Rotor test bed lubricating oil temperature control and monitoring method and related system
By real-time monitoring and analysis of the temperature and pressure of the rotor test bench lubricant, combined with adaptive control algorithms and predictive control strategies, the problem that existing systems cannot detect abnormal temperature trends in advance is solved, and precise adjustment and failure of the lubricant temperature are achieved. Early warning improves the safety and reliability of the system.
Patent Information
- Application Number
- CN202411513387.0
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-10-28
- Publication Date
- 2025-02-07
AI Technical Summary
The existing rotor test bench lubricant temperature control system cannot detect abnormal temperature trends in advance, resulting in poor control effect. The monitoring system lacks effective prediction functions and cannot be promptly warned, which may cause damage to the test bench.
By obtaining the real-time temperature and pressure of the lubricant, converting it into a digital signal, and comparing it with the data in the preset fault library, if a fault is found, fault information is generated, self-tuning optimization control instructions are extracted, and adaptive control algorithms and predictions are used. The control policy is processed, real-time control instructions are generated, and the control policy is updated.
It realizes accurate adjustment of lubricant temperature, improves the safety and reliability of the system, reduces downtime caused by equipment failure, extends the service life of the equipment, and improves the accuracy of temperature monitoring and fault prevention capabilities.
Smart Images
Figure CN119396213A_ABST
Abstract
Description
Technical Field
[0001] The invention belongs to the field of rotor test bench monitoring, and in particular relates to a rotor test bench lubricating oil temperature control and monitoring method and a related system. Background Art
[0002] In the existing rotor test bench, the control and monitoring of lubricating oil temperature is crucial to ensure the normal operation of the test bench and extend the life of the equipment. The temperature of the lubricating oil needs to be kept within a certain range to prevent the lubrication performance from being reduced due to excessively high temperature or the lubrication effect from being affected due to excessively low temperature, thereby affecting the efficiency and safety of the rotor. Therefore, a rotor test bench lubricating oil temperature control and monitoring device is needed. However, traditional control systems often use fixed control parameters and cannot adapt to the various uncertainties that the rotor test bench may encounter during operation, resulting in poor control effects. At the same time, the existing monitoring system lacks effective prediction functions and cannot detect abnormal temperature trends in advance. It can only issue an alarm after the temperature has exceeded the safe range, which may cause damage to the test bench. Summary of the invention
[0003] The purpose of the present invention is to overcome the deficiency of the existing system that it is unable to detect abnormal temperature trends in advance, and to provide a rotor test bench lubricating oil temperature control and monitoring method and a related system.
[0004] In order to achieve the above object, the present invention adopts the following technical solution: In a first aspect, the present invention provides a method for controlling and monitoring the temperature of lubricating oil in a rotor test bench, comprising the following steps: Obtain the real-time temperature and real-time pressure of the lubricating oil, and convert the real-time temperature and real-time pressure into digital signals; Analyze the real-time temperature and real-time pressure, and compare them with the data in the preset fault library. If the comparison result is a fault, generate fault information; otherwise, continue to analyze; According to the fault information, the corresponding self-tuning optimization control instructions are extracted from the fault library; Adopting adaptive control algorithm to process self-tuning optimization control instruction to generate preliminary real-time control instruction, and adopting predictive control strategy to process preliminary real-time control instruction to obtain real-time control instruction; Execute real-time control instructions and update control strategies according to the content of real-time control instructions.
[0005] A further improvement of the present invention is that the specific method of obtaining the real-time temperature and real-time pressure of the lubricating oil and converting the real-time temperature and real-time pressure into digital signals is as follows: Filter and amplify real-time temperature and real-time pressure conversions; The filtered and amplified real-time temperature and real-time pressure are converted to obtain digital signals of the real-time temperature and real-time pressure.
[0006] A further improvement of the present invention is that the real-time temperature and real-time pressure are analyzed and compared with the data in the preset fault library. If the comparison result is a fault, the specific method of generating fault information is as follows: Perform component analysis and wavelet transform on the real-time temperature and real-time pressure to obtain the required feature vectors of the real-time temperature and real-time pressure; Compare the feature vector with the feature model in the preset fault library to obtain the difference index; The difference index is compared with a preset difference threshold range. If the difference index exceeds or falls below the preset difference threshold range, the specific location of the fault is determined by a fault isolation method. Generate fault information based on the specific location of the fault.
[0007] A further improvement of the present invention is that an adaptive control algorithm is used to process the self-tuning optimization control instruction, and the specific method of generating a preliminary real-time control instruction is as follows: Preset control parameters and reference models; Inputting the self-tuning optimization control command into the reference model to generate the expectation of the command; Compare the error between the expected instruction and the preset standard expectation; The self-tuning optimization control instruction is adjusted according to the error to obtain the adjusted self-tuning optimization control instruction; The self-tuning optimization control instructions are input into the control parameters to generate preliminary real-time control instructions.
[0008] A further improvement of the present invention is that the calculation method of the reference model is as follows:
[0009] in, For the expectation of the instruction, is the parameter vector of the reference model, is the adjustable parameter vector of the reference model.
[0010] A further improvement of the present invention is that a predictive control strategy is used to process the preliminary real-time control instruction, and the specific method of obtaining the real-time control instruction is as follows: Build predictive models; Processing the preliminary real-time control instructions to obtain the predicted control instructions within the required time; A multivariable optimization model is used to process the predictive control instructions within the required time to obtain the control quantity and output boundary of the predictive control instructions; The preliminary real-time control instruction is processed according to the control quantity and output boundary of the predicted control instruction to obtain the real-time control instruction.
[0011] A further improvement of the present invention is that a multivariable optimization model is used to process the predictive control instructions within the required time, and a method for obtaining the control amount and output boundary of the predictive control instructions is as follows:
[0012] in, is the control quantity of the predicted control instruction, Based on the current moment The predicted output boundaries, For the current moment The reference trajectory of is the weight coefficient, is the prediction range, For control range.
[0013] In a second aspect, the present invention provides a rotor test bench lubricating oil temperature control and monitoring system, comprising: A data processing module, used for obtaining the real-time temperature and real-time pressure of the lubricating oil, and converting the real-time temperature and real-time pressure into digital signals; The data analysis module is used to analyze the real-time temperature and real-time pressure and compare them with the data in the preset fault library. If the comparison result is a fault, the fault information is generated; otherwise, the analysis continues; A fault extraction module is used to extract the corresponding self-tuning optimization control instructions in the fault library according to the fault information; A real-time control instruction generation module is used to process the self-tuning optimization control instruction using an adaptive control algorithm to generate a preliminary real-time control instruction, and to process the preliminary real-time control instruction using a predictive control strategy to obtain a real-time control instruction; The control strategy update module is used to execute the real-time control instructions and update the control strategy according to the content of the real-time control instructions.
[0014] In a third aspect, the present invention provides an electronic device including a memory and a processor, wherein the memory stores a computer program, and the processor implements the steps of a rotor test bench lubricating oil temperature control and monitoring method when executing the computer program.
[0015] In a fourth aspect, the present invention provides a storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of a method for controlling and monitoring the temperature of lubricating oil on a rotor test bench.
[0016] Compared with the prior art, the present invention has the following beneficial effects: The present invention can instantly grasp the operating status of the system and discover potential anomalies in time by obtaining the real-time temperature and pressure of the lubricating oil. The present invention can compare the real-time data with the fault library, identify the risk of failure in advance, and thus improve the safety and reliability of the system. The present invention adopts an adaptive control algorithm, which can dynamically adjust the control strategy according to different fault types to achieve precise regulation of the lubricating oil temperature. The present invention can achieve more efficient temperature control and improve the overall operating efficiency of the system by extracting self-tuning optimization control instructions and combining predictive control strategies. The present invention can reduce the downtime caused by equipment failures and improve the availability of equipment through timely fault warning and real-time control. The present invention can effectively control the temperature of the lubricating oil, reduce the wear caused by abnormal temperature, and thus extend the service life of the equipment. The present invention can provide valuable basis for future optimization and improvement by continuously parsing and analyzing data, forming a closed-loop feedback mechanism. In summary, the present invention not only improves the accuracy of temperature monitoring, but also brings significant improvements in fault prevention, performance optimization, etc. BRIEF DESCRIPTION OF THE DRAWINGS
[0017] Figure 1 is a flow chart of the present invention; Figure 2 is a system diagram of the present invention; Figure 3 This is a system diagram of Example 5. DETAILED DESCRIPTION
[0018] In order to further understand the content of the present invention, the present invention is described in detail below in conjunction with the accompanying drawings and specific embodiments. It should be understood that the embodiments are only for explaining the present invention and are not intended to limit it.
[0019] Embodiment 1: See also Figure 1 , a rotor test bench lubricating oil temperature control and monitoring method, comprising the following steps: S1, obtains the real-time temperature and real-time pressure of the lubricating oil, and converts the real-time temperature and real-time pressure into digital signals.
[0020] S2, analyze the real-time temperature and real-time pressure, and compare them with the data in the preset fault library. If the comparison result is a fault, generate fault information; otherwise, continue to analyze.
[0021] S3, extracting the corresponding self-tuning optimization control instructions in the fault library according to the fault information.
[0022] S4, using an adaptive control algorithm to process the self-tuning optimization control instruction to generate a preliminary real-time control instruction, and using a predictive control strategy to process the preliminary real-time control instruction to obtain a real-time control instruction.
[0023] S5, execute the real-time control instruction, and update the control strategy according to the content of the real-time control instruction.
[0024] Embodiment 2: See also Figure 2 , rotor test bench lubricating oil temperature control and monitoring system, including: A data processing module, used for obtaining the real-time temperature and real-time pressure of the lubricating oil, and converting the real-time temperature and real-time pressure into digital signals; The data analysis module is used to analyze the real-time temperature and real-time pressure and compare them with the data in the preset fault library. If the comparison result is a fault, the fault information is generated; otherwise, the analysis continues; A fault extraction module is used to extract the corresponding self-tuning optimization control instructions in the fault library according to the fault information; A real-time control instruction generation module is used to process the self-tuning optimization control instruction using an adaptive control algorithm to generate a preliminary real-time control instruction, and to process the preliminary real-time control instruction using a predictive control strategy to obtain a real-time control instruction; The control strategy update module is used to execute the real-time control instructions and update the control strategy according to the content of the real-time control instructions.
[0025] Embodiment 3: This embodiment is a detailed embodiment of a rotor test bench lubricating oil temperature control and monitoring method, comprising the following steps: Step 1: Real-time data acquisition; Obtain the real-time temperature and real-time pressure of the lubricating oil, collect them using sensors, filter and amplify the real-time temperature and real-time pressure signals to ensure the accuracy and stability of the data, and convert the filtered and amplified signals into digital signals for subsequent processing.
[0026] Step 2: Fault detection; Perform component analysis and wavelet transform on the real-time temperature and real-time pressure to extract feature vectors, compare the extracted feature vectors with the feature models in the preset fault library, calculate the difference index, and compare the difference index with the preset difference threshold range: If it exceeds or falls below the threshold, the fault location is determined through fault isolation methods and fault information is generated. Otherwise, real-time data analysis is continued.
[0027] Step 3: Extraction of self-tuning optimization control instructions According to the generated fault information, the corresponding self-tuning optimization control instructions are extracted from the fault library.
[0028] Step 4: Generate preliminary real-time control instructions Preset control parameters and reference model, input self-tuning optimization control instructions into the reference model, and generate the expectation of instructions:
[0029] in, For the expectation of the instruction, is the parameter vector of the reference model, is the adjustable parameter vector of the reference model.
[0030] Calculate the error between the expectation and the preset standard expectation, adjust the self-tuning optimization control instruction according to the error, obtain the adjusted self-tuning optimization control instruction, input the adjusted self-tuning optimization control instruction into the control parameter, and generate a preliminary real-time control instruction.
[0031] Step 5: Real-time control command generation Establish a prediction model, process preliminary real-time control instructions, obtain the prediction control instructions within the required time, use a multivariable optimization model to process the prediction control instructions, and calculate the control amount and output boundary:
[0032] in, is the control quantity of the predicted control instruction, Based on the current moment The predicted output boundaries, For the current moment The reference trajectory of is the weight coefficient, is the prediction range, For control range.
[0033] The preliminary real-time control instruction is processed according to the control quantity and output boundary of the predictive control instruction to obtain the final real-time control instruction.
[0034] Step 6: Execute real-time control instructions and update the control strategy according to the instruction content to adapt to the dynamic changes of the system.
[0035] This embodiment can effectively realize real-time monitoring, fault warning and adaptive control, thereby improving the stability and reliability of the system, and can respond to temperature anomalies in a timely manner to ensure the safe operation of the equipment.
[0036] Embodiment 4: The practical application of the present invention includes the following steps: Step 1: Get the real-time temperature and real-time pressure of the lubricating oil: The temperature sensor measured the real-time temperature of the lubricating oil to be 90°C.
[0037] The pressure sensor measured the real-time lubricating oil pressure to be 1.2 MPa.
[0038] After filtering and amplification, the temperature signal is 90.5°C and the pressure signal is 1.25 MPa. The temperature signal is converted into a digital signal T_d = 90.5, and the pressure signal is converted into a digital signal P_d = 1.25.
[0039] Step 2: Perform wavelet transform on the real-time temperature and pressure data to obtain the feature vector V = [0.8, 1.0, 0.95]. Compare it with the corresponding feature model in the fault library (M = [0.7, 1.1, 0.9]) and calculate the difference index D = 0.3.
[0040] The difference threshold range is [0.1, 0.25]. Since D = 0.3 exceeds the threshold, fault isolation confirms that the fault location is the pump component and generates fault information.
[0041] Step 3: Extract the corresponding self-tuning optimization control instruction from the fault library and obtain the instruction as C opt = [-5, +3], which means lowering the oil temperature by 5°C and increasing the pressure by 3%.
[0042] Step 4: Set the reference model parameters to = [0.2, 0.5], the adjustable parameter vector is = [T_d,P_d] = [90.5, 1.25], the instruction expects to calculate:
[0043] Set the standard expectation to E = -15 and the error to E {error} = -18.9 - (-15) = -3.9. Adjust the self-tuning optimization control command to get C adjusted = [-5 + 0.1 \times (-3.9), +3 + 0.1 \times (-3.9)] = [-5.39, +2.61].
[0044] After inputting the control parameters, the initial real-time control instructions are generated as C initial = [-5.39, +2.61].
[0045] Step 5: Establish a prediction model. The prediction time is 5 seconds. By processing the preliminary control instructions, the predicted control instructions within the required time are obtained. The multivariable optimization model is processed:
[0046] The reference trajectory is = [80, 80, 80, 80, 80], the predicted output is = [82, 81, 81, 80, 79], set the weight coefficient = [1, 1]. The control quantity is calculated to obtain J(k) = 0.5 + 0.1 = 0.6, and the control quantity output boundary is [78, 82]. The preliminary real-time control instruction is processed according to the control quantity and output boundary, and the final real-time control instruction is C real_time = [-5.2, +2.5].
[0047] Step 6: Execute real-time control instructions to reduce the lubricating oil temperature to 85°C and increase the pressure to 1.23 MPa, and update the control strategy according to the instruction content to ensure that the system continues to monitor in the new state.
[0048] This embodiment demonstrates the effectiveness and practicality of the rotor test bench lubricating oil temperature control and monitoring method, which can realize real-time monitoring, fault warning and adaptive control, and improve the stability and reliability of the system.
[0049] Embodiment 5: See also Figure 3 As shown, the present invention also provides an electronic device 100 for a rotor test bench lubricating oil temperature control and monitoring method; the electronic device 100 includes a memory 101, at least one processor 102, a computer program 103 stored in the memory 101 and executable on the at least one processor 102, and at least one communication bus 104.
[0050] The memory 101 can be used to store the computer program 103. The processor 102 implements the steps of the rotor test bench lubricating oil temperature control and monitoring method described in Example 1 by running or executing the computer program stored in the memory 101 and calling the data stored in the memory 101. The memory 101 can mainly include a program storage area and a data storage area, wherein the program storage area can store an operating system, an application required for at least one function (such as a sound playback function, an image playback function, etc.), etc.; the data storage area can store data (such as audio data) created according to the use of the electronic device 100, etc. In addition, the memory 101 can include a non-volatile memory, such as a hard disk, a memory, a plug-in hard disk, a smart memory card (Smart Media Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (Flash Card), at least one disk storage device, a flash memory device, or other non-volatile solid-state storage devices.
[0051] The at least one processor 102 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA) or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The processor 102 may be a microprocessor or any conventional processor, etc. The processor 102 is the control center of the electronic device 100, and uses various interfaces and lines to connect various parts of the entire electronic device 100.
[0052] The memory 101 in the electronic device 100 stores a plurality of instructions to implement a rotor test bench lubricating oil temperature control and monitoring method, and the processor 102 can execute the plurality of instructions to implement: Obtain the real-time temperature and real-time pressure of the lubricating oil, and convert the real-time temperature and real-time pressure into digital signals; Analyze the real-time temperature and real-time pressure, and compare them with the data in the preset fault library. If the comparison result is a fault, generate fault information; otherwise, continue to analyze; According to the fault information, the corresponding self-tuning optimization control instructions are extracted from the fault library; Adopting adaptive control algorithm to process self-tuning optimization control instruction to generate preliminary real-time control instruction, and adopting predictive control strategy to process preliminary real-time control instruction to obtain real-time control instruction; Execute real-time control instructions and update control strategies according to the content of real-time control instructions.
[0053] Embodiment 6: If the module / unit integrated in the electronic device 100 is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the process in the above-mentioned embodiment method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium, and the computer program can implement the steps of the above-mentioned method embodiments when executed by the processor. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory and read-only memory (ROM, Read-Only Memory).
[0054] It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as methods, systems, or computer program products. Therefore, the present invention may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Furthermore, the present invention may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.
[0055] The present invention is described with reference to flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to embodiments of the present invention. It should be understood that each process and / or block in the flowchart and / or block diagram, as well as the combination of processes and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.
[0056] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.
[0057] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.
[0058] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit it. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.
Claims
1. A method for controlling and monitoring the temperature of lubricating oil in a rotor test bench, characterized in that: The following steps are involved: Obtain the real-time temperature and real-time pressure of the lubricating oil, and convert the real-time temperature and real-time pressure into digital signals; Analyze the real-time temperature and real-time pressure, and compare them with the data in the preset fault library. If the comparison result is a fault, generate fault information; Otherwise continue parsing; According to the fault information, the corresponding self-tuning optimization control instructions in the fault library are extracted; Adopting adaptive control algorithm to process self-tuning optimization control instruction to generate preliminary real-time control instruction, and adopting predictive control strategy to process preliminary real-time control instruction to obtain real-time control instruction; Execute real-time control instructions and update control strategies according to the contents of real-time control instructions.
2. The rotor test bench lubricating oil temperature control and monitoring method according to claim 1 is characterized in that: The specific method of obtaining the real-time temperature and real-time pressure of the lubricating oil and converting the real-time temperature and real-time pressure into digital signals is as follows: Filter and amplify real-time temperature and real-time pressure conversions; The filtered and amplified real-time temperature and real-time pressure are converted to obtain digital signals of the real-time temperature and real-time pressure.
3. The rotor test bench lubricating oil temperature control and monitoring method according to claim 1 is characterized in that: Analyze the real-time temperature and real-time pressure, and compare them with the data in the preset fault library. If the comparison result is a fault, the specific method of generating fault information is as follows: Perform component analysis and wavelet transform on the real-time temperature and real-time pressure to obtain the required feature vectors of the real-time temperature and real-time pressure; Compare the feature vector with the feature model in the preset fault library to obtain the difference index; The difference index is compared with a preset difference threshold range. If the difference index exceeds or falls below the preset difference threshold range, the specific location of the fault is determined by a fault isolation method. Generate fault information based on the specific location of the fault.
4. The rotor test bench lubricating oil temperature control and monitoring method according to claim 1 is characterized in that: The specific method of using the adaptive control algorithm to process the self-tuning optimization control instructions and generate preliminary real-time control instructions is as follows: Preset control parameters and reference models; Inputting the self-tuning optimization control command into the reference model to generate the expectation of the command; Compare the error between the expected instruction and the preset standard expectation; The self-tuning optimization control instruction is adjusted according to the error to obtain the adjusted self-tuning optimization control instruction; The self-tuning optimization control instructions are input into the control parameters to generate preliminary real-time control instructions.
5. The rotor test bench lubricating oil temperature control and monitoring method according to claim 4 is characterized in that: The reference model is calculated as follows: in, For the expectation of the instruction, is the parameter vector of the reference model, is the adjustable parameter vector of the reference model.
6. The rotor test bench lubricating oil temperature control and monitoring method according to claim 1 is characterized in that: The specific method of using the predictive control strategy to process the preliminary real-time control instructions and obtain the real-time control instructions is as follows: Build predictive models; Processing the preliminary real-time control instructions to obtain the predicted control instructions within the required time; A multivariable optimization model is used to process the predictive control instructions within the required time to obtain the control quantity and output boundary of the predictive control instructions; The preliminary real-time control instruction is processed according to the control quantity and output boundary of the predicted control instruction to obtain the real-time control instruction.
7. The rotor test bench lubricating oil temperature control and monitoring method according to claim 6 is characterized in that: The method of using a multivariable optimization model to process the predictive control instructions within the required time to obtain the control amount and output boundary of the predictive control instructions is as follows: in, is the control quantity of the predicted control instruction, Based on the current moment The predicted output boundaries, For the current moment The reference trajectory of is the weight coefficient, is the prediction range, For control range.
8. Rotor test bench lubricating oil temperature control and monitoring system, characterized in that: include: A data processing module, used for obtaining the real-time temperature and real-time pressure of the lubricating oil, and converting the real-time temperature and real-time pressure into digital signals; The data analysis module is used to analyze the real-time temperature and real-time pressure and compare them with the data in the preset fault library. If the comparison result is a fault, the fault information is generated; Otherwise continue parsing; A fault extraction module is used to extract the corresponding self-tuning optimization control instructions in the fault library according to the fault information; A real-time control instruction generation module is used to process the self-tuning optimization control instruction using an adaptive control algorithm to generate a preliminary real-time control instruction, and to process the preliminary real-time control instruction using a predictive control strategy to obtain a real-time control instruction; The control strategy update module is used to execute the real-time control instructions and update the control strategy according to the content of the real-time control instructions.
9. An electronic device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the rotor test bench lubricating oil temperature control and monitoring method according to any one of claims 1 to 7 are implemented.
10. A storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the rotor test bench lubricating oil temperature control and monitoring method according to any one of claims 1 to 7 are implemented.