Dynamic cooperative control system of high and low temperature environment chamber and sunlight simulation system

Abstract

The invention provides a dynamic cooperative control system of a high and low temperature environment chamber and a sunlight simulation system, and belongs to the technical field of environment testing. The system establishes a dynamic coupling model, integrates a plurality of core environment parameters, optimizes a combination problem in a high-dimensional parameter space by adopting a quantum annealing algorithm, and adopts edge computing nodes to realize the dynamic cooperative control of the high and low temperature environment chamber and the sunlight simulation system. And real-time issuing of the control instruction is realized. In addition, the refrigerating / heating power and the illumination intensity are dynamically adjusted through real-time sensor data, and closed-loop control over environmental parameters is achieved. The problem that dynamic environment parameters are difficult to cooperatively adjust in real time due to independent control of a high and low temperature environment chamber and a sunlight simulation system in a traditional environment testing system is solved.

Description

Technical Field

[0001] The present invention relates to the field of environmental testing technology, and in particular to a dynamic coordinated control system of a high and low temperature environmental chamber and a sunlight simulation system. Background Art

[0002] In the field of modern environmental testing, high and low temperature environmental chambers and sunlight simulation systems are key equipment for simulating real driving conditions and conducting product performance tests. However, in traditional environmental testing systems, high and low temperature environmental chambers and sunlight simulation systems are mostly controlled independently, making it difficult to achieve real-time coordinated adjustment of dynamic environmental parameters (such as temperature, humidity, and light intensity). In off-season calibration tests, this static environmental simulation method cannot accurately reflect real driving conditions, resulting in large deviations in test data, which in turn affects product test results and performance evaluation.

[0003] Traditional systems also have shortcomings in control accuracy and response speed. For example, temperature control accuracy cannot meet high precision requirements, and uniformity of light intensity cannot be effectively guaranteed. Furthermore, switching between different test scenarios often requires a long time to adjust parameters, which not only reduces test efficiency but also increases energy consumption. Furthermore, traditional systems also have certain issues with electromagnetic compatibility and are susceptible to electromagnetic interference, which affects test accuracy and stability.

[0004] In view of this, this application is filed. Summary of the Invention

[0005] The present invention provides a dynamic coordinated control system for a high and low temperature environment chamber and a sunlight simulation system, which can at least partially improve the above-mentioned problems.

[0006] To achieve the above object, the present invention adopts the following technical solutions:

[0007] A dynamic coordinated control system for a high and low temperature environment chamber and a sunlight simulation system, comprising: a main controller, an execution component, and a feedback component, wherein a data terminal of the main controller is communicatively connected to a data terminal of a host computer, an output terminal of the main controller is electrically connected to an input terminal of the execution component, and an output terminal of the feedback component is communicatively connected to an input terminal of the main controller, the execution component is configured to adjust the temperature, humidity, light intensity, and air pressure in the high and low temperature environment chamber, and the feedback component is configured to collect current temperature and humidity values, light intensity values, and air pressure values ​​in the high and low temperature environment chamber;

[0008] The main controller is configured to implement the following steps by executing a computer program stored therein:

[0009] Obtain the environmental parameter setting data sent by the host computer, call the preset dynamic coupling model to calculate the environmental parameter setting data, and generate the environmental parameter constraint matrix;

[0010] Preprocessing the environmental parameter constraint matrix to generate high and low temperature environmental chamber control instructions and sunlight simulation system control instructions, and controlling the execution component to adjust the temperature, humidity, light and air pressure in the chamber according to the instructions;

[0011] The current environmental data collected by the feedback signal is obtained, the current environmental data is compared, an error correction matrix is ​​generated, a dynamic coupling model is called to calculate the error correction matrix, and corresponding instructions are regenerated according to the calculated value to control the execution component for adjustment.

[0012] In summary, the proposed dynamic collaborative control system for a high- and low-temperature environmental chamber and a solar simulation system aims to address the difficulty in real-time coordinated adjustment of dynamic environmental parameters, a problem inherent in traditional environmental testing systems, due to the independent control of these two systems. This model integrates four core environmental parameters: temperature, humidity, light, and air pressure. It leverages real-time sensor data to dynamically adjust cooling / heating power and light intensity, achieving closed-loop control of these environmental parameters. This approach limits temperature fluctuations to within ±1°C and light uniformity to within 15%. Furthermore, a quantum annealing algorithm is employed to optimize combinatorial problems in a high-dimensional parameter space, automatically generating optimal environmental parameter combinations based on constraints. This allows for rapid switching between test scenarios, significantly reducing switching time and energy consumption. Furthermore, the device utilizes edge computing nodes and deploys a lightweight LinuxRT kernel to preprocess sensor data and issue control commands in real time, reducing communication latency. Through predictive control driven by digital twins, a high-precision 3D thermodynamic simulation model is constructed to optimize temperature uniformity and achieve four-dimensional collaborative control. In addition, the system is also equipped with a photothermal coupling energy recovery system to reduce overall energy consumption, while using adaptive electromagnetic shielding technology to improve the system's anti-interference ability. BRIEF DESCRIPTION OF THE DRAWINGS

[0013] Figure 1 It is a schematic diagram of the process framework of the dynamic collaborative control system of the high and low temperature environment chamber and the sunlight simulation system provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0014] In order to make the purpose, technical solutions and advantages of the present invention more clearly understood, the present invention will be further described in detail below in conjunction with the embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0015] refer to Figure 1As shown, the first embodiment of the present invention discloses a dynamic cooperative control system of a high and low temperature environment chamber and a sunlight simulation system, which includes: a main controller, an execution component and a feedback component, wherein the data end of the main controller is communicatively connected to the data end of the host computer, the output end of the main controller is electrically connected to the input end of the execution component, the output end of the feedback component is communicatively connected to the input end of the main controller, the execution component is configured to adjust the temperature, humidity, light intensity and air pressure in the high and low temperature environment chamber, and the feedback component is configured to collect the current temperature and humidity values, light intensity values ​​and air pressure values ​​in the high and low temperature environment chamber;

[0016] Preferably, the execution component includes a two-stage compression refrigeration unit, an electric heater, an ultrasonic humidifier, a rotary dehumidifier, a xenon lamp array, an adaptive optical lens and a centrifugal fan, and the output end of the main controller is electrically connected to the input end of the two-stage compression refrigeration unit, the input end of the electric heater, the input end of the ultrasonic humidifier, the input end of the rotary dehumidifier, the input end of the xenon lamp array, the input end of the adaptive optical lens and the input end of the centrifugal fan.

[0017] Preferably, the feedback component includes a temperature sensor, a humidity sensor, a light sensor, and a pressure sensor, and the output end of the temperature sensor, the output end of the humidity sensor, the output end of the light sensor, and the output end of the pressure sensor are communicatively connected to the input end of the main controller.

[0018] Specifically, in this embodiment, the hardware architecture of the dynamic collaborative control system of the high and low temperature environmental chamber and the sunlight simulation system adopts a distributed PLC and a real-time industrial computer, using multi-protocol communication (for example, EtherCAT, ModbusTCP, and OPC UA); the corresponding software platform is a LabVIEW-based HMI interface that supports environmental curve settings (such as NEDC and WLTC operating conditions); in addition, the system uses a time series database (InfluxDB) with a sampling frequency of 1kHz for data storage. It should be noted that the performance indicators of the dynamic collaborative control system of the high and low temperature environmental chamber and the sunlight simulation system include dynamic response and illumination uniformity. Among them, the dynamic response: temperature fluctuation ≤±2℃ (steady state), ±3℃ (dynamic); humidity fluctuation ≤±5%RH; illumination uniformity: ≤±15% (ISO 19453 standard).

[0019] The core of this system is the main controller, which communicates with the host computer via a data port and receives environmental parameter setting instructions from the host computer, such as target temperature, humidity, light intensity, and air pressure. The main controller's output is electrically connected to the inputs of the various devices in the actuator component, sending control instructions to the actuator component to drive the device operation and adjust the parameters within the environmental chamber. The feedback component includes a temperature sensor, a humidity sensor, a light sensor, and a pressure sensor. Its output is connected to the input of the main controller to collect the actual parameter values ​​within the environmental chamber in real time and feed this data back to the main controller. Through this closed-loop control structure, the system can monitor the status of the environmental chamber in real time and dynamically adjust the operation of the actuator component based on the feedback data to ensure that the environmental parameters remain stable within the set range.

[0020] The actuator components are key to achieving environmental parameter adjustment. They include a two-stage compression refrigeration unit (COP ≥ 3.2), an electric heater (response time <10s), an ultrasonic humidifier (5L / h), a rotary dehumidifier (dehumidification capacity 30kg / h), a xenon lamp array (color temperature 5500K ± 300K), an adaptive optical lens, and a centrifugal fan (air volume 0-5000m³ / h, frequency conversion accuracy 0.1Hz). The two-stage compression refrigeration unit provides cooling. Its high cooling capacity enables rapid reduction of the temperature within the environmental chamber while maintaining a high energy efficiency ratio. The electric heater is responsible for heating, rapidly responding to temperature changes and ensuring accurate and timely temperature adjustment through precise control of heating power. The ultrasonic humidifier and rotary dehumidifier work together to regulate humidity. The ultrasonic humidifier uses high-frequency vibration to generate water mist, rapidly increasing the ambient humidity, while the rotary dehumidifier effectively reduces humidity through adsorption and desorption processes. Together, they enable wide-range humidity control. The xenon lamp array, the core device for illumination simulation, produces high-intensity illumination with a color temperature close to natural sunlight, providing realistic lighting conditions within the environmental chamber. Adaptive optical lenses adjust the direction and distribution of illumination as needed, further optimizing illumination uniformity. A centrifugal fan regulates air pressure, maintaining a slightly positive pressure within the environmental chamber by controlling the fan speed and ensuring stable air pressure.

[0021] The temperature sensor uses a MEMS thermopile (MLX90640) to achieve temperature field imaging (32x24 pixels, 10Hz frame rate) and converts resistance values ​​to temperature values ​​(-40°C to 60°C, with a resolution of 1 square centimeter per point). The humidity sensor uses a capacitive sensor to obtain capacitive humidity readings (0-100% RH, with an accuracy of ±2%). The light sensor uses a radiometer, and the light intensity output by the silicon photocell ranges from 0 to 2000w / m² with a linearity of ±1%. The pressure sensor collects air pressure data. These sensors have accuracies of ±0.5°C, ±3% RH, ±5% FS, and ±1Pa, respectively. Each sensor transmits data to the main controller via a pre-deployed LoRaWAN wireless sensor network for data fusion and error analysis, reducing wiring costs by 90%.

[0022] The main controller is configured to implement the following steps by executing a computer program stored therein:

[0023] S1, obtain the environmental parameter setting data sent by the host computer, call the preset dynamic coupling model to calculate the environmental parameter setting data, and generate the environmental parameter constraint matrix;

[0024] Specifically, step S1 includes: obtaining environmental parameter setting data sent by the host computer through the TCP / IP communication protocol, wherein the environmental parameter setting data includes a temperature digital signal, a humidity digital signal, a light digital signal, and an air pressure digital signal, and the formats of these digital signals are all in JSON format.

[0025] The environmental parameter constraint matrix is ​​a floating-point array, which includes coupling weights of temperature, humidity, light, and pressure.

[0026] In this embodiment, the main controller first communicates with the host computer to obtain the environmental parameter setting data sent by the host computer. This process is the starting point of the entire control process and ensures that the system can accurately control according to the preset target parameters. Specifically, the main controller receives the environmental parameter setting data sent by the host computer via the TCP / IP communication protocol. This data is transmitted in JSON format and covers digital signals of key environmental parameters such as temperature, humidity, light, and air pressure. This communication method not only ensures the efficiency and accuracy of data transmission, but also facilitates the system to uniformly process and parse data in different formats.

[0027] In practical applications, the method of receiving environmental parameter setting data through the TCP / IP communication protocol enables the system to efficiently interact with external devices or host computers. The adoption of this communication method not only improves the flexibility and scalability of the system, but also facilitates remote monitoring and automated control of the system. At the same time, the data transmission method in JSON format further simplifies the complexity of data parsing and processing, and improves the operating efficiency of the system. The generation of the environmental parameter constraint matrix provides precise input data for the subsequent optimization algorithm, enabling the system to more accurately regulate environmental parameters. This collaborative control method based on the dynamic coupling model not only improves the regulation accuracy of environmental parameters, but also effectively reduces system energy consumption and improves test efficiency. It has important practical application value and broad application prospects.

[0028] Preferably, the dynamic coupling model consists of sub-models of four core environmental parameters: temperature, humidity, light and air pressure;

[0029] Based on the law of conservation of energy and heat transfer equation, a thermodynamic sub-model is constructed to describe the temperature changes in the high and low temperature environment chamber. The formula is: Among them, Q 加热 is the heating power, Q 制冷 is the cooling power, Q 光照 is the light radiation heat, Q 通风 is the heat exchange capacity of the ventilation system, C 舱体 is the heat capacity of the cabin;

[0030] A humidity diffusion sub-model is constructed based on the water vapor mass balance equation to calculate the humidity changes in the high and low temperature environment chambers. The formula is: Among them, M 加湿 is the humidification efficiency, M 除湿 is the dehumidification efficiency, M 通风 is the moisture exchange capacity of the ventilation system, V 舱体 is the cabin volume;

[0031] Based on the light intensity distribution and optical attenuation characteristics, the radiation transfer equation is used to simulate the light uniformity and construct a light propagation sub-model to calculate the light changes in the high and low temperature environment chamber. The formula is: I(x,y)=I0·e -μ·d ′·cosθ, where I0 is the initial intensity of the light source, μ is the attenuation coefficient, d′ is the propagation distance, and θ is the angle of incidence;

[0032] Based on the Bernoulli equation and the air volume regulation principle, a pressure balance sub-model is constructed to calculate the pressure changes in the high and low temperature environment chamber and maintain the pressure balance inside and outside the chamber. The calculation formula is: Where ρ is the air density, v 进 is the air inlet velocity, v 出The air outlet speed.

[0033] In this embodiment, after acquiring the environmental parameter setting data, the main controller immediately invokes a preset dynamic coupling model to calculate this data. Through the synergistic effect of these sub-models, the dynamic environmental parameters within the environmental chamber can be accurately simulated and controlled. Feedforward compensation and fuzzy logic are used to decouple parameters. By calculating light radiation heat in real time, the cooling power is dynamically adjusted to achieve light radiation heat compensation. Based on dew point temperature prediction, humidity is prioritized to prevent condensation, achieving temperature-humidity decoupling. The dynamic coupling model is one of the core technologies of this system and a multi-physics collaborative control model. It comprehensively considers the interactions between temperature, humidity, light, and air pressure to generate an environmental parameter constraint matrix. This constraint matrix is ​​presented as a floating-point array and contains coupling weights for temperature, humidity, light, and pressure. These weights reflect the interdependencies between the environmental parameters and provide a basis for subsequent optimization calculations. This approach enables the system to more accurately understand and process the complex relationships between environmental parameters, thereby achieving more efficient and precise collaborative control.

[0034] The generation of the environmental parameter constraint matrix not only provides the necessary input data for the subsequent optimization algorithm, but also ensures the synergy of various environmental parameters during the control process through precise weight distribution. This calculation method based on the dynamic coupling model can significantly improve the control accuracy and stability of environmental parameters compared to traditional independent control methods. For example, in the coordinated control of temperature and humidity, by considering the coupling relationship between the two, the system can more accurately adjust the cooling or heating power and the humidification or dehumidification rate, thereby avoiding fluctuations in other parameters caused by the adjustment of a single parameter. This collaborative control method not only improves the stability and uniformity of environmental parameters in the environmental chamber, but also effectively reduces system energy consumption and improves test efficiency.

[0035] S2, preprocessing the environmental parameter constraint matrix to generate high and low temperature environmental chamber control instructions and sunlight simulation system control instructions, and controlling the execution component to adjust the temperature, humidity, light and air pressure in the chamber according to the instructions;

[0036] Specifically, step S2 includes: optimizing the environmental parameter constraint matrix using a quantum annealing algorithm based on the D-Wave architecture to optimize its objective function parameters;

[0037] The optimized parameters are downsampled and outliers are eliminated using the edge computing node module, which uses the NVIDIA Jetson Xavier NX computing module to generate control instructions for the high and low temperature environment chamber and the sunlight simulation system.

[0038] Transmitting a 0-10V analog signal to the xenon lamp array to adjust the power supply of the xenon lamp array to optimize uniformity with the reflector, wherein the analog signal linearly corresponds to the light intensity;

[0039] The drive circuit is controlled by PWM signal to adjust the two-stage compression refrigeration unit, the duty cycle is proportional to the compressor speed, and the power of the electric heater is adjusted based on the PID algorithm;

[0040] Combined with dew point temperature feedback, the ultrasonic humidifier and rotary dehumidifier are adjusted in conjunction, and the speed of the centrifugal fan is adjusted based on instructions to maintain a slight positive pressure.

[0041] In this embodiment, the environmental parameter constraint matrix is ​​first preprocessed, and the entire data transmission is based on the system's internal data bus. This process uses a quantum annealing algorithm based on the D-Wave architecture to optimize the objective function parameters in the constraint matrix. The quantum annealing algorithm can efficiently handle combinatorial optimization problems in high-dimensional parameter spaces and quickly find the optimal solution through quantum computing resources. Specifically, combined with constraint conditions (such as device power limits and test cycles), the optimal environmental parameter combination is automatically generated according to test requirements (such as vehicle energy consumption tests and battery thermal management tests). It supports rapid switching of custom test scenarios. Quantum computing resources are called through the cloud platform to process large-scale optimization of 1000+ variables. The test scenario switching time is shortened from 20 minutes to 3 minutes, and energy consumption is reduced by 15%. This optimization method not only improves the system's response speed, but also significantly improves the accuracy and stability of environmental parameter regulation. The optimized parameters are further processed to generate high and low temperature environmental chamber control instructions and sunlight simulation system control instructions.

[0042] Next, to ensure the accuracy and real-time performance of control commands, the optimized control parameters (such as 60% cooling power and light intensity gradient parameters) are encapsulated into CAN frames (ID 0*201, 8-byte data field) and transmitted to the edge computing node module via the CAN bus (ISO 11898-2 standard). The edge computing node module performs data preprocessing. This module uses the NVIDIA Jetson Xavier NX compute module and deploys a lightweight Linux RT kernel, offering powerful computing power and low latency. The edge computing node module downsamples the optimized parameters, removes outliers, and issues control commands in real time (response latency ≤ 50ms). Furthermore, a customized lightweight communication protocol (UDP-based RapidEnv) is used to compress data packets to 128 bytes. This process effectively reduces data transmission volume and communication latency with the main control platform (response time ≤ 100ms), improving system responsiveness and reliability. The preprocessed parameters are converted into specific control commands that drive the actuator components to adjust the temperature, humidity, light intensity, and air pressure within the environmental chamber.

[0043] In this embodiment, for illumination control, the system transmits a 0-10V analog signal to the xenon lamp array to adjust the power supply. This analog signal linearly corresponds to the light intensity (e.g., 0-10V linearly corresponds to 0-1200W / m² of light intensity), enabling precise control of the xenon lamp's output power. By coordinating with the reflector, the uniformity of illumination is further optimized. This precise illumination control capability is crucial for simulating natural lighting conditions and can significantly improve the accuracy and reliability of testing.

[0044] The system uses a variety of advanced technologies to control temperature and humidity. The two-stage compression refrigeration unit is adjusted by controlling the drive circuit through PWM signals, and the duty cycle precisely controls the compressor speed. For example, a duty cycle of 1kHz corresponds to a compressor speed range of 0-100%. At the same time, the power of the electric heater is adjusted based on the PID algorithm to ensure temperature stability and uniformity. For humidity control, the system uses dew point temperature feedback to jointly adjust the ultrasonic humidifier and rotary dehumidifier. This feedback-based control method can effectively avoid excessive humidity fluctuations and ensure that the humidity in the environmental chamber remains stable within the set range. In addition, the system adjusts the speed of the centrifugal fan based on instructions to maintain a slightly positive pressure (50-100Pa) and further optimize the air pressure conditions in the environmental chamber.

[0045] S3, obtaining the current environmental data collected by the feedback signal, comparing the current environmental data, generating an error correction matrix, calling the dynamic coupling model to calculate the error correction matrix, and regenerating corresponding instructions based on the calculated value to control the execution component to adjust.

[0046] Specifically, step S3 includes: obtaining current environmental data collected by the feedback signal, the current environmental data including temperature value, temperature field imaging, humidity value, light intensity and air pressure value;

[0047] Comparing the current environment data with the environment parameter setting data to determine whether they are equal;

[0048] If not, calculate the error values ​​of the two, generate an error correction matrix, and transfer it to the dynamic coupling model via the SPI bus, wherein the error correction matrix includes temperature deviation, humidity compensation coefficient, light deviation, and air pressure deviation;

[0049] If not, the generated judgment result is normal.

[0050] In this embodiment, by real-time feedback of multi-point sensor data in the environmental chamber, the cooling / heating power and light intensity are dynamically adjusted (for example; (temperature fluctuations in the range of -40°C to 60°C are reduced to ±1°C, and light uniformity is ≤15%)), thereby achieving closed-loop control of environmental parameters and overcoming the limitations of fixed parameters in traditional PID control. Specifically, the current environmental data are first obtained through the feedback component. These data include temperature values, temperature field imaging, humidity values, light intensity, and air pressure values. These data are collected in real time by sensors distributed in the environmental chamber and can accurately reflect the current state of the environmental chamber. The temperature sensor uses a high-precision MEMS thermopile, which can realize temperature field imaging and provide detailed temperature distribution information; the humidity sensor uses a capacitive sensor to ensure the accuracy of humidity measurement; the light sensor is based on a silicon photocell and can accurately measure light intensity; and the air pressure sensor is used to monitor air pressure changes in the environmental chamber.

[0051] After acquiring the current environmental data, it is compared with the environmental parameters set by the host computer. This comparison process is completed by the main controller, which compares the current environmental data with the set environmental parameter data item by item to determine whether the two are equal. If the current environmental data is completely consistent with the set data, the parameters in the environmental chamber have met the set requirements, and the system generates a "normal" judgment result, requiring no further adjustment. However, in actual operation, due to environmental changes, equipment aging, or other factors, the current environmental data often deviates from the set data. In this case, the system calculates the error between the two and generates an error correction matrix. The error correction matrix is ​​a floating-point array that contains temperature deviation, humidity compensation coefficient, light deviation, and air pressure deviation. These deviation values ​​reflect the difference between the current environmental data and the set data, providing a basis for subsequent control.

[0052] After the error correction matrix is ​​generated, it is transferred to the dynamic coupling model via shared memory or a high-speed serial bus (such as SPI). The dynamic coupling model recalculates the environmental parameter constraint matrix based on the error correction matrix and generates new control instructions. These new control instructions are sent to the actuator components, driving the two-stage compression refrigeration unit, electric heater, ultrasonic humidifier, rotary dehumidifier, xenon lamp array, adaptive optical lens, and centrifugal fan to make corresponding adjustments to eliminate errors and stabilize the parameters in the environmental chamber within the set range.

[0053] This feedback-based closed-loop control mechanism is one of the key innovations of this system. By monitoring the actual parameters within the environmental chamber in real time and comparing them with the set parameters, the system can quickly respond to environmental changes and promptly adjust the operating status of the actuators. This dynamic control method not only improves the control accuracy of environmental parameters but also enhances the stability and reliability of the system. For example, during testing, if the temperature within the environmental chamber fluctuates due to external interference, the system can quickly detect this change through the feedback mechanism and restore temperature stability by adjusting the cooling or heating power. This rapid response capability is crucial to ensuring test accuracy and repeatability.

[0054] In addition, by generating an error correction matrix and calling a dynamic coupling model for recalculation, the system can achieve refined control of environmental parameters. This model-based control method not only takes into account the mutual influence between various parameters, but also dynamically adjusts the control strategy based on real-time feedback data, thereby achieving more efficient and accurate environmental control. For example, in the coordinated control of humidity and temperature, the system can simultaneously adjust the operating status of the humidifier, dehumidifier, and refrigeration unit based on humidity deviation and temperature deviation, ensuring that the humidity and temperature in the environmental chamber are always maintained at the optimal state.

[0055] It should be noted that the dynamic coordinated control system of the high and low temperature environmental chamber and the sunlight simulation system embeds common-mode chokes (TDK ZJY51 series) in the control signal lines to suppress high-frequency noise. Fiber optic isolation (HFBR-1521Z) is also used to transmit key instructions to avoid electromagnetic pulse (EMP) interference. This ensures a bit error rate of less than 1×10 at an electromagnetic field strength of 10V / m. -9 .

[0056] Preferably, the method further includes: constructing a high-precision 3D thermodynamic simulation model, using digital twin technology to predict the airflow distribution in the cabin based on the high-precision 3D thermodynamic simulation model, and issuing an early warning when the predicted value does not meet the preset conditions;

[0057] Photovoltaic panels are integrated into the sunlight simulation system to convert the waste heat of the xenon lamp array in the actuator into electrical energy. At the same time, a semiconductor thermoelectric power generation module is used to recover the waste heat of the refrigeration system in the high and low temperature environment cabin.

[0058] In this embodiment, the dynamic collaborative control system of the high and low temperature environmental chamber and the sunlight simulation system further constructs a high-precision 3D thermodynamic simulation model (ANSYS Fluent coupling), which can accurately simulate the airflow distribution in the environmental chamber in advance and optimize temperature uniformity. Through digital twin technology, the simulation model is mapped to the actual environmental chamber in real time, and its pressure sensor (MPX5100DP) monitors the air pressure in the cabin, updates and calibrates the simulation model, and realizes the four-dimensional collaborative control of temperature = humidity-light-air pressure. In this way, the simulation model can reflect the airflow state in the environmental chamber in real time and predict future trends. When the prediction results show that the airflow distribution does not meet the preset conditions, the system will automatically issue an early warning signal to remind the operator to take timely measures to adjust. This predictive control method based on digital twins can not only detect potential problems in advance and avoid abnormal fluctuations in environmental parameters, but also effectively improve the stability and reliability of the test and reduce test errors caused by environmental changes.

[0059] At the same time, the system integrates photovoltaic panels (PERC cells) into the sunlight simulation system, using the panels to convert the waste heat generated by the xenon lamp array into electrical energy. Xenon lamps generate a large amount of heat during operation, which is usually wasted. By integrating photovoltaic panels, this waste heat is effectively converted into electrical energy, providing additional power support for the system. In addition, semiconductor thermoelectric power generation modules (TEG modules) are used to recover waste heat generated by the refrigeration system in high and low temperature environment cabins. The refrigeration system generates a large amount of waste heat during operation. This waste heat is converted into electrical energy through the semiconductor thermoelectric power generation module, reducing overall energy consumption by 30%, complying with the ISO 50001 energy management system, and further improving the system's energy efficiency performance. This energy recovery mechanism not only reduces energy waste and lowers the system's operating costs, but also meets modern energy conservation and environmental protection requirements, with significant economic and social benefits.

[0060] In summary, the dynamic collaborative control system for the high- and low-temperature environmental chamber and the solar simulation system aims to achieve high-precision dynamic control of environmental parameters such as temperature, humidity, light intensity, and air pressure within the environmental chamber through innovative control strategies and technical means, while also improving the system's energy efficiency and stability. Regarding the control method, the system receives environmental parameter setting data from the host computer via a main controller. It then calculates this data using a dynamic coupling model to generate an environmental parameter constraint matrix. This model comprehensively considers the interactions between temperature, humidity, light intensity, and air pressure, accurately describing the dynamic changes within the environmental chamber. The constraint matrix is ​​optimized using a quantum annealing algorithm, and data preprocessing is combined with edge computing node modules to generate control commands for the high- and low-temperature environmental chamber and the solar simulation system, enabling precise control of various parameters within the environmental chamber. This control strategy, based on the dynamic coupling model and quantum optimization algorithm, not only improves the accuracy of environmental parameter control but also significantly shortens test scenario switching time and reduces system energy consumption.

[0061] The device design incorporates a variety of advanced technologies and equipment. The actuator components, including a two-stage compression refrigeration unit, an electric heater, an ultrasonic humidifier, a rotary dehumidifier, a xenon lamp array, an adaptive optical lens, and a centrifugal fan, efficiently adjust the temperature, humidity, light intensity, and air pressure within the environmental chamber. The feedback components, including high-precision temperature, humidity, light intensity, and pressure sensors, collect the actual parameter values ​​within the environmental chamber in real time and feed this data back to the main controller. This closed-loop control structure enables the system to rapidly respond to environmental changes and achieve highly precise dynamic coordinated control.

[0062] In addition, a high-precision 3D thermodynamic simulation model and digital twin technology were introduced to provide real-time predictions and early warnings of airflow distribution within the environmental chamber. This predictive control approach, based on digital twins, can proactively identify potential issues and avoid abnormal fluctuations in environmental parameters, thereby improving the stability and reliability of the test. Furthermore, by integrating photovoltaic panels into the sunlight simulation system and using semiconductor thermoelectric power generation modules to recover waste heat from the refrigeration system, the system effectively recycles and utilizes waste heat, significantly improving the system's energy efficiency and reducing operating costs.

[0063] The above is a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications are also considered to be within the scope of protection of the present invention.

Claims

1. A dynamic coordinated control system for a high and low temperature environment chamber and a sunlight simulation system, characterized in that: include: A main controller, an execution component, and a feedback component, wherein the data end of the main controller is communicatively connected to the data end of the host computer, the output end of the main controller is electrically connected to the input end of the execution component, the output end of the feedback component is communicatively connected to the input end of the main controller, the execution component is configured to adjust the temperature, humidity, light intensity, and air pressure in the high and low temperature environment chamber, and the feedback component is configured to collect the current temperature and humidity values, light intensity values, and air pressure values ​​in the high and low temperature environment chamber; The main controller is configured to implement the following steps by executing a computer program stored therein: Obtain the environmental parameter setting data sent by the host computer, call the preset dynamic coupling model to calculate the environmental parameter setting data, and generate the environmental parameter constraint matrix; Preprocessing the environmental parameter constraint matrix to generate high and low temperature environmental chamber control instructions and sunlight simulation system control instructions, and controlling the execution component to adjust the temperature, humidity, light and air pressure in the chamber according to the instructions; The current environmental data collected by the feedback signal is obtained, the current environmental data is compared, an error correction matrix is ​​generated, a dynamic coupling model is called to calculate the error correction matrix, and corresponding instructions are regenerated according to the calculated value to control the execution component for adjustment.

2. The dynamic coordinated control system of the high and low temperature environment chamber and the sunlight simulation system according to claim 1 is characterized in that: The execution component includes a two-stage compression refrigeration unit, an electric heater, an ultrasonic humidifier, a rotary dehumidifier, a xenon lamp array, an adaptive optical lens and a centrifugal fan. The output end of the main controller is electrically connected to the input end of the two-stage compression refrigeration unit, the input end of the electric heater, the input end of the ultrasonic humidifier, the input end of the rotary dehumidifier, the input end of the xenon lamp array, the input end of the adaptive optical lens and the input end of the centrifugal fan.

3. The dynamic coordinated control system of the high and low temperature environment chamber and the sunlight simulation system according to claim 1 is characterized in that: The feedback component includes a temperature sensor, a humidity sensor, a light sensor, and a pressure sensor. The output end of the temperature sensor, the output end of the humidity sensor, the output end of the light sensor, and the output end of the pressure sensor are communicatively connected to the input end of the main controller.

4. The dynamic coordinated control system of the high and low temperature environment chamber and the sunlight simulation system according to claim 1 is characterized in that: Obtain environmental parameter setting data sent by the host computer, specifically: obtain environmental parameter setting data sent by the host computer through the TCP / IP communication protocol, wherein the environmental parameter setting data includes temperature digital signal, humidity digital signal, light digital signal, and air pressure digital signal, and the format of these digital signals is JSON format.

5. The dynamic coordinated control system of the high and low temperature environment chamber and the sunlight simulation system according to claim 1 is characterized in that: The dynamic coupling model consists of sub-models of four core environmental parameters: temperature, humidity, light and air pressure; Based on the law of conservation of energy and heat transfer equation, a thermodynamic sub-model is constructed to describe the temperature changes in the high and low temperature environment chamber. The formula is: Among them, Q 加热 is the heating power, Q 制冷 is the cooling power, Q 光照 is the light radiation heat, Q 通风 is the heat exchange capacity of the ventilation system, C 舱体 is the heat capacity of the cabin; A humidity diffusion sub-model is constructed based on the water vapor mass balance equation to calculate the humidity changes in the high and low temperature environment chambers. The formula is: Among them, M 加湿 is the humidification efficiency, M 除湿 is the dehumidification efficiency, M 通风 is the moisture exchange capacity of the ventilation system, V 舱体 is the cabin volume; Based on the light intensity distribution and optical attenuation characteristics, the radiation transfer equation is used to simulate the light uniformity and construct a light propagation sub-model to calculate the light changes in the high and low temperature environment chamber. The formula is: I(x,y)=I2·e -μ·2′ cosθ, where I2 is the initial intensity of the light source, μ is the attenuation coefficient, d′ is the propagation distance, and θ is the angle of incidence; Based on the Bernoulli equation and the air volume regulation principle, a pressure balance sub-model is constructed to calculate the pressure changes in the high and low temperature environment chamber and maintain the pressure balance inside and outside the chamber. The calculation formula is: Where ρ is the air density, v 进 is the air inlet velocity, v 出 The air outlet speed.

6. The dynamic coordinated control system of the high and low temperature environment chamber and the sunlight simulation system according to claim 1 is characterized in that: The environmental parameter constraint matrix is ​​a floating-point array, which includes coupling weights of temperature, humidity, light, and pressure.

7. The dynamic coordinated control system of the high and low temperature environment chamber and the sunlight simulation system according to claim 1 is characterized in that: The environmental parameter constraint matrix is ​​preprocessed to generate high and low temperature environmental chamber control instructions and sunlight simulation system control instructions, specifically: A quantum annealing algorithm based on the D-Wave architecture is used to optimize the environmental parameter constraint matrix and optimize its objective function parameters; The optimized parameters are downsampled and outliers are eliminated using the edge computing node module, which uses the NVIDIA Jetson Xavier NX computing module.

8. The dynamic coordinated control system of the high and low temperature environment chamber and the sunlight simulation system according to claim 2, characterized in that: According to the instructions, the execution components are controlled to adjust the temperature, humidity, light and air pressure in the cabin. Specifically: Transmitting a 0-10V analog signal to the xenon lamp array to adjust the power supply of the xenon lamp array to optimize uniformity with the reflector, wherein the analog signal linearly corresponds to the light intensity; The drive circuit is controlled by PWM signal to adjust the two-stage compression refrigeration unit, the duty cycle is proportional to the compressor speed, and the power of the electric heater is adjusted based on the PID algorithm; Combined with dew point temperature feedback, the ultrasonic humidifier and rotary dehumidifier are adjusted in conjunction, and the speed of the centrifugal fan is adjusted based on instructions to maintain a slight positive pressure.

9. The dynamic coordinated control system of the high and low temperature environment chamber and the sunlight simulation system according to claim 1, characterized in that: Obtain the current environmental data collected by the feedback signal, compare the current environmental data, and generate an error correction matrix, specifically: Acquiring current environmental data collected by the feedback signal, the current environmental data including temperature value, temperature field imaging, humidity value, light intensity and air pressure value; Comparing the current environment data with the environment parameter setting data to determine whether they are equal; If not, calculate the error values ​​of the two, generate an error correction matrix, and transfer it to the dynamic coupling model via the SPI bus, wherein the error correction matrix includes temperature deviation, humidity compensation coefficient, light deviation, and air pressure deviation; If not, the generated judgment result is normal.

10. The dynamic coordinated control system of the high and low temperature environment chamber and the sunlight simulation system according to claim 1, characterized in that: Also includes: Construct a high-precision 3D thermodynamic simulation model, and use digital twin technology to predict the airflow distribution in the cabin based on the high-precision 3D thermodynamic simulation model. When the predicted value does not meet the preset conditions, an early warning is issued; Photovoltaic panels are integrated into the sunlight simulation system to convert the waste heat of the xenon lamp array in the actuator into electrical energy. At the same time, a semiconductor thermoelectric power generation module is used to recover the waste heat of the refrigeration system in the high and low temperature environment cabin.

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