A precise temperature control system for variable frequency dehumidifiers with dual temperature and humidity modes.

Abstract

This invention belongs to the field of dehumidification equipment technology and discloses a precise temperature control system for a variable frequency dehumidifier with dual-mode temperature and humidity coordination. Through deep collaboration between the dual-mode decision module and the temperature and humidity calibration module, it dynamically switches between dehumidification-priority and temperature-priority modes based on a fuzzy PID fusion algorithm. Adjustment weights are allocated in real time according to temperature and humidity deviations. Combined with dew point temperature modeling and complementary correction technology, it achieves synchronous and precise control of both parameters. A multi-sensor layout covers key locations, coupled with an adaptive self-calibration mechanism and data preprocessing function, ensuring the accuracy and stability of the collected data and adapting to various extreme temperature and humidity conditions. During mode switching, collaborative commands link various execution modules, avoiding humidity rebound after cooling shutdown or dehumidification failure during temperature control. The variable frequency speed control module uses an adaptive fuzzy PID algorithm with multi-input fusion to dynamically optimize speed parameters. Combined with a dynamic frequency step size adjustment mechanism, it achieves rapid response to large deviations and precise fine-tuning for small deviations.

Description

Technical Field

[0001] This invention belongs to the field of dehumidification equipment technology, specifically a variable frequency dehumidifier precise temperature control system with dual temperature and humidity modes. Background Technology

[0002] With the improvement of people's living standards and the increasing demands for precise temperature and humidity control in industrial production, variable frequency dehumidifiers have been widely used in various scenarios such as homes, laboratories, and industrial workshops. Their core requirements have evolved from simple dehumidification to the dual goals of dehumidification and precise temperature control. This is especially true under extreme conditions such as low temperature and high humidity or high temperature and high humidity, where the requirements for the precision and stability of coordinated temperature and humidity control are even more stringent. Currently, existing variable frequency dehumidifier temperature control systems still have the following technical problems: First, temperature and humidity control is fragmented, often using a single fixed mode, with temperature and humidity adjustments being independent of each other. If the humidity does not meet the standard after the cooling is stopped, the unit needs to be restarted, resulting in a response delay. Furthermore, it is prone to overcooling, overheating, and temperature drift, making it impossible to achieve synchronous and precise control of two parameters. Secondly, the frequency conversion regulation accuracy is insufficient and the energy consumption is too high. It relies on a single PID parameter and lacks dynamic step size adaptation and resonant frequency hopping strategy, which easily leads to frequency oscillation and response lag. Moreover, the regulation is not related to temperature and humidity deviation, resulting in poor temperature control accuracy and serious energy waste. Third, it has poor adaptability to low-temperature operating conditions, lacks a dedicated temperature control mechanism, has a single electronic expansion valve adjustment, and the evaporator is prone to frosting and requires shutdown for defrosting, which interrupts operation and exacerbates temperature fluctuations, becoming the main bottleneck for low-temperature applications. Summary of the Invention

[0003] The purpose of this invention is to provide a precise temperature control system for a variable frequency dehumidifier with dual-mode temperature and humidity coordination, in order to solve the problems mentioned in the background art.

[0004] To achieve the above objectives, the present invention provides the following technical solution: a precise temperature control system for a variable frequency dehumidifier with dual-mode temperature and humidity coordination, comprising the following modules: Preferably, the temperature and humidity calibration module is equipped with temperature and humidity sensors at the air inlet, air outlet and inside the dehumidifier body, and integrates operating parameter sensors such as compressor exhaust temperature, suction pressure and electronic expansion valve opening, and sets up an evaporator fin-fitting temperature sensor to realize the acquisition of ambient temperature and humidity, system operating parameters and core component temperature. A deep learning-based dew point temperature modeling algorithm is introduced to construct a correlation model between temperature, humidity, dew point and evaporator temperature. Combined with historical data, complementary correction of temperature and humidity parameters is achieved. At the same time, an adaptive error self-calibration mechanism is designed to dynamically adjust the calibration cycle according to environmental conditions and automatically calibrate sensor deviation by combining environmental reference values ​​and historical data. When abnormal sensor data is detected, the mutual verification process is automatically triggered to identify the faulty sensor and switch to the backup acquisition channel. At the same time, the fault information is recorded and synchronized to the monitoring and protection module. The integrated data preprocessing module filters and reduces noise on the acquired raw data and removes extreme outliers.

[0005] Preferably, the dual-mode decision module constructs a dual-mode dynamic decision model based on a fuzzy PID fusion algorithm, automatically identifies environmental conditions, and dynamically switches operating modes; when the ambient humidity deviation is greater than the temperature deviation, it enters the dehumidification priority mode, prioritizes adjusting the frequency of the variable frequency compressor, and fine-tunes the temperature through the auxiliary temperature control unit; when the ambient temperature deviation is greater than the humidity deviation, it enters the temperature control priority mode, prioritizes adjusting the frequency of the variable frequency compressor and the auxiliary temperature control unit to maintain temperature stability, and dynamically adjusts the dehumidification intensity at the same time. Based on real-time changes in environmental parameters, combined with dew point temperature and historical operating data, the system automatically allocates weights for temperature and humidity regulation and clarifies the weight calculation logic. At the same time, based on historical operating data, it anticipates sudden changes in operating conditions and adjusts the weights and operating modes in advance. When switching modes, it synchronously sends coordination instructions to the frequency conversion speed control module, temperature control and anti-frost module, and heat recovery module. It supports manual forced mode switching and presets dedicated mode parameters for special scenarios such as laboratory temperature control and warehouse dehumidification. It outputs decision instructions and weight allocation results to the frequency conversion speed control module and the temperature control and anti-frost module.

[0006] Preferably, the variable frequency speed control module adopts an adaptive fuzzy PID speed control algorithm and integrates multiple input quantities. It takes temperature and humidity deviation, deviation change rate, compressor operating parameters and heat recovery efficiency, and anti-frost status as input quantities, dynamically optimizes PID parameters, and designs a dynamic frequency step size adjustment and operating condition adaptive threshold mechanism to automatically adjust the frequency adjustment step size according to the magnitude of temperature and humidity deviation and operating condition type. Based on compressor runtime, parameter drift, and vibration sensor data, the compressor's resonant frequency range is identified in real time. When the frequency adjustment approaches the resonant frequency range, it quickly jumps across it. During low-frequency operation, the frequency is maintained by using current compensation technology combined with energy recovery from the heat recovery module. At the same time, an energy consumption optimization factor is added, and the frequency adjustment rate is dynamically adjusted by incorporating temperature and humidity weights during speed regulation. The operating status is fed back to the feedback correction module in real time.

[0007] Preferably, the temperature control and anti-frost module is equipped with a two-stage temperature control compensation unit. The first-stage compensation adopts condensation heat recovery compensation, and the second-stage compensation adopts electric heating. The intelligent switching logic of the compensation mode is designed to automatically switch the compensation mode or combine compensation based on the dew point temperature and temperature deviation. For low-temperature and high-humidity operating conditions, an anti-frost collaborative control logic and machine learning prediction are designed. Combining evaporator temperature, ambient dew point temperature, compressor operating parameters and historical frost data, the XGBoost model is used to predict the frost trend in advance. By dynamically adjusting the compressor frequency, electronic expansion valve opening and fan speed, the evaporator frost rate is reduced. The compressor exhaust hot gas defrosting and heat recovery collaborative technology are integrated. When the evaporator frost amount is detected to reach the preset threshold, defrosting is performed by using the compressor exhaust hot gas combined with the condensation heat stored in the heat recovery module. The compensation unit works in conjunction with the variable frequency speed control module and the temperature and humidity calibration module. Based on the real-time data from the temperature and humidity calibration module and the frequency adjustment commands from the variable frequency speed control module, it adapts to the usage requirements of extreme working conditions such as low temperature and high temperature. The temperature control compensation and anti-frost status are simultaneously fed back to the heat recovery module.

[0008] Preferably, the heat recovery module recovers the compressor condensation heat to the temperature control and compensation unit for temperature compensation; and a total heat exchanger is installed at the fresh air inlet and the exhaust air outlet to recover the sensible heat and latent heat in the exhaust air for pre-treatment of the fresh air and to reduce ambient temperature fluctuations. The system automatically adjusts the energy recovery ratio based on ambient temperature and humidity, system operating mode, and anti-frosting status. For example, under low-temperature anti-frosting conditions, the recovery ratio is increased to 80%~100%; under high-temperature conditions, the recovery ratio is adjusted to 30%~60%. A small energy storage unit is set up to store excess condensation heat, which is released during low-temperature conditions or defrosting to assist in temperature control compensation. The integrated energy consumption real-time monitoring and optimization unit collects energy consumption data from each module of the system in real time, compares it with the ideal energy consumption model, and automatically adjusts the energy recovery ratio and heat exchange efficiency. The heat recovery efficiency is fed back to the variable frequency speed control module in real time to optimize PID parameters and frequency adjustment logic. At the same time, according to the defrosting and compensation instructions of the temperature control and anti-frost module, the direction of condensation heat recovery is adjusted to prioritize meeting the requirements of anti-frost and temperature control. Energy consumption data and recovery efficiency are transmitted synchronously to the feedback correction module.

[0009] Preferably, the feedback correction module collects multi-source feedback data such as temperature and humidity regulation deviation, variable frequency compressor operating status, temperature control compensation effect, evaporator frosting status and heat recovery efficiency, and adopts a weighted fusion algorithm to compare the preset target value in real time and automatically correct the weight allocation, variable frequency speed regulation parameters and temperature control compensation parameters of the dual-mode collaborative decision-making model. The system monitors the runtime, parameter drift, and fault records of core components in real time. It constructs an aging prediction model based on a gradient boosting tree model to predict component aging trends in advance, automatically adjusts control parameters, and compensates for the decrease in accuracy caused by component aging. It supports cloud-based benchmark value synchronization and remote parameter calibration, and can synchronize environmental benchmark values ​​through a wireless communication module to achieve remote self-calibration. It also has a calibration log recording function to record the parameters, time, and operating conditions of each calibration. It receives data collected by the temperature and humidity calibration module in real time, dynamically adjusts the calibration threshold, and sends the calibrated parameters to the frequency converter in real time. The calibration command covers all execution modules, and the calibration status and component aging warning information are synchronized to the monitoring and protection module.

[0010] Preferably, the monitoring and protection module classifies faults and anomalies into three levels, focusing on monitoring and protecting the operation of core components such as compressors, sensors, and evaporators: for minor anomalies, an early warning is issued, and the self-calibration function of the feedback correction module is triggered to ensure uninterrupted operation; for moderate anomalies, a graded early warning is issued, and operating parameters are adjusted to reduce the impact of the anomaly and maintain basic operation; for severe anomalies, an emergency warning is issued, and the safety shutdown protection is activated, while the fault tracing function is used to locate the faulty module and the cause. The system monitors the operating status of core components in real time, records operating data and fault information, and supports fault tracing and remote diagnosis. It integrates temperature and humidity over-limit prediction and early warning functions, combining real-time data from the temperature and humidity calibration module with historical operating data to predict the trend of temperature and humidity parameter changes in advance. When the temperature and humidity approach the preset limit value, it sends intervention commands to the dual-mode decision module and the variable frequency speed control module in advance. When the temperature and humidity are within the target range for a long time, it automatically enters the sleep mode, reduces the speed of the compressor and fan, shuts down redundant heating units, and maintains real-time monitoring of the temperature and humidity calibration module. Based on dew point temperature changes and environmental condition predictions, it wakes up the system in advance. The system monitors energy consumption in real time. When energy consumption exceeds the ideal range, it automatically sends optimization commands to the variable frequency speed control and heat recovery modules to adjust operating parameters. If the optimization still fails to meet the standards, a moderate warning is triggered to remind the user to check the operating conditions or equipment status. At the same time, it integrates differential pressure switch protection to detect the refrigerant pipeline pressure difference. When the pressure difference exceeds the safe range, it dynamically adjusts the compressor frequency or shuts down the system.

[0011] The beneficial effects of this invention are as follows: 1. This invention achieves precise synchronous control of two parameters through deep collaboration between a dual-mode decision module and a temperature and humidity calibration module. Based on a fuzzy PID fusion algorithm, it dynamically switches between dehumidification-priority and temperature control-priority modes, and allocates adjustment weights in real time according to temperature and humidity deviations. Combined with dew point temperature modeling and complementary correction technology, it achieves precise control of two parameters simultaneously. A multi-sensor layout covers key locations, and with an adaptive self-calibration mechanism and data preprocessing function, it ensures the accuracy and stability of the collected data and adapts to various extreme temperature and humidity conditions. When switching modes, it links various execution modules through collaborative commands to avoid humidity rebound after cooling shutdown or dehumidification failure during temperature control, and solves the problems of overcooling, overheating and temperature drift, meeting the temperature and humidity accuracy requirements of different scenarios.

[0012] 2. The variable frequency speed control module of this invention adopts an adaptive fuzzy PID algorithm with multi-input fusion to dynamically optimize speed control parameters. Combined with a dynamic frequency step size adjustment mechanism, it achieves rapid response to large deviations and precise fine-tuning to small deviations. Through resonance frequency identification and avoidance technology, it avoids mechanical wear and noise generation. During low-frequency operation, it ensures frequency stability through current compensation and heat recovery, preventing temperature and humidity fluctuations. The heat recovery module integrates condensation heat recovery, total heat exchange, and energy storage technologies. It dynamically adjusts the energy recovery strategy according to the system operation mode and working conditions. Excess heat is used for auxiliary temperature control, reducing additional energy consumption. The energy consumption monitoring and optimization unit is linked with the feedback correction module in real time to dynamically optimize operating parameters, significantly reducing energy consumption while ensuring adjustment accuracy.

[0013] 3. The temperature control and anti-frost module of this invention adopts a dual-level temperature control compensation mode and intelligent frost prediction technology. It achieves active frost prevention by dynamically adjusting operating parameters. Combined with the compressor exhaust heat and condensation heat for synergistic defrosting, the defrosting operation can be completed without stopping the machine, ensuring continuous operation. The monitoring and protection module constructs a three-level early warning and comprehensive protection system. It takes measures such as early warning, parameter adjustment or safe shutdown for different degrees of abnormality. It also has a fault tracing function to quickly locate the root cause of the problem. The feedback correction module predicts the aging trend based on the operating data of the core components. It compensates for the accuracy loss through remote calibration and dynamic parameter adjustment. Combined with low-power sleep and intelligent wake-up functions, it further improves the system's ability to adapt to complex working conditions. Attached Figure Description

[0014] Figure 1 This is a flowchart of the overall system of the present invention; Figure 2 This is a logic flowchart of the temperature control and anti-frost module of the present invention. Detailed Implementation

[0015] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0016] like Figures 1 to 2 As shown, this embodiment of the invention provides a precise temperature control system for a variable frequency dehumidifier with dual-mode temperature and humidity coordination, comprising the following modules: The temperature and humidity calibration module is equipped with temperature and humidity sensors at the air inlet, air outlet and inside the dehumidifier body (near the evaporator). It also integrates operating parameter sensors such as compressor exhaust temperature, suction pressure and electronic expansion valve opening, and sets up an evaporator fin-fitting temperature sensor to collect ambient temperature and humidity, system operating parameters and core component temperature. A deep learning-based dew point temperature modeling algorithm is introduced to construct a correlation model between temperature, humidity, dew point, and evaporator temperature. A lightweight neural network architecture (such as a multilayer sensor) is selected to adapt to the embedded computing power of the dehumidifier. A sensor installation location correction factor is incorporated into the modeling process. The input parameters include real-time temperature and humidity, evaporator temperature, and historical calibration deviation data. The output is the corrected and accurate dew point temperature and the temperature-humidity correlation coefficient. Complementary correction of temperature and humidity parameters is achieved by combining historical data to improve the accuracy of the collected data. At the same time, an adaptive error self-calibration mechanism is designed to dynamically adjust the calibration cycle according to environmental conditions such as temperature and humidity fluctuations and dew point change rate. The adjustment range is 5~30 minutes. The sensor deviation is automatically calibrated by combining environmental benchmark values ​​and historical data. The environmental condition determination is based on the fluctuation range of temperature and humidity. When the fluctuation range is ≤5%, the calibration cycle is set to 20~30 minutes; when the fluctuation range is 5%~15%, the calibration cycle is set to 10~20 minutes; and when the fluctuation range is >15%, the calibration cycle is set to 5~10 minutes. The fluctuation range is calculated by the standard deviation of 10 consecutive sets of collected data.

[0017] When an anomaly is detected in the data from a sensor, i.e., the deviation from the data collected from the other two points exceeds a preset threshold, the mutual verification process is automatically triggered. The faulty sensor is identified and switched to the backup acquisition channel. At the same time, the fault information is recorded and synchronized to the monitoring and protection module to prevent temperature control failure due to acquisition failure. The integrated data preprocessing module filters and reduces noise in the raw data, removes extreme outliers, ensures data stability, and adapts to the acquisition needs of extreme temperature and humidity conditions such as low temperature and high humidity, and high temperature and high humidity.

[0018] The filtering method uses the moving average filtering method (the window size is set to 5 sets of data). Noise reduction is achieved by removing data that deviate from the mean by ±3 times the standard deviation. Extreme outliers are determined as two consecutive sets of data that exceed the normal collection range by 30%. The processed data retains one decimal place.

[0019] The mutual verification process is achieved by comparing the consistency of the data collected by the backup sensor at the same location with the data collected by other key location sensors. The number of backup acquisition channels is 1 to 2 sets, and the anomaly judgment threshold is set to 3 consecutive sets of data with a deviation of >5%. After the verification is passed, the weighted average method is used to integrate the effective data as the output.

[0020] The dual-mode decision module constructs a dual-mode dynamic decision model based on a fuzzy PID fusion algorithm. It automatically identifies environmental conditions based on temperature and humidity deviations, deviation change rates, dew point temperature, and evaporator temperature, and dynamically switches operating modes. When the ambient humidity deviation is greater than the temperature deviation, it enters a dehumidification priority mode, prioritizing the adjustment of the inverter compressor frequency for rapid dehumidification, while fine-tuning the temperature through an auxiliary temperature control unit to prevent excessive temperature drop during dehumidification. When the ambient temperature deviation is greater than the humidity deviation, it enters a temperature control priority mode, prioritizing the adjustment of the inverter compressor frequency and the auxiliary temperature control unit to maintain temperature stability, while dynamically adjusting the dehumidification intensity to prevent humidity rebound during temperature control. Based on real-time changes in environmental parameters and combined with dew point temperature and historical operating data, the weights for temperature and humidity regulation are automatically allocated, with a weight range of 0.2 to 0.8. The weight calculation logic is clearly defined as: weight = humidity deviation change rate / (temperature deviation change rate + humidity deviation change rate) × 0.6 + dew point correction coefficient × 0.4, achieving seamless coordination of temperature and humidity regulation and avoiding parameter imbalance caused by single regulation. The dew point correction factor is dynamically adjusted according to the ambient temperature range. The value range is 0.3~0.5 for low temperature conditions (≤10℃), 0.2~0.4 for normal temperature conditions (10~30℃), and 0.1~0.3 for high temperature conditions (≥30℃). The adjustment of the factor is based on the difference between the dew point temperature and the target temperature.

[0021] At the same time, based on historical operating data, it can predict sudden changes in operating conditions, such as a sudden increase in humidity during the plum rain season or a sudden drop in temperature in a low-temperature workshop. It can adjust the weights and operating modes in advance, reduce parameter fluctuations, strengthen the linkage logic with other modules, and send collaborative instructions to the frequency conversion speed control module, temperature control and anti-frost module and heat recovery module simultaneously when switching modes. The predicted triggering conditions for sudden changes in operating conditions are temperature and humidity change rate >2℃ / 10min or >5%RH / 10min. The advance adjustment time window is 1~3min. The adjustment strategies include pre-adjusting the frequency step size of the inverter and switching the compensation mode in advance to avoid parameter overshoot caused by sudden changes in operating conditions.

[0022] It supports manual forced mode switching and presets dedicated mode parameters for special scenarios such as laboratory temperature control and warehouse dehumidification. For example, the preset weights for laboratory scenarios are 0.7 for temperature control and 0.3 for dehumidification, and the preset weights for warehouse scenarios are 0.3 for temperature control and 0.7 for dehumidification, which improves scenario adaptability. The decision instructions and weight allocation results are output to the frequency conversion speed control module and the temperature control and anti-frost module.

[0023] New dedicated modes have been added for the precision electronics workshop (temperature control weight 0.8, dehumidification weight 0.2) and the archive room (temperature control weight 0.5, dehumidification weight 0.5). The preset parameters include the target temperature and humidity fluctuation range (±0.5℃, ±2%RH) and the mode switching delay time (≤2min), which can be adapted to the needs of precise control in different scenarios.

[0024] The variable frequency speed control module employs an adaptive fuzzy PID speed control algorithm with multi-input fusion. It uses temperature and humidity deviation, deviation change rate, compressor operating parameters (exhaust temperature, current, pressure), heat recovery efficiency, and anti-frost status (feedback from the temperature control and anti-frost module) as inputs to dynamically optimize PID parameters, including proportional coefficient, integral time, and derivative time. This avoids low-frequency oscillation and high-frequency lag issues. The module also features a dynamic frequency step size adjustment and an adaptive threshold mechanism based on operating conditions. It automatically adjusts the frequency adjustment step size according to the magnitude of the temperature and humidity deviation and the operating condition type (low temperature / high temperature): the step size increases when the deviation is large and decreases when the deviation is small, thus optimizing the frequency adjustment step size range. The step size range is 1~3Hz under low temperature conditions and 2~5Hz under high temperature conditions, enabling rapid adjustment for large deviations and precise fine-tuning for small deviations. Large deviations are judged by humidity deviation > 5%RH or temperature deviation > 3℃, and the corresponding step size is the maximum value under this condition; small deviations are judged by humidity deviation ≤ 2%RH and temperature deviation ≤ 1℃, and the corresponding step size is the minimum value under this condition; intermediate deviations are adjusted by linearly proportionally.

[0025] Based on compressor runtime, parameter drift, and vibration sensor data, the compressor's resonant frequency range is identified in real time. The criteria for resonant frequency identification are vibration acceleration ≥ 0.5g (g is the acceleration due to gravity) and duration ≥ 2s. A database of common compressor resonant frequency ranges (covering the mainstream range of 50~150Hz) is preset, and the range thresholds are dynamically updated in conjunction with real-time operating parameters. When the frequency adjustment approaches the resonant frequency range, it quickly jumps across the range (jumping by 2~3Hz) to avoid increased noise and mechanical wear caused by resonance, thus extending the compressor's service life. During low-frequency operation (≤30Hz), current compensation technology combined with energy recovery from the heat recovery module is used to maintain frequency stability and avoid temperature and humidity fluctuations caused by low-frequency shutdowns. At the same time, an energy consumption optimization factor is added, and the frequency adjustment rate is dynamically adjusted during speed regulation in conjunction with temperature and humidity weights to avoid the phenomenon of large deviations and small adjustments or small deviations and large adjustments, thereby reducing energy consumption. The frequency regulation effect directly affects the compensation accuracy and anti-frost effect of the temperature control and anti-frost module, and the operating status is fed back to the feedback correction module in real time.

[0026] The current compensation technology is automatically triggered when the frequency is ≤30Hz. The compensation current range is 10%~20% of the rated current. The compensation logic is dynamically adjusted according to real-time frequency fluctuations to ensure that the frequency fluctuation amplitude is controlled within ±1Hz and to avoid low-frequency instability.

[0027] The temperature control and anti-frost module is equipped with a two-stage temperature compensation unit. The first stage of compensation uses condensation heat recovery compensation, which recovers the condensation heat of the compressor to the air outlet to regulate the air outlet temperature, which is energy-saving and environmentally friendly. The second stage of compensation uses electric heating, and the power can be steplessly adjusted within a range of 50~300W. The design incorporates intelligent switching logic for compensation modes, which automatically switches between compensation modes or combinations of compensation based on dew point temperature and temperature deviation. For example, in low-temperature conditions, a combination of condensation heat recovery and low-power electric heating is used, while in high-temperature conditions, only condensation heat recovery is used, avoiding temperature fluctuations or energy waste caused by single compensation. The response condition for electric heating adjustment is when the temperature deviation is >2℃ or the condensation heat recovery compensation is insufficient. The adjustment step size is set according to the temperature deviation gradient. When the deviation is 2~5℃, the step size is 50W. When the deviation is >5℃, the step size is 100W. When the temperature deviation is ≤1℃, the electric heating is automatically turned off.

[0028] For low-temperature and high-humidity operating conditions, an anti-frost collaborative control logic and machine learning prediction are designed. Combining evaporator temperature, ambient dew point temperature, compressor operating parameters and historical frost data, a simplified version of the XGBoost model is adopted (avoiding the high hardware computing power requirements of complex machine learning models and adapting to the computing power limitations of embedded control scenarios of dehumidifiers) to predict frost trends in advance. The model simplification scheme limits the number of decision trees to ≤20 and the depth of a single tree to ≤5 layers. Evaporator temperature, ambient dew point temperature and compressor operating frequency are selected as core input features. The training data is updated through a sliding window (window duration of 5 minutes) to ensure that the prediction response speed is ≤3 seconds.

[0029] By dynamically adjusting the compressor frequency, electronic expansion valve opening, and fan speed, the evaporator frosting rate is reduced, achieving active anti-frost. The electronic expansion valve opening adjustment range is 50~480 steps, with an adjustment step size of 10~30 steps / time. When the frosting trend is predicted to be "mild", the opening is increased by 10~20 steps; when it is "moderate", it is increased by 20~30 steps; and when it is "severe", it is increased by 30 steps in conjunction with the compressor frequency reduction, ensuring that the refrigerant flow is adapted to the anti-frost requirements.

[0030] The integrated compressor exhaust hot gas defrosting and heat recovery synergistic technology can quickly defrost when the amount of frost on the evaporator reaches a preset threshold, using the compressor exhaust hot gas combined with the condensation heat stored in the heat recovery module, without stopping the machine, ensuring continuous dehumidification and temperature control operations. The preset threshold for frost amount is determined by the difference between the evaporator fin temperature and the ambient dew point temperature. A difference of ≤2℃ and a duration of ≥10min is the threshold for light frost, and a difference of ≤0℃ and a duration of ≥5min is the threshold for heavy frost, which correspond to different defrosting intensities.

[0031] The compensation unit works in conjunction with the variable frequency speed control module and the temperature and humidity calibration module. Based on the real-time data from the temperature and humidity calibration module and the frequency adjustment commands from the variable frequency speed control module, it achieves closed-loop temperature control, adapting to the usage requirements of extreme operating conditions such as low temperature and high temperature. Temperature control compensation and anti-frost status are simultaneously fed back to the heat recovery module to optimize the energy recovery strategy.

[0032] The heat recovery module recovers the condensing heat of the compressor to the temperature control compensation unit for temperature compensation, reducing the energy consumption of electric heating and helping to improve the temperature control response speed; a total heat exchanger is set at the fresh air inlet and the exhaust air outlet to recover the sensible heat and latent heat in the exhaust air for pre-treatment of fresh air, reducing the energy consumption required for fresh air conditioning and reducing ambient temperature fluctuations. The target heat exchange efficiency of the total heat exchanger is ≥70%. When the detected efficiency is below 60%, the speed of the fresh air fan is automatically adjusted (increased by 10%~20%), and the cleaning reminder function is triggered at the same time. The heat exchange efficiency is calculated by the temperature and humidity difference between the inlet and outlet air.

[0033] Based on ambient temperature and humidity, system operating mode (feedback from the dual-mode decision module), and anti-frost status (feedback from the temperature control and anti-frost module), the energy recovery ratio is automatically adjusted, with an adjustment range of 0~100%. For example, under low-temperature anti-frost conditions, the recovery ratio is increased to 80%~100%, prioritizing heat for anti-frost and temperature control; under high-temperature conditions, the recovery ratio is adjusted to 30%~60% to avoid excessive heat causing temperature rise; a small energy storage unit is set up to store excess condensation heat, which is released during low-temperature conditions or defrosting to assist in temperature control compensation, reduce electric heating energy consumption, and is suitable for small dehumidifier scenarios; The energy storage unit has a capacity of 500~1000Wh, and the heat release rate can be dynamically adjusted according to temperature control requirements. The heat release rate is 50~100W / h under low temperature conditions and increases to 100~200W / h under defrosting conditions. When the temperature of the energy storage unit is below 50℃, it automatically stops releasing heat and starts charging.

[0034] The integrated energy consumption real-time monitoring and optimization unit collects energy consumption data from various modules of the system, including compressors, fans, and electric heaters, in real time. It compares this data with an ideal energy consumption model and automatically adjusts the energy recovery ratio and heat exchange efficiency. This achieves energy-saving goals while ensuring precise temperature control. The ideal energy consumption model is constructed based on the dehumidifier's rated power, ambient temperature and humidity range, and target temperature and humidity parameters. The adjustment is based on the percentage deviation between measured energy consumption and ideal energy consumption (allowable deviation ≤10%). When the deviation exceeds the range, the recovery ratio is gradually optimized in 5% increments. The system strengthens its synergy with the variable frequency drive and temperature control / defrosting modules, providing real-time feedback of heat recovery efficiency to the variable frequency drive module for optimizing PID parameters and frequency adjustment logic. Simultaneously, based on the defrosting and compensation commands from the temperature control / defrosting module, the system adjusts the direction of condensation heat recovery, prioritizing anti-frost and temperature control needs to achieve efficient energy utilization. Energy consumption data and recovery efficiency are simultaneously transmitted to the feedback correction module, providing a basis for parameter correction.

[0035] The feedback correction module collects multi-source feedback data such as temperature and humidity regulation deviation, variable frequency compressor operating status, temperature control compensation effect, evaporator frosting status and heat recovery efficiency. It adopts a weighted fusion algorithm to assign weights according to the importance of each feedback data, compares the preset target value in real time, and automatically corrects the weight allocation, variable frequency speed regulation parameters and temperature control compensation parameters of the dual-mode collaborative decision-making model to avoid correction deviations caused by single feedback. Temperature and humidity regulation deviation accounts for 40%~50% of the weight, compressor operating status and heat recovery efficiency each account for 20%~25%, evaporator frosting status accounts for 10%~15%, and the weight can be dynamically fine-tuned according to the aging degree of the components. The weight of data related to aging components is reduced by 5%~10%.

[0036] The system monitors the runtime, parameter drift, and fault records of core components such as compressors, sensors, and electronic expansion valves in real time. An aging prediction model is built based on a gradient boosting tree model to predict component aging trends in advance. Core input features of the model include component runtime, parameter drift, and the cumulative impact of ambient temperature and humidity. The prediction cycle is updated every 24 hours to assess the aging trend. Aging compensation adjusts control parameters linearly, with each compensation increment not exceeding 5% of the initial parameters to avoid over-correction and system fluctuations. It automatically adjusts control parameters to compensate for accuracy degradation caused by component aging. The system supports cloud-based benchmark synchronization and remote parameter calibration, enabling remote self-calibration by synchronizing environmental benchmark values ​​via a wireless communication module (linked with the monitoring and protection module). A calibration log recording function is also included to record the parameters, time, and operating conditions of each calibration, facilitating fault tracing and algorithm optimization. The wireless communication module uses Wi-Fi or Bluetooth BLE5.0 protocols, and data transmission uses AES-128 encryption. Remote calibration commands must be verified through the device's unique identifier. The original parameters are backed up during the calibration process, and the system automatically rolls back to the backup parameters if the calibration fails.

[0037] It receives data collected by the temperature and humidity calibration module in real time, dynamically adjusts the calibration threshold, and sends the calibrated parameters to the frequency converter in real time. The calibration command covers all execution modules, and the calibration status and component aging warning information are synchronized to the monitoring and protection module to achieve coordination between calibration and safety protection.

[0038] The adjustment of the correction threshold is based on the fluctuation range of temperature and humidity. When the fluctuation range is ≤3%, the threshold is set to ±5% of the target value. When the fluctuation range is 3%~8%, the threshold is relaxed to ±8%. When the fluctuation range is >8%, the threshold is temporarily set to ±10%. The default threshold will be restored after the fluctuation stabilizes.

[0039] The monitoring and protection module categorizes faults and anomalies into three levels: minor, moderate, and severe. It focuses on monitoring and protecting core components such as compressors, sensors, and evaporators for operational anomalies. For minor anomalies, such as slight sensor deviations, an early warning is issued, and the self-calibration function of the feedback correction module is triggered to ensure uninterrupted operation. For moderate anomalies, such as compressor frequency anomalies or slight evaporator frosting, a graded early warning is issued, and operating parameters, such as the frequency of the variable frequency compressor and the compensation mode, are adjusted to reduce the impact of the anomaly and maintain basic operation. For severe anomalies, such as high-pressure over-limits, abnormal current, or sensor malfunctions, an emergency warning is issued, and a safety shutdown protection is activated to prevent component damage. Simultaneously, the fault tracing function locates the faulty module and its cause. The criteria for minor anomalies are: sensor data deviation ≤3% and energy consumption exceeding the ideal range ≤10%; the criteria for moderate anomalies are: sensor data deviation 3%~10%, compressor frequency fluctuation ±10Hz, and evaporator frost reaching 50%~80% of the preset threshold; the criteria for severe anomalies are: sensor data deviation >10%, refrigerant pipeline pressure difference exceeding the safe range ±20%, and compressor current abnormality lasting ≥5s.

[0040] The system monitors the operating status of core components in real time, records operating data and fault information, and supports fault tracing and remote diagnosis (linked with the remote function of the feedback correction module). It integrates temperature and humidity over-limit prediction and early warning functions, combining real-time data from the temperature and humidity calibration module with historical operating data to predict the trend of temperature and humidity parameter changes in advance. When the temperature and humidity approach the preset limit value, it sends intervention commands to the dual-mode decision module and variable frequency speed control module in advance to avoid parameter over-limit. When the temperature and humidity are within the target range for a long time, it automatically enters the sleep mode, reduces the speed of the compressor and fan, shuts down redundant heating units, and maintains real-time monitoring of the temperature and humidity calibration module. Based on changes in dew point temperature and prediction of environmental conditions (such as increased humidity during the plum rain season), it wakes up the system in advance and restores the normal operating mode to ensure that the temperature and humidity are maintained within the target range, avoids parameter deviation and subsequent adjustment, and ensures the continuous and stable operation of precise temperature control and dehumidification. In hibernation mode, the compressor speed is reduced to 30%~40% of the rated speed, the fan speed is reduced to 20%~30% of the rated speed, and only the core temperature and humidity sensor is retained to collect data in real time, with the collection interval extended to 1~2 minutes; the wake-up trigger condition is that the temperature and humidity deviate from the target value by ±1℃ or ±3%RH, and the wake-up process adopts a gradient speed-up strategy to avoid inrush current.

[0041] The system monitors energy consumption in real time. When energy consumption exceeds the ideal range, it automatically sends optimization commands to the variable frequency speed control and heat recovery modules to adjust operating parameters. If the optimization still fails to meet the standards, a moderate warning is triggered to remind the user to check the operating conditions or equipment status to avoid energy waste. At the same time, it integrates differential pressure switch protection to detect the refrigerant pipeline pressure difference. When the pressure difference exceeds the safe range, it dynamically adjusts the compressor frequency or shuts down the system to further ensure safe operation of the system and achieve comprehensive monitoring and abnormal intervention of all modules.

[0042] The safe differential pressure range of the refrigerant pipeline is preset to 0.5~3.0MPa according to the compressor model. If it exceeds this range by ±0.3MPa, the compressor frequency will be dynamically adjusted first (adjustment range ±5Hz). If it does not recover within 30 seconds after adjustment, the shutdown protection will be activated to avoid pipeline damage.

[0043] It should be noted that, in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus.

[0044] Although embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A precise temperature control system for a variable frequency dehumidifier with dual temperature and humidity modes, characterized in that, Includes the following modules: Temperature and humidity calibration module: Various sensors are deployed at key locations of the dehumidifier to achieve comprehensive collection of ambient temperature and humidity, system operating parameters and core component temperatures. It introduces an adaptive error self-calibration and fault self-diagnosis mechanism, and is equipped with data preprocessing functions to adapt to various extreme temperature and humidity conditions. Dual-mode decision module: Based on the fuzzy PID fusion algorithm, a dual-mode dynamic decision architecture is constructed, which can identify environmental conditions and dynamically switch between two operating modes: dehumidification priority and temperature control priority. It is designed with a collaborative weight dynamic allocation and operating condition prediction mechanism, and supports mode preset and manual switching. Variable frequency speed control module: Constructs an adaptive fuzzy PID variable frequency speed control system that integrates multiple input quantities, including dynamic frequency adjustment, resonance avoidance, low frequency stabilization and energy consumption optimization mechanisms, and can dynamically optimize speed control parameters according to environmental conditions and feedback signals from each module. Temperature control and anti-frost module: Constructs a two-level intelligent temperature control compensation and collaborative anti-frost system, adopts a combination compensation mode of condensation heat recovery and electric heating and realizes adaptive switching, combines intelligent prediction technology to perform active anti-frost, and works in conjunction with other related modules to achieve closed-loop temperature control under extreme operating conditions; Heat recovery module: Constructs a collaborative heat recovery system, integrating condensation heat recovery, total heat exchange and energy storage technologies. It can dynamically adjust the energy recovery ratio according to the system operation mode and working conditions, and is equipped with energy consumption monitoring and optimization functions, and transmits relevant operating data to the feedback correction module. Feedback calibration module: Constructs a multi-source feedback closed-loop calibration system, collects operational feedback data from each module and performs dynamic calibration, and is equipped with core component aging prediction and adaptive calibration functions, supporting remote calibration and log recording; Monitoring and Protection Module: Constructs a hierarchical early warning and comprehensive protection system, adopts multiple mechanisms such as hierarchical protection, fault tracing and prediction and early warning, covers core components and operation links, and is equipped with low-power sleep and intelligent wake-up functions, which can monitor the system operation status in real time and perform abnormal intervention.

2. The variable frequency dehumidifier precise temperature control system with dual temperature and humidity modes as described in claim 1, characterized in that, The temperature and humidity calibration module is equipped with temperature and humidity sensors at the air inlet, air outlet, and inside the dehumidifier body. It also integrates a compressor operating parameter sensor and an evaporator fin-mounted temperature sensor to achieve multi-dimensional parameter acquisition. It introduces intelligent modeling and complementary correction technology to build a parameter correlation model and designs an adaptive error self-calibration mechanism, which can dynamically adjust the calibration cycle and automatically calibrate sensor deviations according to environmental conditions. When abnormal sensor data is detected, it automatically triggers the mutual verification process and switches to the backup acquisition channel, synchronously records fault information and feeds it back to the monitoring and protection module. The acquired data is filtered and noise-reducing preprocessed to remove extreme outliers.

3. The variable frequency dehumidifier precise temperature control system with dual temperature and humidity modes as described in claim 2, characterized in that, The dual-mode decision module constructs a dynamic decision model based on a fuzzy PID fusion algorithm. It automatically switches operating modes according to the magnitude of the environmental temperature and humidity deviation: when the environmental humidity deviation is greater than the temperature deviation, it enters the dehumidification priority mode, prioritizing the adjustment of the frequency of the variable frequency compressor and fine-tuning the temperature through the auxiliary temperature control unit; when the environmental temperature deviation is greater than the humidity deviation, it enters the temperature control priority mode, prioritizing the adjustment of the frequency of the variable frequency compressor and the auxiliary temperature control unit, while dynamically adjusting the dehumidification intensity. It can automatically allocate temperature and humidity adjustment weights according to changes in environmental parameters, predict sudden changes in operating conditions based on historical data, and adjust the operating strategy in advance. When switching modes, it synchronously sends collaborative instructions to relevant execution modules, and supports manual forced switching and preset modes for special scenarios.

4. The precise temperature control system for a variable frequency dehumidifier with dual-mode temperature and humidity coordination according to claim 3, characterized in that, The variable frequency speed control module takes temperature and humidity deviation, deviation change rate, and feedback parameters from each module as inputs, and dynamically optimizes the speed control parameters through an adaptive fuzzy PID algorithm; it is designed with a dynamic frequency step size adjustment mechanism, which can automatically adjust the frequency adjustment step size according to the operating condition; it has a resonance frequency identification and avoidance function, which can quickly cross the compressor resonance frequency range, and maintain frequency stability through current compensation and heat recovery during low-frequency operation; energy consumption optimization logic is incorporated into the speed control process, and the frequency adjustment rate is dynamically adjusted in combination with temperature and humidity adjustment weights, and the operating status is fed back to the feedback correction module in real time.

5. The variable frequency dehumidifier precise temperature control system with dual temperature and humidity modes as described in claim 4, characterized in that, The temperature control and anti-frost module features a two-stage temperature compensation unit. The first stage uses condensation heat recovery compensation, and the second stage uses electric heating. It can automatically switch compensation modes or combine compensation based on temperature and humidity deviations and dew point temperature. For low-temperature and high-humidity conditions, it predicts frost trends by combining multi-dimensional parameters and historical data, and reduces the frost rate by dynamically adjusting system operating parameters. It adopts a collaborative defrosting mode of compressor exhaust heat and condensation heat recovery. When the amount of frost reaches a preset threshold, it automatically performs defrosting operation. The compensation unit works in conjunction with the temperature and humidity calibration and variable frequency speed control modules to adapt to the temperature control and anti-frost requirements of various extreme conditions, and the operating status is synchronously fed back to the heat recovery module.

6. The variable frequency dehumidifier precise temperature control system with dual-mode temperature and humidity coordination according to claim 5, characterized in that, The heat recovery module recovers the compressor's condensation heat to the temperature control compensation unit. It recovers sensible and latent heat from the exhaust air through a total heat exchanger for pre-treatment of fresh air. It is equipped with a small energy storage unit to store excess condensation heat, which can be released for auxiliary temperature control under low temperature or defrosting conditions. It can dynamically adjust the energy recovery ratio according to the system's operating mode and conditions. It integrates a real-time energy consumption monitoring and optimization unit, automatically optimizes the recovery efficiency by comparing with an ideal energy consumption model, and adjusts the direction of condensation heat recovery according to the instructions of the temperature control and anti-frost module to prioritize meeting the needs of anti-frost and temperature control. Energy consumption and recovery efficiency data are synchronously transmitted to the feedback correction module.

7. The variable frequency dehumidifier precise temperature control system with dual temperature and humidity modes as described in claim 6, characterized in that, The feedback correction module collects operational feedback data from each module, compares it in real time with preset target values ​​using a weighted fusion algorithm, and automatically corrects the dual-mode decision weights, variable frequency speed control, and temperature control compensation parameters. Based on the operational data of core components, it constructs an aging prediction model to predict aging trends in advance and adjust control parameters to compensate for accuracy loss. It supports cloud-based benchmark synchronization and remote parameter calibration, has a correction log recording function, can dynamically adjust correction thresholds, and sends the corrected parameters to each execution module in real time, while synchronizing correction status and aging warning information to the monitoring and protection module.

8. The variable frequency dehumidifier precise temperature control system with dual-mode temperature and humidity coordination according to claim 7, characterized in that, The monitoring and protection module classifies faults and anomalies into three levels, focusing on monitoring the operating status of core components including the compressor, sensors, and evaporator: For minor anomalies, it issues an early warning and triggers the feedback correction module for self-calibration; for moderate anomalies, it issues a graded early warning and adjusts operating parameters to maintain basic operation; for severe anomalies, it issues an emergency early warning and initiates a safety shutdown protection, while simultaneously tracing the faulty module and its cause. It features temperature and humidity over-limit prediction and early warning, energy consumption monitoring, and refrigerant pipeline differential pressure protection functions. It can automatically switch between low-power sleep and intelligent wake-up modes based on the system's operating status, record operating data and fault information in real time, and support fault tracing and remote diagnosis.

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