1861 results about "Smart surveillance" patented technology

The present application provides a method and system for intelligent monitoring and early warning of meteorological disasters in power grids based on GIS, the method comprising: obtaining historical multi-source data including power grid equipment data, meteorological data and equipment operation data; standardizing the historical multi-source data to obtain a four-dimensional data model including longitude, latitude, altitude and time dimensions; constructing a disaster risk assessment model based on the four-dimensional data model using a deep learning method; inputting real-time multi-source data into the disaster risk assessment model to obtain disaster risk assessment results; making early warning decisions based on the disaster risk assessment results to generate early warning information; and generating emergency response plans based on the early warning information. The system comprises a data processing module, a model building module, a risk assessment module, an early warning decision module and an emergency response module. The technical solution of the present application realizes the effective fusion of multi-source heterogeneous data, improves the accuracy and real-time performance of early warnings, and improves the efficiency of emergency response.

The invention relates to the technical field of machine tool intelligent monitoring, and discloses a numerical control machine tool intelligent monitoring method and system based on the Internet of Things and a storage medium. The method comprises the steps of collecting machine tool multi-source data and transmitting the data to an edge node; original data is processed to obtain a working condition feature set, the working condition feature set is input into a recursive convolution hybrid memory network to construct a common dependency relation graph, and state parameter prediction is realized through time-frequency domain joint analysis; converting the two-dimensional feature map into a two-dimensional feature map, and extracting interaction features through SE-ResNet; fusing the two types of features to execute state classification; and predicting the life and generating a diagnosis report based on the health state judgment result in combination with the process parameters and historical data. According to the method, effective fusion analysis of multi-source heterogeneous data of the numerical control machine tool is realized, and a self-adaptive state recognition and fault early warning mechanism is constructed, so that accurate monitoring of the operation state of the machine tool, intelligent diagnosis of early faults and accurate prediction of residual life are realized.

The invention relates to the technical field of industrial automatic monitoring, in particular to a weftless tape machine intelligent monitoring system and method based on real-time data acquisition. The system comprises a data acquisition module used for acquiring real-time data of a multi-heterogeneous sensor; the data processing module is used for preprocessing the real-time data; the intelligent analysis module covers a feature extraction unit, an anomaly detection unit, a fault diagnosis unit, a service life prediction unit and a data fusion unit and can extract data features, detect anomaly, diagnose faults, predict the service life of a part and fuse multi-unit output to make a decision; and the early warning response module triggers corresponding levels of alarms according to the analysis result and generates a diagnosis report. According to the invention, comprehensive real-time monitoring, early fault early warning, accurate fault diagnosis, residual life prediction, multi-source information fusion and the like of the operation state of the weftless tape machine are realized, the production efficiency and the product quality are effectively improved, and intelligent management of production of the weftless tape machine is assisted.

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 provides a railway line monitoring alarm method and system, and relates to the technical field of computers, and the method comprises the steps: continuously collecting a dynamic monitoring data set comprising a track structure vibration waveform sequence, a track surface optical image sequence and an environment meteorological parameter sequence through a plurality of sensor nodes of a target railway line; carrying out multi-modal feature fusion processing on the dynamic monitoring data set, generating a comprehensive state evaluation matrix containing a vibration energy distribution vector and the like, carrying out risk alarm grade classification on the comprehensive state evaluation matrix by utilizing a preset track safety evaluation model, and outputting an abnormal event type code and an abnormal region position code; then, a multi-stage linkage control instruction set containing a train emergency braking triggering instruction and the like is generated based on the multi-stage linkage control instruction set, the multi-stage linkage control instruction set is synchronously transmitted to a central dispatching system, the central dispatching system is driven to execute rail blocking, train speed regulation and control and equipment maintenance operation according to instruction priorities, and comprehensive and intelligent monitoring and efficient alarm response to railway lines are achieved.

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 fresh corn circulation mildew-proof and quality-guaranteeing method based on intelligent monitoring, and particularly relates to the technical field of agricultural product circulation management.The method comprises the steps that quality data are collected through image, gas, humidity, temperature and microorganism multi-source sensing means, and a quality degradation trend curve and a risk grade score are generated in combination with a time sequence modeling algorithm; identification parameters are automatically adjusted in a high-humidity or oscillation environment, so that early-stage mildew identification accuracy is improved; a high-risk batch is identified through multi-dimensional cross analysis, and a circulation suspension response is triggered; the whole-process data is uploaded to a management platform, so that a traceable quality record is formed, and whole-process dynamic monitoring and closed-loop control are realized; according to the method, recessive mildew is recognized through multi-modal perception, the shelf life is predicted and evaluated through trend, the recognition stability is dynamically adjusted and improved, a chain type data tracing mechanism is constructed to achieve model self-evolution, the quality early warning, risk control and continuous optimization capacity in the whole fresh corn circulation process is improved, and it is ensured that the quality guaranteeing and mildew preventing effects are accurate and reliable.

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 a production process intelligent monitoring method and system based on an intelligent mine, and the method comprises the steps: collecting a target monitoring data set in the real-time production process of mine equipment, covering equipment vibration time sequence signals, environment temperature and humidity distribution data and an equipment energy consumption fluctuation curve, and carrying out the dynamic standardization processing, the method comprises the following steps: obtaining a standardized monitoring data set matched with an equipment type and a production process stage, calling a pre-trained multi-dimensional feature extraction model to carry out joint feature mapping, generating an equipment operation state, environment association and data abnormal fluctuation features, and carrying out dynamic fusion analysis on the features based on a preset abnormal mode identification model, and finally, according to the risk level, an alarm instruction is triggered, an optimization strategy is fed back to a production control terminal to adjust equipment operation parameters, and intelligent monitoring and optimization of the intelligent mine production process are realized.

The invention discloses a booster station monitoring operation and maintenance system, and relates to the technical field of data management. A plurality of heterogeneous intelligent sensors are deployed in a booster station to monitor the operation state of equipment in real time, an electromagnetic field sensor is utilized to sense the electromagnetic interference intensity, and when abnormal high-frequency pulses are detected, a data anomaly detection mode is triggered to detect the abnormal high-frequency pulses; calculating an error compensation value according to the abnormal degree, correcting abnormal data, ensuring the accuracy of the data, performing equipment state evaluation according to the recovered data, and if potential safety hazards are found, sending intelligent early warning to operation and maintenance personnel and providing suggestions for adjusting monitoring strategies or optimizing shielding measures; according to the invention, the anti-interference capability and accuracy of booster station monitoring data are effectively improved, the risk of operation and maintenance misjudgment caused by electromagnetic interference is reduced, the reliability of intelligent monitoring in an ultrahigh-voltage power transmission scene is enhanced, and a powerful guarantee is provided for safe and stable operation of a power system.

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 relates to the technical field of monitoring control, and provides an intelligent monitoring method for a dandelion and golden camellia fermentation process based on the Internet of Things, which comprises the following steps: deploying and self-calibrating a sensor array, extracting and analyzing various data features, and judging a fermentation stage and generating a regulation and control instruction according to the data features. According to the method, a precise fermentation monitoring and control method logic is formed, through omnibearing data acquisition and deep analysis, the change of each parameter in the fermentation process can be precisely grasped, the hypha growth rate, the flora metabolism intensity, the generation of a target product and other key indexes are monitored in real time, the fermentation conditions are adjusted in time, and the fermentation efficiency is improved. And the fermentation process is stabilized in an optimal state, so that the accuracy of fermentation production is greatly improved, the fluctuation of product quality is reduced, and the consistency and stability of the quality of each batch of dandelion and golden camellia are ensured.

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 provides a one-key start-stop operation monitoring method and system for a gas-steam combined cycle unit, and relates to the technical field of equipment state monitoring, and the method comprises the steps: obtaining operation data, carrying out the data cleaning, noise reduction and standardization, and extracting the performance characteristics of the unit; constructing a component correlation model based on the spatial-temporal characteristics, and optimizing operation efficiency parameters; and performing fault diagnosis by using a multi-modal feature enhancement network, constructing a layered optimization control system, and performing control strategy optimization to obtain an optimal control strategy. According to the invention, intelligent monitoring, fault diagnosis and optimal control of the gas-steam combined cycle unit can be realized, the operation efficiency and reliability of the unit are improved, the fault risk is reduced, and the service life of equipment is prolonged.

The invention discloses a three-dimensional panoramic intelligent monitoring method and device for a coal conveying gallery, and relates to the technical field of industrial automation and intelligent monitoring, and the method comprises the steps: obtaining initial point cloud data and an initial multispectral image of the coal conveying gallery through a rotary laser radar, a high-dynamic-range camera and an infrared thermal imager; inertial measurement units are arranged in the devices to collect vibration data, and the vibration data are used for carrying out vibration compensation on point cloud and images to generate target point cloud data and target multispectral images; thirdly, performing three-dimensional modeling and multispectral texture mapping on the coal conveying gallery based on the data to obtain a target three-dimensional model; and finally, mapping equipment, personnel and environment data into the three-dimensional model in real time, displaying each piece of state information, and realizing comprehensive monitoring. According to the application, high-precision three-dimensional panoramic monitoring can be realized in a complex vibration environment of the coal conveying gallery, and the safety management level and the operation and maintenance efficiency of the coal conveying gallery are effectively improved.

The invention provides a comprehensive financial IT operation and maintenance management system and method based on artificial intelligence, and the system comprises an intelligent monitoring and predictive maintenance module which is used for achieving the real-time monitoring, abnormality recognition and performance prediction of an IT system through collection of multi-dimensional time series data, and when an abnormal condition is monitored or a prediction result does not meet a preset requirement, the intelligent monitoring and predictive maintenance module carries out the operation and maintenance of the IT system; if so, triggering intelligent early warning; the AI-driven fault diagnosis and self-healing module is used for carrying out intelligent fault analysis and repair in combination with IT system logs and historical fault data based on the detected abnormal condition; the self-adaptive resource management module is used for predicting a future load trend and a future performance trend, and performing self-adaptive resource management based on the predicted future load trend and the future performance trend in combination with cost and energy consumption factors; and the security operation and maintenance automation module is used for monitoring security threats of the IT system in real time and automatically executing security strategies.

The invention relates to the technical field of engineering project management, and discloses an engineering project progress intelligent monitoring method and system. The method comprises the following steps: acquiring progress parameters including a task completion state, resource consumption and the like in real time through a multi-source data acquisition terminal; inputting the parameters into a preset anomaly detection model to identify and mark abnormal data segments, wherein the model dynamically adjusts a threshold value according to historical data characteristics; inputting the marked parameters into a collaborative prediction model to generate a progress trend prediction result, and carrying out multi-dimensional fitting on the model according to a task dependency relationship and a resource allocation weight; and finally, classifying and integrating the prediction result and the current progress parameter by using a priority scheduling algorithm to form a dynamic monitoring data set for storage. According to the invention, intelligent monitoring of the progress of the engineering project is realized, the monitoring accuracy and timeliness are improved, project delay and cost increase can be effectively prevented, and the project management efficiency is improved.

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 intelligent monitoring system for sewage treatment in the coal chemical industry, and relates to the technical field of sewage treatment.The intelligent monitoring system is characterized in that a water quality monitoring unit is used for collecting parameters such as COD, turbidity, pH and heavy metal ion concentration in real time, the change trend of water quality in a short time is predicted through a long-short term memory (LSTM) network, and then adding parameters of a flocculating agent are dynamically calculated; the defects of a fixed adding curve or artificial experience adjustment are overcome, so that the adding amount, the type and the speed of the flocculating agent can be adaptively adjusted to be matched with the change of sewage components; the addition optimization unit is combined with a CNN-LSTM model to analyze the rheological characteristics of the sludge, so that the addition of the flocculating agent adapts to the rheological change of the sludge, the flocculation effect is optimized, and the sludge settling performance is improved; meanwhile, the stirring control unit adjusts the stirring strength and time in real time based on ultrasonic online monitoring data, so that the flocculating agent is fully dispersed, the particle distribution state is optimized, the flocculation reaction is more uniform, and the subsequent precipitation separation efficiency is 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.