Lubricating oil viscosity monitoring method and system based on parameter coupling and digital twinning

Abstract

The invention discloses a lubricating oil viscosity monitoring method and system based on parameter coupling and digital twinning, and belongs to the technical field of lubricating oil performance monitoring, and the method comprises the following steps: S1, collecting viscosity data of lubricating oil at different temperature-shear rates by using a rheometer; s2, constructing a multi-parameter dynamic coupling viscosity-temperature model; s3, building a digital twinborn platform; s4, outputting a curve based on the model; and S5, performing dynamic correction and performance prediction. By establishing the temperature-shear rate coupling model, the viscosity-temperature curve fitting error is reduced, and the monitoring precision is improved.

Description

Technical Field

[0001] This invention belongs to the field of lubricating oil performance monitoring technology, and particularly relates to a lubricating oil viscosity monitoring method and system based on parameter coupling and digital twin. Background Technology

[0002] In modern industry, the viscosity of lubricating oil, as a core indicator for measuring its lubrication performance, directly relates to the friction and wear characteristics and service life of mechanical systems, and is crucial for the stable and efficient operation of equipment. From the perspective of current technological development, the industry has long relied on traditional viscosity-temperature models to fit and predict the viscosity-temperature characteristics of lubricating oils. These models estimate the viscosity of lubricating oil by establishing the correlation between viscosity and temperature, providing a fundamental method for evaluating lubrication performance.

[0003] However, traditional viscosity-temperature models generally lack accuracy and dynamic performance. Their core flaw lies in considering only the effect of temperature on viscosity, completely ignoring the crucial role of shear rate under actual operating conditions. Studies have shown that when the shear rate exceeds 6000 s⁻¹... -1 When the lubricating oil exhibits significant non-Newtonian characteristics, its viscosity changes dynamically with the shear rate. Traditional models, which do not take this effect into account, result in a large deviation between the fitting results and the actual working conditions, and cannot reflect the true dynamic change of viscosity. Summary of the Invention

[0004] In view of the above-mentioned shortcomings and deficiencies of the prior art, the present invention provides a lubricating oil viscosity monitoring method and system based on parameter coupling and digital twin. By establishing a temperature-shear rate coupling model, the viscosity-temperature curve fitting error is reduced and the monitoring accuracy is improved.

[0005] To achieve the above objectives, the main technical solutions adopted by the present invention include: A lubricating oil viscosity monitoring method based on parameter coupling and digital twins includes the following steps: Step S1: Use a rheometer to collect viscosity data of lubricating oil at different temperatures and shear rates, remove outliers, and retain valid data as model input data for step S2. Step S2: Construct a multi-parameter dynamically coupled viscosity-temperature model as follows: μ(T,γ)=a(T)(logγ)³+b(T)(logγ)²+c(T)logγ+d(T); Where μ is the dynamic viscosity, T is temperature, °C; γ is shear rate, s. -1 a(T), b(T), and c(T) are the logarithmic terms of the shear rate (logγ). 3 (logγ) 2The coefficients of logγ; d(T) is a constant term; a(T), b(T), c(T), and d(T) are determined as follows: Set η 预测 =a(logγ)³+b(logγ)²+c(logγ)+d; Where, η 预测 To predict viscosity, γ is the shear rate, and a, b, c, and d are four parameters. By adding an adaptive damping factor λ to the Levenberg-Marquardt algorithm, the optimized LM algorithm is obtained as follows: ; Among them, J k λ is the partial derivative matrix of the residual with respect to the parameters at the k-th iteration, i.e., the Jacobian matrix; k Let r be the adaptive damping factor in the k-th iteration. k Let I be the residual vector at the k-th iteration, I be the identity matrix, k be the iteration number, and d be the residual vector at the k-th iteration. k The parameter vector to be determined in the k-th iteration The increment; The optimization objective is to minimize the sum of squared residuals: S(x) = Σ[η 实测 -η 预测 ]², where η 实测 This is the actual measured viscosity; The residual ratio is calculated using the following formula: ; in: The residual ratio at the k-th iteration. To minimize the sum of squared residuals in the k-th iteration, For d k Minimize the sum of squared residuals in the k-th iteration of the correction; Based on ρ k Adjust λ k Update the parameter vector x until the sum of squared residuals is less than the preset convergence threshold or the maximum number of iterations is reached, to obtain the optimal parameters [a, b, c, d] at the current temperature T. Calculate the parameters at different temperature points based on the data at different temperature points. The corresponding optimal parameter set , will get , , , As a new dataset, the functional relationships a(T), b(T), c(T), d(T) of parameters a, b, c, d as a function of temperature T were determined by polynomial fitting. Step S3: Build a digital twin platform: The digital twin platform consists of a physical layer, a virtual layer, and a data interaction layer. The physical layer consists of temperature sensors, torque sensors, and a data acquisition card installed on the rheometer to collect real-time temperature and shear rate-viscosity data of the lubricating oil. The virtual layer is based on the multi-parameter dynamic coupling viscosity-temperature model constructed in step S2 to build a virtual simulation model and integrates a sub-model of oil molecule motion under shear. The data interaction layer uses the OPC UA protocol to achieve real-time data synchronization between the physical layer and the virtual layer. Step S4: Based on the model output curve: The output curve is determined to be a "viscosity-temperature curve" under a fixed shear rate. Based on the multi-parameter dynamic coupling viscosity-temperature model in step S2, the corresponding dynamic viscosity μ is calculated in combination with the selected shear rate to obtain the viscosity-temperature curve under that shear rate, which is then displayed through the digital twin platform in step S3. Step S5, Dynamic Correction and Performance Prediction: The digital twin platform compares the measured viscosity μ of the physical layer in real time. 实测 With virtual layer predicted viscosity μ 预测 Calculate the relative error: ; Set an error threshold. When the error exceeds the set threshold, trigger the feedback mechanism and call the optimized LM algorithm to refit the coefficients a, b, c, and d to correct the model until the error is no greater than the set threshold. Based on the corrected model, output the viscosity-temperature curves under different combinations of temperature and shear rate. Generate multi-dimensional early warning information: when the viscosity change rate is >10% / min, trigger "viscosity abnormal fluctuation warning"; when the temperature T exceeds the effective lubrication temperature range of the lubricating oil, issue "critical temperature threshold warning".

[0006] Furthermore, in step S1, the temperature adjustment range of the rheometer covers -40℃ to +150℃; during the data acquisition process, the temperature is gradually changed within the range of 20℃ to 80℃ at a rate of 5℃ / min, and each temperature point is stabilized for more than 10 minutes, with the shear rate set to 10~38000s. -1 For each temperature-shear rate combination, viscosity data was continuously collected for 30 seconds with a sampling interval of 1 second, and the average value was taken as the valid data.

[0007] Furthermore, in step S2, based on ρ k Adjust λ k If 0.75 < ρ k <1, adjust λ k+1 =λ k / 10; if 0 < ρ k <0.25, adjust λ k+1 =10λ k If 0.25≤ρk If ≤0.75, then λ k+1 =λ k ;where ρ k Let λ be the residual ratio at the k-th iteration. k Let λ be the adaptive damping factor in the k-th iteration. k+1 This is the adaptive damping factor at the (k+1)th iteration.

[0008] Furthermore, in step S2, if ρ k If the value is greater than 0, then update parameter x. k+1 =x k +d k , where ρ k x is the residual ratio at the k-th iteration. k+1 Let x be the parameter vector at the (k+1)th iteration. k Let d be the parameter vector at the k-th iteration. k The parameter vector to be determined in the k-th iteration The increment.

[0009] Furthermore, in step S3, the data collected by the physical layer of the digital twin platform is transmitted to the edge computing gateway via an industrial bus for filtering, normalization preprocessing, and then uploaded.

[0010] Furthermore, in step S3, the transmission period of the data interaction layer is ≤100ms.

[0011] Furthermore, in step S3, a real-time database is built on the digital twin platform to store historical data, supporting data backtracking and model iteration.

[0012] Furthermore, in step S4, during the output curve process, the temperature parameter T is selected to be in the range of -40℃ to +150℃, and the shear rate parameter γ is selected to be in the range of 10 to 38000 s. -1 .

[0013] Furthermore, when the error exceeds 5%, a feedback mechanism is triggered, calling the optimized LM algorithm to refit the coefficients a, b, c, and d until the error is ≤5%, thus achieving dynamic model updates.

[0014] A lubricating oil viscosity monitoring system based on parameter coupling and digital twin includes a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the lubricating oil viscosity monitoring method based on parameter coupling and digital twin.

[0015] The beneficial effects of the present invention are as follows: The lubricating oil viscosity monitoring method and system based on parameter coupling and digital twin of the present invention creates a temperature-shear rate coupling model based on the improved LM algorithm, so that the viscosity-temperature curve fitting error is less than 5%, thereby improving the accuracy of viscosity data calculation.

[0016] This invention relies on the three-layer architecture of the digital twin platform to achieve real-time correction at the 50ms level, thereby improving response speed. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the digital twin platform architecture of the present invention; Figure 2 This is the viscosity-temperature curve of SAE5W-30 under ideal conditions predicted in Example 1; Figure 3 The viscosity-temperature curve of SAE5W-30 at a fixed shear rate is shown in Example 1. Detailed Implementation

[0018] To better explain and facilitate understanding of the present invention, the present invention will be described in detail below with reference to the accompanying drawings and specific embodiments.

[0019] This invention provides a method and system for monitoring lubricating oil viscosity based on parameter coupling and digital twins. The method for monitoring lubricating oil viscosity based on parameter coupling and digital twins includes the following steps: Step S1: Use a rheometer to collect viscosity data of lubricating oil at different temperatures and shear rates, remove outliers, and retain valid data as model input data for step S2.

[0020] Specifically, a TADHR-2 rotary rheometer was used for data acquisition. The TADHR-2 rotary rheometer employs a temperature control unit based on Peltier thermoelectric technology, with a temperature adjustment range covering -40℃ to +150℃ and a control accuracy of ±0.1℃. A 40mm circular aluminum plate was used as the fixture, and the sample gap could be adjusted within the range of 100~200μm according to the oil viscosity characteristics. During the data acquisition process, the temperature was gradually varied within the range of 20℃ to 80℃ at a rate of 5℃ / min, and each temperature point was stabilized for more than 10 minutes to ensure that the lubricating oil reached thermal equilibrium. The shear rate was set to 10~38000 s. -1 It covers mechanical operating conditions from low speed to high speed. For each temperature-shear rate combination, viscosity data is continuously collected for 30 seconds with a sampling interval of 1 second, and the average value is taken as the valid data.

[0021] The raw data collected by S1 is preprocessed to remove outliers caused by equipment fluctuations or operational errors, and retain valid samples.

[0022] Step S2: Construct a multi-parameter dynamically coupled viscosity-temperature model as follows: μ(T,γ)=a(T)(logγ)³+b(T)(logγ)²+c(T)logγ+d(T); Where μ is the dynamic viscosity, T is temperature, °C; γ is shear rate, s. -1 a(T), b(T), and c(T) are the logarithmic terms of the shear rate (logγ). 3 (logγ) 2 The coefficients of logγ; d(T) is a constant term; a(T), b(T), c(T), and d(T) are determined as follows: Set η 预测 =a(logγ)³+b(logγ)²+c(logγ)+d; Where, η 预测 To predict viscosity, γ is the shear rate, and a, b, c, and d are four parameters. By adjusting the coefficients a, b, c, and d, the curve shape is adapted to suit different grades of lubricating oil. For (shear rate, viscosity) data at any fixed temperature T, the following iterative optimization process is performed to determine the model parameter set x=[a, b, c, d] at that temperature.

[0023] The optimization objective is to minimize the sum of squared residuals: S(x) = Σ[η 实测 -η 预测 ]², where η 实测 The viscosity is the actual measured value. Adding an adaptive damping factor λ to the Levenberg-Marquardt algorithm yields the optimized LM algorithm as follows: ; Among them, J k λ is the partial derivative matrix of the residual with respect to the parameters at the k-th iteration, i.e., the Jacobian matrix; k Let r be the adaptive damping factor in the k-th iteration. k Let I be the residual vector at the k-th iteration, I be the identity matrix, k be the iteration number, and d be the residual vector at the k-th iteration. k The parameter vector to be determined in the k-th iteration The increment. Set the initial value of the parameter to be determined, x0, and the initial value of the adaptive damping factor λ. Find d k The i-th row of the Jacobian matrix is ​​defined as Where i refers to the i-th experimental data point (γ) i η measured value J i ).

[0024] The residual ratio is calculated using the following formula: ; in: The residual ratio at the k-th iteration. To minimize the sum of squared residuals in the k-th iteration, For d k Minimize the sum of squared residuals in the k-th iteration of the correction; Based on ρ k Adjust λ k Update the parameter vector x until the sum of squared residuals is less than the preset convergence threshold or the maximum number of iterations is reached, to obtain the optimal parameters [a, b, c, d] at the current temperature T. Calculate the parameters at different temperature points based on the data at different temperature points. The corresponding optimal parameter set , will get , , , As a new dataset, the functional relationships a(T), b(T), c(T), d(T) of parameters a, b, c, d as a function of temperature T are determined by polynomial fitting.

[0025] Specifically, based on ρ k Adjust λ k : If 0.75 < ρ k <1, adjust λ k+1 =λ k / 10, the residual is significantly reduced, the damping factor is reduced, and the convergence is accelerated.

[0026] If 0 < ρ k <0.25, adjust λ k+1 =10λ k If the reduction in residual is insufficient or abnormally large, increase the damping factor to enhance stability.

[0027] If 0.25≤ρ k If ≤0.75, then λ k+1 =λ k , where ρ k Let λ be the residual ratio at the k-th iteration. k Let λ be the adaptive damping factor in the k-th iteration. k+1 This is the adaptive damping factor at the (k+1)th iteration.

[0028] If ρ k If the value is greater than 0, then update parameter x. k+1 =x k +d k , where ρ k x is the residual ratio at the k-th iteration. k+1 Let x be the parameter vector at the (k+1)th iteration. kLet d be the parameter vector at the k-th iteration. k The parameter vector to be determined in the k-th iteration The increment.

[0029] Repeat step S2 until the sum of squared residuals is less than the preset convergence threshold or the maximum number of iterations is reached. Output the optimal parameters [a, b, c, d] at the current temperature T, and calculate multiple sets of different temperature points T based on the data at different temperature points. i The corresponding optimal parameter set (a) i b i c i d i ), will obtain (T i , a i ), (T i b i ), (T i c i ), (T i d i As a new dataset, the functional relationships a(T), b(T), c(T), d(T) of parameters a, b, c, d as a function of temperature T are determined by polynomial fitting.

[0030] Substitute the fitted functions a(T), b(T), c(T), d(T) into the model expression η 预测 =a(logγ)³+b(logγ)²+c(logγ)+d, we obtain the global viscosity-temperature model μ(T,γ) = a(T)(logγ)³+b(T)(logγ)²+c(T)logγ+d(T) for viscosity as a function of temperature and shear rate, where μ is the dynamic viscosity. T is temperature (°C), γ is shear rate (s). -1 a(T), b(T), and c(T) are the logarithmic terms of the shear rate (logγ). 3 (logγ) 2 The coefficients of logγ are used to describe the nonlinear effect of shear rate on viscosity; d(T) is a constant term that reflects the basic viscosity level under the effect of temperature alone; with the goal of minimizing the mean square error between the predicted and measured viscosity values, the coefficients a, b, c, and d at different temperatures are determined through multiple iterative calculations to ensure that the model fit R² ≥ 0.999, accurately reflecting the combined effect of temperature and shear rate on viscosity.

[0031] Step S3: Build a digital twin platform: A digital twin platform consists of a physical layer, a virtual layer, and a data interaction layer, such as... Figure 1As shown, the physical layer consists of a temperature sensor, a torque sensor, and a data acquisition card set up by the rheometer. It collects real-time temperature and shear rate-viscosity data of the lubricating oil, which is then transmitted via an industrial bus to an edge computing gateway for filtering and normalization preprocessing before being uploaded. Specifically, the temperature sensor has an accuracy of ±0.1℃; the torque sensor is used to calculate the shear rate with an accuracy of ±0.5%; and the data acquisition card has a sampling rate of ≥1kHz. The virtual layer, based on the multi-parameter dynamically coupled viscosity-temperature model constructed in step S2, builds a virtual simulation model and integrates a sub-model of oil molecular motion under shear action. This refines the simulation of the microscopic molecular behavior of the lubricating oil under temperature and shear action, helping to improve the accuracy and physical consistency of the viscosity-temperature characteristic simulation. By simulating the change in molecular arrangement with temperature and shear rate, the simulation precision is improved. The data interaction layer uses the OPC UA protocol to achieve real-time data synchronization between the physical and virtual layers, with a transmission cycle of ≤100ms. A real-time database is built to store historical data, supporting data backtracking and model iteration.

[0032] Step S4: Based on the model output curve: The output curve type is determined to be a "viscosity-temperature curve" under a fixed shear rate. During the output curve process, the temperature parameter T is selected to be in the range of -40℃ to +150℃, and the shear rate parameter γ is selected to be in the range of 10 to 38000 s. -1 For each temperature point, the corresponding coefficients have been fitted using the optimized LM algorithm. These coefficients are substituted into the model, and the corresponding dynamic viscosity μ is calculated in combination with the selected shear rate. For example, when the shear rate γ0 is fixed, μ corresponding to different temperatures T is calculated, and the viscosity curve under that shear rate as a function of temperature is obtained and displayed through the digital twin platform in step S3.

[0033] Step S5, Dynamic Correction and Performance Prediction: The digital twin platform compares the measured viscosity μ of the physical layer in real time. 实测 With virtual layer predicted viscosity μ 预测 Calculate the relative error: ; An error threshold is set. When the error exceeds the threshold, a feedback mechanism is triggered, and the optimized LM algorithm is called to refit the coefficients a, b, c, and d to correct the model until the error is no greater than the threshold. Based on the corrected model, viscosity-temperature curves under different combinations of temperature and shear rate are output. Specifically, when the error exceeds 5%, the feedback mechanism is triggered, and the optimized LM algorithm is called to refit the coefficients a, b, c, and d until the error is ≤5%, realizing dynamic model updates. Based on the corrected model, viscosity-temperature curves under different combinations of temperature and shear rate are output, intuitively quantifying the viscosity change law of lubricating oil under different temperatures and shear rates, and providing a visual basis for performance evaluation, operating condition optimization, and failure early warning.

[0034] Generate multi-dimensional early warning information: when the viscosity change rate is >10% / min, trigger the "viscosity abnormal fluctuation warning"; when the temperature T exceeds the effective lubrication temperature range of the lubricating oil, issue the "critical temperature threshold warning", further realize the lubrication failure risk assessment, and provide decision support for equipment maintenance.

[0035] The present invention also provides a lubricating oil viscosity monitoring system based on parameter coupling and digital twin, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of the lubricating oil viscosity monitoring method based on parameter coupling and digital twin.

[0036] Example: A representative sample of SAE 5W-30 fully synthetic engine oil was selected. Data acquisition was conducted using a TADHR-2 rotary rheometer, a PT100 temperature sensor, a high-precision torque sensor, and a data acquisition card with a 2kHz sampling rate. The temperature control range was 20℃~120℃, the heating rate was 5℃ / min, and each temperature point was stabilized for 15 minutes to ensure thermal equilibrium. The fixture used a 40mm circular aluminum plate, and the sample loading gap was 150μm. The shear rate covered 10~38000s. -1 For each temperature-shear rate combination, data was continuously collected for 30 seconds and averaged under engine cold start, normal driving, and high-speed, high-load conditions. After three repeated verifications, the data repeatability error was ≤1.5%. Some key data showed that the data was collected in 10 seconds at 20℃. -1 Viscosity at shear rate 385.2 38000s -1 8.6 The viscosity at the corresponding shear rate at 120℃ is 18.3. and 1.9 .

[0037] A cubic polynomial model is used to describe the relationship between viscosity and temperature and shear rate. The model expression is μ = a(T)(logγ)³ + b(T)(logγ)² + c(T)logγ + d(T). The parameters [a(T), b(T), c(T), d(T)] are optimized using an improved Levenberg-Marquardt algorithm. The initial damping factor λ0 = 0.01 is set, and the residual ratio ρ is used as the basis for further optimization. k The damping factor was adjusted to balance the convergence speed and stability. After 120 iterations, the model converged with a goodness of fit R² = 0.9996. The parameter fitting results at different temperatures showed that at 20℃, a = 0.021, b = -0.352, c = 2.105, and d = 5.982. At 60℃ and 100℃, the parameters changed regularly with increasing temperature.

[0038] Generate viscosity-temperature curves at a fixed shear rate, such as... Figure 3 As shown, idling condition (γ=100 s) -1 At 20°C, the viscosity decreased from 125. 15.2 at 100℃ Significant temperature sensitivity; high-speed operation (γ=10000s) -1 At that temperature, the viscosity decreased from 28.6 at 20°C. 6.8 at 100℃ The shear dilution effect is obvious. Analysis shows that the viscosity of SAE 5W-30 lubricating oil is more sensitive to temperature in the low temperature range (<40℃), while the change is slower in the high temperature range (>80℃). Moreover, the viscosity decreases nonlinearly with increasing shear rate, which is consistent with the characteristics of pseudoplastic fluid.

[0039] Real-time comparison of the measured viscosity-temperature curve with the predicted viscosity-temperature curve under ideal conditions. The predicted viscosity-temperature curve under ideal conditions is as follows: Figure 2 As shown, correction is triggered when the error exceeds 5%, for example, at 80℃ and γ=5000s. -1 Under operating conditions, the initial prediction error was 6.5%, which was reduced to 1.1% after correction. In terms of performance warning, a viscosity abnormal fluctuation warning was triggered when the viscosity change rate was >10% / min, and a critical temperature warning was issued when the temperature was >150℃.

[0040] Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention. Any modifications, alterations, substitutions, and variations made by those skilled in the art to the above embodiments are within the scope of the present invention.

Claims

1. A method for monitoring lubricating oil viscosity based on parameter coupling and digital twin, characterized in that, The steps include: Step S1: Use a rheometer to collect viscosity data of lubricating oil at different temperatures and shear rates, remove outliers, and retain valid data as model input data for step S2. Step S2: Construct a multi-parameter dynamically coupled viscosity-temperature model as follows: μ(T,γ)=a(T)(logγ)³+b(T)(logγ)²+c(T)logγ+d(T); Where μ is the dynamic viscosity, T is temperature, °C; γ is shear rate, s. -1 a(T), b(T), and c(T) are the logarithmic terms of the shear rate (logγ). 3 (logγ) 2 The coefficients of logγ; d(T) is a constant term; a(T), b(T), c(T), and d(T) are determined as follows: setting 预测 =a(logγ)³+b(logγ)²+c(logγ)+d; Where, η 预测 To predict viscosity, γ is the shear rate, and a, b, c, and d are four parameters. By adding an adaptive damping factor λ to the Levenberg-Marquardt algorithm, the optimized LM algorithm is obtained as follows: ； Among them, J k λ is the partial derivative matrix of the residual with respect to the parameters at the k-th iteration, i.e., the Jacobian matrix; k Let r be the adaptive damping factor in the k-th iteration. k Let I be the residual vector at the k-th iteration, I be the identity matrix, k be the iteration number, and d be the residual vector at the k-th iteration. k The parameter vector to be determined in the k-th iteration The increment; The optimization objective is to minimize the sum of squared residuals: S(x) = Σ[η 实测 -η 预测 ]², where η 实测 This is the actual measured viscosity; The residual ratio is calculated using the following formula: ； in: The residual ratio at the k-th iteration. To minimize the sum of squared residuals in the k-th iteration, For d k Minimize the sum of squared residuals in the k-th iteration of the correction; Based on ρ k Adjust λ k Update the parameter vector x until the sum of squared residuals is less than the preset convergence threshold or the maximum number of iterations is reached, to obtain the optimal parameters [a, b, c, d] at the current temperature T. Calculate the parameters at different temperature points based on the data at different temperature points. The corresponding optimal parameter set , will get , , , As a new dataset, the functional relationships a(T), b(T), c(T), d(T) of parameters a, b, c, d as a function of temperature T were determined by polynomial fitting. Step S3: Build a digital twin platform: The digital twin platform consists of a physical layer, a virtual layer, and a data interaction layer. The physical layer consists of temperature sensors, torque sensors, and a data acquisition card installed on the rheometer to collect real-time temperature and shear rate-viscosity data of the lubricating oil. The virtual layer is based on the multi-parameter dynamic coupling viscosity-temperature model constructed in step S2 to build a virtual simulation model and integrates a sub-model of oil molecule motion under shear. The data interaction layer uses the OPC UA protocol to achieve real-time data synchronization between the physical layer and the virtual layer. Step S4: Based on the model output curve: The output curve is determined to be a "viscosity-temperature curve" under a fixed shear rate. Based on the multi-parameter dynamic coupling viscosity-temperature model in step S2, the corresponding dynamic viscosity μ is calculated in combination with the selected shear rate to obtain the viscosity-temperature curve under the shear rate, which is then displayed through the digital twin platform in step S3. Step S5, Dynamic Correction and Performance Prediction: The digital twin platform compares the measured viscosity μ of the physical layer in real time. 实测 With virtual layer predicted viscosity μ 预测 Calculate the relative error: ； Set an error threshold. When the error exceeds the set threshold, trigger the feedback mechanism and call the optimized LM algorithm to refit the coefficients a, b, c, and d to correct the model until the error is no greater than the set threshold. Based on the corrected model, output the viscosity-temperature curves under different combinations of temperature and shear rate. Generate multi-dimensional early warning information: when the viscosity change rate is >10% / min, trigger "viscosity abnormal fluctuation warning"; when the temperature T exceeds the effective lubrication temperature range of the lubricating oil, issue "critical temperature threshold warning".

2. The lubricating oil viscosity monitoring method based on parameter coupling and digital twin according to claim 1, characterized in that: In step S1, the temperature adjustment range of the rheometer covers -40℃ to +150℃; during the data acquisition process, the temperature is gradually changed within the range of 20℃ to 80℃ at a rate of 5℃ / min, and each temperature point is stabilized for more than 10 minutes, with the shear rate set to 10~38000s. -1 For each temperature-shear rate combination, viscosity data was continuously collected for 30 seconds with a sampling interval of 1 second, and the average value was taken as the valid data.

3. The lubricating oil viscosity monitoring method based on parameter coupling and digital twin according to claim 1, characterized in that: In step S2, based on ρ k Adjust λ k If 0.75 < ρ k <1, adjust λ k+1 =λ k / 10; if 0 < ρ k <0.25, adjust λ k+1 =10λ k If 0.25≤ρ k If ≤0.75, then λ k+1 =λ k ;where ρ k Let λ be the residual ratio at the k-th iteration. k Let λ be the adaptive damping factor in the k-th iteration. k+1 This is the adaptive damping factor at the (k+1)th iteration.

4. The lubricating oil viscosity monitoring method based on parameter coupling and digital twin according to claim 1, characterized in that: In step S2, if ρ k If the value is greater than 0, then update parameter x. k+1 =x k +d k , where ρ k x is the residual ratio at the k-th iteration. k+1 Let x be the parameter vector at the (k+1)th iteration. k Let d be the parameter vector at the k-th iteration. k The parameter vector to be determined in the k-th iteration The increment.

5. The lubricating oil viscosity monitoring method based on parameter coupling and digital twin according to claim 1, characterized in that: In step S3, the data collected by the physical layer of the digital twin platform is transmitted to the edge computing gateway via the industrial bus for filtering, normalization preprocessing, and then uploaded.

6. The lubricating oil viscosity monitoring method based on parameter coupling and digital twin according to claim 1, characterized in that: In step S3, the transmission period of the data interaction layer is ≤100ms.

7. The lubricating oil viscosity monitoring method based on parameter coupling and digital twin according to claim 1, characterized in that: In step S3, a real-time database is built on the digital twin platform to store historical data, supporting data backtracking and model iteration.

8. The lubricating oil viscosity monitoring method based on parameter coupling and digital twin according to claim 1, characterized in that: In step S4, during the output curve process, the temperature parameter T is selected to be in the range of -40℃ to +150℃, and the shear rate parameter γ is selected to be in the range of 10 to 38000 s. -1 .

9. The lubricating oil viscosity monitoring method based on parameter coupling and digital twin according to claim 1, characterized in that: When the error exceeds 5%, a feedback mechanism is triggered, and the optimized LM algorithm is called to refit the coefficients a, b, c, and d until the error is ≤5%, thereby achieving dynamic model updates.

10. A lubricating oil viscosity monitoring system based on parameter coupling and digital twin, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the lubricating oil viscosity monitoring method based on parameter coupling and digital twin as described in any one of claims 1-9.

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