73 results about "Safety knowledge" patented technology

The invention relates to the technical field of safety monitoring, in particular to a building construction safety monitoring method and system based on artificial intelligence, and the method comprises the steps: obtaining multi-source heterogeneous data of a construction site, carrying out the distributed feature extraction of the multi-source heterogeneous data through employing a federal learning framework, and generating time-space correlated construction site state representation data; based on a preset dynamic risk prediction model, risk prediction is carried out by using the construction site state representation data, a multi-level risk prediction result is output, and the preset dynamic risk prediction model is constructed based on a construction safety knowledge graph and a space-time diagram neural network; and triggering an adaptive feedback mechanism according to the risk level corresponding to the prediction result, generating visual early warning information and an equipment control instruction, and linking a construction site control system to execute emergency response operation. The problems that a traditional monitoring method is tedious in data processing, insufficient in real-time performance, high in cost, lack of prediction capacity and the like are solved.

The invention discloses an engineering safety early warning method and system based on artificial intelligence real-time risk identification, and relates to the technical field of engineering safety, and the method comprises the steps: collecting scattered engineering safety data from each engineering platform in batches, and carrying out the preprocessing of the data, and constructing a dynamic database; according to the engineering safety data collected in batches, an engineering safety knowledge graph is constructed, and different risk levels are preset according to engineering safety standards. According to the method, multi-source engineering safety data are integrated, dynamically changing risk characteristics are analyzed in real time by using an AI risk identification model, the hysteresis of traditional manual inspection and static analysis is overcome, a nonlinear relationship among the risk characteristics is captured by using a random forest model through integrated learning of multiple decision trees, and the risk characteristics are analyzed in real time. And the probability values of high, medium and low risk levels are output in combination with Softmax probability normalization, so that the evaluation precision is remarkably improved, the risk features are positioned, the scientificity of risk traceability is ensured, and data-driven decision support is provided for engineering safety management.

The invention discloses an automatic driving safety key simulation scene generation method based on adversarial generation and co-evolution. The method comprises the following steps: receiving a basic traffic scene described by a natural language, generating an antagonistic element scene containing security threats by using a large language model in combination with a traffic safety knowledge base, and analyzing the antagonistic element scene into an executable scene script; constructing a multi-agent confrontation collaboration diagram based on the meta-scene, and recognizing a key background vehicle through a cross-timing attention mechanism in combination with a time mask and time decay mechanism; and performing disturbance optimization on the key background vehicle trajectory to generate an automatic driving test scene. According to the method, a scientific and systematic solution with engineering operability is provided for safety verification of the automatic driving system when the automatic driving system faces real traffic challenges such as multi-source intervention and dynamic collaborative threat, and the method has wide adaptation capability and important industrial popularization value.

The invention discloses a safe penetration type management method and system for an engineering construction project, and relates to the technical field of constructional engineering, and the method comprises the steps: collecting engineering entity data in real time, carrying out the preprocessing, and generating an engineering state vector; based on the engineering state vector, constructing a digital twinborn model, calling multi-physics field coupling simulation to simulate the whole construction process, and generating a risk early warning report; uploading the risk early warning report and historical construction data to a federated platform, and constructing a dynamic security knowledge graph; performing hierarchical feature learning on nodes in the dynamic security knowledge graph, simulating decision behaviors of a manager, and generating an optimal management and control rule; based on the optimal management and control rule, driving a construction site management mechanism to operate, and forming a closed loop report; by using the digital twin model and the multi-physics field simulation technology, the accurate prediction of potential safety hazards possibly occurring in the construction process is realized, and the accuracy and timeliness of risk early warning are greatly improved.

The invention discloses a gas safety intelligent inspection method and system, and belongs to the crossing field of artificial intelligence technology and safety production management. The method comprises the following steps: acquiring image data through a field data acquisition terminal; a preliminary hidden danger identification module in the cloud server innovatively adopts a scene perception target detection model and a scene reasoning multi-label model for collaborative analysis; and the comprehensive risk assessment module performs deep correlation analysis on the hidden dangers based on a preset gas safety knowledge graph, and determines a quantitative comprehensive risk value by calculating a basic risk accumulated value of each hidden danger and a correlation risk increment generated by a correlated hidden danger pair. According to the method, the defects that traditional inspection depends on subjective experience and composite risks are difficult to quantify are overcome, amplification risks caused by concurrence of multiple hidden dangers can be accurately identified and quantitatively evaluated, continuous self-improvement of system capability is realized through a closed-loop mechanism, and the intelligent level of gas safety management is remarkably improved.

The invention relates to a power grid safety regulation question-answering system and method based on knowledge graph collaborative multi-agent, and belongs to the technical field of power grid safety regulation question-answering. The system comprises a power grid safety knowledge graph construction module and a multi-agent collaborative question and answer module. The power grid safety knowledge graph construction module is constructed according to an electric power safety working regulation in a mode of combining a large model and regularization, and professional knowledge support is provided for questions and answers. The multi-agent collaborative question and answer module comprises a key entity recognition agent group, an intention visual angle analysis agent group, a knowledge evidence retrieval agent group, an evidence feedback agent group and a professional answer generation agent group, all the agent groups cooperate in a labor division mode, and deep understanding and professional answering of electric power safety regulation problems are achieved through a knowledge graph. Power grid employees can be effectively assisted in understanding and applying safety regulations, and the power grid operation safety level is improved.

The invention discloses a heterogeneous AI engine interaction system and method based on a cognitive firewall, and relates to the technical field of artificial intelligence, and the system comprises a cognitive isolation channel which is used for building an isolation communication path between an RAG system and an Agent system, and generating standardized knowledge; the three-layer filtering architecture is used for performing grammar layer verification, semantic layer understanding and pragmatic layer evaluation on the standardized knowledge in sequence and outputting safety knowledge passing verification; the bidirectional value alignment module is used for mapping the safety knowledge to a multi-dimensional value space through a forward value alignment mechanism to calculate value conformity, and generating value alignment knowledge according to the value conformity; and the knowledge-instruction conversion engine is used for converting the value alignment knowledge into a structured instruction which can be executed by the Agent system. The method can actively identify, intercept and avoid potential dangerous, misleading, prejudice or ethical inconformity knowledge applications, and greatly reduce negative effects caused by wrong use of knowledge.

The invention relates to the technical field of intelligent coal mine safety production, and discloses an intelligent coal mine safety early warning method and system based on deep learning, and the method comprises the steps: constructing a difficulty evaluation function, and achieving the progressive learning from simple to complex; based on a difficulty assessment result, a model-independent meta-learning algorithm is realized, so that the model quickly adapts to new mining area characteristics; constructing a privacy protection federated learning framework by using the meta-learning model, and realizing multi-mining-area cooperative training; a continuous learning module is constructed, and original experience is reserved when new knowledge is learned; constructing a meta-knowledge evaluation module to realize cross-mining-area safety knowledge sharing; according to the invention, the security risk identification accuracy is improved; multi-mining-area cooperative training is realized on the premise of protecting data privacy; the method has continuous optimization capability and effectively solves the problem of model drift; and efficient sharing and migration of cross-mining-area safety knowledge are realized.

The invention discloses a sensitive data identification method, system and device based on rule matching and LLM deep semantic analysis, a medium and a product, and relates to the field of artificial intelligence, the method comprises the following steps: preprocessing data in an information data set; the information data set comprises multi-industry corpora, personal information and safety knowledge; the multi-industry corpora cover structured data format features and unstructured text semantic scenes; according to the preprocessed data, a LoRA technology is adopted to carry out field adaptation fine tuning on the large language model; the large language model comprises a rule matching layer and an LLM depth recognition layer; the rule matching layer screens and identifies the structured data; the LLM depth recognition layer recognizes unstructured data and implicit sensitive data; performing semantic recognition on the to-be-tested data according to the fine-tuned large language model, and outputting sensitive data; the sensitive data comprises information such as an identity card number, a home address and a mobile phone number, and the sensitive data can be accurately recognized.

The invention discloses a personalized safety training content generation method, system and device and a medium. The personalized safety training content generation method comprises the steps that answer data, historical performance data and future operation tasks of operators in safety training are collected; safety knowledge weak points and learning behavior modes of the operating personnel are identified, and related risk association points are determined; based on the safety knowledge weak point, the learning behavior mode and the risk association point, constructing a structured user portrait, based on a deep neural network model fused with an attention mechanism, screening basic training content matched with the user portrait from a dynamic training material library, and adjusting the basic training content to obtain a basic training result; according to the method, the personalized target safety training content is generated, and the safety training is performed on the operator according to the target safety training content, so that the generation efficiency of the personalized safety training content can be improved.

The invention discloses an electric power safety knowledge testing method based on a large language model and enhanced retrieval generation. The method mainly comprises the steps of constructing a vector knowledge base, constructing a dynamic interaction generation workflow framework driven by a multi-role large language model, performing safety knowledge testing and quantifying the electric power operation safety awareness of a user. According to the invention, the problem with high scene adaptation degree can be dynamically generated in combination with the scene of the operation work order and the user interaction result, so that the test problem accuracy is improved; the quality of test content is improved through cooperative work of a multi-role large language model and in combination with an enhanced retrieval generation module; a dynamic adjustment strategy is implemented according to a user feedback mechanism, so that knowledge blind spots of the user can be effectively identified; the safety awareness of the user is quantified, targeted consolidation is carried out, and the electric power safety knowledge level of the user is improved.

The invention discloses an interactive biological virtual experiment teaching system based on deep learning, and the system comprises a dynamic scene intelligent construction module, a multi-modal operation perception module, a real-time feedback guidance module, a comprehensive report generation module, and a self-adaptive learning optimization module. Component interaction, state simulation and parameter adaptive adjustment are supported, learning progress and difficulty data are received to realize linkage, a knowledge graph contains principle, specification, equipment characteristics and safety knowledge, staged loading, dangerous operation early warning and real-time knowledge node pushing are supported, and the knowledge graph is used for learning. The multi-mode operation sensing module integrates computer vision and sensor technologies to collect action data, identifies an operation sequence and a pre-judgment intention based on a space-time sequence model, adopts an environment adaptability algorithm to adjust collected parameters, and achieves autonomous optimization of the parameters through historical data analysis.

The invention provides an engineering quality safety digital management method integrated with a large language model, and the method comprises the following steps: S1, extracting engineering feature data from a basic database, and building an engineering quality safety knowledge graph according to the engineering feature data, the engineering feature data including process feature data, material feature data and safety monitoring data; s2, acquiring engineering field data of a current engineering project; and S3, according to the established engineering quality safety knowledge graph, performing engineering quality safety analysis on the engineering field data based on the knowledge graph enhanced large language model, and generating an engineering quality safety evaluation result. According to the method, the pertinence and the application effect of the large language model on quality safety management and control of the engineering project can be improved, and the intelligent level of digital management of the engineering project is improved.

The invention provides a construction site safety management and control method and system based on enhanced retrieval generation, and belongs to the technical field of data processing. Comprising the following steps: constructing a construction safety knowledge base, and vectorizing text data in the knowledge base; field self-adaptive fine tuning is carried out on the visual language large model through a low-rank self-adaptive method, and perception, cognition and decision-making processes of construction site safety management and control are executed: in a perception stage, construction safety hidden dangers are dynamically identified and positioned, and violation descriptions containing hidden danger types are generated; in the cognition stage, an enhanced retrieval generation process is started, and construction specification information associated with hidden dangers is obtained through multi-level semantic matching and retrieval; in a decision-making stage, based on violation description and construction specification information, a visual language large model is driven to automatically generate a structured security log through a preset structured cue word template. According to the invention, unsafe behaviors of the construction site can be timely and accurately warned, and effective data support is provided for the safety of the construction site.

The application provides a full-chain meat safety knowledge graph construction method and retrieval device, which has the following characteristics: steps S1-S2, entity and relationship extraction is performed on all meat safety standard files; step S3, meat product nodes and corresponding subordinate nodes are constructed and relationships are set; steps S4-S6, corresponding multistage nodes are set for each subordinate node; step S7, the extracted entities and relationships are assigned to the corresponding nodes; step S8, CCP selection nodes including specific hazards and corresponding control measures are set according to the HACCP principle; steps S9-S10, non-directly connected nodes are directly connected as needed through artificial relationships. In summary, the method can construct a more comprehensive meat safety knowledge graph, and the retrieval device can more quickly, conveniently and accurately retrieve and display the data of the meat safety knowledge graph.

The invention discloses a method for improving an industrial safety education training effect based on an AI large model. The method comprises the following steps: analyzing, vectorizing and storing latest industrial safety related documents to obtain a safety knowledge base; the related documents comprise industrial standard specifications, laws and regulations and enterprise specifications related to industrial safety; the knowledge base comprises a plurality of sub knowledge bases with different themes and different types; matching an associated target sub-knowledge base from the safety knowledge base based on the training plan, so as to generate an employee training task based on the target sub-knowledge base; based on the training task, utilizing an AI large model to generate a training problem in the training process, and using the training problem for employee training to obtain a training result of the employee; and based on the training result, using an AI large model to generate test questions corresponding to different employees, and examining the employees based on the test questions to obtain an examination result. The training effect of employee safety education can be improved, and potential safety hazards are reduced.

This invention discloses a big data-based safety design management method for chemical projects, relating to the field of big data technology in chemical safety. The method includes: collecting multi-source heterogeneous data and preprocessing it to form a project baseline dataset; collecting design change data and performing differential comparison with a safety knowledge graph to identify differential objects; starting from the differential objects, performing dependency propagation analysis along the safety dependencies in the safety knowledge graph to output affected objects and risk propagation chains; determining the review order of affected objects based on the risk propagation chains, sequentially extracting the specifications to be reviewed that match the affected objects from the specification knowledge base, determining whether the affected objects meet the specifications to be reviewed, and outputting the specification review conclusion. This invention realizes dynamic and precise management and automated compliance review of chemical project safety design, shifting from experience-driven to big data-driven management.

This invention discloses a method and system for power operation safety monitoring and question answering based on a multimodal large model. The method includes the following steps: a data acquisition step, acquiring visual data and safety regulation text data from the power operation site; a multimodal semantic fusion step, generating a unified fused semantic vector through feature extraction and cross-modal alignment fusion technology; a knowledge reasoning step, matching the fused semantic vector with a pre-constructed power safety knowledge graph and performing compliance reasoning based on a graph neural network; and an intelligent question answering generation step, inputting the fused semantic vector and the reasoning results into a large language model to generate natural language question answering information. This invention, through the synergistic innovation of multimodal semantic fusion, knowledge graph reasoning, and a large language model, solves the technical problems of insufficient semantic understanding, disconnect between the question answering system and the field, and unstructured knowledge representation in existing technologies, achieving fully automated and interpretable intelligent safety supervision from perception to cognition.

The application relates to the technical field of safety monitoring, in particular to a building construction safety monitoring method and system based on artificial intelligence, which comprises the following steps: acquiring multi-source heterogeneous data of a construction site, performing distributed feature extraction on the multi-source heterogeneous data by adopting a federal learning framework, and generating site state representation data associated with time and space; performing risk prediction on the site state representation data by using a preset dynamic risk prediction model, outputting a multi-level risk prediction result, and constructing the preset dynamic risk prediction model based on a construction safety knowledge graph and a space-time graph neural network; triggering an adaptive feedback mechanism according to a risk level corresponding to the prediction result, generating visual warning information and equipment control instructions, and linking the construction site control system to execute emergency response operations. The problems of traditional monitoring methods, such as complicated data processing, insufficient real-time performance, high cost and lack of prediction ability, are solved.

The invention discloses a large language model reasoning method, device and equipment and a readable storage medium, and is applied to the technical field of computers, and the method comprises the steps: analyzing a to-be-reasoned problem, and determining a target reasoning scene; determining a target safety knowledge base based on the target reasoning scene; the target security knowledge base is a knowledge base for controlling data based on a dynamic access security policy and an encryption and decryption algorithm; calculating the credibility of the current user in real time based on the historical operation record of the current user, screening encrypted data from the target security knowledge base based on the credibility and a dynamic access security policy, and decrypting the encrypted data based on an encryption and decryption algorithm to obtain a reasoning data set; and based on the reasoning data set and according to the to-be-reasoned problem, reasoning is performed by using the large language model to obtain a reasoning result. According to the method, the target safety knowledge base is set, the safe reasoning data set in the target safety knowledge base is dynamically accessed based on the credibility of the user, reasoning is performed based on the reasoning data set, and the reasoning safety is improved.

The present application relates to the technical field of knowledge graph construction, in particular to a knowledge graph construction method based on semantic network. Production data set of knowledge graph to be constructed is acquired, safety knowledge recognizer is used to perform safety knowledge recognition, and safety knowledge word vector set is obtained; basic safety knowledge graph is constructed; scale coefficient set and proportion coefficient set are obtained; according to training features of the safety knowledge recognizer, accident coefficient set and reason coefficient set are obtained, the proportion coefficient set is corrected, accident labeling coefficient set and reason labeling coefficient set are processed, edge scale of each accident type word vector and reason type word vector is labeled and processed, and safety knowledge graph is obtained. The present application increases the visualization degree of safety knowledge graph, realizes clear presentation of logical association of “accident type-reason type”, and quantifies accident severity, reason influence weight and the association strength of both.

This invention provides a construction AI-based risk analysis and decision-making method for safety management. Through a knowledge base construction mechanism involving intelligent parsing, vectorized storage, and incremental updates, it transforms scattered safety knowledge from projects into digital assets, enabling the continuous accumulation of enterprise safety knowledge assets. A RAG-enhanced large language model question-answering system equips each on-site personnel with a personal AI expert, providing near-instantaneous responses to on-site issues and improving efficiency. By employing a performance quantitative analysis model, an Attention-LSTM time-series risk prediction model, and a hazard clustering analysis algorithm, it uncovers management shortcomings and predicts risk trends from massive amounts of data, making safety management decisions more data-driven and moving away from experience-based, extensive management. AI time-series prediction identifies high-risk areas and hazard trends seven days in advance, generating actionable control recommendations, shifting safety management from post-event remediation to pre-event prevention, significantly reducing the probability of accidents.

The invention relates to the technical field of knowledge graph construction, in particular to a knowledge graph construction method based on a semantic network. Obtaining a production data set of a to-be-constructed knowledge graph, and performing safety knowledge recognition by adopting a safety knowledge recognizer to obtain a safety knowledge word vector set; constructing a basic security knowledge graph; obtaining a scale coefficient set and a proportion coefficient set; according to training features of the safety knowledge recognizer, analyzing to obtain an accident coefficient set and a reason coefficient set, correcting and processing the proportion coefficient set to obtain an accident labeling coefficient set and a reason labeling coefficient set, and labeling edge scales of each accident type word vector and each reason type word vector to obtain a safety knowledge graph. According to the method, the visualization degree of the security knowledge graph is increased, the logic association of'accident type-reason type 'is clearly presented, and the accident severity, the reason influence weight and the association strength of the accident severity and the reason influence weight are quantified.

The invention relates to the technical field of artificial intelligence, in particular to an intelligent evaluation method for a harbor potential safety hazard treatment effect, and the method comprises the steps: constructing a harbor safety knowledge graph based on harbor static data and harbor potential safety hazard treatment dynamic data; performing text analysis based on semantic understanding of a port potential safety hazard treatment scheme and treatment records, and converting the text into a structured action sequence; creating a copy of the port safety knowledge graph, and mapping the structured action sequence to the copy of the port safety knowledge graph to obtain a simulation evaluation initial state graph; performing simulation deduction on the simulation evaluation initial state diagram by using Monte Carlo tree search to obtain probability distribution of a deduction result; and determining an evaluation score of the harbor potential safety hazard treatment effect based on each deduction result included in the probability distribution of the deduction results and the probability score thereof. Therefore, the accuracy of the evaluation score is improved.

The invention relates to the field of petroleum and petrochemical safety, and discloses a JSA operation safety analysis method and system based on an AI agent, and the method comprises the following steps: receiving and complementing operation description through a user layer, and obtaining complete context information; processing the context information to extract key information and converting the key information into structured data; generating an ordered tool calling sequence based on the key information; calling tools in sequence and carrying out verification, evaluation and conflict resolution on a plurality of returned results so as to integrate and form a unified analysis conclusion; and filling the analysis conclusion into a JSA document template to generate a structured report. According to the method, a system architecture of a user layer, an agent layer, a memory storage layer and a tool layer is constructed, the agent layer is utilized to autonomously plan tasks, various tools such as historical experience retrieval and operation safety analysis are called, specific risks in historical cases and general safety knowledge are integrated, and a JSA report with accurate content is generated; and the efficiency and the quality of operation safety analysis are improved.

The invention relates to the technical field of data security training and evaluation, and discloses a holographic perception evaluation method and system for data security, and the method comprises the steps: constructing a security mapping model of multiple sensory channels, and mapping data security risk features to visual, auditory and tactile perception features; personalized perception intensity adjustment and semantic correlation optimization are carried out based on user cognition characteristics; dynamic simulation and interactive experience of known and unknown security threats are realized through a potential safety hazard simulation technology; establishing a unified cross-scene perception evaluation framework; according to the method, the limitation of a traditional single sensory channel is broken through, multi-dimensional cooperative transmission of safety knowledge is realized, the visual understanding and deep cognitive ability of a user on data safety risks are improved, and a systematic solution is provided for improving the overall safety awareness of an organization.

The invention relates to the technical field of Internet of Things and machine learning, and particularly discloses a laboratory safety whole-process dynamic management system based on the Internet of Things. The system comprises an Internet of Things sensing layer, an edge calculation layer, a data fusion and knowledge construction layer, a dynamic risk assessment and decision-making layer and an execution and feedback layer. A dynamic security knowledge graph is constructed by fusing multi-source heterogeneous data, risk assessment is performed by integrating mode recognition and probability graph reasoning, a hierarchical management and control instruction is generated, and finally closed-loop management is formed through an execution layer, so that real-time and comprehensive perception, accurate risk assessment and adaptive dynamic management and control of a laboratory security state are realized.

The embodiment of the invention provides an intelligent auxiliary compilation method and system based on an electric power safety knowledge large model, and belongs to the technical field of electric power auxiliary compilation. The intelligent auxiliary compilation method comprises the following steps: acquiring electric power safety knowledge data; constructing an electric power safety knowledge base according to the electric power safety knowledge data; selecting a basic large model, and finely adjusting the basic large model by adopting the electric power safety knowledge base; acquiring real-time content input by a current user; inputting the real-time content into the basic large model to obtain a compiled first draft; checking the compiled first draft, and obtaining a compiled final draft; and inputting the programmed final draft into the electric power safety knowledge base. According to the method, data updating can be effectively and continuously carried out on the electric power safety knowledge base, so that the first draft compiling accuracy is improved.

The invention relates to the technical field of smart watches, and discloses a child situation learning watch system based on environmental language perception and a safety knowledge graph, and the system comprises a graph construction module which is configured to construct the safety knowledge graph according to safety knowledge; the sensing module is configured to trigger different modes according to the tone judgment result and the sensing judgment result; the sensing module is further configured to analyze the environment language information to obtain environment language content; the analysis interaction module is configured to generate recommendation content according to the retrieval result and in combination with different modes; the analysis interaction module is further configured to perform child answer scoring according to the child language information and the recommended content; and the feedback module is configured to determine the next learning content and the next learning interval duration according to the child answer score. According to the invention, the safety and interactivity of children learning are improved, and the learning efficiency and effect are greatly improved through a personalized feedback mechanism.

The invention discloses a multi-dimensional safe medication intelligent management system for outpatient transfusion patients, and relates to the technical field of medical informatization. The system comprises an interface docking module, a management system module, an infusion monitoring module, a multi-dimensional safety knowledge base module and a personalized propaganda and education module. The infusion state is monitored in real time through the infusion monitor with the weight being only 70 g, blood return is prevented through the double protection technology of graded speed reduction and precise cut-off, and the abnormal dripping speed is automatically adjusted through the AEA intelligent protection technology. The multi-dimensional safety knowledge base integrates three-dimensional information of medicine, diagnosis and patient attributes, and a personalized infusion scheme is intelligently recommended. The system reminds in real time through multiple channels such as large-screen broadcasting and a nurse wristwatch, and medicine dispensing is notified in advance when infusion is about to be completed. The personalized propaganda and education module generates an exclusive two-dimensional code, and the patient scans the code to obtain targeted health education content. The problems of many potential safety hazards, low nursing efficiency and the like in traditional infusion management are effectively solved, and infusion safety and medical experience are comprehensively improved.