55 results about "Pruning algorithm" patented technology

The invention discloses an operation and maintenance workflow cooperation system and method, and relates to the technical field of business process.The method comprises the steps that after a natural language operation and maintenance requirement is received, a subtask set containing task attributes is extracted through a semantic model built based on a pre-training operation and maintenance field language model; inputting the sub-tasks into a causal mining model, capturing an implicit dependency relationship between the tasks through an attention mechanism which takes task types and resource demands as weight regulation factors, and generating an operation and maintenance relationship graph which contains dependency confidence coefficients and dependency types and does not have cyclic conflicts; splitting the atlas into a task chain set and a free task point set by adopting a causal-oriented greedy pruning algorithm based on a directed edge association subtask maximum aggregation and task chain set scale minimization principle; task chains are distributed through a weighted matching algorithm in combination with the chain overlap ratio and the to-be-handled task amount of the intelligent agent, remaining free task points are distributed according to the balance principle, and accurate disassembly and efficient cooperation of operation and maintenance tasks are achieved.

The invention provides a system-level fault analyzing and tracing method and system based on multi-layer causal diagram extraction, and belongs to the technical field of fault diagnosis. Using a multi-level convolutional neural network to convert the time sequence monitoring data features into a feature matrix; by introducing a hierarchical adjacency pruning algorithm and an elastic network regularization constraint, sparse modeling of a multi-level causal matrix is realized, and a causal matrix graph, namely a prediction contribution matrix graph, is obtained; according to a proposed score quantization algorithm, direct propagation and indirect propagation effects are comprehensively considered, prediction information provided by each variable is quantified, a reason score is provided, and a fault reason variable is determined. According to the method, multi-dimensional feature information of the system-level fault can be compared, all useful information is fully utilized, the contribution degree of the system variable fault is accurately evaluated, and the high-level fault reason detection rate is obtained.

The invention relates to the field of environment-friendly packaging boxes, and discloses a multi-modal data fusion environment-friendly packaging box intelligent design auxiliary system which comprises a packaging full-life-cycle heterogeneous atlas database module, a design constraint parameter analysis module, a topological variation index engine module, a compliance and multi-objective optimization module and a parameterization scheme generation module. According to the method, a time dimension is introduced through a topological variation index engine, instantaneous stress in a folding process is calculated in combination with a nonlinear viscoelastic model, physical evolution of a structure is simulated in a virtual design stage, and the fracture risk is predicted; meanwhile, multi-objective optimization is carried out by utilizing a Hash mask mechanism based on laws and regulations and a pruning algorithm, and a compliance design scheme containing production process parameters is output, so that the problems of lack of physical simulation and low compliance verification efficiency in environmental protection material design are solved, and the physical feasibility and the production yield of the design scheme are improved.

The invention relates to the technical field of model lightweight, and particularly discloses a deep neural network model optimization method based on hierarchical reinforcement learning and multi-agent collaborative distillation, and the method comprises the steps: building a structured pruning searcher based on an ABC algorithm, constructing a pruning combination reduction strategy dynamic artificial bee colony pruning algorithm, and carrying out the optimization of a deep neural network model. Performing fitness evaluation to guide a search process, and outputting an optimal pruning network structure under resource constraint; establishing a staged distillation architecture and a multi-dimensional hierarchical loss function, and realizing smooth and progressive knowledge transmission between the teacher model and the assistant model; a fine-grained quantization scheme based on parameter classification is designed, differential bit widths are configured for weights, batch normalization parameters and activation output respectively, a quantization perception training loss function fusing a hardware delay look-up table model is constructed, hardware perception joint fine tuning of the network weights and quantization parameters is achieved, and the quantization precision of the network weights and the quantization parameters is improved. Therefore, the effect of remarkably improving the model compression efficiency on the premise of keeping the precision is achieved.

The invention provides a real-time incremental RAG method and system based on a dual-tense knowledge graph, and belongs to the technical field of artificial intelligence and knowledge graphs. The method comprises the following steps: through dual-tense decoupling modeling of event effective time and system input time, constructing a versioned knowledge graph and establishing a tense index; efficient retrieval is carried out by adopting a time sequence pruning algorithm based on indexes, and dynamic expansion of a graph pattern is realized through a declarative compiler; realizing abstract-free high-precision result sorting by utilizing a multi-modal concurrent retrieval and attention fusion mechanism; incremental updating of the knowledge graph is realized through streaming data processing and a distributed engine; and closed-loop optimization is formed according to user feedback. The system effectively solves the problems that a traditional RAG scheme is high in data updating delay, tense information is lost, retrieval efficiency is low and expansibility is poor, tense query accuracy, retrieval efficiency and system throughput are remarkably improved, and meanwhile operation and maintenance complexity is reduced.

The invention relates to the technical field of purchase demand prediction, and discloses a part purchase demand prediction method and system based on machine learning, and the method comprises the steps: obtaining modular product basic data, and generating a BOM graph structure; calculating prior probability distribution through a Bayesian inference algorithm; executing an adaptive probability pruning algorithm to solve the problem of combinatorial explosion; quantizing uncertainty by using a Bayesian deep learning network; calculating a differentiated safety inventory coefficient based on the value-at-risk model; executing an importance sampling algorithm to carry out Monte Carlo simulation on high-risk low-frequency configuration, and verifying a demand coverage rate in an extreme scene; an incremental learning mechanism is utilized to update probability distribution and a pruning threshold according to the new order data, and a self-adaptive optimization demand prediction result is output; according to the method, the inventory cost is remarkably reduced, the stockout risk is reduced, and the balance between the calculation efficiency and the prediction accuracy is realized.

A method may comprise: converting one or more functions or function call instructions of a first neural network (NN) model into one or more graph modules; analyzing a relationship between one or more inputs and one or more outputs of the one or more graph modules; generating a second NN model including the one or more graph modules as one or more nodes of a directed acyclic graph (DAG) by coupling the one or more inputs and outputs of the graph modules based on the relationship; adding one or more markers corresponding to a weight parameter of one or more layers of the second NN model; and updating the one or more markers according to a pruning algorithm that removes at least a portion of the weight parameter.

The application provides a multiple layout splitting method, system, medium and program product. First, an initial splitting coloring operation is performed based on a saturation priority strategy to generate an initial layout splitting scheme. Then, the global exploration ability of a tabu search algorithm is utilized, and a preset target function is combined to perform multiple rounds of iteration update operations on an initial conflict node set to generate a multiple layout splitting optimization scheme. For ultimate conflict nodes that cannot be completely eliminated by the tabu search algorithm, a backtracking pruning algorithm is started to maximize the attempt to eliminate all ultimate conflicts to generate a final multiple layout splitting target scheme. The application combines the initial optimization ability of the saturation priority strategy, the global exploration ability of the tabu search algorithm, and the quality guarantee of the backtracking pruning algorithm to realize accurate splitting of multiple layouts, guarantee splitting quality and lithography yield, improve splitting efficiency, reduce computational complexity, and adapt to the needs of multiple layout splitting.

The present disclosure discloses a redundant backup deployment method for microservice instances in edge computing environments. Redundant backup deployment of the microservice instances and the selection of a primary instance are acquired using Transformer-based deep reinforcement learning (T-DRL). For a service failing to satisfy the service level agreement (SLA), the actual service reliability is improved by downgrading the switching priority of the microservice instances; for a service satisfying the SLA, the deployment cost of the microservice instances is reduced using a pruning algorithm. The present disclosure effectively reduces the deployment cost of microservice instances and minimizes the number of active edge nodes while ensuring the SLA, thereby effectively reducing the resource consumption in edge computing environments.

The application discloses an operation and maintenance workflow cooperation system and method, relates to the technical field of business processes, and comprises the following steps: after receiving a natural language operation and maintenance requirement, a semantic model based on a pre-trained operation and maintenance field language model is used to extract a subtask set containing task attributes; the subtasks are input into a causal mining model, the hidden dependency relationship between the tasks is captured through an attention mechanism taking the task type and resource requirement as a weight adjustment factor, and an operation and maintenance relationship graph containing dependency confidence, dependency type and no circular conflict is generated; a causal orientation greedy pruning algorithm is used to split the graph into a task chain set and a set of free task points based on the principles of maximum aggregation of the subtasks associated with the directed edges and minimization of the size of the task chain set; the task chains are distributed by a weighted matching algorithm combined with the chain coincidence degree and the to-do task amount of the intelligent agent, the remaining free task points are distributed according to the balance principle, and accurate disassembly and efficient cooperation of the operation and maintenance tasks are realized.

The application relates to a model pruning method of an interpretable CNN classification model, belongs to the field of image compression, and solves the problems of high operation complexity, large time and memory consumption and difficulty in deployment on terminal equipment of an existing deep CNN model, and solves the problem of lack of interpretability of an existing model pruning algorithm. The method comprises the following steps: inputting a training picture into a neural network model to be pruned, and extracting a feature map matrix of each convolution layer; upsampling the feature map matrix to the size of the input picture, and then performing a normalization operation to construct a saliency map; multiplying the saliency map and the input picture element by element to construct a weighted input picture; subtracting the input picture from the weighted picture element by element to construct an attention region occlusion map; inputting the attention occlusion map into the model to be pruned, observing the change of model accuracy as an importance score of the channel, and pruning the channel to obtain a pruned lightweight model. The application realizes high pruning rate of the model and improves the interpretability of the pruning process.

The application provides a mechanical equipment residual service life prediction method and device, and relates to the technical field of equipment management.The application adopts an adaptive pruning algorithm for light processing, and continuously optimizes model performance through error calculation and model evaluation, so that the prediction speed and prediction accuracy are improved; the pruning process automatically prunes redundant elements through an adaptive structured pruning strategy, so that unnecessary operations and searches are avoided; and when different neural network layers are used, different pruning rates are used for automatic structure pruning, so that an optimal light network model is finally obtained; the residual service life prediction accuracy is significantly improved, and less storage space is occupied, so that the application can be conveniently deployed on platforms such as small embedded systems, and timely maintenance and maintenance of maintenance personnel are reminded, so that the application is suitable for preventive maintenance and management of various mechanical equipment, and has good practicability and popularization value.

The invention provides a target detection method and device based on automatic driving, a new energy automobile and electronic equipment, and the method comprises the steps: obtaining a road image; preprocessing the road image to obtain a target detection data set; training the target detection data set according to a pre-constructed first target detection model to obtain output features; optimizing the first target detection model according to a model pruning algorithm to obtain a second target detection model; obtaining distillation loss according to the first target detection model and the second target detection model; and performing knowledge distillation on the output characteristics according to the distillation loss to obtain a detection result. According to the invention, the method can improve the detection capability of a detection model for a target, improves the detection precision and efficiency, reduces the redundancy degree of the model, reduces the calculation amount, and reduces the consumption of calculation power.

This invention discloses a method for solving power system time-series optimization problems, comprising: constructing a dynamic mathematical model of the power system time-series problem and determining the objective function and constraints; generating an initial solution based on the dynamic mathematical model, performing pruning preprocessing on the initial solution using a fast pruning algorithm to select high-quality initial feasible solutions; generating neighborhood solutions based on the high-quality initial feasible solutions using a simulated annealing algorithm, determining whether to accept the neighborhood solutions according to the Metropolis criterion, and marking the completion of one iteration after the determination; performing pruning feedback and search direction optimization after each iteration until a preset iteration convergence condition is reached, and outputting the optimal solution for power system time-series optimization. This invention can effectively improve the efficiency, accuracy, and global optimality of solving power system time-series optimization problems, and is suitable for the complex requirements of high-voltage and high-efficiency power systems.

The application discloses a kind of offshore wind farm group's decentralized coordination control method and device, its method includes: the tail flow wind turbine power and thrust load balance optimization and control model of considering tail flow influence of single wind turbine group are constructed;Original tail flow directed graph is constructed, and original tail flow directed graph is decomposed into completely uncoupled sparse sub-tail flow directed graph using graph weight pruning algorithm and graph depth-first search algorithm, sparse wind farm group-field-machine multi-layer decentralized control system is constructed, and decentralized wind farm power and thrust load balance optimization model is constructed, and the optimal value of power and thrust load balance control parameter is solved.The method and device of the application establish power and thrust balance optimization model, realize a kind of communication burden low, less and scalable wind turbine group decentralized coordination control method and device.

The invention belongs to the technical field of communication, and discloses a pruning and fine tuning troposphere waveguide prediction method and system based on multi-granularity evaluation, and the method comprises the steps: calculating edge loss and task loss; fusing edge loss and task loss to construct a channel importance evaluation system, and performing quantitative evaluation and dynamic sorting on the contribution degree of the atmospheric waveguide prediction channel; on the basis of the sorting of atmospheric waveguide prediction channels, redundant channels are gradually eliminated by adopting an iterative pruning algorithm until a preset pruning rate is reached; and calculating the prediction confidence of the atmospheric waveguide prediction model before and after pruning, positioning a high-sensitivity atmospheric waveguide sample of which the prediction result is remarkably reduced, establishing a dynamic sample weighting mechanism, and performing fine adjustment compensation on the residual channel weight by using error back propagation. According to the method, a multi-granularity importance evaluation cutting mechanism and a prediction information guiding method are adopted, lightweight compression and fine adjustment are performed on the model, the effectiveness of the prediction model is improved, and accurate prediction and interaction of the non-uniform atmospheric waveguide are realized.

The invention discloses an improved YOLO11-based method for detecting breakage of a hair braid of an oil pumping unit in a low-light scene, and the method comprises the following steps: collecting an image data set of the hair braid on the oil pumping unit, and carrying out the preprocessing of the image data set, thereby obtaining a preprocessed image data set of the hair braid of the oil pumping unit; based on a YOLO11 model, a double-trunk module is adopted, and a CBAM attention mechanism and a loss function are fused to improve the model; carrying out training on the improved YOLO11 model; trimming the trained YOLO11 model by using an LAMP amplitude pruning algorithm, taking the trimmed YOLO11 model as a detection model after verification, and deploying the trimmed YOLO11 model on site; acquiring a camera real-time video stream of the hair braid of the oil pumping unit in operation, and detecting the camera real-time video stream in real time by adopting the detection model; real-time judgment of breakage of hair braid of oil pumping unit If so, a fault message is uploaded, and an alarm is given at a webpage end. According to the detection method, the accuracy of the breakage detection of the hair braid of the oil pumping unit under the low-light condition can be remarkably improved.

The invention discloses an urban power distribution network data driving distribution robust optimization scheduling method and device considering uncertainty. The method comprises the following steps: according to a traditional fuzzy set B1 based on a Wasserstein distance, constructing a fuzzy set B2 based on a Copula function and considering uncertainty correlation; according to the fuzzy set B2, constructing a micro-grid dispatching model of the urban power distribution network considering the uncertain relevance; deducing a worst case conversion method in the fuzzy set B2 through a duality theory, McCormick relaxation and conditional value-at-risk approximation of the micro-grid scheduling model, and obtaining a micro-grid linear scheduling model; trimming the collected original data set by adopting a sample trimming algorithm to obtain a trimmed data set; and based on the trimming data set, a solver is adopted to solve the micro-grid linear scheduling model, and an optimal scheduling strategy of the micro-grid is obtained.

This invention provides a continuous subgraph matching method for social networks based on dynamic pruning, belonging to the field of social network behavior detection. The invention proposes a dynamic pruning algorithm that establishes local equivalence theory by calculating the backward neighbors of query points and candidate points, defines subtree-level equivalence criteria in dynamic graph environments, and is applicable to highly isomorphic behavior patterns such as zombie account groups and terminal nodes of propagation chains. The dynamic pruning algorithm skips isomorphic subtree structures to achieve the pruning effect. A query graph decomposition and sorting mechanism is proposed to remove the binding constraint of the matching order on the update edges and candidate sets, actively identifying and prioritizing highly redundant candidate set vertices, further reducing the search space. Ultimately, while ensuring result completeness, it achieves a 16.9-fold performance improvement over RapidFlow and a 100% completion rate for complex queries, providing sub-second decision support for abnormal behavior detection in social networks and promoting the application of dynamic graph analysis in resource-constrained environments such as edge computing.

The invention provides a social network continuous subgraph matching method based on dynamic pruning, and belongs to the field of social network behavior detection. According to the method, a dynamic pruning algorithm is provided, a local equivalence theory is established by calculating backward neighbors of query points and candidate points, a sub-tree-level equivalence judgment criterion in a dynamic graph environment is defined, the method is suitable for high isomorphic behavior modes such as zombie account groups and propagation chain tail end nodes, the dynamic pruning algorithm is designed to skip an isomorphic sub-tree structure, and the pruning effect is achieved; a query graph decomposition sorting mechanism is provided, the binding constraint of a matching sequence on an updating edge and a candidate set is relieved, high-redundancy candidate set vertexes are actively recognized and preferentially processed, and the search space is further reduced. And finally, on the premise of ensuring result completeness, performance improvement which is 16.9 times that of RapidFlow and 100% of complex query completion rate are realized, sub-second decision support is provided for abnormal behavior detection of the social network, and application of dynamic graph analysis in resource-constrained environments such as edge calculation and the like is promoted.

The application provides a multiple layout splitting method, system, medium and program product. First, an initial splitting coloring operation is performed based on a saturation priority strategy to generate an initial layout splitting scheme. Then, the global exploration ability of a tabu search algorithm is utilized, and a preset target function is combined to perform multiple rounds of iteration update operations on an initial conflict node set to generate a multiple layout splitting optimization scheme. For ultimate conflict nodes that cannot be completely eliminated by the tabu search algorithm, a backtracking pruning algorithm is started to maximize the attempt to eliminate all ultimate conflicts to generate a final multiple layout splitting target scheme. The application combines the initial optimization ability of the saturation priority strategy, the global exploration ability of the tabu search algorithm, and the quality guarantee of the backtracking pruning algorithm to accurately split the multiple layout, guarantee the splitting quality and the lithography yield, improve the splitting efficiency, reduce the calculation complexity, and adapt to the demand for multiple layout splitting.

The invention relates to a KAN-based tropical cyclone strength change mechanism analysis method and device, and belongs to the technical field of TC strength change prediction. The method comprises the steps that a factor recursive pruning algorithm based on a KAN2.0 attribution score is adopted, and a high-impact forecasting factor set for TC intensity change forecasting is screened and obtained; performing factor contribution analysis based on symbolization regression by using KAN2.0 trained by a high-impact forecasting factor set, obtaining a complete linear equation from high-impact forecasting factors to TC intensity change, and calculating the TC intensity change according to the symbols and absolute values of various linear coefficients in the equation; and quantitatively analyzing the contribution direction and contribution degree of each high-influence forecasting factor to the TC intensity change. According to the method, the stability, the accuracy, the generalization ability and the interpretability of TC intensity forecasting can be effectively improved.

The invention relates to the technical field of data processing and artificial intelligence, discloses a historical detection data accurate retrieval method based on a knowledge graph, and aims at solving the problems that existing automobile historical detection data retrieval is low in accuracy and poor in semantic understanding ability, and multi-source heterogeneous data cannot be effectively fused. The method comprises the following steps: acquiring and processing multi-source heterogeneous historical detection data; based on the processed data, an automobile detection knowledge graph is constructed through entity extraction and relation extraction; based on the knowledge graph, generating a knowledge routing library through vectorization and a path pruning algorithm; finally, according to user query, intention recognition, multi-mode retrieval and result verification are conducted through the knowledge routing library, and structured answers are output through large language model reasoning. According to the method, the domain knowledge graph is constructed, and knowledge routing and multi-mode reasoning are combined, so that the retrieval accuracy and efficiency can be remarkably improved, and deep semantic understanding and high-precision response to massive historical detection data are realized.

The application discloses a kind of lightweight collaborative detail enhancement small target detection method, belong to small target detection field, this method includes the improvement to YOLOv11n network model, obtains collaborative detail enhancement SDE-YOLO network model;The improvement includes the network connection structure between each C3K2 feature extraction module of Neck neck network of YOLOv11n network model is replaced as IGB multiscale collaborative optimization module;Each detection head is replaced as SDDH collaborative dynamic detail enhancement detection head;Using layer adaptive amplitude pruning algorithm LAMP to the collaborative detail enhancement SDE-YOLO network model of training completion is restructured, and the collaborative detail enhancement network model after reconstruction is obtained;The collaborative detail enhancement network model after reconstruction is deployed on unmanned aerial vehicle, and using scale adaptive non-maximum suppression algorithm ASD from detection result screening optimal small target detection result.The application solves the problem that the existing unmanned aerial vehicle-oriented method cannot balance target detection accuracy and computational overhead under limited computing power conditions.

This invention provides a method for analyzing negative Weibo comment data based on a cosine similarity-C4.5 decision tree, comprising: Step S1, preprocessing the original Weibo comment data, identifying whether the comments contain keywords to form a complete descriptive attribute set using the AC automaton algorithm, and labeling the influence category based on the number of comment interactions; Step S2, using the AC automaton algorithm to perform multi-keyword fast matching on the comment text; Step S3, based on the improved C4.5 algorithm, first merging attribute values ​​with similar information entropy using cosine similarity measurement, then calculating the information gain rate of each attribute and recursively constructing a decision tree; Step S4, using a pessimistic error pruning algorithm based on confidence interval estimation to perform post-pruning on the decision tree and extract classification rules. This invention improves retrieval efficiency through the AC automaton, reduces model redundancy through cosine similarity merging, and enhances generalization ability through pessimistic pruning. The three synergistically achieve a simultaneous improvement in classification accuracy, model simplicity, and processing efficiency.

This invention discloses a structure-preserving compression method for aero-engine blade defect detection model, belonging to the field of intelligent detection technology. It includes constructing a teacher model with linear deformable convolution and deformable attention mechanisms; employing a gradient-sensitivity-weighted layer-adaptive amplitude pruning algorithm to calculate the gradient sensitivity of weights to the defect focusing loss function, and combining this with a linear deformable convolution structure decoupling protection mechanism for pruning, generating a sparse student model architecture; constructing an initial student model based on this architecture; and training the model using a classification and regression branch co-distillation strategy to output a lightweight student model and deploy it on a detection terminal. This invention solves the technical challenge of deploying high-precision detection models on resource-constrained terminals, reducing the number of model parameters by 40% while maintaining over 96% mAP, and increasing the inference speed to 68.5 FPS, achieving real-time online high-precision detection of minute defects in aero-engine turbine blades.

The application provides a novel wafer layout and cutting calculation method, relates to the technical field of semiconductor chip manufacturing, and optimizes wafer layout by distinguishing between Shot pitch and Die pitch and combining a graphic periodic layout tree structure, significantly improves the number of chips (DPW), effectively improves wafer utilization, breaks through the limitations of traditional methods, inputs independent pitch constraints in Sp1, constructs a periodic tree structure in Sp2, and searches for an optimal layout through iteration and traversal in Sp3 and Sp4, so that the DPW is increased from 624 to 637, and the increase is about 2.1%, thanks to the innovative application of adaptive step length and pruning algorithm, not only the edge space is accurately captured, but also the waste is eliminated through secondary fine adjustment, the geometric conversion of non-rectangular Die shape is supported, the wafer area potential is further released, the breakthrough pitch optimization capability provides a more efficient solution for semiconductor manufacturing, and has remarkable creative significance.

The embodiment of the invention provides a model pruning method and device and computing equipment. The method comprises the steps that a model declaration file is acquired, the model declaration file defines the forward propagation process of each decoding layer in a model after structured pruning, and the forward propagation process comprises a first module of a first residual module under the condition that an attention module participates in pruning and a second module under the condition that the attention module does not participate in pruning, the second residual module is a third module under the condition that the multi-layer sensor participates in pruning and a fourth module under the condition that the multi-layer sensor does not participate in pruning; for input data of the model, traversing a target decoding layer to be pruned in N decoding layers from front to back, calling a structured pruning algorithm for the target decoding layer traversed each time, and executing a forward propagation part corresponding to the target decoding layer so as to perform structured pruning on an attention module and / or a multi-layer sensor in the target decoding layer, and determining modules executed by the first residual module and the second residual module in the target decoding layer according to the model declaration file.

The invention discloses a lightweight corn leaf disease and insect pest detection method based on VOLOv8, and relates to the technical field of computer vision and agricultural information. The method comprises the following steps: an improvement mode of an initial corn leaf disease and insect pest detection model is that a C2f module in a backbone network of a YOLOv8 model is replaced by a C2f-RVBSE module; a convolution module in the neck network is replaced by the lightweight convolution GSConv, and a C2f module in the neck network is replaced by the VoV-GSCSP; replacing an up-sampling module in the neck network by a dynamic up-sampling module; adding a SegNext Attention module to the front of each detection head so as to capture context information of the corn leaf disease and insect pest image; compressing the initial corn leaf disease and insect pest detection model through a pruning algorithm and a knowledge distillation method in sequence; detecting the to-be-detected corn leaf data through the corn leaf disease and pest detection model to obtain a disease and pest detection result. According to the method, high-precision detection of corn leaf diseases and insect pests is realized through a lightweight model.

The invention belongs to the technical field of aeronautical manufacturing and assembly detection, and relates to a rapid intelligent identification method and system for an aeronautical assembly on-machine operation tool. The method comprises the steps that an aviation assembly on-machine operation tool image database is constructed, a tool recognition-oriented instance segmentation neural network model is designed, and the neural network model can recognize tool type feature information of on-machine operation tool images; training the instance segmentation neural network model by using a computer operation tool image database to obtain a primary computer operation tool recognition model, and processing the primary computer operation tool recognition model through a neural network distillation algorithm and a single-step rapid pruning algorithm to obtain a lightweight computer operation tool recognition model; the method comprises the following steps: acquiring a machine operation tool image in real time, performing instance segmentation on various tools in the image by utilizing a light-weight machine operation tool identification model, and marking various tools on the image by utilizing different colors to obtain a marked machine operation tool image.