3349results about How to "Reduce failure" patented technology

The invention discloses an intelligent adjusting system of an adaptive multi-environment ship electric control valve actuator, which relates to the technical field of control valve adjustment and comprises an environment judgment module, a real-time monitoring module, an adaptive adjusting module and a feedback optimization module. The environment judgment module collects environment data through various sensors distributed on an electric control valve and around the electric control valve, and the environment data are processed through a signal conditioning and microprocessor application algorithm. According to the intelligent adjusting system of the self-adaptive multi-environment ship electric control valve actuator, the adaptability and the stability of the ship electric control valve actuator in variable environments are remarkably improved; the electric control valve actuator can monitor and respond to environment changes such as temperature, humidity, vibration and electromagnetic interference in real time, it is ensured that the electric control valve actuator can be accurately regulated and controlled under the extreme working condition, and therefore the reliability and safety of a ship system are improved.

The invention provides a power plant intelligent early warning method and system based on big data, and relates to the technical field of power systems, and the method comprises the steps: generating a high-dimensional feature map, generating a multi-modal feature tensor through adaptive chaotic mapping, constructing a feature relation network, generating a time sequence multi-dimensional feature vector, and reconstructing a multi-dimensional state vector based on topological entropy. In combination with a topological data analysis method, carrying out high-dimensional topological space mapping on the multi-dimensional state vector, identifying an operation mode of the power plant equipment, calculating a fuzzy region and a mode abrupt change point of the operation state of the equipment, and obtaining a dynamic evaluation result of the health state of the equipment; a multi-level recurrent neural network is combined, a prediction result is obtained through multi-scale time sequence prediction, uncertainty corresponding to the prediction result is quantitatively solved based on a quantum probability graph model, quantum probability distribution of an early warning signal is generated, a real-time operation strategy of equipment is adjusted, and an intelligent early warning signal of the power plant equipment is generated.

The invention, which relates to the technical field of the industrial internet, discloses an intelligent equipment data interaction system based on the industrial internet, comprising an industrial internet platform. The industrial internet platform is in communication connection with a data acquisition and preprocessing module, an edge calculation module, a data transmission module, an anomaly analysis module, an intelligent decision module, a feedback control module and a man-machine interaction module, and all the modules are in electric signal connection. According to the intelligent equipment data interaction system based on the industrial internet, the production efficiency is remarkably improved by monitoring and optimizing the production process in real time, data are collected through a sensor and an equipment state monitor, and abnormal states in the production process are quickly recognized and responded through preprocessing and analysis of an edge calculation module; and the intelligent decision-making module generates an optimization control scheme according to an analysis result and feeds back the implementation scheme of the control module, so that the risks of equipment failure and production interruption are reduced.

The invention discloses a screening efficiency online monitoring method, device and system based on a vibrating screen, and relates to the technical field of vibrating screen efficiency monitoring, and the screening efficiency online monitoring method comprises the following steps: collecting working parameter data of the vibrating screen in real time through a sensor arranged on the vibrating screen; the accuracy and stability of screening efficiency are improved by monitoring key parameters such as humidity, particle size distribution and vibration frequency in real time and combining a screening effect model and a dynamic correction algorithm. The system automatically adjusts equipment parameters, equipment overload or screen damage is avoided, the material passing rate is optimized, and production losses are reduced. Meanwhile, manual intervention is reduced through an automatic control mechanism, the automation level of a production line is improved, human errors are reduced, the equipment fault and shutdown time is shortened, the production efficiency is improved, and the service life of the equipment is prolonged.

The invention relates to the field of transformer parameter regulation and control, in particular to a parameter optimization method and device for a coupling type transformer. The method comprises the following steps: acquiring multi-dimensional real-time working monitoring parameters and real-time working environment state parameters of the coupling type transformer; performing nonlinear dynamic analysis and global state sensing modeling on the multi-dimensional real-time working monitoring parameters so as to construct a transformer global state sensing model; performing multi-parameter combination definition on the multi-dimensional real-time working monitoring parameters, performing dynamic operation simulation based on a transformer global state sensing model, and collecting operation simulation response data of a plurality of schemes; performing multi-target dynamic balance analysis on the operation simulation response data of the plurality of schemes to obtain an optimal transformer working parameter combination; and carrying out environment dynamic parameter change analysis on the real-time working environment state parameters, and constructing a real-time environment condition distribution field. According to the invention, efficient and self-adaptive adjustment transformer parameter optimization is realized.

The invention discloses an intelligent self-adaptive industrial robot control system and method, and the method comprises the steps: carrying out the automatic recognition and self-inspection of a sensor, an actuator and a controller module through a standardized interface when the system is started; various sensors are used for environment monitoring, acquired data are stored in a cloud end, and a sensor fusion technology is applied for integration; the system state and the environment condition are monitored in real time, machine learning and an artificial intelligence algorithm are used for analyzing the operation state and the task effect, and a corresponding operation strategy is formulated; based on task execution and environment information, an execution path is generated by adopting a path planning algorithm, and an operation strategy and parameters are dynamically adjusted according to real-time feedback; a real-time safety monitoring mechanism is introduced, and a safety threshold value is set; sensor data are analyzed regularly, potential faults are identified, and fault prediction is carried out; according to the intelligent self-adaptive industrial robot control system and method, when the robot is designed, the safety of man-machine cooperation is considered, and safety protection measures are added.

The invention provides a different types of database visualization table management method and system, and relates to the technical field of computers.The method comprises the steps that a database general operation system comprising a general operation interface and a dialect abstract base class is constructed, and a query performance evaluation system is established based on performance data; and predicting an optimal database connection mode through deep reinforcement learning and generating a multi-stage execution plan. Meanwhile, an anomaly feature knowledge base based on a graph neural network is constructed, and anomaly prediction and preventive processing are carried out based on the anomaly feature knowledge base. A multi-stage execution plan is executed after a database operation request is subjected to grammar adaptation, and a distributed transaction processor dynamically adjusts a transaction isolation level and a concurrency control parameter. And finally, according to the terminal and the data volume, selecting a proper data display mode to carry out visual presentation.

The invention relates to an electric power meteorological disaster risk early warning method and system based on artificial intelligence. The method comprises the following steps: cleaning and preprocessing basic meteorological data including temperature, humidity, wind speed and precipitation to obtain a cleaned basic meteorological data set; on the basis of satellite cloud picture or radar image data, meteorological changes are modeled through a neural network model, assimilation features are obtained, and the basic meteorological data and the assimilation features are spliced to form a joint feature vector; and constructing a comprehensive disaster risk assessment model based on the meteorological factors, the power grid factors and the geographic information, carrying out training and multi-objective optimization on the comprehensive disaster risk assessment model, and carrying out real-time electric power meteorological disaster risk early warning by using the optimized comprehensive disaster risk assessment model. According to the invention, meteorological data, power grid equipment states and geographic information can be monitored in real time, meteorological disasters possibly suffered by power equipment can be accurately warned, and the safety and stability of a power system are improved.

The invention relates to the technical field of power distribution network dispatching, in particular to an intelligent dispatching method and system for a power distribution network in an industrial park, and the method comprises the following steps: S1, data collection and preprocessing: collecting the real-time operation data of the power distribution network in the industrial park through an intelligent electric meter, a sensor and Internet of Things equipment; s2, performing real-time load prediction: performing load prediction on the industrial park power distribution network by using the load prediction model; s3, dynamic electric energy quality management: identifying electric energy quality abnormity and potential problems; s4, power supply scheme optimization and scheduling strategy generation: generating an optimal power supply scheme and scheduling strategy; s5, real-time monitoring and feedback: the running state of the power distribution network is monitored in real time; and S6, self-learning and optimization: analyzing historical scheduling data and an actual operation effect, and summarizing a scheduling rule and experience. According to the invention, the optimal power supply scheme is generated, and the economical efficiency and reliability of power supply are ensured.

The invention discloses a safe airtight cold-resistant low-smoke flame-retardant control cable used in an explosion environment, and relates to the technical field of cables, a first hot melting pipe is filled with expanded graphite, the outer side of a wrapping tape is uniformly sleeved with protective iron rings at intervals, the outer sides of the protective iron rings are uniformly provided with fixing holes at intervals, and a communication port is formed between every two adjacent fixing holes; according to the invention, the heat insulation cotton, the compression-resistant rib, the protection iron ring, the arc-shaped elastic sheet, the arc-shaped frame and the elastic roll absorb and buffer external force, so that the compression resistance and impact resistance of the cable are enhanced; the aluminum hydroxide powder absorbs a large amount of heat and delays combustion, the expanded graphite in the first hot melting pipe is heated to expand, the expanded graphite is diffused to a gap between the shielding layer and the wrapping layer, flame is prevented from combusting to the interior of the cable, the working time is prolonged, and the economic loss is reduced.

The embodiment of the invention discloses a code defect prediction method, a readable storage medium and a computer program product. The method comprises the following steps: constructing a plurality of prediction models and a plurality of agents; when the code library is updated, determining a plurality of update items, and obtaining new version codes of the plurality of update items; obtaining potential code defect types and potential code defect descriptions of a plurality of update items based on the new version code and the plurality of prediction models; generating defect reports of a plurality of update items based on the potential code defect types, the potential code defect descriptions and the plurality of agents; and carrying out authenticity verification on the defect report, and repairing the new version code after the authenticity verification is passed. According to the method, code defects can be efficiently and accurately predicted, and a defect report is given for repairing related defects, so that software research and development are facilitated.

The invention relates to the technical field of the Internet of Things, in particular to a short circuit fault monitoring system and method based on the Internet of Things. According to the method, fault mode identification is carried out based on current characteristic waveforms, prediction is carried out based on historical data, current waveform data are captured in real time by using a high-speed sampling current sensor, and abnormal changes in the current waveforms are identified and marked; an RNN deep learning model is constructed, features are extracted from waveforms, the features are compared with known fault modes, potential short circuit faults are recognized, the constructed deep learning model conducts reasoning based on real-time data, abnormal waveform features are quickly recognized, and therefore quick response is achieved, the fault detection time is greatly shortened, and the fault detection efficiency is improved. According to the method, short-circuit faults in a high-speed circuit are effectively handled, power flow data of multiple nodes are processed in real time by adopting a distributed data aggregation and analysis technology, a Bayesian network model is constructed, potential short-circuit faults are identified when power flow is abnormal, and specific nodes and paths where the faults occur are positioned.

The invention relates to the technical field of energy storage configuration, in particular to an energy storage configuration method and system for photovoltaic power generation. The method comprises the following steps: acquiring information data of a photovoltaic power generation energy station; performing distributed synchronous energy storage network construction on the photovoltaic power generation energy station information data to obtain a distributed synchronous energy storage network; performing real-time power generation data acquisition on the distributed synchronous energy storage network to obtain standard real-time photovoltaic power generation data; performing photovoltaic power generation prediction on the standard real-time photovoltaic power generation data to generate real-time photovoltaic power generation prediction data; performing historical electricity load data acquisition on the distributed synchronous energy storage network to obtain historical electricity load data; and performing load curve curvature trend analysis on the historical electricity load data to generate load curve curvature trend data. According to the invention, through real-time photovoltaic power generation prediction, load demand dynamic analysis and adaptive energy storage strategy optimization, the accuracy and flexibility of photovoltaic energy storage configuration are improved.

The invention provides a tap water treatment coagulating basin dosing stirring monitoring device which comprises a stirring motor and a stirring shaft, a mounting platform is erected on a coagulating basin, the stirring motor is mounted on the mounting platform, the stirring motor and the stirring shaft are in transmission connection through a coupler, and the tap water treatment coagulating basin dosing stirring monitoring device further comprises a dosing device and a sensor assembly. The chemical adding device comprises a chemical box, a chemical adding pump, a chemical conveying pipe and a rotary chemical conveying device, a plurality of chemical adding openings are formed along the stirring shaft, the chemical adding pump conveys chemicals in the chemical box to each chemical adding opening through the rotary chemical conveying device and the chemical conveying pipe, and the chemicals are put into the coagulating basin through the chemical adding openings. The dosing, stirring and monitoring device for the tap water treatment coagulating basin provided by the invention adopts an integrated design, integrates stirring and dosing to realize a compact structure and function fusion, can obtain key parameters of a water body in the coagulating basin in real time through the sensor assembly, realizes accurate and automatic adjustment of the stirring speed and the dosing amount based on monitoring data, and improves the working efficiency. The stability and the reliability of the coagulation effect are ensured.

The invention relates to the technical field of well lid machining, and particularly discloses an automatic grinding machine for a well lid, the automatic grinding machine comprises a supporting seat and a well lid body, a mounting frame is fixedly connected to the upper surface of the supporting seat, a motor is fixedly connected to the inner wall of the top of the mounting frame, and the output end of the motor penetrates through the mounting frame and is fixedly connected with a third gear; the top of the mounting frame is rotationally connected with a second gear, and the second gear and the third gear are meshed with each other; the well lid body can be stably clamped on the supporting base through the supporting block and the pressing disc, the lower surface of the well lid is tightly attached to the rolling wheel, when the motor drives the mounting base to rotate, the rolling wheel rolls to drive the grinding roller to rotate, the peripheral face of the well lid is ground, and during grinding, the grinding roller slides in the fixing plate to adapt to protrusions or burrs of different specifications; and the three groups of grinding rollers work in sequence, so that larger bulges and burrs can be quickly removed. In addition, the three sets of grinding rollers with different mesh numbers can meet the grinding requirement more quickly, and efficiency and quality are greatly improved.

The invention relates to the technical field of software application testing, in particular to an automatic intelligent testing system and method for software application. The method comprises the following steps: acquiring test environment parameters of software to be tested, calculating a dynamic waiting time reference value, and generating waiting mechanism configuration data; performing element positioning information collection and element positioning strategy generation on a user interface of the software application based on the waiting mechanism configuration data to obtain element positioning configuration data; performing conditional waiting strategy processing according to the element positioning configuration data and the waiting mechanism configuration data to generate conditional waiting mapping data; and performing visual AI auxiliary positioning processing according to the conditional waiting mapping data to generate visual positioning feature data. Through real-time monitoring of test environment parameters, the system automatically calculates and configures appropriate waiting time, so that the test process can flexibly adapt to various operation environments.

The invention relates to the technical field of thermal power plant production management, in particular to a thermal power plant DCS production process intelligent early warning system based on artificial intelligence, which comprises a data acquisition module for receiving real-time production data of each production link of a thermal power plant acquired from a DCS; the data storage module is connected with the data acquisition module and is used for storing the acquired real-time production data; the data preprocessing module is used for preprocessing the stored data; the feature extraction module is used for extracting key feature parameters from the preprocessed data; the model training module is used for training an artificial intelligence model based on historical data and the extracted key feature parameters; the real-time monitoring module is used for monitoring the production process in real time; according to the invention, the method captures the dynamic change of the time sequence data and the mutual relation between the multi-dimensional features, and achieves the more accurate prediction of the future time.

The invention relates to the field of hoisting equipment, in particular to a crane monitoring system and a crane monitoring method, and the operation efficiency, the safety and the reliability of the system are remarkably improved through real-time monitoring analysis, trend analysis and fault analysis. Real-time monitoring and analysis can respond to abnormal conditions in time, operation parameters are optimized, and the equipment utilization rate is improved. Trend analysis supports predictive maintenance and resource optimization, and downtime is reduced. Fault reasons are accurately diagnosed through fault analysis, detailed reports and prevention measures are provided, and safety is guaranteed. Data quality and analysis accuracy are improved through data preprocessing, and long-term stable operation of the system is ensured through historical data analysis and trend prediction. And the comprehensive report and data visualization assist in management decision making, and the operation cost is reduced. On the whole, the data analysis module brings comprehensive optimization and remarkable benefit improvement to the crane monitoring system.

The invention provides a sewage treatment progress monitoring method and system, and the method comprises the steps: collecting and processing a first water quality parameter value of a key node of a sewage treatment facility in a historical time period, constructing a dynamic model of a sewage treatment process through a deep neural network, and introducing a genetic algorithm to optimize the structure and parameters of the dynamic model. An optimized dynamic model is obtained, the optimized dynamic model is used for predicting the sewage treatment effect, and a prediction result and operation data of the sewage treatment facility are obtained; using a fuzzy logic control algorithm to adjust the operation parameter setting value of the sewage treatment system, and using a support vector machine algorithm to classify and identify the fault mode of the sewage treatment system to obtain a fault classification result; based on the prediction result, the operation data, the optimized operation parameter setting value and the fault classification result, a comprehensive monitoring management system based on the cloud platform is constructed; according to the invention, comprehensive monitoring and fault identification of the sewage treatment process are realized, and efficient, stable and reliable operation of sewage treatment is ensured.

The invention relates to the technical field of performance simulation analysis, in particular to a performance simulation method of a power module. The method comprises the following steps: acquiring initial physical characteristic data and initial electrical characteristic data of a power module; the initial physical characteristic data and the initial electrical characteristic data are preprocessed and simulated, and physical field distribution characteristics in the power module are obtained; performing multi-physical field simulation analysis on the power module based on physical field distribution characteristics in the power module by using an adaptive grid generation algorithm and sparse matrix solution to obtain simulation result data; performing parameter adjustment and optimization on a simulation model of the power module according to the simulation result data to generate optimized simulation model parameter data; and according to the optimization simulation model parameter data, performing simplification processing on the design of the power module to establish a simplified model. Through multi-stage simulation and optimization, the performance and the control precision of the power module can be remarkably improved, and the stability and the efficiency of the system are improved.

The invention relates to an intelligent fault prediction system based on deep learning, and aims to improve the fault prediction capability and operation and maintenance efficiency of an IT system. The system comprises a data collection module, a preprocessing module, a deep learning model module, a fault prediction module and an alarm feedback module. The system collects data, performs cleaning, normalization and feature extraction on the data, and performs training and prediction by using deep learning algorithms such as a convolutional neural network (CNN) and a long short-term memory (LSTM) network. The core technology is to analyze operation data in real time by using a deep learning model and generate a fault prediction result and early warning information. The fault prediction module analyzes data in real time, and the alarm and feedback module provides feedback for system optimization. According to the system, the accuracy and the real-time performance of fault prediction are remarkably improved, manual intervention is reduced, the maintenance cost is reduced, and the system stability and the operation and maintenance efficiency are improved. The method is widely applicable to the fields of data centers, network equipment and the like, and has remarkable technical and commercial values.

The invention relates to a rapid self-calibration method for field maintenance of a water quality sensor, and belongs to the technical field of water quality monitoring, and the method comprises the steps of starting the water quality sensor, recording initial data, collecting actually measured data, calculating an autocorrelation function, calibrating and adjusting, verifying a calibration result, and recording and reporting. Calibration parameters are dynamically adjusted in combination with on-site environment data, and fault pre-judgment is performed by utilizing machine learning, so that rapid self-calibration without a standard solution is realized, dependence on the standard solution is reduced, the calibration process is simplified, the problem of difficulty in on-site calibration of the water quality sensor is solved, and the accuracy of on-site calibration of the water quality sensor is improved. The method has the advantages of simplicity and convenience in operation, no need of standard solution, adaptability to field environment, real-time monitoring performance and self-adaptability.

The invention belongs to the field of machine tool machining, and relates to a tooth turning machine tool rest main shaft split type automatic tool changing mechanism which comprises a split type main shaft, a tool handle, a disc spring, a pull claw and a detection device. The split main shaft comprises a first main shaft and a second main shaft which are connected through a loose screw; the cutter handle is connected with the first main shaft through a pull claw, the pull claw tensions a blind rivet on the cutter handle through tensioning force generated by a disc spring, and the conical surface of the cutter handle is attached to the conical surface of the second main shaft; the detection device comprises a first detection switch and a second detection switch and is used for detecting whether the cutter handle is installed on the second main shaft or not. By adopting the split type main shaft design, even under the condition that the cutter is locked, the cutter handle can be easily ejected out by loosening the connecting screw, and the main shaft does not need to be integrally disassembled; by arranging the detection switch and the detection point used in cooperation with the detection switch, whether the tool is installed on the main shaft or not can be accurately judged, signal feedback is provided for the automatic tool changing process, and the accuracy and safety of the tool changing action are ensured.

The invention discloses a cable with a liquid cooling heat dissipation function. The cable comprises a closed cable section, an input cable section and an output cable section, the closed cable section is a completely-sealed cable section and is mainly used for liquid cooling heat dissipation of the cable; the input cable section is mainly used for transition connection between a power supply end and the closed cable section; and the output cable section is mainly used for transition connection between the closed cable section and a power utilization end. Through the synergistic effect of the insulating liquid cooling oil, the annular cooling fins, the complete through holes, the one-way through holes and the like, heat generated by operation of the cable can be rapidly absorbed and dissipated, local overheating is avoided, stable operation of the cable is ensured, and the heat dissipation efficiency is improved; compared with a mode of additionally arranging a spiral metal tube on the whole cable, the material and transportation cost is reduced, and the flexibility is not sacrificed; the high-quality material selection and the reasonable structural design ensure the electrical insulation performance, reduce the faults caused by the heat dissipation problem, and prolong the service life of the cable.

The invention discloses an unmanned aerial vehicle traffic jam and personnel gathering detection method based on AI identification, and the method comprises the following steps: S1, obtaining a real-time video and three-dimensional space data of a target region, and constructing a data set; s2, preliminarily processing the data set, and extracting key features; s3, the extracted key features are transmitted to a cloud server in real time through a 5G network, and an AI model is constructed and optimized; s4, performing multi-target detection and classification on the received key features to generate a multi-target detection result; s5, performing global scheduling optimization by adopting an intelligent scheduling algorithm of fuzzy logic control and particle swarm optimization; s6, identifying possible equipment faults in advance by using a graph neural network and an adaptive lifting algorithm; s7, monitoring the flight state and the recognition result of the unmanned aerial vehicle in real time; and S8, displaying the identification result and the analysis report through a visual interface. According to the invention, AI identification and intelligent scheduling are combined, unmanned aerial vehicle path planning and maintenance are optimized, and the method has the advantages of high efficiency, safety and high adaptability.

The invention discloses a power equipment defect identification method, system and device and a storage medium, and belongs to the technical field of power equipment defect identification. The method comprises the following steps: acquiring and processing a target infrared light image and a target visible light image of target power equipment to obtain an infrared light sub-image set and a visible light sub-image set; obtaining a feature similarity matrix, obtaining a target element set with the maximum sum of similarity values in the feature similarity matrix, carrying out key point calculation and optimization processing to obtain a target key point set, and constructing a mapping matrix of the target key point set; registering and fusing the target infrared light image and the target visible light image to obtain a target detection image; inputting the target detection image into the trained defect detection model for detection, and outputting a defect detection result of the target detection image; by combining the information of the infrared light and visible light images, the state of the power equipment can be reflected more comprehensively, so that the defect detection precision is improved.

The invention relates to the technical field of power system fault monitoring, in particular to a power system fault monitoring and active control system and method.The method includes the steps that a three-dimensional model of a power distribution network in a target area is constructed, the power distribution network is divided in a refined mode, power parameters of all segments are analyzed in real time, and the operation fluctuation value of the power distribution network is calculated; the method comprises the following steps: determining whether abnormity exists or not, identifying and determining an abnormal segment when the abnormity of the power distribution network is detected, then analyzing the influence degree of the abnormal segment and carrying out grade division to evaluate the potential threat of the fault to the power distribution network, automatically matching a corresponding fault processing channel according to the grade of the influence degree of the abnormal segment, and executing corresponding control measures. Therefore, the fault source can be accurately identified, the control strategy is actively adjusted according to the actual influence condition of the fault, the influence of the fault on the power system is effectively reduced, and the stability and reliability of the system are improved.

The invention relates to a park equipment operation monitoring method and system, computer equipment and a storage medium, and the method comprises the steps: carrying out the real-time collection of operation parameters of energy nodes in a park, carrying out the denoising and standardization processing of the operation parameters through signal processing, and obtaining standard parameter data; obtaining a park task list, monitoring the standard parameter data in real time based on a preset operation rule, and generating a state evaluation report of the power distribution system; key operation data in the ground source heat pump system are collected, evaluation is carried out through a trend analysis algorithm in combination with historical operation data, and an equipment operation prediction report is generated; acquiring real-time environment monitoring data and video monitoring data of a preset important area, performing environment state evaluation and safety state evaluation on the park, and generating an environment safety monitoring report; and constructing a virtual three-dimensional model of the park, and integrating the monitoring parameters to the virtual three-dimensional model. The method and the device have the effect of improving the monitoring efficiency of the equipment in the park.

The invention belongs to the technical field of aging state evaluation, and discloses a high-voltage dry-type bushing insulation aging state evaluation method based on frequency domain characteristics. According to the method, the FDS is adopted, and the aging state of the insulating material is evaluated according to the change of the complex capacitance, the complex dielectric constant, the dielectric loss factor and the like of a medium along with the frequency under a low-voltage sine alternating electric field. The FDS shows remarkable superiority in a plurality of aspects. The method comprises the following steps: firstly, measuring a dielectric constant and dielectric loss of an insulating medium in a wide frequency range by an FDS method so as to obtain response characteristics of the insulating material under different frequencies; according to the method, the frequency domain dielectric spectroscopy FDS is combined with the advantages of wide frequency range measurement, frequency domain feature recognition, comprehensive reflection of electrical characteristics and the like, so that accurate evaluation of the insulation aging state of the high-voltage dry-type bushing is realized. The safety and the stability of a power system can be improved, and faults and accidents caused by insulation aging can be reduced.

The invention belongs to the technical field of network self-adaption, and particularly relates to a weak network self-adaption algorithm system based on dynamic network monitoring, which comprises the following steps of: deploying a monitoring agent on a network node; extracting network bandwidth, delay and packet loss rate from the network state report, preprocessing the extracted data, and extracting feature vectors from the preprocessed data; adjusting the size of a buffer area for data transmission according to the network bandwidth and delay; and controlling the sending and receiving of data according to the adjusted algorithm and parameter, monitoring the data transmission state in real time in the data transmission process, feeding back the data transmission state information, and adjusting the algorithm and parameter in time according to the feedback information. The problem of how to improve the efficiency and reliability of data transmission under the conditions of complex and changeable network environment, low bandwidth, high delay, serious packet loss and the like in the prior art is solved.