Intelligent regulation and control method and system for central air conditioner terminal environment comfort degree

Abstract

The invention discloses an intelligent regulation and control method and system for the environmental comfort degree of a central air conditioner terminal, and relates to the technical field of air conditioner regulation and control. The intelligent regulation and control method comprises the following steps that a pressure fluctuation track generated by aerodynamic disturbance is collected in the operation process of a draught fan, and the pressure fluctuation track is converted into energy spectrum distribution in a time sequence; establishing dynamic energy representation of the fan frequency; based on dynamic energy representation of fan frequency, a sound vibration field is projected in a spatial range, excitation response of inherent frequency is synchronously recorded through a room structure sensing array, and instantaneous energy coupling fingerprints of a room structure are formed. Through dynamic energy characterization and structural response coupling analysis, real-time identification and quantitative control of the standing wave noise risk are realized, a phase drift mechanism is introduced at a risk critical point, a fan disturbance phase is actively adjusted, standing wave superposition is effectively blocked, and acoustic safety and operation stability of a tail end environment are improved.

Description

Technical Field

[0001] This invention relates to the field of air conditioning control technology, specifically to an intelligent control method and system for the environmental comfort of central air conditioning terminals. Background Technology

[0002] The central air conditioning terminal environment refers to the localized air environment formed when air, after being processed by the central air conditioning unit's cooling and heating sources, finally enters the room through terminal devices such as fan coil units, diffusers, and air vents. It typically includes multiple parameters such as indoor temperature, humidity, air velocity, noise level, and air cleanliness, all of which collectively determine human comfort within the room. The role of intelligent control of the comfort of the central air conditioning terminal environment lies in: collecting real-time terminal environment data through sensors, combining it with human thermal comfort models and artificial intelligence algorithms to achieve dynamic adjustment of temperature, humidity, wind speed, and fresh air volume. This avoids problems such as uneven heating and cooling, humidity imbalance, or excessive energy consumption that occur with traditional control methods. Intelligent control not only improves the comfort of residents and office workers but also allows for personalized adjustments based on the needs of different groups and scenarios. Simultaneously, it reduces operating costs through energy efficiency optimization, achieving a balance between energy saving and comfort. This approach breaks through the limitations of relying on manual temperature settings or fixed operating modes, enabling the terminal environment to maintain stable comfort under dynamically changing outdoor climate and indoor load conditions, truly achieving harmonious adaptation between humans, machines, and the environment.

[0003] The existing technology has the following shortcomings: In existing technologies, when there is a sudden increase in indoor heating or cooling load, the operating frequency of the central air conditioning terminal fans often changes drastically within a short period of time. If this frequency transiently couples with the natural frequency of the room structure, it can easily induce local standing wave noise. This type of standing wave noise not only causes a sharp decline in environmental comfort but also generates continuous vibration and impact on the ceiling joists and their connecting components. Long-term stress accumulation may lead to structural loosening, and in severe cases, even cause the entire ceiling to collapse, posing a safety risk.

[0004] The information disclosed in the background section is only intended to enhance the understanding of the background of this disclosure, and therefore may include information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0005] The purpose of this invention is to provide an intelligent control method and system for the environmental comfort of central air conditioning terminals, so as to solve the problems in the background art mentioned above.

[0006] To achieve the above objectives, the present invention provides the following technical solution: an intelligent control method for the environmental comfort of central air conditioning terminals, comprising the following steps: During the operation of the wind turbine, the pressure fluctuation trajectory generated by aerodynamic disturbance is collected, and the pressure fluctuation trajectory is converted into an energy spectrum distribution in the time series to establish a dynamic energy characterization of the wind turbine frequency. Based on the dynamic energy characterization of the fan frequency, an acoustic vibration field is projected in the space and the excitation response of the natural frequency is synchronously recorded through the room structure sensing array, thereby forming the instantaneous energy coupling fingerprint of the room structure. Based on the instantaneous energy coupling fingerprint of the structure, phase drift analysis is introduced to synchronously compare the dynamic energy characterization of the wind turbine frequency with the structural response signal to generate a joint curve of frequency difference and phase deviation. In the process of the joint curve evolution of frequency difference and phase deviation, a nonlinear residual amplification mechanism is introduced to further enhance the coupling characteristics, and the critical point distribution of transient energy superposition effect is extracted from the enhanced coupling characteristics. Based on the critical point distribution of the transient energy superposition effect, a transient coupling response function is constructed, and the dynamic energy characterization of the wind turbine frequency is normalized and fitted with the structural response signal to calculate the transient coupling coefficient. When the transient coupling coefficient approaches the standing wave noise risk threshold, a phase drift mechanism is introduced based on the normalized fitting curve to make a slight adjustment to the phase angle of the fan airflow disturbance, so that the fan airflow disturbance and the structural response maintain a continuous phase deviation, thereby blocking the superposition of standing wave peaks.

[0007] Preferably, the dynamic energy characterization of the wind turbine frequency by collecting the pressure fluctuation trajectory generated by aerodynamic disturbances during wind turbine operation includes the following steps: Piezoelectric pressure sensors with a range of ±500Pa, a resolution better than 0.01Pa, and a sampling frequency of 20kHz are installed at the fan outlet, the bend in the fan coil unit's air supply duct, and the front end of the air outlet. After processing by a low-noise amplifier and an anti-aliasing filter, the data is digitally acquired by a 24-bit analog-to-digital converter to obtain a high-fidelity pressure fluctuation trajectory. The pressure fluctuation trajectory is stored as a time series signal with a step size of 1 millisecond, and an energy spectrum distribution map containing three-dimensional coordinates of time, frequency and energy is generated by combining short-time Fourier transform and Hilbert-Huang transform. By comparing the energy spectrum distribution map with the wind turbine speed signal, and by matching the wind turbine blade passing frequency and its higher harmonics, characteristic frequency components are identified, and the energy amplitude is normalized, thus obtaining a dynamic energy characterization curve that can reflect the changes in the wind turbine's operating state.

[0008] Preferably, the dynamic energy characterization based on the fan frequency projects an acoustic vibration field in the spatial range and forms an instantaneous energy coupling fingerprint of the room structure, including the following steps: The dynamic energy representation of the fan frequency is used as the sound source input. A composite sound field is projected into the room through the array loudspeaker set at the air supply duct outlet and the fan self-excited vibration in a dual-channel manner, so that the frequency and energy distribution of the sound field correspond to the dynamic energy representation of the fan. A sensor array consisting of a triaxial accelerometer and a capacitive sound pressure sensor was arranged at key locations in the room structure, and the excitation response at the natural frequency was recorded using a synchronous sampling clock trigger and a 10kHz sampling frequency. The structural response signal is mapped point by point to the dynamic energy characterization of the wind turbine, and a spatial mapping relationship is established through fast Fourier transform and phase correction. By fusing the intrinsic frequency response results from different spatial locations with dynamic energy characterization, an instantaneous energy coupling fingerprint in the form of a three-dimensional matrix with time, frequency, and energy as coordinates is generated.

[0009] Preferably, based on the instantaneous energy coupling fingerprint of the structure, phase drift analysis is introduced to generate a joint curve of frequency difference and phase deviation, including the following steps: The time series, frequency components and energy response intensity of the instantaneous energy coupling fingerprint of the structure are extracted and matched with the main frequency curve of the dynamic energy characterization of the wind turbine. The time series alignment is achieved by dynamic time warping and the sampling difference is compensated by interpolation and windowing. The aligned signal is subjected to Hilbert transform to obtain the instantaneous phase curve, and a continuous phase trajectory is formed by phase expansion and bandpass filtering. The phase trajectory is compared point by point with the dynamic energy characterization. The instantaneous phase difference and frequency difference are calculated and a joint curve is generated. The joint curve is fitted by a sliding window segment and an energy weighting factor is introduced to form a comprehensive risk index of energy-frequency-phase three parameters, which is used to identify the critical interval of standing wave noise and as input for subsequent processing.

[0010] Preferably, during the sliding window segmented fitting process, when the root mean square of the frequency difference and the standard deviation of the phase difference are both lower than the set threshold, and the instantaneous energy intensity at the corresponding point exceeds twice the average energy value, it is determined to be the critical interval of standing wave noise risk.

[0011] Preferably, the process of introducing a nonlinear residual amplification mechanism and extracting the critical point distribution of transient energy superposition effect during the evolution of the joint curve of frequency difference and phase deviation includes the following steps: The joint curve is divided into intervals on the time axis and the residual signal is obtained by polynomial fitting. The positive residual is cubically calculated, and the negative residual is superimposed by the absolute value and the square operation, thereby amplifying the nonlinear coupling characteristics and suppressing irrelevant noise. The residual signal after nonlinear amplification is superimposed with the original signal of the joint curve in a weighted manner. The weighting coefficients are adaptively adjusted according to the instantaneous energy intensity. Wavelet decomposition and reconstruction are performed on the enhanced coupled characteristic curve to highlight the nonlinear response during the energy concentration period. The enhanced coupling characteristic curve is analyzed by first and second derivatives. The effective inflection point is identified by combining the instantaneous energy threshold discrimination condition, thereby marking the critical point of the transient energy superposition effect and generating a critical point distribution map.

[0012] Preferably, constructing the transient coupled response function and calculating the transient coupling coefficient based on the critical point distribution of the transient energy superposition effect includes the following steps: The frequency difference, phase deviation, and energy intensity parameters in the critical point distribution are extracted and mapped to a three-dimensional coordinate system. Radial basis function interpolation is used to establish a continuous surface between each critical point to form the framework of the transient coupled response function. The function is normalized by normalizing the frequency difference, phase deviation and energy intensity to the interval between 0 and 1, and then weighted and fused according to the energy threshold results to obtain a unified normalized response function. The dynamic energy characterization of the wind turbine frequency and the structural response signal are compared point by point using the same normalization method. The transient coupling coefficient is calculated by multivariate regression fitting, and the goodness of fit is measured by the coefficient of determination R². The transient coupling coefficient is smoothed by time window and dynamically tracked. When it approaches the preset standing wave noise risk threshold, it is used as the trigger condition for subsequent phase drift control.

[0013] Preferably, when the transient coupling coefficient approaches the standing wave noise risk threshold, introducing a phase drift mechanism based on the normalized fitting curve includes the following steps: The dynamic curve of the transient coupling coefficient is monitored in real time. When it remains above 0.85 and approaches 0.9 within three consecutive time windows, the phase deviation information is extracted by normalizing the fitting curve. When the phase difference is less than 15°, the target phase angle Δφ is generated by combining the frequency difference and the instantaneous energy value. The target phase angle range is limited to within ±30°. Based on the target phase angle Δφ, a frequency correction signal is applied to the fan motor speed, causing the motor operating frequency to shift slightly by 0.5% to 1%. This is then smoothly adjusted within 100 to 200 milliseconds using a gradual control strategy, thereby achieving the cumulative drift of the fan airflow disturbance phase angle. After completing the phase adjustment, the phase difference is recalculated and multiple time windows are tracked. When the phase difference remains in the range of 20° to 40° and the transient coupling coefficient drops below 0.7, the phase drift is deemed effective. During the verification process, the air volume fluctuation is monitored to be less than 5% and the temperature fluctuation is less than 0.3 degrees Celsius to ensure stable comfort.

[0014] The intelligent control system for the environmental comfort of central air conditioning terminals includes a dynamic energy characterization module, an acoustic vibration projection acquisition module, a phase comparison and analysis module, a residual amplification and extraction module, a coupling function fitting module, and a phase drift control module. The dynamic energy characterization module collects the pressure fluctuation trajectory generated by aerodynamic disturbances during wind turbine operation, and converts the pressure fluctuation trajectory into an energy spectrum distribution in the time series to establish a dynamic energy characterization of wind turbine frequency. The acoustic vibration projection acquisition module projects an acoustic vibration field in the space based on the dynamic energy characterization of the fan frequency, and synchronously records the excitation response of the natural frequency through the room structure sensor array to form the instantaneous energy coupling fingerprint of the room structure. The phase comparison and analysis module introduces phase drift analysis on the basis of instantaneous energy coupling fingerprint, and performs synchronous phase comparison between the dynamic energy characterization of the wind turbine frequency and the structural response signal to generate a joint curve of frequency difference and phase deviation. The residual amplification extraction module introduces a nonlinear residual amplification mechanism during the evolution of the joint curve of frequency difference and phase deviation, further enhancing the coupling characteristics, and extracting the critical point distribution of transient energy superposition effect from the enhanced coupling characteristics. The coupling function fitting module constructs a transient coupling response function based on the critical point distribution of the transient energy superposition effect, and normalizes and fits the dynamic energy characterization of the wind turbine frequency with the structural response signal to calculate the transient coupling coefficient. The phase drift control module introduces a phase drift mechanism based on the normalized fitting curve when the transient coupling coefficient approaches the standing wave noise risk threshold. This mechanism makes a slight adjustment to the phase angle of the fan airflow disturbance, ensuring that the fan airflow disturbance and the structural response maintain a continuous phase deviation.

[0015] The technical effects and advantages provided by the present invention in the above technical solution are as follows: This invention achieves precise coupling identification of wind turbine operating disturbances and building structure acoustic and vibration characteristics by constructing a dynamic energy characterization of wind turbine frequency and performing real-time acquisition and analysis of the structure's natural frequency response within a spatial range. By introducing phase drift analysis and nonlinear residual amplification mechanisms, it can not only identify potential standing wave superposition trends in advance but also extract the transient evolution characteristics of coupled energy with high sensitivity. This allows for real-time quantification of the complex coupling relationship between wind turbine operating status and room structural response, greatly improving the monitorability and predictability of terminal environmental acoustic safety.

[0016] This invention, at a critical juncture where the transient coupling coefficient approaches the risk threshold, introduces a phase drift control strategy based on a normalized fitting curve. This enables dynamic, micro-adjustment of the phase angle of the fan airflow disturbance, thereby breaking the condition of in-phase and in-frequency operation at the physical level and effectively blocking the superposition of standing wave peaks. This proactive intervention mechanism not only possesses high real-time performance and flexible control capabilities but also significantly suppresses the formation of structural standing wave responses while ensuring stable airflow. This avoids fatigue failure and loosening caused by long-cycle vibration of ceiling components, improving the operational stability and long-term safety of the central air conditioning terminal environment. Attached Figure Description

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or the prior art, the drawings used in the embodiments will be briefly introduced below. Obviously, the drawings described below are only some embodiments recorded in this invention. For those skilled in the art, other drawings can be obtained based on these drawings.

[0018] Figure 1 This is a flowchart of the intelligent control method for environmental comfort at the central air conditioning terminal of the present invention.

[0019] Figure 2 This is a schematic diagram of the modules of the intelligent control system for the environmental comfort of the central air conditioning terminal of the present invention. Detailed Implementation

[0020] Exemplary embodiments will now be described more fully with reference to the accompanying drawings. However, these exemplary embodiments can be implemented in many forms and should not be construed as limited to the examples set forth herein; rather, they are provided so that the description of this disclosure will be more complete and fully convey the concept of the exemplary embodiments to those skilled in the art.

[0021] This invention provides, for example Figure 1 The intelligent control method for the environmental comfort of the central air conditioning terminal unit, as shown, includes the following steps: During the operation of the wind turbine, the pressure fluctuation trajectory generated by aerodynamic disturbance is collected, and the pressure fluctuation trajectory is converted into an energy spectrum distribution in the time series to establish a dynamic energy characterization of the wind turbine frequency. To accurately establish a dynamic energy characterization of the wind turbine frequency in response to the aerodynamic disturbance and pressure fluctuations generated during wind turbine operation, the following steps are specifically included: High-sensitivity dynamic pressure sensors are installed at the fan outlet, the bends in the fan coil unit's air supply duct, and the front end of the air outlet. The pressure sensors are piezoelectric sensors with a range of ±500Pa and a resolution better than 0.01Pa. The sampling frequency of the sensors is set to 20kHz to ensure that pressure fluctuation signals within a range up to 10kHz during fan operation can be captured. Each sensor is directly mounted on the inner wall of the air supply duct using a stainless steel mounting bracket. The mounting points are distributed at three different distances along the airflow direction: 0.5 meters, 1.5 meters, and 3 meters, to comprehensively record the propagation process of airflow disturbances in space. During data acquisition, the voltage signal output from the pressure sensor is first amplified to a standard ±5V level by a low-noise amplifier, then processed by an anti-aliasing filter to remove stray components above the Nyquist frequency, and finally sent to a 24-bit analog-to-digital converter for digital acquisition. To avoid interference from environmental electromagnetic noise, all signal acquisition lines use twisted-pair shielded cables, and an active filter is added at the power supply end to suppress power frequency interference. Through this series of acquisition and preprocessing steps, high-fidelity aerodynamic disturbance pressure fluctuation trajectories are obtained, providing stable input for subsequent time series analysis.

[0022] The preprocessed pressure fluctuation trajectory data is stored in the computer in 1-millisecond steps to form a continuous time series signal. To avoid aliasing during time-domain sampling, the sampling frequency is strictly set to more than twice the highest frequency component of the signal, i.e., maintained at 20kHz. When processing the time series signal, the Short Time Fourier Transform (STFT) method is first used to divide the time series into window function segments of length 2048 points, with a 50% overlap between each segment to ensure a balance between frequency and time resolution. For each segment, the Fast Fourier Transform (FFT) is used to calculate its spectral distribution, and then the energy intensity of each frequency component is obtained through the squared energy integral. After completing the STFT, to capture the instantaneous characteristics of the non-stationary signal, the Hilbert-Huang Transform (HHT) is further applied to the time series signal. Empirical Mode Decomposition (EMD) is used to decompose the original signal into a series of intrinsic mode functions (EMFs), and then the Hilbert Transform is applied to each EMF to obtain the two-dimensional distribution of instantaneous frequency and instantaneous energy. Finally, the global frequency energy distribution obtained from the short-time Fourier transform is combined with the instantaneous energy distribution obtained from the Hilbert-Huang transform to generate an energy spectrum distribution map containing three-dimensional coordinates of time, frequency, and energy. This map can intuitively display the dominant frequency components of the wind turbine at different time periods and their corresponding energy intensities, thus comprehensively revealing the time-frequency evolution law of pressure fluctuations.

[0023] After obtaining the energy spectrum distribution map, the frequency axis of the energy spectrum is compared with the real-time speed data of the fan. The fan speed is acquired in real time by a Hall sensor installed at the motor shaft end, and the speed signal is transmitted to the data processing unit at a frequency of 1kHz. By matching the frequency of the fan blades (obtained by multiplying the speed by the number of blades) with the peak value of the dominant frequency in the energy spectrum, the characteristic frequency components caused by the fan operation can be identified. Furthermore, the harmonic components in the energy spectrum are compared with the higher-order harmonics of the fan operation to establish a mapping relationship between the fan operating state and the pressure disturbance energy. After completing the frequency correspondence, the amplitude in the energy spectrum is normalized, scaling the energy amplitude under different operating conditions to between 0 and 1 according to the maximum amplitude ratio, so that the energy intensity under different times and different loads is comparable. By continuously tracking the normalized energy curve, a dynamic energy characterization curve of the fan frequency changing with time can be obtained. This curve shows a periodic distribution when the fan is running smoothly, while a significant energy peak drift occurs when the indoor heating and cooling loads suddenly increase. This dynamic energy characterization can not only reflect the frequency response characteristics of the wind turbine under normal operation, but also accurately capture the abnormal energy fluctuations during sudden load changes, providing a complete and reliable input data basis for subsequent acoustic and vibration field projection and transient coupling fingerprint formation.

[0024] Through the above steps, it is possible to accurately acquire the trajectory of aerodynamic disturbance pressure fluctuations during wind turbine operation, perform multi-dimensional time-frequency analysis of the signal and generate an energy spectrum distribution map, and form a dynamic energy characterization curve by combining wind turbine speed and blade parameters. This implementation not only achieves quantitative and dynamic characterization of pressure disturbance energy, but also provides sufficient prior data support for the prediction and phase control of standing wave noise.

[0025] Based on the dynamic energy characterization of the fan frequency, an acoustic vibration field is projected in the space and the excitation response of the natural frequency is synchronously recorded through the room structure sensing array, thereby forming the instantaneous energy coupling fingerprint of the room structure. To further apply the dynamic energy characterization of the fan frequency to the acoustic and vibration environment within a spatial range, and to accurately record the excitation response of the room structure at its natural frequencies, in order to form a representative instantaneous energy coupling fingerprint of the structure, the process includes the following steps: Using the dynamic energy characterization of the fan frequency established in the previous step as the sound source input, a sound vibration field with a specific frequency and energy distribution is projected into the room space through a dual-channel method of array loudspeakers installed at the air outlet and the fan's self-excited vibration. During implementation, the array loudspeakers are evenly distributed around the air outlet, and their frequency output is modulated in real time by a digital signal processor to ensure that their frequency and energy distribution perfectly correspond to the characteristic curves in the dynamic energy characterization. Simultaneously, the aerodynamic disturbance signal generated by the fan blade rotation is also used as the initial input, superimposed on the loudspeaker output within the space to form a composite sound vibration field highly consistent with the actual operating state, ensuring that the spatial projection results accurately reflect the fan's operating characteristics.

[0026] Sensor arrays were deployed at key locations in the room structure to synchronously record the excitation response at the natural frequencies. The sensor arrays consisted of a combination of high-sensitivity triaxial accelerometers and broadband capacitive sound pressure sensors. Accelerometers were placed at the intersections of ceiling joists, wall-to-beam connections, and load-bearing nodes around air vents to capture structural vibration signals. Sound pressure sensors were placed in room corners, the center, and areas with high foot traffic to record the sound pressure intensity distribution during standing wave formation. During data acquisition, all sensors were triggered by a synchronous sampling clock, with the sampling frequency set to 10kHz to ensure compatibility with the frequency range of the dynamic energy characterization. The sensor data was amplified with low noise and filtered against anti-aliasing before being transmitted in real-time to the central processing unit for comparison with the fan frequency energy characterization.

[0027] The structural response signals acquired by the sensor array are mapped point-by-point to the dynamic energy characterization of the wind turbine frequency, forming a spatial mapping relationship through frequency matching and phase correction. Specifically, a Fast Fourier Transform (FFT) is first performed on the sensor-acquired signals to obtain the natural frequency excitation spectra at different spatial locations. These spectra are then compared with the characteristic frequency components in the dynamic energy characterization to determine at what frequency the structure is strongly excited. To improve the comparison accuracy, a phase correction method is introduced. By comparing the phase difference between the sensor signals and the dynamic energy characterization signals, time delay compensation is applied to the sampled data, allowing the response signals at different locations to be superimposed and analyzed under a unified reference frame. Through this process, a precise coupling mapping between wind turbine energy and structural response can be established within a spatial range.

[0028] After constructing the spatial mapping relationship, the natural frequency response results recorded at different spatial locations are fused with the dynamic energy characterization to generate the instantaneous energy coupling fingerprint of the structure. This coupling fingerprint is stored in the form of a three-dimensional matrix, where the horizontal axis represents the fan operating time, the vertical axis represents the frequency components, and the vertical axis represents the corresponding structural energy response intensity. This fingerprint matrix visually presents the transient coupling process between fan frequency changes and the natural frequencies of the room structure, and can identify dangerous coupling points with concentrated energy and consistent phase. This coupling fingerprint not only preserves the dynamic characteristics of fan operation but also includes the synchronous response of the spatial structure at multiple locations, thus providing a complete input data foundation for subsequent phase drift analysis and nonlinear residual amplification processing.

[0029] Through the implementation of the above steps, the dynamic energy characterization of the wind turbine frequency was projected in the spatial range. By using multi-point synchronous recording of the room structure sensor array, an energy fingerprint that can reflect the instantaneous acoustic-vibration coupling state was generated. This not only revealed the coupling law between wind turbine operation and structural response, but also laid the foundation for suppressing the superposition of standing wave peaks, improving indoor environmental comfort, and reducing structural risks.

[0030] Based on the instantaneous energy coupling fingerprint of the structure, phase drift analysis is introduced to synchronously compare the dynamic energy characterization of the wind turbine frequency with the structural response signal to generate a joint curve of frequency difference and phase deviation. Based on the instantaneous energy coupling fingerprint of the structure, the interaction between the dynamic energy characterization of the wind turbine frequency and the room structural response signal is further revealed, and the joint curve of frequency difference and phase deviation is generated by phase drift analysis. This process includes the following steps: Based on the established structural instantaneous energy coupling fingerprint, the time series, frequency components, and corresponding energy response intensities in the fingerprint matrix are extracted one by one and matched with the dominant frequency curve in the wind turbine's dynamic energy characterization. To ensure matching accuracy, dynamic time warping is first used to align the two sets of data in time, ensuring that the time nodes in the wind turbine energy characterization strictly correspond to the time points in the structural fingerprint matrix. During this process, interpolation is used to compensate for sampling frequency differences, and a windowing function is employed to avoid energy distortion introduced by boundary effects. In this way, a one-to-one correspondence between the wind turbine energy signal and the structural response signal can be established in the time dimension, ensuring that subsequent phase drift analysis has a unified reference coordinate.

[0031] After time alignment, phase expansion processing is performed simultaneously on the wind turbine frequency dynamic energy characterization and structural response signals. Specifically, the two sets of time-domain signals are first converted into analytic signals using Hilbert transform. Instantaneous phase information is extracted from the analytic signals, and the instantaneous phase curves are expanded over the entire time series. To avoid discontinuities caused by phase jumps, the phase values ​​are expanded point-by-point, and phase values ​​exceeding ±π are cumulatively corrected to form a continuous and monotonic phase trajectory curve. In this process, each frequency component corresponds to an independent phase curve, and the wind turbine energy characterization and structural response each form multiple phase trajectories. Subsequently, bandpass filtering is applied to the phase curves to filter out high-frequency phase jitter unrelated to wind turbine operation, thereby retaining the main phase information that reflects the acoustic-vibration coupling characteristics. This step ensures that the phase characteristics of both the wind turbine signal and the structural signal are expanded under the same time reference, creating conditions for the difference analysis between the two.

[0032] After obtaining the phase trajectories of the wind turbine signal and the structural signal, a point-by-point phase comparison is performed at the same frequency to obtain an instantaneous phase difference sequence. Simultaneously, the frequency curve in the dynamic energy characterization is compared with the resonant frequency in the structural fingerprint matrix to calculate the instantaneous frequency difference. To improve the accuracy of the analysis, a synchronous alignment method is adopted, whereby the phase difference and frequency difference are calculated simultaneously at each time point, and the result is recorded with that time point as the abscissa. This synchronous processing generates a three-dimensional evolution surface plot with time as the abscissa, frequency difference as the ordinate, and phase difference as the color intensity. In this surface plot, it can be visually observed that in certain time periods, the wind turbine signal and structural response not only have similar frequencies but also a phase difference approaching zero. This indicates extremely high acoustic-vibration coupling, which may lead to an increased risk of standing wave noise. To enhance the identification effect, the phase difference and frequency difference are normalized to the interval between 0 and 1, and a joint curve is formed in the same coordinate system. This joint curve reflects both the degree of frequency matching and the degree of phase synchronization, serving as an important basis for determining the transient coupling strength.

[0033] After generating the joint curve of frequency difference and phase deviation, an evolution analysis is performed on the curve to identify potential dangerous coupling points. Specifically, a sliding window method is used to fit the joint curve piecewise. Within each window, the root mean square of the frequency difference and the standard deviation of the phase difference are calculated. When both are below a set threshold, high-intensity in-phase coupling is determined to exist within that time period. To further quantify the risk level, instantaneous energy intensity is superimposed on the vertical axis of the joint curve as a weighting factor. This ensures that each point on the curve includes not only information on frequency difference and phase deviation but also the weight of energy concentration, thus forming a comprehensive risk index with three parameters: energy, frequency, and phase. In this way, an evolution curve with predictive capabilities can be generated, clearly marking the critical intervals where standing wave noise may occur. Finally, this evolution curve is used as input for subsequent nonlinear residual amplification and transient coupling response function calculations, enabling the entire control method to provide early warning and control before the acoustic vibration risk develops.

[0034] Through the above steps, not only was the phase drift analysis between the dynamic energy characterization of wind turbine frequency and the instantaneous response of the structure achieved, but also a joint curve of frequency difference and phase deviation was successfully constructed through synchronous comparison and normalization. This joint curve can comprehensively reflect the instantaneous intensity and evolution law of acoustic-vibration coupling, providing a data foundation and methodological support for identifying standing wave risks and implementing phase control.

[0035] In the process of the joint curve evolution of frequency difference and phase deviation, a nonlinear residual amplification mechanism is introduced to further enhance the coupling characteristics, and the critical point distribution of transient energy superposition effect is extracted from the enhanced coupling characteristics. To further amplify the potential acoustic-vibration coupling characteristics during the evolution of the joint curve of frequency difference and phase deviation, enhance sensitivity to dangerous conditions, and accurately extract the critical point distribution of transient energy superposition effect from the amplified signal, the process includes the following steps: The joint curve of frequency difference and phase deviation is used as the input signal, and residual decomposition and nonlinear modeling are performed on it. In the specific implementation, the joint curve is first divided into several equally spaced intervals on the time axis, and a polynomial fitting method is used to approximate the curve in each interval. By comparing with the original curve, the residual signal in that interval can be obtained. Since the acoustic-vibration coupling process is not completely linear, the residual signal often contains high-order nonlinear characteristics. In order to avoid these characteristics being submerged in subsequent analysis, the residual signal needs to be nonlinearly amplified. Specifically, a cubic nonlinear enhancement function is used to cube the part of the residual value greater than zero, so that small positive deviations are highlighted after amplification; at the same time, the part of the residual value less than zero is superimposed by the absolute value and square operation, so that sudden changes in negative deviations are also enhanced. After this processing, the weak coupling characteristics in the joint curve are amplified, while background noise unrelated to coupling is mostly suppressed after polynomial fitting residual processing due to its unstable frequency distribution, thereby significantly improving the signal-to-noise ratio of the signal.

[0036] Based on the nonlinearly amplified residual signal, it is weighted and superimposed with the original signal of the joint curve to highlight the coupling characteristics in the overall curve. Specifically, at each time point, the weighted values ​​of the residual signal and the original joint curve are calculated, and the weighting coefficients are adaptively adjusted according to the instantaneous energy intensity. When the energy level is high, the residual signal is given a larger weight to amplify potentially dangerous coupling characteristics; when the energy level is low, the original joint curve is given a larger weight to ensure that the overall trend of the curve is not distorted. Through this adaptive weighted superposition, an enhanced coupling characteristic curve can be generated. On this curve, small phase deviations and frequency differences that were originally difficult to identify are significantly amplified near the energy peak, making potential standing wave superposition risk points appear obvious. Subsequently, wavelet transform analysis is performed on the enhanced curve, and the transient characteristics in different frequency bands are extracted using a multi-scale decomposition method. By reconstructing the high-frequency and low-frequency signals, the nonlinear response characteristics during periods of concentrated energy are further highlighted. This processing ensures that the enhanced curve not only maintains consistency with the original joint curve in the macroscopic trend but also amplifies the key information of nonlinear coupling in local details.

[0037] In the enhanced coupling characteristic curve, precursor information of standing wave risk is extracted by identifying transient critical points of energy superposition. The specific implementation method is as follows: First, the first derivative of the enhanced curve is calculated to obtain the distribution curves of the rate of change of phase difference and frequency difference over time; then, second derivative analysis is used to find the inflection points where the rate of change of the curve changes from positive to negative or vice versa. These inflection points often correspond to the critical points in the energy superposition process, i.e., the instant when acoustic and vibration energy changes from slow accumulation to rapid enhancement. To improve the identification accuracy, an energy threshold discrimination condition is introduced simultaneously with inflection point identification; that is, only when the instantaneous energy level at the inflection point exceeds twice the average value of the dynamic energy characterization curve is it considered a valid energy superposition critical point. After the determination, all valid critical points are marked with a time coordinate and correlated with the wind turbine operating state, thus forming a critical point distribution map of the transient energy superposition effect. This distribution map can clearly show under what operating conditions and within what time periods there is a high risk of high coupling between wind turbine frequency and structural response, thus providing basic data for the subsequent construction of the transient coupled response function.

[0038] By implementing the above steps, the weak coupling characteristics in the joint curve of frequency difference and phase deviation are effectively enhanced using residual decomposition and nonlinear amplification methods. Furthermore, through adaptive weighted superposition and multi-scale wavelet analysis, the potential dangerous coupling characteristics are highlighted. Based on this, the critical point distribution of transient energy superposition effects is accurately extracted by combining derivative analysis and energy threshold discrimination. This not only improves the sensitivity and accuracy of standing wave risk prediction but also enables the central air conditioning terminal to identify potential dangerous conditions in advance during operation.

[0039] Based on the critical point distribution of the transient energy superposition effect, a transient coupling response function is constructed, and the dynamic energy characterization of the wind turbine frequency is normalized and fitted with the structural response signal to calculate the transient coupling coefficient. Based on the critical point distribution of the transient energy superposition effect, a transient coupling response function that can truly reflect the relationship between the dynamic energy characterization of wind turbine frequency and the structural response signal is constructed. The transient coupling coefficient is then calculated by normalization fitting. This process includes the following steps: Based on the extracted distribution of critical points for the transient energy superposition effect, all critical points are sorted according to their temporal order and energy level, and used as key anchor points for constructing the transient coupled response function. Specifically, three types of parameters are extracted for each critical point: the frequency difference, the phase deviation, and the energy intensity. Then, the parameter set of all critical points is mapped to a three-dimensional coordinate system, with time on the horizontal axis, frequency difference on the vertical axis, and energy intensity on the horizontal axis. The phase deviation is visualized as a color weight for each point. This method yields a multi-dimensional distribution map centered on the critical points. Based on this distribution map, a radial basis function interpolation method is used to establish a continuous surface between the critical points, thus forming a preliminary framework for the transient coupled response function. This framework not only includes the dynamic changes in the wind turbine's energy characterization but also incorporates the instantaneous coupling performance of the structural response signal, laying the foundation for subsequent normalization fitting.

[0040] After establishing the initial framework of the transient coupled response function, the function is normalized to ensure that parameters with different dimensions can be compared and fitted on the same scale. Specifically, the frequency difference is normalized first, mapping the maximum frequency difference to 1 and the minimum frequency difference to 0; second, the phase deviation is normalized by dividing the absolute value of the phase deviation by π, limiting its range to between 0 and 1; third, the energy intensity is normalized by dividing all energy values ​​by the maximum energy value, distributing them within the range of 0 to 1. After normalizing these three types of parameters, all critical point parameters are weighted and fused. The weight allocation is based on the energy threshold determination results from the previous step: the weight for high-energy critical points is set to 0.6, the weight for medium-energy critical points is set to 0.3, and the weight for low-energy critical points is set to 0.1. Through weighted fusion, a multi-dimensional unified normalized response function is obtained. This function continuously varies within the range of 0 to 1 and can simultaneously reflect the combined effects of frequency difference, phase deviation, and energy level.

[0041] Based on the normalized transient coupling response function, the dynamic energy characterization of the wind turbine frequency is curve-fitted with the structural response signal to calculate the transient coupling coefficient. In this process, the dynamic energy characterization curve of the wind turbine frequency is first processed using the same normalization method to ensure that its frequency components and energy distribution are scaled to the 0-1 range. Then, the normalized curve of the structural response signal is compared point-by-point with the wind turbine energy characterization curve, and the least squares method is used for fitting to obtain the best-fit curve. During the fitting process, to avoid bias caused by a single variable, a multivariate regression model is used, with frequency difference, phase deviation, and energy intensity as independent variables, and the normalized amplitude of the structural response signal as the dependent variable. The parameter matrix of the fitted curve is obtained through regression calculation. The final goodness of fit is measured by the coefficient of determination R². When R² is greater than 0.9, it indicates that the fitting result can truly reflect the relationship between the wind turbine energy characterization and the structural response. The parameter matrix generated in this fitting process forms the basis for calculating the transient coupling coefficient.

[0042] Finally, after fitting, the transient coupling coefficient is calculated and dynamically tracked and thresholded. Specifically, the weighted average of the standardized regression coefficients of the three independent variables in the regression model is calculated as the initial value of the transient coupling coefficient. This initial value is then smoothed according to time windows, with the length of each window set as an integer multiple of the wind turbine blade's travel period to ensure the physical continuity of the transient coupling coefficient. The smoothed transient coupling coefficient curve accurately depicts the coupling strength between the wind turbine and the structure under different operating conditions. When the transient coupling coefficient rapidly approaches the preset standing wave noise risk threshold within a short period, it indicates a dangerous energy superposition between the wind turbine's energy disturbance and the structure's natural frequency response. In this case, the dynamic curve of the transient coupling coefficient serves not only as a real-time monitoring indicator of coupling risk but also as a trigger condition for subsequent phase drift control mechanisms, thereby achieving early suppression of standing wave noise.

[0043] Through the above steps, using the critical point distribution of the transient energy superposition effect as anchor points, a transient coupled response function that can comprehensively describe the relationship between the energy characterization and structural response of the wind turbine is constructed, and the transient coupling coefficient is calculated through a normalized fitting method. This implementation realizes the transformation from local critical point information to the overall coupling function, enabling the acoustic-vibration coupling characteristics to be quantitatively expressed in mathematical form, thereby providing data support for risk prediction and control in the environmental comfort regulation of central air conditioning terminals.

[0044] When the transient coupling coefficient approaches the standing wave noise risk threshold, a phase drift mechanism is introduced based on the normalized fitting curve to make a slight adjustment to the phase angle of the fan airflow disturbance, so that the fan airflow disturbance and the structural response maintain a continuous phase deviation, thereby blocking the superposition of the standing wave peak. When the transient coupling coefficient gradually approaches the preset standing wave noise risk threshold, a phase drift mechanism is introduced based on the normalized fitting curve to slightly adjust the phase angle of the wind turbine airflow disturbance, ensuring a continuous phase deviation between the wind turbine airflow disturbance and the structural response, thereby blocking the energy superposition of the standing wave peak. This process specifically includes the following steps: Based on the calculated transient coupling coefficient dynamic curve, the curve is monitored in real time and compared with a threshold. When the transient coupling coefficient remains above 0.85 for three consecutive time windows and approaches the set risk threshold of 0.9, it is determined that there is a high-intensity transient coupling between the dynamic energy characterization of the wind turbine frequency and the structural response signal. At this time, the current phase deviation information needs to be extracted by normalizing the fitted curve. Specifically, the dynamic energy characterization curve of the wind turbine and the normalized curve of the structural response are compared point by point to calculate the instantaneous phase difference. When the phase difference is less than 15°, it indicates that the wind turbine disturbance and the structural response are tending to be in phase, and standing wave superposition is very likely to occur. Under this condition, the phase difference, frequency difference, and instantaneous energy value are jointly input into the phase drift decision-maker to generate the fine-tuning target phase angle Δφ. The value range of this target phase angle is limited to ±30° to ensure that the in-phase condition can be effectively broken after phase adjustment without causing a significant deviation in the wind turbine's air delivery performance.

[0045] Based on the target phase angle Δφ, the phase angle of the airflow disturbance in the fan is slightly adjusted. In practice, the current blade passage frequency is first determined by real-time monitoring of the fan motor's speed signal. Then, a small frequency correction signal is applied to the fan's variable frequency drive, causing a slight shift in the motor's actual operating frequency compared to the original set frequency. This physically adjusts the phase angle of the airflow disturbance. For example, when a +10° phase drift is required, the motor's operating frequency is temporarily increased to 0.5% to 1% higher than the target frequency and maintained for several blade passage cycles; then it is gradually restored to the original target frequency. In this way, the fan airflow disturbance signal is stretched or compressed over time, achieving a cumulative phase drift effect. The advantage of this method is that it eliminates the need for additional hardware intervention in the airflow, utilizing precise control of the motor frequency to achieve phase adjustment, resulting in both speed and stability. To avoid excessive airflow fluctuations caused by frequency adjustments, a gradual control strategy is introduced during the process. This involves applying frequency correction signals in stages over a period of 100 to 200 milliseconds to ensure a smooth transition in the fan phase angle, thereby guaranteeing that indoor comfort is not significantly affected.

[0046] After the phase angle adjustment is completed, the adjustment effect is verified in real time to ensure that a continuous phase deviation is maintained between the fan airflow disturbance and the structural response. Specifically, the phase difference between the fan energy characterization curve and the structural response signal is recalculated and tracked over several time windows. If the phase difference remains within the range of 20° to 40° and the transient coupling coefficient drops below 0.7, it indicates that the phase drift mechanism has successfully broken the in-phase superposition condition, thereby blocking the energy accumulation of the standing wave peak. During the verification process, the stability of the fan air volume and room temperature must also be monitored simultaneously. When the air volume fluctuation is less than 5% and the indoor temperature fluctuation is less than 0.3 degrees Celsius, it is considered that the phase drift adjustment will not have a negative impact on the basic comfort of the terminal environment. If, during the verification process, the phase difference is found to gradually fall back to within 15°, or the transient coupling coefficient approaches the risk threshold again, the target phase angle Δφ is immediately recalculated and the adjustment process is repeated until the coupling strength is effectively weakened. This closed-loop verification mechanism ensures that the wind turbine disturbance always maintains a certain degree of phase misalignment with the structural response, thereby physically blocking the formation of standing wave peaks.

[0047] Through the above steps, when the transient coupling coefficient approaches the standing wave noise risk threshold, a phase drift mechanism can be introduced based on the normalized fitting curve. Precise phase angle fine-tuning ensures a continuous phase deviation between the fan airflow disturbance and the structural response. This method not only intervenes and breaks the in-phase condition before noise risk develops, but also guarantees the smooth operation of the fan and the stability of indoor comfort through gradual control and real-time verification.

[0048] This invention achieves precise coupling identification of wind turbine operating disturbances and building structure acoustic and vibration characteristics by constructing a dynamic energy characterization of wind turbine frequency and performing real-time acquisition and analysis of the structure's natural frequency response within a spatial range. By introducing phase drift analysis and nonlinear residual amplification mechanisms, it can not only identify potential standing wave superposition trends in advance but also extract the transient evolution characteristics of coupled energy with high sensitivity. This allows for real-time quantification of the complex coupling relationship between wind turbine operating status and room structural response, greatly improving the monitorability and predictability of terminal environmental acoustic safety.

[0049] This invention, at a critical juncture where the transient coupling coefficient approaches the risk threshold, introduces a phase drift control strategy based on a normalized fitting curve. This enables dynamic, micro-adjustment of the phase angle of the fan airflow disturbance, thereby breaking the condition of in-phase and in-frequency operation at the physical level and effectively blocking the superposition of standing wave peaks. This proactive intervention mechanism not only possesses high real-time performance and flexible control capabilities but also significantly suppresses the formation of structural standing wave responses while ensuring stable airflow. This avoids fatigue failure and loosening caused by long-cycle vibration of ceiling components, improving the operational stability and long-term safety of the central air conditioning terminal environment.

[0050] This invention provides, for example Figure 2 The intelligent control system for the environmental comfort of the central air conditioning terminal shown includes a dynamic energy characterization module, an acoustic vibration projection acquisition module, a phase comparison and analysis module, a residual amplification and extraction module, a coupling function fitting module, and a phase drift control module. The dynamic energy characterization module collects the pressure fluctuation trajectory generated by aerodynamic disturbances during wind turbine operation, and converts the pressure fluctuation trajectory into an energy spectrum distribution in the time series to establish a dynamic energy characterization of wind turbine frequency. The acoustic vibration projection acquisition module projects an acoustic vibration field in the space based on the dynamic energy characterization of the fan frequency, and synchronously records the excitation response of the natural frequency through the room structure sensor array to form the instantaneous energy coupling fingerprint of the room structure. The phase comparison and analysis module introduces phase drift analysis on the basis of instantaneous energy coupling fingerprint, and performs synchronous phase comparison between the dynamic energy characterization of the wind turbine frequency and the structural response signal to generate a joint curve of frequency difference and phase deviation. The residual amplification extraction module introduces a nonlinear residual amplification mechanism during the evolution of the joint curve of frequency difference and phase deviation, further enhancing the coupling characteristics, and extracting the critical point distribution of transient energy superposition effect from the enhanced coupling characteristics. The coupling function fitting module constructs a transient coupling response function based on the critical point distribution of the transient energy superposition effect, and normalizes and fits the dynamic energy characterization of the wind turbine frequency with the structural response signal to calculate the transient coupling coefficient. The phase drift control module introduces a phase drift mechanism based on the normalized fitting curve when the transient coupling coefficient approaches the standing wave noise risk threshold. This mechanism makes a slight adjustment to the phase angle of the fan airflow disturbance, ensuring that the fan airflow disturbance and the structural response maintain a continuous phase deviation.

[0051] The intelligent control method for the environmental comfort of central air conditioning terminals provided in this embodiment of the invention is implemented through the aforementioned intelligent control system for the environmental comfort of central air conditioning terminals. For details of the specific methods and processes of the intelligent control system for the environmental comfort of central air conditioning terminals, please refer to the embodiments of the intelligent control method for the environmental comfort of central air conditioning terminals, which will not be repeated here.

[0052] The foregoing has only described certain exemplary embodiments of the present invention by way of illustration. Undoubtedly, those skilled in the art can modify the described embodiments in various ways without departing from the spirit and scope of the present invention. Therefore, the foregoing drawings and descriptions are illustrative in nature and should not be construed as limiting the scope of protection of the claims of the present invention.

Claims

1. A method for intelligent control of environmental comfort at central air conditioning terminal units, characterized in that, Includes the following steps: During the operation of the wind turbine, the pressure fluctuation trajectory generated by aerodynamic disturbance is collected, and the pressure fluctuation trajectory is converted into an energy spectrum distribution in the time series to establish a dynamic energy characterization of the wind turbine frequency. Based on the dynamic energy characterization of the fan frequency, an acoustic vibration field is projected in the space and the excitation response of the natural frequency is synchronously recorded through the room structure sensing array to form the instantaneous energy coupling fingerprint of the room structure. Based on instantaneous energy coupling fingerprinting, phase drift analysis is introduced to synchronously compare the dynamic energy characterization of wind turbine frequency with the structural response signal to generate a joint curve of frequency difference and phase deviation. In the process of the joint curve evolution of frequency difference and phase deviation, a nonlinear residual amplification mechanism is introduced to enhance the coupling characteristics, and the critical point distribution of transient energy superposition effect is extracted from the enhanced coupling characteristics. Based on the critical point distribution of the transient energy superposition effect, a transient coupling response function is constructed, and the dynamic energy characterization of the wind turbine frequency is normalized and fitted with the structural response signal to calculate the transient coupling coefficient. When the transient coupling coefficient approaches the standing wave noise risk threshold, a phase drift mechanism is introduced based on the normalized fitting curve to make a slight adjustment to the phase angle of the fan airflow disturbance, so that the fan airflow disturbance and the structural response maintain a continuous phase deviation.

2. The intelligent control method for the environmental comfort of central air conditioning terminals according to claim 1, characterized in that, The process of collecting pressure fluctuation trajectories generated by aerodynamic disturbances during wind turbine operation and establishing a dynamic energy characterization of wind turbine frequency includes the following steps: Piezoelectric pressure sensors are installed at the fan outlet, the bend in the fan coil unit's air supply duct, and the front end of the air outlet. The pressure fluctuation trajectory is obtained by digital acquisition through a 24-bit analog-to-digital converter after processing by a low-noise amplifier and an anti-aliasing filter. The pressure fluctuation trajectory is used to generate an energy spectrum distribution map containing three-dimensional coordinates of time, frequency, and energy by combining short-time Fourier transform and Hilbert-Huang transform. By comparing the energy spectrum distribution map with the wind turbine speed signal, and by matching the wind turbine blade passing frequency and its higher harmonics, the characteristic frequency components are identified, and the energy amplitude is normalized to obtain a dynamic energy characterization curve that reflects the changes in the wind turbine's operating state.

3. The intelligent control method for the environmental comfort of central air conditioning terminals according to claim 2, characterized in that, Dynamic energy characterization based on fan frequency projects an acoustic vibration field across a spatial range and forms an instantaneous energy coupling fingerprint of the room structure, including the following steps: The dynamic energy representation of the fan frequency is used as the sound source input. A composite sound field is projected into the room through a dual-channel method of array loudspeakers and fan self-excited vibration, so that the frequency and energy distribution of the sound field correspond to the dynamic energy representation of the fan. Sensor arrays were placed at key locations in the room structure, and the excitation response at the natural frequency was recorded using a synchronous sampling clock trigger and a 10kHz sampling frequency. The structural response signal is mapped point by point to the dynamic energy characterization of the wind turbine, and a spatial mapping relationship is established through fast Fourier transform and phase correction. By fusing the intrinsic frequency response results from different spatial locations with dynamic energy characterization, an instantaneous energy coupling fingerprint in the form of a three-dimensional matrix with time, frequency, and energy as coordinates is generated.

4. The intelligent control method for the environmental comfort of central air conditioning terminals according to claim 3, characterized in that, Based on the instantaneous energy coupling fingerprint of the structure, phase drift analysis is introduced to generate a joint curve of frequency difference and phase deviation, including the following steps: The time series, frequency components and energy response intensity of the instantaneous energy coupling fingerprint of the structure are extracted and matched with the main frequency curve of the dynamic energy characterization of the wind turbine. The time series alignment is achieved by dynamic time warping and the sampling difference is compensated by interpolation and windowing. The aligned signal is subjected to Hilbert transform to obtain the instantaneous phase curve, and a continuous phase trajectory is formed by phase expansion and bandpass filtering. The phase trajectory is compared point by point with the dynamic energy characterization. The instantaneous phase difference and frequency difference are calculated and a joint curve is generated. The joint curve is fitted by a sliding window segment and an energy weighting factor is introduced to form a comprehensive risk index of energy-frequency-phase three parameters, which is used to identify the critical interval of standing wave noise.

5. The intelligent control method for the environmental comfort of central air conditioning terminal units according to claim 4, characterized in that, During the sliding window piecewise fitting process, when the root mean square of the frequency difference and the standard deviation of the phase difference are both lower than the set threshold, and the instantaneous energy intensity at the corresponding point exceeds twice the average energy value, it is determined to be the critical interval of standing wave noise risk.

6. The intelligent control method for the environmental comfort of central air conditioning terminal units according to claim 1, characterized in that, In the process of the joint curve evolution of frequency difference and phase deviation, a nonlinear residual amplification mechanism is introduced and the critical point distribution of transient energy superposition effect is extracted, including the following steps: The joint curve is divided into intervals on the time axis and the residual signal is obtained by polynomial fitting. The positive residual is cubically calculated, and the negative residual is superimposed by the absolute value and the square operation to amplify the nonlinear coupling characteristics and suppress irrelevant noise. The residual signal after nonlinear amplification is superimposed with the original signal of the joint curve in a weighted manner. The weighting coefficients are adaptively adjusted according to the instantaneous energy intensity. Wavelet decomposition and reconstruction are performed on the enhanced coupled characteristic curve to highlight the nonlinear response during the energy concentration period. The enhanced coupling characteristic curve is analyzed by first and second derivatives. The effective inflection point is identified by combining the instantaneous energy threshold discrimination condition. The critical point of the transient energy superposition effect is marked and the critical point distribution map is generated.

7. The intelligent control method for the environmental comfort of central air conditioning terminal units according to claim 6, characterized in that, Constructing the transient coupled response function and calculating the transient coupling coefficient based on the critical point distribution of the transient energy superposition effect includes the following steps: The frequency difference, phase deviation, and energy intensity parameters in the critical point distribution are extracted and mapped to a three-dimensional coordinate system. Radial basis function interpolation is used to establish a continuous surface between each critical point to form the framework of the transient coupled response function. The function is normalized by normalizing the frequency difference, phase deviation and energy intensity to the interval between 0 and 1, and then weighted and fused according to the energy threshold results to obtain a unified normalized response function. The dynamic energy characterization of the wind turbine frequency and the structural response signal are compared point by point using the same normalization method. The transient coupling coefficient is calculated by multivariate regression fitting, and the goodness of fit is measured by the coefficient of determination R².

8. The intelligent control method for the environmental comfort of central air conditioning terminal units according to claim 1, characterized in that, When the transient coupling coefficient approaches the standing wave noise risk threshold, a phase drift mechanism is introduced based on the normalized fitting curve, including the following steps: The dynamic curve of the transient coupling coefficient is monitored in real time. When it remains above 0.85 and approaches 0.9 within three consecutive time windows, the phase deviation information is extracted by normalizing the fitting curve. When the phase difference is less than 15°, the target phase angle Δφ is generated by combining the frequency difference and the instantaneous energy value. The target phase angle range is limited to within ±30°. Based on the target phase angle Δφ, a frequency correction signal is applied to the fan motor speed, causing the motor operating frequency to shift slightly by 0.5% to 1%. This is then smoothly adjusted within 100 to 200 milliseconds using a gradual control strategy, thereby achieving the cumulative drift of the fan airflow disturbance phase angle. After completing the phase adjustment, the phase difference is recalculated and multiple time windows are tracked. When the phase difference remains in the range of 20° to 40° and the transient coupling coefficient drops below 0.7, the phase drift is deemed valid.

9. An intelligent control system for the environmental comfort of central air conditioning terminals, used to implement the intelligent control method for the environmental comfort of central air conditioning terminals as described in any one of claims 1-8, characterized in that, It includes a dynamic energy characterization module, an acoustic vibration projection acquisition module, a phase comparison and analysis module, a residual amplification and extraction module, a coupling function fitting module, and a phase drift control module; The dynamic energy characterization module collects the pressure fluctuation trajectory generated by aerodynamic disturbances during wind turbine operation, and converts the pressure fluctuation trajectory into an energy spectrum distribution in the time series to establish a dynamic energy characterization of wind turbine frequency. The acoustic vibration projection acquisition module projects an acoustic vibration field in the space based on the dynamic energy characterization of the fan frequency, and synchronously records the excitation response of the natural frequency through the room structure sensor array to form the instantaneous energy coupling fingerprint of the room structure. The phase comparison and analysis module introduces phase drift analysis on the basis of instantaneous energy coupling fingerprint, and performs synchronous phase comparison between the dynamic energy characterization of the wind turbine frequency and the structural response signal to generate a joint curve of frequency difference and phase deviation. The residual amplification extraction module introduces a nonlinear residual amplification mechanism during the evolution of the joint curve of frequency difference and phase deviation, further enhancing the coupling characteristics, and extracting the critical point distribution of transient energy superposition effect from the enhanced coupling characteristics. The coupling function fitting module constructs a transient coupling response function based on the critical point distribution of the transient energy superposition effect, and normalizes and fits the dynamic energy characterization of the wind turbine frequency with the structural response signal to calculate the transient coupling coefficient. The phase drift control module introduces a phase drift mechanism based on the normalized fitting curve when the transient coupling coefficient approaches the standing wave noise risk threshold. This mechanism makes a slight adjustment to the phase angle of the fan airflow disturbance, ensuring that the fan airflow disturbance and the structural response maintain a continuous phase deviation.

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