount of engine oil historical data, the accuracy of predicting the life of the engine oil is further improved, the percentage of the life of the engine oil, the traveled mileage of a vehicle and the last replacement time of the engine oil are input into the pre-trained engine oil prediction model to obtain the remaining maintenance mileage and time of the engine oil, the service life of the engine oil can be accurately predicted, and the effect of accurate maintenance is achieved.
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In order to more clearly illustrate the technical solutions in the embodiments or the conventional technologies of the present application, the drawings used in the descriptions of the embodiments or the conventional technologies will be briefly introduced below, it is obvious that the drawings in the following descriptions are only some embodiments of the present application, and for those skilled in the art, other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a first schematic diagram illustrating a method for predicting a lifetime of engine oil according to an embodiment of the present disclosure;
FIG. 2 is a schematic diagram of a method for predicting a lifetime of engine oil according to an embodiment of the present disclosure;
FIG. 3 is a third schematic diagram of a method for predicting a lifetime of engine oil according to an embodiment of the present disclosure;
FIG. 4 is a fourth schematic diagram of a method for predicting a lifetime of engine oil according to an embodiment of the present disclosure;
FIG. 5 is a schematic diagram of an engine oil life prediction apparatus according to an embodiment of the present disclosure;
fig. 6 is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure.
Detailed Description
In order to make the aforementioned objects, features and advantages of the present application more comprehensible, embodiments accompanying the present application are described in detail below with reference to the accompanying drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present application. This application is capable of embodiments in many different forms than those described herein and those skilled in the art will be able to make similar modifications without departing from the spirit of the application and it is therefore not intended to be limited to the embodiments disclosed below.
Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application belongs. The terminology used herein in the description of the present application is for the purpose of describing particular embodiments only and is not intended to be limiting of the application. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
Fig. 1 is a first schematic view of a method for predicting a lifetime of engine oil according to an embodiment of the present disclosure.
In a first aspect, with reference to fig. 1, an embodiment of the present application provides a method for predicting a lifetime of engine oil, including four steps S101 to S104:
s101: and acquiring the traveled mileage of the vehicle and the last time of oil replacement.
Specifically, in the embodiment of the application, the traveled mileage of the vehicle and the last oil replacement time are obtained, the traveled mileage is the mileage after oil replacement, such as 100km, and the last oil replacement time can be any natural date of oil replacement, such as No. 6/20/2021.
S102: and determining the total working time of the engine oil and the standard working time of the engine oil.
Specifically, in the embodiment of the application, the total working time of the engine oil is determined after multiplying correction and adding correction are performed on the working basic time of the engine oil on the basis of the working basic time of the engine oil; the standard engine oil operating time is determined according to the style factor of the vehicle owner and the engine oil life threshold.
S103: determining the percentage of the service life of the engine oil according to the total working time of the engine oil and the standard working time of the engine oil;
specifically, in the embodiment of the present application, the percentage of oil life is determined according to the ratio of the total operating time of the engine oil to the standard operating time of the engine oil.
S104: and inputting the service life percentage of the engine oil, the traveled mileage of the vehicle and the last time of engine oil replacement into a pre-trained engine oil prediction model to obtain the remaining maintenance mileage and time of the engine oil.
Specifically, in the embodiment of the application, the remaining engine oil maintenance mileage and time can be obtained by inputting the percentage of the engine oil life, such as 90%, the traveled mileage of a vehicle is 120km, and the last engine oil replacement time is 2021, 6 months and 20, into a pre-trained engine oil prediction model.
Fig. 2 is a schematic diagram of a method for predicting the service life of engine oil according to an embodiment of the present application.
Further, in the oil life prediction method, with reference to fig. 2, determining the total operating time of the oil includes three steps S201 to S203:
s201: and determining the base time of the engine oil operation.
Specifically, in the embodiment of the present application, the oil operation base time is determined according to the operation state of the oil pump or the rotation speed of the engine.
S202: and multiplying and correcting the engine oil working basic time according to the first coefficient to obtain the total working time of the first engine oil.
Specifically, in the embodiment of the present application, the engine oil working base time is multiplied and corrected according to the first coefficient, such as the engine oil working load coefficient, the engine oil working temperature coefficient, and the engine oil dilution coefficient, to obtain the first total engine oil working time.
S203: and performing addition correction on the total working time of the first engine oil according to a second coefficient to obtain the total working time of the engine oil.
Specifically, in the embodiment of the application, the total operating time of the engine oil is obtained by performing addition correction on the total operating time of the first engine oil according to the second coefficient, such as the pure electric drive operating time coefficient and the natural time coefficient.
It should be understood that after the engine oil working basic time is determined, the engine oil working basic time is corrected to obtain the total engine oil working time by considering the influences of the engine oil due to factors such as engine oil pressure, engine oil temperature, engine oil dilution, natural time and vehicle pure electric drive running time, and the accuracy of engine oil service life prediction is improved.
Wherein the first coefficient includes at least: the engine oil working load coefficient, the engine oil working temperature coefficient and the engine oil dilution coefficient; the second coefficients include at least: and the pure electric drive running time coefficient and the natural time coefficient.
The engine oil working load coefficient, the engine oil working temperature coefficient, the engine oil dilution coefficient, the pure electric drive running time coefficient and the natural time coefficient are described in sequence below.
The engine oil work load factor includes two cases:
in the first case: firstly, reading an engine oil pressure value, and obtaining an engine oil working load coefficient according to the engine oil pressure value; defining the engine oil pressure under the idle working condition as the base pressure, wherein the engine oil work load coefficient is 1 under the base pressure, and the engine oil work load coefficient value is slightly increased along with the increase of the engine oil pressure, such as 1.1, 1.2, 1.3 and the like; the engine oil workload coefficient may also be derived from machine learning.
In the second case: if the inorganic oil pressure value in the signal can be collected, the engine speed can be used instead. And obtaining the working load coefficient of the engine oil for different engine rotating speed values. Regarding the setting method of the table FEGSPD, the engine oil work load coefficient under the idle working condition is defined to be 1, and the engine oil work load coefficient is 1 when the normal rotating speed is 2000rpm and below, and the engine oil work load coefficient value is slightly increased along with the increase of the rotating speed, such as 1.1, 1.2, 1.3 and the like; the engine oil workload coefficient may also be derived from machine learning.
Engine oil working temperature coefficient:
in the first case: firstly, the temperature of the engine oil is read, and the working temperature coefficient of the engine oil is given from large to small along with the temperature of the engine oil from low to high.
In the second case: in the absence of oil temperature, engine coolant temperature is used instead, giving a large to small oil operating temperature coefficient as the coolant temperature goes from low to high.
Engine oil dilution factor: reading a pre-oxygen voltage signal, and determining an engine oil dilution coefficient according to the degree that the air-fuel ratio represented by the pre-oxygen voltage signal is less than 1, wherein the engine oil dilution coefficient is 1 when the air-fuel ratio is greater than 0.8, and the engine oil dilution coefficient is properly greater than 1, such as 1.2, when the air-fuel ratio is less than 0.8, and is adjusted according to the conditions of the items; the engine oil dilution factor can also be obtained from machine learning.
Pure electric drive running time coefficient: in the hybrid vehicle, when the engine does not operate, the vehicle can be driven by the motor, and at the moment, certain influence can be caused on engine oil due to vehicle shaking, and the determined coefficient is a pure electric drive operation time coefficient.
Natural time coefficient: the engine oil in the oil pan is deteriorated with the lapse of time even if the vehicle is not moving all the time, and then a certain proportion of attenuation of the engine oil in the vehicle is required with the lapse of natural time, and the determined coefficient is a natural time coefficient.
It should be understood that after the engine oil working basic time is determined, the influence of factors such as engine oil pressure, engine oil temperature, engine oil dilution, natural time and vehicle pure electric drive running time on the engine oil working basic time is considered, the engine oil working basic time is corrected through an engine oil working load coefficient, an engine oil working temperature coefficient, an engine oil dilution coefficient, a pure electric drive running time coefficient and a natural time coefficient to obtain the total engine oil working time, and the accuracy of engine oil service life prediction is improved.
Further, in the oil life prediction method, determining the base time of the oil operation includes the following two cases:
in the first case: acquiring the working state of an engine oil pump; and when the engine oil pump is in a working state, summing the engine oil working time to obtain the engine oil working basic time.
Specifically, in the embodiment of the application, the working state of the oil pump is read, and when the oil pump is read to be in the working state, the working time of the oil is summed to obtain the working basic time of the oil.
In the second case: and when the working state of the engine oil pump is not obtained, summing the engine oil working time when the rotating speed of the engine is greater than a preset threshold value to obtain the engine oil working basic time.
Specifically, in the embodiment of the present application, when the engine speed is greater than the preset threshold value, for example, 40, that is, it is considered that the engine oil is working, the engine oil working time is accumulated, so as to obtain the engine oil working base time.
Fig. 3 is a third schematic diagram of a method for predicting the service life of engine oil according to an embodiment of the present application.
Further, in the oil life prediction method, with reference to fig. 3, determining the standard operating time of the oil includes three steps S301 to S303:
s301: acquiring a style coefficient of a vehicle owner; the owner style coefficients at least include: conservative coefficient, popular coefficient and economic coefficient.
Specifically, in the embodiment of the present application, if the car owner style coefficient is conservative, the conservative coefficient is slightly smaller than 1, for example, 0.95, otherwise, if the car owner style coefficient is economical, the economical coefficient is slightly larger than 1, for example, 1.05, and the car owner style coefficient is used for multiplicatively correcting the oil life threshold.
S302: and determining an engine oil life threshold according to the engine oil variety.
Specifically, in the present embodiment, the threshold value of the oil life is related to the type of the oil, and the oil life is usually 200 hours in the case of a fully synthetic oil, and 150 hours in the case of a normal oil.
S303: and determining the standard working time of the engine oil according to the product of the style coefficient of the vehicle owner and the service life threshold of the engine oil.
Specifically, in the embodiment of the present application, if the owner style coefficient is conservative and the service life of the used general engine oil is 150 hours, the standard operating time of the engine oil is 0.95 × 150 — 142.5 hours.
It should be understood that the service life threshold value of the engine oil variety and the style coefficient of the vehicle owner are considered in the embodiment of the application to obtain the standard working time of the engine oil, so that the accuracy of engine oil service life prediction is further improved.
Further, in the oil life prediction method, determining the percentage of the oil life according to the total operating time of the oil and the standard operating time of the oil includes:
and calculating the ratio of the total working time of the engine oil to the standard working time of the engine oil to obtain the percentage of the service life of the engine oil.
Fig. 4 is a fourth schematic view of a method for predicting the service life of engine oil according to an embodiment of the present application.
Further, in the oil life prediction method, with reference to fig. 4, the training steps of the oil prediction model trained in advance are three steps S401 to S403:
s401: and acquiring historical engine oil data.
S402: determining the relation between the percentage of the service life of the engine oil and the traveled mileage and the time for replacing the engine oil according to the historical data of the engine oil;
s403: and training the model according to the relation between the percentage of the service life of the engine oil and the traveled mileage and the engine oil replacement time to obtain a pre-trained engine oil prediction model.
Specifically, the engine oil historical data in the embodiment of the application is a large amount of engine oil historical data obtained through big data; and performing trend analysis on a large amount of historical data to determine the relationship between the percentage of the service life of the engine oil and the traveled mileage and the relationship between the percentage of the service life of the engine oil and the time for replacing the engine oil, and training a model according to the relationship between the percentage of the service life of the engine oil and the traveled mileage and the relationship between the percentage of the service life of the engine oil and the time for replacing the engine oil to obtain a pre-trained engine oil prediction model.
It should be appreciated that the pre-trained oil prediction model obtained by training is supported by a large amount of historical data, and the accuracy of model prediction is improved.
Further, the oil life prediction method further includes:
the accuracy of the pre-trained oil prediction model is optimized through machine learning.
Specifically, in the embodiment of the application, after the pre-trained oil prediction model is obtained, the precision of the pre-trained oil prediction model is optimized through machine learning, and the accuracy of oil life prediction is further improved.
Fig. 5 is a schematic view of an engine oil life prediction apparatus according to an embodiment of the present application.
In a second aspect, an embodiment of the present invention further provides an engine oil life prediction apparatus, including:
the obtaining module 501: the method is used for acquiring the traveled mileage of the vehicle and the last time of oil replacement.
Specifically, in this embodiment of the application, the traveled mileage acquired by the acquisition module 501 is the traveled mileage after oil replacement, such as 100km, and the last oil replacement time acquired by the acquisition module 501 may be any natural date of oil replacement, such as # 6/20/2021.
The first determination module 502: the method is used for determining the total working time of the engine oil and the standard working time of the engine oil.
Specifically, in the embodiment of the present application, the total operating time of the engine oil is determined by the first determining module 502 after performing multiplication correction and addition correction on the operating basic time of the engine oil on the basis of the operating basic time of the engine oil; the standard oil operating time is determined by the first determination module 502 based on the owner style factor and the oil life threshold.
The second determination module 503: and the method is used for determining the percentage of the service life of the engine oil according to the total working time of the engine oil and the standard working time of the engine oil.
Specifically, in the embodiment of the present application, the second determining module 503 determines the percentage of oil life according to a ratio of the total operating time of the oil to the standard operating time of the oil.
Specifically, in the embodiment of the present application, the input module 504 inputs the percentage of the oil life, such as 90%, the traveled mileage of the vehicle is 120km, and the last oil replacement time is 2021 year, 6 month and 20 # into the pre-trained oil prediction model to obtain the module 505, so that the remaining oil maintenance mileage and time can be obtained.
In a third aspect, an embodiment of the present invention further provides an electronic device, including: a processor and a memory;
the processor is used for executing the engine oil life prediction method according to any one of the above items by calling the program or the instructions stored in the memory.
In a fourth aspect, the present invention further provides a computer-readable storage medium, which stores a program or instructions for causing a computer to execute the method for predicting engine oil life as described in any one of the above.
Fig. 6 is a schematic block diagram of an electronic device provided by an embodiment of the present disclosure.
As shown in fig. 6, the electronic apparatus includes: at least one processor 601, at least one memory 602, and at least one communication interface 603. The various components in the electronic device are coupled together by a bus system 604. A communication interface 603 for information transmission with an external device. It is understood that the bus system 604 is used to enable communications among the components. The bus system 604 includes a power bus, a control bus, and a status signal bus in addition to a data bus. But for the sake of clarity the various busses are labeled in fig. 6 as the bus system 604.
It will be appreciated that the memory 602 in this embodiment can be either volatile memory or nonvolatile memory, or can include both volatile and nonvolatile memory.
In some embodiments, memory 602 stores the following elements, executable units or data structures, or a subset thereof, or an expanded set thereof: an operating system and an application program.
The operating system includes various system programs, such as a framework layer, a core library layer, a driver layer, and the like, and is used for implementing various basic services and processing hardware-based tasks. The application programs, including various application programs such as a Media Player (Media Player), a Browser (Browser), etc., are used to implement various application services. The program for implementing any one of the methods for predicting the service life of the engine oil provided by the embodiments of the present application may be included in the application program.
In the embodiment of the present application, the processor 601 is configured to execute the steps of the embodiments of the engine oil life prediction method provided by the embodiments of the present application by calling a program or an instruction stored in the memory 602, which may be specifically a program or an instruction stored in an application program.
Acquiring the traveled mileage of the vehicle and the last time of engine oil replacement;
determining total working time of engine oil and standard working time of the engine oil;
determining the percentage of the service life of the engine oil according to the total working time of the engine oil and the standard working time of the engine oil;
and inputting the service life percentage of the engine oil, the traveled mileage of the vehicle and the last time of engine oil replacement into a pre-trained engine oil prediction model to obtain the remaining maintenance mileage and time of the engine oil.
Any method of the engine oil life prediction method provided by the embodiment of the application can be applied to the processor 601, or can be realized by the processor 601. The processor 601 may be an integrated circuit chip having signal processing capabilities. In implementation, the steps of the above method may be performed by integrated logic circuits of hardware or instructions in the form of software in the processor 601. The Processor 601 may be a general-purpose Processor, a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), an off-the-shelf Programmable Gate Array (FPGA) or other Programmable logic device, discrete Gate or transistor logic device, or discrete hardware components. A general purpose processor may be a microprocessor or the processor may be any conventional processor or the like.
The steps of any method in the engine oil life prediction method provided by the embodiment of the application can be directly implemented by a hardware decoding processor, or implemented by combining hardware and software units in the decoding processor. The software elements may be located in ram, flash, rom, prom, or eprom, registers, among other storage media that are well known in the art. The storage medium is located in a memory 602, and a processor 601 reads information in the memory 602 and performs the steps of a method for predicting engine oil life in combination with hardware thereof.
Those skilled in the art will appreciate that although some embodiments described herein include some features included in other embodiments instead of others, combinations of features of different embodiments are meant to be within the scope of the application and form different embodiments.
Those skilled in the art will appreciate that the description of each embodiment has a respective emphasis, and reference may be made to the related description of other embodiments for those parts of an embodiment that are not described in detail.
While the invention has been described with reference to specific embodiments, it will be understood by those skilled in the art that various changes in form and details may be made therein without departing from the spirit and scope of the invention as defined by the appended claims. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. A method for predicting the life of engine oil, comprising:
acquiring the traveled mileage of the vehicle and the last time of engine oil replacement;
determining total working time of engine oil and standard working time of the engine oil;
determining the percentage of the service life of the engine oil according to the total working time of the engine oil and the standard working time of the engine oil;
and inputting the oil life percentage, the traveled mileage of the vehicle and the last time of oil replacement into a pre-trained oil prediction model to obtain the remaining oil maintenance mileage and time.
2. The oil life prediction method of claim 1, wherein the determining the total oil on-time comprises:
determining the basic time of engine oil work;
multiplying and correcting the engine oil working basic time according to a first coefficient to obtain a first engine oil total working time; adding and correcting the total working time of the first engine oil according to a second coefficient to obtain the total working time of the engine oil;
wherein the first coefficients include at least: the engine oil working load coefficient, the engine oil working temperature coefficient and the engine oil dilution coefficient; the second coefficients include at least: and the pure electric drive running time coefficient and the natural time coefficient.
3. The oil life prediction method of claim 2, wherein the determining an oil service base time comprises:
acquiring the working state of an engine oil pump; when the engine oil pump is in a working state, summing the engine oil working time to obtain the engine oil working basic time;
and when the working state of the engine oil pump is not obtained, summing the engine oil working time when the rotating speed of the engine is greater than a preset threshold value to obtain the engine oil working basic time.
4. The oil life prediction method of claim 1, wherein the determining an oil standard on-time comprises:
acquiring a style coefficient of a vehicle owner;
determining an engine oil life threshold according to the engine oil variety;
determining the standard working time of the engine oil according to the product of the owner style coefficient and the engine oil life threshold;
wherein the owner style coefficient at least comprises: conservative coefficient, popular coefficient and economic coefficient.
5. The oil life prediction method of claim 1, wherein the determining an oil life percentage based on the total operating time of the oil and the standard operating time of the oil comprises:
and calculating the ratio of the total working time of the engine oil to the standard working time of the engine oil.
6. The oil life prediction method of claim 1, wherein the pre-trained oil prediction model is trained by:
obtaining historical engine oil data;
determining the relation between the percentage of the service life of the engine oil and the traveled mileage and the time for replacing the engine oil according to the historical data of the engine oil;
and training a model according to the relation between the percentage of the service life of the engine oil and the traveled mileage and the engine oil replacement time to obtain a pre-trained engine oil prediction model.
7. The oil life prediction method of claim 6, further comprising:
optimizing the accuracy of the pre-trained oil prediction model by machine learning.
8. An engine oil life prediction apparatus, comprising:
an acquisition module: the engine oil changing device is used for acquiring the traveled mileage of the vehicle and the last engine oil changing time;
a first determination module: the system is used for determining the total working time of the engine oil and the standard working time of the engine oil;
a second determination module: the engine oil service life percentage is determined according to the total engine oil working time and the standard engine oil working time;
an input module and an obtaining module: and the engine oil remaining maintenance mileage and time are obtained by inputting the percentage of the service life of the engine oil, the historical driving mileage of the life cycle of the engine oil and the last time of replacing the engine oil into an engine oil prediction model.
9. An electronic device, comprising: a processor and a memory;
the processor is used for executing the engine oil life prediction method according to any one of claims 1 to 7 by calling the program or the instructions stored in the memory.
10. A computer-readable storage medium storing a program or instructions for causing a computer to execute the oil life prediction method according to any one of claims 1 to 7.
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