710 results about "Template match" patented technology

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 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 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.

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.

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.

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 provides a multi-platform informatization construction system and method based on a computer, and solves the problems that existing multi-platform data synchronization is not timely, formats are not compatible and the like. The system comprises a real-time data capture module, and a data change event is generated in real time through database transaction log monitoring; the intelligent routing decision module optimizes a transmission path in combination with rules and machine learning; the data conversion adaptation module realizes automatic adaptation of a format and an interface by utilizing dynamic template matching; the conflict detection and resolution module guarantees data consistency based on version control and priority judgment; the data synchronization execution module transmits data according to an optimized path, and the monitoring management module monitors the whole process in real time. The method comprises the steps of data capture, routing decision, format conversion, synchronous execution and monitoring feedback. According to the scheme, the data synchronization efficiency and accuracy are improved, the multi-platform integration operation and maintenance cost is reduced, and the method is suitable for multi-scene informatization construction of enterprises, government affairs and the like.

The invention discloses a multi-rule template quintuple rule matching method and device based on an FPGA, and the method comprises the steps: storing a plurality of rule templates through a high-bandwidth memory, carrying out the cascading of the rule templates, and carrying out the rule matching of quintuple information in a to-be-processed message, carrying out the matching in a non-cascading / cascading manner, and carrying out the rule matching of the quintuple information in a to-be-processed message. And obtaining an action strategy of the to-be-processed message. The matching information of each template is flexible and configurable, a complex matching function is realized through cascading of the templates, the number of the templates is effectively reduced, and system resources are reduced.

The invention provides a target positioning method and system based on a distributed networking radar system, and belongs to the technical field of radars, and the method comprises the steps: controlling a plurality of radar nodes to transmit stepping linear frequency modulation continuous wave pulse signals, and collecting target echoes; carrying out band-pass filtering, pulse compression and fast time synchronization processing on the echoes, and generating interference-free data through a moving target extraction algorithm; realizing cross-node clock synchronization based on a moving target behavior cognition model and a template matching algorithm; constructing a multi-node distance estimation equation set, generating an accurate distance value through calibration broadband synthesis and FFT, and determining a target three-dimensional coordinate; and generating a motion track and speed prediction through Kalman filtering based on the target position sequence. The system comprises a signal emission and acquisition module, a signal processing module, a clock synchronization module, a distance estimation and coordinate fusion module and a trajectory prediction module. The method supports multi-target tracking, improves positioning precision and time synchronization precision, has few errors, and is compatible with any node deployment form.

The invention discloses a formatted document intelligent generation method and system, and the method comprises the steps: obtaining a multi-source formatted document sample, carrying out the structural analysis through a deep learning technology, and constructing a hierarchical document structure knowledge base; performing template matching through neural manifold representation learning and comparison self-supervised learning on the basis of the knowledge base in combination with document requirements input by a user, and outputting an optimal document template; performing structure analysis on the template, generating document content through a diffusion model in combination with user content, and performing quality diagnosis and optimization by adopting optimal loss value estimation; carrying out format self-adaptive adjustment and style consistency check through a value-free strategy optimization algorithm of the reward partition; and performing multi-format conversion export and quality evaluation on the finished document. The technical problems that an existing system is insufficient in flexibility, format adjustment is lack of intelligence, and document semantic coherence and style consistency are difficult to maintain are solved.

The invention discloses an intelligent medical inquiry and medical record generation method and system based on multi-technology fusion, and the system comprises a specialized dynamic pre-inquiry module which is used for sequentially executing the analysis of a patient chief complaint, the matching of a dynamic template, the intelligent inquiry and the completion and verification of a knowledge graph according to a chief complaint text inputted by a patient, and obtaining the comprehensive information of the patient; the dual-drive medical risk control module is used for performing dominant and recessive risk identification based on a rule engine and a large model in combination with an RAG knowledge base according to the comprehensive information of the patient, and realizing a collaborative early warning closed loop; the voice structuring and medical record generation module is used for executing double-track voice separation and transcription, specialized structuring processing and multi-source data fusion and EMR template matching in the dialogue process of the patient and the doctor to generate a structured electronic medical record; and the system integration and performance optimization module is used for adapting to a hospital HIS / EMR system through a standardized interface and executing data preloading and cache optimization to guarantee the response performance of the system.

The invention provides a distributed computing task office resource allocation method and device, equipment and a medium. The method comprises the following steps: in response to a received distributed computing task of an office scene, obtaining a type identifier of the distributed computing task, and associating the type identifier with a preset task stage template; monitoring a current stage identifier of the task process based on the constructed global state graph, and generating an execution stage migration event containing a target stage identifier when detecting that the logic state identifier is changed; matching a resource demand baseline of the target stage identifier according to the task stage template, and generating a target stage resource demand prediction value in combination with the global state map; and traversing the residual resource quantity of all the nodes in the global state map, screening a candidate node set meeting the resource demand predicted value of the target stage, and generating an optimal scheduling strategy based on the data locality parameter and the hardware suitability parameter of each candidate node in the candidate node set. By adopting the method, the execution efficiency of the distributed computing task can be improved.

A method and apparatus for video coding using MMVD mode are disclosed. According to this method, a base merge MV is determined from a merge list. A set of expanded merge candidates is determined according to a set of steps and a set of directions by adding pairs of the step and direction to the base MV. At least one combination of the set of steps and the set of directions is excluded in a partial set of expanded merge candidates. The partial set of expanded merge candidates are reordered according to template matching costs measured between a template of the current block and a template of a corresponding reference block for in the partial set of expanded merge candidates. The current block is encoded or decoded by using motion information comprising a reordered partial set of expanded merge candidates.

Embodiments of the disclosure provide a solution for video processing. A method for video processing is proposed. The method includes: determining, for a conversion between a video unit of a video and a bitstream of the video, motion information of the video unit based on template matching for intra block copy (IBC) or intra template matching prediction (IntraTMP), wherein the template matching for IBC or IntraTMP is different from template matching for inter prediction; and performing the conversion based on the prediction or reconstruction of the video unit.

The invention discloses an intelligent workflow generation method and device, equipment and a storage medium, and the method comprises the steps: obtaining multi-modal demand information inputted by a user, carrying out the semantic understanding of the multi-modal demand information, and obtaining a semantic tag set and a workflow node link; according to the semantic tag set and the workflow node link, screening a basic template matched with the multi-modal demand information from a plurality of workflow generation templates, and expanding nodes in the basic template to obtain a target template; and acquiring hardware resource information of the user equipment, adjusting the node configuration information in the target template, and generating a target workflow file according to the adjusted target template. According to the technical scheme provided by the embodiment of the invention, the technical difficulty of a workflow generation scheme can be reduced, the generation efficiency of the workflow and the quality of a workflow generation result are improved, the problem that the template cannot normally operate due to the fact that the hardware of the user equipment is not supported or the plug-in is missing is avoided, and the workflow can smoothly fall to the ground.

Disclosed in embodiments of the present application are an encoding method, a decoding method, a code stream, an encoder, a decoder, and a storage medium. The decoding method includes: decoding a relevant syntax element of a current block; according to the relevant syntax element, determining to use an intra-template matching prediction (TMP) (IntraTMP) merged intra prediction mode to perform prediction on the current block; determining a matching block of the current block on the basis of a TMP mode, and determining a first predicted block of the current block according to the matching block; determining a second predicted block of the current block on the basis of a non-template matching intra prediction mode; and merging the first predicted block and the second predicted block to determine a final predicted block of the current block.

The invention relates to the technical field of mechanical fault diagnosis, in particular to a gearbox fault diagnosis method and system based on graph structure signal processing, and the method comprises the following steps: calling a vibration data sequence, detecting slope change and marking nodes, constructing edge connection and edge weight to obtain a graph structure, calculating a spectral coefficient to recognize a high-density frequency interval, and carrying out the fault diagnosis. Extracting a spectrum boundary, obtaining a gradient difference value of a spectrum edge graph, positioning a node sequence, extracting a peak value, judging a fault type, and outputting a gearbox fault diagnosis result. According to the method, graph structure reconstruction is carried out through a node slope hopping trend, an edge connection direction and a weight reference quantity are constructed through inter-node amplitudes and time intervals, the sensitivity of a graph structure to weak impact characteristics is enhanced, disturbance frequency band screening is carried out based on spectrum coefficient slope inversion frequency, and the node response degree is mapped through spectrum edge amplitude gradient differences. And establishing a node amplitude direction sequence, matching the node amplitude direction sequence with a standard direction template, judging a fault boundary and a graph structure breakpoint position, and realizing accurate identification of an early fault of the gearbox.

The embodiment of the invention discloses an intelligent driving scheduling method and device based on intention recognition and a medium, and relates to the technical field of driving scheduling, and the method comprises the steps: receiving a natural language query from a user terminal to call a pre-trained domain intention recognition model, processing the natural language query, and generating and outputting an intention classification result, based on the intention classification result, a retrieval strategy is dynamically generated through a preset intelligent retrieval agent, at least one corresponding related knowledge fragment is retrieved from a preset driving scheduling knowledge base according to the retrieval strategy, and the retrieval strategy comprises a to-be-retrieved target knowledge sub-base type and a retrieval priority; and selecting a target cue word template matched with the intention classification result from a cue word template library to combine the at least one related knowledge fragment and the target cue word template, determining an input instruction of a large language model, and generating a driving intelligent scheduling answer through the large language model.

The invention relates to the field of internet search service, and discloses a big data-based investment attraction clue analysis system, which comprises a query analysis unit, a query analysis unit, a query analysis unit, a query analysis unit and a query analysis unit, wherein the query analysis unit calls an event sequence template for defining a meta event type and a standard relative time window constraint interval; the distributed retrieval and aggregation unit is used for retrieving and aggregating meta event types to entity objects; the sequential logic verification unit is used for counting the global document frequency of the meta event, reversely mapping the global document frequency to generate a zoom coefficient, dynamically correcting a time window, and judging that only an entity object in a dynamic verification interval after correction is passed; the object sorting unit generates sorting scores according to template matching integrity, and by constructing a density and window reverse coupling mechanism, the sequential logic boundary is adaptively compressed in a high background noise environment, and the retrieval signal-to-noise ratio and the logic confidence coefficient are maintained.

The embodiment of the invention discloses a literature interpretation method and device based on multi-Agent collaboration, a medium and a program product. The method comprises the following steps: performing feature extraction on a target document, generating a document feature vector, screening a target processing template matched with the target processing template from a preset processing template set, and constructing a directed acyclic graph by using the target processing template; key information of the target literature is extracted and stored in a shared work area; on the basis of the execution sequence of the directed acyclic graph, calling a corresponding functional agent to read input data from the shared working area, executing a processing task on the basis of the input data, and writing an execution result back to the shared working area; and after the execution of each functional agent is completed, outputting a literature interpretation report integrated by the execution results stored in the shared working area. According to the method, dynamic adaptation of a literature interpretation strategy can be realized through feature-driven dynamic scheduling and a multi-Agent cooperation mechanism, flexible expansion of system functions is supported, and the intelligent level and comprehensive efficiency of complex literature interpretation are greatly improved.

The invention relates to the technical field of bus protocol analysis and network monitoring, provides an ARINC739 protocol aviation bus data analysis method and system, and solves the problems of low accuracy and poor real-time performance of aviation bus network fault diagnosis. The method comprises the following steps: acquiring a protocol data stream of an aviation bus, node connection data of an aviation bus network and delay index data; performing graph embedding on the node connection data and the delay index data to obtain a topological feature matrix, and generating a modulation feature flow based on the topological feature matrix; aggregating the modulation feature flow by using a graph neural network to generate enhanced topological features; extracting a message header identification field from the protocol data stream, and performing hierarchical analysis on the message header identification field through dynamic template matching; and fusing the enhanced topological features and a hierarchical analysis result, performing anomaly detection on a fusion result, and identifying a link fault mode. According to the invention, the accuracy and real-time performance of aviation bus network fault diagnosis are improved.

The invention discloses a food business review method and device based on an intelligent agent, equipment and a storage medium, and relates to the technical field of large models, and the method comprises the steps: receiving a to-be-reviewed food business document through a scheme generation intelligent agent, calling a food field review agent, matching a template matching instruction issued by the scheme generation agent with the first cue word template library, and determining a target rule retrieval tool; performing secondary matching in a second cue word template library by utilizing a target rule retrieval tool and a template matching instruction issued by a food field review agent, obtaining an initial retrieval result from a preset rule database, and performing supplementary retrieval to obtain a final target retrieval result if the initial retrieval result does not meet a matching condition; and comprehensively analyzing the target retrieval result and the first template matching result by the food field review agent, generating a compliance judgment result, returning the compliance judgment result to the scheme generation agent, and outputting a business review result and a solution according to the compliance judgment result. Therefore, the efficiency of reviewing the document can be improved.

A video bitstream is received. The video bitstream includes a current block comprising a plurality of subblocks and a template region of the current block comprising a plurality of template subblocks adjacent to at least one of a top side and a left side of the current block. A motion vector (MV) located in a center position of the current block is determined. The MV is determined based on at least one MV of the plurality of subblocks of the current block. A MV for each of the plurality of template subblocks is determined based on the MV located in the center position of the current block and a respective MV of a corresponding subblock of the plurality of subblocks that is adjacent to the respective template subblock. The current block is reconstructed based on the determined MVs for the plurality of template subblocks.

A method, an apparatus and non-transitory computer-readable storage medium for video data process are provided. The method includes receiving a bitstream comprising a coding unit coded in a geometric partition mode (GPM); decoding a first parameter associated with the coding unit, the first parameter indicating whether template matching being applied to the coding unit; and determining, according to the first parameter, motion information for the coding unit, wherein when the first parameter indicates the template matching is applied to the coding unit, the motion information is refined using the template matching.