2687 results about "Information system" patented technology

Automatically investigating security incidents and generating security incident reports using a Large Language Model (LLM). A computerized system receives an incoming Security Alert Message pertaining to a possible security-related incident. The system automatically feeds into the LLM at least: the content of the Security Alert Message; the metadata of the Security Alert Message; context information describing a security domain; and organization context information pertaining to users and machines of that organization. The system automatically prompts the LLM to automatically investigate the Security Alert Message and to automatically generate a detailed Incident Report pertaining to the Security Alert Message.

A database system generates a prompt for an LLM or other machine learning (ML) model to narrow the search space to highly relevant information about a database. A distinct instance of a classifier, a clustering algorithm, or a topic modeling model can be trained based on information from ML automation within the database system, respectively for each column or table in the database. Model instances can then be used during generative LLM inferencing to identify relevant sources of data to answer the user's question. Thus, the prompt generation combines ML automation and other ML models or an LLM for topic modeling and schema description.

Systems, devices, and methods are provided for determining contextually relevant user-based product recommendations based on scene information. In at least one embodiment, techniques described herein may be used to determine, using a first machine-learning model, first information associated with a first object within a first image of digital content, determine, using a second machine-learning model, similarity scores between the first object and a first plurality of products of an online purchasing system, detect, in association with the first image of the digital content, performance of a first computer-based action by a user, determine, using a third machine-learning model and based on contextual data of the user, one or more affinity scores for the user, select a first product based on the one or more affinity scores, and present a recommendation to the user to perform a second computer-based action in association with the first product.

The invention provides a micro-grid cooperative scheduling method and device, and relates to the technical field of smart grids, and the method comprises the steps: obtaining historical operation data and real-time operation data of a micro-grid system, and data of an external information system; generating load demand and energy equipment output prediction information based on the historical operation data and the data of the external information system; constructing a layered multi-time-scale decision architecture, and performing decision optimization on each layer of agents by adopting a reinforcement learning algorithm; constructing a plurality of heterogeneous agents, and carrying out cooperative scheduling on the plurality of heterogeneous agents by adopting a centralized training and distributed execution multi-agent reinforcement learning algorithm; inputting the prediction information and the real-time operation data into a decision framework, and outputting a real-time control instruction; and setting a security constraint condition, and realizing optimization of the security constraint in combination with a Lyapunov function, a Lagrange multiplier method, a security layer mechanism and a reinforcement learning algorithm. According to the method provided by the invention, the safe, efficient and reliable operation of the micro-grid in the grid-connected / off-grid mode can be realized.

The invention discloses a cloud information system index collection method, system and device and a medium, and relates to the technical field of cloud information system operation and maintaining.The method comprises the steps that information collection interfaces of a cloud system are integrated, data flow information entropy is quantized, and an entropy value sequence is constructed; an interface calling serial number is traced based on a historical log, a calling chain is formed, a directed graph topological structure is established, an influence propagation path of an entropy mutation node is identified, and a collection regulation priority of an interface cluster is determined; an acquisition strategy is formulated in combination with entropy time sequence data and a load level, a state-strategy mapping relation matrix is optimized, and a time period driving type strategy adjustment parameter set is generated; and issuing the strategies in sequence, dynamically regulating and controlling the acquisition frequency, and if state transition or fitting degree abnormity occurs, triggering strategy rollback and optimizing the learning weight. According to the method, on the premise of guaranteeing the integrity of the monitoring data of the power equipment, the utilization rate of edge computing resources is remarkably optimized, and high-timeliness and high-fidelity data support is provided for safe and stable operation of a power grid.

The invention discloses a road intelligent induction and dynamic early warning method and system integrated with meteorological perception, and relates to the technical field of intelligent traffic and road safety. The method comprises the following steps: acquiring real-time weather, traffic and road data, performing multi-source data fusion by adopting an improved Kalman filtering and attention mechanism, and generating unified state estimation; dynamic risk assessment is carried out in combination with Bayesian reasoning and a Markov model, and speed-limiting adaptive adjustment is realized based on safety, traffic efficiency and energy consumption multi-objective optimization; and further calculating the length and position of the dynamic early warning area, and controlling devices such as intelligent spikes to issue induction information. The system comprises a data acquisition unit, a fusion estimation unit, a risk prediction unit, a speed adjustment unit, an early warning calculation unit and an induction unit. According to the invention, real-time monitoring, risk prediction and intelligent regulation and control of the road traffic environment in complex weather are realized, and the driving safety and the traffic efficiency are improved.

The invention discloses a cargo transportation real-time path planning method based on artificial intelligence, and relates to the technical field of artificial intelligence and intelligent traffic, and the method comprises the following steps: collecting real-time data from a traffic monitoring system, a vehicle-mounted sensor, a road event platform and a weather information system in a transportation process, forming an original data set containing multi-source information; time synchronization and space alignment are carried out on source data in an original data set, and a standardized data frame is constructed to ensure the consistency of different source information in a space-time dimension. According to the method, the accuracy, the real-time performance and the intelligence of path planning are effectively improved through multi-source data consistency identification, weighted fusion and a dynamic re-planning mechanism. The system can timely identify data conflicts, dynamically adjust paths and enhance transportation safety and efficiency, has a self-learning capability, continuously optimizes path strategies and adapts to complex traffic environments.

The invention discloses a battlefield target behavior prediction method capable of being guided by micro-physical representation and a thinking chain, and the method comprises the steps: obtaining satellite images, radar / communication detection and open source text multi-source time sequence data, completing the entity recognition, relation extraction and event detection, and constructing a dynamic space-time knowledge graph; the maneuverability, sensor detection, weapon range and terrain accessibility mechanism are micronized to serve as a physical consistency constraint embedded prediction model; generating an intention-action-result causal priori chain and parameterizing the causal priori chain into a computable structure; performing multi-branch long-time-sequence situation deduction, and outputting a future target behavior track and a scene probability; and evaluating and explaining by integrating the causal confidence coefficient, the physical consistency and the data goodness of fit, and giving a key event probability and situation evolution report. According to the method, unified modeling of semantic causal and physical constraints is realized, and the method has explainable, verifiable and robust prediction capabilities, and is suitable for target behavior prediction and command information system decision support in a complex environment.

The invention provides an automatic pavement settlement monitoring method and system based on a shield undercrossing airport runway. The dynamic database is constructed by collecting operation parameters such as shield tunneling machine propelling pressure, cutterhead torque and synchronous grouting pressure in real time and combining three-dimensional deformation data obtained through laser scanning. Strain sensing units are arranged in the soil layers to form a layered monitoring network, strain changes of the soil layers of different depths are collected, and spatial correlation is established between the strain changes and the tunneling position. And analyzing a spatial attenuation rule through a dynamic association processing unit, and generating a multi-dimensional feature data set. And establishing a settlement prediction model, fusing the shield dynamic load sequence and the layered strain conduction characteristics, and outputting settlement distribution information. And the system automatically compares the settlement difference value of the adjacent areas with a preset safety threshold value, and generates a graded early warning signal after verification of the three-dimensional deformation data space. According to the technical scheme provided by the invention, real-time three-dimensional prediction and spatial verification type graded early warning of settlement of the airport runway are realized.

The present invention provides an unmanned aerial vehicle-based electric power patrol inspection control method and system. The method comprises: creating a flight path on the basis of an electric power patrol inspection area, and collecting images and position information of electric power devices by means of various types of devices carried by an unmanned aerial vehicle, the images and the position information being transmitted to a receiving terminal at a ground station in a wireless transmission mode; the ground station receiving the images and the position information that are collected in real time, processing and analyzing the images to obtain analytic status, such as defects, damage and corrosion, of the electric power devices; and, on the basis of the analytic status from the ground station, using the position information as distinguishing features to generate an electronic patrol inspection report, providing service lives, maintenance costs and maintenance measures of the electric power devices on the basis of the electronic patrol inspection report, and notifying maintenance staff of the position information of the electric power devices. The system comprises an unmanned aerial vehicle control module, an image processing and analysis module and an electronic patrol inspection report generation module. The present invention is of great significance to maintenance and management of electric power devices.

The invention provides an operation risk early warning method and system based on a power grid information system, and relates to the technical field of power grid operation risk assessment. According to the method, equipment risk characterization and future risk score prediction are realized by constructing a risk perception graph fusing node and edge features; and identifying a high-risk region and a diffusion path by combining a clustering algorithm, and triggering a hierarchical response strategy based on a rule base. According to the invention, real-time early warning and intelligent management and control of the operation risk of the power grid are realized.

The invention discloses an interface authentication method, system and device based on a national cryptographic algorithm and a medium, belongs to the technical field of computer security, and aims to solve the technical problem of how to realize identity authentication, data encryption, integrity verification and replay attack protection of interface interaction and meet the security requirement of a key information system. According to the technical scheme, the method comprises the following steps: system initialization: a server and a client respectively generate SM2 key pairs, the client and the server exchange public keys through a secure channel, and a mapping relation between the public key of the opposite side and an identity label is stored; session key negotiation: exchanging a temporary public key based on an SM2-ECDH algorithm, calculating a shared secret value, and deriving an SM4 session key through a KDF; the client side carries out SM3 hash and SM2 signature on request parameters, and the server side verifies the signature and verifies a timestamp and a random number to resist replay attacks; and session key updating: triggering key updating according to a preset condition, and encrypting the new key negotiation message by using the original session key.

The invention relates to a multi-source data fusion management method and system for medical record information, and the method comprises the following steps: carrying out the data collection of an electronic medical record system, a medical image workstation and an inspection information system of a medical institution, and obtaining an original medical record data set; performing semantic mapping and concept association on the original medical record data set based on a preset medical ontology knowledge graph to obtain a semantic association medical record information network; performing dynamic time sequence feature analysis on the semantic association medical record information network through a time sequence feature extractor to obtain a time sequence medical record feature sequence; and carrying out heterogeneous data fusion on the time sequence medical record feature sequence to obtain a unified medical record data view, so that the technical problem that the capability of deeply understanding and mining medical record information is limited due to the fact that an existing medical record management method usually ignores semantic relevance between data is solved.

The invention discloses an electric power information operation violation risk supervision system based on a knowledge graph, which relates to the field of violation risk supervision and comprises a dynamic graph construction module, a causal analysis module, a strategy analysis module, a strategy modeling module and a supervision decision module. The method comprises the following steps: obtaining multi-type electric power operation field sensing data and information system data, and carrying out data processing and knowledge graph dynamic construction to obtain a dynamic knowledge graph; performing risk situation quantification and risk decision point inference based on the dynamic knowledge graph to obtain a key causal decision point set; based on the key causal decision point set, through strategy logic analysis, obtaining a logic rule set which can be directly deployed and executed; according to the method, a logic rule set which can be directly deployed and executed is subjected to dynamic strategy evolution of a stochastic differential equation to obtain a dynamic strategy model, and the dynamic strategy model is subjected to decision optimal screening of measurement transformation to obtain an optimal supervision decision set, so that a risk value can be accurately calculated, and the supervision response speed can be increased.

The invention discloses a multi-agent automatic image retouching system based on content analysis, and the method comprises the steps: 1, receiving the input of an original image, calling a finely-adjusted multi-mode large model to carry out the combined semantic-visual analysis of the image, extracting key semantic elements in the image, and carrying out the recognition of the key semantic elements; comprising but not limited to background coordination degree, illumination condition, figure hair style definition, weather quality and composition layout rationality content information. Based on these information, the system performs comprehensive image quality scoring on the image, which combines image style, aesthetic level, definition and subjective quality multi-dimensional evaluation criteria. Meanwhile, based on a multi-dimensional image quality scoring result and understanding of image semantic content, the system automatically generates a repair and optimization strategy set for specific defects so as to guide a subsequent image quality enhancement process. The invention relates to the field of multi-modal model and multi-agent cooperation, and can meet the increasing requirements of rapid optimization and high-quality propagation of image contents.

The invention provides a power grid cable temperature monitoring and alarming method, device and equipment and a storage medium. The method comprises the following steps: acquiring cable outer sheath temperature data through a temperature sensor and acquiring cable current load data and health condition indexes; calculating the temperature of the conductor according to the temperature data of the outer sheath, the current load data and the environmental parameter data by using a superposition principle calculation model based on time attenuation coefficient correction; according to the cable health condition index, calculating a grading early warning threshold value, and comparing the conductor temperature with the threshold value to determine an early warning grade; and when the early warning level reaches a preset condition, matching the sheath temperature data with a template in an abnormal mode library, identifying an abnormal reason through multiple algorithms, and generating a disposal suggestion in combination with geographic information system data. According to the invention, the conductor temperature can be quickly and accurately calculated, the early warning threshold value is dynamically adjusted, the abnormal reason is intelligently identified, and the accuracy and effectiveness of cable temperature monitoring and early warning are greatly improved.

The invention provides a full-coverage cleaning modular control method and system for a photovoltaic cleaning robot. According to the method, a photovoltaic panel three-dimensional terrain model is constructed by collecting laser ranging, photovoltaic panel surface temperature distribution, environment airflow and position relation data; and generating an obstacle distribution map based on the model, and dynamically analyzing gap width and attachment height parameters of the cleaning area. Therefore, the bending angle and the telescopic amplitude of a connecting part of the cleaning module are adjusted in real time, and robot pose information is synchronously updated. And in combination with the position data, the obstacle map and the pose of the whole robot, an uncleaned area identifier and a dynamic coverage path are generated, and information is shared through a wireless network. The system dynamically distributes working areas and advancing directions of the multiple robots, and efficient cooperative full-coverage cleaning of the photovoltaic array is achieved. According to the technical scheme provided by the invention, through multi-source environment perception and dynamic pose adjustment, intelligent path cooperation and full-coverage operation of multiple cleaning robots are realized.

The invention relates to an LLM-based power distribution network knowledge graph construction and fault intelligent diagnosis method. The method comprises the steps of constructing a knowledge system, constructing a normal operation state model and an anomaly detection threshold value, performing fault diagnosis through an LSTM-BP cascade network, and performing fault classification. The abnormal parameters and the fault classification result are input into an LLM model, the fault type is judged, corresponding diagnosis information is obtained, and the system generates a scheduling instruction and an emergency decision suggestion according to the diagnosis information; and generating a detailed diagnosis report. According to the system, statistical analysis and deep learning algorithms are adopted for operation parameters such as voltage and current after preprocessing, and automatic detection and fault classification of abnormal parameters are realized in combination with a CNN recognition module; a preset rule base in the knowledge system is combined to accurately determine a fault type, generate a scheduling instruction and an emergency decision suggestion, and assist operation and maintenance personnel in rapid response and dynamic management; finally, a detailed diagnosis report is generated, thereby improving the fault handling efficiency and the guarantee capability of safe and stable operation of a power grid.

The invention relates to a detection and analysis method and system for an energy storage system, and aims to improve the state monitoring precision, the fault early warning capability and the response intelligence level of the system. The method comprises the following steps: firstly, synchronously acquiring and transmitting multi-source data in real time through a sensor network deployed in a battery module, a power conversion unit and a cooling system; and then, key dynamic features are extracted from the multi-source data, and dynamic feature state information of the energy storage system is comprehensively formed. And inputting the state information into a pre-trained deep learning model, and outputting a health state score and a fault probability of the system. Based on the evaluation result and the fault traceability information, the system triggers a multi-level early warning mechanism and executes an active response strategy, and after response execution, the system re-evaluates the health state in a closed loop mode and verifies the response effect; and if the improvement does not reach the expectation, making a decision to start an artificial diagnosis protocol. According to the invention, a complete process from dynamic perception, intelligent diagnosis and accurate response to closed-loop verification is realized, and the safety and reliability of operation of the energy storage system are improved.

According to various embodiments, a system and method for verifying a user's identity using both document-based and biometric data is disclosed. The system may prompt a user to upload an image of a government-issued identification document and extract user information from the image using optical character recognition (OCR). The system may also extract the embedded face image from the ID and prompt the user to take a real-time selfie while performing one or more randomized actions or poses. A facial recognition engine may compare the extracted ID image to the live selfie to determine a similarity score. Based on this match, along with optional document authenticity checks, the system may confirm the user's identity in real-time for use cases such as account access, onboarding, and regulatory KYC compliance.

The invention provides a non-invasive electronic medical record generation method and system, and relates to the technical field of medical informatization and artificial intelligence. The method comprises the following steps: acquiring a doctor-patient dialogue audio, and extracting structured medical record information based on a natural language processing model after speaker role distinguishing and voice-to-text conversion; comparing the information with a medical record standard to check the integrity, and generating a prompt to guide a doctor to supplement inquiry when the information is lost; finally, information is non-intrusively filled in in an analog input manner by identifying a graphical user interface of an existing medical information system. The method solves the problems of low medical record input efficiency and difficulty in integration and transformation in the prior art, and has the beneficial effects of non-invasive deployment, low cost, high efficiency, capability of ensuring medical record quality and the like.

The invention discloses an intelligent diagnosis and risk assessment method and system for cerebral apoplexy related to atrial fibrillation, and relates to the technical field of atrial fibrillation detection.The method comprises the steps that multi-dimensional data of atrial fibrillation patients in a clinical information system are integrated, a structured database is generated, and a standardized data set is output; a standardized data set is adopted to train a first machine learning model, and model performance is optimized through parameter joint search and a training set-verification set convergence dynamic monitoring mechanism; performing cross validation on a feature weight sorting result in the optimized diagnosis model and a clinical index risk association degree calculated by a second machine learning model to generate an interaction map; and based on clinical event data containing timestamps in the structured database, adopting a third machine learning model to extract time sequence features, and combining with a survival probability analysis model to generate a risk assessment report. According to the invention, through an intelligent model adjusting and optimizing mechanism, multi-dimensional medical data are effectively integrated, and the recognition precision of the atrial fibrillation related cerebral apoplexy is greatly improved.

The invention discloses an electronic information system multi-fault diagnosis method and system based on artificial intelligence, and belongs to the technical field of electronic information system fault diagnosis, and the method comprises the steps: data collection and preprocessing, feature extraction and selection, multi-fault diagnosis model construction, real-time fault diagnosis and analysis, and fault recovery and feedback. The system comprises a data acquisition module, a preprocessing module, a feature extraction and selection module, a multi-fault diagnosis model module and a fault analysis and repair module, and all the modules cooperate to achieve full-process automation from data acquisition to fault repair. The method has a self-learning optimization capability, can significantly improve the fault diagnosis efficiency and reliability of a complex electronic information system, and is suitable for real-time fault diagnosis scenes of various types of electronic equipment.

A system receives, from a server, information about a first set of remotely-configured options for an unattended machine. In response to receiving the information about the first set of remotely-configured options, the system displays user interface objects that allow for selection of respective options in the first set of remotely-configured options. After detecting a selection of a first user interface object, the system receives, from the server, specifications regarding electric pulses to be provided to the unattended machine by a pulse-providing device. After sending an authorization grant and the specifications to the pulse-providing device, the system receives an indication that the electric pulses were provided to the unattended machine according to the specifications.

The invention discloses a high-precision three-dimensional point cloud visualization method, electronic equipment and a storage medium, and belongs to the technical field of geographic space information systems.The high-precision three-dimensional point cloud visualization method comprises the steps that S1, a point cloud file is input, and meta-information of the point cloud file is obtained; s2, performing scalable data geographic gridding processing on the point cloud file meta-information to obtain a basic grid object; s3, adopting a data stratified sampling method to obtain a multi-level grid object; s4, constructing a multi-level index file based on the multi-level grid, and outputting the multi-level index file; s5, performing high-availability, high-concurrency and compression processing on the output multi-level index file; and S6, performing real-time hierarchical scheduling rendering on the point cloud based on the processed multi-level index file. A point cloud is quickly split into small blocks by using a gridding technology, then parallel processing is performed, a hierarchical rendering level is generated by performing sub-sampling on a whole data set from bottom to top by using a plurality of sampling strategies, and meanwhile, a quick layering strategy is introduced, so that the throughput is remarkably improved.

The invention discloses a multi-department medical sample collaborative management system and method, and relates to the technical field of medical information, and the system comprises an integrated information interaction platform which is used for achieving the real-time sharing of basic information, clinical diagnosis information and inspection reports of patients; the unique identification module is used for carrying out full-life-cycle tracking on a patient sample through a bar code or a radio frequency tag and dynamically associating the patient sample with patient information; the Internet of Things monitoring module is used for collecting and uploading temperature and humidity parameters of the sample storage environment in real time and triggering abnormal early warning; the automatic storage equipment executes sample classified storage and calling based on a platform instruction, and synchronously updates a sample state with the information system; and the data security module is used for realizing data encryption, dynamic authority management and operation traceability by adopting role-based access control and a block chain technology. According to the technical scheme, the core problems of data islands, low operation efficiency, weak safety and the like in traditional medical management can be systematically solved.

The invention discloses a cross-domain identity authentication and access authorization method, system and device and a medium, and belongs to the field of Internet of Things. According to the method, a unified identity authentication mechanism and a dynamic authority control model are constructed based on a decentralized identifier and a block chain technology. Firstly, an Internet of Things entity submits decentralized identifier information through an identity authentication request; the system generates identity metadata according to the request and records the identity metadata to an identity registry, and meanwhile, constructs a decentration identification document and stores the decentration identification document to a verifiable data registry. And then, the authorizer executes point-to-point trust evaluation based on the identity data and the on-chain traceability information, and generates a verifiable certificate with an encrypted signature. And the verifier completes certificate verification through an intra-domain or cross-domain verification mode, and dynamically grants the authority according to the trust value and the access policy directory. Cross-domain trust migration and dynamic permission adjustment are supported, and the identity authentication security and the access control flexibility in the multi-domain environment of the Internet of Things are improved.

The invention discloses an intelligent fusion and standardization processing system and method for multi-source heterogeneous operation and maintenance data, and belongs to the technical field of operation and maintenance data. The system comprises a data acquisition module, a data preprocessing module, a data fusion module and an information extraction module; the data acquisition module is used for collecting original multi-source heterogeneous operation and maintenance data from different data sources, and the data sources comprise facilities, operating systems, basic components, middleware, databases and / or information systems; the data preprocessing module is used for cleaning, denoising and standardizing the acquired data; the data fusion module is used for selecting a low-level data fusion model, a middle-level data fusion model or a high-level data fusion model according to the fusion scene and fusing the preprocessed data; and the information extraction module is used for carrying out feature extraction on the fused data. According to the method, deep fusion and standardized processing of the multi-source heterogeneous operation and maintenance data are realized, and a unified and high-quality data basis is provided for data analysis and decision making.

The invention discloses a power distribution network fault differentiation operation and maintenance first-aid repair decision recommendation method, system and device based on a knowledge graph and a medium, and belongs to the technical field of operation and maintenance recommendation, and the method comprises the steps: obtaining the real-time monitoring data of a power distribution network, and constructing a fault feature data set; based on the fault feature data set, combining with a power distribution network knowledge graph to identify a historical fault entity; extracting a corresponding operation and maintenance first-aid repair strategy from the historical fault entity, presetting an evaluation index, and calculating a strategy effect score; evaluating a fault processing priority; and sorting the candidate first-aid repair strategies according to the strategy effect score and the fault processing priority, and screening and outputting a target operation and maintenance strategy suitable for the current fault and matters needing attention of the target operation and maintenance strategy. According to the method, the geographic information system is used for positioning the fault, the distance between the fault and the first-aid repair personnel is evaluated, the processing priority is determined in combination with the emergency degree of the fault, resource allocation is effectively optimized, and response efficiency is improved.

The invention provides an intelligent medical information system based on multi-system integration, a data security management method and device, electronic equipment and a storage medium, and is applied to the technical field of medical data processing. According to the method, three types of information including core security elements, multi-system heterogeneous adaptation (including HIS / LIS / EMR interface specifications and the like) and full-process auditing (including operation logs and the like) are obtained. Normalizing the core security elements by using technologies such as AI multi-mode privacy identification and the like to generate a standardized security baseline file; heterogeneous adaptation information is processed through a lightweight API connector and the like, and a multi-system security access scheme is formed. Associating the baseline file with the access scheme, and generating a full-life-cycle protection strategy in a zero-trust architecture; and processing the audit information to generate a dynamic security audit report. And finally, based on a security-adaptation linkage decision engine and compliance verification, outputting a cross-system data security sharing scheme and a privacy disclosure emergency response instruction, and realizing a medical data security management closed loop.