267 results about "Dynamic feature" patented technology

The invention relates to the technical field of data analysis, and discloses a multi-modal time sequence anomaly analysis method, device, equipment and medium, and the method comprises the steps: collecting visual data, audio data and process text data, carrying out the preprocessing and standardization processing of different types of data, constructing a multi-modal data set with aligned timestamps, and storing the multi-modal data set in a database; the method comprises the following steps: extracting a time-frequency dynamic feature and a semantic vector feature, extracting a map structure feature, a time-frequency dynamic feature and a semantic vector feature, fusing the features by using a cross-modal attention mechanism to generate a fused feature vector, finally performing analysis processing based on the fused feature vector, and outputting an analysis result. According to the method, the multi-modal data set with consistent time is constructed, the structural features of various modals are extracted, and the cross-modal attention mechanism is introduced to realize deep fusion of the feature level, so that the problems of single information utilization and weak feature relevance of the existing detection means are solved, and the comprehensiveness of defect detection and the accuracy of fault diagnosis are improved.

The invention relates to a robust unmanned aerial vehicle detection method based on dynamic feature fusion and context attention, and belongs to the technical field of image processing. Aiming at the problems of small target feature loss, semantic gap, background noise interference and the like caused by a fixed convolution kernel scale, one-way feature fusion and a static attention mechanism in an existing unmanned aerial vehicle aerial image target detection method, the method comprises the following steps: constructing a detection model comprising a backbone network, a neck network and a detection head network; a feature rearrangement and extraction module is designed in the backbone network to enhance feature learning, an enhanced double-flow feature fusion pyramid is designed in the neck network to optimize multi-scale feature fusion, and a dynamic multi-scale context attention mechanism is designed in the detection head network to suppress irrelevant background noise. The method effectively improves the accuracy and robustness of small target detection, and achieves a clearer and more stable detection effect in a complex environment.

The invention relates to the technical field of face verification, and discloses a face identity verification data processing method based on dynamic feature extraction, which comprises the following steps: acquiring a continuous face video frame sequence and calculating a full-pixel instantaneous velocity vector to generate an original dense optical flow field, selecting rigid region anchor points to calculate a rigid affine transformation matrix and construct a theoretical rigid motion field, performing differential stripping on the theoretical rigid motion field from the original dense optical flow field, and extracting a non-rigid micro-motion residual field; mapping the non-rigid micro-motion residual field to a facial muscle topological grid to generate a time sequence feature tensor, and calculating a geodesic line distance between a covariance matrix of the time sequence feature tensor and a reference dynamic feature in a Riemannian manifold space; when the geodesic distance is smaller than a threshold value, verification is passed, through a rigid-non-rigid orthogonal decomposition mechanism, the special viscoelastic micro-motion and cooperation law of biological soft tissue is captured by utilizing a residual field, and the high-simulation mask is effectively defended.

The invention relates to the technical field of mineral processing visual detection, and discloses a visual analysis system for detecting the grade of a phosphorite flotation froth layer. The system comprises an image acquisition and decomposition module, a parallel feature extraction module, a dynamic feature fusion module, a foam evolution analysis module and a grade decision output module. The system performs multi-scale decomposition on a foam image, extracts physical and semantic features in parallel, constructs a dynamic fusion network based on bidirectional mapping to perform iterative interaction, and generates a multi-modal feature descriptor. Therefore, self-organizing growth of a foam evolution graph is driven, an evolution track of a key foam primitive is positioned and tracked, a foam grade state vector is formed, and finally, a regulation and control decision is output in combination with external control parameters. According to the system, deep fusion of physical and semantic features and deep analysis of the foam dynamic evolution process are achieved, the accuracy and predictability of foam grade state sensing are improved, and an effective means is provided for accurate control over the flotation process.

The invention provides a weak supervision video anomaly detection method and system based on potential energy field damping dynamics. The method comprises the following steps: inputting a video into an anomaly detection model to obtain an original video feature sequence; calculating fluctuation potential energy by using the original video feature sequence and further obtaining a global inertia proxy vector; calculating interaction potential energy through the global inertia proxy vector and the original video feature vector; nonlinear self-adaptive damping force is generated through interaction potential energy; applying a nonlinear adaptive damping force to the original video feature vector to obtain a purified dynamic feature vector; constructing a loss function by using the purified dynamic feature vector, and training the model to obtain an optimized model; and inputting the video into the optimized model to obtain a final frame-level anomaly detection result. According to the method, anomaly detection is reconstructed from a classification problem to a signal decoupling and energy dissipation problem in a physical system, and the physical nature of a depolarization mechanism is explained from the theoretical level.

The invention relates to a pressing plate operation behavior analysis and error prevention method and system based on image recognition and machine learning. The method comprises the following steps: acquiring a pressing plate operation area video stream through image acquisition equipment, and segmenting a video into independent operation event segments based on motion detection and trajectory analysis; extracting key point time sequence data of hands of an operator by using a human body posture estimation model, and constructing a dynamic feature sequence fusing a spatial relationship, kinematics and posture semantics; performing multi-level similarity comparison on the dynamic feature sequence and a standard operation template, and judging operation compliance by a machine learning model in combination with a dynamic threshold value; triggering graded early warning and intervention according to a judgment result; and an incremental learning mechanism is adopted, and the template and the threshold value are adaptively optimized based on historical data. According to the invention, accurate and intelligent analysis and active error prevention of the whole operation process of the pressing plate are realized, and the safety level of electric power operation is effectively improved.

The invention discloses an SAR directed target detection method based on multi-scale context sensing, and belongs to the technical field of remote sensing image processing and computer vision. According to the method, firstly, through a scattering characteristic guided multi-scale dynamic characteristic enhancement network, a multi-branch structure and a dynamic fusion mechanism are utilized to enhance characteristic expressions of targets of different scales; and secondly, designing a task-adaptive regional context sensing module, fusing local details and semantic contexts, and improving the target discrimination capability under a complex background. And a decoupling type progressive refining detection head is adopted to predict a target category, a directed bounding box and a direction angle, and the positioning precision is optimized through progressive regression. Semantic consistency loss is introduced in the training stage, and end-to-end optimization is achieved in combination with a multi-task joint strategy. According to the method, the problems of large target scale difference, strong background interference, changeable directions and the like in the SAR image are effectively solved, and the accuracy and robustness of directed target detection are remarkably improved.

The invention relates to the technical field of deep learning, in particular to a small sample fine-grained image classification method based on multilayer feature dynamic interactive fusion, which comprises the following steps: inputting a support set and a query set into an image classification model, and outputting a prediction category of a query sample; the image classification model processing steps are as follows: S201, extracting to obtain multi-scale features; s202, obtaining interaction features after interaction optimization of each support sample through a multi-layer feature interaction module; s203, obtaining fusion features of each support sample through a dynamic feature fusion module; s204, projecting the fusion features to a corresponding feature space; s205, calculating category prototype representation of each category in each feature space; s206, calculating the cosine similarity between the query sample and the category prototype representation of each category in each feature space; and S207, determining a prediction category of the query sample based on the cosine similarity. According to the method, efficient cross-layer feature aggregation can be realized, and the capability of capturing fine-grained differences is enhanced through an attention mechanism of spatial perception.

The embodiment of the invention relates to a gesture recognition method and system based on cross-modal feature alignment. The method comprises the following steps: acquiring a radar echo signal and a visual image of a gesture action; performing feature extraction on the radar echo signal based on a fusion attention mechanism to obtain a radar feature vector; performing multi-layer convolution processing on the visual image to extract a high-level semantic feature, performing dynamic feature extraction on the visual image based on a fusion space-time attention mechanism, and fusing the extracted high-level semantic feature and the dynamic feature to obtain a visual feature vector; performing time sequence alignment, hidden space alignment and semantic alignment on the radar feature vector and the visual feature vector under the constraint of total alignment loss; performing dynamic weighted fusion processing on the aligned radar feature vector and visual feature vector based on a cross-modal attention mechanism to obtain a fusion feature; and recognizing a gesture category according to the fused feature to obtain a gesture recognition result. According to the embodiment of the invention, the data of each sensor are effectively fused, and the recognition precision is improved.

The invention discloses a pipeline defect positioning method, device and equipment, a medium and a product. An SPPF-LSKA optimization instance segmentation model is combined with an ORB-SLAN3 framework, and dynamic feature points are eliminated by using information of an RGB-D depth map and an instance segmentation mask; secondly, a sliding window confidence evaluation mechanism is introduced, the credibility of feature points can be dynamically evaluated, wrong feature points generated due to instantaneous false detection are removed in time, and high-precision dynamic point removal is achieved; and finally, in a back-end optimization stage, a double-layer map strategy is adopted, a static mapping layer participates in BA optimization to reduce a positioning error, and a dynamic observation layer independently records an optical flow speed and a segmentation result of a dynamic region for dynamic interference analysis and does not participate in global optimization, so that interference of dynamic factors on positioning precision is avoided.

The invention relates to the technical field of equipment fault prediction and diagnosis, and discloses an equipment fault prediction and diagnosis method and system based on dynamic feature fusion. Comprising the steps of collecting a multi-modal monitoring signal, preprocessing to generate a standardized time series data stream, extracting a feature vector through a local detail sensing network and a global trend capturing network, generating a comprehensive feature vector by using an adaptive feature fusion module, and outputting an operation state category and a confidence score through a classification decision network. According to the method, the multi-modal time sequence features can be effectively fused, the feature weight is dynamically adjusted, the equipment operation state is accurately identified, and the accuracy and reliability of fault prediction and diagnosis are improved.

The invention provides a multimedia interaction control method and system based on a man-machine feedback closed loop, and relates to the technical field of financial science and technology, and the method comprises the steps: obtaining the multi-mode original feedback data of a user in an interaction environment in real time; preprocessing the multi-modal original feedback data to obtain preprocessed multi-modal feedback data; performing space-time coupling processing on the preprocessed multi-modal feedback data to generate multi-modal fusion features; dynamically determining a plurality of feature reference points based on feedback data of different modalities in a feature space corresponding to the multi-modal fusion features; the feature reference points at least comprise a user pupil center coordinate point in the visual feedback data, a fundamental frequency energy peak point in the voice feedback data and a pressure distribution gravity center point in the tactile feedback data. According to the method, space-time cooperative processing, dynamic feature quantification and closed-loop adaptive compensation of multi-modal feedback in a financial scene can be realized, and the instruction precision and information response timeliness of financial interaction are improved.

The invention relates to the technical field of internet advertisement services, and particularly provides an internet advertisement service method and system, and the method comprises the steps: generating a multi-dimensional dynamic feature vector; according to the combination and weight of the three types of core features in the multi-dimensional dynamic feature vector, dynamically selecting and synthesizing audio-visual materials and information copywriting matched with the combination and weight from the creative element library to form a personalized advertisement creative instance; when the personalized advertisement creative instance is displayed and triggers a deep interaction behavior event defined by an interaction tracking mechanism, capturing the deep interaction behavior event and specific parameters thereof, and encoding the deep interaction behavior event into a real-time feedback data stream; the scene-feedback feature vectors are used for adjusting element selection rules and synthesis logic parameters in the creative element library in real time, and a new round of generation and delivery of personalized advertisement creative instances is driven. The system comprises a vector analysis module, a creative instance module and a real-time feedback module. According to the invention, closed-loop optimization of advertisement creative generation and delivery is realized.

The invention belongs to the technical field of non-intrusive load identification or decomposition, and discloses a non-intrusive load decomposition method fusing bidirectional Mamb-Attention and dynamic feature enhancement, which comprises the following steps: acquiring household electric appliance consumption data within a period of time, and preprocessing the electric appliance consumption data; according to the preprocessed electric appliance consumption data, dynamically selecting one-dimensional convolution or multi-scale depth separable convolution to perform feature enhancement, and outputting a feature vector; a bidirectional Mamb-Attention fusion model is constructed, and the feature vectors are processed; the constructed bidirectional Mamb-Attention fusion model is trained and tested, and an optimal model is obtained; and performing non-intrusive load decomposition on the electric appliance consumption data acquired in real time by using the optimal model. According to the method, through collaborative design of a bidirectional Mamba module and a multi-head attention mechanism, joint modeling of long-term dependence and local features is realized.

The invention discloses a product recommendation method and device, electronic equipment and a storage medium, and relates to the technical field of data processing, basic information and demand preferences of a user are obtained, a multi-dimensional dynamic feature vector is constructed, the vector can dynamically capture multiple demands of the user in different stages of a life cycle, and the user experience is improved. The method does not depend on a fixed preset rule or single-dimension data; and generating a recommendation request based on the dynamic vector, and inputting the recommendation request into a recommendation algorithm module to screen and sort products, so that recommendation logic can closely fit real-time demand change of a user, and limitation of static analysis of a traditional model is avoided, and therefore, deviation between a recommendation result and an actual demand of the user caused by an existing preset rule or a traditional machine learning model can be solved; the technical problems that the recommendation accuracy and the user satisfaction are affected are solved, and the technical effects of improving the recommendation accuracy of the personal pension insurance products, effectively matching the actual demands of the users in different stages and improving the satisfaction of the users to recommendation services are achieved.

The invention discloses a lightweight CAN intrusion detection method based on dynamic feature engineering, and the method comprises the steps: firstly obtaining vehicle-mounted CAN bus flow data and carrying out the preprocessing, and forming an initial feature vector; secondly, on the basis of the initial feature vector, a self-adaptive dynamic feature enhancement module is constructed, and an enhanced dynamic feature vector fused with time sequence change characteristics is generated; and then, aiming at the enhanced dynamic feature vector, constructing a lightweight detection model based on space-time anomaly score optimization and a hierarchical attention mechanism, and outputting probability distribution corresponding to each attack category. And finally, through knowledge distillation, migrating knowledge of the teacher model to the constructed lightweight detection model, training the lightweight detection model after knowledge migration, and outputting a lightweight CAN intrusion detection result. According to the method, abnormal changes in network traffic can be captured in real time, random threats such as fuzzy attacks can be effectively identified, and the long-term operation requirement of a vehicle-mounted network is met.

The invention discloses a self-adaptive compression and lossless fast transmission method and system for high-frequency test data. The method comprises the following steps: extracting multi-dimensional dynamic feature vectors from high-frequency test data in real time and marking feature points; based on the features, the network bandwidth and the target compression ratio, dynamically selecting a compression algorithm combination and parameters through a pre-training decision model; performing data framing and typed preprocessing according to the algorithm combination; compression is executed through a multi-stage assembly line, and a correction link based on statistical characteristics is inserted between stages; constructing a three-dimensional transmission state matrix fusing priorities, links and quality scores, dynamically allocating links and adaptively adding forward error correction codes; and after error correction and decompression of a receiving end, realizing lossless verification through multi-dimensional comparison of feature point sequences of the reconstructed data and the original data. According to the method, the whole-course self-adaption of the compression strategy and the transmission scheduling is realized, and the lossless and reliable recovery of the data is ensured while the compression and transmission efficiency is improved.

The invention relates to the technical field of equipment operation tests, particularly discloses a simple scene equipment operation action rapid processing method based on a test video, and is used for solving the problems of low action recognition precision, insufficient spatial-temporal feature fusion, low detection speed and more redundant data in the prior art. Comprising the following steps: accurately intercepting motion start-stop frames, extracting the frames according to a preset frame rate, implementing geometric transformation and color augmentation on the frame-extracted images, respectively extracting spatio-temporal dynamic features and static spatial features by adopting a 3D convolutional network and a 2D convolutional network in parallel, integrating the spatio-temporal features by a channel fusion attention module based on a Gram matrix, and extracting the spatial and temporal dynamic features and the static spatial features by adopting the channel fusion attention module based on the Gram matrix. A thermodynamic diagram is generated based on Grad-CAM to assist in key area positioning, a RefineDet network is input to generate a detection result through ARM coarse regression and negative anchor filtering, ODM fine regression and non-maximum suppression in sequence, and efficient and accurate positioning and classification of operation actions are carried out with assistance of a dynamic frame abandoning and time sequence correction strategy; according to the invention, through the multi-scale residual error convolution and the anchor frame network, accurate positioning of slow action of equipment is realized.

The invention belongs to the technical field of large language models, and particularly discloses a large language model illusion detection method and system, a terminal and a medium. Comprising the steps of obtaining a middle hidden state of each token in a process of generating an answer by a large language model, and organizing and forming a hidden state sequence according to a generation sequence; performing time sequence preprocessing on the hidden state sequence to complete the preprocessing of the sequence; inputting the pre-processed hidden state sequence and a dynamic feature formed by a difference vector, a trend drift degree or local fluctuation intensity into a dynamic sequence modeling network, and enabling the network to extract a time dependence feature reflecting an internal state change mode based on an evolution relation of a hidden state along with a generation process; and generating an illusion risk score according to the comprehensive time sequence characteristics output by the dynamic sequence modeling network, and determining that the answer contains the illusion content when the score exceeds a preset threshold. The reliability of the output content of the large language model can be improved.

The invention relates to a commodity sales volume prediction method and device based on multi-modal feature fusion and a storage medium, and is applied to the technical field of store commodity sales volume prediction, and the method comprises the steps: obtaining static and dynamic multi-modal features affecting the store commodity sales volume, and carrying out the unique coding representation of a store and a commodity; the feature processing layer converts unique coding representation into dynamic embedded features and then performs weighted fusion on the dynamic embedded features and static features through feature gating, the feature gating has the advantage of dynamic weight distribution, and adaptive adjustment cannot be performed according to input sample features due to the fact that static weight distribution is adopted in a traditional method; then, the dynamic features continue to be fused through a cross attention mechanism, and soft alignment and weighted fusion of cross-modal features are achieved through the attention mechanism; then, the incidence relation between fusion features is learned through a multi-layer feedforward neural network of a calculation layer; and inputting the final fusion feature into a task layer to obtain a sales prediction result.

This application provides a method and system for facial asymmetry analysis, relating to the interdisciplinary fields of computer vision and machine learning, and particularly to a method for facial asymmetry analysis, comprising: acquiring multiple frames of facial images to be evaluated; performing inter-frame difference operations on the multiple frames of facial images to obtain difference images, and inputting the difference images into a pre-trained first classification model to obtain probability values ​​predicted as pathological facial asymmetry, as dynamic features; detecting facial key points based on the multiple frames of facial images and a preset facial key point detection algorithm, and using the facial key points to calculate eyebrow tilt, the ratio of eyebrow height to inner canthus distance, mouth corner tilt, and mouth corner angle, as static features; fusing the dynamic and static features to obtain fused features; and inputting the fused features into a pre-trained second classification model to obtain classification results. This application can improve the accuracy of facial asymmetry analysis.

The application provides a transformer fault diagnosis method and device based on a multi-modal self-attention mechanism, and the method comprises the following steps: collecting multi-modal monitoring data of a transformer to obtain a data sequence matrix of multiple modes; pre-processing the matrix to obtain pre-processed data; calculating the change rate and change point index to obtain a change rate and change point feature set; performing time sequence attention calculation on each mode based on the pre-processed data and the feature set to obtain single-mode feature representation; performing cross-modal attention interaction calculation based on the single-mode feature representation to obtain global cross-modal fusion feature representation; fusing the single-mode feature representation and the global cross-modal fusion feature representation to obtain fusion representation; and inputting the fusion representation into a pre-trained classifier to obtain a transformer fault diagnosis result. The application realizes reliable mutual verification of multi-modal information by extracting dynamic feature indexes and performing multi-modal self-attention mechanism processing, and improves the transformer fault diagnosis accuracy and early warning capability.

The application provides a video file description text generation method and device and a storage medium, and relates to the technical field of big data. The method comprises the following steps: acquiring a video file to be analyzed, extracting global static features, global dynamic features and local features in the video file to be analyzed; determining action behavior features of a target object in the video file to be analyzed according to the global static features, the global dynamic features and the local features; and generating a description text according to the global static features, the global dynamic features, the local features and the action behavior features of the target object. By using the technical solution, the staff can be assisted to quickly locate the required video file, and then the user's behavior can be quickly obtained through the video file, so that the work efficiency is improved.

The invention provides a large-scale complex software system test case recommendation method based on deep reinforcement learning, which forms deep cognition of a model on a case by providing analyzable case-code-defect multi-layer features, and constructs dynamic features for describing selected cases at the same time, so as to recommend the test cases of the large-scale complex software system. The complementarity and redundancy among the cases are comprehensively considered by the model, and the group-level test case recommendation ability is improved. In addition, through a multi-cycle adaptive training reinforcement learning structure, the method has high adaptability to a dynamic environment, and automatic use case recommendation can be achieved according to code fireworks. The improvement of the capabilities realizes more intelligent and more efficient test case recommendation, and is helpful for improving the defect detection efficiency and the test resource utilization rate of software testing in a continuous integration environment.

The invention discloses a dynamic environment visual map construction method and system based on OpenSeeD (Open SeeD). Relates to the technical field of computer vision and robot autonomous navigation. Comprising the following steps: acquiring a continuous RGB image sequence of a dynamic object; performing instance segmentation and optical flow estimation on the continuous RGB image sequence to obtain an initial mask and optical flow information; fusing and optimizing the initial mask and the optical flow information, and performing dynamic object recognition on an optimized fusion result to obtain a dynamic mask; and removing the dynamic feature points of the dynamic mask, retaining the static feature points of the static region, and inputting the static feature points into the ORB-SLAN3 library to complete pose estimation and map construction. According to the method, the problem of pose estimation failure or precision reduction caused by feature pollution of the traditional visual SLAM in a dynamic environment can be remarkably improved, and the method has important theoretical value and engineering application prospect in the aspects of reliable autonomous navigation and persistent map construction in a complex dynamic scene.

The invention discloses a facial expression detection method based on a Transform dual-channel network and related equipment, and can be applied to the technical field of deep learning. Static features of a video sequence to be processed are extracted through a video sequence feature extractor to form a video sequence feature map carrying global information and local information, and dynamic features of an optical flow sequence to be processed are extracted through an optical flow sequence feature extractor to form an optical flow sequence feature map. Fusing features of different levels in the video sequence feature map through a multi-level feature fusion module to obtain multi-level static fusion features, and carrying out feature refinement on the optical flow sequence feature map through a self-adaptive convolution attention module to obtain space channel refinement dynamic features; the two features are fused through a cross attention fusion module to obtain a cross fusion feature, and then facial expression recognition is performed according to the cross fusion feature, so that the accuracy of expression detection can be effectively improved.

The invention discloses an intelligent evaluation method for operation of an energy-saving facility based on artificial intelligence. The method comprises the following steps: step 1, collecting multi-source time sequence data of the energy-saving facility; 2, extracting a supervision label and binding the supervision label with the paired sample; step 3, inputting the paired samples into an improved TSMixer model, generating feature mixing weight parameters and bias parameters in a feature mixing layer through a HyperNetwork sub-module, and embedding an SE-block channel attention structure to obtain intermediate feature representation; 4, the step 3 is executed repeatedly; 5, constructing a dynamic feature fusion network; 6, inputting the comprehensive feature set into the LightGBM model, and outputting a result sequence; and 7, comparing the result sequence with the corresponding reference data in the reference library, and outputting an energy-saving facility operation intelligent evaluation result. The method has the advantages of comprehensive evaluation, efficient processing and intelligent result.

The invention provides an image editing and fusing method based on a structured state space sequence model, and the method comprises the steps: S1, obtaining at least one input image, and carrying out the time sequence feature extraction of the input image through employing the structured state space sequence model, and obtaining a background feature; s2, background features are separated through mask operation, foreground features output by a conditional generation model are introduced, and latent space feature coding is performed on the background features and the foreground features through a variational auto-encoder to obtain coded background features and coded foreground features; and S3, inputting the encoded background features and the encoded foreground features into a Mamba fusion network for feature fusion to obtain a fusion image, and dynamically adjusting fusion weights of the encoded background features and the encoded foreground features by using a selective scanning mechanism in a fusion process to perform feature space alignment. The method has the beneficial effects that the dynamic feature modeling capability can be realized, the feature fusion accuracy is improved, and the calculation efficiency is improved.

A search engine-based user portrait construction method, system, medium and device, relating to the technical field of Internet, the method comprises: obtaining target search behavior data of a user; determining data corresponding to static feature labels and dynamic feature labels of the target user according to the target search behavior data; calculating first feature weights of the user according to the data corresponding to each dynamic feature label; correcting the first feature weights based on a preset time decay formula to obtain second feature weights; and generating a user portrait of the user based on each static feature label and the second feature weights. The technical solution provided by the present application can construct a more accurate user portrait according to the search behavior of the user, thereby facilitating subsequent recommendation of more accurate related information to the user based on the user portrait.

This invention discloses a crowdsourcing testing platform system and method based on intelligent matching. The method includes: receiving test task requests containing task description text; using a hierarchical attention network model to perform semantic understanding of the task description text, generating structured skill tags, and dynamically updating the tag confidence of testers using a Bayesian probabilistic framework; calculating the initial matching degree using a cosine similarity algorithm that incorporates a recent task completion quality correction factor, and then filtering and constructing a candidate pool; for each candidate, constructing a dynamic feature matrix, and using an improved K-nearest neighbor algorithm based on Mahalanobis distance for ranking and recommendation, where the K value is dynamically determined based on the number of candidates in the pool and the task dwell time. This invention significantly improves the efficiency and accuracy of task allocation through a capability assessment mechanism, achieving optimized allocation of testing resources.