3147 results about "Concurrent computation" patented technology

The invention discloses a distributed real-time monitoring and early warning system for a temperature field of a smelting furnace, and relates to the technical field of industrial process intelligent monitoring. The problems of accumulated measurement errors and non-stationary hotspot escape reconstruction hysteresis caused by static emissivity setting in an existing system are solved. Collecting multiband radiation intensity and voltage signals through time domain alignment of the multispectral sensor array and the thermocouple array; iterating emissivity parameters in real time by adopting a dynamic ash body spectrum ratio algorithm in combination with flue gas absorption characteristics; fusing non-contact and contact temperature measurement data based on weighted Kalman filtering and complementary filtering; constructing a space-time variable covariance function to carry out non-stationary Kriging interpolation; dynamically optimizing the local grid resolution by combining an adaptive grid module; the processing flow is accelerated through the parallel computing module; early warning is triggered based on abnormal probability judgment and is fed back to emissivity correction and grid optimization; according to the invention, the monitoring precision and real-time performance of the temperature field are obviously improved, and the risks of false alarm, missing alarm and equipment melting loss are effectively inhibited.

The invention discloses an automatic driving system based on real-time holographic modeling and dynamic shielding compensation and a vehicle, and aims to solve the problem of complicated scene perception. The system comprises a real-time holographic environment modeling module for constructing a dynamic 3D environment model; the dynamic shielding compensation module outputs compensated environment data; the sensing data fusion module is used for integrating multi-source data; the high-precision positioning and mapping module is used for generating a centimeter-level environment map; the edge computing node is used for cooperatively processing fusion data; the AI self-adaptive decision making system generates a driving instruction, and the decision making efficiency is optimized and improved through a satellite auxiliary decision making unit; the V2Xcommunication module supports low-delay and high-security data interaction; the execution control unit is used for accurately controlling the vehicle; an FPGA + GPU parallel computing framework is adopted, and decision making and control are supported after modeling and compensation data are fused. According to the invention, optimal decision making and high-precision real-time control in a complex environment can be realized, and development of an automatic driving technology is promoted.

The invention belongs to the field of parallel computing architecture optimization, and relates to a matrix multiplication acceleration method and system based on dynamic computing resource mapping, and the method comprises the steps: constructing a dynamic topology model driven by tensor dimension features, and generating a thread block distribution mode according to matrix parameters and GPU hardware information; constructing a multi-dimensional resource scheduling strategy library, dynamically selecting an optimal thread block distribution strategy from the multi-dimensional resource scheduling strategy library, and generating a binding relationship between the thread blocks and the data blocks; calculating collaborative access logic of thread blocks and storage hierarchies based on block parameters and dynamic mapping function optimization; distributed calculation is carried out, calculation and data transmission are parallelized through pipelining and a double-buffering mechanism, and result aggregation across calculation units is completed synchronously through atomic operation and a barrier. According to the method, discontinuous memory access conflicts can be effectively reduced, the execution efficiency of the calculation instruction and the utilization rate of the cache space are improved, the parallel calculation process of accelerating and optimizing the general matrix multiplication is realized, and the data processing efficiency is improved.

The invention discloses a tensor core-based method for accelerating arbitrary precision sparse matrix multiplication and addition operation, which comprises the following steps of: aiming at an architecture of a tensor core on A100 and a programming model thereof, combining program characteristics of deep learning parallel processing sparse matrix, combining an arbitrary precision sparse matrix multiplication and addition operation operator with parallel computing acceleration equipment, and performing parallel computing acceleration on the sparse matrix. The method comprises the following steps: decomposing a general matrix into a sparse matrix in a set format, partitioning the sparse matrix and assigning a corresponding parallel computing hardware unit for the sparse matrix, reducing memory conflicts and access delay by using a shared memory, calling a high-level language API function library to execute loading, computing and storing processes of a computing data volume, and finally performing specification on output data of a function. Therefore, the final result of the random-precision sparse matrix multiplication and addition operation on the parallel computing acceleration equipment is obtained. According to the method, parallel computing resources such as the tensor core on the A100 can be fully utilized, the program calculation amount and the communication overhead are effectively reduced, and the program operation performance is greatly improved.

The invention relates to the technical field of low-power-consumption AI processors, and discloses a novel real-time data processing method and chip for calculating a low-power-consumption AI processor, and the method comprises the steps: carrying out the neural mimicry classification processing of a calculation task inputted by the low-power-consumption AI processor, and obtaining a calculation task feature matrix; performing multi-dimensional mapping operation on the calculation task feature matrix to obtain a heterogeneous calculation space vector; performing core resource allocation based on the heterogeneous calculation space vector to obtain a dual-mode processing unit configuration matrix; constructing a storage and calculation integrated data path based on the dual-mode processing unit configuration matrix to obtain a parallel calculation data flow diagram; executing dynamic voltage frequency mapping on the parallel computing data flow diagram to obtain a power consumption optimization control vector; and inputting the power consumption optimization control vector into the trained pulse neural network model to carry out computing power load balancing calculation and generate computing power load balancing parameters, and the method improves the real-time data processing capability of the low-power AI processor.

The invention discloses a computing task scheduling method and device in a multivariate heterogeneous environment and a medium, and relates to the technical field of parallel computing. The method comprises the following steps: monitoring resource states of various computing resources in a multivariate heterogeneous environment in real time, and quantifying the resource states to obtain corresponding resource information; under the condition that the to-be-processed calculation task is generated in the scheduling task list, performing demand analysis on the to-be-processed calculation task to determine a task demand report; calculating the task demand report and the resource information by using a preset scheduling strategy generation model to generate an intelligent scheduling strategy; according to the intelligent scheduling strategy, various computing resources are called to execute the to-be-processed computing task; and continuously monitoring the scheduling task list and the resource state so as to update the intelligent scheduling strategy corresponding to the to-be-processed calculation task based on the scheduling strategy strategy optimization logic.

The invention relates to the field of cloud computing and distributed computing, and discloses a cloud data processing system based on an artificial intelligence algorithm, which comprises a data input module, a task decomposition module, a resource scheduling module, a parallel computing module and a result reconstruction module, the data input module receives high-dimensional data and converts the high-dimensional data into a tensor structure; the task decomposition module decomposes the tensor, generates a core tensor and an orthogonal matrix, and generates a plurality of sub-tasks; the resource scheduling module is used for carrying out resource allocation and scheduling on the sub-tasks, and task execution time and resource consumption are minimized; the parallel computing module distributes subtasks to the computing nodes for execution, and the task execution sequence is guaranteed; and the result reconstruction module combines and verifies the execution results of the subtasks to generate a final calculation result. The cloud data processing efficiency and the resource utilization rate are effectively improved, the accuracy and consistency of calculation results are ensured, and the method is suitable for the fields of artificial intelligence training, large-scale data analysis, scientific calculation and the like.

A parallel-in-time disturbance region update method for dynamic characteristics of flight vehicles includes reading-in the data; obtaining the components of Chebyshev transformation matrix; initializing the flow field; establishing a dynamic computational domain; solving the flow-governing equations of all time layers simultaneously in the dynamic computational domains; updating the dynamic computational domain; judging whether the parallel computation in the current time period converges and whether the computation completes; if so, outputting the results; if the computation converges but not completes, jumping to the step that initializing the flow field, and performing the computation in next time period; if not, jumping to the step that solving the flow-governing equations of all time layers simultaneously in the dynamic computational domains, and entering the next iterative step.

The invention provides a many-core cache consistency system, a many-core cache consistency method, electronic equipment, a storage medium and a product, and the system is based on a network-on-chip architecture, and a plurality of nodes, corresponding routers and distributed directories deployed in the routers are introduced. Each node comprises one or more processor cores and a distributed last-stage cache, and a memory address is divided into a plurality of address blocks and is associated with the distributed last-stage cache through a dynamic address mapping rule. The distributed directories and the distributed last-stage caches are in one-to-one correspondence and used for recording the states of cache lines, the router is responsible for receiving and responding to requests from local or remote nodes, querying the distributed directories and updating the states of the cache lines, and efficient management of cache consistency is achieved. The technical scheme belongs to the technical field of communication, effectively solves the problems of high load and communication delay of a main node in a traditional cache consistency protocol, and improves data consistency and access efficiency in a multiprocessor system and a large-scale parallel computing environment.

The invention relates to the technical field of scene rendering, in particular to a building information model scene rendering method and system based on parallel computing, and the method comprises the following steps: based on scene data stored in a building information model, analyzing space coordinates of geometric structure nodes of a building, and associating the geometric structure nodes with material attributes; and analyzing a data partition relationship according to the illumination distribution, and generating a geometric material illumination analysis result. According to the method, deep integration of geometry, materials and illumination is realized by analyzing spatial coordinates of geometric nodes and material attributes and combining illumination partition analysis, the data organization efficiency of a complex scene and the analysis precision of an illumination path are improved, light energy in a noise area is optimized by analyzing light propagation and energy distribution in a segmented manner, refraction and reflection paths are corrected, and the accuracy of the light path analysis is improved. And through texture boundary and scene geometry conjoint analysis, texture fracture is repaired, detail expression is enhanced, efficient analysis and optimization of complex building scene data are realized, and the high-precision rendering requirement is met.

The invention belongs to the field of computer graphics, and discloses a fast parallel multi-scale smooth symbol distance curved surface reconstruction method and system, and the method comprises the steps: carrying out the local fitting of point cloud data through employing a moving least square method, and calculating the Gaussian curvature of a point cloud; constructing a curvature adaptive octree based on Gaussian curvature, wherein the octree is used for dynamically adjusting local resolution and capturing geometric details to improve curved surface reconstruction precision; global fitting is carried out through an implicit function, and a zero contour surface is obtained through an octree contour surface extraction algorithm; and multiple threads are introduced in the process of iteratively solving the linear system to carry out parallel computing. The adaptive octree based on Gaussian curvature can effectively capture complex geometric details, and the algorithm realizes high-precision curved surface reconstruction under different scales. In addition, the local and overall calculation efficiency is remarkably improved through parallel calculation, and the high efficiency of the algorithm in large-scale data processing is ensured. The method provided by the invention provides an efficient and accurate solution for complex curved surface reconstruction.

The invention provides an urban inland inundation simulation method based on CPU-GPU heterogeneous parallel calculation, and aims to reduce the number of calculation grids by adopting a self-adaptive non-uniform grid, consider pipe network calculation and improve the calculation efficiency of low-fluctuation urban area flood simulation at the same time, and comprises the following steps: performing one-dimensional pipe network runoff CPU calculation, constructing a rainwater pipe network model, and constructing a rainwater pipe network model. Extracting an overflow process of an overflow node of the pipeline; dividing a non-uniform grid based on the original raster data and a gradient threshold; non-uniform grid parallel calculation is carried out; a CPU-GPU heterogeneous parallel one-dimensional and two-dimensional coupling model is constructed; and coupling water depth calculation. The method can flexibly adapt to topographic changes and reduce the number of computational grids, and solves the limitation that precision and efficiency are difficult to consider in waterlogging simulation of a uniform grid in a low-fluctuation area in combination with CPU-GPU heterogeneous parallel calculation.

The invention specifically relates to the technical field of edge computing, and discloses a multi-platform concurrent transmission edge computing data acquisition method, which comprises the following steps: S1, concurrent access of multi-platform equipment: establishing a mapping relationship between equipment identity and a communication link; s2, data concurrent collection and preprocessing: obtaining a preprocessed data stream; s3, concurrent transmission priority scheduling: allocating a transmission bandwidth for each data stream; s4, edge node parallel computing: distributing the subtasks to a plurality of computing nodes; s5, cloud collaboration and global optimization: generating a global optimization strategy; s6, carrying out full-link monitoring and dynamic regulation and control; concurrent access of heterogeneous equipment is realized by generating data streams in a uniform format, multi-platform compatibility is effectively enhanced, bandwidth is dynamically allocated to the data streams according to priority weight indexes by calculating the priority weight indexes of the data streams, data transmission efficiency is improved, and the data transmission efficiency is improved by allocating sub-tasks to a plurality of computing nodes. And the resource utilization rate of the edge computing gateway is improved.

The invention provides a full-layout defect rapid detection system based on pattern matching and classification, which relates to the technical field of semiconductor manufacturing and comprises a data acquisition and multi-source fusion module, a self-adaptive preprocessing module, a dynamic pattern matching module, a multi-source feature fusion module and a closed-loop optimization and output module, the dynamic mode matching module dynamically generates a defect template from real-time data through a clustering algorithm (such as DBSCAN) and a generative adversarial network (GAN), the speed and accuracy of defect detection are remarkably improved in combination with reinforcement learning filtering and region focusing technologies of the self-adaptive preprocessing module, a detection strategy can be adaptively adjusted according to the real-time data, and the defect detection accuracy is improved. According to the method, a high-risk area is preferentially matched, and GPU parallel computing acceleration processing is performed, so that the detection time is shortened to 50% or below of that of a traditional method, deformation or fuzzy defects can be effectively identified, the false alarm rate is reduced by about 30%, and high-quality input is provided for subsequent matching and classification.

The invention provides a railway signal monitoring data generation method and system, and relates to the technical field of railways, and the method comprises the steps: carrying out the time-space correlation fusion processing of obtained carrier frequency characteristics, response coding information, the receiving time of transponder signal data, train operation speed data and train positioning data, and generating a monitoring data record under a unified coordinate system; adopting a rule-based data compression algorithm to adaptively select a compression rule and a compression rate to perform data compression on the monitoring data record; packaging the compressed monitoring data record into a monitoring data file in a standard format; and the data management server decompresses and decodes the monitoring data file, cleans, classifies and indexes the data in the monitoring data file by adopting a MapReduce parallel computing framework, stores the structured monitoring data into a distributed database, and performs fragmentation and redundant backup management on the database.

The invention discloses a data production and application method based on index management, and the method comprises the steps: receiving an index query request containing a target index identifier, a dimension constraint condition and a time range parameter, carrying out the analysis and semantic verification of the target index identifier based on a global index asset library, and obtaining the index definition information; generating a standardized query statement according to the index definition information, and selecting an optimal data calculation engine to execute query; performing multi-level cache optimization and parallel computing acceleration on the query process to obtain an original data set; processing the original data set in real time according to a preset analysis model to generate structured index data containing trend analysis, anomaly detection or attribution inference results; and filtering and desensitizing the data based on a fine-grained permission control strategy, only returning contents in a permission range and recording an audit log. By means of the method, unified management, efficient query and intelligent analysis of the index data are achieved, the automation level and query performance of data production are improved, and meanwhile data safety and access controllability are guaranteed.

The invention discloses an expert network automatic hybrid distributed algorithm based on cloud side end cross-domain data, and belongs to the field of large model distributed training. According to the method, an innovative method is introduced in the field of large model distributed training, an OSDP is used for training an MOE model in a distributed mode, distribution of different experts in a GPU cluster is optimized, and higher parallel computing efficiency is ensured. The combined motivation is used for improving the training efficiency under the condition that the model performance is not sacrificed. Through hybrid parallel accelerated training and hardware resource saving, the training cost of a large-scale model is reduced, and training of a super-large-scale deep learning model becomes more economical and feasible. And an automatic hybrid distributed algorithm based on an expert network is introduced, so that cloud, edge and end collaborative cross-domain heterogeneous data fusion is realized, and the complex requirements of the industrial intelligent field in a multi-base and multi-data-source environment can be met.

The invention relates to an NPU model optimization method and device based on TensorFlow and a storage medium. The method comprises the steps that a model analysis module reads and extracts the structure and parameters of a TensorFlow model; the model conversion module is used for converting the TensorFlow model into an intermediate representation supported by the NPU; through operator fusion, memory optimization and quantization, the model optimization module optimizes the converted TensorFlow model, and deploys the optimized TensorFlow model to the NPU execution module; and the performance monitoring module monitors the operation performance of the TensorFlow model on the NPU and outputs performance data. According to the method, an efficient model conversion tool is provided, the TensorFlow model is quickly converted into the format supported by the NPU, and the model conversion efficiency is improved; the hardware characteristics of low-precision calculation, parallel calculation and the like of the NPU are fully utilized, the calculation efficiency is improved, and the conversion time and the development cost are reduced by means of operator fusion, memory optimization, quantization and the like; manual operation of developers is reduced, the model deployment process is simplified, and the deployment difficulty is reduced; large-scale model deployment is supported, and actual application requirements are met.

The invention provides a self-adaptive efficient simulation method and system for electric propulsion plasma oscillation, and relates to the technical field of electric propulsion plasma oscillation simulation. According to the method, a high-precision electric propulsion plasma numerical simulation model is adopted to ensure the precision of a simulation result; according to the method, the calculation tasks of the simulation model are decomposed through various adjustment parameters, that is, all the calculation tasks are completed in a parallel calculation mode, and therefore the simulation efficiency can be effectively improved. Moreover, when the simulation model is subjected to simulation solving, the electromagnetic field solving model is solved by adopting a multi-grid method, and the simulation calculation efficiency can be further improved based on the solving mode. Besides, the target neural network model is utilized to process the simulation result so as to update all the adjustment parameters in the process, so that a plurality of adjustment parameters used in the simulation process are continuously and adaptively optimized, and the technical effect of considering the simulation efficiency and precision of the electric propulsion plasma oscillation simulation model is achieved.

The invention discloses a power grid real-time data reasoning analysis system and method based on Rete algorithm optimization, and belongs to the technical field of smart power grids. In order to solve the problems of low rule matching efficiency and insufficient event triggering and operation optimization capability, a data acquisition module efficiently accesses a multi-source heterogeneous data stream, a streaming data processing module realizes high-throughput real-time data processing based on DAG task fragmentation and parallel computing, a rule engine module optimizes a dynamic rule network through a Rete algorithm, and a data processing module realizes real-time data processing based on DAG task fragmentation and parallel computing. The reasoning analysis module is combined with rule matching and event triggering, power grid faults are rapidly positioned, operation state evaluation and dynamic optimization suggestions are provided, and the graphical rule construction module reduces rule design complexity and enhances rule adaptability through visual rule editing and verification tools; the optimization monitoring module guarantees the system stability and the resource utilization efficiency through real-time performance monitoring, load prediction and automatic tuning.

The invention relates to the technical field of heterogeneous unmanned aerial vehicle cluster task planning and intelligent scheduling, and discloses a heterogeneous unmanned aerial vehicle cluster intelligent task planning method, which is characterized by comprising task and unmanned aerial vehicle information initialization, task preliminary allocation based on a quantum annealing algorithm, real-time adjustment based on reinforcement learning, and a communication and cooperation mechanism. Executing and monitoring a task; the super-strong parallel computing capability and the quantum tunneling effect of a quantum annealing algorithm are utilized to quickly process a large-scale task allocation problem and find an approximate range of a globally optimal solution, and then an unmanned aerial vehicle cluster continuously learns and adjusts a task execution strategy according to real-time environment feedback in a task execution process by means of reinforcement learning, so that the task execution efficiency is improved. Therefore, the method adapts to dynamically changing scenes.

The invention belongs to the field of high-performance computing, and relates to an integer parallel computing method and device based on distributed storage and computer equipment, and the method comprises the steps of collecting real-time resource indexes, dynamically identifying fault nodes, triggering task migration, and performing data verification and hard disk fault detection. The weight value of each node is calculated, the nodes are arranged according to the descending order of the weight values, and the nodes with high load capacity are selected to distribute tasks; dynamically distributing a data generation task to a computing node, executing parallel computing, and performing distributed storage on a result; obtaining an operand, converting the operand into a first-order tensor form of a basic operand, serializing tensor data, and sending the serialized tensor data to a parallel computing layer; distributing a search task to a computing node, retrieving storage data in parallel, reading effective data from a storage layer, and combining search results into a partial sum; and summarizing and then outputting. The system has dynamic resource management and fault-tolerant capabilities, and can realize efficient task allocation and load balancing.

The invention discloses a resource optimization scheduling method for a high-performance parallel computing data center, and relates to the technical field of resource optimization scheduling, and the method comprises the steps: collecting multi-dimensional parameters of the high-performance parallel computing data center, and carrying out the preprocessing; calculating a comprehensive scheduling target index according to the preprocessed parameters; calculating a comprehensive resource scheduling index according to the comprehensive scheduling target index, setting the comprehensive resource scheduling index as a target function, defining a constraint condition, and constructing a multi-target optimization model; and solving the multi-objective optimization model according to a multi-objective optimization algorithm, generating a resource allocation scheme, and allocating tasks to corresponding resource nodes to complete resource optimization scheduling. The comprehensive resource scheduling index is constructed, and the optimal solution is efficiently searched in the complex high-dimensional solution space by using the multi-objective optimization algorithm, so that the comprehensiveness of resource evaluation, the construction efficiency of the scheduling index and the optimization quality and practicability of the resource allocation scheme are remarkably improved.

The invention discloses a knowledge graph reasoning method based on dynamic rule perception memory. The method aims at solving the problems that an existing neural combination rule learning method is insufficient in expression ability and prone to splitting global semantics and local relation modes. The method comprises the following steps: introducing a lightweight dynamic relationship memory module, and executing self-attention on all relationships in a knowledge graph to capture global semantics; meanwhile, local relation interaction features are extracted through convolution, semantic fusion is carried out through a Transform encoder with multi-head attention and relative position coding, and unified and parallel-computing high-quality combination representation is constructed for each pair of relations in parallel. And meanwhile, a closed high-quality reasoning path is generated in combination with bidirectional breadth-first search. Through global semantic and local interactive collaborative modeling, the extendibility is ensured, the understanding of global semantics is enhanced, and the reasoning accuracy is improved. Global and local relation dependence is fused, an interpretable reasoning path is generated, and reasoning accuracy, expandability and robustness are improved.

The invention relates to the technical field of computer architecture and parallel computing, and discloses a parallel task scheduling algorithm for a heterogeneous multi-core processor, which comprises the steps of task modeling, resource mapping, dynamic load balancing, communication optimization, task scheduling decision and execution monitoring. Task allocation is adjusted in real time through dynamic load balancing, cross-core communication delay is reduced in combination with communication optimization, and an efficient task allocation sequence is generated by using an improved genetic algorithm. According to the method, the resource utilization rate and the task execution efficiency of the heterogeneous multi-core processor in a high-performance computing scene can be improved, meanwhile, the robustness and adaptability of an algorithm are enhanced, and the task allocation problem in a complex computing scene is effectively solved.

The invention relates to a fraud website identification early warning method, device and equipment and a storage medium thereof. The method comprises the following steps: firstly, acquiring network address information, then extracting feature data from dimensions such as structure, semantics, behavior and vision, accelerating similarity detection by applying a graphics processor parallel computing technology, and generating a composite feature vector containing a domain name similarity score and a registration feature; constructing fraud features in combination with the composite feature vector, mining a data association relationship through a graph neural network, and forming multi-dimensional risk feature data; and finally, processing the risk feature data by using a deep learning model, generating a risk probability, and when the risk probability exceeds a preset threshold, determining that the network address is a high-risk network address and generating an early warning report. By adopting the method, the comprehensiveness and accuracy of fraud website identification can be improved, complex fraud means such as domain name variation and semantic camouflage can be effectively dealt with, the real-time early warning capability for potential risks is enhanced, and efficient technical support is provided for network security protection.

The invention discloses a gas pipeline and subway safety distance optimization method based on numerical simulation, and relates to the technical field of subway tunnel safety, and the method comprises the steps: simulating gas pipeline explosion data through a KFX Exsim gas explosion model; establishing a fluid-solid coupling model, a soil dynamic model and a thermodynamic coupling model, and coupling the three models to obtain a multi-physics coupling model; based on a multi-physics field coupling model, performing decomposition and region division on a numerical simulation task in combination with a parallel computing framework, and simulating the influence of gas pipe explosion on the subway; based on a numerical simulation result, evaluating the safety of the tunnel under the action of the explosion load; based on a safety evaluation result, optimizing a safety distance between the gas pipeline and the tunnel; according to the method, the fluid-solid coupling model, the soil dynamic model and the thermodynamic coupling model are established and coupled to form a multi-physical field coupling model comprehensively covering the explosion environment, and accurate simulation of complex behaviors of the subway tunnel in the explosion environment is achieved.

The invention discloses an optimization method for a multi-head attention mechanism operator oriented to a domestic heterogeneous processor, which improves the efficiency of the multi-head attention mechanism operator from four aspects, and comprises the following steps of: firstly, combining the general computing power of a CPU (Central Processing Unit) and the advantages of a DSP (Digital Signal Processor) in the aspect of parallel computing; a high-performance multi-head attention mechanism operator operation method on a CPU + DSP heterogeneous processor is constructed; secondly, a previous softmax optimization algorithm is used for fusing the two batch processing matrix multiplication and a softmax operator, so that the memory access overhead of DSP equipment is reduced; then, a vector operation instruction of a DSP calculation core is used in the calculation process of a multi-head attention operator, and SIMD calculation characteristics of DSP equipment are fully utilized; and finally, during data transmission and calculation, a double-flow water mechanism is adopted, the data transmission time is covered, and the multi-head attention mechanism operator calculation efficiency is improved.

The invention provides a numerical control machining defect real-time detection method and system based on AI image recognition and a medium, and belongs to the technical field of intelligent manufacturing and machine vision crossing. The method comprises the following steps: collecting part surface image data according to a preset frequency, and separating a part from a background through noise reduction, contrast enhancement and a dynamic threshold algorithm of a data preprocessing module; inputting the processed image into a multi-modal feature fused AI detection model, and extracting and fusing multi-modal features to judge the type and position of a defect; and processing the image by using a parallel computing architecture, feeding back a detection result to a numerical control processing control system in real time, and visually displaying the detection result on a monitoring interface. According to the method, real-time and accurate detection and feedback control of numerical control machining defects are realized, and the machining quality and efficiency are effectively improved.

The invention discloses a multi-module-based analog simulation calculation method, and relates to the technical field of analog simulation calculation, and the method comprises the following steps: S1, system demand analysis and module division; s2, establishing a module model and setting parameters; s3, constructing a parallel computing environment; s4, module parallel computing task allocation; s5, data exchange and synchronization; s6, summarizing results; and S7, feedback optimization. According to the analog simulation calculation method based on the multiple modules, through modular design, a complex system is decomposed into the multiple relatively simple modules, each module can independently conduct simulation and calculation, and the maintainability and expandability of the system are improved; data and information exchange and cooperative work can be carried out among different modules through interfaces, so that comprehensive simulation and optimization of the whole system are realized; in addition, parallel computing resources are fully utilized, parallel computing is carried out on the multiple modules, the computing time is greatly shortened, and the computing efficiency is improved.