35 results about "Time frequency spectrum" patented technology

Methods and systems for detecting anomalous software behavior by monitoring frequency spectrums emanating from an electronic device are provided. A method includes storing frequency spectrum profiles for software applications and operational modes on the device. During execution of a software application, the real-time emanating frequency spectrum is measured and compared to the stored spectrum profile for that application and operational mode. Deviations between the real-time and reference spectrums indicate anomalous behavior from unknown software executing. The device determines a deviation exists and performs remedial actions like alerting the user, disconnecting from the network, shutting down, or switching application execution to another device. Frequency profiles are measured for new applications installed and stored to keep the data updated. Monitoring real-time frequency spectrums emanating from a device provides a computationally lightweight technique for detecting malicious software behavior without requiring complex analysis of application code.

The invention relates to the technical field of biological recognition, in particular to an intelligent wearable device identity authentication method and system based on biological characteristics, and the method comprises the following steps: collecting a user voice sample, removing background noise from the voice sample through a spectral subtraction method and a Wiener filter technology, and generating a denoised voice sample; separating the time sequence and frequency spectrum information of the voice signal from the de-noised voice sample by using a convolutional neural network, and extracting a time frequency spectrum feature vector; according to the method, background noise is eliminated from a voice sample by using spectral subtraction and a Wiener filter, and time spectrum features are extracted in combination with the convolutional neural network, so that the user voice capturing and analyzing capability is improved. In addition, the gating circulation unit deeply learns the intonation and rhythm of the voice data, so that the recognition of the voice mode is more accurate. The sensor data is subjected to independence analysis through the isolated forest algorithm, abnormal data can be recognized and corrected, and the accuracy of data output is ensured.

The invention discloses a heat dissipation unit control method and electronic equipment, and relates to the technical field of heat dissipation unit control, and the method comprises the steps: obtaining acoustic signals and vibration signals of a heat dissipation unit in a server in various operation states, and determining a baseline embossing feature and a resonance frequency spectrum of the heat dissipation unit, so as to establish a heat dissipation acoustic archive; acquiring and analyzing a real-time acoustic signal and a real-time vibration signal of the heat dissipation unit to obtain a real-time embossing feature and a real-time frequency spectrum, and judging whether the running state of the heat dissipation unit is abnormal or not; in response to abnormal operation of the heat dissipation unit, judging whether the heat dissipation unit has a resonance risk or not; and in response to existence of the resonance risk, generating a fusion adjustment instruction based on the feature comparison result and the spectrum comparison result. The operation state of the heat dissipation unit can be sensed in real time and pre-controlled, the resonance risk and the abnormal state are avoided, noise is reduced, and the problems that noise control lags behind, depends on hardware structure modification and cannot avoid the resonance risk in advance in the prior art are solved.

The invention relates to a false fire point data identification method and device, equipment and a storage medium. The method comprises the following steps: acquiring target fire point monitoring data of a target area within a preset historical duration; performing spatial clustering on multiple pieces of fire point data in the target fire point monitoring data based on a preset clustering mode to obtain multiple clustering objects of the target fire point monitoring data; performing time spectrum analysis on each clustering object to obtain power spectrum density distribution of each clustering object; based on the power spectral density distribution of the clustering objects, identifying fire point types of the clustering objects, and determining the clustering objects of which the fire point types are false fire points as target clustering objects; and determining the fire point data in the target clustering object as the false fire point data of the target area in the preset historical duration, so that the false fire point data in the fire point monitoring data can be quickly identified by effectively utilizing the spatial distribution rule and the time spectrum characteristics of the historical fire point data. The identification efficiency and the identification accuracy of the false fire point data can be improved.

The invention relates to the technical field of motor control, in particular to an electric bed motor control method and an electric bed, and the method comprises the steps: sampling a current ripple signal, carrying out the spectrum analysis of the current ripple signal, extracting a load resonant frequency, generating an anti-phase harmonic current, and injecting the anti-phase harmonic current into a motor winding at the starting moment of a motor; damping harmonic waves are injected into the motor winding in the motor pre-braking stage, and phase-synchronous reverse currents are injected into the motor winding in the motor main braking stage. Low-impact control over starting and stopping of the electric bed is achieved through an electromechanical energy hedging mechanism, and an existing electric bed electromechanical framework is compatible; at the starting moment of the motor, high-frequency resonance excitation force can be neutralized by injecting anti-phase harmonic current; kinetic energy is dissipated through damping harmonic waves in the pre-braking stage, and current reverse locking is applied in the main braking stage, so that the in-place residual amplitude approaches zero; and dynamic tuning control parameters are analyzed based on a real-time spectrum, so that the start-stop time under a variable load is relatively constant, and the problems of light-load overshoot and heavy-load hysteresis are solved.

The invention discloses an AI-based LED display screen interactive display method, and relates to the technical field of intelligent display, and the method comprises the following steps: a microwave radar sensor monitors electromagnetic spectrum information in a surrounding environment in real time through a built-in spectrum sensing module, and receives and analyzes electromagnetic wave signals emitted from different electronic devices in real time; collected electromagnetic wave signal data are preprocessed to construct a data set, and key features reflecting the risk of overlapping between the working frequency band of the microwave radar sensor and the frequency band of surrounding electronic equipment are extracted from the data set. Through real-time frequency spectrum monitoring, intelligent interference evaluation and dynamic frequency band switching, it is ensured that the radar system automatically avoids interference in a complex environment, misjudgment is reduced, and the target positioning precision, the response speed and the system stability are improved. According to the scheme, technical guarantee is provided for application of the microwave radar sensor in a high electromagnetic interference environment, and the reliability and the adaptive capacity of a system are enhanced.

The invention relates to a method for realizing full-time frequency spectrum monitoring based on a nest unmanned aerial vehicle and a fixed monitoring station. The method comprises the following steps: issuing a full-time frequency spectrum monitoring task; continuously scanning the monitoring target frequency band; extracting a signal frequency, and performing parameter measurement; signal parameters which are not matched with the known signal library are stored, and the equipment state is recovered; setting a time interval, and generating a direction finding task list; the unmanned aerial vehicle advances according to a preset track and performs a monitoring direction finding task; the unmanned aerial vehicle returns to enter the nest, and charging and heat dissipation reconditioning are automatically completed; and updating the signal positioning data. By adopting the method, the system, the device, the processor and the computer readable storage medium for realizing full-time frequency spectrum monitoring based on the nest unmanned aerial vehicle and the fixed monitoring station, a reliable positioning result is given through a multi-point direction finding result of the unmanned aerial vehicle on the whole track, high-frequency weak signals are easy to capture, the monitoring utilization rate of the unmanned aerial vehicle is greatly improved, and the real-time performance of the unmanned aerial vehicle is improved. The total element information of the signal is collected, and the management is scientific and accurate.

The application discloses a teaching method for the characteristics of the singing tune of the Liuqin opera based on sound processing, and comprises the following steps: constructing a singing tune audio library, and marking the time axis characteristics by experts to form a first positive sample; carrying out human voice separation, band filtering and differential frame processing based on rhythm types on the sample to generate a time-spectrum graph as a third positive sample; extracting a singing tune characteristic sequence through a characteristic extraction network; finally, using the trained network to carry out multi-dimensional evaluation on the singing tune of students, calculating quantitative indexes such as overall similarity, rhythm deviation and time sequence deviation, and providing objective singing feedback and individualized correction guidance for students. The application realizes the change of the teaching of the Liuqin opera from traditional experience transmission to modern quantitative analysis, and significantly improves the teaching efficiency and precision.

The invention relates to the technical field of voice recognition, and provides an intelligent device soft wakeup system and method based on 5G CPE. A 5G CPE module; the intelligent awakening module is connected with the customized configuration end and is used for acquiring a spectrum feature code model corresponding to an awakening word from a background server end, performing Fourier transform on an audio stream acquired in real time to extract a spectrum feature code, calculating a spectrum overlapping ratio of the real-time spectrum feature code and the spectrum feature code model, and when the overlapping ratio exceeds a preset threshold value, starting the awakening module; transmitting the audio data to a background server; and the background server is in two-way communication connection with the intelligent wake-up module and is used for extracting the audio data as pinyin data and matching the pinyin data with the pinyin data generated by the customized configuration end. According to the method, the function of customizing the wake-up word by the user is realized, the recognized tone can be customized, the method is completely realized by software, and the overall cost is greatly reduced.

The invention discloses an insulator internal defect identification method and system based on a terahertz technology, and relates to the technical field of nondestructive detection.The method comprises the steps that firstly, a frequency spectrum obtained after echo signal processing of an insulator is obtained in the healthy state of the insulator and serves as a reference healthy fingerprint; then, echo signals are monitored in real time, the real-time frequency spectrum is compared with the reference frequency spectrum through a state difference value method, and newly-added reflection caused by internal defects such as microcracks is recognized. Furthermore, the invention provides an optimized deployment strategy for installing the sensors at electric field change sensitive positions in the insulator string, and provides a scheme for carrying out data cross validation by adopting multiple sensors for a long insulator string, so that the detection accuracy is improved to the maximum. The system is also integrated with a LoRa wireless communication module, and reliable remote transmission of data in a high-voltage strong electromagnetic interference environment is realized. According to the invention, on-line, lossless and high-precision monitoring of internal defects of the insulator is realized.

The application provides a self-supervised abnormal sound detection method based on spectrum and time information combination, comprising the following steps: collecting original audio clips of each target device in advance, and storing the original audio clips in association with machine ID as an identifier; using a preset mel filter to extract a mel spectrum graph of the original audio clips; using a preset time feature extraction network to extract a time spectrum feature graph of the original audio clips; performing feature fusion on the mel spectrum graph and the time spectrum feature graph of each target device respectively; inputting the mel and time fusion features of each target device into a classifier, taking the machine ID of each target device as a category label, and training to obtain a target classification model; in the test, inputting the to-be-tested sound information into the obtained target classification model to obtain real-time feature sound indicators of each target device, and judging whether the target device is in an abnormal state according to the comparison result of the real-time feature sound indicators and a critical value.

The application discloses a real-time recursive discrete Fourier transform implementation method and system based on FPGA. The method comprises the following steps: obtaining an initial frequency spectrum result; buffering an input data window, obtaining a removed sampling value and a newly entered sampling value through sliding update; for a target frequency point, performing differential update on the current frequency spectrum result, the removed sampling value and the newly entered sampling value, and performing complex multiplication in combination with a rotation factor lookup table to obtain an updated frequency spectrum result; buffering and feeding back the updated frequency spectrum result, and outputting a continuous real-time frequency spectrum analysis result corresponding to the target frequency point. The method does not need to recalculate all frequency points every window, significantly reduces the calculation amount and resource consumption, supports an arbitrary window length, has strong real-time performance, and is suitable for embedded spectrum analysis scenes.

The invention belongs to the technical field of power electronics, and discloses a harmonic suppression optimization method and device for an electric energy quality treatment device. The method comprises the following steps: synchronously acquiring a three-phase signal of a power grid target node, and preprocessing to obtain a normalized signal; analyzing to obtain a current power spectral density, a fundamental wave parameter and a real-time spectrum map; carrying out harmonic blind separation on the normalized signal, and extracting a feature vector from a separated signal; searching frequency spectrum holes based on a real-time frequency spectrum map, evaluating the interference cost in combination with the feature vector, and generating an optimal suppression strategy according to interference cost maximization; generating a modulation voltage according to a suppression strategy, and injecting the modulation voltage into the IGBT as a switching signal to realize harmonic suppression; the method effectively breaks through the limitation that the exogenous harmonic waves are useless in the prior art, remarkably improves the adaptability and harmonic suppression accuracy of the device in a complex power grid environment, ensures that the total harmonic distortion rate of the power grid meets the national standard requirement, and guarantees the stable electric energy quality.

To separately collect a sound field radiated from a sound source in an area and a sound field coming from the outside of the area by using a distributed spherical microphone array.SOLUTION: A separation matrix calculation part 10 of a sound collection device 1 calculates a separation matrix in which position information and size are reflected and which is expressed by a spherical harmonic function on the basis of preset distributed spherical microphone array position information. The frequency analysis unit 11 obtains a time-frequency spectrum by performing Fourier transform on a sound collection signal from the distributed spherical microphone array M in a time domain. A spherical harmonic spectrum vector calculation part 12 calculates a spherical harmonic spectrum vector having a spherical harmonic spectrum of a sound field radiated from a sound source S in an area and a spherical harmonic spectrum of a sound field due to an interference wave coming from the outside of the area as elements on the basis of a separation matrix and a time frequency spectrum of a sound collection signal.SELECTED DRAWING: Figure 3

The invention discloses a badminton flying strength detection method based on deep learning, and relates to the technical field of intelligent detection of kinematics, and the method comprises the steps: converting ball hitting window data containing speed information into four recurrence plots and short-time spectrograms, splicing the four recurrence plots and short-time spectrograms into an unchecked strength image tensor, carrying out the phase consistency and edge checking, and carrying out the detection of the strength image tensor; obtaining a force image tensor; and inputting the force image tensor into a convolution and attention combined deep learning network to perform feature extraction, fitting air resistance and effective quality parameters in combination with a speed change curve, and performing constraint in a micro physical layer according to a momentum conservation relationship to obtain a physically corrected badminton hitting force result and generate a detection report. According to the invention, visual representation and depth feature extraction of the multi-source motion signal are realized, the accuracy of ball hitting force detection is improved, and the physical consistency and stability of a force result are realized.

This invention discloses a time-frequency energy resource allocation system based on an intelligent reflective surface for an underground Internet of Things (IoT) architecture. Belonging to the field of resource allocation, this invention designs a frequency-division duplex IoT architecture for underground spaces by jointly using distributed energy access points and a Resource Allocation System (RIS). To achieve efficient utilization of channel and energy resources, based on this IoT architecture, this invention models the resource allocation problem as a joint allocation of time, spectrum, and energy to meet QoS requirements, along with RIS phase shift configuration. This problem is a mixed-integer nonlinear programming problem, difficult to solve in polynomial time. To reduce the complexity of the solution, this invention constructs a multi-agent collaborative resource allocation algorithm based on near-end policy optimization. Overall, the purpose of this invention is to build an efficient monitoring IoT in underground spaces, achieving an energy-saving and highly efficient resource allocation scheme.

The invention relates to the technical field of data processing, in particular to a micro-particle shot blasting equipment running state monitoring method based on multi-source data fusion. The method comprises the following steps: fusing the same frequency bands in a plurality of abnormal frequency spectrums in the same abnormal state to obtain a comprehensive frequency spectrum, and connecting the comprehensive frequency spectrums of the plurality of frequency bands to form an abnormal fusion frequency spectrum; acquiring various characteristic signals in the operation process of the equipment in real time and acquiring a real-time frequency spectrum; fusing the frequency bands of the plurality of real-time frequency spectrums to obtain a real-time fused frequency spectrum; comparing the difference between each abnormal fusion spectrum and the real-time fusion spectrum to obtain a frequency domain similarity; and calculating an abnormal score based on the frequency domain similarity, and judging an equipment operation state based on the abnormal score. The method and the device have the effect of improving the detection accuracy of the equipment running state.

Computing systems and methods for estimation and correction of audio pitch variations resulting from eccentric rotation in the playback of phonograph records are disclosed. The methods include simulation of phonograph playback to generate eccentric and non-eccentric time bases for correction. Estimation of eccentricity parameters is done by a parameter optimization through evaluation of a spectral sharpness detection function on time-spectral content of audio data. Candidate audio data are generated using phonograph simulation while conducting an optimal parameter search. The disclosed technology further provides systems and methods to correct audio using such parameters to correct the induced pitch variations from eccentric phonograph record playback in order to improve the pitch stability of audio recordings. In addition, the physical properties of groove radius and playback speed variations in phonograph playback may be estimated from the eccentric record playback.

The invention discloses an unmanned aerial vehicle countering signal identification method and a signal identification device, and belongs to the technical field of unmanned aerial vehicle countering signal identification, and the method comprises the following steps: obtaining a high-precision three-dimensional building and terrain three-dimensional model of a task area, collecting the historical spectrum data of an existing monitoring node of the area, and carrying out the collection of the historical spectrum data; constructing an electromagnetic environment baseline model of the region through a machine learning algorithm; in the grouping operation process of the unmanned aerial vehicles, continuously receiving and monitoring communication signal states between all the unmanned aerial vehicles and a control station, and comparing the real-time frequency spectrum with a pre-established baseline model; according to the method, abnormal signals are accurately recognized through the baseline model, high-precision positioning of the interference source is realized by means of three-dimensional modeling and reverse tracking, calculation is simplified through path comparison, the real-time performance is improved, the problems of high interference recognition misjudgment rate, inaccurate positioning and response lag in a complex environment can be effectively solved, and the operation safety and reliability of the unmanned aerial vehicle are guaranteed.

The invention discloses a five-axis numerical control engraving and milling machine machining regulation and control method and system based on machine vision, and relates to the technical field of engraving and milling machine machining regulation and control. The method comprises the steps that three-dimensional point cloud data of a workpiece are obtained and subjected to registration comparison with a preset workpiece theoretical CAD model, and the clamping position deviation and the posture deviation of the workpiece are calculated; a preset numerical control machining track is corrected, and a first compensation machining path is generated; real-time vibration signals of a machine tool machining area are collected in real time through a vibration sensor, real-time spectrum analysis is carried out, and vibration related signal data are generated; and calculating and generating a vibration suppression driving signal, and outputting the vibration suppression driving signal to a vibration suppression execution device for vibration suppression control. The technical problems that in the prior art, due to workpiece clamping deviation, a five-axis numerical control engraving and milling machine is poor in machining path adaptability, cutting vibration is difficult to restrain accurately, and then machining precision, efficiency and reliability are affected are solved, and the technical effect of improving the machining precision, efficiency and reliability of the five-axis numerical control engraving and milling machine is achieved.

The present application relates to the technical field of motor control, and particularly relates to a motor control method for an electric bed and the electric bed, which comprises the following steps: at the moment of starting the motor, sampling a current ripple signal, performing frequency spectrum analysis on the current ripple signal, extracting a load resonance frequency, generating an anti-phase harmonic current, and injecting the anti-phase harmonic current into a motor winding; at a motor pre-braking stage, injecting a damping harmonic into the motor winding, and at a motor main-braking stage, injecting a phase-synchronous reverse current into the motor winding. The present application realizes low-impact control of starting and stopping of the electric bed through an electromechanical energy hedging mechanism, and is compatible with the existing electromechanical architecture of the electric bed. At the moment of starting the motor, the anti-phase harmonic current is injected to neutralize high-frequency resonance exciting force. At the pre-braking stage, the damping harmonic is used to dissipate kinetic energy, and at the main-braking stage, the current is reversed to lock, so that the residual amplitude at the destination approaches zero. Based on real-time frequency spectrum analysis, control parameters are dynamically tuned to ensure that the starting and stopping time is relatively constant under varying loads, and the problems of overshoot under light load and delay under heavy load are solved.

The application discloses an anchor rod machine self-adaptive damping control method and system and a medium, and relates to the technical field of intelligent control. The method comprises the following steps: an interactive monitoring device is used to collect vibration signals and working condition signals of multiple parts of an anchor rod machine from a drill bit to an operating room; real-time frequency spectrum analysis is performed according to the vibration signals, vibration type identification is performed on the obtained frequency spectrum characteristics and working condition signals, and favorable vibration and unfavorable vibration are distinguished; self-adaptive control is performed according to the favorable vibration and the unfavorable vibration, wherein a generation maintenance or conduction instruction is generated for the favorable vibration, and a reverse damping instruction is generated for the unfavorable vibration; the vibration state of the anchor rod machine after the control instruction is executed is monitored, the vibration amplitude of the operating room and the mast resonance peak amplitude are used as main performance indexes, and the control parameters are self-adaptively adjusted and optimized. The technical problem of poor damping effect of the anchor rod machine in the prior art is solved, and the technical effect of improving the damping control effect of the anchor rod machine by identifying the vibration type and self-adaptively adjusting the control parameters is achieved.

A method and system for visualizing defect detection. This method includes the following steps. Output a test audio signal to a device under test, and receive a response signal of the device under test to the test audio signal to generate a received audio signal. Perform signal processing on the received audio signal to generate a time-frequency spectrum diagram. According to the time-frequency spectrum diagram, visually determine whether the device under test has unacceptable defects in a pre-established auditory standard.

This invention discloses a low-power FFT chip for real-time spectrum analysis of electroencephalogram (EEG) signals and its usage method. The chip includes an analog front-end module, a digital processing module, an interface and control module, and a low-power management and clock control module. The analog front-end module amplifies, filters, performs power frequency notch filtering, and performs analog-to-digital conversion on the input microvolt-level analog EEG signals. This invention integrates a low-noise analog front-end, a dedicated digital processing module, and a fine-grained power management module into a single chip, constructing a dual anti-interference link that combines analog and digital processing. While ensuring signal purity, the digital processing module accelerates real-time processing speed, and the overall power consumption is controlled through fully custom circuitry and dynamic power management. This solves the three major problems of poor real-time performance, high power consumption, and weak noise immunity in general-purpose processor solutions for EEG spectrum analysis, thus meeting the demanding requirements of wearable devices.

The present invention relates to the field of biometric technologies, and specifically to an identity authentication method and system for intelligent wearable devices based on biometric features, comprising the following steps: collecting a user's voice sample, removing background noise from the voice sample using spectral subtraction and Wiener filter techniques to generate a denoised voice sample; applying a convolutional neural network to the denoised voice sample to separate the time series and spectral information of the voice signal and extract a time-frequency spectral feature vector. The present invention uses spectral subtraction and Wiener filter to remove background noise from the voice sample, and combines a convolutional neural network to extract time-frequency spectral features, improving the ability to capture and analyze the user's voice. In addition, the gated recurrent unit deeply learns the intonation and rhythm of the voice data, making the recognition of voice patterns more accurate. The isolation forest algorithm performs an independence analysis on the sensor data, can identify and correct abnormal data, and ensures the accuracy of data output.

The present invention discloses a deep learning decoding method and system for hand movements driven by neural representations, comprising: acquiring electroencephalogram (EEG) signals, performing feature extraction on the EEG signals to obtain movement-related cortical potential features and event-related synchronization / desynchronization oscillation features; obtaining a time-spectrum-space feature map based on the movement-related cortical potential features and the event-related synchronization / desynchronization oscillation features; performing single-hand and double-hand movement intention decoding on the time-spectrum-space feature map, obtaining decoding results, and completing deep learning decoding of hand movements driven by neural representations. The present invention designs a deep learning model driven by neurophysiological features, improves the decoding performance of multi-classification single-hand and double-hand movements, and for the first time attempts to improve movement decoding performance by fusing movement-related cortical potentials with neural representations of time-related synchronization / desynchronization in deep learning.

The application belongs to the technical field of power electronics, and discloses a harmonic suppression optimization method and device of a power quality treatment device; the method comprises the following steps: synchronously collecting three-phase signals of a target node of a power grid, and obtaining normalized signals through preprocessing; current power spectral density, fundamental wave parameters and a real-time frequency spectrum map are obtained through analysis; harmonic blind separation is performed on the normalized signals, and a characteristic vector is extracted from the separated signals; spectrum holes are searched based on the real-time frequency spectrum map, the interference cost is evaluated in combination with the characteristic vector, the optimal suppression strategy is generated according to the maximum interference cost; modulation voltages are generated according to the suppression strategy, and IGBTs are injected as switching signals to realize harmonic suppression; the application effectively breaks through the limitation of the prior art that such exogenous harmonics cannot be suppressed, significantly improves the adaptability and harmonic suppression accuracy of the device in a complex power grid environment, ensures that the total harmonic distortion rate of the power grid meets the national standard requirements, and guarantees the stability of power quality.

The invention provides a miniaturized real-time frequency spectrum digital fluorescence maintaining method which specifically comprises the following steps: carrying out broadband sampling analog-to-digital conversion on an input signal to convert the input signal into time domain data; inputting the time domain data into an FPGA (Field Programmable Gate Array) for orthogonal transformation and digital down conversion; the data is subjected to low-pass digital filtering; performing real-time discrete Fourier transform on the data to obtain frequency domain data of the input signal; frequency domain data is subjected to frequency spectrum matrix mapping, columns of a frequency spectrum matrix represent signal amplitudes, and rows of the matrix represent signal frequencies; merging the plurality of frequency spectrum matrixes, and counting the number of repetitions of each site to obtain a statistical matrix; and carrying out digital fluorescence maintaining processing point by point. According to the technical scheme, the problem that in the prior art, the digital fluorescence maintaining processing speed of a real-time spectrum analysis system is low is solved.

The application discloses a heat dissipation unit control method and electronic equipment, relates to the technical field of heat dissipation unit control, and comprises the following steps: acquiring acoustic signals and vibration signals of a heat dissipation unit in a server under multiple operating states, determining baseline pressure mark characteristics and resonance frequency spectrum of the heat dissipation unit, and establishing a heat dissipation acoustic archive; acquiring and analyzing real-time acoustic signals and real-time vibration signals of the heat dissipation unit to obtain real-time pressure mark characteristics and real-time frequency spectrum, and judging whether the operating state of the heat dissipation unit is abnormal; in response to abnormal operation of the heat dissipation unit, judging whether the heat dissipation unit has resonance risk; and in response to the existence of resonance risk, generating a fusion adjustment instruction based on a feature comparison result and a frequency spectrum comparison result. The application can realize real-time sensing of the operating state of the heat dissipation unit and pre-control, avoid resonance risk and abnormal state, and reduce the generation of noise, so as to solve the problems of noise control lag, dependence on hardware structure modification and inability to avoid resonance risk in advance in the related art.

The invention discloses an underground Internet of Things architecture time-frequency energy resource allocation system based on an intelligent reflecting surface, which belongs to the field of resource allocation and comprises the step of designing an underground space-oriented frequency division duplex Internet of Things architecture by combining distributed energy access points and RIS (Radio Information System). In order to realize efficient utilization of channel resources and energy resources, based on the architecture of the Internet of Things, a resource allocation problem is modeled as a time-frequency spectrum-energy joint allocation and RIS phase shift configuration problem meeting QoS requirements. The problem is a mixed integer nonlinear programming problem and is difficult to solve in polynomial time. In order to reduce the solving complexity, a multi-agent collaborative resource allocation algorithm is constructed based on near-end strategy optimization. Generally speaking, the invention aims to construct an efficient monitoring Internet of Things in an underground space and realize an energy-saving and high-resource scheduling efficient resource allocation scheme.