691 results about "Continuous optimization" patented technology

The invention provides an intelligent question and answer method and system based on a knowledge graph, and relates to the technical field of knowledge graphs, the method comprises the following steps: S1, integrating multi-source heterogeneous data to construct an initial knowledge graph; s2, analyzing the questions of the user, executing single-hop query and judging whether a result meets requirements or not; if not, entering S3, performing multi-hop dynamic search based on an adaptive path extension algorithm, and generating a reasoning path and evidence; s4, calling a large language model to combine with an attention mechanism to perform semantic matching on a result or a path, and screening an optimal answer; and S5, correcting the knowledge graph and performing closed-loop feedback to the query process to realize continuous optimization. Through cooperation of the knowledge graph and the large language model and combination of dynamic path search and semantic matching, the problems that a traditional method is insufficient in knowledge coverage, low in reasoning efficiency and poor in answer interpretability are solved, the method has the advantages of being efficient, accurate and self-optimized, and the performance and user experience in a complex scene are remarkably improved.

The invention provides an industrial control network security advanced threat detection system fused with artificial intelligence. The system comprises a multi-source data acquisition module, an intelligent analysis engine, a threat detection module, a dynamic defense module and a self-evolution learning system which perform data interaction in sequence. The industrial control network security advanced threat detection system fused with artificial intelligence realizes collaborative decision-making among the modules through a dynamic knowledge graph. Through multi-source data fusion, dynamic knowledge graph and lightweight model design, the core problems of protocol analysis, threat association, defense collaboration and model adaptability in the industrial control network security field are solved, and a full-stack protection system covering'perception-analysis-decision-response-evolution 'is constructed. The deep analysis capability of an industrial protocol is improved, the dynamic threat association analysis is broken through, the agility of a defense strategy is enhanced, and the feasibility of continuous optimization of a model is improved, so that a systematic solution is provided for advanced threat defense in a complex industrial control environment.

The invention discloses a predictive maintenance method for an optical storage and charging integrated power station based on deep learning, and the method comprises the steps: constructing an efficient equipment state evaluation and prediction model based on multi-source data fusion, an intelligent prediction algorithm and a closed-loop optimization feedback mechanism, collecting multi-source data, and carrying out the fusion processing, an improved Attention-LSTM model is utilized to evaluate and predict the state of equipment, a transfer learning method is adopted to improve generalization ability, Bayesian optimization and an adaptive sliding window technology are combined at the same time, dynamic threshold adjustment is performed, a deep reinforcement learning algorithm based on a Markov decision process is adopted to optimize a maintenance strategy, and the maintenance efficiency is improved. Weibull distribution is introduced for failure probability modeling, the maintenance cost and the fault risk are balanced, continuous optimization and dynamic adaptive adjustment of a predictive maintenance scheme are realized through a closed-loop feedback mechanism, the prediction accuracy and the intelligent level of maintenance decision are remarkably improved, planned maintenance and sudden fault maintenance are reduced, and the maintenance efficiency is improved. And the reliability of the charging station is improved.

The invention discloses an automatic instrument fault prediction system and method based on big data analysis, and belongs to the technical field of fault detection. The system comprises the following modules: an intelligent data processing and normalizing module which collects multi-source heterogeneous data of an instrument and an environment sensor in real time and performs data cleaning, standardization and quality evaluation; the working condition environment characteristic analysis module is used for identifying the current working condition state and quantitatively evaluating the influence degree of environmental factors on instrument operation; the multi-monitoring-parameter coupling analysis module is used for calculating and analyzing the mutual influence relationship among the monitoring parameters of the instrument and evaluating the coupling strength and influence links among the monitoring parameters in real time; the dynamic threshold calculation module is used for dynamically calculating and adjusting an early warning threshold system of each monitoring parameter; the fault prediction decision module is used for comprehensively evaluating various monitoring parameters and calculating a fault risk probability; and the early warning output and feedback module optimizes early warning output through an intelligent filtering mechanism, and collects early warning effect feedback for continuous optimization.

The invention provides an energy-saving temperature control optimization method and system based on a central air-conditioning simulation platform, and relates to the field of central air-conditioning simulation platforms, and the method comprises the steps: collecting the operation parameters of a central air-conditioning system in real time through an Internet of Things sensor network, constructing a multi-dimensional dynamic data set in combination with historical data and outdoor meteorological data, and carrying out the calculation of the multi-dimensional dynamic data set; a physical model and machine learning hybrid driven simulation engine is adopted, a dynamic thermodynamic model of the central air-conditioning system is established, and a multi-objective optimization function of a simulation platform model is defined according to user comfort requirements, energy consumption cost constraints and environmental policy indexes; and adopting a hybrid optimization strategy of fusion of reinforcement learning and a genetic algorithm, iteratively optimizing control parameters in the simulation platform model, deploying a digital twin system according to the energy-saving strategy set, predicting potential faults through a virtual sensor, correcting the parameters of the simulation platform model, and realizing closed-loop control and continuous optimization. The method is used for overcoming the defect that a closed-loop feedback mechanism is lacked in the prior art.

The invention provides an industrial control network security service security guarantee system based on behavior analysis, which belongs to the technical field of industrial control network security, and comprises a multi-source data fusion acquisition module, a dynamic behavior modeling engine, a federal learning analysis cluster, an attack chain prediction module, a self-adaptive protection strategy executor and a model evolution feedback ring, wherein the multi-source data fusion acquisition module synchronously acquires industrial control network flow (including OPC UA / Modbus / DNP3 protocol analysis), equipment operation logs, user operation behavior fingerprints and physical interface state data, and the physical interface state data comprises electrical characteristic fluctuation monitoring of USB / network interfaces. According to the scheme, through multi-technology fusion and closed-loop design, the problems of static performance, single-dimension analysis defects and response lag of a traditional industrial control security scheme are effectively solved, a comprehensive protection system with dynamic modeling, intelligent decision making, privacy protection and continuous optimization is constructed, and the security and service reliability of an industrial control network are remarkably improved.

The invention discloses a territorial space planning method based on big data, and belongs to the technical field of territorial space planning. Comprising the following steps: step 1, constructing a territorial space knowledge graph; step 2, constructing a multi-dimensional territorial space planning solution space, identifying key constraints and determining an optimization path; 3, searching and generating an optimal planning scheme in a solution space; 4, performing quantification and grading processing on hard constraints and soft constraints in territorial space planning to realize multi-objective comprehensive optimization; step 5, performing multi-dimensional confidence evaluation and validity verification on the planning scheme generated by optimization; and step 6, realizing continuous optimization and adaptive evolution of territorial space planning.

The invention provides a learning performance evaluation-driven teaching management method constructed based on a capability graph. The method comprises the following steps: S1, constructing a multi-dimensional capability graph; s2, a step of operating a multi-modal data acquisition system; s3, performing a dynamic capability value calculation model; s4, a step of constructing a real-time capability early warning system; s5, generating a self-adaptive teaching strategy; s6, a step of carrying out interdisciplinary ability association analysis; s7, generating a personalized learning path; s8, dynamically evaluating the teaching effect; and S9, dynamically optimizing the teaching resources. According to the method, the core problems of data lag, inaccuracy in intervention and one-sided evaluation in traditional education management are solved by constructing a dynamic capability evaluation model, and a quantifiable decision support system is provided for precise teaching. The innovation of the method is that differential equation modeling and reinforcement learning are combined, and continuous optimization and adaptive adjustment of the education process are realized.

The invention discloses a process automation execution method based on a large language model, and belongs to the technical field of artificial intelligence and process automation. User intention is analyzed through multi-modal input, and a structured task definition is constructed; the semantic reasoning layer is used for performing task layering, complexity evaluation and sorting optimization; the task execution layer completes subtask scheduling and execution; and the feedback and optimization layer performs performance evaluation and model updating based on execution data to realize closed loop and continuous optimization of the process, so that the technical problems of dynamically analyzing unstructured instructions, automatically optimizing a complex task dependency relationship and adapting to business changes in real time by a process automation tool are solved; according to the method, end-to-end conversion from an unstructured instruction to a structured task is realized, a subtask execution path is dynamically optimized, cross-platform tool calling is supported, the existing system integration cost of an enterprise is reduced, visual display task decomposition logic and prediction and execution time consumption comparison are provided, and the system credibility is enhanced.

The invention relates to the technical field of supply chain management, in particular to a supply chain middle station collaborative management method and system based on an AI intelligent decision engine. Comprising the steps of accessing multi-source data of each participant of a supply chain; utilizing a deep learning model to predict market demands and dynamically adjust the market demands; based on the prediction result, using an optimization algorithm to realize global optimization configuration and scheduling of resources; a machine learning risk assessment model is constructed, various risks are monitored in real time, and early warning is performed in time; a collaborative decision-making platform is established, online communication, negotiation and decision-making of participants are supported, and real-time data sharing is achieved; defining a performance indicator system, evaluating the performance of the supply chain in real time, and automatically adjusting a strategy for continuous improvement. The method can improve the demand prediction accuracy, optimize the resource configuration, enhance the risk response capability, improve the collaborative decision-making efficiency, realize the continuous optimization of the supply chain, and effectively solve the problems of unsmooth data circulation, low collaborative efficiency and the like in the traditional supply chain management.

The invention relates to the technical field of mining industry, and discloses an intelligent prediction method for gold ore beneficiation process parameters based on cloud and edge fusion, which realizes space-time correlation modeling of beneficiation process parameters and accurately depicts dynamic interaction influence among equipment. The cloud edge collaborative architecture considers global optimization and real-time response requirements, and the prediction stability under complex working conditions is effectively improved. The introduction of physical constraints enhances the applicability of the model in an actual production environment, a bidirectional feedback mechanism ensures the adaptive ability of the system in a dynamic change environment, and through the joint reasoning of a knowledge graph and a neural network, the consistency of a prediction result and a process principle is enhanced, and the risk of misjudgment under an abnormal working condition is reduced; the man-machine cooperation mechanism significantly improves the labeling efficiency of high-value samples, shortens the model iteration period, and ensures the continuous optimization capability of the prediction system in the actual production environment.

The invention relates to the technical field of data processing, in particular to a standardized detection result calibration method based on multi-modal fusion, which comprises the following steps of: performing high-precision space-time synchronization and standardization on different modal data, performing dynamic compensation by utilizing timestamp alignment, a cross-correlation function and an IMU (Inertial Measurement Unit), and performing dynamic parameter adjustment by adopting a local abnormal factor algorithm. Carrying out cross validation by utilizing inherent relevance of multi-modal data, constructing and continuously optimizing a high-confidence system state representation model, and realizing real-time identification and self-adaptive calibration of model parameters by adopting a Bayesian online learning framework; the method further comprises the steps that closed-loop recalibration is conducted through multi-sensor cross validation and residual analysis, an optimal action sequence is generated through a reinforcement learning agent, accurate prediction of key indexes of a monitored object is achieved, instant calibration and situational prediction can be conducted according to a specific event, and the intelligent level and maintenance efficiency of system monitoring are comprehensively improved.

The invention discloses an artificial intelligence-based industrial production line dynamic scheduling method and system, and relates to the technical field of intelligent manufacturing and industrial intelligent scheduling, and the method comprises the steps: collecting real-time state data, carrying out the preprocessing, taking the maximization of productivity, the shortest delivery time and the lowest energy consumption as optimization objectives, constructing a multi-objective reinforcement learning model, and carrying out the optimization of the multi-objective reinforcement learning model; scheduling priority data is generated, and operation distribution of each process node is adjusted in combination with a current equipment load threshold value and production bottleneck node information; when an abnormal condition is detected, triggering a rescheduling mechanism according to scheduling priority data, and updating an operation sequence and a resource allocation result; and synchronously feeding back the updated job allocation result and execution effect to the multi-target reinforcement learning model, and carrying out iterative optimization on the multi-target reinforcement learning model through a priority experience playback mechanism to realize continuous optimization of a scheduling strategy. According to the method, through a mode of combining multi-target reinforcement learning and dynamic scheduling, the learning efficiency and the optimization effect are improved.

The invention, which relates to the technical field of payment security, discloses a payment-scene-oriented interaction intention identification and error correction system comprising an input analysis module, an intention simulation module, a dynamic decision module, a biological verification module, an audit evidence storage module, and a cross-scene knowledge migration module. According to the method, multi-modal data such as voice, texts, images and touch tracks are integrated, structured feature vectors are generated through a cross-modal attention network, the problem of incomplete single-modal coverage is solved, cross-modal data consistency verification is achieved based on a unified semantic tag system, and the reliability of input sources is graded by combining equipment fingerprints and geographic positions, so that the reliability of the input sources is improved. A high-risk transaction protection capability is enhanced, a generative adversarial network is utilized to construct a virtual attack sample library, attacks such as tampering with characters similar in shape and AI faking voiceprints are simulated, unknown threats are actively defended through cosine similarity matching, a user historical behavior statistical model is integrated, and known risks such as high-frequency small-amount transfer are passively intercepted. And a closed-loop incremental learning continuous optimization model is supported.

The invention provides a question and answer system construction method and system based on a large language model, and the method comprises the steps: obtaining multi-modal data, constructing a question and answer knowledge base and a knowledge graph, and carrying out the dynamic updating of the question and answer knowledge base; obtaining a query text, and respectively carrying out vectorization processing on the query text and the multi-modal data to generate a corresponding query semantic vector and a multi-modal vector; an entity in the query text is extracted by using the recognition model, a triple associated with the entity is extracted from the knowledge graph, the query text and the triple are spliced and vectorized, and a query semantic enhancement vector is generated; according to the method, through a dynamic knowledge base incremental updating mechanism, a context-aware hybrid retrieval strategy, a cross-modal semantic enhancement technology and a user feedback-driven continuous optimization method, real-time processing requirements of various modal data such as texts, images and voices can be effectively met, and accurate semantic understanding and answer generation of complex queries are achieved.

The invention discloses a low-code development automatic generation method based on a large language model, which comprises the following steps: collecting natural language description information input by a user, and preprocessing; inputting the standardized demand corpus set into a large language model, and executing semantic understanding and context modeling; matching the low-code component library based on the structured semantic representation to generate component assembly description information; generating and verifying an engineering skeleton according to the assembly description information, and outputting an executable low-code application initial version; running the executable low-code application initial version, and monitoring and analyzing execution difference to generate an increment adjustment instruction; and inputting the increment adjustment instruction into the large language model, performing reconstruction and adaptive optimization, and outputting an executable low-code application final version. According to the method, large language model semantic understanding and adaptive optimization technologies are fused, automatic generation and continuous optimization of low-code applications are realized, and the method has the advantages of intelligence, high precision and engineering reliability.

The invention relates to the technical field of industrial safety, in particular to a factory safety intelligent management and control method based on a heterogeneous multi-system cross service fusion technology, and the method comprises the steps: collecting factory multi-source heterogeneous data, and carrying out the time-space alignment and fusion; constructing a risk assessment model based on a deep learning algorithm, and performing quantitative risk analysis on personnel behaviors, environmental parameters and equipment states; a dynamic grading early warning mechanism is established, and warning and automatic handling are achieved according to the risk grade; introducing risk trend prediction, and analyzing a risk evolution trend; and a safe closed-loop optimization mechanism is constructed, and self-adaptive adjustment of the management and control strategy is realized. According to the method, a full-process and dynamically-optimized intelligent safety management and control scheme is constructed, and comprehensive perception, quick response and continuous optimization of factory safety management are realized.

The invention belongs to the field of artificial intelligence, and discloses a scheduling strategy selection large model training method based on reinforcement learning, and the method comprises the steps: constructing a virtualized cluster environment; generating training data; carrying out large model base pre-training; learning environment interaction is reinforced; performing priority experience playback training; performing near-end strategy optimization; carrying out multi-expert strategy distillation; policy security verification is carried out; carrying out progressive online deployment; and feedback driving continuous optimization is carried out. According to the method, the resource topology, the task portrait and the dynamic load feature are decomposed to the special agent, and structured semantic tags such as a high-bandwidth demand task and a critical overheat node are generated, so that the heterogeneous resource state recognition accuracy is improved by 40%, and the multi-dimensional feature fusion efficiency is improved by 35%.

The invention relates to a data delivery risk early warning and intelligent management and control method and system. The method comprises the following steps: collecting flow characteristic data, user behavior data and environment characteristic data of data delivery in real time; establishing a dynamic baseline model, and generating reference range data of normal delivery behaviors based on historical data and industrial standards; performing anomaly detection on the user behavior data and the environment characteristic data by using a dynamic baseline model, and generating a risk early warning signal; when a risk early warning signal is received, identifying a risk type, and generating a corresponding risk level; and matching management and control actions in a preset strategy library based on the risk type and the risk level, executing the management and control actions, monitoring a feedback effect, and continuously optimizing dynamic baseline model parameters. According to the method, through dynamic reference generation, multi-dimensional data fusion and a continuous optimization mechanism, the problem of pain points of a traditional model in adaptability, comprehensiveness and maintenance cost is solved.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a product decision optimization method, device, equipment and medium based on a knowledge graph. And generating a user feature portrait in combination with the user related information, taking the user feature portrait and the information in the knowledge graph as an input state, taking the product information set as an action space, training and generating a decision model based on a preset incentive mechanism, outputting a target item by using the decision model, and updating the knowledge graph and the decision model based on user feedback information. The comprehensiveness of an input state is improved through a fusion modeling mode containing user information, product information and environment information, a dynamic updating mechanism is constructed in combination with a knowledge graph and user feedback, a decision model is driven to be continuously optimized, and then the pertinence of a recommendation result and the self-adaptive capacity of a system are improved.

The invention discloses a space-time large model intelligent decision support system, and belongs to the technical field of artificial intelligence and engineering. Aiming at the problems of insufficient speciality, lack of space-time processing capability and low decision-making reliability of a general large model in the engineering field, a system comprising a data processing module, a space-time large model module, an intelligent decision-making module, an interaction and integration module and a feedback learning module is constructed; the method comprises the following steps: formulating a multi-source heterogeneous data processing standard and specification of discrete storage in the engineering field, constructing a structured and specialized field data set and a knowledge graph, training a large space-time model with space-time processing capability by adopting technologies such as multi-modal alignment, and verifying and enhancing the reliability of a decision in combination with the knowledge graph; meanwhile, by means of a user feedback continuous optimization method and the like, an intelligent decision support system and application of space-time vector data processing, semantic alignment and illustrative and graphical aspects are achieved, and the accuracy, reliability and intelligent level of decision support in the engineering fields of mines, water conservancy, cities, traffic, factories, buildings and the like can be remarkably improved.

The invention discloses a cable life dynamic evaluation system based on multi-physics field coupling, and particularly relates to the field of industrial automation and control systems, which comprises a multi-physics field sensing module, a coupling analysis engine module, a dynamic life evaluation module, a digital twin interaction module and an environmental interference suppression module, through a distributed optical fiber temperature sensor, a capacitive electric field sensor and a magnetostrictive stress sensor, temperature, electric field, magnetic field and mechanical stress data of a cable are collected in real time, multi-physical field characteristics and a cable defect database are matched in real time by using a cross-scale dynamic association algorithm, a damage state is evaluated, and the cable defect detection accuracy is improved. A time sequence neural network architecture is adopted to predict the remaining life, model self-correction is achieved through digital twin comparison, interference is suppressed in combination with an environment-physical field coupling compensation matrix, sensing and evaluation of the health state of the cable, life prediction and continuous optimization of the model are achieved, and the efficiency of cable life evaluation is improved.

The invention provides a construction site safety information supervision system based on the Internet of Things, and relates to the technical field of data processing, the risk assessment accuracy is remarkably improved and the data noise interference is inhibited through a deep reinforcement learning dynamic optimization grading early warning strategy and in combination with a multi-source data compression and federated learning framework; a risk coupling model is constructed based on a complex network theory, a conduction path and a linkage effect between regions are analyzed, a global early warning scheme is optimized, and the adaptability of the system to a complex construction environment is enhanced; the model is continuously optimized through a dynamic weight updating mechanism, high adaptability is kept under the environment change of a construction site, meanwhile, a resource dynamic allocation strategy is adopted, the problem of low high-concurrency processing efficiency is effectively solved, and the overall accident early warning accuracy is improved.

The invention relates to the technical field of science and technology operation management and control, and discloses a science and technology operation intelligent management and control method and system based on big data. According to the method, firstly, heterogeneous data sources in the scientific and technological operation process are collected, and a standardized operation data set is generated through multi-modal fusion processing; performing dynamic feature classification on the key operation indexes, extracting time sequence features and spatial correlation features of the key operation indexes, and constructing a multi-level operation state graph based on feature importance weights; then matching a service rule base with the atlas, identifying abnormal nodes and resource conflict paths, and generating an optimization instruction set containing node repair priorities and conflict resolution strategies; an executable management and control operation sequence is generated; and finally, collecting a feedback data stream, and updating the service rule base and the feature importance weight through an incremental learning mechanism to form a closed-loop optimization link. According to the method, heterogeneous data can be effectively integrated, the operation problem can be accurately identified, the optimization strategy can be quickly generated, and intelligent management and control and continuous optimization of scientific and technological operation can be realized.

The invention relates to the technical field of bridge structure health monitoring, and discloses a bridge key position disease prediction method and system based on a graph convolutional neural network. According to a bridge design drawing and real-time sensor data, a dynamic topological graph reflecting an actual mechanical state of a bridge is constructed, and a real-time incremental graph convolutional network is designed to realize continuous optimization of a model. The mechanical relationship of the components is quantified into dynamically adjustable edge weights, so that the graph structure can reflect the real state of bridge load transmission in real time. The dynamic representation mode breaks through the limitation of a traditional fixed topological graph, so that the model can capture a disease propagation path more accurately. Meanwhile, an online gradient updating mechanism ensures that model parameters are continuously optimized along with new data, the inherent problem that a traditional offline training mode is difficult to adapt to data distribution drift and data lag is avoided, and prediction precision and timeliness are further improved.

The invention provides a robot control instruction analysis method and system fusing continuous instructions, and relates to the technical field of electric digital data processing, and the method comprises the steps: carrying out the quantitative evaluation of the instruction execution quality and resource consumption according to prediction data, and generating a quality score and a consumption estimation; constructing a multi-objective optimization framework based on the quality score and the consumption estimation, and determining a candidate path set; switching cost and execution time are calculated according to the candidate path set, and optimized path schemes meeting constraint conditions are screened; the instruction execution priority and the resource allocation proportion are adjusted in combination with the real-time environment data, and a scheduling strategy is generated. According to the method, environment changes and task quality are monitored in real time, instruction priorities and resource allocation are dynamically adjusted, when execution efficiency is lower than a threshold value, online updating of a prediction model is triggered, model parameters are finely adjusted to adapt to new conditions, and through continuous optimization and monitoring, the execution efficiency and task quality of an instruction sequence can be improved, and the execution efficiency is improved. And intelligent instruction scheduling and resource allocation are realized.

The invention provides a shield tunnel dynamic settlement compensation construction method based on an adaptive optimization algorithm, and the method comprises the steps: collecting the ground surface settlement, soil stress and underground water level data in real time through an Internet of Things sensor, achieving the data preprocessing and feature extraction in combination with an edge calculation node, constructing a three-dimensional geologic model, integrating the historical engineering data through transfer learning, and achieving the dynamic settlement compensation of a shield tunnel. The method comprises the following steps: identifying a high-risk area by using a clustering algorithm, designing a hybrid adaptive optimization framework with fusion of random forest and incremental learning, dynamically adjusting shield tunneling speed and soil bin pressure construction parameters, introducing an adaptive step length mechanism to cope with geological complexity change, and identifying a settlement abnormal mode through Fourier transform. Precise compensation is achieved in combination with a layered grouting strategy, the pressure of a soil bin is dynamically adjusted based on a hydraulic system, a closed-loop feedback mechanism is established, the predicted deviation rate is compared with an actual monitoring value, model parameters and the compensation strategy are continuously optimized, the settlement control precision is improved, and the construction risk is reduced.

The invention discloses an automatic code security optimization method based on a large language model, and belongs to the technical field of code security optimization. Acquiring large-scale programming data to establish a programming data set, establishing a primary training model, generating an initial code and establishing an initial code set; the predefined vulnerability mode set scans the initial code set, vulnerabilities in the initial code set are identified and defined as initial vulnerabilities, and the initial vulnerabilities are positioned and classified; static analysis is matched with a semantic discriminator to evaluate the code snippets to obtain a new generation strategy, and the primary generation strategy is adjusted to obtain an optimized training model; capturing potential vulnerabilities as training signals, and optimizing the optimization training model to obtain a continuous optimization training model; and establishing a vulnerability library and a repair strategy library, and optimizing vulnerability time and a repair algorithm by analyzing data of the vulnerability library and the repair strategy library. Potential vulnerabilities in generated codes can be automatically recognized and repaired, a large amount of manual intervention is not needed, and code generation safety is improved.

The invention relates to the technical field of combination of communication network management and artificial intelligence, in particular to an intelligent network traffic anomaly monitoring system fused with artificial intelligence, which comprises a data acquisition unit, a feature extraction and processing unit, an intelligent anomaly detection unit, a fault prediction and health management evaluation unit, a decision response unit and a continuous model optimization unit. Through multi-modal intelligent fusion, lightweight modeling and hybrid modeling technologies, real-time accurate identification and potential threat positioning of network flow abnormal behaviors are realized, network equipment faults and health states are predicted based on interpretable AI and physical law fusion, a transparent and prospective decision basis is provided for network operation and maintenance, and the network flow abnormal behavior prediction method has the advantages of high reliability and high reliability. The anomaly detection accuracy is greatly improved, the fault prediction advance exceeds 24 hours, and the network security and reliability are remarkably enhanced.

The invention discloses an intelligent management system applied to a convention and exhibition center, relates to the technical field of convention and exhibition intelligent management, and realizes unified and high-precision data input through protocol conversion and multi-mode denoising. Second-level alarm and topology reconstruction are supported in cooperation with anomaly detection and self-adaptive management on the edge side; the comprehensive interpretation precision of the passenger flow and the environment situation is improved by using a multi-modal fusion algorithm; then mapping the perception information into a virtual venue situation by means of a digital twinning and self-learning mechanism, and realizing continuous optimization of sensor calibration and algorithm weight; finally, multi-Agent complex network dynamics is introduced, interaction evolution of a large-scale crowd in an emergency scene is simulated, closed-loop management of active prediction and emergency layout is provided for intelligent exhibition, and leap-type improvement in multiple levels of monitoring precision, safety disposal, resource scheduling and the like is expected to be achieved so as to cope with exhibition layout adjustment.