30423results about How to "Easy to handle" patented technology

The invention provides an oil depot tank field inspection robot task allocation method and system based on the Internet of Things, and relates to the technical field of the Internet of Things. Constructing a multi-target collaborative scheduling model, extracting features by adopting a space-time attention mechanism, establishing a task priority evaluation and path planning sub-model, and generating an optimal task allocation strategy; and an inspection task instruction is issued, the execution condition is monitored in real time, and when abnormity is detected, the emergency module is started for optimization adjustment. According to the invention, intelligent and collaborative routing inspection task distribution is realized, and the routing inspection efficiency and safety are improved.

The invention relates to the technical field of database construction, in particular to a knowledge base question-answering system construction method and device, equipment and a storage medium. The method comprises the following steps: acquiring a user question instruction, and performing part-of-speech tagging and keyword vectorization on a target text to generate a retrieval text keyword vector; performing semantic linear embedding and spatial projection processing on the keyword vectors to obtain keyword semantic vectors; logic mining is conducted on the keywords, a logic relation set among the keywords is obtained, logic recognition is conducted on the target text based on the relations, and retrieval text logic is generated; performing matching retrieval with semantic vectors in a knowledge base to obtain a candidate semantic query data set; and performing coherent logic retrieval on the candidate data set according to the retrieval text logic and the keyword vector to generate a final retrieval target answer. According to the method and the system, more accurate and efficient knowledge base question-answering system construction is realized.

The invention discloses a method and system for generating enhanced knowledge questions and answers for heterogeneous database mixed retrieval, and the method comprises the following steps: (1) processing text data, and constructing a heterogeneous database; (2) receiving user query, and performing mixed retrieval in the heterogeneous database; the retrieval results are merged, and documents and triple information related to user query are obtained; (3) reordering the retrieved documents and triple information by using a reordering model, and selecting K retrieval results with the highest relevancy with user query; and (4) combining user query and retrieval results, constructing cue words, inputting the cue words into the large language model, and generating coherent and accurate natural language answers. By means of the method and device, more comprehensive and accurate information retrieval can be achieved, and the accuracy and reliability of knowledge question and answer results are improved.

The invention belongs to the field of network security, and discloses a network security evaluation system and method based on a dynamic attack and defense game model, and the evaluation system comprises four functional modules of attack and defense game construction, risk prediction, dynamic defense optimization and security situation awareness. According to the method, the idea of dynamic attack and defense game is introduced, multi-level and multi-scene attack and defense simulation is carried out according to historical states, strategy data and real-time data, and the optimal attack path and attack strategy are calculated and analyzed through a game equilibrium solution; according to the method, network resources and network security resources are dynamically adjusted by relying on reinforcement learning and deep learning technologies and a defense rule base generated by pre-training, network configuration is optimized according to a real-time evaluation result, and waste of system resources is reduced. By implementing the system and the method provided by the invention, the security of the target network can be dynamically evaluated, the security defense strategy of the network can be adjusted in real time, and unknown attacks such as advanced persistent threat (APT) and zero-day attack can be effectively resisted.

The invention discloses a highway construction safety monitoring system and method, and belongs to the technical field of highway construction. The method comprises the following steps of 1, collecting construction site data in real time, wherein the construction site data comprises environment data, video data and construction management data; step 2, generating global situation awareness data; 3, evaluating the safety risk probability and severity in real time, and predicting a risk propagation path and a cascade effect; step 4, generating a plurality of risk disposal schemes for sorting and screening, and selecting an optimal disposal suggestion; 5, precise early warning and differentiated management are achieved, the false alarm rate is reduced, and high-risk events are found in time; and step 6, displaying the construction site through three-dimensional reconstruction, superposing construction site data and a risk assessment result, and providing remote monitoring and emergency command of a mobile terminal.

The invention relates to the technical field of electric power short-term load prediction, in particular to an industrial park short-term load prediction method and system based on a Diffusion-Transform-AR model. Acquiring non-stationary historical load data and meteorological data of the industrial park, and performing missing value processing on the data; performing correlation analysis on the data by using the maximum information coefficient, selecting meteorological data with relatively high correlation, and reprocessing time data; performing time sequence decomposition on the load data by using a seasonal trend decomposition method to obtain trend term, seasonal term and residual term features; constructing a diffusion Transform prediction model, and taking the residual term, the meteorological data and the time data as input prediction residual; constructing an autoregression prediction model and taking the trend term as an input prediction trend; according to the method, the advantages of various models are combined, time sequence features are deeply mined, the prediction precision and the model generalization ability are effectively improved, and the method is suitable for complex industrial park load prediction scenes.

The invention discloses a self-adaptive power grid state court intelligent fusion terminal and a control method thereof, relates to the technical field of intelligent power grids, and solves the defects that an existing fusion terminal is insufficient in self-adaptive capability and limited in data processing and analysis capability. Learning power grid state change in real time through a deep power grid dynamic control algorithm and a prediction analysis engine combining LSTM and random forest, and autonomously adjusting a control strategy; the edge calculation and distributed processing module is utilized, so that the data processing flow is optimized, and the data processing speed and efficiency are greatly improved; the application of time sequence analysis and a machine learning model further excavates and understands a complex data mode; the resource configuration efficiency is improved through an intelligent scheduling algorithm, and a cross-layer coordination mechanism is established through a distributed coordination algorithm, so that rapid issuing and feedback collection of a control instruction are ensured; according to the invention, the intelligent degree of the existing fusion terminal is greatly improved.

The invention relates to the technical field of intelligent building management, in particular to an intelligent building full-life-cycle monitoring management method and system, and the system comprises a data collection module, a multi-source data fusion and analysis module, a full-life-cycle management model construction module, an intelligent early warning module, a self-adaptive feedback regulation and control module, a data storage module and a remote visual monitoring module. According to the system, environment parameters and equipment operation state data of a building are acquired in real time through the data acquisition module, key operation indexes are extracted by using the multi-source data fusion and analysis module, and accurate risk assessment and early warning are realized under the support of the full-life-cycle management model. The self-adaptive feedback regulation and control module can dynamically adjust key operation parameters according to real-time data to ensure stable operation of equipment under different environment conditions, the data storage module supports long-term performance analysis, and the remote visual monitoring module realizes remote monitoring and multi-party collaborative management. According to the invention, the operation safety, stability and management efficiency of hydraulic facilities are effectively improved.

The invention relates to the technical field of security knowledge maps, and discloses a city security knowledge map construction method based on an AI large model and a related device, and the method comprises the steps: carrying out the vectorization feature extraction of multi-source city security event data, obtaining a multi-dimensional feature matrix, carrying out the feature analysis through a BERT encoder, and obtaining a multi-dimensional feature matrix; performing key value pair generation and feature processing on each group of query vectors in the obtained grouped query vector set; performing spatial-temporal correlation calculation on the event association feature matrix to obtain an initial security knowledge graph, and performing graph neural network learning and event classification on the initial security knowledge graph to obtain an event type division result; performing situation assessment index calculation on the event type division result to obtain security situation early warning data; and performing knowledge graph updating on the security situation early warning data to obtain an urban security knowledge graph, thereby realizing accurate classification and situation evaluation of security events and dynamic updating of the urban security knowledge graph.

The invention discloses an expressway service area sewage treatment system based on the Internet of Things, and relates to the technical field of sewage treatment.The expressway service area sewage treatment system is provided with a multi-modal sensor and an edge computing unit, real-time monitoring and data processing of sewage flow and water quality parameters are achieved, the water quality change trend and the parameter change rate are rapidly recognized, and the sewage treatment efficiency is improved. The sensing capacity of the system to load changes is remarkably improved, the system conducts real-time adjustment according to the water quality changes, fluctuation of the treatment effect and waste of energy consumption are avoided, an LSTM model is adopted for conducting dynamic prediction on water quality parameters, in combination with real-time monitoring data, the aeration strategy is optimized through intelligent self-adaptive adjustment and closed-loop fine control, and the aeration efficiency is improved. The LSTM model can predict the future sewage load and ammonia nitrogen concentration change, the aeration intensity and time are adjusted in advance, the fluctuation of the ammonia nitrogen concentration is quickly responded through PID control and closed-loop control of a feedback mechanism, and the ammonia nitrogen removal efficiency and the aeration accuracy are improved.

The invention relates to the technical field of electrical safety, in particular to an electrical operation risk assessment and early warning system based on big data analysis, which comprises a data acquisition module, a data fusion module, a feature extraction module, a risk assessment module, a real-time monitoring and early warning module, a historical data storage and analysis module and a visual monitoring module. Multi-source data are collected in real time by deploying multiple sensors, risk assessment is carried out through Kalman filtering and support vector regression algorithm fusion processing, feature variable extraction and combination of a support vector machine and a random forest algorithm, real-time monitoring and risk level judgment of an electrical working environment are achieved, long-term risk trend prediction is supported, and the risk assessment accuracy is improved. The visual monitoring module provides a basis for optimizing alarm rules and decision support, and displays an evaluation result, early warning information and an analysis result through a visual interface; the system has the advantages of being accurate in data processing, efficient in risk assessment, timely in early warning and the like, and can be widely applied to risk monitoring and safety management of electrical operation.

The invention discloses a multi-layer semantic understanding large model agent construction and application method. The method comprises the following steps: S1, preprocessing input data; s2, analyzing the preprocessed data by using a large language model and dialogue history, identifying user intentions and extracting deep semantic features; s3, key information is extracted from data intending to be recognized, structured representation is carried out, and model configuration is adjusted through dynamic hyper-parameter optimization; s4, querying the knowledge graph, associating and organizing the extracted key information with the existing data, and generating a structured knowledge representation; s5, vectorizing the structured knowledge representation, and storing the vectorized structured knowledge representation in a vector database to support rapid similarity search; and S6, in combination with the generated unified semantic representation and the large model, performing deep analysis on user input, generating coherent and logical personalized answers, and returning the personalized answers to the user. The method can support various application scenes such as intelligent question and answer, content generation, sentiment analysis and multi-modal search.

The invention relates to a real-time fault detection and automatic repair system for a power grid. The real-time fault detection and automatic repair system comprises a multi-level sensing module, a data processing module, an automatic repair module and a monitoring and logging module. The multi-level sensing module collects data such as voltage, current, frequency and temperature in operation of a power grid by deploying multiple types of sensors, and sends the data to the data processing module. And the data processing module analyzes the collected data through an embedded intelligent algorithm, identifies potential faults and generates fault diagnosis results. And the automatic repair module executes repair operations of isolating a fault area, reconstructing a power grid topological structure and recovering normal power supply according to a fault diagnosis result. The monitoring and log module records the operation state of the power grid and the fault diagnosis and repair process, and provides fault analysis and performance evaluation functions through a user interface. According to the system, real-time detection, accurate positioning and automatic repair of power grid faults and comprehensive monitoring of operation data are realized, and the safety and reliability of power grid operation are remarkably improved.

The invention provides a satellite measurement and control data anomaly detection and intelligent diagnosis early warning method and system, and relates to the technical field of satellite measurement and control, and the method comprises the steps: obtaining multi-source heterogeneous data in a satellite measurement and control system, and carrying out the preprocessing of the multi-source heterogeneous data; constructing a multi-level space-time fusion network model, inputting the preprocessed multi-source heterogeneous data into the multi-level space-time fusion network model, and extracting high-dimensional space-time features; based on the extracted high-dimensional spatial-temporal features, designing a dynamic adaptive anomaly detection algorithm, and performing anomaly detection on the satellite measurement and control data; according to an anomaly detection result, performing intelligent diagnosis by using a pre-constructed multi-level satellite system knowledge graph; based on the intelligent diagnosis result, multi-level early warning information is generated; a distributed edge computing architecture is designed, and partial data processing and anomaly detection tasks are deployed to a ground station and a satellite end; and presenting the generated multi-level early warning information through a visual interface, and automatically triggering a corresponding emergency plan according to the early warning level.

The invention discloses a real-time video quality optimization and enhancement method based on deep learning. The method comprises the following steps: S1, collecting a video stream and extracting continuous frames; s2, extracting motion area features by using an optical flow estimation algorithm, and generating a multi-modal feature matrix; s3, carrying out normalization processing; s4, outputting a de-noising optimization frame by using an adaptive multi-dimensional residual network and a frequency domain analysis technology; s5, generating a detail enhancement frame by using a progressive generative adversarial network; s6, constructing an inter-frame relation graph through locality sensitive hashing, correcting the frame by using a double-flow time sensing network, optimizing the frame sequence based on a dynamic time warping method, and outputting the corrected frame sequence; s7, inputting the corrected frame sequence into a dynamic feedback optimization module to generate an optimized frame sequence; and S8, recombining the optimized frame sequence into a continuous video, and transmitting the continuous video to a target platform. According to the method, the video definition and the time consistency are comprehensively optimized through methods of multi-modal feature fusion, dynamic optimization, progressive network generation and the like.

The invention discloses a construction method and device of a multi-component data agent, and the construction method specifically comprises the following steps: obtaining sample data, and constructing a vector knowledge base; extracting a knowledge service flow according to the sample data, determining an ontology through knowledge representation, and forming a knowledge graph; training an initial language model based on the vector knowledge base, and giving a data processing model; and performing modularization arrangement and linking on the knowledge graph and the data processing model in combination with a logic tool chain and a thinking graph of the data intelligent agent, and constructing to obtain the multi-component data intelligent agent. According to the method, the knowledge graph is fused to construct the business rule engine, training data support is provided, the thinking chain process is modeled into the thinking graph of a graph structure, and the data processing model and the knowledge graph associated with the workflow node are executed, so that the accuracy of result reasoning of the data agent is enhanced, and the knowledge boundary is expanded; and particularly for financial auditing data, key information can be better extracted, and the working efficiency of power grid auditing personnel is improved.

The invention discloses a task integrity judgment method for multi-device cooperative work under complex constraint conditions. The method comprises the steps that a comprehensive data collection system is established, multi-dimensional data of cooperative devices are collected in real time and preprocessed, constraint modeling is combined, and the complex constraint conditions are quantified into a computable constraint condition model; a multi-agent collaborative evaluation model is constructed, each device participating in collaborative work is regarded as an agent by the multi-agent collaborative evaluation model, and evaluation of the integrity of the whole task is achieved by simulating the collaborative process between the devices; automatically extracting advanced features from the multi-dimensional data by using a deep learning technology; and a distributed real-time analysis system is designed, and large-scale data processing and continuous model optimization are realized in combination with an adaptive optimization mechanism. According to the method, the reliability and efficiency of the cooperative task can be improved, powerful support can be provided for equipment task decision making, and the method plays an important role in improving the intelligent level of application guarantee and the decision making accuracy.

The invention discloses a method for inverting key parameters in an underground water pollutant biodegradation numerical model based on a neural network algorithm. Carrying out preprocessing and refined dimension reduction on the data, and processing high-dimensional multi-scale data; building a multilayer neural network architecture under a physical information neural network framework, embedding a related reaction equation of a pollutant natural attenuation process, and coupling a biochemical reaction mechanism and a data driving model; a neural network model is trained, errors in the training process are calculated, and the accuracy of an inversion result is improved by using a sub-when optimization algorithm; integrating observation data and a model prediction result, determining final parameter setting of a numerical model, and carrying out underground water damage real object quantification; comparative verification of a neural network parameter inversion model is carried out, and feasibility and accuracy of the parameter inversion method are ensured. According to the method, the problem that the key parameters of the biodegradation numerical model are difficult to accurately invert in the real object quantification process of underground water pollution under complex geological conditions is solved.

The invention discloses an intelligent monitoring method and system based on the Internet of Things, a medium and a program product, and the method comprises the steps: abstracting a monitoring region into a dynamic graph structure, and obtaining a self-adaptive monitoring scene graph; fusing at least one piece of video data and at least one kind of data of other sensors at each node to form a multi-mode sensing network; carrying out distributed feature extraction on the multi-modal sensing data to obtain data features, and compressing the data features into low-dimensional vectors; constructing a space-time knowledge graph; executing an anomaly detection algorithm on a preset number of spatial scales; matching a corresponding adjustment mode in a preset adjustment scheme according to the type and the degree of the abnormal event, and adjusting the numerical weights of the nodes and the edges to obtain an adjusted adaptive monitoring scene graph; and according to the adjusted adaptive monitoring scene graph and the numerical value weight corresponding to the node, synchronously adjusting the working parameters of all the cameras of the Internet of Things and other sensors. According to the invention, the integrity and comprehensiveness of the monitoring data acquired by the Internet of Things camera equipment are improved.

The invention relates to the technical field of environmental sound event detection, in particular to an environmental sound event detection method based on multi-modal data fusion, which utilizes a microphone array, a camera and a sensor to synchronously acquire audio, video and environmental data to form a multi-modal data source. And the collected multi-modal data is preprocessed, so that the data quality and the accuracy of subsequent analysis are improved. And key features are extracted from the processed data, and the features are fused to form multi-modal features. And a deep learning model is constructed, and multi-modal features are used for training, so that a model capable of identifying an environmental sound event can be obtained. And performing optimization such as pruning, quantification and knowledge distillation on the trained model, and deploying the model on edge equipment to realize real-time preliminary feature extraction and event detection. The problems that an environment sound event detection method in the prior art is poor in robustness, low in real-time performance and limited in generalization ability are solved.

The invention relates to the field of environment monitoring, and discloses an efficient and accurate pollution source monitoring quality control system and method, and the system comprises a multi-modal data collection module, a high-dimensional data modeling module, a time-space correlation analysis module, a time sequence prediction module, a multi-task learning module and an anomaly detection feedback module. The method comprises the following steps: collecting data of pollutants such as air, water quality and noise, performing high-dimensional modeling and optimization on the data, and capturing space-time propagation paths of the pollutants among different sensor nodes; the system predicts the short-term change and the long-term trend of the pollutant concentration in real time; the multi-task learning model is used for predicting concentration changes of multiple pollutants at the same time, multi-scale decomposition is carried out on a time sequence through wavelet transform, abnormal changes in data are detected, and a feedback control strategy is generated. According to the invention, the precision and real-time performance of pollution source monitoring are improved, the dynamic change of multiple pollutants can be effectively handled, and the high efficiency and reliability of environment quality monitoring are ensured.

The invention discloses an estuary and gulf early warning management method and system based on digital twinning, and relates to the technical field of early warning management, and the method comprises the following steps: collecting multi-source data of an estuary and gulf region, and integrating the multi-source data into a unified digital twinning system; building a digital twinborn model of the estuary and gulf by utilizing the collected multi-source data, and analyzing dynamic trends of a physical water quality environment, a hydrological environment and an ecological environment of the estuary and gulf; the estuary bay environment is monitored in real time by deploying a sensor and monitoring equipment. By scientifically identifying the potential ecological problems of the ecological system in the estuary and gulf area, evaluating and predicting the performance of the ecological system, optimizing land-sea overall planning, river-sea linkage comprehensive treatment and preventive maintenance schemes, the ecological function of the gulf is improved, the decision risk is reduced, the response effect and management efficiency are improved, and the economic benefit is increased. And a new technical approach is explored and technical support is provided for sustainable high-quality development of an estuary and gulf ecological system.

The invention belongs to the technical field of data security, and discloses a data management method based on a data resource security identification level, and the method achieves the automatic and intelligent decision making: achieves the data classification and the automatic identification of the security level through a machine learning and deep learning model, remarkably reduces the manual intervention, and improves the efficiency and accuracy. And dynamic real-time performance: in combination with a risk assessment model, the system can detect abnormal behaviors in real time and dynamically adjust a security policy in a data flow process, so as to ensure that the sensitive data is properly protected in different scenes. And security and compliance: a log auditing module ensures the traceability of all data operations, improves the response capability to potential risks through an anomaly detection algorithm, and provides effective compliance guarantee for data security governance. And the system can be compatible with various data sources, supports configuration and execution of various security policies, and is suitable for data security management requirements of different industries (such as finance and medical treatment).

The invention provides a sewage treatment data optimization method and system based on cloud computing, and relates to the technical field of data processing.The method comprises the steps that real-time key information, including water quality indexes, equipment operation parameters and energy consumption data, collected by monitoring equipment in real time is received, the key information is preprocessed, and a processed data set is obtained; constructing a cloud database according to the processed data set, and performing data analysis including trend prediction and anomaly detection by using the cloud database to obtain an analysis result; and according to the analysis result, constructing a sewage treatment data optimization model, and inputting the water quality standard, the equipment operation efficiency and the energy consumption cost parameters into the sewage treatment data optimization model to generate an optimization scheme. By collecting and analyzing sewage treatment key information in real time and constructing and optimizing the data model, intelligent adjustment and optimization of the sewage treatment process are realized, the water quality is improved, the energy consumption is reduced, and the equipment operation efficiency and the overall treatment effect are improved.

The invention provides a structured data modeling analysis method based on Transform, and relates to the technical field of data analysis, and the method comprises the steps: carrying out the piecewise linear transformation and adaptive quantum coding of numeric features and category features through a double-flow feature encoder, and carrying out the fusion through a gating mechanism to obtain fusion features; inputting the enhanced training sample into a feature extraction network comprising a hierarchical attention module and a self-calibration module, and extracting global feature representation through a feature affinity graph and a causal attention mechanism; and constructing an information bottleneck feature filter to select a key feature subset, performing prediction and reconstruction by adopting a double-branch network, and analyzing the feature contribution degree by using a structured gradient method. According to the method, heterogeneous features can be effectively processed, the model performance is improved, and meanwhile an interpretable feature importance evaluation mechanism is provided.

The invention relates to the technical field of sleep aiding, in particular to a method and a system for extracting feature information of a sleep state monitoring model. The method comprises the following steps: carrying out multi-mode physiological signal acquisition on a detected object to obtain original sleep monitoring data; performing noise removal and signal enhancement processing on the original sleep monitoring data, and performing signal segmentation to obtain multi-channel sleep signal fragment data; performing feature extraction based on electroencephalogram, eye movement diagram, electromyogram, electrocardiogram and accelerometer signals on the multi-channel sleep signal fragment data to obtain multi-dimensional feature data; and performing ultrasonic feature and acoustic feature extraction on the multichannel sleep signal fragment data to generate acoustic feature data. According to the method, dynamic modeling and trend prediction are carried out on the conversion process of the sleep state, potential sleep disorder events can be accurately recognized, influence evaluation is carried out on the potential sleep disorder events through deep learning, and support is provided for personalized sleep diagnosis.

The invention discloses a multi-sensor fusion positioning mapping and thermal imaging device and a computer readable medium. The equipment integrates positioning mapping sensors such as a thermal imaging camera, a visible light camera, a laser radar and an inertial navigation system, and is specially designed for high-risk and complex fire emergency environments. Through an end-cloud integrated cluster cooperation system and a multi-link stable transmission technology and in combination with intelligent decision support, high-precision real-time positioning and dynamic 3D temperature measurement mapping can be realized in an environment in which satellite navigation signals are weak or unavailable. The multi-mode portability and environmental adaptability design of the system ensures the high efficiency and safety of an operator / unmanned system under extreme conditions, and the advanced data processing and analysis technology of the system significantly improves the accuracy and response speed of rescue operation. Due to the innovative characteristics, the method has remarkable practical application value and market prospect in the field of fire-fighting emergency rescue.

The invention belongs to the technical field of remote sensing image imaging, and provides a remote sensing image unsupervised domain adaptation method based on comparative learning and multi-prototype alignment, and the method specifically comprises the steps: constructing a teacher-student network, and setting a cross-domain multi-prototype alignment module and a pixel-level cross-domain comparative learning module which are needed in training in the teacher-student network; during teacher-student network training, a cross-domain multi-prototype alignment module is used for extracting prototypes of all categories in a source domain and a target domain and minimizing the feature distance of the prototypes, and a pixel-level cross-domain contrast learning module is used for explicitly modeling the global semantic relationship of cross-domain pixel samples; and an embedding space with high intra-class compactness and high inter-class separability is established.

The embodiment of the invention provides a mining area vehicle path planning method and device based on a digital twin map, and belongs to the technical field of vehicle path planning, and the method comprises the steps: obtaining map data and physical scene data of a mining area; creating a dynamic twin map of the mining area based on the map data and the physical scene data; obstacle information is identified and marked based on the dynamic twin map, and obstacles comprise natural obstacles and artificial obstacles; and importing related data of the vehicle into the dynamic twin map, and carrying out obstacle avoidance path planning on the vehicle based on the marked obstacle information to obtain an optimal obstacle avoidance path. According to the scheme, the running efficiency of mining area vehicles can be improved, and collision and obstacle detouring time is shortened.