Data acquisition and cooperative control method and system of piston oscillation cooling test system

Abstract

The invention relates to the technical field of internal combustion engine testing and industrial automation control, in particular to a data acquisition and cooperative control method and system of a piston oscillation cooling test system. The system comprises a multi-source heterogeneous data acquisition and synchronization module, a cooperative control logic engine, an intelligent algorithm execution module, a safety interlocking management module and a man-machine interaction and data management module. According to the method, through a predictive pre-cooling strategy, the industrial problem of temperature overshoot of the inertial system is fundamentally solved, temperature control is achieved, and the control precision reaches + / -0.5 DEG C; besides, by constructing a parameter self-tuning PID cluster based on multi-source information, predictive suppression of coupling disturbance in the system is achieved, the hysteresis problem of head pain and foot pain of a multivariable control system is solved, interactive interference in the dynamic adjustment process is reduced by 60% or above, and the overall stability of the system is remarkably improved.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of internal combustion engine testing and industrial automation control, and particularly relates to a data acquisition and collaborative control method and system for a piston oscillation cooling test system. BACKGROUND

[0002] Piston oscillation cooling technology is one of the key technologies of modern high-performance engines, and its performance verification relies on high-precision simulation test benches. The existing piston cooling test bench control systems have the following defects: Control isolation: The oil temperature control, oil supply pressure control, and piston drive speed control subsystems usually operate independently, lacking a collaborative linkage mechanism. For example, when the oil temperature or pressure is not stable, the piston motor may have started in advance, causing the test conditions to be inaccurate, and even damaging the equipment.

[0003] Insufficient control precision and response speed: The traditional PID control parameters are fixed, and for nonlinear and time-varying hydraulic systems such as oil supply systems, the adjustment process is prone to overshoot or oscillation, and the pressure stabilization time is long, affecting test efficiency.

[0004] Low degree of automation and intelligence: The test process relies heavily on manual intervention by operators, such as data recording, stage switching, fault judgment, etc., with high risk of human error, and complex multi-condition trigger control logic cannot be implemented.

[0005] Dispersed data acquisition: Different types of sensors (analog, digital, and bus communication) are handled by different acquisition modules, lacking a unified time scale and centralized management platform, which is not conducive to subsequent synchronous analysis and deep mining of data.

[0006] In addition, existing technologies such as patent CN116358875A focus on the mechanical structure and integration method of the piston test device, focusing on solving the generality and authenticity of physical simulation, and do not use multiple data acquisition and control algorithms to ensure control accuracy.

[0007] Therefore, the present application proposes a data acquisition and collaborative control method and system for a piston oscillation cooling test system. SUMMARY

[0008] To make up for the deficiencies of the prior art and solve the technical problems in the background art, the present application proposes a data acquisition and collaborative control method and system for a piston oscillation cooling test system.

[0009] The present application is implemented through the following technical solutions: The data acquisition and collaborative control method for a piston oscillation cooling test system includes the following steps: A hardware platform is built with an industrial control computer as the core, a time sequence logic based on temperature-stable start pressure and pressure-stable start rotation is used to construct a multi-system condition-triggered collaborative control process, so that the oil constant temperature control system, the oil pressure control system, the piston rotation speed control system and the air heating control system are started in order and linked according to the preset conditions; Through the hardware platform, multi-thread architecture is adopted to collect temperature, pressure, flow, rotation speed and weight type multi-source heterogeneous sensor data in parallel, a unified time scale with a precision of not less than 1ms is added to the collected data, after real-time digital filtering and wild value elimination, data quality evaluation is completed by calculating the signal-to-noise ratio and the fluctuation coefficient, and qualified data is used for control decision and storage; In the oil constant temperature control process, a simplified thermodynamic model of the system including the oil heat capacity, the circulation flow, the heater power and the environmental heat dissipation coefficient is established, the rolling time domain method is used to predict the future thermal disturbance and overshoot risk of the system according to the current system state and the real-time load power of the motor, the air cooler is dynamically triggered according to the prediction result, and the heat dissipation power and the oil circulation flow are adjusted synchronously, a feedforward-feedback composite control is formed to suppress the temperature overshoot, and the temperature is stabilized within the range of ±0.5℃ of the set value. An online self-tuning PID cluster integrating the oil constant temperature PID controller, the oil supply pressure PID controller and the piston motor rotation speed PID controller is constructed, a cross-controller multi-source parameter collaboration mechanism is established, the PID parameters of any controller are dynamically adjusted according to the real-time running state information of at least one other controller, and a multi-modal adaptive PID control algorithm is configured for the PID controllers of the pressure and rotation speed control to realize high-precision control of the pressure and rotation speed.

[0010] Preferably, the cross-controller multi-source parameter collaboration mechanism includes at least one of a feedforward cross-loop tuning and a coupled parameter compensation. The feedforward cross-loop tuning is that the PID parameters of the oil supply pressure controller are dynamically pre-tuned according to the set value change trend or the real-time load state of the piston motor rotation speed controller. The coupled parameter compensation is that the PID parameters of the oil supply pressure controller or the piston motor rotation speed controller are compensated online based on the change of the physical properties of the oil according to the output of the oil constant temperature controller or the real-time value of the system oil temperature.

[0011] Preferably, the implementation process of the multi-modal adaptive PID control algorithm is as follows: The system control error is calculated in real time According to the numerical range of The control mode is divided according to the numerical range of When |>0.2MPa, enter the large error area, use high-gain proportional control and set the integral coefficient =0; When 0.05MPa < | When |≤0.2MPa, it enters the small error region and adopts standard PID control; When | When |≤0.05MPa, it enters the precise stability region, adopts anti-overshoot fine control parameters and introduces an anti-integral saturation compensation mechanism; During operation of each control mode, errors are monitored in real time using a dynamic performance sensor. Error change rate The absolute value of the integral of the error and the overshoot are output by the adaptive strategy optimizer based on the built-in expert knowledge base and optimization rule set to determine the PID controller parameters. , , The optimal correction value is then obtained, and the PID controller parameters are dynamically updated through an online parameter correction actuator.

[0012] Preferably, it also includes constructing a multi-level condition monitoring safety interlocking mechanism: The system collects temperature, pressure, rotation speed, and liquid level parameters in real time and compares them with preset safety thresholds; When parameter over-limits, communication interruption or equipment failure is detected, the system will automatically trigger audible and visual alarms, sequential shutdown or emergency power-off protection actions according to the fault level. In the event of an emergency power outage, maintain power supply to the cooling fans and industrial control computer to ensure safe cooling of the equipment and the integrity of the collected data.

[0013] Preferably, the multi-system condition-triggered collaborative control process is managed through a state machine architecture, which includes six states: initialization, preparation, debugging, experimentation, weighing, and termination. Initialization status: After system startup, sensor calibration and device self-test are performed automatically; Ready state: Receives and stores the test parameters input by the operator; Commissioning status: Supports independent operation, commissioning, and functional verification of actuators such as heaters, oil pumps, and piston motors; Experimental status: Executing a multi-system coordinated control process of temperature-stable start-up and pressure-stable start-up; Weighing status: After the test, the weight sensor will automatically start to measure the weight of the engine oil and record the data; End status: Completion of test data classification and storage, and equipment reset operation; Automatic transitions between states are achieved based on preset operating condition judgment logic and safety conditions.

[0014] Systems applicable to the data acquisition and coordinated control methods of the above-mentioned piston oscillation cooling test system include: Multi-source heterogeneous data acquisition and synchronization module: configure multi-thread acquisition unit, time scale generation unit and data preprocessing unit, the multi-thread acquisition unit is used for parallel acquisition of temperature, pressure, flow, speed, weight sensor data, the time scale generation unit is used for generating unified time scale with precision not less than 1ms and attaching to the acquisition data, the data preprocessing unit is used for real-time filtering, wild value elimination and quality evaluation of data; Cooperative control logic engine: built-in state machine management unit and condition trigger control unit, the state machine management unit is used for managing initialization, preparation, debugging, experiment, weighing, end state and state conversion logic, the condition trigger control unit is used for executing the multi-system cooperative control logic of temperature stabilization enabling pressure and pressure stabilization enabling rotation; Intelligent algorithm execution module: contains prediction precooling strategy unit and intelligent PID cluster control unit, the prediction precooling strategy unit is used for realizing feedforward-feedback compound control of engine oil constant temperature control, and the intelligent PID cluster control unit is used for integrating engine oil constant temperature, engine oil pressure and piston speed PID controllers, and realizing dynamic adjustment of PID parameters through cross-controller parameter cooperative mechanism; Safety interlock management module: configure parameter monitoring unit, fault level determination unit and protection action execution unit, the parameter monitoring unit is used for real-time monitoring of key parameters of the system, the fault level determination unit is used for determining the fault level according to the parameter overrun degree, and the protection action execution unit is used for triggering corresponding protection actions according to the fault level; Man-machine interaction and data management module: contains parameter setting interface, real-time data visualization interface, historical data storage unit and data export unit, which is used for test parameter setting, real-time data display, data storage and export.

[0015] Preferably, the intelligent PID cluster control unit is configured with a feedforward cross-loop tuning subunit and a coupled parameter compensation subunit: The feedforward cross-loop tuning subunit is used to obtain the set value change rate of the piston motor speed controller or the real-time load state, and generate the PID parameter adjustment instruction of the engine oil supply pressure controller; The coupled parameter compensation subunit is used to obtain the real-time value of engine oil temperature or the output of engine oil constant temperature controller, and generate the PID parameter compensation instruction of engine oil supply pressure controller or piston motor speed controller based on the engine oil temperature-viscosity correlation model.

[0016] Preferably, the multi-source heterogeneous data acquisition and synchronization module is further configured with a sensor interface adaptation unit, which supports analog interface, digital interface and RS485 communication interface, and is used for adapting different types of temperature, pressure, flow, speed and weight sensors.

[0017] The beneficial effects of the present application are: 1、The present application realizes intelligent full-automatic process through conditional trigger mechanism and state machine management, greatly reduces human intervention, guarantees accurate reproduction and consistency of test working condition, improves test efficiency by more than 50%, and realizes high coordination and full-process automation.

[0018] 2、The present application fundamentally solves the industry problem of temperature overshoot of inertial system through prediction precooling strategy, realizes temperature control, and the control precision reaches ±0.5℃; in addition, by constructing a parameter self-tuning PID cluster based on multi-source information, the predictability of the internal coupling disturbance of the system is realized, the hysteresis problem of the multivariable control system is solved, the interactive interference in the dynamic adjustment process is reduced by more than 60%, and the global stability of the system is significantly improved.

[0019] 3、The present application greatly improves the safety of equipment and personnel by constructing a multi-level and deep defense safety system from initialization self-checking, real-time overrun judgment to hardware emergency stop and linkage protection, realizes system-level safety and reliability; at the same time, based on a unified time scale and centralized management platform, the synchronous collection and centralized management of multi-source heterogeneous data are realized, the data format is standardized, and a complete and reliable data basis is provided for subsequent data analysis, model verification and report generation, realizing strong data integrity and traceability; in addition, the software-defined measurement and control architecture is solidified in software and algorithm logic rather than specific hardware, so that the system has good scalability, portability, and can adapt to the iteration of future hardware technology. BRIEF DESCRIPTION OF DRAWINGS

[0020] Figure 1 is the overall hardware architecture schematic diagram of the present application; Figure 2 is the overall flowchart of the present application; Figure 3 is the rolling prediction and decision flowchart of the present application; Figure 4 is the software PID cluster cooperative control architecture diagram of the present application; Figure 5 is the multi-modal adaptive PID control algorithm flowchart of the present application; Figure 6 is the main interface schematic diagram of the host computer software of the present application; Figure 7 is the debugging interface schematic diagram of the host computer software of the present application; Figure 8 is the oil supply pressure stability situation diagram of the present application. DETAILED DESCRIPTION

[0021] The application will be further described in connection with the following specific examples. It should be understood that these examples are intended to illustrate the application and are not intended to limit the scope of the application. The experimental methods in the following examples, unless otherwise specified, are generally performed according to conventional conditions or according to the conditions recommended by the manufacturer.

[0022] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art. The reagents or materials used in the present application can be purchased by conventional routes. Unless otherwise specified, the reagents or materials used in the present application are used according to the conventional methods in the art or according to the product instructions. In addition, any method and material similar or equivalent to those described can be used in the present application. The preferred methods and materials described in the present application are only used for demonstration, according to the drawings and specific embodiments of the present application.

[0023] The data acquisition and collaborative control method and system of the piston oscillation cooling test system are as shown in Figures 1-8 Example 1: System hardware integration Taking an industrial computer as an example, the hardware platform of the present system takes a high-performance industrial control computer (configuration: Intel Core i5, 16GB DDR5, 500GB SSD) as the core. On the PCIe expansion slot of the industrial computer, three kinds of professional data acquisition cards are installed: Multifunctional analog quantity acquisition card (such as PCIe-5654): used for connecting pressure transmitters (0-10V), turbine flow meters and other analog quantity sensors, and providing analog quantity output to control the frequency of the frequency converter.

[0024] Optoelectronic isolation digital quantity I / O card (such as PCIe-2313A): used for receiving digital signals such as emergency stop buttons and float switches, and controlling the start and stop of high-power execution elements such as gear pumps, air coolers and electromagnetic valves through intermediate relays to realize strong and weak electric isolation.

[0025] Multi-protocol serial communication card (such as PCI6200): exchanges data with intelligent instruments such as frequency converters, temperature control boxes, tachometer instruments and electronic scales through RS485 Modbus-RTU protocol.

[0026] Example 2: Core algorithm software implementation and PID cluster construction ​The core algorithm of the system takes the National Instruments LabVIEW platform as an example. This platform is particularly suitable for building high-reliability, complex parallel data acquisition and control systems due to its graphical programming and data flow execution characteristics. To ensure the real-time and stability of the system, the software uses modular design and producer / consumer design patterns. Under this architecture, data acquisition, key control algorithm execution, data recording, and user interface updates are distributed to multiple independent parallel loops for execution. The loops communicate and synchronize data through message queues, notifiers, or global function variables. This design effectively avoids the risk of the entire system stalling due to a blocked task, ensuring the accuracy of the control loop timing and laying the software foundation for high-precision control.

[0027] 1. Detailed implementation of predictive pre-cooling strategy The predictive pre-cooling strategy of the invention is essentially proactive intervention and prevention. Its core idea is to predict future states using system models and take action in advance. Its software implementation can be divided into three core steps: model construction, rolling prediction, and feedforward-feedback composite control.

[0028] a. Model construction To implement prediction, a mathematical model that can approximate the thermal dynamic behavior of the system is needed. Based on the law of conservation of energy, we established a centralized parameter model with first-order inertia and pure lag. This model is discretized in software to facilitate real-time calculations by digital controllers. Its core thermal balance equation is executed once every control period (e.g., Δt = 100 ms) and has the following form: T-predicted(k+1)= T-actual(k)+((P-heater(k)- K-cool* Cooler-Power(k)-K-loss * (T-actual(k)-T-ambient)) / (M-oil*C-oil))*Δt; Detailed explanation of variables and parameters T-predicted(k+1): Predicted temperature value at the next sampling time (k+1). This is the most direct basis for our intelligent decision-making.

[0029] T-actual(k): The actual measured engine oil temperature at the current k time, sourced from the Pt100 platinum resistance installed in the main oil tank, transmitted to the industrial computer via the temperature transmitter and analog acquisition card.

[0030] P-heater(k): The actual output power of the current heater. This value can be fed back by the temperature control box through communication or calculated based on the control instruction percentage sent by the industrial computer to the temperature control box.

[0031] Cooler-Power(k): The equivalent cooling power of the current air cooler, which is a key control variable, usually normalized in software to 0 (representing the air cooler off) to 1 (representing the air cooler running at maximum power).

[0032] K-cool: The equivalent cooling coefficient of the air cooler. This is a parameter that needs to be calibrated through experiments (such as step response tests), reflecting the cooling capacity of the air cooler per unit power.

[0033] K-loss: The natural heat dissipation coefficient of the system pipeline, oil tank wall, etc. to the external environment, also obtained by experimental calibration.

[0034] T-ambient: Ambient temperature, measured by an independent sensor, used to compensate for the impact of environmental changes on the system.

[0035] M-oil*C-oil: The total heat capacity of the oil circulating in the system (the total mass of the oil multiplied by its specific heat capacity), which is a system constant that can be pre-calculated or calibrated.

[0036] Δt: The sampling period of the control algorithm, which needs to balance between computational load and control accuracy, usually set to 100ms to 500ms.

[0037] a. Rolling prediction and decision (risk of overshoot judgment) A separate prediction thread runs independently of the main control loop in the software, which executes at a slightly slower period (such as 1 second). Its workflow is a simplified embodiment of model predictive control. This thread continuously calls the above model recursively within a limited time window (prediction horizon, e.g. 30 seconds in the future) based on current measurements and assuming constant future control, rolling out the temperature change trajectory in the future.

[0038] This prediction trajectory is compared with a set "safety corridor". The upper limit of the safety corridor is usually set slightly higher than the target temperature, for example T-set + 0.3℃. Once the prediction trajectory touches or exceeds the upper limit of this corridor at a certain point in the future, the algorithm determines that there is an overshoot risk and must intervene immediately.

[0039] a. Feedforward-feedback composite control When the prediction logic determines that there is an overshoot risk, the controller no longer passively waits for the error (T-set-T-actual) to appear, but immediately activates the feedforward control channel: Feedforward control calculation: According to the idea of inverse model, the algorithm dynamically calculates the additional power of the cooler Cooler_Power_ff needed to exactly offset the predicted overshoot. At the same time, in order to maximize the heat exchange efficiency, the software will increase the speed of the gear pump through the analog output card or communication to increase the oil circulation flow. The two actions are coordinated.

[0040] Superimposed with feedback loop: This calculated feedforward control Cooler_Power_ff will not be executed alone, but will be superimposed with the output of the traditional PID feedback controller (calculated according to the current temperature deviation) to serve as the final control instruction sent to the cooler (adjust power) and circulating pump (adjust speed).

[0041] 2. Implementation of parameter self-tuning PID cluster architecture based on multi-source information: The algorithm is implemented in software as a highly configurable and reusable PID function VI (Virtual Instrument), which is called by the pressure control and speed control loop at a high frequency (such as 1 kHz).

[0042] The core innovation of the invention - the software PID cluster of multi-source information parameter online self-tuning, is specifically implemented as a carefully designed cluster management object at the software level. This object uses object-oriented programming ideas to encapsulate the three PID controllers of oil constant temperature, oil supply pressure and piston motor speed, which are physically independent but tightly coupled at the system level, into a unified and intelligent control entity in software logic.

[0043] a. Implementation of cluster coordination logic The core of this cluster management object is to maintain a shared data area in memory. This data area serves as a global information hub that continuously receives and updates key state information from various control loops, including: set values, real-time process feedback values, control outputs, current working modes (such as running, pausing, fault), and key states obtained from other system modules, such as real-time oil temperature, real-time load power of the piston motor, etc.

[0044] The cooperative intelligence of the cluster is reflected in the execution logic of each controller. Taking the oil supply pressure control loop as an example, in its high-frequency (such as 1 kHz) control loop, in addition to reading the local pressure sensor signal and calculating the error, it also executes a complete set of cross-controller parameter coordination logic: Firstly, the pressure controller will actively obtain the latest state of the piston motor speed control loop from the shared data area of the cluster, including its set value and the trend of the set value. Through simple differential calculation, the pressure controller can know in real time whether the speed is in a transient process of rapid rise or fall.

[0045] Then, the pressure controller dynamically pre-adjusts its control parameters according to the preset feedforward cross-loop tuning rule. For example, when the algorithm judges that the speed set value is rapidly rising, it will predictively realize that the acceleration of the piston will soon lead to a sharp increase in the demand for oil flow, thereby forming a huge pull-down disturbance to the oil supply pressure. In order to actively respond to this predictable disturbance, the pressure controller will immediately start the feedforward tuning mechanism to dynamically and temporarily increase its proportional gain coefficient. This operation is equivalent to "injecting" the pressure controller with stronger control ability in advance before the disturbance actually arrives, so that it can more quickly and powerfully drive the oil supply pump to compensate, thereby suppressing the potential pressure drop in the embryonic state.

[0046] At the same time, the pressure controller also obtains real-time oil temperature information from the shared data area. Since oil viscosity is extremely sensitive to temperature, and viscosity directly affects the damping characteristics and flow resistance of the hydraulic system, the present application eliminates this influence through a coupling parameter compensation mechanism. A "temperature-viscosity-parameter compensation" mapping table based on experimental data calibration is preset in the software. The pressure controller queries this table according to the current oil temperature to obtain a compensation coefficient for its PID parameters (mainly proportional gain and integral time), and makes real-time corrections to the parameters. This ensures that the pressure control loop can maintain optimal dynamic performance and steady-state accuracy regardless of whether the oil is in a cold, hot, or any intermediate temperature state.

[0047] b. Multi-modal adaptive PID algorithm implementation at the bottom of the cluster Under the above cooperative architecture, each independent PID controller in the PID cluster is itself a highly intelligent algorithm unit that integrates a multi-modal adaptive control strategy based on dynamic error partitioning.

[0048] The control accuracy and response speed of the system benefit from its core - the multi-modal adaptive PID control algorithm based on state perception. The following takes the oil pressure control as an example to elaborate the implementation of the algorithm.

[0049] (1) Algorithm structure: Controller output Determined by the proportional, integral, and differential terms and the anti-saturation compensation term: ; Where, This represents the current pressure error. The sampling period is This is a compensation factor to combat integral saturation. The innovation of this algorithm lies in the proportionality coefficient. Integral coefficient and differential coefficients It is not a fixed value, but rather the real-time state of the system. (Mainly due to error) The function that determines.

[0050] (2) Multimodal switching logic: Mode 1 (Large Error Region) (>0.2MPa): Fast approach mode When the pressure error is large, in order to quickly approach the target value and completely prevent integral saturation, the algorithm sets the integral coefficient. =0, and use a larger one A value of 1 constitutes a high-gain proportional controller. At this point, the control output mainly depends on the proportional term of the error, driving the oil supply pump motor to run at high speed and rapidly reduce the pressure difference.

[0051] Mode 2 (small error region 0.05 MPa <| |≤0.2 MPa): Standard PID control mode When the pressure enters the region near the set value, the algorithm switches to a set of pre-optimized PID parameters. 2, 2, 2) Activate the complete PID control law to smoothly and quickly adjust the system to near the target pressure.

[0052] Mode 3 (Precise Stable Region) |≤0.05 MPa): High-precision anti-overshoot mode When the pressure is very close to the target value, the algorithm enters a high-precision steady state. At this point, to avoid overshoot due to system inertia, the algorithm fine-tunes the parameters, for example, by using a slightly smaller scaling factor. 3. Integral action is maintained to eliminate steady-state error. Furthermore, the error trend will be closely monitored during this stage, and appropriate measures will be introduced as needed. Anti-integral saturation compensation completely eliminates steady-state fluctuations.

[0053] (3) Intelligent anti-windup: When the calculated control quantity When the power exceeds the physical limit that the inverter can perform (such as 100% power), the algorithm no longer blindly accumulates integral terms, but instead calculates using a specific compensation algorithm. , for offsetting the excessive integral action, and once the controller exits the saturation region, it can immediately restore normal control, effectively avoiding large overshoot.

[0054] To further enhance the adaptability of the controller, the above multi-modal framework is also integrated with a dynamic perceiver and an adaptive strategy optimizer. With the control error and its rate of change as inputs, the standard PID parameters in the "small error region" are dynamically fine-tuned online through the built-in fuzzy rule base based on expert experience. This allows the controller not only to switch modes according to the "size" of the error, but also to intelligently optimize parameters according to the "trend" of the error, thereby adapting to more complex working condition changes.

[0055] Typical test procedure operation example Taking a complete piston oscillation cooling performance test as an example, the automatic operation process of the system is demonstrated: Test preparation: The operator completes the hardware connection and power-up, starts the host computer software, and the system self-check passes.

[0056] Parameter setting: Set T-set=90℃, P-set=0.7 MPa, N-set=1500 rpm in the software interface Test execution and monitoring: Click "Start Test", the system automatically executes according to the above S3 steps.

[0057] Observe the real-time curve in the interface, it can be seen that when the oil temperature rises smoothly to 85℃, the prediction and pre-cooling strategy is triggered, the air cooler is automatically started, and the temperature is stably controlled at 90±0.5℃ without overshoot.

[0058] After the temperature is stable for 60 seconds, the cooperative logic engine automatically starts the oil supply pump. The multi-modal PID pressure controller works, and the pressure is stably controlled at 0.701 MPa after about 25 seconds.

[0059] After the pressure fluctuates less than 0.005 MPa for 300 consecutive sampling points, the cooperative logic engine automatically starts the piston motor. The multi-modal adaptive PID controller for speed works, and quickly stabilizes the speed at 1498 rpm.

[0060] After all the systems are stable, the industrial computer starts synchronous sampling and storage of all sensor data with a unified time scale.

[0061] Test end and data analysis: After the test reaches the set time, the system automatically shuts down in reverse order: first stop the piston motor, then stop the oil supply pump, and finally stop heating. The gear pump and air cooler continue to run to cool the system until the oil temperature drops to a safe value and the system is completely turned off.

[0062] The operator can use the data playback function of the software to view the historical curves of all parameters, and can export data files in standard formats (such as.csv) for in-depth performance analysis and report generation.

[0063] V. Implementation Effects Through the system and method of the embodiment, a highly automated and intelligent piston oscillation cooling test platform is successfully constructed. Experimental data show that the system achieves: Machine oil temperature control accuracy: ± 0.5℃ Oil supply pressure control accuracy: ± 0.01MPa Piston speed control accuracy: ± 5rpm Especially, after introducing the PID cluster collaborative mechanism, the dynamic anti-interference ability of the system to the main disturbance of speed mutation is significantly improved, and the maximum dynamic deviation of pressure caused thereby is reduced by more than 60%.

[0064] Full-process automation linkage significantly reduces human intervention, improves test efficiency and reproducibility.

[0065] Multi-level safety interlocking ensures the safety of equipment and personnel. The invention not mentioned is applicable to the prior art.

Claims

1. A data acquisition and coordinated control method for a piston oscillation cooling test system, characterized in that, Includes the following steps: A hardware platform centered on an industrial control computer is built. Based on the timing logic of temperature-stable start-up and pressure-stable start-up, a multi-system condition-triggered collaborative control process is constructed to enable the oil constant temperature control system, oil pressure control system, piston speed control system and air heating control system to start and work together in an orderly manner according to preset conditions. The hardware platform employs a multi-threaded architecture to collect data from multiple heterogeneous sensors, including temperature, pressure, flow rate, rotation speed, and weight, in parallel. A unified timescale with an accuracy of no less than 1ms is added to the collected data. After real-time digital filtering and outlier removal, the data quality is assessed by calculating the signal-to-noise ratio and fluctuation coefficient. Qualified data is then selected for control decisions and storage. In the process of constant temperature control of engine oil, a simplified thermodynamic model of the system is established, which includes engine oil heat capacity, circulation flow rate, heater power and ambient heat dissipation coefficient. Combining the current system state and the real-time load power of the motor, the rolling time domain method is used to predict the future thermal disturbance and overshoot risk of the system. Based on the prediction results, the air cooler is dynamically triggered and its heat dissipation power and engine oil circulation flow rate are adjusted synchronously to form a feedforward-feedback composite control to suppress temperature overshoot and stabilize the temperature within the set value ±0.5℃ range. An online self-tuning PID cluster integrating an oil constant temperature PID controller, an oil supply pressure PID controller, and a piston motor speed PID controller is constructed. A cross-controller multi-source parameter collaboration mechanism is established, enabling the PID parameters of any controller to be dynamically adjusted based on the real-time operating status information of at least one other controller. At the same time, a multi-modal adaptive PID control algorithm is configured for the PID controllers of pressure and speed control to achieve high-precision control of pressure and speed.

2. The data acquisition and coordinated control method for the piston oscillation cooling test system according to claim 1, characterized in that, The cross-controller multi-source parameter coordination mechanism includes at least one of feedforward cross-loop tuning and coupled parameter compensation: The feedforward cross-loop tuning is as follows: the PID parameters of the oil supply pressure controller are dynamically pre-tuned based on the trend of the set value change of the piston motor speed controller or the real-time load status. The coupling parameter compensation is as follows: the PID parameters of the oil supply pressure controller or piston motor speed controller are compensated online based on the output of the oil constant temperature controller or the real-time value of the system oil temperature, according to the changes in the physical properties of the oil.

3. The data acquisition and coordinated control method for the piston oscillation cooling test system according to claim 1, characterized in that, The implementation process of the multimodal adaptive PID control algorithm is as follows: The system control error e(k) is calculated in real time, and the control modes are divided according to the numerical range of |e(k)|: When |e(k)|>0.2MPa, it enters the large error region, and high-gain proportional control is adopted with the integral coefficient Ki=0. When 0.05MPa < |e(k)| ≤ 0.2MPa, it enters the small error region and standard PID control is adopted; When |e(k)|≤0.05MPa, it enters the precise stability region, adopts anti-overshoot fine control parameters and introduces an anti-integral saturation compensation mechanism; During operation of each control mode, the error e(k), error rate of change ec(k), absolute value of error integral, and overshoot are monitored in real time by the dynamic performance sensor. The adaptive strategy optimizer outputs the optimal correction values ​​of PID controller parameters Kp, Ki, and Kd based on the built-in expert knowledge base and optimization rule set. The PID controller parameters are then dynamically updated by the online parameter correction actuator.

4. The data acquisition and coordinated control method for the piston oscillation cooling test system according to claim 1, characterized in that, This also includes building a multi-level condition monitoring and safety interlocking mechanism: The system collects temperature, pressure, rotation speed, and liquid level parameters in real time and compares them with preset safety thresholds; When parameter over-limits, communication interruption or equipment failure is detected, the system will automatically trigger audible and visual alarms, sequential shutdown or emergency power-off protection actions according to the fault level. In the event of an emergency power outage, maintain power supply to the cooling fans and industrial control computer to ensure safe cooling of the equipment and the integrity of the collected data.

5. The data acquisition and coordinated control method for the piston oscillation cooling test system according to claim 1, characterized in that, The multi-system condition-triggered collaborative control process is managed through a state machine architecture, which includes six states: initialization, preparation, debugging, experimentation, weighing, and termination. Initialization status: After system startup, sensor calibration and device self-test are performed automatically; Ready state: Receives and stores the test parameters input by the operator; Commissioning status: Supports independent operation, commissioning, and functional verification of actuators such as heaters, oil pumps, and piston motors; Experimental status: Executing a multi-system coordinated control process of temperature-stable start-up and pressure-stable start-up; Weighing status: After the test, the weight sensor will automatically start to measure the weight of the engine oil and record the data; End status: Completion of test data classification and storage, and equipment reset operation; Automatic transitions between states are achieved based on preset operating condition judgment logic and safety conditions.

6. A system applicable to the data acquisition and coordinated control method of the piston oscillation cooling test system according to any one of claims 1-5, characterized in that, include: Multi-source heterogeneous data acquisition and synchronization module: configured with a multi-threaded acquisition unit, a time stamp generation unit and a data preprocessing unit. The multi-threaded acquisition unit is used to acquire temperature, pressure, flow, speed and weight sensor data in parallel. The time stamp generation unit is used to generate a unified time stamp with an accuracy of not less than 1ms and attach it to the acquired data. The data preprocessing unit is used to perform real-time filtering, outlier removal and quality assessment on the data. Collaborative control logic engine: It has a built-in state machine management unit and a condition trigger control unit. The state machine management unit is used to manage the initialization, preparation, debugging, experimentation, weighing, end state and state transition logic. The condition trigger control unit is used to execute the multi-system collaborative control logic of temperature stability start-up and pressure stability start-up. The intelligent algorithm execution module includes a predictive pre-cooling strategy unit and an intelligent PID cluster control unit. The predictive pre-cooling strategy unit is used to realize feedforward-feedback composite control for constant oil temperature control. The intelligent PID cluster control unit is used to integrate PID controllers for constant oil temperature, oil pressure, and piston speed, and realizes dynamic adjustment of PID parameters through a cross-controller parameter coordination mechanism. Safety interlock management module: configured with parameter monitoring unit, fault level determination unit and protection action execution unit. The parameter monitoring unit is used to monitor key system parameters in real time. The fault level determination unit is used to determine the fault level according to the degree of parameter exceeding the limit. The protection action execution unit is used to trigger the corresponding protection action according to the fault level. Human-computer interaction and data management module: includes parameter setting interface, real-time data visualization interface, historical data storage unit and data export unit, used for setting test parameters, real-time data display, data storage and export.

7. The system of data acquisition and coordinated control method for piston oscillation cooling test system according to claim 6, characterized in that, The intelligent PID cluster control unit is configured with a feedforward cross-loop tuning subunit and a coupling parameter compensation subunit: The feedforward cross-loop tuning subunit is used to obtain the set value change rate of the piston motor speed controller or the real-time load status, and generate PID parameter adjustment instructions for the oil supply pressure controller. The coupling parameter compensation subunit is used to obtain the real-time value of the engine oil temperature or the output of the engine oil constant temperature controller, and to generate PID parameter compensation instructions for the engine oil supply pressure controller or piston motor speed controller based on the engine oil temperature-viscosity correlation model.

8. The system of data acquisition and coordinated control method for the piston oscillation cooling test system according to claim 6, characterized in that, The multi-source heterogeneous data acquisition and synchronization module is also equipped with a sensor interface adapter unit, which supports analog interfaces, digital interfaces and RS485 communication interfaces to adapt to different types of temperature, pressure, flow, speed and weight sensors.

Images