A method for constant temperature and humidity control of a cigar cabinet based on fuzzy control algorithm

Abstract

This invention discloses a method for constant temperature and humidity control of a cigar cabinet based on a fuzzy control algorithm, comprising the following steps: setting a target temperature and humidity range; collecting environmental data of each layer through a distributed sensor array and comparing it with the target range to obtain the temperature and humidity deviation of each layer; inputting the deviation into a temperature and humidity decoupling control module and outputting a weight adjustment signal; inputting the weight adjustment signal into a fuzzy controller to construct a multi-objective fitness function, optimizing it with the WFPSO algorithm, and outputting hierarchical fuzzy controller parameters; inputting the hierarchical fuzzy controller parameters into a hierarchical temperature and humidity model to monitor the status of each layer in real time, and outputting a drive command when the limit is exceeded; inputting the drive command and hierarchical parameters into an actuator, adjusting it, and outputting new environmental data; and using a feedforward control module to adjust in advance when a door opening / closing disturbance warning is issued, dynamically adjusting the status of the affected space layer. This invention improves the temperature and humidity control accuracy and disturbance adaptability of the cigar cabinet through hierarchical optimization and feedforward intervention control mechanisms.

Description

Technical Field

[0001] This invention relates to the field of intelligent environmental control and embedded system technology, and in particular to a method for constant temperature and humidity control of a cigar cabinet based on a fuzzy control algorithm. Background Technology

[0002] The storage environment for cigars requires extremely stable temperature and humidity. Existing cigar cabinets mostly employ single-point temperature and humidity detection and traditional PID or simple fuzzy control algorithms, controlling the compressor, heater, humidifier, and fan through simple start-stop mechanisms. However, these methods generally suffer from the following shortcomings:

[0003] The complex spatial distribution within the cigar cabinet makes it prone to uneven temperature and humidity gradients and localized anomalies across different layers. Single-point data collection and overall adjustment cannot guarantee the uniformity and responsiveness of the entire cabinet environment. Secondly, traditional parameter settings are mostly static and fixed, making it difficult to cope with dynamic disturbances caused by factors such as cigar batches, shelf materials, and external climate. The control effect relies on human experience, resulting in limited adjustment precision and intelligence. Although some high-end cigar cabinets support multi-point zone detection, their temperature and humidity control strategies still primarily focus on overall cabinet optimization, failing to achieve independent adaptive optimization for each spatial layer, resulting in insufficient local response and anomaly repair capabilities. Finally, existing control methods lack a closed-loop mechanism for adaptive optimization and rapid repair of cigar cabinet environmental anomalies such as door opening and cigar insertion / removal, which can easily lead to quality fluctuations and energy waste.

[0004] Therefore, how to provide a constant temperature and humidity control method for cigar cabinets based on fuzzy control algorithms is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] One objective of this invention is to propose a method for temperature and humidity control in cigar cabinets based on a fuzzy control algorithm. This invention introduces a hierarchical, clustered WFPSO optimization algorithm, combined with decoupled temperature and humidity control, piecewise nonlinear fitness function construction, material moisture absorption memory index modeling, and a feedforward disturbance prediction mechanism, to achieve precise temperature and humidity control across multiple spatial layers within the cigar cabinet. Compared to traditional control methods, this invention can dynamically adapt to different disturbance intensities and material properties, improving the environmental response speed and adjustment accuracy of each layer, reducing temperature and humidity overshoot and energy consumption fluctuations, and significantly enhancing recovery capabilities under disturbance scenarios such as door opening and closing. It possesses beneficial effects such as reasonable structure, high response efficiency, strong adaptability, and excellent stability.

[0006] A method for controlling the temperature and humidity of a cigar cabinet based on a fuzzy control algorithm according to an embodiment of the present invention includes the following steps:

[0007] Set a target temperature and humidity range, collect raw environmental datasets for each layer through a distributed sensor array, compare the raw environmental datasets for each layer with the target temperature and humidity range, calculate the temperature and humidity deviations for each layer, input the temperature and humidity deviations for each layer into the temperature and humidity decoupling control module, and output the temperature and humidity weight adjustment signals for each layer.

[0008] The temperature and humidity weight adjustment signals of each layer are input into the fuzzy controller to construct a multi-objective fitness function. The fitness function results are input into the WFPSO algorithm to output the set of parameters for the whole cabinet layered fuzzy controller.

[0009] The parameter set of the full cabinet layered fuzzy controller is input into the vertical layered temperature and humidity distribution model. Combined with the environmental data of each layer, when the humidity or temperature deviation of a certain layer exceeds the limit, the drive command is output.

[0010] The drive command for the fan or dehumidifier, together with the parameter set of the cabinet-wide hierarchical fuzzy controller, is input to the actuator control module to output new environmental status data.

[0011] A feedforward control module is set up so that when an opening or closing event is detected, the feedforward regulation of the affected space layer is triggered in advance to adjust the operating status of the affected space layer.

[0012] Furthermore, the original environmental dataset includes temperature and humidity data for each space layer inside the cabinet, temperature and humidity of the top space, temperature and humidity of the external environment, and average temperature and humidity data for the entire cabinet;

[0013] The temperature and humidity decoupling control includes the following steps: comparing the actual temperature and humidity of each spatial layer with their respective target temperature and humidity, calculating the temperature deviation and humidity deviation of each layer, and outputting the actual temperature and humidity deviation signal of each layer.

[0014] Determine the temperature deviation of each layer, switch the main control mode of this layer according to the preset range, including humidity control mode, mixed control mode and relative humidity priority control mode, and output the control mode signal of each layer;

[0015] Based on the control mode signal, the temperature control weight and humidity control weight are obtained, and the temperature and humidity weight adjustment signals of each layer are output to the fuzzy controller.

[0016] Furthermore, the fitness function is a piecewise nonlinear combination structure. After normalization, each control performance index is evaluated using a quadratic function when it is less than a preset threshold, and penalized using an exponential function when it exceeds the preset threshold. The weight factors of each index are dynamically adjusted in real time based on the accuracy of the prediction model, the frequency of historical disturbance events, and the moisture content of the material. The construction of the fitness function specifically includes:

[0017] Continuously collect moisture content data of wooden materials inside the cabinet and calculate the material's moisture absorption memory effect index;

[0018] The overshoot value, predicted settling time, energy consumption data, and material memory effect index were comprehensively considered and normalized.

[0019] For the normalized overshoot value, predicted settling time, energy consumption data and material memory effect index, a piecewise nonlinear function is used for evaluation. When the overshoot value, predicted settling time, energy consumption data and material memory effect index do not reach the preset threshold, a quadratic function is used for evaluation, and when they exceed the preset threshold, an exponential penalty function is used for evaluation.

[0020] The weights of the indicators are dynamically adjusted based on real-time environmental conditions, historical disturbance frequency, prediction model accuracy, and material moisture content to obtain a dynamic adaptive weight factor.

[0021] Based on the dynamically adjusted adaptive weighting factor and piecewise nonlinear function, the various indicators are integrated into a piecewise nonlinear combined fitness function.

[0022] Furthermore, the WFPSO algorithm adopts a hierarchical cluster evolutionary structure, and its steps include:

[0023] The space of the cigar cabinet is divided into multiple vertical layers, and each layer is set with an independent subset of fuzzy control parameters.

[0024] The particle swarm is divided into multiple subgroups, each corresponding to a spatial layer of the cigar cabinet. Each subgroup independently initializes particle parameters and performs a search. The particle parameters of each subgroup include four items: temperature fuzzy quantization factor, humidity fuzzy quantization factor, temperature scaling factor, and humidity scaling factor. The current position of each particle represents a specific set of fuzzy controller parameter values. The fitness of the particle is jointly determined by the temperature and humidity control performance of the current layer under the action of the corresponding parameters.

[0025] During each iteration, each subgroup independently updates particle parameters and maintains the optimal solution based on the temperature and humidity data and fitness index of its corresponding space layer.

[0026] Set up an information exchange phase between subgroups, periodically select the current optimal particle parameters of each subgroup, and share and recombine some particle parameters between different subgroups through information exchange operations;

[0027] During the optimization termination phase, the optimal particle parameters of all subgroups are output, forming the parameter set of the full-cabinet hierarchical fuzzy controller.

[0028] Furthermore, the information interaction phase includes:

[0029] Periodically collect the quantization factor and scaling factor parameters of the optimal fuzzy controller obtained from the current optimization of each subgroup;

[0030] Randomly select some parameter components from the optimal parameter group of each subgroup, wherein the partial parameter components include one or more of the following: temperature fuzzy quantization factor, humidity fuzzy quantization factor, temperature scaling factor, and humidity scaling factor.

[0031] Take the current value of the same parameter component from two different subgroups, and then take the weighted average of the two parameter values ​​according to a preset mixing ratio. The new parameter value is equal to the parameter value of the first subgroup multiplied by the mixing ratio, plus the parameter value of the second subgroup multiplied by one minus the mixing ratio. The mixing ratio can be dynamically adjusted according to the fitness performance of each subgroup in the historical optimization process, or it can be set to a fixed constant in advance.

[0032] For the new parameter components generated by the mixture, a validity check is performed. If the parameter exceeds the allowable range of the fuzzy controller, it is truncated to the range boundary or rolled back to the optimal parameter value of the previous round.

[0033] After the new parameter components generated by the recombination are combined, they replace the corresponding components in the original subgroup parameter group respectively. The updated values ​​are used as the initial values ​​of the particle parameters in the next iteration, which are then used for subsequent independent optimization and fitness evaluation.

[0034] Selected parameter components are interchanged or replaced between different subgroups according to a cross-recombination strategy, and the updated parameter group is used as the starting point for a new round of optimization in the optimization iteration.

[0035] Furthermore, the vertical stratified temperature and humidity distribution model specifically includes the following steps:

[0036] The parameter set of the full cabinet layered fuzzy controller is input into the control unit of each space layer of the cigar cabinet;

[0037] For each spatial layer, the temperature and humidity control output is independently calculated based on the current ambient temperature and humidity data of that layer and the corresponding fuzzy controller parameters.

[0038] Collect and update real-time temperature and humidity data for each space layer in the vertical direction of the cabinet;

[0039] The temperature and humidity control outputs of each layer are compared with the target temperature and humidity to determine whether the temperature and humidity deviations of each layer exceed the preset thresholds.

[0040] When the temperature or humidity deviation of any space layer is detected to exceed the threshold, a drive command is output.

[0041] Furthermore, the actuator control module includes: receiving drive commands from the fan or dehumidifier and input of a set of parameters from the cabinet-wide layered fuzzy controller; controlling the execution of the compressor, heater, humidifier, and fans on each layer based on the drive commands and layered fuzzy control parameters; and outputting new environmental status data based on the adjustment results.

[0042] Furthermore, the feedforward control module includes the following steps:

[0043] Multiple types of proximity sensors and mechanical motion detection sensors are installed on the cigar cabinet door frame to detect hand proximity signals, door contact signals, and hinge angle change signals in the door frame area in real time, respectively.

[0044] When any type of sensor continuously detects that the signal change trend meets the set threshold condition, it determines that an opening or closing event is about to occur, and outputs a disturbance prediction event signal through the event discrimination logic unit.

[0045] The disturbance prediction event signal is analyzed and mapped to the spatial layering structure of the cigar cabinet. It is then matched with the historical disturbance impact model to locate the target spatial layer that may be significantly affected, thus forming the disturbance prediction layer identification result.

[0046] For the identified affected space layers, a feedforward control signal is output in advance to improve the control response sensitivity of the affected space layers and expand the output adjustment range;

[0047] The number of information interactions in the affected spatial layer during the WFPSO optimization process will be temporarily increased.

[0048] After the disturbance event ends, the time process and recovery curve of the temperature and humidity of the affected space layer returning to a stable state are used to determine whether the disturbance has been resolved. If it has been resolved, the temperature control weight, humidity control weight and information interaction number are automatically restored to the normal state.

[0049] Furthermore, the historical disturbance impact model matching step specifically includes:

[0050] The specific characteristics of the disturbance event include the location, duration, signal amplitude, and historical frequency of occurrence. These characteristics are matched with the historical disturbance impact model established in advance. For each characteristic, if they are completely identical, they are scored as 1 point; otherwise, they are scored as 0 points. Numerical characteristics such as signal amplitude and duration can be assigned scores according to the difference. The scores of all characteristic items are accumulated. The higher the score, the more similar the two disturbance events are.

[0051] Based on the type and intensity of the disturbance event, prioritize finding historical event records most similar to the current disturbance conditions, call up the actual temperature and humidity changes and recovery trends of each spatial layer under the corresponding disturbance in the model, compare with the current real-time temperature and humidity distribution in the cabinet, determine which spatial layers have experienced overshoot, lag, slow recovery or amplified fluctuations when subjected to similar disturbances in the past, and combine with the current real-time data to predict the possible range and severity of the impact of this disturbance on each spatial layer.

[0052] The beneficial effects of this invention are:

[0053] This invention uses a distributed sensor array to collect real-time temperature and humidity information from each spatial layer, the top, and the outside of the cabinet. A temperature and humidity decoupled control module dynamically determines the master control mode and control weight for each layer. After initial processing by a fuzzy controller, the fitness function uses a piecewise nonlinear combination structure to fuse multi-dimensional indicators such as predicted disturbance overshoot, settling time, energy consumption, and the moisture absorption memory effect of the wooden shelving material. The WFPSO algorithm is then used to optimize the controller parameters for each layer, outputting a set of fuzzy controller parameters for the entire cabinet. Finally, based on a vertically layered temperature and humidity distribution model, local fan or dehumidification control is implemented for the over-limit spatial layers, achieving dynamic correction of the spatial gradient distribution.

[0054] Furthermore, this invention incorporates a feedforward control module, deploying proximity, contact, and hinge motion detection sensors on the cigar cabinet door frame. Utilizing a disturbance prediction model, it proactively identifies door opening and closing events, instantly triggering adjustments to the operational state of the affected spatial layers. It also temporarily increases the WFPSO optimization particle weights and iteration step size, automatically reverting to the optimization rhythm after disturbance recovery, thus achieving closed-loop control of disturbance prevention, rapid response, and steady-state regression. This method, combining real-time sensing, fuzzy regulation, intelligent optimization, and feedforward intervention, effectively improves the environmental stability, layered response speed, and global energy efficiency of the cigar cabinet, making it suitable for high-end cigar storage environments with stringent requirements for precise and stable microclimate control. Attached Figure Description

[0055] The accompanying drawings are provided to further illustrate the invention and form part of the specification. They are used in conjunction with embodiments of the invention to explain the invention and do not constitute a limitation thereof. In the drawings:

[0056] Figure 1 The flowchart shows a method for controlling the constant temperature and humidity of a cigar cabinet based on a fuzzy control algorithm proposed in this invention.

[0057] Figure 2 This is a schematic diagram of the module relationships of a cigar cabinet constant temperature and humidity control method based on fuzzy control algorithm proposed in this invention.

[0058] Figure 3This is a detailed operation diagram of the feedforward control module of a cigar cabinet constant temperature and humidity control method based on fuzzy control algorithm proposed in this invention. Detailed Implementation

[0059] The present invention will now be described in further detail with reference to the accompanying drawings. These drawings are simplified schematic diagrams, illustrating only the basic structure of the invention, and therefore only show the components relevant to the invention.

[0060] refer to Figure 1-3 A method for controlling the temperature and humidity of a cigar cabinet based on a fuzzy control algorithm includes the following steps:

[0061] Set a target temperature and humidity range, collect raw environmental datasets for each layer through a distributed sensor array, compare the raw environmental datasets for each layer with the target temperature and humidity range, calculate the temperature and humidity deviations for each layer, input the temperature and humidity deviations for each layer into the temperature and humidity decoupling control module, and output the temperature and humidity weight adjustment signals for each layer.

[0062] The temperature and humidity weight adjustment signals of each layer are input into the fuzzy controller to construct a multi-objective fitness function. The fitness function results are input into the WFPSO algorithm to output the set of parameters for the whole cabinet layered fuzzy controller.

[0063] The parameter set of the full cabinet layered fuzzy controller is input into the vertical layered temperature and humidity distribution model. Combined with the environmental data of each layer, when the humidity or temperature deviation of a certain layer exceeds the limit, the drive command is output.

[0064] The drive command for the fan or dehumidifier, together with the parameter set of the cabinet-wide hierarchical fuzzy controller, is input to the actuator control module to output new environmental status data.

[0065] A feedforward control module is set up so that when an opening or closing event is detected, the feedforward regulation of the affected space layer is triggered in advance to adjust the operating status of the affected space layer.

[0066] In this embodiment, the original environmental dataset includes temperature and humidity data of each space layer inside the cabinet, temperature and humidity of the top space, temperature and humidity of the external environment, and average temperature and humidity data of the entire cabinet;

[0067] The temperature and humidity decoupling control includes the following steps: comparing the actual temperature and humidity of each spatial layer with their respective target temperature and humidity, calculating the temperature deviation and humidity deviation of each layer, and outputting the actual temperature and humidity deviation signal of each layer.

[0068] Determine the temperature deviation of each layer, switch the main control mode of this layer according to the preset range, including humidity control mode, mixed control mode and relative humidity priority control mode, and output the control mode signal of each layer;

[0069] Based on the control mode signal, the temperature control weight and humidity control weight are obtained, and the temperature and humidity weight adjustment signals of each layer are output to the fuzzy controller.

[0070] The temperature and humidity decoupling control module includes:

[0071] The layered temperature and humidity deviation calculation unit is used to calculate the temperature and humidity deviation of each layer based on the actual temperature and humidity of each layer and the corresponding target temperature and humidity.

[0072] The layer discrimination logic unit is used to determine the control mode based on the temperature deviation of each layer, including the moisture content control mode, the mixed control mode and the relative humidity priority control mode, and output the control mode signal of that layer.

[0073] The hierarchical weighted factor generation unit is used to generate temperature control weights and humidity control weights for each layer based on the signal.

[0074] The hierarchical main control logic switching unit is used to switch the main control logic path of the layer according to the control mode signal output by the discrimination logic unit of each layer and the weight signal of the weighting factor generation unit, and output the temperature and humidity weight adjustment signal of the layer to the fuzzy controller of the same layer.

[0075] In this embodiment, the fitness function is a piecewise nonlinear combination structure. After normalization, each control performance index is evaluated using a quadratic function when it is less than a preset threshold, and penalized using an exponential function when it exceeds the preset threshold. The weight factors of each index are dynamically adjusted in real time based on the accuracy of the prediction model, the frequency of historical disturbance events, and the moisture content of the material. The construction of the fitness function specifically includes:

[0076] First, temperature and humidity response curves after historical disturbance events are collected to construct a historical database of disturbance events, and an environmental disturbance prediction model is established based on this database.

[0077] Continuously collect moisture content data of wood materials inside the cabinet, calculate the material's moisture absorption memory effect index, and reflect the impact of long-term moisture absorption of the material on the environment inside the cabinet.

[0078] The overshoot value, predicted settling time, energy consumption data, and material memory effect index were comprehensively considered and normalized.

[0079] For the normalized overshoot value, predicted settling time, energy consumption data, and material memory effect index, a piecewise nonlinear function is used for evaluation. When the overshoot value, predicted settling time, energy consumption data, and material memory effect index do not reach the preset threshold, a quadratic function is used for evaluation; when they exceed the preset threshold, an exponential penalty function is used for evaluation. The quadratic function is: Fitness Function = Weight 1 × (Overshoot Value) 2 +Weight 2 × (Predicted Adjustment Time) 2+Weight 3 × (Energy Consumption) 2 +Weight 4 × (Material Memory Effect) 2 The fitness function is defined as: weight1 × exp(overshoot value - threshold) + weight2 × (predicted settling time). 2 +Weight 3 × (Energy Consumption) 2 +Weight 4 × (Material Memory Effect) 2 The weights 1, 2, 3, and 4 of the two functions are the same and are all set by the operator;

[0080] The weights of the indicators are dynamically adjusted based on real-time environmental conditions, historical disturbance frequency, prediction model accuracy, and material moisture content to obtain a dynamic adaptive weight factor.

[0081] Based on the dynamically adjusted adaptive weighting factor and piecewise nonlinear function, the various indicators are integrated into a piecewise nonlinear combined fitness function, which serves as the evaluation criterion for optimizing the fuzzy controller parameters using the hierarchical and clustered WFPSO algorithm.

[0082] In this invention, to achieve independent and efficient control of the temperature and humidity in the multi-layered space of a cigar cabinet, a weighted fitness particle swarm optimization algorithm (WFPSO) with a hierarchical and swarm-evolutionary structure is employed. Based on the physical spatial structure of the cigar cabinet, this algorithm divides the overall optimization task into several independent sub-tasks and integrates cross-subgroup information interaction mechanisms during particle evolution to improve global optimization capabilities and hierarchical control accuracy.

[0083] In this embodiment, the WFPSO algorithm adopts a hierarchical cluster evolutionary structure, and its steps include:

[0084] The space of the cigar cabinet is divided into multiple vertical layers, and each layer is set with an independent subset of fuzzy control parameters.

[0085] The particle swarm is divided into multiple subgroups, each corresponding to a spatial layer of the cigar cabinet. Each subgroup independently initializes particle parameters and performs a search. The particle parameters of each subgroup include four items: temperature fuzzy quantization factor, humidity fuzzy quantization factor, temperature scaling factor, and humidity scaling factor. The current position of each particle represents a specific set of fuzzy controller parameter values. The fitness of the particle is calculated by the comprehensive performance of the current parameter configuration in the temperature and humidity control process of the spatial layer. The fitness function is defined by the aforementioned construction method.

[0086] During each iteration, each subgroup performs velocity update and position update operations based on the real-time temperature and humidity data of the corresponding space layer and the fitness function evaluation value, independently maintains the local optimum of its subgroup, and records the current optimal particle parameters.

[0087] To improve overall search efficiency and the coordination of cabinet control, an information interaction stage is set up between subgroups. The current optimal particle parameters of each subgroup are periodically selected. Through information exchange operations, some particle parameters are shared and recombined between different subgroups. For example, the "temperature ratio factor" or "humidity fuzzy quantization factor" can be randomly selected and exchanged between two subgroups. The new particles are constructed with the combined new parameters as the initial state for the next iteration to avoid getting trapped in local optima.

[0088] After the termination conditions are met (such as the upper limit of the number of iterations, fitness convergence, etc.), the optimal particle parameters of all subgroups are output to form the parameter set of the whole cabinet hierarchical fuzzy controller, which is used to drive the overall temperature and humidity multi-layer control of the cigar cabinet.

[0089] This hierarchical and clustered evolutionary strategy can take into account both the personalized control needs of each spatial layer and the overall environmental control of the entire cabinet. While maintaining local adaptability, it can effectively improve the convergence efficiency of the global optimal solution and has good real-time performance and scalability.

[0090] In this embodiment, to enhance the diversity of optimization for each subgroup and improve global convergence performance, the weighted fitness particle swarm optimization algorithm incorporates an information interaction phase during execution. This phase is triggered by a fixed iteration cycle and performs cross-layer parameter fusion operations on the particle subgroups corresponding to each spatial layer, enabling the sharing and recombination of optimization information between different regions.

[0091] In this embodiment, the information interaction stage includes:

[0092] In each round of interaction, the optimal fuzzy controller quantization factor and scaling factor parameters obtained by each subgroup are periodically collected. The parameter set of each particle consists of four items: temperature fuzzy quantization factor, humidity fuzzy quantization factor, temperature scaling factor, and humidity scaling factor.

[0093] Randomly select some parameter components from the optimal parameter group of each subgroup, wherein the partial parameter components include one or more of the following: temperature fuzzy quantization factor, humidity fuzzy quantization factor, temperature scaling factor, and humidity scaling factor.

[0094] Selected parameter components are interchanged or replaced between different subgroups according to a cross-recombination strategy to construct new initial states of particles. This allows each subgroup to maintain its independent search advantage in its main control region while incorporating the advantageous features of other subgroups, thereby improving the risk of getting trapped in local search.

[0095] In this embodiment, the cross-recombination strategy adopts a weighted average hybrid recombination method. Specifically, during the information interaction phase, after selecting the corresponding parameter components of the current best particle from each of the two subgroups, for each pair of selected parameters of the same type, a weighted average of the two parameter values ​​is calculated according to a set ratio coefficient, which is used as the parameter component of the newly generated particle.

[0096] Taking the temperature fuzzy quantization factor as an example, if the current optimal particle of subgroup A has a parameter value of A1 and the current optimal particle of subgroup B has a parameter value of B1, and the set mixing weight is 60%, then the new parameter value after recombination is obtained by weighting A1 and B1 in a 6:4 ratio. All selected parameter components are recombinated in this way.

[0097] The weighting ratio can be preset to a fixed value or dynamically adjusted according to the current iteration cycle or the historical convergence state of the subgroup. The new parameter components after recombination will, together with the unrecombined parts, constitute a new set of particle initialization parameters, participating in subsequent fitness evaluation and evolutionary iteration processes.

[0098] By adopting a weighted mean mixing strategy, not only are the advantageous features of the current optimization results of each subgroup preserved, but cross-layer information fusion is also introduced, which effectively improves the search diversity and global convergence ability.

[0099] The recombined particle parameters will serve as the starting point for updates in the next optimization iteration, continuing the particle velocity and position update process, and participating in fitness evaluation and optimal solution maintenance. Through the aforementioned information interaction mechanism, the WFPSO algorithm described in this invention can introduce cross-regional information flow while maintaining a hierarchical independent optimization structure, enhancing global search capabilities and improving the quality of fuzzy control parameters in finding optimal solutions.

[0100] In this embodiment, in order to achieve independent control of the temperature and humidity of each space layer inside the cigar cabinet, a vertical layer temperature and humidity distribution model is established. This model is based on the parameters of the whole cabinet layer fuzzy controller and combined with the real-time environmental data of each space layer to achieve dynamic adaptation and anomaly detection of the multi-layer control strategy.

[0101] In this embodiment, the vertical stratified temperature and humidity distribution model specifically includes the following steps:

[0102] The parameter set of the whole cabinet layered fuzzy controller is input into the control unit of each space layer of the cigar cabinet. Each control unit corresponds to an independent fuzzy controller instance, which is responsible for calculating the temperature and humidity adjustment instructions of that layer.

[0103] For each spatial layer, the temperature and humidity control output is independently calculated based on the current ambient temperature and humidity data of that layer and the corresponding fuzzy controller parameters.

[0104] Collect and update real-time temperature and humidity data for each space layer in the vertical direction of the cabinet;

[0105] The temperature and humidity control outputs of each layer are compared with the target temperature and humidity. It is determined whether the temperature deviation and humidity deviation of each layer exceed the preset threshold. If the deviation exceeds the limit, the space layer is immediately determined to be an "abnormal control layer".

[0106] For the space location identified as an abnormal control layer, the current temperature and humidity status, control output and deviation information of that layer are submitted to the actuator decision module, which outputs drive commands for the fan or dehumidification device to achieve local response control only for the over-limit layer, maintaining the stable operation of other space layers in the entire cabinet.

[0107] This hierarchical temperature and humidity distribution model enables multi-layer parallel sensing and judgment, possesses high responsiveness and regional independence, and significantly improves the hierarchical dynamic control capability of cigar cabinets under complex disturbance conditions.

[0108] In this embodiment, the actuator control module includes: receiving drive commands from the fan or dehumidifier and inputting a set of parameters from the whole cabinet layered fuzzy controller; based on the drive commands and layered fuzzy control parameters, controlling the execution of the compressor, heater, humidifier, and fans on each layer, including controlling the compressor to cool and adjust its operating frequency when the overall or local temperature needs to be lowered; controlling the heater to work and setting the corresponding power when the temperature needs to be raised; driving the humidifier and controlling its atomization intensity when the detected humidity is low; controlling the fan operation status and wind speed of the corresponding space layer when the local humidity or temperature is abnormal, to achieve regional airflow disturbance; outputting a new environmental state dataset based on the adjustment results, and feeding this dataset back to the periodic monitoring module and the WFPSO optimization stage to provide a state basis for the next round of control.

[0109] Through the configuration and response mechanism of the aforementioned actuator control module, a closed-loop control process of software and hardware collaboration can be efficiently realized, ensuring the continuity and stability of the constant temperature and humidity strategy.

[0110] In this embodiment, to enhance the response capability to sudden disturbance events, a feedforward control module is set up to predict, identify and prepare for local control in advance before the cigar cabinet is detected to be about to open or close, thereby reducing the impact of environmental disturbances and improving the foresight of the response.

[0111] In this embodiment, the feedforward control module includes the following steps:

[0112] Multiple types of proximity sensors and mechanical motion detection sensors are installed on the cigar cabinet door frame to detect hand proximity signals, door contact signals, and hinge angle change signals in the door frame area in real time, forming a multimodal disturbance sensing channel.

[0113] When any type of sensor continuously detects that the signal change trend meets the set threshold condition, it determines that an opening or closing event is about to occur, and outputs a disturbance prediction event signal through the event discrimination logic unit.

[0114] The disturbance prediction event signal is analyzed and mapped to the spatial layering structure of the cigar cabinet. It is matched with the historical disturbance impact model. The target spatial layer that may be significantly affected by the disturbance is identified through pattern matching, forming the disturbance prediction layer identification result.

[0115] For the identified affected spatial layers, feedforward control signals are output in advance to dynamically adjust the control strategy, including improving the response sensitivity of the fuzzy controller output and expanding the control output amplitude range, so that it has a stronger disturbance response capability.

[0116] The number of information interactions in the affected spatial layer during the WFPSO optimization process will be temporarily increased.

[0117] After the disturbance event ends, the time process and recovery curve of the temperature and humidity of the affected space layer returning to a stable state are used to determine whether the disturbance has been resolved. If it has been resolved, the temperature control weight, humidity control weight and information interaction number are automatically restored to the normal state.

[0118] Through this feedforward mechanism, the present invention can predict and defend against sudden operational disturbances in real time, and is particularly suitable for constant temperature and humidity scenarios where cabinet doors are frequently opened or where high precision is required.

[0119] In this embodiment, the historical disturbance impact model matching step specifically includes:

[0120] The specific characteristics of the disturbance event include the location, duration, signal amplitude, and historical frequency of occurrence. These characteristics are matched with the historical disturbance impact model established in advance. For each characteristic, if they are completely identical, they are scored as 1 point; otherwise, they are scored as 0 points. Numerical characteristics such as signal amplitude and duration can be assigned scores according to the difference. The scores of all characteristic items are accumulated. The higher the score, the more similar the two disturbance events are.

[0121] Based on the type and intensity of the disturbance event, prioritize finding historical event records most similar to the current disturbance conditions, call up the actual temperature and humidity changes and recovery trends of each spatial layer under the corresponding disturbance in the model, compare with the current real-time temperature and humidity distribution in the cabinet, determine which spatial layers have experienced overshoot, lag, slow recovery or amplified fluctuations when subjected to similar disturbances in the past, and combine with the current real-time data to predict the possible range and severity of the impact of this disturbance on each spatial layer.

[0122] Example 1:

[0123] To verify the feasibility of this invention in practice, it was applied to a high-end cigar cabinet with a three-tiered structure of approximately 150L (96cm high, 52cm wide, and 30cm deep). The cabinet is equipped with a high-precision temperature and humidity sensor, a door frame multimodal disturbance sensor, a fan, a humidifier, a dehumidification module, and a tiered execution unit. Each tier has a set of temperature and humidity acquisition points, which automatically collect data such as temperature, humidity, and wind speed on each tier at 10-second intervals. All environmental and execution parameters are uploaded to the edge computing unit in real time via a local area network.

[0124] Data acquisition includes parameters such as temperature, humidity, ambient wind speed, door frame disturbance status, equipment operating current, and cabinet energy consumption for each floor. Data is normalized using historical maximum and minimum values ​​over a continuous 7-day period, with a sliding window of 60 seconds. The mean, standard deviation, and maximum / minimum values ​​for each parameter are calculated. The system automatically detects sudden changes in slope and duration of door frame disturbance signals to identify door opening / closing events. In case of abnormal temperature or humidity, emergency control of the stratified fans / humidifiers / dehumidifiers is automatically triggered, and actuator parameters are adjusted based on the fuzzy controller output.

[0125] A hierarchical WFPSO algorithm is used to dynamically optimize the parameters of the fuzzy controllers at each layer. The fitness function includes maximum overshoot due to temperature and humidity, recovery time, energy consumption, and material moisture memory factor. The parameter weights are dynamically adjusted based on the disturbance frequency and material moisture content. When the feedforward control module detects the disturbance trend of the door frame, it increases the response and optimization weight of the fan in the disturbed layer in advance. After 30 days of operation, the key performance comparison results with the traditional "threshold + timing" control mode are shown in the table below:

[0126]

[0127]

[0128] As shown in the table, after adopting the method of this invention, the average temperature and humidity recovery time of each layer in the cigar cabinet under disturbances such as door opening and closing is shortened by approximately 31% and 35%, respectively. The maximum overshoot is reduced to less than one-third of the original value. The number of fan responses, material moisture content fluctuations, and the frequency of manual intervention are all significantly reduced, resulting in an 11% reduction in energy consumption. Throughout the entire operation, the steady-state error of temperature and humidity in all layers remains within ±0.3℃ / ±1.5%RH, with no abnormal over-limit alarms. This indicates that the method of this invention can effectively improve the constant temperature and humidity stability, energy efficiency, and intelligent automation level of the cigar cabinet in multi-layer heterogeneous spaces, making it particularly suitable for the microclimate management needs of long-term storage of high-end cigars.

[0129] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. A humidor thermostat control method based on fuzzy control algorithm, characterized in that, Includes the following steps: Set a target temperature and humidity range, collect raw environmental datasets for each layer through a distributed sensor array, compare the raw environmental datasets for each layer with the target temperature and humidity range, calculate the temperature and humidity deviations for each layer, input the temperature and humidity deviations for each layer into the temperature and humidity decoupling control module, and output the temperature and humidity weight adjustment signals for each layer. The temperature and humidity weight adjustment signals of each layer are input into the fuzzy controller to construct a multi-objective fitness function. The fitness function results are input into the WFPSO algorithm to output the set of parameters for the whole cabinet layered fuzzy controller. The fitness function is a piecewise nonlinear combination structure. After normalization, each control performance index is evaluated using a quadratic function when it is less than a preset threshold, and penalized using an exponential function when it exceeds the preset threshold. The weight factors of each index are dynamically adjusted in real time based on the accuracy of the prediction model, the frequency of historical disturbance events, and the moisture content of the material. The construction of the fitness function specifically includes: Continuously collect moisture content data of wooden materials inside the cabinet and calculate the material's moisture absorption memory effect index; The overshoot value, predicted settling time, energy consumption data, and material moisture memory effect index were comprehensively considered and normalized. For the normalized overshoot value, predicted settling time, energy consumption data and material moisture memory effect index, a piecewise nonlinear function is used for evaluation. When the overshoot value, predicted settling time, energy consumption data and material moisture memory effect index do not reach the preset threshold, a quadratic function is used for evaluation. When they exceed the preset threshold, an exponential penalty function is used for evaluation. The weights of the indicators are dynamically adjusted based on real-time environmental conditions, historical disturbance frequency, prediction model accuracy, and material moisture content to obtain a dynamic adaptive weight factor. Based on the dynamically adjusted dynamic adaptive weighting factor and piecewise nonlinear function, the various indicators are integrated into a piecewise nonlinear combined fitness function. The WFPSO algorithm employs a hierarchical cluster evolutionary structure, and its steps include: The space of the cigar cabinet is divided into multiple vertical layers, and each layer is set with an independent subset of fuzzy control parameters. The particle swarm is divided into multiple subgroups, each corresponding to a spatial layer of the cigar cabinet. Each subgroup independently initializes particle parameters and performs a search. The particle parameters of each subgroup include four items: temperature fuzzy quantization factor, humidity fuzzy quantization factor, temperature scaling factor, and humidity scaling factor. The current position of each particle represents a specific set of fuzzy controller parameter values. The fitness of the particle is jointly determined by the temperature and humidity control performance of the current layer under the action of the corresponding parameters. During each iteration, each subgroup independently updates particle parameters and maintains the optimal solution based on the temperature and humidity data and fitness index of its corresponding space layer. Set up an information exchange phase between subgroups, periodically select the current optimal particle parameters of each subgroup, and share and recombine some particle parameters between different subgroups through information exchange operations; During the optimization termination phase, the optimal particle parameters of all subgroups are output, forming the parameter set of the full-cabinet hierarchical fuzzy controller; The parameter set of the full cabinet layered fuzzy controller is input into the vertical layered temperature and humidity distribution model. Combined with the environmental data of each layer, when the humidity or temperature deviation of a certain layer is detected to exceed the limit, the drive command is output. The drive command for the fan or dehumidifier, together with the parameter set of the cabinet-wide hierarchical fuzzy controller, is input to the actuator control module to output new environmental status data. A feedforward control module is set up so that when an opening or closing event is detected, the feedforward regulation of the affected space layer is triggered in advance to adjust the operating status of the affected space layer.

2. The humiture regulation method of the humidor based on the fuzzy control algorithm according to claim 1, characterized in that, The original environmental dataset includes temperature and humidity data for each space layer inside the cabinet, temperature and humidity of the top space, temperature and humidity of the external environment, and average temperature and humidity data for the entire cabinet. The temperature and humidity decoupling control includes the following steps: comparing the actual temperature and humidity of each spatial layer with their respective target temperature and humidity, calculating the temperature deviation and humidity deviation of each layer, and outputting the actual temperature and humidity deviation signal of each layer. Determine the temperature deviation of each layer, switch the main control mode of this layer according to the preset range, including humidity control mode, mixed control mode and relative humidity priority control mode, and output the control mode signal of each layer; Based on the control mode signal, the temperature control weight and humidity control weight are obtained, and the temperature and humidity weight adjustment signals of each layer are output to the fuzzy controller.

3. The humiture regulation method of the humidor based on the fuzzy control algorithm according to claim 1, characterized in that, The information exchange phase includes: Periodically collect the quantization factor and scaling factor parameters of the optimal fuzzy controller obtained from the current optimization of each subgroup; Randomly select some parameter components from the optimal parameter group of each subgroup, wherein the partial parameter components include one or more of the following: temperature fuzzy quantization factor, humidity fuzzy quantization factor, temperature scaling factor, and humidity scaling factor. Take the current value of the same parameter component from two different subgroups, and then take the weighted average of the two parameter values ​​according to a preset mixing ratio. The new parameter value is equal to the parameter value of the first subgroup multiplied by the mixing ratio, plus the parameter value of the second subgroup multiplied by one minus the mixing ratio. The mixing ratio used is dynamically adjusted according to the fitness performance of each subgroup in the historical optimization process, or is set to a fixed constant in advance. For the new parameter components generated by the mixture, a validity check is performed. If the parameter exceeds the allowable range of the fuzzy controller, it is truncated to the range boundary or rolled back to the optimal parameter value of the previous round. After the new parameter components generated by the recombination are combined, they replace the corresponding components in the original subgroup parameter group respectively. The updated values ​​are used as the initial values ​​of the particle parameters in the next iteration, which are then used for subsequent independent optimization and fitness evaluation. Selected parameter components are interchanged or replaced between different subgroups according to a cross-recombination strategy, and the updated parameter group is used as the starting point for a new round of optimization in the optimization iteration.

4. The method for constant temperature and humidity control of a cigar cabinet based on a fuzzy control algorithm according to claim 1, characterized in that, The vertical stratified temperature and humidity distribution model specifically includes the following steps: The parameter set of the full cabinet layered fuzzy controller is input into the control unit of each space layer of the cigar cabinet; For each spatial layer, the temperature and humidity control output is independently calculated based on the current ambient temperature and humidity data of that layer and the corresponding fuzzy controller parameters. Collect and update real-time temperature and humidity data for each space layer in the vertical direction of the cabinet; The temperature and humidity control outputs of each layer are compared with the target temperature and humidity to determine whether the temperature and humidity deviations of each layer exceed the preset thresholds. When the temperature or humidity deviation of any space layer is detected to exceed the threshold, a drive command is output.

5. The method for constant temperature and humidity control of a cigar cabinet based on a fuzzy control algorithm according to claim 1, characterized in that, The actuator control module includes: receiving drive commands from the fan or dehumidifier and inputting a set of parameters from the cabinet-wide layered fuzzy controller; controlling the execution of the compressor, heater, humidifier, and fans on each layer based on the drive commands and layered fuzzy control parameters; and outputting new environmental status data based on the adjustment results.

6. The method for constant temperature and humidity control of a cigar cabinet based on a fuzzy control algorithm according to claim 1, characterized in that, The feedforward control module includes the following steps: Multiple types of proximity sensors and mechanical motion detection sensors are installed on the cigar cabinet door frame to detect hand proximity signals, door contact signals, and hinge angle change signals in the door frame area in real time, respectively. When any type of sensor continuously detects that the signal change trend meets the set threshold condition, it determines that an opening or closing event is about to occur, and outputs a disturbance prediction event signal through the event discrimination logic unit. The disturbance prediction event signal is analyzed and mapped to the spatial layering structure of the cigar cabinet. It is then matched with the historical disturbance impact model to locate the target spatial layer that may be significantly affected, thus forming the disturbance prediction layer identification result. For the identified affected space layers, a feedforward control signal is output in advance to improve the control response sensitivity of the affected space layers and expand the output adjustment range; The number of information interactions in the affected spatial layer during the WFPSO optimization process will be temporarily increased. After the disturbance event ends, the time process and recovery curve of the temperature and humidity of the affected space layer returning to a stable state are used to determine whether the disturbance has been resolved. If it has been resolved, the temperature control weight, humidity control weight and information interaction number are automatically restored to the normal state.

7. A method for constant temperature and humidity control of a cigar cabinet based on a fuzzy control algorithm according to claim 6, characterized in that, The historical disturbance impact model matching step specifically includes: The specific characteristics of the disturbance event include the location, duration, signal amplitude, and historical frequency of occurrence. These characteristics are matched with a pre-established historical disturbance impact model. For each characteristic, if they are completely identical, they are scored as 1 point; otherwise, they are scored as 0 points. Numerical characteristics such as signal amplitude and duration are assigned scores according to the difference. The scores of all characteristic items are summed up. The higher the score, the more similar the two disturbance events are. Based on the type and intensity of the disturbance event, prioritize finding historical event records most similar to the current disturbance conditions, call up the actual temperature and humidity changes and recovery trends of each spatial layer under the corresponding disturbance in the model, compare with the current real-time temperature and humidity distribution in the cabinet, determine which spatial layers have experienced overshoot, lag, slow recovery or amplified fluctuations when subjected to similar disturbances in the past, and combine with the current real-time data to predict the possible range and severity of the impact of this disturbance on each spatial layer.

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