1119 results about "Granularity" patented technology

Approaches presented herein provide for the ability to process, store, index, and search geospatial information such as maps with flexible granularity. A set of observations, such as may include sensor data captured for a region of an environment, can be fed as input to a language model. The language model can generate a tokenized description of the region, as may include a text string of tokens encapsulating semantics, topology, geometry, and / or other aspects of the region. A feature vector or embeddings for the region can be generated based on the tokenized description, and a similarity search performed against a vector database, for example, to identify similar feature vectors corresponding to similar regions or domains. Labels or other information associated with these similar feature vectors can be automatically applied to the example region. Clustering of feature vectors or other embeddings can also be performed based in part on the similarity.

The invention provides a multi-feature fusion rumor detection method, system and device based on knowledge distillation, and mainly solves the problems that an existing model is high in calculation overhead, insufficient in feature fusion and insufficient in emotion utilization. The method comprises the steps of firstly obtaining multi-dimensional data such as social media original texts and comments; extracting deep semantic representation by using a pre-training model, and analyzing comment emotion features in combination with a hybrid neural network; then, features such as semantics, emotions, emoticons and populations are input into a hierarchical gating interactive fusion network (GIFN), and weights are dynamically adjusted to achieve effective fusion of multi-granularity features; in order to reduce complexity, a knowledge distillation framework is designed: a deep GIFN is used as a teacher network to generate a soft label, and a lightweight student network (LSTM) is guided to perform training. According to the trained student model, the parameter quantity is remarkably reduced, meanwhile, good detection performance is kept, the student model can be conveniently deployed in an actual content auditing system or edge equipment, and social content rumors can be efficiently recognized and judged.

The invention discloses a large model knowledge base construction method based on multi-granularity retrieval. The method comprises the steps of obtaining a target text and analyzing a document structure, splitting and reorganizing text data according to three levels of titles, fragments and sentences, and constructing three levels of knowledge of the titles, the fragments and the sentences; using a large language model to construct a generative index as question-level knowledge according to the sentence-level knowledge; using a vector model to generate a first vector representing index keys of fragment-level knowledge and / or sentence-level knowledge, and a second vector representing index keys of title-level knowledge and / or question-level knowledge; and respectively storing the first vector and the second vector into a vector database, and storing the index values and the index types corresponding to the first vector and the second vector into a relational database. According to the method, multi-granularity document knowledge base construction is achieved, the expression modes of knowledge under different granularities are expanded, the accuracy of user query and knowledge matching is improved, and the problem that information is lost or incomplete in an existing method is solved.

The invention relates to the technical field of data processing, and discloses a multi-granularity extraction and enhancement method, device and equipment for multi-source heterogeneous data. The method comprises the following steps: performing multi-level feature extraction on a multi-modal data set and original feature parameters to obtain a multi-modal feature set; performing correlation analysis on the multi-modal feature set to obtain a feature correlation matrix and optimized feature representation; performing cross-modal knowledge migration processing on the feature incidence matrix and the optimized feature representation to obtain migration feature parameters; performing dynamic fusion processing on the migration feature parameters to obtain target fusion features; and performing multi-task segmentation processing according to the target fusion features to obtain a plurality of sub-task sequences, performing time sequence enhancement and multi-task fusion analysis on each sub-task sequence to obtain target extraction data, and performing visual display on the target extraction data. According to the method and the device, the efficiency and the accuracy of multi-granularity extraction and enhancement for multi-source heterogeneous data are improved.

The invention provides an enterprise production data micro-service granularity optimization method, a medium and a system, belongs to the technical field of production data management, and establishes a micro-service granularity evaluation equation set by obtaining a dependency relationship and business flow data of micro-services in an enterprise production data system, constructing a coupling degree and cohesion degree matrix and establishing a micro-service granularity evaluation equation set. According to the method, a hybrid optimization algorithm is adopted, the search direction of a grey wolf algorithm and group evolution of a genetic algorithm are combined, and a composite fitness function containing system response time, resource utilization rate, maintenance cost and micro-service granularity balance degree is constructed. And continuously updating the micro-service division scheme through iterative optimization until the preset number of iterations is reached and the composite fitness function meets the optimization target, and finally outputting the optimal micro-service division scheme. The technical problem that the micro-service granularity is difficult to determine in the prior art is solved according to the division scheme of multiple indexes such as the maintenance cost and the granularity balance degree.

The invention discloses a scene granularity interface test case generation method based on multiple agents, and the method comprises a plurality of large language model agents and related components, namely context memory, an observer, a decision maker, an executor, a supervisor, a recorder and a to-be-tested application. An observer identifies the interface control, detection and attribute identification of the control are effectively focused, and it is ensured that accurate data input is provided for subsequent testing steps. Based on the output of an observer, a decision maker generates a detailed plan of the next operation according to the current page state, and an operation strategy can be independently formulated, so that test generation is supported by business scene logic. And an executor analyzes and executes the generated plan operation, carries out test operation strictly according to the plan, and ensures the traceability and repeatability of the test process. Furthermore, the supervisor verifies whether the operation is executed according to the plan and meets the requirements of the target scene, and makes corresponding feedback according to the check result, thereby ensuring that the wrong operation is corrected in time, avoiding the continuous accumulation of errors, and ensuring the effectiveness of each test step. The recorder is responsible for storing the execution instruction and the context information of the test process to the context memory, and the operation history and result in the test process can be traced through the test context information, so that a better decision can be made in the subsequent iteration, and the result export after the test is completed can be effectively supported. And after the recording is finished, the test generation enters the next iteration until the coverage of the test scene is finished.

The invention discloses an auto-filling method and device of a northbound FTP (File Transfer Protocol) performance file for a telecommunication network management system. The method comprises the following steps of: setting the granularity state of granularity of performance data according to the reported time of the performance data from a foreground network element by a performance loading module of the telecommunication network management system; when the performance loading module receives information reported subsequently by the foreground network element, wherein the information contains the performance data, if judging that the granularity of the performance data is in a completed state according to the set granularity state, then sending filling information containing the performance data to a northbound FTP performance module; and carrying out the processing that the performance data is filled to a previously generated northbound FTP performance file with the granularity by the northbound FTP performance module according to the filling information. Through the auto-filling method, the information reported by the foreground network element is analyzed to judge whether the northbound FTP performance file is complete or not, and the auto-filling of the performance data is carried out when the judgment result is that the northbound FTP performance file is incomplete.

The invention discloses an information resource efficient collaborative optimization method for a distributed computer system, which belongs to the technical field of information resource optimization control and comprises the following steps: executing a transaction according to a target lock element information set, monitoring popularity information of each resource segment, generating self-adaptive lock granularity decision information of each resource segment, and executing dynamic adjustment of lock granularity. And obtaining load sensing optimization parameters to complete lock competition optimization, and completing real-time iterative updating of lock element information and strategies. According to the method, the problems of resource competition and deadlock in the distributed system can be effectively solved, and the system performance and the resource utilization efficiency are improved. Transaction pre-allocation resources are reasonably allocated, the deadlock risk is reduced, lock granularity is dynamically adjusted according to resource segmentation popularity, lock competition optimization is completed based on load awareness optimization parameters, an adjustment result and the optimization parameters are synchronized to a distributed lock management library for real-time iteration updating, and it is ensured that a system always operates with an optimal strategy; and the distributed computer system efficiently and stably processes information resources in a cooperative manner.

The invention relates to the technical field of natural language processing, knowledge engineering and artificial intelligence, and particularly discloses a method and system for enhancing understanding of professional domain knowledge by a large model. The method comprises the steps that a professional domain entity classification system composed of a core entity, an auxiliary entity and a relation entity is constructed, attributes are expressed in a layered labeling and multi-granularity modeling mode, and semantic vectors are generated through ontology modeling and an embedding algorithm; based on a mixed extraction framework fusing expert rules and a neural network model, high-quality extraction of professional domain knowledge is realized; the method comprises the following steps: integrating multi-source heterogeneous data, and constructing a dynamically updated domain knowledge graph through semantic mapping, entity normalization and metadata weighting strategies; a knowledge graph is embedded into a Transform architecture, a knowledge perception attention mechanism and a multi-hop inference engine driven by reinforcement learning are introduced, and the knowledge fusion and inference ability of a large model is improved; and meanwhile, a triple check mechanism is designed to ensure entity consistency, relation logicality and numerical reasonability of the generated content. According to the method, the knowledge understanding and reasoning capability of a large model in professional scenes such as water conservancy is effectively improved, and the method has good universality and engineering application prospects.

The invention discloses a GPU computing power resource scheduling method and device based on load awareness and a medium, and relates to the technical field of computing power scheduling. The method comprises the steps that according to the video memory capacity and the number of calculation cores of the GPU, the video memory capacity is divided into a plurality of continuous fragments, the calculation cores are divided into a plurality of logic calculation groups, and sub-resource units are obtained; recording occupation states and load indexes of the sub-resource units in real time to obtain a resource pool; analyzing a job submitted by a user, and collecting an execution period of a kernel function, a video memory access mode and an instruction pipeline blocking rate when the job runs; and on the basis of a reinforcement learning algorithm, predicting a resource demand inflection point of the job according to the load fingerprint model, performing hierarchical scheduling on the sub-resource units based on virtualization isolation and multi-dimensional resource quantitative evaluation, and generating a preemptive allocation strategy of the sub-resource units. According to the method, efficient utilization and secure sharing of GPU resources are realized through collaborative design of dynamic granularity segmentation, load aware scheduling and hardware-level security isolation.

The invention discloses a noise text open intention classification method and system based on a large model, and belongs to the technical field of natural language processing and artificial intelligence, and the method mainly comprises the steps: extracting text features through a pre-trained large language model, carrying out the clustering of the text features through unsupervised granular ball clustering, and constructing intra-class structure representation in a feature space; dividing the training sample into a clean sample, an internal distribution noise sample, an external distribution noise sample and an uncertain sample based on a global particle-ball-level OOD tendency index and a sample-level consistency index; constructing a joint loss function guided by beneficial noise, and performing differentiation training on the model by using a sample division result; and constructing a multi-granularity decision boundary based on a granular ball structure, wherein the multi-granularity decision boundary is used for judging the category of the input sample. According to the method, multi-granularity structure information, a noise pattern recognition mechanism and a joint representation learning strategy are fused, and the recognition robustness and generalization ability of the model in a complex open environment are effectively improved.

Action and / or process determination and recommendations for Robotic Process Automation (RPA) using semantic action graphs is disclosed. Semantic action graphs are graphs that store individual actions, and potentially graphical elements and / or text associated with the actions, as nodes, as well as the relationships between nodes as edges. Metadata to develop the semantic action graphs may be derived from task mining applications that can monitor the interactions of users with computing systems, workforce intelligence, etc. The semantic action graphs may be for a user, an organization, an industry, product-wide, etc. At their lowest level of granularity, the recommendations may be for mouse clicks, key presses, Application Programming Interface (API) calls, system events, etc. At higher levels of granularity, the recommendations may be for opening an order, creating a lead, approving a work item, etc.

The invention discloses a dynamic printing data minimization splitting method based on topological entropy mapping. The method comprises the steps that S1, a complete data set of a printing task is acquired; s2, establishing a topological network for the divided data blocks; s3, calculating the three-dimensional fractal dimension of each data block in the topological network by using an improved spatial fractal algorithm; s4, recording topological entropy values of the data blocks and priority indexes of the data blocks in the network; s5, calculating the comprehensive priority of the data blocks in a weighted fusion mode; s6, performing dynamic minimization splitting on the data blocks according to the comprehensive priority; s7, dynamically adjusting the transmission sequence and the splitting granularity; s8, carrying out local compensation on missing data generated by splitting; s9, optimizing subsequent transmission and splitting strategies; and S10, performing supplementary transmission on the data blocks marked as low priorities according to the integrity requirement of the task. According to the invention, efficient resource allocation is realized, and the integrity and accuracy of the printing task are ensured.

Systems and methods for training and using large language models to respond to queries and cascading to new models when their performance falls below a threshold during the response generation cycle are described. The methods involve receiving and analyzing a query using heuristics to determine the query's categories and a level of granularity at which its response is to be evaluated. Selecting a language model based on the query analysis and during the generation of the response, evaluating the model's performance for its ability to predict the next segment in a response. The methods score the evaluation and use the score to determine whether the language model's performance exceeds a confidence threshold. If it does not, then during the response is being generated, e.g., at a point before the response generation is completed, cascading from the model to a different model that can perform at a higher confidence level.

The invention provides a multi-agent strategic scenario generation system based on a large language model, and the system comprises the following steps: carrying out the modeling of a scene and a system, and carrying out the system modeling of an attempt to record a case, deduce a situation, an event, a scenario, a strategic-battle-tactical-level red-blue party subsystem and a white party subsystem; the method comprises the following steps: initializing a red party attempt to record cases and a blue party attempt to record cases, when a user uses the system, prompting that relevant information of military deduction needs to be input, and after the information is input according to requirements, the system uses a global attempt record generation agent to receive the information input by the user and generate an attempt record in a JSON format. According to the multi-agent strategic scenario generation system based on the large language model provided by the invention, the strategy-battle-tactical multi-level agent vertical cooperation relationship-based game subsystem is designed, and the task granularity of scenarios designed by the large language model is refined through the multi-level game subsystem; and the scenario design quality is effectively improved.

The invention provides a power big data-based sparsity feature adaptive compression method, which comprises the steps of adaptively optimizing a sensing matrix through a dictionary learning method aiming at a screened multi-granularity feature subset, introducing a sparse regularization item to enhance the sparsity of data reconstruction, and according to a missing mode of a missing value and context information, carrying out adaptive compression on the sparse feature of the power big data. Establishing a mapping relationship among the compression ratio, the reconstruction error and the feature subset, and determining a self-adaptive compression strategy; based on a self-adaptive compression strategy, a compressed sensing method is adopted to carry out self-adaptive compression on the multi-time-granularity power data, the compression ratio is dynamically adjusted according to reconstruction error feedback, the data compression and reconstruction quality is balanced, and the reconstructed multi-granularity power data is synchronized according to timestamp precision.

The invention discloses a code graph construction method and device, electronic equipment and a computer storage medium. The method comprises the steps that a target code warehouse is obtained, and the target code warehouse comprises a target directory and a target file; generating a target directory tree according to the target directory; extracting a first target entity and a first target entity relationship in the target file; and according to the target directory tree, the first target entity and the first target entity relationship, constructing a target code graph corresponding to the target code warehouse. According to the method, a code graph is constructed according to a target directory tree and a first target entity and a first target entity relationship in a target file to realize overall overview and cross-file understanding of a target code warehouse, and information of different granularities of the target code warehouse is acquired, analyzed and integrated from macroscopic and local perspectives. Developers can master the content and the hierarchical structure of the target code warehouse more quickly and conveniently, and global overview and quick retrieval of the code warehouse are achieved.

Computer-implemented systems and methods implement semantic search in high-dimensional vector spaces, specifically tailored for use with large language models (LLMs). In particular, clustering is combined with Euclidean distance measurements to facilitate real-time vector searches. By implementing clustering, the invention reduces the computational complexity and costs associated with Euclidean distance calculations, which are typically more resource-intensive than other methods such as cosine similarity. This reduction is achieved by limiting the scope of distance calculations to within clusters, thereby avoiding the inefficiencies and diminished accuracy otherwise encountered by existing systems when using Euclidean distance in high-dimensional spaces. As a result, the invention retains the benefits of Euclidean distance, such as its superior granularity and precision in measuring semantic relevance, without succumbing to the usual drawbacks of high computational demands and poor scalability.

The invention discloses a Token level cache matching method and system of a large language model and a storage medium. The method comprises the following steps: constructing a local context fragment; generating a context embedding vector of the current Token, and calculating a context entropy value, a semantic consistency index and a semantic change gradient; for the target Token, determining a semantic category of the target Token; inputting the semantic category to which the target Token belongs into a Hash decision maker to obtain a Hash granularity level; if the Hash is the first-level Hash, executing fixed-length Hash and mapping the Hash to a semantic cache bucket based on a semantic theme or a context range of the first-level Hash; if the first-level hash is the second-level hash, dynamically adjusting the hash length according to the semantic similarity with the adjacent Token; if the Hash granularity level of the target Token is a third-level Hash, constructing a high-dimension context representation and executing a fine Hash operation; and matching the hash result with the KV in the pre-stored cache, and executing corresponding operation according to the matching result.

The invention discloses an intelligent question and answer method and a related device, and relates to the technical field of natural language processing, and the method comprises the steps: obtaining a question input by a user and a target document corresponding to the question; the method comprises the following steps: firstly, determining a vector of a problem and vectors of a plurality of text fragments corresponding to a target document, wherein the plurality of text fragments corresponding to the target document are obtained by segmenting the target document from a plurality of granularities based on a document structure; on the basis of the similarity between the vector of the problem and vectors of a plurality of text segments corresponding to the target document, candidate text segments are determined; and finally, based on the candidate text fragments, generating answers corresponding to the questions. According to the method and the device, through the multi-granularity segmentation strategy based on the document structure, the target document can be more efficiently analyzed and analyzed in the subsequent steps on the premise that the integrity of original text information of the target document is not lost, so that information related to the question can be more accurately retrieved, and the accuracy and the reliability of answers are improved.

The invention discloses a reasoning optimization method and device for a code generation large model, equipment and a medium, and relates to the technical field of model reasoning, and the method comprises the steps: in the reasoning process of a code generation task of a target code generation large model, executing a multi-granularity uncertainty quantification step in parallel every time a new Token is generated, obtaining a multi-granularity uncertainty score; constructing a state space vector, and utilizing a preset reinforcement learning strategy network to evaluate the selection probability of a plurality of preset reasoning optimization strategies based on a preset smooth decision mechanism so as to determine a target reasoning optimization strategy; if the strategy is a preset reasoning acceleration strategy, optimization processing is carried out through speculation decoding; if the strategy is a preset exploration optimization strategy, performing optimization processing by using a preset multi-path sampling technology and a preset knowledge enhancement technology; if the strategy is the preset fuzzy processing strategy, taking the plurality of candidate outputs as target reasoning outputs for optimization processing; and evaluating the decision effect according to the reasoning result to optimize the preset reinforcement learning strategy network.

The invention discloses a processor, a chip product, computer equipment and a tensor calculation method, and relates to the technical field of processors. A first tensor is stored in a first storage unit of the processor; a plurality of scaling factors corresponding to the first tensor are stored in a second storage unit of the processor, the scaling factor set comprises a plurality of scaling factors, each scaling factor corresponds to one sub-block in the first tensor, each sub-block comprises at least one element in the first tensor, and each sub-block comprises at least one element in the first tensor; the scaling factor is used for adjusting the dynamic range of each element in the same sub-block; a tensor calculation engine of the processor is used for reading a first tensor from the first storage unit and reading a scaling factor set from the second storage unit; and for any sub-block in the first tensor, executing logical operation on elements in the sub-block according to the scale factor corresponding to the sub-block to obtain an operation result. According to the method and the device, the tensor calculation engine can realize accurate operation of the low-precision tensor through the scale factor of the sub-block granularity.

The invention relates to the technical field of engineering process control, and discloses a ball mill granularity soft measurement method based on a time sequence large model. Constructing a multi-feature fusion module for extracting multi-scale features based on Convld K3, Convld K5 and Convld K1, and constructing a soft measurement model in combination with multi-head attention and a large language module with a fixed weight; the soft measurement model generates a first feature and a query matrix, generates a key matrix and a value matrix according to a fixed weight of the large language module, generates a second feature based on the query matrix, the key matrix and the value matrix, and fuses the second feature with the first feature to obtain a fused feature; and then a granularity prediction result corresponding to the field data is obtained through a large language module, so that the problems that an existing soft measurement model is too high in dependence on large-scale sample data, insufficient in modeling capability for complex nonlinear process parameters and low in prediction precision are solved.

The invention discloses an equipment operation and maintenance decision-making method based on large-scale knowledge graph multi-granularity learning reasoning, which comprises the following steps of: firstly, extracting a multi-granularity sub-graph of a subject entity in an equipment operation and maintenance knowledge graph, and integrating multilevel information such as an equipment state, a fault mode and an environmental factor to form a relation path set; secondly, performing joint coding on a question text and a relation path through an attention enhanced word embedding module based on a graph convolutional network, forming a word embedding vector, calculating a similarity score between a relation in the path and the question, pruning a sub-graph, and reducing the calculation amount; and finally, fusing a plurality of particle size sub-graphs after pruning processing, inputting the fused particle size sub-graphs into a reasoning decision module, extracting feature information from graph structure data by the reasoning decision module through graph convolution operation, dynamically adjusting a GCN learning process in combination with LSTM, finally realizing question reasoning and answer generation, and ensuring the accuracy of an equipment operation and maintenance decision.

The invention discloses a time sequence prediction method and system based on a multi-granularity time sequence large model, which can improve the accuracy of time sequence prediction, effectively cope with the uncertainty of data, and have excellent iteration capability and generalization capability. According to the technical scheme, a multi-granularity time sequence large model is reconstructed and constructed based on a language class basic large model; performing data enhancement on the time series data by applying a mixed data enhancement method, and expanding the size of a time series data set; for each batch of time sequence data, constructing a template cue word of each batch for context cue, and assisting the multi-granularity time sequence large model to understand the time sequence; converting a one-dimensional time sequence into a plurality of groups of two-dimensional vectors according to different granularities by using an original time sequence data set, and splicing the plurality of groups of two-dimensional vectors to form new data representation; and performing fine tuning training on the multi-granularity time sequence large model to enable the pre-trained multi-granularity time sequence large model to solidify knowledge of the data set and complete time sequence prediction.

The invention provides a method and a device for constructing a three-dimensional random geologic model based on machine learning. The construction method comprises the following steps: constructing a geologic model area range and discretizing the geologic model area range into n voxels; constructing an initial geologic model based on the label value of the known voxel determined by the drilling data and the initial label value of the unknown voxel determined by the cubic learning domain; constructing a neighborhood system of each voxel, and setting spatial constraints in 13 directions; based on the label value of each voxel in the initial geologic model, obtaining a prediction result according to a label probability prediction formula and an updating iteration formula of a granularity coefficient; based on the prediction result, obtaining the marginal probability of each unknown voxel and the prediction label value of each unknown voxel; and updating the initial geologic model based on the predicted label value of each unknown voxel, and constructing a three-dimensional random geologic model. The three-dimensional random geologic model constructed by adopting the construction method disclosed by the invention is accurate in simulation result, and meanwhile, the uncertainty of the stratum can be represented.

The invention provides a data state updating method and device, a storage medium and computer equipment, and the method comprises the steps: determining an updating object and an updating operation corresponding to a preset action when the triggering of the preset action is detected; determining a related component corresponding to the updated object according to the subscription list of the updated object; determining a target data state corresponding to the update object in a preset data state library and updating the target data state; when updating is completed, updating information is generated, and the updating information is sent to the monitors corresponding to all the related components, so that all the components are rendered again according to the updating information. According to the embodiment of the invention, the related components influenced by the update object can be determined through the subscription mechanism, and the update information can be generated and sent to the monitors corresponding to the related components after the update is completed, so that the related components can be re-rendered according to the update information, and the re-rendering is only carried out on the related components; and data state updating with finer granularity is realized, so that the efficiency and the performance of data state updating are improved.

The invention belongs to the technical field of time sequence data streams, and particularly relates to a multi-granularity anomaly detection system for a time sequence data stream, which comprises a data acquisition and preprocessing module used for firstly performing multi-source data acquisition, then cleaning and standardizing the data and then generating data with different time granularities; the multi-granularity feature extraction module is used for firstly performing time sequence feature extraction, then performing window feature extraction and then performing multi-granularity feature fusion; and the anomaly detection model module is used for detecting whether the data fused by the multi-granularity feature extraction module is abnormal or not, and then improving the accuracy and stability of detection according to an integrated learning model. Compared with a traditional detection method, the method has the advantages that by arranging the anomaly detection model module, the detection accuracy and timeliness of complex mode anomalies can be greatly improved, and economic losses caused by the fact that enterprises do not find anomalies in time are avoided.

The invention relates to a question and answer method and system fusing a multi-granularity knowledge graph and a self-adaptive agent. The question and answer method comprises the steps of knowledge governance and extraction, knowledge generation and expansion, knowledge retrieval and management, and knowledge rule mining and self-adaptive retrieval. Through cooperation of the multi-granularity knowledge graph and the self-adaptive agent, questions of the user can be more comprehensively understood, and more relevant knowledge can be retrieved from the knowledge base, so that the question and answer accuracy is remarkably improved, and wrong answers are reduced. Meanwhile, the multi-granularity knowledge graph can support more complex knowledge application requirements such as reasoning and prediction, so that the application capability of the question and answer system is enhanced, and the question and answer system can process more complex question and answer tasks such as multi-round dialogues and multi-task question and answer.