112 results about "Adaptive filtering algorithm" patented technology

The invention relates to a detection-interference integrated unmanned aerial vehicle electromagnetic interference device, which belongs to the technical field of electronic countermeasure, and comprises an integrated design of an antenna module, a radio frequency front end module, a digital signal processing module and a main control module, the radio frequency front-end module comprises a detection receiving channel and an interference transmitting channel to process signal conversion, the digital signal processing module executes signal detection and analysis, intelligent interference decision and anti-self-interference signal processing based on a self-adaptive filtering algorithm, and the main control module is responsible for overall control and power supply. According to the invention, the technical problems of large response delay, complex system cooperation and incapability of continuous reconnaissance in the interference process of traditional anti-unmanned aerial vehicle equipment are solved. Real-time signal cancellation is achieved through the normalized least mean square algorithm, the reconnaissance capability is kept while interference is applied, the method has the advantages of being rapid in response, accurate in interference, low in power consumption and the like, and the countering efficiency is remarkably improved.

The invention discloses a radio frequency chip phase deviation calibration method based on multichannel sampling data analysis, and relates to the technical field of radio frequency signal processing and multichannel data synchronous calibration. Processing the sampling sequence of each channel by adopting a phase estimation algorithm to calculate a phase value to obtain initial phase distribution; according to the initial phase distribution, if the number of detection channels is increased or decreased, the reference phase is recalculated by fusing the time sequence relation adjustment information to obtain an updated reference phase; acquiring an updated reference phase, and extracting a reference signal from the residual channels and compensating a missing part through an adaptive filtering algorithm to obtain compensated phase distribution if a failure channel signal is lost according to partial channel failure judgment; the radio frequency chip phase deviation calibration method based on multichannel sampling data analysis has long-term self-calibration and self-adaption capabilities, and the robustness, reliability and signal processing precision of a system are remarkably improved.

The invention provides a low-altitude logistics aircraft take-off and landing point and vehicle dynamic connection positioning calibration method, and aims to solve the problems of insufficient positioning precision, poor collaboration and single path optimization in dynamic connection. The method comprises the following steps: firstly, constructing a multi-source fusion positioning module, fusing multi-sensor data through hardware terminal integration and data synchronization and an adaptive filtering algorithm, and realizing high-precision positioning by combining environment adaptive signal switching; secondly, a vehicle-aircraft cooperative calibration link is established, and real-time cooperation is guaranteed through low-delay data transmission, dynamic deviation prediction calibration and communication exception handling; and finally, establishing a multi-agent collaborative path optimization model, modeling based on multi-target parameters, modifying a path in real time by an improved algorithm, and improving a connection effect. The method is suitable for high shielding scenes such as an operation area, centimeter-level positioning calibration can be realized, and the accuracy and collaboration of dynamic connection are enhanced.

The invention discloses an anti-interference method suitable for a satellite navigation signal, and belongs to the field of satellite navigation signal anti-interference, and the method comprises the steps: collecting multi-channel satellite navigation signal data through a receiver array, processing an initial signal through a self-adaptive filtering algorithm to preliminarily reduce noise interference, and obtaining a preliminarily purified signal; extracting a spatial correlation matrix according to the preliminary purification signal, decomposing the matrix by adopting a principal component analysis algorithm to identify dominant spatial features, and determining an interference direction vector; obtaining the spectrum distribution of the direction suppression signal, further separating the spectrum overlapping part by adopting a principal component analysis algorithm, and judging the residual interference residue; through fusion of the direction suppression signal and the residual interference residue, an adaptive filtering algorithm is adopted to iteratively optimize the weight so as to enhance the navigation signal strength, and an optimized enhanced signal is obtained; and calculating a positioning parameter according to the optimized enhanced signal, judging whether the parameter stability meets a preset threshold value, and obtaining a final stable signal.

The invention discloses a ship waste heat utilization system and an energy recovery method, and the method comprises the steps: monitoring and collecting waste heat parameters in real time through a multi-sensor array, processing data fluctuation through a self-adaptive filtering algorithm, and carrying out the quality evaluation in combination with an effective energy model; according to the real-time effective energy value, the fluctuation index and the host load, the waste heat resource allocation priority is dynamically adjusted, and a multi-mode waste heat resource allocation strategy is synchronously formulated based on the output predicted by the extended Kalman filtering algorithm; a full-state prediction model based on an ORC system is constructed, and a prediction-optimization-compensation adaptive control framework is established; a heat charging and discharging sequence is optimized based on model prediction control, and energy loss is minimized; according to the method, performance indexes of the whole waste heat utilization system are monitored, control parameters are optimized by using a reinforcement learning algorithm based on real-time data, the optimized parameters are applied to the system, and closed-loop control is performed. The problem of insufficient waste heat utilization is solved, energy loss is remarkably reduced, and the waste heat utilization efficiency of the system is improved.

The invention relates to the technical field of automobile chassis dynamics control, and discloses a hydrogen energy heavy truck omnidirectional degree-of-freedom optimization method, which comprises the following steps: acquiring multi-source sensor data and hydrogen fuel cell system state data of a hydrogen energy heavy truck, and executing time synchronization and noise preprocessing; carrying out real-time identification on the key state parameters of the vehicle by utilizing a nineteen-degree-of-freedom vehicle dynamics model and a self-adaptive filtering algorithm; a layered omnidirectional coordination control architecture is constructed and comprises a main control layer, a scheduling layer and an execution layer, and the main control layer generates a global motion control target; carrying out multi-objective optimization solution on the scheduling layer, and calculating to obtain an expected acting force distribution instruction; and the execution layer converts the instruction into a bottom layer execution control signal and drives the subsystems to cooperatively act so as to realize omni-directional degree-of-freedom optimization control. According to the invention, the limitations of poor coordination and difficulty in multi-target optimization caused by independent control of subsystems of a traditional chassis can be broken through, and accurate control of the hydrogen energy heavy truck under complex working conditions is realized.

The invention discloses an intelligent shock absorption and noise reduction control method for rail transit, and belongs to the technical field of rail transit noise and vibration control. The method aims at solving the problems that traditional passive control measures are poor in adaptive capacity and limited in frequency band, and effective prospective offset is difficult to achieve due to system delay and inaccurate reference signals in existing active control. The method comprises the following steps: synchronously acquiring vibration and noise signals through a sensor array; calculating the contribution weight of vibration to noise by using a cross-domain modal correlation module based on an attention mechanism, and fusing the features; predicting an evolution sequence of vibration and noise in a specific time period in the future by adopting a long-short-term memory network model; synthesizing a target reference signal according to the prediction sequence, and generating an anti-phase control signal through an adaptive filtering algorithm; finally, it is ensured that the total delay of the system is smaller than the feedforward advance time, and an actuator is driven to achieve accurate foresight counteracting. The method is mainly used for improving the self-adaptive capacity and the control effect of the rail transit damping and noise reduction system.

The invention provides a human body posture recognition and rehabilitation training evaluation method and system based on deep learning, and relates to the technical field of human body posture recognition. According to the method, multiple human body key points are extracted from a pre-acquired user image by using a deep learning attitude estimation model, then measurement data of human body key angles are acquired based on the human body key points, and then dynamic smoothing processing is performed on the human body key angle data based on an adaptive filtering algorithm introduced with a multi-level smoothing factor switching mechanism. And finally, outputting the smoothed key angle data of the human body in real time, and performing human body posture recognition and rehabilitation training evaluation according to the smoothed key angle data of the human body. Compared with the prior art, different dimensions such as real-time performance, measurement precision, equipment portability, functional integrity and user experience are remarkably improved.

The invention discloses a belt-disengaging prevention combined control method and device for an electric crawler and a medium, and relates to the technical field of crawler control. The method comprises the following steps: S1, acquiring multi-source sensing data of the electric crawler, wherein the multi-source sensing data at least comprises vehicle attitude data, crawler tension data, crawler sag data, driving wheel speed data and environment sensing data which take a quantum inertial sensor as a reference; and S2, carrying out space-time alignment and consistency correction on the multi-source sensing data, and fusing the corrected data by adopting an adaptive filtering algorithm to obtain a real-time fusion state quantity of the vehicle. And S3, on the basis of the real-time fusion state quantity, a comprehensive risk index is calculated through multi-dimensional feature coupling analysis, and a current off-band risk level is determined. And S4, a preset hierarchical control strategy is matched according to the determined belt disengaging risk level, a combined control instruction is generated and sent to an execution mechanism of the vehicle, and the execution mechanism comprises a motor driving system and a mechanical tensioning system.

The invention provides a variable-speed vibration signal feature extraction method and system. The method comprises the following steps: acquiring vibration signal data under a variable-speed condition; preprocessing the vibration signal data; carrying out feature extraction on the preprocessed vibration signal data by adopting order analysis; optimizing the feature data by adopting a self-adaptive filtering algorithm; constructing a fault feature physical model, and performing classification or regression analysis on feature data based on a machine learning or deep learning algorithm; and inputting the optimized feature data into the fault feature physical model, and carrying out bearing fault diagnosis. According to the variable-speed vibration signal feature extraction method and system provided by the invention, an equal-angle resampling technology is adopted, the accuracy and consistency of signal analysis are ensured, and in combination with an intelligent feature extraction and mode recognition algorithm, mild, moderate and severe classification evaluation of faults such as bearing wear is realized.

The invention discloses a cardiovascular system joint variability index detection method which comprises the following steps: synchronously acquiring an electrocardiosignal, an arterial blood pressure signal and a respiratory signal through a multi-channel physiological signal acquisition device, and carrying out denoising and baseline correction on each signal by adopting a self-adaptive filtering algorithm to obtain a preprocessed multi-modal signal sequence; respectively extracting a heart rate variability time domain index, a blood pressure variability frequency domain index and a breath variability nonlinear index according to the multi-modal signal sequence, mapping the three indexes into a unified sliding window by adopting a timestamp alignment mechanism, and generating an aligned variability feature set; performing Z-score-based standardization processing on the variability feature set, and constructing a dynamic weight distribution matrix in combination with individual physiological parameters; the objective of the invention is to solve the problems of time sequence mismatch, dimension difference, insufficient individual adaptation and historical data continuity deficiency in the process of acquisition, synchronization, feature extraction and fusion of multi-source physiological signals in the prior art.

The invention provides a digital pre-distortion coefficient updating method and device for a frequency hopping communication system, and the method comprises the steps: obtaining a corresponding historical digital pre-distortion coefficient when the system hops to a target frequency point, and collecting training data at the initial stage of a current hop; based on the historical digital pre-distortion coefficient and the training data, obtaining an intermediate vector used for representing the change degree of the system through an adaptive filtering algorithm, and determining a change metric based on the intermediate vector; comparing the change measurement with a preset threshold value so as to divide the system state change into different levels, and dynamically triggering a matched updating mode; wherein the updating mode comprises a holding mode, a lightweight tracking mode and a precise reconstruction mode. According to the method, the system state change is divided into different levels by quantitatively evaluating the change measurement of the coefficient update vector, and the update mode which is most matched with the change is dynamically triggered, so that the global optimization of the system performance and the resource overhead is realized.

This invention discloses an adaptive filtering method based on the multi-kernel Nystrom method, belonging to the field of kernel adaptive filtering. The multi-kernel Nystrom method combines multiple kernel functions, which can include not only Gaussian kernels but also other different kernel functions, mapping the original data to multiple different independent feature spaces. Compared with the traditional single-kernel Nystrom method, the multi-kernel Nystrom method has better filtering accuracy and becomes less sensitive to the choice of kernel parameters. Compared with other multi-kernel adaptive filtering methods, it can significantly reduce computational complexity, time, and memory consumption. Subsequently, this invention introduces the proposed multi-kernel Nystrom method into kernel adaptive filters for the first time. Experiments show that this algorithm can achieve better filtering accuracy than current state-of-the-art kernel adaptive filtering algorithms with relatively low computational complexity.

The present application relates to epidemic prevention communication technology field, especially to a duplex cluster communication interaction method and system and epidemic prevention helmet, which comprises: using the air guide pickup component arranged in the helmet body to obtain the voice signal of the wearer, and outputting the audio signal through the air guide sound production component arranged in the helmet body; performing echo cancellation and noise reduction processing on the obtained audio signal; using an adaptive filtering algorithm to suppress the acoustic feedback between the air guide pickup component and the air guide sound production component; based on a distributed channel access mechanism, allocating communication resources for multiple communication nodes to access in duplex; relying on a mesh self-organizing network topology without a central node to perform multi-hop forwarding and route maintenance of data packets, solving the problems of unclear communication, howling and signal attenuation in the strong noise, wind noise and shielding scene in the epidemic prevention helmet, effectively guaranteeing the clarity, stability and safety of cluster communication in harsh working environment.

The application provides an active noise reduction method and system for road noise and wind noise of an automobile, and the active noise reduction method comprises the following steps: S1, collecting a dynamic parameter signal and a total mixed noise signal during automobile driving; S2, constructing a dynamic calculation model of a Strouhal number according to a Reynolds number, deducing a real-time calculation model of a characteristic frequency of each noise source i according to the dynamic calculation model of the Strouhal number, and outputting a real-time characteristic frequency sequence corresponding to each noise source i; S3, constructing a global signal dictionary D, using an orthogonal matching pursuit sparse representation algorithm, and separating a single noise source signal in the total mixed noise signal by iteratively screening a dictionary atom with the highest matching degree with the characteristic frequency; S4, using a frequency tracking type fast adaptive filtering algorithm to generate a corresponding anti-phase cancellation signal; and S5, outputting the anti-phase cancellation signal in the automobile cabin, so that noise reduction can be performed on each noise source i.

This application relates to pressure detection technology and discloses a flexible pressure acquisition system, device, and method based on a parallel architecture. It includes a signal acquisition and control module, a pressure sensor array, and an environmental monitoring module. The signal acquisition and control module includes an MCU and analog front-end circuitry. The analog front-end circuitry includes K parallel analog multiplexers. The MCU internally has K corresponding ADC channels. The pressure sensor array is electrically connected to the signal acquisition and control module, and its M×N pressure sensing units are divided into K parallel acquisition sub-regions. Each sub-region's column electrodes are connected to an analog multiplexer input channel. The environmental monitoring module acquires real-time environmental parameters. The MCU synchronously triggers all ADC channels to synchronously convert the pressure signals of the K sub-regions, and combines the environmental parameters with an adaptive filtering algorithm for compensation and correction, outputting pressure distribution data. This application can improve the sampling rate and measurement accuracy of the pressure acquisition system.

The application provides a high-density surface electromyography signal hardware filtering device and method based on FPGA, comprising: an sEMG signal input module for collecting multi-channel noisy surface electromyography signals and at least one reference noise signal; an LMS adaptive filtering calculation module for receiving the noisy surface electromyography signals and the reference noise signal, adopting a fixed-point number operation system and executing an LMS adaptive filtering algorithm through a parallel pipeline structure to output a filtered error signal; a data buffer and output interface module for buffering and outputting the filtered surface electromyography signals; and an FPGA main control module connected with the sEMG signal input module, the LMS adaptive filtering calculation module and the data buffer and output interface module for controlling the work of each module. The application realizes the LMS adaptive filtering algorithm on the FPGA platform, and solves the problems of insufficient real-time performance and large processing delay of software filtering when facing high-density multi-channel sEMG signal processing.

This invention provides a method and system for autonomous plunger hovering control based on an expansion mechanism in a natural gas well. The invention utilizes a multi-point pressure sensor array mounted on the plunger to monitor real-time pressure distribution changes on the wellbore. Noise is removed and the optimal expansion degree is predicted using an adaptive filtering algorithm and machine learning model in an edge computing node, generating an expansion diameter adjustment scheme. The plunger's expansion mechanism is adjusted according to this scheme, and a three-dimensional flow field simulation environment is constructed by combining fluid dynamics parameters and high-resolution environmental information. Particle swarm optimization and Kalman filters are used for modeling and prediction to determine the plunger's position and resistance, improving the plunger's operational accuracy and response speed in complex downhole environments, enhancing the system's flexibility and adaptability, and thus improving the safety and efficiency of natural gas extraction.

The invention discloses an active noise reduction method, device and equipment for a take-off and landing point of a hovercar and a program product. The method comprises the steps that real-time noise signals and real-time environment parameters around a take-off and landing platform and real-time attitude parameters of the hovercar are acquired; performing propagation attenuation compensation on the real-time noise signal to obtain a corrected noise signal; matching in the model noise model library to obtain a corresponding rotor noise reference spectrum, an airflow noise reference spectrum and a power system noise reference spectrum; performing feature analysis on the corrected noise signal to obtain a rotor noise component, an airflow noise component and a power system noise component; predicting the change trend of the noise component through an adaptive filtering algorithm to obtain a predicted noise signal, and predicting the next airspace position of the hovercar according to the real-time attitude parameters; and generating an offset sound wave signal according to the predicted noise signal, and transmitting the offset sound wave signal to a next airspace position. The method realizes active noise reduction of the take-off and landing point of the hovercar, and can be applied to the technical field of hovercars.

This invention relates to an intelligent monitoring system and deployment method for building module data interfaces based on the Internet of Things (IoT), belonging to the field of building information monitoring technology. The system consists of distributed sensing units, an edge computing gateway, a cloud platform, and a visualization terminal. The edge computing gateway incorporates an adaptive filtering algorithm and an anomaly pattern recognition model. The cloud platform uses a time-series database to construct a multi-dimensional data warehouse and performs structural health assessment by fusing LSTM neural networks and random forest algorithms. The deployment method includes: optimizing sensor placement strategies based on BIM models, establishing a wireless mesh self-organizing network communication architecture, and configuring a hierarchical early warning mechanism and fault tracing function. The innovation lies in proposing a dynamic threshold adjustment algorithm and an interface performance degradation prediction model to achieve real-time monitoring and lifespan prediction of building module connection status. This system has the advantages of flexible deployment, high detection accuracy, and low maintenance costs, effectively improving the intelligent level of building structural safety monitoring.

The invention relates to the technical field of dark-red enameled pottery temperature monitoring, and discloses a dark-red enameled pottery inner cavity temperature real-time monitoring system based on digital processing. The system comprises a dynamic temperature sensing module, a multi-dimensional temperature field reconstruction module, an abnormal heat source positioning module, a temperature field evolution prediction module and a self-adaptive sampling control module. The dynamic temperature sensing module acquires original temperature data of different depths of an inner cavity by using a distributed thermocouple array, and de-noising is performed through an adaptive filtering algorithm to obtain a real-time temperature waveform; the multi-dimensional temperature field reconstruction module combines the geometric parameters and the heat conductivity coefficient of the kettle, builds a model by means of a three-dimensional interpolation network, and outputs a four-dimensional temperature tensor with space-time coordinates; the abnormal heat source positioning module carries out tensor measurement on the abrupt change area and inversely calculates the position of an abnormal heat source; the temperature field evolution prediction module predicts a diffusion path by means of a time sequence convolutional network and generates a trend chart; the self-adaptive sampling control module adjusts sampling parameters according to a graph, and sets encryption points in a high-gradient area to adapt to the temperature change of the inner cavity.

The invention relates to the technical field of new energy automobile battery data analysis, and discloses a data enhancement method for improving the reliability of a battery system, and the method comprises the following steps: S1, carrying out the regularization processing of collected original battery operation data; s2, based on the regularized data, dynamically suppressing noise by adopting an adaptive filtering algorithm, and reserving key transient characteristics in the charging and discharging process of the battery, so as to obtain basic data; s3, inputting the basic data into a generative AI model fused with physical constraints; the generative AI model is embedded into a model training and reasoning process by taking an electrochemical physical rule for describing an internal process of the battery as a constraint condition, input data is reconstructed and enhanced, and enhanced battery data with low-noise characteristics and electrochemical rationality is generated. The method can ensure that the enhanced data has low noise characteristic and electrochemical rationality, and is beneficial to comprehensively improving the operation reliability and prolonging the service life of a battery system.

This invention discloses a method and system for adaptive cancellation of strong and weak signals based on complex structures. The method includes: using a complex RLS adaptive filtering algorithm to track and filter strong interference signals in a complex observation signal to obtain a filtered output signal; during filtering, applying a new filter coefficient vector obtained iteratively from the current complex observation signal to the input complex observation signal after a delay of N periods, thereby decoupling the filter coefficient update from the signal filtering in time, allowing the filtering and filter coefficient update processes to be executed asynchronously; subtracting the filtered output signal from the desired signal to achieve adaptive cancellation of strong signals; and extracting weak signal features from the cancelled signal. This invention fully utilizes the complete information of complex signals in amplitude and phase, overcoming the limitations of traditional real-number processing methods in separating overlapping spectrum signals. It is particularly suitable for complex scenarios where strong and weak signals have similar spectra and large power differences, and has the advantages of high precision, low distortion, and strong real-time performance.

The present application relates to the technical field of ocean current monitoring, and discloses a kind of ocean current meter data quality control method and system based on adaptive filtering.The original data containing ocean current direction and speed are obtained, after being decomposed into eastward and northward flow velocity components, frequency spectrum distribution is obtained by Fourier transform, dynamic filtering window is determined by adaptive filtering algorithm, components are preprocessed and feature vector is extracted to calculate flow direction;According to whether the amplitude of the feature vector changes is over the threshold, select Kalman filter to smooth the component or directly obtain the corrected flow direction data;The dynamic trend is extracted by principal component analysis method for the corrected data, and the main direction feature set is obtained;Based on the analysis of the influence of tides and topography, the environmental influence weight is obtained;The filtering parameter update frequency is adjusted by the weight, and the real-time component is filtered twice to output high-precision ocean current direction data.This method can improve the accuracy of ocean current meter data quality control and improve the monitoring accuracy.

The invention provides an unmanned aerial vehicle noise elimination wave signal generation method, electronic equipment and a program product, and relates to the technical field of sound wave noise elimination. The method disclosed by the invention comprises the following steps: receiving an audio signal and performing spectral analysis to obtain characteristic frequency information corresponding to rotor noise and environment noise; based on the characteristic frequency information, respectively extracting signals corresponding to the rotor noise and the environmental noise from the audio signal to obtain a first rotor noise signal and a first environmental noise signal; based on the first rotor noise signal, performing prediction through an adaptive filtering algorithm to obtain a second rotor noise signal; based on the second rotor noise signal, a reverse sound wave signal opposite to the second rotor noise signal in phase is generated; and adding the reverse sound wave signal and the first environment noise signal, and multiplying an addition result by an adjustment quantity obtained based on the flight state adjustment function to obtain a noise elimination wave signal.

The present application relates to the technical field of electric variable measurement, in particular to a power meter harmonic metering method and system based on adaptive filtering, comprising: acquiring parameter data of a power meter at a current time node and its historical multiple time nodes to construct a target parameter sequence; decomposing the target parameter sequence by using a time series decomposition algorithm to obtain a target residual sequence; decomposing the target residual sequence into a short-time residual sequence and a long-time residual sequence; performing adaptive filtering processing on the target parameter sequence by using an improved adaptive filtering algorithm to acquire harmonic components, and completing metering analysis of power meter harmonics based on the harmonic components. The present application solves the problem of low accuracy of harmonic component extraction and power metering.

The application discloses an infrared temperature difference sensing image processing method and system and electronic equipment, and relates to the technical field of image processing.The processing steps are as follows: S1, acquiring surrounding images based on a three-proofing mobile phone, automatically adjusting filter parameters for the features of the images based on an adaptive filtering algorithm, and obtaining infrared images; S2, performing layered processing on the infrared images based on a guided filtering technology, dividing the images into different levels based on the local features of the images, and including background images and subject images; and S3, for the subject images, mainly enhancing the edge and detail information thereof, and for the background images.The adaptive filtering algorithm can automatically adjust parameters according to image features, can accurately remove noises of different degrees, can retain details, can avoid the problems of excessive blurring or incomplete denoising in traditional filtering, can quickly discover temperature difference abnormalities during outdoor exploration, can timely detect equipment temperature faults in industrial scenes, and can meet the application requirements in multiple scenes.

A processor is configured to determine, using an adaptive filter algorithm, an adaptive feedforward acoustic noise cancellation, ANC, filter, filter a headphone internal microphone signal using a feedback filter to produce a feedback audio signal, drive a headphone speaker with the feedback audio signal, and modify the feedback filter based on a characteristic of the adaptive feedforward ANC filter. Other embodiments are also described.

The invention provides a fine dried noodle pressing equipment operation monitoring method and system, and the method comprises the steps: obtaining real-time pressure data in a pressing process, carrying out the denoising and standardization of the pressure data through a data preprocessing technology, and obtaining a smooth pressure data flow; according to the smooth pressure data flow, a time sequence analysis method is adopted to extract the pressure change trend in the pressing process, and pressure feature points of all stages are determined; according to the relationship between the pressure feature points and the pressing time, calculating the optimal residence time of each pressing stage by adopting a dynamic programming algorithm to obtain a time distribution scheme; according to the time distribution scheme, the operation rhythm of the pressing equipment is adjusted in real time, the force grading arrangement is synchronously updated, and dynamic cooperative control parameters are obtained; and according to the real-time change of the deformation effect data, updating pressure gradient setting, and smoothing force transition by adopting an adaptive filtering algorithm to obtain optimized suppression control parameters.

The invention relates to the technical field of 5G video customer service, and discloses an intelligent video customer service access model establishment method based on 5G. The method comprises the following steps: collecting real-time video stream data transmitted by a user terminal through a 5G network, extracting multi-dimensional session features such as facial expression change, voice intonation, gesture action and the like, and constructing a dynamic intention weight matrix in combination with a historical session record intention classification result; performing noise reduction on the video stream by using an adaptive filtering algorithm, extracting a key action frame and calculating a time sequence correlation degree; the dynamic intention weight matrix and the key action frame time sequence correlation degree are fused, and an enhanced user intention vector is generated; based on the matching degree of the vector and historical session record intentions, dividing user intention categories through a hierarchical clustering algorithm, and establishing a mapping relationship between the user intention categories and a customer service strategy; and finally, generating an access instruction according to the mapping relation, dynamically allocating optimal customer service resources, and adapting to 5G network characteristics.