A method and system for burned oil detection

Abstract

The application relates to the technical field of vehicle fault diagnosis, and discloses a burnt engine oil detection method and system, which collects engine operation parameters; identifies engine operation events according to the engine operation parameters; extracts dynamic consumption characteristics of engine oil liquid level data during the engine operation events according to the engine operation events; evaluates scene parameters affecting engine oil consumption according to the engine operation parameters; dynamically generates a health reference mode of engine oil consumption according to the scene parameters; and judges whether a burnt engine oil fault exists according to the dynamic consumption characteristics and the health reference mode, so that the health reference mode of engine oil consumption is dynamically adjusted according to the actual operation state and the scene parameters of the engine, the burnt engine oil fault is more accurately judged, and the problem of high false alarm rate of a traditional fixed threshold detection method is effectively solved.

Description

Technical Field

[0001] This application relates to the field of vehicle fault diagnosis technology, and more specifically, to a method and system for detecting excessive oil consumption. Background Technology

[0002] In daily operation, internal combustion engines consume engine oil to a certain extent, in addition to its vital functions of lubrication, cooling, and cleaning. However, if mechanical problems occur within the engine, such as worn piston rings, aged valve stem seals, or scratches on the cylinder walls, engine oil may inadvertently enter the combustion chamber and burn with the fuel, a phenomenon commonly known as "oil burning." This not only causes excessive oil consumption, increasing vehicle operating costs, but also produces carbon deposits due to incomplete combustion, which can clog the catalytic converter and produce blue fumes containing harmful substances, damaging both the environment and the engine itself. Therefore, accurately determining whether an engine is burning oil previously relied primarily on manual methods, such as periodically checking the oil level on the dipstick or observing for blue fumes from the exhaust pipe. These methods are highly subjective and cannot provide early warnings of problems. With the increasing electronic sophistication of vehicles, on-board diagnostic systems have begun to play a role. A basic electronic detection method utilizes an oil level sensor. This sensor monitors the oil level in the oil pan in real time and sends this data to the engine control unit. The control unit records the vehicle's mileage and calculates the rate at which the oil level drops per unit mileage. When this rate exceeds a pre-set fixed value, such as oil consumption exceeding one liter per thousand kilometers, the system assumes there may be a risk of oil burning and warns the driver. However, in actual vehicle use, engineers quickly discovered that this method, relying solely on a fixed consumption rate, has significant limitations. Engine oil consumption is not constant and is closely related to the engine's operating conditions. For example, oil consumption differs significantly between low-speed driving in urban areas and high-speed cruising on highways. Under high-speed, high-load conditions, the temperature and pressure inside the cylinder rise sharply, the oil viscosity decreases, its fluidity increases, and the piston rings move more vigorously. These factors all lead to more oil entering the combustion chamber through the tiny gaps between the piston rings and the cylinder walls. If a car has experienced a prolonged period of high-speed driving or frequent rapid acceleration, its oil consumption will be significantly higher than normal in a short period. In this situation, the previously used simple fixed-value detection method may generate false alarms, misinterpreting this normal increase in consumption due to aggressive driving as an oil burning problem, causing unnecessary worry and repair costs for the car owner.

[0003] To address this issue, the new detection method no longer uses a single fixed value but attempts to take the engine's operating status into account. The control unit begins collecting more information, such as engine speed, throttle opening, intake manifold pressure, and coolant temperature, using this information to comprehensively determine the engine's current workload level. The system establishes a correlation between oil consumption and engine load. Simply put, the system assumes that oil consumption should be very low under low load conditions, while allowing for a higher oil consumption rate under high load conditions. Only when the actual detected consumption rate significantly exceeds the normal range allowed by this correlation within a specific load range will the system determine that oil is being burned. This improvement significantly reduces false alarms caused by changes in driving style, resulting in an initial improvement in detection accuracy.

[0004] However, considering only the engine's load under stable operating conditions is insufficient. The entire process from cold start to reaching stable operating temperature has a significant impact on oil consumption. During the cold start phase, the engine components have not yet reached their normal operating clearances, especially the relatively large clearance between the piston and cylinder wall. Simultaneously, the oil temperature is low, viscosity is high, fluidity is poor, and the ability to establish and maintain an oil film is weak. During this phase, oil is more likely to enter the combustion chamber. As the engine gradually warms up, the metal components expand due to heat, the clearances return to normal, and the oil reaches its optimal operating temperature. At this point, oil consumption tends to reach a relatively stable, lower level. This means that oil consumption exhibits a non-linear variation throughout the driving journey, especially in the initial stages. If a driver frequently takes short trips, their vehicle will experience multiple cold starts and warm-up cycles, potentially resulting in significantly higher cumulative oil consumption compared to a vehicle taking a single long-distance trip of the same distance. The previous testing method, which only considered stable loads, did not distinguish the special transition process of warm-up. Therefore, it may have mistakenly identified this normal consumption accumulation caused by short-distance driving habits as an oil burning problem.

[0005] Furthermore, ambient temperature is a crucial factor. In cold winter regions, such as when the ambient temperature drops below -20 degrees Celsius, the engine warm-up process becomes extremely lengthy. The time required for the engine oil to reach its normal operating temperature is significantly extended, meaning the engine will operate under suboptimal lubrication and sealing conditions for a longer period, naturally resulting in higher oil consumption compared to warmer climates. Conversely, in hot summers, the warm-up process is rapid, and the initial peak in oil consumption is short-lived. If the detection system lacks the ability to sense the external ambient temperature and adjust its judgment accordingly, it may arrive at completely different diagnostic conclusions in winter and summer. A vehicle judged as normal in summer might be incorrectly reported as burning oil in winter simply because the environment is colder and warm-up is slower, leading to increased consumption—this is clearly unreasonable. Another often overlooked but crucial factor is the condition of the engine oil itself. The physicochemical properties of brand-new engine oil and old engine oil used for thousands of kilometers have undergone significant changes. During use, engine oil gradually ages due to high-temperature oxidation, shearing, and the introduction of combustion byproducts. Its viscosity decreases, and its anti-wear and sealing abilities weaken. Older, less viscous oil is more likely to enter the combustion chamber and burn through gaps in components like piston rings. Therefore, towards the end of its lifespan, the normal consumption rate of engine oil is naturally higher than when the oil is new. Furthermore, when car owners discover the oil level is low, they may add oil themselves, and the brand and grade of oil added may differ from the original oil. Mixing these two oils alters their overall properties. Existing testing methods typically assume that the vehicle is using oil in a consistent state; they cannot distinguish whether increased oil consumption is due to mechanical wear in the engine or simply due to oil aging or a change to a different type of oil. This constitutes a blind spot in the testing process, making the system highly susceptible to making incorrect judgments when faced with changes in the oil's condition.

[0006] In the complex real-world vehicle usage environment, the challenge currently facing oil consumption detection technology is how to accurately identify the actual oil consumption failure caused by the wear of key components such as piston rings and valve stem seals, while effectively eliminating normal oil consumption fluctuations caused by drastic changes in driving conditions, engine cold start and warm-up processes, differences in external ambient temperature, and the aging or mixing of engine oils. It is also crucial to avoid misjudging the normal increase in consumption caused by the combined effect of these complex factors as a mechanical failure. Summary of the Invention

[0007] To address the shortcomings of existing technologies, this application provides a method and system for detecting excessive oil consumption.

[0008] Firstly, this application provides a method for detecting excessive oil consumption, including:

[0009] Collect engine operating parameters;

[0010] Identify engine operating events based on engine operating parameters;

[0011] Based on engine operation events, extract the dynamic consumption characteristics of engine oil level data during engine operation events;

[0012] Based on engine operating parameters, assess the scenario parameters that affect oil consumption;

[0013] Based on scenario parameters, a healthy reference model for oil consumption is dynamically generated;

[0014] Determine if there is an oil consumption problem based on dynamic consumption characteristics and health reference patterns.

[0015] By dynamically adjusting the health reference mode for oil consumption based on the engine's actual operating status and scenario parameters, the system can more accurately determine oil consumption faults and effectively solve the problem of high false alarm rate in traditional fixed threshold detection methods.

[0016] Furthermore, this application also proposes that the steps for dynamically generating a healthy reference model for oil consumption based on scenario parameters include:

[0017] Real-time monitoring of the rate of change of scene parameters;

[0018] Based on the rate of change of scene parameters, identify whether the scene parameters are in a state of rapid change;

[0019] When the scene parameters are detected to be changing rapidly, the parameters of the health reference mode are continuously calculated based on the scene parameters using a preset non-linear adjustment function.

[0020] Generate a health reference model based on the parameters of the health reference model;

[0021] When it is detected that the scene parameters are not in a rapidly changing state, a healthy reference mode is selected from the preset healthy engine behavior set based on the scene parameters.

[0022] By flexibly selecting the generation method of the health reference mode based on the rate of change of scenario parameters, accurate health references can be provided under different operating conditions, thus improving the adaptability and accuracy of detection.

[0023] Furthermore, this application also proposes that the engine operating parameters include engine oil level data, vehicle lateral acceleration, vehicle longitudinal acceleration, vehicle steering angle rate, engine oil temperature, external ambient temperature, and engine speed; and the scenario parameters include external ambient temperature, engine oil condition, and driving style.

[0024] Furthermore, this application also proposes a step prior to the step of extracting dynamic consumption characteristics of the oil level data during the engine operating event based on the engine operating event:

[0025] Based on the vehicle's lateral acceleration, longitudinal acceleration, and steering angle rate, the oil level data is dynamically filtered to suppress oil sloshing fluctuations in the oil level data.

[0026] Furthermore, this application also proposes a step prior to the step of extracting dynamic consumption characteristics of the oil level data during the engine operating event based on the engine operating event:

[0027] Based on the oil temperature and the external ambient temperature, viscosity compensation processing is performed on the oil level data to suppress oil sloshing and fluctuation, in order to correct the level reading deviation caused by high viscosity in the oil level data.

[0028] Furthermore, this application also proposes a step prior to the step of extracting dynamic consumption characteristics of the oil level data during the engine operating event based on the engine operating event:

[0029] Based on the oil temperature and engine speed, the degree of oil foaming is inferred, and foam compensation processing is performed on the oil level data after correcting for the level reading deviation caused by high viscosity, so as to correct the level reading deviation caused by foaming in the oil level data.

[0030] Furthermore, this application proposes a method for determining whether an oil consumption problem exists based on dynamic consumption characteristics and a health reference model, including the following steps:

[0031] Set the consumption fluctuation range based on the health reference model;

[0032] Analyze whether the dynamic consumption characteristics exceed the consumption fluctuation range to determine if there is an oil burning problem.

[0033] By setting a dynamic range for oil consumption fluctuations, the diagnosis of oil burning faults becomes more flexible and accurate, and it can adapt to the oil consumption characteristics under different operating conditions.

[0034] Furthermore, this application also proposes that the steps for setting the consumption fluctuation range according to a health reference model include:

[0035] Based on the health reference mode and scenario parameters, the preset initial consumption fluctuation range is weighted and adjusted to obtain the consumption fluctuation range.

[0036] By weighting the initial consumption fluctuation range, the consumption fluctuation range can better reflect the oil consumption characteristics under the current scenario parameters, thus improving the accuracy of fault diagnosis.

[0037] Furthermore, this application also proposes a step for obtaining the consumption fluctuation range by weighting and adjusting a preset initial consumption fluctuation range based on a health reference mode and scenario parameters, including:

[0038] A multidimensional lookup table is obtained based on the health reference model. This multidimensional lookup table stores the adjustment coefficients corresponding to the combination of scenario parameters.

[0039] Based on the scenario parameters, the adjustment coefficient is obtained from the multidimensional lookup table;

[0040] The initial consumption fluctuation range is obtained by weighting the adjustment coefficients and adjusting them accordingly.

[0041] Secondly, this application also proposes an oil consumption detection system, which includes:

[0042] The parameter acquisition module is used to collect engine operating parameters;

[0043] The event recognition module is used to identify engine operating events based on engine operating parameters;

[0044] The consumption feature extraction module is used to extract the dynamic consumption features of engine oil level data during engine operation events based on engine operation events.

[0045] The scenario parameter evaluation module is used to evaluate scenario parameters that affect oil consumption based on engine operating parameters;

[0046] The health reference mode generation module is used to dynamically generate a health reference mode for oil consumption based on scenario parameters.

[0047] The fault analysis module is used to determine whether there is an oil burning fault based on dynamic consumption characteristics and health reference mode.

[0048] In summary, the oil consumption detection method and system provided in this application collect engine operating parameters, identify engine operating events, and extract the dynamic consumption characteristics of oil level data during these events. Simultaneously, it assesses scenario parameters affecting oil consumption and dynamically generates a health reference model for oil consumption based on these parameters. By comparing the dynamic consumption characteristics with the health reference model, it determines whether an oil consumption fault exists. This overcomes the limitations of existing technologies that rely solely on a fixed consumption rate to determine oil consumption faults, effectively solving the problem of high false alarm rates caused by significant differences in oil consumption under different operating conditions and driving styles. By dynamically generating a health reference model, it can more accurately reflect the normal oil consumption level of the engine under specific operating conditions, thereby avoiding misjudging normal increases in consumption as oil consumption faults. This significantly improves the accuracy and reliability of oil consumption detection, providing a more scientific and intelligent basis for vehicle daily maintenance and upkeep. Attached Figure Description

[0049] Figure 1 This is a flowchart illustrating a method for detecting excessive oil consumption, as provided in an embodiment of this application.

[0050] Figure 2 This is a schematic diagram of a system for detecting excessive oil consumption, provided in an embodiment of this application.

[0051] Labeling Explanation: 210, Parameter Acquisition Module; 220, Event Recognition Module; 230, Consumption Feature Extraction Module; 240, Scene Parameter Evaluation Module; 250, Health Reference Mode Generation Module; 260, Fault Analysis Module. Detailed Implementation

[0052] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0053] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0054] Traditional methods for detecting excessive oil consumption rely primarily on manual observation or simple oil level sensor data, using a fixed consumption threshold for assessment. However, this approach has significant limitations in practical applications, failing to accurately distinguish between normal oil consumption and abnormal oil burning, especially under varying engine operating conditions. This can easily lead to false alarms or missed alarms, causing inconvenience and additional costs for vehicle maintenance.

[0055] Regarding this, firstly, see... Figure 1 This application proposes a method for detecting excessive oil consumption, comprising:

[0056] Collect engine operating parameters;

[0057] Identify engine operating events based on engine operating parameters;

[0058] Based on engine operation events, extract the dynamic consumption characteristics of engine oil level data during engine operation events;

[0059] Based on engine operating parameters, assess the scenario parameters that affect oil consumption;

[0060] Based on scenario parameters, a healthy reference model for oil consumption is dynamically generated;

[0061] Determine if there is an oil consumption problem based on dynamic consumption characteristics and health reference patterns.

[0062] This application dynamically assesses scenario parameters affecting oil consumption and dynamically generates a health reference pattern for oil consumption accordingly. This allows for more accurate determination of whether oil consumption is present, effectively overcoming the shortcomings of fixed threshold judgments in existing technologies and improving the accuracy and reliability of detection. Engine operating parameters refer to physical quantities or state quantities acquired in real time by various sensors during engine operation, such as oil level data, vehicle lateral acceleration, vehicle longitudinal acceleration, vehicle steering angle rate, engine oil temperature, ambient temperature, and engine speed. These parameters are the basic data for assessing engine operating status and oil consumption. Engine operating events refer to a period of time during which the engine operates under specific conditions, such as vehicle starting, idling, acceleration, deceleration, and constant speed driving. Identifying these events helps to correlate oil consumption with specific operating scenarios. The dynamic consumption characteristics of oil level data during engine operating events refer to the trend, rate, or pattern of oil level changes over time during a specific engine operating event, reflecting the actual oil consumption under specific operating conditions. Scenario parameters refer to external or internal environmental factors that affect oil consumption, such as ambient temperature, oil condition (e.g., viscosity, aging), and driving style (e.g., aggressive driving, smooth driving). Changes in these parameters directly affect the normal oil consumption. The healthy oil consumption reference mode refers to the normal oil consumption pattern of a healthy engine under different scenario parameters. It is a dynamically changing benchmark used for comparison with actual consumption characteristics. Oil burning refers to an abnormality inside the engine that causes oil to abnormally enter the combustion chamber and burn.

[0063] The core of this application lies in achieving accurate diagnosis of oil consumption faults by dynamically evaluating scenario parameters and generating health reference models. Specifically, the necessary engine operating data can be acquired through various methods. For example, it can connect to the vehicle's electronic control unit (ECU) via an on-board diagnostic (OBD) interface to read various sensor data in real time, including but not limited to oil level sensors, vehicle speed sensors, engine speed sensors, and temperature sensors. Alternatively, additional sensors can be installed on the vehicle for direct measurement, such as high-precision oil level sensors, multi-axis accelerometers, and gyroscopes. These sensors continuously collect data and transmit it to the processing unit for further analysis. Machine learning algorithms or rule-based expert systems can be used to identify different engine operating events. For example, by analyzing the changing trends of parameters such as vehicle speed, engine speed, and throttle opening, it can be determined whether the vehicle is currently idling, accelerating, decelerating, or driving at a constant speed. Preset thresholds and logical judgments can also be used; for example, when the vehicle speed is below a certain threshold and the engine speed is stable, it is identified as an idling event; when the vehicle speed and engine speed increase rapidly simultaneously, it is identified as an acceleration event. Once a specific engine operating event is identified, the focus shifts to the oil level data during that event. For example, the amount and rate of oil level drop during a complete acceleration event can be calculated. Time-series analysis of the oil level data can also be performed to extract patterns of change during specific operating events, such as linear or non-linear drops. These dynamic consumption characteristics provide a more precise reflection of oil consumption under specific operating conditions. Collected engine operating parameters are comprehensively utilized to assess scenario parameters affecting oil consumption. For example, ambient temperature can be directly obtained from an external temperature sensor. Oil condition can be indirectly assessed by analyzing parameters such as oil temperature, engine running time, and mileage. For example, excessively high oil temperature or prolonged running time may indicate decreased oil viscosity or aging. Driving style can be assessed by analyzing parameters such as lateral acceleration, longitudinal acceleration, steering angle rate, and the frequency and depth of accelerator and brake pedal application. For example, frequent rapid acceleration and braking may indicate an aggressive driving style. Based on the assessed scenario parameters, a baseline pattern reflecting the oil consumption of a healthy engine under the current scenario is dynamically generated. For example, a database containing various scenario parameter combinations and corresponding healthy oil consumption patterns can be pre-established. When the current scenario parameter combination is evaluated, the corresponding healthy reference pattern can be obtained from the database or through interpolation. Alternatively, model-based prediction methods can be used, such as neural networks or regression models, to predict the oil consumption curve or consumption rate of a healthy engine under that scenario, given the current scenario parameters. The extracted actual dynamic consumption characteristics will be compared with the dynamically generated healthy reference pattern; for example, the deviation between the actual consumption characteristics and the healthy reference pattern can be calculated.If the actual consumption characteristics deviate significantly from the healthy reference mode and exceed the preset reasonable fluctuation range, it can be determined that there is an oil consumption fault. This comparison method can effectively avoid the drawbacks of traditional fixed threshold judgment and improve the accuracy of detection.

[0064] The overall working principle of this application lies in constructing a dynamically adaptable oil consumption assessment system that adapts to the engine's operating environment. Traditional methods often use fixed oil consumption thresholds to determine oil consumption problems. This approach ignores the influence of various factors such as engine operating conditions, ambient temperature, and driving habits on normal oil consumption, leading to false alarms or missed alarms in practical applications. For example, under high-speed, high-load operating conditions, the normal oil consumption of the engine will increase significantly. If a fixed threshold under low-speed conditions is still used for judgment, normal consumption may be misjudged as oil consumption problems. This application obtains comprehensive data by collecting engine operating parameters, including not only oil level but also vehicle motion status, engine internal temperature, and external environmental information. Subsequently, the complex continuous operating process is divided into a series of events with distinct characteristics, such as acceleration, deceleration, and idling, which helps to correlate oil consumption with specific operating conditions. In each operating event, the dynamic consumption characteristics of engine oil level data during the engine operation event are extracted. This makes the analysis of oil consumption no longer a simple total calculation, but delves into the changing trends and rates within a specific time period, thereby capturing more refined consumption patterns. Based on dynamically changing scenario parameters, a healthy reference model for oil consumption is dynamically generated. This means that the oil consumption benchmark for a healthy engine is no longer fixed, but can be adjusted and optimized in real time according to the current actual operating environment. For example, in cold environments, the oil viscosity is higher, and normal consumption may be lower; while under high temperature and high load driving, oil consumption will increase accordingly. By dynamically generating a healthy reference model, a more reasonable "healthy" benchmark for oil consumption under different operating conditions is provided. By comparing the actually measured dynamic consumption characteristics with the dynamically generated healthy reference model under the current operating conditions, it is accurately determined whether the actual consumption exceeds the healthy range. This dynamic and adaptive judgment mechanism effectively avoids the drawbacks of traditional fixed threshold methods and significantly improves the accuracy and reliability of oil consumption fault detection. The various technical features work together to form a closed-loop intelligent detection system. From data collection to final judgment, each step provides support for more accurate fault diagnosis, thus effectively solving the problem of inaccurate oil burning detection in existing technologies.

[0065] Furthermore, the steps for dynamically generating a healthy reference mode for oil consumption based on scenario parameters include:

[0066] Real-time monitoring of the rate of change of scene parameters;

[0067] Based on the rate of change of scene parameters, identify whether the scene parameters are in a state of rapid change;

[0068] When the scene parameters are detected to be changing rapidly, the parameters of the health reference mode are continuously calculated based on the scene parameters using a preset non-linear adjustment function.

[0069] Generate a health reference model based on the parameters of the health reference model;

[0070] When it is detected that the scene parameters are not in a rapidly changing state, a healthy reference mode is selected from the preset healthy engine behavior set based on the scene parameters.

[0071] Specifically, the current values ​​of scene parameters are continuously acquired and compared with historical values ​​to determine their rate of change over time. For example, the rate of change can be obtained by calculating the difference or slope of the scene parameters within a certain time window. Identifying whether a scene parameter is in a rapidly changing state based on its rate of change can be understood as comparing the calculated rate of change with a preset threshold. When the rate of change exceeds the threshold, the scene parameter is considered to be in a rapidly changing state; otherwise, it is considered not to be in a rapidly changing state. This threshold can be set according to actual application scenarios and experience. In practical applications, when a rapidly changing scene parameter is detected, the parameters of the health reference mode are continuously calculated based on the scene parameter using a preset nonlinear adjustment function. The nonlinear adjustment function can be a polynomial function, exponential function, logarithmic function, or other mathematical model that reflects the complex relationship between the scene parameters and the health reference mode parameters. Through continuous calculation, it can be ensured that the health reference mode can smoothly and promptly adapt to rapidly changing scene conditions. The parameters of the health reference mode can be slopes, intercepts, fluctuation ranges, etc., describing the trend of oil consumption. Based on these parameters, a specific health reference pattern can be generated, such as a dynamically changing baseline oil consumption curve. When it is detected that the scenario parameters are not in a rapidly changing state, a health reference pattern is selected from a preset set of healthy engine behaviors, based on the scenario parameters. The set of healthy engine behaviors can be a database or a lookup table, which stores typical oil consumption patterns of a healthy engine under different stable scenario parameter combinations. By selecting this pattern, a health reference pattern suitable for the current stable scenario can be quickly and accurately obtained, avoiding unnecessary complex calculations.

[0072] This application introduces real-time monitoring and identification of the rate of change of scene parameters, enabling the differentiation of the dynamic degree of change in scene parameters. When scene parameters change rapidly, a more refined and real-time nonlinear adjustment function is used for continuous calculation, ensuring that the generated health reference model can quickly and accurately track the healthy oil consumption trend of the engine under drastic operating conditions. When scene parameters are relatively stable, a model is selected from a preset set of healthy engine behaviors, avoiding unnecessary computational complexity while ensuring the accuracy of the reference model. This dynamic adaptation mechanism effectively solves the problem of inaccurate health reference model generation under complex and variable operating conditions in traditional methods. This application can significantly improve the generation accuracy and adaptability of the oil consumption health reference model. Especially when scene parameters such as external ambient temperature, oil condition, or driving style change rapidly, the generated health reference model can more accurately reflect the true health consumption level of the engine, effectively avoiding misjudgments or omissions caused by lagging or inaccurate reference models, thereby improving the reliability and robustness of oil consumption fault detection.

[0073] In some preferred embodiments, it is assumed that during vehicle operation, the ambient temperature rapidly drops from 20°C to -5°C within a short period, while the driving style changes from smooth driving to aggressive driving. Real-time monitoring detects that the rate of change of both the ambient temperature and driving style exceeds preset thresholds, thus identifying a rapidly changing scenario parameter. At this point, instead of simply selecting a mode from a preset set, the parameters of a healthy reference mode are continuously calculated based on the current ambient temperature, oil condition (e.g., increased oil viscosity at low temperatures), and aggressive driving style, using a preset nonlinear adjustment function (e.g., a multivariable function considering temperature, viscosity, and engine speed). For example, this function can calculate how the baseline for healthy engine oil consumption should be adjusted under current low-temperature and aggressive driving conditions, and how its fluctuation range should change. Based on these dynamically calculated parameters, a reference mode is generated that can reflect healthy oil consumption behavior under current extreme operating conditions in real time. For example, this reference mode might show a slight increase in oil consumption at the beginning of aggressive driving at low temperatures, followed by a stabilization. In this way, even under complex operating conditions with drastic changes in scene parameters, the accuracy of the health reference mode can be ensured, thus providing a reliable basis for accurate diagnosis of oil burning faults.

[0074] Furthermore, engine operating parameters can include oil level data, vehicle lateral acceleration, vehicle longitudinal acceleration, vehicle steering angle rate, engine oil temperature, ambient temperature, and engine speed. Among these, oil level data directly reflects oil consumption; vehicle lateral acceleration, longitudinal acceleration, and steering angle rate can be used to assess the vehicle's motion state, thus helping to determine the impact of oil sloshing on the oil level data; engine oil temperature and ambient temperature significantly affect oil viscosity, foaming degree, and consumption rate; and engine speed is closely related to engine operating conditions and oil consumption.

[0075] Furthermore, scenario parameters can include ambient temperature, oil condition, and driving style. Ambient temperature directly affects the engine's thermal load and the physical properties of the oil; oil condition, such as viscosity and oxidation level, affects its consumption characteristics; and driving style, such as aggressive or smooth driving, significantly alters the engine's operating load and oil consumption patterns.

[0076] This application, by clearly defining the aforementioned engine operating parameters and scenario parameters, provides a more comprehensive and refined data foundation for oil consumption detection methods. Specifically, by collecting engine operating parameters including oil level data, vehicle motion parameters, temperature parameters, and engine speed, engine operating events can be identified more accurately, and the dynamic consumption characteristics of oil level data during these events can be extracted. This also provides necessary data support for subsequent evaluation of scenario parameters affecting oil consumption. For example, the introduction of vehicle lateral acceleration, longitudinal acceleration, and steering angle rate allows for the consideration and compensation of oil sloshing caused by vehicle movement when extracting oil level data, thus obtaining a more realistic level change. The introduction of oil temperature and ambient temperature helps to understand the impact of factors such as oil viscosity and foaming on oil level readings and provides a basis for subsequent compensation processing. Furthermore, by evaluating scenario parameters including ambient temperature, oil condition, and driving style, the external and internal factors affecting oil consumption can be more accurately characterized. The introduction of these scenario parameters allows the generation of the health reference mode to fully consider the complexity of the actual operating environment and conditions of the vehicle, thereby dynamically generating an oil consumption health reference mode that is more in line with the actual situation. For example, the evaporation and consumption rate of engine oil will be different under different external ambient temperatures; the degree of aging of engine oil (engine oil condition) will also affect its consumption characteristics; and differences in driving style will directly affect the engine load and speed, thus affecting the amount of engine oil consumed.

[0077] Furthermore, it also includes steps prior to the step of extracting dynamic consumption characteristics of engine oil level data during engine operating events based on engine operating events:

[0078] Based on the vehicle's lateral acceleration, longitudinal acceleration, and steering angle rate, the oil level data is dynamically filtered to suppress oil sloshing fluctuations in the oil level data.

[0079] Dynamic filtering refers to the process of real-time correction or smoothing of collected oil level data based on the vehicle's real-time motion. The vehicle's lateral acceleration, longitudinal acceleration, and steering angular rate are key parameters reflecting its dynamic motion, directly indicating the forces and attitude changes in different directions. These parameters change significantly during acceleration, deceleration, and cornering, directly causing oil sloshing within the oil pan. By utilizing these parameters, the degree and direction of oil sloshing can be accurately identified and quantified. In practical applications, dynamic filtering can be implemented using various algorithms, such as Kalman filtering, extended Kalman filtering, moving average filtering, or adaptive filtering. For example, a physical or empirical model can be constructed, using the vehicle's lateral acceleration, longitudinal acceleration, and steering angular rate as input to predict the theoretical amount of oil level sloshing. Then, the actual measured oil level data is compared and corrected with the predicted sloshing amount to obtain more stable and accurate oil level data. Its purpose is to eliminate or significantly reduce fluctuations in engine oil level readings caused by vehicle movement, ensuring the accuracy of subsequent data analysis.

[0080] This application effectively solves the problem of oil sloshing affecting the accuracy of oil level data in traditional methods by introducing dynamic filtering before extracting the dynamic consumption characteristics of oil level data. Specifically, when a vehicle is in a dynamic driving state, changes in lateral acceleration, longitudinal acceleration, and steering angle rate directly cause inertial movement of the oil in the oil pan, resulting in oil level sloshing. By monitoring and utilizing these motion parameters in real time, the impact of oil sloshing on the oil level sensor reading can be accurately estimated and compensated. For example, when the vehicle accelerates rapidly, the oil will accumulate to the rear, causing the sensor reading to be higher; the opposite is true during rapid deceleration. By inputting these motion parameters into a preset filtering model, the oil level deviation caused by sloshing can be calculated and removed from the original oil level data, thereby obtaining a value closer to the true oil level. This application can significantly improve the accuracy and stability of oil level data, especially during dynamic vehicle driving. Because the interference caused by oil sloshing is eliminated, the extracted dynamic consumption characteristics of oil level will more realistically reflect the actual oil consumption, avoiding misjudgments caused by data fluctuations. This not only improves the reliability of the oil burning detection method, but also makes the fault diagnosis results more accurate, which helps to detect and solve engine oil burning problems in a timely manner, extend engine life, and reduce maintenance costs.

[0081] Furthermore, it also includes steps prior to the step of extracting dynamic consumption characteristics of engine oil level data during engine operating events based on engine operating events:

[0082] Based on the oil temperature and the external ambient temperature, viscosity compensation processing is performed on the oil level data to suppress oil sloshing and fluctuation, in order to correct the level reading deviation caused by high viscosity in the oil level data.

[0083] Specifically, oil temperature refers to the actual temperature of the engine oil inside the engine, which can be measured in real time by a temperature sensor installed in the engine oil pan or oil passage. Ambient temperature refers to the temperature of the environment outside the vehicle, which can be obtained by a temperature sensor outside the vehicle. Viscosity compensation processing refers to the process of correcting the oil level data to suppress oil sloshing fluctuations based on the oil temperature and ambient temperature. High viscosity-induced level reading deviation refers to the fact that the high viscosity of the oil causes an adhesion layer to the surface of the level sensor or within the measurement channel, resulting in a higher reading than the actual level and thus measurement error. In practical applications, viscosity compensation processing can be based on a pre-established model of the relationship between oil viscosity and temperature (oil temperature and ambient temperature). For example, a multi-dimensional lookup table can be constructed to store the influence of oil viscosity on level reading deviation under different combinations of oil temperature and ambient temperature. When the current oil temperature and ambient temperature are obtained, the corresponding compensation coefficient or compensation amount can be looked up from the lookup table and applied to the oil level data to suppress oil sloshing fluctuations, thereby obtaining the corrected oil level data.

[0084] This application effectively addresses the impact of high viscosity on the accuracy of engine oil level measurement by introducing viscosity compensation processing. Engine oil viscosity is a crucial indicator of its fluidity and changes significantly with temperature. When the oil temperature or ambient temperature is low, the viscosity of the engine oil typically increases. High-viscosity engine oil may exhibit stronger adhesion or slower flow velocity in the level sensor or measurement channel, leading to a systematic deviation between the value read by the level sensor and the actual level. By monitoring the oil temperature and ambient temperature in real time, and combining this with a preset viscosity-temperature relationship model, the current viscosity state of the engine oil can be accurately inferred, and the level reading deviation caused by this viscosity state can be calculated. Appropriate compensation corrections are applied to the engine oil level data to suppress oil sloshing fluctuations, ensuring that the acquired engine oil level data more accurately reflects the actual oil level under different temperature and viscosity conditions, thus providing more accurate input for subsequent dynamic consumption feature extraction. This application effectively corrects the level reading deviation caused by high engine oil viscosity, significantly improving the measurement accuracy of engine oil level data. Compared to solutions that only suppress oil sloshing fluctuations, this application further considers the influence of oil physical properties (viscosity) on the measurement results, making the obtained oil level data closer to the true value. This improves the accuracy and reliability of oil burning fault diagnosis, avoids misjudgments or omissions caused by measurement errors, and further enhances the robustness of the oil burning detection method.

[0085] Furthermore, it also includes steps prior to the step of extracting dynamic consumption characteristics of engine oil level data during engine operating events based on engine operating events:

[0086] Based on the oil temperature and engine speed, the degree of oil foaming is inferred, and foam compensation processing is performed on the oil level data after correcting for the level reading deviation caused by high viscosity, so as to correct the level reading deviation caused by foaming in the oil level data.

[0087] Specifically, oil temperature refers to the actual temperature of the engine oil inside the engine, which can be measured in real time by a temperature sensor installed in the engine oil pan or oil passages. Engine speed refers to the number of revolutions per minute of the engine crankshaft, which can be obtained through the engine control unit (ECU). Inferring the degree of oil foaming can be understood as estimating the content or volume percentage of air bubbles in the current oil based on these two key parameters: oil temperature and engine speed, combined with a preset oil foaming model or empirical data. For example, when the oil temperature is high and the engine speed is high, the oil is more prone to foaming. Foam compensation processing refers to correcting the oil level data based on the inferred degree of oil foaming to eliminate or reduce the falsely high level readings caused by foaming. Oil level data corrected for high viscosity-induced level reading deviations refers to oil level data that has undergone dynamic filtering and viscosity compensation processing, eliminating the influence of oil sloshing and viscosity changes.

[0088] This application improves the accuracy of oil level data by introducing foam compensation processing. During engine operation, oil temperature and engine speed are key factors affecting the degree of oil foaming. When the oil temperature rises or the engine speed increases, the shear force inside the oil increases, making it easier for air to be entrained and form bubbles, causing the oil level sensor to read a level higher than the actual level. By monitoring oil temperature and engine speed in real time and combining this with a preset foaming model, the current degree of oil foaming can be accurately inferred. Based on this inference, the oil level data, which has already been corrected for deviations caused by high viscosity, is reversed through foam compensation processing, thereby eliminating the influence of foaming on the level reading. This ensures that the oil level data used for subsequent dynamic consumption feature extraction is closer to the true value, providing a more reliable data basis for accurate diagnosis of oil consumption faults. This application can effectively correct the level reading deviation caused by foaming in oil level data. Compared to solutions that only compensate for viscosity, this application further considers the impact of oil foaming, a complex factor, on oil level measurement, significantly improving the accuracy of oil level data. This not only avoids misjudging oil consumption as too low due to falsely high oil levels caused by foaming, but also ensures the reliability and accuracy of oil consumption detection under various complex operating conditions, especially high speeds and high temperatures where foaming is prone to occur, thereby improving the sensitivity and accuracy of fault diagnosis.

[0089] Furthermore, the steps to determine whether there is an oil consumption problem based on dynamic consumption characteristics and health reference patterns include:

[0090] Set the consumption fluctuation range based on the health reference model;

[0091] Analyze whether the dynamic consumption characteristics exceed the consumption fluctuation range to determine if there is an oil burning problem.

[0092] Setting a consumption fluctuation range refers to determining a reasonable range of oil consumption variation based on a dynamically generated oil consumption health reference model. This health reference model reflects the normal oil consumption behavior of a healthy engine under specific scenario parameters. The purpose of setting the consumption fluctuation range is to define the boundary between normal and abnormal consumption, providing a benchmark for subsequent fault diagnosis. In practical applications, this consumption fluctuation range can be a preset fixed range or a range dynamically adjusted based on the health reference model and current scenario parameters. Furthermore, the dynamic consumption characteristics of the oil level data extracted during engine operation are compared with the aforementioned set consumption fluctuation range. If the dynamic consumption characteristics, such as the oil consumption rate or total consumption, exceed the set consumption fluctuation range, an oil burning fault can be preliminarily determined. Conversely, if the dynamic consumption characteristics are within the consumption fluctuation range, the oil consumption is considered to be normal.

[0093] This application establishes a quantitative benchmark for normal oil consumption behavior by setting a consumption fluctuation range based on a health reference model. The health reference model comprehensively considers the natural impact of various scenario parameters on oil consumption, enabling the set consumption fluctuation range to more accurately reflect normal consumption levels under different operating conditions. Subsequently, by comparing the actual extracted dynamic consumption characteristics with this fluctuation range, abnormal consumption behavior exceeding the normal fluctuation range can be effectively identified. This dynamically set fluctuation range based on the health reference model allows fault diagnosis to adapt to changes in the engine's operating environment and conditions, rather than relying on a single fixed threshold, thus improving the accuracy and robustness of oil consumption fault detection. By setting a consumption fluctuation range that matches the health reference model, normal oil consumption and abnormal oil consumption can be more accurately distinguished, avoiding misjudgments caused by changes in environment or operating conditions. This improves the sensitivity and specificity of oil consumption fault detection, enabling timely and accurate fault identification, thereby providing a reliable basis for vehicle maintenance and repair.

[0094] Furthermore, the steps for setting the consumption fluctuation range based on the health reference model include:

[0095] Based on the health reference mode and scenario parameters, the preset initial consumption fluctuation range is weighted and adjusted to obtain a more accurate and adaptive consumption fluctuation range.

[0096] Specifically, when determining the fluctuation range of engine oil consumption, a dynamic adjustment mechanism is introduced instead of relying solely on a fixed or simple reference value. The health reference mode can be understood as the baseline behavior pattern of engine oil consumption under ideal or normal operating conditions, reflecting the expected consumption trend of a healthy engine under specific operating conditions. Scenario parameters cover external and internal factors affecting oil consumption, such as ambient temperature, oil condition (e.g., viscosity, aging), and driving style (e.g., aggressive driving, smooth driving). The preset initial consumption fluctuation range can be a range with a certain width determined based on general healthy engine data, serving as the starting point for subsequent adjustments. In practical applications, this initial range can be initially set based on empirical data or statistical analysis results. Furthermore, weighted adjustment refers to correcting the upper and lower limits of the aforementioned initial consumption fluctuation range based on the current engine health reference mode and real-time evaluated scenario parameters. This correction is not a simple linear addition, but rather introduces weighting factors, allowing the initial range to dynamically expand or shift according to actual operating conditions. For example, under certain scenario parameters (such as high temperature and high load), normal oil consumption may be slightly higher than the average level. In this case, weighted adjustment can appropriately widen the upper limit of the consumption fluctuation range. Conversely, under low load and stable operation scenarios, the consumption fluctuation range may be narrowed to improve the sensitivity of detection.

[0097] This application effectively solves the problems of inflexible and unsuitable oil consumption fluctuation range settings in traditional methods by introducing a health reference mode and scenario parameters to weight and adjust the initial oil consumption fluctuation range. The health reference mode provides baseline oil consumption information under healthy engine conditions, while scenario parameters reflect the actual impact of the current operating environment on oil consumption. This weighted adjustment allows these dynamic factors to be incorporated into the fluctuation range setting. Consequently, the resulting oil consumption fluctuation range more accurately reflects the normal oil consumption level of the engine under specific operating conditions, avoiding misjudgments caused by fixed ranges and improving the accuracy and robustness of oil consumption fault detection. This application enables dynamic and adaptive setting of the oil consumption fluctuation range. Compared to fixed or simply set fluctuation ranges, the weighted adjustment mechanism proposed in this application can more accurately capture the normal oil consumption characteristics of the engine under different operating scenarios, significantly reducing false alarm and false negative rates.

[0098] Furthermore, the steps for obtaining the consumption fluctuation range by weighting and adjusting the preset initial consumption fluctuation range based on the health reference mode and scenario parameters include:

[0099] A multidimensional lookup table is obtained based on the health reference model. The multidimensional lookup table stores the adjustment coefficients corresponding to the combination of scenario parameters.

[0100] Based on the scenario parameters, the adjustment coefficient is obtained from the multidimensional lookup table;

[0101] The initial consumption fluctuation range is obtained by weighting the adjustment coefficients and adjusting them accordingly.

[0102] Specifically, the multidimensional lookup table can be pre-built, and its construction process can be based on a large amount of healthy engine operating data, trained and optimized through machine learning algorithms or expert experience. This lookup table aims to capture the influence of various scenario parameter combinations (e.g., ambient temperature, oil condition, and driving style) on the oil consumption fluctuation range under different health reference modes. The adjustment coefficient is a quantitative value used to correct the initial consumption fluctuation range; its magnitude and sign reflect the trend and amplitude of oil consumption fluctuation relative to the baseline under a specific scenario parameter combination. Once the current health reference mode is obtained, a corresponding multidimensional lookup table is selected or generated based on this mode. Subsequently, based on the currently evaluated scenario parameters, such as the current ambient temperature, oil condition, and driving style, a query is performed in the multidimensional lookup table to obtain the adjustment coefficient that precisely matches the current scenario parameter combination.

[0103] This application introduces a multidimensional lookup table, enabling a more refined and intelligent weighted adjustment process for the initial oil consumption fluctuation range. Traditional simple weighted adjustments may fail to effectively handle the complex nonlinear relationship between the healthy reference mode and variable scenario parameters, leading to deviations in the adjustment results. However, by pre-constructing and storing a multidimensional lookup table containing adjustment coefficients corresponding to combinations of scenario parameters, the most suitable adjustment coefficients for the current operating conditions can be quickly and accurately obtained based on the real-time healthy reference mode and scenario parameters. Therefore, the initial oil consumption fluctuation range can be precisely corrected, thus more accurately reflecting the oil consumption fluctuation characteristics of the engine under healthy conditions. This data-driven and pre-defined adjustment method significantly improves the accuracy and adaptability of the oil consumption fluctuation range setting. Since the adjustment coefficients are obtained from the healthy reference mode and scenario parameters based on the multidimensional lookup table, the impact of various complex operating conditions on oil consumption can be fully considered, avoiding errors that may arise from simple weighted adjustments. Therefore, the diagnosis of oil consumption faults will be more accurate and reliable, effectively reducing the risk of false alarms and false negatives, and improving the overall performance and user experience of the detection system.

[0104] Secondly, see Figure 2 This application also discloses an oil consumption detection system, comprising:

[0105] Parameter acquisition module 210 is used to acquire engine operating parameters;

[0106] The event recognition module 220 is used to identify engine operating events based on engine operating parameters;

[0107] The consumption feature extraction module 230 is used to extract the dynamic consumption features of engine oil level data during engine operation events based on engine operation events.

[0108] The scenario parameter evaluation module 240 is used to evaluate scenario parameters that affect oil consumption based on engine operating parameters;

[0109] The health reference mode generation module 250 is used to dynamically generate a health reference mode for oil consumption based on scenario parameters.

[0110] The fault analysis module 260 is used to determine whether there is an oil burning fault based on dynamic consumption characteristics and health reference mode.

[0111] Traditional oil consumption detection systems often rely on fixed oil consumption thresholds or manual observation. Their core flaw lies in failing to adequately consider the impact of various dynamic factors, such as engine operating conditions, environmental conditions, and driving habits, on normal oil consumption, resulting in low accuracy and reliability. For example, existing systems may misinterpret increased normal oil consumption as oil consumption during prolonged high-speed, high-load operation, leading to unnecessary concerns and repair costs. The core innovation of this application lies in introducing a mechanism of "dynamically generating a healthy reference model for oil consumption." Compared to the closest existing technology (i.e., systems based on fixed consumption rates), this application no longer relies on a single, static consumption threshold. Instead, by acquiring key factors affecting oil consumption in real time, it dynamically constructs a healthy engine oil consumption benchmark based on the current actual operating environment and conditions. For example, under aggressive driving conditions, the healthy reference model will correspondingly increase the expected normal oil consumption; while under stable driving conditions, it will decrease it. This dynamic adaptability is a significant advantage of this application compared to existing systems. By comparing the extracted actual dynamic consumption characteristics with the dynamically generated health reference model, this application can more accurately determine whether the actual oil consumption exceeds the normal range under the current operating conditions, effectively distinguishing between normal consumption increases caused by changes in operating conditions and actual oil burning faults, thereby significantly reducing the false alarm rate and improving the accuracy and reliability of detection.

[0112] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for detecting excessive engine oil consumption, characterized in that, include: Collect engine operating parameters; Based on the engine operating parameters, engine operating events are identified. The engine operating parameters include oil level data, vehicle lateral acceleration, vehicle longitudinal acceleration, vehicle steering angle rate, engine oil temperature, external ambient temperature, and engine speed. Based on the engine operation events, extract the dynamic consumption characteristics of the engine oil level data during the engine operation events, which include vehicle start-up, idling, acceleration, deceleration, and constant speed driving. Based on the engine operating parameters, assess the scenario parameters that affect oil consumption, including ambient temperature, oil condition, and driving style. Based on the scenario parameters, a healthy reference pattern for oil consumption is dynamically generated. The healthy reference pattern for oil consumption refers to the normal oil consumption pattern of a healthy engine under different scenario parameters. It is a dynamically changing benchmark used to compare with actual consumption characteristics. The step of dynamically generating a healthy reference mode for oil consumption based on the scenario parameters includes: Real-time monitoring of the rate of change of the scene parameters; Based on the rate of change of the scene parameters, identify whether the scene parameters are in a state of rapid change; When the scene parameters are detected to be changing rapidly, the parameters of the health reference mode are continuously calculated based on the scene parameters using a preset nonlinear adjustment function. The health reference pattern is generated based on the parameters of the health reference pattern. When it is detected that the scene parameters are not in a rapidly changing state, a healthy reference mode is selected from a preset set of healthy engine behaviors based on the scene parameters. The presence of an oil consumption problem is determined based on the dynamic consumption characteristics and the health reference mode.

2. The method for detecting excessive oil consumption according to claim 1, characterized in that, The engine operating parameters include engine oil level data, vehicle lateral acceleration, vehicle longitudinal acceleration, vehicle steering angle rate, engine oil temperature, ambient temperature, and engine speed; the scenario parameters include ambient temperature, engine oil condition, and driving style.

3. The method for detecting excessive oil consumption according to claim 2, characterized in that, The method further includes a step performed prior to the step of extracting the dynamic consumption characteristics of the oil level data during the engine operating event based on the engine operating event: The oil level data is dynamically filtered based on the vehicle's lateral acceleration, longitudinal acceleration, and steering angle rate to suppress oil level fluctuations.

4. The method for detecting excessive oil consumption according to claim 3, characterized in that, The method further includes a step performed prior to the step of extracting the dynamic consumption characteristics of the oil level data during the engine operating event based on the engine operating event: Based on the oil temperature and the external ambient temperature, viscosity compensation processing is performed on the oil level data to suppress oil sloshing fluctuations, so as to correct the level reading deviation caused by high viscosity in the oil level data.

5. The method for detecting excessive oil consumption according to claim 4, characterized in that, The method further includes a step performed prior to the step of extracting the dynamic consumption characteristics of the oil level data during the engine operating event based on the engine operating event: Based on the oil temperature and the engine speed, the degree of foaming of the engine oil is inferred, and foam compensation processing is performed on the engine oil level data after correcting the level reading deviation caused by high viscosity, so as to correct the level reading deviation in the engine oil level data caused by foaming.

6. The method for detecting excessive oil consumption according to claim 1, characterized in that, The step of determining whether an oil consumption fault exists based on the dynamic consumption characteristics and the health reference mode includes: Based on the aforementioned health reference model, set the consumption fluctuation range; Analyze whether the dynamic consumption characteristics exceed the consumption fluctuation range to determine whether there is an oil burning fault.

7. The method for detecting excessive oil consumption according to claim 6, characterized in that, The step of setting the consumption fluctuation range according to the health reference model includes: Based on the health reference mode and the scenario parameters, the preset initial consumption fluctuation range is weighted and adjusted to obtain the consumption fluctuation range.

8. The method for detecting excessive oil consumption according to claim 7, characterized in that, The step of adjusting the preset initial consumption fluctuation range according to the health reference mode and the scenario parameters to obtain the consumption fluctuation range includes: A multidimensional lookup table is obtained based on the health reference model, and the multidimensional lookup table stores the adjustment coefficients corresponding to the combination of scenario parameters. The adjustment coefficient is obtained from the multidimensional lookup table based on the scenario parameters; The consumption fluctuation range is obtained by weighting and adjusting the preset initial consumption fluctuation range according to the adjustment coefficient.

9. A system for detecting excessive oil consumption, characterized in that, The system includes: The parameter acquisition module is used to collect engine operating parameters; The event recognition module is used to identify engine operating events based on the engine operating parameters, including engine oil level data, vehicle lateral acceleration, vehicle longitudinal acceleration, vehicle steering angle rate, engine oil temperature, external ambient temperature, and engine speed. The consumption feature extraction module is used to extract the dynamic consumption features of the engine oil level data during the engine operation event, which includes vehicle start-up, idling, acceleration, deceleration, and constant speed driving. The scenario parameter evaluation module is used to evaluate the scenario parameters that affect oil consumption based on the engine operating parameters. The scenario parameters include external ambient temperature, oil condition, and driving style. The health reference mode generation module is used to dynamically generate a health reference mode for oil consumption based on the scenario parameters. The health reference mode for oil consumption refers to the normal oil consumption mode of a healthy engine under different scenario parameters. It is a dynamically changing benchmark used to compare with actual consumption characteristics. The step of dynamically generating a healthy reference mode for oil consumption based on the scenario parameters includes: Real-time monitoring of the rate of change of the scene parameters; Based on the rate of change of the scene parameters, identify whether the scene parameters are in a state of rapid change; When the scene parameters are detected to be changing rapidly, the parameters of the health reference mode are continuously calculated based on the scene parameters using a preset nonlinear adjustment function. The health reference pattern is generated based on the parameters of the health reference pattern. When it is detected that the scene parameters are not in a rapidly changing state, a healthy reference mode is selected from a preset set of healthy engine behaviors based on the scene parameters. The fault analysis module is used to determine whether there is an oil burning fault based on the dynamic consumption characteristics and the health reference mode.

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