113 results about "SNePS" patented technology

The invention provides a dynamic tool integration method and system for an intention-driven AI dialogue system, and the method comprises the steps: S5, extracting resource occupation information from a transaction log according to a state persistence record, carrying out the resource release and temporary context cleaning of each heterogeneous component, and obtaining a released resource list; s6, searching a breakpoint state snapshot and a transaction log from the distributed database according to the released resource list, and reconstructing a tool chain execution flow historical state to obtain a fault context analysis result; and S8, according to the context recovery state and the semantic feature vector, updating a tool chain execution stream, and reallocating computing resources through a heterogeneous component coordination mechanism to obtain a task continuity confirmation result. According to the method, the recovery efficiency and the resource utilization rate of the tool chain in an abnormal scene are remarkably improved, and the task continuity and the system robustness in a high-concurrency environment are guaranteed.

The invention provides a low-code application logic generation method and system based on a large language model, and the method comprises the steps: obtaining a natural language demand and an operation environment snapshot of a user, and constructing a prompt project context of the large language model based on the natural language demand and the operation environment snapshot; based on the context of the prompt project, performing retrieval by utilizing a large language model to obtain a logic component; processing the logic component based on a pre-constructed agent to generate a logic DSL text; analyzing and rendering the logic DSL text to obtain a visual logic flow graph, and updating the logic DSL text in response to a modification operation of a user on the visual logic flow graph; based on the logic DSL text, verifying application logic corresponding to the logic DSL text in the sandbox environment, and deploying the application logic to the current application after verification is passed. According to the invention, complex business requirements can be met, and logic development thresholds are reduced.

Techniques are described for techniques for an actionable artificial intelligence bot based on data security correlations. An example method comprises determining, by a data platform implemented by a computing system, a plurality of tags for a snapshot executed by the data platform, detecting, by the data platform, an indication of a security breach relating to the snapshot, processing, by the data platform and using a machine learning model, a plurality of attributes of the security breach and the plurality of tags to identify a potential compromise of the snapshot, processing, by the data platform and using a large language model, at least the plurality of attributes to generate an actionable prompt including a natural language description of at least one security response, and outputting, by the data platform, the actionable prompt.

The invention discloses a method and system for automatically generating headless test cases based on behavior description. The method comprises the following steps: obtaining an original behavior description text, and carrying out standardization processing on the original behavior description text to generate a standardized behavior description text; performing semantic analysis on the standardized behavior description text to generate a structured intermediate representation; matching each IR step in the structured intermediate representation with a preset step library, and generating a corresponding target step for each IR step; according to all the target steps, corresponding code templates are selected from a preset template library and combined in sequence, and headless automatic test cases are generated; and for an interface on which the headless automatic test case depends, selecting a matched Mock data version from the Mock snapshot warehouse according to the interface contract hash, and performing contract consistency verification. According to the method, through semantic analysis, step library matching and template combination, BDD description is fully automatically converted into executable scripts, and testers are liberated from repeated coding work.

The invention discloses an incremental learning method of a graph neural network model, a related device and a storage medium, and the method comprises the steps: screening a plurality of target boundary nodes from a historical snapshot graph based on an incremental data set; constructing a knowledge graph based on the plurality of target boundary nodes, the historical snapshot graph and the incremental data set, and generating an incremental sub-graph; inputting the incremental sub-graph into an incremental learning network to obtain a node new representation; inputting the historical snapshot graph into the original graph neural network model to obtain a node historical representation; fusing the node historical representation of the plurality of target boundary nodes with the node new representation of each node in the increment sub-graph to obtain the final node representation of each node in the increment sub-graph; iteratively training the incremental learning network based on the final node representation; and processing the to-be-predicted data set based on the incremental learning network and the original graph neural network model, and performing reasoning to obtain a target reasoning result. According to the method, the reasoning accuracy of the graph neural network model can be improved under the condition of avoiding total retraining.

The invention relates to the field of intrusion detection, and provides an APT attack detection method and system based on a dynamic multilayer semantic enhancement traceability graph. The APT attack detection method based on the dynamic multi-layer semantic enhancement traceability graph comprises the following steps: constructing the dynamic multi-layer semantic enhancement traceability graph which is sequentially subjected to layer-by-layer feature fusion of an original event layer, a behavior mode layer and a threat technology mapping layer, carrying out deep semantic enhancement on nodes and edges in the graph, and adopting an increment updating mechanism to update the threat technology mapping layer in the dynamic multi-layer semantic enhancement traceability graph; capturing an attack behavior, extracting a semantic context, and constructing a graph snapshot of the dynamic multilayer semantic enhanced traceability graph; adopting a pre-trained H-STFT model to embed all nodes in the graph snapshot into a feature space to obtain a node embedding vector set; aggregating all node embedding vectors in the node embedding vector set to obtain a graph level feature vector; based on the graph level feature vector, obtaining the probability of whether the APT attack exists in the current graph snapshot; and accurate detection of the APT attack is realized.

The invention discloses a smart home control method and system based on graph retrieval enhanced generation and multi-agent gaming, and the method comprises the steps: constructing a basic knowledge graph based on a current smart home environment, carrying out the semantic enrichment of service entities in the basic knowledge graph, generating an enhanced knowledge graph, and storing the enhanced knowledge graph in a vector database; constructing an example library containing a plurality of formalized observation-logic hypothesis pairs; acquiring a situation snapshot of the user, and outputting a structured scene analysis report based on the situation snapshot and the vectorization database through a front-end LLM; collaborative review is carried out by combining a factor-tracing reasoning agent, a user preference agent and a conflict detection agent with a scene analysis report and an example library, and a final decision is output by a core decision LLM and mapped into an equipment control instruction to call corresponding equipment for execution. According to the method, the fuzzy instruction of the user is combined with the knowledge graph, so that the intention recognition accuracy is improved; the multi-agent parallel auditing mechanism improves the robustness and safety of decision making.

The invention discloses an AI policy consultation database and an AI consultation method, and belongs to the technical field of artificial intelligence and government affair information services. The invention aims to solve the problems of unreliable information source, large semantic understanding deviation and low policy matching accuracy of the existing general AI tool in the field of policy consultation. According to the technical scheme, the method comprises the steps that a multi-level, multi-dimension and versioning management policy database is constructed, and entity version snapshots and a multi-version concurrency control (MVCC) mechanism are adopted to process time sequence conflicts caused by policy revision; the retrieved trusted policy text and an original query request of a user are integrated into an enhanced Prompt (Augmented Prompt); and receiving and analyzing a preliminary conclusion returned by the LLM, and generating a standardized policy consultation report containing a structured table and attachments by using a natural language generation (NLG) and chapter structure analysis technology. And the accuracy, timeliness and reliability of policy consultation are remarkably improved.

The invention discloses a low-code process automatic arrangement method and system, and particularly relates to the technical field of automatic arrangement, and the method comprises the steps: carrying out the unified collection and verification of an external system, resources and system boundaries before deployment, generating an operation environment fingerprint with a signature, and carrying out the strong binding of the fingerprint with a process instance; process DAG modeling is completed on the visual canvas, system and semantic double validity verification is carried out, and a post-curing parameter snapshot is used as a subsequent unique parameter source; in the online simulation period, observable measurement is collected, a deviation perception index is formed, and steady selection and switching of an execution kernel are achieved by combining hysteresis and a minimum residence strategy; in a production state, a canary release strategy, a degradation strategy and a rollback strategy are used for communicating simulation and real disk, and the SLA and the key KPI are continuously monitored; normalized collection and immutable archiving are carried out on structured and semi-structured data in the operation period, an evidence chain and a minimum reproduction experiment package are generated based on hash and signature, and the comparability and traceability of cross-version results are achieved.

The invention belongs to the technical field of computers, and discloses a heterogeneous SoC task adaptive distribution system. Comprises: a migration determination module for identifying a load imbalance state between cores in a running process and screening migration candidate tasks; the migration preparation module is used for generating baseline snapshots for the running states of the migration candidate tasks and carrying out hierarchical division on the baseline snapshots; the semantic annotation module is used for carrying out semantic annotation on memory pages occupied by the migration candidate tasks, calculating regularized semantic scores and distributing levels to memory page data according to the regularized semantic scores; the compression migration module is used for carrying out mixed compression and self-adaptive reduction on the layered state data in the migration process; the preheating loading module is used for executing preheating loading of codes and data of a high-priority function on a high-speed cache of the target core according to the preheating priority sequence on the migrated target core; and through semantic annotation and hierarchical compression, the transmission data volume is effectively reduced, and the migration dwell time is shortened.

The invention relates to the technical field of natural language processing, and discloses a diagnosis and treatment path prediction method based on time sequence knowledge graph completion, which comprises the following steps: acquiring a diagnosis and treatment knowledge graph snapshot sequence; dividing the snapshot sequence of the diagnosis and treatment knowledge graph into long-term, middle-term and short-term diagnosis and treatment information sub-graphs according to the time sequence and the clinical path characteristics, and performing structure coding on the three types of diagnosis and treatment information sub-graphs to obtain diagnosis and treatment information graph structure representations of different time scales; performing weighted fusion on the diagnosis and treatment information graph structure representations of different time scales to obtain a diagnosis and treatment information query representation; and inputting the diagnosis and treatment information query representation into a scoring function to score and sort all candidate tail diagnosis and treatment information entities to obtain a diagnosis and treatment path prediction result. According to the invention, the accuracy and robustness of complementing the complex application task for the diagnosis and treatment path can be improved.

The invention relates to the technical field of substation operation and maintenance. The invention provides a transformer substation hidden risk identification method, system and device and a storage medium. The method comprises the following steps: performing deep semantic vectorization processing on an operation and maintenance text, and constructing an equipment time sequence semantic snapshot; generating an equipment state change trend vector; defining nodes and edges based on the equipment state, the regional topology and the risk type, constructing a transformer substation heterogeneous graph, and designing a meta-path; aggregating heterogeneous neighbor information by using a graph attention mechanism, configuring an independent parameter matrix for different relationships, and expanding a sensing range through multi-round iteration until a node state and a risk entropy are converged; and finally, quantizing and outputting equipment or regional risk indexes, and generating an interpretable traceability report by combining attention weight backtracking key paths and abnormal states. The problems that in the prior art, unstructured text fuzzy semantics cannot be deeply analyzed, the multi-dimensional topological relation between devices is difficult to model, and therefore multiple low-risk events cannot be recognized and hidden dangers are formed through network conduction aggregation are solved.

The invention discloses an automatic file analysis method based on a semantic evolution graph. The method comprises the following steps: generating a policy document standardized corpus; obtaining a policy document structured corpus unit set; constructing an initial semantic map snapshot containing a version stamp; establishing a time slice semantic evolution graph sequence; obtaining a multi-time sequence fusion vector; generating a multi-time sequence optimization word embedding vector matrix; performing time continuous regularization on the multi-time-sequence optimization word embedding vector matrix, outputting time continuous word embedding vectors, and mapping the time continuous word embedding vectors to nodes and edges of the time slice semantic evolution graph sequence to form a global semantic evolution graph fusion representation sequence; and taking the global semantic evolution graph fusion representation sequence as feature input to obtain a policy document analysis result set. According to the method, the timeliness, the accuracy and the interpretability of automatic analysis of the policy document are greatly improved.

The invention relates to the technical field of multi-agent collaboration, in particular to a task execution method and system based on multi-agent collaboration, and the method comprises the steps: carrying out the semantic analysis and multi-granularity decomposition of a user task instruction, generating a subtask dependence structure, and storing the subtask dependence structure into a dynamic task pool; performing collaborative planning and rehearsal execution on the to-be-executed subtasks, generating context snapshots, executing Push, and writing the Push into a historical state stack; retrieving an evidence chain based on the domain knowledge base and extracting a fact triple set, performing consistency comparison based on structured evidence triples on the candidate reasoning paths, and outputting conflict confidence and consistency scores; when the consistency score is lower than a threshold value, triggering a projectile stack, physically clearing wrong branch intermediate data, recovering a historical context, injecting a negative feedback prompt to generate a new inference branch, and returning to continue rehearsal execution; if the verification is passed, outputting a result and entering a next subtask; therefore, the controllability and auditing performance of multi-agent cooperative task execution are improved.

The application discloses a research report generation method based on natural language processing and relates to the technical field of intelligent document analysis and generation, which comprises the following steps: version storage of source documents and generation of timestamp snapshots; paragraph-level and sentence-level segmentation of the timestamp snapshots and extraction of evidence segments carrying original text positioning indexes; core claim extraction of the evidence segments to generate conclusion units containing claim texts and confidence; establishment of the association relationship between the conclusion units and the evidence segments; generation of chapter texts according to the associated conclusion units and calculation of evidence segment coverage; and structural adjustment and regeneration of the texts when the evidence segment coverage is lower than a preset threshold. The method realizes evidence traceability, multi-type semantic association and automatic optimization based on coverage feedback.

A common data model is created for an automation system, which includes an ontology of semantic relationships between entities of the automation system captured from multiple simulation tools based on a simulation design of the automation system. Artifacts, such as simulation and automation artifacts may be attached to corresponding entities in the common data model. New automation artifacts may be generated utilizing logic definitions from the simulation artifacts in the common data model. Changes to an artifact may be propagated to affected artifacts in the common data model utilizing dependencies defined by the semantic relationships. A snapshot of the common data model, including the newly generated or modified artifacts are committed to a version control system. A continuous integration execution engine executes virtual commissioning for validating each automation artifact against a simulation environment in a simulation of the automation system, based on the commit of the common data model.

This invention relates to the field of data management technology, specifically to a method, apparatus, computer equipment, and storage medium for multi-source data consistency management. The method includes: acquiring data records from at least two source systems to generate change events; converting the change events into evidence records containing original values ​​and contextual information based on a semantic rule registration library, and generating a standardized value view; performing entity parsing based on the standardized value view to obtain a set of candidate entity matching relationships, and outputting a credible candidate set through semantic equivalence transformation stability testing and evidence conflict measurement; performing constraint consistency inference based on the credible candidate set and the semantic rule registration library to generate a consistency state snapshot and a correction action plan; generating a synchronous execution rule set based on the correction action plan and performing a pre-rehearsal acceptance test; executing correction and re-collecting verification when successful, and rolling back when unsuccessful. This invention improves the traceability of cross-source consistency determination and the verifiability of correction execution, and reduces the risk of erroneous corrections.

This invention relates to the fields of computer data processing and network communication technology, specifically to a method and system for real-time synchronization of paperless interactive information across multiple terminals. The method includes: a logic construction step: acquiring the original interactive command stream, constructing a semantic causal logic tree based on a vector clock, and dividing strong barriers and weak streaming events; a backpressure monitoring step: establishing a rendering backpressure model to calculate the rendering backpressure value of the target terminal; a dynamic shaping step: responding to a backpressure value exceeding a threshold, reducing the dimensionality of the command stream and reorganizing its timing, attaching weak streaming events to strong barrier events and merging them into an atomic state snapshot for transmission; and a consistency verification step: comparing the screen content-aware hash value, and if a mismatch occurs, performing full state synchronization. This invention solves the problem of visual semantic confusion caused by differences in rendering capabilities between heterogeneous terminals.

The invention provides a territorial space knowledge evolution model construction method, and belongs to the technical field of territorial space knowledge evolution models.The territorial space knowledge evolution model construction method includes the steps that a distributed graph storage framework is constructed, sub-graph partitions are divided through a community detection algorithm, and external event information is collected and converted into a graph operation instruction sequence; positioning a k-hop neighborhood sub-graph by using an incremental sub-graph matching algorithm and executing local updating to generate an intermediate knowledge graph, calculating node state embedding representation of a fusion time coding vector, capturing a time sequence dependence mode through a time perception graph attention aggregation algorithm, executing semantic consistency constraint detection to identify conflicting nodes, and obtaining a time perception graph attention aggregation algorithm; when the conflict degree exceeds a threshold value, the multi-version concurrency control protocol is triggered to roll back to the snapshot version, otherwise, the intermediate knowledge graph is reserved as final output, and the problem that it is difficult to give consideration to updating efficiency and semantic consistency guarantee when the territorial space knowledge graph responds to an external event for evolution updating is solved.

The invention discloses an employee subject authorization verification and authorization change verifiable control method and system. The method comprises the following steps: segmenting a natural language authorization task and establishing session isolation for each subtask; calling a neural network model in the first session to output an employee subject name set, an action enumeration and a task phrase set in a fixed format; generating a candidate task subset based on the cross index, calling a neural network model in a second session to carry out constrained semantic alignment, and allowing to output candidate inner task codes or newly creating task names; the execution layer rejects border-crossing output through a hard check gate; a compensation transaction log is generated before writing, a pre-change snapshot and a fingerprint value of an influence domain file are stored, the fingerprint value is recorded after writing, a submission mark is written, and rollback recovery can be performed according to a transaction state during abnormal restart; the authorized cache is maintained or reconstructed based on the increments and the differential set, providing a withdrawal or compensation transaction based on fingerprint value collision detection.

The application relates to the technical field of computers, can be applied to financial and medical scenarios, and discloses a front-end code automatic alignment and change verification method, system, device and medium. The method comprises the following steps: constructing a semantic mapping index table of old interface fields and business codes in response to a package management script command; performing interface field level difference comparison on a remote interface protocol and a local historical snapshot to generate a change list; constructing a prompt word based on the index table and the change list, generating an adapter code segment, and non-invasively injecting the business code, updating the index table and the snapshot; and completing consistency verification and controlling submission before submission. The application realizes automatic perception of interface changes, non-invasive adaptation of inventory codes, and submission quality forced verification, and improves front-end engineering efficiency and system stability.

The application discloses a method and device for sensing, judging and processing data structure change. The method comprises the following steps: collecting a data structure change event based on a change data capture tool; constructing a data structure change abstract syntax tree through a SQL parser to accurately identify the change operation type and content; obtaining structure snapshots before and after the change; executing compatibility judgment in combination with a compatibility rule library to output the compatibility state and risk level; adopting a hierarchical message notification strategy according to the risk level; and automatically triggering or arranging corresponding processing actions, such as automatically synchronizing fields, generating a compatible view or suspending a high-risk task. The application realizes semantic-level analysis of data structure change, automatic compatibility judgment, refined risk grading and automatic processing, significantly reduces the cost of manual intervention, effectively avoids downstream synchronization interruption and data quality abnormalities caused by structure change, and improves the stability and automatic operation and maintenance level of a medium or large data platform.

The invention provides a code snapshot management method and device and related equipment. The method comprises the following steps: monitoring a change event of a code file; obtaining multiple pieces of evaluation dimension information corresponding to the change event, and fusing the multiple pieces of evaluation dimension information to obtain a semantic importance score corresponding to the change event; when it is determined that the score is larger than a preset score threshold value, semantic analysis is conducted on codes in the change file, corresponding code semantic information is obtained, and according to the code semantic information, a code snapshot is generated and stored. According to the method, an automatic triggering mechanism based on semantic importance evaluation is adopted for code change evaluation, whether snapshots are generated or not is intelligently decided on the basis of an automatic snapshot triggering mechanism, problem positioning is converted into semantic retrieval through natural language semantic matching, the analysis period is greatly shortened, the uncertainty of manual judgment is eliminated, and the analysis efficiency is improved. According to the generated code snapshots, the storage cost is reduced on the premise of ensuring that any historical snapshots can be completely reconstructed.

This invention relates to the field of artificial intelligence technology and discloses a drilling operation assistance and fault analysis method and system based on multi-agent collaboration. By acquiring problem text data, it jointly processes problem semantic vector data and equipment parameter status snapshot data to generate knowledge-enhanced result data. Parallel reasoning is performed using operation assistance agents and fault detection agents. Inconsistent data states are processed for consistency based on a multi-agent collaboration mechanism. Finally, a comprehensive evaluation agent performs fusion reasoning to generate drilling operation assistance and fault handling suggestions. This invention combines a vector knowledge base and a knowledge graph dual-knowledge engine. Through multi-agent collaborative consistency processing, and with division of labor and cooperation under a unified scheduling mechanism, it continuously optimizes decision quality through context sharing and memory feedback loops. This provides on-site operators with full-process, traceable intelligent support, from operation guidance to fault tracing and handling suggestions.

The invention relates to a building engineering scheme natural language interactive editing and auditing method oriented to multi-role cooperation. The method comprises the steps of receiving natural language project description, generating a structured task workflow and allocating an initial responsibility role; receiving a multi-modal natural language instruction, and performing domain intention recognition and entity extraction; analyzing a to-be-executed construction engineering scheme editing and auditing task from the task workflow; generating an interactive response message containing the collaborative decision; receiving an interaction instruction, executing a compiling and auditing task corresponding to the interaction instruction, generating a task result, and performing dynamic updating and rendering; and synchronously loading and visualizing task workflow snapshots at moments corresponding to the historical dialogue records. The method has the advantages that microscopic task flow and responsibility division can be intelligently deduced from a macroscopic project target, the initial planning efficiency of scheme compilation is improved by one order of magnitude, and project management is fundamentally converted from experience driving to data and AI driving.

The invention relates to the technical field of computer data processing and network communication, in particular to a paperless interaction information multi-terminal real-time synchronous processing method and system, and the method comprises the following steps: a logic construction step: obtaining an original interaction instruction stream, constructing a semantic causal logic tree based on a vector clock, and dividing strong barrier and weak stream events; a back pressure monitoring step: establishing a rendering back pressure model to calculate a rendering back pressure value of the target terminal; a dynamic shaping step: in response to a condition that the backpressure value is higher than a threshold value, performing dimension reduction and time sequence recombination on an instruction stream, mounting a weak stream event to a strong barrier event, and combining the weak stream event and the strong barrier event into an atomic state snapshot for sending; a consistency verification step: comparing the screen content perception hash values, and if the screen content perception hash values are not matched, executing full-amount state synchronization; according to the method, the problem of visual semantic disorder caused by different rendering capabilities of heterogeneous terminals is solved.

The invention provides an intelligent data processing method and system supporting multi-source heterogeneous data, and the method comprises the steps: aggregating structured data from at least two business systems, and constructing a data snapshot unit comprising a task identifier, a time window and an organization set; performing standardization and semantic category labeling on the fields, calculating semantic deviation in combination with a task historical mean value, generating role strength values representing field action strength, and forming semantic segments; matching a task category template based on the semantic fragment, identifying a target task and constructing a task structure; constructing a causal connection graph according to the causal template, extracting a maximum causal intensity path as a causal chain, and generating suggestion confidence; and finally, generating a structured strategy suggestion according to the causal chain and a preset mapping template, and converting the structured strategy suggestion into an executable instruction of the target business system. According to the method, closed-loop generation from multi-source data to explainable and executable strategies is realized, and the accuracy of the strategies and the self-adaptive capability of the system are improved.

The embodiment of the invention discloses a multi-data-source data fusion method and device and a medium, and relates to the technical field of data fusion, the method comprises the following steps: receiving a triggered data query request to determine a required target source data entity and associated fusion calculation logic; according to the target source data entity, screening a standardized change event associated with the target source data entity from a persistently stored standardized change event stream, so as to map the changed data in the standardized change event into a standardized data record based on a pre-constructed unified semantic data model and a mapping rule base; and performing data quality verification and cleaning on the standardized data record, determining a target data record, and determining a fusion data result corresponding to the data query request according to the fusion calculation logic.

This application relates to the field of data processing technology and discloses an adaptive fusion method for multi-source heterogeneous data based on fine-tuning alignment. The invention constructs a common aligned time axis by identifying anchor events in multi-source heterogeneous event data, and generates aligned semantic representation results and source-level time offset boundaries by combining source-specific lightweight adapters. It further reconstructs the time intervals in which events can be established, and generates versioned event fragments by combining object compatibility relationships, event sequence constraints, and joint judgment by multiple sub-learners. When a new access event arrives, the affected subgraph is located using a local back-repair index, and version rollback, local re-fusion, and historical snapshot update are performed. This solves the problems of difficulty in comparing multi-source events, easy separation and merging, and overall recalculation and historical result fluctuations caused by late events, thereby improving fusion accuracy, result traceability, and online operation stability.

This invention discloses a method, electronic device, and storage medium for processing the interactive context of a language model. The method includes: real-time monitoring of the length of the dialogue context and the access frequency of each data fragment in the session context cache; if there are data fragments with an access frequency below a threshold, they are migrated to external storage, and step transformation data containing semantic summaries, key findings, and file indexes is generated; if there are no such data fragments and the context length exceeds the limit, historical content is summarized and compressed based on preset anchor points, retaining recent content; then, the processed content is reorganized into a structured context with type labels according to a preset structure; finally, a context state snapshot associated with a version identifier is generated and stored. This method achieves adaptive optimization management of the context, significantly reduces computational load and cost, improves response speed and decision quality, and enhances the traceability of the state.