325 results about "Network analytics" patented technology

The invention provides a big data platform scheduling task and data collaborative smooth migration method and system, and relates to the technical field of big data, and the method comprises the steps: constructing a digital twin environment of a target system, collecting task operation state data of a source system, extracting task features through a deep neural network, and carrying out the clustering and grouping. And deploying the task group to a digital twin environment, analyzing a task association relationship and a resource competition relationship based on a graph neural network, and generating a task stability score in combination with a task operation index. And constructing a migration risk prediction model to perform quantitative evaluation on the migration risk, and determining a migration priority and an execution sequence according to the quantitative evaluation. And making corresponding resource configuration, data synchronization and fault recovery plans for each task group, carrying out migration drilling in a digital twinning environment, and optimizing a migration scheme by utilizing a reinforcement learning algorithm. The migration risk can be effectively reduced, the smoothness and stability of the migration process are guaranteed, and the migration efficiency is improved.

The invention provides an oil extraction equipment fault monitoring system and method, and belongs to the technical field of oil extraction equipment fault monitoring. The method comprises the following steps: acquiring operation data of oil extraction equipment, and performing feature extraction on the acquired operation data to obtain a target feature vector; fusing the obtained target feature vector with a historical fault case library and an oil extraction equipment physical constraint equation, and constructing a dynamically updated knowledge graph; based on the space-time causal adversarial network, analyzing the distribution offset of the target feature vector in the space-time dimension, detecting an abnormal event and outputting an abnormal type label; and according to the output abnormity type label, combining with a knowledge graph, tracing a propagation path of an abnormal event, and calculating a fault probability of a root cause component through a Bayesian network so as to carry out monitoring and early warning on the oil extraction equipment. According to the method, accurate fault detection and root cause positioning are realized through multi-modal data fusion and the dynamic causal knowledge graph, and the equipment shutdown risk and the operation and maintenance cost are remarkably reduced.

The invention relates to the technical field of order management, and discloses a supply chain-oriented intelligent order management method, which comprises the steps of obtaining corresponding multi-modal data through an order demand flow, a production equipment state, logistics sensor dynamic information and an inventory topological graph; analyzing relevance between orders and equipment based on a space-time diagram convolutional network, and generating a capacity allocation scheme; calculating a logistics path planning scheme, predicting a stock stockout risk and generating a replenishment suggestion; if the high-priority order exists, inserting a productivity plan and adjusting an equipment process chain; if resource conflicts occur, dynamically allocating resources; if the path risk value exceeds the threshold value, standby path switching is triggered; adjusting weighting parameters through an adaptive federation algorithm, generating a global strategy and issuing the global strategy to the client; the client dynamically adjusts local configuration and uploads execution effect data in real time; and if abnormity is detected, triggering global strategy regeneration and updating the model through federated learning increment. According to the invention, efficient management of supply chain orders can be realized.

The invention discloses a CCER forestry carbon sink project development system based on artificial intelligence technology monitoring, and relates to the technical field of carbon sink monitoring and optimization. According to the CCER forestry carbon sink project development system based on artificial intelligence technology monitoring, space-time estimation is carried out on CCER forestry carbon sink project data through the deep learning model, and the dynamic change of the carbon sink amount is accurately predicted. Spatial features are extracted through a convolutional neural network, time sequence data are analyzed in combination with a long-short-term memory network, and a carbon sink prediction model is constructed. And through statistical analysis and a spatial interpolation algorithm, carbon sink quantity change is monitored in real time, an abnormal trend is identified, and early warning is provided. Based on a mixed integer linear programming algorithm, forest land resource allocation and vegetation types are optimized, a carbon sink optimization scheme is generated, and carbon sink benefit maximization is promoted. And through an intelligent carbon sink management scheme, sustainable ecological development and green and economic popularization are assisted.

The invention discloses a cross-border e-commerce compliance intelligent auditing platform and a multi-language contract analysis method, and relates to the field of cross-border contract compliance auditing. In the multi-modal data access step, customs codes, laws and regulations and other multi-source data are collected, and 18 kinds of language contract texts are analyzed; in the cross-language semantic alignment step, a knowledge graph is constructed, and multi-language legal concept mapping is achieved; in the compliance risk reasoning step, a rule engine and an agent cooperatively check a contract, and the compliance conclusion confidence is calculated; the dynamic risk assessment step adopts an LSTM network to analyze historical data and predict a risk trend; in the multi-language report generation step, a multi-format bilingual or multilingual report is generated based on a template engine, encrypted and archived. According to the invention, cross-border contracts are audited efficiently and intelligently, dynamic adaptation laws and regulations are analyzed in multiple languages, compliance risks are identified accurately, and a multi-language report is generated quickly; therefore, the checking efficiency is improved, the manual workload is reduced, the compliance risk is reduced, and the enterprise cross-border business competitiveness and the risk response capability are enhanced.

The invention provides a BIM intelligent management platform oriented to a whole life cycle of project construction. A building information model, Internet of Things sensing data and a block chain evidence storage mechanism are integrated through a multi-source data fusion technology, and a whole-process data chain of association planning, design, construction, operation and maintenance is associated. The platform adopts space optimization Huffman coding to realize model lightweight, combines a constraint genetic algorithm to optimize a construction path, and applies a bidirectional long-short-term memory network to analyze an equipment state. A three-chain block chain system is reconstructed on the architecture, intelligent association of engineering quantity and payment nodes is realized through cooperation of a main chain, a calculation quantity side chain and an auditing side chain, and mobile terminal offline interaction is supported based on a digital-analog separation technology. The platform covers an intelligent design management unit, a block chain investment management unit, a dynamic correction management unit, a quality safety responsibility tracing unit, an NLP risk management unit and a digital twin operation and maintenance unit. The units achieve cross-system cooperation through a unified data bus, and a closed-loop management architecture covering the whole life cycle of project construction is formed.

The invention discloses a self-adaptive intelligent teaching content recommendation system, and relates to the technical field of intelligent teaching, and the system comprises a data collection and analysis module which is used for collecting multiple types of data of students in the learning process, including learning behavior data, physiological signals, environment data and learning achievement cognition feedback data of the students, and after collection, sending the data to a database; performing preprocessing of noise reduction, standardization and analysis feature extraction on the multi-type data to generate multi-modal data; according to the method, the three-dimensional knowledge graph of knowledge points, error types and thinking paths is constructed, and the TCN analysis is combined, so that explicit knowledge defects can be identified, implicit knowledge vulnerabilities can be diagnosed, students can accurately know knowledge system vulnerabilities of themselves, and targeted defect checking, leak repairing and intensified training are carried out; learning interest information is extracted from multi-modal data, an explicit and implicit multi-dimensional interest model is established, and teaching content is screened in combination with a knowledge short board diagnosis result.

The invention relates to a data processing method and device based on big data and advertisement pushing, and the method comprises the following steps: obtaining the historical behavior data of a user on a multi-channel platform, constructing a dynamic interest label map according to the historical behavior data, and depicting a user interest evolution process. Combining with a social relation network to analyze an interest propagation path, forming a user social interest diffusion trajectory, and introducing a time decay weighting mechanism to generate a dynamic interest decay curve. According to the method, user interests and advertisement materials are subjected to semantic similarity matching, a personalized advertisement recommendation list is generated, an optimal advertisement putting strategy is determined through multi-target optimization configuration and comprehensive consideration of display positions, opportunities and forms, accurate and efficient advertisement pushing is achieved, and the problems that a traditional user portrait method often depends on a static label system, and the user experience is poor are solved. The dynamic characteristic that the user interest changes along with time is difficult to reflect, so that the advertisement recommendation content lags behind the real intention of the user.

The invention discloses a brain image analysis method and system based on multi-modal fusion, and relates to the technical field of brain image processing. A brain image analysis system based on multi-modal fusion comprises a data acquisition module, a data preprocessing module, a feature extraction and fusion module, a brain network analysis module, a comprehensive analysis module and a focus detection module. The comprehensive analysis model adopts a double-branch structure, deep processing is performed on multi-modal features and brain network features, and interactive fusion of the two types of features is realized through a cross-modal attention mechanism; the model is further combined with a classification branch and a regression branch to cooperatively complete brain disease classification and focus quantitative analysis, and the adaptive capacity and diagnosis performance of an existing model in a complex task scene are improved.

The invention discloses a market supervision data asset multi-dimensional evaluation method and system based on a hierarchy-network dynamic fusion model, and relates to the technical field of data asset evaluation. According to the method, a semantic association and index alignment relationship between data assets is comprehensively described by constructing a semantic map of the data assets; a hierarchy-network dynamic fusion model is formed by combining an analytic hierarchy process and a network analysis process, so that the determination of the index weight is more scientific and reasonable; a graph neural network is utilized to perform embedded representation learning on a data asset graph structure, comprehensive feature expression of each data asset is extracted, a multi-objective optimization method is combined, a Pareto optimal solution set is generated, scoring, sorting and grading are performed on the data assets, and reliability and interpretability of an evaluation result are ensured; the evaluation report is output in a structured data format and is provided for a market supervision system through an interface, thereby facilitating effective management and decision support for data assets by a supervision department, and having a wide application prospect.

In one implementation, a device obtains a natural language-based description of a network via a user interface. The device generates, based on the natural language-based description, network configuration parameters for the network using a generative model. The device conducts a simulation of traffic in the network using the network configuration parameters, to obtain telemetry data. The device uses the telemetry data to train a machine learning model to perform network analytics.

The invention relates to the technical field of intelligent decision making of production equipment, and discloses a decision making method and system based on a knowledge graph, and the decision making method based on the knowledge graph comprises the following steps: processing input data through a multi-granularity knowledge graph construction system, and obtaining a multi-scale knowledge graph; processing the real-time sensor data and the multi-scale knowledge graph through a time sequence knowledge dual representation learning framework to obtain a dynamic representation model; processing the dynamic representation model and the multi-scale knowledge graph through a time-varying causal propagation network decision system to obtain a decision analysis result; processing the decision analysis result and the multi-scale knowledge graph through a multi-hypothesis reasoning algorithm to obtain a root cause analysis report; a multi-scale knowledge graph is constructed by fusing multi-source heterogeneous data, and intelligent decision with high accuracy and strong interpretation is realized by combining time sequence knowledge dual representation learning and time-varying causal propagation network analysis.

The invention relates to the technical field of anesthesia effect evaluation, in particular to an anesthesia effect evaluation system based on big data, which comprises a physiological data processing module, a patient grouping and analysis module, an anesthesia risk evaluation module and an anesthesia adjustment support module. And collecting physiological indexes and types and doses of anesthetic drugs. According to the method, the real-time processing capacity of physiological data is enhanced by integrating historical health records and operation information, accurate evaluation of the instant effect of anesthetic is provided by deeply analyzing time sequence changes of heart rate and blood pressure, the response of different patient groups to the anesthetic can be recognized through grouping analysis, and the accuracy of the anesthetic is improved. Therefore, a targeted anesthesia scheme is realized, the anesthetic injection time and dosage are analyzed by using the LSTM network, the dynamic prediction of drug response is optimized, a doctor is supported to adjust the anesthetic dosage according to real-time data, the precision and safety of anesthesia management are greatly improved, and the anesthesia side effect risk is reduced.

The invention discloses a virtualized container environment monitoring device and method based on eBPF, and aims to solve the problems of poor isolation, insufficient security and platform adaptation deficiency in a virtualized container environment. According to the method, time sequence data of monitored events in a virtualized container environment are collected in real time through an eBPF technology, the time sequence data are transmitted to a data analysis module through eBPF mapping and message queues, data are analyzed through a long-short-term memory network, the deviation degree of the current time sequence data and historical data is calculated, abnormal behaviors are judged, and an alarm is given. According to the method, the user-defined program is operated under the condition that kernel codes do not need to be modified, intelligent analysis and abnormal behavior recognition are conducted on the collected data, comprehensive monitoring and dynamic detection of complex threats are achieved, the safety, stability and compatibility of the system are improved, and the method is suitable for virtual environments under cloud computing and big data scenes.

The invention relates to the technical field of intelligent teaching, in particular to an intelligent teaching system for traditional Chinese medicine clinical skill cultivation. The system comprises a virtual reality simulation module, an operation interaction module, an operation data analysis module, an adaptive learning feedback module and a teaching evaluation module. The virtual reality simulation module constructs a vivid traditional Chinese medicine skill training scene through three-dimensional modeling, ray tracing and physical simulation technologies, and the vivid traditional Chinese medicine skill training scene covers pulse feeling, acupuncture and moxibustion, massage and other operations. The operation interaction module collects user operation data and extracts features in combination with a variational auto-encoder. The operation data analysis module analyzes user operation deviation by using a graph neural network and a generative adversarial network, the adaptive learning feedback module generates a personalized learning strategy, and the teaching evaluation module evaluates a learning effect through fuzzy evaluation and analytic hierarchy process. According to the invention, intelligence, individuation and high efficiency of traditional Chinese medicine clinical skill cultivation are realized, the fidelity of teaching and the accuracy of feedback are improved, and the skill level and the training effect of learners are also improved.

The invention provides an influence maximization method and system based on deep reinforcement learning and a graph neural network, and belongs to the field of influence maximization in social network analysis. The method aims to optimize selection of seed nodes through a reinforcement learning technology so as to realize maximum propagation of information in a network, and the graph neural network can accurately capture an interaction relationship between nodes in a graph structure by virtue of a message transmission mechanism of the graph neural network, which is highly matched with an information propagation process in a cascade propagation model. Therefore, in the influence maximization model based on the deep reinforcement learning and the graph neural network, the graph neural network is combined with the deep reinforcement learning, the embedded representations of the nodes are learned by using the graph neural network, and the embedded representations are used as the input of the deep Q network so as to fully capture the complex structure and node relationship in the network. In addition, a multi-head attention mechanism is introduced to aggregate information of each round, expression of key features is enhanced, and information redundancy is effectively reduced.

The invention relates to the technical field of environmental monitoring, in particular to an ecological environment and environmental pollution monitoring system and method, and a multi-source heterogeneous data acquisition module comprehensively acquires water quality, weather and space monitoring data. The system comprises a topological mapping data preprocessing module which is used for abnormal data detection and data vacancy filling; the state evolution analysis module is used for constructing an environment state transition network and analyzing an environment state evolution rule; the environment comprehensive evaluation module is used for calculating a water safety level index and a water pollution condition index and generating an environment quality evaluation report; the pollution diffusion prediction module is used for predicting a pollutant diffusion path and concentration distribution and identifying a high-risk area; the intelligent early warning and visualization module is used for generating graded early warning information and visually displaying the environment state and the pollution diffusion condition through multi-dimensional data, so that the coverage and the data integrity of environment monitoring are remarkably improved, the environmental pollution problem can be timely found and dealt with, and the ecological environment safety is guaranteed.

The invention discloses a detection method for printing effect verification, and the method comprises the steps: collecting scanning image information and environment sensing information, employing an improved OTSU algorithm and an illumination compensation module to process the scanning image information, and extracting a standardized image feature matrix containing the characteristics of character integrity, edge sharpness and the like; identifying a defect mode by using an EAST text detection algorithm and morphological operation, and generating a quantitative evaluation vector; the vector and environment sensing information are subjected to space-time alignment, a printing quality degradation model is constructed through a density peak value clustering algorithm, and a multi-dimensional quality index set is established; and finally, based on the graph attention network and the time sequence convolutional network, analyzing the incidence relation between the printing quality and the equipment state, and outputting a verification report containing a quality score, a defect positioning graph and a life prediction curve. According to the method, accurate quantitative evaluation of the printing quality is realized, a dynamic association model of the quality, the environmental parameters and the equipment aging is established, and an intelligent decision basis is provided for printing quality maintenance.

The invention provides an Alzheimer disease auxiliary accurate identification method and system based on a multi-omics marker network and an adaptive support vector machine. Accurate identification of Alzheimer's disease and other types of dementia is realized through a marker network based on five modal data and an adaptive support vector machine model. According to the method, multi-source heterogeneous data such as blood, urine, neuroimaging, electrophysiology and clinical evaluation are fused, specific markers are analyzed and screened by adopting a weighted gene co-expression network, and cross-modal feature interactive learning is realized through a self-attention mechanism. An online learning mechanism is introduced to enable the model to adapt to new data distribution, and the contribution degree of each marker to diagnosis is output in combination with an interpretability module. Finally, multi-center data synchronization and model optimization are realized by means of a cloud platform, the accuracy, specificity and early diagnosis capability of AD diagnosis and identification and the adaptability of the model are remarkably improved, the limitation of the prior art is overcome, and doctors are assisted to diagnose and treat the Alzheimer's disease.

The invention relates to the technical field of social network analysis and influence prediction, and discloses a large-scale social network influence prediction system and method.The large-scale social network influence prediction method comprises the steps that a multi-language knowledge graph alignment system is constructed, and accurate mapping of cross-language concept nodes is achieved; constructing a culture vector space representation system, and extracting culture features from the social network user behavior data; the resonance intensity calculation between the content and the culture vector is realized, and the resonance intensity of the content in a specific culture environment is quantified; realizing culture gene transmission dynamics simulation, decomposing the content into transmissible culture gene units, and simulating the transmission process of the culture gene units; fusing prediction results to realize accurate influence evaluation; the technical problems that an existing social network influence prediction technology is inaccurate in prediction in a cross-language environment and neglects a culture resonance effect and culture dynamics are solved, and more accurate prediction support is provided for applications such as social media marketing and public opinion analysis.

The invention relates to the technical field of data processing, and discloses a child autism behavior recognition method and system based on data analysis and a medium. The method comprises the steps of collecting and preprocessing child multi-modal behavior data; extracting nonlinear features to obtain a time sequence feature matrix; identifying a repeated behavior mode through a three-dimensional convolutional network; analyzing behavior time sequence change by using a time convolutional network; fusing a plurality of feature representations to obtain a comprehensive feature vector; a multi-classifier system is applied to identify autism behavior types and evaluate severity. By extracting the nonlinear time sequence features, constructing the deep spatial-temporal feature extraction network and designing a feature fusion mechanism and a multi-classifier integration system, the method can overcome the limitations of a single mode, a linear feature and a single algorithm in the prior art, and improves the accuracy and interpretability of autism behavior recognition.

The invention discloses a content recommendation method and system based on an industry knowledge graph and reinforcement learning, and relates to the technical field of advertisement recommendation, and the method comprises the steps: collecting original advertisement data and user behavior data, carrying out the entity recognition, relation extraction and attribute extraction, and constructing the industry knowledge graph; a graph attention network is adopted to analyze association strength among different modal entity nodes in the industry knowledge graph, advertisement elements are dynamically recombined according to the association strength, and a personalized advertisement material package is generated; mapping the personalized advertisement material package into a reinforcement learning action space, extracting a user historical behavior path from the industry knowledge graph to construct a state space, and obtaining a reinforcement learning environment; in a reinforcement learning environment, mapping the user behavior nodes into a relation chain of an industry knowledge graph, allocating reward values to intermediate nodes, and constructing an RL strategy model; deep modeling and strategy optimization of user behavior intentions are realized, and relevance of recommended content and intelligence of decision making are enhanced.

Apparatuses, methods, and systems are disclosed for improving confidence of network analytics using a digital twin. An apparatus (800) includes a transceiver (825) that receives a request for analytics that includes a threshold minimum confidence level for the analytics and a specified time period for delivering the analytics and a processor (805) that determines a set of additional data to use for satisfying the threshold minimum confidence level available from a set of physical network resources associated with at least one digital twin instance. The transceiver (825) receives the set of additional data from the at least one digital twin instance and delivers the analytics at the specified time period in response to the analytics, derived based on the set of additional data from the at least one digital twin instance, satisfying the threshold minimum confidence level.

Various embodiments comprise a wireless communication network configured to anonymize network Key Performance Indicators (KPIs) to train a machine learning model. In some examples, the wireless communication network comprises a network analytics system and a KPI store. The network analytics system retrieves the network KPIs generated by network KPI sources. The analytics system filters the network KPIs based on an intended function of the machine learning model. The analytics system identifies correlated ones of the filtered KPIs and groups the correlated ones of the filtered KPIs into KPI groups based on a network condition. For each KPI group, the analytics system sorts the filtered KPIs into KPI ranges. For each KPI range, the analytics system tokenizes the KPI range by converting the filtered KPIs that compose the KPI range into strings. The KPI store stores the tokenized KPIs in a KPI database accessible by the machine learning model for training.

The invention discloses a public opinion event multi-mode semantic fusion modeling and abstract generation method and system, and relates to the field of natural language processing and social network analysis. Through the multi-mode semantic fusion technology, the short text understanding ability is improved, and the problems of semantic fuzziness and network language diversification are solved. Meanwhile, through a cross-window event cluster matching technology, an event evolution path with time continuity is constructed, and comprehensive capture of event dynamic characteristics is realized. Besides, the structured event abstract is automatically generated by utilizing the generative model, so that the consistency and the information density of the abstract are improved, and the actual application requirements are met. Through the innovations, the defects in the aspects of semantic comprehension, dynamic modeling and abstract generation in the prior art can be effectively overcome, a more efficient and accurate solution is provided for monitoring and analysis of public opinion events, and the method has wide application prospects in the fields of public opinion monitoring, emergency early warning, social media data analysis and the like.

In one implementation, a device pauses generation of computer code by a language model. The device matches a block of the computer code to a hint regarding a portion of the block of computer code. The device inserts the hint into the computer code. The device resumes generation of the computer code by the language model, wherein the language model uses the hint to generate a new portion of the computer code.

The invention discloses a blood, urine and body fluid collecting, detecting, analyzing and uploading system based on multi-sensor integration, which comprises the following modules: a blood collecting module for collecting a blood sample; the urine collection module is used for detecting urine; the body fluid collecting module is used for detecting body fluid; the data processing module is used for preprocessing data; the feature extraction and classification module is used for constructing a multi-modal feature matrix based on a neural structure search network, a support vector machine and a random forest algorithm; the anomaly detection module is used for generating a weighted anomaly score in combination with an isolated forest and a depth anomaly detection model; the time sequence analysis module is used for analyzing the health trend by using a variational auto-encoder-generative adversarial network; the health portrait and risk assessment module is used for constructing a health map by adopting a graph convolutional network; and the encryption and uploading management module is used for encrypting by using AES and RSA and managing the data access authority based on the intelligent contract. According to the invention, a multi-modal sensor and an intelligent algorithm are integrated, and collection, detection, analysis, encryption and uploading of blood, urine and body fluid are realized.

The invention provides a knowledge graph-based public security multi-source risk factor association identification analysis method, which relates to the technical field of risk identification, and comprises the steps of obtaining multi-source risk factor data, extracting information from unstructured data, constructing an initial association network, performing feature analysis and calculating a similarity matrix; and the close association subgroups are identified through community discovery, a multi-level association network is constructed, a conduction path is analyzed, a weight is calculated, and finally risk early warning information is generated. According to the invention, the complex association between public security risk factors can be effectively identified, and the risk prediction accuracy is improved.

The invention discloses a GNN-based multi-mode brain imaging infantile autism spectrum disorder detection method, which is characterized in that a function connection network which is different from traditional characteristics and takes an edge as a center is introduced, higher-order information interaction in a brain interval is mined, and a brain function abnormity mode of an infantile autism patient is accurately captured. Meanwhile, integrating brain imaging data and clinical text information into a unified multi-modal graph by utilizing the graph structure learning ability of GNN, capturing a complex nonlinear relationship, and realizing deep combination of multi-modal features; according to the method, the accuracy and robustness of autism detection are effectively improved, technical support is provided for personalized diagnosis and treatment, key technical problems in multi-modal data fusion and brain network analysis are solved, and the accuracy and reliability of ASD early diagnosis are improved.

The invention discloses an organization internal communication efficiency improvement method based on social network analysis. The method comprises the following steps: S1, collecting internal communication data of an organization; s2, preprocessing and standardizing the collected data; s3, constructing a communication network graph, and identifying communication frequency and intensity; s4, identifying communication bottlenecks and key nodes in combination with a social physics model and a graph attention network; s5, introducing a network fluid dynamic model and a swarm intelligence method to optimize a communication strategy; s6, continuously optimizing the network structure based on an adaptive optimization method and a context awareness technology; and S7, generating personalized communication optimization suggestions, and dynamically displaying communication network changes. According to the invention, the communication efficiency and decision-making ability in the organization are effectively improved.