Sensor-based Digital Intelligent Control System for Oil Filter

Abstract

The present invention relates to the technical field of oil filter control. Specifically, it relates to a digital intelligent control system for an oil filter based on sensors, which includes a fusion monitoring unit, an oil temperature control unit, and a feedback adjustment unit. In the present invention, the fusion monitoring unit uses multiple sensors to monitor the oil pressure, flow rate, and temperature. The oil temperature control unit calculates the optimal heating rate and target temperature range through an adaptive algorithm based on the environment and the initial oil temperature, compares with the actual oil temperature during the heating process, and adjusts the heating power by pulse heating according to the result. At the same time, it judges the working load based on the specific heat capacity of the oil product and adjusts the frequency of the vacuum pump. The feedback adjustment unit evaluates the system stability based on the target operating frequency and heating power of the vacuum pump, generates an optimization strategy, and feeds it back to the oil temperature control unit to achieve precise control of the oil temperature, improve the equipment stability and energy utilization efficiency, and ensure the efficient operation of the oil filter.

Description

Technical Field

[0001] The present invention relates to the technical field of oil filter control, and more specifically, to a digital intelligent control system for an oil filter based on sensors. Background Art

[0002] Industrial production is an important technology. In the field of industrial production, oil filters are crucial for maintaining the quality of oil and ensuring the normal operation of equipment. With the development of technology, the performance requirements for oil filters are constantly increasing.

[0003] The traditional oil filter control system has obvious limitations in oil temperature control. Among them, the performance of sensors will shift under different working environments and temperature conditions, that is, the deviation between the output value and the true value will change, making it difficult to accurately calculate based on the ambient temperature and the initial temperature of the oil, and unable to obtain the optimal heating rate and a reasonable target temperature range. As a result, the oil temperature control lacks precision. In actual operation, the oil temperature is likely to be too high or too low. When the oil temperature is too high, the seals will age rapidly, reducing the service life of the equipment; when the oil temperature is too low, the viscosity of the oil will increase, thereby increasing the working load of the equipment and significantly reducing the filtration efficiency. To solve this technical problem, we provide a digital intelligent control system for an oil filter based on sensors. Summary of the Invention

[0004] The purpose of the present invention is to provide a digital intelligent control system for an oil filter based on sensors to solve the problems raised in the above background art.

[0005] The present invention uses an adaptive oil temperature control algorithm to calculate the optimal heating rate and the target temperature range, continuously compares the oil temperature during heating, and adjusts the heater power in a pulse heating manner according to the temperature state. At the same time, it combines the specific heat capacity of the oil, the historical operation data of the equipment, and the oil flow rate to judge the working load and adjust the operating frequency of the vacuum pump, thereby solving the oil temperature control problem.

[0006] To achieve the above purpose, a digital intelligent control system for an oil filter based on sensors is provided, including a fusion monitoring unit, an oil temperature control unit, and a feedback adjustment unit;

[0007] The fusion monitoring unit is composed of a fusion of multiple sensors. Among them, a pressure sensor and a flow sensor are used to monitor the pressure and flow rate values of the oil in real time, and a temperature sensor is used to obtain the external ambient temperature value and the oil temperature;

[0008] The oil temperature control unit calculates the optimal heating rate and target temperature range according to the ambient temperature and the initial oil temperature by using an adaptive oil temperature control algorithm. During the heating process, it continuously compares the actual oil temperature with the target temperature range to obtain the temperature state of the oil temperature, and automatically adjusts the heater power in a pulse heating mode according to the temperature state of the oil temperature. At the same time, it automatically judges the working load of the oil filter according to the specific heat capacity of different oils, the historical operation data of the equipment, and the oil flow rate, and adjusts the operating frequency of the vacuum pump according to the load condition to obtain the target operating frequency of the vacuum pump;

[0009] The feedback adjustment unit constructs a comprehensive performance evaluation model based on the target operating frequency and heating power of the vacuum pump, evaluates the stability of the current system according to this model, and finally generates an optimization adjustment strategy according to the evaluation result and sends it to the oil temperature control unit.

[0010] As a further improvement of this technical solution, the oil temperature control unit includes a data processing module. In the adaptive oil temperature control algorithm, the data processing module is used to calculate the difference between the ambient temperature and the initial oil temperature, specifically as follows:

[0011] Use a temperature sensor to collect the ambient temperature and the initial oil temperature, and calculate their initial difference;

[0012] Set a temperature offset characteristic curve, which is used to describe the performance changes of the oil under different temperature conditions, and use the temperature offset characteristic curve to correct the ambient temperature and the initial oil temperature, and recalculate the difference between the corrected ambient temperature and the initial oil temperature.

[0013] As a further improvement of this technical solution, the oil temperature control unit includes an analysis and calculation module. The method for calculating the optimal heating rate by the analysis and calculation module in the adaptive oil temperature control algorithm is specifically as follows:

[0014] Determine the oil quality, heat transfer area, heat transfer distance, and initial oil specific heat capacity according to the equipment data, obtain the temperature difference from the data processing module, finally set the heating-up time and the target temperature range, and calculate the difference between the target temperature and the initial temperature;

[0015] Preset an initial heating rate and start an iterative loop. Calculate the temperature change amount according to the current heating rate and the heating-up time, then calculate the current specific heat capacity according to the current temperature and the formula for the change of oil specific heat capacity with temperature, substitute it into the heat conduction formula to calculate the estimated heat transfer amount, and finally calculate the theoretical heat demand;

[0016] Compare the estimated heat transfer amount with the theoretical heat demand, and obtain the optimal heating rate according to the comparison result.

[0017] As a further improvement of this technical solution, the analysis and calculation module calculates the target temperature range by using an adaptive oil temperature control algorithm, specifically as follows:

[0018] Extract the viscosity-temperature characteristic curve data of the current oil product and the current ambient temperature from the oil product database, and set the upper and lower limits of the oil temperature range corresponding to the optimal viscosity range for the equipment operation;

[0019] On the viscosity-temperature characteristic curve, select three data points adjacent to the current ambient temperature value, use the piecewise linear interpolation algorithm for calculation, obtain the linear equations between the points, and substitute the upper and lower limits of the optimal viscosity range of the equipment into the above linear equations respectively, calculate the corresponding temperature values, and finally determine the target temperature range for this heating process accordingly.

[0020] As a further improvement of this technical solution, the oil temperature control unit includes a pulse heating module. The pulse heating module automatically adjusts the heater power in a pulse heating manner according to the temperature state of the oil temperature, specifically as follows:

[0021] Obtain the actual oil temperature value of the oil fluid from the fusion monitoring unit, and set a sampling period. At each sampling moment, the temperature sensor collects the current actual oil temperature value;

[0022] Then compare the collected actual oil temperature value with the upper and lower limits of the target temperature range, and divide it into a low-temperature state and a high-temperature state according to the comparison result;

[0023] When in the low-temperature state, calculate the difference between the actual oil temperature and the lower limit of the target temperature range, adjust the proportional coefficient according to the size of the difference, and then calculate the power adjustment amount according to the proportional control algorithm formula;

[0024] When in the high-temperature state, determine the degree to which the oil temperature exceeds the upper limit of the target temperature range by calculating the difference between the actual oil temperature and the upper limit of the target temperature range, determine the integral time according to the degree to which the oil temperature exceeds the upper limit, and then calculate the heating power adjustment amount by using the integral control algorithm formula.

[0025] As a further improvement of this technical solution, the oil temperature control unit includes a load judgment module. The load judgment module judges the working load of the oil filter according to the specific heat capacity of different oil products, the equipment operation historical data, and the oil fluid flow rate, specifically as follows:

[0026] Obtain the specific heat capacity value of the current oil product from the oil product database , and measure the oil fluid flow rate value through the flow sensor , and then calculate the heat absorption of the oil fluid per unit time by using the law of conservation of energy formula;

[0027] Then, extract the standard heat absorption per unit time from the device operation historical data, compare the actual heat absorption with the standard heat absorption per unit time to obtain a difference, and finally, use the weighted average algorithm based on the difference to obtain the working load coefficient.

[0028] As a further improvement of this technical solution, the operation frequency adjustment algorithm of the vacuum pump is based on the working load coefficient, specifically as follows:

[0029] Obtain the current working load coefficient of the vacuum pump , and set a high load threshold and a low load threshold ;

[0030] When , use the exponential growth algorithm to calculate the difference between the maximum tolerable vacuum degree and the current vacuum degree of the vacuum pump. The vacuum degree is the degree to which the gas pressure is lower than the atmospheric pressure. Determine the exponential base according to this difference, and then calculate the target operation frequency of the vacuum pump according to the exponential growth formula, and adjust the operation frequency of the vacuum pump to the target operation frequency of the vacuum pump;

[0031] When , use the linear decreasing algorithm to calculate the difference between the lowest stable operation vacuum degree and the current vacuum degree of the vacuum pump. Determine the decreasing slope of the linear decreasing algorithm according to this difference, and finally calculate the target operation frequency of the vacuum pump according to the linear decreasing formula, and adjust the operation frequency of the vacuum pump to the target operation frequency of the vacuum pump.

[0032] As a further improvement of this technical solution, the feedback adjustment unit constructs a comprehensive performance evaluation model based on the target operation frequency and heating power of the vacuum pump, and evaluates the stability of the current system according to this model, specifically as follows:

[0033] Obtain the target operation frequency and the current heating power of the vacuum pump from the oil temperature control unit, set a time interval at the same time, and construct a comprehensive performance evaluation model;

[0034] Extract the specific heat capacity, mass flow rate of the current oil, and the change in oil temperature within a fixed time, calculate the system energy loss according to the law of conservation of energy, then calculate the stability coefficient according to the system energy loss, and finally compare it with the preset stability coefficient threshold to judge the current stability state of the system.

[0035] Compared with the prior art, the beneficial effects of the present invention:

[0036] In the sensor-based digital intelligent control system of the oil filter, the oil temperature control unit adopts an adaptive oil temperature control algorithm to accurately calculate the optimal heating rate and the target temperature range, effectively avoiding the adverse effects on the equipment caused by too high or too low oil temperature, greatly improving the stability and reliability of the equipment operation, extending the service life of the equipment, and continuously comparing the oil temperature and automatically adjusting the heater power during the heating process to ensure that the oil temperature is always in an ideal state, improving the quality of oil treatment. At the same time, the operating frequency of the vacuum pump is adjusted according to various factors to judge the working load, realizing the rational use of energy and reducing energy consumption. Brief Description of the Drawings

[0037] Figure 1 This is the overall block diagram of the present invention.

[0038] The meanings of the various reference numerals in the figure are as follows:

[0039] 1. Fusion monitoring unit; 2. Oil temperature control unit; 21. Data processing module; 22. Analysis and calculation module; 23. Pulse heating module; 24. Load judgment module; 3. Feedback adjustment unit. Detailed Embodiments

[0040] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0041] The present invention provides a sensor-based digital intelligent control system for an oil filter. Please refer to Figure 1 as shown, which includes a fusion monitoring unit 1, an oil temperature control unit 2, and a feedback adjustment unit 3;

[0042] The fusion monitoring unit 1 is composed of a variety of sensors. Among them, the pressure sensor and the flow sensor are used to monitor the pressure and flow values of the oil in real time, and the temperature sensor is used to obtain the external environmental temperature value and the oil temperature.

[0043] The oil temperature control unit 2 calculates the optimal heating rate and the target temperature range according to the environmental temperature and the initial oil temperature by using an adaptive oil temperature control algorithm. Among them, the oil temperature control unit 2 includes a data processing module 21, an analysis and calculation module 22, a pulse heating module 23, and a load judgment module 24.

[0044] The data processing module 21 is used to calculate the difference between the environmental temperature and the initial oil temperature in the adaptive oil temperature control algorithm, specifically as follows:

[0045] In the oil temperature control unit 2, the difference between the ambient temperature and the initial oil temperature is one of the key parameters for subsequent calculation of the optimal heating rate and the target temperature range. A tiny temperature measurement error will be amplified in the subsequent complex control algorithm, resulting in inaccurate oil temperature control and failing to meet the requirements of the equipment for oil temperature accuracy.

[0046] Therefore, the temperature sensor is pre-calibrated and tested in a laboratory environment, that is, at different known standard temperature points, the actual output values of the sensor are recorded. Based on these measurement data, a mathematical model of the temperature offset characteristic curve is established by using the method of polynomial fitting. The expression of the mathematical model is ; where y is the measurement error of the sensor, x is the measured temperature value, are the coefficients obtained by fitting to ensure that the model can describe the temperature offset characteristic of the sensor.

[0047] During the actual operation process, when the sensor obtains the ambient temperature value and the initial oil temperature value , they are substituted into the mathematical model of the temperature offset characteristic curve to calculate the corresponding measurement errors and .

[0048] Since the ambient temperature changes rapidly, the original measured values need to be corrected to obtain the corrected ambient temperature value and the initial oil temperature value . Finally, the difference between the corrected ambient temperature value and the initial oil temperature value is calculated ; in this way, the measurement error of the temperature sensor can be effectively reduced, the accuracy of calculating the difference between the ambient temperature and the initial oil temperature can be improved, and thus more reliable basic data can be provided for the subsequent adaptive oil temperature control algorithm, ensuring the accuracy and stability of the entire oil temperature control process.

[0049] The method for the analysis and calculation module 22 to calculate the optimal heating rate in the adaptive oil temperature control algorithm is as follows:

[0050] After completing the correction of the difference between the ambient temperature and the initial oil temperature, the difference between the corrected ambient temperature value and the initial oil temperature value is extracted, and the heating-up time is set as . Based on the heat conduction formula ; where is the heat, is the mass of the oil, is the specific heat capacity of the oil, and the heat power formula ; is the heat power, and an expression related to the heating rate can be initially constructed to provide a reasonable framework for the heating rate calculation.

[0051] Then start iterative calculation and initialize an estimated value of the heating rate. In each iteration:

[0052] According to the currently estimated heating rate and the time step calculate the estimated increase in the oil temperature after this time step ; By making a prediction to judge the heating effect and knowing the temperature change trend in advance, it helps to adjust the heating rate strategy in a timely manner.

[0053] Consider the correction coefficient for the change of the oil heat capacity with temperature which can be obtained by fitting the experimental measurement data of the oil heat capacity at different temperatures in advance, and correct the estimated increase value to obtain the corrected temperature increase value where is the current oil temperature. Since the oil characteristics change with temperature, accurate correction is required to improve the accuracy of temperature calculation, make the oil temperature control more in line with the actual oil situation. According to the corrected temperature increase value, update the current oil temperature ; where is the updated oil temperature, follow the temperature change in real time, ensure that the calculation is based on the latest state, provide an accurate basis for subsequent judgment, calculate the distance between the updated oil temperature and the lower limit value of the target temperature range The lower limit value of the target temperature range is obtained by determining the target temperature range through the adaptive oil temperature control algorithm, clarify the gap with the target, facilitate measuring the heating progress, and help judge whether the expected heating effect is achieved.

[0054] If , then update the currently estimated heating rate according to the iterative update formula . If , then the currently estimated heating rate is the required optimal heating rate, where is the preset temperature error range, is the weight coefficient obtained based on historical experience. By iteratively approaching the optimal value, gradually optimize the calculation result, accurately obtain the optimal heating rate to achieve efficient oil temperature control. Repeat the above iterative process until the convergence criterion is met, and finally obtain the optimal heating rate that can make the oil temperature reach the target temperature range within the preset time, ensure the reliability of the result, and effectively ensure the stable operation of the entire oil temperature control unit 2.

[0055] Among them, the specific way for the analysis and calculation module 22 to calculate the target temperature range using the adaptive oil temperature control algorithm is as follows:

[0056] After obtaining the optimal heating rate, it is necessary to define the target temperature range for this heating process. In an oil filter, if the oil temperature is too high, it may cause the seals to age; if the oil temperature is too low, the viscosity of the oil will increase, increasing the working load of the equipment and reducing the filtration efficiency.

[0057] First, retrieve the viscosity-temperature characteristic curve data of the currently processed oil from the pre-stored oil product database. The viscosity-temperature characteristics of different oils vary, and relying on specific oil product data can ensure calculation accuracy and provide an accurate basis for oil product characteristics for subsequent calculations.

[0058] Next, obtain the current ambient temperature value , as the ambient temperature affects the heat dissipation of the oil. Obtaining its value can determine the target temperature range more in line with the actual working conditions, helping to improve the adaptability of the entire oil temperature control system and making the calculated target temperature range more in line with the requirements of the actual operating environment.

[0059] Then, based on the design parameters and operating requirements of the equipment, determine the upper and lower limits of the oil temperature range corresponding to the optimal viscosity range for equipment operation, denoted as and , because the equipment operates with the highest efficiency and the least wear in a specific viscosity range. Determining the oil temperature range in this way can ensure the good operation of the equipment, effectively extend the service life of the equipment, and improve the operating stability of the equipment.

[0060] After that, on the viscosity-temperature characteristic curve, select three data points close to the current ambient temperature value , denoted as point , point , and point , where and , represents viscosity. By using local data points, the calculation can be simplified and the viscosity-temperature relationship near the current ambient temperature can be better reflected. The calculation amount is relatively small and the accuracy can meet the requirements, providing a suitable data basis for piecewise linear interpolation.

[0061] Use the piecewise linear interpolation algorithm for calculation. First, calculate the straight-line equations from point to point and from point to point . For the straight-line equation from point to point , denoted as ; where the slope ; similarly, the straight-line equation from point to point is , ; where is the current ambient temperature value, are the ambient temperature values of point , point and point respectively, is the viscosity of point , point and point respectively, is expressed as the slope.

[0062] Substitute the lower limit of the optimal viscosity range of the device into the above linear equation, and calculate the corresponding temperature values and of point 1 and point 2 respectively. If the temperature value obtained by substituting into the first linear equation is greater than , then discard this result and adopt the temperature value calculated by the second linear equation. Similarly, substitute the upper limit of the optimal viscosity range of the device into the linear equation to calculate the corresponding temperature values and of point 3 and point 4. Finally, determine that the target temperature range for this heating process is , where , , is expressed as the lower limit of the target temperature range, is expressed as the upper limit of the target temperature range. By determining the target temperature range in this way, the characteristics of the oil product, ambient temperature and equipment requirements are comprehensively considered, and the target temperature range that meets multiple conditions can be accurately determined, enabling the oil temperature control to adapt to different oils and environments while ensuring the normal operation of the equipment, improving the versatility and reliability of the system, and ensuring that the entire oil system operates in the best state.

[0063] During the heating process, the pulse heating module 23 continuously compares the actual oil temperature with the target temperature range, obtains the temperature state of the oil temperature, and automatically adjusts the heater power in a pulse heating manner according to the temperature state of the oil temperature, as follows:

[0064] First, set a high-precision temperature sensor to collect the actual oil temperature value of the oil. Since accurate oil temperature measurement is the basis of the entire comparison algorithm, its high-precision characteristic can provide a reliable data source for subsequent judgments, reduce the influence of measurement errors on control, and enable the system to more accurately grasp the actual situation of the oil temperature.

[0065] Next, set the sampling period according to the pre-analysis of the oil temperature change rate and the control accuracy required by the system. At each sampling moment , is the th sampling moment, and the temperature sensor collects the current actual oil temperature value , is represented as the actual oil temperature value, and then the collected actual oil temperature value is compared with the upper and lower limits of the target temperature range. When occurs, the current oil temperature state is marked as the low temperature state, and the direction of the oil temperature deviation from the target range is clarified, which is convenient for subsequent adoption of targeted heating control strategies, making the control logic clear and concise, and can quickly start the corresponding heating power improvement mechanism to promote the rise of the oil temperature. When occurs, the current oil temperature state is marked as the high temperature state, and the situation of too high oil temperature is detected in time, so as to take cooling measures, enhance the early warning ability of the system to abnormal high temperature, and effectively prevent problems such as equipment damage or deterioration of oil performance caused by too high oil temperature, and ensure the safe and stable operation of the entire system.

[0066] When in the low temperature state, calculate the difference from the lower limit of the target oil temperature range ; Only by clarifying the deviation magnitude can the adjustment range of the heating power be reasonably determined, making the control targeted and being able to accurately increase the heating power according to the oil temperature situation.

[0067] Then, according to dynamically adjust the proportional coefficient , where ; is a constant. The larger the difference, the larger, then a larger power improvement amplitude is required to quickly make up for the insufficient oil temperature, which can flexibly adjust the heating intensity according to the actual demand and can efficiently make the oil temperature approach the target range.

[0068] Next, according to the proportional control algorithm formula ; calculate the heating power adjustment amount, is the heating power adjustment amount. Adjust the pulse width according to this formula. Because the pulse width is proportional to the heating power, the power can be adjusted by changing the pulse width, and the heating power can be accurately controlled by using the pulse signal to effectively heat the oil temperature in the low temperature state and make the oil temperature gradually rise to the target range.

[0069] When in the high temperature state, first determine the degree to which the oil temperature exceeds the upper limit of the target temperature range, and calculate ; In order to measure the degree of too high oil temperature to determine a suitable power reduction strategy, different exceeding degrees require different cooling rates to make the cooling control more adaptable and avoid adverse effects on the system caused by too fast or too slow cooling.

[0070] Determine the integral time according to the degree to which the oil temperature exceeds the upper limit, so that it can stably and effectively reduce the oil temperature and make the oil temperature stable within the target range to ensure the normal operation of the system.

[0071] Adopt the integral control algorithm formula ; Calculate the adjustment amount of the heating power, where is the integral time variable, and then adjust the pulse width according to this to reduce the power. Through integral control, it is possible to comprehensively consider the over-temperature situation of the oil temperature within a period of time, avoid excessive adjustment caused by instantaneous fluctuations, achieve stable control of the oil temperature in the high-temperature state, and gradually bring the oil temperature back to the target range.

[0072] At the same time, the load judgment module 24 automatically judges the working load of the oil filter according to the specific heat capacity of different oils, the historical operation data of the equipment, and the oil flow rate, and adjusts the operating frequency of the vacuum pump according to the load situation, specifically as follows:

[0073] First, obtain the specific heat capacity value of the currently processed oil , then accurately measure the oil flow rate value through the flow sensor , and use the law of conservation of energy formula ; Calculate the heat absorption of the oil per unit time , where the temperature difference between the inlet and outlet of the oil is . The reason for calculating based on the law of conservation of energy is to follow the physical principle to ensure the scientific nature of the calculation, accurately reflect the energy absorption situation of the oil, and obtain the heat absorption data closely related to the actual working conditions.

[0074] Then, extract the standard heat absorption per unit time from the equipment operation historical data storage module , and compare the calculated heat absorption per unit time with the standard heat absorption per unit time to obtain the difference ; Comparing the difference can intuitively reflect the deviation degree of the current heat absorption from the standard situation, which is a key step in judging the change of the working load. Measuring the change of the working load through the deviation can simply and directly reflect the working load state, providing effective data input for the subsequent weighted average algorithm.

[0075] Finally, adopt the weighted average algorithm to obtain the working load coefficient . The weighted average algorithm is conducive to comprehensively judging the working load by considering multiple factors, avoiding the one-sidedness of single-factor judgment, and obtaining a coefficient that can accurately reflect the comprehensive working load situation of the oil filter, so as to reasonably adjust the operating frequency of the vacuum pump according to this coefficient to ensure the efficient and stable operation of the equipment.

[0076] Among them, the vacuum pump operating frequency adjustment algorithm is based on the working load coefficient, specifically as follows:

[0077] Obtain the working load coefficient of the current vacuum pump , and set the high load threshold and the low load threshold , clarify the boundary of the workload to determine when to adjust the operating frequency of the vacuum pump and what adjustment strategy to adopt, ensure that the vacuum pump operates within a suitable workload range, and avoid overworking or underworking.

[0078] When it is the case, first calculate the maximum vacuum degree that the vacuum pump can withstand and the current vacuum degree difference ; Determine the exponential base , ; is a functional relationship determined according to the equipment characteristics. The reason for determining the base in this way is that a larger difference means that a greater increase in the performance of the vacuum pump is required to cope with high loads. The exponential growth algorithm can quickly increase the operating frequency of the vacuum pump and rapidly enhance the pumping capacity of the vacuum pump under high loads, meeting the vacuum degree requirements of the system under high load conditions.

[0079] Then, according to the exponential growth formula ; Calculate the target operating frequency of the vacuum pump, and adjust the current operating frequency of the vacuum pump to the target operating frequency , where is the initial operating frequency of the vacuum pump, is a variable related to time.

[0080] When it is the case, calculate the difference between the lowest stable operating vacuum degree of the vacuum pump and the current vacuum degree , and determine the decreasing slope of the linear decreasing algorithm according to this difference , is a functional relationship determined according to the equipment characteristics. The reason for determining the slope is that a larger difference indicates that the vacuum pump can reduce the operating frequency by a greater margin to save energy and reduce wear. It can smoothly reduce the operating frequency of the vacuum pump under low loads, while meeting the basic vacuum degree requirements of the system, reducing equipment energy consumption and operating noise, and extending the service life of the vacuum pump.

[0081] Finally, according to the linear decreasing formula ; Calculate the target operating frequency of the vacuum pump, where is the time variable, and adjust the current operating frequency of the vacuum pump to the target operating frequency .

[0082] The feedback regulation unit 3 obtains the target operating frequency of the vacuum pump and the heating power adjustment amount from the oil temperature control unit 2 Meanwhile, set the time interval for data acquisition so as to obtain a sufficient number of data points to accurately evaluate the system performance.

[0083] Next, construct a comprehensive performance evaluation model. For the determination of the system stability evaluation index, adopt a method based on energy balance and equipment operation characteristics, and define a stability coefficient whose calculation is related to the operating power of the vacuum pump , the adjustment amount of heating power and the thermophysical properties of the oil. Let the specific heat capacity of the oil be , the mass flow rate be , and the change in oil temperature within a certain period of time be . According to the law of conservation of energy , quantify the system stability through mathematical relationships, be able to early warn of unstable states, and provide a scientific basis for system optimization.

[0084] Then, substitute the real-time collected data of the operating frequency and heating power of the vacuum pump into the constructed model to calculate the current stability coefficient , and by comparing it with the preset stability threshold , judge the current stability state of the system. If , the system is in a stable state. If , it is determined that the system needs to be optimized and adjusted to ensure the continuous and efficient operation of the system. Finally, generate an optimization and adjustment strategy according to the evaluation results and send it to the oil temperature control unit 2.

[0085] In the present invention, the monitoring unit 1 uses a variety of sensors to monitor the oil pressure, flow rate, and temperature. The oil temperature control unit 2 calculates the optimal heating rate and target temperature range according to the environment and the initial oil temperature through an adaptive algorithm. During heating, it compares the oil temperature and adjusts the power with pulse heating according to the state. It also judges the working load and adjusts the operating frequency of the vacuum pump according to the specific heat capacity of the oil product. The feedback adjustment unit 3 evaluates the system stability based on the vacuum pump frequency and heating power, generates an optimization strategy and sends it back to the oil temperature control unit 2, realizing precise control of the oil temperature, improving the equipment stability and energy utilization efficiency, and ensuring the efficient operation of the oil filter.

[0086] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of protection claimed by the present invention is defined by the appended claims and their equivalents.

Claims

1. A sensor-based digital intelligent control system for an oil filter, characterized in that, It includes a fusion monitoring unit (1), an oil temperature control unit (2), and a feedback adjustment unit (3); The fusion monitoring unit (1) is composed of the fusion of multiple sensors. Among them, the pressure sensor and the flow sensor are used to monitor the pressure and flow values of the oil in real time, and the temperature sensor is used to obtain the external environmental temperature value and the oil temperature; The oil temperature control unit (2) calculates the optimal heating rate and the target temperature range according to the environmental temperature and the initial oil temperature by using the adaptive oil temperature control algorithm. During the heating process, it continuously compares the actual oil temperature with the target temperature range to obtain the temperature state of the oil temperature, and according to the temperature state of the oil temperature, it automatically adjusts the heater power by using the pulse heating method. At the same time, it automatically judges the working load of the oil filter according to the specific heat capacity of different oil products, the equipment operation historical data, and the oil flow, and adjusts the operating frequency of the vacuum pump according to the load condition to obtain the target operating frequency of the vacuum pump; The oil temperature control unit (2) includes a load judgment module (24). The load judgment module (24) judges the working load of the oil filter according to the specific heat capacity of different oil products, the equipment operation historical data, and the oil flow, specifically as follows: Obtain the specific heat capacity value of the current oil product from the oil product database and measure the oil flow value through a flow sensor Then use the law of conservation of energy formula to calculate the heat absorption of the oil per unit time; Then, extract the standard heat absorption per unit time from the equipment operation historical data, compare the actual heat absorption with the standard heat absorption per unit time to obtain a difference, and finally, obtain the working load coefficient by using the weighted average algorithm based on the difference; The operating frequency adjustment algorithm of the vacuum pump is based on the working load coefficient, specifically as follows: Obtain the working load coefficient of the current vacuum pump , and set a high load threshold and a low load threshold ; When it is the case, the exponential growth algorithm is used to calculate the difference between the maximum tolerable vacuum degree and the current vacuum degree of the vacuum pump. The vacuum degree is the degree to which the gas pressure is lower than the atmospheric pressure. According to this difference, the exponential base is determined, and then the target operating frequency of the vacuum pump is calculated according to the exponential growth formula, and the operating frequency of the current vacuum pump is adjusted to the target operating frequency of the vacuum pump; When is true, the difference between the lowest stable operating vacuum degree of the vacuum pump and the current vacuum degree is calculated using a linear decreasing algorithm. The decreasing slope of the linear decreasing algorithm is determined based on this difference. Finally, the target operating frequency of the vacuum pump is calculated according to the linear decreasing formula, and the operating frequency of the current vacuum pump is adjusted to the target operating frequency of the vacuum pump; The feedback adjustment unit (3) constructs a comprehensive performance evaluation model based on the target operating frequency and the heating power of the vacuum pump, evaluates the stability of the current system according to this model, and finally generates an optimization adjustment strategy according to the evaluation result and sends it to the oil temperature control unit (2).

2. The sensor-based digital intelligent control system for oil filters according to claim 1, wherein: The oil temperature control unit (2) includes a data processing module (21). The data processing module (21) is used to calculate the difference between the environmental temperature and the initial oil temperature in the adaptive oil temperature control algorithm, specifically as follows: Use the temperature sensor to collect the environmental temperature and the initial oil temperature, and calculate their initial difference; Set the temperature offset characteristic curve, which is used to describe the performance change of the oil under different temperature conditions, and use the temperature offset characteristic curve to correct the environmental temperature and the initial oil temperature, and recalculate the difference between the corrected environmental temperature and the initial oil temperature.

3. The sensor-based digital intelligent control system for oil filters according to claim 2, characterized in that: The oil temperature control unit (2) includes an analysis and calculation module (22). The method for calculating the optimal heating rate by the analysis and calculation module (22) in the adaptive oil temperature control algorithm is specifically as follows: Determine the oil quality, heat transfer area, heat transfer distance, and initial oil specific heat capacity according to the equipment data, obtain the temperature difference from the data processing module (21), and finally set the heating-up time and the target temperature range, and calculate the difference between the target temperature and the initial temperature; Preset the initial heating rate and start the iterative loop. Calculate the temperature change amount according to the current heating rate and the heating-up time, then calculate the current specific heat capacity according to the current temperature and the formula for the change of the oil specific heat capacity with temperature, substitute it into the heat conduction formula to calculate the estimated heat transfer amount, and finally calculate the theoretical heat demand; Compare the estimated heat transfer amount with the theoretical heat demand, and obtain the optimal heating rate according to the comparison result.

4. The sensor-based digital intelligent control system for oil filters according to claim 3, wherein: The analysis and calculation module (22) calculates the target temperature range by using the adaptive oil temperature control algorithm, and the specific method is as follows: Extract the viscosity-temperature characteristic curve data of the current oil product and the current ambient temperature from the oil product database, and set the upper and lower limits of the oil temperature range corresponding to the optimal viscosity range of the equipment operation; On the viscosity-temperature characteristic curve, select three data points adjacent to the current ambient temperature value, use the piecewise linear interpolation algorithm for calculation, obtain the linear equation between points, and substitute the upper and lower limits of the oil temperature in the optimal viscosity range of the equipment into the above linear equation respectively, calculate the corresponding temperature values, and finally determine the target temperature range of this heating process accordingly.

5. The sensor-based digital intelligent control system for oil filters according to claim 4, characterized in that: The oil temperature control unit (2) includes a pulse heating module (23), and the pulse heating module (23) automatically adjusts the heater power in a pulse heating manner according to the temperature state of the oil temperature, and the specific method is as follows: Obtain the actual oil temperature value of the oil from the fusion monitoring unit (1), and set the sampling period. At each sampling moment, the temperature sensor collects the current actual oil temperature value; Then compare the collected actual oil temperature value with the upper and lower limits of the target temperature range, and divide it into a low-temperature state and a high-temperature state according to the comparison result; When in the low-temperature state, calculate the difference between the actual oil temperature and the lower limit of the target temperature range, adjust the proportional coefficient according to the size of the difference, and then calculate the power adjustment amount according to the proportional control algorithm formula; When in the high-temperature state, determine the degree to which the oil temperature exceeds the upper limit of the target temperature range by calculating the difference between the actual oil temperature and the upper limit of the target temperature range, determine the integral time according to the degree to which the oil temperature exceeds the upper limit, and then calculate the heating power adjustment amount by using the integral control algorithm formula.

6. The sensor-based digital intelligent control system for an oil filter, characterized in that: The feedback adjustment unit (3) constructs a comprehensive performance evaluation model based on the target operating frequency and heating power of the vacuum pump, and evaluates the stability of the current system according to this model, and the specific method is as follows: Obtain the target operating frequency of the vacuum pump and the current heating power from the oil temperature control unit (2), set the time interval at the same time, and construct a comprehensive performance evaluation model; Extract the specific heat capacity, mass flow rate of the current oil, and the change in oil temperature within a fixed time, calculate the system energy loss according to the law of conservation of energy, then calculate the stability coefficient according to the system energy loss, and finally compare it with the preset stability coefficient threshold to judge the current stability state of the system.

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