Intelligent pre-control system and method for engine cold-heat shock test

Abstract

The application provides an intelligent pre-control system and method for engine cold-heat shock test, wherein the method comprises the following steps: receiving real-time state data through a prediction module, combining a working condition instruction generated by a main working condition control module, and online simulating and predicting a future thermal state of a measured engine under a current to-be-executed working condition instruction; an intelligent scheduling unit dynamically decides whether to insert a pre-adjustment step before execution of the current to-be-executed working condition instruction based on the future thermal state, and determines a starting time and a duration of pre-adjustment; if it is decided to insert, an adaptive control instruction is generated, otherwise the current to-be-executed working condition instruction is taken as a to-be-executed instruction; and an execution control module drives an execution mechanism group to operate a temperature control system according to the adaptive control instruction or the to-be-executed instruction. The application is suitable for a working condition switching process of engine cold-heat shock test, can inhibit temperature overshoot caused by system thermal inertia through active prediction and advanced intervention, and guarantees test data accuracy and improves test efficiency.

Description

Technical Field

[0001] This invention relates to the field of engine testing and automatic control technology, specifically to an intelligent pre-control system and method for engine thermal shock testing. Background Technology

[0002] In the field of engine research and development and reliability verification, thermal shock durability testing is a core test item for evaluating the thermomechanical reliability of engines under extreme temperature alternation environments. The accuracy of its test data directly affects the effectiveness of engine thermal system design optimization and life assessment. Currently, the industry generally adopts a control mode with a preset fixed test procedure for engine thermal shock testing. That is, the test process is executed in a fixed rhythm sequence such as "load increase stage - high load heat preservation stage - load decrease stage - low load heat preservation stage". The corresponding temperature control actions such as opening and closing of cooling water valves are only triggered after the test enters a specific stage. In essence, it is a passive, event-triggered control scheme.

[0003] However, the aforementioned existing technical solutions have several inherent defects that are incompatible with experimental requirements. First, the engine body and cooling system have significant thermal inertia. When the cooling action is initiated only after the test has entered the cooling phase, a large amount of heat has already accumulated in the system, and the cooling system's response has a time delay, resulting in significant overshoot of the temperature in key engine components. It takes a long time for the temperature to drop back to the target range, which not only prolongs the test stabilization time and reduces test efficiency but may also affect the accuracy of test data due to overheating. Second, the core control parameters, such as the opening degree of the temperature control actuator and the parameters of the proportional-integral-derivative controller, largely depend on engineers' decisions. Offline calibration is used in testing, but these fixed parameters cannot adapt to individual differences between different engines, performance degradation due to component aging, and disturbances such as ambient temperature fluctuations. This results in poor control stability and makes it difficult to achieve optimal test control accuracy. Thirdly, the engine temperature control system encompasses multiple subsystems, including the coolant system, intercooler system, and oil cooling system. The dynamic response characteristics of each subsystem differ, but traditional control methods use a unified timetable to drive the start and stop of each subsystem. This lacks a global, intelligent, and coordinated scheduling mechanism, making it impossible to achieve globally optimal control at the system level. In fact, the actions of different subsystems may even interfere with each other. These problems directly affect the accuracy and efficiency of engine thermal shock testing.

[0004] A patent search revealed invention patent CN114278423A, which discloses a coolant temperature prediction control algorithm based on a predictive extended state observer. This algorithm establishes a predictive model of engine heat dissipation to anticipate the impact of varying engine operating conditions on coolant temperature, thereby adjusting the water pump and fan speeds in advance. Addressing the need for coolant temperature tracking control, and considering unmeasurable disturbances in the cooling system such as coolant transmission delay and model errors, it treats the deviation of the prediction model as an equivalent total disturbance, employs a predictive extended state observer for active observation, and compensates for the effects of large time delays, thus improving control performance and ultimately achieving high-precision control of coolant temperature. However, this patent is limited to the coolant system, lacks an online learning mechanism, and cannot adapt to individual engine differences and performance degradation; it also lacks dynamic pre-adjustment decision-making, resulting in insufficient adaptability to all operating conditions under thermal shock.

[0005] In summary, given the problems of the existing technologies, researching an intelligent pre-control system and method for engine thermal shock testing has become a critical task that urgently needs to be addressed. Summary of the Invention

[0006] In view of the deficiencies in the prior art, the purpose of this invention is to provide an intelligent pre-control system and method for engine thermal shock testing.

[0007] An intelligent pre-control system for engine thermal shock testing, provided by the present invention, includes: The temperature control system is used to provide a temperature environment for the engine under test; The sensor array is used to collect real-time status data of the engine and temperature control system under test. The actuator assembly is used to regulate the temperature control system; The main operating condition control module is used to generate a sequence of operating condition commands arranged in chronological order according to a preset test cycle program; The prediction module, connected to the sensor group, is used to perform online simulation based on real-time status data, predict the future thermal state of the engine under test under the current operating condition command requirements, and obtain thermal state prediction data. The intelligent scheduling unit connects the main operating condition control module and the prediction module. It is used to decide whether to insert a pre-adjustment step before executing the current operating condition command based on the current operating condition command to be executed and the thermal state prediction data. If the decision is to insert, an adaptive control command corresponding to the current operating condition command to be executed is generated based on the thermal state prediction data. If the decision is not to insert, the current operating condition command to be executed is used as the command to be executed. The execution control module, connected to the intelligent scheduling unit and the actuator group, is used to drive the actuator group to move according to adaptive control instructions or instructions to be executed.

[0008] Preferably, the prediction module is a digital twin model that operates synchronously with the engine under test.

[0009] Preferably, the intelligent pre-control system further includes an online learning module, which compares the real-time state data collected by the sensor group with the corresponding thermal state prediction data generated by the prediction module, and uses an optimization algorithm to correct the model parameters of the prediction module online based on the comparison results.

[0010] Preferably, when the decision is inserted into the pre-adjustment step, the start time and duration of the pre-adjustment step are dynamically determined based on the engine thermal inertia time constant and the response speed of the temperature control system calculated in real time by the prediction module.

[0011] Preferably, when the decision is inserted into the pre-adjustment step, the control objective of the pre-adjustment step is to make the thermal state of the engine under test fall into the preset optimal initial state range at the end of the pre-adjustment. The optimal initial state range is defined at least according to the dynamic characteristics of the controller to be used after the current operating condition command to be executed, and includes a combination of temperature value range and temperature change rate range.

[0012] Preferably, the adaptive control command is a sequence of commands generated based on thermal state prediction data and controlling the actuator group to act according to a preset time function. The command sequence is used to adjust the temperature control system from the current state to the initial state required to execute the current operating condition command.

[0013] Preferably, the intelligent pre-control system further includes a safety monitoring and intervention module, which is used to determine the operating status parameters of the tested engine based on the real-time status data of the sensor group, and monitor the operating status parameters. When any operating status parameter reaches or exceeds the corresponding preset safety boundary, the safety monitoring and intervention module is configured to override the instructions issued by the intelligent scheduling unit with the highest priority, and send preset safety control instructions to the execution control module.

[0014] Preferably, the intelligent scheduling unit generates adaptive control instructions through a hybrid decision-making strategy that combines rule-based and model predictive control. The model predictive control process includes: using the prediction module as the prediction engine, in each control cycle, under the condition of satisfying multiple safety constraints of the tested engine and the physical limitations of the actuator group, continuously solving for the optimal control sequence in the future finite time domain; the intelligent scheduling unit integrates the solved control actions of the current control cycle into the adaptive control instructions and outputs them.

[0015] This invention also provides an intelligent pre-control method for engine thermal shock testing, based on the aforementioned intelligent pre-control system for engine thermal shock testing, comprising the following steps: Step S1: Construct a prediction module, enabling the prediction module to operate synchronously with the engine under test, receive real-time status data from the sensor group, and generate a sequence of operating condition commands arranged in chronological order according to the preset test cycle program. Step S2: Based on real-time status data, the prediction module simulates and predicts the future thermal state of the engine under test under the current operating condition command requirements, and obtains thermal state prediction data. Step S3: Based on the current operating condition command to be executed and the thermal state prediction data, the intelligent scheduling unit decides whether to insert a pre-adjustment step before starting the current operating condition command to be executed. If the decision is to insert a pre-adjustment step, the intelligent scheduling unit generates an adaptive control command based on the thermal state prediction data to execute the current operating condition command to be executed. If the decision is not to insert a pre-adjustment step, the intelligent scheduling unit uses the current operating condition command to be executed as the command to be executed. In step S4, the intelligent scheduling unit sends adaptive control instructions or instructions to be executed to the execution control module. The execution control module drives the actuator group to operate the temperature control system according to the received instructions.

[0016] Preferably, the intelligent pre-control method further includes step S5, comparing the real-time state data collected by the sensor group with the thermal state prediction data predicted by the prediction module at the same time, and based on the comparison results, using an optimization algorithm to correct the model parameters of the prediction module online.

[0017] Compared with the prior art, the present invention has the following beneficial effects: 1. This application achieves online simulation prediction of thermal state through a prediction module, and combines this with the intelligent scheduling unit's decision-making and pre-adjustment steps, transforming traditional passive response control into an active prediction and proactive intervention control mode, fundamentally solving the control delay problem of thermal inertial systems. This invention, through the matching design of the prediction module, intelligent scheduling unit, and pre-adjustment steps, can significantly reduce temperature overshoot during operating condition switching, shorten temperature stabilization time, thereby improving the accuracy of experimental data and shortening the overall experimental cycle.

[0018] 2. This application utilizes an online learning module to continuously compare the real-time state data collected by the sensor array with the thermal state prediction data output by the prediction module. An optimization algorithm is then used to correct the model parameters of the prediction module online, ensuring the long-term high fidelity of the prediction model for the tested engine. The intelligent scheduling unit dynamically generates control strategies based on accurate thermal state prediction data, without relying on offline calibrated fixed empirical parameters. This allows it to adapt to individual differences in different engines, performance degradation due to component aging, and disturbances such as ambient temperature fluctuations, improving the system's control stability and robustness.

[0019] 3. This invention uses an intelligent scheduling unit as a unified decision-making center, which can coordinate the actions of multiple temperature control subsystems such as the coolant system, intercooler system, and oil cooling system. This design fully considers the different dynamic response characteristics of each subsystem, and achieves coordinated action of each subsystem by generating adaptive control commands. This avoids the action conflicts or low control efficiency problems that may be caused by the independent start-stop of each subsystem in traditional control, and achieves globally optimal control at the temperature control system level. Attached Figure Description

[0020] Other features, objects, and advantages of the present invention will become more apparent from the following detailed description of non-limiting embodiments with reference to the accompanying drawings: Figure 1 This is a diagram of an intelligent pre-control system architecture for an engine thermal shock test according to an embodiment of the present invention. Detailed Implementation

[0021] The present invention will now be described in detail with reference to specific embodiments. These embodiments will help those skilled in the art to further understand the present invention, but do not limit the invention in any way. It should be noted that those skilled in the art can make several changes and improvements without departing from the concept of the present invention. These all fall within the protection scope of the present invention.

[0022] This application provides an intelligent pre-control system and method for engine thermal shock testing, aiming to solve the technical problems in existing engine thermal shock tests, such as control response lag and temperature overshoot caused by system thermal inertia, and the inability of fixed control strategies to adapt to individual engine differences, performance degradation, and environmental disturbances. The method includes: receiving real-time status data collected by a sensor group through a prediction module, and combining it with the operating condition command generated by the main operating condition control module to online simulate and predict the future thermal state of the engine under test under the current operating condition command to be executed; an intelligent scheduling unit dynamically decides whether to insert a pre-adjustment step before the execution of the current operating condition command based on the future thermal state, and determines the start time and duration of the pre-adjustment; if the decision is made to insert, an adaptive control command is generated; otherwise, the current operating condition command to be executed is used as the command to be executed; the execution control module drives the actuator group to operate the temperature control system according to the adaptive control command or the command to be executed. This application, through a control mode of active prediction and proactive intervention, significantly reduces the temperature overshoot during operating condition switching, shortens the temperature stabilization time, and can adapt to individual engine differences, performance degradation, and environmental changes, achieving global collaborative optimization of multiple temperature control subsystems, effectively improving test accuracy and efficiency. This application is applicable to the operating condition switching process of engine thermal shock testing. It can suppress temperature overshoot caused by system thermal inertia through proactive prediction and advance intervention, thereby ensuring the accuracy of test data and improving test efficiency.

[0023] Example 1: Figure 1 This is a diagram of an intelligent pre-control system architecture for an engine thermal shock test according to an embodiment of the present invention.

[0024] like Figure 1 As shown, this embodiment provides an intelligent pre-control system for engine thermal shock testing, including: The temperature control system is used to provide a temperature environment for the engine under test; The sensor array is used to collect real-time status data of the engine and temperature control system under test. The actuator assembly is used to regulate the temperature control system; The main operating condition control module is used to generate a sequence of operating condition commands arranged in chronological order according to a preset test cycle program; The prediction module, connected to the sensor group, is used to perform online simulation based on real-time status data, predict the future thermal state of the engine under test under the current operating condition command requirements, and obtain thermal state prediction data.

[0025] In this embodiment, the prediction module is a digital twin model that runs synchronously with the engine under test.

[0026] The intelligent scheduling unit connects the main operating condition control module and the prediction module. It is used to decide whether to insert a pre-adjustment step before executing the current operating condition command based on the current operating condition command to be executed and the thermal state prediction data. If the decision is to insert, an adaptive control command corresponding to the current operating condition command to be executed is generated based on the thermal state prediction data. If the decision is not to insert, the current operating condition command to be executed is used as the command to be executed.

[0027] Specifically, when a decision is made to insert a pre-adjustment step, the start time and duration of the pre-adjustment step are dynamically determined based on the engine thermal inertia time constant calculated in real time by the prediction module and the response speed of the temperature control system.

[0028] Furthermore, when the decision is inserted into the pre-adjustment step, the control objective of the pre-adjustment step is to make the thermal state of the engine under test fall into the preset optimal initial state range at the end of the pre-adjustment. The optimal initial state range is defined at least according to the dynamic characteristics of the controller to be used after the current operating condition command to be executed, and includes a combination of temperature value range and temperature change rate range.

[0029] Specifically, the goal of the pre-conditioning step is to guide the tested engine into its optimal initial state range, rather than reaching a fixed temperature value, so that the closed-loop controller of the execution control module can achieve a smooth, overshoot-free, seamless takeover. For example, when the subsequent operating condition is high-precision constant temperature control using a PID controller, the intelligent scheduling unit determines the required optimal initial state range by analyzing the dynamic characteristics of the PID controller. This optimal initial state range is defined by the temperature value and its rate of change (first derivative). At the moment of operating condition switching, if the system state is a temperature slightly lower than the target value with a small positive rate of change, the PID controller can achieve the fastest and smoothest zero-overshoot takeover. Therefore, the control of the pre-conditioning step is to ensure that the system state falls precisely into this optimal initial state range at the switching point. To achieve this goal, both the temperature and its rate of change need to be controlled simultaneously. Therefore, the intelligent scheduling unit generates fine, non-linear control commands to actively shape the temperature curve and its slope that approximate the switching point. This scheme reduces the adjustment burden on the execution control module, achieves a smoother, faster, and overshoot-free operating condition switch, and reflects a global optimization of the control process.

[0030] Specifically, the adaptive control command is a sequence of commands generated based on thermal state prediction data and controlled by the actuator group according to a preset time function. The command sequence is used to adjust the temperature control system from the current state to the initial state required to execute the current operating condition command.

[0031] In this embodiment, the intelligent scheduling unit generates adaptive control instructions through a hybrid decision-making strategy that combines rules and model predictive control (MPC). The model predictive control (MPC) process includes: using the prediction module as the prediction engine, in each control cycle, under the condition of satisfying the multiple safety constraints of the tested engine and the physical limitations of the actuator group, rollingly solving for the optimal control sequence in the future finite time domain; the intelligent scheduling unit integrates the solved control actions of the current control cycle into the adaptive control instructions and outputs them.

[0032] Specifically, hybrid decision-making strategies are used to handle complex operating condition transitions with multiple constraints. For example, when switching from high load to low load, the coolant temperature needs to decrease as quickly as possible, while the engine oil temperature must not fall below a safe threshold. Using a single-objective optimization strategy might lead to excessive oil temperature reduction, triggering the safety boundary. The optimization process of the Model Predictive Control (MPC) algorithm is as follows: First, the optimization objective is defined as maximizing the achievement of the target value for key temperatures (such as coolant temperature). Second, constraints are set, with other key parameters (such as engine oil temperature not falling below the threshold) as hard constraints, including physical limitations on the actuator group. Then, using the prediction module as the prediction engine, the optimal control sequence (e.g., the optimal opening sequence of the coolant valve and oil cooler valve) is solved within a finite prediction time domain in each control cycle. Finally, only the optimal control action for the current cycle is executed, and this rolling optimization process is repeated in the next cycle. The resulting adaptive control command is a dynamic control curve that changes over time, thus achieving coordinated and precise control of multiple variables while satisfying multiple safety constraints, avoiding the risk of parameter exceeding limits.

[0033] The execution control module, connected to the intelligent scheduling unit and the actuator group, is used to drive the actuator group to move according to adaptive control instructions or instructions to be executed.

[0034] The online learning module connects the sensor group and the prediction module. It compares the real-time state data collected by the sensor group with the corresponding thermal state prediction data generated by the prediction module. Based on the comparison results, it uses an optimization algorithm to correct the model parameters of the prediction module online, ensuring that the model maintains high fidelity over a long period of time and achieves adaptive adjustment.

[0035] The safety monitoring and intervention module is connected to the sensor group and is used to determine the operating status parameters of the engine under test based on the real-time status data of the sensor group, and monitor the operating status parameters. When any operating status parameter reaches or exceeds the corresponding preset safety boundary, the safety monitoring and intervention module is configured to override the instructions issued by the intelligent scheduling unit with the highest priority, and send preset safety control instructions to the execution control module.

[0036] Specifically, this safety monitoring and intervention module is an independent, parallel-operating unit with higher command output authority than the intelligent scheduling unit, used to ensure system safety in abnormal situations. Its specific operation is illustrated using a sensor failure as an example: When the main cooling temperature sensor in the sensor group malfunctions and outputs a persistently low erroneous reading, this erroneous data causes the prediction module to be incorrectly corrected, further leading the intelligent scheduling unit to generate a dangerous stop-cooling command based on an erroneous prediction of impending overcooling. At this time, the safety monitoring and intervention module, through its independently monitored redundant sensor data, detects that the actual temperature of the tested engine is rapidly rising and approaching the alarm value, creating a serious logical conflict with the stop-cooling command issued by the intelligent scheduling unit. The module determines that the system is at risk of loss of control and immediately executes the highest priority intervention, including: a) command overwriting, sending a preset safety mode command (such as forcibly executing basic cooling operations) to the execution control module; b) shielding the risk source, temporarily blocking the command channel from the intelligent scheduling unit; c) alarm and recording, issuing a high-level alarm to the operator and recording event data. Through the above mechanism, the security monitoring and intervention module constitutes the key security defense line of the system, which can avoid serious accidents caused by the amplification of underlying faults through the intelligent decision chain, and ensure the reliability and robustness of the entire solution.

[0037] In summary, the working logic of the system in this embodiment can be further illustrated by the following example: The test program presets a timeline, for example, requiring the execution of the core operating condition command of "150℃ heat preservation" to begin at 10:00. At 9:58, the intelligent scheduling unit, based on the prediction data from the prediction module, determines that if the current state is directly executed, the actual temperature of the tested engine at 10:00 will be far below 150℃, leading to a decrease in control quality. Therefore, the intelligent scheduling unit decides to insert a pre-adjustment step and generates a corresponding adaptive control command. This command performs a dynamic preheating operation on the temperature control system between 9:58 and 10:00, ensuring that by the time the command is officially launched at 10:00, the thermal state of the tested engine has smoothly transitioned to an optimized starting point close to the target value, thereby achieving precise and overshoot-free operating condition switching.

[0038] The core of the intelligent pre-control mechanism of this invention lies in dynamically optimizing the dynamic process path to achieve and maintain the target through prediction and decision-making. For example, for the instruction to "rapidly drop from room temperature to -40°C", the system may first slightly increase the temperature to release thermal stress through pre-adjustment, and then start cooling, thereby "softening" the severe thermal shock curve, effectively protecting the engine and improving the test quality while ensuring the test target.

[0039] Example 2: This embodiment provides an intelligent pre-control method for engine thermal shock testing, which is implemented on the intelligent pre-control system for engine thermal shock testing described in the above embodiment. That is, those skilled in the art can understand the intelligent pre-control method for engine thermal shock testing as the operation mode of the intelligent pre-control system for engine thermal shock testing.

[0040] Specifically, the intelligent pre-control method for the engine's thermal shock test includes the following steps: Step S1: Construct a prediction module, enabling the prediction module to operate synchronously with the engine under test, receive real-time status data from the sensor group, and generate a sequence of operating condition commands arranged in chronological order according to the preset test cycle program. Step S2: Based on real-time status data, the prediction module simulates and predicts the future thermal state of the engine under test under the current operating condition command requirements, and obtains thermal state prediction data. Step S3: Based on the current operating condition command to be executed and the thermal state prediction data, the intelligent scheduling unit decides whether to insert a pre-adjustment step before starting the current operating condition command to be executed. If the decision is to insert a pre-adjustment step, the intelligent scheduling unit generates an adaptive control command based on the thermal state prediction data to execute the current operating condition command to be executed. If the decision is not to insert a pre-adjustment step, the intelligent scheduling unit uses the current operating condition command to be executed as the command to be executed. In step S4, the intelligent scheduling unit sends adaptive control instructions or instructions to be executed to the execution control module. The execution control module drives the actuator group to operate the temperature control system according to the received instructions.

[0041] Step S5: Compare the real-time state data collected by the sensor group with the thermal state prediction data predicted by the prediction module at the same time, and based on the comparison results, use the optimization algorithm to correct the model parameters of the prediction module online.

[0042] Those skilled in the art will understand that, besides implementing the system and its various devices, modules, and units provided by this invention in the form of purely computer-readable program code, the same functions can be achieved entirely through logical programming of the method steps, making the system and its various devices, modules, and units of this invention function in the form of logic gates, switches, application-specific integrated circuits, programmable logic controllers, and embedded microcontrollers. Therefore, the system and its various devices, modules, and units provided by this invention can be considered as a hardware component, and the devices, modules, and units included therein for implementing various functions can also be considered as structures within the hardware component; alternatively, the devices, modules, and units for implementing various functions can be considered as both software modules implementing the method and structures within the hardware component.

[0043] Specific embodiments of the present invention have been described above. It should be understood that the present invention is not limited to the specific embodiments described above, and those skilled in the art can make various changes or modifications within the scope of the claims, which do not affect the essence of the present invention. Unless otherwise specified, the embodiments and features described in this application can be arbitrarily combined with each other.

Claims

1. An intelligent pre-control system for engine cold-heat shock test, characterized in that, include: The temperature control system is used to provide a temperature environment for the engine under test; The sensor array is used to collect real-time status data of the engine under test and the temperature control system. An actuator assembly is used to regulate the temperature control system; The main operating condition control module is used to generate a sequence of operating condition commands arranged in chronological order according to a preset test cycle program; The prediction module, connected to the sensor group, is used to perform online simulation based on the real-time status data, predict the future thermal state of the engine under test under the current operating condition command requirements, and obtain thermal state prediction data. The intelligent scheduling unit, connected to the main operating condition control module and the prediction module, is used to decide whether to insert a pre-adjustment step before executing the current operating condition command based on the current operating condition command to be executed and the thermal state prediction data. If a decision is inserted, an adaptive control instruction corresponding to the current operating condition instruction to be executed is generated based on the thermal state prediction data; if no decision is inserted, the current operating condition instruction to be executed is used as the instruction to be executed. An execution control module, connected to the intelligent scheduling unit and the execution mechanism group, is used to drive the execution mechanism group to operate according to the adaptive control command or the command to be executed.

2. The intelligent pre-control system for engine thermal shock testing according to claim 1, characterized in that, The prediction module is a digital twin model that operates synchronously with the engine under test.

3. The intelligent pre-control system for engine thermal shock testing according to claim 1, characterized in that, The intelligent pre-control system also includes an online learning module, which is used to compare the real-time state data collected by the sensor group with the thermal state prediction data generated by the prediction module, and to use an optimization algorithm to correct the model parameters of the prediction module online based on the comparison results.

4. The intelligent pre-control system for engine thermal shock testing according to claim 1, characterized in that, When a decision is inserted into the pre-adjustment step, the start time and duration of the pre-adjustment step are dynamically determined based on the engine thermal inertia time constant calculated in real time by the prediction module and the response speed of the temperature control system.

5. The intelligent pre-control system for engine thermal shock testing according to claim 4, characterized in that, When a decision is inserted into the pre-adjustment step, the control objective of the pre-adjustment step is to make the thermal state of the engine under test fall into a preset optimal initial state range at the end of the pre-adjustment. The optimal initial state range is defined at least according to the dynamic characteristics of the controller that will be used after the current operating condition command to be executed, and includes a combination of temperature value range and temperature change rate range.

6. The intelligent pre-control system for engine thermal shock testing according to claim 1, characterized in that, The adaptive control command is a sequence of commands generated based on the thermal state prediction data and used to control the actuator group to act according to a preset time function. The command sequence is used to adjust the temperature control system from the current state to the initial state required to execute the current working condition command.

7. The intelligent pre-control system for engine thermal shock testing according to claim 1, characterized in that, The intelligent pre-control system also includes a safety monitoring and intervention module, which is used to determine the operating status parameters of the tested engine based on the real-time status data of the sensor group, and monitor the operating status parameters. When any of the aforementioned operating status parameters reaches or exceeds the corresponding preset safety boundary, the safety monitoring and intervention module is configured to override the instructions issued by the intelligent scheduling unit with the highest priority, and send preset safety control instructions to the execution control module.

8. The intelligent pre-control system for engine thermal shock testing according to claim 1, characterized in that, The intelligent scheduling unit generates the adaptive control command through a hybrid decision-making strategy that combines rule-based and model predictive control. The model predictive control process includes: using the prediction module as the prediction engine, in each control cycle, under the condition of satisfying the multiple safety constraints of the tested engine and the physical limitations of the actuator group, continuously solving for the optimal control sequence in the future finite time domain; the intelligent scheduling unit integrates the solved control action of the current control cycle into the adaptive control command and outputs it.

9. An intelligent pre-control method for engine thermal shock testing, based on the intelligent pre-control system for engine thermal shock testing as described in any one of claims 1-8, characterized in that, Includes the following steps: Step S1: Construct a prediction module, which operates synchronously with the engine under test, receives real-time status data from the sensor group, and the main operating condition control module generates a sequence of operating condition commands arranged in chronological order according to a preset test cycle program. Step S2: Based on the real-time status data, the prediction module simulates and predicts the future thermal state of the engine under test under the current operating condition command requirements, and obtains thermal state prediction data. Step S3: Based on the current operating condition command to be executed and the thermal state prediction data, the intelligent scheduling unit decides whether to insert a pre-adjustment step before starting the current operating condition command to be executed. If the decision is made to insert the pre-adjustment step, the intelligent scheduling unit generates an adaptive control command based on the thermal state prediction data to execute the current operating condition command to be executed. If the decision is not to insert the pre-adjustment step, the intelligent scheduling unit treats the current operating condition command to be executed as the command to be executed. In step S4, the intelligent scheduling unit sends the adaptive control command or the command to be executed to the execution control module, and the execution control module drives the actuator group to operate the temperature control system according to the received command.

10. The intelligent pre-control method for engine thermal shock testing according to claim 9, characterized in that, The intelligent pre-control method further includes step S5, which compares the real-time state data collected by the sensor group with the thermal state prediction data predicted by the prediction module at the same time, and based on the comparison results, uses an optimization algorithm to correct the model parameters of the prediction module online.

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