Fan sound insulation, noise reduction and heat dissipation integrated device with remote monitoring and intelligent regulation

Abstract

The application discloses a fan sound insulation, noise reduction and heat dissipation integrated device for remote monitoring and intelligent regulation and control, relates to the technical field of fan noise control and heat dissipation integration, and comprises a signal acquisition module, an acoustic analysis module, an interference identification module, a parameter adjustment module and a dynamic anti-misoperation triggering control module.The signal acquisition module acquires impact sound signals, vibration signals and airflow change data in a fan operation environment, arranges all the acquired signals in time sequence, eliminates stable background noise and generates impact sound time sequence data sets.Through multi-source signal acquisition and time sequence analysis, the application realizes the differentiation of external impact sound and equipment operation sound, prevents frequent start and stop caused by misoperation self-checking, adjusts operation rhythm through interference coupling calibration, introduces double confirmation sampling, a silent window and flexible start control, and ensures the stable operation of the fan in a complex environment and prolongs the service life.

Description

Technical Field

[0001] This invention relates to the field of integrated wind turbine noise control and heat dissipation technology, specifically to an integrated wind turbine sound insulation, noise reduction and heat dissipation device with remote monitoring and intelligent control. Background Technology

[0002] The integrated sound insulation, noise reduction, and heat dissipation device for remote monitoring and intelligent control is a comprehensive fan equipment that integrates noise suppression, heat dissipation, data acquisition and control, and remote intelligent management. Based on traditional fans, this device effectively reduces operating noise by arranging sound-absorbing composite cavities, vibration-damping guide channels, and sound energy dissipation materials at the air inlet and outlet. For heat dissipation, it features a thermally conductive composite shell and multi-stage heat exchange airflow paths to improve the heat exchange efficiency of high-power equipment. Simultaneously, it incorporates built-in sensors, data acquisition and control, and communication units to monitor airflow, temperature, noise level, and vibration status in real time. The intelligent control system remotely adjusts operating parameters to achieve an adaptive balance between noise control and heat dissipation performance, making it suitable for applications such as computer rooms, factories, and energy equipment where both noise reduction and heat dissipation are critical.

[0003] The existing technology has the following shortcomings:

[0004] In existing technologies, intelligent control systems for wind turbines typically rely on noise sensing signals to identify their operating status. When abnormal acoustic characteristics are detected, they automatically initiate a self-test program to investigate potential faults. However, in complex operating environments, when there are transient impact noises, such as pipe collisions, foreign object impacts, or short-term strong noises like airflow bursts, the control system can easily misidentify these transient noises, which are not generated by the equipment itself, as wind turbine fault signals, thus continuously triggering the self-test program. Because the self-test process involves shutdown, testing, and restart operations, the system will repeatedly start and stop in a short period of time. The motor and drive power supply are frequently subjected to high current surges, which can easily cause heat accumulation and stress fatigue in the drive components, ultimately leading to burnout of the drive power supply or damage to the control board, seriously affecting the operational stability and service life of the equipment.

[0005] 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

[0006] The purpose of this invention is to provide an integrated device for remote monitoring and intelligent control of fan sound insulation, noise reduction and heat dissipation, so as to solve the problems in the background art mentioned above.

[0007] To achieve the above objectives, the present invention provides the following technical solution: a remote monitoring and intelligent control integrated device for fan sound insulation, noise reduction, and heat dissipation, comprising a signal acquisition module, an acoustic analysis module, an interference identification module, a parameter adjustment module, and a dynamic anti-false triggering control module:

[0008] The signal acquisition module collects impact sound signals, vibration signals, and airflow change data in the wind turbine operating environment, arranges all the collected signals in chronological order, eliminates stable background noise, and generates an impact sound time series dataset.

[0009] The acoustic analysis module identifies the impact start interval and attenuation interval based on the impact sound time series dataset, calculates the attenuation change of the sound wave peak over time and extracts the echo delay parameter, and generates impact sound event distribution information based on the above parameters.

[0010] The interference identification module calibrates the timing of the system's self-test based on the distribution information of the impact sound events, compares the impact sound characteristics with the fan start-up and shutdown time series, analyzes the overlap relationship between the impact sound and the start-up and shutdown rhythm, and extracts the interference coupling scale.

[0011] The parameter adjustment module sets the signal buffer period, self-test interval, and sound recognition threshold according to the interference coupling scale, and adjusts the minimum time interval for the fan to start and stop, thus forming an operation rhythm control scheme.

[0012] The dynamic anti-false triggering control module executes wind turbine operation adjustment according to the operation rhythm control scheme. Within the interference coupling scale range, it implements dual confirmation sampling, reverse silent window control and flexible start strategy to block false triggering cycle and stabilize wind turbine operation status.

[0013] Preferably, the steps for generating the impact sound time series dataset are as follows:

[0014] The signal acquisition conditions of the wind turbine operating environment are initialized and configured. Under the normal operating condition of the wind turbine, the impact sound signal, structural vibration signal and airflow velocity change data are acquired through the multi-channel sensing unit. The sampling frequency, sampling time window and sampling resolution of various signals are uniformly set to ensure time synchronization.

[0015] The collected impact sound signals, vibration signals and airflow change data are aligned according to the collection time label, and a data index is established on the same time axis. A continuous multi-dimensional time series data matrix is ​​formed by time interpolation.

[0016] The background noise characteristic range is determined based on the sound pressure signal and airflow fluctuation signal during the steady-state operation of the wind turbine, and the stable background noise is eliminated by the sliding range method to retain the dynamic change information;

[0017] After background noise removal, acoustic signals, vibration signals and airflow change data are paired according to time index to form an impact sound time series dataset, and a time index table is established to achieve time-consistent expression of multi-physics field signals.

[0018] Preferably, all sensing units are synchronously triggered with a unified time reference, so that the impact sound signal, vibration signal and airflow change data maintain a one-to-one correspondence in the time dimension. After the acquisition is completed, the signals are smoothed in chronological order to eliminate transient high-frequency noise caused by external random disturbances, thereby ensuring the consistency of the formed impact sound time series dataset in terms of continuity and comparability, and providing a stable data foundation for subsequent analysis.

[0019] Preferably, the steps for generating impact sound event distribution information are as follows:

[0020] Energy characteristics were scanned on the time series dataset of impact sound, and the change trend of sound pressure amplitude over time was continuously read. The initial response interval of the impact event was determined as the impact starting point interval based on the time range of energy surge.

[0021] After the impact starting point interval is determined, energy attenuation analysis is performed on its subsequent time series. The maximum sound pressure peak is used as the starting reference to monitor the decreasing trend of sound wave energy and determine the attenuation interval of the impact sound based on the time for energy to return to steady state.

[0022] After obtaining the impact start-point interval and attenuation interval, the continuous change of the sound pressure peak value over time is recorded for each impact event segment. The peak attenuation characteristics are extracted and the echo peak value generated by sound wave reflection is captured to obtain the echo delay parameter.

[0023] After extracting the various feature parameters, the impact start time, energy peak, attenuation duration and echo delay time are integrated in chronological order to generate impact sound event distribution information and establish an event index table to present the temporal pattern of impact events.

[0024] Preferably, when generating impact sound event distribution information, the starting time, energy peak, attenuation duration and echo delay time of each impact event are arranged sequentially with the time axis as the main line. The intensity characteristics of the impact event are represented by the energy change curve, and a time correspondence is established in the event index table to realize continuous tracking and distribution pattern identification of impact sound in the time dimension.

[0025] Preferably, the interference coupling scale extraction steps are as follows:

[0026] Based on the distribution information of impact sound events, the self-inspection time points within the wind turbine operating cycle are calibrated, the wind turbine self-inspection trigger time is aligned with the impact event time index, and the correspondence between impact events and self-inspection time is established under a unified time reference.

[0027] After completing the self-test time calibration, the impact sound characteristics in the impact sound event distribution information are compared with the wind turbine start-up and shutdown time series. By analyzing the time overlap and event duration, impact sound events that overlap with the self-test cycle are identified.

[0028] After identifying the time overlap relationship, the time distribution characteristics of the impact sound event and the start-up and shutdown operation time of the wind turbine were analyzed and a time correlation table was established by using the starting time of the impact event and the start-up and shutdown operation time of the wind turbine as the time coordinates.

[0029] Based on the overlapping data of impact sound and start-stop rhythm recorded in the time correlation table, the interference coupling scale is extracted and the concentrated interval of interference events is determined by the time difference distribution, so as to reflect the influence of impact sound events on the wind turbine self-test process.

[0030] Preferably, in the process of extracting the interference coupling scale, the starting time, peak intensity, attenuation duration and echo delay time of the impact sound event are used as the basic parameters, and matched with the wind turbine self-test trigger time, shutdown duration and restart interval. The concentrated interval of the interference event on the time axis is determined by comparing the time difference distribution, and the time influence range of the interference coupling scale is characterized by the ratio of the interval length to the duration of the impact event.

[0031] Preferably, the steps for generating the rhythm control scheme are as follows:

[0032] Based on the distribution law of impact sound interference in the interference coupling scale, a signal buffer period corresponding to the self-test behavior of the fan is set, and a buffer link is added to the self-test trigger logic to filter short-term impact interference signals.

[0033] After the signal buffer period is set, the fan self-test interval is set according to the interference cycle information of the interference coupling scale, and the self-test operation is made to avoid the concentrated distribution range of interference events in time.

[0034] Based on the self-test interval time, the sound recognition threshold is set according to the energy distribution information in the interference coupling scale, and the recognition range is determined with reference to the peak energy of the impact event and the average energy of the steady-state noise.

[0035] After the signal buffer period, self-test interval and sound recognition threshold are set, the minimum time interval for starting and stopping the fan is adjusted according to the time coupling relationship between the three parameters, and an adaptive operating rhythm control scheme is formed to stabilize the operating status of the fan.

[0036] Preferably, the signal buffer period, self-test interval, and sound recognition threshold set in the operation rhythm control scheme are correlated in a time ratio, and the minimum start-stop time interval is dynamically adjusted based on the time span of the interference coupling scale, so that the fan can maintain stable operation during the continuous impact sound interference, and avoid the self-test logic being repeatedly triggered within the interference time interval, thereby achieving time coordination and continuous balance of the operation rhythm.

[0037] Preferably, the wind turbine operation is adjusted according to the operating rhythm control scheme. Within the interference coupling scale range, dual confirmation sampling, reverse silent window control, and flexible start-up strategies are implemented to block false triggering cycles and maintain stable wind turbine operation. The steps are as follows:

[0038] After the operation rhythm control scheme is determined, the operating status of the wind turbine is double-confirmed by sampling. Within the interference coupling scale range, the sound pressure signal, vibration signal and airflow change signal are independently collected and compared twice to confirm the continuity and stability of the signal characteristics.

[0039] After completing the double confirmation sampling, a reverse silence window control is introduced based on the time characteristics of the interference coupling scale. The silence time window is set with the echo delay time as a reference, and the self-test trigger judgment is paused within the silence window to eliminate echo interference.

[0040] After completing the reverse silent window control, a flexible start strategy is executed according to the running rhythm control scheme. The motor torque rise speed is adjusted according to the ambient acoustic state of the interference coupling scale to achieve smooth start.

[0041] With the combined effects of dual-confirmation sampling, reverse silent window control, and flexible start-up strategy, an operation adjustment process is formed to block false triggering cycles and ensure that the wind turbine operates stably within the interference coupling scale range.

[0042] The technical effects and advantages provided by the present invention in the above technical solution are as follows:

[0043] This invention establishes a multi-source signal acquisition and timing analysis mechanism during wind turbine operation, enabling dynamic correlation of acoustic, vibration, and airflow signals under a unified time reference. By identifying the impact sound initiation point, attenuation range, and echo delay characteristics, it distinguishes between external instantaneous noise and the equipment's own operating sound. This process forms a closed-loop judgment in the acoustic analysis and interference identification stages, effectively avoiding erroneous self-check operations triggered by external impact sounds. This allows the wind turbine to maintain a stable operating rhythm in complex acoustic environments, thereby reducing the number of invalid start-stop cycles and preventing heat accumulation and fatigue damage to the drive unit due to frequent current surges.

[0044] This invention establishes an operational rhythm control mechanism centered on interference coupling scale, dynamically adjusting the signal buffer period, self-test interval, and start-stop time interval. It also introduces dual-confirmation sampling, a reverse silent window, and a flexible start-up strategy during the control process to achieve adaptive regulation of the wind turbine's start-stop rhythm. This method can suppress the cyclic triggering phenomenon during the self-test process, ensuring that the self-test operation only occurs under actual fault conditions. This improves the reliability and service life of the wind turbine's intelligent control, enabling the equipment to possess comprehensive operational performance with anti-interference, noise reduction, and heat dissipation synergy under remote monitoring. Attached Figure Description

[0045] 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.

[0046] Figure 1 This is a schematic diagram of the integrated device for remote monitoring and intelligent control of fan sound insulation, noise reduction and heat dissipation of the present invention. Detailed Implementation

[0047] 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.

[0048] This invention provides, for example Figure 1 The remote monitoring and intelligent control integrated fan sound insulation, noise reduction and heat dissipation device shown includes a signal acquisition module, an acoustic analysis module, an interference identification module, a parameter adjustment module and a dynamic anti-false triggering control module.

[0049] The signal acquisition module collects impact sound signals, vibration signals, and airflow change data in the wind turbine operating environment, arranges all the collected signals in chronological order, eliminates stable background noise, and generates an impact sound time series dataset.

[0050] To construct a complete time-series dataset of impact sound, acoustic, vibration, and airflow signals from the wind turbine operating environment were continuously acquired and time-series processed. This ensured that the acquired data reflected the actual state changes during wind turbine operation, thus providing a foundation for subsequent feature extraction and interference analysis. The specific implementation steps are as follows:

[0051] The signal acquisition conditions for the wind turbine's operating environment are initialized and configured. Under normal wind turbine operation, impact sound signals, structural vibration signals, and airflow velocity change data are acquired through multi-channel sensing units. During acquisition, the sampling frequency, sampling time window, and sampling resolution for each type of signal are uniformly set to ensure that each acquisition channel obtains continuous signal waveforms on the same time axis. To avoid data misalignment due to phase deviations between different sensing points during signal acquisition, all signal sampling is synchronously triggered using a unified time reference, thus ensuring a one-to-one correspondence between sound signals, vibration signals, and airflow signals in the time dimension. To ensure that the acquired data covers the entire lifecycle of wind turbine operation, the acquisition period should include the entire process of wind turbine startup, steady-state operation, and deceleration and shutdown, so that the data used for subsequent analysis can reflect the complete dynamic changes from transient to stable operation.

[0052] The collected impact sound signals, vibration signals, and airflow change data are arranged in a unified time sequence. During this process, different types of signals are time-aligned according to their acquisition time labels, and a data index is established on the same time axis. To ensure the continuity of the arrangement results, time interpolation is performed to fill in any sampling delays or data loss, maintaining a consistent sampling interval between signals. In this way, sound, vibration, and airflow signals can be integrated into a continuous multi-dimensional time-series data matrix. Each time node in the matrix contains the corresponding sound pressure amplitude, vibration acceleration, and airflow velocity change. After the time sequence arrangement is completed, the data undergoes preliminary smoothing to eliminate transient high-frequency noise caused by external random disturbances, making the signal waveforms more continuous and comparable. The resulting signal data set can fully reflect the dynamic response of the wind turbine when subjected to external impacts in the operating environment.

[0053] Based on a time-series sequence of signal data, stable background noise in the wind turbine operating environment is eliminated. This process begins by selecting background sound pressure signals and steady-state airflow fluctuation signals under conditions of no external impact or structural vibration interference as benchmarks, according to the steady-state period of wind turbine operation. The statistical distribution range of background noise is determined by calculating the average energy distribution and amplitude variation range of various signals during the steady-state phase. After obtaining the background noise characteristics, this characteristic range is applied to the entire time series. Signal segments exceeding the upper limit of steady-state noise are filtered, and intervals belonging to stable background noise are removed from the signal series. Background noise suppression is performed on the entire signal series using a continuous sliding interval method, ensuring that the retained signal portion mainly contains dynamic changes such as instantaneous impact sound, structural response, and airflow disturbances, thereby effectively removing stable background noise. After background noise elimination, the waveform characteristics of the signal series more clearly reflect changes in the wind turbine's operating state, especially the response process during external impact events.

[0054] After removing background noise, the processed signal data is structured and organized to generate an impact sound time series dataset. At this point, the acoustic signal is used as the main sequence, and vibration signals and airflow change data at the same time point are paired as subordinate sequences using time as the index, forming a complete time series sample set. Each time sample unit contains sound pressure, vibration acceleration, and airflow velocity change values, maintaining a strict time correspondence. In the constructed time series dataset, the impact sound signal reflects the acoustic characteristics of transient impact events in the wind turbine operating environment, the vibration signal reflects the mechanical response of the wind turbine structure under impact, and the airflow change data reflects the aerodynamic fluctuations of the wind turbine under impact. To make the time series more suitable for subsequent feature extraction and interference analysis, the entire dataset is uniformly numbered according to time sequence, and a time index table is established to ensure that acoustic, vibration, and airflow data can be accessed synchronously at any time point, achieving time-consistent representation of multi-physics field signals. After these steps, the resulting impact sound time series dataset can accurately reflect the dynamic response characteristics of the wind turbine under complex operating conditions, providing a sufficient data foundation for subsequent analysis of the impact initiation point, attenuation range, interference coupling scale, and operation rhythm optimization.

[0055] The acoustic analysis module identifies the impact start interval and attenuation interval based on the impact sound time series dataset, calculates the attenuation change of the sound wave peak over time and extracts the echo delay parameter, and generates impact sound event distribution information based on the above parameters.

[0056] To accurately identify the starting and attenuation intervals of impact sound in the wind turbine operating environment, and to further calculate the attenuation characteristics of the sound peak over time and extract echo delay parameters, thereby generating impact sound event distribution information that reflects the time-dimensional distribution pattern of impact events, the entire process is based on a constructed impact sound time series dataset. Through segment-by-segment analysis and feature extraction of this dataset, the temporal correlation between sound wave propagation and energy attenuation is established, providing a precise basis for subsequent refinement of interference coupling scales and control of operating rhythm. The specific implementation steps are as follows:

[0057] Energy feature scanning was performed on the time series dataset of impact sound to determine the initial response interval of the impact event on the time axis. By continuously reading the change trend of sound pressure amplitude over time in the time series, the sound energy corresponding to each time point was integrated and statistically analyzed to obtain a time-series energy distribution curve reflecting the concentration of sound energy. In the energy distribution curve, the steady-state sound energy during the normal operation of the wind turbine is usually stable, while when an external impact event occurs, the sound energy will rise sharply in a very short time and form an instantaneous peak. By identifying the time range of this energy surge, the initial response interval of the impact event, i.e., the impact starting point interval, can be determined. To ensure the accuracy of the impact starting point interval, the duration for which the sound pressure energy continuously rises and exceeds the upper limit of the steady-state background sound energy is used as the criterion during the identification process, so that the impact starting point can accurately reflect the entire process from the generation of the sound wave to reaching the maximum sound energy. Through this energy scanning method, not only can the starting point interval of a single impact event be identified, but also the temporal interval distribution of multiple consecutive impact events can be distinguished, providing a time reference for subsequent attenuation interval extraction.

[0058] After determining the initial impact interval, energy attenuation analysis is performed on the subsequent time series to determine the attenuation interval of the impact sound. In this process, the maximum sound pressure peak at the end of the initial interval is used as the starting reference, and the continuous changes in sound wave energy afterward are monitored, with the decreasing trend of sound pressure amplitude over time serving as the criterion. When the sound energy gradually decreases and returns to the steady-state noise energy range, the end time of the attenuation interval can be determined. In this way, the entire process of an impact event, from sound wave generation and energy peak to energy stabilization, can be divided into a complete time segment. The duration and rate of sound energy change of this segment reflect the intensity and duration of the impact event. To make the division of the attenuation interval more precise, airflow change data and vibration response signals are combined during the analysis. The time difference between sound wave energy attenuation and structural vibration attenuation is identified through time comparison, thereby eliminating errors caused by environmental noise and making the impact sound attenuation process closer to the actual acoustic response characteristics of the wind turbine.

[0059] After obtaining the impact starting point and attenuation intervals, the peak attenuation characteristics and echo delay phenomena of the impact sound are quantitatively extracted. First, the peak sound pressure and its continuous value over time are recorded within each impact event segment. The time span of peak attenuation and the rate of amplitude decrease are used as characteristic indicators to measure the attenuation of sound wave energy. In practical applications, the energy of the impact sound does not disappear instantaneously but will generate multiple reflections in the fan casing, ducts, or air, forming echoes. To extract the echo delay parameter, the sound pressure change curve after the attenuation interval is continuously tracked, capturing the time nodes of the secondary rise or fluctuation of sound pressure to determine the echo response generated by reflection during the spatial propagation of the sound wave. By recording the interval between the time of each echo peak occurrence and the time of the original impact peak, the echo delay parameter can be obtained. This parameter reflects the complexity of the impact sound propagation path and spatial reflection characteristics. Combining the echo delay information of multiple impact events, the reflection patterns of sound waves on different structural surfaces in the fan operating environment can be further analyzed, providing a basis for the temporal induction of the subsequent impact sound event distribution.

[0060] After extracting the impact start-up interval, attenuation interval, peak acoustic wave attenuation variation, and echo delay parameters, these characteristic data are integrated in chronological order to generate impact acoustic event distribution information. Specifically, using the time axis as the main line, the start time, peak energy, attenuation duration, and echo delay time of each impact event are arranged sequentially, and an event index table is established to present the frequency and distribution characteristics of impact events over a complete time range. For ease of subsequent analysis, the event distribution information is represented in a combination of time coordinates and energy changes, ensuring that each impact event has a traceable start and end point in the time dimension, and its intensity characteristics are reflected through energy attenuation curves. In this way, the impact acoustic event distribution information not only includes the time location and duration characteristics of each impact but also comprehensively reflects the physical processes of sound wave propagation and energy attenuation. This information can intuitively display the temporal patterns and energy change patterns of impact events in the wind turbine operating environment, providing complete data basis for the subsequent extraction of interference coupling scales.

[0061] The interference identification module calibrates the timing of the system's self-test based on the distribution information of the impact sound events, compares the impact sound characteristics with the fan start-up and shutdown time series, analyzes the overlap relationship between the impact sound and the start-up and shutdown rhythm, and extracts the interference coupling scale.

[0062] Based on the distribution information of impact sound events, the temporal characteristics of the wind turbine during its self-test process are determined. By comparing the impact sound characteristics with the wind turbine start-up and shutdown time series, the overlap between the two in the time dimension is identified, thereby extracting an interference coupling scale reflecting the interaction between acoustic interference and operating rhythm. This process, based on the generated impact sound event distribution information and wind turbine start-up and shutdown time series, constructs a correlation mapping relationship between impact noise and start-up / shutdown behavior through multi-layer time alignment, event comparison, and pattern summarization, providing fundamental data support for the subsequent development of operating rhythm control schemes. The specific implementation steps are as follows:

[0063] The self-check time points of the wind turbine during its operating cycle are calibrated based on the impact sound event distribution information. During long-term wind turbine operation, the self-check operation is often automatically triggered by the control program when abnormal sound wave characteristics are detected. Therefore, the self-check time is usually manifested as instantaneous fluctuations in the wind turbine power curve, speed curve, or current curve. By aligning the time variation curves of these operating parameters with the impact sound event distribution information, the self-check trigger time can be precisely located on the time axis. During calibration, the start-stop switching time points in the wind turbine self-check process are mapped to the same time coordinate system using the time index in the impact event distribution information as a reference, ensuring that each wind turbine self-check behavior corresponds to a specific impact sound event time range. In this process, the continuity and uniformity of the time calibration are ensured, meaning that the timing sequence of all signals under the same time reference is completely consistent, thereby establishing a one-to-one correspondence between impact events and wind turbine self-check times.

[0064] After calibrating the wind turbine self-test timing, the impact sound characteristics in the impact sound event distribution information are compared with the wind turbine start-up and shutdown time series. The wind turbine start-up and shutdown time series includes the entire time record of the wind turbine from normal operation to shutdown and then to restart, with each start-up and shutdown operation corresponding to an operating state switching cycle. By comparing the impact sound event distribution information with the start-up and shutdown time series, it can be analyzed whether each impact sound event occurs within the time interval before and after the wind turbine self-test start-up and shutdown cycle. When the start point or attenuation interval of the impact sound event overlaps with the wind turbine's shutdown or start-up time, it indicates that the impact sound event has a temporal interference relationship with the wind turbine start-up and shutdown behavior. During the comparison process, the degree of temporal overlap and the event duration are used as the judgment criteria. That is, when the duration of the impact sound event covers the wind turbine self-test operation time period, or the time interval between the start and end of the two is less than the set synchronization tolerance, it is considered that the impact event has an interference effect on the self-test process. In this way, all impact sound events that overlap with the self-test process can be identified, providing a raw data set for subsequent interference relationship analysis.

[0065] Based on the identified temporal overlap, a temporal correlation analysis was performed on the interference patterns between impact noise and start-stop rhythm. In this process, the starting time of each impact event and the corresponding wind turbine start-stop operation time were used as time coordinates. By comparing the time intervals between the two, the distribution characteristics of impact noise events before, during, and after the wind turbine self-test were analyzed. If impact noise events are concentrated in the time interval before the wind turbine self-test starts, it indicates that external impact noise may be the trigger for the self-test program. If impact noise events are concentrated in the time interval from wind turbine shutdown to restart, it indicates that the equipment start-stop vibration during the self-test process causes additional acoustic disturbances. If impact noise events continuously overlap with the self-test cycle, it indicates that the wind turbine control logic is repeatedly triggered under the influence of interference noise. By analyzing the correspondence between impact events and start-stop cycles in the above different time periods, the temporal coupling mode between impact noise and self-test behavior can be summarized. To ensure the continuity of the analysis results, the time difference between each impact event and the corresponding start-stop cycle, the duration of overlap, and the energy distribution parameters were recorded together, and a temporal correlation table was established for subsequent extraction and parameterization of interference scales.

[0066] Based on the overlapping data of impact noise and start-stop rhythm recorded in the time correlation table, an interference coupling scale is extracted. The interference coupling scale reflects the impact of impact noise events on the wind turbine's self-test triggering and the temporal coupling pattern between the two. During extraction, the start time, peak intensity, attenuation duration, and echo delay time of the impact noise event are used as basic parameters, matched with the wind turbine's self-test triggering time, shutdown duration, and restart interval. By comparing the time difference distribution of multiple impact events and start-stop cycles, the concentrated interval of interference events on the time axis is determined, and the ratio of this interval length to the impact event duration is used as the quantitative basis for the interference coupling scale. The interference coupling scale reflects the impact range and period of the impact noise event on the self-test process in the time domain. When the scale range highly overlaps with the self-test trigger interval, it indicates that the system is in a sensitive interval susceptible to external impacts. By summarizing the impact noise events of multiple operating cycles, a comprehensive scale diagram reflecting the interference distribution pattern under different operating conditions can be formed. This scale not only reflects the interaction characteristics between the impact sound and the self-test process in the time dimension, but also provides a quantitative basis for setting the subsequent signal buffer period, self-test interval time, and identification threshold.

[0067] The parameter adjustment module sets the signal buffer period, self-test interval, and sound recognition threshold according to the interference coupling scale, and adjusts the minimum time interval for the fan to start and stop, thus forming an operation rhythm control scheme.

[0068] Based on the interference coupling scale, the self-test triggering rhythm during wind turbine operation is rationally controlled to prevent frequent start-stops caused by impact noise interference in complex noise environments. This is achieved by coordinating the signal buffer period, self-test interval, and sound recognition threshold, and further adjusting the minimum time interval for wind turbine start-stop, ultimately forming an operating rhythm control scheme that adapts to the characteristics of the operating environment. This process is based on obtaining the interference coupling scale. By comprehensively considering the temporal distribution and energy characteristics of interference features, the operating rhythm control scheme effectively avoids the impact of external impact interference on the triggering of the self-test logic without affecting the equipment's heat dissipation performance and operational safety. The specific implementation steps are as follows:

[0069] Based on the distribution pattern of impact noise reflected in the interference coupling scale, a signal buffer period corresponding to the wind turbine's self-test behavior is set. In the wind turbine operating environment, external impact noise often appears in the form of instantaneous bursts, and its duration is usually shorter than the wind turbine's self-test cycle. To avoid these short-duration impact noises directly affecting the self-test triggering, the signal buffer period should cover the time range of concentrated occurrence of interference events in the interference coupling scale. In specific implementation, based on the duration and time concentration interval of impact events recorded in the interference coupling scale, a signal buffer period is added to the wind turbine's self-test triggering logic, so that the system does not respond to single acoustic energy mutations during this period. When the acoustic signal experiences a short-term surge during the buffer period, the system continues to monitor its energy persistence. Only when the sound pressure change continues to exceed the length of the buffer period is the self-test program allowed to enter the triggering judgment stage. By introducing a signal buffer period into the self-test triggering logic, short-duration impact interference signals corresponding to the interference coupling scale can be effectively filtered out, thereby suppressing unnecessary self-test actions caused by instantaneous external noise.

[0070] After setting the signal buffer period, the self-test interval of the fan is reasonably set according to the frequency and duration distribution characteristics of interference reflected by the interference coupling scale. When the fan is running in a complex environment, a self-test interval that is too short can cause the control program to frequently enter the detection process under multiple impact interferences, while an interval that is too long may delay the identification of equipment faults. Therefore, when determining the self-test interval, the interference period information in the interference coupling scale should be combined, and the self-test interval should be set to an integer multiple of the interference period, so that the self-test operation avoids the time concentration area of ​​interference events as much as possible. In the implementation process, first determine the average interval of interference events on the time axis, and then set the self-test time period according to the interval length, so that there is enough buffer space between two adjacent self-tests, and avoid the impact sound that has not been fully attenuated in the previous cycle from affecting the judgment of the next self-test. At the same time, in the initial stage of the self-test cycle, the sampling and judgment time of the acoustic signal by the system is delayed to ensure that the ambient sound energy in the self-test start-up stage is in a stable range. In this way, the self-test operation can be effectively staggered with the distribution of interference events in time, thereby further improving the smoothness of the operation rhythm.

[0071] Based on the determined self-test interval, the sound recognition threshold is dynamically set according to the energy distribution information in the interference coupling scale. The acoustic signal intensity in the wind turbine operating environment is affected by various factors, including external air disturbances, equipment load changes, and structural vibrations. Therefore, setting a fixed recognition threshold at different operating stages often leads to misjudgments. To avoid this, the recognition threshold range applicable to the current operating environment should be determined based on the difference between the upper limit of the impact event energy and the lower limit of steady-state noise in the interference coupling scale. In specific implementation, the peak energy of the impact event is used as the upper limit reference, the average steady-state noise energy is used as the lower limit reference, and the intermediate value between the two that can distinguish between interference signals and real fault signals is selected as the recognition threshold. The system only enters the self-test trigger judgment process when the acoustic signal exceeds this threshold; if the acoustic signal energy fluctuation does not reach the upper limit of the threshold, it is considered environmental noise, and the self-test operation is not triggered. In this way, the accuracy of the wind turbine's discrimination of acoustic signals can be effectively improved, making the system's response to environmental impact sounds more stable, thus enabling the self-test logic to have anti-interference characteristics in complex noise environments.

[0072] After setting the signal buffer period, self-test interval, and sound recognition threshold, the minimum time interval for fan start-up and shutdown is adjusted based on the time coupling relationship between the aforementioned three parameters to form a complete operating rhythm control scheme. In actual operation, the fan start-up and shutdown frequency directly affects the thermal load of the drive power supply and control circuit. To prevent frequent start-ups and shutdowns in a short period from causing current surges and component stress accumulation, a minimum start-up and shutdown time interval should be set in the operating logic. In implementation, based on the time span in the interference coupling scale, the minimum start-up and shutdown time interval is set as the combined length of the interference event duration and echo delay time, ensuring sufficient buffer intervals between each start-up and shutdown operation. During this time interval, the system pauses a new round of self-test trigger judgment, allowing the fan operating state to stabilize completely. To make the operating rhythm control more coordinated, the signal buffer period, self-test interval, and minimum start-up and shutdown time interval are uniformly mapped to the operating rhythm control scheme according to time proportions, ensuring consistency among the three in the time dimension, thereby forming an adaptive operating rhythm control logic. In this way, the wind turbine can maintain stable operation in an environment with frequent external interference noise. The self-check operation is only triggered when there is a fault signal, which effectively avoids heat accumulation and component fatigue caused by frequent start-stop.

[0073] The dynamic anti-false triggering control module executes wind turbine operation adjustment according to the operation rhythm control scheme. Within the interference coupling scale range, it implements dual confirmation sampling, reverse silent window control and flexible start strategy to block false triggering cycle and stabilize wind turbine operation status.

[0074] To achieve dynamic adjustment of the wind turbine under the guidance of the operating rhythm control scheme, effectively blocking false triggering cycles caused by external impact noise in complex noise environments, and ensuring stable operation of the wind turbine within the interference coupling scale range through the synergistic effect of dual-confirmation sampling, reverse silent window control, and a flexible start-up strategy, this process is based on the established operating rhythm control scheme. It combines the two-dimensional characteristics of time and energy in the interference coupling scale to achieve coordinated matching of wind turbine operation control in terms of time and signal response, thereby enabling the entire operation process to possess comprehensive capabilities of anti-interference, balance, and adaptive adjustment. The specific implementation steps are as follows:

[0075] After the operation rhythm control scheme is determined, the wind turbine's operating status is subjected to dual confirmation sampling to ensure the reliability of self-test triggering and anti-interference capability. Dual confirmation sampling refers to independently acquiring and comparing acoustic signals twice within the same time period to confirm the continuity and stability of signal characteristics. In implementation, firstly, within the time range limited by the interference coupling scale, according to the signal buffer period and identification threshold set in the operation rhythm control scheme, the sound pressure signal, vibration signal, and airflow change signal in the wind turbine operating environment are acquired in the first round. Subsequently, a second round of acquisition is conducted under the condition that the time interval does not exceed one sound wave propagation cycle, and the signal waveforms obtained from the two samplings are superimposed and compared on the time axis. When the two sampling results are consistent in the trend of sound energy change, vibration response mode, and airflow disturbance amplitude, the signal can be identified as the real equipment operating status signal, and the operation logic is allowed to proceed to the next stage of judgment. When there is a difference between the two sampling results or only one sampling shows a sudden signal, the signal is classified as an external short-term interference signal and ignored during the buffer period. This double-confirmation sampling method ensures the system's stability in response to external acoustic stimuli, allowing the fan's operation control logic to respond only to continuous and physically relevant signals, fundamentally reducing the probability of false triggering.

[0076] After completing double-confirmation sampling, to further stabilize the fan's operational response, a reverse silence window control is introduced into the operational rhythm control scheme based on the time characteristics of the interference coupling scale. Reverse silence window control refers to setting a silence time window after the impact sound event ends. During this time window, the system temporarily does not respond to new acoustic signal trigger requests to prevent repeated triggering caused by impact sound echoes or environmental reflections. In specific implementation, the echo delay time recorded in the interference coupling scale is used as a reference to determine the length of the silence window, ensuring that this time window completely covers the entire process of impact sound energy decaying from its peak to the steady-state noise level. Within this time window, even if the acoustic signal experiences short-term fluctuations again, the system does not perform self-check triggering but continues to maintain the current operating state. Real-time response to acoustic signals is resumed after the silence window ends. By introducing a reverse silence window within the interference coupling scale range, the secondary echo effects caused by sound wave reflections around the fan casing, ventilation ducts, or equipment can be effectively eliminated, thereby preventing the system from repeatedly performing self-checks and start-stop operations during the echo duration. This technology enables the wind turbine to maintain a stable operating state during the attenuation of external acoustic interference, creating a stable foundation for the subsequent implementation of flexible start-up strategies.

[0077] After completing the reverse silent window control, a flexible start-up strategy is implemented based on the operating rhythm control scheme and the actual operating state of the fan. This ensures a smooth transition between energy output and mechanical response during the fan's start-up process. The implementation of the flexible start-up strategy aims to avoid current surges and mechanical vibrations caused by sudden power-on, thereby extending the service life of the motor and drive power supply. Specifically, when the fan enters the start-up phase from a stationary state, the control logic determines whether it is currently in a disturbance-sensitive range based on the environmental acoustic conditions recorded in the disturbance coupling scale. If it is in a disturbance-sensitive range, a gradual increase in motor torque is used to achieve flexible acceleration, allowing the fan to smoothly transition to its rated speed over a longer period. If it is in a disturbance-insensitive range, a faster acceleration curve can be used to shorten the start-up time. Simultaneously, during the flexible start-up process, the system continuously monitors the synchronous changes in sound pressure and vibration signals to ensure that the fan is not subjected to new external shocks during the acceleration phase. When external acoustic disturbances are detected, the flexible start-up strategy automatically slows down the power increase rate, further reducing mechanical shocks during the start-up process. In this way, the wind turbine can achieve a balance between energy release and environmental disturbance during startup, ensuring stable and reliable operation.

[0078] Through the combined effects of dual-confirmation sampling, reverse silent window control, and a flexible start-up strategy, a complete operation adjustment process is formed, enabling the wind turbine to operate stably within the interference coupling scale range. Specifically, during the continuous operation phase of the wind turbine, the system continuously monitors the acoustic signals dynamically according to the operation rhythm control scheme. When a new impact event is detected, the authenticity of the signal is first determined through dual-confirmation sampling. If a short-term impact signal occurs during the confirmation phase, a silent window is immediately triggered to temporarily suppress the response. After the silent window ends, if no continuous interference signal is detected, the system maintains the existing operating state. If the wind turbine is waiting to start while stationary, the acceleration curve is adjusted according to the flexible start-up strategy, and the acoustic response is monitored simultaneously, thereby maintaining the continuity and anti-interference of the operating rhythm in different operating phases. Through this coordinated operation mechanism, the start-up and shutdown behavior of the wind turbine corresponds to the time distribution of environmental noise in a staggered manner, thereby effectively blocking false triggering cycles and ensuring the stability of the control logic in complex operating environments. This operation adjustment process realizes closed-loop management from signal acquisition to operation control, enabling the wind turbine to maintain thermal balance and electrical stability under conditions of frequent changes in external noise, and ensuring that the operating rhythm and interference distribution are always time-isolated.

[0079] This invention establishes a multi-source signal acquisition and timing analysis mechanism during wind turbine operation, enabling dynamic correlation of acoustic, vibration, and airflow signals under a unified time reference. By identifying the impact sound initiation point, attenuation range, and echo delay characteristics, it distinguishes between external instantaneous noise and the equipment's own operating sound. This process forms a closed-loop judgment in the acoustic analysis and interference identification stages, effectively avoiding erroneous self-check operations triggered by external impact sounds. This allows the wind turbine to maintain a stable operating rhythm in complex acoustic environments, thereby reducing the number of invalid start-stop cycles and preventing heat accumulation and fatigue damage to the drive unit due to frequent current surges.

[0080] This invention establishes an operational rhythm control mechanism centered on interference coupling scale, dynamically adjusting the signal buffer period, self-test interval, and start-stop time interval. It also introduces dual-confirmation sampling, a reverse silent window, and a flexible start-up strategy during the control process to achieve adaptive regulation of the wind turbine's start-stop rhythm. This method can suppress the cyclic triggering phenomenon during the self-test process, ensuring that the self-test operation only occurs under actual fault conditions. This improves the reliability and service life of the wind turbine's intelligent control, enabling the equipment to possess comprehensive operational performance with anti-interference, noise reduction, and heat dissipation synergy under remote monitoring.

[0081] 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 remote monitoring and intelligent control integrated device for fan sound insulation, noise reduction, and heat dissipation, characterized in that, It includes a signal acquisition module, an acoustic analysis module, an interference identification module, a parameter adjustment module, and a dynamic anti-false trigger control module. The signal acquisition module collects impact sound signals, vibration signals, and airflow change data in the wind turbine operating environment, arranges all the collected signals in chronological order, eliminates stable background noise, and generates an impact sound time series dataset. The acoustic analysis module identifies the impact start interval and attenuation interval based on the impact sound time series dataset, calculates the attenuation change of the sound wave peak over time and extracts the echo delay parameter, and generates impact sound event distribution information based on the above parameters. The interference identification module calibrates the timing of the system's self-test based on the distribution information of the impact sound events, compares the impact sound characteristics with the fan start-up and shutdown time series, analyzes the overlap relationship between the impact sound and the start-up and shutdown rhythm, and extracts the interference coupling scale. The parameter adjustment module sets the signal buffer period, self-test interval, and sound recognition threshold according to the interference coupling scale, and adjusts the minimum time interval for the fan to start and stop, thus forming an operation rhythm control scheme. The dynamic anti-false triggering control module executes wind turbine operation adjustment according to the operation rhythm control scheme. Within the interference coupling scale range, it implements dual confirmation sampling, reverse silent window control, and flexible start-up strategy to block false triggering cycles and stabilize the wind turbine operation status.

2. The integrated device for remote monitoring and intelligent control of fan sound insulation, noise reduction, and heat dissipation according to claim 1, characterized in that, The steps for generating the impact sound time series dataset are as follows: The signal acquisition conditions of the wind turbine operating environment are initialized and configured. Under the normal operating condition of the wind turbine, the impact sound signal, structural vibration signal and airflow velocity change data are acquired through the multi-channel sensing unit. The sampling frequency, sampling time window and sampling resolution of various signals are uniformly set to ensure time synchronization. The collected impact sound signals, vibration signals and airflow change data are aligned according to the collection time label, and a data index is established on the same time axis. A continuous multi-dimensional time series data matrix is ​​formed by time interpolation. The background noise characteristic range is determined based on the sound pressure signal and airflow fluctuation signal during the steady-state operation of the wind turbine, and the stable background noise is eliminated by the sliding range method to retain the dynamic change information; After background noise removal, acoustic signals, vibration signals and airflow change data are paired according to time index to form an impact sound time series dataset, and a time index table is established to achieve time-consistent expression of multi-physics field signals.

3. The integrated device for remote monitoring and intelligent control of fan sound insulation, noise reduction, and heat dissipation according to claim 2, characterized in that, All sensing units are synchronously triggered with a unified time reference, ensuring a one-to-one correspondence between the impact sound signal, vibration signal, and airflow change data in the time dimension. After acquisition, the signals are smoothed in chronological order to eliminate transient high-frequency noise caused by random external disturbances, thereby ensuring the consistency of the formed impact sound time series dataset in terms of continuity and comparability, and providing a stable data foundation for subsequent analysis.

4. The integrated device for remote monitoring and intelligent control of fan sound insulation, noise reduction, and heat dissipation according to claim 2, characterized in that, The steps for generating impact sound event distribution information are as follows: Energy characteristics were scanned on the time series dataset of impact sound, and the change trend of sound pressure amplitude over time was continuously read. The initial response interval of the impact event was determined as the impact starting point interval based on the time range of energy surge. After determining the impact starting point range, energy attenuation analysis is performed on the subsequent time series. Taking the peak value of the maximum sound pressure as the starting reference, the decreasing trend of sound wave energy is monitored, and the attenuation range of the impact sound is determined based on the time for energy to return to steady state. After obtaining the impact start-point interval and attenuation interval, the continuous change of the sound pressure peak value over time is recorded for each impact event segment. The peak attenuation characteristics are extracted and the echo peak value generated by sound wave reflection is captured to obtain the echo delay parameter. After extracting the various feature parameters, the impact start time, energy peak, attenuation duration and echo delay time are integrated in chronological order to generate impact sound event distribution information and establish an event index table to present the temporal pattern of impact events.

5. The integrated device for remote monitoring and intelligent control of fan sound insulation, noise reduction, and heat dissipation according to claim 4, characterized in that, When generating impact sound event distribution information, the starting time, energy peak, attenuation duration and echo delay time of each impact event are arranged sequentially with the time axis as the main line. The intensity characteristics of the impact event are represented by the energy change curve, and a time correspondence is established in the event index table to realize the continuous tracking and distribution pattern identification of impact sound in the time dimension.

6. The integrated device for remote monitoring and intelligent control of fan sound insulation, noise reduction, and heat dissipation according to claim 5, characterized in that, The steps for extracting interference coupling scales are as follows: Based on the distribution information of impact sound events, the self-inspection time points within the wind turbine operating cycle are calibrated, the wind turbine self-inspection trigger time is aligned with the impact event time index, and the correspondence between impact events and self-inspection time is established under a unified time reference. After completing the self-test time calibration, the impact sound characteristics in the impact sound event distribution information are compared with the wind turbine start-up and shutdown time series. By analyzing the time overlap and event duration, impact sound events that overlap with the self-test cycle are identified. After identifying the time overlap relationship, the time distribution characteristics of the impact sound event and the start-up and shutdown operation time of the wind turbine were analyzed and a time correlation table was established by using the starting time of the impact event and the start-up and shutdown operation time of the wind turbine as the time coordinates. Based on the overlapping data of impact sound and start-stop rhythm recorded in the time correlation table, the interference coupling scale is extracted and the concentrated interval of interference events is determined by the time difference distribution, so as to reflect the influence of impact sound events on the wind turbine self-test process.

7. The integrated device for remote monitoring and intelligent control of fan sound insulation, noise reduction, and heat dissipation according to claim 6, characterized in that, In the process of extracting the interference coupling scale, the starting time, peak intensity, attenuation duration and echo delay time of the impact sound event are used as the basic parameters, and matched with the wind turbine self-test trigger time, shutdown duration and restart interval. By comparing the time difference distribution, the concentrated interval of the interference event on the time axis is determined, and the ratio of the interval length to the duration of the impact event is used to characterize the time influence range of the interference coupling scale.

8. The integrated device for remote monitoring and intelligent control of fan sound insulation, noise reduction, and heat dissipation according to claim 6, characterized in that, The steps for generating the rhythm control scheme are as follows: Based on the distribution law of impact sound interference in the interference coupling scale, a signal buffer period corresponding to the self-test behavior of the fan is set, and a buffer link is added to the self-test trigger logic to filter short-term impact interference signals. After the signal buffer period is set, the fan self-test interval is set according to the interference cycle information of the interference coupling scale, and the self-test operation is made to avoid the concentrated distribution range of interference events in time. Based on the self-test interval time, the sound recognition threshold is set according to the energy distribution information in the interference coupling scale, and the recognition range is determined with reference to the peak energy of the impact event and the average energy of the steady-state noise. After the signal buffer period, self-test interval and sound recognition threshold are set, the minimum time interval for starting and stopping the fan is adjusted according to the time coupling relationship between the three parameters, and an adaptive operating rhythm control scheme is formed to stabilize the operating status of the fan.

9. The integrated device for remote monitoring and intelligent control of fan sound insulation, noise reduction, and heat dissipation according to claim 8, characterized in that, The signal buffer period, self-test interval, and sound recognition threshold set in the operation rhythm control scheme are kept in a time ratio relationship. The minimum start-stop time interval is dynamically adjusted based on the time span of the interference coupling scale, so that the fan can maintain stable operation during the continuous impact sound interference and avoid the self-test logic being repeatedly triggered within the interference time interval, thereby achieving time coordination and continuous balance of the operation rhythm.

10. The integrated device for remote monitoring and intelligent control of fan sound insulation, noise reduction, and heat dissipation according to claim 8, characterized in that, The operation of the wind turbine is adjusted according to the operating rhythm control scheme. Within the interference coupling scale range, dual confirmation sampling, reverse silent window control, and flexible start-up strategies are implemented to block false triggering cycles and maintain stable wind turbine operation. The steps are as follows: After the operation rhythm control scheme is determined, the operating status of the wind turbine is double-confirmed by sampling. Within the interference coupling scale range, the sound pressure signal, vibration signal and airflow change signal are independently collected and compared twice to confirm the continuity and stability of the signal characteristics. After completing the double confirmation sampling, a reverse silence window control is introduced based on the time characteristics of the interference coupling scale. The silence time window is set with the echo delay time as a reference, and the self-test trigger judgment is paused within the silence window to eliminate echo interference. After completing the reverse silent window control, a flexible start strategy is executed according to the running rhythm control scheme. The motor torque rise speed is adjusted according to the ambient acoustic state of the interference coupling scale to achieve smooth start. With the combined effect of dual-confirmation sampling, reverse silent window control, and flexible start-up strategy, an operation adjustment process is formed to block false triggering cycles and ensure that the wind turbine operates stably within the interference coupling scale range.

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