Control methods, devices, equipment and storage media for heat recovery rotary dehumidification systems

Abstract

The present application relates to the field of dehumidification control, and particularly relates to a control method, device and equipment of a heat recovery rotary dehumidification system and a storage medium. The method comprises the following steps: collecting real-time temperature and humidity parameters of a target indoor area, performing temperature and humidity position distribution interpolation calculation, and constructing a temperature and humidity distribution fluctuation diagram; performing indoor and outdoor humidity difference calculation and dehumidification demand prediction according to the temperature and humidity distribution fluctuation diagram, so as to obtain a dehumidification demand prediction result; performing full-cycle operation state monitoring on the heat recovery rotary dehumidification system, so as to obtain dehumidification load intensity of a moisture absorption area and temperature recovery efficiency of a regeneration area; extracting historical information of rotary material based on a dehumidification system operation log, and performing dynamic estimation of material effective adsorption ratio and regeneration efficiency, so as to generate a dehumidification performance attenuation index. The present application realizes the minimization of dehumidification energy consumption and the high-efficiency and comfortable indoor adaptive dehumidification effect.

Description

Technical Field

[0001] This invention relates to the field of dehumidification control, and in particular to a control method, apparatus, equipment and storage medium for a heat recovery rotary dehumidification system. Background Technology

[0002] With the continuous improvement of building energy conservation and indoor environmental quality standards, dehumidification technology is playing an increasingly prominent role in HVAC systems, especially in high-humidity climates and environments requiring high-precision temperature and humidity control (such as museums, cleanrooms, and data centers). Dehumidification systems have become a crucial component in ensuring environmental stability and safe equipment operation. Among various dehumidification technologies, heat recovery rotary dehumidification systems are increasingly widely used in various industrial and civil buildings due to their high energy efficiency, strong operational stability, and significant energy recovery capabilities.

[0003] Heat recovery rotary dehumidifier systems primarily rely on adsorption materials to physically dehumidify the air, and then regenerate the adsorption capacity of these materials through high-temperature airflow, achieving continuous circulation. Compared to traditional refrigeration dehumidification methods, this system not only achieves stable dehumidification under low-temperature and low-humidity conditions but also effectively recovers the heat energy generated during regeneration, thus significantly improving the energy efficiency ratio. However, as the application of these systems deepens, the complexity of their control methods and the requirements for their intelligence level are also constantly increasing. In practical applications, due to large fluctuations in indoor loads and frequent changes in environmental conditions, heat recovery rotary dehumidifier systems using fixed parameters or static control strategies often suffer from problems such as response lag, insufficient dehumidification, or excessive energy consumption, making it difficult to adapt to the real-time adjustment needs of dynamic environments. Summary of the Invention

[0004] To address the aforementioned technical problems, this invention proposes a control method, apparatus, equipment, and storage medium for a heat recovery rotary dehumidification system, thereby resolving at least one of the aforementioned technical problems.

[0005] To achieve the above objectives, the present invention provides a control method for a heat recovery rotary dehumidification system, comprising the following steps:

[0006] Step S1: Collect real-time temperature and humidity parameters of the target indoor area, perform interpolation calculation of temperature and humidity location distribution, and construct a temperature and humidity distribution fluctuation map;

[0007] Step S2: Calculate the indoor and outdoor humidity difference and predict dehumidification demand based on the temperature and humidity distribution fluctuation map to obtain the dehumidification demand prediction result;

[0008] Step S3: Monitor the full-cycle operation status of the heat recovery rotary dehumidification system to obtain the dehumidification load intensity of the moisture absorption zone and the temperature recovery efficiency of the regeneration zone;

[0009] Step S4: Extract historical information of the rotor material based on the dehumidification system operation log, and dynamically estimate the effective adsorption ratio and regeneration efficiency of the material to generate a dehumidification performance degradation index;

[0010] Step S5: Based on the dehumidification performance attenuation index, perform dehumidification power attenuation compensation on the predicted dehumidification demand, and dynamically balance and coordinate the dehumidification load intensity of the absorption zone and the temperature recovery efficiency of the regeneration zone to drive dynamic real-time dehumidification control operation.

[0011] This specification provides a control device for a heat recovery rotary dehumidification system, used to execute the control method for the heat recovery rotary dehumidification system described above, including:

[0012] The temperature and humidity distribution calculation module is used to collect real-time temperature and humidity parameters of the target indoor area, perform interpolation calculation of temperature and humidity location distribution, and construct a temperature and humidity distribution fluctuation map.

[0013] The dehumidification demand prediction module is used to calculate the indoor and outdoor humidity difference and predict the dehumidification demand based on the temperature and humidity distribution fluctuation map, thereby obtaining the dehumidification demand prediction result.

[0014] The status monitoring module is used to monitor the full-cycle operation status of the heat recovery rotary dehumidification system, and obtain the dehumidification load intensity of the moisture absorption zone and the temperature recovery efficiency of the regeneration zone.

[0015] The dehumidification performance degradation module is used to extract historical information of the rotor material based on the dehumidification system operation log, and to dynamically estimate the effective adsorption ratio and regeneration efficiency of the material to generate a dehumidification performance degradation index.

[0016] The attenuation compensation module is used to compensate for the attenuation of dehumidification power based on the dehumidification performance attenuation index and to dynamically balance and coordinate the dehumidification load intensity of the moisture absorption zone and the temperature recovery efficiency of the regeneration zone to drive dynamic real-time dehumidification control operations.

[0017] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the control method of the heat recovery rotary dehumidification system described in any of the above claims.

[0018] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the control method of the heat recovery rotary dehumidification system described in any of the preceding claims.

[0019] The beneficial effects of this invention are as follows: By collecting temperature and humidity data of the target area, the system can instantly understand the environmental conditions at various locations indoors, avoiding information bias caused by relying on single-point measurements. Interpolation calculations generate temperature and humidity distribution fluctuation maps, making spatial differences in temperature and humidity readily apparent, helping to identify humidity hotspots or dry areas. These temperature and humidity distribution maps provide a precise basis for subsequent dehumidification demand prediction, improving prediction accuracy. Analyzing temperature and humidity fluctuations can assist in determining optimal sensor placement schemes, improving monitoring accuracy and reducing costs. Calculating the indoor and outdoor humidity difference allows for accurate estimation of the intensity and timing of dehumidification, reducing energy consumption from blindly turning on the dehumidification system. Prediction results guide the dehumidification system to operate rationally, avoiding over-dehumidification and thus saving electricity and heat. Precise dehumidification maintains indoor humidity within a comfortable range for humans, reducing mold growth or discomfort caused by excessive humidity. Prediction results can be used to adjust the operating status of the heat recovery rotor in advance, achieving proactive control rather than passive response. Monitoring the dehumidification load intensity of the moisture absorption zone and the temperature recovery efficiency of the regeneration zone allows for dynamic understanding of the system's operating status, ensuring that dehumidification capacity meets demand. Continuous monitoring of operating parameters allows for timely detection of issues such as decreased rotor efficiency and insufficient temperature recovery, preventing system failure. The obtained load and efficiency data provide a foundation for subsequent dynamic optimization, making the dehumidification process more intelligent and energy-efficient. Analysis of historical operating data objectively reflects the trend of rotor moisture-absorbing material degradation over time. Estimation of the material's effective adsorption ratio and regeneration efficiency enables the system to understand the current actual dehumidification performance of the rotor. The generated dehumidification performance degradation index can be used to determine whether maintenance or operational strategy adjustments are needed, reducing comfort or energy losses caused by performance degradation. Quantitative monitoring of material degradation allows for the scientific formulation of maintenance cycles, avoiding excessive wear or premature material replacement. Power degradation compensation ensures that the actual dehumidification effect matches predicted demand, ensuring that even partial degradation of the rotor material does not affect indoor comfort. Dynamic and coordinated adjustment of the load and temperature efficiency of the moisture-absorbing and regeneration zones achieves optimal overall system performance, avoiding localized overload or energy waste. Driving dynamic real-time dehumidification operations enables automated control, reducing manual intervention and improving system response speed and flexibility. Through degradation compensation and coordinated optimization, energy consumption is minimized while maintaining comfortable humidity levels, achieving the dual goals of energy saving and comfort. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the steps of the control method of the heat recovery rotary dehumidification system of the present invention;

[0021] Figure 2 This is a detailed flowchart illustrating the implementation steps of step S1.

[0022] Figure 3 This is a flowchart illustrating the detailed implementation steps of step S2. Detailed Implementation

[0023] It should be understood that the specific embodiments described herein are for illustrative purposes only and are not intended to limit the scope of the invention.

[0024] This application provides a control method, apparatus, device, and storage medium for a heat recovery rotary dehumidification system. The execution entities of the control method, apparatus, device, and storage medium for the heat recovery rotary dehumidification system include, but are not limited to, mechanical equipment, data processing platforms, cloud server nodes, and network upload devices mounted on the system, which can be considered as general-purpose computing nodes in this application. The data processing platform includes, but is not limited to, at least one of an audio-visual management system, an information management system, and a cloud-based data management system.

[0025] Please see Figures 1 to 3 This invention provides a control method for a heat recovery rotary dehumidification system, comprising the following steps:

[0026] Step S1: Collect real-time temperature and humidity parameters of the target indoor area, perform interpolation calculation of temperature and humidity location distribution, and construct a temperature and humidity distribution fluctuation map;

[0027] Step S2: Calculate the indoor and outdoor humidity difference and predict dehumidification demand based on the temperature and humidity distribution fluctuation map to obtain the dehumidification demand prediction result;

[0028] Step S3: Monitor the full-cycle operation status of the heat recovery rotary dehumidification system to obtain the dehumidification load intensity of the moisture absorption zone and the temperature recovery efficiency of the regeneration zone;

[0029] Step S4: Extract historical information of the rotor material based on the dehumidification system operation log, and dynamically estimate the effective adsorption ratio and regeneration efficiency of the material to generate a dehumidification performance degradation index;

[0030] Step S5: Based on the dehumidification performance attenuation index, perform dehumidification power attenuation compensation on the predicted dehumidification demand, and dynamically balance and coordinate the dehumidification load intensity of the absorption zone and the temperature recovery efficiency of the regeneration zone to drive dynamic real-time dehumidification control operation.

[0031] In the embodiments of the present invention, see Figure 1 The diagram below illustrates the steps of a control method for a heat recovery rotary dehumidification system according to the present invention. In this example, the control method for the heat recovery rotary dehumidification system includes the following steps:

[0032] Step S1: Collect real-time temperature and humidity parameters of the target indoor area, perform interpolation calculation of temperature and humidity location distribution, and construct a temperature and humidity distribution fluctuation map;

[0033] In this embodiment, a high-precision temperature and humidity sensor array is deployed in the target indoor area with a grid spacing of 3m × 3m. The sensors are the Swiss Sensirion SHT85 model, with a measurement accuracy of ±0.1℃ for temperature and ±1.5%RH for relative humidity. The sensors are installed at a height of 1.5m above the ground to avoid direct sunlight and heat sources. The data acquisition frequency is set to once every 30 seconds to ensure the capture of rapid changes in environmental parameters. The wireless Zigbee communication protocol is used to aggregate the data from each sensor to the central processing unit, with a communication distance of up to 100m to ensure coverage of a large space.

[0034] Spatial interpolation was performed on the collected multi-point temperature and humidity data, using a hybrid interpolation method combining Kriging interpolation and inverse distance weighting (IDW). First, a sensor spatial coordinate system was established, with a corner point indoors as the origin, to determine the X and Y coordinate positions of each sensor. For temperature field interpolation, a spherical variogram model was used, with nugget value C0 = 0.2, sill value C = 1.8, and range a = 5.0 m. For humidity field interpolation, an exponential variogram model was used, with parameters set to C0 = 0.15, C = 2.1, and a = 4.5 m. The interpolation grid density was set to 0.5 m × 0.5 m, generating a refined contour map of indoor temperature and humidity distribution.

[0035] The time-series analysis employed a sliding window technique with a window length of 1 hour and a sliding step of 5 minutes to analyze trends in historical temperature and humidity data. Wavelet transform was used to extract multi-scale features of temperature and humidity changes, decomposing them into two components: high-frequency fluctuations and low-frequency trends. The high-frequency component reflects short-term environmental disturbances (such as human activity and equipment start-up / shutdown), while the low-frequency component reflects long-term trends (such as diurnal temperature range and seasonal variations). Combining spatial distribution characteristics with temporal variation patterns, a three-dimensional temperature and humidity distribution fluctuation map was constructed. The X and Y axes represent spatial location, the Z axis represents time, and color intensity represents the magnitude of temperature and humidity values, forming an intuitive visualization of the spatiotemporal changes in environmental parameters.

[0036] Step S2: Calculate the indoor and outdoor humidity difference and predict dehumidification demand based on the temperature and humidity distribution fluctuation map to obtain the dehumidification demand prediction result;

[0037] In this embodiment, based on the constructed temperature and humidity distribution fluctuation map, the average humidity value of each area indoors is extracted, and the overall indoor humidity representative value is calculated using the area-weighted average method. Simultaneously, real-time outdoor humidity parameters are obtained from an outdoor weather station equipped with a Vaisala HMP155 humidity sensor, with a measurement range of 0-100%RH and an accuracy of ±1.0%RH. The indoor-outdoor humidity difference ΔRH = RHindoor - RHoutdoor is calculated. When ΔRH > 0, it indicates that the indoor humidity is higher than the outdoor humidity, requiring dehumidification.

[0038] A dehumidification demand forecasting model was established, employing a hybrid forecasting method combining multiple linear regression and neural networks. Input variables included 12 factors such as the current indoor-outdoor humidity difference, indoor temperature, number of people, and equipment moisture dissipation load. Historical data showed that dehumidification demand began to appear when indoor relative humidity exceeded 60%; demand increased rapidly above 65%; and emergency dehumidification was required when it reached above 70%. Analysis of dehumidification demand patterns across different seasons and time periods revealed that the highest dehumidification demand occurred during the hot and humid summer months (June-September), with an average dehumidification capacity of 8-12 kg / h; moderate dehumidification demand occurred during transitional seasons (April-May and October-November), approximately 4-6 kg / h; and the lowest dehumidification demand occurred during the winter heating season, typically below 2 kg / h.

[0039] The prediction algorithm employs an LSTM (Long Short-Term Memory) network with an input layer, hidden layer 1 (64 neurons), hidden layer 2 (32 neurons), and output layer. The training dataset contains 4320 samples from six consecutive months of data. The prediction time window is set to the next two hours, and the prediction accuracy has been verified to exceed 85%. Considering the differences in dehumidification needs across different areas, the indoor space is divided into several functional zones, such as office areas, meeting areas, and equipment areas, and a sub-zone dehumidification demand prediction model is established for each zone. Finally, a comprehensive dehumidification demand prediction result is generated, including information on the total dehumidification demand, the distribution of demand in each zone, and the demand time series, providing an accurate load prediction basis for subsequent system control.

[0040] Step S3: Monitor the full-cycle operation status of the heat recovery rotary dehumidification system to obtain the dehumidification load intensity of the moisture absorption zone and the temperature recovery efficiency of the regeneration zone;

[0041] In this embodiment, a comprehensive monitoring network for the rotary dehumidification system is established, with temperature, humidity, pressure, and wind speed sensors installed at key locations such as the rotary inlet / outlet, the moisture absorption zone, and the regeneration zone. A Testo 6681 temperature and humidity transmitter is installed at the rotary inlet, with a measurement range of -40 to +60℃ and 0 to 100%RH, and a response time of less than 15 seconds. The rotary outlet is equipped with dual-channel monitoring to measure parameter changes in the treated air and the regenerated air respectively. The rotary speed is monitored in real time by a photoelectric encoder with an accuracy of 0.1 rpm and an adjustable speed range of 0-20 rpm. The power of the regeneration heater is monitored by a smart meter with a sampling frequency of 1 Hz and a measurement accuracy of ±0.5%.

[0042] The dehumidification load intensity of the absorption zone is calculated using the enthalpy difference method. This involves measuring the temperature and humidity parameters of the air at the inlet and outlet of the rotor to calculate the change in air enthalpy. The formula for calculating air enthalpy is h = 1.005t + d(2501 + 1.84t), where t is the dry-bulb temperature and d is the moisture content. The dehumidification load intensity η1 = (h1 - h2) / A × t, where h1 and h2 are the inlet and outlet air enthalpies, A is the effective absorption area of ​​the rotor (typically 60-70% of the total rotor area), and t is the residence time of the air in the absorption zone. Actual measured data shows that under standard operating conditions (inlet air temperature 27℃, relative humidity 70%, air volume 3000 m³ / h), the dehumidification load intensity of the absorption zone is approximately 0.8-1.2 kg / (m²·h).

[0043] The temperature recovery efficiency of the regeneration zone is determined by analyzing the temperature change curve of the regenerated air. The regenerated air is heated from ambient temperature (typically 25-30℃) to a set temperature (generally 80-120℃), then desorbed by the impeller, and finally discharged at a temperature reduced to 60-80℃. The temperature recovery efficiency η2 is defined as (T_discharge - T_ambient) / (T_set - T_ambient) × 100%, and this efficiency should be maintained between 65-75% during normal operation. Continuous monitoring revealed that excessively high regeneration temperatures (>130℃) accelerate impeller material aging, while excessively low temperatures (<70℃) result in insufficient desorption, affecting dehumidification performance. A database of impeller operating conditions is established to record optimal operating parameters under different environmental conditions, including rotational speed, regeneration temperature, and airflow distribution ratio, providing a reference benchmark for automatic system adjustment.

[0044] Step S4: Extract historical information of the rotor material based on the dehumidification system operation log, and dynamically estimate the effective adsorption ratio and regeneration efficiency of the material to generate a dehumidification performance degradation index;

[0045] In this embodiment, key operating parameters of the rotor material are extracted from the system operation log, including cumulative operating time, adsorption-desorption cycle count, average operating temperature, and historical humidity load. The operation log records parameters including rotor speed, inlet and outlet temperature and humidity, air volume, regeneration temperature, and 16 other key indicators, with an hourly time granularity. Data mining techniques are used to identify key influencing factors on material performance changes, revealing that cumulative operating time and high-temperature exposure time are the main factors affecting material performance.

[0046] The rotary adsorption material uses a silica gel-lithium chloride composite material with an initial adsorption capacity of 0.35 kg water / kg desiccant. Regular performance tests revealed that the effective adsorption ratio of the material decreases exponentially with operating time. A material performance degradation model was established: α(t) = α0 × e^(-λt), where α(t) is the adsorption ratio after t hours of operation, α0 is the initial adsorption ratio (0.35), and λ is the degradation constant (measured value 2.8 × 10^-6 h^-1). This model shows that after 8760 hours (one year) of operation, the material's adsorption capacity decreases to 97.6% of the initial value, and after 5 years of operation, it decreases to 87.8%.

[0047] The dynamic estimation of regeneration efficiency is achieved by analyzing the moisture balance during the desorption process. Under standard regeneration conditions (regeneration temperature 100℃, regeneration air volume 1200 m³ / h), the theoretical regeneration efficiency of the new material can reach over 95%. With prolonged use, the microporous structure of the material changes, and some adsorption sites are occupied by pollutants, leading to a gradual decrease in regeneration efficiency. By comparing and analyzing the moisture mass balance during adsorption and desorption processes, the actual regeneration efficiency is calculated as: βt = (Wdes / Wads) × 100%, where Wdes is the desorbed water content and Wads is the adsorbed water content from the previous cycle.

[0048] Based on the changing trends of the effective adsorption ratio and regeneration efficiency of the comprehensive materials, a Dehumidification Performance Index (DPI) is established: DPI = α(t) × β(t) × 100%. The DPI value of a new system is typically between 90-95%, decreasing to 85-90% after one year of operation and further to 75-80% after three years. When the DPI falls below 70%, it is recommended to replace the rotor material. Real-time monitoring of DPI changes allows for accurate understanding of the rotor material's health status, providing a scientific basis for maintenance decisions. A material performance database is established to record the performance degradation patterns of rotor materials from different brands and specifications, providing a reference for material selection and replacement cycle determination.

[0049] Step S5: Based on the dehumidification performance attenuation index, perform dehumidification power attenuation compensation on the predicted dehumidification demand, and dynamically balance and coordinate the dehumidification load intensity of the absorption zone and the temperature recovery efficiency of the regeneration zone to drive dynamic real-time dehumidification control operation.

[0050] In this embodiment, a power attenuation compensation model is established based on the dehumidification performance degradation index (DPI) to dynamically correct the system's dehumidification capacity. When the DPI drops from the initial value of 90% to the current value of 85%, the actual dehumidification capacity of the system decreases by 5.6%, requiring compensation by increasing the operating power. The compensation strategy adopts a multi-parameter coordinated adjustment method: firstly, the impeller speed is appropriately increased from the standard 12 rpm to 13-14 rpm to enhance the contact time between the air and the adsorption material; secondly, the regeneration temperature is adjusted from 90℃ to 95-100℃ to improve the desorption driving force; finally, the air volume distribution is optimized, increasing the air volume in the moisture absorption zone from the standard 3000 m³ / h to 3200-3400 m³ / h.

[0051] The power compensation calculation formula is: Pcomp = Pstd × (1 / DPI - 1) × K, where Pstd is the standard operating power (usually 8-12kW), and K is the compensation coefficient (value 0.6-0.8). In practical applications, when DPI = 85%, the compensation power is approximately 1.0-1.5kW; when DPI = 80%, the compensation power increases to 2.0-2.8kW. Power compensation is mainly achieved by increasing the power of the regenerative heater and the fan, while considering the total system power limit to avoid excessive compensation leading to a surge in energy consumption.

[0052] The dynamic balance optimization between the desiccation zone and the regeneration zone employs a multi-objective optimization algorithm, establishing an optimization function that includes multiple objectives such as desiccation efficiency, energy consumption level, and equipment lifespan. The load intensity adjustment of the desiccation zone is achieved by changing the residence time of the impeller in the desiccation zone. In the standard configuration, the desiccation zone occupies 75% of the total impeller area, and the regeneration zone occupies 25%. When the desiccation load is high, the proportion of the desiccation zone can be increased to 80%, and the regeneration zone reduced accordingly to 20%; when the load is low, the ratio is adjusted to 70%:30%. The temperature recovery efficiency optimization of the regeneration zone is achieved through precise control of the regeneration temperature curve, employing a segmented heating strategy: rapid heating to the set value in the initial stage, maintaining a constant temperature in the middle stage, and appropriate cooling in the final stage, forming a trapezoidal temperature curve that ensures both desorption efficiency and energy savings.

[0053] The collaborative optimization control employs a model predictive control (MPC) algorithm with a prediction time window of 1 hour and a control time step of 5 minutes. The algorithm calculates the optimal control sequence in real time, including parameters such as impeller speed, regeneration temperature, and airflow distribution. Constraints are set during optimization: impeller speed range of 6-20 rpm, regeneration temperature range of 70-130℃, and total power not exceeding 110% of the rated value. Through real-time feedback control, the system can automatically adjust operating parameters based on environmental changes and performance degradation, ensuring stable and reliable dehumidification. A control performance evaluation mechanism is established; by comparing the actual dehumidification capacity with the target requirement, control accuracy and response time are calculated, continuously optimizing control algorithm parameters and improving the system's intelligence level.

[0054] In this embodiment, see Figure 2 The diagram below illustrates the detailed implementation steps of step S1. In this embodiment, the detailed implementation steps of step S1 include:

[0055] Real-time temperature and humidity parameters of the target indoor area are collected based on a high-precision distributed temperature and humidity sensor array.

[0056] Calculate the spatial deployment coordinates of each sensor node in the temperature and humidity sensor array;

[0057] Based on the spatial deployment coordinates, the real-time temperature and humidity parameters are mapped to their locations, and interpolation calculations are performed to calculate the temperature and humidity location distribution, resulting in a temperature and humidity location distribution map.

[0058] The real-time temperature and humidity parameters are tracked at multiple time points to obtain multi-time point temperature and humidity fluctuation curves;

[0059] Based on the temperature and humidity fluctuation curves at multiple time points, the temperature and humidity location distribution map is dynamically rendered to construct a temperature and humidity distribution fluctuation map.

[0060] In this embodiment, a high-precision distributed temperature and humidity sensor array is deployed within the target indoor space to collect environmental parameters in real time. The recommended measurement accuracy of these sensors is ±0.1℃ for temperature and ±1%RH for humidity, ensuring the subsequent control algorithm's responsiveness to minor fluctuations. The number and spacing of the sensors are determined based on the indoor area and floor height. For example, in a space of approximately 50 m², 16-25 sensor nodes can be deployed on a uniform grid to form a regular array, ensuring comprehensive data coverage. The acquisition frequency can be set to 1-5 Hz, reflecting the dynamic characteristics of temperature and humidity in real time while avoiding redundant data. Each node is connected to the central control unit via a wired bus (e.g., RS485, Modbus) or a wireless protocol (e.g., ZigBee, LoRa), and the data is aggregated and appended with a unified timestamp. To ensure the stability and reliability of the raw data, preliminary preprocessing is required, including median filtering and chi-square test to remove outliers, to reduce deviations caused by occasional interference. After the sensor array is deployed, each node needs to be spatially calibrated to correlate its monitored values ​​with its position in the three-dimensional space of the room. This process typically uses the geometry of the target indoor area as a reference to establish a three-dimensional coordinate system, defining the room's length, width, and height as the X, Y, and Z directions, respectively. For example, if the space is 10 meters long, 5 meters wide, and 3 meters high, a corner of the floor can be set as the origin, and the position of each sensor node in the three-dimensional coordinate system can be obtained using measuring tools. The coordinate accuracy should be controlled within ±1 cm to ensure the accuracy of interpolation and mapping. If the sensors are installed on walls or ceilings, their installation height and orientation information also need to be recorded for subsequent correction using airflow organization models. All coordinate information is stored in a database, corresponding to a unique node ID, thus realizing the mapping relationship of "sensor ID—spatial position—real-time parameters".

[0061] After spatial coordinate calibration, the collected real-time temperature and humidity parameters are mapped to their corresponding coordinates to form discrete sampling points indoors. Due to the limited number of sensor nodes, a continuous spatial distribution cannot be directly constructed; therefore, interpolation methods are needed to generate a complete temperature and humidity field. Common methods include inverse distance weighted (IDW), Kriging, and radial basis function (RBF) interpolation. IDW can quickly generate results when the node distribution is dense; however, Kriging is more suitable when the node distribution is relatively sparse or when there are obvious airflow directions indoors, to obtain a smooth distribution that conforms to physical laws. The interpolation grid resolution can be set according to actual needs, for example, 0.25 m × 0.25 m × 0.5 m, to control the computational load while ensuring accuracy. After interpolation, the temperature and humidity values ​​at any point in the indoor space can be obtained, and further generated into two-dimensional contour maps or three-dimensional color volume plots. A spatial distribution at only one moment cannot reflect the dynamic changes of the environment over time; therefore, time-series tracking of the sensor-collected parameters is necessary. By continuously recording temperature and humidity values ​​at each node, fluctuation curves covering multiple time points can be generated. For example, with a sampling period of 1 second, any node will generate 3600 data points within one hour. To ensure curve smoothness and readability, moving averages or low-pass filtering can be introduced during data processing to suppress high-frequency noise caused by short-term disturbances. The resulting curves can show the characteristics of temperature and humidity changes over time at different spatial locations, and by comparing curves at different nodes, differences and lags in spatial distribution can be identified. Simultaneously, these curves maintain consistency with the interpolation distribution results in the time dimension, ensuring synchronization during subsequent rendering. The interpolation distribution results at continuous time points are organized into a frame sequence, with each frame corresponding to the temperature and humidity field at a specific moment, and then an animation is created through visualization rendering. During rendering, pseudo-color encoding can be used to map the temperature or humidity value range to gradient colors, such as blue for low values, red for high values, and transitional colors for intermediate values. This visually displays the trend and spatial differences over time. Playing these image sequences demonstrates the dynamic fluctuation process of temperature and humidity indoors. Furthermore, fluctuation curves representing local nodes can be overlaid on the distribution map to create a composite visualization effect combining images and curves. This method allows for a direct reflection of the impact of the heat recovery rotary dehumidifier system on indoor humidity control before and after operation, such as the spatial range, uniformity, and stability of humidity reduction. This provides a clear visual basis for system operation control and strategy optimization.

[0062] In this embodiment, see Figure 3 The diagram below illustrates the detailed implementation steps of step S2. In this embodiment, the detailed implementation steps of step S2 include:

[0063] Collect outdoor humidity parameters; detect and remove outliers from the outdoor humidity parameters to obtain outlier-removed humidity parameters;

[0064] Based on the temperature and humidity distribution fluctuation map, the humidity difference in different regions is calculated for the abnormal humidity parameters to obtain the indoor and outdoor humidity difference in different regions.

[0065] Identify the intended use, personnel activity density, and equipment moisture dissipation of the target indoor area, and define a dehumidification control threshold.

[0066] The target humidity deviation is calculated based on the indoor and outdoor humidity difference according to the dehumidification control threshold, and the dehumidification demand is predicted to obtain the dehumidification demand prediction result.

[0067] In this embodiment, when controlling the heat recovery rotary dehumidification system, monitoring only the indoor environment is insufficient; real-time monitoring of outdoor humidity is also essential. Outdoor humidity parameters can be obtained by deploying high-precision humidity sensors around the building's perimeter. A measurement accuracy of at least ±1%RH is recommended to ensure direct comparison with indoor data. Sensors should be installed away from direct sunlight, rain, and strong winds, ideally in well-ventilated but sheltered areas, such as the north side of the building facade or the rear of ventilation louvers. The acquisition frequency is typically set to 1-2 Hz to ensure synchronization with the indoor monitoring system. The acquired humidity parameters are transmitted to the central control unit via network or bus, and timestamped to allow for simultaneous comparison with the indoor temperature and humidity distribution map. To ensure reliability, redundancy can be incorporated into the acquisition layer, such as by installing two outdoor sensors at different heights for cross-comparison, preventing data deviations caused by single-point failures. Since the outdoor environment is more susceptible to sudden wind speed changes, raindrop adhesion, or electronic interference, outliers may appear in the raw humidity data. Without proper handling, the calculation of the indoor-outdoor humidity difference will be inaccurate, affecting the reliability of dehumidification control. Outlier detection and removal of outdoor humidity parameters are necessary. Common methods include sliding window detection, the 3σ criterion, box plots, and robust statistical methods based on the median. For example, a 60-second sliding time window can be set, and the mean and standard deviation can be calculated within this interval. When a value deviates from the mean by more than three times the standard deviation, it is identified as an outlier and removed. For continuous data, median filtering can be used for further smoothing to eliminate occasional interference. After removing outliers, a stable humidity curve will be obtained, with significantly reduced data noise and abrupt changes, ensuring that the comparison with the indoor humidity distribution is based on a true and effective foundation.

[0068] At any given moment, the humidity values ​​at each monitoring grid point indoors are extracted and subtracted from the corresponding outdoor humidity parameters to obtain a difference matrix. Due to spatial non-uniformity indoors—for example, humidity changes may be faster in areas near exterior walls, windows, or air conditioning vents—it is necessary to calculate humidity for each area individually, rather than simply using an overall average. The indoor space can be divided into multiple functional zones, such as areas of primary human activity, concentrated equipment areas, and areas near the external building envelope, with humidity differences calculated separately for each zone. This accurately reflects the humidity load at different locations. For example, the humidity difference in densely populated areas may be significantly greater than in corner areas, while areas near windows are more susceptible to external infiltration. This process yields a multi-dimensional set of humidity difference data, including both the overall indoor-outdoor humidity difference and the humidity differences in various local areas, providing refined input for subsequent calculations based on control thresholds. The intended use of different spaces directly determines humidity control requirements. For example, office areas have moderate personnel density and high comfort requirements, generally needing humidity control within the range of 40%RH to 60%RH; server rooms or equipment rooms, due to equipment moisture dissipation and heat dissipation, have stricter humidity requirements, needing to be controlled below 40%RH; while warehouses or special process areas may require humidity to be stable at even lower levels. To establish reasonable dehumidification control thresholds, it is first necessary to identify the personnel activity density based on the space's functional attributes (which can be determined through space design values ​​or actual monitoring data), and estimate the moisture dissipation per person, typically approximately 40-60 g / h. Simultaneously, it is also necessary to identify the moisture dissipation from equipment during operation, such as the moisture load from printers, humidifiers, or other process equipment. Through the comprehensive identification of these parameters, humidity threshold ranges can be defined for different usage scenarios. For example, for high-density office areas, 55%RH can be set as the upper limit threshold and 45%RH as the lower limit threshold; for storage areas, 50%RH can be set as the upper limit and 35%RH as the lower limit.

[0069] Determine whether the humidity value in each area exceeds the upper threshold. If so, calculate the extent of the exceedance. Simultaneously, refer to outdoor humidity levels to assess the feasibility of air treatment after dehumidification. Based on this, a predictive model can be used to estimate the humidity trend over a certain period. The model can employ time series analysis (such as ARIMA), regression prediction, or data-driven grey prediction methods. The prediction timescale can be set to 10-30 minutes to meet real-time control requirements. The prediction results will show the trend of indoor humidity changes at different time points and clarify whether dehumidification is needed and its intensity. For example, if the prediction curve shows that the humidity in a certain area will exceed the threshold of 5%RH in 20 minutes, the system can adjust the rotary dehumidifier operating parameters in advance to avoid exceeding the humidity limit.

[0070] In this embodiment, step S3 includes the following steps:

[0071] Full-cycle operation status monitoring of the heat recovery rotary dehumidification system, and extraction of multi-dimensional monitoring parameters;

[0072] Based on multi-dimensional monitoring parameters, the temperature / humidity difference of the airflow at the inlet and outlet of the rotor, the rotor speed, the air temperature in the regeneration zone, and the airflow switching time between the two zones of the rotor are calculated to obtain a number of key indicators.

[0073] Identify the moisture absorption zone and regeneration zone of the heat recovery rotary dehumidification system;

[0074] Based on multiple key indicators, the dehumidification load intensity of the dehumidification zone and the temperature recovery efficiency of the regeneration zone are calculated to obtain the characteristics of the dehumidification zone and the regeneration zone.

[0075] In this embodiment, a full-cycle monitoring mechanism must be established during the operation of the heat recovery rotary dehumidification system to ensure a comprehensive understanding of the system's dynamics. "Full-cycle" refers to the coverage of the rotary dehumidification system's status under different time periods and operating conditions, including temperature and humidity, airflow conditions, rotary dehumidification zone and regeneration zone, rotary dehumidification speed, regeneration zone heat source temperature, and switching frequency. Therefore, sensors need to be installed at key system locations, such as the rotary dehumidification zone inlet and outlet, regeneration air inlet and outlet, air supply ducts, and regeneration heating section. Monitoring parameters should include at least temperature, humidity, air velocity, rotary dehumidification speed, motor power, and regeneration zone heater power. A sampling frequency of 1-2 Hz is recommended to ensure the capture of short-term fluctuations during rotary dehumidification operation. All collected data is transmitted in real-time to the central control platform, processed with unified timestamps, and stored in a database format for subsequent calculations and modeling. The temperature and humidity differences between the inlet and outlet airflows of the rotary dehumidification zone are calculated. This difference directly reflects the rotary dehumidification capacity; a larger humidity difference indicates a higher dehumidification load in the dehumidification zone. The temperature difference reflects the exchange of latent and sensible heat. Secondly, the rotor speed parameter is extracted. Rotation speed is a crucial variable controlling dehumidification and regeneration efficiency, generally ranging from 8 to 20 revolutions per hour. Different speeds affect the residence time of airflow in the absorption and regeneration zones. Thirdly, the regeneration zone air temperature is monitored, as this parameter directly relates to regeneration efficiency, with a common operating range of 60℃ to 120℃. Insufficient regeneration air temperature prevents the adsorbent from fully releasing moisture, leading to a decrease in subsequent dehumidification capacity. Finally, the airflow switching time between the two zones of the rotor is calculated, i.e., the time interval for a unit of air to transfer from the absorption zone to the regeneration zone. This indicator is determined by both the rotor speed and structural dimensions. By comprehensively considering these key indicators, a multi-dimensional performance profile of the system's operating status can be established, providing a foundational input for subsequent regional characteristic calculations.

[0076] The heat recovery rotor consists of alternating moisture absorption and regeneration zones, and their identification is a necessary prerequisite for subsequent calculations. The moisture absorption zone is typically located between the fresh air and supply air circuits, and its main function is to remove some moisture from the incoming humid air through the surface of the adsorbent. The regeneration zone, on the other hand, uses high-temperature drying air to release moisture from the adsorbent, restoring its moisture absorption capacity. To identify these two zones, the rotor's geometric partitions and operating parameters must be considered. First, the physical partitions can be clearly defined based on the rotor's structural drawings; these are generally disc-shaped partitions, with the moisture absorption and regeneration zones each occupying a certain proportion angularly (commonly 3:1 or 4:1). Second, sensor data is used to verify the partitions. For example, if a significant decrease in humidity is measured at the inlet and outlet of a certain area, that area can be identified as the moisture absorption zone; if the inlet and outlet temperatures increase while the humidity changes in the opposite direction, it is the regeneration zone. This dual identification through physical structure and operating data ensures the accuracy of the zone division. After completing the zone identification, feature calculations need to be performed on the moisture absorption and regeneration zones separately to quantify their operational effectiveness. For the moisture absorption zone, the dehumidification load intensity can be obtained by multiplying the inlet and outlet humidity difference by the air volume. This value reflects the total amount of moisture removed by the moisture absorption zone per unit time. For example, when the inlet air humidity is 65%RH, the outlet air humidity is 50%RH, and the air volume is 2000 m³ / h, the humidity difference can be calculated and further converted into dehumidification capacity, serving as a load intensity indicator. For the regeneration zone, the focus is on calculating the temperature recovery efficiency, which is the ratio of the regeneration air temperature rise to the theoretical heating temperature rise. If the regeneration air temperature is 90℃, while the theoretical heating requirement is 100℃, then the recovery efficiency is 90%. Simultaneously, the completeness of adsorbent recovery in the regeneration zone can be analyzed by combining the impeller speed and airflow switching time. Insufficient regeneration efficiency will lead to a gradual decrease in the moisture absorption zone load during long-term operation. Through this series of calculations, two core indicators can be formed: the dehumidification load intensity of the moisture absorption zone and the temperature recovery efficiency of the regeneration zone.

[0077] In this embodiment, step S4 includes the following steps:

[0078] Historical information of the rotor material was extracted from the dehumidification system's operation log; the historical information of the rotor material included the number of adsorption-desorption cycles, cumulative operating time, and operating temperature history.

[0079] The dehumidification power per unit time is calculated based on the historical information of the impeller material;

[0080] Based on the historical information of the rotor material, the changes in pore structure, the reduction of active sites, and surface contamination were analyzed to obtain the material degradation mechanism.

[0081] Based on the material degradation mechanism and the dynamic estimation of the current effective adsorption ratio and regeneration efficiency of the material using the dehumidification power, a dehumidification performance degradation index is generated.

[0082] In this embodiment, during the long-term operation of the heat recovery rotary dehumidification system, the rotary adsorption material undergoes numerous moisture absorption and regeneration cycles, and its performance gradually degrades over time. Therefore, it is necessary to establish an archiving and extraction mechanism for operation log data to track the material's historical information. The operation log typically includes the following types of data: adsorption-desorption cycle count, cumulative operating time, and operating temperature change history. The cycle count can be obtained by accumulating the rotary speed and operating time; for example, at a speed of 12 rpm and continuous operation for 2000 hours, the cumulative cycle count can reach 24,000 times. The cumulative operating time comes directly from the system operation monitoring records and is a fundamental indicator for measuring material fatigue. The operating temperature history is obtained through long-term monitoring by temperature sensors installed in the regeneration zone, forming a complete temperature curve that reflects the material's historical load under different heating intensities. This information is categorized with timestamps and cleaned to form a material operation database for subsequent analysis. The dehumidification capacity over a period of time is calculated using the inlet and outlet humidity difference and air volumetric flow rate of the rotary desiccation zone. Dehumidification capacity can be obtained by multiplying the difference in air humidity by the airflow rate, with units of grams of water per hour or kilograms of water per hour. Combining this with time information from the system operation log, the dehumidification capacity is divided by the corresponding operating time to obtain the average dehumidification power per unit time. For example, when the inlet air humidity is 60%RH, the outlet air humidity is 45%RH, and the air volumetric flow rate is 2000 m³ / h, the corresponding air humidity difference is 3 g / kg, then the dehumidification power for that stage can reach approximately 6 kg / h. Through continuous calculations from long-term operation logs, dehumidification power curves can be obtained for different time periods, reflecting the changing trend of material performance. When analyzed in conjunction with cumulative operating time and cycle count, it can also reveal whether the dehumidification power decreases under the same operating conditions, thus providing a quantitative basis for inferring the degradation mechanism.

[0083] The number of adsorption-desorption cycles and the high-temperature regeneration temperature history affect the stability of the pore structure. More cycles may lead to structural collapse or pore size enlargement in micropores and mesopores, resulting in a decrease in specific surface area. Secondly, the reduction in active sites is a major reason for the decline in material adsorption capacity. The high-temperature regeneration process causes some surface chemical bonds to break, thereby reducing the active sites available for adsorbing water molecules. Furthermore, during long-term operation, dust, organic matter, or corrosive gases in the air may adhere to the material surface, forming a coating layer and causing surface contamination, making it difficult for the adsorbent to fully contact with moisture in the air. These mechanisms can be analyzed by the correlation between historical operating conditions and performance data. For example, frequent occurrences in the high-temperature operating range often indicate that pore structure and active site degradation are dominant, while high dust concentrations in the operating environment make surface contamination the main factor. The current effective adsorption ratio is obtained by calculating the ratio of the historically calculated dehumidification power to the theoretical dehumidification power under the material's design conditions. If this ratio is consistently below 80%, it indicates a significant decline in material performance. Simultaneously, based on the temperature history and recovery effect in the regeneration zone, the regeneration efficiency is calculated, which is the ratio of the actual ability of the regenerated air to restore the material to the theoretical complete regeneration capacity. By combining the effective adsorption ratio and regeneration efficiency, a "dehumidification performance degradation index" can be constructed, with a value typically between 0 and 1. 1 represents completely normal material performance, while values ​​close to 0 indicate severe performance degradation. This index can be dynamically updated, for example, automatically recalculated every 24 hours based on log data. This not only allows for real-time tracking of material performance but also predicts future degradation trends, enabling advance planning for maintenance, replacement, or optimization of operating parameters.

[0084] In this embodiment, step S5 includes the following steps:

[0085] The overall dehumidification load for the region is calculated based on the dehumidification demand forecast results, resulting in the overall dehumidification demand.

[0086] The maximum power requirement of the dehumidification system is calculated based on the overall dehumidification capacity requirement, and the maximum dehumidification power requirement is generated.

[0087] Based on the dehumidification performance degradation index, the maximum dehumidification power requirement is predicted to reduce dehumidification performance, and power degradation compensation is performed to obtain the degradation compensation power value.

[0088] Based on the attenuation compensation power value, the dehumidification load intensity of the moisture absorption zone and the temperature recovery efficiency of the regeneration zone are dynamically balanced and optimized in a coordinated manner to drive dynamic real-time dehumidification control operations.

[0089] In this embodiment, the predicted humidity excess values ​​for each area are combined with the corresponding air volumetric flow rate to calculate the mass of water vapor that needs to be removed from each area per unit time. The calculation uses the difference in air humidity as the core parameter; that is, by comparing the target threshold humidity with the predicted humidity level, the excess humidity is obtained, and then multiplied by the area's airflow to obtain the dehumidification requirement for that area. Then, the results for all areas are summed to obtain the total dehumidification requirement for the entire target space. For example, in a 1000 cubic meter space, if the predicted excess humidity corresponds to a dehumidification requirement of 2.5 kg / h for the office area, 1.8 kg / h for the equipment area, and 1.2 kg / h for the peripheral infiltration area, then the total dehumidification requirement is 5.5 kg / h. This calculation clearly quantifies the total dehumidification load that the system needs to handle at a given predicted time point. Dividing the total dehumidification requirement by the Coefficient of Performance for Dehumidification (COPd) allows for an estimation of the input power required to achieve that dehumidification level. For example, with a COPd of 2.5, if the total dehumidification demand is 5.5 kg / h, the corresponding maximum dehumidification power demand is approximately 2.2 kW. Secondly, adjustments need to be made based on system operating characteristics, such as impeller speed, regeneration air heating efficiency, and fan power consumption, to ensure the calculation results reflect the actual system load. This power demand represents the maximum output capacity the system must possess under current environmental conditions and within the predicted timeframe. Through this process, the system control logic can obtain a clear power upper limit target, providing a basis for energy consumption planning, operation scheduling, and dynamic compensation.

[0090] After obtaining the maximum dehumidification power requirement, adjustments need to be made based on the degradation of material performance. Because the adsorption capacity and regeneration efficiency of the impeller material decrease over long-term operation, its actual usable power is usually lower than the theoretically calculated value. Therefore, the previously calculated "dehumidification performance degradation index" is introduced. Multiplying the maximum dehumidification power requirement by this index predicts the actual achievable power under deteriorated material performance. For example, if the maximum dehumidification power requirement is 2.2 kW and the degradation index is 0.85, the predicted actual achievable power is only 1.87 kW. To meet the actual dehumidification demand, power compensation is necessary. This involves adding a "degradation compensation power value" during the calculation process. The compensation amount is equal to the difference between the maximum required power and the predicted achievable power, i.e., 0.33 kW. This compensation value will be allocated as an additional load in system operation scheduling to increase fan speed, raise regeneration air temperature, or extend the working time of the moisture absorption zone. A portion of the compensation power value is used to increase the dehumidification load intensity of the moisture absorption zone, for example, by appropriately reducing the supply air temperature or increasing the airflow time, allowing the air to stay on the adsorbent surface longer, thereby increasing the dehumidification rate per unit time. Secondly, another portion of the compensation power is allocated to the regeneration zone to increase the regeneration air temperature or extend the regeneration airflow duration, ensuring that the adsorbent's recovery efficiency does not decrease due to excessive load. The optimization process requires a dynamic balance; that is, enhanced dehumidification in the moisture absorption zone cannot come at the expense of recovery in the regeneration zone, otherwise, performance will further degrade over long periods of operation. Therefore, a multi-objective optimization method can be adopted, with "maximizing the load intensity of the moisture absorption zone" and "stabilizing the recovery efficiency of the regeneration zone" as dual objectives, and coordinated adjustments made using real-time monitoring data. The final result is a dynamically optimized operating state, enabling the dehumidification system to maintain stable and reliable dehumidification capacity even under performance degradation conditions. This optimization strategy directly drives real-time control operations, achieving a dual balance between energy saving and performance.

[0091] In this embodiment, the specific steps for dynamically balancing and coordinating the dehumidification load intensity of the moisture absorption zone and the temperature recovery efficiency of the regeneration zone based on the attenuation compensation power value to drive the dynamic real-time dehumidification control operation are as follows:

[0092] Based on the attenuation compensation power value, the dehumidification load intensity of the moisture absorption zone and the temperature recovery efficiency of the regeneration zone are dynamically balanced and optimized to obtain the dehumidification and regeneration balance parameters.

[0093] Based on the dehumidification and regeneration balance parameters, the global system dehumidification parameters of the heat recovery rotary dehumidification system are adjusted, and global adjustment control parameters are generated.

[0094] Dynamic real-time dehumidification control is performed based on global adjustment of control parameters.

[0095] In this embodiment, after obtaining the attenuation compensation power value, it needs to be rationally allocated to the moisture absorption zone and the regeneration zone to form a dynamic balance optimization mechanism. The optimization goal of the moisture absorption zone is to improve the dehumidification capacity per unit time. For example, during periods of high inlet air humidity, the fan speed can be moderately increased to 110% of the rated value, or the contact time of air on the adsorbent surface can be extended, thereby increasing the load intensity. At the same time, the goal of the regeneration zone is to maintain the regeneration efficiency of the impeller material and prevent the material from failing to recover its moisture absorption performance due to excessive compensation load. By adjusting the regeneration air heating temperature, for example, dynamically increasing it from the normal operating temperature of 90℃ to 95~100℃, the insufficient regeneration caused by material performance degradation can be compensated. The optimization process requires the use of a multi-objective collaborative calculation method, setting "moisture absorption enhancement" and "regeneration assurance" as parallel objectives, and finding the balance point through an iterative algorithm to obtain a set of dehumidification and regeneration balance parameters. The parameters corresponding to the load intensity of the moisture absorption zone and the temperature recovery efficiency of the regeneration zone are input into the central controller, which calculates key parameters such as the impeller speed range, the target value of the inlet and outlet air humidity difference, and the set value of the regeneration air temperature under optimal operating conditions. Secondly, these parameters are integrated into a set of global adjustment and control parameters. For example, with a total dehumidification requirement of 6 kg / h, the controller might provide a global parameter combination of 16 rpm for the dehumidifier, 2100 m³ / h for the absorption zone, 95℃ for the regeneration air temperature, and 1800 m³ / h for the regeneration zone. Finally, the controller dynamically corrects these parameters based on real-time monitoring data to ensure stability under different operating loads. Through this process, optimizations originally scattered in the absorption and regeneration zones are integrated into a system-level adjustment scheme, ensuring that the heat recovery dehumidifier system achieves an optimal balance between overall energy efficiency and performance. The central controller distributes the global parameters to each execution unit, including the fan inverter, heater power regulation module, and dehumidifier drive motor. In actual operation, the fan speed is dynamically adjusted by ±5% based on real-time humidity fluctuations to maintain the stability of the target humidity curve. The regeneration zone heater uses power-level control to achieve rapid switching between 90℃ and 100℃, ensuring sufficient material regeneration under different loads. The rotary drive motor maintains a specified speed range and dynamically switches between the moisture absorption zone and the regeneration zone through time control of the airflow switching valve. Simultaneously, a sensor network continuously collects data such as inlet and outlet humidity, regeneration temperature, and power consumption, feeding this data back to the central controller. The controller compares this real-time feedback with global parameters, and if a deviation occurs, it immediately triggers a secondary correction, achieving true closed-loop control. Ultimately, the system maintains a dynamic balance between moisture absorption and regeneration, ensuring that the overall dehumidification effect meets preset requirements even if material performance degrades. This control mode not only improves the system's response speed but also achieves dual optimization in energy efficiency and lifespan management.

[0096] In this embodiment, a control device for a heat recovery rotary dehumidification system is provided, used to execute the control method for the heat recovery rotary dehumidification system as described above, including:

[0097] The temperature and humidity distribution calculation module is used to collect real-time temperature and humidity parameters of the target indoor area, perform interpolation calculation of temperature and humidity location distribution, and construct a temperature and humidity distribution fluctuation map.

[0098] The dehumidification demand prediction module is used to calculate the indoor and outdoor humidity difference and predict the dehumidification demand based on the temperature and humidity distribution fluctuation map, thereby obtaining the dehumidification demand prediction result.

[0099] The status monitoring module is used to monitor the full-cycle operation status of the heat recovery rotary dehumidification system, and obtain the dehumidification load intensity of the moisture absorption zone and the temperature recovery efficiency of the regeneration zone.

[0100] The dehumidification performance degradation module is used to extract historical information of the rotor material based on the dehumidification system operation log, and to dynamically estimate the effective adsorption ratio and regeneration efficiency of the material to generate a dehumidification performance degradation index.

[0101] The attenuation compensation module is used to compensate for the attenuation of dehumidification power based on the dehumidification performance attenuation index and to dynamically balance and coordinate the dehumidification load intensity of the moisture absorption zone and the temperature recovery efficiency of the regeneration zone to drive dynamic real-time dehumidification control operations.

[0102] The present invention also provides a computer device, including a memory and a processor, wherein the memory stores a computer program, and the processor executes the computer program to implement the steps of the control method of the heat recovery rotary dehumidification system described in any of the above claims.

[0103] The present invention also provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the steps of the control method of the heat recovery rotary dehumidification system described in any of the preceding claims.

[0104] Therefore, the embodiments should be considered as exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description. Thus, all variations falling within the meaning and scope of the equivalents of the application are intended to be included within the invention.

[0105] The above description is merely a specific embodiment of the present invention, enabling those skilled in the art to understand or implement it. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein are implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the present invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features of the invention herein.

Claims

1. A control method of a heat recovery rotary dehumidification system, characterized by, The method comprises the following steps: Step S1: Collecting real-time temperature and humidity parameters of the target indoor area, performing temperature and humidity position distribution interpolation calculation, and constructing a temperature and humidity distribution fluctuation map; Step S2: Calculating the indoor and outdoor humidity difference and predicting the dehumidification demand according to the temperature and humidity distribution fluctuation map, thereby obtaining a dehumidification demand prediction result; Step S3: Monitoring the full-cycle operation state of the heat recovery rotary dehumidification system, obtaining the dehumidification load intensity of the moisture absorption zone and the temperature recovery efficiency of the regeneration zone; Step S4: Extracting the rotary material historical information based on the dehumidification system operation log, and dynamically estimating the effective adsorption ratio and regeneration efficiency of the material to generate a dehumidification performance attenuation index; Step S5: According to the dehumidification performance attenuation index, the dehumidification demand prediction result is compensated for dehumidification power attenuation, and the dehumidification load intensity of the moisture absorption zone and the temperature recovery efficiency of the regeneration zone are dynamically balanced and optimized to drive dynamic real-time dehumidification control operation; Wherein, the specific steps of step S3 are: Monitoring the full-cycle operation state of the heat recovery rotary dehumidification system, extracting multi-dimensional monitoring parameters; According to the multi-dimensional monitoring parameters, the temperature / humidity difference of the rotary inlet and outlet air flow, the rotary speed, the regeneration zone air temperature, and the rotary two-zone air flow switching time are calculated as multiple key indicators; Identify the moisture absorption zone and the regeneration zone of the heat recovery rotary dehumidification system; According to the multiple key indicators, the characteristics of the moisture absorption zone and the regeneration zone are calculated to obtain the dehumidification load intensity of the moisture absorption zone and the temperature recovery efficiency of the regeneration zone; The specific steps of step S4 are: Extracting the rotary material historical information based on the dehumidification system operation log; The rotary material historical information includes the number of adsorption-desorption cycles, the cumulative running time, and the working temperature history; Based on the rotary material historical information, the dehumidification power per unit time is calculated; According to the rotary material historical information, the pore structure change, active site reduction, and surface pollution are analyzed to obtain the material degradation mechanism; According to the material degradation mechanism and the dehumidification power, the current material effective adsorption ratio and regeneration efficiency are dynamically estimated to generate a dehumidification performance attenuation index; Wherein, the specific steps of step S5 are: According to the dehumidification demand prediction result, the total dehumidification load of the area is calculated to obtain the total dehumidification amount demand; According to the total dehumidification amount demand, the maximum power demand of the dehumidification system is calculated to generate the maximum dehumidification power demand; According to the dehumidification performance attenuation index, the maximum dehumidification power demand is predicted for dehumidification performance attenuation, and power attenuation compensation is performed to obtain the attenuation compensation power value; Based on the attenuation compensation power value, the dehumidification load intensity of the moisture absorption zone and the temperature recovery efficiency of the regeneration zone are dynamically balanced and optimized to drive dynamic real-time dehumidification control operation.

2. The control method of the heat recovery rotary dehumidifying system according to claim 1, characterized by, The specific steps of step S1 are: Collecting real-time temperature and humidity parameters of the target indoor area based on a high-precision distributed temperature and humidity sensor array; Calculate the spatial deployment coordinates of each sensor node of the temperature and humidity sensor array; According to the spatial deployment coordinates, the real-time temperature and humidity parameters are mapped by position and the temperature and humidity position distribution interpolation calculation is performed to obtain a temperature and humidity position distribution map; Tracking the changes of the real-time temperature and humidity parameters at multiple time points to obtain a multi-time point temperature and humidity fluctuation curve; According to the multi-time point temperature and humidity fluctuation curve, a dynamic fluctuation rendering is performed on the temperature and humidity position distribution map to construct a temperature and humidity distribution fluctuation map.

3. The control method of the heat recovery rotary dehumidifying system according to claim 1, characterized by, The specific steps of step S2 are as follows: Collecting outdoor humidity parameters; performing abnormal value detection on the outdoor humidity parameters and eliminating the abnormal values to obtain abnormal-eliminated humidity parameters; According to the temperature and humidity distribution fluctuation map, the humidity difference in different regions is calculated based on the abnormal-eliminated humidity parameters to obtain the indoor and outdoor humidity difference in different regions; The use purpose, personnel activity density and equipment moisture emission amount of the target indoor region are identified, and a dehumidification control threshold is defined; According to the dehumidification control threshold, target humidity deviation calculation is performed on the indoor and outdoor humidity difference, and dehumidification demand prediction is performed to obtain a dehumidification demand prediction result.

4. The control method of the heat recovery rotary dehumidifying system according to claim 1, characterized by, The specific steps of driving the dynamic real-time dehumidification control operation based on the attenuation compensation power value to dynamically balance and cooperatively optimize the dehumidification load intensity of the moisture absorption area and the temperature recovery efficiency of the regeneration area are as follows: Based on the attenuation compensation power value, the dehumidification load intensity of the moisture absorption area and the temperature recovery efficiency of the regeneration area are dynamically balanced and cooperatively optimized to obtain dehumidification and regeneration balance parameters; Based on the dehumidification and regeneration balance parameters, the global system dehumidification parameters of the heat recovery rotary dehumidification system are adjusted to generate global adjustment control parameters; According to the global adjustment control parameters, dynamic real-time dehumidification control operation is performed.

5. A control device of a heat recovery rotary dehumidifying system, characterized by, The control method of the heat recovery rotary dehumidification system as claimed in claim 1 comprises: A temperature and humidity distribution calculation module is configured to collect real-time temperature and humidity parameters of a target indoor region, perform temperature and humidity position distribution interpolation calculation, and construct a temperature and humidity distribution fluctuation map; A dehumidification demand prediction module is configured to perform indoor and outdoor humidity difference calculation and dehumidification demand prediction based on the temperature and humidity distribution fluctuation map to obtain a dehumidification demand prediction result; A state monitoring module is configured to perform full-cycle operation state monitoring on the heat recovery rotary dehumidification system to obtain the dehumidification load intensity of the moisture absorption area and the temperature recovery efficiency of the regeneration area; A dehumidification performance attenuation module is configured to extract rotary material historical information based on dehumidification system operation logs, and perform dynamic estimation of material effective adsorption ratio and regeneration efficiency to generate a dehumidification performance attenuation index; An attenuation compensation module is configured to perform dehumidification power attenuation compensation on the dehumidification demand prediction result based on the dehumidification performance attenuation index, and dynamically balance and cooperatively optimize the dehumidification load intensity of the moisture absorption area and the temperature recovery efficiency of the regeneration area to drive dynamic real-time dehumidification control operation. 6.A computer device, comprising a memory and a processor, wherein the memory stores a computer program, and the computer device is configured to perform the method according to any one of claims 1-5. The processor executes the computer program to implement the steps of the control method of the heat recovery rotary dehumidification system as claimed in any one of claims 1 to 4.

7. A computer readable storage medium having stored thereon a computer program, characterized in that, The computer program is executed by the processor to implement the steps of the control method of the heat recovery rotary dehumidification system as claimed in any one of claims 1 to 4.

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