Combined humidifier precise control method and system based on trend change

Abstract

The invention provides a combined type humidifier precise control method and system based on trend change, and the method comprises the steps that environment parameters of a controlled area and state parameters of a humidifier set are obtained, and the humidifier set comprises a high-pressure micro-mist humidifier, a steam humidifier and a wet film humidifier; constructing a trend prediction model to predict the humidity change rate in the future delta t time and predict the humidity distribution of the local area; preferential starting of the humidifier set is determined according to different scenes, and the target load of the humidifier set is distributed in proportion according to the trend prediction result. According to the invention, through multi-dimensional environmental parameter acquisition, dynamic trend prediction and differential load distribution, humidity dynamic and accurate control is realized.

Description

Technical Field

[0001] The present invention relates to the technical field of air humidity control, and in particular to a method and system for accurately controlling a combined humidifier based on trend changes. Background Art

[0002] During tobacco processing, tobacco leaves need to be humidified and conditioned to ensure the relative humidity of the tobacco leaves, so as to increase leaf temperature, increase toughness and reduce crushing. Therefore, the accuracy of tobacco leaves to humidity is also high. Traditional humidifier control methods mostly use fixed thresholds or simple feedback control (such as PID algorithm), but are limited by the differences in humidifier types and the complexity of dynamic changes in the environment, and have the following problems:

[0003] 1. Response hysteresis: When humidity fluctuation triggers control instructions, the humidifier is difficult to adjust in time due to mechanical inertia;

[0004] 2. Insufficient adaptability: The humidification efficiency and energy consumption characteristics of different humidifiers (high-pressure mist, steam, wet film) vary significantly, and a single control logic is difficult to take into account performance optimization;

[0005] 3. Over-regulation and oscillation: Frequent start-stop or sudden load changes can easily lead to humidity over-regulation or periodic fluctuations.

[0006] Due to the above-mentioned defects, the humidifier cannot be controlled dynamically and accurately. In this regard, the present application provides a method and system for precise control of a combined humidifier based on trend changes. Summary of the invention

[0007] The purpose of the present invention is to provide a method and system for precise control of a combined humidifier based on trend changes, which can achieve dynamic and precise control of humidity through multi-dimensional environmental parameter collection, dynamic trend prediction and differentiated load distribution.

[0008] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0009] A method for precise control of a combined humidifier based on trend changes, comprising:

[0010] Acquiring environmental parameters of the controlled area and state parameters of a humidifier group, wherein the humidifier group includes a high-pressure mist humidifier, a steam humidifier, and a wet film humidifier;

[0011] Construct a trend prediction model to predict the rate of change of humidity within the next Δt time and predict the humidity distribution in the local area;

[0012] The priority start-up of the humidifier group is determined according to different scenarios, and the target load of the humidifier group is proportionally allocated according to the trend prediction results.

[0013] Furthermore, the environmental parameters of the controlled area and the state parameters of the humidifier group are obtained, wherein the environmental parameters of the controlled area include: temperature, relative humidity and air flow rate;

[0014] The status parameters of the humidifier group include:

[0015] Water pressure, atomization particle size and nozzle status parameters of high-pressure micro-mist humidifier;

[0016] Heating power, steam output and water conductivity parameters of steam humidifiers;

[0017] Water film saturation, fan speed and evaporation efficiency parameters of wet film humidifier.

[0018] Furthermore, the construction of a trend prediction model to predict the humidity change rate within the future Δt time and the humidity distribution in the local area includes:

[0019] Time series analysis: trend fitting of historical humidity data is performed through a sliding window algorithm to predict the rate of change of humidity within the next Δt period;

[0020] The coupling factor modeling was carried out to establish the temperature and humidity coupling equation and the model of the influence of air flow on humidity diffusion, and to predict the humidity distribution in the local area.

[0021] Furthermore, the trend fitting of the historical humidity data is performed by a sliding window algorithm, wherein the calculation formula of the sliding window algorithm is:

[0022]

[0023] In the formula, ω i is the weight coefficient, RH _t For historical humidity data.

[0024] Furthermore, the establishment of the temperature and humidity coupling equation and the influence model of air flow on humidity diffusion to predict the humidity distribution in the local area includes: constructing the temperature and humidity coupling equation based on the wet-bulb temperature equation, and the formula is:

[0025]

[0026] In the formula, T is temperature and RH is relative humidity;

[0027] A simplified fluid mechanics model is introduced to calculate the effect of air velocity on local humidity distribution. The formula is:

[0028]

[0029] In the formula, K v is the diffusion coefficient, RH pred is the corrected predicted value, V is the air velocity.

[0030] Furthermore, the method of determining the priority start-up of the humidifier groups according to different scenarios and allocating the target load of the humidifier groups in proportion according to the trend prediction results includes:

[0031] Pre-store dynamic characteristic parameters of three types of humidifiers, including dynamic response curve, maximum humidification capacity Q_max, energy efficiency ratio COP and inertia delay parameters;

[0032] Scene judgment: if the humidity change rate is greater than the threshold, the quick response mode is activated. At this time, the high-pressure mist humidifier and steam humidifier are turned on first; if the humidity change rate is less than the threshold, the steady-state maintenance mode is activated. At this time, the wet film humidifier is switched to the main one, supplemented by fine-tuning instructions to other types of humidifiers;

[0033] Load instructions are generated, and target loads of the three types of humidifiers are proportionally allocated based on trend prediction results.

[0034] Furthermore, the load distribution in the fast response mode satisfies:

[0035]

[0036] Where L1 is the load of the high pressure mist humidifier, L2 is the load of the steam humidifier, ΔQ demand is the amount of humidification required for the current humidity deviation, and k is the trend compensation coefficient;

[0037] The load distribution in the steady-state maintenance mode satisfies:

[0038] L3 ≥ 70%;

[0039] L1 / L2≤10%;

[0040] In the formula, L1 is the load of the high-pressure mist humidifier, L2 is the load of the steam humidifier, and L3 is the load of the wet film humidifier;

[0041] Furthermore, the priority start-up of the humidifier groups is determined according to different scenarios and the target load of the humidifier groups is proportionally distributed according to the trend prediction results, and then closed-loop feedback optimization is also included, and the closed-loop feedback optimization includes:

[0042] Adaptive parameter adjustment: dynamically modify the trend prediction model weight and load distribution ratio through fuzzy logic or reinforcement learning algorithm;

[0043] Oscillation suppression mechanism, introducing hysteresis control to reduce control sensitivity when humidity approaches the set threshold;

[0044] Furthermore, when adjusting parameters through the fuzzy logic, the input variable humidity deviation e and the deviation change rate ec, the output variable trend compensation coefficient k and the load distribution weight are defined, and the parameters are dynamically adjusted through the fuzzy rule base;

[0045] When adjusting parameters through the reinforcement learning algorithm, the Q-learning model is constructed with energy consumption minimization as the objective function:

[0046] Q(s,a)=(1-α)Q(s,a)+α[R+γmaxQ(s']

[0047] In the formula, s includes environmental parameters and equipment status, action a is the load distribution ratio, and reward R = -(energy consumption + humidity fluctuation);

[0048] In the oscillation suppression mechanism, a threshold is set to ±1% RH, and when the humidity enters the set threshold, the load adjustment instruction is frozen.

[0049] On the other hand, based on the control method, the present application also provides a combined humidifier precision control system based on trend changes, including:

[0050] The sensor layer is used to obtain the environmental parameters of the controlled area and the status parameters of the humidifier group;

[0051] The control layer is used to execute the construction of a trend prediction model to predict the humidity change rate within the future Δt time and the humidity distribution in the local area, and to determine the priority start-up of the humidifier group according to different scenarios and to distribute the target load of the humidifier group in proportion according to the trend prediction results;

[0052] The execution layer includes high-pressure mist humidifiers, steam humidifiers, and wet film humidifiers;

[0053] The sensor layer includes a temperature and humidity sensor, an air flow rate sensor, an integrated humidifier state monitoring unit and a data acquisition module. The integrated humidifier state monitoring unit is used to obtain the state parameters of the humidifier group.

[0054] The temperature and humidity sensor, the air flow rate sensor, the integrated humidifier state monitoring unit and the control layer are respectively connected to the data acquisition module; the control layer is connected to the execution layer.

[0055] Compared with the prior art, the present invention has at least the following beneficial effects:

[0056] 1. The present invention can more accurately predict the humidity change trend through multi-dimensional environmental parameter collection and dynamic trend prediction, thereby adjusting the working state of the humidifier in advance, effectively reducing the humidity fluctuation range, and thus improving the control accuracy;

[0057] 2. The present invention can dynamically adjust the load instructions according to the type of humidifier and environmental changes through differentiated load distribution strategies to adapt to different humidity control requirements. For example, in the rapid response scenario, the high-pressure mist humidifier is preferentially enabled, while in the steady-state maintenance scenario, the wet film humidifier is switched to enhance the adaptability of humidity control, resulting in lower energy consumption and higher accuracy;

[0058] 3. The oscillation suppression mechanism and adaptive parameter adjustment are introduced in the closed-loop feedback optimization. The oscillation suppression mechanism reduces the frequent start and stop of the equipment, thereby reducing energy consumption. The comprehensive energy consumption can be reduced by 15%-30%, and the number of equipment starts and stops can be reduced by more than 50%; the adaptive parameter adjustment can dynamically correct the model parameters and load distribution ratio, adapt to seasonal changes and equipment aging and other factors, and improve the long-term stability and reliability of the system. BRIEF DESCRIPTION OF THE DRAWINGS

[0059] Figure 1 The figure is a flow chart of the control method of the present invention.

[0060] Figure 2 This is a flow chart of the trend prediction and load distribution algorithm in the present invention.

[0061] Figure 3 It is a schematic diagram of the priority start-up and load distribution process of the humidifier group of the present invention.

[0062] Figure 4 This is a diagram of the system architecture of the present invention.

[0063] Figure 5 This is a timing diagram of coordinated control of three types of humidifiers in the present invention. DETAILED DESCRIPTION

[0064] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not intended to limit the present invention.

[0065] A precise control method for a combined humidifier based on trend changes of the present invention is described below in conjunction with the accompanying drawings.

[0066] See also Figure 1-2 As shown, the control method includes:

[0067] Step S101, obtaining environmental parameters of the controlled area and state parameters of a humidifier group, wherein the humidifier group includes a high-pressure mist humidifier, a steam humidifier, and a wet film humidifier;

[0068] Step S102, constructing a trend prediction model to predict the humidity change rate within the next Δt time and predict the humidity distribution in the local area;

[0069] Step S103, determining the priority start-up of the humidifier groups according to different scenarios and allocating the target load of the humidifier groups in proportion according to the trend prediction results.

[0070] Furthermore, in step S101, the environmental parameters of the controlled area include: temperature T, relative humidity RH and air flow rate V. To obtain the environmental parameters, temperature and humidity sensors and air flow rate sensors may be deployed in the controlled area. In addition, in step S101, the state parameters of the humidifier group include:

[0071] Water pressure, atomization particle size and nozzle status parameters of high-pressure micro-mist humidifier;

[0072] Heating power, steam output and water conductivity parameters of steam humidifiers;

[0073] Water film saturation, fan speed and evaporation efficiency parameters of wet film humidifier.

[0074] In order to obtain the status parameters of the humidifier group, an integrated humidifier status monitoring unit can be configured, and the data ports of the high-pressure mist humidifier, steam humidifier and wet film humidifier can be connected to the integrated humidifier status monitoring unit, so as to collect the status parameters of the humidifier group at one time. Through multi-dimensional data such as various environmental parameters and various status parameters of the humidifier group, it can provide multi-faceted basis and feedback for the precise control of humidity.

[0075] In step S102, specifically, the humidity change rate within the future Δt time can be predicted by time series analysis, that is, trend fitting of historical humidity data is performed by a sliding window algorithm, and the calculation formula is:

[0076]

[0077] In the formula, ω i is the weight coefficient, RH _t For historical humidity data.

[0078] It is worth noting that the weight coefficient ω i As the data becomes less time-sensitive, recent trends are highlighted.

[0079] The prediction of humidity distribution in a local area is achieved by modeling the coupling factors. Specifically, the temperature and humidity coupling equation and the influence model of air flow on humidity diffusion are established. The temperature and humidity coupling equation is constructed based on the wet-bulb temperature equation, and its formula is:

[0080]

[0081] In the formula, T is temperature and RH is relative humidity;

[0082] For the model of the influence of air flow on humidity diffusion, this application introduces a simplified fluid mechanics model to calculate the influence of air flow rate on local humidity distribution, and the formula is:

[0083]

[0084] In the formula, K v is the diffusion coefficient, which can be calibrated experimentally; RH pred is the corrected predicted value; V is the air flow rate.

[0085] Through step S102, the humidity fluctuation direction can be predicted 3-5 minutes in advance, compensating for the lag of traditional feedback control.

[0086] In step S103, refer to Figure 3 As shown, the more specific steps are:

[0087] S103a: pre-storing dynamic characteristic parameters of three types of humidifiers, the dynamic characteristic parameters including dynamic response curve, maximum humidification amount Q_max, energy efficiency ratio COP and inertia delay parameter;

[0088] In this embodiment, the pre-stored parameters of the humidifier group are as follows:

[0089] Humidifier Type Response time(s) Maximum humidification capacity (g / h) Energy efficiency ratio (cop) High pressure mist humidifier 5-10 2000 3.2 Steam humidifier 15-20 1500 2.5 Wet film humidifier 30-60 800 4.8

[0090] S103b: Scene judgment: if the humidity change rate is greater than the threshold, the quick response mode is started. At this time, the high-pressure mist humidifier and steam humidifier are turned on first; if the humidity change rate is less than the threshold, the steady-state maintenance mode is started. At this time, the wet film humidifier is switched to the main one, supplemented by fine-tuning instructions to other types of humidifiers. The three types of humidifier coordinated control timing diagram can be referred to Figure 5 As shown;

[0091] More specifically, the load distribution in the fast response mode satisfies:

[0092]

[0093] Where L1 is the load of the high pressure mist humidifier, L2 is the load of the steam humidifier, ΔQ demand is the amount of humidification required for the current humidity deviation, and k is the trend compensation coefficient;

[0094] The load distribution in the steady-state maintenance mode satisfies:

[0095] L3 ≥ 70%;

[0096] L1 / L2≤10%;

[0097] In the formula, L1 is the load of the high-pressure mist humidifier, L2 is the load of the steam humidifier, and L3 is the load of the wet film humidifier;

[0098] Through differentiated load distribution strategies, load instructions can be dynamically adjusted according to the type of humidifier and environmental changes to adapt to different humidity control requirements, resulting in lower energy consumption and higher accuracy.

[0099] S103c: Load instruction generation, and allocating target loads of the three types of humidifiers in proportion according to the trend prediction results.

[0100] In addition, after step S103, closed-loop feedback optimization is also included, and the closed-loop feedback optimization includes:

[0101] Adaptive parameter adjustment: through fuzzy logic or reinforcement learning algorithms, dynamically correct trend prediction model weights and load distribution ratios to adapt to long-term factors such as seasonal changes and equipment aging;

[0102] Specifically, in this process, when adjusting parameters through the fuzzy logic, the input variable humidity deviation e and the deviation change rate ec, the output variable trend compensation coefficient k and the load distribution weight are defined, and the parameters are dynamically adjusted through the fuzzy rule base;

[0103] When adjusting parameters through the reinforcement learning algorithm, the Q-learning model is constructed with energy consumption minimization as the objective function:

[0104] Q(s,a)=(1-α)Q(s,a)+α[R+γmaxQ(s'

[0105] In the formula, s includes environmental parameters and equipment status, action a is the load distribution ratio, and reward R = -(energy consumption + humidity fluctuation);

[0106] In actual applications, through this adaptive parameter adjustment process, the system's adaptive optimization efficiency increased by 20% after 3 months of operation;

[0107] Oscillation suppression mechanism, introducing hysteresis control to reduce control sensitivity when humidity approaches the set threshold;

[0108] In the oscillation suppression mechanism, a threshold is set at ±1% RH. When the humidity reaches the set threshold, the load adjustment instruction is frozen to avoid frequent starts and stops.

[0109] The overall energy consumption is reduced by 15%-30%, and the number of equipment starts and stops is reduced by more than 50%.

[0110] In actual applications, the oscillation suppression mechanism avoids frequent start and stop of the humidifier, reduces the comprehensive energy consumption by 15%-30%, and reduces the number of equipment start and stop times by more than 50%, greatly reducing energy consumption and equipment loss.

[0111] refer to Figure 4 In addition, based on the control method, the present invention also provides a combined humidifier precision control system based on trend changes, including:

[0112] Sensor layer 1, used to obtain environmental parameters of the controlled area and status parameters of the humidifier group;

[0113] Control layer 2 is used to execute the construction of a trend prediction model to predict the humidity change rate within the future Δt time and the humidity distribution in the local area, and to determine the priority start-up of the humidifier group according to different scenarios and to distribute the target load of the humidifier group in proportion according to the trend prediction results;

[0114] Execution layer 3, including high-pressure mist humidifier 3a, steam humidifier 3b, wet film humidifier 3c;

[0115] The sensor layer 1 includes a temperature and humidity sensor 1a, an air flow rate sensor 1b, an integrated humidifier state monitoring unit 1c and a data acquisition module 1d. The integrated humidifier state monitoring unit 1c is used to obtain the state parameters of the humidifier group;

[0116] The temperature and humidity sensor 1a, the air flow rate sensor 1b, the integrated humidifier state monitoring unit 1c and the control layer 2 are respectively connected to the data acquisition module 1d; the control layer 2 is connected to the execution layer 3.

[0117] The "one embodiment", "another embodiment", "embodiment", "preferred embodiment" and the like mentioned in this specification refer to the specific features, structures or characteristics described in conjunction with the embodiment included in at least one embodiment generally described in this application. The same expression appearing in multiple places in the specification does not necessarily refer to the same embodiment. Further, when a specific feature, structure or characteristic is described in conjunction with any embodiment, it is claimed that the realization of such feature, structure or characteristic in conjunction with other embodiments also falls within the scope of the present invention.

[0118] Although the present invention is described herein with reference to a number of illustrative embodiments of the present invention, it will be appreciated that those skilled in the art may devise many other modifications and implementations that fall within the scope and spirit of the principles disclosed herein. More specifically, within the scope of the present disclosure, drawings, and claims, a variety of variations and modifications may be made to the components or layout of the subject combination layout. In addition to variations and modifications made to the components or layout, other uses will also be apparent to those skilled in the art.

Claims

1. A method for precise control of a combined humidifier based on trend changes, characterized in that: include: Acquiring environmental parameters of the controlled area and state parameters of a humidifier group, wherein the humidifier group includes a high-pressure mist humidifier, a steam humidifier, and a wet film humidifier; Construct a trend prediction model to predict the rate of change of humidity within the next Δt time and predict the humidity distribution in the local area; The priority start-up of the humidifier group is determined according to different scenarios, and the target load of the humidifier group is proportionally allocated according to the trend prediction results.

2. A method for precise control of a combined humidifier based on trend changes according to claim 1, characterized in that: The environmental parameters of the controlled area and the state parameters of the humidifier group are obtained, wherein the environmental parameters of the controlled area include: temperature, relative humidity and air flow rate; The status parameters of the humidifier group include: Water pressure, atomization particle size and nozzle status parameters of high-pressure micro-mist humidifier; Heating power, steam output and water conductivity parameters of steam humidifiers; Water film saturation, fan speed and evaporation efficiency parameters of wet film humidifier.

3. The method for precise control of a combined humidifier based on trend change according to claim 1, characterized in that: The trend prediction model is constructed to predict the humidity change rate within the future Δt time and the humidity distribution in the local area, including: Time series analysis: trend fitting of historical humidity data is performed through a sliding window algorithm to predict the rate of change of humidity within the next Δt period; The coupling factor modeling was carried out to establish the temperature and humidity coupling equation and the model of the influence of air flow on humidity diffusion, and to predict the humidity distribution in the local area.

4. A method for precise control of a combined humidifier based on trend changes according to claim 3, characterized in that: The trend fitting of the historical humidity data is performed by a sliding window algorithm, wherein the calculation formula of the sliding window algorithm is: In the formula, ω i is the weight coefficient, RH _t For historical humidity data.

5. The method for precise control of a combined humidifier based on trend change according to claim 3, characterized in that: The method of establishing a temperature-humidity coupling equation and an air flow influence model on humidity diffusion to predict humidity distribution in a local area includes: constructing a temperature-humidity coupling equation based on a wet-bulb temperature equation, and the formula is: In the formula, T is temperature and RH is relative humidity; A simplified fluid mechanics model is introduced to calculate the effect of air velocity on local humidity distribution. The formula is: In the formula, K v is the diffusion coefficient, RH pred is the corrected predicted value, V is the air velocity.

6. The method for precise control of a combined humidifier based on trend change according to claim 1, characterized in that: The method of determining the priority start-up of the humidifier groups according to different scenarios and allocating the target load of the humidifier groups in proportion according to the trend prediction results includes: Pre-store dynamic characteristic parameters of three types of humidifiers, including dynamic response curve, maximum humidification capacity Q_max, energy efficiency ratio COP and inertia delay parameters; Scene judgment: if the humidity change rate is greater than the threshold, the quick response mode is activated. At this time, the high-pressure mist humidifier and steam humidifier are turned on first; if the humidity change rate is less than the threshold, the steady-state maintenance mode is activated. At this time, the wet film humidifier is switched to the main one, supplemented by fine-tuning instructions to other types of humidifiers; Load instructions are generated, and target loads of the three types of humidifiers are proportionally allocated based on trend prediction results.

7. A method for precise control of a combined humidifier based on trend changes according to claim 6, characterized in that: The load distribution in the fast response mode satisfies: Where L1 is the load of the high pressure mist humidifier, L2 is the load of the steam humidifier, ΔQ demand is the amount of humidification required for the current humidity deviation, and k is the trend compensation coefficient; The load distribution in the steady-state maintenance mode satisfies: L3≥70％； L1 / L2≤10%; Where L1 is the load of the high-pressure mist humidifier, L2 is the load of the steam humidifier, and L3 is the load of the wet film humidifier.

8. The method for precise control of a combined humidifier based on trend change according to claim 1, characterized in that: The method determines the priority start-up of the humidifier group according to different scenarios and distributes the target load of the humidifier group in proportion according to the trend prediction result, and then includes closed-loop feedback optimization, which includes: Adaptive parameter adjustment: dynamically modify the trend prediction model weight and load distribution ratio through fuzzy logic or reinforcement learning algorithm; The oscillation suppression mechanism introduces hysteresis control to reduce the control sensitivity when the humidity approaches the set threshold.

9. A method for precise control of a combined humidifier based on trend change according to claim 8, characterized in that: When adjusting parameters through the fuzzy logic, the input variable humidity deviation e and deviation change rate ec, the output variable trend compensation coefficient k and load distribution weight are defined, and the parameters are dynamically adjusted through the fuzzy rule base; When adjusting parameters through the reinforcement learning algorithm, the Q-learning model is constructed with energy consumption minimization as the objective function: Q(s,a)=(1-α)Q(s,a)+α[R+γmaxQ(s',a')] In the formula, s includes environmental parameters and equipment status, action a is the load distribution ratio, and reward R = -(energy consumption + humidity fluctuation); In the oscillation suppression mechanism, a threshold is set to ±1% RH, and when the humidity enters the set threshold, the load adjustment instruction is frozen.

10. A combined humidifier precision control system based on trend changes, characterized in that: include: The sensor layer is used to obtain the environmental parameters of the controlled area and the status parameters of the humidifier group; The control layer is used to execute the construction of a trend prediction model to predict the humidity change rate within the future Δt time and the humidity distribution in the local area, and to determine the priority start-up of the humidifier group according to different scenarios and to distribute the target load of the humidifier group in proportion according to the trend prediction results; The execution layer includes high-pressure mist humidifiers, steam humidifiers, and wet film humidifiers; The sensor layer includes a temperature and humidity sensor, an air flow rate sensor, an integrated humidifier state monitoring unit and a data acquisition module. The integrated humidifier state monitoring unit is used to obtain the state parameters of the humidifier group. The temperature and humidity sensor, the air flow rate sensor, the integrated humidifier state monitoring unit and the control layer are respectively connected to the data acquisition module; the control layer is connected to the execution layer.

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