Method for predicting remaining useful mileage of engine oil, electronic device, and readable storage medium

Abstract

The application discloses a method for predicting the remaining service mileage of engine oil, an electronic device and a readable storage medium. The method comprises the following steps: obtaining a plurality of running state parameters of an engine of a vehicle under a current running condition, wherein the running state parameters are used to represent the running state of the engine under the current running condition from a target dimension; determining the attenuation coefficient of the engine oil in the engine based on the running state parameters, and detecting the engine oil to obtain the oil deterioration coefficient of the engine oil, wherein the attenuation coefficient is used to represent the degree of influence of the current running condition on the deterioration rate of the engine oil, and the oil deterioration coefficient is used to represent the degree of oil deterioration of the engine oil; and predicting the remaining service mileage of the engine oil based on the attenuation coefficient and the oil deterioration coefficient, wherein the remaining service mileage is used to represent the mileage of the vehicle allowed to run by the engine oil. The application solves the technical problem that the remaining service mileage of the engine oil of the engine cannot be effectively predicted in the related art.

Description

Technical Field

[0001] This application relates to the field of vehicle technology, and more specifically, to a method for predicting remaining oil usage mileage, an electronic device, and a readable storage medium. Background Technology

[0002] Currently, with the rapid development of vehicle intelligence and vehicle networking technology, engine oil maintenance has become a key aspect of improving vehicle operating efficiency and reducing maintenance costs.

[0003] In related technologies, algorithmic models based on vehicle operating parameters (such as mileage, engine speed, load, temperature, etc.) are commonly used to predict the remaining service mileage of engine oil. However, these models often rely on fixed mileage cycles (such as oil changes every 20,000 kilometers) or fitting of a single operating condition. In contrast, vehicles operate under a variety of actual conditions, and this method cannot effectively predict the remaining service mileage of engine oil under different operating conditions.

[0004] There is currently no effective solution to the technical problem of not being able to effectively predict the remaining service mileage of engine oil. Summary of the Invention

[0005] This application provides a method, electronic device, and readable storage medium for predicting the remaining service mileage of engine oil, so as to at least solve the technical problem in the related art that it is impossible to effectively predict the remaining service mileage of engine oil.

[0006] According to one aspect of the embodiments of this application, a method for predicting the remaining service mileage of engine oil is provided. The method may include: acquiring multiple operating state parameters of a vehicle's engine under current operating conditions, wherein the operating state parameters are used to characterize the engine's operating state under the current operating conditions from a target dimension, and different operating state parameters correspond to different target dimensions; determining the degradation coefficient of the engine oil based on the operating state parameters, and detecting the engine oil to obtain an oil quality deterioration coefficient, wherein the degradation coefficient is used to characterize the degree of influence of the current operating conditions on the deterioration rate of the engine oil, and the oil quality deterioration coefficient is used to characterize the degree of oil quality deterioration; and predicting the remaining service mileage of the engine oil based on the degradation coefficient and the oil quality deterioration coefficient, wherein the remaining service mileage represents the distance the engine oil allows the vehicle to travel.

[0007] Optionally, the degradation coefficient of engine oil in the engine is determined based on operating state parameters, including: obtaining weight coefficients corresponding to multiple operating state parameters, wherein the weight coefficients are used to characterize the degree of influence of the operating state parameters on the degradation rate of engine oil under the current operating conditions; and performing a weighted calculation on multiple operating state parameters and their corresponding weight coefficients to obtain the degradation coefficient of engine oil in the engine.

[0008] Optionally, the engine oil is tested to obtain its degradation coefficient, including: using oil testing equipment to sample the engine oil and obtaining sampled engine oil; using oil testing equipment to test the oil composition of the sampled engine oil and obtaining test results; and determining the engine oil degradation coefficient based on the test results.

[0009] Optionally, based on the test results, the oil degradation coefficient of the engine oil is determined, including: extracting the test results of wear factor, additive factor, and contaminant factors from the test results, wherein the wear factor test results are used to characterize the content of wear factors in the engine oil, the additive factor test results are used to characterize the content of additive factors in the engine oil, and the contaminant factor test results are used to characterize the content of contaminants in the engine oil; and determining the oil degradation coefficient of the engine oil based on the wear factor test results, additive factor test results, contaminant factor test results, and engine oil consumption.

[0010] Optionally, based on the results of wear factor detection, additive factor detection, contaminant factor detection, and oil consumption, the oil quality degradation coefficient of the engine oil is determined, including: determining the wear coefficient of the engine oil based on the wear factor detection results and oil consumption, wherein the wear coefficient is used to characterize the wear degree of the key friction pairs of the engine; determining the additive coefficient of the engine oil based on the additive factor detection results and oil consumption, wherein the additive coefficient is used to characterize the degree of loss of additive factors in the engine oil; determining the contamination coefficient of the engine oil based on the contaminant factor detection results and oil consumption, wherein the contamination coefficient is used to characterize the degree of contamination of the engine oil by contaminants; and determining the wear coefficient, additive coefficient, and contamination coefficient as the oil quality degradation coefficient.

[0011] Optionally, based on the attenuation coefficient and the oil deterioration coefficient, the remaining service mileage of the engine oil is predicted, including: predicting a first remaining service mileage of the engine oil based on the attenuation coefficient, the wear coefficient in the oil deterioration coefficient, and the service mileage of the engine oil in its initial state, wherein the first remaining service mileage represents the service mileage of the engine oil under the current operating conditions and degree of wear; predicting a second remaining service mileage of the engine oil based on the attenuation coefficient, the additive coefficient in the oil deterioration coefficient, and the service mileage of the engine oil in its initial state, wherein the second remaining service mileage represents the service mileage of the engine oil under the current operating conditions and degree of wear; predicting a third remaining service mileage of the engine oil based on the attenuation coefficient, the contamination coefficient in the oil deterioration coefficient, and the service mileage of the engine oil in its initial state, wherein the third remaining service mileage represents the service mileage of the engine oil under the current operating conditions and degree of contamination; and determining the remaining service mileage of the engine oil based on the first remaining service mileage, the second remaining service mileage, and the third remaining service mileage.

[0012] Optionally, the remaining service mileage of the engine oil is determined based on the first remaining service mileage, the second remaining service mileage, and the third remaining service mileage, including: determining the minimum value among the first remaining service mileage, the second remaining service mileage, and the third remaining service mileage as the remaining service mileage of the engine oil.

[0013] Optionally, the method further includes: generating a reminder strategy based on the remaining mileage, wherein the reminder strategy is used to characterize the rule for reminding the driver of the vehicle to change the engine oil; and reminding the driver according to the reminder strategy.

[0014] According to another aspect of the embodiments of this application, a device for predicting the remaining service mileage of engine oil is also provided. The device may include: an acquisition unit, configured to acquire multiple operating state parameters of a vehicle's engine under current operating conditions, wherein the operating state parameters characterize the engine's operating state under current operating conditions from a target dimension, and different operating state parameters correspond to different target dimensions; a determination unit, configured to determine the degradation coefficient of the engine oil based on the operating state parameters, and to detect the engine oil to obtain an oil quality deterioration coefficient, wherein the degradation coefficient characterizes the degree of influence of the current operating conditions on the deterioration rate of the engine oil, and the oil quality deterioration coefficient characterizes the degree of oil quality deterioration; and a prediction unit, configured to predict the remaining service mileage of the engine oil based on the degradation coefficient and the oil quality deterioration coefficient, wherein the remaining service mileage represents the mileage that the engine oil allows the vehicle to travel.

[0015] According to another aspect of the embodiments of this application, an electronic device is also provided, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods in various embodiments of this application when it runs.

[0016] According to another aspect of the embodiments of this application, a computer-readable storage medium is also provided, the computer-readable storage medium including a stored executable program, wherein, when the executable program is running, it controls the device where the computer-readable storage medium is located to perform the methods of various embodiments of this application.

[0017] According to another aspect of the embodiments of this application, a computer program product is also provided, including a computer program that, when executed by a processor, implements the methods of various embodiments of this application.

[0018] According to another aspect of the embodiments of this application, a computer program product is also provided, including a non-volatile computer-readable storage medium storing a computer program that, when executed by a processor, implements the methods in various embodiments of this application.

[0019] According to another aspect of the embodiments of this application, a computer program is also provided, which, when executed by a processor, implements the methods of the various embodiments of this application.

[0020] In this embodiment, multiple operating state parameters of the vehicle's engine under current operating conditions are obtained. These operating state parameters characterize the engine's operating state under current operating conditions from a target dimension, with different operating state parameters corresponding to different target dimensions. Based on the operating state parameters, the degradation coefficient of the engine oil is determined, and the engine oil is tested to obtain its oil quality deterioration coefficient. The degradation coefficient characterizes the degree of influence of the current operating conditions on the rate of oil deterioration, while the oil quality deterioration coefficient characterizes the degree of oil quality deterioration. Based on the degradation coefficient and the oil quality deterioration coefficient, the remaining service mileage of the engine oil is predicted, where the remaining service mileage represents the distance the engine oil allows the vehicle to travel. In other words, in this embodiment, the degradation coefficient of the engine oil is determined using multiple operating state parameters of the vehicle under current operating conditions, quantifying the degree of influence of the current operating conditions on the rate of oil deterioration. Furthermore, the oil quality deterioration coefficient is obtained by testing the engine oil, thus accurately reflecting the degree of oil deterioration. Then, by combining the oil degradation coefficient and the oil quality deterioration coefficient, the remaining service mileage of the oil is predicted. This takes into account both the actual operating conditions of the vehicle's engine and the degree of oil deterioration, which can greatly improve the prediction accuracy of the remaining service mileage of the oil. This enables a fundamental shift from "experience-based oil changes" to "quality-based maintenance," thereby solving the technical problem of the inability to effectively predict the remaining service mileage of engine oil in related technologies. Attached Figure Description

[0021] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings:

[0022] Figure 1 This is a flowchart of a method for predicting remaining oil usage mileage according to an embodiment of this application;

[0023] Figure 2 This is a flowchart of another method for predicting remaining oil usage mileage according to an embodiment of this application;

[0024] Figure 3 This is a schematic diagram of a device for predicting the remaining mileage of engine oil according to an embodiment of this application;

[0025] Figure 4 This is a schematic diagram of an electronic device according to an embodiment of this application. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present application, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present application, and not all embodiments. Based on the embodiments in the present application, all other embodiments obtained by those of ordinary skill in the art without creative effort 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, claims, and accompanying drawings of this application are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of this application described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion; for example, a process, method, system, functional component, or device that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, functional components, or devices.

[0028] According to an embodiment of this application, an embodiment of a method for predicting the remaining mileage of engine oil is provided. It should be noted that the steps shown in the flowchart in the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions. Furthermore, although a logical order is shown in the flowchart, in some cases, the steps shown or described may be executed in a different order than that shown here.

[0029] Figure 1 This is a flowchart of a method for predicting remaining oil usage mileage according to an embodiment of this application, such as... Figure 1 As shown, the method may include the following steps.

[0030] Step S101: Obtain multiple operating status parameters of the vehicle's engine under the current operating conditions.

[0031] In the technical solution provided in step S101 of this application, the aforementioned multiple operating state parameters include, but are not limited to: engine speed, engine torque percentage, cumulative engine idling time, cumulative number of engine start-stop cycles, engine oil temperature, engine oil pressure, and engine oil consumption percentage. These operating state parameters are used to characterize the engine's operating state under current operating conditions from a target dimension, with different operating state parameters corresponding to different target dimensions. For example, the aforementioned engine speed is used to characterize the engine's operating status under the current operating conditions from the perspective of engine speed; the aforementioned engine torque percentage under the current operating conditions is used to characterize the engine's operating status under the current operating conditions from the perspective of torque; the aforementioned cumulative engine idling time is used to characterize the engine's operating status under the current operating conditions from the perspective of idling time; the aforementioned cumulative number of engine start-stop cycles is used to characterize the engine's operating status under the current operating conditions from the perspective of start-stop cycles; the aforementioned engine oil temperature is used to characterize the engine's operating status under the current operating conditions from the perspective of oil temperature; the aforementioned engine oil pressure is used to characterize the engine's operating status under the current operating conditions from the perspective of oil pressure; and the aforementioned percentage of engine oil consumption is used to characterize the engine's operating status under the current operating conditions from the perspective of oil consumption.

[0032] In this embodiment, multiple operating state parameters of the vehicle's engine under the current operating conditions are obtained. These multiple operating state parameters characterize the engine's operating state under the current operating conditions from different dimensions.

[0033] Optionally, the aforementioned engine operating parameters under current conditions can be obtained from a vehicle big data platform. Among these, engine speed and engine torque percentage together reflect the engine's power output and mechanical stress level; cumulative engine idling time and cumulative engine start-stop cycles characterize the engine's frequent and inefficient operation, which can easily lead to incomplete combustion and fuel dilution; engine oil temperature and engine oil pressure reflect the changing trends of the lubrication system's thermal load and oil film carrying capacity, respectively; and the engine oil consumption percentage is used to correct for concentration shifts caused by oil loss, improving the accuracy of subsequent analyses. These parameters are not isolated but work together to reflect the engine's operating status under current conditions.

[0034] Step S102: Based on the operating status parameters, determine the degradation coefficient of the engine oil in the engine, and test the engine oil to obtain the oil quality deterioration coefficient.

[0035] In the technical solution provided in step S102 of this application, the attenuation coefficient is used to characterize the degree of influence of the current operating conditions on the deterioration rate of the engine oil, and can quantify the accelerating or inhibiting effect of the current engine operating state on the deterioration rate of the engine oil; the oil deterioration coefficient is used to characterize the degree of deterioration of the engine oil, including three major categories: wear coefficient, additive coefficient and contamination coefficient. The wear coefficient is characterized by the content of metal elements such as iron (Fe), copper (Cu), aluminum (Al), and lead (Pb) in the engine oil, reflecting the wear degree of key friction pairs of the engine (such as cylinder liners, bearings, and rocker arms). The additive coefficient is characterized by the changes in the content of antioxidant, anti-wear, and detergent-dispersant elements such as phosphorus (P), sulfur (S), zinc (Zn), calcium (Ca), magnesium (Mg), and boron (B), reflecting the consumption rate of functional additives in the engine oil. The contamination coefficient covers the content of external contaminants such as silicon (Si), sodium (Na), and potassium (K), as well as the fuel dilution rate w_fuel, soot content w_smoke content, and water content w_water, representing the damage to oil performance caused by the penetration of external contaminants and the accumulation of combustion byproducts.

[0036] In this embodiment, after obtaining multiple operating state parameters of the engine oil in step S101, these multiple operating state parameters can be preprocessed. Then, based on the preprocessed multiple operating state parameters, the degradation coefficient of the engine oil can be determined. Furthermore, the composition of the engine oil can be detected to obtain the oil degradation coefficient.

[0037] For example, after obtaining multiple operating state parameters, each operating state parameter can be discretized according to a preset operating condition range division rule. For instance, the engine speed can be divided into multiple speed ranges (e.g., 500–600 r / min, 600–700 r / min…3400–3500 r / min) by dividing each 100 r / min into multiple torque ranges (e.g., 0–5%, 5–10%, 10–15%…95–100%) by dividing each 5% into multiple torque ranges (e.g., 0–5%, 5–10%, 10–15%…95–100%) by dividing each 2°C into multiple oil temperature ranges (e.g., 100–102°C, 102–104°C…) by dividing each 20 kPa into multiple oil pressure ranges (e.g., 500–520 kPa, 520–540 kPa…). In addition, the cumulative engine idling time is measured in hours, and the cumulative number of engine start-stop cycles is measured in tens of thousands. Then, the duration percentage of each operating parameter in each interval is statistically analyzed under the current operating conditions. Combined with the contribution of each operating condition interval to the oil degradation coefficient as determined by experiments, the weighting coefficients corresponding to each operating parameter under the current operating conditions are determined. For example, the weighting coefficients are: engine speed (D1), engine torque percentage (D2), cumulative engine idling time (D3), cumulative engine start-stop cycles (D4), engine oil temperature (D5), and engine oil pressure (D6).

[0038] Optionally, after obtaining the weighting coefficients of multiple operating state parameters under the current operating conditions, the proportions of the multiple operating state parameters in each interval and their corresponding weighting coefficients can be linearly weighted and summed to obtain a comprehensive engine oil degradation coefficient D. This degradation coefficient D serves as a dynamic correction factor for calculating the remaining service mileage of the engine oil, achieving a key leap from "fixed mileage prediction" to "operating condition adaptive prediction".

[0039] For example, the attenuation coefficient D of computer oil can be obtained by the following formula (1).

[0040] (1)

[0041] Where D1 represents the weighting coefficient corresponding to the engine speed. D2 is used to represent the percentage of engine speed in each speed range; D2 is used to represent the weighting coefficient corresponding to the percentage of engine torque. D3 represents the percentage of engine torque across different torque ranges; c represents the cumulative engine idling time; D4 represents the cumulative number of engine start-stop cycles; d represents the cumulative number of engine start-stop cycles; and D5 represents the weighting factor corresponding to the engine oil temperature. D6 is used to represent the percentage of engine oil temperature in each oil temperature range; D6 is used to represent the weighting coefficient corresponding to engine oil pressure. Used to indicate the percentage of engine oil pressure in each oil pressure range.

[0042] Optionally, when testing engine oil to obtain its degradation coefficient, oil testing equipment deployed at service stations or cooperative repair points can be used to sample the engine oil in the engine, obtaining a sampled oil (usually 3–5 mL). Then, rapid analysis techniques such as atomic absorption spectroscopy, infrared spectroscopy, or electrochemical sensors are used to simultaneously determine the content of wear factors, additive factors, and contaminants in the oil sample within a short time. The wear factor can also be called a wear element, the additive factor can also be called an additive element, and the contaminant can also be called a contaminant element. Then, the wear coefficient of the computer oil is calculated using the following formula (2). The additive coefficient of computer oil is obtained through the following formula (3). And the pollution coefficient of computer oil is obtained through the following formula (4). .

[0043] (2)

[0044] (3)

[0045] (4)

[0046] in, This represents the percentage of engine oil consumed, or the amount of engine oil used. To correspond to the content of wear-causing agents in the engine oil, This corresponds to the initial content of the additive factors in the engine oil. This corresponds to the mass fraction of contaminants in the engine oil. This serves as a baseline value for the total wear factor in engine oil. This serves as the baseline value for changes in the total value of additive factors in engine oil. This serves as a baseline value for changes in the total amount of contaminants in engine oil. This serves as the benchmark value for the total mass fraction of pollutants in the engine.

[0047] Optionally, wear coefficient The wear coefficient reflects the degree to which metallic elements in engine oil accumulate to a critical level. A higher wear coefficient indicates more severe wear on critical friction pairs in the engine; additive coefficient... The additive coefficient reflects the loss ratio of additive factors (such as P, Zn, and Ca) in engine oil. A higher additive coefficient indicates a more significant decline in oil performance and a shorter mileage range. The contamination coefficient... It is used to reflect the cumulative relative levels of contaminants in engine oil (such as Si, Na, K, w_fuel, w_smoke, w_water).

[0048] Optionally, after obtaining the degradation coefficient and the oil quality deterioration coefficient of the engine oil, the degradation coefficient and the oil quality deterioration coefficient can be used together to form a two-dimensional input of "operating condition driving + chemical measurement", providing a complete, reliable and high signal-to-noise ratio data basis for the next step of comprehensively evaluating the remaining life of the engine oil.

[0049] Step S103: Based on the attenuation coefficient and the oil deterioration coefficient, predict the remaining service mileage of the engine oil.

[0050] In the technical solution provided in step S103 of this application, after obtaining the attenuation coefficient and the oil deterioration coefficient, the remaining service mileage of the engine oil can be predicted based on these coefficients. This remaining service mileage characterizes the distance the engine can allow the vehicle to travel under the combined effects of the engine oil's degradation under the vehicle's current operating conditions. In other words, it represents the maximum mileage the vehicle can continue to travel, provided that the engine can still operate safely and reliably without abnormal wear or failure due to oil performance degradation, even under the combined effects of the expected oil degradation under the vehicle's current operating conditions.

[0051] In this embodiment, a triple remaining mileage assessment model can be constructed based on the attenuation coefficient and the wear coefficient, additive coefficient, and contamination coefficient in the oil deterioration coefficient to determine the remaining mileage of the engine oil under different wear coefficients, and the minimum value among them is taken as the final remaining mileage, so as to achieve high-precision prediction under multi-dimensional collaborative constraints.

[0052] Optionally, the computer oil's first remaining service mileage is determined based on the wear coefficient within the attenuation coefficient and oil deterioration coefficient; the computer oil's second remaining service mileage is determined based on the additive coefficient within the attenuation coefficient and oil deterioration coefficient; and the computer oil's third remaining service mileage is determined based on the contamination coefficient within the attenuation coefficient and oil deterioration coefficient.

[0053] For example, the process of the first remaining service mileage of computer oil can be represented by the following formula (5) based on the attenuation coefficient and the wear coefficient in the oil deterioration coefficient.

[0054] (5)

[0055] in, Used to indicate the first remaining mileage. The reference mileage used to indicate engine oil is, that is, the mileage of engine oil in its initial state.

[0056] For example, the process of the second remaining service mileage of computer oil can be expressed by the following formula (6) based on the degradation coefficient and the additive coefficient in the oil deterioration coefficient.

[0057] (6)

[0058] in, Used to indicate the second remaining usage mileage.

[0059] For example, the process of the third remaining service mileage of computer oil can be represented by the following formula (7) based on the degradation coefficient and the pollution coefficient in the oil deterioration coefficient.

[0060] (7)

[0061] in, Used to indicate the third remaining usage mileage.

[0062] Optionally, after obtaining the first remaining usage mileage, the second remaining usage mileage, and the third remaining usage mileage, the minimum value among these three can be determined as the remaining usage mileage L of the engine oil. That is, .

[0063] In steps S101 to S103 of this application, the degradation coefficient of the engine oil is determined by using multiple operating parameters of the vehicle under current operating conditions. This quantifies the impact of current operating conditions on the rate of oil deterioration. Furthermore, by testing the engine oil, the oil quality degradation coefficient is obtained, accurately reflecting the degree of oil degradation. Subsequently, by combining the degradation coefficient and the oil quality degradation coefficient, the remaining service mileage of the engine oil is predicted. This approach considers both the actual operating conditions of the vehicle's engine and the degree of oil degradation, significantly improving the accuracy of predicting the remaining service mileage. This achieves a fundamental shift from "experience-based oil changes" to "quality-based maintenance," thereby solving the technical problem in related technologies where the remaining service mileage of engine oil cannot be effectively predicted.

[0064] The method described in this embodiment will be further described below.

[0065] As an optional embodiment, step S102, determining the degradation coefficient of engine oil based on operating state parameters, includes: obtaining weight coefficients corresponding to multiple operating state parameters, wherein the weight coefficients are used to characterize the degree of influence of operating state parameters on the degradation rate of engine oil under the current operating conditions; and performing weighted calculation on multiple operating state parameters and their corresponding weight coefficients to obtain the degradation coefficient of engine oil.

[0066] In this embodiment, the key to determining the engine oil degradation coefficient lies in identifying and quantifying the differentiated impact of various operating parameters on the oil degradation rate. This impact is characterized by the weighting coefficients corresponding to each operating parameter under the current operating conditions. These weighting coefficients are not fixed values ​​set arbitrarily, but rather are engineering experience coefficients obtained through statistical regression and machine learning calibration based on a large amount of engine bench testing, road durability testing, and oil chemical analysis data. They have clear physical meaning and technical reliability.

[0067] Optionally, after obtaining multiple operating state parameters of the vehicle's engine under the current operating conditions, each operating state parameter is divided into preset discretization intervals. For example, engine speed is divided into intervals of 100 r / min (e.g., 500–600 r / min, 600–700 r / min, etc.), engine torque percentage is divided into intervals of 5% (e.g., 0–5%, 5–10%, etc.), oil temperature is divided into intervals of 2°C, and oil pressure is divided into intervals of 20 kPa. Then, the duration percentage of each operating state parameter within each discrete interval is statistically analyzed to obtain the distribution probability of each operating state parameter within each discrete interval under the current operating conditions. Based on this, the operating condition weight coefficient table obtained through bench testing and road calibration is called, and corresponding weight coefficients (e.g., D1, D2…D6) are matched according to the distribution percentage of each operating state parameter within its interval. These weighted coefficients are then summed to finally calculate the attenuation coefficient D, which reflects the comprehensive impact of the current operating conditions on the oil degradation rate. This process enables an engineered mapping from continuous operating data to quantified degradation influencing factors, providing a dynamic and personalized basis for subsequent life prediction based on operating condition corrections.

[0068] Optionally, the weighting coefficient D1 corresponding to engine speed is used to reflect the accelerating effect of engine on oil oxidation and thermal decomposition during engine operation in different speed ranges. At high speeds (e.g., above 3000 r / min), oil film shear intensifies and oil temperature rises, leading to the breakage of base oil molecular chains; this range is assigned a higher weighting coefficient. The weighting coefficient D2 corresponding to engine torque percentage is used to reflect the coupled effect of engine load level on mechanical stress and oil contamination. Under high torque conditions (e.g., above 80% load), combustion temperature rises and soot generation increases; therefore, its weighting is significantly higher than in the low load range. The weighting coefficient D2 corresponding to the cumulative engine idling time is... The weighting coefficient D3 and the weighting coefficient D4 corresponding to the cumulative number of engine start-stop cycles jointly reflect the cumulative effect of "inefficient operation" on oil degradation. Prolonged idling causes incomplete fuel combustion, diluting the oil, while frequent start-stop cycles result in cold-start wear and oil temperature fluctuations; both are assigned high weights. The weighting coefficients D5 and D6 corresponding to engine oil temperature and oil pressure, respectively, reflect the thermal load and oil film stability of the lubrication system. When the temperature exceeds 110°C, the oxidation rate increases exponentially, and the weight increases sharply. Conversely, excessively low oil pressure indicates insufficient lubrication, exacerbating wear on the friction pairs; this range is also assigned high weights. These weighting coefficients are not isolated but constitute a "condition-degradation response" mapping matrix that highly matches the actual operating characteristics of the vehicle, providing a scientific basis for subsequent comprehensive calculations.

[0069] Optionally, after obtaining the weight coefficients corresponding to the multiple operating state parameters, the multiple operating state parameters and their corresponding weight coefficients can be weighted and calculated to obtain the oil degradation coefficient in the engine, as described in the aforementioned formula (1).

[0070] In this step, the engine oil degradation coefficient is calculated by weighting and summing multiple operating parameters of the vehicle under the current operating conditions with their corresponding weighting coefficients. This achieves a transparent, traceable, and reproducible transformation from "raw operating data" to "deterioration stress intensity." By introducing this multi-dimensional, weighted, and dynamic degradation coefficient calculation method, the system overcomes the limitations of traditional "fixed oil change interval" or "single parameter fitting" methods, enabling the remaining service life of the engine oil to accurately perceive and adaptively correct actual driving behavior.

[0071] As an optional embodiment, testing the engine oil to obtain its degradation coefficient includes: using oil testing equipment to sample the engine oil and obtaining sampled engine oil; using oil testing equipment to analyze the oil composition of the sampled engine oil and obtaining test results; and determining the engine oil degradation coefficient based on the test results.

[0072] In this embodiment, when testing the engine oil to determine its degradation coefficient, the engine oil can first be sampled using oil testing equipment deployed at service stations or cooperative repair outlets. The sampled oil, typically 3–5 ml, is obtained. For example, the sampled oil can be obtained by slightly draining it through the engine dipstick hole or drain plug. This process does not interrupt normal vehicle operation and does not affect the user experience. It also avoids the drawbacks of traditional laboratory oil sampling, which requires disassembling the oil pan or prolonged engine shutdown.

[0073] Optionally, after sampling, the sampled oil can be automatically sent to an oil testing device with a built-in analysis module. This device uses multimodal detection technology to comprehensively analyze the oil composition, including atomic absorption spectrometry to determine the absolute concentration of metallic wear elements such as Fe, Cu, Al, and Pb; infrared spectroscopy and electrochemical sensors to jointly detect fuel dilution rate (w_fuel), soot content (w_soil), and water content (w_water); and inductively coupled plasma or X-ray fluorescence technology to obtain the content of key additive elements such as P, S, Zn, Ca, Mg, and B. The entire testing process is completed in a short time, combining high efficiency and accuracy, and its results can achieve a signal-to-noise ratio and reliability level similar to traditional laboratory testing.

[0074] Optionally, after obtaining the test results, the oil degradation coefficient of the engine oil can be determined based on these results. This degradation coefficient includes a wear coefficient, an additive coefficient, and a contamination coefficient. The wear coefficient is calculated from the relative change rate of the measured total content of elements such as Fe, Cu, Al, and Pb compared to the initial reference value of the engine oil, reflecting the abnormal wear trend of engine friction pairs (e.g., cylinder liners, connecting rod bearings, valve mechanisms). The additive coefficient is calculated from the current content of effective components such as P, S, Zn, Ca, Mg, and B compared to the loss ratio of additives at the initial reference value, characterizing the degree of decline in the engine oil's antioxidant, anti-wear, and detergency / dispersant functions. The contamination coefficient comprehensively considers fuel dilution rate, soot accumulation, and moisture intrusion, comparing it with the initial contaminant reference value to quantify the degree of damage to the physicochemical properties of the oil caused by external contaminants. The wear coefficient, additive coefficient, and contamination coefficient are all output in dimensionless form; the closer their values ​​are to 1, the more severe the oil degradation and the closer its function is to the failure threshold.

[0075] Optionally, after obtaining the above-mentioned wear coefficient, additive coefficient, and contamination coefficient, the above-mentioned wear coefficient, additive coefficient, and contamination coefficient can be determined as the oil deterioration coefficient of the engine oil.

[0076] As an optional embodiment, the oil degradation coefficient of the engine oil is determined based on the test results, including: extracting the wear factor test results, additive factor test results, and contaminant test results of the engine oil from the test results, wherein the wear factor test results are used to characterize the content of wear factors in the engine oil, the additive factor test results are used to characterize the content of additive factors in the engine oil, and the contaminant test results are used to characterize the content of contaminants in the engine oil; and determining the oil degradation coefficient of the engine oil based on the wear factor test results, additive factor test results, contaminant test results, and engine oil consumption.

[0077] In this embodiment, after obtaining the test results, the wear factor test results, additive factor test results, and contamination factor test results can be accurately extracted from the test results. Among them, the wear factor test results can reflect the absolute content of wear factors (Fe, Cu, Al, Pb) in the engine oil. These wear factors, also known as wear metal elements, originate from the particle shedding of key engine friction pairs (such as cylinder liners, piston rings, bearings, and valve mechanisms) during operation. Their concentration level directly reflects the degree of accumulation of internal mechanical wear. The additive factor test results characterize the residual content of functional additive elements (P, S, Zn, Ca, Mg, B) in the engine oil. These elements are the core carriers of the engine oil's antioxidant, anti-wear, detergency, dispersion, and rust prevention properties. A decrease in their content means a continuous decline in the oil's protective ability. The contaminant factor test results cover foreign impurities (Si, Na, K) and combustion byproducts (fuel dilution rate w_fuel, soot content w_smoke, water content w_water). These substances enter the engine oil through sealing failure, incomplete combustion, or condensation intrusion, damaging the oil film stability, accelerating oxidation, and causing acid corrosion. They are important causes of sudden changes in oil performance.

[0078] Optionally, after obtaining the above-mentioned wear factor test results, additive factor test results and contaminant factor test results, the oil quality degradation coefficient of the engine oil can be determined by further combining the oil consumption.

[0079] As an optional embodiment, the oil deterioration coefficient of the engine oil is determined based on the results of wear factor detection, additive factor detection, contaminant factor detection, and oil consumption. This includes: determining the wear coefficient of the engine oil based on the wear factor detection results and oil consumption, wherein the wear coefficient characterizes the wear degree of key friction pairs in the engine; determining the additive coefficient of the engine oil based on the additive factor detection results and oil consumption, wherein the additive coefficient characterizes the degree of loss of additive elements in the engine oil; determining the contamination coefficient of the engine oil based on the contaminant factor detection results and oil consumption, wherein the contamination coefficient characterizes the degree of contamination of the engine oil by contaminant elements; and finally, the wear coefficient, additive coefficient, and contamination coefficient are used as the oil deterioration coefficient.

[0080] In this embodiment, after obtaining the above-mentioned wear factor test results, additive factor test results, and contaminant factor test results, the oil quality deterioration coefficient of the engine oil can be determined by combining the oil consumption. The oil consumption can be expressed as a percentage, that is, the percentage of oil consumption.

[0081] Optionally, as described above, the wear factor detection results are used to characterize the content of wear factors in the engine oil. These wear factors, also known as wear elements, are metallic elements that are lost into the engine oil due to friction from key friction pairs during operation. These key friction pairs are used to characterize components that affect the engine's operating condition, such as cylinder liners, piston rings, crankshaft bearings, and valve mechanisms. The aforementioned wear factors (elements) may include metallic elements such as Fe, Cu, Al, and Pb.

[0082] Optionally, although the content of wear elements in the engine oil in the wear factor test results can directly reflect the absolute concentration of wear elements in the engine oil, if the reduction in oil volume due to natural consumption during use (such as piston ring scraping, evaporation, or seal leakage) is not considered, a "falsely high concentration" phenomenon will occur. Even if wear is not aggravated, the reduction in oil volume will passively increase the concentration of metal elements, causing misjudgment. Therefore, the oil consumption is introduced as a correction factor to normalize the total content of wear factors obtained from the test, so that the wear coefficient only reflects the actual wear increment, rather than the interference of oil volume changes. The higher the value of the wear coefficient, the more severe the wear of the key friction pairs in the engine, indicating that the engine may have abnormal wear risks, such as cylinder wall scoring, bearing spalling, etc., providing a direct basis for early warning of mechanical failures. The wear coefficient of the engine oil can be determined based on the wear test results and the oil consumption using the above formula (2).

[0083] Optionally, as described above, the above additive factor detection results are used to characterize the content of additive factors in engine oil, which can also be called additive elements. Among them, the additive factors are key functional additives that maintain the engine oil's ability to resist oxidation, wear, detergency, dispersion, and neutralize acidic substances, including but not limited to P, S, Zn, Ca, Mg, and B. As the mileage increases, the additive elements are continuously consumed under the action of high temperature, high pressure, and combustion by-products, and their concentration decrease is an early signal of engine oil failure. However, if the degree of loss is judged only by the current concentration, the concentration dilution effect caused by engine oil consumption will be ignored. Therefore, the additive factor content can be compensated and corrected in combination with the engine oil consumption, and the true loss rate of the additive factor content relative to the initial benchmark value can be calculated to accurately characterize the "actual consumption" of the additive. Among them, the additive coefficient of the engine oil can be determined by the above formula (3) based on the additive factor detection results and the engine oil consumption. When the additive coefficient is close to or exceeds the preset threshold, it indicates that the engine oil has lost its key protective function. Even if there is no obvious discoloration in appearance, its anti-wear performance has been significantly reduced, and timely intervention is necessary to avoid irreversible engine damage.

[0084] Optionally, as described above, the above-mentioned contaminant detection results are used to characterize the content of contaminants in engine oil, which can also be called contaminant elements. Among them, the contaminants include, but are not limited to: Si, Na, and K of airborne dust, soot produced by incomplete combustion (w_smoke), dilution caused by fuel leakage (w_fuel), and water content formed by condensate intrusion (w_water). These contaminants can lead to abnormal oil viscosity, increased acid value, oil film rupture, and aggravated corrosion. Unlike the former two, the contaminants do not originate from internal engine wear or additive reactions, but from external intrusion and the accumulation of combustion byproducts. Since some contaminants (such as fuel and water) have volatile or dilutive properties, their concentration changes are easily affected by the reduction of oil volume. Therefore, the engine oil consumption is also introduced to dynamically correct the contaminant detection results, so as to eliminate the interference of oil volume changes on the concentration of contaminants in engine oil and truly reflect the "net accumulation" of contaminants in engine oil. Among them, the contamination coefficient of engine oil can be determined by the above formula (4) based on the contaminant detection results and the engine oil consumption. The higher the pollution coefficient, the more serious the oil contamination. If it is not replaced, it will lead to filter overload, oil circuit blockage, and accelerated corrosion, and may even cause serious failures such as cylinder scoring and bearing seizure.

[0085] Optionally, after obtaining the aforementioned wear coefficient, additive coefficient, and contamination coefficient, these three coefficients can be uniformly defined as the oil degradation coefficient, serving as the three core indicators for measuring the health status of engine oil. Each coefficient independently represents a degradation mechanism, with no direct dependence on each other, avoiding misjudgments caused by distortion from a single parameter.

[0086] As an optional embodiment, step S103, predicting the remaining service mileage of the engine oil based on the attenuation coefficient and the oil deterioration coefficient, includes: predicting a first remaining service mileage of the engine oil based on the attenuation coefficient, the wear coefficient in the oil deterioration coefficient, and the service mileage of the engine oil in its initial state, wherein the first remaining service mileage represents the service mileage of the engine oil under the current operating conditions and degree of wear; predicting a second remaining service mileage of the engine oil based on the attenuation coefficient, the additive coefficient in the oil deterioration coefficient, and the service mileage of the engine oil in its initial state, wherein the second remaining service mileage represents the service mileage of the engine oil under the current operating conditions and degree of wear; predicting a third remaining service mileage of the engine oil based on the attenuation coefficient, the contamination coefficient in the oil deterioration coefficient, and the service mileage of the engine oil in its initial state, wherein the third remaining service mileage represents the service mileage of the engine oil under the current operating conditions and degree of contamination; and determining the remaining service mileage of the engine oil based on the first remaining service mileage, the second remaining service mileage, and the third remaining service mileage.

[0087] In this embodiment, when predicting the remaining service mileage of engine oil based on the attenuation coefficient and the oil deterioration coefficient, the core strategy of "multi-dimensional independent evaluation and minimum value constraint decision" can be adopted. Starting from the three failure mechanisms of engine friction pair wear, additive function consumption and contaminant accumulation, the remaining service mileage of engine oil is collaboratively modeled in combination with the attenuation coefficient of engine oil under the current operating conditions.

[0088] Optionally, the wear coefficient is determined based on the attenuation coefficient D, the wear coefficient in the oil degradation coefficient, and the baseline service mileage set for the engine oil in its initial state. The first remaining mileage of computer oil The first remaining service mileage represents the maximum mileage that the engine oil can effectively support under the combined effects of the current vehicle operating conditions (represented by D) and the degree of wear accumulation inside the engine (quantified by the wear coefficient), and can still effectively protect the friction pairs and avoid failure caused by abnormal wear. Its essence is a reflection of the "mechanical durability limit". When the wear coefficient is close to 1, it indicates that a large number of metal particles have been deposited and the oil film carrying capacity is approaching the critical point. At this time, even if the operating conditions are mild, the remaining mileage will be shortened sharply. If the operating conditions are harsh (D>1), even if the degree of wear is low, it will fail prematurely due to the accelerated oxidation of the oil. The benchmark service mileage set by the engine oil in the initial state is dynamically compressed by the oil decay coefficient, so as to realize the coupled evaluation of operating conditions and wear degree. The method for calculating the first remaining service mileage can refer to the aforementioned formula (5), which will not be repeated here.

[0089] Optionally, the degradation coefficient D, the additive coefficient in the oil deterioration coefficient, and the baseline service mileage set for the engine oil in its initial state can be used. The second remaining mileage of computer oil This second remaining mileage characterizes the extent to which core additive elements (such as Zn, Ca, P, and Mg) in the engine oil, providing antioxidant, anti-wear, and cleaning benefits, have been depleted under current operating conditions, allowing the oil to continue operating beyond its critical protective function. Additives are not disposable; they slowly decay due to continuous reactions with temperature, shear, and combustion byproducts. When the additive coefficient approaches 1, it indicates that the effective components are nearly exhausted, and the oil will lose its ability to prevent carbon buildup, inhibit oxidation, and neutralize acidic substances. Even with minor wear and contamination, it no longer meets the safety margin for continued use. This is achieved by using the baseline mileage... Dividing by the attenuation coefficient D, a punitive correction is made for conditions that exacerbate additive wear, such as high load and high temperature, so that the prediction results are closer to the actual usage scenario. The method for calculating the second remaining usage mileage can be referred to the aforementioned formula (6), which will not be repeated here.

[0090] Optionally, the baseline service mileage is set based on the degradation coefficient D, the contamination coefficient in the oil deterioration coefficient, and the reference service mileage of the engine oil in its initial state. The third remaining mileage of computer oil The third remaining service mileage is used to characterize the last mileage that can be tolerated under the current operating environment due to the accumulation of pollutants such as fuel dilution, soot accumulation, and moisture intrusion, leading to abnormal oil viscosity, oil film rupture, and increased corrosion, thus causing lubrication failure. The pollutants are not generated inside the engine, but rather originate from external factors such as combustion efficiency, sealing performance, and ambient humidity. Their harm is sudden and cumulative. When the pollution coefficient exceeds the threshold, even if additives are still present and wear does not increase significantly, the engine oil may still cause catastrophic failures such as cylinder scoring and bearing seizure due to the collapse of physical properties. The method for calculating this third remaining service mileage can be referred to the aforementioned formula (7), which will not be repeated here.

[0091] Optionally, after obtaining the first remaining usage mileage, the second remaining usage mileage, and the third remaining usage mileage, the final remaining usage mileage L of the engine oil can be determined by combining the three remaining usage mileages.

[0092] The following section will further explain the process of determining the final remaining mileage of the engine oil by combining the above three remaining mileage values.

[0093] As an optional implementation, determining the remaining service mileage of the engine oil based on the first remaining service mileage, the second remaining service mileage, and the third remaining service mileage includes: determining the minimum value among the first remaining service mileage, the second remaining service mileage, and the third remaining service mileage as the remaining service mileage of the engine oil.

[0094] In this embodiment, the final remaining mileage of the engine oil is not determined by a single-dimensional linear calculation, but rather based on the first remaining mileage obtained above. Second remaining usage mileage With the third remaining usage mileage The "shortest path first" principle is used for comprehensive judgment, that is, the minimum value among the three is taken as the final remaining service mileage L of the engine oil. .

[0095] Optionally, the minimum value is used as the final remaining mileage L, essentially following the "barrel effect" in engineering safety design. That is, the remaining service mileage of the engine oil is determined by the degradation dimension that reaches the failure threshold first, rather than the average or weighted value. For example, even if 70% of the additives remain and the level of contamination is low, if the wear coefficient indicates a risk of severe cylinder liner scoring, then even if other indicators are acceptable, the oil must be replaced immediately, otherwise irreversible engine damage will occur. Conversely, if only contaminants exceed the standard but wear and additives are still within the safe range, cleaning or filtration can be prioritized, delaying the oil change. This strategy effectively avoids the risks of "over-maintenance" or "under-maintenance" caused by traditional predictions based on a single indicator (such as total mileage, total time, or single element concentration), truly shifting oil change decisions from "experience-driven" to "condition-driven." At the same time, this minimum value judgment mechanism has strong robustness. Even if a certain test data deviates due to accidental error, as long as the other two dimensions still simultaneously point to a short lifespan trend, it can still accurately trigger a warning, ensuring safety redundancy. The final output of remaining mileage L is not only a quantitative expression of the remaining fuel capacity, but also a comprehensive diagnostic conclusion of the engine's current overall health status, providing an accurate, reliable, and executable decision-making basis for subsequent graded warning and power limiting strategies.

[0096] As an optional implementation, the method further includes: generating a reminder strategy based on the remaining mileage, wherein the reminder strategy is used to characterize the rule for reminding the driver of the vehicle to change the engine oil; and reminding the driver according to the reminder strategy.

[0097] In this embodiment, after obtaining the final remaining mileage L of the engine oil, a tiered, dynamic, and safety-priority reminder strategy is constructed based on this value to accurately guide the driver to change the engine oil in a timely manner, realizing an intelligent transformation from "passive maintenance based on mileage" to "proactive warning based on status." This reminder strategy is not a simple triggering mechanism with a fixed threshold, but rather sets three levels of response rules based on the distribution of the remaining mileage, each corresponding to different risk levels and intervention intensities, ensuring that engine safety is guaranteed while maximizing oil usage efficiency and user operating economy.

[0098] Optionally, when the remaining mileage L≥5000km, the engine oil is determined to be still within the safe operating range and has not yet entered a high-risk critical state. At this time, the cloud platform only pushes the current remaining oil change mileage information to the vehicle terminal (such as the dashboard or in-vehicle APP) as a status reminder for the driver's reference, without triggering any mandatory intervention, so as to avoid excessive reminders causing user fatigue or neglect. This stage aims to maintain the normal operating rhythm and support users to reasonably arrange maintenance plans, which is especially suitable for commercial vehicle scenarios that are sensitive to downtime, such as long-distance transportation and continuous operation.

[0099] Optionally, when 0 ≤ L < 5000km, the system enters a "warning response" state, indicating that the engine oil has entered the final stage of its efficient use, with accelerated functional degradation and a high risk of failure. At this time, the cloud platform not only continuously updates the remaining mileage data but also proactively pushes replacement reminders to the driver, including messages such as "It is recommended to change the engine oil soon" and "Less than 5000km remaining." It also automatically links the vehicle's current location, intelligently recommends nearby partner service stations, and provides navigation routes and appointment options, achieving an integrated closed loop of "reminder—location—service." This stage of the design fully considers the user's actual operational convenience, achieving precise scheduling of service resources through the vehicle networking platform, reducing the risk of failure due to missed oil change opportunities, and improving the customer conversion efficiency of service stations.

[0100] Optionally, when L < 0, the engine oil is determined to be completely ineffective, and the engine is in a dangerous operating state of severe insufficient lubrication. Continued use could potentially lead to irreversible damage such as cylinder liner scoring, bearing erosion, and carbon deposit detonation. In this case, the warning strategy is upgraded to a mandatory intervention mode: the cloud platform immediately sends a high-priority alarm to the driver (e.g., red flashing on the dashboard, voice broadcast, APP push notification), clearly stating "the engine oil is ineffective and must be replaced immediately," and simultaneously sends control commands to the vehicle controller to limit engine output power, such as reducing maximum torque, limiting maximum speed, or entering limp mode, to slow down wear and maintain basic driving capability until the oil change is completed. This mandatory intervention mechanism is the core innovation of this invention in safety design, upgrading "suggestive reminders" to "mandatory protection," fundamentally preventing serious malfunctions caused by human negligence.

[0101] In addition, the service station simultaneously receives vehicle information for oil changes where L < 0, forming a closed-loop maintenance tracking system. Service station personnel can proactively contact vehicle owners to confirm the oil change status. After the oil change is completed, the new oil test data is uploaded, the system automatically resets the oil's baseline mileage, and starts the next monitoring cycle. This strategy not only achieves closed-loop management of a single maintenance service but also builds an engine oil failure database by continuously accumulating oil change data and failure cases, providing a data foundation for future model optimization and personalized maintenance recommendations.

[0102] The method for predicting the remaining mileage of engine oil will be further described below with reference to the preferred embodiments of this application.

[0103] Figure 2 This is a flowchart of another method for predicting the remaining mileage of engine oil according to an embodiment of this application, such as... Figure 2 As shown, the method includes the following steps.

[0104] In step S201, the vehicle network data platform calculates the engine oil degradation coefficient D based on the received engine driving status parameters.

[0105] In this embodiment, after obtaining the engine driving status parameters, the vehicle terminal (Telematics Box, or T-Box for short) can transmit the engine status parameters to the vehicle network data platform. The vehicle network data platform calculates the engine oil degradation coefficient D based on the received engine driving status parameters using the aforementioned formula (1).

[0106] Optionally, engine operating status parameters are obtained from a vehicle big data platform. The actual engine torque percentage is divided into 5% intervals, such as 0-5%, 5-10%, 10-15%...95-100%. Engine speed is divided into 100 rpm intervals, such as 500-600 rpm, 600-700 rpm, 700-800 rpm...3400-3500 rpm. Engine oil temperature is divided into 2°C intervals, such as 100-102°C, 102-104°C... Oil pressure is divided into 20 kcal intervals, such as 500-520 kcal, 520-540 kcal... The intervals and ranges for actual engine torque percentage, engine speed, oil pressure, and oil temperature can be adjusted according to the actual vehicle distribution, balancing computational efficiency and accuracy. Actual vehicle driving involves relatively many operating conditions, but most of these conditions account for less than 0.01% of the total time, having a minimal impact on the overall calculation results. To improve computational efficiency, typical operating conditions, representing no less than 0.01% of the total, were selected, covering the vehicle's operating status under various conditions. Idle time was measured in hours, and start-stop cycles were measured in tens of thousands of times, a unit that facilitates the calibration of operating condition data. D1, D2, D3, D4, D5, and D6 are the influence coefficients on oil degradation under various operating conditions, obtained from experimental calibration data.

[0107] Step S202: Perform a rapid test on the engine oil and classify the test results according to wear factor, additive factor, and contamination factor.

[0108] In this embodiment, the engine oil is rapidly tested to obtain the test results, which are then categorized into wear factors, additive factors, and contaminant factors. Wear factors include the content of Fe, Cu, Al, and Pb elements; additive factors include the content of P, S, Zn, Ca, Mg, and B; and contaminant factors include the content of Si, Na, and K, fuel content (w_fuel), soot content (w_soybean), and water content (w_water).

[0109] Optionally, atomic absorption spectrometry can be used for rapid testing of engine oil. The testing equipment is installed at service stations, allowing for on-site analysis with results similar to laboratory analysis. Users can bring samples to the nearest service station or mail them in. The rapid oil testing equipment requires small samples, typically 3-5 ml, which does not affect normal use and is fast, usually within one minute. After testing, the service station uploads the data to the vehicle network data platform. The platform categorizes the results into wear factors, additive factors, and contamination factors, calculating the wear coefficient, additive coefficient, and contamination coefficient of the engine oil. The wear coefficient reflects the degree of wear on key engine friction pairs. A high wear coefficient indicates severe wear on these pairs, and the type and size of severely worn metals can help determine the location and cause of engine failure. The additive coefficient reflects the degree of loss of key engine oil additives. Significant fluctuations in the additive coefficient indicate severe consumption of the corresponding additives, determining whether the engine oil can continue to be used. Users can choose to change the oil directly or add additives to bring the additive coefficient back to a reasonable range. The contamination coefficient reflects the degree of contamination of engine oil by external pollutants. Currently, the impact of contaminant generation on oil change intervals is becoming increasingly significant. When the contamination coefficient is high, the source of the contaminants should be investigated, and corresponding treatment measures should be taken to prevent sudden changes in oil performance. When contaminants reach a certain level, users should change the oil promptly to prevent irreversible damage to the engine.

[0110] Step S203: Calculate the wear coefficient of the engine based on the wear factor, calculate the additive coefficient of the engine oil based on the additive factor, and calculate the contamination coefficient of the engine oil based on the contamination factor.

[0111] In this embodiment, the wear coefficient of the engine can be calculated based on the wear factor, the additive coefficient of the engine oil can be calculated based on the additive factor, and the contamination coefficient of the engine oil can be calculated based on the contamination factor. The specific calculation process can be referred to the aforementioned formulas (2)-(4), and will not be repeated here.

[0112] Step S204: Calculate the first remaining mileage of the engine, calculate the second remaining mileage of the engine, and calculate the third remaining mileage of the engine.

[0113] In this embodiment, after obtaining the attenuation coefficient, the wear coefficient of the engine, the additive coefficient of the engine oil, and the contamination coefficient of the engine oil, the first remaining service mileage, the second remaining service mileage, and the third remaining service mileage of the engine can be calculated by the aforementioned formulas (5)-(7).

[0114] Step S205: Determine the remaining operating mileage L of the engine based on the first remaining operating mileage, the second remaining operating mileage, and the third remaining operating mileage.

[0115] In this embodiment, after obtaining the first remaining usage mileage, the second remaining usage mileage, and the third remaining usage mileage, the minimum value among the first remaining usage mileage, the second remaining usage mileage, and the third remaining usage mileage can be used to determine the engine's remaining usage mileage L.

[0116] Step S206: When L≥5000km, send the remaining engine oil usage mileage; when 0≤L<5000km, send the remaining engine oil change mileage and remind the user to choose the nearest service station for oil change; when L<0, remind the user that the engine oil must be changed immediately and limit the engine output power.

[0117] In this embodiment, when L < 5000km, the cloud platform sends the remaining engine oil change mileage to the vehicle's driver and reminds the driver to choose the nearest service station for engine oil change. The vehicle big data platform recommends nearby service stations to the user, allowing the user to choose a convenient one for maintenance. When L < 0, the cloud platform reminds the driver that the engine oil must be changed immediately and immediately limits the engine output power. Service station personnel will track whether vehicles with L < 0 have completed the oil change, and after the change, the service station will upload the new oil data for monitoring the next maintenance cycle. The vehicle big data platform uses the collected information to form a large database, collecting various types of engine oil failure cases and detailed failure characteristics and parameter representations. When the data accumulates to a certain amount, the platform builds an engine oil failure database, establishing a comparison table of failure types and characteristics, allowing users to view similar failure cases and parameter characteristics to obtain corresponding handling suggestions.

[0118] In steps S201 to S206 above, the degradation coefficient of the engine oil is determined by using multiple operating parameters of the vehicle under current operating conditions. This quantifies the impact of the current operating conditions on the rate of oil deterioration. Furthermore, the oil quality degradation coefficient is obtained by testing the engine oil, accurately reflecting the degree of oil degradation. Subsequently, by combining the degradation coefficient and the oil quality degradation coefficient, the remaining service mileage of the engine oil is predicted. This approach considers both the actual operating conditions of the vehicle's engine and the degree of oil degradation, significantly improving the accuracy of predicting the remaining service mileage. This achieves a fundamental shift from "experience-based oil changes" to "quality-based maintenance," thereby solving the technical problem of the inability to effectively predict the remaining service mileage of engine oil in related technologies.

[0119] According to an embodiment of this application, a device for predicting the remaining oil usage mileage is also provided. It should be noted that this device for predicting the remaining oil usage mileage can be used to execute the method for predicting the remaining oil usage mileage in the embodiments.

[0120] Figure 3 This is a schematic diagram of a device for predicting remaining oil usage mileage according to an embodiment of this application. Figure 3 As shown, the oil remaining usage mileage prediction device 300 may include: an acquisition unit 301, a determination unit 302, and a prediction unit 303.

[0121] The acquisition unit 301 is used to acquire multiple operating state parameters of the vehicle's engine under the current operating conditions. The operating state parameters are used to characterize the engine's operating state under the current operating conditions from a target dimension, and different operating state parameters correspond to different target dimensions.

[0122] The determining unit 302 is used to determine the degradation coefficient of the engine oil based on the operating state parameters, and to detect the engine oil to obtain the oil quality deterioration coefficient. The degradation coefficient is used to characterize the degree of influence of the current operating conditions on the deterioration rate of the engine oil, and the oil quality deterioration coefficient is used to characterize the degree of oil quality deterioration.

[0123] The prediction unit 303 is used to predict the remaining service mileage of the engine oil based on the attenuation coefficient and the oil deterioration coefficient, wherein the remaining service mileage is used to represent the mileage that the engine oil allows the vehicle to travel.

[0124] Optionally, the determining unit 302 is further configured to: obtain the weighting coefficients corresponding to multiple operating state parameters, wherein the weighting coefficients are used to characterize the degree of influence of the operating state parameters on the deterioration rate of the engine oil under the current operating conditions; and perform weighted calculation on the multiple operating state parameters and their corresponding weighting coefficients to obtain the degradation coefficient of the engine oil in the engine.

[0125] Optionally, the determining unit 302 is further configured to: call an oil testing device to sample the engine oil and obtain sampled engine oil; call an oil testing device to perform oil composition testing on the sampled engine oil and obtain test results; and determine the oil deterioration coefficient of the engine oil based on the test results.

[0126] Optionally, the determining unit 302 is further configured to: extract the wear factor test results, additive factor test results, and contaminant test results of the engine oil from the test results, wherein the wear factor test results are used to characterize the content of wear factors in the engine oil, the additive factor test results are used to characterize the content of additive factors in the engine oil, and the contaminant test results are used to characterize the content of contaminants in the engine oil; and determine the oil deterioration coefficient of the engine oil based on the wear factor test results, additive factor test results, contaminant test results, and engine oil consumption.

[0127] Optionally, the determining unit 302 is further configured to: determine the wear coefficient of the engine oil based on the wear factor detection results and the oil consumption, wherein the wear coefficient is used to characterize the wear degree of the key friction pairs of the engine; determine the additive coefficient of the engine oil based on the additive factor detection results and the oil consumption, wherein the additive coefficient is used to characterize the degree of loss of additive factors in the engine oil; determine the contamination coefficient of the engine oil based on the contamination factor detection results and the oil consumption, wherein the contamination coefficient is used to characterize the degree of contamination of the engine oil by contaminants; and determine the wear coefficient, additive coefficient, and contamination coefficient as the oil deterioration coefficient.

[0128] Optionally, the prediction unit 303 is further configured to: predict a first remaining service mileage of the engine oil based on the attenuation coefficient, the wear coefficient in the oil deterioration coefficient, and the service mileage of the engine oil in its initial state, wherein the first remaining service mileage represents the service mileage of the engine oil under the current operating conditions and degree of wear; predict a second remaining service mileage of the engine oil based on the attenuation coefficient, the additive coefficient in the oil deterioration coefficient, and the service mileage of the engine oil in its initial state, wherein the second remaining service mileage represents the service mileage of the engine oil under the current operating conditions and degree of wear; predict a third remaining service mileage of the engine oil based on the attenuation coefficient, the contamination coefficient in the oil deterioration coefficient, and the service mileage of the engine oil in its initial state, wherein the third remaining service mileage represents the service mileage of the engine oil under the current operating conditions and degree of contamination; and determine the remaining service mileage of the engine oil based on the first remaining service mileage, the second remaining service mileage, and the third remaining service mileage.

[0129] Optionally, the prediction unit 303 is further configured to: determine the minimum value among the first remaining usage mileage, the second remaining usage mileage, and the third remaining usage mileage as the remaining usage mileage of the engine oil.

[0130] Optionally, the device 300 is further configured to: generate a reminder strategy based on the remaining mileage, wherein the reminder strategy is used to characterize the rule for reminding the driver of the vehicle to change the engine oil; and remind the driver according to the reminder strategy.

[0131] This device determines the engine oil degradation coefficient by using multiple operating parameters of the vehicle under current operating conditions. This quantifies the impact of current operating conditions on the oil's deterioration rate. Furthermore, by testing the engine oil, it obtains the oil quality degradation coefficient, accurately reflecting the degree of oil degradation. Then, by combining the oil degradation coefficient and the oil quality degradation coefficient, the remaining service mileage of the engine oil is predicted. This approach considers both the actual operating conditions of the vehicle's engine and the degree of oil degradation, significantly improving the accuracy of predicting the remaining service mileage. This represents a fundamental shift from "experience-based oil changes" to "quality-based maintenance," thereby solving the technical problem of effectively predicting the remaining service mileage of engine oil in related technologies.

[0132] Embodiments of this application also provide an electronic device. Figure 4 This is a schematic diagram of an electronic device according to an embodiment of this application. Figure 4 As shown, the electronic device 400 may include a memory 401 and a processor 402. The memory 401 stores an executable program; the processor 402 is used to run the executable program stored in the memory 401, wherein the program executes the methods described in various embodiments of this application during runtime.

[0133] Embodiments of this application also provide a vehicle, including: a memory storing an executable program; and a processor for running the program, wherein the program executes the methods described in various embodiments of this application when it runs.

[0134] Embodiments of this application also provide a computer-readable storage medium including a stored executable program, wherein, when the executable program is executed, it controls the device where the computer-readable storage medium is located to execute the oil remaining usage mileage prediction method of various embodiments of this application.

[0135] Embodiments of this application also provide a computer program product, including a computer program that, when executed by a processor, implements the method for predicting remaining oil usage mileage in various embodiments of this application.

[0136] Embodiments of this application also provide a computer program product, including a non-volatile computer-readable storage medium for storing a computer program. When the computer program is executed by a processor, it implements the oil remaining mileage prediction method in various embodiments of this application.

[0137] The embodiments of this application also provide a computer program that, when executed by a processor, implements the method for predicting remaining oil usage mileage in the various embodiments of this application described above.

[0138] The sequence numbers of the embodiments in this application are for descriptive purposes only and do not represent the superiority or inferiority of the embodiments.

[0139] In the above embodiments of this application, the descriptions of each embodiment have different focuses. For parts not described in detail in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0140] In the several embodiments provided in this application, it should be understood that the disclosed technical content can be implemented in other ways. The device embodiments described above are merely illustrative; for example, the division of units can be a logical functional division, and in actual implementation, there may be other division methods. For instance, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the displayed or discussed mutual coupling, direct coupling, or communication connection may be through some interfaces; the indirect coupling or communication connection between units or modules may be electrical or other forms.

[0141] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0142] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0143] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, read-only memory (ROM), random access memory (RAM), portable hard drives, magnetic disks, or optical disks.

[0144] The above description is only a preferred embodiment of this application. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the principle of this application, and these improvements and modifications should also be considered within the scope of protection of this application.

Claims

1. A method for predicting remaining mileage of engine oil, characterized in that, include: The vehicle engine is acquired under the current operating conditions. The operating state parameters are used to characterize the operating state of the engine under the current operating conditions from a target dimension. Different operating state parameters correspond to different target dimensions. Based on the operating state parameters, the degradation coefficient of the engine oil in the engine is determined, and the engine oil is tested to obtain the oil quality deterioration coefficient of the engine oil. The degradation coefficient is used to characterize the degree of influence of the current operating conditions on the deterioration rate of the engine oil, and the oil quality deterioration coefficient is used to characterize the degree of oil quality deterioration of the engine oil. Based on the attenuation coefficient and the oil deterioration coefficient, the remaining service mileage of the engine oil is predicted, wherein the remaining service mileage is used to represent the mileage that the engine allows the vehicle to travel with the engine enabled by the engine.

2. The method according to claim 1, characterized in that, Based on the aforementioned operating state parameters, the degradation coefficient of the engine oil is determined, including: Obtain weight coefficients corresponding to multiple operating state parameters, wherein the weight coefficients are used to characterize the degree of influence of the operating state parameters on the deterioration rate of the engine oil under the current operating conditions; The degradation coefficient of the engine oil is obtained by weighting multiple operating state parameters with their corresponding weighting coefficients.

3. The method according to claim 1, characterized in that, The engine oil is tested to obtain its oil degradation coefficient, including: The engine oil is sampled using oil testing equipment to obtain sampled engine oil. The oil testing equipment is used to analyze the composition of the sampled oil, and the test results are obtained. Based on the test results, the oil degradation coefficient of the engine oil is determined.

4. The method according to claim 3, characterized in that, Based on the test results, the oil degradation coefficient of the engine oil is determined, including: The wear factor test results, additive factor test results, and contaminant test results of the engine oil are extracted from the test results. The wear factor test results are used to characterize the content of wear factors in the engine oil, the additive factor test results are used to characterize the content of additive factors in the engine oil, and the contaminant test results are used to characterize the content of contaminants in the engine oil. Based on the test results of the wear factor, the test results of the additive factor, the test results of the contaminant factor, and the consumption of the engine oil, the oil deterioration coefficient of the engine oil is determined.

5. The method according to claim 4, characterized in that, Based on the wear factor test results, the additive factor test results, the contaminant factor test results, and the oil consumption, the oil degradation coefficient of the engine oil is determined, including: Based on the wear factor detection results and the oil consumption, the wear coefficient of the oil is determined, wherein the wear coefficient is used to characterize the wear degree of the key friction pairs of the engine; Based on the detection results of the additive factors and the consumption of the engine oil, the additive coefficient of the engine oil is determined, wherein the additive coefficient is used to characterize the degree of loss of the additive factors in the engine oil. Based on the detection results of the pollutants and the consumption of the engine oil, the pollution coefficient of the engine oil is determined, wherein the pollution coefficient is used to characterize the degree of pollution of the engine oil by the pollutants. The wear coefficient, the additive coefficient, and the contamination coefficient are determined as the oil deterioration coefficient.

6. The method according to claim 5, characterized in that, Based on the attenuation coefficient and the oil degradation coefficient, the remaining service mileage of the engine oil is predicted, including: Based on the attenuation coefficient, the wear coefficient in the oil deterioration coefficient, and the service mileage of the engine oil in the initial state, the first remaining service mileage of the engine oil is predicted, wherein the first remaining service mileage is used to represent the service mileage of the engine oil under the current operating conditions and the degree of wear. Based on the attenuation coefficient, the additive coefficient in the oil deterioration coefficient, and the service mileage of the engine oil in the initial state, the second remaining service mileage of the engine oil is predicted, wherein the second remaining service mileage is used to represent the service mileage of the engine oil under the current operating conditions and the degree of wear; Based on the attenuation coefficient, the contamination coefficient in the oil deterioration coefficient, and the service mileage of the engine oil in the initial state, the third remaining service mileage of the engine oil is predicted, wherein the third remaining service mileage is used to characterize the service mileage of the engine oil under the current operating conditions and the degree of contamination. The remaining mileage of the engine oil is determined based on the first remaining mileage, the second remaining mileage, and the third remaining mileage.

7. The method according to claim 6, characterized in that, Determining the remaining mileage of the engine oil based on the first remaining mileage, the second remaining mileage, and the third remaining mileage includes: The minimum value among the first remaining usage mileage, the second remaining usage mileage, and the third remaining usage mileage is determined as the remaining usage mileage of the engine oil.

8. The method according to any one of claims 1 to 7, characterized in that, The method further includes: Based on the remaining mileage, a reminder strategy is generated, wherein the reminder strategy is used to characterize the rule for reminding the driver of the vehicle to change the engine oil; The driver is reminded according to the aforementioned reminder strategy.

9. An electronic device, characterized in that, include: Memory, which stores executable programs; A processor for running the program, wherein the program, when running, performs the method according to any one of claims 1 to 8.

10. A computer-readable storage medium, characterized in that, The computer-readable storage medium includes a stored executable program, wherein, when the executable program is executed, it controls the device on which the storage medium is located to perform the method according to any one of claims 1 to 8.

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