62 results about "Joint evaluation" patented technology

The invention relates to the technical field of data processing, and discloses an operation state monitoring method and system for vibration test equipment. The method comprises the following steps: generating a multi-dimensional physical characteristic data stream through vibration waveform calibration according to operation parameters collected by each sensor of vibration test equipment; performing frequency response analysis on the data stream to obtain physical response characteristics; based on this, a test calibration reference is established; outputting test state parameters according to the reference analysis real-time response characteristics; and performing performance attenuation analysis according to the parameters, and generating an equipment quality evaluation report. According to the comprehensive analysis method based on the multi-dimensional physical characteristic data flow, real-time monitoring, multi-parameter joint evaluation and performance attenuation trend analysis of the dynamic characteristics of the equipment are achieved, so that the accuracy, comprehensiveness and predictability of state monitoring of the vibration test equipment are improved, and the reliability and consistency of test results are ensured.

The invention discloses a diabetic foot assessment method based on wound image recognition. The method comprises the following steps: processing a foot infrared thermal imaging image to generate a temperature anomaly graph; fusing the foot wound image and the temperature anomaly graph to generate a fused image; constructing a foot blood flow analysis model, extracting a blood flow influence factor, and correcting the fused image through the blood flow influence factor; and constructing a joint assessment model, taking the corrected fusion image and historical data of the diabetic foot patient as input of the joint assessment model, predicting a risk assessment level of the diabetic foot, and outputting a disease assessment result of the diabetic foot patient according to the risk assessment level. According to the method, multi-modal medical image fusion analysis is realized, the accuracy of wound area positioning and pathological state expression is improved, the accuracy of lesion area recognition is improved, the illness worsening risk can be predicted in advance, clinical decision can be assisted, the risk of serious complications such as amputation of a patient is reduced, and the prognosis and life quality of the patient are improved.

The invention is suitable for the technical field of 3D scanning, and provides a figure model 3D scanning and dynamic attitude capturing method and system, which dynamically triggers 3D scanning by monitoring joint movement intensity in real time, continuously collects continuous movement tracks by using an inertial sensor in a non-scanning period, and reversely deduces space-time parameters of a shielded part through multi-light-source shadow boundary evolution. And constructing a spatio-temporal joint evaluation function to adaptively adjust the scanning frequency and the sampling rate, and finally performing spatio-temporal alignment and confidence weighted fusion on the discrete point cloud, the continuous trajectory and the reverse reckoning data to generate a dynamic biomechanical model. The scheme breaks through the bottleneck of resource waste and blind area shielding of traditional fixed frequency scanning, realizes space-time complementation and precision optimization of motion monitoring, and is suitable for dynamic scenes such as movie and television animation, medical rehabilitation, physical training and the like. According to the system, through multi-modal data fusion and self-adaptive resource scheduling, the data integrity, the real-time performance and the biomechanical analysis capability are remarkably improved, and an efficient solution is provided for human motion digitization.

The invention discloses a standing and walking test method and system based on an inertial sensor and a myoelectricity technology, and relates to the technical field of gait evaluation and rehabilitation detection, and the method comprises the following steps: completing data acquisition by using the inertial sensor and the myoelectricity technology; comprehensively evaluating the difference between the left and right support phases and the swing phase through a first algorithm, and judging whether the gait is symmetrical and stable or not; and evaluating the muscle coordination of the patient through a second algorithm. According to the method, synchronous data fusion of the behavior layer and the neural control layer is achieved, a data foundation is laid for follow-up construction of an accurate and multi-dimensional gait and myoelectricity evaluation model, joint evaluation of two core indexes of gait symmetry and stability is achieved, the detection capacity of neurological gait disorders is improved, and the method is suitable for being used for the field of neurological gait disorders. Reliable criteria are provided for individualized rehabilitation intervention, modeling and difference measurement of time sequence regulation and control characteristics are achieved, and the recognition capacity for neuromuscular control disorders is improved.

The invention discloses a battery SOC-SOH joint evaluation method and device, and the method comprises the steps: screening standing working condition points of an energy storage battery SOC in a non-plateau period, building a dynamic SOC-OCV function and an SOH experience attenuation model, taking the minimum SOC jump of a calibration point as a target optimization parameter, updating the model according to an optimization result, calculating the subsequent SOC and SOH, monitoring the SOC calibration value jump of a subsequent standing point, and carrying out the SOC-SOH joint evaluation. And when the threshold value is exceeded, the dynamic OCV estimation is restarted. The device comprises a working condition point screening module, a model construction and parameter optimization module, an SOC and SOH calculation module, and a monitoring and restarting module. By dynamically updating model parameters and a jump monitoring mechanism, the problem of insufficient evaluation precision under complex working conditions is solved, the method is suitable for scenes such as new energy automobiles and energy storage systems, and the accuracy and adaptability of battery state evaluation are improved.

The invention discloses a multi-modal image fusion method and system based on feature uncertainty joint evaluation, and the method comprises the steps: splicing a pair of input images to obtain a spliced image, inputting a diffusion model, and extracting S features of different scales from N time steps, inputting the obtained N * S features into a full connection layer neural network model to obtain feature potential uncertainty weights of features of different time steps, and performing weighted splicing compression on the features of the same scale of different time steps according to the feature potential uncertainty weights to obtain S preliminary fusion features of different scales, and performing layer-by-layer up-sampling according to the scale, adjusting the number of channels by using an attention module to obtain a final fusion feature, and obtaining a fusion image by using a decoder. The invention aims to solve the problems of insufficient feature extraction and unstable fusion result caused by modal difference and potential uncertainty of features in image fusion, and overcome the problems of fusion result detail missing and unstable performance caused by insufficient feature extraction and unreasonable fusion.

This disclosure provides methods, components, devices and systems for traffic classifier for service aware Wi-Fi operation. The techniques described herein support the creation of a common traffic detection model on an access point (AP) or client. In some examples, the model may reclassify traffic over time to avoid detection failure during an atypical traffic pattern period of an interested flow. The traffic classifier may provide traffic detection results and traffic activity such that a power saving operation may be enabled. Some aspects more specifically relate to mechanisms according to which the AP or client may select power save parameters associated with a classified traffic flow to the AP or client.

The invention discloses an equipment identification and quality grade joint evaluation method for a transformer substation inspection image, and the method comprises the steps: inputting a collected original transformer substation image into an image identification and quality grade joint evaluation model; the model comprises a detection perception image enhancement module, a target detection module, an ROI extraction module and an image quality evaluation module which are connected in sequence. Performing enhancement processing on the original transformer substation image by using a detection perception image enhancement module to obtain an enhanced image; performing target detection on the enhanced image by using a target detection module to obtain a power equipment category label and ROI frame coordinate information; an ROI extraction module is utilized to cut the original substation image according to the ROI frame coordinate information to obtain a corresponding equipment ROI image; performing no-reference image quality evaluation on the ROI image of the equipment by using an image quality evaluation module to obtain a quality grade label; according to the method, the equipment identification accuracy is improved, and the image quality grade is credibly output.

This disclosure provides methods, components, devices and systems for traffic classifier for service aware Wi-Fi operation. The techniques described herein support the creation of a common traffic detection model on an access point (AP) or client. In some examples, the model may reclassify traffic over time to avoid detection failure during an atypical traffic pattern period of an interested flow. The traffic classifier may provide traffic detection results and traffic activity such that a power saving operation may be enabled. Some aspects more specifically relate to mechanisms according to which the AP or client may select power save parameters associated with a classified traffic flow to the AP or client.

This invention discloses a method for joint evaluation and optimization of buckling and bending of arbitrary quadrilateral laminates based on physical information neural networks, relating to the field of composite material structure design technology. The method, for arbitrary quadrilateral laminates, first constructs a method based on classical laminate theory... USA The stiffness matrix explicitly reflects the influence of ply parameters, and a physical domain is transformed to a reference domain through a bitriangular affine mapping to establish a physical information neural network mechanical model. The model is trained with the goal of minimizing the total potential energy or Rayleigh quotient, solving for the buckling factor and layer-by-layer strain limits, and adapting to four typical boundary conditions through a boundary embedding function. Subsequently, using ply angle and thickness as variables, optimization is performed using a genetic algorithm, with the goal of minimizing mass, and the buckling factor and strain limits as constraints. The final output is the optimal ply scheme that satisfies specific boundary conditions. This invention can optimize ply angle and thickness while satisfying buckling stability and strain strength constraints.

This application provides a method and apparatus for jointly evaluating security boundaries and communication performance based on 5G-A industrial internet. The method includes: acquiring current network load data and current security policy parameter combinations, and inputting the current network load data and current security policy parameter combinations into a nonlinear latency prediction model to obtain the current overall latency; utilizing the current security policy parameter combinations and the current overall latency to generate a current security-communication global score; when the current security-communication global score is lower than a preset threshold, determining the fine-tuning amount of each security parameter from a multidimensional coupling relationship matrix library based on the current network load data and the current security policy parameter combinations to obtain the optimal security policy parameter combination. Through the nonlinear latency prediction model and the multidimensional coupling relationship matrix library, coupled modeling of security and communication is achieved; it also enables comparison and integration of security and communication in the same dimension, achieving synergistic optimization of security and communication resources.

The invention discloses a multi-parameter joint evaluation method for transmission performance of ultra-high performance concrete, and belongs to the technical field of concrete durability evaluation. The multi-parameter joint evaluation method comprises the following steps: preparing an ultra-high performance concrete test piece with a gradient function; calculating a steel fiber distribution coefficient; obtaining a slice test piece; performing a gas permeation test on the test piece with the undamaged slices; performing a capillary water absorption experiment on the test piece with the undamaged slices; carrying out a splitting tensile strength test to obtain slice test pieces with different damage degrees; performing a gas permeation test and a capillary water absorption test on the damaged slice test piece; and calculating and fitting the experimental results to obtain a concrete transmission performance multi-parameter joint evaluation equation set under the steel fiber distribution coefficient. According to the method, the gas transmission performance and the capillary water absorption are tested at different slice positions of the ultra-high performance concrete test piece with a certain mixing ratio, the evaluation process of the concrete transmission performance is obtained, the process of permeability evaluation through multiple parameters is introduced, and the accuracy of the evaluation result is improved.

The invention discloses a double-domain joint evaluation method for imaging quality in wide-spectrum coherent diffraction imaging, which belongs to the related technical field of wide-spectrum coherent diffraction imaging, and comprises the following steps: 1, measuring light source spectrum distribution for wide-spectrum coherent diffraction imaging, and imaging a wide-spectrum diffraction field through an imaging algorithm; 2, obtaining a current reconstructed image and actually measured diffraction light intensity after each iteration of the imaging algorithm, constructing wide spectrum diffraction light intensity by using spectral distribution and reconstructed image values, and using and calculating diffraction field errors; 3, performing no-reference quality evaluation on the reconstructed image after each iteration, and calculating an image error; and 4, using weighting to form a double-domain joint evaluation result. The method can effectively solve the problem that the existing wide-spectrum laminated diffraction imaging lacks a unified and accurate evaluation method, thereby improving the reliability and consistency of reconstruction quality evaluation.

The invention discloses a model training method and device, a video processing method and device, equipment and a medium, which are applied to the technical field of computers, and comprise the following steps: obtaining a training sample with a label and scene data; the training sample is self-recording video data of the target object; respectively evaluating the training samples by using a single-mode model and a multi-mode model, and screening the training samples according to evaluation results to obtain screened training samples; and performing stage training on the to-be-trained model by using the screened training samples and scene data, and adjusting the training data and model parameters of the next stage by using the training result of the previous stage until an end condition is met, thereby obtaining a trained model. According to the method, through single-mode and multi-mode joint evaluation, the single-mode quality is ensured, cross-mode alignment is ensured, and the training data quality is improved; the staged dynamic adjustment mechanism can optimize training data and parameters in real time according to the model performance, the training data are fully utilized, and the model training accuracy is improved.

This invention relates to the fields of civil engineering structural safety inspection and machine learning technology, and discloses a machine learning-based collaborative evaluation method and system for the mechanical properties of concrete components. The method constructs a coupled graph of mechanical indices with confidence levels, trains a missing-node-aware joint evaluation model, and suppresses the propagation of noise from missing nodes through joint attenuation of observability masks and edge confidence weights. Then, it performs weighted SHAP interaction effect analysis and hierarchical verification, and generates a supplementary detection scheme with comprehensive information gain ranking based on the D-optimal criterion. Through iterative closed-loop optimization, it outputs the collaborative evaluation results and evaluation confidence levels of the concrete component's compressive strength, splitting tensile strength, and elastic modulus.

This disclosure provides methods, components, devices and systems for workload scheduling in edge computing. Some aspects more specifically relate to workload scheduling in distributed computing scenarios, which may include various devices including one or more workload scheduler devices, one or more host devices, and one or more client devices. A workload scheduler device may receive a workload request from a client device and may assign the workload to a host device from a set of host devices, with such a set of host devices including host devices capable of satisfying expectations of the workload. To assign the workload, the workload scheduler device may perform joint evaluations for the set of host devices, obtain scores for the set of host devices in accordance with the joint evaluations, rank the set of host devices in accordance with their respective scores, and assign the workload to the host device having the greatest ranking.

The present invention discloses a multimodal image fusion method and system based on joint feature uncertainty assessment. A pair of input images are spliced together to obtain a spliced image. A diffusion model is input to extract S features of different scales from N time steps. The resulting N×S features are input into a fully connected neural network model to obtain the feature potential uncertainty weights of the features at different time steps. Features of the same scale at different time steps are weighted and spliced and compressed according to the feature potential uncertainty weights to obtain S preliminary fused features of different scales. The system then upsamples layer by layer based on the scale and uses an attention module to adjust the number of channels to obtain the final fused features. The decoder then obtains the fused image. The present invention aims to address the issues of insufficient feature extraction and unstable fusion results caused by modal differences and feature potential uncertainty in image fusion, and to overcome the issues of missing details and unstable performance in fusion results caused by insufficient feature extraction and unreasonable fusion.

The invention belongs to the technical field of space target monitoring and data processing, and particularly relates to a space debris short arc data quality evaluation method and system based on a multi-dimensional standard, and the method comprises the steps: obtaining space target ephemeris data, a short arc observation data set and a target identification mapping relation, binding the ephemeris data of the space target and the short arc observation data set to the same space target; judging whether the space target ephemeris data is available or not; analyzing the successfully matched short arc observation data to obtain observation data features; executing a full-dimension quality evaluation process for available space target ephemeris data, and executing a degradation quality evaluation process for unavailable space target ephemeris data; and performing normalization processing and weighted fusion on each index to obtain a comprehensive quality score, and outputting an evaluation report. According to the invention, through joint evaluation and fusion scoring of multi-dimensional indexes, stable and interpretable quality determination of short arc observation data can be realized.

The invention discloses an advertisement recommendation effect evaluation system based on multi-dimensional user portraits, and relates to the technical field of artificial intelligence, functional modules of the system comprise a user portrait quantification module, a space-time correction module, a sample weight correction module, a joint evaluation score calculation module and a normalization quantification output module. According to the method, a user portrait abnormal risk measurement mechanism based on space-time correction is set, normalized modeling is carried out in combination with the sample weight, a unified abnormal risk combined evaluation score is formed, the defect of abnormal risk isolated evaluation in the prior art is effectively overcome, and the stability and credibility of advertisement recommendation effect evaluation are improved. According to the technical means, a solid data basis is provided for risk identification and effect quantification of the recommendation model, and an advertisement recommendation system is promoted to develop towards a more accurate and intelligent direction.

The invention provides an image determination method and device, electronic equipment and a computer program product, and relates to the technical field of machine vision. The method comprises the steps of obtaining a to-be-labeled image set, wherein the to-be-labeled image set comprises a plurality of target labeled images; receiving an image annotation result of the annotation personnel for the target annotation image, wherein the image annotation result is obtained by carrying out image annotation by the annotation personnel based on an image annotation standard generated after the annotation training operation; obtaining a reference annotation image, and performing cross verification on the image annotation result based on the reference annotation image to obtain an annotation verification result; and based on the annotation verification result, determining a target quality image from the plurality of target annotation images. According to the method and the device, through a multi-round joint evaluation and cross validation mechanism, the annotation consistency is remarkably improved, the problem that the quality of the annotated high-quality image is unstable can be effectively solved, and meanwhile, the construction efficiency of the high-quality image set can be improved.

The invention provides an aircraft bearing end face parallelism joint evaluation method with a common mode rejection function, and belongs to the technical field of aircraft engine bearing end face parallelism measurement. The method comprises the following steps: step 1, acquiring end face data of an inner ring and an outer ring of a bearing through a parallelism measurement system to obtain an original observation vector; 2, by introducing a weighing parameterization method, the absolute plane parameters of the inner ring and the outer ring are decoupled and recombined into common-mode tilt parameters and differential-mode shape parameters; step 3, error correlation modeling: capturing statistical characteristics of the system by constructing an equivalent covariance matrix containing cross correlation coefficients; step 4, performing robust judgment based on mahalanobis distance; and step 5, performing adjustment calculation in combination with a Gauss-Helmert model to obtain a final parallelism evaluation result. According to the method, the high-order residual problem of a traditional linearization model is solved, while common-mode vibration signals are reserved, real outliers are accurately recognized, and the numerical stability and the result reliability of parallelism evaluation are remarkably improved.

The invention relates to the technical field of medical monitoring, and discloses an orthopedic postoperative limb swelling monitoring method and system, and the method comprises the steps: carrying out the interaction relation mining of multi-source heterogeneous data in a target limb region, and obtaining a coupling relation group of the target limb region; performing multi-scale physiological signal separation on the multi-source heterogeneous data to obtain an integrated physiological state parameter of the target limb region; analyzing local microcirculation impedance characteristics of the target limb region, and performing spatial topological mapping on the local microcirculation impedance characteristics to obtain an impedance distribution diagram of the target limb region; performing path deduction on the individualized swelling trend of the target limb region to obtain a reference track of the individualized swelling trend; performing feedback type dynamic correction on the reference track to obtain a dynamic synchronous track of the individualized swelling trend; performing multi-modal joint evaluation on the individualized swelling trend to obtain a swelling report of the target limb region; the orthopedic postoperative limb swelling monitoring efficiency can be improved.

Disclosed herein is a method and system for knee joint evaluation and balancing. The method can include the steps of tracking a knee joint during a patellar tendon reflex with a sensor to generate sensor data, generating image data related to the knee joint during the patellar tendon reflex, and determining a patellar tendon score from the sensor data and the imaging data. The patellar tendon score can represent a knee joint condition. The system can include a sensor configured to track a knee joint during a patellar tendon reflex, an imaging device configured to generate image data related to the knee joint, a processor in communication with the at least one sensor and the imaging device, and a user interface configured to display a patellar tendon score.

The application discloses a standing-up and walking test method and system based on inertial sensors and electromyography technology, relates to the technical field of gait assessment and rehabilitation detection, and comprises the following steps: data acquisition is completed by using inertial sensors and electromyography technology; through a first algorithm, differences between left and right support phases and swing phases are comprehensively evaluated to determine whether the gait is symmetrical and stable; and through a second algorithm, muscle coordination of a patient is evaluated. The method realizes synchronous data fusion of a behavior layer and a neural control layer, lays a data foundation for subsequent construction of a precise and multi-dimensional gait and electromyography evaluation model, realizes joint evaluation of two core indexes of gait symmetry and stability, improves detection capability of neurological gait disorders, provides reliable criteria for individualized rehabilitation intervention, realizes modeling and difference measurement of timing control characteristics, and improves recognition capability of neuromuscular control disorders.

The invention provides a laser light source line width stability multi-parameter joint evaluation method and system. The method comprises the following steps: acquiring junction temperature parameters of a laser, environmental sound spectrum data and optical interference original signals in a power abrupt change period; performing energy accumulation operation on the environmental sound spectrum data to generate an acoustic characteristic energy value sequence; performing phase demodulation processing on the optical interference original signal to generate a line width drift value sequence; performing frequency domain correlation operation on the junction temperature parameter, the acoustic characteristic energy value sequence and the line width drift value sequence to generate a joint transfer function matrix; calculating the amplitude response modulus length of the matrix to obtain a dynamic weight vector; performing Fourier transform on the line width drift value sequence to obtain an initial line width frequency domain distribution vector, and calculating a multi-parameter joint evaluation value in combination with the dynamic weight vector; according to the invention, dynamic weighted comprehensive evaluation of the line width stability of the laser light source is realized, and the accuracy and robustness of evaluation are improved.

This application provides a method and system for assessing the lifespan and tiered utilization of wind turbine blades. The method involves acquiring operational monitoring data and damage detection information of the wind turbine blades; based on the operational monitoring data, constructing a joint evaluation framework of a data-driven time-series prediction model and a cumulative damage calculation model based on material fatigue characteristics; determining the remaining lifespan of the wind turbine blades by cross-validating and weighted summation of the outputs of both models; generating targeted repair decisions based on the remaining lifespan and the damage type, location, and severity contained in the damage detection information; and performing repair treatment on the wind turbine blades according to these decisions; evaluating the performance of the repaired wind turbine blades; and classifying the blades into different tiered utilization levels based on the evaluation results and preset performance thresholds for corresponding reuse treatment. This approach improves the accuracy of lifespan assessment and the efficiency of tiered utilization.

The present invention discloses a tactile information feedback processing device and method for lower limb rehabilitation training. The device includes: a display module, a lower limb movement acquisition module, a training evaluation module, a feedback processing module, a lower limb rehabilitation training module, and a muscle information acquisition module; the lower limb movement acquisition module is configured to acquire a set of key point spatial position matrices during the process of the user using the lower limb rehabilitation training module for human lower limb muscle strength training; the training evaluation module is configured to perform joint evaluation processing on the force value sequence and the set of key point spatial position matrices to obtain a training effect evaluation value sequence; the lower limb rehabilitation training module is configured to perform rehabilitation training on the user's lower limbs; the feedback processing module is configured to perform feedback calculation processing to obtain a feedback output value; the muscle information acquisition module is configured to acquire a set of the user's muscle information.

The present application is suitable for the field of 3D scanning technology, and provides a character model 3D scanning and dynamic posture capturing method and system. The method dynamically triggers three-dimensional scanning by real-time monitoring of joint movement intensity, continuously collects continuous motion trajectories using inertial sensors during non-scanning periods, and inversely calculates the space-time parameters of the occluded part through multi-light source shadow boundary evolution. A space-time joint evaluation function is constructed to adaptively adjust the scanning frequency and sampling rate. Finally, the discrete point cloud, continuous trajectory and inverse calculation data are space-time aligned and confidence weighted fused to generate a dynamic biomechanical model. This scheme breaks through the resource waste and occlusion blind zone bottleneck of traditional fixed frequency scanning, realizes the space-time complementation and precision optimization of motion monitoring, and is suitable for dynamic scenes such as film and television animation, medical rehabilitation, sports training, etc. Through multi-modal data fusion and adaptive resource scheduling, the system significantly improves the data integrity, real-time performance and biomechanical analysis capability, providing an efficient solution for human motion digitization.

This application relates to the field of medical neuroimaging processing and computer-aided diagnosis technology, and discloses a method based on normal white matter. 18 An Alzheimer's Disease (AD) Auxiliary Assessment System Based on F-florbetapir Retention Features. This system includes a data acquisition module for acquiring multimodal brain imaging data of subjects; an image preprocessing and segmentation module for impurity removal and target localization of the image data; a feature extraction module for extracting image features from the processed images; and a joint assessment and prediction module for AD auxiliary assessment based on the extracted features, incorporating a subject performance characteristic model and a logistic regression system. This Alzheimer's Disease (AD) auxiliary assessment system can significantly improve the accuracy of early AD diagnosis, accurately predict the rate of cognitive decline, and dynamically monitor the effect of immunotherapy.

The invention relates to the field of geotechnical engineering monitoring, in particular to a half-excavated and half-filled roadbed and slope stability combined monitoring and evaluation method and system, and the method comprises the following steps: building a data combined monitoring array, and carrying out the data synchronous collection through the data combined monitoring array, so as to obtain multi-source distributed monitoring data; performing quality control and preprocessing on the multi-source distributed monitoring data to obtain a multi-source standardized monitoring data set; constructing a roadbed-slope stability joint evaluation model based on the multi-source standardized monitoring data set, and obtaining a stability collaborative evaluation index according to the roadbed-slope stability joint evaluation model; and constructing a multi-stage stability evaluation mechanism, and combining the stability collaborative evaluation index and the multi-stage stability evaluation mechanism to obtain a stability joint monitoring evaluation result. According to the method, the comprehensiveness and the accuracy of combined monitoring and evaluation of the stability of the half-excavated and half-filled roadbed and the slope are improved, and effective data support is provided for engineering decision and engineering safety.