579results about "Pyrometry for temperature profile" patented technology

The invention belongs to the technical field of photovoltaic panel surface defect detection, and discloses a photovoltaic panel surface defect detection method and system based on physical property analysis. The method comprises the following steps: firstly, acquiring a surface temperature distribution image, surface deformation data, ultrasonic echo data, a spectral image, spectral characteristic data and eddy current signal characteristic data of the photovoltaic panel by respectively utilizing an infrared thermal imager, a laser speckle interferometer, an ultrasonic flaw detector, a visible light multi-band imager and eddy current detection equipment; various abnormal regions such as temperature, deformation, ultrasonic echo, spectral characteristics and eddy current signals are determined; and then determining defect positions by integrating various abnormal regions, and determining the types and sizes of the surface defects of the photovoltaic panel by combining various data corresponding to the defect positions. According to the method, accurate positioning and identification of the surface defects of the photovoltaic panel are realized through a multi-physical property detection means, and the detection accuracy and reliability are effectively improved.

The invention relates to the technical field of intelligent detection in the electronic manufacturing industry, in particular to a PCB warping degree intelligent evaluation system integrating visual positioning and multi-modal sensing, which comprises a multi-modal sensing unit, a visual positioning unit, an intelligent evaluation engine and a closed-loop control unit, the multi-modal sensing unit integrates laser displacement, infrared thermal imaging and strain sensors to acquire three-dimensional deformation, temperature and stress data; the visual positioning unit realizes sub-pixel-level positioning by using a high-resolution industrial camera and a feature point matching algorithm, and compensates vibration errors; the intelligent evaluation engine fuses data based on a time-space synchronization protocol, predicts a thermal deformation trend through an improved multi-modal convolutional neural network, and dynamically adjusts a qualified threshold value; and the closed-loop control unit executes sorting and rechecking according to an evaluation result, and optimizes warping and leveling parameters. According to the system, multi-dimensional accurate detection and intelligent control are realized, the PCB warping degree detection accuracy is effectively improved, the process can be dynamically optimized according to the production working condition, and the equipment fault risk is reduced.

The invention discloses an intelligent monitoring and early warning system for the safety state of an electrical cabinet, and belongs to the technical field of electrical variable measurement, and the system comprises a data collection module which is used for obtaining multi-source sensing data of the electrical cabinet, and the multi-source sensing data comprises a current waveform parameter, an infrared temperature distribution parameter and a mechanical vibration spectrum parameter; the health feature evaluation module is used for generating a health state feature vector of the electrical cabinet by using the multi-source sensing data; the instruction generation module is used for outputting an early warning decision instruction according to a matching result of the health state feature vector and a preset fault propagation rule base; and the safety protection module is used for executing a safety protection action when the early warning decision instruction meets a preset execution condition. A multi-source sensing data fusion analysis technology is adopted to construct an electrothermal mechanical composite feature vector, a fault propagation rule base is coupled to realize cross-domain conduction path modeling, composite hidden dangers such as contact deterioration and the like can be accurately identified in a fault incubation period, and graded active safety protection measures are triggered.

The invention discloses a multi-source information collaborative power equipment three-dimensional temperature field construction method. Firstly, an infrared camera, an IMU and a laser radar are utilized to obtain an accurate external parameter relation through joint calibration, point cloud distortion is eliminated, angular points and plane points are extracted, a re-projection residual error, a distance sampling residual error and an IMU pre-integration residual error are constructed, an error state iteration Kalman filter is adopted to optimize a global pose, and positioning is achieved. Providing a self-supervised depth completion network, combining an infrared temperature image and a sparse depth map generated by a laser radar as input, adopting a depth completion strategy guided by an infrared image, estimating relative motion of adjacent frames by using pose information, introducing a feature alignment module to reduce alignment errors, and combining the depth map, the infrared image and IMU data to obtain a self-supervised depth completion algorithm; and efficient construction of the three-dimensional temperature field of the power equipment is realized. According to the invention, the three-dimensional temperature field of the power equipment is constructed more accurately, and the capability of the substation inspection robot for state monitoring and fault diagnosis of the power equipment is improved.

The invention relates to the technical field of building appearance defect detection, in particular to a building appearance defect detection method and system based on an unmanned aerial vehicle, and the method comprises the steps: obtaining the building information of a target building, and generating a hierarchical scanning path and a three-dimensional obstacle avoidance flight path, a visible light image, an infrared thermodynamic diagram and laser radar point cloud information are collected for space-time alignment processing, and an attention mechanism neural network is used for extracting multi-scale features to generate a detection report containing defect three-dimensional coordinates, damage levels and safety risk assessment. The method achieves the purpose of efficiently and accurately detecting the building appearance defects, can adapt to complex building structures and environmental conditions, supports defect trend prediction and maintenance decision, and remarkably improves the building safety management efficiency.

The invention provides an unmanned aerial vehicle identification early warning method and system. The method comprises the following steps: acquiring RGB image data, thermal radiation data and spectral data of an unmanned aerial vehicle no-fly zone through a visible light camera, an infrared thermal imager and a multispectral imager; performing transmission preprocessing on the RGB image data, the thermal radiation data and the spectral data, and adaptively adjusting multi-level fusion of a fusion weight based on real-time environmental parameters to generate target fusion data; based on a deep learning model and a tracking prediction algorithm, performing unmanned aerial vehicle identification early warning on the target fusion data, and generating early warning data; and transmitting the target fusion data and the corresponding abnormal event log to a cloud server, and updating the deep learning model by adopting the target fusion data and the abnormal event log. Through cooperative work of a multi-mode sensor, visible light, infrared, multispectral and other wave bands are covered, all-weather and full-scene unmanned aerial vehicle detection is achieved, the fusion weight is adjusted in real time based on real-time environment parameters, and the accuracy of the recognition result under the complex air situation is ensured.

The invention discloses a temperature measurement precision optimization method based on multiple sensors, and belongs to the technical field of temperature measurement, and the method specifically comprises the steps: deploying a temperature monitoring array which comprises a fixed position basic sensor group and a position adjustable auxiliary sensor group; scanning the target area through an infrared thermal imaging device to generate a thermal field temperature gradient distribution map; identifying a high dynamic change region boundary based on the thermal field temperature gradient distribution map, and extracting geometric feature parameters of the boundary; constructing a heat flow propagation prediction model according to the boundary geometric feature parameters, and calculating a key monitoring node space coordinate set on a heat conduction path; generating a sensor deployment instruction according to the key monitoring node space coordinate set, and dynamically scheduling an auxiliary sensor group to move to a target position; fusing the temperature data streams of the basic sensor group and the auxiliary sensor group, and executing space-time calibration calculation to generate an optimized temperature field distribution diagram; according to the invention, the coverage integrity and the result accuracy of temperature measurement in a dynamic scene are improved.

The invention discloses a grinding machine internal part temperature anomaly detection method based on vibration signal analysis, which comprises the following steps that grinding data are collected through sensor deployment, and the sensors comprise a temperature sensor, a vibration sensor, an infrared thermal imaging sensor, a magnetic resistance current sensor and an inductance type particle sensor; carrying out preprocessing and feature extraction on the collected data; model construction and training are carried out based on data of preprocessing and feature extraction; according to the method, the abnormal condition of the temperature of the part is predicted by detecting the vibration signal of the internal part, the content of metal particles in lubricating oil is detected through the oil analysis sensor, and the abrasion degree of the bearing is judged in combination with the vibration signal. And motor current harmonic characteristics are monitored, and overload or rotor imbalance problems are identified.

The invention provides a power management chip high-low side driving signal time sequence optimization method and system. The method comprises the following steps: acquiring temperature data of a power management chip under a switch switching transient working condition through an infrared thermal imaging technology, and synchronously acquiring three-dimensional temperature field distribution of junction temperatures on the surface and inside a packaging layer; based on the space continuity of a temperature field, extracting the temperature change characteristics of a power device area, converting the temperature gradient into Seebeck voltage by using a thermoelectric material embedded in a hot spot area, and constructing a dynamic thermoelectric feedback network; according to Seebeck voltage data, a coupling relation between hot carrier concentration and driving signal phase deviation is established, and disturbance of hot carrier mobility change on MOSFET switching delay is quantified; the rising / falling edge slope of the grid voltage is adjusted based on the coupling relation, the phase deviation caused by the hot carrier effect is eliminated through dynamic compensation, and the switching performance is optimized. The dynamic response precision of the power management chip is improved.

The invention relates to the technical field of power equipment state monitoring, in particular to a lightning arrester state monitoring method, system and equipment based on multi-physics field coupling and a storage medium. Acquiring current-voltage characteristic parameters, temperature distribution data and mechanical stress data of the lightning arrester, performing electro-thermal-mechanical coupling analysis based on multi-physical field monitoring data, identifying overlapping positions of an electric field distortion area, a temperature abnormal area and a stress concentration area, and determining the overlapping positions as a degradation key area; establishing a correlation response relationship between the leakage current and the temperature and a transfer response relationship between the temperature and the mechanical stress for the deteriorated key area; on the basis of the leakage current change trend of the degradation key area and the coupling influence of the superimposed temperature field and stress field, a degradation feature fusion factor is constructed, the evolution law of the degradation feature fusion factor in the time sequence is analyzed, and the degradation threshold value under the multi-field synergistic effect is determined in combination with the electric-thermal coupling acceleration effect and the thermal-mechanical coupling weakening effect.

The invention discloses a nondestructive testing method for infrared thermal imaging spatio-temporal information fusion, which relates to the technical field of nondestructive testing and comprises the following steps: establishing a linear laser heat source scanning infrared thermal imaging nondestructive testing system; collecting a dynamic infrared thermal image sequence; and calculating and analyzing dynamic infrared thermal image sequence data, and carrying out data processing on the acquired dynamic infrared thermal image sequence by using an infrared thermal imaging spatio-temporal information fusion post-processing method to obtain a full-field thermal response image with surface crack defects. According to the method, spatio-temporal information can be fused to obtain data with higher resolution, more comprehensive and accurate full-field thermal response characteristics are obtained while the calculation efficiency is improved, the method has better flexibility and wider applicability, and the purpose of nondestructive detection of metal surface defects is achieved.

The invention relates to the technical field of electrical equipment detection, and discloses an electrical equipment real-time state live detection method and system, and the system comprises a non-contact multi-source sensing array, an edge calculation unit, a high-frequency pulse excitation module, a self-adaptive installation structure, an intelligent diagnosis platform, and a self-energy-taking power supply unit. When electrical equipment state live-line detection is carried out, infrared thermodynamic characteristics, ultraviolet corona intensity and ultrasonic discharge signals are fused and collected through a non-contact multi-source sensing array, multi-parameter real-time sensing under the live-line condition is achieved, the problem of signal distortion caused by electromagnetic interference in traditional detection is solved, and the accuracy of state parameter extraction is improved; and meanwhile, the multi-dimensional feature data is subjected to standardization processing in combination with an edge calculation unit, so that the system can eliminate interference of environmental factors on detection results, the consistency of evaluation results under different working conditions is guaranteed, and state diagnosis errors are reduced.

The invention relates to the technical field of industrial intelligent inspection driven by artificial intelligence, and discloses a multi-modal property management scene risk point detection system based on artificial intelligence, and the system comprises a multi-modal data collection module which is used for synchronously obtaining visible light images, infrared thermal imaging and Internet of Things sensor data in building facilities; the domain self-adaptive defect generation module is based on a decoupling type generator and a StyleGAN2-ADA framework; a multi-modal feature fusion module; an attention enhancement detection module; a lightweight model compression frame; a real-time detection module; and a continuous learning module. High-fidelity defect samples are generated through the decoupling generator and the StyleGAN2-ADA, the problems of sample scarcity and class imbalance are solved, samples are generated through the cyclic generative adversarial network to cover multiple defect types, the acquisition period of the defect samples is shortened, and the requirement for training real samples is reduced.

The invention relates to the technical field of substation equipment fault diagnosis, in particular to a substation equipment thermal fault diagnosis method and system based on an infrared image. The invention discloses a substation equipment thermal fault diagnosis method based on an infrared image. The method comprises the following steps: acquiring an equipment temperature distribution image through an infrared thermal imager; carrying out denoising, contrast enhancement and normalization preprocessing on the image; extracting features such as temperature anomaly, temperature gradient and hot spot areas; a YOLOv13 model is adopted to identify the equipment type; inputting the features into a deep learning model for fault classification; and implementing multi-level alarm according to the classification result confidence. According to the method, temperature gradient analysis and a regional dynamic contrast enhancement technology are creatively fused, so that the fault detection precision and the early warning capability are remarkably improved, and intelligent diagnosis and graded early warning of the thermal fault of the substation equipment are realized.

The invention discloses an urban heat island effect decision-making method based on satellite data and artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the steps: obtaining satellite remote sensing data and urban basic data of a target urban region, the urban basic data comprising population density distribution, historical intervention effects, infrastructure information and urban function partition data; preprocessing the satellite remote sensing data to generate a standardized surface temperature distribution image, and based on the surface temperature distribution image, identifying an urban heat island area and dividing the intensity grade of the urban heat island area; the method has the advantages that satellite remote sensing data and urban multi-dimensional basic data are fused, dynamic scoring and optimization algorithms are combined, the heat island area is accurately recognized, the optimal intervention strategy is generated, the problems that a traditional method is insufficient in data utilization and poor in decision-making scientificity are solved, and the method has the advantages of improving treatment accuracy and resource utilization efficiency.

The invention relates to the technical field of unmanned aerial vehicle navigation, and discloses an unmanned aerial vehicle navigation method and system based on computer vision, and the unmanned aerial vehicle navigation method based on computer vision comprises the following steps: collecting a current visual field image of an airborne camera of an unmanned aerial vehicle, and carrying out adaptive image enhancement; performing scene semantic understanding; semantic perception feature extraction is carried out; feature matching and outer point elimination are carried out; floor identification is carried out, and multi-sensor fusion positioning is carried out; plane segmentation, structural constraint extraction and map normalization are carried out; performing visual navigation evaluation, judging a navigation mode according to an evaluation result, and generating an exploration control instruction quantity based on the navigation mode; generating a cross-floor path sequence, optimizing a track in real time, and performing control fusion in combination with a navigation mode identifier and an exploration control vector; according to the method, the navigation problem of the unmanned aerial vehicle in complex scenes such as smoke interference, multi-story buildings and illumination dramatic change in a GPS denial environment is solved, and high-precision autonomous navigation is realized.

The invention relates to the technical field of power monitoring, in particular to an urban rail power supply system fault early warning method based on multi-source data fusion, which comprises the following steps: acquiring a thermogram, extracting a temperature drift trajectory, marking a thermal abnormal point, identifying abnormal data by combining voltage sampling, analyzing the overlapping property of abnormal time and direction, and positioning a fault overlapping point. And identifying the linkage early warning area through spatial statistics. According to the method, the continuous drift trajectory of the temperature gravity center in the traction transformer thermogram is recognized, the thermal abnormal points are extracted, the voltage sudden change features are analyzed in combination with the voltage sampling sequences in the same time period, the composite recognition mode of time synchronization and trend direction comparison is achieved, the fault symptom positioning accuracy is effectively improved, and the fault symptom positioning accuracy is improved. By aggregating the fault coincidence points in the plurality of power supply sections and executing the spatio-temporal clustering operation, the capability of sensing common anomalies in the region can be significantly improved, and a potential linkage fault region can be clearly identified based on joint calculation of a spatial adjacent structure and a statistical aggregation degree.

The invention relates to the technical field of quality detection, in particular to a gas atomization powder sphericity degree multi-dimensional online analysis system and a method thereof.The gas atomization powder sphericity degree multi-dimensional online analysis system comprises a dynamic dual-mode imaging module, a transverse wind field auxiliary detection unit and a multi-dimensional feature fusion analysis module; the transverse wind field auxiliary detection unit is used for applying controllable transverse wind power through an airflow nozzle orthogonal to the powder falling direction and measuring the deflection track of gas atomization powder ball particles in combination with a laser displacement sensor. The online calibration module is used for spraying standardized spherical particles to the powder flow according to a preset period; the traditional density detection depends on destructive sampling and off-line measurement, the efficiency is low, and the whole production line particles cannot be covered; through an orthogonal wind field trajectory inversion technology, a density value and an internal defect mark are synchronously output through non-contact dynamic measurement, 100% lossless online full inspection of a production line is realized, and sampling limitation and aging bottleneck of a traditional means are broken through.

The invention relates to the technical field of data processing, and discloses a fire scene situation awareness and early warning method and system. The method comprises the following steps: performing gridding segmentation through infrared thermal imaging and visible light fusion, identifying similar electrical equipment and calculating a temperature baseline deviation value and an abnormal propagation coefficient, constructing a heat conduction coupling matrix to predict an abnormal diffusion probability, determining a fire development stage and generating a graded coloring situation visualization graph. According to the invention, the problem that a traditional infrared thermal imaging technology cannot realize intelligent identification of similar electrical equipment and prediction of a temperature anomaly propagation path is solved, and the accuracy of electrical fire hazard monitoring is improved.

The invention relates to the technical field of soil detection and multi-source information fusion, in particular to a soil moisture content cooperative detection method and system.The method comprises the steps that a target area is determined, and a target thermal infrared image of surface soil of the target area is obtained; extracting target characteristic parameters related to the moisture content from the target thermal infrared image, inputting the target characteristic parameters into the trained BP neural network, and predicting to obtain a surface soil moisture content distribution diagram; based on the surface soil moisture content distribution diagram, determining a target range region with abnormal moisture content through threshold comparison; after the air coupling stepping radar is driven to be aligned with the thermal infrared imaging system in a space-time mode, scanning is conducted in a target range area, and a radar reflection coefficient extracted from an obtained radar image and phase difference information serve as radar characteristic parameters; and performing modeling analysis on the radar characteristic parameters based on a support vector regression (SVR) model, and obtaining soil profile moisture content distribution of the moisture content abnormal region by constructing a nonlinear mapping relation and optimizing model hyper-parameter inversion.

The invention discloses an additive manufacturing defect detection method and system based on deep learning, and relates to additive manufacturing, and the method comprises the steps: collecting temperature field data and process parameters in an additive manufacturing process; the process parameters comprise a scanning path, energy input and wire feeding speed; the scanning path refers to the motion trail of the deposition head in the additive manufacturing process, and the energy input refers to heat needed by metal melting; the wire feeding speed refers to the speed of feeding metal wires into a molten pool; the temperature field data are preprocessed, and time sequence features are constructed; establishing a multi-branch fused detection model, wherein multiple branches comprise a crack detection branch and a pore detection branch; performing feature analysis on the time sequence features by using the detection model, and identifying defect types including cracks and pores; adjusting process parameters according to the identified defect type; aiming at the low defect detection precision in the additive manufacturing process in the prior art, the detection precision of linear cracks and dotted pores is improved by establishing two special crack and pore detection branches and the like.

The invention relates to an industrial gas tank storage place early warning method and system. The method comprises the following steps: acquiring image time sequence data at least representing temperature field distribution of a target monitoring area, generating a temperature image sequence, determining area temperature change characteristics and tank body surface reflection characteristics in the area, and generating reflection area probability distribution of the target monitoring area to determine initial gas leakage probability data of the target monitoring area; generating baseline feature data at least representing background thermal field distribution of the target monitoring area; and performing association processing on the initial gas leakage probability data and the baseline feature data to generate fusion time sequence features and determine a gas leakage diffusion weight so as to correct the initial gas leakage probability data to obtain corrected gas leakage probability data. By adopting the method, the gas leakage probability can be accurately judged by comprehensively utilizing temperature field time sequence information, tank body surface reflection characteristics and background thermal field distribution characteristics under the condition of a complex background thermal field, so that early warning is realized.

The invention provides a data co-processing method and system for a linear servo actuator. The method comprises the following steps: firstly, acquiring a multi-level temperature data set of a key heat source area in the linear servo actuator; performing dynamic coupling analysis processing on the multi-level temperature data set based on a thermal field prediction model to generate a temperature change trend prediction result; a dynamic heat dissipation strategy grade is matched according to the temperature change trend prediction result and real-time load current data, a heat dissipation execution control instruction is generated, and temperature feedback data in the heat dissipation process is monitored in real time; and based on a comparison result of the temperature feedback data and a preset temperature threshold, triggering a thermal failure protection mechanism to adjust the execution priority of the composite heat dissipation operation. According to the invention, power output can be reduced, external active heat dissipation control is triggered, equipment shutdown and other related instructions which are in effective contact with an abnormal working state can be triggered, so that the risk of equipment damage caused by abnormal operation of the actuator is effectively reduced, and the reliability and stability of the system are improved.

The invention relates to the technical field of packaging quality detection, and discloses an aluminum foil sealing quality detection system, which comprises a transmission module, a detection module and a control module, the thermal imaging acquisition module is used for acquiring temperature distribution data of a sealing area on the to-be-detected container; the image processing module is used for analyzing the temperature distribution data based on an image recognition algorithm and extracting defect characteristics of the aluminum foil seal; the temperature calibration module is used for dynamically calibrating the thermal imaging acquisition module according to preset parameters; the sorting module is used for removing defective products from the conveying module according to the extracted defect features; and the intelligent decision module is used for integrating the detection data and the production parameters, generating process optimization suggestions and outputting regulation and control instructions. According to the system and the method, high-precision real-time detection and classification of aluminum foil sealing defects are realized, and meanwhile, the automation level, the detection stability and the quality control efficiency of a production line are improved through dynamic sorting and process optimization suggestion output.

The invention discloses a photovoltaic module mismatch diagnosis system and method based on dual-mode thermal detection and AI dynamic optimization, and belongs to the technical field of fault detection of a photovoltaic power generation system.The photovoltaic module mismatch diagnosis system comprises a thermocouple sensor, an infrared thermal imaging module, a wireless data acquisition and transmission module, an AI intelligent data analysis system and a remote operation and maintenance management platform, and the inverter optimization control unit is respectively connected with the remote operation and maintenance management platform and the wireless data acquisition and transmission module. According to the invention, infrared thermal imaging large-range scanning and a surface ultra-thin flexible thermocouple sensor are combined, the temperature of key parts of the assembly is monitored in real time, and mismatch faults are accurately screened; adopting temperature gradient analysis, K-means clustering and a time sequence based on transfer learning to predict and recognize faults such as shadows, microcracks and poor welding, and performing remote early warning; mPPT parameters are dynamically adjusted based on a particle swarm algorithm, and power loss is reduced; the method improves the stability of a photovoltaic system, reduces the inspection cost, and is suitable for a large-scale power station and a distributed photovoltaic system.

The invention provides an intelligent target identification method and system based on an unmanned intelligent turntable. According to the method, an unmanned intelligent rotary table is used for collecting a thermal imaging video at night, a thermal radiation map is generated through compensation, and a background heat conduction coefficient is extracted. After the high-temperature heat source is positioned, calculating a centroid trajectory, and combining a background coefficient and an environment gradient to establish a dynamic reference temperature field; the heat source is divided into a contact sub-region and a non-contact sub-region according to the heat exchange rate difference, and the boundary mutation of the contact sub-region is marked. And analyzing the collaborative response of the temperature gradient change rate and the boundary deformation rule of the contact sub-region to judge an abnormal behavior. If abnormal, tracking parameters are generated based on the mass center acceleration to adjust the posture of the rotary table to keep the target in the middle, and non-contact subarea temperature diffusion is utilized to verify continuity so as to optimize tracking. According to the method, intelligent discrimination and adaptive closed-loop tracking of night abnormal behaviors are realized by analyzing the dynamic thermodynamic characteristics of the heat source contact area and the deformation rule cooperative response.

The invention discloses an infrared and visible light dynamic temperature measurement method based on multispectral fusion. The method specifically comprises the following steps: S1, synchronously acquiring infrared thermal radiation data and visible light image data through a multi-modal data synchronous acquisition module; s2, performing feature matching and region segmentation on the acquired data based on a multispectral data fusion module; s3, environment interference and heat conduction errors are corrected through a dynamic compensation algorithm module; s4, a visual temperature distribution diagram is generated based on the temperature field reconstruction module, and abnormal temperature early warning is triggered, the method is based on a transfer learning method, on the basis of an original task domain model, small sample transfer to a new task domain is achieved, the limitation of sample dependence is relieved, and the universal generalization of the model is improved.

The invention discloses a capacitor detection method and system, and the method comprises the steps: obtaining an original thermal image set of a capacitor in a power-on state, determining the temperature change rate of each point position of the capacitor based on the original thermal image set, and accurately describing the dynamic change condition of the temperature of the capacitor; performing abnormal point identification on the capacitor based on the temperature change rate distribution diagram to obtain a hot spot distribution diagram corresponding to the capacitor; based on the hot spot distribution diagram, heat flow transmission path analysis is carried out on the capacitor, the propagation track of the temperature change rate of the capacitor is determined, and an abnormal heat flow path diagram of the capacitor is obtained; according to the method, the abnormal heat flow and the internal hot spots are accurately identified, the key feature points are extracted from the abnormal heat flow path diagram, and the fault detection result of the capacitor is determined based on the difference between the key feature points and the preset template of the capacitor, so that the potential fault of the capacitor can be accurately and effectively detected, and the detection accuracy of the capacitor is improved.

The invention discloses a lightning arrester fault positioning method, which comprises the following steps of S1, installing an intelligent monitoring terminal externally connected with multiple sensors near a lightning arrester, monitoring the lightning arrester by adopting the multiple sensors, and preprocessing monitored data; s2, transmitting the monitoring data to a power operation and maintenance center by using the Internet of Things (IoT) to realize remote real-time monitoring; and S3, comparing long-term monitoring data with a normal lightning arrester database, and analyzing whether an abnormal trend exists or not. According to the invention, the fault condition of the lightning arrester is analyzed by combining the data monitored by multi-sensor fusion and adopting an intelligent analysis method, the problem that a slight fault and aging are difficult to distinguish is effectively solved, the fault position is corrected through the AI model, the fault positioning precision is improved, and the fault positioning accuracy is improved. In addition, the service life of the lightning arrester can be effectively prolonged in cooperation with the mode of intelligently adjusting the maintenance period through the AI prediction model, and the use requirement is met.

The invention discloses a power storage performance attenuation and variation detection method for an energy storage battery, and relates to the field of energy storage batteries. The method comprises the following steps: deploying a terahertz time-domain spectroscopy system and a microphone array on the surface of the energy storage battery, based on a terahertz time-domain spectroscopy system and a microphone array, acquiring a three-dimensional imaging map and a voiceprint signal of the energy storage battery at the beginning stage of charging the energy storage battery each time; creating a cloud database, and storing the three-dimensional imaging map and the voiceprint signal of the energy storage battery by using the cloud database; according to the method, terahertz time-domain spectrum three-dimensional imaging and voiceprint signal bimodal data are fused, battery global information acquisition is realized through distributed array scanning and matrix deployment, and a multi-dimensional attenuation evaluation model is constructed by combining dynamic storage and comparative analysis of a cloud database, so that the accuracy of battery attenuation evaluation is improved. A dynamic weight adjustment mechanism and ontology state information correction are introduced, so that the attenuation index estimation precision is remarkably improved.