911 results about "Template match" patented technology

Methods and systems for generating a sharable script related to an input digital model on a digital platform are provided. The method includes receiving a user request indicative of a digital task involving an input digital model, and retrieving a corresponding input digital model file. Then, determining characteristic attributes of the input digital model, where the characteristic attributes include digital artifacts generated from the input digital model file. Then, selecting from a collection of templates, using a machine learning (ML) engine, a template matching the characteristic attributes of the input digital model. The ML engine may be trained on documentations of digital tools integrated into the digital platform, a resource-capability mapping of the digital platform, and sample digital thread orchestration scripts collected through past uses of the digital platform. Finally, the method includes generating the sharable script that implements the digital task, based on the selected template.

The invention provides an audit report automatic generation method based on natural language processing, and relates to the technical field of natural languages, and the method comprises the steps: obtaining audit document data passing the examination and approval, the audit document data comprising an audit manuscript, an audit problem, an audit evidence obtaining sheet and an audit verification sheet; performing semantic analysis on the audit document data through a natural language processing model, and extracting key information of an audit subject, a risk point and a compliance conclusion; inputting the key information into a structured processing algorithm to generate structured data matched with a preset auditing template; and inputting the structured data into a pre-training text generation model for language reconstruction, generating an audit report text, automatically filling the audit report text into a corresponding field of an audit template, and outputting a complete audit report.

The invention discloses a structured query language generation method and device based on a large model, equipment and a medium, and relates to the technical field of artificial intelligence. The method comprises the steps of performing unified index definition and dimension definition for different business scenes, and combining indexes and dimensions in the business scenes according to the index definition and the dimension definition to obtain different structured query language templates; obtaining a natural language query statement input by a user in the current service scene, and performing semantic analysis on the natural language query statement by using a preset large model to obtain a semantic analysis result; and determining a target structured query language template matched with the natural language query statement from the structured query language templates according to the semantic analysis result, and filling the target structured query language template to generate a structured query language statement result. According to the technical scheme, the natural language query of the user can be efficiently and accurately converted into the structured query language statement.

The invention discloses an AI virtual coach training system based on standard action matching and deviation feedback, which relates to the technical field of AI virtual coach training systems and comprises a user modeling module, an action acquisition module, a template matching module, a deviation calculation module, a feedback generation module, an interactive presentation module and a learning optimization module. The user modeling module is used for modeling a registered user by adopting a body parameter acquisition and health data analysis method to obtain a user personalized feature vector; the action acquisition module is used for capturing actions executed by a user in real time by adopting a multi-source sensor fusion method to obtain a time sequence containing key point coordinates; and the template matching module is used for comparing the time sequence of the key point coordinates with corresponding actions in a preset standard action template library by adopting an improved dynamic time warping (DTW) algorithm to obtain an optimal matching path and a corresponding minimum matching cost.

The invention discloses a deep learning artificial intelligence security vulnerability detection platform, and relates to the technical field of intelligent detection, and the platform comprises an information processing module which collects heterogeneous data in real time, carries out the labeling, format unification and modal aggregation processing of the data, and generates a sample set; the feature learning module is used for performing feature unwrapping on the sample set by using a variational auto-encoder, extracting modal data features and potential space representation learning, and outputting a potential space vector; the response generation module is used for generating a vulnerability response strategy through a modal consistency verification and response template matching mechanism based on the vulnerability risk level vector in combination with a response generation engine; and the repair feedback module is used for executing automatic vulnerability repair operation in combination with federal reinforcement learning and Bayesian optimization, performing feedback optimization according to an execution result, and outputting the vulnerability repair operation and a feedback result. According to the method, the response strategy is combined with intelligent matching of the real-time risk level, so that the accuracy and adaptability of vulnerability repair are improved.

The invention discloses a rescue method and system of a rescue robot for exploration, and relates to the technical field of underground space rescue, and the method comprises the following steps: obtaining multi-path acoustic echo data of a karst cave and motion track data of the robot, and constructing a three-dimensional point cloud model according to the multi-path acoustic echo data and the motion track data of the robot; and identifying unmatched data in the multi-path acoustic echo data and the robot motion trail data, and taking an area where the unmatched data is located as an abnormal area. According to the method, the spatial resolution of signal acquisition is enhanced through the multi-microphone array, robust acoustic fingerprints are extracted in combination with a noise reduction algorithm and short-time Fourier transform, high-confidence human body sound source signals are screened out by using a feature template matching mechanism, accurate extraction and recognition of human body acoustic features in a karst cave complex noise environment are realized, and the accuracy of human body acoustic feature recognition is improved. The problem of misjudgment caused by confusion of sound source features and environmental noise in a traditional method is effectively solved.

The invention discloses a power grid data acquisition and analysis system based on big data, particularly relates to the technical field of power network monitoring, and is used for solving the problem of key feature loss caused by cross-hierarchy data semantic association missing of an existing hierarchical aggregation mechanism. Performing standardized filling and time label layered alignment processing on the original data through the equipment acquisition module; the feature extraction module screens an abnormal waveform fragment and a steady-state parameter offset based on the equipment type and the real-time fluctuation amplitude; the semantic association module generates a space-time association weight in combination with the regional physical topological relation and the reactive circulation path sensitivity; the feature clustering module reconstructs a region-level feature aggregation packet through kernel density estimation and spatial weighted fusion; the cross-layer analysis module dynamically corrects the aggregation weight based on the current flow direction and the transient energy distribution; and the decision generation module generates an equipment positioning instruction and load scheduling strategy combination through alarm template matching, and finally realizes accurate positioning of fault equipment and generation of a scheduling strategy.

Methods and systems for generating a sharable script related to an input digital model on a digital platform are provided. The method includes receiving a user request indicative of a digital task involving an input digital model, and retrieving a corresponding input digital model file. Then, determining characteristic attributes of the input digital model, where the characteristic attributes include digital artifacts generated from the input digital model file. Then, selecting from a collection of templates, using a machine learning (ML) engine, a template matching the characteristic attributes of the input digital model. The ML engine may be trained on documentations of digital tools integrated into the digital platform, a resource-capability mapping of the digital platform, and sample digital thread orchestration scripts collected through past uses of the digital platform. Finally, the method includes generating the sharable script that implements the digital task, based on the selected template.

The invention relates to the technical field of text processing, and provides an engineering management report generation method and system based on natural language processing, which are used for realizing deep analysis of construction logs and providing a comprehensive and accurate decision basis for engineering management. The method comprises the steps that a construction log text set generated in the engineering construction process is acquired, natural language processing is conducted on the construction log text set, key entities and progress events in each construction stage description unit are extracted, and a construction feature set containing entity identification information and event time sequence information is obtained; performing semantic alignment processing on the construction feature set and a preset engineering specification knowledge base to generate specification association information between the construction stage description unit and an engineering specification requirement; and performing structured integration processing on the standard associated information based on a hierarchical template matching algorithm to generate a structured report text meeting the engineering management requirements.

The invention provides a satellite networking communication method, device and equipment, and the method comprises the steps: carrying out the space-time window decomposition processing of a satellite constellation orbit parameter based on orbit phase synchronization, and decomposing a long-period scheduling problem into a plurality of short-period sub-problems; performing state transition conflict detection processing on the double-feed antenna system according to the space-time window decomposition result, and determining the working state of the feed antenna; parallel neighborhood search processing based on reinforcement learning is carried out according to the feed antenna resource configuration scheme, and a communication link scheduling strategy is generated through multi-thread collaborative optimization; and performing dynamic fusion processing based on state prediction according to the parallel optimization scheduling scheme, and adjusting a satellite-ground station communication link establishment time sequence through learning type template matching. Through space-time decomposition, intelligent conflict detection, reinforcement learning optimization and dynamic fusion, the problems of complexity and real-time performance of large-scale satellite networking communication scheduling are solved, and the scheduling efficiency and the communication quality are remarkably improved.

The invention provides a complex question and answer method and system based on adaptive task deconstruction and multi-modal evidence aggregation, belongs to the technical field of natural language processing, and designs a dynamic Few-shot prompt construction method based on dependency syntax fingerprints to ensure that a prompt template is matched with a question structure; the invention discloses a dynamic problem deconstruction method based on confidence evaluation and auto-reflection. The method comprises the following steps: recursively decomposing a problem tree by using a large language model; converting the problem tree into a standardized linear task execution sequence by a problem tree context dependence specification and task sequence generation method; obtaining a high-correlation evidence set of each task based on an evidence generation method of two-way recall and cross encoder rearrangement; and the task sequence is reasoned and dynamically optimized by a question answer extraction method based on double-strategy aggregation reasoning. According to the method, the accurate complex question and answer result can be provided on the premise of ensuring the question disassembling quality, restraining error propagation and comprehensively recalling evidences.

The invention discloses a multi-agent task arrangement method and system, and relates to the technical field of artificial intelligence. In the method, firstly, a task demand is obtained, key information and intention of the task demand are extracted, and task semantics are obtained; secondly, based on the task semantics and the business standard process template, obtaining a business standard process template matched with the task demand; then, according to the matched business standard process template and task semantics, a task context is constructed, and a domain specific language DSL is generated through reasoning according to the task context; thirdly, the DSL is analyzed, a task dependency graph is constructed according to the DSL analysis result, and the task dependency graph is converted into BPMN process model data; and finally, sub-task scheduling and execution of the sub-agents are carried out according to the BPMN process model data. According to the method provided by the invention, the dependence on professional developers or process engineers can be reduced, the response and planning time of complex tasks can be shortened, the result predictability can be improved, and the reasoning cost can be obviously reduced.

The invention relates to the technical field of software development tools, and discloses a micro-application generation system and method based on natural language interaction, and the system comprises four core components: a natural language understanding module, a demand modeling module, a code generation engine and a deployment and integration platform. The natural language understanding module generates structured semantic representation through intention recognition, entity extraction and context analysis; the demand modeling module constructs a data model, a process logic and an interface prototype based on the semantic representation, and optimizes model parameters by utilizing reinforcement learning; the code generation engine generates a cross-platform code of the micro-service architecture through template matching and modularization assembly; and the deployment and integration platform realizes seamless integration of the external system and the Internet of Things equipment through a standardized interface. According to the method, the problems that a traditional low-code platform depends on visual dragging operation, demand analysis is rough and cross-domain adaptation is insufficient can be solved.

Methods for video decoding and encoding, apparatuses and non-transitory computer-readable storage media thereof are provided. In one method for video decoding, a decoder may obtain a current coding unit (CU) that is coded based on Intra Block Copy (IBC) mode combined with Geometric Partitioning Mode (GPM). Additionally, the decoder may obtain from at least one of regular merge candidates, Template Matching (TM) refined merge candidates, or merge candidates with block vector difference (MBVD candidates), a first IBC merge prediction and a second IBC merge prediction. Further, the decoder may obtain a prediction for the current CU based on the first IBC merge prediction and the second IBC merge prediction.

The invention relates to a context-driven fault solution suggestion generation method, which specifically comprises the following steps of: firstly, collecting various log data generated in system operation, and preprocessing the data as a basis for subsequent analysis; then, a BERTomaly model based on a multi-head cross attention mechanism is constructed, feature extraction, feature fusion and template matching can be performed on the log data, anomaly detection of system faults can be realized, and then, based on the system operation log data, the system fault detection efficiency is improved. The method comprises the following steps of: firstly, constructing a fault solution library which is of a context-driven type and has a dynamic updating capability by using a large language model in combination with the output of a BERTomally model and the output of an FP-Growth association analysis algorithm, and carrying out real-time detection on each fault, so that the fault solution library has a dynamic updating capability. And a targeted solution is output.

There is provided a system and method of defect detection on a semiconductor specimen based on template matching or machine learning (ML). Template matching is performed between a set of template patches and a set of runtime images, by selectively performing at least two of the following: matching a defect template patch in an inspection image, matching a reference template patch in a reference image, or matching a difference template patch in a difference image, so as to provide likelihood of target of interest (TOI) presence in the inspection image. The ML-based approach includes feeding an inspection patch and a reference patch together to a trained ML model, to generate a feature vector representative of a given TOI candidate, and evaluating the feature vector of the given TOI candidate to provide a likelihood of the given TOI candidate being a TOI or non-TOI.

The invention discloses an intelligent text report automatic generation method and system based on safety production data, and belongs to the technical field of big data and artificial intelligence, and the method comprises the steps: multi-source heterogeneous data collection and structured processing: collecting safety production event data including violation operation, violation command and violation of labor discipline, carrying out structured processing on the data; semantic enhancement prompt engineering and large language model reasoning: constructing prompt words, inputting structured data into a large language model, and performing semantic understanding and text generation on the data by utilizing a prompt engineering technology guide model; and dynamic report template adaptation and multi-format output: realizing structured report output through template matching, outputting a structured text report by the model, filling the structured text report into a preset template, and exporting the structured text report. The enterprise safety management efficiency is remarkably improved, the manual writing burden is reduced, informatization, standardization and intelligent processing of safety production data is achieved, and the safety management digitization and automation level of enterprises is improved.

The invention discloses a defect detection method and detection equipment for a photovoltaic module, and the method comprises the steps: obtaining a first infrared image and a first visible light image which comprise the photovoltaic module, and carrying out the template matching of an area where the photovoltaic module in the first infrared image is located and an area where the photovoltaic module in the first visible light image is located, generating a second infrared image and a second visible light image; pairing the battery pieces in the second infrared image and the battery pieces in the second visible light image, and generating a third infrared image and a third visible light image; acquiring spatial common characteristics and spatial difference characteristics of the cells in the third infrared image and the third visible light image; combining the spatial generality features and the spatial difference features to generate a fusion modal feature map; and on the basis of the fused modal characteristic pattern, defect detection of the photovoltaic module is realized. The detection equipment can execute the defect detection method. Through the arrangement, the defect detection accuracy of the defect detection method can be improved.

The invention discloses a complex terrain-oriented mobile geological survey data real-time acquisition and analysis system, and relates to the technical field of geological survey, and the acquisition and analysis system comprises a mobile acquisition terminal, a multi-platform acquisition device and a sensor integrated with a self-adaptive complex terrain, and a historical data template library. The storage module is used for storing multi-dimensional historical geological data subjected to standardization processing, standardization processing comprises data cleaning, feature extraction and space-time coordinate calibration, template retrieval and reuse based on feature similarity and a historical data reuse module are supported, and the historical geological data are stored through template matching, data fusion and incremental updating. And multiplexing of a historical template, semantic association of multi-source data and real-time adjustment of new data of a basic template are realized. According to the method, redundant analysis of newly-collected geological data is reduced by multiplexing the overlapped part of the historical geological survey data, and more efficient geological survey data collection and analysis can be completed only by matching the multiplexing data model and combining the new data to update the model.

A device for decoding video data comprises one or more processors configured to: obtain a syntax element from a bitstream that includes an encoded representation of the video data; determine, based on the syntax element, that a template-matching tool is enabled; based on the template-matching tool being enabled, applying the template-matching tool to generate a prediction block for a current coding unit (CU) of the video data; and reconstruct the current CU based on the prediction block for the current CU.

According to the automatic software development method and device and the computer program, intelligent demand analysis is achieved through a natural language processing technology, dynamic task arrangement is conducted through a finite state automaton, collaborative development is completed by means of a multi-specialized role agent, intelligent verification is implemented based on a template matching mechanism, and the development efficiency is improved. And a complete automatic closed loop from demand to delivery is constructed. According to the scheme, full-process automation is realized, manual intervention is greatly reduced, and the development efficiency is remarkably improved; through division and cooperation of multiple agents, the professionality and decision-making quality of each link are ensured, and the capability limitation of a single agent is overcome; flexible and reliable process control is provided based on state machine management, and development state changes are dynamically adapted; and the standard consistency of the delivery result is ensured by combining template verification. According to the method, the problems of long development period, large quality fluctuation and the like caused by chain splitting, low intelligent level and excessive dependence on manpower of an existing development tool are effectively solved, and a feasible path is provided for comprehensive intelligence of software development.

An example device includes one or more processors configured to determine a first search area in a first reference picture for a current block of the video data. The one or more processors are configured to determine a first initial reference block in the first search area. The one or more processors are configured to apply a local illumination compensation model to the first search area to generate a refined first search area. The one or more processors are configured to apply template matching to the refined first search area to determine a first candidate motion vector having a lowest template matching cost for the refined first search area. The one or more processors are configured to decode the current block based on the first candidate motion vector.

The invention relates to the technical field of software architecture design, and discloses a software architecture design method based on intelligent bionic implementation of a large model group. The method comprises the steps of obtaining software design requirement input; performing multi-level semantic analysis on software design requirement input to generate an initial component set and context environment description; based on the context environment description, candidate component knowledge related to the initial component set is retrieved from a distributed knowledge base; performing consistency verification on the candidate component knowledge and the initial component set by utilizing a dynamic inference engine to generate a verified component map; according to the verified component atlas, candidate software architectures are generated through an architecture template matching algorithm; and performing iterative adjustment on the candidate software architecture by adopting an optimization strategy, and outputting a target software architecture design. Through collaborative analysis and intelligent reasoning of the large model group, automation and intelligence of software architecture design are achieved, and the efficiency and quality of software design are improved.

The invention discloses a large language model LLM-based hierarchical workflow automatic generation method. The method comprises the following steps: firstly, processing natural language task description input by a user through a pre-trained large language model to obtain a structured task component; then, related workflow modes and optimal practices are retrieved from a pre-constructed domain knowledge base based on the structured task component, and the workflow modes and the optimal practices are fused to form knowledge-enhanced workflow description; based on the knowledge-enhanced workflow description, automatically converting the knowledge-enhanced workflow description into a standardized directed acyclic graph (DAG) through the steps of node extraction, type classification, dependency analysis, structure verification and the like; and finally, performing quality evaluation and iterative optimization on the generated DAG, and finally realizing end-to-end automatic generation from a natural language to an executable workflow. The technical problems that an existing workflow generation method based on template matching seriously depends on a preset template, is insufficient in flexibility and is difficult to adapt to complex and changeable user requirements can be solved.

The invention provides an enterprise-level application rapid development and deployment method and system based on model driving, and relates to the technical field of data processing, the method comprises the following steps: obtaining structured modeling data, carrying out semantic disassembly and relationship classification, and constructing an intermediate model structure; performing traversal analysis on the flow path to generate an execution mapping configuration table; extracting a form type, a data field definition and subsequent flow path information corresponding to each flow node, and matching and generating a front-end rendering component file and a rear-end interface description file in combination with a preset front-end template library and a service interface template; automatically splicing the front-end page code and the rear-end controller code by using a template engine to generate a target deployment structure; executing code construction and dependent compilation operation, and outputting a deployment package file conforming to enterprise-level application specifications; according to the method, the autonomy and the accuracy of rapid development and deployment of enterprise-level applications are improved.

According to the statement conversion method based on the fine tuning model, semantic information and query intention of the query text are analyzed from two aspects of slot positions and operation types through the multi-task model, so that the accuracy of the query statement is improved, and semantic completion is performed on the query statement by reconstructing the slot position relation graph, so that the accuracy of the query statement is improved. Therefore, a relation topology basis is provided for subsequent template matching, and the semantic analysis integrity of the complex query statement is improved. And finally, according to the slot position relation graph, an SQL template conforming to the query intention and semantic information corresponding to the original query statement is determined, and the target SQL statement is obtained by splicing and assembling the target SQL template based on the slot position relation graph, so that the writing difficulty of the query statement is reduced. When the method is applied to a data query function of a system in the financial business field or the medical field, the input convenience of query statements can be improved, and the statement conversion accuracy is improved.

The invention relates to an X corner detection method based on multilevel matching and feature optimization, and belongs to the technical field of computer vision and image processing. The method comprises the following steps: making a high-precision template corresponding to a predetermined X corner mark, so that the high-precision template can be accurately focused on a corner area; carrying out noise reduction and filtering operations on the image before identifying the mark so as to remove noise interference; carrying out template matching on the preprocessed image by adopting a self-adaptive threshold value and a multi-scale search strategy; and performing multi-level detection on the basis of template matching, including ORB feature extraction, Harris corner optimization and sub-pixel level positioning, and outputting the accurate position of the corner and a feature descriptor. The method can still maintain high precision and stability in a complex scene, is suitable for the fields of visual positioning, three-dimensional reconstruction and the like, and has the advantages of high detection efficiency, strong anti-interference capability, wide applicability and the like.

Provided in the embodiments of the present application are a decoding method, an encoding method, a decoder, and an encoder. The decoding method comprises: determining at least one reference block of the current block based on an intra template matching prediction (IntraTMP) mode; determining a target prediction block of the current block based on the at least one reference block; and determining a reconstructed block of the current block based on a residual block of the current block and the target prediction block.

The invention provides a management method of a packet filtering strategy and a data packet filtering method and device, and the management method comprises the steps: enabling the packet filtering strategy to be matched with a strategy template, and storing the matched packet filtering strategy to the strategy template of a corresponding type; each category of strategy template is configured with value ranges of parameters of at least two dimensions, and the combination modes of the dimensions of the parameters of different strategy templates are different; screening out candidate packet filtering strategies from the packet filtering strategies which are not matched with the strategy template, wherein the candidate packet filtering strategies are packet filtering strategies which coincide with the value range of the parameters of the strategy template; splitting a value range of a parameter coinciding with the strategy template from the candidate packet filtering strategy, generating a new packet filtering strategy, and storing the new packet filtering strategy in the corresponding strategy template; and taking the value range of the residual parameter which is not overlapped with the strategy template in the candidate packet filtering strategy as another new packet filtering strategy, and storing the new packet filtering strategy and other packet filtering strategies which are not matched with the strategy template in the strategy tree together.

The invention relates to a message rule searching method. The method comprises the following steps: searching a first candidate quintuple rule in a preliminary screening hash table according to address field data of a target message; the storage position of each quintuple rule in the preliminary screening hash table is determined according to the hash value of the address field data in each quintuple rule; searching a second candidate quintuple rule in the collaborative screening hash table according to a quintuple combination template matched with the address field data in the collaborative screening hash table and the quintuple field data of the target message; the storage position of each quintuple rule in the collaborative screening hash table is determined according to the hash value of each quintuple rule; and determining the first candidate quintuple rule and the second candidate quintuple rule as the quintuple rule of the target message. By adopting the method, the rule searching process of the message can be simplified, and the processing performance of the message can be improved.