86 results about "Dynamic noise" patented technology

The invention discloses a self-adaptive filtering method and system for an intelligent electric energy meter, and relates to the technical field of power grid signal processing, and the method comprises the steps: obtaining a power grid voltage and current signal set collected by a target intelligent electric energy meter historically, and obtaining a signal noise spectrum data set and a noise level evaluation result through spectrum analysis and noise level evaluation; if the result exceeds a preset noise threshold value, identifying an interference type and building a noise adaptive filtering multi-channel, integrating and deploying the noise adaptive filtering multi-channel into a target intelligent electric meter to complete signal filtering test and effect evaluation, and dynamically optimizing and generating an electric energy meter adaptive filtering multi-channel based on the obtained multi-channel filtering effect index parameter; and finally, signal filtering closed-loop control is realized. The technical problem that a traditional electric energy meter is weak in filtering pertinence and cannot adapt to dynamic noise of a power grid in electric power related scenes with complex dynamic interference is solved, and the technical effects that the electric variable measurement accuracy of the electric energy meter is improved, and the adaptive capacity of the electric energy meter to different interference scenes is enhanced are achieved.

The invention discloses a household electrical appliance fault diagnosis system based on artificial intelligence, and relates to the technical field of household electrical appliance fault diagnosis and state monitoring. Working condition parameters aligned with a time window are obtained through working condition sensing, and fault features and the working condition parameters are linked in an order form to be converted into a target frequency track; fault characterization is converted into track characterization capable of being continuously tracked along with working conditions from a fixed frequency point, and disturbance to fault positioning in the speed change process is reduced; in order to solve the problem that weak fault components are covered by the same frequency band due to strong dynamic background noise jointly formed by inverter switch harmonic waves, current harmonic waves and load fluctuation, a dynamic noise reference spectrum is output under the current working condition through a condition generation model, and a current spectrum and the dynamic noise reference spectrum form a residual spectrum; the background spectrum base related to the working condition is suppressed, and the separability of the weak fault component in the residual domain is improved from the source.

The invention relates to the technical field of gas concentration detection, in particular to a gas concentration detection method and system for a ring main unit. The method comprises the following steps: acquiring a concentration time sequence of a gas sensor in the ring main unit; constructing an instantaneous fluctuation coefficient; analyzing the instantaneous fluctuation coefficient and a gas physical diffusion limit rate constant to obtain an environmental interference factor representing electromagnetic interference intensity; obtaining an environment interference curve, and combining the environment interference factor and the integral operation value of the environment interference curve to obtain a dynamic adjustment parameter; a dynamic noise covariance used for Kalman filtering is obtained; and performing Kalman filtering processing on the concentration time sequence by using the dynamic noise covariance to obtain an optimal estimated gas concentration value. The problems of false alarm and missing detection in the gas concentration detection process are solved.

The invention discloses a weak fault sound source monitoring and early warning method based on deep learning and an acoustic array, and belongs to the technical field of equipment state monitoring and fault diagnosis. In order to solve the problem of early fault detection caused by easy submergence of weak fault voiceprint signals, incomplete feature extraction and scarcity of fault samples in a strong noise environment, the method comprises the following steps: collecting multichannel signals through an acoustic array, establishing a dynamic noise baseline by adopting a Gaussian mixture model, and performing noise suppression in combination with a spectral subtraction method; extracting a composite feature vector containing time domain, frequency domain and time-frequency domain features; and carrying out fault identification by using the pre-trained and fine-tuned transfer learning model, and finally outputting an early warning according to an identification result. The method is suitable for online monitoring and early warning of early weak faults of industrial equipment.

An SRAM architecture is disclosed that provides a process- and temperature-compensated word-line underdrive scheme enabling stable, low-voltage operation. Each row decoder generates an initial word-line signal, derives an inverse word-line signal, and drives the corresponding word line through driver circuitry including a word-line underdrive p-channel transistor. The transistor's gate is controlled by the inverse word-line signal and optionally receives a dynamically generated negative bias to reduce device size while maintaining performance. Control circuitry includes a word-line underdrive sink circuit having dummy memory cells that replicate bit-cell behavior to generate a compensated driver output signal tracking process and temperature variations. A negative bump generator produces a transient below-ground potential at the gate node to enhance underdrive efficiency. This improves bit-cell stability, dynamic noise-margin yield, and performance uniformity across process corners and temperature, while minimizing silicon area.

This invention discloses a dynamic noise active suppression suspension system for air-to-ground electromagnetic detection, belonging to the field of geophysical exploration technology. It includes a coil vibration suppression system connected to a UAV, a receiving coil sensor, and a coil attitude control system. The coil vibration suppression system is connected to the pitch frame of the coil attitude control system via a carbon fiber tube assembly. The pitch frame is connected to the receiving coil sensor via two roll shafts. One roll shaft is connected to a first motor, and a second motor is mounted on the frame edge of the pitch frame perpendicular to the roll shaft. Both the first and second motors are connected to a controller. The coil vibration suppression system suppresses the vibration amplitude of the receiving coil and absorbs the motion noise generated by the receiving coil vibration. The controller collects and controls the first and second motors to adjust the attitude of the receiving coil, suppressing the motion noise generated by the attitude change of the receiving coil. This application significantly reduces the motion noise of air-to-ground transient electromagnetic reception data, enabling low-noise detection using air-to-ground transient electromagnetic methods in complex environments.

The application discloses a call voice real-time noise reduction method and system based on dynamic noise perception, acquires a noisy original voice signal, carries out frame division and windowing pretreatment on the digital voice signal with noise, extracts the spectral characteristics of the noise, divides the noise into steady-state noise, non-steady-state noise and burst noise according to the spectral characteristics of the noise, constructs a lightweight neural network structure through model pruning and quantization, adjusts the parameters and strategies of the types of the steady-state noise, the non-steady-state noise and the burst noise to realize optimized reduction, carries out post-processing such as window removal and overlap addition on the voice signal after noise reduction, and outputs the voice signal after noise reduction; the application effectively removes the background noise in a complex noise environment through dynamic noise perception and an adaptive noise reduction strategy, significantly improves the voice quality, meets the real-time requirement, realizes real-time noise reduction processing under low delay conditions through lightweight model design, and meets the real-time requirement of voice communication and voice control.

Embodiments of the present application disclose a kind of group adaptive integrated control method, device, electronic equipment and storage medium, involve embodied intelligent technical field, wherein, the method includes: according to target scene characteristic, utilize physical modeling and domain randomization technology to generate diversified environment task set, and pre-processing is carried out to ensure data quality, train reinforcement learning coach based on these tasks, combine decoupling and backtracking policy distillation technology to synthesize training sequence, enhance the generalization and adaptive ability of model, adopt long-time memory model structure design base model, integrate multi-agent observation module to capture time sequence and spatial correlation, dynamic noise injection and asynchronous optimization are carried out in training process, the trained field base model is deployed to target scene, form collaborative network, and set fault-tolerant mechanism to deal with agent offline and other sudden situations, continuously collect actual data to optimize model parameters.The present application effectively solves the problems of high customization, weak migration and high maintenance cost in the prior art.

The application discloses a 12-lead electrocardiosignal denoising method and system based on a dual generative adversarial network, which comprises the following steps: obtaining time-adjacent clean electrocardiosignal segments and noisy electrocardiosignal segments; constructing a noise generative adversarial network, wherein the generator takes a random noise vector as input and simulates noise as output, the discriminator takes the noisy electrocardiosignal segment as a positive sample and a synthetic signal composed of the clean electrocardiosignal segment and the simulated noise as a negative sample for adversarial training, and a noise generator snapshot library is generated; performing dynamic noise injection on the clean electrocardiosignal segment to obtain 'noisy-clean' paired data; constructing a denoising network comprising a denoiser; obtaining the 12-lead electrocardiosignal containing noise and inputting it into the denoiser to output the denoised 12-lead electrocardiosignal. The application aims to overcome the defects in the prior art, such as the lack of real 12-lead electrocardiosignal denoising paired data and the poor model generalization ability caused by excessive dependence on the Gaussian noise assumption.

A voice capture system is provided that includes a first voice beamformer and a second voice beamformer, a voice mixer, a voice-rejecting noise beamformer, a noise beamformer adjuster, an interference suppressor, and a speech enhancer. The first voice beamformer and the second voice beamformer and the voice mixer generate a voice-enhanced reference signal based on a first frequency-domain microphone signal and a second frequency-domain microphone signal. The voice-rejecting noise beamformer includes filter weights and generates a noise reference signal based on the first frequency-domain microphone signal and the second frequency-domain microphone signal. The noise beamformer adjuster adjusts one or more filter weights of the voice-rejecting noise beamformer to account for fit variation. The interference suppressor generates an interference-suppressed signal based on the voice-enhanced reference signal and the noise reference signal. The speech enhancer dynamically generates an output voice signal by applying a dynamic noise suppression signal to each frequency bin of the interference-suppressed signal.

A voice capture system including a first and second voice beamformer, a voice mixer, a voice rejected noise beamformer, a noise beamformer adjustor, a jammer suppressor, and a speech enhancer is provided. The first and second voice beamformer and the voice mixer generate a voice enhanced reference signal based on a first and second frequency domain microphone signal. The voice rejected noise beamformer includes filter weights and generates a noise reference signal based on the first and second frequency domain microphone signal. The noise beamformer adjustor adjusts the one or more filter weights of the voice rejected noise beamformer to account for fit variation. The jammer suppressor generates a jammer suppressed signal based on the voice enhanced reference signal and the noise reference signal. The speech enhancer dynamically generates an output voice signal by applying a dynamic noise suppression signal to each frequency bin of the jammer suppressed signal.

The invention belongs to the technical field of mining dust collector noise control, and relates to a mining wet dust collector noise reduction method, which comprises the steps of S1, vibration sensor and electromagnetic damper installation, S2, vibration source monitoring and noise correlation analysis, and S3, adaptive damping adjustment and feedback optimization. A three-step closed loop of hardware installation, intelligent analysis and self-adaptive execution and feedback optimization is applied to the mining wet dust collector, real dynamic noise reduction is achieved, an electromagnetic damper and a gradual change damping material work cooperatively, an active and passive composite noise reduction mode is formed, and the mining wet dust collector can adapt to complex working conditions such as underground air quantity change and dust concentration sudden change in real time.

The application provides an audio noise reduction method and a monitoring device, and relates to the technical field of monitoring, wherein the method comprises the following steps: acquiring video data and audio data of at least one direction in a current time period; for each direction, determining motion information of each sound source target in the current time period based on the video data of the direction, and determining spectral change information of the current time period relative to a previous time period based on the audio data of the direction; based on the spectral change information and the motion information of each sound source target in the current time period, predicting a noise reduction frequency band in a next time period; and reducing noise of each noise reduction frequency band in target audio data in the next time period. The application can realize the association of the sound source target and the spectral change information based on the spectral change information and the motion information of each sound source target in the current time period, and can further dynamically predict the noise reduction frequency band in the next time period, so as to realize the dynamic noise reduction of the audio data.

The application discloses a boost circuit capable of realizing adjustable charging and discharging frequency, comprising a charging and discharging circuit main body, three groups of optional passive RC filter circuits, a secondary passive filter and source follower circuit, a combination logic and differential pulse generation circuit and a negative feedback circuit. The charging and discharging circuit main body comprises two sub-circuits, the three groups of optional filter circuits are combined by switches and resistors and capacitors, and the negative feedback circuit controls the differential pulse signal by a comparator to adjust the charging and discharging. The circuit can realize the switching of three charging and discharging frequencies of 30 MHz, 100 MHz and 400 MHz, is stably boosted to 1.6 V-2.2 V, effectively controls dynamic noise and is suitable for application scenarios of low power supply voltage.

The application discloses an audio loudness improvement and dynamic range optimization method and related device, and the method comprises the following steps: obtaining an audio signal to be optimized, performing frequency response correction and dynamic noise suppression filtering processing on the audio signal to be optimized based on a multi-stage IIR filter, and obtaining a filtered audio signal; designing an adaptive dynamic compressor based on an LUFS loudness evaluation model, performing dynamic compression processing on the filtered audio signal by using the adaptive dynamic compressor, and obtaining a compressed audio signal; performing filtering optimization processing on the compressed audio signal based on a multi-stage low-pass filter, and outputting a filtered and optimized audio signal. In the embodiment of the application, the loudness is improved, the dynamic details of the audio signal are retained, the high-frequency noise amplification and the excessive compression of the low-frequency signal are avoided, the automatic dynamic compression of the audio signal is realized, and the problems such as "breathing effect" and "peak leakage" are eliminated.

The application relates to the technical field of gas concentration detection, in particular to a gas concentration detection method and system for a ring main unit; the method comprises the following steps: acquiring a concentration time sequence of a gas sensor in the ring main unit; constructing an instantaneous fluctuation coefficient; analyzing the instantaneous fluctuation coefficient and a gas physical diffusion limit rate constant to acquire an environmental interference factor representing the intensity of electromagnetic interference; acquiring an environmental interference curve, combining the environmental interference factor and an integral operation value of the environmental interference curve to acquire a dynamic adjustment parameter; obtaining a dynamic noise covariance for Kalman filtering; performing Kalman filtering processing on the concentration time sequence by using the dynamic noise covariance to obtain an optimal estimated gas concentration value. The problems of false alarms and missed detection in the gas concentration detection process are solved.

The invention discloses a low-illumination image dynamic noise reduction method for a daytime effect night vision device based on image recognition, and the method comprises the following steps: obtaining a low-illumination image sequence, and determining a current frame as a target frame; performing noise level estimation and image recognition on the target frame to obtain a noise parameter and a recognition confidence map; generating an initial edge set based on the target frame, and determining an initial edge weight; performing semantic boundary constraint reconstruction on the initial edge set, and outputting a reconstructed edge set; performing Felzenszwalb graph segmentation on the reconstructed edge set, sorting the reconstructed edge set according to the reconstructed edge weight, taking edges in sequence according to the sorting sequence, and outputting a final region label graph and a segmented region set according to a merging criterion; determining a region-level noise reduction intensity parameter for each region, and generating a noise reduction control chart; and performing regional dynamic noise reduction processing on the target frame according to the noise reduction control chart to obtain a noise reduction output frame. According to the method, the target edge and detail fidelity is improved, and the excessive smoothing risk of cross-semantic boundaries is reduced.

The present application relates to a kind of computing method for realizing wide range dynamic noise map system fast update, comprising the following steps: using the data information of system database, based on existing day and night period and city road grade, simplify the structure of traditional road sound level mechanism model;Using big data technology, using hybrid modeling method, regression each grade road sound source (day and night) sound level contribution amount prediction model;Road noise fast calculation method based on sound level contribution amount prediction model is constructed, the fast update of dynamic noise map is realized.Compared with prior art, while ensuring the calculation precision, the operation and update efficiency of noise map system is greatly improved, and the technical constraints of foreign core computing software are broken, the localization of noise map core computing technology is realized, which provides technical support for wide range deep development of noise map.

The application relates to the technical field of voice noise reduction, in particular to a microphone array voice noise reduction method and system for a video conference room, which comprises the following steps: acquiring a multi-channel frequency domain observation vector containing a main speaker voice and a burst interference, and performing an algebraic left multiplication operation on the multi-channel frequency domain observation vector by using a main speaker direction space suppression matrix to separate out a multi-channel residual vector after suppression; independently determining the update weight of each frequency point in a cross-verification manner of spatial and frequency domain characteristics; driving a bottom layer dynamic noise covariance matrix to perform a weighted recursive replacement with a frequency point by frequency point and independent step length. A beam space filtering weight vector is solved by using the updated covariance matrix, and then a time domain voice after noise reduction is reconstructed. The application aims to improve the voice purity of a video conference room in a complex acoustic environment.

The invention discloses an AI-based children language development evaluation method and system, and belongs to the technical field of voice analysis and processing, and the method comprises the steps: obtaining a voice signal and situation data of a child, generating multi-mode perception data, carrying out the dynamic noise reduction processing, and generating a preprocessed voice signal; extracting multi-dimensional characteristic parameters from the preprocessed voice signals; based on the multi-dimensional feature parameters and a preset personal language development digital shadow, using a diagnosis model to generate a language development diagnosis result; and generating a real-time feedback instruction according to the language development diagnosis result. According to the comprehensive technical scheme, dynamic noise reduction is carried out by fusing situation information, personalized diagnosis is carried out based on personal language development digital shadows, and a real-time feedback instruction is generated, so that intelligent evaluation with accurate and dynamic language development and closed-loop intervention capability for children can be realized.

This invention discloses a real-time processing method based on voice SMS, relating to the field of communication technology. The specific steps of the method are: voice preprocessing, voice transcription and text standardization, semantic analysis and processing, information processing, and result feedback. This invention achieves dynamic noise reduction and precise segmentation of voice signals through an adaptive filtering formula in voice preprocessing and a segmentation mechanism based on voice pause intervals and intonation changes. It can analyze the frequency and amplitude of noise signals and the corresponding characteristics of voice signals in real time. By dynamically adjusting the filtering coefficients through the adaptive filtering formula, it effectively filters out non-voice signal components while preserving complete voice information. By capturing pause intervals and intonation changes in the voice signal, it accurately segments continuous voice streams, forming standardized voice segments with timestamps and sequence identifiers, providing a high-quality data foundation for subsequent voice transcription and semantic analysis.

A time-shifting high-density electrical method data temperature and humidity noise reduction method fused with a KNN algorithm comprises the following steps: 1, arranging a field acquisition system, and obtaining original observation data; 2, data preprocessing; and 3, constructing a noise reduction model and outputting. The method comprises the following steps: establishing an apparent resistivity and temperature and humidity data set aligned in time and depth dimensions by constructing a dynamic noise reduction system; by introducing a time shift reference and machine learning driven interpolation strategy, noise reduction is realized through coupling calculation of time shift data and influence factors, the problem of error accumulation in traditional static correction is effectively reduced, time-space dynamic accurate correction of temperature and humidity interference is realized, and the accuracy of temperature and humidity interference correction is improved. The data quality and the interpretation accuracy of the high-density electrical method in a complex environment are obviously improved; a model with site specificity is fitted by using a long-term monitoring time-shifting data set, the dynamic relationship between temperature and humidity and apparent resistivity is quantified, and the separation of environmental noise is realized, so that the data signal-to-noise ratio of a high-density electrical method and the detectability of weak signals are improved.

The present application relates to simultaneous interpretation technical field, disclose a kind of simultaneous interpretation data processing method and system based on POE microphone array.The method includes the synchronous acquisition of multilingual original audio stream and meeting environment noise spectrum characteristics by power over Ethernet distributed microphone array;After audio stream time domain framing, using dynamic noise reduction weight coefficient executes adaptive filtering and obtains preliminary clean speech segment;It is segmented into independent speech unit with language label by pre-training multilingual speech endpoint detection model, and phoneme level time alignment sequence is generated by matching corresponding acoustic model;Output terminology replacement instruction stream in real time by combining simultaneous terminology library comparison, generate intermediate semantic representation vector after fusion;It is converted into target language speech parameter sequence by low-delay encoder, and final simultaneous interpretation audio is generated by driving waveform synthesizer.The method optimizes whole process processing, and simultaneous interpretation accuracy and real-time performance are considered, and it is suitable for multilingual meeting scene.

The invention discloses a two-stage sample detoxication backdoor attack defense method based on a diffusion model. The method comprises the following steps: step 1, global lightweight noise injection and diffusion detoxication; 2, carrying out structure similarity difference positioning and noise intensity mapping; 3, local high-intensity noise injection and selective denoising are carried out; and 4, accelerating reasoning and resource optimization. According to the method, a black box defense framework without model parameters and training data is constructed, any third-party service scene is adapted, and the application limitation of a traditional white box scheme is solved; a dynamic noise intensity distribution mechanism is designed, damage thresholds of backdoor triggers with different visibility are accurately matched based on trigger visibility-robustness association, and the problem of excessive or insufficient purification of an existing method is avoided; rapid detection and local purification of a high-visibility trigger are realized through a structural similarity difference positioning technology, original sample semantics are reserved to the maximum extent, and the classification precision of detoxicated samples is guaranteed.

The invention discloses an anti-interference method of a mobile fire detection system, and belongs to the technical field of fire detection. A comprehensive solution integrating active vibration compensation, dynamic noise cancellation and sub-pixel accurate positioning is provided. Through vibration detection and real-time reverse driving compensation, the offset of the optical system is controlled within 0.02 mm, and the over-distance positioning precision of 0.3 m outside 150 m is achieved; by constructing a noise feature library capable of being dynamically updated and combining real-time noise matching and a reverse counteracting algorithm, vibration and electromagnetic interference are effectively suppressed, the noise reduction effect is improved by more than 30dB, and signal fluctuation is reduced to 0.05 V; through narrowband filtering and a temperature and humidity compensation mechanism, a real fire source is distinguished from vehicle high temperature, illumination and other interferences, and the monthly average false alarm frequency is reduced from more than 5 times to less than 1 times. The comprehensive performance of the flame detection system under severe working conditions is improved, and reliable technical support is provided for early accurate detection and positioning of highway tunnel fires.

The invention discloses a signal noise reduction and power supply optimization method for an earphone internal circuit, and the method comprises the core steps: collecting a real-time power supply voltage, a real-time interference signal frequency, a real-time circuit temperature and a real-time load resistance based on a synchronous collection time sequence control logic, correcting a reference noise reduction coefficient based on a parameter coupling effect, and obtaining a dynamic noise reduction coefficient; and adjusting the reference power supply current based on the dynamic noise reduction coefficient and the real-time load resistance to obtain a self-adaptive power supply current, and calculating a noise reduction-power supply comprehensive performance index based on the dynamic noise reduction coefficient and the self-adaptive power supply efficiency. According to the method, through synchronous acquisition and multi-parameter coupling linkage adjustment, the core problem of independent optimization of noise reduction and power supply in the prior art is solved, collaborative adaptation of noise reduction and power supply is realized, and the operation stability of an earphone circuit is improved.

The application provides a DAS system audio adaptive noise reduction method, device, medium and product, the method comprises the following steps: demodulating the light signals of each spatial sampling channel of the DAS system to obtain vibration signals, and calculating short-time energy values; constructing a global dynamic noise floor baseline reflecting the energy space-time distribution characteristics based on the short-time energy values of all channels; comparing the short-time energy values of each channel with the baseline, determining the target channel when the difference is greater than the threshold value, and using it to delimit the reference channel search range and the protection interval area; after eliminating the channels in the protection area, selecting the reference channels from the candidate set; using the reference channels to determine the environmental noise characteristics, and performing frequency domain noise reduction on the target channel. By implementing the application, the spatial correlation of the DAS array can be fully utilized to construct a robust noise reference, the effective signal of the adjacent channel can be prevented from leaking into the noise reference by setting the protection interval, and the signal-to-noise ratio and the listening clarity of the DAS restored audio in a complex environment can be improved.

The utility model relates to an integrated medium-frequency band-pass filter which comprises a shell and a filter body, a channel selector is arranged on the left side in the shell, a lifting circuit device is arranged above the channel selector, the filter body is arranged on the right side of the lifting circuit device, and a dynamic noise suppressor is arranged on the right side of the filter body. A group of automatic adjustable dynamic heat dissipation devices are symmetrically arranged on the two sides of the upper part in the shell; the utility model has the advantages of reasonable structure, reduced number of components, dynamic heat dissipation adjustment, miniaturized integrated structure, low cost and easy processing.

This invention discloses a two-degree-of-freedom coupled loading control method and system for a centrifuge. The method includes: real-time acquisition of measured load and displacement signals during centrifugal loading; calculation of the dynamic parasitic force caused by the centrifugal environment and subtraction from the load signal to obtain the true load feedback value; calculation of the net additional bending moment caused by geometric coupling due to model deformation based on the displacement signal, and conversion into a single equivalent force correction; obtaining the updated target load by combining the equivalent force correction with a pre-constructed parameterized target equation; calculating the normal deviation of the current loading point from the target path in a normalized load coordinate system based on the true load and the updated target load; and executing inter-channel decoupling control and adaptively adjusting the advance rate of the dimensionless loading parameters based on this deviation. This invention eliminates dynamic noise from hypergravity and interference from large deformation eccentricity, achieving high-fidelity tracking closed-loop control in a multi-dimensional load space.

The application relates to the technical field of medical instruments, and discloses a ward round vehicle medical patient communication information online synchronization system, which comprises a data acquisition module, a data preprocessing module, a sound quality analysis module, a tone analysis module, a patient comprehensive analysis module, a report review module and a report generation module. Compared with traditional ward round technology, the system realizes comprehensive intelligentization and dataization of the ward round process through the integrated multi-module collaborative work. In terms of sound quality processing, the system solves the speech distortion problem in a high-noise environment through dynamic noise reduction and audio enhancement technology; in terms of dialect recognition, the system significantly improves the accuracy of dialect communication through tone analysis technology; in terms of report generation, the system greatly improves the efficiency and accuracy through intelligent decision-making and automatic generation mechanism. Overall, the system optimizes the ward round process, improves the medical patient communication quality, reduces the medical error rate, and provides more reliable support for clinical decision-making.