Temperature change rate intelligent control method of condensation-free climate test box

Abstract

The invention discloses a temperature change rate intelligent control method for a non-condensation climate test box, and belongs to the technical field of climate test boxes, and the method comprises the following steps: S1, obtaining information such as temperature, humidity and pressure in the climate test box through a sensor group; s2, adjusting the temperature, humidity and temperature change rate in the climate test box by using a hardware control system; s3, adopting a temperature and humidity feedback control strategy to realize closed-loop control of temperature and humidity in the climate test box; s4, a fruit fly optimization algorithm is adopted, PID parameter setting in the temperature and humidity feedback control strategy process is carried out in an upper computer through codes, and the system response speed and the control stability are improved. The temperature, the humidity and the temperature change rate in the climate test box are adjusted through the hardware control system, fine regulation and control over the temperature and the humidity are achieved, and therefore the accuracy of the test result is improved, PID parameter setting in the temperature and humidity feedback control strategy process is conducted through the fruit fly optimization algorithm, and the system response speed and the control stability are improved.

Description

Technical Field

[0001] The invention relates to the technical field of climate test chambers, and in particular to an intelligent control method for the temperature change rate of a non-condensation climate test chamber. Background Art

[0002] At present, with the rapid development of society, the requirements for temperature and humidity environmental test chambers are getting higher and higher. The temperature changes in climate test chambers are also increasing, but climate test chambers will cause damage to the test products due to condensation when the temperature changes. The definition of condensation in GB / T 2422 is: a physical phenomenon in which moisture precipitates on the surface when the surface of the test product is lower than the dew point temperature of the surrounding air. When the test product is quickly transferred from a low-temperature environment to a high-temperature environment, due to the influence of the thermal inertia of the object, the cold inside the product gradually transfers from the inside to the surface. The temperature of the product surface may be lower than the temperature of the humid air surrounding it for a period of time, and the humid air absorbs the cold released by the product to produce condensation. In order to better improve the temperature and humidity environmental test chamber, the temperature change rate of the temperature and humidity environmental test chamber can be well controlled when the temperature changes; therefore, we propose a temperature change rate intelligent control method for a non-condensing climate test chamber to solve this problem. Summary of the invention

[0003] The object of the present invention is to provide a method for intelligently controlling the temperature change rate of a non-condensing climate test chamber to solve the problems raised in the above-mentioned background technology.

[0004] In order to achieve the above object, the present invention adopts the following technical solutions:

[0005] A method for intelligently controlling the temperature change rate of a non-condensing climate test chamber comprises the following steps:

[0006] S1. Obtain information such as temperature, humidity and pressure in the climate test chamber through the sensor group;

[0007] S2. Use the hardware control system to adjust the temperature, humidity and temperature change rate in the climate test chamber;

[0008] S3, adopt temperature and humidity feedback control strategy to achieve closed-loop control of temperature and humidity in the climate test chamber;

[0009] S4. Use the fruit fly optimization algorithm and implement it through code in the host computer to adjust the PID parameters in the process of temperature and humidity feedback control strategy to improve the system response speed and control stability.

[0010] Preferably, in S1, the sensor group includes a temperature sensor, a humidity sensor, and a pressure sensor;

[0011] The temperature sensor and humidity sensor are provided at the air outlet of the box only when the box volume is less than or equal to 1 cubic meter;

[0012] When the volume of the box is greater than 1 cubic meter and less than or equal to 3 cubic meters, install a set of temperature and humidity sensors at the left, middle and right positions of the box, 20 cm from the top and 20 cm from the back wall of the box; install a set of temperature and humidity sensors at 20 cm from the top and 40 cm from the door;

[0013] When the test object is placed in the climate test chamber, place temperature and humidity sensors at the connection of the test object's components, on the same plane as the test object, or inside the test object.

[0014] Preferably, in S2, the hardware control system includes a host computer, a controller, a filter and a sampling circuit;

[0015] The sampling circuit transmits the electrical signal collected by the sensor group to the host computer after filtering. The control software in the host computer calculates and sends the control behavior to the controller, which controls the device to perform humidity control.

[0016] The host computer is connected to the controller and the filter, the sampling circuit is connected to the filter and the test box, and the controller is connected to the test box.

[0017] Preferably, in S3, the temperature and humidity feedback control strategy establishes a fitness function model according to the climate test chamber parameters to calculate whether condensation will occur at a certain temperature change rate. The present invention selects the condensation humidity calculation formula as:

[0018]

[0019] In the above formula, τ is the thermal time constant of the test object, and is the temperature change rate. The thermal time constant of the test object is calculated according to the following formula:

[0020]

[0021] In the above formula, m is the mass of the test sample, c is the specific heat capacity of the test sample, Kt is the heat dissipation constant, and A is the surface area of ​​the test sample;

[0022] The temperature change rate is used as the system control quantity, and the humidity in the climate test chamber is used as the feedback signal;

[0023] The humidity signal is calculated through the feedback function to obtain the actual temperature change rate, which is compared with the set temperature change rate to obtain the temperature change rate error. The error is used as the input of the PID module, and the temperature change rate increment is calculated through the PID module. At the same time, the fruit fly optimization algorithm is used to adjust the PID parameters to improve the calculation accuracy and control stability of the PID module and avoid condensation during the temperature change process.

[0024] When using formula (2) to calculate the thermal time constant of the test product, if the parameters such as the specific heat capacity and heat dissipation constant of the test product are difficult to obtain, the thermal time constant is determined by experimental methods; the specific method is: determine the thermal time constant of the test product according to the thermal stabilization time of the test product, and the calculation formula of the stabilization time and the time constant is:

[0025]

[0026] The temperature stabilization time of the test sample is also obtained through experiments. For example, a test sample at 25°C is placed in a constant temperature box at 50°C. At this time, the temperature of the constant temperature box is approximately the step input to the test sample, that is:

[0027]

[0028] The time required when the difference between the measured temperature of the test object and the set temperature is within 2% is the stabilization time.

[0029] Preferably, in S4, the fruit fly optimization algorithm aims to solve the Kp, KI, Kd parameter selection problem in the PID module, and is implemented by software design to improve the response speed and stability of the control system in step 3. The purpose of the fruit fly optimization algorithm is to perform PID parameter tuning and solve the Kp, Ki, Kd parameter selection problem in the PID module.

[0030] Preferably, in S4, the fruit fly optimization algorithm takes the temperature change rate error e as input and searches for the optimal PID parameters through iterative calculation.

[0031] Preferably, in S4, the specific process of the fruit fly optimization algorithm includes:

[0032] S41. Initialize the information of the fruit fly group, including: the size of the fruit fly group, the maximum number of iterations, the effective search radius, the exploration radius of the fruit fly individual, the initial position radius, and the center position of the fruit fly group. Initialize the fruit fly individual information:

[0033]

[0034] S42, calculate the fitness function:

[0035]

[0036] S43, find the current optimal value of odor concentration and the corresponding fruit fly individual information, compare with the historical optimal value of odor concentration and update the historical optimal value and the corresponding fruit fly individual information:

[0037] [Smell best *i best ]=max(Smell i ) Formula (7);

[0038] S44, other individuals in the group update their positions to form a new fruit fly group center:

[0039]

[0040] S45, iterate and search until the iteration ends, and output the final Kp, Ki, and Kd values ​​to the PID module.

[0041] The beneficial effects of the present invention are:

[0042] 1. In the present invention, the temperature change rate intelligent control method of a non-condensing climate test chamber obtains information such as temperature, humidity and pressure in the climate test chamber through a sensor group to improve the test accuracy. The sensor can monitor the environmental parameters in the test chamber in real time to ensure that the temperature, humidity and pressure conditions in the test process meet the preset standards, thereby ensuring the accuracy of the test results; real-time data monitoring is achieved: the sensor group can continuously collect data and provide real-time monitoring, so that the operator can timely understand the environmental changes in the test chamber, which is convenient for timely adjustment and control of the test conditions; the data recorded by the sensor can analyze the changing trends of temperature, humidity and pressure, which is very helpful for studying the impact of environmental changes on test samples, and the automated sensor reduces the need for manual measurement, reduces the errors caused by improper human operation, and improves the reliability of data. The automated data collection and recording of the sensor can save a lot of time and human resources and improve work efficiency;

[0043] 2. In the present invention, the temperature change rate intelligent control method of a non-condensing climate test chamber uses a hardware control system to adjust the temperature, humidity and temperature change rate in the climate test chamber to achieve fine control of temperature and humidity, thereby improving the accuracy of the test results. The hardware control system can automatically execute a preset program without human intervention, which not only reduces the possibility of human error, but also improves the efficiency of the test process. The hardware control system can provide stable performance and maintain its accuracy and reliability even under long-term operation or extreme conditions;

[0044] 3. In the present invention, the temperature change rate intelligent control method of a non-condensing climate test chamber realizes closed-loop control of temperature and humidity in the climate test chamber by adopting a temperature and humidity feedback control strategy, which can effectively respond to external disturbances and internal changes of the system and maintain the stability of temperature and humidity, which is crucial to ensure the reliability of the test results. It can also quickly respond to environmental changes, adjust temperature and humidity in time, reduce the time to reach a steady state, and improve test efficiency. Since the climate test chamber may need to simulate a variety of different environmental conditions, the closed-loop control system can provide multiple temperature control points to meet the needs of different tests. The closed-loop control system can also provide multiple safety protection measures, such as over-temperature protection, short-circuit protection, etc., to ensure the safety of personnel and equipment during the test.

[0045] 4. In the present invention, the temperature change rate intelligent control method of a non-condensing climate test chamber adopts the fruit fly optimization algorithm to adjust the PID parameters in the process of temperature and humidity feedback control strategy, thereby improving the system response speed and control stability. The fruit fly optimization algorithm can perform a global search in the entire search space, thereby avoiding falling into a local optimal solution, and improving the possibility of finding the optimal or near-optimal PID parameters. Traditional PID parameter tuning methods usually require experienced engineers to perform manual adjustments, while the fruit fly optimization algorithm can automatically perform parameter optimization, reduce manual intervention, and improve efficiency. The PID parameters adjusted by the fruit fly optimization algorithm can reduce the steady-state error of the control system, shorten the rise time, and reduce the overshoot, thereby obtaining a better system response. The fruit fly optimization algorithm can dynamically adjust the search step size and direction according to the characteristics of the controlled object, making it more adaptable to different control environments and requirements. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] Figure 1 This is a schematic diagram of the arrangement of the sensor group of this example;

[0047] Figure 2 This is a simplified schematic diagram of the hardware control system of this example;

[0048] Figure 3 This is the flow chart of the temperature and humidity feedback strategy for this example;

[0049] Figure 4 This is the flow chart of the fruit fly optimization algorithm for this example;

[0050] Figure 5 This is the temperature curve diagram before and after optimization of this example. DETAILED DESCRIPTION

[0051] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0052] Reference Figure 1-Figure 5 , a temperature change rate intelligent control method of a non-condensing climate test chamber, comprising the following steps:

[0053] S1. Obtain information such as temperature, humidity and pressure in the climate test chamber through the sensor group;

[0054] S2. Use the hardware control system to adjust the temperature, humidity and temperature change rate in the climate test chamber;

[0055] S3, adopt temperature and humidity feedback control strategy to achieve closed-loop control of temperature and humidity in the climate test chamber;

[0056] S4. Use the fruit fly optimization algorithm and implement it through code in the host computer to adjust the PID parameters in the process of temperature and humidity feedback control strategy to improve the system response speed and control stability.

[0057] In this embodiment, in S1, the sensor group includes a temperature sensor, a humidity sensor, and a pressure sensor;

[0058] Temperature sensor and humidity sensor: when the volume of the box is less than or equal to 1 cubic meter, only one set of temperature sensor and humidity sensor is provided at the air outlet of the box;

[0059] When the volume of the box is greater than 1 cubic meter and less than or equal to 3 cubic meters, install a set of temperature and humidity sensors at the left, middle and right positions of the box, 20 cm from the top and 20 cm from the back wall of the box; install a set of temperature and humidity sensors at 20 cm from the top and 40 cm from the door;

[0060] When the test object is placed in the climate test chamber, place temperature and humidity sensors at the connection of the test object's components, on the same plane as the test object, or inside the test object.

[0061] In this embodiment, in S2, the hardware control system includes a host computer, a controller, a filter and a sampling circuit;

[0062] The sampling circuit transmits the electrical signal collected by the sensor group to the host computer after filtering. The control software in the host computer calculates and sends the control behavior to the controller, which controls the device to perform humidity control.

[0063] The host computer is connected to the controller and the filter, the sampling circuit is connected to the filter and the test box, and the controller is connected to the test box.

[0064] In this embodiment, in S3, the temperature and humidity feedback control strategy establishes a fitness function model based on the climate test chamber parameters to calculate whether condensation will occur at a certain temperature change rate. The present invention selects the condensation humidity calculation formula as:

[0065]

[0066] In the above formula, τ is the thermal time constant of the test object, and is the temperature change rate. The thermal time constant of the test object is calculated according to the following formula:

[0067]

[0068] In the above formula, m is the mass of the test sample, c is the specific heat capacity of the test sample, Kt is the heat dissipation constant, and A is the surface area of ​​the test sample;

[0069] The temperature change rate is used as the system control quantity, and the humidity in the climate test chamber is used as the feedback signal;

[0070] The humidity signal is calculated through the feedback function to obtain the actual temperature change rate, which is compared with the set temperature change rate to obtain the temperature change rate error. The error is used as the input of the PID module, and the temperature change rate increment is calculated through the PID module. At the same time, the fruit fly optimization algorithm is used to adjust the PID parameters to improve the calculation accuracy and control stability of the PID module and avoid condensation during the temperature change process.

[0071] When using formula 2 to calculate the thermal time constant of the test product, if the parameters such as the specific heat capacity and heat dissipation constant of the test product are difficult to obtain, the thermal time constant is determined by experimental methods; the specific method is: determine the thermal time constant of the test product according to the thermal stabilization time of the test product, and the calculation formula of the stabilization time and the time constant is:

[0072]

[0073] The temperature stabilization time of the test sample is also obtained through experiments. For example, a test sample at 25°C is placed in a constant temperature box at 50°C. At this time, the temperature of the constant temperature box is approximately the step input to the test sample, that is:

[0074]

[0075] The time required when the difference between the measured temperature of the test object and the set temperature is within 2% is the stabilization time.

[0076] In this embodiment, in S4, the fruit fly optimization algorithm aims to solve the Kp, KI, Kd parameter selection problem in the PID module, and is implemented through software design to improve the response speed and stability of the control system in step 3. The purpose of the fruit fly optimization algorithm is to perform PID parameter tuning and solve the Kp, Ki, Kd parameter selection problem in the PID module.

[0077] In this embodiment, in S4, the fruit fly optimization algorithm uses the temperature change rate error e as input and searches for the optimal PID parameters through iterative calculation.

[0078] In this embodiment, in S4, the specific process of the fruit fly optimization algorithm includes:

[0079] S41. Initialize the information of the fruit fly group, including: the size of the fruit fly group, the maximum number of iterations, the effective search radius, the exploration radius of the fruit fly individual, the initial position radius, and the center position of the fruit fly group. Initialize the fruit fly individual information:

[0080]

[0081] S42, calculate the fitness function:

[0082]

[0083] S43, find the current optimal value of odor concentration and the corresponding fruit fly individual information, compare with the historical optimal value of odor concentration and update the historical optimal value and the corresponding fruit fly individual information:

[0084] [Smell best *i best ]=max(Smell i ) Formula 7;

[0085] S44, other individuals in the group update their positions to form a new fruit fly group center:

[0086]

[0087] S45, iterate and search until the iteration ends, and output the final Kp, Ki, and Kd values ​​to the PID module.

[0088] In this embodiment, the temperature, humidity, pressure and other information in the climate test chamber are obtained through the sensor group to improve the test accuracy. The sensor can monitor the environmental parameters in the test chamber in real time to ensure that the temperature, humidity and pressure conditions in the test process meet the preset standards, thereby ensuring the accuracy of the test results; real-time data monitoring is achieved: the sensor group can continuously collect data and provide real-time monitoring, so that the operator can timely understand the environmental changes in the test chamber, which is convenient for timely adjustment and control of the test conditions; the data recorded by the sensor can be used to analyze the changing trends of temperature, humidity and pressure, which is very helpful for studying the impact of environmental changes on test samples, and the automated sensor reduces the need for manual measurement, reduces the errors caused by improper human operation, and improves the reliability of data. The automated data collection and recording of the sensor can save a lot of time and human resources and improve work efficiency; the hardware control system is used to adjust the temperature, humidity and temperature change rate in the climate test chamber to achieve fine control of temperature and humidity, thereby improving the accuracy of the test results. The hardware control system can automatically execute the preset program without manual intervention, which not only reduces the possibility of human error, but also improves the efficiency of the test process. The hardware control system can provide stable performance and maintain its accuracy and reliability even under long-term operation or extreme conditions; by adopting the temperature and humidity feedback control strategy, the The closed-loop control of temperature and humidity in the climate test chamber can effectively respond to external disturbances and internal changes in the system and maintain the stability of temperature and humidity, which is crucial to ensuring the reliability of the test results. It can also quickly respond to environmental changes, adjust temperature and humidity in time, reduce the time to reach a steady state, and improve test efficiency. Since the climate test chamber may need to simulate a variety of different environmental conditions, the closed-loop control system can provide multiple temperature control points to meet the needs of different tests. The closed-loop control system can also provide multiple safety protection measures, such as over-temperature protection and short-circuit protection, to ensure the safety of personnel and equipment during the test. The fruit fly optimization algorithm is used to adjust the PID parameters in the process of temperature and humidity feedback control strategy to improve the system The fruit fly optimization algorithm can perform a global search in the entire search space to improve the system response speed and control stability, thus avoiding falling into the local optimal solution and increasing the possibility of finding the optimal or near-optimal PID parameters. The traditional PID parameter tuning method usually requires experienced engineers to perform manual adjustments, while the fruit fly optimization algorithm can automatically optimize the parameters, reduce manual intervention and improve efficiency. The PID parameters tuned by the fruit fly optimization algorithm can reduce the steady-state error, rise time and overshoot of the control system, thereby obtaining a better system response. The fruit fly optimization algorithm can dynamically adjust the search step size and direction according to the characteristics of the controlled object, making it more adaptable to different control environments and requirements.

[0089] The above is a detailed introduction to the temperature change rate intelligent control method of a non-condensing climate test chamber provided by the present invention. Specific embodiments are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea. It should be pointed out that for ordinary technicians in this technical field, without departing from the principle of the present invention, several improvements and modifications can be made to the present invention, and these improvements and modifications also fall within the scope of protection of the claims of the present invention.

Claims

1. A method for intelligently controlling the temperature change rate of a non-condensing climate test chamber, characterized in that: The steps include: S1. Obtain information such as temperature, humidity and pressure in the climate test chamber through the sensor group; S2. Use the hardware control system to adjust the temperature, humidity and temperature change rate in the climate test chamber; S3, adopt temperature and humidity feedback control strategy to achieve closed-loop control of temperature and humidity in the climate test chamber; S4. Use the fruit fly optimization algorithm and implement it through code in the host computer to adjust the PID parameters in the process of temperature and humidity feedback control strategy to improve the system response speed and control stability.

2. The method for intelligently controlling the temperature change rate of a non-condensing climate test chamber according to claim 1, characterized in that: In S1, the sensor group includes a temperature sensor, a humidity sensor, and a pressure sensor; The temperature sensor and humidity sensor are provided at the air outlet of the box only when the box volume is less than or equal to 1 cubic meter; When the volume of the box is greater than 1 cubic meter and less than or equal to 3 cubic meters, install a set of temperature and humidity sensors at the left, middle and right positions of the box, 20 cm from the top and 20 cm from the back wall of the box; install a set of temperature and humidity sensors at 20 cm from the top and 40 cm from the door; When the test object is placed in the climate test chamber, place temperature and humidity sensors at the connection of the test object's components, on the same plane as the test object, or inside the test object.

3. The method for intelligently controlling the temperature change rate of a non-condensing climate test chamber according to claim 1, characterized in that: In S2, the hardware control system includes a host computer, a controller, a filter and a sampling circuit; The sampling circuit transmits the electrical signal collected by the sensor group to the host computer after filtering. The control software in the host computer calculates and sends the control behavior to the controller, which controls the device to perform humidity control. The host computer is connected to the controller and the filter, the sampling circuit is connected to the filter and the test box, and the controller is connected to the test box.

4. The method for intelligently controlling the temperature change rate of a non-condensing climate test chamber according to claim 1, characterized in that: In S3, the temperature and humidity feedback control strategy establishes a fitness function model according to the climate test chamber parameters to calculate whether condensation will occur at a certain temperature change rate. The present invention selects the condensation humidity calculation formula as follows: In the above formula, τ is the thermal time constant of the test object, and is the temperature change rate. The thermal time constant of the test object is calculated according to the following formula: In the above formula, m is the mass of the test sample, c is the specific heat capacity of the test sample, Kt is the heat dissipation constant, and A is the surface area of ​​the test sample; The temperature change rate is used as the system control quantity, and the humidity in the climate test chamber is used as the feedback signal; The humidity signal is calculated through the feedback function to obtain the actual temperature change rate, which is compared with the set temperature change rate to obtain the temperature change rate error. The error is used as the input of the PID module, and the increment of the temperature change rate is calculated by the PID module. At the same time, the fruit fly optimization algorithm is used to adjust the PID parameters to improve the calculation accuracy and control stability of the PID module and avoid condensation during the temperature change process. When using formula (2) to calculate the thermal time constant of the test product, if the parameters such as the specific heat capacity and heat dissipation constant of the test product are difficult to obtain, the thermal time constant is determined by experimental methods; the specific method is: determine the thermal time constant of the test product according to the thermal stabilization time of the test product, and the calculation formula of the stabilization time and the time constant is: The temperature stabilization time of the test sample is also obtained through experiments. For example, a test sample at 25°C is placed in a constant temperature box at 50°C. At this time, the temperature of the constant temperature box is approximately the step input to the test sample, that is: The time required when the difference between the measured temperature of the test object and the set temperature is within 2% is the stabilization time.

5. The method for intelligently controlling the temperature change rate of a non-condensing climate test chamber according to claim 1, characterized in that: In S4, the fruit fly optimization algorithm aims to solve the Kp, KI, Kd parameter selection problem in the PID module, and is implemented through software design to improve the response speed and stability of the control system in step 3. The purpose of the fruit fly optimization algorithm is to perform PID parameter tuning and solve the Kp, Ki, Kd parameter selection problem in the PID module.

6. The method for intelligently controlling the temperature change rate of a non-condensing climate test chamber according to claim 4, characterized in that: In S4, the fruit fly optimization algorithm takes the temperature change rate error e as input and searches for the optimal PID parameters through iterative calculation.

7. The method for intelligently controlling the temperature change rate of a non-condensing climate test chamber according to claim 4, characterized in that: In S4, the specific process of the fruit fly optimization algorithm includes: S41. Initialize the information of the fruit fly group, including: the size of the fruit fly group, the maximum number of iterations, the effective search radius, the exploration radius of individual fruit flies, the initial position radius, the center position of the fruit fly group, and initialize the information of individual fruit flies: S42, calculate the fitness function: S43, find the current optimal value of odor concentration and the corresponding fruit fly individual information, compare with the historical optimal value of odor concentration and update the historical optimal value and the corresponding fruit fly individual information: [Smell best *i best = max(Smell i ) Equation (7); S44, other individuals in the group update their positions to form a new fruit fly group center: S45, iterate and search until the iteration ends, and output the final Kp, Ki, and Kd values ​​to the PID module.

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