A cleanroom temperature and humidity regulation and energy-saving control method

Abstract

This invention discloses an energy-saving control method for adjusting the temperature and humidity of a cleanroom, comprising the following steps: S1, acquiring real-time temperature and humidity data, equipment operating status data, and outdoor meteorological data at multiple locations within the cleanroom; S2, generating a dynamic temperature and humidity load distribution cloud map based on the indoor temperature and humidity data and a cleanroom space model; S3, logically dividing the cleanroom into at least two virtual areas with different control priorities based on the dynamic temperature and humidity load distribution cloud map. The beneficial effects of this invention are: by constructing a dynamic load distribution cloud map and dividing virtual areas, a shift from overall uniform control to precise, on-demand local control is achieved. By matching and directionally intervening in dynamically occurring local load areas with the lowest-cost available adjustment methods in real time, energy waste caused by overall adjustment to meet local needs is avoided, significantly reducing the total system energy consumption while ensuring the control accuracy of key areas.

Description

Technical Field

[0001] This invention relates to the field of cleanroom environmental control technology, specifically to an energy-saving control method for adjusting the temperature and humidity of a cleanroom. Background Technology

[0002] Cleanrooms, especially those used for food preservation, require strict temperature and humidity parameters to ensure the quality of goods. Traditional control methods involve using air conditioning systems to regulate the indoor environment as a whole to achieve the set temperature and humidity target values.

[0003] Existing cleanroom temperature and humidity control methods typically employ feedback control with fixed setpoints. This uniform control mode fails to respond to the uneven spatial and temporal distribution of temperature and humidity loads caused by personnel activity, goods storage, and equipment operation. Often, to meet the needs of the most unfavorable local point, the entire space is over-cooled or over-dehumidified, resulting in energy waste. Furthermore, the control logic is simplistic, usually relying solely on high-quality energy sources such as compressor refrigeration and electric reheat for regulation. It fails to intelligently and preferentially utilize low-quality energy sources such as natural cooling sources and equipment heat recovery based on real-time operating conditions (e.g., outdoor climate conditions). The response to sudden, localized temperature and humidity disturbances is delayed, and the adjustment actions affect the entire system, leading to a contradiction between control accuracy and energy consumption. Therefore, this application proposes an energy-saving control method for cleanroom temperature and humidity regulation. Summary of the Invention

[0004] The purpose of this invention is to provide a cleanroom temperature and humidity regulation energy-saving control method that can effectively identify and distinguish between steady-state loads and dynamic disturbance loads in a space, and intelligently match the most economical solution path for dynamic disturbance loads, thereby achieving the control objective of "on-demand allocation and precise supply". It balances control accuracy and energy-saving requirements under complex working conditions, thus solving the problems mentioned in the background art.

[0005] To achieve the above objectives, the present invention provides the following technical solution: a cleanroom temperature and humidity regulation and energy-saving control method, comprising the following steps: S1. Real-time acquisition of temperature and humidity data, equipment operating status data, and outdoor weather data from multiple locations within the cleanroom; S2. Based on the indoor temperature and humidity data, and combined with the cleanroom space model, generate an indoor dynamic temperature and humidity load distribution cloud map; S3. Based on the dynamic temperature and humidity load distribution cloud map, the clean room is logically divided into at least two virtual areas with different control priorities. The virtual area includes at least one local adjustment area whose boundary and intensity are dynamically determined according to the real-time load. S4. Establish an energy cost sequence that includes multiple regulation methods, including the use of natural cold sources, the use of low-grade energy, the intervention of local regulation equipment, and the regulation of the central host. S5. Match the spatial location information and load intensity information of the local regulation zone with the supply characteristics of the currently available regulation means in the energy cost sequence to generate a priority regulation instruction for the local regulation zone. S6. Execute the priority control command to drive the actuator corresponding to the selected adjustment means to perform targeted environmental adjustment on the local adjustment area.

[0006] Preferably, the step S2 of generating the indoor dynamic temperature and humidity load distribution cloud map specifically involves: S21. Input the real-time collected discrete point sensor data into the preset cleanroom three-dimensional space model; S22. Calculate the estimated temperature and humidity values ​​of each grid point in the model using a spatial interpolation algorithm; S23. Combine the basic flow field data of computational fluid dynamics to generate a three-dimensional cloud map reflecting the degree of deviation of temperature and humidity from the set value and its spatial distribution.

[0007] Preferably, the division of the virtual region in step S3 specifically includes: S31. Areas that are used for long-term storage of high-value items and are sensitive to temperature and humidity fluctuations should be designated as core protection zones. S32. In the dynamic temperature and humidity load distribution cloud map, the continuous spatial range where the temperature and humidity parameters exceed the preset threshold is identified and marked as the local adjustment zone in real time; S33. The remaining areas of the cleanroom, excluding the core protection area and the local adjustment area, serve as a background buffer zone.

[0008] Preferably, the energy cost sequence is established in step S4 as follows: S41. Based on real-time outdoor meteorological data, determine the feasibility and expected effect of introducing fresh outdoor air for cooling or dehumidification, and use it as the first priority adjustment method. S42. Calculate the recoverable cooling capacity or waste heat of condensate based on the operating status of the air conditioning system and use it as the second priority adjustment method. S43. Use independently controllable local temperature and humidity control equipment as the third priority control method; S44. The central air conditioning refrigeration unit, dehumidifier and reheat device are used as the fourth priority adjustment means. The above priorities are ranked from low to high based on the comprehensive operating cost per unit of adjustment.

[0009] Preferably, the matching rule in step S5 is: first, determine whether the spatial location of the local adjustment zone is within the air supply range of the first priority adjustment means or the heat exchange influence range of the second priority adjustment means; If so, the corresponding low-cost adjustment method will be matched first and instructions will be generated; If not, determine whether the third priority adjustment means is in place, and generate equipment scheduling and start instructions; If none of the aforementioned methods are available or have insufficient capacity, then the fourth priority adjustment method is matched and a central host operating parameter adjustment instruction is generated.

[0010] Preferably, a prediction step is included between steps S1 and S2: based on historical operation data, scheduling plans and real-time trends, the local temperature and humidity load changes that may occur in the cleanroom within a specific future time period are predicted. In step S5, the local adjustment zone is pre-matched based on the prediction results, and the corresponding adjustment measures are scheduled to enter the preparation state or start pre-adjustment in advance before the predicted load change occurs.

[0011] As a preferred option, outdoor air quality parameters are introduced as constraints in the feasibility assessment of the first priority adjustment method. When the concentration of particulate matter or microorganisms in the outdoor air exceeds the allowable standard for the cleanroom, the adjustment method is deemed unavailable at present, even if the thermal parameters are suitable.

[0012] Preferably, in step S6, when performing directional adjustment for the local adjustment zone, the air supply or exhaust parameters of adjacent areas are adjusted simultaneously to maintain the design pressure difference between different areas of the cleanroom and prevent cross-contamination of air.

[0013] Preferably, the indoor data obtained in step S1 also includes positioning data for locating indoor mobile heat sources or moisture sources. In step S3, when dividing the local adjustment zone, the influence range of the moving heat source or moisture source is included in the calculation of the dynamic boundary by combining the positioning data.

[0014] Compared with the prior art, the beneficial effects of the present invention are: by constructing a dynamic load distribution cloud map and dividing virtual areas, the transformation from overall uniform control to local precise on-demand control is realized. By matching and directionally intervening with dynamically appearing local load areas in real time with the lowest cost available adjustment means, the energy waste caused by overall adjustment to meet local needs is avoided. While ensuring the control accuracy of key areas, the total energy consumption of the system is significantly reduced.

[0015] By dynamically identifying dynamic adjustment zones through zoning and matching them with low-cost solutions such as natural cooling with fresh air, recovery of cold energy utilization, or mobile equipment processing, precise matching of energy quality and load location is achieved. At the same time, the introduction of a pre-adjustment mechanism based on short-term forecasts enables energy to be scheduled in advance to smooth out disturbances, further improving control quality and energy efficiency. In addition, by setting multi-level control priorities and flexible floating ranges, stability is provided for the core area while providing flexible optimization space for the entire system. Detailed Implementation

[0016] The present invention provides a method for energy-saving control of cleanroom temperature and humidity regulation, with reference to specific embodiments. It should be noted that, to make the embodiments more detailed, the following embodiments are the best and preferred embodiments; however, those skilled in the art can use other alternative methods to implement some known technologies.

[0017] It should be noted that the use of terms such as "an embodiment," "an embodiment," "an exemplary embodiment," and "some embodiments" in the specification indicates that the described embodiment may include a specific feature, structure, or characteristic, but not every embodiment necessarily includes that specific feature, structure, or characteristic. Furthermore, when a specific feature, structure, or characteristic is described in connection with an embodiment, implementing such a feature, structure, or characteristic in conjunction with other embodiments (whether explicitly described or not) should be within the knowledge of those skilled in the art.

[0018] Generally, terms can be understood at least partially from their use in context. For example, depending at least partially on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in a singular sense, or a combination of features, structures, or characteristics in a plural sense. Additionally, the term "based on" can be understood not necessarily to convey an exclusive set of factors, but can instead, depending at least partially on the context, allow for the presence of other factors that are not necessarily explicitly described. All other embodiments obtained by those skilled in the art without inventive effort are within the scope of this invention.

[0019] This invention provides a technical solution: a cleanroom temperature and humidity regulation and energy-saving control method, comprising the following steps: S1. Real-time acquisition of temperature and humidity data, equipment operating status data, and outdoor weather data from multiple locations within the cleanroom; S2. Based on the indoor temperature and humidity data, and combined with the cleanroom space model, generate an indoor dynamic temperature and humidity load distribution cloud map; S3. Based on the dynamic temperature and humidity load distribution cloud map, the clean room is logically divided into at least two virtual areas with different control priorities. The virtual area includes at least one local adjustment area whose boundary and intensity are dynamically determined according to the real-time load. S4. Establish an energy cost sequence that includes multiple regulation methods, including the use of natural cold sources, the use of low-grade energy, the intervention of local regulation equipment, and the regulation of the central host. S5. Match the spatial location information and load intensity information of the local regulation zone with the supply characteristics of the currently available regulation means in the energy cost sequence to generate a priority regulation instruction for the local regulation zone. S6. Execute the priority control command to drive the actuator corresponding to the selected adjustment means to perform targeted environmental adjustment on the local adjustment area.

[0020] Example 1: Taking a clean cold storage warehouse for storing fruits and vegetables as an example, the complete workflow of this method is explained. The warehouse has a fixed storage area for high-value products (core area) and a regular goods turnover area.

[0021] Initial state: At night, the warehouse is operating at a low load, the outdoor temperature is low, and the system is in basic maintenance mode, using only the minimum fresh air and low-power fans to maintain the overall environment; Disturbance occurred: In the early morning, staff drove electric forklifts into the turnover area to carry out outbound operations (lasting 20 minutes). The heat dissipation of the forklift motors and the activities of the staff created a gradually increasing local heat source in a corner of the turnover area. Sensing and Recognition: The high-density sensor network detected that the temperature in the area was rising at a rate of 0.1℃ per minute. The load cloud map generation module immediately identified the area with abnormally high temperature in the 3D model. The area was about 10 square meters and the temperature was 1.2℃ higher than the set value. The partitioning algorithm then defined it as the "dynamic adjustment zone" for the current cycle. Energy Assessment and Matching: The energy assessment module determines that the current outdoor air temperature is 15℃ and humidity is 60%, and its enthalpy value is significantly lower than that of the air inside the warehouse, which is a high-quality natural cold source. The system searches the duct layout diagram and finds that there is a fresh air branch duct outlet 3 meters above the dynamic adjustment zone. The match is successful, and the lowest cost solution is determined to be: introduce outdoor fresh air for cooling. Command generation and execution: The system generates and issues commands. a. Increase the opening of the electric regulating valve on the fresh air branch pipe from 20% to 70%; b. Adjust the spherical nozzle of the air outlet downwards by 15 degrees, directly aiming it at the hot spot area below; c. To prevent excessive positive pressure inside the warehouse, the frequency of exhaust fans in adjacent areas will be increased slightly by 5% simultaneously; Effects and Adjustments: Low-temperature fresh air was precisely delivered to the hot spot area, where the temperature stopped rising within 5 minutes and then dropped back to the allowable fluctuation range within the next 10 minutes. Throughout the process, the temperature fluctuation in the core area of ​​the warehouse did not exceed ±0.3℃, the central refrigeration unit did not start, and after 20 minutes, the operation stopped, the dynamic heat source disappeared, the system detected a reduction in load, and gradually restored the fresh air valve and air outlet to their initial state. Data recording: This event (including disturbance characteristics, matching strategy, control effect, and energy consumption data) is recorded in the historical database for use in optimizing future prediction and matching models.

[0022] Example 2: This example focuses on addressing the impact of wet loads on warehousing operations and demonstrates the application of forecasting and mobile devices.

[0023] Scenario setting: The cleanroom needs to receive a batch of fresh products with high moisture content. The operation time is estimated to be 30 minutes. The operation area is located in the loading and unloading buffer zone on the east side of the cleanroom. This buffer zone is a "background buffer zone" during non-operation periods. Forecasting and pre-adjustment: 15 minutes before the start of the planned operation, the system activates the forecasting mechanism based on the scheduling information. The forecasting module combines historical data of similar operations to predict that a high humidity load area will appear in the operation area. Pre-matching and resource preparation: The system assesses the current energy sequence: outdoor air humidity is high, natural cold sources are unavailable, and the central dehumidifier is operating at low load. The system decides to intervene with "local precision equipment". A mobile environmental conditioning robot with dehumidification function in the warehouse receives the scheduling instruction and moves in advance to the vicinity of the loading and unloading buffer to stand by. Dynamic response: After the operation begins, the sensor confirms that the humidity in the loading and unloading buffer zone rises rapidly. The system classifies it as a "dynamic adjustment zone". Since the mobile robot has been pre-matched, the system immediately instructs the robot to enter the edge of the work area, start the dehumidification function, and direct its air supply direction to the point of highest humidity. At the same time, the system slightly reduces the overall air supply temperature of the area to assist in dehumidification. Collaboration and Isolation: To prevent the diffusion of high-humidity air in the loading and unloading area, the system increases the air supply volume in the buffer zone between this area and the internal clean area to form an air curtain isolation. The load of the central dehumidification unit only increases slightly due to the local processing by the robot, avoiding a significant increase in the unit load to cope with local peaks. Operation completion and recovery: After the operation is completed and the humidity load disappears, the robot continues to work until the humidity in the area returns to the set value, and then returns to the charging station. The system gradually restores the air supply parameters of each area to normal.

[0024] As can be seen from the above two embodiments, the method of the present invention can effectively identify and distinguish between steady-state loads and dynamic disturbance loads in space, and intelligently match the most economical solution path for dynamic disturbance loads, thereby achieving the control objective of "on-demand allocation and precise supply" and balancing control accuracy and energy-saving requirements under complex operating conditions.

[0025] While embodiments of the invention have been shown and described, it will be understood by those skilled in the art that various changes, modifications, substitutions and alterations can be made to these embodiments without departing from the principles and spirit of the invention, the scope of which is defined by the appended claims and their equivalents.

Claims

1. A method for energy-saving control of cleanroom temperature and humidity regulation, characterized in that, Includes the following steps: S1. Real-time acquisition of temperature and humidity data, equipment operating status data, and outdoor weather data from multiple locations within the cleanroom; S2. Based on the indoor temperature and humidity data, and combined with the cleanroom space model, generate an indoor dynamic temperature and humidity load distribution cloud map; S3. Based on the dynamic temperature and humidity load distribution cloud map, the clean room is logically divided into at least two virtual areas with different control priorities. The virtual area includes at least one local adjustment area whose boundary and intensity are dynamically determined according to the real-time load. S4. Establish an energy cost sequence that includes multiple regulation methods, including the use of natural cold sources, the use of low-grade energy, the intervention of local regulation equipment, and the regulation of the central host. S5. Match the spatial location information and load intensity information of the local regulation zone with the supply characteristics of the currently available regulation means in the energy cost sequence to generate a priority regulation instruction for the local regulation zone. S6. Execute the priority control command to drive the actuator corresponding to the selected adjustment means to perform targeted environmental adjustment on the local adjustment area.

2. The cleanroom temperature and humidity regulation and energy-saving control method according to claim 1, characterized in that: In step S2, the indoor dynamic temperature and humidity load distribution cloud map is generated as follows: S21. Input the real-time collected discrete point sensor data into the preset cleanroom three-dimensional space model; S22. Calculate the estimated temperature and humidity values ​​of each grid point in the model using a spatial interpolation algorithm; S23. Combine the basic flow field data of computational fluid dynamics to generate a three-dimensional cloud map reflecting the degree of deviation of temperature and humidity from the set value and its spatial distribution.

3. The cleanroom temperature and humidity regulation and energy-saving control method according to claim 1, characterized in that: The step S3 of dividing the virtual region specifically includes: S31. Areas that are used for long-term storage of high-value items and are sensitive to temperature and humidity fluctuations should be designated as core protection zones. S32. In the dynamic temperature and humidity load distribution cloud map, the continuous spatial range where the temperature and humidity parameters exceed the preset threshold is identified and marked as the local adjustment zone in real time; S33. The remaining areas of the cleanroom, excluding the core protection area and the local adjustment area, serve as a background buffer zone.

4. The cleanroom temperature and humidity regulation and energy-saving control method according to claim 1, characterized in that: The establishment of the energy cost sequence in step S4 is specifically as follows: S41. Based on real-time outdoor meteorological data, determine the feasibility and expected effect of introducing fresh outdoor air for cooling or dehumidification, and use it as the first priority adjustment method. S42. Calculate the recoverable cooling capacity or waste heat of condensate based on the operating status of the air conditioning system and use it as the second priority adjustment method. S43. Use independently controllable local temperature and humidity control equipment as the third priority control method; S44. The central air conditioning refrigeration unit, dehumidifier and reheat device are designated as the fourth priority adjustment means.

5. The cleanroom temperature and humidity regulation and energy-saving control method according to claim 4, characterized in that: The matching rule in step S5 is as follows: First, determine whether the spatial location of the local adjustment zone is within the air supply range of the first priority adjustment means or the heat exchange influence range of the second priority adjustment means; If so, the corresponding low-cost adjustment method will be matched first and instructions will be generated; If not, determine whether the third priority adjustment means is in place, and generate equipment scheduling and start instructions; If none of the aforementioned methods are available or have insufficient capacity, then the fourth priority adjustment method is matched and a central host operating parameter adjustment instruction is generated.

6. The cleanroom temperature and humidity regulation and energy-saving control method according to claim 1, characterized in that: Between steps S1 and S2, there is also a prediction step: based on historical operation data, scheduling plans and real-time trends, predict the possible local temperature and humidity load changes in the cleanroom within a specific future time period. In step S5, the local adjustment zone is pre-matched based on the prediction results, and the corresponding adjustment measures are scheduled to enter the preparation state or start pre-adjustment in advance before the predicted load change occurs.

7. The cleanroom temperature and humidity regulation and energy-saving control method according to claim 4, characterized in that: In the feasibility assessment of the first priority adjustment method, outdoor air quality parameters are introduced as constraints. When the concentration of particulate matter or microorganisms in the outdoor air exceeds the allowable standard for the cleanroom, the adjustment method is deemed unavailable at present, even if the thermal parameters are suitable.

8. The cleanroom temperature and humidity regulation and energy-saving control method according to claim 1, characterized in that: In step S6, when performing directional adjustment for the local adjustment zone, the air supply or exhaust parameters of adjacent areas are adjusted simultaneously to maintain the design pressure difference between the cleanroom areas and prevent cross-contamination of air.

9. The cleanroom temperature and humidity regulation and energy-saving control method according to claim 1, characterized in that: The indoor data obtained in step S1 also includes positioning data for locating indoor mobile heat sources or moisture sources. In step S3, when dividing the local adjustment zone, the influence range of the moving heat source or moisture source is included in the calculation of the dynamic boundary by combining the positioning data.