Automatic temperature detection method based on dynamic feedback and intelligent cooling control system

Abstract

The invention discloses an automatic temperature detection method based on dynamic feedback and an intelligent cooling control system, a plurality of temperature sensors are arranged in an array mode, a fuzzy PID controller outputs PID parameters according to a fuzzy PID control algorithm, the PID parameters can be dynamically adjusted and adapt to dynamic environment fluctuation, and the PID parameter output accuracy is improved, so that the decision accuracy is improved; and therefore, a graded refrigeration strategy is accurately implemented, the refrigeration and heat dissipation effects are ensured, and energy consumption is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of medical machinery, and particularly to an automatic temperature detection method and an intelligent cooling control system based on dynamic feedback. Background Art

[0002] Thermostatic boxes mostly adopt mechanical temperature control switches or single-sensor control, resulting in inaccurate measured temperature values and high hysteresis, and being unable to respond to temperature changes in real time;

[0003] The cooling system of the thermostatic box relies on fixed threshold triggering and is difficult to adapt to dynamic environmental fluctuations;

[0004] The thermostatic box also lacks a multi-stage linked refrigeration control strategy, with high energy consumption and low efficiency;

[0005] The abnormal state warning mechanism of the thermostatic box is imperfect, easily leading to equipment damage or sample failure. Summary of the Invention

[0006] The purpose of the present invention is to provide an automatic temperature detection method and an intelligent cooling control system based on dynamic feedback. A plurality of temperature sensors are arranged in an array, and a fuzzy PID controller outputs PID parameters according to the fuzzy PID control algorithm. The PID parameters can be dynamically adjusted to adapt to dynamic environmental fluctuations, improve the accuracy of the PID parameter output, thereby improving the accuracy of decision-making, and then accurately implementing a hierarchical refrigeration strategy.

[0007] To achieve this purpose, the present invention adopts the following technical solutions:

[0008] An automatic temperature detection method based on dynamic feedback, the method comprising the following steps:

[0009] Input a set temperature;

[0010] Arrange a plurality of temperature sensors in an array, measure the values of the real-time temperature, and form a set of values of the real-time temperature;

[0011] Collect the values of the threshold temperature and the real-time temperature, and output PID parameters according to the fuzzy PID control algorithm;

[0012] Execute a multi-stage refrigeration mode according to the PID parameters.

[0013] In some embodiments, the number of the temperature sensors is at least set to 5;

[0014] The values of the real-time temperature are output after Kalman filtering denoising.

[0015] In some embodiments, the fuzzy PID control algorithm comprises the following sub-steps:

[0016] Collect and calculate values: Collect the values of the threshold temperature and the real-time temperature, and calculate the real-time temperature difference and the real-time temperature change rate;

[0017] Fuzzification processing: Map the real-time temperature difference and the real-time temperature change rate into fuzzy language variable values;

[0018] Form a fuzzy rule base: Based on fuzzy logic rules, convert the fuzzy language variable values into an IF-THEN fuzzy rule base;

[0019] Fuzzy inference: Generate a fuzzy value set according to the fuzzy logic rules from the fuzzy language variable values;

[0020] Defuzzification processing: Convert the fuzzy values into precise PID parameters;

[0021] Dynamic adjustment of PID parameters: Update the PID control quantity and dynamically adjust the PID parameters;

[0022] Output the PID parameters.

[0023] In some embodiments, in the fuzzification processing step, a Gaussian function is applied for fuzzification processing.

[0024] In some embodiments, in the step of forming the fuzzy rule base, 25 IF-THEN rule tables and an integral anti-windup algorithm are applied to form the IF-THEN fuzzy rule base.

[0025] In some embodiments, in the fuzzy inference step, a Mamdani model is applied to generate the fuzzy value set.

[0026] In some embodiments, in the defuzzification processing step, a gravity algorithm is applied to generate the precise PID parameters.

[0027] In some embodiments, in the step of dynamically adjusting the PID parameters, the PID control quantity is updated according to the humidity value and the air pressure value of the environment, so as to dynamically adjust the PID parameters.

[0028] In some embodiments, the multi-stage refrigeration includes at least three stages of refrigeration, namely air flow refrigeration, solid refrigeration and liquid refrigeration.

[0029] Intelligent temperature control system, including:

[0030] Set temperature input module, used for setting the threshold temperature;

[0031] Temperature measurement module, including a plurality of temperature sensors arranged in an array, used for measuring the values of the real-time temperature and forming an array of real-time temperature value sets;

[0032] The fuzzy PID controller outputs PID parameters according to the fuzzy PID control algorithm, and the PID parameters are output after being fuzzified and dynamically adjusted;

[0033] Among them, the fuzzy PID controller includes a collection and calculation module, a fuzzification processing module, a fuzzy rule base formation module, a fuzzy inference engine module, a defuzzification processing module, a PID parameter dynamic adjustment module, and an output PID parameter module that are sequentially connected by data;

[0034] The refrigeration control module generates a refrigeration control signal according to the PID parameters;

[0035] And a multi-stage refrigeration module that executes a multi-stage refrigeration mode according to the refrigeration control signal.

[0036] The beneficial effects of the present invention: Multiple temperature sensors are arranged in an array, the fuzzy PID controller outputs PID parameters according to the fuzzy PID control algorithm, the PID parameters can be dynamically adjusted to adapt to dynamic environmental fluctuations, improve the accuracy of PID parameter output, thereby improve the accuracy of decision-making, and then accurately implement a hierarchical refrigeration strategy, ensure the refrigeration effect, and reduce energy consumption. Description of the Drawings

[0037] Figure 1 One of the flowcharts of the automatic temperature detection method based on dynamic feedback of the present invention;

[0038] Figure 2 Another flowchart of the automatic temperature detection method based on dynamic feedback of the present invention;

[0039] Figure 3 The structure diagram of the intelligent temperature control system of the present invention;

[0040] Figure 4 The structure diagram of the temperature control box of the present invention;

[0041] Figure 5 The exploded view of the temperature control box of the present invention;

[0042] Wherein: 100 - temperature control box; 10 - set temperature input module; 1 - temperature measurement module: 11 - temperature sensor; 12 - Kalman filter; 2 - fuzzy PID controller; 21 - collection and calculation module; 22 - fuzzification processing module; 23 - fuzzy rule base formation module; 24 - fuzzy inference engine module; 25 - defuzzification processing module; 26 - PID parameter dynamic adjustment module; 27 - output PID parameter module; 3 - refrigeration control module; 4 - multi-stage refrigeration module; 41 - air-cooled fan; 42 - semiconductor refrigeration chip; 43 - liquid cooling component; 5 - environmental compensation module; 51 - humidity sensor; 52 - air pressure sensor; 6 - closed-loop feedback and anomaly detection module; 7 - wireless communication module. Detailed implementation manners

[0043] The present invention will be further described in detail below with reference to the accompanying drawings.

[0044] Reference Figure 1 And Figure 2 Based on the dynamic feedback automatic temperature detection method, the method includes the following steps:

[0045] S1: Input the set temperature T set , the unit is °C;

[0046] S2: Arrange a plurality of temperature sensors 11 in an array, measure the value of the real-time temperature T real , the unit is °C, and form a numerical set of the real-time temperature; the array can be understood as a uniform arrangement or arranged in a certain rule, aiming to measure the temperature values at more positions, so that the numerical set of the temperature is more and more comprehensive;

[0047] Among them, steps S1 and S2 are not in sequence and can also be executed simultaneously;

[0048] S3: Collect the values of the threshold temperature T set and the real-time temperature T real , and output the PID parameters according to the fuzzy PID control algorithm; among them, the PID parameters can be dynamically adjusted to adapt to the fluctuations of the environment, such as changes in humidity and air pressure, etc.;

[0049] S4: Execute a multi-stage refrigeration mode according to the PID parameters, for example, set at least three levels of refrigeration modes, namely air flow refrigeration, solid-state refrigeration and liquid-state refrigeration; the air flow refrigeration can be the cold energy formed by the air flow of the air-cooled fan, the solid-state refrigeration can be the cold energy formed by the semiconductor refrigeration chip, and the liquid-state refrigeration can be the cold energy such as liquid nitrogen; when the temperature is low, air flow refrigeration can be used, when the temperature is medium, solid-state refrigeration can be used, and when the temperature is high, liquid-state refrigeration can be used. Of course, the above three levels of refrigeration methods can cooperate with each other in any combination of two or three. Thus, different refrigeration modes can be executed according to different temperatures.

[0050] Therefore, the fuzzy logic rule processing can improve the accuracy of the PID parameters, improve the accuracy of the decision-making, and the PID parameters can be dynamically adjusted to further improve the accuracy, and can adapt to the fluctuations of the dynamic environment, so that the corresponding refrigeration mode can be accurately and timely executed, ensuring the refrigeration and heat dissipation effect, and facilitating the reduction of energy consumption.

[0051] In step S2, the number of the temperature sensors 11 is at least set to 5, and they are evenly arranged or arranged in an array at different positions of the object to be measured or the environment to be measured.

[0052] In step S2, the value of the real-time temperature is output after Kalman filtering and denoising, which can eliminate the local temperature fluctuation noise and thus improve the accuracy of numerical measurement.

[0053] Reference Figure 2 , in step S3, the fuzzy PID control algorithm includes the following sub-steps: (S31 to S37)

[0054] S31: Collect and calculate values: Receive or collect the threshold temperature T set and the real-time temperature T real values, calculate the real-time temperature difference e = T set -T real (unit: °C) and the real-time temperature change rate (unit: °C / s).

[0055] S32: Fuzzification processing: Map the real-time temperature difference and the real-time temperature change rate into fuzzy language variable values to realize the fuzzification of the input variables.

[0056] Among them, the fuzzy language variable of e can be: {negative large, negative medium, zero, positive medium, positive large};

[0057] The fuzzy language variable of Δe can be: {rapid decline, slow decline, stable, slow rise, rapid rise};

[0058] Specifically: The fuzzification processing can be carried out through the Gaussian function;

[0059] Membership function: Gaussian function:

[0060] μ: Mean value (central value) of the membership function, μ ∈ [-5, +5] μ ∈ [-5, +5];

[0061] σ: Standard deviation (width) of the membership function, σ ∈ [0.3, 1.5] σ ∈ [0.3, 1.5];

[0062] The following takes the temperature deviation e as an example to briefly illustrate:

[0063] 1. NB (negative large): μ = -5.0, σ = 1.2;

[0064] 2. ZO (zero): μ = 0.0, σ = 0.8.

[0065] Thus, the fuzzy language variable is generated through the Gaussian function fuzzification processing, improving the accuracy of numerical processing.

[0066] S33: Form a fuzzy rule base: Based on fuzzy logic rules, convert the numerical values of fuzzy language variables into an IF-THEN fuzzy rule base, thereby forming a decision-making scheme based on fuzzy logic rules, and adjusting the PID parameters accordingly to accurately output the PID parameters.

[0067] Specifically: 25 IF-THEN rule tables and an integral anti-windup algorithm can be applied to form an IF-THEN rule base from the numerical values of fuzzy language variables, and then adjust the PID parameters based on the 25 IF-THEN rules.

[0068] Rule example:

[0069] IF e = PB (Positive Big) AND Δe = FU (Fast Rising) THEN ΔKp = VL (Very Large Increase), ΔKi = VS (Very Small Inhibition);

[0070] IF e = ZO (Zero) AND Δe = ST (Stable) THEN enable the integral anti-windup algorithm.

[0071] Table 1: Fuzzy rule base (IF-THEN rule table): NB (Negative Big), NM (Negative Medium), ZO (Zero), PM (Positive Medium), PB (Positive Big); Control variables: Kp, Ki, Kd;

[0072]

[0073] Table 1

[0074] Thus, based on fuzzy logic rules, form a decision-making scheme, adjust the PID parameters accordingly, and accurately output the PID parameters.

[0075] S34: Fuzzy inference: Generate a fuzzy value set from the numerical values of fuzzy language variables according to fuzzy logic rules.

[0076] Specifically: The Mamdani model can be applied to generate a fuzzy value set from the numerical values of fuzzy language variables according to the IF-THEN fuzzy logic rules.

[0077] For example: IF e is [fuzzy value set] AND Δe is [fuzzy value set] THEN ΔKp =..., ΔKi =..., ΔKd =...

[0078] Example rule R1:

[0079] IF e = PB (μPB(e)) AND Δe = FU (μFU(Δe)) THEN ΔK p = VL, ΔK i = VS, ΔK d = L

[0080] Trigger intensity: αR1 = min(μPB(e), μFU(Δe));

[0081] Output membership degree: μΔK p (y) = αR1 · μVL(y);

[0082] Output variables: Fuzzy values of Kp, Ki, Kd (such as "large", "medium", "small").

[0083] S35: Defuzzification: Convert the fuzzy values into precise PID parameters.

[0084] Specifically: The gravity algorithm can be applied to generate precise PID parameters.

[0085] The gravity algorithm (COG) generates PID parameters according to the following formula;

[0086]

[0087] Similarly:

[0088] Similarly:

[0089] Thus, the precision and stability of PID parameter generation are improved through the gravity algorithm (COG).

[0090] S36: Dynamic adjustment of PID parameters: Update the PID control quantity and dynamically adjust the PID parameters according to the change of the PID control quantity.

[0091] Specifically:

[0092] Formula for updating the PID control quantity:

[0093]

[0094] And the integral anti-windup algorithm can be applied to limit the integral term to reduce or avoid cumulative errors;

[0095] Environmental compensation: The PID control quantities Kp, Ki, Kd can be corrected or adjusted according to the humidity, air pressure and other values of the environment; for example, compensation can be carried out through a compensator, and the real-time temperature can also be compensated according to the RBF neural network or neural network training model to correct or adjust the PID control quantities Kp, Ki, Kd.

[0096] It can be seen that by using the PID control quantity calculation formula and environmental compensation, etc., the PID parameters are dynamically adjusted to improve the accuracy of the output value, so as to adapt to the fluctuations of the dynamic environment.

[0097] S37: Output the PID parameters. Output the PID parameters so that corresponding refrigeration control can be performed according to the PID parameters, and a multi-stage refrigeration mode can be achieved.

[0098] Therefore, the numerical values are processed by fuzzy logic rules to improve the accuracy of the output, enhance the accuracy and scientific nature of decision-making, and can adjust the PID parameters through humidity, air pressure, etc. to achieve dynamic adjustment, adapt to environmental fluctuations, and improve the accuracy and reliability of the output. Then, according to this output, a multi-stage refrigeration mode (strategy) is executed to achieve scientific and intelligent refrigeration, ensure the refrigeration effect, and reduce energy consumption.

[0099] Reference Figure 3 , an intelligent temperature control system, comprising:

[0100] A set temperature input module 10 for inputting the set temperature T set ;

[0101] A temperature measurement module 1, including a plurality of temperature sensors 11 arranged in an array, for measuring the real-time temperature T real value, forming an array of real-time temperature T real value set; wherein, it further includes a Kalman filter 12 for denoising the real-time temperature T real value.

[0102] A fuzzy PID controller 2 for performing the above-mentioned step S3, for receiving or collecting the set temperature T set and the real-time temperature T real , and outputting PID parameters according to the fuzzy PID control algorithm, and the PID parameters can be dynamically adjusted;

[0103] Among them, the fuzzy PID controller 2 includes a collection and calculation module 21, a fuzzification processing module 22, a fuzzy rule base formation module 23, a fuzzy inference engine module 24, a defuzzification processing module 25, a PID parameter dynamic adjustment module 26, and an output PID parameter module 27 that are sequentially connected by data;

[0104] The collection and calculation module 21: used for performing the above-mentioned step S31;

[0105] The fuzzification processing module 22: used for performing the above-mentioned step S32;

[0106] The fuzzy rule base formation module 23: used for performing the above-mentioned step S33;

[0107] The fuzzy inference engine module 24: used for performing the above-mentioned step S34;

[0108] The defuzzification processing module 25: used for performing the above-mentioned step S35;

[0109] PID parameter dynamic adjustment module 26: used to execute the above step S36;

[0110] Output PID parameter module 27: used to execute the above step S36.

[0111] Refrigeration control module 3, which receives PID parameters and generates a refrigeration control signal according to the PID parameters;

[0112] And a multi-stage refrigeration module 4, which executes a multi-stage refrigeration mode according to the refrigeration control signal; wherein, the multi-stage refrigeration module 4 includes an air-cooled fan 41, a thermoelectric cooler 42 and a liquid-cooling component 43, and the above three can all work independently, or work in cooperation in any combination of two or three.

[0113] The control mode or grading strategy of refrigeration can be expressed as follows:

[0114]

[0115] Of course, the trigger condition can be appropriately set according to different application scenarios to implement the multi-stage refrigeration mode.

[0116] The intelligent temperature reduction control system further includes:

[0117] An environment compensation module 5, including a humidity sensor 51 and a barometric pressure sensor 52, which are respectively used to measure the humidity value and the barometric pressure value; both the humidity sensor 51 and the barometric pressure sensor 52 are connected to the fuzzy PID controller 2; and

[0118] A closed-loop feedback and anomaly detection module 6, which is connected to the fuzzy PID controller 2, and the real-time temperature data can be re-input to the detection module to form a closed-loop control. When it is detected that the PID control quantities Kp, Ki, Kd are exceeded or the current is abnormal, a fault tolerance mechanism is triggered, for example, switching to a standby refrigeration module, and an alarm signal can be issued.

[0119] A wireless communication module 7, which is connected to the closed-loop feedback and anomaly detection module 6, and can send the alarm signal to a remote control platform or a terminal device through technologies such as WiFi / 4G / 5G / bluetooth.

[0120] Reference Figure 4 And Figure 5 , a temperature control box 100, including the above intelligent temperature reduction control system, and the intelligent temperature reduction control system can be arranged inside the temperature control box 100.

[0121] Through the temperature sensor 11 array and the fuzzy PID control algorithm, the intelligent temperature reduction control system can achieve a temperature control accuracy of ±0.1 °C, meeting the high-precision scenario requirements such as laboratories and medical equipment.

[0122] The multi - stage refrigeration cooperation strategy (air cooling → TEC → liquid cooling) dynamically adjusts the refrigeration intensity according to the temperature deviation, reducing the energy consumption by about 20%.

[0123] The high - precision temperature control ability of the intelligent temperature - reduction control system can improve the performance of devices that rely on temperature stability. For example:

[0124] Laboratory equipment: The experimental repeatability and accuracy of equipment such as PCR machines and incubators are improved.

[0125] Medical equipment: The temperature stability of blood storage boxes and drug refrigerators is improved, ensuring medical safety.

[0126] Electronic test equipment: The reliability of chip aging tests is enhanced, reducing test errors.

[0127] The above - disclosed are only some embodiments of the present invention. For those of ordinary skill in the art, without departing from the inventive concept of the present invention, several variations and improvements can still be made, and these all fall within the protection scope of the invention.

Claims

1. An automatic temperature detection method based on dynamic feedback, characterized in that The method includes the following steps: Input the set temperature; Arrange multiple temperature sensors in an array to measure the real-time temperature values and form a set of real-time temperature values; Collect the threshold temperature and real-time temperature values, and output PID parameters according to the fuzzy PID control algorithm; Execute a multi-stage refrigeration mode according to the PID parameters.

2. The automatic temperature detection method based on dynamic feedback according to claim 1, wherein The number of the temperature sensors is at least set to 5; The real-time temperature values are output after Kalman filtering for denoising.

3. The automatic temperature detection method based on dynamic feedback according to claim 1, wherein The fuzzy PID control algorithm includes the following sub-steps: Collect and calculate values: Collect the threshold temperature and real-time temperature values, and calculate the real-time temperature difference and the real-time temperature change rate; Fuzzification processing: Map the real-time temperature difference and the real-time temperature change rate into fuzzy language variable values; Form a fuzzy rule base: Based on fuzzy logic rules, convert the fuzzy language variable values into an IF-THEN fuzzy rule base; Fuzzy inference: Generate a set of fuzzy values according to the fuzzy logic rules from the fuzzy language variable values; Defuzzification processing: Convert the fuzzy values into accurate PID parameters; Dynamic adjustment of PID parameters: Update the PID control quantity and dynamically adjust the PID parameters; Output the PID parameters.

4. The automatic temperature detection method based on dynamic feedback according to claim 3, wherein In the fuzzification processing step, the Gaussian function is applied for fuzzification processing.

5. The automatic temperature detection method based on dynamic feedback according to claim 3, wherein In the step of forming the fuzzy rule base, 25 IF-THEN rule tables and an integral anti-windup algorithm are applied to form the IF-THEN fuzzy rule base.

6. The automatic temperature detection method based on dynamic feedback according to claim 3, wherein In the fuzzy inference step, the Mamdani model is applied to generate a set of fuzzy values.

7. The automatic temperature detection method based on dynamic feedback according to claim 3, characterized in that In the defuzzification processing step, the gravity algorithm is applied to generate accurate PID parameters.

8. The automatic temperature detection method based on dynamic feedback according to claim 3, characterized in that, In the step of dynamically adjusting the PID parameters, the PID control quantity is updated according to the humidity value and the air pressure value of the environment, so as to dynamically adjust the PID parameters.

9. The automatic temperature detection method based on dynamic feedback according to claim 1, wherein The multi-stage refrigeration includes at least three stages of refrigeration, namely air flow refrigeration, solid refrigeration and liquid refrigeration.

10. Intelligent cooling control system, characterized in that, It includes: A set temperature input module for setting the threshold temperature; A temperature measurement module, including multiple temperature sensors arranged in an array, for measuring the real-time temperature values and forming a set of array real-time temperature values; A fuzzy PID controller, which outputs PID parameters according to the fuzzy PID control algorithm, and the PID parameters are output after fuzzification processing and dynamic adjustment; Among them, the fuzzy PID controller includes a collection and calculation module, a fuzzification processing module, a fuzzy rule base formation module, a fuzzy inference engine module, a defuzzification processing module, a PID parameter dynamic adjustment module, and an output PID parameter module that are sequentially connected by data; A refrigeration control module for generating a refrigeration control signal according to the PID parameters; and A multi-stage refrigeration module for executing a multi-stage refrigeration mode according to the refrigeration control signal.

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