Internet of Things automatic control method and system for medical temperature and humidity automatic control box

Abstract

The present invention discloses an Internet of Things (IoT) automatic control method and system for a medical temperature and humidity automatic control box, and relates to the technical field of medical cold chain. The present invention comprises the following steps: setting a target temperature and a target humidity, initializing coefficients of a dynamic weight allocation algorithm, and calibrating sensors; performing preprocessing based on the calibrated sensors, collecting data in the box in real time, and calculating external disturbance factors; calculating temperature and humidity deviations and their rates of change based on the preprocessed data, generating control weights through a dynamic weight allocation algorithm in combination with external disturbance factors, and performing dynamic priority allocation for multi-objective control; selecting a priority mode for temperature or humidity regulation based on the calculated weights, and driving an actuator; uploading execution results and environmental data to the cloud through an IoT module, supporting users to remotely adjust target parameters to form closed-loop control; and cyclically executing the above steps until the temperature and humidity are stable within the target range for three consecutive cycles, or the user actively terminates the control and enters a low-power consumption mode.

Description

Technical Field

[0001] The present invention belongs to the field of medical cold chain technology, and in particular relates to an Internet of Things automatic control method and system for a medical temperature and humidity automatic control box. Background Art

[0002] In the medical transportation sector, pharmaceuticals, vaccines, and biological specimens require extremely stable temperature and humidity in their storage environments. Traditional medical temperature and humidity control boxes typically use fixed-parameter control systems that rely on preset thresholds to trigger heating or cooling operations, making them difficult to cope with complex and changing real-world application scenarios. Existing technologies generally suffer from a single temperature and humidity control strategy and poor dynamic coupling adaptability. This inability to effectively coordinate multivariable adjustment priorities leads to control lag or overshoot. Furthermore, traditional systems lack deep integration with IoT technologies, making it difficult to achieve remote real-time monitoring and data-driven parameter optimization. This results in low operation and maintenance efficiency. External disturbances, such as door opening and closing frequency and ambient temperature and humidity differences, are often overlooked, further reducing control accuracy and increasing energy consumption. The inability to dynamically allocate actuator weights based on real-time deviations, rates of change, and external disturbances can easily lead to resource conflicts or redundant operations. Furthermore, the lack of a closed-loop optimization mechanism prevents the full utilization of historical data, potentially leading to parameter drift or performance degradation during long-term operation. These issues not only increase the risk of deterioration of medical supplies during transportation but also lead to energy waste and increased equipment maintenance costs. Therefore, we propose an IoT-based automatic control method and system for medical temperature and humidity control boxes. Summary of the Invention

[0003] To solve the above technical problems, the present invention is achieved through the following technical solutions:

[0004] The present invention provides an Internet of Things automatic control method for a medical temperature and humidity automatic control box, comprising the following steps:

[0005] Step S1: Set the target temperature and target humidity, initialize the coefficients of the dynamic weight allocation algorithm, set the control period, and calibrate the temperature sensor, humidity sensor, and door magnetic sensor;

[0006] Step S2: Preprocessing is performed based on the calibrated sensors to collect real-time data on the temperature, humidity, door opening and closing times inside the box, and the external ambient temperature and humidity. Noise is eliminated through sliding average filtering to calculate the external disturbance factor.

[0007] Step S3: Based on the preprocessed data, the temperature and humidity deviations and their change rates are calculated. Combined with the external disturbance factors, the control weights of heating, cooling, humidification, and dehumidification are generated through a dynamic weight allocation algorithm to perform dynamic priority allocation for multi-objective control.

[0008] Step S4: Selecting a priority mode for temperature or humidity regulation based on the calculated weights, driving the corresponding actuators, and dynamically adjusting the power or working time based on the weight values;

[0009] Step S5: Upload the execution results and environmental data to the cloud via the IoT module, allowing users to remotely adjust target parameters. At the same time, the coefficients in step 1 are optimized based on historical data to form a closed-loop control.

[0010] Step S6: Execute the above steps repeatedly until the temperature and humidity are stable within the target range for three consecutive cycles, or the user actively terminates the process, and the system enters a low-power standby mode.

[0011] The step S3 includes the following steps:

[0012] Step S31: Calculate the temperature and humidity deviation based on the collected data. The temperature deviation formula is as follows:

[0013] E T =T current -T target ;

[0014] The humidity deviation formula is as follows:

[0015] E H =H current -H target ;

[0016] Where, T current The temperature value measured in real time inside the box is collected by the PT1000 temperature sensor. current The real-time humidity value measured inside the box is collected by a capacitive humidity sensor;

[0017] Step S32: Calculate the deviation change rate using the following formula:

[0018] Temperature deviation change rate:

[0019]

[0020] Humidity deviation change rate:

[0021]

[0022] Where, dt is the control period;

[0023] Step S33: Implement dynamic priority allocation for heating, cooling, humidification, and dehumidification control modes, combining real-time deviation, deviation change rate, and external disturbance factor F. ext , define the dynamic weight allocation algorithm, its formula is as follows:

[0024]

[0025] Where α is the basic weight coefficient, β is the nonlinear adjustment coefficient, γ is the change rate weight coefficient, δ is the external disturbance weight coefficient, and Ei is the temperature and humidity deviation, dE i is the instantaneous change of temperature and humidity deviation, is the rate of change of deviation.

[0026] Furthermore, the step S1 includes the following steps:

[0027] Step S11: Input T via user terminal or set it by default target With H target , initialize coefficients α, β, γ, δ; T target is the target temperature value of the automatic control box, H target is the target humidity value of the automatic control box;

[0028] Step S12: calibrate the zero drift of the temperature sensor and the humidity sensor, and confirm the online status of the device through the Internet of Things module.

[0029] Furthermore, in step S2, the comprehensive impact of the external environmental changes on the internal environment is quantified, and the external disturbance factor F is defined. ext , the calculation formula is as follows:

[0030]

[0031] Where, T ext is the external ambient temperature, which is collected by the temperature and humidity sensor deployed outside the box. target is the target temperature value of the automatic control box, H ext The external environment humidity is collected by the temperature and humidity sensor deployed outside the box. target is the target humidity value of the automatic control box, N door The number of times the door was opened and closed in the past five minutes.

[0032] Furthermore, the step S4 includes the following steps:

[0033] Step S41: compare the maximum values ​​of the temperature control weight and the humidity control weight, and prioritize the type of adjustment with the higher weight;

[0034] Step S42: Map the weight value to the specific operating parameters of the actuator, and define the control rule formula as follows:

[0035] Heating power calculation formula:

[0036] P heat =K p ×W heat (K P =0.1);

[0037] Cooling power limit function to avoid overload operation. The calculation formula is as follows:

[0038] Pcool =min(100%,W cool ×15%);

[0039] Humidification time calculation formula:

[0040] t humid =2×W humid ;

[0041] Dehumidification fan calculation formula:

[0042] V fan =200×W dehumid ;

[0043] Where, P heat is the output power of the heater, expressed as a percentage, K p is the proportional coefficient, the value is a fixed constant 0.1, W heat is the heating control weight, reflecting the priority of the current heating demand, P cool is the output power of the semiconductor refrigeration chip, the unit is percentage, min (100%, ) is the minimum function, ensuring that the cooling power does not exceed the rated maximum value (100%), W cool is the cooling control weight, t humid The humidification time indicates the running time of the ultrasonic atomizer in each control cycle, in seconds, W humid is the humidification control weight, V fan is the dehumidification fan speed, in m 3 / h,W dehumid Control weight for dehumidification.

[0044] Furthermore, in step S5, in order to adapt to different environmental scenarios and reduce energy consumption, the cloud uses the gradient descent method to iteratively update the coefficients α, β, γ, δ based on historical data, and pushes them to the device end through wireless remote update.

[0045] Furthermore, in step S6, when the temperature and humidity are stable at T for three consecutive cycles, target ±0.5℃ and H target The control process is terminated when the humidity is within the range of ±2%RH or the user actively shuts down the system.

[0046] An Internet of Things (IoT) automatic control system for a medical temperature and humidity control box, comprising a configuration management module, a sensor calibration module, a data acquisition module, a data processing module, a control algorithm module, an actuator control module, an IoT communication module, a cloud interaction module, a circulation control module, and a low-power management module.

[0047] The output end of the configuration management module is unidirectionally connected to the input end of the sensor calibration module, the output end of the sensor calibration module is unidirectionally connected to the input end of the data acquisition module, the output end of the data acquisition module is unidirectionally connected to the input end of the data processing module, the output end of the data processing module is unidirectionally connected to the input ends of the control algorithm module and the cycle control module respectively, the output end of the control algorithm module is unidirectionally connected to the input end of the actuator control module, the output end of the actuator control module is unidirectionally connected to the input end of the Internet of Things communication module, the output end of the Internet of Things communication module is unidirectionally connected to the input end of the cloud interaction module, and the output end of the cloud interaction module is unidirectionally connected to the input end of the control algorithm module; the output end of the cycle control module is unidirectionally connected to the input end of the actuator control module and the input end of the low power management module respectively;

[0048] The configuration management module is used to receive user input or load target temperature, humidity, and control period, initialize the coefficients of the dynamic weight allocation algorithm, and set system operating parameters;

[0049] The sensor calibration module includes calibration of temperature sensors, capacitive humidity sensors, and door magnetic sensors, which are used to verify the online status of the device;

[0050] The data acquisition module is used to collect the temperature, humidity, door opening and closing times inside the box and the external environment temperature and humidity in real time, and transmit the original data to the data processing module;

[0051] The data processing module is used to pre-process the raw data, including sliding average filtering and denoising, calculating external disturbance factors, temperature and humidity deviations, and deviation change rates;

[0052] The control algorithm module is used to calculate the control weights of heating, cooling, humidification, and dehumidification based on preprocessed data through a dynamic weight allocation algorithm and dynamically assign priorities. It specifically includes the following steps:

[0053] Based on the collected data, calculate the temperature and humidity deviation. The temperature deviation formula is as follows:

[0054] E T =T current -T target ;

[0055] The humidity deviation formula is as follows:

[0056] E H =H current -H target ;

[0057] Where, T current The temperature value measured in real time inside the box is collected by the PT1000 temperature sensor. current The real-time humidity value measured inside the box is collected by a capacitive humidity sensor;

[0058] The formula for calculating the deviation change rate is as follows:

[0059] Temperature deviation change rate:

[0060]

[0061] Humidity deviation change rate:

[0062]

[0063] Where, dt is the control period;

[0064] Realize dynamic priority allocation for heating, cooling, humidification and dehumidification control modes, combining real-time deviation, deviation change rate and external disturbance factor F ext , define the dynamic weight allocation algorithm, its formula is as follows:

[0065]

[0066] Where α is the basic weight coefficient, β is the nonlinear adjustment coefficient, γ is the change rate weight coefficient, δ is the external disturbance weight coefficient, and E i is the temperature and humidity deviation, dE i is the instantaneous change of temperature and humidity deviation, is the rate of change of deviation;

[0067] The actuator control module is used to select the priority of temperature and humidity adjustment according to the weight value, drive the actuators such as heater, refrigerator, humidifier and dehumidification fan, and map the weight value to the specific operation parameter;

[0068] The IoT communication module is used to upload the temperature, humidity, door opening and closing times, and execution results inside the box to the cloud, and receive remote user commands from the cloud.

[0069] The cloud interaction module is used to store historical data, optimize the coefficients of the dynamic weight allocation algorithm based on the gradient descent method, and push the optimized parameters to the device end; it supports users to remotely adjust target parameters;

[0070] The cycle control module is used to control the process cycle execution, monitoring whether the temperature and humidity are stable within the target range for three consecutive cycles, or whether the user actively terminates it, triggering the low-power standby mode;

[0071] The low power management module is used to reduce energy consumption and enter standby mode when the system meets the termination conditions.

[0072] The present invention has the following beneficial effects:

[0073] 1. The present invention uses a dynamic weight allocation algorithm, combined with real-time temperature and humidity deviations, deviation change rates and external disturbance factors, to dynamically adjust the control weights of heating, cooling, humidification and dehumidification, solving the problems of single control strategy and rigid priority allocation in traditional systems. The algorithm flexibly allocates actuator resources based on multi-variable coupling relationships, avoiding control lag or overshoot while quickly responding to environmental changes, significantly improving the accuracy and stability of temperature and humidity control, ensuring that medical supplies are always within the target environmental range during transportation, and reducing the risk of deterioration.

[0074] 2. The present invention uploads environmental data and execution results to the cloud in real time, supports users' remote monitoring and parameter adjustment, and uses historical data to optimize the dynamic weight distribution coefficient through the gradient descent method to form a closed-loop self-learning mechanism. This design not only improves operation and maintenance efficiency, but also continuously optimizes system performance, reduces the risk of parameter drift in long-term operation, reduces energy waste and equipment maintenance costs, and realizes data-driven intelligent control and sustainable energy efficiency management.

[0075] 3. The present invention introduces a quantitative model of external disturbance factors, comprehensively considers the impact of external environmental temperature and humidity differences and the frequency of door opening and closing on the environment inside the box, and incorporates it into the control algorithm decision-making process. By dynamically adjusting the weights of the actuators, it effectively offsets the temperature and humidity fluctuations caused by external interference, solving the problem of decreased control accuracy caused by traditional systems ignoring external disturbances, and further ensuring the safety of the medical supplies storage environment.

[0076] Of course, any product implementing the present invention does not necessarily need to achieve all of the advantages described above at the same time. BRIEF DESCRIPTION OF THE DRAWINGS

[0077] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0078] Figure 1 This is a flow chart of an Internet of Things automatic control method for a medical temperature and humidity automatic control box according to the present invention;

[0079] Figure 2 The figure is a flow chart of an Internet of Things automatic control system for a medical temperature and humidity automatic control box according to the present invention. DETAILED DESCRIPTION

[0080] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making any creative efforts shall fall within the scope of protection of the present invention.

[0081] See also Figure 1-2 As shown, the present invention is an Internet of Things automatic control method for a medical temperature and humidity automatic control box, comprising the following steps:

[0082] Step S1: Set the target temperature and target humidity, initialize the coefficients of the dynamic weight allocation algorithm, set the control period, and calibrate the temperature sensor, humidity sensor, and door magnetic sensor;

[0083] Step S2: Preprocessing is performed based on the calibrated sensors to collect real-time data on the temperature, humidity, door opening and closing times inside the box, and the external ambient temperature and humidity. Noise is eliminated through sliding average filtering to calculate the external disturbance factor.

[0084] Step S3: Based on the preprocessed data, the temperature and humidity deviations and their change rates are calculated. Combined with the external disturbance factors, the control weights of heating, cooling, humidification, and dehumidification are generated through a dynamic weight allocation algorithm to perform dynamic priority allocation for multi-objective control.

[0085] Step S4: Selecting a priority mode for temperature or humidity regulation based on the calculated weights, driving the corresponding actuators, and dynamically adjusting the power or working time based on the weight values;

[0086] Step S5: Upload the execution results and environmental data to the cloud via the IoT module, allowing users to remotely adjust target parameters. At the same time, the coefficients in step 1 are optimized based on historical data to form a closed-loop control.

[0087] Step S6: Execute the above steps repeatedly until the temperature and humidity are stable within the target range for three consecutive cycles, or the user actively terminates the process, and the system enters a low-power standby mode.

[0088] Step S3 includes the following steps:

[0089] Step S31: Calculate the temperature and humidity deviation based on the collected data. The temperature deviation formula is as follows:

[0090] E T =T current -T target ;

[0091] The humidity deviation formula is as follows:

[0092] E H =H current-H target ;

[0093] Where, T current The temperature value measured in real time inside the box is collected by the PT1000 temperature sensor. current The real-time humidity value measured inside the box is collected by a capacitive humidity sensor;

[0094] Step S32: Calculate the deviation change rate using the following formula:

[0095] Temperature deviation change rate:

[0096]

[0097] Humidity deviation change rate:

[0098]

[0099] Where, dt is the control period;

[0100] Step S33: Implement dynamic priority allocation for heating, cooling, humidification, and dehumidification control modes, combining real-time deviation, deviation change rate, and external disturbance factor F. ext , define the dynamic weight allocation algorithm, its formula is as follows:

[0101]

[0102] Where α is the basic weight coefficient, β is the nonlinear adjustment coefficient, γ is the change rate weight coefficient, δ is the external disturbance weight coefficient, and E i is the temperature and humidity deviation, dE i is the instantaneous change of temperature and humidity deviation, is the rate of change of deviation.

[0103] Step S1 includes the following steps:

[0104] Step S11: Input T via user terminal or set it by default target With H target , initialize coefficients α, β, γ, δ; T target is the target temperature value of the automatic control box, H target is the target humidity value of the automatic control box;

[0105] Step S12: calibrate the zero drift of the temperature sensor and the humidity sensor, and confirm the online status of the device through the Internet of Things module.

[0106] In step S2, the comprehensive impact of external environmental changes on the internal environment is quantified, and the external disturbance factor F is defined. ext , the calculation formula is as follows:

[0107]

[0108] Where, T ext is the external ambient temperature, which is collected by the temperature and humidity sensor deployed outside the box. target is the target temperature value of the automatic control box, H ext The external environment humidity is collected by the temperature and humidity sensor deployed outside the box. target is the target humidity value of the automatic control box, N door The number of times the door was opened and closed in the past five minutes.

[0109] Step S4 includes the following steps:

[0110] Step S41: compare the maximum values ​​of the temperature control weight and the humidity control weight, and prioritize the type of adjustment with the higher weight;

[0111] Step S42: Map the weight value to the specific operating parameters of the actuator, and define the control rule formula as follows:

[0112] Heating power calculation formula:

[0113] P heat =K p ×W heat (K P =0.1);

[0114] Cooling power limit function to avoid overload operation. The calculation formula is as follows:

[0115] P cool =min(100%,W cool ×15%);

[0116] Humidification time calculation formula:

[0117] t humid =2×W humid ;

[0118] Dehumidification fan calculation formula:

[0119] V fan =200×W dehumid ;

[0120] Where, P heat is the output power of the heater, expressed as a percentage, K p is the proportional coefficient, the value is a fixed constant 0.1, W heat is the heating control weight, reflecting the priority of the current heating demand, P cool is the output power of the semiconductor refrigeration chip, the unit is percentage, min (100%, ) is the minimum function, ensuring that the cooling power does not exceed the rated maximum value (100%), W cool is the cooling control weight, thumid The humidification time indicates the running time of the ultrasonic atomizer in each control cycle, in seconds, W humid is the humidification control weight, V fan is the dehumidification fan speed, in m 3 / h,W dehumid Control weight for dehumidification.

[0121] In step S5, to adapt to different environmental scenarios and reduce energy consumption, the cloud uses the gradient descent method to iteratively update the coefficients α, β, γ, δ based on historical data, and pushes them to the device end through wireless remote update.

[0122] In step S6, when the temperature and humidity are stable at T for three consecutive cycles, target ±0.5℃ and H target The control process is terminated when the humidity is within the range of ±2%RH or the user actively shuts down the system.

[0123] An Internet of Things (IoT) automatic control system for a medical temperature and humidity control box, comprising a configuration management module, a sensor calibration module, a data acquisition module, a data processing module, a control algorithm module, an actuator control module, an IoT communication module, a cloud interaction module, a circulation control module, and a low-power management module.

[0124] The output end of the configuration management module is unidirectionally connected to the input end of the sensor calibration module, the output end of the sensor calibration module is unidirectionally connected to the input end of the data acquisition module, the output end of the data acquisition module is unidirectionally connected to the input end of the data processing module, the output end of the data processing module is unidirectionally connected to the input ends of the control algorithm module and the loop control module respectively, the output end of the control algorithm module is unidirectionally connected to the input end of the actuator control module, the output end of the actuator control module is unidirectionally connected to the input end of the Internet of Things communication module, the output end of the Internet of Things communication module is unidirectionally connected to the input end of the cloud interaction module, and the output end of the cloud interaction module is unidirectionally connected to the input end of the control algorithm module; the output end of the loop control module is unidirectionally connected to the input end of the actuator control module and the input end of the low power management module respectively;

[0125] The configuration management module is used to receive user input or load target temperature, humidity, and control period, initialize the coefficients of the dynamic weight allocation algorithm, and set system operating parameters;

[0126] The sensor calibration module includes calibration of temperature sensors, capacitive humidity sensors, and door magnetic sensors to verify the online status of the device;

[0127] The data acquisition module is used to collect the temperature, humidity, door opening and closing times inside the box and the external environment temperature and humidity in real time, and transmit the original data to the data processing module;

[0128] The data processing module is used to pre-process the raw data, including sliding average filtering and denoising, calculating external disturbance factors, temperature and humidity deviations, and deviation change rates;

[0129] The control algorithm module is used to calculate the control weights of heating, cooling, humidification, and dehumidification based on preprocessed data through a dynamic weight allocation algorithm and dynamically assign priorities. It specifically includes the following steps:

[0130] Based on the collected data, calculate the temperature and humidity deviation. The temperature deviation formula is as follows:

[0131] E T =T current -T target ;

[0132] The humidity deviation formula is as follows:

[0133] E H =H current -H target ;

[0134] Where, T current The temperature value measured in real time inside the box is collected by the PT1000 temperature sensor. current The real-time humidity value measured inside the box is collected by a capacitive humidity sensor;

[0135] The formula for calculating the deviation change rate is as follows:

[0136] Temperature deviation change rate:

[0137]

[0138] Humidity deviation change rate:

[0139]

[0140] Where, dt is the control period;

[0141] Realize dynamic priority allocation for heating, cooling, humidification and dehumidification control modes, combining real-time deviation, deviation change rate and external disturbance factor F ext， Define the dynamic weight allocation algorithm, the formula is as follows:

[0142]

[0143] Where α is the basic weight coefficient, β is the nonlinear adjustment coefficient, γ is the change rate weight coefficient, δ is the external disturbance weight coefficient, and E i is the temperature and humidity deviation, dE i is the instantaneous change of temperature and humidity deviation, is the rate of change of deviation;

[0144] The actuator control module is used to select the priority of temperature and humidity adjustment according to the weight value, drive the actuators such as heater, refrigerator, humidifier and dehumidification fan, and map the weight value to the specific operation parameter;

[0145] The IoT communication module is used to upload the temperature, humidity, door opening and closing times, and execution results inside the box to the cloud, and receive remote user commands from the cloud.

[0146] The cloud interaction module is used to store historical data, optimize the coefficients of the dynamic weight allocation algorithm based on the gradient descent method, and push the optimized parameters to the device end; it supports users to remotely adjust target parameters;

[0147] The cycle control module is used to control the process cycle execution, monitoring whether the temperature and humidity are stable within the target range for three consecutive cycles, or whether the user actively terminates it, triggering the low-power standby mode;

[0148] The low power management module is used to reduce energy consumption and enter standby mode when the system meets the termination conditions.

[0149] A specific application of this embodiment is:

[0150] System hardware settings

[0151] Sensor module: A PT1000 high-precision temperature sensor (measuring range: -50°C to 100°C, accuracy of ±0.1°C) and a capacitive humidity sensor (measuring range: 0% to 100% RH, accuracy of ±1.5% RH) are installed inside the box; an ambient temperature and humidity sensor (model SHT35) is deployed outside the box; and a door magnetic sensor is used to monitor the number of times the door is opened and closed.

[0152] Actuators: semiconductor heating element (maximum power 50W), semiconductor cooling element (maximum power 60W), ultrasonic humidifier (humidification rate 0.5L / h), centrifugal dehumidification fan (air volume 200m / h);

[0153] Control core: STM32F407 microcontroller with built-in dynamic weight allocation algorithm program; IoT communication module uses NB-IoT module;

[0154] Cloud platform: Alibaba Cloud IoT platform, used for data storage and analysis, supporting gradient descent method to optimize algorithm parameters;

[0155] Implementation steps

[0156] 1. Initialization and parameter setting:

[0157] The user sets the target temperature T through the mobile phone APP target =5℃ (vaccine storage scenario), target humidity H target=40%RH; initialize dynamic weight coefficients α=0.6, β=0.8, γ=0.4, δ=0.3; control period dt=30s;

[0158] Calibrate the sensors: The PT1000 temperature sensor is calibrated at two points by immersing it in an ice-water mixture (0°C) and boiling water (100°C). The capacitive humidity sensor is calibrated using a saturated salt solution (75% RH and 33% RH) at 25°C.

[0159] 2. Data collection and preprocessing:

[0160] Real-time collection of box temperature T current =6.2℃, humidity H current =45%RH; external ambient temperature T ext =25℃, humidity H ext =60%RH;N number of door openings and closings in the past 5 minutes door =3;

[0161] Sliding average filter: take the average of 5 samplings of temperature data to eliminate random noise;

[0162] Calculate the external disturbance factor:

[0163]

[0164] 3. Dynamic weight calculation and priority matching:

[0165] Calculate temperature and humidity deviation:

[0166] E T =6.2-5=1.2℃;E H =45-40=5%RH;

[0167] Deviation change rate, last cycle E T (t-dt)=0.8, EH(t-dt)=3%RH:

[0168]

[0169] Dynamic weight distribution (taking humidity control as an example):

[0170] W H =0.6×(1-e -0.8×5 )+0.4×0.067+0.3×6=0.6×0.98+0.0268+1.8=2.44;

[0171] Calculate the temperature weight W T =1.92, because W T >W H , humidity adjustment is performed first;

[0172] 4. Actuator control

[0173] Select the dehumidification mode according to the weight mapping formula:

[0174] V fan =200×W dehumid =200×2.44=488m 3 / h;

[0175] Limited by the maximum air volume of the fan, the actual operating air volume is 200m / h (full load).

[0176] At the same time, the semiconductor refrigeration sheet is started to assist in cooling, and the power limit is:

[0177] P cool =min(100%,2.44×15%)=36.6%;

[0178] 5. Data upload and closed-loop optimization:

[0179] Upload the execution results (dehumidification fan running for 30 seconds, cooling power 22W) and environmental data to the cloud;

[0180] Based on historical data, the cloud optimizes the coefficients α and β using the gradient descent method to update them to α = 0.62 and β = 0.78, and pushes them to the device.

[0181] 6. Cycle control and low power management

[0182] After three cycles of continuous monitoring (90 seconds), the temperature inside the box stabilized to 5°C ± 0.3°C, and the humidity stabilized to 40% RH ± 1.5% RH. The system entered low-power standby mode and only maintained periodic sensor sampling.

[0183] Throughout this specification, references to terms such as "one embodiment," "example," or "specific example" indicate that the specific features, structures, materials, or characteristics described in conjunction with that embodiment or example are included in at least one embodiment or example of the present invention. In this specification, schematic representations of these terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

[0184] The preferred embodiments of the present invention disclosed above are intended only to help illustrate the present invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the present invention to the specific embodiments described. Obviously, many modifications and variations are possible based on the content of this specification. These embodiments are selected and described in detail in this specification to better explain the principles and practical applications of the present invention, thereby enabling those skilled in the art to better understand and utilize the present invention. The present invention is limited only by the claims and their full scope and equivalents.

Claims

1. An Internet of Things automatic control method for a medical temperature and humidity automatic control box, characterized by: The following steps are involved: Step S1: Set the target temperature and target humidity, initialize the coefficients of the dynamic weight allocation algorithm, set the control period, and calibrate the temperature sensor, humidity sensor, and door magnetic sensor; Step S2: Preprocessing is performed based on the calibrated sensors to collect real-time data on the temperature, humidity, door opening and closing times inside the box, and the external ambient temperature and humidity. Noise is eliminated through sliding average filtering to calculate the external disturbance factor. Step S3: Based on the preprocessed data, the temperature and humidity deviations and their change rates are calculated. Combined with the external disturbance factors, the control weights of heating, cooling, humidification, and dehumidification are generated through a dynamic weight allocation algorithm to perform dynamic priority allocation for multi-objective control. Step S4: Selecting a priority mode for temperature or humidity regulation based on the calculated weights, driving the corresponding actuators, and dynamically adjusting the power or working time based on the weight values; Step S5: Upload the execution results and environmental data to the cloud via the IoT module, allowing users to remotely adjust target parameters. At the same time, the coefficients in step 1 are optimized based on historical data to form a closed-loop control. Step S6: Execute the above steps repeatedly until the temperature and humidity are stable within the target range for three consecutive cycles, or the user actively terminates the process, and the system enters a low-power standby mode. The step S3 includes the following steps: Step S31: Calculate the temperature and humidity deviation based on the collected data. The temperature deviation formula is as follows: E T =T current -T target ; The humidity deviation formula is as follows: E H =H current -H target ; Where, T current The temperature value measured in real time inside the box is collected by the PT1000 temperature sensor. current The real-time humidity value measured inside the box is collected by a capacitive humidity sensor; Step S32: Calculate the deviation change rate using the following formula: Temperature deviation change rate: Humidity deviation change rate: Where, dt is the control period; Step S33: Implement dynamic priority allocation for heating, cooling, humidification, and dehumidification control modes, combining real-time deviation, deviation change rate, and external disturbance factor F. ext , define the dynamic weight allocation algorithm, its formula is as follows: In the formula, α is the basic weight coefficient, β is the nonlinear adjustment coefficient, γ is the change rate weight coefficient, δ is the external disturbance weight coefficient, and E i is the temperature and humidity deviation, dE i is the instantaneous change of temperature and humidity deviation, is the rate of change of deviation.

2. The Internet of Things automatic control method for a medical temperature and humidity automatic control box according to claim 1, characterized in that: The step S1 includes the following steps: Step S11: Input T via user terminal or set it by default target With H target , initialize coefficients α, β, γ, δ; T target is the target temperature value of the automatic control box, H target is the target humidity value of the automatic control box; Step S12: calibrate the zero drift of the temperature sensor and the humidity sensor, and confirm the online status of the device through the Internet of Things module.

3. The Internet of Things automatic control method for a medical temperature and humidity automatic control box according to claim 2, characterized in that: In step S2, the comprehensive impact of external environmental changes on the internal environment is quantified, and the external disturbance factor F is defined. ext , the calculation formula is as follows: Where, T ext is the external ambient temperature, which is collected by the temperature and humidity sensor deployed outside the box. target is the target temperature value of the automatic control box, H ext The external environment humidity is collected by the temperature and humidity sensor deployed outside the box. target is the target humidity value of the automatic control box, N door The number of times the door was opened and closed in the past five minutes.

4. The Internet of Things automatic control method for a medical temperature and humidity automatic control box according to claim 3, characterized in that: The step S4 includes the following steps: Step S41: compare the maximum values ​​of the temperature control weight and the humidity control weight, and prioritize the type of adjustment with the higher weight; Step S42: Map the weight value to the specific operating parameters of the actuator, and define the control rule formula as follows: Heating power calculation formula: P heat =K p ×W heat (K P =0.1); Cooling power limit function to avoid overload operation. The calculation formula is as follows: P cool =min(100%,W cool ×15%); Humidification time calculation formula: t humid =2×W humid ; Dehumidification fan calculation formula: In fan =200×W dehumid ; Where, P heat is the output power of the heater, expressed as a percentage, K p is the proportional coefficient, the value is a fixed constant 0.1, W heat is the heating control weight, reflecting the priority of the current heating demand, P cool is the output power of the semiconductor refrigeration chip, the unit is percentage, min (100%, ) is the minimum function, ensuring that the cooling power does not exceed the rated maximum value (100%), W cool is the cooling control weight, t humid The humidification time indicates the running time of the ultrasonic atomizer in each control cycle, in seconds, W humid is the humidification control weight, V fan is the dehumidification fan speed, the speed is m 3 / h,W dehumid Control weight for dehumidification.

5. The Internet of Things automatic control method for a medical temperature and humidity automatic control box according to claim 4, characterized in that: In step S5, to adapt to different environmental scenarios and reduce energy consumption, the cloud uses the gradient descent method to iteratively update the coefficients α, β, γ, δ based on historical data, and pushes them to the device end via wireless remote update.

6. The Internet of Things automatic control method for a medical temperature and humidity automatic control box according to claim 5, characterized in that: In step S6, when the temperature and humidity are stable at T for three consecutive cycles, target ±0.5℃ vs. H target The control process is terminated when the humidity is within the range of ±2%RH or the user actively shuts down the system.

7. An Internet of Things automatic control system for a medical temperature and humidity automatic control box, characterized in that: It includes configuration management module, sensor calibration module, data acquisition module, data processing module, control algorithm module, actuator control module, Internet of Things communication module, cloud interaction module, cycle control module and low power management module; The output end of the configuration management module is unidirectionally connected to the input end of the sensor calibration module, the output end of the sensor calibration module is unidirectionally connected to the input end of the data acquisition module, the output end of the data acquisition module is unidirectionally connected to the input end of the data processing module, the output end of the data processing module is unidirectionally connected to the input ends of the control algorithm module and the cycle control module respectively, the output end of the control algorithm module is unidirectionally connected to the input end of the actuator control module, the output end of the actuator control module is unidirectionally connected to the input end of the Internet of Things communication module, the output end of the Internet of Things communication module is unidirectionally connected to the input end of the cloud interaction module, and the output end of the cloud interaction module is unidirectionally connected to the input end of the control algorithm module; the output end of the cycle control module is unidirectionally connected to the input end of the actuator control module and the input end of the low power management module respectively; The configuration management module is used to receive user input or load target temperature, humidity, and control period, initialize the coefficients of the dynamic weight allocation algorithm, and set system operating parameters; The sensor calibration module includes calibration of temperature sensors, capacitive humidity sensors, and door magnetic sensors, which are used to verify the online status of the device; The data acquisition module is used to collect the temperature, humidity, door opening and closing times inside the box and the external environment temperature and humidity in real time, and transmit the original data to the data processing module; The data processing module is used to pre-process the raw data, including sliding average filtering and denoising, calculating external disturbance factors, temperature and humidity deviations, and deviation change rates; The control algorithm module is used to calculate the control weights of heating, cooling, humidification, and dehumidification based on preprocessed data through a dynamic weight allocation algorithm and dynamically assign priorities. It specifically includes the following steps: Based on the collected data, calculate the temperature and humidity deviation. The temperature deviation formula is as follows: E T =T current -T target ; The humidity deviation formula is as follows: E H =H current -H target ; Where, T current The temperature value measured in real time inside the box is collected by the PT1000 temperature sensor. current The real-time humidity value measured inside the box is collected by a capacitive humidity sensor; The formula for calculating the deviation change rate is as follows: Temperature deviation change rate: Humidity deviation change rate: Where, dt is the control period; Realize dynamic priority allocation for heating, cooling, humidification and dehumidification control modes, combining real-time deviation, deviation change rate and external disturbance factor F ext , define the dynamic weight allocation algorithm, its formula is as follows: In the formula, α is the basic weight coefficient, β is the nonlinear adjustment coefficient, γ is the change rate weight coefficient, δ is the external disturbance weight coefficient, and E i is the temperature and humidity deviation, dE i is the instantaneous change of temperature and humidity deviation, is the rate of change of deviation; The actuator control module is used to select the priority of temperature and humidity adjustment according to the weight value, drive the actuators such as heater, refrigerator, humidifier and dehumidification fan, and map the weight value to the specific operation parameter; The IoT communication module is used to upload the temperature, humidity, door opening and closing times, and execution results inside the box to the cloud, and receive remote user commands issued by the cloud; The cloud interaction module is used to store historical data, optimize the coefficients of the dynamic weight allocation algorithm based on the gradient descent method, and push the optimized parameters to the device end; it supports users to remotely adjust target parameters; The cycle control module is used to control the process cycle execution, monitoring whether the temperature and humidity are stable within the target range for three consecutive cycles, or whether the user actively terminates it, triggering the low-power standby mode; The low power management module is used to reduce energy consumption and enter standby mode when the system meets the termination conditions.

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