Intelligent Zoning Control BIPV Curtain Wall Comprehensive Utilization System

Abstract

The present invention discloses an intelligent partition control BIPV curtain wall comprehensive utilization system, which relates to the technical field of BIPV curtain walls. The system realizes continuous monitoring of condensation perception data by setting up a multi-dimensional perception module, arranging temperature and humidity, heat flux, and differential thermocouple sensor groups in each curtain wall area, and through a high-frequency data acquisition cycle of 5 seconds. At the same time, the central control processing module synchronizes, aligns, eliminates anomalies, and performs dimensionless normalization processing on the collected data in the central control system, calculates the humidity fluctuation amplitude ▽RH and the heat flux offset △Q in combination with the feature extraction unit, forms a standardized data set, and further calculates and outputs the dew point temperature Tdp of each partition. This process realizes the accurate data modeling of the air state at the partition level of the BIPV curtain wall and the construction of the basis for trend recognition, providing high-efficiency and high-precision data support for subsequent condensation potential analysis and response control.

Description

Technical Field

[0001] The present invention relates to the technical field of BIPV curtain wall, and specifically to an intelligent zoning-controlled BIPV curtain wall comprehensive utilization system. Background Art

[0002] Intelligent zoning-controlled BIPV curtain wall belongs to the field of BIPV curtain wall technology, specifically involving an intelligent temperature control, shading and power generation control technology applied to photovoltaic integrated curtain wall BIPV building structures, especially a multi-dimensional perception control system that can dynamically identify and link responses to the condensation trend and thermal inertia behavior of the curtain wall zoning level. Based on the "condensation potential" modeling, the system integrates dew point estimation, hygrothermal disturbance trend analysis and structural thermal response mechanism to construct a curtain wall comprehensive utilization and protection mechanism with predictive, zoning and structural coordination, which is mainly used to suppress condensation failures of curtain wall systems in high humidity environments, improve building thermal comfort and extend the service life of curtain wall ancillary equipment.

[0003] At present, in high-humidity or hot-cold alternating climate areas, the common condensation phenomenon in photovoltaic integrated curtain wall systems BIPV has become a key issue affecting its power generation efficiency, optical performance and internal structural durability. Existing BIPV curtain wall systems mostly rely on air-conditioning linkage or ventilation volume adjustment to adjust temperature and humidity. Its control method is mainly based on the absolute value of temperature judgment, lacks dynamic identification of the "imminent condensation" trend, and cannot respond in detail to thermal disturbances or microclimate changes in local partitions. In addition, the existing control system does not fully utilize the response capabilities of structural layers, such as laminated glass and air layers, resulting in the inability to form effective intervention in the early stages of condensation, resulting in hidden and sustainable condensation problems on the curtain wall surface or internal equipment area, affecting the system's operating safety and energy-saving effects. Summary of the invention

[0004] In view of the deficiencies in the prior art, the present invention provides an intelligent zoning-controlled BIPV curtain wall comprehensive utilization system, which solves the problems mentioned in the background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: including a multi-dimensional perception module, a central control processing module, a condensation potential identification module, a partitioned thermal inertia perception module and a comprehensive control decision module;

[0006] The multi-dimensional perception module sets a sensor group in each curtain wall area and sets a collection period to collect the condensation perception data of the curtain wall in real time, and at the same time builds a central control system to transmit the condensation perception data to the central control system;

[0007] The central control processing module receives condensation sensing data in real time in the central control system, performs preprocessing and feature extraction on the condensation sensing data to obtain a standardized data set, and calculates and outputs the dew point temperature Tdp of different areas based on the standardized data set;

[0008] The condensation potential identification module calculates and outputs the condensation potential index CPI based on the dew point temperature Tdp in combination with the dimensionless data set, and sets the first condensation threshold F1 and the second condensation threshold F2 to perform preliminary comparative evaluation and risk level classification with the condensation potential index CPI;

[0009] The partition thermal inertia perception module calculates and outputs the thermal inertia index HCI by analyzing the thermal hysteresis and thermal imbalance degree of the curtain wall partition;

[0010] The comprehensive control decision module extracts the condensation potential index CPI and the thermal inertia index HCI of each area, performs comprehensive calculation to output the response control function U of each curtain wall area, and performs hierarchical control based on the output result of the response control function U.

[0011] Preferably, the multidimensional perception module includes a multidimensional data acquisition unit and a multidimensional data transmission unit;

[0012] The multi-dimensional data acquisition unit collects condensation perception data of different curtain wall areas in real time by installing sensor groups in all curtain wall areas and setting the collection cycle of the sensor groups to collect data once every 5 seconds;

[0013] The sensor group includes a humidity sensor, a temperature sensor, a heat flow sensor and a differential thermocouple array;

[0014] The condensation sensing data includes temperature T, humidity RH, heat flux Q and thermal power P;

[0015] The multi-dimensional data transmission unit constructs a central control system of the BIPV curtain wall and wirelessly connects the communication module of each sensor in the sensor group to the central control system through local area network technology. After the wireless connection, the condensation perception data of different curtain wall areas collected in real time are transmitted to the central control system.

[0016] Preferably, the central control processing module includes a data processing unit, a feature extraction unit and a dew point temperature analysis unit;

[0017] The data processing unit receives the condensation sensing data in real time in the central control system and preprocesses the condensation sensing data, wherein the preprocessing includes data synchronization and timestamp unification, denoising and dimensionless processing;

[0018] The data synchronization and timestamp unification are based on the control period of the central control system for all condensation perception data, and the intermediate data are aligned by linear interpolation;

[0019] The denoising is performed by using sliding window Z-score anomaly detection to judge the outliers in the condensation perception data, and the outliers are replaced by the mean method to remove the outliers in the condensation number perception data;

[0020] The dimensionless processing is carried out by using the Min-Max normalization method to eliminate the influence of dimensions in the condensation perception data;

[0021] The feature extraction unit extracts features from the preprocessed condensation perception data, obtains the humidity fluctuation amplitude ▽RH and the heat flux offset △Q of each curtain wall area respectively, and summarizes the humidity fluctuation amplitude ▽RH and the heat flux offset △Q with the preprocessed condensation perception data to obtain a standardized data set;

[0022] The humidity fluctuation amplitude ▽RH is obtained by calculating the standard deviation by saving the current humidity RH and the historical values within a fixed time period time window;

[0023] The heat flux offset △Q is obtained by interpolation calculation through the current heat flux Q and the average heat flow at the past t-△t moment, where △t represents the time interval.

[0024] Preferably, the dew point temperature analysis unit constructs a dew point analysis formula, extracts the temperature T and humidity RH from the standardized data set, inputs them into the dew point analysis formula, and performs joint calculation to output the dew point temperature Tdp of each curtain wall area to analyze the dew point conditions of the air in different curtain wall areas.

[0025] Preferably, the condensation potential identification module includes a condensation potential analysis unit and a condensation potential evaluation unit;

[0026] The condensation potential analysis unit calculates the temperature change and humidity change per unit time respectively through the temperature T and humidity RH in the standardized data set, and then associates with the dew point temperature Tdp of each curtain wall area to perform correlation calculation to output the condensation potential index CPI to measure the air condensation conditions of each curtain wall area.

[0027] Preferably, the condensation potential evaluation unit sets the first condensation threshold F1 and the second condensation threshold F2 respectively by comparing the condensation potential index CPI of different curtain wall areas and analyzing the condensation potential index CPI before condensation occurs and when the condensation trend exists but condensation has not occurred. Then, the obtained condensation potential index CPI of each area is preliminarily compared and evaluated with the first condensation threshold F1 and the second condensation threshold F2, and the condensation risk level division value L of the i-th curtain wall area is output i , to judge the condensation risk of the curtain wall, and based on the preliminary comparison and evaluation results, conduct condensation risk level division. The specific evaluation content is as follows;

[0028] When the condensation potential index CPI of the i-th curtain wall area i ≤ the first condensation threshold F1, at this time, the condensation risk level division value L of the i-th curtain wall area i The output result is 0, indicating that there is no current condensation risk, and the current curtain wall area is marked as the safe level;

[0029] When the first condensation threshold F1 < the condensation potential index CPI of the i-th curtain wall area i ≤ the second condensation threshold F2, at this time, the condensation risk level division value L of the i-th curtain wall area i The output result is 1, indicating that the current is in a critical risk, and the current curtain wall area is marked as the warning area, and the acquisition period is adjusted to be acquired once every 2 seconds for continuous monitoring;

[0030] When the condensation potential index CPI of the i-th curtain wall area i > the second condensation threshold F2, at this time, the condensation risk level division value L of the i-th curtain wall area i The output result is 2, indicating that the current is in an abnormal risk, and the current curtain wall area is marked as the abnormal risk area. At this time, the partition thermal inertia perception module is executed for active thermal regulation and shading coordination.

[0031] Preferably, the partition thermal inertia perception module extracts the heat power P, temperature T, and humidity fluctuation amplitude ▽RH of different areas in the standard dataset, performs correlation calculation to output the thermal inertia index HCI, and analyzes the internal heat retention effect of all curtain wall areas due to the continuous heat generation of BIPV.

[0032] Preferably, the comprehensive control decision module includes a response analysis unit, a decision control unit, and a thermal buffer unit;

[0033] The response analysis unit extracts the condensation potential index CPI and the thermal inertia index HCI of the current curtain wall area, and jointly calculates and outputs the response control function U by combining the risk level division value L and the heat flux offset △Q of each curtain wall area.

[0034] Preferably, the decision control unit performs a secondary comparison and evaluation based on the output result of the response control function U, and performs hierarchical control based on the secondary comparison and evaluation result to execute different control behaviors. The specific evaluation content is as follows;

[0035] If the response control function U of the i-th curtain wall area i ∈[0, 1), at this time, the first-level control is executed on the current curtain wall area. The first-level control reduces the light transmittance of the curtain wall glass by 10% and reduces the heating efficiency of the BIPV module by 10%, and natural ventilation is assisted;

[0036] If the response control function U of the i-th curtain wall area i ∈[1, 2), at this time, the second-level control is executed on the current curtain wall area. The second-level control adjusts the angle of the sunshade device to 45°, controls the BIPV module to reduce by 30%, and mechanical air supply is assisted;

[0037] If the response control function U of the i-th curtain wall area i ∈[2, 3), at this time, the third-level control is executed on the current curtain wall area. After the third-level control is triggered, the ATFB active thermal buffer mechanism is started for structural heat conduction;

[0038] If the response control function U of the i-th curtain wall area i ∈[3, 4), at this time, the fourth-level control is executed on the current curtain wall area. After the fourth-level control is triggered, the current curtain wall area is completely closed, and full shading and power generation suspension are performed. The ATFB active thermal buffer mechanism switches to the maximum heat conduction mode, the alarm is marked as the condensation black area, and the user is notified to intervene for pre-maintenance.

[0039] Preferably, after starting the ATFB active thermal buffer mechanism, the thermal buffer unit performs an intelligent heat transfer response and automatically selects the heat conduction and heat insulation states;

[0040] The ATFB active thermal buffer mechanism includes a microchannel air interlayer, a phase change heat conductive material layer PCM, and a microstructural thermal valve plate array;

[0041] The microchannel air interlayer controls the air heat conduction and air adiabatic on-off by setting a sealed air layer with a thickness of 20 mm behind the curtain wall glass. At the same time, a microchannel network is designed inside the sealed air layer to guide the air flow and stasis;

[0042] The phase change heat conductive material layer PCM controls the condensation caused by sudden temperature changes by adding a phase change material layer with a thickness of 5 mm in the microchannel air interlayer;

[0043] The microstructured thermal valve sheet array is formed by uniformly arranging micro thermal response devices in the microchannel air interlayer. The thermal response devices include shape memory alloys and bimetallic thermal sheets. Based on the shape memory alloys and bimetallic thermal sheets, the microstructured thermal valve sheet array automatically responds to temperature changes and opens and closes the local thermal channel.

[0044] The present invention provides an intelligent partition control BIPV curtain wall comprehensive utilization system. It has the following beneficial effects:

[0045] (1) The system sets up a multi-dimensional perception module, deploys temperature and humidity, heat flow and differential thermocouple sensor groups in each curtain wall area, and realizes continuous monitoring of condensation perception data through a high-frequency data acquisition cycle of 5 seconds. At the same time, the central control processing module synchronizes, eliminates anomalies and performs dimensionless normalization processing on the collected data in the central control system, and combines the feature extraction unit to calculate the humidity fluctuation amplitude ▽RH and the heat flux offset △Q to form a standardized data set, and further calculates and outputs the dew point temperature Tdp of each partition. This process realizes the accurate data modeling and trend identification foundation of the air state at the partition level of the BIPV curtain wall, providing high-efficiency and high-precision data support for subsequent condensation potential analysis and response control.

[0046] (2) The system introduces a condensation potential index CPI calculation model based on temperature T, humidity RH and its change rate through the condensation potential identification module, integrates the dew point temperature Tdp, and constructs a dynamic evaluation parameter that reflects the "rate of change of air state towards condensation trend". Combined with the first condensation threshold F1 and the second condensation threshold F2 set by the condensation potential evaluation unit, the intelligent judgment of the condensation risk level can be realized. Further, through the partitioned thermal inertia perception module, the thermal power P, the current temperature T and the humidity fluctuation amplitude ▽RH are extracted to calculate the thermal inertia index HCI, which characterizes the internal heat retention phenomenon caused by the continuous heating of BIPV in the curtain wall. The dual modules work together to identify the "heat and moisture coupling critical state" in the curtain wall that is about to enter the condensation critical zone in advance without relying on the traditional air-conditioning system, and make intelligent judgments based on its evolution rate and heat accumulation trend, so as to achieve predictive and proactive optimization of partitioned thermal safety management.

[0047] (3) The system constructs a response control function U through a comprehensive control decision module, integrating the condensation potential index CPI, thermal inertia index HCI, risk level L and thermal disturbance term T·△Q, and uses the response smoothing factor Form a stable and controllable numerical output. The system executes a step-by-step response strategy from level one to level four based on the magnitude interval of the response control function U. When U ∈ [2, 3) or above, the system will activate the thermal buffer unit and initiate the ATFB active thermal buffer mechanism. This mechanism includes a microchannel air interlayer, a phase change heat conduction material layer PCM, and a microstructured thermal valve plate array, which can switch between heat conduction and heat insulation modes according to the thermal disturbance state. Compared with the traditional temperature regulation-based control method, this solution breakthroughly introduces the structural response dimension, uses the physical structure as the heat control response body to participate in the intelligent regulation process, effectively alleviates the condensation risk in the curtain wall area, reduces the probability of dew formation on the equipment surface, extends the service life of the BIPV system, and realizes comprehensive energy consumption optimization. Brief Description of the Drawings

[0048] Figure 1 It is a schematic flow chart of the intelligent partition control BIPV curtain wall comprehensive utilization system of the present invention;

[0049] Figure 2 It is a data flow and processing flow chart of the intelligent partition control BIPV curtain wall comprehensive utilization system of the present invention;

[0050] Figure 3 It is a time curve graph of the condensation potential index CPI of the present invention. Detailed Embodiments

[0051] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0052] Embodiment 1

[0053] Please refer to Figure 1 、 Figure 2 and Figure 3 The present invention provides an intelligent partition control BIPV curtain wall comprehensive utilization system. To achieve the above objectives, the present invention is realized through the following technical solutions: including a multi-dimensional perception module, a central control processing module, a condensation potential identification module, a partition thermal inertia perception module, and a comprehensive control decision module;

[0054] The multi-dimensional perception module sets a sensor group in each curtain wall area, sets a collection period to collect the condensation perception data of the curtain wall in real time, and constructs a central control system to transmit the condensation perception data to the central control system;

[0055] The central control processing module receives the condensation perception data in real time in the central control system, preprocesses the condensation perception data and extracts features to obtain a standardized data set, and calculates and outputs the dew point temperature Tdp of different regions based on the standardized data set;

[0056] The condensation potential identification module calculates and outputs the condensation potential index CPI by combining the dew point temperature Tdp with the dimensionless data set, and sets the first condensation threshold F1 and the second condensation threshold F2 to conduct a preliminary comparison evaluation and risk level division with the condensation potential index CPI;

[0057] The partition thermal inertia perception module calculates and outputs the thermal inertia index HCI by analyzing the thermal lag and thermal imbalance degree of the curtain wall partition;

[0058] The comprehensive control decision-making module extracts the condensation potential index CPI and the thermal inertia index HCI of each region, conducts comprehensive calculations to output the response control function U of each curtain wall region, and conducts hierarchical control based on the output results of the response control function U.

[0059] In this embodiment, the overall system is composed of a multi-dimensional perception module, a central control processing module, a condensation potential identification module, a partition thermal inertia perception module, and a comprehensive control decision-making module, constructing a curtain wall environment self-regulation system based on high-frequency perception, intelligent modeling, and multi-level response linkage. The system first arranges various environmental sensors in each curtain wall region through the multi-dimensional perception module, collects key parameters such as temperature T, humidity RH, and heat flux Q in real time, and completes data integration and transmission through the central control system; subsequently, the central control processing module preprocesses, standardizes, and extracts features from the collected raw data, calculates and outputs the dew point temperature Tdp of each region, and establishes a physical baseline for condensation trend judgment. The condensation potential identification module combines the dew point temperature Tdp with the temperature and humidity change rate, outputs the condensation potential index CPI, and divides the condensation risk level through double-threshold comparison; at the same time, the partition thermal inertia perception module calculates the thermal inertia index HCI through heat power, temperature offset, and humidity disturbance amplitude, and identifies the internal heat retention state caused by BIPV heating. Finally, the comprehensive control decision-making module fuses two types of indicators, CPI and HCI, combines the risk level and the thermal disturbance term, constructs the response control function U, and allocates multi-level control behaviors according to its value, from shading adjustment to structural thermal response, forming a closed-loop adjustment mechanism. Compared with the traditional passive air-conditioning linkage system based only on temperature or humidity, the present invention realizes the predictive identification of condensation trends, the structural guidance of thermal inertia responses, and the adaptive control of partition behaviors, with the advantages of high response accuracy, timely intervention, and rich regulation methods. The system significantly improves the anti-condensation ability and operation stability of the curtain wall, effectively extends the service life of curtain wall components, reduces the maintenance frequency, and at the same time improves the photovoltaic power generation efficiency and thermal energy management level.

[0060] Example 2

[0061] See also Figure 1 and Figure 2 ,Specifically: the multi-dimensional perception module includes a multi-dimensional data acquisition unit and a multi-dimensional data transmission unit;

[0062] The multi-dimensional data acquisition unit collects condensation perception data of different curtain wall areas in real time by installing sensor groups in all curtain wall areas and setting the collection cycle of the sensor groups to collect data every 5 seconds;

[0063] The sensor set includes a humidity sensor, a temperature sensor, a heat flow sensor, and a differential thermocouple array;

[0064] Condensation sensing data include temperature T, humidity RH, heat flux Q and thermal power P;

[0065] The multi-dimensional data transmission unit builds a central control system for the BIPV curtain wall and uses local area network technology to wirelessly connect the communication module of each sensor in the sensor group to the central control system. After the wireless connection, the condensation perception data of different curtain wall areas collected in real time is transmitted to the central control system.

[0066] In this embodiment, the multi-dimensional perception module of the system is composed of a multi-dimensional data acquisition unit and a multi-dimensional data transmission unit. Humidity sensors, temperature sensors, heat flow sensors and differential thermocouple arrays are deployed in all curtain wall areas to achieve high-frequency acquisition of key condensation perception data such as temperature T, humidity RH, heat flux Q and thermal power P every 5 seconds. The system builds a central control system through local area network technology, and uses the wireless communication module of the sensor to achieve real-time data transmission with the central processing node, ensuring the ability to collect heat and humidity data in multiple regions, with low latency and high synchronization. This module effectively solves the problems of single perception dimension, low acquisition frequency and high data transmission delay in the existing BIPV curtain wall system, and improves the ability to capture rapid fluctuations in microclimate and the interactive state of heat and humidity. Compared with the traditional method of relying only on indoor temperature and humidity monitoring points or centralized air conditioning parameters for judgment, this module realizes the real-time acquisition of multi-physical field linkage data at the curtain wall partition level, providing high-resolution and high-responsive basic data support for subsequent condensation potential analysis and thermal inertia identification. This technology significantly enhances the system's advance perception of condensation trends and improves the accuracy of early warning, providing data support for active intelligent heat and humidity control, and ultimately improving the overall environmental adaptability, operating efficiency and energy-saving level of the curtain wall structure.

[0067] Example 3

[0068] See also Figure 1 and Figure 2, specifically: the central control processing module includes a data processing unit, a feature extraction unit, and a dew point temperature analysis unit;

[0069] The data processing unit receives the condensation perception data in real time in the central control system and preprocesses the condensation perception data. The preprocessing includes data synchronization and timestamp unification, denoising, and dimensionless processing;

[0070] For data synchronization and timestamp unification, all the condensation perception data are unified based on the control period of the central control system, and the intermediate data are aligned by linear interpolation to ensure the consistency of data input at the same control moment;

[0071] For denoising, the sliding window Z-score anomaly detection is used to judge the outliers in the condensation perception data, and the outliers are replaced by the mean method to remove the outliers in the condensation perception data;

[0072] For dimensionless processing, the Min-Max normalization method is used to eliminate the influence of dimensions in the condensation perception data;

[0073] The feature extraction unit extracts features from the preprocessed condensation perception data, obtains the humidity fluctuation amplitude ▽RH and the heat flux offset △Q of each curtain wall area respectively, and summarizes the humidity fluctuation amplitude ▽RH and the heat flux offset △Q with the preprocessed condensation perception data to obtain a standardized data set;

[0074] Among them, the humidity fluctuation amplitude ▽RH and the heat flux offset △Q are calculated and obtained by eliminating two pieces of the preprocessed condensation perception data, that is, dimensionless parameters;

[0075] The humidity fluctuation amplitude ▽RH is obtained by calculating the standard deviation by saving the current humidity RH and the historical values within a fixed time period time window;

[0076] The heat flux offset △Q is obtained by interpolation calculation through the current heat flux Q and the average heat flow value at the past t-△t moment, where △t represents the time interval.

[0077] The dew point temperature analysis unit constructs a dew point analysis formula, extracts the temperature T and humidity RH in the standardized data set, inputs them into the dew point analysis formula, and performs joint calculation to output the dew point temperature Tdp of each curtain wall area, and analyzes the dew point conditions of the air in different curtain wall areas;

[0078] The dew point temperature Tdp is calculated and output through the following dew point analysis formula;

[0079] ;

[0080] In the formula, "ln" represents the natural logarithm function, "a" represents the slope control factor, which is the slope control factor for the sensitivity of the saturated water vapor pressure in the air to temperature, "b" represents the vapor constant of water, reflecting the critical curvature at which water vapor reaches saturation at a specific temperature point, and Tdp i represents the dew point temperature of the i-th curtain wall area, and T i represents the temperature of the i-th curtain wall area, and RH i represents the humidity of the i-th curtain wall area. Both "a" and "b" are dimensionless;

[0081] The physical meaning of the formula is that it maps the temperature T and humidity RH in the air together to the critical temperature at which the air reaches the saturation state.

[0082] In this embodiment, the system receives the condensation perception data from the multi-dimensional perception module through the central control system by the data processing unit, and performs data synchronization, anomaly elimination, and dimensionless processing on it. Through timestamp alignment and linear interpolation, the system realizes a unified data acquisition period between different sensors, ensuring the timeliness consistency of multi-source input within the same control cycle. With the help of the sliding window Z-score algorithm, the system can accurately identify and correct sudden outliers, significantly improving data stability and analysis reliability. Through Min-Max normalization, the system realizes the dimensionless processing of parameters, making the subsequent calculation model have strong generality and comparability. On this basis, the feature extraction unit extracts the humidity fluctuation amplitude ▽RH and the heat flux offset △Q of each curtain wall partition from the preprocessed data, constructs a standardized data set, and effectively captures the thermal and moisture disturbance trends in the curtain wall system. Further, the dew point temperature analysis unit inputs the standardized temperature T and humidity RH, and based on the dew point temperature calculation model with clear physical meaning, outputs the dew point temperature Tdp of each partition, thereby realizing the quantitative modeling of the critical state of air condensation. Compared with the traditional method of roughly judging the condensation risk by absolute temperature and humidity values, this module realizes a predictive analysis mechanism based on trend recognition and physical formula fusion, enabling the system to accurately identify whether a local area of the curtain wall is evolving towards the direction of condensation, and then deploying response measures in advance. Through this module, the system has been significantly improved in terms of data accuracy, processing stability, trend sensitivity, and modeling physicality, laying a high-quality foundation data and modeling ability for realizing multi-partition dynamic condensation trend recognition and intelligent linkage control, and further improving the operation intelligence level and environmental adaptability of the entire BIPV curtain wall system.

[0083] Embodiment 4

[0084] Please refer to Figure 2 and Figure 3 , specifically: The condensation potential identification module includes a condensation potential analysis unit and a condensation potential evaluation unit;

[0085] The condensation potential analysis unit standardizes the temperature T and humidity RH in the dataset, calculates the temperature change and humidity change per unit time respectively, and then combines with the dew point temperature Tdp of each curtain wall area to perform a correlation calculation to output the condensation potential index CPI, which is used to measure the air condensation situation of each curtain wall area;

[0086] The condensation potential index CPI is calculated and output through the following algorithm formula;

[0087] ;

[0088] In the formula, CPI i represents the condensation potential index of the i-th curtain wall area, k represents the weight factor of the temperature change rate, which controls the amplification effect of temperature change on the condensation risk, u represents the weight factor of the humidity change rate, which controls the amplification degree of humidity rise on the risk assessment, d represents the calculus variable, and dt represents the time calculus variable;

[0089] Among them, represents the distance between the current temperature and the dew point temperature. The smaller the difference, the greater the condensation risk;

[0090] represents the temperature change rate per unit time, represents whether the temperature is dropping rapidly. A rapid temperature drop amplifies CPI and is more likely to cause condensation;

[0091] represents the humidity change rate per unit time, represents whether the humidity is rising rapidly. An increase in moisture leads to faster saturation and amplifies CPI;

[0092] The physical meaning of the formula is a numerical index used to judge whether there is a risk of impending condensation in a certain curtain wall partition. Its essence is: combining the current temperature and humidity state T, RH at the spatial position with their change trends to form a dynamic response value, which is used to express whether the area is rapidly approaching the condensation point.

[0093] The condensation potential evaluation unit sets the first condensation threshold F1 and the second condensation threshold F2 respectively by comparing the condensation potential index CPI of different curtain wall areas and analyzing the condensation potential index CPI before the re-condensation occurs and when the condensation trend exists but has not occurred. That is, the condensation potential index CPI at the time of re-dew is set as the second condensation threshold F2, and the condensation potential index CPI at the critical dew is set as the first condensation threshold F1. Then, the condensation potential index CPI of each obtained area is preliminarily compared and evaluated with the first condensation threshold F1 and the second condensation threshold F2, and the condensation risk level division value L of the i-th curtain wall area is output i, determine the condensation risk of the curtain wall and classify the condensation risk level based on the preliminary comparison and evaluation results. The specific evaluation content is as follows;

[0094] When the condensation potential index CPI of the i-th curtain wall area i ≤ the first condensation threshold F1, at this time, the condensation risk level classification value L of the i-th curtain wall area i The output result is 0, indicating that there is no current condensation risk, and the current curtain wall area is marked as the safety level;

[0095] When the first condensation threshold F1 < the condensation potential index CPI of the i-th curtain wall area i ≤ the second condensation threshold F2, at this time, the condensation risk level classification value L of the i-th curtain wall area i The output result is 1, indicating that the current is in a critical risk, the current curtain wall area is marked as the warning area, and the acquisition period is adjusted to be acquired once every 2 seconds for continuous monitoring;

[0096] When the condensation potential index CPI of the i-th curtain wall area i > the second condensation threshold F2, at this time, the condensation risk level classification value L of the i-th curtain wall area i The output result is 2, indicating that the current is in an abnormal risk, the current curtain wall area is marked as the abnormal risk area, and at this time, the partition thermal inertia perception module is executed for active thermal regulation and shading coordination.

[0097] In this embodiment, the condensation potential recognition module of the system consists of a condensation potential analysis unit and a condensation potential evaluation unit. By modeling and calculating the exponential values of the dynamic relationships among the standardized temperature T, humidity RH, and dew point temperature Tdp of each curtain wall area, a predictive quantitative index, the condensation potential index CPI, is constructed. This module can capture the change rates of temperature and humidity parameters in each partition in real time and introduce a weighted correction mechanism for the temperature change rate and humidity increase amplitude, realizing an algorithm upgrade from absolute value perception to change trend perception. Compared with the traditional static method that relies on the over-limit judgment of single-point temperature and humidity to determine condensation, this module can give early warnings of the dynamic process in which the humid and hot state of the air rapidly approaches the condensation point, effectively enhancing the forward-looking judgment ability of the system. Through the first threshold F1 and the second threshold F2 set by the condensation potential evaluation unit, the system can discretize the CPI value into three categories of condensation risk levels, realizing the qualitative classification of the safety level, warning level, and abnormal level of each curtain wall area. Combining with the level feedback mechanism, the data acquisition frequency and control strategy can be dynamically adjusted. This method breaks through the technical limitation of the existing control strategy that only responds passively after condensation occurs, realizing the predictive response characteristic of "the system takes action before the risk becomes apparent". Finally, this module not only improves the recognition lead and accuracy of the system for potential condensation trends but also provides accurate, quantifiable, and traceable partition condensation risk input data for downstream thermal inertia analysis and response control, significantly enhancing the operation stability, anti-condensation ability, and intelligent linkage efficiency of the curtain wall system.

[0098] Embodiment 5

[0099] Please refer to Figure 1 and Figure 2 , specifically: The partition thermal inertia perception module extracts the thermal power P, temperature T, and humidity fluctuation amplitude ▽RH of different regions from the standard dataset, performs correlation calculations to output the thermal inertia index HCI, and analyzes the internal heat retention effect of all curtain wall areas due to the continuous heat generation of BIPV.

[0100] The thermal inertia index HCI is calculated and output through the following algorithm formula;

[0101] ;

[0102] In the formula, HCI i represents the thermal inertia index of the i-th curtain wall area, t0 represents the initial moment, P i (t) represents the BIPV thermal power of the i-th curtain wall area at time t, Tavg represents the historical average temperature from the initial moment t0 to time t, w1 represents the sensitivity of heat accumulation, w2 represents the temperature offset penalty coefficient, and w3 represents the humidity fluctuation amplification factor, all of which take dimensionless values;

[0103] represents the heat accumulation amount and represents the total time of heat energy accumulation;

[0104] It represents the difference between the current temperature T and the average temperature of the curtain wall area. If the current temperature T is much higher, it means that the heat has not dissipated yet, that is, the heat retention is serious; if it is lower than the average temperature, it indicates that the area is starting to cool down, which can be used as a basis for judging the natural cooling trend.

[0105] In this embodiment, the thermal inertia perception module of the system extracts the thermal power P, temperature T, and humidity fluctuation amplitude ▽RH of each curtain wall area from the standardized dataset, and calculates and outputs the thermal inertia index HCI based on the relationship between the cumulative heat and the current thermal state offset within a time period, for identifying and quantifying the local area heat retention effect caused by the continuous heat generation of BIPV components. This module not only considers the accumulation trend of the regional heat generation power, but also introduces the weighted coefficients of temperature offset and humidity disturbance factors, realizing the comprehensive coupling modeling of three factors: "energy accumulation", "thermal field imbalance", and "microclimate instability". By judging the deviation direction and intensity between the current temperature and the historical average temperature, the system can accurately judge whether the partition is in a state of un-recovered thermal imbalance or the start of natural cooling release. Compared with the traditional BIPV system that relies on fixed-time temperature sampling or surface temperature rise judgment, this module has the composite perception advantages of dynamic analysis + trend modeling + multi-factor regulation, breaking through the technical bottleneck of "being unable to identify the risk of thermal inertia residue" in the existing technology. In the case where the condensation potential is high and the heat retention has not been released, this module can intervene in time and serve as an important trigger basis for structural-level response control, effectively preventing the occurrence of lagging condensation caused by the un-dissipated internal waste heat. At the same time, this module provides the control system with a more accurate understanding of the partition thermal environment, supporting the system to perform more forward-looking and structurally adaptable shading, heat conduction, or energy management responses, significantly improving the heat control accuracy, response timeliness, and condensation suppression effect of the BIPV curtain wall system.

[0106] Embodiment 6

[0107] Please refer to Figure 1 and Figure 2 , specifically: The comprehensive control decision module includes a response analysis unit, a decision control unit, and a thermal buffer unit;

[0108] The response analysis unit extracts the condensation potential index CPI and the thermal inertia index HCI of the current curtain wall area, and jointly calculates and outputs the response control function U by combining the risk level division value L and the heat flux offset △Q of each curtain wall area;

[0109] The response control function U is calculated and output through the following algorithm formula;

[0110] ;

[0111] In the formula, U iIt represents the response control function of the i-th curtain wall area. It represents the response smoothing factor, which is used to map the input value to a continuous, stable, and controllable output range as the basis for system behavior decision-making. It represents the thermal disturbance linkage term. a1, a2, a3, and a4 respectively represent the condensation potential index CPI, the heat output inertia index HCI, the risk level division value L, and the weight coefficient of thermal disturbance linkage. Among them, a1 + a2 + a3 + a4 = 1, and its specific value is set by the user.

[0112] The decision control unit conducts a secondary comparison and evaluation based on the output result of the response control function U, and conducts hierarchical control based on the secondary comparison and evaluation result to execute different control behaviors. The specific evaluation content is as follows:

[0113] If the response control function U of the i-th curtain wall area i ∈[0, 1), at this time, the first-level control is executed for the current curtain wall area. The first-level control reduces the light transmittance of the curtain wall glass by 10%, reduces the heat generation efficiency of the BIPV module by 10%, and performs natural ventilation assistance.

[0114] If the response control function U of the i-th curtain wall area i ∈[1, 2), at this time, the second-level control is executed for the current curtain wall area. The second-level control adjusts the angle of the sunshade device to 45°, controls the BIPV module to reduce by 30%, and performs mechanical air supply assistance.

[0115] If the response control function U of the i-th curtain wall area i ∈[2, 3), at this time, the third-level control is executed for the current curtain wall area. After the third-level control is triggered, the ATFB active thermal buffer mechanism is started for structural heat conduction.

[0116] If the response control function U of the i-th curtain wall area i ∈[3, 4), at this time, the fourth-level control is executed for the current curtain wall area. After the fourth-level control is triggered, the current curtain wall area is completely closed, full shading and power generation suspension are performed, the ATFB active thermal buffer mechanism switches to the maximum heat conduction mode, the alarm is marked as the condensation black zone, and the user is notified to intervene for pre-maintenance.

[0117] After starting the ATFB active thermal buffer mechanism, the thermal buffer unit conducts intelligent heat transfer response and automatically selects the heat conduction and heat insulation states.

[0118] The ATFB active thermal buffer mechanism includes a microchannel air interlayer, a phase change heat conduction material layer PCM, and a microstructural thermal valve plate array.

[0119] The micro-channel air interlayer controls air heat conduction and air insulation by setting a 20mm thick closed air layer behind the curtain wall glass. At the same time, a micro-channel network, such as honeycomb and grid shapes, is designed inside the closed air layer to guide air flow and stillness.

[0120] Phase change thermal conductive material layer PCM controls condensation caused by sudden temperature changes by adding a 5mm thick phase change material layer, such as paraffin, PEG composite lipid and saline gel, into the microchannel air interlayer;

[0121] The microstructured thermal valve sheet array is achieved by evenly arranging micro thermal response devices in the microchannel air interlayer. The thermal response devices include shape memory alloys and bimetallic thermal sheets. Based on the shape memory alloys and bimetallic thermal sheets, they automatically respond to temperature changes and open and close local thermal channels.

[0122] In this embodiment, the comprehensive control decision module of the system is composed of a response analysis unit, a decision control unit and a thermal buffer unit, and a partition linkage intelligent response decision system for the BIPV curtain wall system is constructed. The response analysis unit integrates key parameters such as the condensation potential index CPI, the thermal inertia index HCI, the condensation risk level L and the thermal disturbance index △Q, and constructs the response control function U through weighted combination, and introduces the response smoothing factor to modulate the function output so that it remains in a stable, continuous and controllable range, as the logical basis for the hierarchical control behavior of the whole system. Compared with the traditional mode based on a single environmental threshold trigger, this method realizes multi-dimensional factor fusion and dynamic response output, and enhances the decision accuracy and environmental adaptability of the control logic. On the basis of the response control function U, the decision control unit sets the interval classification standard and executes a step-by-step control strategy from level one to level four, covering the whole process response mechanism such as mild shading, heating limit control, ventilation intervention, structural heat conduction and final condensation black area closure. In particular, in the hierarchical control, the system will link the thermal buffer unit to start the ATFB active thermal buffer mechanism, automatically select the thermal conduction or insulation state, and realize the structural level response behavior. ATFB is composed of a microchannel air interlayer, a phase change thermal conductive material layer PCM and a microstructured thermal valve array. It does not rely on traditional air conditioning or energy-consuming equipment, and can quickly adjust and guide the local heat flux state under severe thermal disturbance or condensation risk. It has the characteristics of structural self-control, independent partitioning, and passive activation. The introduction of this module enables the system to have a full-link intelligent closed-loop capability from parameter identification to trend modeling to hierarchical control, and then to structural response. It completely breaks through the limitations of the existing technology such as single response means, insufficient structural participation ability, and drastic control behavior jumps, and realizes the intelligent transition from "detection environment" to "control physics". Ultimately, it significantly improves the BIPV curtain wall system's resistance to condensation risks, the efficiency of eliminating thermal disturbances, and the level of coordinated optimization of overall energy efficiency, and has high scalability and practical engineering application value.

[0123] Although embodiments of the present invention have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention.

Claims

1. Intelligent partition control BIPV curtain wall comprehensive utilization system, characterized in that: It includes multi-dimensional perception module, central control processing module, condensation potential identification module, partition thermal inertia perception module and comprehensive control decision module; The multi-dimensional perception module sets a sensor group in each curtain wall area and sets a collection period to collect the condensation perception data of the curtain wall in real time, and at the same time builds a central control system to transmit the condensation perception data to the central control system; The central control processing module receives condensation sensing data in real time in the central control system, performs preprocessing and feature extraction on the condensation sensing data to obtain a standardized data set, and calculates and outputs the dew point temperature Tdp of different areas based on the standardized data set; The condensation potential identification module calculates and outputs the condensation potential index CPI based on the dew point temperature Tdp in combination with the dimensionless data set, and sets the first condensation threshold F1 and the second condensation threshold F2 to perform preliminary comparative evaluation and risk level classification with the condensation potential index CPI; The condensation potential identification module includes a condensation potential analysis unit and a condensation potential evaluation unit; The condensation potential evaluation unit sets the first condensation threshold F1 and the second condensation threshold F2 respectively by comparing the condensation potential index CPI of different curtain wall areas and analyzing the condensation potential index CPI before condensation occurs and during the condensation trend but before it occurs. Then, the obtained condensation potential index CPI of each area is preliminarily compared and evaluated with the first condensation threshold F1 and the second condensation threshold F2, and the condensation risk level division value L of the i-th curtain wall area is output i , to judge the condensation risk of the curtain wall and classify the condensation risk level based on the preliminary comparison and evaluation results. The specific evaluation content is as follows; When the condensation potential index CPI of the i-th curtain wall area i ≤ the first condensation threshold F1, at this time, the condensation risk level division value L of the i-th curtain wall area i The output result is 0, indicating that there is no current condensation risk, and the current curtain wall area is marked as the safe level; When the first condensation threshold F1 < the condensation potential index CPI of the i-th curtain wall area i ≤ the second condensation threshold F2, at this time, the condensation risk level division value L of the i-th curtain wall area i The output result is 1, indicating that the current is in a critical risk. Mark the current curtain wall area as a warning area, and adjust the collection period to collect once every 2 seconds for continuous monitoring; When the condensation potential index CPI of the i-th curtain wall area i > the second condensation threshold F2, at this time, the condensation risk level division value L of the i-th curtain wall area i The output result is 2, indicating that the current is in an abnormal risk. Mark the current curtain wall area as an abnormal risk area. At this time, execute the partition thermal relationship perception module to perform active thermal regulation and shading coordination; The partition thermal inertia perception module calculates and outputs the thermal inertia index HCI by analyzing the thermal hysteresis and thermal imbalance degree of the curtain wall partition; The comprehensive control decision module extracts the condensation potential index CPI and the heat inertia index HCI of each area, performs comprehensive calculation to output the response control function U of each curtain wall area, and performs hierarchical control based on the output result of the response control function U.

2. The intelligent partition-controlled BIPV curtain wall comprehensive utilization system according to claim 1, wherein: The multi-dimensional perception module includes a multi-dimensional data acquisition unit and a multi-dimensional data transmission unit; The multi-dimensional data acquisition unit collects condensation perception data of different curtain wall areas in real time by installing sensor groups in all curtain wall areas and setting the collection cycle of the sensor groups to collect data once every 5 seconds; The sensor group includes a humidity sensor, a temperature sensor, a heat flow sensor and a differential thermocouple array; The condensation sensing data includes temperature T, humidity RH, heat flux Q and thermal power P; The multi-dimensional data transmission unit constructs a central control system of the BIPV curtain wall and wirelessly connects the communication module of each sensor in the sensor group to the central control system through local area network technology. After the wireless connection, the condensation perception data of different curtain wall areas collected in real time are transmitted to the central control system.

3. The intelligent partition-controlled BIPV curtain wall comprehensive utilization system according to claim 1, wherein: The central control processing module includes a data processing unit, a feature extraction unit and a dew point temperature analysis unit; The data processing unit receives the condensation sensing data in real time in the central control system and preprocesses the condensation sensing data, wherein the preprocessing includes data synchronization and timestamp unification, denoising and dimensionless processing; The data synchronization and timestamp unification are achieved by unifying all condensation sensing data based on the control cycle of the central control system, and the intermediate data are aligned by linear interpolation; The denoising is performed by using a sliding window Z-score anomaly detection to determine the outliers in the condensation perception data, and the outliers are replaced by a mean method to remove the outliers in the condensation number perception data; The dimensionless processing is performed by using a Min-Max normalization method to eliminate the dimension effect in the condensation perception data; The feature extraction module extracts features from the preprocessed condensation perception data, obtains the humidity fluctuation amplitude ▽RH and the heat flux offset △Q of each curtain wall area respectively, and summarizes the humidity fluctuation amplitude ▽RH and the heat flux offset △Q with the preprocessed condensation perception data to obtain a standardized data set; The humidity fluctuation amplitude ▽RH is obtained by calculating the standard deviation by saving the current humidity RH and the historical values within the time window of a fixed time period; The heat flux offset △Q is obtained by interpolating the current heat flux Q and the average heat flow at the past t - △t moment, where △t represents the time interval.

4. The intelligent partition control BIPV curtain wall comprehensive utilization system according to claim 3, characterized in that: The dew point temperature analysis unit constructs a dew point analysis formula, extracts the temperature T and humidity RH from the standardized data set, inputs them into the dew point analysis formula, and performs joint calculation to output the dew point temperature Tdp of each curtain wall area, and analyzes the dew point conditions of the air in different curtain wall areas.

5. The intelligent partition-controlled BIPV curtain wall comprehensive utilization system according to claim 4, characterized in that: The condensation potential analysis unit calculates the temperature change and humidity change per unit time respectively through the temperature T and humidity RH in the standardized data set, and then combines with the dew point temperature Tdp of each curtain wall area to perform correlation calculation to output the condensation potential index CPI, and measures the air condensation conditions of each curtain wall area.

6. The intelligent partition-controlled BIPV curtain wall comprehensive utilization system according to claim 1, characterized in that: The partition heat pipe sensing module extracts the heat power P, temperature T and humidity fluctuation amplitude ▽RH of different areas from the standard data set, performs correlation calculation to output the heat inertia index HCI, and analyzes the internal heat retention effect due to the continuous heat generation of BIPV in all curtain wall areas.

7. The intelligent partition-controlled BIPV curtain wall comprehensive utilization system according to claim 6, wherein: The comprehensive control decision module includes a response analysis unit, a decision control unit and a heat buffer unit; The response analysis unit extracts the condensation potential index CPI and the heat inertia index HCI of the current curtain wall area, and combines with the risk level division value L and the heat flux offset △Q of each curtain wall area to perform joint calculation to output the response control function U.

8. The intelligent partition-controlled BIPV curtain wall comprehensive utilization system according to claim 7, characterized in that: The decision control unit performs a secondary comparison and evaluation based on the output result of the response control function U, and performs hierarchical control based on the secondary comparison and evaluation result to execute different control behaviors. The specific evaluation content is as follows; If the response control function U of the i-th curtain wall area i ∈[0, 1), at this time, primary control is performed on the current curtain wall area. The primary control is achieved by reducing the light transmittance of the curtain wall glass by 10%, reducing the heat generation efficiency of the BIPV module by 10%, and assisting with natural ventilation; If the response control function U of the i-th curtain wall area i ∈[1, 2), at this time, secondary control is performed on the current curtain wall area. The secondary control is achieved by adjusting the angle of the sunshade device to 45°, controlling the BIPV module to decrease by 30%, and assisting with mechanical ventilation; If the response control function U of the i-th curtain wall area i ∈[2, 3), at this time, three-level control is executed on the current curtain wall area. After the three-level control is triggered, the ATFB active thermal buffering mechanism is started for structural heat conduction; If the response control function U of the i-th curtain wall area i ∈[3, 4), at this time, four-level control is performed on the current curtain wall area. After the four-level control is triggered, the current curtain wall area is completely closed, full shading and power generation suspension are carried out, the ATFB active thermal buffering mechanism switches to the maximum heat conduction mode, the alarm is marked as the condensation black area, and the user is notified to intervene for pre-maintenance.

9. The intelligent partition-controlled BIPV curtain wall comprehensive utilization system according to claim 8, wherein: The heat buffer unit performs intelligent heat flux response and automatically selects the heat conduction and heat insulation states after starting the ATFB active heat buffer mechanism; The ATFB active heat buffer mechanism includes a microchannel air interlayer, a phase change heat conduction material layer PCM and a microstructured thermal valve array; The microchannel air interlayer controls the on-off of air heat conduction and air adiabaticity by setting a sealed air layer with a thickness of 20 mm behind the curtain wall glass. At the same time, a microchannel network is designed inside the sealed air layer to guide the air flow and stillness; The phase change heat conduction material layer PCM controls the condensation caused by sudden temperature changes by adding a phase change material layer with a thickness of 5 mm in the microchannel air interlayer; The microstructured thermal valve array evenly arranges micro thermal response devices in the microchannel air interlayer. The thermal response devices include shape memory alloys and bimetallic thermal sheets, and automatically respond to temperature changes based on the shape memory alloys and bimetallic thermal sheets to open and close local heat channels.

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