Intelligent breathing curtain wall ventilation control system and energy-saving optimization method

Abstract

This application discloses an intelligent breathing-type curtain wall ventilation control system and energy-saving optimization method, including a main controller and several sets of curtain wall ventilation louver actuators. The main controller integrates a vertical layered and zoned sensing and acquisition module, an actuator hysteresis dynamic compensation module, a thresholdless transient smooth prediction and control module, and a start / stop micro-energy consumption adaptive suppression module. This application achieves differentiated monitoring of vertical layering in the breathing-type curtain wall cavity and independent ventilation control of each zone by setting up a vertical layered and zoned sensing and acquisition module. This effectively overcomes the technical defects of traditional single-point unified control methods, which cannot adapt to the vertical heat storage and layering characteristics of curtain walls and are prone to excessive ventilation and energy consumption in the upper part and insufficient ventilation and condensation in the lower part. It significantly improves the regional adaptation accuracy of curtain wall ventilation control and the accuracy of environmental parameter acquisition. By configuring the actuator hysteresis dynamic compensation module, dynamic advance correction can be made for the mechanical response hysteresis characteristics of ventilation louvers under different operating conditions.

Description

Technical Field

[0001] This application relates to the technical field, and in particular to an intelligent breathing curtain wall ventilation control system and energy-saving optimization method. Background Technology

[0002] Double-layer breathing curtain walls are widely used in energy-saving projects for the facades of public buildings due to their advantages of cavity insulation and natural ventilation. Existing intelligent curtain wall ventilation systems generally rely on temperature threshold logic to control the opening and closing of ventilation louvers, and rely on single-point environmental sensors to collect environmental parameters to uniformly manage the ventilation opening of the entire curtain wall.

[0003] Existing conventional control schemes have several inherent technical defects in practical engineering applications:

[0004] The environmental sensors adopt a single-point deployment mode, which does not take into account the heat storage and stratification characteristics formed by the vertical height of the breathing curtain wall cavity. The heat storage rate and heat dissipation conditions of the upper, middle and lower cavities of the curtain wall are significantly different. Unified ventilation control is prone to causing problems such as over-ventilation in the upper layer and insufficient ventilation in the lower layer, resulting in condensation and an imbalance in regional energy consumption distribution.

[0005] The existing control algorithm assumes that the opening degree issued by the controller is consistent with the actual opening degree of the louver, without considering the opening degree deviation caused by the gear back of the electric louver and the mechanical response lag of the motor. Under the conditions of frequent small temperature fluctuations and high-frequency fine-tuning of the controller during the spring and autumn transition seasons, the opening degree lag error continues to cause cold and hot air flow interference in the cavity, resulting in a large amount of ineffective heat exchange energy consumption.

[0006] The mainstream ventilation control system in the industry adopts a fixed temperature upper and lower limit start-stop logic. During the transition season, when the day and night temperatures rise and fall sharply and the set threshold is repeatedly crossed in a short period of time, the ventilation mechanism starts and stops frequently, and the opening degree opens and closes suddenly. The cavity temperature fluctuates continuously, which not only aggravates heat loss, but also shortens the service life of the actuator.

[0007] Current research on energy-saving optimization of curtain walls mainly focuses on optimizing the energy consumption of ventilation and heat exchange, generally ignoring the impact energy consumption of frequent start-stop of louver motors and the long-term standby power consumption of controllers. These micro-energy consumptions have small single-point losses and a high proportion of long-term cumulative energy consumption, becoming a key bottleneck restricting the further improvement of the overall energy-saving rate of curtain wall systems.

[0008] Therefore, an intelligent breathing-type curtain wall ventilation control system and energy-saving optimization method are proposed. Summary of the Invention

[0009] This application aims to at least partially solve one of the technical problems in the aforementioned technologies.

[0010] To achieve the above objectives, the first aspect of this application proposes an intelligent breathing curtain wall ventilation control system, including a main controller and several sets of curtain wall ventilation louver actuators. The main controller integrates a vertical layered and zoned sensing and acquisition module, an actuator hysteresis dynamic compensation module, a thresholdless transient smooth prediction and control module, and a start-stop micro-energy consumption adaptive suppression module.

[0011] The vertical layered zoning sensor acquisition module is used to divide the curtain wall cavity vertically into three independent control zones: upper, middle, and lower. It collects the temperature of each cavity and the micro-pressure difference between the inside and outside of the cavity in each zone, constructs a vertical layered temperature difference coupling model, and outputs the independent ventilation requirement parameters corresponding to each zone.

[0012] The actuator lag dynamic compensation module has a pre-stored database of nonlinear lag parameters of the actuator adapted to different working conditions. It can correct the timing of the control command output in advance according to the real-time target opening degree and environmental conditions, and compensate for the mechanical response lag error of the ventilation louver actuator.

[0013] The thresholdless transient smoothing prediction and control module eliminates the traditional fixed temperature start and stop threshold. It takes the rate of change of ambient temperature and the real-time heat storage margin of the cavity as the core control input, and outputs a stepless continuous opening control command with opening change slope constraint. Combined with the prediction of the thermal evolution trend of the cavity, it realizes the advanced fine-tuning control of the ventilation opening.

[0014] The start-stop micro-energy consumption adaptive suppression module is used to distinguish between the effective ventilation and heat exchange energy consumption of the system and the ineffective micro-energy consumption generated by the start-stop and standby of the actuator. Under the constraint of ensuring equivalent ventilation effect, it optimizes the action strategy of the actuator and triggers the low-power sleep mechanism of the main controller when the environmental parameters are in a small steady-state fluctuation state.

[0015] In addition, the intelligent breathing curtain wall ventilation control system proposed in this application may also have the following additional technical features:

[0016] As a further description of the above technical solution:

[0017] The vertically layered and partitioned sensing and acquisition module has temperature sensors and micro-differential pressure sensors independently installed in the upper, middle and lower control partitions. The sensing data collected by each partition is independently connected to the main controller to achieve accurate data acquisition in a layered and partitioned manner.

[0018] As a further description of the above technical solution:

[0019] The actuator hysteresis dynamic compensation module includes a working condition calibration unit and an adaptive correction unit. The working condition calibration unit is used to calibrate the actuator hysteresis time constants corresponding to different opening degrees and different ambient temperatures offline, and input the data into the parameter database to complete data iteration. The adaptive correction unit is used to fine-tune the working conditions for high frequency during the transition season, dynamically adapt and correct the hysteresis compensation coefficient.

[0020] As a further description of the above technical solution:

[0021] The thresholdless transient smoothing prediction and control module integrates a transient thermal condition identification unit, an opening slope limiting unit, and a short-term heat storage prediction unit. The transient thermal condition identification unit is used to identify and distinguish three types of transient conditions: gradual temperature change, sudden rise, and sudden drop. The opening slope limiting unit is used to limit the maximum opening change of the ventilation louvers per unit time. The short-term heat storage prediction unit is used to predict the temperature change trend of the cavity in the subsequent preset period.

[0022] As a further description of the above technical solution:

[0023] The start-stop micro-energy consumption adaptive suppression module includes an energy consumption splitting unit, an action frequency constraint unit, and a sleep control unit. The action frequency constraint unit is configured with an upper limit threshold for the opening and closing frequency of the actuator, and adopts a small opening degree long-term continuous ventilation mode to replace the short-term frequent opening and closing ventilation mode, thereby reducing the energy consumption of ineffective actions.

[0024] The second aspect of this application proposes a method for optimizing energy-saving ventilation in intelligent breathing curtain walls, including the following steps:

[0025] 1) Layered and zoned data acquisition: The temperature and micro-pressure difference data of each cavity are collected in different areas by the vertical layered and zoned sensing acquisition module, a vertical layered temperature difference coupling model is constructed, and the baseline parameters of ventilation requirements for each zone are generated.

[0026] 2) Command lag compensation preprocessing: Call the pre-stored actuator lag parameters to perform timing advance compensation on the original opening control command, and eliminate the opening control deviation caused by the mechanical response lag of the actuator;

[0027] 3) Threshold-free smooth prediction and control: The fixed temperature start and stop threshold is eliminated. The optimal louver opening is continuously calculated based on the real-time temperature change rate and the heat storage capacity of the cavity. The dynamic change rate of the opening is constrained, and the ventilation volume is adjusted in advance based on the prediction results of the cavity thermal state.

[0028] 4) Micro-energy consumption adaptive control: Separate and identify the effective ventilation energy consumption and ineffective micro-energy consumption of the system, constrain the frequency of opening and closing actions of the actuator, and start the controller's low-power sleep strategy when the environmental conditions are stable and there is no need for control.

[0029] The intelligent breathing-type curtain wall ventilation energy-saving optimization method proposed in this application may also have the following additional technical features:

[0030] As a further description of the above technical solution:

[0031] In step 1), based on the actual measured working conditions data, the heat storage coefficient and ventilation response delay parameters of each height partition cavity are quantitatively fitted to complete the parameter calibration and fitting of the vertical layered temperature difference coupling model.

[0032] As a further description of the above technical solution:

[0033] In step 2), the calibration test of the actuator lag time constant is completed offline under full opening range and all ambient temperature conditions, and a complete actuator lag parameter database is constructed.

[0034] As a further description of the above technical solution:

[0035] In step 3), a multi-level control gradient is divided according to the real-time temperature change rate. The degree of temperature fluctuation is negatively correlated with the opening change constraint limit, which suppresses the hot and cold flow in the cavity caused by the instantaneous large opening of the louver and reduces transient ineffective energy consumption.

[0036] Advantages of this invention:

[0037] According to the intelligent breathing curtain wall ventilation control system and energy-saving optimization method of this application, by setting up a vertical layered and zoned sensing and acquisition module, the system realizes differentiated monitoring of vertical layering of the breathing curtain wall cavity and independent ventilation control of each zone. This effectively overcomes the technical defects of traditional single-point unified control methods, which cannot adapt to the vertical heat storage and layering characteristics of the curtain wall, and are prone to excessive ventilation and energy consumption in the upper part and insufficient ventilation and condensation in the lower part. It significantly improves the regional adaptation accuracy of curtain wall ventilation control and the accuracy of environmental parameter acquisition. By configuring a dynamic compensation module for actuator lag, it can address the mechanical response lag of ventilation louvers under different operating conditions. The system dynamically and proactively corrects performance to offset execution deviations caused by gear backlash and motor response delays. This solves the problem of hot and cold airflow interference caused by the mismatch between command opening and actual opening during high-frequency fine-tuning conditions in the transition season, ensuring precise execution of control commands. By eliminating the traditional fixed temperature start-stop threshold and adopting thresholdless stepless smooth predictive control logic based on the rate of temperature change and the heat storage margin of the cavity, the system effectively avoids frequent start-stops, sudden changes in opening, and cavity temperature oscillations caused by transient temperature changes during the day and night in the transition season, significantly improving the operational stability of the curtain wall ventilation system and indoor thermal comfort.

[0038] By adding an adaptive suppression mechanism for micro-energy consumption during start-stop operation, the system accurately distinguishes between effective ventilation and heat exchange energy consumption and ineffective micro-energy consumption generated by actuator start-stop impact and controller standby. It replaces high-frequency start-stop actions with continuous ventilation at a small opening degree and is combined with a low-load sleep strategy, filling the technical gap of existing technologies that only optimize ventilation air volume energy consumption while ignoring micro-accumulated energy consumption. Without modifying the existing curtain wall main hardware structure, this invention upgrades the system from multiple dimensions such as sensing accuracy, execution reliability, control stability, and micro-energy consumption suppression, effectively reducing the overall operating energy consumption of the curtain wall ventilation system during the transition season, reducing mechanical wear of actuators, extending equipment lifespan, and reducing building operation and maintenance costs.

[0039] Additional aspects and advantages of this application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of this application. Attached Figure Description

[0040] The above and / or additional aspects and advantages of this application will become apparent and readily understood from the following description of the embodiments taken in conjunction with the accompanying drawings, wherein:

[0041] Figure 1 This is a schematic diagram of an intelligent breathing curtain wall ventilation control system and energy-saving optimization method according to an embodiment of this application;

[0042] Figure 2 This is a schematic diagram of an intelligent breathing curtain wall ventilation control system and energy-saving optimization method according to an embodiment of this application. Detailed Implementation

[0043] The embodiments of this application are described in detail below. Examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and intended to explain this application, and should not be construed as limiting this application.

[0044] The intelligent breathing curtain wall ventilation control system and energy-saving optimization method of this application embodiment are described below with reference to the accompanying drawings.

[0045] The intelligent breathing curtain wall ventilation control system of Embodiment 1 of this application includes a main controller and several sets of curtain wall ventilation louver actuators. The main controller integrates a vertical layered and zoned sensing and acquisition module, an actuator hysteresis dynamic compensation module, a thresholdless transient smooth prediction and control module, and a start-stop micro-energy consumption adaptive suppression module.

[0046] In this embodiment, the main controller is an STM32F429IGT6 industrial-grade embedded edge control unit, equipped with an RS485 industrial communication bus to realize data interaction with the partition sensors and three partition louver actuators. One independent main controller is configured for a single span curtain wall, and three independent controllers are set up for a three-span pilot curtain wall. Each controller performs local operation and control, and does not depend on the upper-level centralized computer room, avoiding the control error caused by the long-distance bus transmission delay.

[0047] The cavity temperature sensor uses an MF58-3950 NTC thermistor temperature probe with a temperature range of -30℃ to 80℃ and a temperature accuracy of ±0.3℃.

[0048] The differential pressure sensor inside and outside the cavity is an SDP3-500Pa digital differential pressure sensor with a range of -500Pa to +500Pa and a differential pressure acquisition accuracy of ±1Pa. All sensors are IP65 waterproof and dustproof encapsulated to adapt to the service environment of varying temperature and humidity inside the curtain wall cavity.

[0049] The ventilation louver actuator is an SK-2406 DC geared window opening motor with a rated operating current of 0.35A. The louver's mechanical full stroke is 0°~90° full opening. The motor's no-load response has an inherent mechanical lag range of 80ms~320ms. The lag time changes non-linearly under different opening load conditions, providing a measured calibration basis for the execution compensation module in this embodiment.

[0050] The vertically layered and zoned sensing module is deployed in this embodiment. Sensing points are independently deployed in three zones: upper, middle, and lower. Within each zone, two sets of temperature probes and one micro-differential pressure sensor are evenly distributed along the horizontal direction of the curtain wall. A total of six temperature sensors and three micro-differential pressure sensors are deployed per span of the curtain wall. The upper zone sensors are positioned at a vertical height of 9.2m within the cavity, the middle zone sensors at 5.4m, and the lower zone sensors at 1.8m. Sensing data from each zone is independently connected to the local main controller via an RS485 bus. The controller has a built-in layered temperature difference coupling model, and the model parameters are calibrated based on 72 hours of continuous steady-state measurement data from the field. After fitting, the heat storage coefficient of the upper zone cavity is 0.72 W / (m³・℃) with a ventilation response delay of 112 s; the heat storage coefficient of the middle zone cavity is 0.58 W / (m³・℃) with a ventilation response delay of 165 s; and the heat storage coefficient of the lower zone cavity is 0.41 W / (m³・℃) with a ventilation response delay of 238 s. These coefficients are fixed and written into the controller program. The controller calculates the independent ventilation demand parameters of each zone in real time based on the real-time collected temperature and pressure difference data of the three zones. It outputs a larger ventilation volume command for the upper zone with its fast heat storage and a smaller ventilation command for the lower zone with its easy condensation condition. This eliminates the regional operating condition imbalance caused by traditional single-point temperature measurement and unified control of the whole area from the hardware acquisition level.

[0051] The hardware and parameter configuration of the actuator lag dynamic compensation module are divided into a working condition calibration unit and an adaptive correction unit within the main controller program. The working condition calibration unit constructs a nonlinear lag parameter database based on a combination of offline laboratory calibration and on-site working condition retesting. The calibration working conditions are divided into 5 ambient temperatures (5℃, 12℃, 19℃, 26℃, 33℃) and 9 louver target openings (5%, 15%, 25%, 35%, 45%, 55%, 65%, 75%, 90%). A total of 45 sets of measured lag time constants are calculated for all working conditions. Among them, the motor lag is 316ms under the low temperature (5℃) small opening (5%) working condition, and 172ms under the normal temperature (19℃) medium opening (45%) working condition. At a temperature of 33℃ and a 90% opening, the motor lags by 84ms. All calibration values ​​are entered into the controller's built-in parameter database. The adaptive correction unit adds a dynamic compensation coefficient specifically for the high-frequency, small-amplitude fine-tuning conditions during the spring and autumn transition seasons. The system identifies the fine-tuning condition during the transition season if the actuator operates more than 12 times within 1 hour. The compensation coefficient is increased by 1.15 times based on the baseline lag time. When the controller outputs control commands, it retrieves the corresponding lag time based on the target opening and real-time ambient temperature, and sends the command timing ahead of the corresponding number of milliseconds to the actuator motor. This offsets the actual opening lag deviation caused by mechanical backlash and motor response delay, and solves the energy consumption problem caused by the inconsistency between the command opening and the actual opening.

[0052] The thresholdless transient smoothing prediction and control module program configuration integrates a transient thermal condition identification unit, an opening slope limiting unit, and a short-term heat storage prediction unit. The module completely eliminates the traditional 22℃ fixed start-stop temperature threshold, using a 1-minute sampling period to collect the temperature change rate dT / dt and the real-time heat storage margin of the cavity as control input parameters. The transient thermal condition identification unit sets three levels of condition judgment thresholds: a temperature change ≤0.3℃ per minute is judged as a slow-change condition; 0.3℃ < temperature change <1.2℃ per minute is judged as a gradual temperature rise / fall condition; and a temperature change ≥1.2℃ per minute is judged as a sudden temperature change condition. The opening slope limiting unit limits the louvers. The maximum opening rate of the louvers is set at 2% per second for gradual change, 1.2% per second for gradual change, and 0.5% per second for sudden change, with strict constraints preventing the louvers from opening too wide instantaneously. The short-term heat storage prediction unit uses a first-order differential prediction algorithm to predict the temperature change trend of the cavity over the next 15 minutes based on continuous 15-minute temperature acquisition data, and makes small adjustments to the louver opening in advance to achieve advanced fine-tuning control. This avoids the drawbacks of frequent start-stop cycles caused by traditional threshold control when the temperature exceeds the limit. The entire control logic is embedded into the main control hardware and can be automatically calculated and output continuously stepless opening commands locally without the need for an external host computer.

[0053] The system features an adaptive suppression module for micro-energy consumption during start-stop operation, incorporating an energy consumption splitting unit, an action frequency constraint unit, and a sleep management unit. The energy consumption splitting unit distinguishes between effective ventilation and heat exchange energy consumption and ineffective micro-energy consumption based on measured parameters. The system is calibrated to have a single motor start-stop impact energy consumption of 0.12Wh and a daily average standby power consumption of 1.8Wh for the controller. The action frequency constraint unit sets a daily upper limit threshold of 32 start-stop actions for the zone actuators. Under the premise of meeting the same ventilation volume, the system prioritizes a long-term continuous ventilation operation mode with a small opening of 5%~20% to replace the ventilation strategy of repeated start-stop operations in short periods. The sleep management unit sets sleep trigger conditions: when the temperature fluctuation range of the three zones is ≤±0.4℃ for 40 consecutive minutes and there is no need for ventilation control, the main controller automatically switches to a low-power sleep mode. In sleep mode, the controller power consumption is reduced from 1.2W during normal operation to 0.18W, cutting off unnecessary power supply to chip peripherals and suppressing hidden accumulated micro-energy consumption from the equipment operation side.

[0054] After 10 days of continuous no-load trial operation, the data interaction of each module was stable. The four functions of hierarchical acquisition, hysteresis compensation, smooth control and energy consumption suppression can all operate autonomously according to the design logic without any program crashes or sensor data errors. It is ready for long-term on-site use.

[0055] Example 2: Intelligent breathing curtain wall ventilation energy-saving optimization method. This example relies on the three-span pilot curtain wall system already assembled in Example 1 to carry out on-site process implementation, specifically including the following steps:

[0056] Step 1: Layered and zoned data acquisition and on-site fitting of the layered temperature difference coupling model. Continuous data acquisition throughout the entire cycle began on the first day of the experiment. Temperature and differential pressure sensors in each zone uploaded data to the local main controller in real time at a sampling frequency of 1 minute / time. The first 7 days were dedicated to fine-tuning the model parameters. Technicians manually compared the sensor readings with the actual ambient temperature inside the cavity three times daily (8:00 AM, 2:00 PM, and 8:00 PM) to correct for sensor zero-point offset errors. Based on 30,240 layers of layered environmental data accumulated over 7 × 24 hours, the heat storage coefficients and ventilation response delay parameters for the lower, middle, and upper layers were further fine-tuned. The final optimized parameters were: upper layer heat storage coefficient 0.71 W / (m³・℃), response delay 110 s, middle layer 0.57 W / (m³・℃). (m³・℃), response delay 163s, lower layer 0.40W / (m³・℃), response delay 235s. After the parameters are solidified, the controller outputs the independent ventilation demand benchmark parameters of the three zones in real time according to the layered model. During the test period, the average ventilation opening of the upper zone is maintained in the range of 18%~32%, the average opening of the middle zone is 12%~22%, and the average opening of the lower zone is 4%~11%, realizing differentiated ventilation of the upper, middle and lower zones. Compared with the control group, the entire curtain wall has a uniform opening (daily average opening of 15%~35%), which completely avoids the problem of excessive ventilation and heat dissipation in the upper layer and insufficient ventilation in the lower layer that causes condensation on the inner wall. During the 30-day test, there was no visible condensation in the lower cavity of the pilot curtain wall of this invention, while the lower glass inner wall of the control group showed condensation and water accumulation in the early morning multiple times.

[0057] Step 2: On-site retesting of actuator lag parameters and preprocessing of command compensation. Based on the offline calibration parameter database pre-stored in Example 1, on-site full-condition retesting and calibration were carried out 3 days before the test. The actual lag time of the motor corresponding to the 9 opening degrees was tested sequentially at three typical time periods: morning low temperature 12℃, midday normal temperature 20℃, and afternoon high temperature 25℃. The database was updated for 4 sets of operating condition parameters with a deviation of >5% between the on-site measured and offline calibration parameters. All updated parameters were stored in the controller storage unit. Before each output opening command, the system automatically retrieved the corresponding operating condition lag value and issued the drive signal ahead of the corresponding time to complete the timing compensation. During the 30-day test period, the difference between the actual louver opening and the controller target command opening was measured hourly for 10 days. According to statistics, the average opening deviation after compensation by this invention was ≤0.7%, while the average deviation between the command and the actual opening of the control group without lag compensation reached 7.3%, which greatly eliminated the air volume imbalance and ineffective energy consumption caused by mechanical lag.

[0058] Step 3: Threshold-free smooth prediction and control throughout the entire cycle. This step completely abandons fixed start-stop temperature thresholds. The system dynamically and continuously adjusts the louver opening daily based on the real-time temperature change rate and the cavity's heat storage capacity. It matches the corresponding opening change constraint slope according to three operating conditions: gradual change, incremental change, and sudden change. Simultaneously, it utilizes a 15-minute short-term prediction function to fine-tune the ventilation volume in advance. During the test period, there were 12 instances of sudden daily temperature changes (hourly temperature change > 1.2℃). In the control group, due to the temperature briefly crossing the 19℃~23℃ threshold range, the average daily louver start-stop time was 18~26 minutes. Secondly, the louvers frequently open and close dramatically. Under the same sudden weather changes, the control scheme of this invention achieves a gradual increase or decrease in the opening degree by relying on the opening slope limit. The average frequency of louver operation per day is only 3 to 7 times, with no instantaneous full closing or full opening. The temperature fluctuation range of the cavity is reduced from ±3.2℃ per day in the control group to ±0.9℃ per day in the pilot project of this invention, significantly improving indoor thermal comfort and avoiding heat exchange losses caused by temperature fluctuations. The prediction module finely adjusts the opening degree 15 minutes in advance each day according to the temperature trend. According to statistics, the daily ineffective ventilation time of a single span of curtain wall is reduced by about 112 minutes.

[0059] Step 4: Full-cycle micro-energy consumption adaptive management and control is implemented. The system separates effective ventilation energy consumption from ineffective micro-energy consumption during start-up, shutdown, and standby in real time. Based on the control rule of a maximum action frequency limit of 32 times / day, frequent start-up and shutdown are replaced by long-term ventilation with small opening. During the 30-day test period, the pilot of this invention had an average of 21.6 start-up and shutdown times per day for a single-span louver, which did not exceed the set threshold. The control group had an average of 79.3 start-up and shutdown times per day for a single-span louver. The sleep control strategy is frequently triggered during the stable ambient temperature period from 2:00 AM to 5:30 AM every day. During this period, the main controller automatically enters low power consumption mode. During hibernation, the average daily standby power consumption of the pilot controller decreased from the conventional 1.8Wh to 0.32Wh. After the test period, the energy consumption data were summarized: the total monthly power consumption of ventilation for a single-span curtain wall in the control group was 31.72kWh, while the total monthly power consumption of a single-span curtain wall using the optimization method of this invention was 19.35kWh, with a monthly comprehensive energy saving rate of 39.0%. Among them, the energy consumption of actuator start-stop impact was reduced by 61.2%, and the micro-energy consumption of controller standby was reduced by 82.2%. This not only achieves macro-level energy saving in ventilation and heat exchange, but also completes the refined suppression of ineffective energy consumption of micro-equipment.

[0060] This method has been validated over 30 days and fully realizes the coordinated operation of the entire chain, including layered precise data acquisition, hysteresis error compensation, threshold-free smooth control, and micro-energy consumption suppression. The measured energy-saving data, equipment operating frequency, and cavity temperature stability index are all superior to the traditional threshold control process. The entire method has clear steps and well-defined parameters, and can be directly replicated and applied to intelligent energy-saving renovation projects of similar breathing curtain walls.

[0061] In the description of this specification, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one of that feature. In the description of this application, "multiple" means at least two, such as two, three, etc., unless otherwise explicitly specified.

[0062] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, without contradiction, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples.

[0063] Although embodiments of this application have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting this application. Those skilled in the art can make changes, modifications, substitutions and variations to the above embodiments within the scope of this application.

Claims

1. An intelligent breathing curtain wall ventilation control system, comprising a master controller and a plurality of curtain wall ventilation shutter actuator groups, characterized in that: The main controller integrates a vertical layered and partitioned sensing and acquisition module, an actuator hysteresis dynamic compensation module, a thresholdless transient smooth prediction and control module, and a start-stop micro-energy consumption adaptive suppression module. The vertical layered zoning sensor acquisition module is used to divide the curtain wall cavity vertically into three independent control zones: upper, middle, and lower. It collects the temperature of each cavity and the micro-pressure difference between the inside and outside of the cavity in each zone, constructs a vertical layered temperature difference coupling model, and outputs the independent ventilation requirement parameters corresponding to each zone. The actuator lag dynamic compensation module has a pre-stored database of nonlinear lag parameters of the actuator adapted to different working conditions. It can correct the timing of the control command output in advance according to the real-time target opening degree and environmental conditions, and compensate for the mechanical response lag error of the ventilation louver actuator. The thresholdless transient smoothing prediction and control module eliminates the traditional fixed temperature start and stop threshold. It takes the rate of change of ambient temperature and the real-time heat storage margin of the cavity as the core control input, and outputs a stepless continuous opening control command with opening change slope constraint. Combined with the prediction of the thermal evolution trend of the cavity, it realizes the advanced fine-tuning control of the ventilation opening. The start-stop micro-energy consumption adaptive suppression module is used to distinguish between the effective ventilation and heat exchange energy consumption of the system and the ineffective micro-energy consumption generated by the start-stop and standby of the actuator. Under the constraint of ensuring equivalent ventilation effect, it optimizes the action strategy of the actuator and triggers the low-power sleep mechanism of the main controller when the environmental parameters are in a small steady-state fluctuation state.

2. The intelligent breathing-type curtain wall ventilation control system according to claim 1, characterized in that: The vertically layered and partitioned sensing and acquisition module has temperature sensors and micro-differential pressure sensors independently installed in the upper, middle and lower control partitions. The sensing data collected by each partition is independently connected to the main controller to achieve accurate data acquisition in a layered and partitioned manner.

3. The intelligent breathing-type curtain wall ventilation control system according to claim 1, characterized in that: The actuator hysteresis dynamic compensation module includes a working condition calibration unit and an adaptive correction unit. The working condition calibration unit is used to calibrate the actuator hysteresis time constants corresponding to different opening degrees and different ambient temperatures offline, and input the data into the parameter database to complete data iteration. The adaptive correction unit is used to fine-tune the working conditions for high frequency during the transition season, dynamically adapt and correct the hysteresis compensation coefficient.

4. The intelligent breathing-type curtain wall ventilation control system according to claim 1, characterized in that: The thresholdless transient smoothing prediction and control module integrates a transient thermal condition identification unit, an opening slope limiting unit, and a short-term heat storage prediction unit. The transient thermal condition identification unit is used to identify and distinguish three types of transient conditions: gradual temperature change, sudden rise, and sudden drop. The opening slope limiting unit is used to limit the maximum opening change of the ventilation louvers per unit time. The short-term heat storage prediction unit is used to predict the temperature change trend of the cavity in the subsequent preset period.

5. The intelligent breathing-type curtain wall ventilation control system according to claim 1, characterized in that: The start-stop micro-energy consumption adaptive suppression module includes an energy consumption splitting unit, an action frequency constraint unit, and a sleep control unit. The action frequency constraint unit is configured with an upper limit threshold for the opening and closing frequency of the actuator, and adopts a small opening degree long-term continuous ventilation mode to replace the short-term frequent opening and closing ventilation mode, thereby reducing the energy consumption of ineffective actions.

6. The intelligent breathing curtain wall ventilation energy-saving optimization method according to any one of claims 1-5, characterized in that, Includes the following steps: 1) Layered and zoned data acquisition: The temperature and micro-pressure difference data of each cavity are collected in different areas by the vertical layered and zoned sensing acquisition module, a vertical layered temperature difference coupling model is constructed, and the baseline parameters of ventilation requirements for each zone are generated. 2) Command lag compensation preprocessing: Call the pre-stored actuator lag parameters to perform timing advance compensation on the original opening control command, and eliminate the opening control deviation caused by the mechanical response lag of the actuator; 3) Threshold-free smooth prediction and control: The fixed temperature start and stop threshold is eliminated. The optimal louver opening is continuously calculated based on the real-time temperature change rate and the heat storage capacity of the cavity. The dynamic change rate of the opening is constrained, and the ventilation volume is adjusted in advance based on the prediction results of the cavity thermal state. 4) Micro-energy consumption adaptive control: Separate and identify the effective ventilation energy consumption and ineffective micro-energy consumption of the system, constrain the frequency of opening and closing actions of the actuator, and start the controller's low-power sleep strategy when the environmental conditions are stable and there is no need for control.

7. The intelligent breathing curtain wall ventilation energy-saving optimization method according to claim 6, characterized in that: In step 1), based on the actual measured working conditions data, the heat storage coefficient and ventilation response delay parameters of each height partition cavity are quantitatively fitted to complete the parameter calibration and fitting of the vertical layered temperature difference coupling model.

8. The intelligent breathing curtain wall ventilation energy-saving optimization method according to claim 6, characterized in that: In step 2), the calibration test of the actuator lag time constant is completed offline under full opening range and all ambient temperature conditions, and a complete actuator lag parameter database is constructed.

9. The intelligent breathing curtain wall ventilation energy-saving optimization method according to claim 6, characterized in that: In step 3), a multi-level control gradient is divided according to the real-time temperature change rate. The degree of temperature fluctuation is negatively correlated with the opening change constraint limit, which suppresses the hot and cold flow in the cavity caused by the instantaneous large opening of the louver and reduces transient ineffective energy consumption.

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