8360 results about "Data harvesting" patented technology

The invention provides a power distribution network communication network fault diagnosis and self-adaptive reconstruction method, which comprises the steps of formulating corresponding layering and partitioning strategies aiming at different network scales and service types, including modular management of a large-scale multi-layer network and centralized response of a small-scale network; the faults of various networks can be effectively positioned and isolated; recognizing a reconstruction object after the fault positioning is completed, and formulating a network reconstruction scheme according to the recognized reconstruction object and the current state of the network, including but not limited to replacing a fault node, reconstructing a connection or modifying a route by starting a standby resource; after the reconstruction scheme is executed, the data collection and analysis period is adjusted according to the fault frequency, and the reconstruction interval is controlled to reduce redundant network structure adjustment and potential network fluctuation, including active detection and passive monitoring of the power distribution network and bidirectional monitoring of the network condition.

The invention relates to the technical field of data processing, in particular to a decision-making method and system for multi-source information fusion. The method comprises the following steps: obtaining hyperspectral image data, visible light image data and infrared image data collected by an unmanned aerial vehicle, and collecting ground environment monitoring data collected by a ground sensor; the hyperspectral image data, the visible light image data and the infrared image data are subjected to image preprocessing, so that a standardized multispectral data set is obtained, and image preprocessing comprises geometric correction, radiation correction and atmospheric correction; and color and texture feature extraction is carried out on the visible light image in the multispectral data set, and appearance feature and growth density identification of crops is carried out so as to obtain morphological feature data. Through integrated analysis of multi-source data, abnormal growth of crops and potential pest and disease damage risks can be identified more accurately, so that engineers are assisted to make irrigation and fertilization decisions.

The invention belongs to the technical field of knowledge maps and artificial intelligence, and discloses a building safety risk identification method of a big language model assisted knowledge map. According to the technical scheme, the overall process comprises the steps of data collection and preprocessing, construction of urban building structured table data, construction of an urban building safety knowledge graph, integration of the structured table data and knowledge graph data, model fine adjustment, model training, model performance evaluation and model recognition effect verification. According to the technical scheme, the large language model with the high semantic modeling capacity and the knowledge graph with the high graph structure expression capacity are integrated, the related knowledge of urban building safety is automatically extracted, constructed and integrated, high-risk events such as fire disasters and structural hidden dangers are recognized, and the informatization level and the intelligent level of urban building safety management are improved.

The invention provides a production scheduling calculation method of a production plan scheduling platform. The method comprises the steps of data collection, demand prediction intelligent analysis, productivity planning and bottleneck analysis, production scheduling plan making, real-time monitoring and dynamic adjustment, plan execution and feedback analysis and report generation and decision support. Through distributed scheduling and real-time dynamic adjustment, the production plan can respond to emergencies such as equipment failure and order change in production in time, the continuity and stability of the production process can be ensured, production stagnation or delay caused by the emergencies is avoided, meanwhile, the production scheduling plan can be rapidly adjusted through real-time data feedback, and the production efficiency is improved. Resource utilization is optimized, waste is reduced, production efficiency is improved, intelligent productivity analysis and bottleneck identification are utilized, an enterprise can comprehensively know the use condition of each production resource, and possible bottleneck problems are found and solved in advance.

The invention discloses a method for realizing high-efficiency low-consumption micro-aerobic hydrolytic acidification of petrochemical wastewater by regulating and controlling the aeration rate in combination with a space-time diagram neural network and reinforced learning, and relates to a method for realizing high-efficiency low-consumption micro-aerobic hydrolytic acidification of petrochemical wastewater by regulating and controlling the aeration rate. The method comprises the following steps of: 1, collecting and cleaning data; 2, constructing a graph data set; 3, training the ST-GNN model; 4, constructing a deep Q reinforcement learning model; 5, the trained ST-GNN model is embedded into an automatic control system of a sewage treatment plant, and the model outputs the optimal aeration rate of the current time step and predicts the micro-aerobic hydrolytic acidification effect through interaction of the current working condition and historical data; and adjusting operation parameters of aeration equipment according to the output of the model, and carrying out hydrolytic acidification treatment. The method is high in regulation and control efficiency and low in operation error, and can be applied to the field of digital and intelligent management of sewage treatment plants.

The invention belongs to the technical field of structure monitoring systems, and discloses a structure monitoring system and method for civil engineering, and the method comprises the steps that a data collection module collects structure data; the cruise monitoring mechanism collects a high-definition image, an infrared image and laser point cloud data; the environment data acquisition module acquires environment data; the image analysis module identifies abnormal conditions; the infrared data analysis module detects internal defects; the laser data analysis module accurately measures the deformation of the structure; the comprehensive analysis module performs comprehensive evaluation; the risk prediction module fuses multi-source data and predicts potential problems and risks; when an abnormal condition or a potential problem risk is monitored, the alarm module gives an alarm in time. According to the invention, through combination of sensor monitoring and unmanned aerial vehicle cruise monitoring, the structure of civil engineering is monitored in an omnibearing manner; the comprehensive analysis module is used for integrating the multi-source monitoring data to carry out comprehensive evaluation and abnormity judgment; and the risk prediction module fuses monitoring results of multiple modules and predicts potential problems and risks.

A system and methodology for automated security assessment of microservices includes a microservice composition model, data gathering, security assessment, and labeling components. It treats microservices as separate projects, collecting source code, dependencies, and runtime information. The security assessment employs tools to analyze code, track vulnerabilities, and identify risks. Predefined rules categorize microservices and assign security state labels. A hidden Markov model predicts security states based on historical data, enabling proactive security management and risk mitigation.

The invention discloses a substation network security defense system based on artificial intelligence. The system comprises a data collection module for collecting transformer substation network data; the anomaly detection module is used for identifying an abnormal mode and an intrusion behavior; the threat intelligence analysis module is used for identifying potential threat and vulnerability information; the intelligent security monitoring and response module is used for evaluating the network security state; the malicious code detection module is used for detecting, analyzing and isolating malicious software; the intelligent security prediction module is used for predicting a network security event and carrying out early warning prompt; the self-adaptive security policy module is used for automatically adjusting or updating an optimal security policy; the system updating and maintaining module is used for updating and maintaining system modules and functions; the user interface and alarm module is used for providing safety state display and alarm for a user; the data backup and recovery module is used for backing up and recovering key data; and a compliance auditing module which makes operation and configuration accord with security standards. According to the invention, hierarchical, intelligent and active protection of the transformer substation network is realized.

The invention discloses a ship arrival and departure prediction method based on a multi-modal neural network and an adaptive LSTM, and the method comprises the following steps: S1, collecting data, including ship position data, environment data and historical arrival and departure data; s2, data preprocessing: cleaning and standardizing the data; s3, feature extraction optimization is carried out on various types of data; s4, performing multi-modal data fusion, including data splicing fusion, data weighted fusion and data deep fusion; s5, training and constructing a self-adaptive LSTM model, wherein the self-adaptive LSTM model predicts the port entering and leaving behaviors and time of the ship; s6, evaluating and optimizing a self-adaptive LSTM (Long Short Term Memory) model; s7, performing real-time prediction and scheduling; and S8, dynamically predicting and providing scheduling suggestions. Based on the ship position data, the environment data and the historical port entering and leaving data, the multi-mode neural network is used for data fusion, the port entering and leaving prediction model based on the self-adaptive LSTM is constructed, and the prediction accuracy and real-time performance are improved.

System and methods are provided for dynamically consolidating interaction points in a distribution ecosystem. The method involves integrating multiple touchpoints of communication between distributors, resellers, end-users, vendors, and suppliers into a unified interactive interface. This interface enables the management of end-to-end partner lifecycle, systematic data collection, analysis using advanced statistical algorithms, deployment of artificial intelligence and machine learning algorithms, and continuous updates based on user feedback. The system includes modules for communication integration, consolidation, lifecycle management, data collection, data analysis, and artificial intelligence. The disclosed method and system enhance supply chain operations, generate actionable insights, and provide personalized user experiences, ultimately driving business growth and efficiency.

System and methods are provided for dynamically consolidating interaction points in a supply chain and distribution ecosystem. The method involves integrating multiple communication channels (i.e., touchpoints) between distributors, resellers, end-users, vendors, and suppliers into a unified interactive interface. This interface enables the management of end-to-end partner lifecycle, systematic data collection, analysis using advanced statistical algorithms, deployment of artificial intelligence and machine learning algorithms, and continuous updates based on user feedback. The system includes modules for communication integration, consolidation, lifecycle management, data collection, data analysis, and artificial intelligence. The disclosed method and system enhance supply chain and distribution operations, generate actionable insights, and provide personalized user experiences, ultimately driving business growth and efficiency.

The invention discloses a medical history recognition method and system based on a large model and an expert strategy, and the method comprises the steps: collecting data in a medical network, and carrying out the cleaning and standardization processing of the data; extracting medical entities and relationships thereof from the text, and optimizing the rule set; the extracted entities and relations are fused into a knowledge graph, a multi-relation graph is constructed, and attributes are defined; storing the knowledge graph in a graph database, and receiving new data through a real-time data stream processing system; basic information and symptom description of a patient are collected, and key information is extracted by using a large language model; an inquiry path is dynamically adjusted, and specific symptoms are clearly and fuzzily answered or deeply discussed by questioning; and generating a structured electronic medical record by using the large language model. Through an intelligent pre-inquiry process, the system can collect detailed medical history information and generate a standardized electronic medical record before a patient officially sees a doctor, so that the information verification and supplement time when a doctor receives the doctor is greatly shortened.

The invention discloses an electric power Internet of Things industrial control equipment safety assessment method based on a multi-modal large model. The method mainly comprises the steps of data collection and preprocessing, multi-modal large model processing, safety risk assessment and result display and decision support. The method comprises the following steps: firstly, collecting multi-modal data of the power Internet of Things industrial control equipment, and preprocessing to ensure the consistency and accuracy of the data; secondly, performing deep fusion and feature extraction on the multi-source heterogeneous data by adopting a multi-modal large model technology and combining an orthogonal sequential fusion (OSF) strategy; then, establishing and training a security risk assessment model, quantifying the fault probability, the risk level and the potential influence, and providing more accurate security assessment in combination with background information provided by the knowledge graph; and finally, displaying an evaluation result through a visual interface. According to the invention, the automation level and accuracy of safety risk assessment of the power Internet of Things industrial control equipment are effectively improved, and stable operation and safety management of a power system are ensured.

The invention relates to the technical field of structure health monitoring, in particular to a basement prestress bottom plate health monitoring system based on the internet of things, which comprises a stress distribution analysis module, a defect diffusion analysis module, a damage characteristic evaluation module and a safety monitoring evaluation module. According to the method, accurate stress data collection and real-time monitoring are realized by capturing spatial position information and node stress values, so that stress change is carefully tracked and evaluated, stress conduction and edge weight between monitoring points are analyzed, the internal stress distribution state of the structure is accurately revealed, the defect diffusion range and path are simulated, and the damage trend is predicted in time; according to damage characteristic evaluation, through analysis of stress mutation and material strain, key data support is provided, maintenance decision making and maintenance plan optimization are effectively guided, threshold value classification and early warning implementation of safety monitoring evaluation are effectively guided, the recognition capacity of an overproof stress concentration area is improved, and the reliability of the system is improved. And the overall safety and stability of the structure are ensured.

The invention discloses a distributed optical storage micro-grid control system based on a large model and an energy management method, and the system collects various data through a data collection module, captures a time sequence long-term dependence relation based on a self-attention mechanism through a large model prediction system, and predicts the photovoltaic power generation amount, the load demand and the energy storage charging and discharging demand. The network-forming inverter integration module dynamically adjusts the output power, the energy storage strategy and the interaction power of the power generation system according to a prediction result, the distributed control strategy module adopts a distributed consensus algorithm to realize information sharing and collaborative decision making, and the energy management module makes a multi-time scale plan and introduces an economic optimization model. The energy management method comprises the steps of data collection, real-time monitoring, prediction modeling, plan making, distributed control, economic optimization, system monitoring, fault processing and the like. The method can improve the new energy utilization rate, the electric energy quality and the system stability, adapts to the change of environmental factors, and maximizes the economic and environmental benefits of the micro-grid.

The invention relates to a knowledge graph and RAG-based science and technology service field knowledge base construction method and system. A science and technology knowledge graph and a personal knowledge graph are constructed through data collection and preprocessing, cleaning, labeling and standardization of multi-source science and technology data (such as patents, papers and technical documents), and the science and technology knowledge graph adopts an entity-relation-entity-attribute tetrad structure and supports efficient knowledge management and retrieval. An RAG model is designed and trained, a retrieval module and a generation module are combined, related knowledge is retrieved from the knowledge graph, a natural language reply is generated, a dynamically updated knowledge base is constructed, new data are accessed in real time, retrieval and generation efficiency is optimized, scientific and technological achievements can be retrieved according to user query and in combination with the personal knowledge graph, and a multi-modal reply is generated; and meanwhile, the timeliness and accuracy of knowledge are ensured by iteratively optimizing the model and the knowledge base through user feedback. The intelligent level and the user experience of science and technology services are remarkably improved.

The invention relates to a power log intelligent analysis system based on a knowledge graph and a large language model. The power log intelligent analysis system comprises a data collection and preprocessing module, a knowledge graph construction and management module, a context understanding and correlation analysis module, a personalized report generation module and a multi-modal data integration module. According to the method, information such as equipment, events and operations in the power logs is structured through the knowledge graph and is associated with domain knowledge, so that deep understanding and association analysis of log contents are realized, and the problem of insufficient context understanding in a traditional method is solved. By utilizing the strong generation capability of a large language model and based on context information provided by a knowledge graph, a personalized report meeting roles and requirements of a user can be automatically generated, and the user experience and practicability of log analysis are improved; log data in various formats are integrated into the knowledge graph, a comprehensive summary report containing information such as charts and timelines is generated through a large language model, and the problem that heterogeneous data are difficult to integrate is solved.

A value chain network control tower system comprises a processor and memory configured to execute a know your model system that manages the complete lifecycle of Al models in enterprise environments. The know your model system performs model intake and registration actions including model documentation collection, registration procedures, metadata collection, input / output interface standardization, legal and licensing validation checks, and security validation. The system conducts comprehensive model evaluation and risk assessment actions by analyzing foundational properties, task performance, safety and risk management, alignment and compliance characteristics, operational metrics, and tooling transparency capabilities. The know your model system executes model deployment actions through automated environment validation, predeployment approval processes, and controlled production deployment with continuous monitoring.

The invention discloses a contract compliance examination method and platform based on multi-agent cooperation, relates to the field of artificial intelligence, and is used for improving the safety and accuracy of contract examination. The method comprises the following steps: acquiring latest rule data by a data collection agent; the data construction agent constructs retrieval data and a rule knowledge graph based on the rule data; the document analysis agent extracts key information of the target contract and retrieves related rules from the retrieval data according to the key information; the compliance review agent performs risk item assessment on the target contract according to the related rules and the rule knowledge graph; the risk management and control agent generates a response measure of the risk item based on the retrieval data; the report generation agent generates a compliance review report based at least on the evaluated risk items and corresponding countermeasures. According to the invention, the security, accuracy and efficiency of contract review are improved.

Apparatuses, methods, and systems are disclosed for determining application data and / or analytics. One method includes associating, at an application enablement unit, an application data analytics event with a combination of data collection events and network data analytics events based on an application request for end-to-end application related performance analytics. The method includes obtaining a first set of data from data producing entities and / or data analytics producing entities based on the application data analytics event. The method includes configuring a second set of data based on the first set of data. The second set of data includes at least a portion of and / or an abstraction of the first set of data based on the application data analytics event. The method includes deriving application data analytics corresponding to the application data analytics event based on the second set of data.

The invention relates to a coal mine potential safety hazard characterization method and system based on a knowledge graph, and belongs to the technical field of coal mine safety. The method comprises the following steps: data collection: collecting coal mine daily safety inspection records including hidden danger types, occurrence places, time and related descriptions; data preprocessing: cleaning, classifying and standardizing the collected data so as to facilitate subsequent knowledge extraction and graph construction; and knowledge graph construction: based on the preprocessed data, identifying key entities, extracting relationships among the entities, and constructing a coal mine potential safety hazard knowledge graph. Through the hidden danger correlation analysis and risk assessment module in the knowledge graph, the system can assess the risk degrees of different hidden dangers in real time and generate corresponding early warning information according to the assessment result, and the dynamic early warning mechanism enables managers to master the safety condition of a coal mine in time and quickly respond to potential safety threats.

The invention relates to the technical field of laser cutting, in particular to a laser cutting device and a control method thereof. The method comprises the following steps: collecting data of a laser cutting machine to obtain data of the laser cutting machine; cutting head moving speed data of the laser cutting machine data are extracted, and the cutting head moving speed data are obtained; performing power calculation on the moving speed data of the cutting head to obtain laser power data; spatial modeling is carried out based on the cutting head moving speed data and the laser power data, and a laser cutting machine three-dimensional model is generated; performing cutting path identification on the three-dimensional model of the laser cutting machine to generate cutting path data; and extracting first cutting data of the laser cutting machine data to obtain the first cutting data. Through the Internet of Things technology, the data mapping technology, the data analysis technology and the intelligent control technology, self-adaptive laser cutting control over different materials and thickness conditions is achieved, and therefore the cutting quality and efficiency are improved.

A cellular network having radio access network (RAN) nodes where each RAN node includes (i) a central unit (CU) that resides on a public cloud of the cellular network, (ii) a distributed unit (DU) that resides on a private cloud of the cellular network such that the DU is in communication with the CU on the public cloud of the cellular network, and (iii) a radio unit (RU) under control of the DU. The cellular network also has network repository functions (NRFs) that are distributed on the cellular network and reside on at least the public cloud of the cellular network where the NRFs control operation of cell sites and local data centers (LDCs) on the cellular network. The network also has processors configured to control data collection edge applications residing with the NRFs that are distributed on the cellular network.

The invention provides an AI-assisted large-model intelligent question and answer accuracy improvement method, and belongs to the technical field of intelligent question and answer. Comprising the steps of data collection, data cleaning, data labeling, dynamic knowledge base construction, knowledge base updating trigger mechanism establishment, pre-training language model improvement, post-processing language understanding enhancement, combination of rule-based and machine learning reasoning, reasoning result verification and optimization, dialogue strategy optimization, emotion perception and response, multi-language search and language recognition search. And multi-dimensional evaluation index setting and regular evaluation and feedback improvement are realized. The intelligent question and answer accuracy improving method is high in understanding ability, timely in data updating, outstanding in reasoning ability and high in language recognition and dialogue ability.

The invention belongs to the technical field of e-commerce, and particularly relates to a big data-based recommendation algorithm design in an e-commerce platform, which comprises the following steps: completing data preprocessing based on data collection and preprocessing; constructing user portraits to generate personalized user portraits, and classifying the users by using a clustering algorithm to find group features of the users; analyzing the user similarity and the commodity similarity, and calculating the similarity between the users; carrying out user-based collaborative filtering and article-based collaborative filtering, and carrying out dimensionality reduction processing on user and commodity features by using an SVD or latent semantic model; modeling user behaviors by using a neural network, and performing data compression and feature extraction by using an auto-encoder; the user feedback data is collected for continuous optimization and iteration of the model, and online learning and A / B test are adopted to adjust the recommendation strategy, so that the method has the effects of realizing more accurate and personalized commodity recommendation, and improving the shopping experience of the user and the sales conversion rate of the platform.

An AI-driven system for personalized advertising in real time, where: ◯ an analytics unit to monitor and analyze user behavior in real time across multiple digital platforms such as websites, mobile applications, social media, and smart devices; the unit collects data on user engagement, browsing patterns, time spent, and content preferences to enable targeted, personalized advertising; ◯ a prediction module that predicts preferences and interests of a user, operatively connected to the user interaction data collection unit, wherein the prediction module uses machine learning models such as deep learning, recurrent neural networks (RNNs) and transformer-based architectures to predict interests of users based on historical interactions and inferred preferences; ◯ an emotion and sentiment analysis unit that assesses the user's mood in real time through computer vision, natural language processing (NLP) and voice analysis, whereby the analysis of facial expressions, voice pitch and linguistic mood is used to determine emotional states and receptivity to advertising content; ◯ an embodiment of a context awareness component in operational communication with the emotion analysis and mood unit, in some cases further augmented by various environmental and situational data such as the device type and its physical location, date and time, and the content processed in the device, processing methods, etc., in establishing adaptability and automatic ad placement to be as relevant as possible to the user and their status as prescribed; ◯ Use reinforcement learning algorithms and generative AI models to drive advertising with personalization engines. Creative elements, messaging, and presentations are dynamically adjusted in real time based on user responses to ensure advertising is personalized and always optimized for best performance; o a privacy-focused AI system with federated learning, differential privacy methods, and on-device AI processing to reduce targeted advertising while complying with international data protection laws such as the General Data Protection Regulation (GDPR) and California consumer privacy laws; o an operationally adapted ad delivery mechanism to engage with real-time bidding (RTB) networks, programmatic advertising exchanges and demand-side platforms (DSPs) and place advertisements through digital advertising networks, which guarantees the delivery of tailored advertising to the most relevant audience in real time; and o a contextual feedback loop in which the machine learning models used in the preference and interest prediction module and in advertising personalization The engine is continuously updated to reflect the latest user engagement data, improving personalization over time and optimizing advertising performance.

The invention discloses a text keyword desensitization method, system and equipment based on a large model and a medium, belongs to the technical field of large model and natural language processing, and aims to solve the technical problem of how to automatically identify and desensitize sensitive information, improve the accuracy of sensitive information identification and further improve the text processing efficiency. According to the technical scheme, the method comprises the steps of data collection; preprocessing the data; marking sensitive information; segmenting the data; selecting a basic model; finely adjusting the model; training the fine-tuned large language model, and obtaining and storing the trained fine-tuned large language model; preprocessing the to-be-processed text data; detecting sensitive information, formulating a desensitization strategy and implementing desensitization processing, performing desensitization verification on the text after desensitization processing, and optimizing and improving the fine-tuned large language model according to a verification result; and packaging the optimized and improved fine-tuned large language model into an API (Application Program Interface) service for an application program to call.

The invention relates to the technical field of supply chain management, and discloses a product supply chain carbon footprint accounting method, which comprises the following steps: (1) data collection and arrangement: collecting time sequence activity data of each link of a supply chain, including production data, transportation data and supplier data; acquiring corresponding emission factors including emission factors with specific areas and time; (2) constructing a dynamic life cycle evaluation model: establishing the dynamic life cycle evaluation model containing time dynamics, and taking activity data and emission factors as time-dependent input parameters; by introducing dynamic life cycle evaluation, the method can capture the time change of carbon emission of each link of the supply chain, including seasonal fluctuation, policy change, market demand change and other factors, and considers the influence of geographic positions, transportation routes and regional energy structures on carbon emission in detail in combination with the GIS technology, so that the method has the advantages of high efficiency and high reliability. The supply chain carbon footprint accounting precision is significantly improved, and the result is more accurate and reliable.

An intelligent anomaly detection system (100) for IoT networks using edge computing and deep learning, comprising: (a) a data collection and pre-processing module configured to collect data from a variety of heterogeneous IoT devices and perform pre-processing operations, including normalization, denoising, and feature extraction; b) an edge intelligence and model deployment module configured to deploy deep learning models optimized for edge devices to perform local data analysis; (c) an anomaly detection and classification module configured to identify abnormal behavior in the processed data using deep neural networks and to classify the anomalies into predefined threat categories; (d) an adaptive learning and model update module configured to update the deployed models through incremental or federated learning mechanisms without transmitting raw data to a centralised server; (e) an alert and response management module configured to generate alerts and execute predefined mitigation actions based on the type and severity of the detected anomalies; f) and a system monitoring and visualization module configured to display real-time insights, alerts and system analysis through a user interface, g) the system operates in a decentralised manner using edge computing to achieve scalable and privacy-preserving anomaly detection in IoT environments in real time.