Dynamic scheduling method of rotating wheel dehumidification system and rotating wheel dehumidification system

Abstract

The invention discloses a dynamic scheduling method of a rotary dehumidification system and the rotary dehumidification system. A multi-dimensional parameter control matrix is constructed by performing multi-functional region division on a rotary wheel dehumidification unit, a system dynamic energy distribution diagram is generated in combination with real-time monitoring, and a multi-section dehumidification coupling coefficient is calculated to obtain a system performance quantitative index. And a dynamic regulation and control instruction set is generated according to the indexes, local frost prevention and defrosting management is executed, a self-adaptive frost layer management scheme is formed, overall optimization is carried out on all the functional areas according to the scheme, and finally optimized operation parameters are output. Local frosting can be efficiently restrained under the deep dehumidification working condition, and regeneration energy consumption is reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of rotary dehumidification systems, and in particular to a dynamic scheduling method of a rotary dehumidification system and a rotary dehumidification system. Background Art

[0002] The rotary dehumidification system is an air treatment solution that uses hygroscopic materials to efficiently remove moisture from the air. It is commonly used in places with extremely strict requirements on humidity indicators. In some harsh pharmaceutical plants or high-cleanliness industrial sites, in order to meet the extremely low temperature and low humidity process requirements, rotary dehumidification units with multifunctional modules such as "pre-cooling zone, rotary adsorption zone, regeneration zone, and reheating zone" are usually equipped. The unit first deeply cools and dehumidifies the air, then uses the rotary material for secondary dehumidification, and uses a heat source to regenerate the saturated rotary wheel, thereby ensuring stable control of air humidity in a continuous cycle. Since rotary dehumidification can achieve deep drying at a lower temperature and greatly reduce dependence on traditional refrigeration dehumidification methods, it is widely used in industrial production or scientific research experiments that require strict control of environmental humidity.

[0003] However, in low temperature and low humidity conditions, conventional methods often have difficulty in achieving both efficient dehumidification and energy utilization: on the one hand, local frost is easily formed on the rotor surface or in the pipeline, hindering moisture absorption and mass transfer, resulting in a significant decrease in the dehumidification efficiency of the rotor; on the other hand, large-scale temperature increases to deal with the frost layer will cause a surge in overall energy consumption and affect product quality, and thus cannot simultaneously meet the dual needs of deep dehumidification and energy saving. With the increasing attention paid by various industries to the stability of the production environment and the economic efficiency of energy consumption, how to efficiently suppress local frost on the rotor and accurately allocate system energy under harsh working conditions has become a core technical problem that needs to be solved in the field of rotary dehumidification. Summary of the invention

[0004] The main purpose of the present invention is to solve the technical problem that the existing rotary dehumidification system is difficult to balance deep dehumidification and energy utilization under low temperature and low humidity conditions.

[0005] A first aspect of the present invention provides a dynamic scheduling method for a rotary dehumidification system, the dynamic scheduling method for the rotary dehumidification system comprising: The deep dehumidification system is divided into multifunctional zones, including pre-cooling zone, rotor adsorption zone, rotor regeneration zone, reheat zone and anti-frost zone, and the temperature, humidity and flow parameters of each functional zone are independently controlled to form a multi-dimensional parameter control matrix; According to the multi-dimensional parameter control matrix, each functional area is monitored in real time to obtain energy balance data including sensible heat exchange, latent heat exchange, heat required for wheel regeneration and frost layer status information, and generate a dynamic energy distribution diagram of the system; Based on the dynamic energy distribution diagram, a multi-stage dehumidification coupling coefficient is calculated, where the multi-stage dehumidification coupling coefficient is the ratio of the total energy regulation amount in the precooling and reheating process to the total energy consumption of the rotor regeneration zone and the anti-frost zone, to obtain a quantitative index of system performance; According to the quantitative index of the system performance, the multi-stage dehumidification coupling coefficient is evaluated in real time, and when the multi-stage dehumidification coupling coefficient deviates from the preset normal range, a dynamic control instruction set including adjusting the regeneration temperature, optimizing the anti-frost strategy, and adjusting the operating parameters of the pre-cooling zone is generated; Based on the dynamic control instruction set, priority scheduling of local anti-frost and defrost management is performed, and differentiated processing strategies are adopted according to the degree of frost formation to form an adaptive frost management solution; According to the adaptive frost management scheme, the system is optimized and adjusted as a whole, the temperature and humidity set values ​​and the regeneration heat source target value of each functional zone are updated, the multi-stage dehumidification coupling coefficient is iteratively calculated until it returns to the preset normal range, and the final optimized operating parameters are output.

[0006] Optionally, the deep dehumidification system is divided into multifunctional zones, the functional zones including pre-cooling zone, rotor adsorption zone, rotor regeneration zone, reheat zone and anti-frost zone, and the temperature, humidity and flow parameters of each functional zone are independently controlled to form a multi-dimensional parameter control matrix, including: The deep dehumidification system is dynamically identified in terms of functional area boundaries. The spatial boundaries of the pre-cooling area, the rotor adsorption area, the rotor regeneration area, the reheating area and the anti-frost area are adjusted in real time according to the system operation status and dehumidification requirements to obtain an adaptive functional area distribution map. Based on the adaptive functional area distribution map, extract the microenvironment parameter characteristics of each functional area, obtain the spatial distribution characteristics and time variation laws of temperature, humidity and flow parameters, and form a dynamic characteristic spectrum of the functional area microenvironment; According to the dynamic characteristic spectrum of the microenvironment of the functional area, nonlinear coupling relationship analysis is performed on the temperature, humidity and flow parameters of each functional area, the mutual influence coefficient between the parameters is calculated, and a parameter coupling degree evaluation matrix is ​​obtained; Based on the parameter coupling evaluation matrix, multi-objective optimization calculations are performed on the temperature, humidity and flow parameters of each functional area, while considering dehumidification efficiency, energy consumption control and anti-frost requirements to form a parameter collaborative optimization strategy; According to the parameter collaborative optimization strategy, intelligent prediction and analysis are performed on the temperature, humidity and flow parameters of each functional area, and historical data are used to predict parameter change trends and perform adaptive adjustments to form a multi-dimensional parameter control matrix.

[0007] Optionally, based on the adaptive functional area distribution map, extracting microenvironment parameter characteristics of each functional area, obtaining the spatial distribution characteristics and time variation laws of temperature, humidity and flow parameters, and forming a dynamic characteristic spectrum of the functional area microenvironment, including: According to the adaptive functional area distribution map, each functional area is divided into multiple points, a three-dimensional coordinate system is established, and a high-precision monitoring point array of temperature, humidity and flow parameters is determined to obtain a three-dimensional monitoring network of functional area parameters; Based on the functional area parameter three-dimensional monitoring network, the temperature field of the pre-cooling area is dynamically simulated, the temperature gradient and isothermal surface distribution from the cooling coil surface to the air flow outlet are calculated, and a three-dimensional characteristic model of the temperature field of the pre-cooling area is obtained; According to the three-dimensional characteristic model of the temperature field in the pre-cooling zone, the humidity field in the adsorption zone of the rotor is dynamically scanned, and the relative humidity distribution at different positions on the surface and inside of the rotor is calculated by using an interpolation algorithm to form a three-dimensional dynamic map of the humidity field in the adsorption zone; Based on the three-dimensional dynamic map of the humidity field in the adsorption zone, computational fluid dynamics analysis is performed on the regeneration zone and the reheat zone to simulate the airflow velocity vector field at different cross sections and spatial positions to obtain a three-dimensional flow characteristic model of the airflow; According to the three-dimensional flow characteristic model of airflow, a multi-dimensional spatiotemporal correlation analysis is performed on the parameters of each functional area. Using wavelet analysis, the variation patterns and periodic characteristics of temperature, humidity and flow parameters in spatial and temporal dimensions are calculated to comprehensively generate a dynamic characteristic spectrum of the functional area microenvironment.

[0008] Optionally, the multi-dimensional parameter control matrix is ​​used to monitor each functional area in real time, obtain energy balance data including sensible heat exchange, latent heat exchange, heat required for wheel regeneration, and frost layer status information, and generate a system dynamic energy distribution diagram, including: According to the multi-dimensional parameter control matrix, the dew point temperature of the pre-cooling zone is analyzed, the temperature difference between the intake air and the cooling surface is calculated, and the pre-cooling efficiency evaluation index is obtained; Based on the precooling efficiency evaluation index, the hygroscopic performance of the rotor adsorption zone is evaluated, and the relationship curve between the water vapor adsorption amount of the adsorbent and the relative humidity is calculated to form a rotor hygroscopic efficiency curve diagram; According to the wheel moisture absorption efficiency curve, the heat demand analysis of the wheel regeneration zone is performed, the desorption energy consumption at different regeneration temperatures is calculated, and the regeneration energy consumption optimization parameter table is obtained; Based on the regeneration energy consumption optimization parameter table, the temperature and humidity adjustment energy consumption of the reheating zone is calculated, the energy consumption at different target dew point temperatures is evaluated, and a reheating process energy efficiency evaluation report is formed; According to the reheat process energy efficiency evaluation report, dynamically monitor the frost point temperature of the anti-frost area, calculate the difference between the surface temperature and the dew point temperature, and obtain the frost layer growth risk index; Based on the frost growth risk index, a comprehensive analysis is performed on the sensible heat exchange capacity, latent heat exchange capacity and heat required for wheel regeneration in each functional zone, the energy balance state and distribution characteristics are calculated, and a dynamic energy distribution diagram of the system is generated.

[0009] Optionally, based on the dynamic energy distribution diagram, a multi-stage dehumidification coupling coefficient is calculated, and the multi-stage dehumidification coupling coefficient is the ratio of the total energy regulation amount in the precooling and reheating process to the total energy consumption of the wheel regeneration zone and the anti-frost zone, to obtain a quantitative index of system performance, including: According to the dynamic energy distribution diagram, energy regulation analysis is performed on the precooling zone and the reheating zone, and the sum of the sensible heat exchange and latent heat exchange of each zone is calculated to obtain a total energy regulation data set; Based on the total energy regulation data set, the regeneration energy consumption of the rotor regeneration zone is evaluated, the heat consumption at different regeneration temperatures is calculated, and an energy consumption curve of the regeneration zone is formed; According to the energy consumption curve of the regeneration zone, the defrosting energy consumption of the anti-frost zone is analyzed, the heat consumption under different frost layer thicknesses is calculated, and the energy consumption distribution diagram of the anti-frost zone is obtained; Based on the anti-frost zone energy consumption distribution diagram, the energy consumption data of the rotor regeneration zone and the anti-frost zone are summarized, the total energy consumption of the rotor regeneration zone and the anti-frost zone is calculated, and a system energy consumption evaluation report is formed; According to the system energy consumption evaluation report and the total energy regulation data set, a ratio calculation is performed between the total energy regulation in the precooling and reheating process and the total energy consumption in the wheel regeneration zone and the anti-frost zone to obtain a multi-stage dehumidification coupling coefficient; Based on the multi-stage dehumidification coupling coefficient, the system performance is evaluated, and the trend of the multi-stage dehumidification coupling coefficient changing with time and working conditions is calculated to obtain a quantitative index of system performance.

[0010] Optionally, the multi-stage dehumidification coupling coefficient is evaluated in real time according to the system performance quantitative index, and when the multi-stage dehumidification coupling coefficient deviates from a preset normal range, a dynamic control instruction set including adjusting the regeneration temperature, optimizing the anti-frost strategy, and adjusting the pre-cooling zone operating parameters is generated, including: According to the system performance quantitative index, the multi-stage dehumidification coupling coefficient is monitored in real time, the deviation value of the multi-stage dehumidification coupling coefficient and the preset normal range is calculated, and the multi-stage dehumidification coupling coefficient deviation evaluation result is obtained; Based on the multi-stage dehumidification coupling coefficient deviation evaluation result, the multi-stage dehumidification coupling coefficient deviation is graded into three levels: slight deviation, moderate deviation and severe deviation, forming a multi-stage dehumidification coupling coefficient deviation grade table; According to the multi-stage dehumidification coupling coefficient deviation level table, the system operation status is diagnosed and analyzed, the main influencing factors causing the multi-stage dehumidification coupling coefficient deviation are identified, and a list of system abnormal factors is obtained; Based on the list of abnormal factors of the system, dynamically adjust and calculate the regeneration temperature, determine the optimal regeneration temperature range, and form a regeneration temperature control strategy; According to the regeneration temperature control strategy, the frost growth trend of the anti-frost area is analyzed, the defrost cycle and intensity are calculated, and the optimized anti-frost strategy is obtained; Based on the optimized anti-frost strategy, the operating parameter sensitivity analysis of the pre-cooling zone is performed, the optimal combination of temperature, humidity and flow parameters is calculated, and a parameter adjustment scheme for the pre-cooling zone is formed; According to the pre-cooling zone parameter adjustment scheme, the regeneration temperature control strategy and the optimized anti-frost strategy, the control parameters of each functional zone are collaboratively optimized to generate a dynamic control instruction set including adjusting the regeneration temperature, optimizing the anti-frost strategy and adjusting the pre-cooling zone operating parameters.

[0011] Optionally, the priority scheduling of local anti-frost and defrost management is performed based on the dynamic control instruction set, and a differentiated processing strategy is adopted according to the degree of frost formation to form an adaptive frost management solution, including: According to the dynamic control instruction set, the frost thickness of the rotor surface is analyzed, the frost growth rate of different areas is calculated, and the frost distribution map of the rotor is obtained; Based on the rotor frost distribution map, the rotor is divided into sectors to determine a heavy frost area, a moderate frost area and a light frost area, and a rotor anti-frost priority table is formed; According to the rotor anti-frost priority table, local hot air defrosting design is performed for the heavy frost layer area, the optimal defrosting temperature and wind speed are calculated, and the defrosting plan for key areas is obtained; Based on the defrosting scheme for key areas, a progressive anti-frost strategy is formulated for moderate and light frost areas, and the frost growth inhibition effect at different speeds is calculated to form an anti-frost scheduling plan for the entire rotor; According to the full-rotor anti-frost scheduling plan, the system defrost-operation cycle is optimized, the optimal defrost start time and duration are calculated, and a dynamic defrost execution strategy is obtained; Based on the dynamic defrost execution strategy, the frost layer growth trend is predicted in real time, the critical point of frost layer formation under different working conditions is calculated, and an adaptive frost layer management solution is formed.

[0012] Optionally, according to the adaptive frost management scheme, the system is optimized and adjusted as a whole, the temperature and humidity set values ​​and the regeneration heat source target value of each functional zone are updated, the multi-stage dehumidification coupling coefficient is iteratively calculated until it is restored to a preset normal range, and the final optimized operating parameters are output, including: According to the adaptive frost management scheme, the frost structure analysis is performed on the heavy frost area, the frost density and adhesion strength at different depths are calculated, and the frost characteristic profile is obtained; Based on the frost layer characteristic profile, the hot air penetration process is simulated, the heat transfer coefficient at different depths of the frost layer is calculated, and a heat penetration efficiency map is formed; According to the heat penetration efficiency map, the hot air delivery system is optimized and designed, and the best hot air distribution method is calculated to achieve uniform heat distribution, thereby obtaining a directional hot air delivery solution; Based on the directional hot air delivery scheme, the phase change process of the frost layer is analyzed, the melting and evaporation rates under different temperature and wind speed combinations are calculated, and the defrosting dynamics characteristics are determined; Utilizing the defrosting dynamics, multi-parameter optimization is performed to balance the defrosting effect, energy consumption and potential damage to the adsorption coating, calculate the optimal defrosting temperature and wind speed, and update the temperature and humidity set values ​​and regeneration heat source target values ​​of each functional area; According to the updated set value and target value, the multi-stage dehumidification coupling coefficient is iteratively calculated until it returns to the preset normal range, and the final optimized operating parameters are output.

[0013] A second aspect of the present invention provides a rotary dehumidification system, comprising: a memory and at least one processor, wherein instructions are stored in the memory, and the memory and the at least one processor are interconnected via lines; the at least one processor calls the instructions in the memory so that the rotary dehumidification system executes the steps of the above-mentioned dynamic scheduling method of the rotary dehumidification system.

[0014] This solution first divides the deep dehumidification system into multifunctional zones, separates the processes of precooling, adsorption, regeneration, reheating and anti-frost, and facilitates fine control for different working conditions. On this basis, the temperature, humidity and flow parameters of each functional zone are monitored in real time, and multi-dimensional data such as sensible heat exchange, latent heat exchange and heat required for rotor regeneration are integrated to construct an energy distribution map that can be dynamically updated. Since the energy distribution map can present the interaction between heat and humidity in each functional zone, when a potential frosting risk or abnormal energy consumption occurs in a certain zone, the system can capture these subtle changes in time. By further calculating the multi-stage dehumidification coupling coefficient, the total energy regulation generated by "precooling + reheating" is compared with the total energy consumption of "rotor regeneration zone + anti-frost zone" in real time, and the signs of energy imbalance, frosting tendency or dehumidification efficiency decline can be found in the shortest time, and then the adjustment signal can be fed back to different functional zones. In this way, the system does not need to perform large-scale and high-intensity overall heating or cooling as in traditional practices, but relies on data-driven to achieve precise anti-frost and defrost operations.

[0015] After obtaining the energy balance state, this solution will generate dynamic control instructions according to the interval where the coupling coefficient is located. If the coupling coefficient deviates from the normal range, differentiated adjustments will be made to the regeneration temperature, pre-cooling zone operating parameters, and anti-frost strategies. At the same time, local anti-frost and defrost management adopts priority scheduling, which can quickly locate the area where the frost layer has formed or is about to form, and through short-term and efficient heating or dry wind distribution, the frost layer can be suppressed or removed before it spreads on a large scale. In this process, the system will also track the growth degree of the frost layer, and perform overall optimization and adjustment at the appropriate time, and gradually correct the operating parameters of each functional area. Finally, the coupling coefficient is continuously repaired in the iterative calculation to return to the normal range, which not only ensures the stability of deep dehumidification, but also avoids excessive energy consumption. Through this comprehensive solution of dynamic monitoring, zoning management and energy coupling allocation, efficient removal of moisture and accurate response to frost can be achieved under low temperature conditions, which fundamentally solves the dilemma of high energy consumption and frost accumulation in the traditional dehumidification process in harsh environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the structures shown in these drawings without paying creative work.

[0017] Figure 1 A schematic diagram of an embodiment of a dynamic scheduling method for a rotary dehumidification system in an embodiment of the present invention; Figure 2 It is a schematic diagram of an embodiment of a rotary dehumidification system in an embodiment of the present invention.

[0018] The realization of the purpose, functional features and advantages of the present invention will be further explained in conjunction with embodiments and with reference to the accompanying drawings. DETAILED DESCRIPTION

[0019] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0020] It should be noted that if the embodiments of the present invention involve directional indications (such as up, down, left, right, front, back...), the directional indications are only used to explain the relative position relationship, movement status, etc. between the components under a certain specific posture (as shown in the accompanying drawings). If the specific posture changes, the directional indication will also change accordingly.

[0021] In addition, the descriptions of "first", "second", etc. in the present invention are only used for descriptive purposes and cannot be understood as indicating or implying their relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined as "first" and "second" may explicitly or implicitly include at least one of the features. In addition, "and / or" in the full text includes three solutions. Taking A and / or B as an example, it includes technical solution A, technical solution B, and technical solution that satisfies both A and B. In addition, the technical solutions between the various embodiments can be combined with each other, which must be based on the ability of ordinary technicians in the field to implement. When the combination of technical solutions is contradictory or cannot be implemented, it should be considered that such combination of technical solutions does not exist and is not within the scope of protection required by the present invention.

[0022] An embodiment of the present application provides a dynamic scheduling method for a rotary dehumidification system. Figure 1 A flow chart of a dynamic scheduling method for a rotary dehumidification system provided in an embodiment of the present application. In this embodiment, the method includes: See also Figure 1 , the deep dehumidification system is divided into multifunctional zones, including pre-cooling zone, rotor adsorption zone, rotor regeneration zone, reheat zone and anti-frost zone, and the temperature, humidity and flow parameters of each functional zone are independently controlled to form a multi-dimensional parameter control matrix; In one embodiment of the present invention, the deep dehumidification system is divided into multifunctional zones, and the functional zones include a pre-cooling zone, a rotor adsorption zone, a rotor regeneration zone, a reheating zone and an anti-frost zone. The temperature, humidity and flow parameters of each functional zone are independently controlled to form a multi-dimensional parameter control matrix, including: dynamic functional zone boundary identification of the deep dehumidification system, real-time adjustment of the spatial boundaries of the pre-cooling zone, the rotor adsorption zone, the rotor regeneration zone, the reheating zone and the anti-frost zone according to the system operation status and dehumidification requirements, and obtaining an adaptive functional zone distribution map; based on the adaptive functional zone distribution map, microenvironment parameter feature extraction is performed on each functional zone to obtain the spatial distribution characteristics and time variation of temperature, humidity and flow parameters. According to the dynamic characteristic spectrum of the microenvironment of the functional area, the nonlinear coupling relationship of the temperature, humidity and flow parameters of each functional area is analyzed, and the mutual influence coefficient between the parameters is calculated to obtain the parameter coupling evaluation matrix; based on the parameter coupling evaluation matrix, the temperature, humidity and flow parameters of each functional area are multi-objective optimized, and the dehumidification efficiency, energy consumption control and anti-frost requirements are considered to form a parameter collaborative optimization strategy; according to the parameter collaborative optimization strategy, the temperature, humidity and flow parameters of each functional area are intelligently predicted and analyzed, and the historical data are used to predict the parameter change trend and adaptively adjust to form a multi-dimensional parameter control matrix.

[0023] Specifically, when the deep dehumidification system is dynamically identified in the functional zone boundaries, it is necessary to arrange temperature, humidity and flow sensors in each potential area, and transmit these sensor information to the data processing end in real time for comprehensive analysis. In order to more accurately divide the spatial coverage of the pre-cooling area, the rotor adsorption area, the rotor regeneration area, the reheating area and the anti-frost area, an adaptive partitioning algorithm can be used on the data processing end. For example, using a partitioning method based on fuzzy clustering, the entire system is divided into several initial partitions according to the similarity of temperature and humidity and the law of airflow distribution, and the fuzzy weights are adjusted for the mutation areas of temperature and humidity at each iteration to continuously correct the boundaries of the functional areas. When the target process places higher requirements on the adsorption capacity or anti-frost safety factor, the algorithm will increase the weight of the relevant parameters in the iteration, thereby making a larger spatial division of the adsorption area or anti-frost area. Taking the storage site of biological agents at -5℃ as an example, when the local humidity around the adsorption zone rises suddenly and there is still surplus heat in the regeneration zone, the algorithm will expand the adsorption zone toward the boundary of the regeneration zone to allow more moisture to enter the rotary dehumidification process as soon as possible; at the same time, the anti-frost zone is kept within a suitable minimum range so that targeted heating facilities can be accessed in a short distance when the frost layer appears. Through the above fuzzy clustering strategy, the adaptive division of multifunctional areas in low temperature and low humidity scenarios is realized, reducing energy waste and reduced dehumidification efficiency caused by overall overheating or local frosting out of control.

[0024] After obtaining the adaptive functional area distribution map, it is necessary to extract the microenvironment parameter characteristics of each functional area to obtain the spatial and temporal variation patterns of temperature, humidity and flow, and thus construct the dynamic characteristic spectrum of the functional area microenvironment. In order to complete this construction process, the temperature, humidity and flow data of each area collected in different time periods can be interpolated so that the discrete monitoring points form a continuous distribution surface in three-dimensional space. Subsequently, the wavelet transform can be used to perform multi-scale decomposition of the evolution of these surfaces over time, so as to identify the main periodic components and potential mutation points of the temperature and humidity changes in each functional area. For example, if the temperature on the surface of the condensing coil at a certain point in the pre-cooling area continues to fluctuate, the system will know the main frequency band of the fluctuation after wavelet decomposition, so as to apply fine adjustment to the position in subsequent control. Taking the -10℃ industrial plant as an example, when the load of the pre-cooling area drops sharply after entering the night mode, the wavelet analysis may show that some local temperatures have higher frequency fluctuations. The data processing end will remind the system to change the local air volume or refrigerant flow, thereby reducing overcooling in a short period of time. Through this process, the dynamic characteristic spectrum of the functional area microenvironment can accurately reveal the detailed heat and humidity distribution of each functional area under different operating conditions, which is convenient for subsequent analysis and scheduling.

[0025] After completing the construction of the characteristic spectrum, the nonlinear coupling relationship analysis of temperature, humidity and flow rate can be performed to further obtain the mutual influence coefficients between the parameters, thereby forming a parameter coupling evaluation matrix. In order to accurately quantify these coupling relationships, a correlation analysis method based on kernel functions can be used: first, each parameter is mapped to a high-dimensional feature space, and then the similarity measure between them is calculated to obtain a more refined degree of coupling. For example, if a certain operation shows that a slight increase in the temperature of the adsorption zone of the rotor leads to an increase in energy consumption in the reheating zone, a high-coupling correlation channel can be seen in the coupling evaluation matrix, suggesting that the temperature of the adsorption zone needs to be restricted or the regeneration strategy needs to be dynamically changed in the next stage. Through this analysis method, the linkage mode between the functional zones can be quickly determined without large-scale trial and error, reducing the waste of resources caused by blind heating or cooling.

[0026] After obtaining the parameter coupling evaluation matrix, it is necessary to perform multi-objective optimization calculations to include key objectives such as dehumidification efficiency, energy consumption control, and frost suppression in the evaluation. Optional implementation methods include a multi-objective optimization process based on genetic algorithms, such as the non-dominated sorting genetic algorithm (NSGA-II). The algorithm randomly generates multiple sets of functional area operation parameter combinations in the initial population, and selects better solutions according to the fitness function at each iteration. The fitness function is composed of multiple dehumidification efficiencies, total energy consumption, and frost risk index. The algorithm will continuously cross and mutate the genes of each offspring (i.e., the set values ​​of temperature, humidity, and flow) during the iteration process to find the optimal scheduling solution that takes into account deep dehumidification and energy saving in the solution space. If a combination performs extremely well in terms of deep dehumidification efficiency but consumes too much energy, the algorithm will tend to filter this combination in the next round of iterations; if a combination has reasonable energy consumption but is prone to frost diffusion, the algorithm will correct it by adjusting the priority configuration of the local anti-frost zone. Taking the freeze-drying workshop at -5℃ as an example, if the humidity fluctuates due to frequent equipment switching at night, the algorithm will automatically extend the working time difference between the adsorption area and the regeneration area to stabilize the continuity of moisture absorption of the rotor, while retaining the minimum heat input in the anti-frost area to prevent the spread of frost.

[0027] After the multi-objective optimization is completed, it is necessary to make further intelligent prediction and analysis of the parameters of each functional area based on historical data. The ARIMA model or LSTM deep learning model based on time series can be used to estimate the humidity demand and load conditions of the next time window, so as to determine whether it is necessary to change the air volume of the reheating zone in advance, increase the regeneration temperature of the rotor, or expand the proportion of the anti-frost zone. The prediction process is combined with the results of the multi-objective optimization to output a multi-dimensional parameter control matrix. This matrix lists in detail the temperature setting, humidity setting, and flow restriction indicators of each functional area in different time periods, ensuring that efficient dehumidification can be maintained in extremely low temperature and high cleanliness environments, and energy consumption can be reduced through differentiated frost layer management. If the frost layer signal is detected to begin to appear within a certain period of time, and the prediction model estimates that the regeneration zone is difficult to provide more heat in a short time, the system will automatically dispatch other auxiliary heat sources or local hot air injection modules to alleviate the growth of the frost layer, ensuring that the temperature and humidity in the entire workshop do not fluctuate significantly. The above implementation method can effectively avoid the overall energy consumption surge caused by overall temperature rise in harsh working conditions, and can also prevent the decrease in adsorption efficiency caused by the spread of local frost layers, which has significant energy-saving effects and stability improvement value. Through this adaptive scheduling mechanism, the dehumidification efficiency and defrosting process in the low-temperature and low-humidity production environment are optimized simultaneously.

[0028] In one embodiment of the present invention, based on the adaptive functional area distribution map, the microenvironment parameter feature extraction is performed on each functional area, the spatial distribution characteristics and time variation laws of temperature, humidity and flow parameters are obtained, and a dynamic feature spectrum of the functional area microenvironment is formed, including: according to the adaptive functional area distribution map, each functional area is divided into multiple points of grids, a three-dimensional coordinate system is established, and a high-precision monitoring point array of temperature, humidity and flow parameters is determined to obtain a three-dimensional monitoring network of functional area parameters; based on the three-dimensional monitoring network of functional area parameters, a temperature field dynamic simulation is performed on the pre-cooling area, and the temperature gradient and isothermal surface distribution from the surface of the cooling coil to the air flow outlet are calculated to obtain a three-dimensional feature model of the temperature field of the pre-cooling area; based on According to the three-dimensional characteristic model of the temperature field in the pre-cooling zone, the humidity field in the adsorption zone of the rotor is dynamically scanned, and the relative humidity distribution at different positions on the surface and inside of the rotor is calculated using an interpolation algorithm to form a three-dimensional dynamic map of the humidity field in the adsorption zone; based on the three-dimensional dynamic map of the humidity field in the adsorption zone, computational fluid dynamics analysis is performed on the regeneration zone and the reheating zone, and the airflow velocity vector field at different cross-sections and spatial positions is simulated to obtain a three-dimensional flow characteristic model of the airflow; according to the three-dimensional flow characteristic model of the airflow, a multi-dimensional spatiotemporal correlation analysis is performed on the parameters of each functional zone, and wavelet analysis is used to calculate the changing laws and periodic characteristics of the temperature, humidity and flow parameters in the spatial and temporal dimensions, and a dynamic characteristic spectrum of the microenvironment of the functional zone is comprehensively generated.

[0029] Specifically, when multi-point grid division is performed on each functional area, it is necessary to arrange temperature, humidity and flow sensors in each functional area according to the pre-set fine scale according to the spatial range shown in the adaptive functional area distribution map, and use the visual interface to construct a three-dimensional coordinate system. By corresponding the coordinate axis to the length, width and height of the functional area, a high-precision monitoring point array can be recorded in each grid unit, thereby generating a three-dimensional monitoring network for functional area parameters. Taking the pharmaceutical cold storage room operating in a -10℃ environment as an example, if the area of ​​the rotor adsorption area and the pre-cooling area in the environment is large, the grid division density of the coordinate system will be moderately increased to obtain more accurate data in areas where the local thermal and humidity gradients change drastically. This can provide a solid data foundation for subsequent spatial interpolation and flow analysis, and can reduce the blind spots for the overall performance evaluation of the system. Under certain extreme low temperature conditions, the sensor installation can refer to conventional industrial pipeline temperature measurement or non-contact infrared detection methods, and each collection point can be output to the control module with a digital interface for real-time synchronization.

[0030] After building a three-dimensional monitoring network for functional area parameters, it is necessary to dynamically simulate the temperature field in the pre-cooling area. The goal is to capture the temperature gradient and isothermal surface distribution in the entire path from the surface of the cooling coil to the airflow outlet. In order to achieve this process, the finite volume or finite element method can be used in combination with the meshing data to numerically solve the air flow and heat transfer in the pre-cooling area. This type of numerical solution runs in the control module, with the help of calculation software to discretize the flow field and temperature field, and update the heat exchange amount of the airflow in each iteration. When the temperature around the cooling coil is significantly lower than the average ambient temperature, the module will mark this area as a key cooling zone and form a visual output of the isothermal surface in the final result, thereby obtaining a three-dimensional characteristic model of the temperature field in the pre-cooling area. Taking a large drying workshop in a -15℃ environment as an example, if the temperature gradient in the end area of ​​the cooling coil drops sharply during a certain test, the model will prompt that there is a possible overcooling phenomenon here, and the system will adjust the refrigerant flow or air volume accordingly to prevent local frost or energy waste.

[0031] After obtaining the three-dimensional characteristic model of the temperature field in the pre-cooling zone, it is necessary to dynamically scan the humidity field of the rotor adsorption zone according to the model, and use the interpolation algorithm to calculate the relative humidity distribution at different positions on the rotor surface and inside, so as to form a three-dimensional dynamic map of the humidity field in the adsorption zone. To achieve this humidity field scan, the humidity sensor data arranged in the rotor adsorption zone can be solved jointly with the temperature field model, combined with Kriging interpolation or high-order polynomial interpolation methods, so as to obtain a continuous humidity distribution between the rotor surface and each discrete sampling point inside. When the relative humidity measured by a sensor at a certain location is high, the interpolation algorithm will mark the wet area range and wet area change trend in the dynamic map, which is convenient for subsequent identification of possible adsorption saturation or micro-condensation in the rotor. If a biological agent plant has a significant humidity difference on both sides of the rotor, the dynamic map can be used to determine whether the difference is caused by uneven air volume distribution or uneven aging of the rotor adsorption material, and targeted corrections can be made in the subsequent dehumidification strategy.

[0032] After obtaining the three-dimensional dynamic map of the humidity field in the adsorption zone, it is necessary to perform computational fluid dynamics analysis on the regeneration zone and the reheat zone to simulate the air flow velocity vector field at different cross-sections and spatial positions, thereby forming a three-dimensional flow characteristic model of the air flow. In this part, CFD software can be used to make detailed divisions of the flow fields in the air duct, reheater, and regeneration heating section, and the flow boundary conditions of the adsorption zone can be added to the initial conditions. Through the numerical solution of the velocity vector distribution, eddy zone morphology, and boundary layer development process, it can be determined whether the regeneration heat can be smoothly transferred to the surface of the runner material, and the fluid coupling relationship between the reheat zone and the adsorption zone can also be understood. In a -5℃ environment, if the numerical results show that there is eddy retention near the outlet of the reheat zone, the area may accumulate more moisture or waste heat, which will have a direct impact on the subsequent anti-frost zone layout. The system will perform heat source reallocation or air duct optimization based on the actual monitored velocity vector field distribution.

[0033] After completing the above analysis, the spatial and temporal correlation of the parameters of each functional area can be more intuitively expressed through wavelet analysis, so as to calculate the change law and periodic characteristics in different frequency domains and time series, and finally comprehensively generate the dynamic characteristic spectrum of the functional area microenvironment. Wavelet analysis will perform multi-scale decomposition on the changes of the three-dimensional temperature field, humidity field and flow field in the time series, and obtain the amplitude and phase information in the high-frequency, low-frequency and medium-frequency sections respectively. If the flow in the regeneration zone shows a certain periodic fluctuation in the night shift cycle and changes synchronously with the humidity field in the adsorption zone, the wavelet analysis will identify this implicit coupling and associate the two as homologous triggers in the characteristic spectrum. Afterwards, the associated parameters in the characteristic spectrum will be marked as high coupling, indicating that the fluctuations of both should be paid attention to when adjusting the anti-frost zone, so as to better achieve local or even overall deep dehumidification and energy consumption balance. This multi-dimensional analysis process based on three-dimensional coordinates and grid-based high-precision monitoring methods can significantly reduce the energy consumption fluctuations caused by small airflow interference or accidental frost formation in low temperature and low humidity scenarios, and can also ensure that the adsorption efficiency and regeneration efficiency of the rotor are maximized. By combining such a systematic numerical simulation with real-time monitoring, a solid foundation can be laid for subsequent optimization and control strategies.

[0034] Please continue reading Figure 1 , according to the multi-dimensional parameter control matrix, each functional area is monitored in real time, energy balance data including sensible heat exchange, latent heat exchange, heat required for wheel regeneration and frost layer status information are obtained, and a dynamic energy distribution diagram of the system is generated; In one embodiment of the present invention, the multi-dimensional parameter control matrix is ​​used to monitor each functional area in real time, obtain energy balance data including sensible heat exchange, latent heat exchange, heat required for wheel regeneration, and frost layer status information, and generate a system dynamic energy distribution diagram, including: performing dew point temperature analysis on the pre-cooling area according to the multi-dimensional parameter control matrix, calculating the temperature difference between the intake air and the cooling surface, and obtaining a pre-cooling efficiency evaluation index; based on the pre-cooling efficiency evaluation index, evaluating the hygroscopic performance of the wheel adsorption area, calculating the relationship curve between the water vapor adsorption amount of the adsorbent and the relative humidity, and forming a wheel hygroscopic efficiency curve diagram; according to the wheel hygroscopic efficiency curve diagram, The heat demand analysis is performed on the wheel regeneration zone, the desorption energy consumption at different regeneration temperatures is calculated, and a regeneration energy consumption optimization parameter table is obtained; based on the regeneration energy consumption optimization parameter table, the temperature and humidity adjustment energy consumption of the reheat zone is calculated, the energy consumption at different target dew point temperatures is evaluated, and a reheat process energy efficiency evaluation report is formed; according to the reheat process energy efficiency evaluation report, the frost point temperature of the anti-frost zone is dynamically monitored, the difference between the surface temperature and the dew point temperature is calculated, and the frost layer growth risk index is obtained; based on the frost layer growth risk index, a comprehensive analysis is performed on the sensible heat exchange amount, latent heat exchange amount and heat required for wheel regeneration of each functional zone, the energy balance state and distribution characteristics are calculated, and a system dynamic energy distribution diagram is generated.

[0035] Specifically, when the dew point temperature analysis of the pre-cooling zone is performed based on the multi-dimensional parameter control matrix, the system first reads the key information such as temperature, humidity and flow recorded in the matrix, and compares it with the temperature of the cooling coil surface to calculate the temperature difference between the intake air and the cooling surface. To ensure that this calculation process is more accurate, a digital dew point temperature sensor can be set at the air flow inlet, and a temperature probe can be equipped on the surface of the cooling coil. The data of the two are synchronously transmitted to the control module via the digital interface. If a large temperature difference is detected, it means that the pre-cooling dehumidification potential at this stage is high, but if the refrigerant flow exceeds the reasonable range, it may cause excessive cooling or frost accumulation. The system combines the dew point temperature equation to give a pre-cooling efficiency evaluation index to determine whether it is necessary to appropriately reduce the coil refrigerant flow or adjust the air volume. Taking the storage place of pharmaceutical intermediates in a -5℃ environment as an example, if the evaluation index shows that the difference between the intake air temperature and the coil surface temperature continues to expand, and the difference is close to the condensation point, the control module will immediately reduce the coil refrigerant input to avoid local energy waste or potential frost risk.

[0036] After obtaining the pre-cooling efficiency evaluation index, the system inputs the index and the temperature and humidity parameters of the adsorption zone into the hygroscopic performance evaluation model to calculate the real-time adsorption of water vapor by the adsorbent, and associates it with the relative humidity of the environment, thereby forming a wheel hygroscopic efficiency curve. Here is a further explanation of the hygroscopic performance evaluation model: the model collects data such as the equilibrium moisture content and actual adsorption rate of the wheel material at different temperatures and humidities, and then uses mathematical fitting (such as least squares method, polynomial fitting or common Langmuir, Freundlich and other adsorption isotherm equations) to obtain the multidimensional functional relationship between "adsorption amount-relative humidity-temperature". When there is a significant deviation between the measured value and the fitted value, the model will be dynamically corrected in combination with the air volume, temperature change trend and relative humidity distribution to better reflect the impact of material aging or wind field distortion. Taking the -10℃ environment as an example, if the overall downward movement of the curve exceeds the original standard range, it indicates that the adsorption efficiency is weakened, and it is necessary to increase the air volume in the adsorption zone or adjust the wheel regeneration cycle in time to maintain the deep drying effect required by the system.

[0037] After further analysis of the wheel moisture absorption efficiency curve, the desorption energy consumption of the wheel regeneration zone under different regeneration temperature conditions can be obtained. The system sets a humidity probe and a calorimeter at the wheel outlet, and generates multiple sets of matching data by synchronously recording the increased energy consumption and desorption efficiency gain for each 1°C increase in regeneration temperature, and fills in the regeneration energy consumption optimization parameter table. If the improvement in desorption efficiency at a certain regeneration temperature has reached saturation, but the corresponding energy consumption has increased significantly, it will be marked as "not recommended" or "high energy consumption range". Taking the ultra-low temperature drug freeze-drying process area as an example, if a 60°C regeneration temperature can ensure a desorption rate of 90%, and 70°C only increases the efficiency by 3% but consumes more heat, the parameter table will prompt that 60°C is more cost-effective, and it is recommended to control the regeneration temperature at around 60°C in subsequent operations.

[0038] Subsequently, the system evaluates the energy consumption of the reheat zone at different target dew point temperatures based on the regeneration energy consumption optimization parameter table. The reheat zone must provide sensible heat and coordinate with the heat and humidity conditions of the precooling and regeneration zones. To complete this evaluation, the segmented integral heat transfer balance method can be used to quantify the reheat air volume, air temperature, and economy of various dew point temperature settings under different loads, and finally generate a reheat process energy efficiency evaluation report. If a lower dew point temperature meets the dehumidification requirements but has a greater impact on the overall energy consumption, the report will explicitly list the operating costs caused by this temperature setting in order to balance the deep drying requirements and the economic burden.

[0039] After obtaining the energy efficiency evaluation report of the reheat process, the system will also dynamically monitor the frost point temperature in the anti-frost area, and calculate the frost growth risk index by comparing the surface temperature with the dew point temperature. When the index is close to or below zero, it means that the frost layer is very easy to form, and it is necessary to strengthen local heating or adjust the air volume in time; if the difference is much greater than zero, there is no need to intervene significantly in this area for the time being. Taking the production site of biological agents in an environment of -15℃ as an example, if the risk index continues to rise at night, the system will give priority to allocating waste heat to increase the temperature of the anti-frost area, and reduce part of the power in the reheat area to avoid being forced to increase the temperature on a large scale due to a large amount of frost in the later stage.

[0040] Finally, the system combines the frost growth risk index to comprehensively analyze the sensible heat exchange, latent heat exchange, and heat required for wheel regeneration, and then obtains the energy balance state and distribution characteristics, thereby drawing a dynamic energy distribution diagram of the system. The diagram visualizes the energy distribution of the five functional areas of precooling, adsorption, regeneration, reheating, and anti-frost, so as to quickly show whether there is an area with excessive heat load, obvious incoordination, or potential frost risk. If the monitoring finds that the efficiency of the adsorption area decreases or the heat of the anti-frost area is insufficient, the system will dynamically adjust the intake flow and regeneration temperature in the next operation cycle to ensure that the entire system maintains a better balance between deep dehumidification and energy saving. Through this coherent analysis and control process, the rotary dehumidifier unit in a low-temperature and low-humidity environment can stably and efficiently complete the drying task, while reducing the frost risk to a low level and significantly reducing the overall energy consumption, which has important application value for production lines with strict requirements for temperature and humidity control.

[0041] Please continue reading Figure 1 , based on the dynamic energy distribution diagram, calculating the multi-stage dehumidification coupling coefficient, the multi-stage dehumidification coupling coefficient is the ratio of the total energy regulation amount in the precooling and reheating process to the total energy consumption of the wheel regeneration zone and the anti-frost zone, and obtaining a quantitative index of system performance; In one embodiment of the present invention, the multi-stage dehumidification coupling coefficient is calculated based on the dynamic energy distribution diagram, and the multi-stage dehumidification coupling coefficient is the ratio of the total energy regulation amount in the pre-cooling and reheating process to the total energy consumption of the rotor regeneration zone and the anti-frost zone, so as to obtain a quantitative index of system performance, including: according to the dynamic energy distribution diagram, the energy regulation amount of the pre-cooling zone and the reheating zone is analyzed, and the sum of the sensible heat exchange amount and the latent heat exchange amount of each zone is calculated to obtain a total energy regulation amount data set; based on the total energy regulation amount data set, the regeneration energy consumption of the rotor regeneration zone is evaluated, and the heat consumption at different regeneration temperatures is calculated to form a regeneration zone energy consumption curve; according to the regeneration zone energy consumption curve, the anti-frost zone is evaluated. Perform defrost energy consumption analysis, calculate the heat consumption under different frost layer thicknesses, and obtain the energy consumption distribution diagram of the anti-frost zone; based on the energy consumption distribution diagram of the anti-frost zone, summarize the energy consumption data of the rotor regeneration zone and the anti-frost zone, calculate the total energy consumption of the rotor regeneration zone and the anti-frost zone, and form a system energy consumption evaluation report; according to the system energy consumption evaluation report and the total energy regulation data set, calculate the ratio of the total energy regulation in the pre-cooling and reheating process to the total energy consumption of the rotor regeneration zone and the anti-frost zone to obtain the multi-stage dehumidification coupling coefficient; based on the multi-stage dehumidification coupling coefficient, perform system performance evaluation, calculate the trend of the multi-stage dehumidification coupling coefficient over time and operating conditions, and obtain the quantitative index of system performance.

[0042] Specifically, when analyzing the dynamic energy distribution diagram, it is necessary to first extract the sensible heat exchange and latent heat exchange based on the actual operating data of the precooling zone and the reheating zone, and then add the two together to obtain a comprehensive energy regulation. This part of the result can be called the "total energy regulation data set". The sensible heat exchange here mainly focuses on the heat absorption or release caused by the rise and fall of air temperature, such as the cooling process caused by the contact between the refrigerant and the high-temperature air in the precooling zone, while the latent heat exchange involves the energy released or absorbed during the phase change process. For example, if the humidity in the reheating zone is high, some water may be further evaporated or carried away by the hot air, thereby forming latent heat exchange. Taking the -5℃ environment as an example, if the air in the precooling zone drops significantly, the sensible heat exchange will increase accordingly; if condensation water is precipitated in this area at the same time, the latent heat exchange will also increase, resulting in a higher value of the total energy regulation in the data set at this time.

[0043] After obtaining the total energy regulation data set, it is necessary to evaluate the regeneration energy consumption of the rotor regeneration zone to calculate the heat consumption at different regeneration temperatures and form the regeneration zone energy consumption curve. The regeneration zone applies heat to the adsorption material inside the rotor to make it evaporate and discharge the adsorbed water again. When the regeneration temperature is increased, the desorption efficiency may increase accordingly, but the energy consumption will also increase significantly in a nonlinear manner. To analyze this relationship, the regeneration temperature can be measured and calculated with its corresponding energy input and desorption efficiency step by step, and recorded in the energy consumption curve. This curve can be used to determine whether the regeneration efficiency and energy consumption ratio in a certain temperature range is reasonable. If in a -10℃ low-temperature workshop, a regeneration operation at 60℃ can maintain a high desorption efficiency, and the effect of heating up gradually tends to saturation, it can be seen on the curve that the curve after 60℃ begins to smooth or even steepen, which means that the increase in energy consumption input has not brought considerable desorption benefits.

[0044] Subsequently, it is necessary to analyze the defrosting energy consumption of the anti-frost zone according to the energy consumption curve of the regeneration zone, so as to calculate the heat consumption under different frost layer thicknesses and obtain the energy consumption distribution diagram of the anti-frost zone. The main goal of the anti-frost zone is to prevent the large-scale accumulation of frost or to melt the frost in time, and the heat required for melting or evaporation of frost layers of different thicknesses often varies significantly. If the frost layer is still in its infancy, a small amount of local heating or auxiliary wind can quickly remove it; when the thickness of the frost layer is already considerable, the system often requires a relatively high temperature or a longer heating time, resulting in a rapid increase in energy consumption. By dividing the thickness of the frost layer into several intervals, such as 0.5 mm, 1 mm, 2 mm, etc., and testing or estimating the defrosting time and the corresponding heat input, a defrosting energy consumption curve can be drawn on the coordinate graph, or the energy consumption distribution under each thickness can be presented in the form of a heat consumption cloud map, which helps to determine under what circumstances the defrosting operation should be intervened earlier.

[0045] After obtaining the energy consumption distribution diagram of the anti-frost zone, it is necessary to summarize the energy consumption data of the rotor regeneration zone and the anti-frost zone in the same period, and regard the sum of the energy inputs of the two as the "total energy consumption of the rotor regeneration zone and the anti-frost zone", and form a system energy consumption evaluation report. Such a report usually brings together the regeneration heat load, defrost energy consumption and their corresponding operating efficiency indicators in the time series or working condition dimension. For example, if the report shows that the heat input of the regeneration zone in a certain period of time is significantly higher than the average level, and the energy consumption of the anti-frost zone also increases synchronously, it means that the system has paid a greater energy cost to resist changes in the external environment during this period. If similar fluctuations are still predicted in the future, it can be considered to adjust the energy distribution in advance or shorten some heating time, so as to avoid running too many links at high load in the same time period.

[0046] After mastering the "total energy regulation data set" and the "total energy consumption of the rotor regeneration zone and anti-frost zone", the ratio of these two indicators can be calculated to obtain the multi-stage dehumidification coupling coefficient. The so-called multi-stage dehumidification coupling coefficient is the ratio of the total energy regulation in the pre-cooling and reheating process (that is, the total heat input into the sensible heat and latent heat of the air) to the energy consumed in the regeneration zone and the anti-frost zone to measure the overall energy balance level of the system during deep dehumidification and frost suppression. If the coefficient is too high, it means that the heat input in regeneration and defrosting is insufficient, and the system may frequently fail to dry thoroughly or some frost layers cannot be removed in time; if the coefficient is too low, it means that the energy consumed by regeneration and anti-frost is too large, resulting in the hidden worry of energy waste in maintaining deep dehumidification in the system. Taking the freeze-drying production line under -15℃ as an example, if the multi-stage dehumidification coupling coefficient is found to be significantly lower after several consecutive shifts, it may be that the regeneration temperature is set too high or the defrosting operation in the anti-frost zone is frequent and time-consuming, which needs to be adjusted in the next stage of the control strategy.

[0047] Based on the multi-stage dehumidification coupling coefficient, the system can further carry out performance evaluation, and calculate the change trend of the coefficient in combination with the time axis or different working conditions, so as to obtain the quantitative index of system performance. If the coefficient is in the pre-set balance range in most of the operation stages, it means that the deep dehumidification operation and anti-frost operation are relatively reasonable in energy consumption, and there is no large-scale frost accumulation or heat waste at the regeneration end; if the coefficient fluctuates greatly or deviates significantly from the range, it means that the system is seriously unbalanced, and it is necessary to trace back the energy consumption assessment report to find the specific reasons, such as long-term high temperature at the regeneration end or excessive heating at the anti-frost end. Finally, through the long-term monitoring and trend analysis of the multi-stage dehumidification coupling coefficient, the overall energy utilization of the rotary dehumidifier unit can be quantitatively evaluated, which can not only ensure the quality of deep dehumidification, but also achieve the lowest possible energy consumption in low temperature and low humidity occasions.

[0048] Please continue reading Figure 1 , based on the system performance quantitative index, the multi-stage dehumidification coupling coefficient is evaluated in real time, and when the multi-stage dehumidification coupling coefficient deviates from the preset normal range, a dynamic control instruction set including adjusting the regeneration temperature, optimizing the anti-frost strategy, and adjusting the pre-cooling zone operating parameters is generated; In one embodiment of the present invention, the multi-stage dehumidification coupling coefficient is evaluated in real time according to the system performance quantitative index. When the multi-stage dehumidification coupling coefficient deviates from the preset normal range, a dynamic control instruction set including adjusting the regeneration temperature, optimizing the anti-frost strategy, and adjusting the pre-cooling zone operating parameters is generated, including: according to the system performance quantitative index, the multi-stage dehumidification coupling coefficient is monitored in real time, and the deviation value of the multi-stage dehumidification coupling coefficient and the preset normal range is calculated to obtain the multi-stage dehumidification coupling coefficient deviation evaluation result; based on the multi-stage dehumidification coupling coefficient deviation evaluation result, the multi-stage dehumidification coupling coefficient deviation is graded and divided into three levels of slight deviation, moderate deviation and severe deviation to form a multi-stage dehumidification coupling coefficient deviation grade table; according to the multi-stage dehumidification coupling coefficient deviation grade table, the system operation status is diagnosed. Analyze and identify the main influencing factors that cause the deviation of the multi-stage dehumidification coupling coefficient, and obtain a list of system abnormal factors; based on the list of system abnormal factors, dynamically adjust and calculate the regeneration temperature, determine the optimal regeneration temperature range, and form a regeneration temperature control strategy; according to the regeneration temperature control strategy, analyze the frost growth trend of the anti-frost zone, calculate the defrost cycle and intensity, and obtain the optimized anti-frost strategy; based on the optimized anti-frost strategy, perform operating parameter sensitivity analysis on the pre-cooling zone, calculate the optimal combination of temperature, humidity and flow parameters, and form a pre-cooling zone parameter adjustment plan; according to the pre-cooling zone parameter adjustment plan, the regeneration temperature control strategy and the optimized anti-frost strategy, coordinately optimize the control parameters of each functional zone, and generate a dynamic control instruction set including adjusting the regeneration temperature, optimizing the anti-frost strategy, and adjusting the pre-cooling zone operating parameters.

[0049] Specifically, in the process of real-time monitoring of the multi-stage dehumidification coupling coefficient according to the quantitative indicators of system performance, it is necessary to set a normal interval in the monitoring module to measure whether the current deep dehumidification and energy consumption distribution are in an ideal balance. Once the sensor or data acquisition terminal continuously updates the multi-stage dehumidification coupling coefficient, the control module will immediately calculate the difference between the coefficient and the preset normal interval, that is, the deviation value. If the multi-stage dehumidification coupling coefficient is still within the ideal range, it means that the energy distribution of each subsystem of pre-cooling, regeneration, anti-frost and reheating is relatively reasonable; if the deviation is obvious, it means that the energy consumption and dehumidification efficiency of a certain area or multiple links are unbalanced.

[0050] After obtaining the multi-stage dehumidification coupling coefficient deviation assessment results, the deviation needs to be graded and divided into three levels: slight deviation, moderate deviation and severe deviation, and recorded in the multi-stage dehumidification coupling coefficient deviation level table. Slight deviation means that it can be compensated by a small parameter correction, such as lowering the regeneration temperature or slightly increasing the adsorption air volume to return the coupling coefficient to normal; moderate deviation often requires the regeneration temperature and the anti-frost zone working strategy to be adjusted at the same time, and it may be necessary to adjust the wheel speed or change the air duct layout; severe deviation means that the system may be in a state of high energy consumption or low dehumidification efficiency for a long time, and a comprehensive evaluation is required and some process links may be suspended for in-depth adjustment.

[0051] Next, when the system operating status is diagnosed and analyzed according to the rating table, the operating data of each functional area will be checked one by one, such as the current load level of the pre-cooling area, the moisture absorption of the adsorption area, the heat source consumption of the regeneration area, and the temperature monitoring record of the anti-frost area, and compared with the historical trend or the set threshold, so as to identify the main influencing factors that cause the deviation of the multi-stage dehumidification coupling coefficient, and summarize these factors into a list of system abnormal factors. If the regeneration heat consumption is continuously observed to increase significantly in the -10℃ pharmaceutical workshop and the defrost burden has not increased significantly, this list may indicate problems such as excessive temperature increase in the regeneration area, aging of the rotor material, or uneven air volume distribution.

[0052] After mastering the main influencing factors, it is necessary to dynamically adjust the regeneration temperature to determine an optimal regeneration temperature range, thereby forming a regeneration temperature control strategy. To achieve this goal, a multi-objective optimization algorithm can be used in combination with the information in the abnormal factor list to test the regeneration temperature in a certain range by increasing or decreasing it, and observe the desorption efficiency of the adsorption zone, the energy consumption of the system, and the development of the local frost layer. If it is found that a regeneration temperature of 65°C can achieve a high desorption rate with a lower energy input, the system will give priority to recommending a small interval around 65°C in this strategy. Through this calculation method, it is possible to take into account both the deep dehumidification effect and the prevention of energy waste under extremely low temperature conditions.

[0053] Subsequently, according to the determined regeneration temperature control strategy, the frost growth trend of the anti-frost area is analyzed, and the defrost cycle and intensity are calculated to obtain the optimized anti-frost strategy. If the air dew point is detected to be close to or lower than the surface temperature in a certain period of time, the system will predict that the frost layer may accumulate in the short term, so as to appropriately heat or increase the air volume in advance. At the same time, if it is detected that the frost layer always stays at a thin layer, the control module will avoid overheating and save energy expenditure.

[0054] After completing the anti-frost strategy revision, the pre-cooling zone can be further subjected to the sensitivity analysis of the operating parameters to calculate the best combination of temperature, humidity and flow parameters, and form a pre-cooling zone parameter adjustment plan. If the electricity price of some industrial sites is lower during the night shift and the temperature rises slightly, the system will appropriately increase the air volume of the pre-cooling zone or reduce the refrigerant flow at night according to the sensitivity analysis results, thereby achieving better comprehensive energy consumption economy, while maintaining a good air supply to the rotor adsorption zone.

[0055] Finally, the pre-cooling zone parameter adjustment scheme, regeneration temperature control strategy and optimized anti-frost strategy need to be integrated through a coordination algorithm to generate a dynamic control instruction set and execute it in the next cycle or immediate stage. This instruction set will accurately describe how to adjust the regeneration temperature, how to allocate the heating time and load of the anti-frost zone, and how to fine-tune the air volume and temperature settings of the pre-cooling zone. Through this collaborative optimization process, not only can the deviation of the multi-stage dehumidification coupling coefficient be quickly resolved, but also the operating efficiency and deep dehumidification quality of the rotary dehumidifier unit can be fundamentally improved, avoiding the risk of high energy consumption or frosting out of control for a long time.

[0056] Please continue reading Figure 1 , based on the dynamic control instruction set, priority scheduling of local anti-frost and defrost management is performed, and differentiated processing strategies are adopted according to the degree of frost formation to form an adaptive frost management solution; In one embodiment of the present invention, the priority scheduling of local anti-frost and defrost management is performed based on the dynamic control instruction set, and a differentiated processing strategy is adopted according to the degree of frost formation to form an adaptive frost management plan, including: according to the dynamic control instruction set, the frost thickness of the rotor surface is analyzed, the frost growth rate of different areas is calculated, and the frost distribution map of the rotor is obtained; based on the rotor frost distribution map, the rotor is sectored to determine the heavy frost area, the medium frost area and the light frost area to form a rotor anti-frost priority table; according to the rotor anti-frost priority table, the heavy frost area is locally Hot air defrost design calculates the optimal defrost temperature and wind speed to obtain the defrost plan for key areas; based on the defrost plan for key areas, a progressive anti-frost strategy is formulated for moderate and light frost areas, the frost growth inhibition effect at different speeds is calculated, and a full-rotor anti-frost scheduling plan is formed; according to the full-rotor anti-frost scheduling plan, the system's defrost-operation cycle is optimized, the optimal defrost start time and duration are calculated, and a dynamic defrost execution strategy is obtained; based on the dynamic defrost execution strategy, the frost growth trend is predicted in real time, the critical point of frost formation under different working conditions is calculated, and an adaptive frost management plan is formed.

[0057] Specifically, when analyzing the frost thickness on the rotor surface according to the dynamic control instruction set, it is necessary to first deploy temperature and humidity sensors in various areas of the rotor (such as the edge, center or other key parts of the rotor), and combine visual detection methods (such as infrared thermal imaging or visual recognition) to observe the accumulation of frost. By comparing the local surface temperature with the known frost point parameters, and combining the real-time data of the rotor moisture absorption efficiency and surface humidity, the frost growth rate at different locations can be calculated, and these rate information can be mapped to the entire rotor surface in the control module, and finally a frost distribution map of the rotor is generated. If a high-density frost layer is detected in a certain area and the growth rate is fast, the system will mark the area as a key focus on the distribution map, indicating that the subsequent defrosting or anti-frost strategy needs to be tilted here.

[0058] Based on the frost distribution map of the rotor, the rotor can be divided into sectors and several adjacent areas. In each area, the category is determined based on the thickness and growth rate of the frost layer, and finally the heavy frost area, medium frost area and light frost area are determined to form a rotor anti-frost priority table. The heavy frost area refers to the area where a relatively thick frost layer has accumulated or has continued to grow rapidly in a short period of time. The medium frost area refers to the area with a certain frost risk but can still be controlled. The light frost area is the area where the frost layer is newly formed or basically stable. Using this priority table, defrosting resources can be allocated with higher efficiency, so that high-energy defrosting means are given priority to the heavy frost area that needs to be dealt with urgently, while only necessary fine-tuning or observation is performed on the light frost area.

[0059] After completing the priority division, it is necessary to design local hot air defrosting for the heavy frost layer area according to the anti-frost priority table, and calculate the optimal defrosting temperature and wind speed to obtain the defrosting plan for key areas. This process can use a numerical model based on heat transfer and fluid mechanics, taking the hot air temperature, flow rate, frost layer thickness and actual ambient temperature as the main input, and evaluate the balance point of defrosting time and energy consumption through discretization simulation. If it is found that the hot air temperature is too high, it is easy to cause local overheating of the adsorption material or energy waste, and if it is too low, the defrosting efficiency is insufficient. The algorithm will find an optimal temperature and wind speed combination in iterative testing, so as to effectively reduce the heavy frost layer in a relatively short period of time.

[0060] Based on the defrosting scheme for key areas, a progressive anti-frost strategy can be adopted for moderate and light frost areas, and the frost growth inhibition effect at different speeds can be calculated to form a full-rotor anti-frost scheduling plan. If the rotor can effectively disperse the condensed water attached to the surface at a higher speed, the frost layer will not easily accumulate on a large scale; if the adsorption time is longer at a low speed, it is necessary to increase the micro-heat source compensation in these areas to prevent frost. By evaluating the frost inhibition efficiency of different speeds in simulation or field tests, the accumulation of frost can be delayed without significantly increasing the overall energy consumption, thus gaining a valuable time window for subsequent defrosting work.

[0061] On this basis, according to the full rotor anti-frost scheduling plan, the system's defrost-operation cycle must be further optimized to clarify the optimal start time and duration of each defrost operation and obtain a dynamic defrost execution strategy. This strategy compares the comprehensive data of the rotor anti-frost priority table, local hot air defrost design, and progressive anti-frost strategy to determine at what time to enter the defrost mode to minimize the impact on production or deep dehumidification, and to avoid excessive heating before a serious frost layer appears on the rotor, resulting in energy waste.

[0062] Finally, based on the dynamic defrosting execution strategy, the frost growth trend is predicted in real time, and the critical point of frost formation under different working conditions is calculated, thereby forming an adaptive frost management plan. The prediction can use a time series model or a mechanism model to analyze the temperature and humidity fluctuations of the rotor in several future time periods, and determine whether the frost layer will exceed the set safety threshold. If the estimate shows that the frost layer may surge after a certain time window, the system will call appropriate local heating resources or adjust the speed in advance to prepare for the next wave of defrosting; if the frost layer is expected to grow slowly or even weaken, defrosting can be postponed to avoid consuming energy too frequently. This adaptive management can dynamically adapt to changes in production rhythm in harsh environments, maintaining the deep dehumidification effect while controlling the hidden dangers of frost at a low level.

[0063] Please continue reading Figure 1 According to the adaptive frost management scheme, the system is optimized and adjusted as a whole, the temperature and humidity set values ​​and the regeneration heat source target value of each functional zone are updated, the multi-stage dehumidification coupling coefficient is iteratively calculated until it returns to the preset normal range, and the final optimized operating parameters are output.

[0064] In one embodiment of the present invention, the system is optimized and adjusted as a whole according to the adaptive frost management scheme, the temperature and humidity set values ​​and the regeneration heat source target value of each functional area are updated, the multi-stage dehumidification coupling coefficient is iteratively calculated until it is restored to the preset normal range, and the final optimized operating parameters are output, including: according to the adaptive frost management scheme, the frost layer structure is analyzed in the heavy frost layer area, the frost layer density and adhesion strength at different depths are calculated, and the frost layer characteristic profile is obtained; based on the frost layer characteristic profile, the hot air penetration process is simulated, the heat transfer coefficient at different depths of the frost layer is calculated, and the heat penetration efficiency map is formed; according to the heat penetration efficiency map, the hot air transmission is simulated. The hot air delivery system is optimized and designed, the optimal hot air distribution method is calculated to achieve uniform heat distribution, and a directional hot air delivery plan is obtained; based on the directional hot air delivery plan, the phase change process of the frost layer is analyzed, the melting and evaporation rates under different temperature and wind speed combinations are calculated, and the defrosting dynamics characteristics are determined; using the defrosting dynamics characteristics, multi-parameter optimization is performed to balance the defrosting effect, energy consumption and potential damage to the adsorption coating, calculate the optimal defrosting temperature and wind speed, and update the temperature and humidity set values ​​and regeneration heat source target values ​​of each functional area; according to the updated set values ​​and target values, the multi-stage dehumidification coupling coefficients are iteratively calculated until they are restored to the preset normal range, and the final optimized operating parameters are output.

[0065] Specifically, when analyzing the frost structure in the heavy frost area according to the adaptive frost management scheme, it is necessary to conduct multi-point or layered sampling in the area to collect the density and adhesion strength data of the frost at different depths. The sampling can be done by conventional detection means such as non-contact thickness gauge, local probe or thermal induction scanning, and the temperature and moisture content information corresponding to each depth layer can be recorded together. Subsequently, the analysis module establishes a frost characteristic profile in the vertical direction based on these measured or estimated data, and uses the coordinate axis to depict the frost thickness and the corresponding density, adhesion strength and other indicators. If the frost density in a certain area increases significantly, it means that the lower part of the frost layer is more tightly combined with the rotor surface, and a higher temperature or longer time is required to loosen it or completely detach it during defrosting. At the pharmaceutical freeze-drying process site in a -10℃ environment, if the frost density detected in the anti-frost zone at 3 mm above the rotor surface is significantly greater than that at 1 mm, the profile will indicate that there is still loose space there, and the lower part needs to strengthen heat penetration to achieve significant results.

[0066] After generating the frost layer characteristic profile, it is necessary to simulate the hot air penetration process and calculate the heat transfer coefficient at different depths to form a heat penetration efficiency map. The simulation will treat the frost layer as a porous medium, and combine the thermal conductivity and phase change characteristics to discretize or numerically iterate the hot air penetration path. If the density and adhesion strength of the frost layer at a certain place are too large, it will be difficult for the hot air to transfer heat to the bottom layer in a short time, resulting in low defrosting efficiency. By gradually increasing the hot air temperature or adjusting the wind speed in the simulation, the penetration rate and arrival temperature of heat at each layer depth can be observed, and represented in the form of curves or contour lines in the heat penetration efficiency map. If the map shows that the frost layer can be penetrated deeply and significant melting can be brought about under the conditions of 60°C hot air and moderate wind speed, it indicates that this condition has high applicability in subsequent defrosting strategies.

[0067] After clarifying the heat penetration efficiency map, the hot air delivery system can be optimized and designed, and the optimal hot air distribution method can be calculated in order to obtain uniform and efficient heat distribution in the entire heavy frost area, thereby obtaining a directional hot air delivery solution. This process can be achieved by iteratively solving the nozzle shape, angle and layout position, so that the hot air can reach the defrosted area with the shortest path and minimum energy consumption. If a local area is shown in the simulation to be difficult for hot air to reach, the solution will recommend adding guide vanes or optimizing the direction of the nozzle near that location to avoid adverse consequences such as scalding the rotor material and local condensation water refreezing. In a deep dehumidification system in a -5°C environment, if the hot air is sprayed from above the rotor and cannot fully penetrate into the lower layer of frost, the directional delivery solution will recommend tilting the nozzle angle slightly downward and increasing the local air volume in the air duct to improve the overall penetration efficiency.

[0068] After obtaining the directional hot air delivery solution, a more in-depth analysis of the frost layer phase change process is required, including calculating the melting rate and evaporation rate under different temperature and wind speed combinations, so as to determine the defrosting dynamics. At this stage, the model uniformly inputs data such as hot air temperature, wind speed, frost layer thickness layer, surface temperature, etc., and solves the frost melting rate and evaporation latent heat release rate under given conditions in real time. If the wind temperature is too low or the wind speed is too low, the frost layer will remain solid for a long time, resulting in a decrease in defrosting efficiency; if the temperature is too high or the wind speed is too high, it may cause the rotor adsorption coating to be subjected to thermal fatigue shock in a short period of time. By searching for critical points in a simulation or test environment, the defrosting dynamics can be more clearly understood, and the temperature and wind speed can be set in a targeted manner during actual deployment.

[0069] When optimizing multiple parameters using the defrosting dynamics, it is necessary to balance the defrosting effect, energy consumption, and potential damage to the rotor adsorption coating, and finally calculate the optimal defrosting temperature and wind speed, and write this result back to the temperature, humidity set values, and regeneration heat source target values ​​of each functional area. If it is found that in some cases, increasing the wind speed can significantly shorten the defrosting time but brings higher energy consumption or coating aging risks, the system will abandon this solution in the iterative search and instead tend to use a combination of medium wind speed and suitable temperature. At this time, if the heat penetration efficiency map shows that the deep defrosting efficiency can be improved by increasing the number of hot air nozzles, this solution can also be included in the balance calculation to further reduce the damage of local frost accumulation to the rotor material.

[0070] After completing the process of updating the set value and target value, the system will iteratively calculate the multi-stage dehumidification coupling coefficient again to see whether the new strategy restores the relationship between deep dehumidification efficiency and energy consumption control to the previously set ideal range. If it is monitored during this process that the coupling coefficient still deviates from the reasonable range, it means that the introduced defrosting improvement measures or hot air delivery optimization solutions are still unbalanced in energy distribution, and it is necessary to continue to fine-tune the air volume, temperature or nozzle distribution. When the iterative results show that the multi-stage dehumidification coupling coefficient has returned to the preset normal range, the system can output the final optimized operating parameters, allowing each functional area to enter a stable, energy-saving deep dehumidification and anti-frost operation mode. Through such a full-process closed-loop scheduling that incorporates frost layer structure analysis, directional hot air penetration and defrosting dynamics, the rotary dehumidifier unit can operate stably and efficiently in extremely low temperature and low humidity environments, and effectively reduce the energy consumption surge and production line risks caused by frost accumulation.

[0071] The rotary dehumidification system in the embodiment of the present invention is described in detail from the perspective of hardware processing.

[0072] Figure 2 It is a structural schematic diagram of a rotary dehumidification system provided by an embodiment of the present invention. The rotary dehumidification system 200 may have relatively large differences due to different configurations or performances, and may include one or more processors (central processing units, CPU) 210 (for example, one or more processors) and a memory 220, and one or more storage media 230 (for example, one or more mass storage device terminals) storing application programs 233 or data 232. Among them, the memory 220 and the storage medium 230 can be short-term storage or permanent storage. The program stored in the storage medium 230 may include one or more modules (not shown in the figure), and each module may include a series of instruction operations in the rotary dehumidification system 200. Furthermore, the processor 210 can be configured to communicate with the storage medium 230, and execute a series of instruction operations in the storage medium 230 on the rotary dehumidification system 200 to implement the steps of the dynamic scheduling method of the rotary dehumidification system described above.

[0073] The rotary dehumidification system 200 may also include one or more power supplies 240, one or more wired or wireless network interfaces 250, one or more input and output interfaces 260, and / or one or more operating systems 231, such as Windows Serve, Mac OS X, Unix, Linux, FreeBSD, etc. It will be appreciated by those skilled in the art that Figure 2 The structure of the rotary dehumidification system shown does not constitute a limitation on the rotary dehumidification system provided by the present invention, and may include more or fewer components than shown in the figure, or a combination of certain components, or a different arrangement of components.

[0074] The present invention also provides a computer-readable storage medium, which may be a non-volatile computer-readable storage medium or a volatile computer-readable storage medium. Instructions are stored in the computer-readable storage medium. When the instructions are executed on a computer, the computer executes the steps of the dynamic scheduling method of the rotary dehumidification system.

[0075] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system, device, or unit can refer to the corresponding process in the aforementioned method embodiment and will not be repeated here.

[0076] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art or the whole or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions to enable a computer device (which can be a personal computer, server, or network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM), random access memory (RAM), disk or optical disk and other media that can store program code.

[0077] The above description is only a preferred embodiment of the present invention, and does not limit the patent scope of the present invention. All equivalent structural changes made by using the contents of the present invention specification and drawings under the inventive concept of the present invention, or directly / indirectly applied in other related technical fields are included in the patent protection scope of the present invention.

Claims

1. A dynamic scheduling method for a rotary dehumidification system, characterized in that: include: The deep dehumidification system is divided into multifunctional zones, including pre-cooling zone, rotor adsorption zone, rotor regeneration zone, reheat zone and anti-frost zone, and the temperature, humidity and flow parameters of each functional zone are independently controlled to form a multi-dimensional parameter control matrix; According to the multi-dimensional parameter control matrix, each functional area is monitored in real time to obtain energy balance data including sensible heat exchange, latent heat exchange, heat required for wheel regeneration and frost layer status information, and generate a dynamic energy distribution diagram of the system; Based on the dynamic energy distribution diagram, a multi-stage dehumidification coupling coefficient is calculated, where the multi-stage dehumidification coupling coefficient is the ratio of the total energy regulation amount in the precooling and reheating process to the total energy consumption of the rotor regeneration zone and the anti-frost zone, to obtain a quantitative index of system performance; According to the quantitative index of the system performance, the multi-stage dehumidification coupling coefficient is evaluated in real time, and when the multi-stage dehumidification coupling coefficient deviates from the preset normal range, a dynamic control instruction set including adjusting the regeneration temperature, optimizing the anti-frost strategy, and adjusting the operating parameters of the pre-cooling zone is generated; Based on the dynamic control instruction set, priority scheduling of local anti-frost and defrost management is performed, and differentiated processing strategies are adopted according to the degree of frost formation to form an adaptive frost management solution; According to the adaptive frost management scheme, the system is optimized and adjusted as a whole, the temperature and humidity set values ​​and the regeneration heat source target value of each functional zone are updated, the multi-stage dehumidification coupling coefficient is iteratively calculated until it returns to the preset normal range, and the final optimized operating parameters are output.

2. The dynamic scheduling method of the rotary dehumidification system according to claim 1, characterized in that: The deep dehumidification system is divided into multifunctional zones, which include a pre-cooling zone, a rotor adsorption zone, a rotor regeneration zone, a reheat zone and an anti-frost zone. The temperature, humidity and flow parameters of each functional zone are independently controlled to form a multi-dimensional parameter control matrix, including: The deep dehumidification system is dynamically identified in terms of functional area boundaries. The spatial boundaries of the pre-cooling area, the rotor adsorption area, the rotor regeneration area, the reheating area and the anti-frost area are adjusted in real time according to the system operation status and dehumidification requirements to obtain an adaptive functional area distribution map. Based on the adaptive functional area distribution map, extract the microenvironment parameter characteristics of each functional area, obtain the spatial distribution characteristics and time variation laws of temperature, humidity and flow parameters, and form a dynamic characteristic spectrum of the functional area microenvironment; According to the dynamic characteristic spectrum of the microenvironment of the functional area, nonlinear coupling relationship analysis is performed on the temperature, humidity and flow parameters of each functional area, the mutual influence coefficient between the parameters is calculated, and a parameter coupling degree evaluation matrix is ​​obtained; Based on the parameter coupling evaluation matrix, multi-objective optimization calculations are performed on the temperature, humidity and flow parameters of each functional area, while considering dehumidification efficiency, energy consumption control and anti-frost requirements to form a parameter collaborative optimization strategy; According to the parameter collaborative optimization strategy, intelligent prediction and analysis are performed on the temperature, humidity and flow parameters of each functional area, and historical data are used to predict parameter change trends and perform adaptive adjustments to form a multi-dimensional parameter control matrix.

3. The dynamic scheduling method of the rotary dehumidification system according to claim 2, characterized in that: Based on the adaptive functional area distribution map, the microenvironment parameter feature extraction is performed on each functional area to obtain the spatial distribution characteristics and time variation law of temperature, humidity and flow parameters to form a dynamic feature spectrum of the functional area microenvironment, including: According to the adaptive functional area distribution map, each functional area is divided into multiple points, a three-dimensional coordinate system is established, and a high-precision monitoring point array of temperature, humidity and flow parameters is determined to obtain a three-dimensional monitoring network of functional area parameters; Based on the functional area parameter three-dimensional monitoring network, the temperature field of the pre-cooling area is dynamically simulated, the temperature gradient and isothermal surface distribution from the cooling coil surface to the air flow outlet are calculated, and a three-dimensional characteristic model of the temperature field of the pre-cooling area is obtained; According to the three-dimensional characteristic model of the temperature field in the pre-cooling zone, the humidity field in the adsorption zone of the rotor is dynamically scanned, and the relative humidity distribution at different positions on the surface and inside of the rotor is calculated by using an interpolation algorithm to form a three-dimensional dynamic map of the humidity field in the adsorption zone; Based on the three-dimensional dynamic map of the humidity field in the adsorption zone, computational fluid dynamics analysis is performed on the regeneration zone and the reheat zone to simulate the airflow velocity vector field at different cross sections and spatial positions to obtain a three-dimensional flow characteristic model of the airflow; According to the three-dimensional flow characteristic model of airflow, a multi-dimensional spatiotemporal correlation analysis is performed on the parameters of each functional area. Using wavelet analysis, the variation patterns and periodic characteristics of temperature, humidity and flow parameters in spatial and temporal dimensions are calculated to comprehensively generate the dynamic characteristic spectrum of the functional area microenvironment.

4. The dynamic scheduling method of the rotary dehumidification system according to claim 1, characterized in that: According to the multi-dimensional parameter control matrix, each functional area is monitored in real time to obtain energy balance data including sensible heat exchange, latent heat exchange, heat required for wheel regeneration and frost layer status information, and generate a system dynamic energy distribution diagram, including: According to the multi-dimensional parameter control matrix, the dew point temperature of the pre-cooling zone is analyzed, the temperature difference between the intake air and the cooling surface is calculated, and the pre-cooling efficiency evaluation index is obtained; Based on the precooling efficiency evaluation index, the hygroscopic performance of the rotor adsorption zone is evaluated, and the relationship curve between the water vapor adsorption amount of the adsorbent and the relative humidity is calculated to form a rotor hygroscopic efficiency curve diagram; According to the wheel moisture absorption efficiency curve, the heat demand analysis of the wheel regeneration zone is performed, the desorption energy consumption at different regeneration temperatures is calculated, and the regeneration energy consumption optimization parameter table is obtained; Based on the regeneration energy consumption optimization parameter table, the temperature and humidity adjustment energy consumption of the reheating zone is calculated, the energy consumption at different target dew point temperatures is evaluated, and a reheating process energy efficiency evaluation report is formed; According to the reheat process energy efficiency evaluation report, dynamically monitor the frost point temperature of the anti-frost area, calculate the difference between the surface temperature and the dew point temperature, and obtain the frost layer growth risk index; Based on the frost growth risk index, a comprehensive analysis is performed on the sensible heat exchange capacity, latent heat exchange capacity and heat required for wheel regeneration in each functional zone, the energy balance state and distribution characteristics are calculated, and a dynamic energy distribution diagram of the system is generated.

5. The dynamic scheduling method of the rotary dehumidification system according to claim 1, characterized in that: Based on the dynamic energy distribution diagram, the multi-stage dehumidification coupling coefficient is calculated, and the multi-stage dehumidification coupling coefficient is the ratio of the total energy regulation amount in the precooling and reheating process to the total energy consumption of the rotor regeneration zone and the anti-frost zone, and the system performance quantitative index is obtained, including: According to the dynamic energy distribution diagram, energy regulation analysis is performed on the precooling zone and the reheating zone, and the sum of the sensible heat exchange and latent heat exchange of each zone is calculated to obtain a total energy regulation data set; Based on the total energy regulation data set, the regeneration energy consumption of the rotor regeneration zone is evaluated, the heat consumption at different regeneration temperatures is calculated, and an energy consumption curve of the regeneration zone is formed; According to the energy consumption curve of the regeneration zone, the defrosting energy consumption of the anti-frost zone is analyzed, the heat consumption under different frost layer thicknesses is calculated, and the energy consumption distribution diagram of the anti-frost zone is obtained; Based on the anti-frost zone energy consumption distribution diagram, the energy consumption data of the rotor regeneration zone and the anti-frost zone are summarized, the total energy consumption of the rotor regeneration zone and the anti-frost zone is calculated, and a system energy consumption evaluation report is formed; According to the system energy consumption evaluation report and the total energy regulation data set, a ratio calculation is performed between the total energy regulation in the precooling and reheating process and the total energy consumption in the wheel regeneration zone and the anti-frost zone to obtain a multi-stage dehumidification coupling coefficient; Based on the multi-stage dehumidification coupling coefficient, the system performance is evaluated, and the trend of the multi-stage dehumidification coupling coefficient changing with time and working conditions is calculated to obtain a quantitative index of system performance.

6. The dynamic scheduling method of the rotary dehumidification system according to claim 1, characterized in that: According to the system performance quantitative index, the multi-stage dehumidification coupling coefficient is evaluated in real time. When the multi-stage dehumidification coupling coefficient deviates from the preset normal range, a dynamic control instruction set including adjusting the regeneration temperature, optimizing the anti-frost strategy, and adjusting the pre-cooling zone operating parameters is generated, including: According to the system performance quantitative index, the multi-stage dehumidification coupling coefficient is monitored in real time, the deviation value of the multi-stage dehumidification coupling coefficient and the preset normal range is calculated, and the multi-stage dehumidification coupling coefficient deviation evaluation result is obtained; Based on the multi-stage dehumidification coupling coefficient deviation evaluation result, the multi-stage dehumidification coupling coefficient deviation is graded into three levels: slight deviation, moderate deviation and severe deviation, forming a multi-stage dehumidification coupling coefficient deviation grade table; According to the multi-stage dehumidification coupling coefficient deviation level table, the system operation status is diagnosed and analyzed, the main influencing factors causing the multi-stage dehumidification coupling coefficient deviation are identified, and a list of system abnormal factors is obtained; Based on the list of abnormal factors of the system, dynamically adjust and calculate the regeneration temperature, determine the optimal regeneration temperature range, and form a regeneration temperature control strategy; According to the regeneration temperature control strategy, the frost growth trend of the anti-frost area is analyzed, the defrost cycle and intensity are calculated, and the optimized anti-frost strategy is obtained; Based on the optimized anti-frost strategy, the operating parameter sensitivity analysis of the pre-cooling zone is performed, the optimal combination of temperature, humidity and flow parameters is calculated, and a parameter adjustment scheme for the pre-cooling zone is formed; According to the pre-cooling zone parameter adjustment scheme, the regeneration temperature control strategy and the optimized anti-frost strategy, the control parameters of each functional zone are collaboratively optimized to generate a dynamic control instruction set including adjusting the regeneration temperature, optimizing the anti-frost strategy and adjusting the pre-cooling zone operating parameters.

7. The dynamic scheduling method of the rotary dehumidification system according to claim 1, characterized in that: The method of executing priority scheduling of local anti-frost and defrost management based on the dynamic control instruction set, taking differentiated processing strategies according to the degree of frost formation, and forming an adaptive frost management solution includes: According to the dynamic control instruction set, the frost thickness of the rotor surface is analyzed, the frost growth rate of different areas is calculated, and the frost distribution map of the rotor is obtained; Based on the rotor frost distribution map, the rotor is divided into sectors to determine a heavy frost area, a moderate frost area and a light frost area, and a rotor anti-frost priority table is formed; According to the rotor anti-frost priority table, local hot air defrosting design is performed for the heavy frost layer area, the optimal defrosting temperature and wind speed are calculated, and the defrosting plan for key areas is obtained; Based on the defrosting scheme for key areas, a progressive anti-frost strategy is formulated for moderate and light frost areas, and the frost growth inhibition effect at different speeds is calculated to form an anti-frost scheduling plan for the entire rotor; According to the full-rotor anti-frost scheduling plan, the system defrost-operation cycle is optimized, the optimal defrost start time and duration are calculated, and a dynamic defrost execution strategy is obtained; Based on the dynamic defrost execution strategy, the frost layer growth trend is predicted in real time, the critical point of frost layer formation under different working conditions is calculated, and an adaptive frost layer management solution is formed.

8. The dynamic scheduling method of the rotary dehumidification system according to claim 1, characterized in that: According to the adaptive frost management scheme, the system is optimized and adjusted as a whole, the temperature and humidity set values ​​and the regeneration heat source target value of each functional zone are updated, the multi-stage dehumidification coupling coefficient is iteratively calculated until it is restored to the preset normal range, and the final optimized operating parameters are output, including: According to the adaptive frost management scheme, the frost structure analysis is performed on the heavy frost area, the frost density and adhesion strength at different depths are calculated, and the frost characteristic profile is obtained; Based on the frost layer characteristic profile, the hot air penetration process is simulated, the heat transfer coefficient at different depths of the frost layer is calculated, and a heat penetration efficiency map is formed; According to the heat penetration efficiency map, the hot air delivery system is optimized and designed, and the best hot air distribution method is calculated to achieve uniform heat distribution, thereby obtaining a directional hot air delivery solution; Based on the directional hot air delivery scheme, the phase change process of the frost layer is analyzed, the melting and evaporation rates under different temperature and wind speed combinations are calculated, and the defrosting dynamics characteristics are determined; Utilizing the defrosting dynamics, multi-parameter optimization is performed to balance the defrosting effect, energy consumption and potential damage to the adsorption coating, calculate the optimal defrosting temperature and wind speed, and update the temperature and humidity set values ​​and regeneration heat source target values ​​of each functional area; According to the updated set value and target value, the multi-stage dehumidification coupling coefficient is iteratively calculated until it returns to the preset normal range, and the final optimized operating parameters are output.

9. A rotary dehumidification system, characterized in that: The rotary dehumidification system comprises: a memory and at least one processor, wherein instructions are stored in the memory; The at least one processor calls the instructions in the memory to enable the rotary dehumidification system to execute the steps of the dynamic scheduling method of the rotary dehumidification system according to any one of claims 1 to 8.

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