34 results about "Parallel generation" patented technology

The invention discloses a transformer over-capacitance threshold value dynamic adjustment method based on real-time monitoring. The method comprises the following steps: S1, multi-dimensional total element real-time acquisition; s2, data layering preprocessing and directional transmission; s3, preliminarily judging an edge end basic threshold value and a cluster; s4, calculating insulation, environment and operation three-dimensional coupling threshold values; s5, optimizing a small sample causal interpretable AI threshold value; s6, new energy consumption and cluster cooperation threshold correction; according to the multi-dimensional strategy parallel generation method, by constructing an insulation, environment and operation three-dimensional coupling threshold calculation system, the suitability of super-capacity protection and the actual operation state of the transformer is improved; an AI threshold optimization framework can be explained by means of small sample causality, and transparency and reliability of super-capacity threshold adjustment logic are guaranteed; through the new energy consumption and cluster collaborative threshold correction and multi-strategy fusion optimization mechanism, the new energy consumption efficiency is guaranteed, and unnecessary protection actions are reduced through cluster load linkage.

The invention discloses a hydrological electronic achievement archive batch output method and system and a medium, and the method comprises the following steps: data extraction: reading annual calibration data of specified equipment from a database, including calibration time, a standard value, a measured value and an error rate field; template rendering: reading an Excel template by using a POI (Point of Interest), and mapping data to a template placeholder; and PDF generation: creating a Document object by the iTextPDF, setting a page margin, inserting a page header, dynamically rendering a table, and outputting the PDF through HttpServletResponse. And in combination with intelligent excel-to-pdf of the StirlingPDF, a three-level processing normal form of'type shunting-parallel generation-dynamic assembly 'of the equipment detection document is realized in a breakthrough manner.

The invention discloses a dual reasoning acceleration method based on draft prediction and compressed memory, which comprises the following steps of: 1) acquiring an input context of model reasoning and a token sequence generated by a model at each stage of reasoning; 2) according to an input context of model reasoning and a token sequence generated by the model, predicting subsequent candidate tokens in parallel as draft tokens; the method comprises the following steps of (1) performing parallel generation, (2) performing main model verification, (3) organizing draft tokens according to a tree structure, (4) performing main model verification, (5) compressing each reasoning block into compressed tokens with a fixed length s, and (6) performing context reconstruction. According to the method, through the combination of parallel generation and context compression, the problem of serial low efficiency of traditional autoregression is solved, and the redundant overhead of long-sequence reasoning is relieved.

The application relates to an acceleration method of an AES key expansion algorithm and an instruction set processor, wherein the acceleration method is based on a VAESKEYE extension instruction set, a plurality of independent AES key expansion algorithms are accelerated in parallel by pipelining execution of instructions in the VAESKEYE extension instruction set; the VAESKEYE extension instruction set adopts a RISC architecture and is used for generating any one of AES key expansion words in the AES key expansion algorithm according to a source operand; the VAESKEYE extension instruction set adopts a plurality of AES key expansion word parallel generation algorithms, the plurality of AES key expansion word parallel generation algorithms refer to algorithms for taking a key word or a key expansion word and an AES key expansion round number in 8 independent AES key expansion algorithms as inputs and respectively completing generation of 8 independent AES key expansion words in parallel. The application greatly improves the speed of executing the AES key expansion algorithm.

The application discloses a kind of parallel random number generation devices based on pulse driving micro-ring resonator cavity, by adopting pulse mode driving micro-ring resonator cavity, improve the instantaneous power level in cavity, enhance nonlinear effect, to effectively expand the effective bandwidth of chaotic entropy source, improve the complexity of entropy source.Meanwhile, using the characteristics that micro-ring resonator cavity naturally supports multi-wavelength output, and combining with filter demultiplexing structure, the parallel output of multiple chaotic entropy sources is realized, which is beneficial to improve the parallel generation ability of random number.Finally, by combining post-processing methods such as difference, quantization and decorrelation, the overall generation rate of random bits is improved while ensuring randomness quality.The application has relatively simple structure, is suitable for integrated implementation, and can meet the application requirements of high-speed parallel random number generation.

The invention provides an aircraft manufacturing process instruction intelligent generation method based on hybrid retrieval enhanced generation, and the method guarantees that a generated instruction has professional sentence patterns with historical experience and compliance indexes with standard specifications through a hybrid retrieval mechanism. A large model is limited in a specific process generation link through the agent workflow, strict placeholder filling logic is matched, the illusion problem that key parameters are pinched by the model is thoroughly eradicated, meanwhile, the parallel generation architecture enables the generation time of a long-flow process instruction to be greatly shortened, and unification of high accuracy, high controllability and high efficiency is achieved.

This invention provides a method, apparatus, electronic device, and storage medium for generating multiple candidate responses, belonging to the field of artificial intelligence technology. The method includes: inputting a target prompt into a large language model; generating multiple candidate responses for the target prompt through multiple parallel generation threads; during the generation of multiple candidate responses word-by-word, obtaining hidden state features corresponding to each parallel generation thread using the network layer of the large language model; performing batch-dimensional attention calculations on each hidden state feature using a cross-thread shared layer to obtain shared state features; and using the output layer of the large language model to predict the output words of each parallel generation thread based on the shared state features. This invention breaks the independence between parallel generation threads by introducing a cross-thread shared layer between the network layer and the output layer, enabling interaction and fusion of feature dimensions among the threads during the generation process. This effectively utilizes the global information of the parallel group, improving the diversity and quality of generated candidate responses.

The invention discloses an auto-regression and mask diffusion double-normal-form fusion language model hardware acceleration system based on an FPGA (Field Programmable Gate Array), and relates to the field of FPGA and machine learning. The invention provides a model core calculation process-oriented hardware acceleration architecture by aiming at a language model fusing double normal forms of an autoregression model and a mask diffusion model and adopting the characteristics of revising an attention mechanism and parallel generation of a KV cache. The method disclosed by the invention is implemented by taking an Eso-LMs (Esolic Language Models) model as an example. The system comprises a layer normalization and adaptive layer normalization modulation module, a QKV projection module, a rotation position coding application module, a KV cache management module, a multi-head attention calculation module, an output projection and residual connection module and a multi-layer perceptron module. By optimizing the calculation sequence and the data flow of each module in the model calculation process and adopting the pipeline parallel and resource reuse technology, the efficient reasoning acceleration of the language model fusing the autoregression and mask diffusion double normal forms on the FPGA platform is realized, and the model reasoning speed is obviously improved. According to the system, the advantage of FPGA customizable hardware acceleration is fully exerted, the utilization efficiency of hardware resources is improved, data pipeline blockage is eliminated, calculation delay and storage overhead are reduced by reconstructing the data flow direction and constructing a whole-process pipeline processing architecture, and the system is suitable for the deployment requirement of an edge calculation scene.

The embodiment of the invention relates to the technical field of artificial intelligence, and provides a parallel generation method of response texts and electronic equipment. The method comprises the following steps: receiving a group of tokens, and obtaining a representation vector corresponding to each token based on at least one historical text block and the token; denoising the representation vectors corresponding to the group of tokens in parallel, and obtaining a prediction text block corresponding to the group of tokens based on the denoised representation vectors; in response to the fact that the reasoning task is not completed, taking the prediction text block as a new historical text block, generating a group of new tokens based on at least one historical text block, and returning to the step of obtaining the representation vector corresponding to each token based on the at least one historical text block and the token by using the coding module; and in response to completion of the reasoning task, obtaining a response text corresponding to the reasoning task based on the at least one historical text block and the predicted text block. According to the method provided by the invention, the token of the response text can be generated in a parallel mode, and the problems of long reasoning delay and low response speed in the prior art are solved.

This application proposes an automatic method and apparatus for generating distillation data in underground coal mines. The method includes: acquiring unlabeled data and combining it with instruction data to form a distillation dataset, and distributing it to multiple parallel generation channels; forming a structured candidate inference result set; fusing and calibrating the candidate inference result set to generate labeled distillation data, performing multi-dimensional qualification checks, and selecting valid labeled results; constructing an unbiased distillation training dataset, and inputting it into the target model for supervised fine-tuning training to obtain a distillation model adapted to underground coal mine scenarios. By automatically generating highly diverse distillation data that conforms to the characteristics of underground scenarios, the problem of high labeling costs and uneven data quality is effectively solved; the model's distillation performance is improved through preference consistency constraints and multi-dimensional quality checks, significantly enhancing the generalization ability and recognition accuracy of the distillation model in complex underground environments.

The invention provides an underground mine live-action three-dimensional modeling system and method based on unmanned aerial vehicle SLAM and multi-sensor fusion. The underground mine live-action three-dimensional modeling system comprises a hardware platform and a software platform. The hardware platform takes an unmanned aerial vehicle as a main body, and integrates a visual sensor, a laser radar, an IMU, a self-adaptive lighting system, a visual laser acquisition control point device, a roof control point identification camera and a wireless communication module; the wireless communication module is used for transmitting the acquired data back to a software system in real time and transmitting remote control instructions among the components; the software platform comprises a front-end SLAM module and a modeling and rendering module; the front-end SLAM module adopts a vision-laser radar-IMU tight coupling SLAM algorithm and fuses control point data acquired by a top plate control point identification camera to realize real-time point cloud registration and map construction of absolute coordinate constraint in a GPS-free environment; the core of the modeling and rendering module is a Mesh and 3DGS double-model parallel generation technology, and the modeling and rendering module has the parallel operation capability of synchronously completing Mesh model generation and 3DGS model training.

The invention discloses a content generation data processing method and system based on multi-agent collaboration, and the method comprises the steps: carrying out the semantic deconstruction of a multi-modal content demand inputted by a user through a task analysis agent, and generating a structured task description vector containing a theme dimension, a style element and an emotional tendency; generating a multi-modal content draft based on the structured task description vector; inputting the multi-modal content draft into a quality evaluation agent network, and generating a quality evaluation matrix containing semantic coherence scores and style matching degrees; and dynamically adjusting a content generation strategy through a decision coordination agent according to the quality evaluation matrix, optimizing an agent cooperation weight by adopting a reinforcement learning mechanism, and outputting a final optimized multi-modal content work. By utilizing the embodiment of the invention, high-quality and high-consistency parallel generation and deep fusion of multi-modal contents such as texts, images and audios can be realized, and the creativity and semantic coherence of final works and the matching degree with user requirements are improved.

The invention provides a multi-machine surveying and mapping management method and device, electronic equipment and a storage medium, and relates to the technical field of surveying and mapping. The method comprises the following steps: receiving a task instruction comprising a target ground resolution, a minimum overlapping degree, a task priority and a landform situation; according to the target ground resolution and a preset algorithm, calculating to obtain a navigation height, generating a plurality of navigation routes in combination with the landform situation, the lowest overlapping degree and the preset algorithm, and then generating control instructions corresponding to the unmanned aerial vehicles in combination with task priorities to control the unmanned aerial vehicles to perform surveying and mapping; and during surveying and mapping, surveying and mapping data returned by the unmanned aerial vehicles are spliced in real time. According to the method, the navigation height of the unmanned aerial vehicle is determined through the target ground resolution, and the navigation route is correspondingly generated according to the landform condition by combining the lowest overlapping degree, so that parallel generation of the multi-vehicle navigation route is realized, and surveying and mapping data returned by each unmanned aerial vehicle are spliced in real time; therefore, the reasonability of route planning and task allocation during unmanned aerial vehicle surveying and mapping is improved, and the surveying and mapping accuracy and efficiency are improved.

The application discloses a text candidate screening method and device. The method comprises the following steps: obtaining initial candidate texts generated in parallel by a language model, and calculating the quality scores of the initial candidate texts; performing an iterative screening step to determine final candidate texts, which firstly takes the text with the highest quality score as the first final candidate text, and then in the repeatedly performed selection operation, for each candidate text, calculates the difference score between the candidate text and the selected final candidate set, and calculates a comprehensive score based on the quality score and the difference score, and determines the candidate text with the highest comprehensive score as the new final candidate text. The application guarantees efficiency through parallel generation, and solves the problem that it is difficult to balance the quality and diversity of generated texts in the prior art through the iterative screening strategy combining the quality score and the dynamic difference score, so that the diversity of the final candidate set can be significantly improved while ensuring high quality.

This invention discloses a cloud-based dialogue response method, device, and medium for AI toys, relating to the field of natural language processing technology. The method includes: performing structured task translation processing on a semantic constraint guidance graph and writing corresponding fields to generate a task-driven script; using a constraint projection hierarchical construction method to perform hierarchical response channel orchestration and parallel generation processing on the task-driven script and the semantic constraint guidance graph to obtain hierarchical candidate response clusters; performing convergence and optimization compensation on the hierarchical candidate response clusters to obtain the target response content, and combining the target response content with the task-driven script to encapsulate an executable response, generating a cloud-based response result package; and performing terminal response delivery and memory storage write-back based on the cloud-based response result package to generate response execution information. This invention, by performing semantic deconstruction and relational constraint construction on the natural language requests of AI toys, helps improve the accuracy, coherence, and terminal execution adaptability of the final response content.

The application discloses a kind of application log fragment parallel generation and multi-level materialized view aggregation processing method, predefine application log general framework using standard log format and carry out encapsulation, at least including application identification, log type and log detailed content;In application, the log generator instance of encapsulated application log general framework is introduced, and it is matched to log recording demand, and the API provided by application log general framework is called to generate log record at the log recording point of the program code of application, and application log general framework establishes mapping relationship with application by application identification;When debugging or running application, application log is generated, and saved as original log file;In response to log aggregation instruction, according to preset aggregation strategy, multi-level materialized view aggregation processing is carried out to original log file, and saved as aggregation log file.The application can solve the technical problems that application log format is not standard, content is difficult to analyze, and mass log is difficult to compress.

The invention discloses a high-order grid parallel generation method based on GPU acceleration and fine projection, and the method comprises the steps: firstly inputting an original volume grid and a surface grid, and carrying out the order lifting processing, and obtaining a high-order volume grid and a high-order surface grid; secondly, calculating physical offsets of all nodes on the high-order surface grid relative to the high-order surface grid through projection by utilizing an AABB tree structure, and storing the offsets as displacement potential function values; then, on the basis of a boundary integral equation, physical offset vectors of all internal nodes in the volume mesh are calculated in parallel through a GPU; and finally, traversing all grid nodes, and updating all point coordinates according to the physical offset vectors of the internal nodes to obtain a generated high-order grid. According to the method, the calculation time of the boundary offset is greatly shortened, and the method has the advantages of high efficiency, high expandability and high precision and is suitable for high-precision CFD simulation pretreatment of industrial-grade complex shapes.

The invention discloses a long-time-history large model processing method based on asynchronous reinforcement learning, and relates to the technical field of large language models.The method comprises the steps that the problem complexity is improved through dual-operation iteration, high-quality training data is obtained through triple verification, a training end and a track generation end are decoupled through a two-stage track parallel generation mechanism, and the problem is solved; after a training end collects a preset batch track, long-time-history large model updating is started, then historical data is managed by depending on a basic tool set sub-model, end-to-end asynchronous reinforcement learning optimization is executed on the whole process of an intelligent agent, finally, the problem of long-time-history reward sparseness is solved based on a GRPO algorithm, and a model strategy is optimized. According to the scheme, the core bottlenecks of weak long-range exploration, low training efficiency and insufficient data quality of an open-source search intelligent body are solved, the search intelligence of fuzzy query analysis, key information extraction by noise, cross-document reasoning verification and super-long-range tool calling is unlocked, the limitation of a traditional search round is broken through, and a long-time-range task is supported.

The invention relates to a seismic wave simulation method based on terrain point cloud data hexahedral mesh parallel generation. The method comprises the following steps: acquiring terrain point cloud data, and setting parallel hexahedral mesh generation parameters; in each process, a terrain quadrilateral surface grid is generated based on the point cloud, a non-terrain surface grid is obtained through 2D over-limit interpolation, then the longitude upper and lower scale range of 3D over-limit interpolation is calculated, and a geologic body hexahedral grid is generated through 3D over-limit interpolation. And determining the number of grids in the longitude direction of each process according to the superscript and superscript ranges, calculating the number of grids in the latitude direction and the number of grids in the elevation direction, further obtaining each subdivision grid and the number of subdivision grids generated by the process, and finally generating a geologic body hexahedron subdivision grid. And finally, importing into seismic wave numerical solution software, setting parameters, carrying out parallel solution, and analyzing seismic wave propagation characteristics according to a result. By adopting the method, the seismic wave simulation precision can be improved.

The application discloses an anti-interference millimeter wave radar dynamic target detection system based on deep learning, relates to the technical field of radar signal processing and deep learning fusion, and comprises a preprocessing module, a feature extraction module and a long-time sequence global dependence modeling module.The preprocessing module is used for high-fidelity preprocessing of original millimeter wave radar signals, removing static clutter, completing phase correction, and outputting high signal-to-noise ratio dynamic signals after double-sided filtering denoising.The feature extraction module is used for front-end feature extraction of the high signal-to-noise ratio dynamic signals, parallel generation of a modulated phase stream and a feature stream jointly represented by covariance and trajectory through separation of amplitude and phase information, and output of two heterogeneous feature streams.The long-time sequence global dependence modeling module is used for feature fusion of the two heterogeneous feature streams, and the two heterogeneous feature streams are fused into a unified fusion feature map through an element-by-element addition operation.The fusion feature map is input into a Transform network, and a dynamic target detection result is output.The dynamic target detection precision and robustness in a complex environment are effectively improved.

The invention relates to the technical field of perovskite solar cells, and discloses a preparation process of a perovskite layer and a perovskite solar cell, the process comprises the following steps: S1, acetonitrile, 1.8-diamineheterocyclic [5, 4, 0] decaene-7, phenylpropyl amine iodide and isopropanol are mixed and stirred to obtain a uniform mother solution; s2, introducing carbon dioxide gas into the mother liquor, and then adding a dynamic coordination agent to obtain a reversible complex; s3, adding perovskite precursor salt and tetrabutylammonium iodide into the reversible complex, uniformly mixing, and filtering through a polytetrafluoroethylene filter membrane to obtain a precursor solution. An alkaline component capable of forming reversible complexation with carbon dioxide is introduced into a precursor system, a dynamic coordination agent is combined to regulate and control a perovskite crystallization path before film formation, parallel generation of multiple metastable phases is reduced, and meanwhile, the film formation efficiency is improved by cooperating with subsequent two-stage heat treatment. And the long-term behavior stability of the device under the illumination or bias voltage condition can be improved.

The invention discloses a humanoid robot teaching behavior generation method based on a hierarchical auto-encoder, and the method achieves the independent coding and cooperative generation of the movement of different parts of a body through the multi-stream design and hierarchical quantification. A user can accurately control details of gestures, types of gaits and overall postures by adjusting discrete codes of different levels; parallel decoding is carried out by adopting non-autoregressive Transform, so that the reasoning speed of long sequence generation is greatly improved; a two-stage generation and memory enhancement mechanism is combined, so that the common problem of monotony or poor coordination in parallel generation is effectively solved, and the fluency and naturalness of actions are ensured; inverse kinematics constraints are introduced in the representation learning stage, so that the whole generation process is established on the basis of physically feasible action representation, and the possibility of generating abnormal postures violating ergonomics is fundamentally reduced.

The invention provides a cultural relic interactive repair method and system, and the method comprises the steps: carrying out the on-site three-dimensional scanning of a damaged cultural relic through an augmented reality terminal, so as to obtain a three-dimensional geometric model, surface texture information and spatial position coordinates of the damaged cultural relic, and constructing a cultural relic prior knowledge base; generating constraint conditions corresponding to the damaged cultural relics according to a cultural relic prior knowledge base, adopting a constraint-driven three-dimensional generative model, and taking the three-dimensional geometric model and the surface texture information as input; in the generation process, sampling constraint conditions for verification and guidance so as to generate a plurality of restoration hypothesis schemes conforming to the constraint conditions in parallel, and calling a large language model to automatically extract basis literatures and reference examples of each restoration hypothesis scheme so as to generate restoration reason elaboration; and on the basis of the spatial position coordinates, the restoration scheme and the restoration reason are elaborated and superposed on the damaged cultural relic through an augmented reality technology, so that restoration of the damaged cultural relic is completed. The cultural relic repairing efficiency can be effectively improved.

This invention discloses an adaptive mind map generation method, apparatus, device, and storage medium. Its features include: responding to a user-submitted mind map generation request and identifying the mind map data corresponding to the request; performing routing decisions on the mind map data based on a routing decision engine to determine the mind map generation path corresponding to the data; if the mind map generation path is a parallel generation path, then performing parallel task decomposition and merging on the mind map data to determine the target mind map. This invention can effectively handle all types of data and extremely long documents, possessing strong universality and high information retention. The parallel architecture significantly improves the generation efficiency in multi-source scenarios, ensuring that the mind map results always meet the optimal requirements for mind map visualization and user reading experience.

The invention relates to the technical field of voice interaction, and particularly discloses an AI robot low-delay voice interaction method and system, and the method comprises the steps: obtaining a streaming acoustic feature vector sequence and voice metadata, extracting lexical elements based on the sequence, obtaining lexical element embedded vectors, determining a to-be-predicted position according to the difference between a preset reply length and the number of recognized lexical elements, and carrying out the recognition of the lexical elements. Selecting mask identifier embedding vectors to form an initial mask sequence, positioning a first identifier position of the lexical element embedding vector and a second identifier position corresponding to a mask, constructing a dynamic attention mask, utilizing a language model to generate candidate text marks in parallel, combining the candidate text marks into a reply sequence, and correcting the reply sequence through iterative optimization, so as to obtain a replay sequence. According to the method, a replacement mask sequence is obtained, emotion semantics obtained through emotion recognition are obtained, a final text is generated in combination with the replacement mask sequence, and the final text is output as a low-delay voice result, so that through parallel generation and iterative optimization, the problem of serial dependence of existing autoregression decoding is solved, and response delay can be remarkably reduced.

The invention relates to a three-dimensional volume data generation method and system based on angular deformation and secondary peak constraint. The three-dimensional volume data generation method comprises the following steps: constructing a three-dimensional equidistant grid and calculating a radial distance and a spherical coordinate angle of grid points; designing a controllable random coefficient, acquiring an angular deformation factor based on a Legendre polynomial, and acquiring a coordinate point of the original coordinate point after angular deformation by combining the original radius; constructing a main peak point, designing a secondary peak point center constraint to generate a secondary peak point, calculating intensity contribution and accumulating the intensity contribution to a total intensity field; carrying out NaN value replacement and total intensity field normalization; carrying out volume data storage and metadata file output; shared variables are pre-calculated through outer-layer control circulation, inner-layer samples generate circulation sampling, variable parameters are updated, and the volume data generation efficiency is improved; the system supports a parallel generation mode and GPU acceleration. According to the method, the problems that in traditional volume data synthesis, secondary peak merging and angular asymmetric expression are limited, and controllability and reproducibility are poor are solved.

The invention relates to a finite difference uniform grid parallel generation method for an STL (Standard Template Library) model in the technical field of finite difference numerical simulation grid subdivision, which comprises the following steps of: importing an STL model file of a workpiece, and reading all triangular surface information of the STL model; calculating a bounding box according to the STL model, initializing an octree root node and initializing a grid array; according to the information of the bounding box, the size of a preset grid of the STL model and information of all triangular facets, a plurality of threads are used for parallel computing and recursive creation of octree black child nodes, a surface grid of the STL model is obtained, and a result is recorded to a grid array; and performing internal filling on the surface grid by using a ray method to obtain a finite difference uniform grid of the STL model, recording a result to a grid array, and storing the grid array and related information to a file to obtain a grid file. According to the method provided by the invention, the finite difference uniform grid can be quickly generated, the gridding efficiency is improved, and the method has a great application value.

The invention relates to the technical field of road route planning, in particular to a road route planning system based on multi-search and dynamic cost function optimization. The system comprises a topographic feature partition processing module, a multi-dimensional cost function construction module, a multi-path parallel generation module, a dynamic path fusion processing module, a post-processing optimization module and a project amount and cost calculation module. The method comprises the following steps: establishing a climbing / downhill differentiated dynamic cost model; a four-path parallel search and partition fusion mechanism is provided; integrating real-time cross detection and a gradient preserving algorithm; and developing a full life cycle cost optimization model. The method has the technical effects that the gradient control precision is + / -1%, path crossing is eliminated, the earth volume is reduced by 25-30%, the downhill accident risk is reduced by 35%, and the design efficiency is improved by 40%. The method is especially suitable for mountainous roads, mine roads and other complex terrain scenes.

The invention discloses a multi-level parallel tetrahedral mesh generation method and device for large-scale numerical simulation. The method comprises the steps that a triangular curved surface mesh is generated according to an original CAD geometric model; generating an interface and constructing two independent sub-region curved surface grids through a multi-thread region decomposition method based on OpenMP, and continuing to decompose a plurality of sub-regions through multiple threads; a manager-employee communication framework oriented to multiple processes is constructed, and a manager serving as a main process distributes tasks to all employees; performing efficient multi-thread tetrahedral mesh generation on each sub-region by a working process receiving a task; and the host process monitors and schedules the overall task progress, and summarizes final grid data after the tasks of all the working processes are completed, thereby completing the multi-level parallel tetrahedron generation process. According to the method, the expandability and robustness of parallel generation of the tetrahedral mesh can be improved, multi-stage parallel generation of large-scale tetrahedral meshes of 10 billions and above is realized, and the generation efficiency is improved.

The invention provides a multi-dimensional associative memory method based on a deep autoregressive neural network and a terminal. The method comprises the steps of receiving input of a target word, and generating structured input data based on the target word; inputting the structured input data into the target model to obtain a pronunciation candidate sequence, a font candidate sequence and a story candidate sequence; calculating an internal confidence score of each candidate sequence, and determining an optimal mnemonic sequence from the candidate sequences based on the internal confidence scores; and judging whether the inherent confidence score of the optimal mnemonic sequence meets a preset quality condition or not, and outputting an association result according to a judgment result. By constructing an integrated mechanism of multi-dimensional parallel generation and inherent confidence coefficient preferential evaluation, intelligent screening and personalized recommendation of mnemonic schemes are realized, the automation level and output quality of vocabulary association assistance are effectively improved, and meanwhile, the reliability and applicability of mnemonic contents are ensured through confidence coefficient threshold value control.