1465 results about "Smart surveillance" patented technology

The present invention relates to the technical field of power system equipment and control. The method of the present invention comprises: installing sensors to acquire data for online real-time monitoring, and constructing a comprehensive condition online monitoring model for comprehensive data analysis; comprehensively evaluating the health condition of a generator on the basis of a comprehensive analysis result, and identifying fault causes; and constructing a generator comprehensive data intelligent monitoring and dynamic early warning model to perform insulation degradation trend analysis and prediction on the generator. In the present invention, by comprehensively monitoring the condition of a turbine generator, various key indicators of the generator are captured in real time, and trends and patterns underlying the data are revealed, providing technical support for accurately evaluating the health condition of the generator; and continuous monitoring for the insulation condition of the generator allows for proactive identification of potential risks, thereby providing decision-making support for preventive maintenance, helping operators take measures promptly, preventing faults, improving the reliability and safety of the generator.

The invention discloses an intelligent monitoring management method and system based on archive digitization, and relates to the technical field of data management, and the method comprises the steps: collecting and preprocessing multi-source archive data, employing a multi-mode BERT model to carry out the feature fusion of different data sources, and generating a unified semantic representation; semantic labeling is performed on archive data through a multi-label classification model, a semantic graph of archive content is constructed by using a graph database, an association relationship between archives is represented, a semantic index tree is constructed based on the semantic graph, and rapid positioning and calling of the archive content are optimized; and recording the change of each file version, positioning the change position based on a semantic index tree, identifying the semantic change of the file through a semantic difference comparison algorithm, recording hash, carrying out granularity division on the file content through the semantic boundary of each level of node in the index tree, and generating a user access strategy. According to the invention, dynamic perception and risk early warning of user behaviors are realized, and the intellectualization and safety of the archive management system are effectively improved.

The invention discloses a multi-target detection and tracking method, and relates to the technical field of computer vision and intelligent monitoring. The method comprises the following steps: acquiring multi-source video data of an unmanned aerial vehicle and a middle-high point fixed camera, and after scene adaptation preprocessing, outputting a target detection frame by using a multi-scale detection model fused with scene context; block enhanced appearance features and geometrical relationship features of the target are extracted to construct a dynamic feature library, and an initial track is generated based on a multi-stage adaptive association mechanism; through a child-mother type multi-machine collaborative optimization track, linkage control is triggered in combination with abnormal behavior analysis, and close-range evidence obtaining of the unmanned aerial vehicle and linkage of fixed equipment recording are controlled. According to the method, the multi-source data fusion capability, the multi-scale target detection precision and the trajectory association robustness in a complex scene are improved, and intelligent management and control requirements in the fields of traffic, forestry and the like can be efficiently supported.

The invention discloses a multi-level storage control method based on access popularity, and relates to the technical field of information, the method comprises the following steps: building a storage resource monitoring module, and collecting data volume change, read-write frequency and capacity occupation proportion information of each node from a storage system in real time; the method comprises the following steps: collecting original data, carrying out preliminary cleaning and formatting processing on the collected original data to obtain a standardized resource state data set, comparing predicted data with operation parameters of a current storage system, and if a predicted demand exceeds a current capacity limit, automatically generating a capacity expansion scheduling task to obtain a final resource optimization configuration scheme; according to the multi-level storage control method and device based on the access popularity, intelligent monitoring, load balancing and capacity planning of storage resources are achieved, the resource utilization efficiency and expandability of a storage system are improved, and an effective solution is provided for stable operation and performance optimization of a large-scale storage system.

The invention discloses a coal mine power supply intelligent monitoring system based on the Internet of Things, belongs to the field of coal mine power supply monitoring, and aims to solve the problems that an existing coal mine power supply intelligent monitoring system is lagged in response, high in false alarm rate and large in manual dependence degree. According to the invention, through the end-side global sensing module, the data advanced analysis module, the edge data processing module, the data transmission module, the cloud data analysis and model construction module and the fault early warning and closed-loop control module, the real-time acquisition of the equipment state is realized by deploying multiple types of intelligent sensors; local data preprocessing and abnormal pre-judgment are carried out by combining edge computing nodes, an equipment health degree model is established by adopting a time sequence data association analysis algorithm, closed-loop control of overload prediction, electric leakage positioning and energy consumption optimization is realized through multi-source data fusion analysis, and finally a three-level intelligent monitoring system of end side sensing-edge computing-cloud decision is formed. The system response efficiency and accuracy are improved, and the personal labor intensity is reduced.

The invention provides an intelligent monitoring and early warning method and device for photovoltaic energy storage equipment, and the method comprises the steps: carrying out the multi-time scale sliding window feature extraction of the operation data of a photovoltaic energy storage system, calculating a power coupling degree quantitative index and a system response feature index, and generating a real-time feature data set; based on the real-time feature data set, performing deviation propagation path identification under thermal dynamic constraint to obtain a real-time deviation propagation path diagram marking deviation sensitive nodes; performing multi-level dynamic deduction in combination with the real-time feature data set and the deviation propagation path map to obtain a dynamic deviation accumulation situation map displaying a deviation accumulation risk level and a development trend; and performing adaptive threshold early warning judgment according to the dynamic deviation accumulation situation map, and executing response control according to a hierarchical early warning mechanism to obtain a hierarchical early warning response control instruction. According to the method, the cumulative effect of the power prediction deviation can be effectively identified, and early warning of coordination imbalance of the photovoltaic energy storage system is realized.

The invention discloses a power distribution equipment remote diagnosis method based on edge computing, and particularly relates to the technical field of intelligent monitoring of power equipment, and the method comprises the steps: an edge computing node collects the operation state data of the power distribution equipment in real time; performing diagnosis analysis locally at the node to generate a preliminary diagnosis result and key data; uploading the structured data to a cloud according to a preset strategy, and checking the integrity; and the cloud platform performs association analysis on the multi-node data to identify common anomalies, dynamically optimizes a diagnosis algorithm, automatically triggers alarms and work order distribution in a grading manner, and realizes rapid closed-loop processing in combination with the positions and skills of operation and maintenance personnel. Through cooperation of the edge and the cloud, communication bandwidth occupation is reduced, diagnosis accuracy and real-time performance are improved, fault response time is shortened, and the method is suitable for line-level monitoring and operation and maintenance management of the power distribution network.

The invention discloses an urban road mobile source intelligent monitoring method and system based on multi-source data coupling, and relates to the technical field of environment monitoring and intelligent traffic. Utilizing a machine learning algorithm to construct a pollutant emission, carbon emission and energy consumption prediction model; acquiring vehicle inventory data in the city, and calculating the pollutant emission, energy consumption and carbon emission of the whole city; the method comprises the following steps of: constructing a dynamic distribution diagram of automobile emission in a city by using real-time position data of vehicles and urban road network information, introducing an atmospheric diffusion model, simulating pollutant migration by combining urban geographic information and environmental data, comparing and verifying a simulated migration result with actually measured data of a national control site, and optimizing parameters of the atmospheric diffusion model; and storing the result into a real-time database, and displaying the pollutant distribution, emission, energy consumption and carbon emission of the urban road network in real time through a visual interface. According to the invention, the temporal-spatial resolution and prediction precision of data are effectively improved.

The invention belongs to the technical field of intelligent monitoring, and particularly relates to a 5G Internet of Things fused video monitoring intelligent early warning method, which comprises the following steps of: acquiring vehicle attribute information and environment perception data, constructing a 5G Internet of Things perception network, fusing a vehicle movement track and road condition environment data through a cross-modal attention mechanism, generating an enhanced environment perception graph, and simultaneously, carrying out early warning on the vehicle movement track and the road condition environment data. Optimizing the transmission efficiency by adopting a dynamic resolution adjustment algorithm; the method comprises the following steps: constructing a dynamic behavior prediction model by using a space-time diagram convolutional network, predicting a vehicle abnormal behavior probability and an evolution trajectory, calculating an early warning level through an adaptive risk quantification algorithm, simulating a risk diffusion coefficient by using a dynamic risk propagation model according to the road section vehicle density, the average vehicle speed and the road traffic capacity, and correcting early warning sensitivity parameters in real time. And transmitting to a command platform, performing situation deduction, triggering a grading early warning instruction, and generating thermodynamic diagram warning information. Therefore, the problems of insufficient positioning precision, weak analysis capability, poor transmission efficiency and the like in the prior art are solved.

The invention relates to the technical field of cable asset management and fault prediction, and discloses a cable fault positioning method based on deep learning clustering analysis test waveform characteristics, and the method comprises the steps: collecting waveform and environment data in a cable operation period, and constructing a historical feature library comprising waveform, environment and position features; a self-adaptive detection model is adopted, and parameters are dynamically adjusted to adapt to different working conditions; multi-dimensional feature fusion and matching analysis are combined; a fault point distance is calculated through a signal propagation model and a time difference positioning algorithm, precise positioning is realized by fusing environment compensation and multi-point cross validation, and a three-dimensional geographic coordinate is generated by combining a laying path; and after multiple verifications, a structured report containing a fault type, a risk level, a prediction position, confidence and operation and maintenance suggestions is generated. According to the system, intelligent monitoring, fault risk prediction, asset optimization management and operation and maintenance decision support of a cable operation state are realized, and scientificity and economy of cable management in a complex environment are improved.

The invention relates to the field of image recognition, in particular to a wind power construction intelligent safety management method and system based on intelligent AI monitoring. The method comprises the following steps: obtaining an omnibearing real-time image flow of a wind power construction area, carrying out super-resolution deep convolution optimization and operator three-dimensional image segmentation, and extracting an operator three-dimensional image frame; three-dimensional point cloud modeling of the construction area is carried out based on the image flow, real-time image frame position positioning rendering is carried out according to a three-dimensional image frame, and a real-time twinborn model of the construction area is constructed; performing operation dynamic behavior analysis and behavior deviation degree quantitative analysis based on a twin model to obtain the behavior deviation degree of the operator; and according to the behavior deviation degree, carrying out early prediction analysis on illegal behaviors, making an adaptive risk early warning decision, and constructing an operation behavior risk early warning strategy. According to the invention, through real-time operation behavior identification and environmental risk analysis, the intelligence and safety level of wind power construction are improved.

The invention discloses an automatic soft soil foundation settlement monitoring system based on multi-source data fusion, and relates to the technical field of soft soil foundation monitoring, the system comprises an information acquisition module, a fusion processing module, a settlement prediction module and an intelligent monitoring module; the information acquisition module is used for acquiring foundation settlement sensing data and inputting the acquired data into the fusion processing module; the fusion processing module is used for preprocessing and integrating the collected data; the settlement prediction module is used for soft soil foundation settlement prediction; the intelligent monitoring module comprises a self-adaptive processing module and an interaction alarm module, the self-adaptive processing module is used for generating an optimization strategy, and the interaction alarm module is used for carrying out user interaction and multi-mode abnormal alarm reminding. An early warning response window is provided for engineering personnel, and the occurrence rate of sudden settlement accidents is reduced.

The invention belongs to the technical field of new energy power generation monitoring, and discloses a photovoltaic power station intelligent monitoring method and device, and the method comprises the steps: carrying out the feature extraction of a standardized multi-modal data set, constructing an attention weighting matrix, carrying out the weighted fusion of multi-modal features, and forming a multi-modal fusion feature vector; a hybrid prediction network of a residual connection structure and a bidirectional long-short-term memory network is adopted to predict the future operation state of the photovoltaic power station to obtain a predicted value, and real-time operation data are synchronously collected to obtain an actual value; by comparing the residual error of a predicted value and an actual value, identifying a potential abnormal mark and abnormal duration by adopting an adaptive dynamic threshold mechanism; and according to the amplitude, the change rate and the abnormal duration of the residual error, evaluating the severity of the fault, and generating early warning information. According to the method, the multi-modal data is collected and standardized, so that the data quality and consistency are effectively improved, and the reliability of subsequent analysis is ensured.

The invention relates to the technical field of photovoltaic power generation, in particular to a flexible photovoltaic intelligent monitoring and management system and method based on the Internet of Things, multi-source heterogeneous data are comprehensively collected through deployed multiple types of Internet of Things sensor nodes, the data are uploaded to a cloud platform after being cleaned and standardized through edge nodes, a big data processing architecture integrated with flow and batch is adopted, and the intelligent monitoring and management system and method based on the Internet of Things are established. The method comprises the following steps: performing real-time analysis and state judgment on a real-time data stream, performing deep batch processing and feature mining on historical data, extracting high-order features such as a performance attenuation trend and an abnormal mode, fusing real-time and historical features, and realizing comprehensive scoring of a health state of a component and accurate prediction of residual life by utilizing a machine learning model. And based on an evaluation result and a preset knowledge base, automatically generating a differentiated precise operation and maintenance instruction, and issuing and executing the differentiated precise operation and maintenance instruction to form closed-loop management. According to the invention, the monitoring depth and breadth of the flexible photovoltaic system are effectively improved, the conversion from passive alarm to active predictive maintenance is realized, and the operation reliability of the system is significantly enhanced.

The invention relates to the technical field of road construction quality control, in particular to an asphalt mixing station intelligent monitoring method and system based on Internet of Things data, and aims to solve the problems that in the prior art, technological parameters of an asphalt mixing station cannot be dynamically optimized, state vectors cannot be structured and defined as action spaces, and the working efficiency of the asphalt mixing station cannot be improved. The stability and convergence efficiency of strategy updating cannot be ensured, and long-term optimal control cannot be realized; the state and action space is constructed through the reinforcement learning strategy construction module, the multi-target reward function is combined, the reinforcement learning model is trained through the PPO algorithm, dynamic optimization of the technological parameters of the asphalt mixing station is achieved, the environment perception and regulation and control capacity of the model is enhanced through the structured state and the executable action, and the dynamic optimization of the technological parameters of the asphalt mixing station is achieved. The weighted reward mechanism overall plans quality, energy consumption and stability, and the PPO algorithm ensures efficient and stable training and supports long-term optimal control.

The invention provides an intelligent monitoring system of a liquid cooling cabinet and processing equipment thereof, and relates to the technical field of data processing, and the system comprises a cold source self-control module which is used for dynamically adjusting the operation state of a cooling tower of primary side circulation and the flow of a circulating water pump according to cooling water parameters, and achieving the variable flow control of cooling water; the dynamic environment monitoring module is used for monitoring the secondary side pipeline state and the cabinet environment in real time through the cooling capacity distribution unit and the distributed sensor network; and the comprehensive monitoring sub-module is used for realizing leakage positioning through a built-in liquid leakage sensor, a temperature sensor and a pressure sensor, and automatically supplementing liquid by using a liquid storage tank. Through multi-mode sensor fusion, dynamic threshold value self-adaption and multi-parameter collaborative analysis, accurate leakage positioning and graded alarm are achieved, automatic liquid supplementing closed-loop control and variable flow adjustment are combined, the system reliability is improved, and energy consumption is reduced.

The invention provides an interaction method based on artificial intelligence equipment, belongs to the technical field of artificial intelligence, and remarkably optimizes the efficiency of an AI equipment interaction system through distributed resource dynamic scheduling, multi-framework heterogeneous collaboration and full-link intelligent monitoring. Based on an elastic capacity expansion and contraction and priority scheduling strategy of Kubernetes, GPU resource islands are eliminated, and the computing power distribution efficiency is improved; a Flink checkpoint mechanism and Spark RDD blood relationship tracking are integrated, training task breakpoint continuous calculation and millisecond-level fault recovery are achieved, and repeated data processing is avoided; gPU node performance indexes are collected in real time through Prometheus Exporter, and in combination with Grafana visual early warning, the anomaly detection response speed is shortened to the second level; seamless access of frames such as TensorFlow / PyTorch is supported, heterogeneous device protocol conversion is achieved through a unified API gateway, and the cross-platform migration cost is reduced.

The invention discloses a vacuum circuit breaker state online monitoring method and system based on digital twinning, and relates to the field of intelligent operation and maintenance of high-voltage power equipment. According to the method, various sensors are arranged at key parts of the vacuum circuit breaker, breaking current, shielding case potential and vibration signals are collected in real time, and a digital twinborn model is constructed by combining multi-source data preprocessing, three-dimensional modeling, behavior rule definition and fault simulation analysis; and on-line identification and trend prediction of electrical wear, vacuum degree deterioration and mechanical abnormity are realized. The system has the functions of real-time monitoring, three-dimensional visualization, model self-learning and intelligent early warning, can be widely applied to intelligent monitoring and state evaluation of circuit breaker equipment in a power system, and remarkably improves the operation safety and operation and maintenance efficiency of the equipment.

The invention relates to the technical field of industrial process intelligent monitoring, and provides a digital twinning-based gas pressure regulation self-regulation intelligent monitoring system, which comprises a data acquisition unit, a digital twinning unit and an execution and feedback unit. The data acquisition unit acquires operation data of the gas pressure regulating equipment in real time, and the operation data are preprocessed and then transmitted to the digital twinning unit. The digital twinning unit constructs a physical mechanism sub-model based on the geometric topology and heat flow coupling characteristics of the gas pressure regulating equipment, and loads a data driving sub-model to form a mixed digital twinning model; and in combination with real-time data mapping and a disturbance-safety boundary condition set, executing predictive iterative simulation to generate a plurality of groups of voltage regulation schemes, and screening an optimal scheme. And the execution and feedback unit converts the scheme into a standardized control instruction to drive pressure regulating equipment and return operation data to realize closed-loop optimization, so that real-time monitoring, high-precision prediction and self-adaptive regulation and control of the gas pressure regulating process are realized under complex working conditions, and the safety, the stability and the energy utilization efficiency are improved.

The invention provides a lottery store violation detection method and system based on multi-modal data fusion, and relates to the technical field of intelligent monitoring, and the method comprises the steps: detecting a current abnormal event, carrying out time sequence perception target detection on monitoring video data, and recognizing violation electric equipment in a video image frame based on adaptive feature enhancement and multi-target tracking. The method comprises the following steps: acquiring time sequence data of current abnormity and illegal electric equipment detection, constructing a time sequence incidence relation of current abnormity, equipment detection and an electric state based on a dynamic causal network, calculating a time-varying weight and instantaneous causal intensity by utilizing conditional entropy increment, acquiring optimal time lag in combination with eigenvector conversion and a dynamic programming algorithm, and determining the current abnormity and illegal electric equipment detection according to the optimal time lag. And weighting the integral of the instantaneous causal intensity and the exponential function of the optimal time delay to obtain an event matching score, and distinguishing a temporary power utilization event and a continuous illegal power utilization event based on the event matching score.

The invention provides a port tallying vehicle monitoring management method and system, and relates to the technical field of port automation management, and the method comprises the steps: collecting the multi-dimensional tallying feature data of a port tallying vehicle through an Internet of Things sensing network, and carrying out the preprocessing; extracting a multi-dimensional tallying feature vector, and constructing a dynamic weighted directed graph model in combination with the port traffic situation; planning an optimal tallying driving path for the vehicle based on the dynamic weighted directed graph model, generating an optimal tallying operation plan, and pushing the optimal tallying operation plan to the vehicle-mounted intelligent terminal; when the vehicle executes the plan, monitoring vehicle position posture change and cargo state change through a multi-sensor fusion technology, and when an abnormal event is monitored, triggering a grading early warning mechanism and adjusting the plan; and after the vehicle completes the plan, the operation process data is verified based on the block chain distributed account book, and a digital twin report is generated and synchronized to the monitoring end, so that efficient, safe and intelligent monitoring management of the port tallying vehicle can be realized, and the operation efficiency and safety of the port are improved.

The invention discloses a road roller construction quality real-time monitoring system based on digital twinning, and relates to the technical field of road roller construction intelligent monitoring, and the road roller construction quality real-time monitoring system comprises a data acquisition module which uses a multi-modal fusion sensor network and edge calculation to comprehensively acquire and preprocess data; the digital twinborn model building module is used for modeling by combining physical-data dual drive with geological characteristics; the data transmission module is used for ensuring efficient and safe transmission by using a software defined network and a block chain; the real-time monitoring and analysis module is used for carrying out multi-scale space-time correlation analysis and generating virtual data; and the decision support module fuses deep reinforcement learning and a knowledge graph, supports man-machine cooperation decision, and provides intelligent suggestions for construction. According to the invention, the advantages are obvious, multi-modal acquisition and edge calculation ensure accurate and real-time data, a dual-drive model truly simulates construction, an advanced transmission technology ensures data safety, multi-scale analysis comprehensively evaluates quality, full-process coverage improves construction quality and management intelligence, and cost reduction and efficiency improvement are realized.

The invention relates to the technical field of power grid state monitoring, in particular to an intelligent monitoring and early warning method and system for a high-voltage power grid, and the method comprises the steps: collecting the multi-dimensional parameter data of a power grid node, and obtaining the multi-dimensional parameter data of the power grid node based on the relative deviation of the data of each dimension in a local window and the mean value of the data of each dimension in combination with the correlation coefficient of the data of each dimension; calculating parameter fluctuation attention at a target moment so as to correct parameter data of each dimension; processing the data points through a clustering algorithm to obtain a plurality of clusters, and selecting the cluster center of the cluster with the most data points as a power stability index; and calculating the relative deviation between the data point and the index, and generating a state early warning coefficient so as to estimate and evaluate the abnormality of the power grid node region and generate an early warning signal. According to the method, parameter fluctuation is accurately quantified by fusing the deviation degree and correlation of the multi-dimensional data of the local window, and a foundation is built for monitoring and early warning.

The invention discloses a multi-modal collaborative video sequence segmentation method. The method comprises the following steps: obtaining a multi-scale local feature matrix and a multi-scale global feature matrix of an image sequence; obtaining a multi-scale text feature matrix of the text sequence; obtaining a multi-scale local-global fusion feature matrix of the multi-scale local feature matrix and the multi-scale global feature matrix; obtaining a multi-modal fusion feature matrix of the multi-scale local-global fusion feature matrix and the multi-scale text feature matrix; and utilizing a decoder of the pre-trained large model to predict and generate a segmentation mask, and outputting a semantic segmentation map. The video sequence segmentation method is stable in performance when facing complex and changeable scenes, does not need to depend on a large amount of labeled data, reduces the training cost, and is suitable for various practical application fields including intelligent monitoring, automatic driving, medical image analysis and the like.

The invention discloses a hospital Internet of Things equipment intelligent monitoring and fault early warning system based on edge computing, relates to the technical field of equipment monitoring and early warning, and aims to solve the technical problem that fault discovery lags in a high-real-time scene of an existing intelligent monitoring and fault early warning system. The system is used for collecting various data of hospital Internet of Things equipment and sending the data to a preset storage position, and comprises a local database, a data processing module and an abnormal data judgment unit; the Internet of Things base station is used for being connected with Internet of Things equipment, collecting equipment data and achieving intelligent monitoring and fault early warning through an algorithm model, and the algorithm model is constructed based on a core algorithm and rules; and the hospital Internet of Things edge computing platform is used for carrying out edge computing management on the data sent by the Internet of Things base station, feeding back an analysis result and storing the data. The method has the advantage of improving the fault discovery speed of the Internet of Things equipment.

The invention relates to the field of industrial intelligent monitoring and fault detection, and discloses a coal conveying system intelligent monitoring and online anomaly detection system which comprises a data processing module, a feature extraction module, a dynamic tensor fusion module, an anomaly scoring module, a depth detection module and a decision control module. Carrying out pretreatment; the feature extraction module extracts features and converts the features into multi-dimensional feature vectors; the dynamic tensor fusion module fuses the multi-modal data into a unified tensor; the abnormal scoring module constructs a Markov decision process model and optimizes a scoring strategy; the depth detection module is used for extracting correlation characteristics among sensor modes and analyzing a time sequence dependency relationship of data; and the decision control module executes an intelligent control strategy according to the abnormal score and the depth detection result. According to the invention, the whole-process intelligent monitoring of the coal conveying system can be realized, the accuracy and real-time performance of anomaly detection are improved, the manual inspection cost is reduced, and the operation safety and stability of the system are enhanced.

The invention relates to the technical field of intelligent monitoring, in particular to a multi-thread low-power-consumption intelligent monitoring system based on an AI processor. The method has the advantages that aiming at the problems of unbalanced computing power and power consumption, high multi-task processing delay and strong hardware dependence in the prior art, the NPU module of the RK3588 processor is combined with the INT8 quantitative model, so that the power consumption is lower than 10W under the 6TOPS computing power; a dynamic multi-thread scheduling mechanism is designed, parallel processing of more than eight paths of video streams is supported through binding of a priority queue and an NPU core, and end-to-end delay is compressed to be within 200 ms; a zero-copy video stream architecture is constructed, data transfer is eliminated through memory mapping, and preprocessing time consumption is reduced by 90%; an energy efficiency control module is integrated, the NPU voltage frequency is dynamically adjusted according to the load, and the energy efficiency ratio reaches 0.83 TOPS / W; space-time alignment of multi-model reasoning results is realized by adopting a frame ID synchronization technology, and the mismatching rate is lower than 0.1%.

The invention relates to the field of multi-source data management, in particular to an intelligent security collaborative management system based on multi-source perception and a large language model. Comprising a multi-source data acquisition module which is accessed to various intelligent monitoring devices and is output and converted into a unified standard tuple format through a mapping function; the information fusion module is used for screening a candidate alarm set according to the space-time tolerance, and generating composite alarm information by adopting confidence ranking and semantic embedding weighted averaging; the RAG knowledge base module is used for generating a composite event description through a large language model and vectorizing the composite event description as a retrieval index; the intelligent retrieval module is used for acquiring related historical events by adopting a mixed retrieval strategy and constructing structured cue words; the LLM decision module is used for outputting root cause analysis and classification disposal suggestions based on the composite alarm and the priori knowledge; the double-path response module is used for distributing decision suggestions to management personnel and an agent system to realize collaborative execution; and the feedback optimization module is used for collecting disposal data and adjusting the weight of the knowledge base and the decision template to realize continuous optimization.

The invention belongs to the technical field of image processing, and particularly relates to an intelligent campus monitoring method for preventing campus bullying, which comprises the following steps: acquiring audio and video data of campus monitoring, preprocessing at an edge end and screening out suspected bullying segments; the method comprises the following steps: calculating an interaction anomaly index reflecting limb conflict non-equivalence by analyzing spatial and temporal changes of human body key points in a video; vocabulary extraction and analysis are carried out on the audio, and a semantic bullying index for evaluating speech threats is obtained; according to the signal-to-noise ratio of the field environment, dynamic weighting is carried out on the two indexes, and the two indexes are fused into a more accurate comprehensive bullying index; the system carries out graded early warning and accurate positioning on the bullying event based on the comprehensive index, and immediately pushes an alarm containing a position, a target and a real-time picture to a related person in charge, so that rapid and accurate intelligent identification and intervention on the bullying behavior are realized.

The invention discloses a strain culture monitoring method and system based on artificial intelligence, and relates to the technical field of data processing, and the method comprises the following steps: collecting multi-dimensional original data in a fermentation process, and carrying out the preprocessing to construct a multi-dimensional feature data set; training a bidirectional LSTM model through the multi-dimensional feature data to obtain a high-dimensional feature vector; performing dimension reduction processing on the high-dimensional feature vector through a sparse self-encoding technology to obtain a low-dimensional feature vector, and processing the low-dimensional feature vector through phase mapping and an extreme learning machine to obtain a target classification result; and constructing a Markov decision model based on the low-dimensional feature vector and a target classification result, and solving the Markov decision model through a reinforcement learning algorithm to obtain an optimal adjustment strategy. According to the scheme, intelligent monitoring and precise regulation and control of strain culture based on artificial intelligence are achieved, the defects of a traditional method in data utilization, stage adaptability and model generalization ability are overcome, and the intelligent level and production efficiency of strain culture are improved.