12935 results about "Electric signal" patented technology

The invention relates to the technical field of discharge detection, in particular to a high-precision detection method and device for partial discharge of totally-enclosed GIS equipment. The method comprises the following steps: carrying out metal shell three-dimensional structure modeling on the GIS equipment, and carrying out electromagnetic shielding evaluation so as to obtain a multi-point electromagnetic shielding evaluation value; acquiring an original discharge signal of each sampling point based on the electromagnetic wave sensor, and performing interference suppression processing and back propagation attenuation compensation according to a multi-point electromagnetic shielding evaluation value, so as to obtain an attenuation compensation discharge signal waveform; micro-vibration response parameters are collected based on an MEMS micro-vibration sensor, actual influence waveform point detection marking and physical vibration interference filtering are carried out on attenuation compensation discharge signal waveforms, and therefore pure optimized discharge signal waveforms are obtained. According to the invention, through efficient and accurate partial discharge detection, real-time early warning of the state of the GIS equipment is realized, and the stability and safety of the equipment are improved.

The invention discloses a self-adaptive filtering and intelligent separation method for a multi-mode partial discharge signal, and relates to the technical field of insulation state monitoring and signal processing of power equipment, and the method comprises the following steps: S1, collecting the partial discharge signal of the power equipment in real time, and synchronously obtaining an equipment operation parameter and an environment noise signal; according to the adaptive filtering and intelligent separation method for the multi-modal partial discharge signal, the problems of modal aliasing and feature distortion caused by signal and noise time-frequency coupling in a dynamic noise environment in a traditional method are effectively solved through a cooperative mechanism of dynamic coupling degree modeling and dual-channel adversarial decoupling. The dynamic coupling path sensing module quantifies interaction characteristics of noise and signals in real time, and realizes accurate suppression of high-frequency transient interference and low-frequency periodic noise in combination with a double-channel architecture of complex field phase sensitive filtering and vibration trajectory matched filtering.

An outside handle device for a vehicle door comprises a handle main body provided at the outside of the vehicle door, a handle frame fixed to the vehicle door, a first connector provided at a vehicle body side and electrically connected with an electric signal cable at the vehicle body side, and a second connector provided at the handle main body and electrically connected with the electric component accommodated in the handle main body. The first connector and the second connector are placed with a predetermined distance when the handle main body is assembled in the handle.

The invention discloses a multi-parameter measurement system and method for a power frequency non-partial discharge test transformer, and relates to the technical field of state monitoring of high-voltage test equipment and power equipment, and the system comprises a synchronous trigger unit which is connected to a power frequency voltage signal collection end and is used for detecting a zero crossing point of a power frequency voltage signal and generating a synchronous trigger signal, a power frequency period is divided into a plurality of sub-windows with equal phase angles. According to the multi-parameter measurement system and method for the power frequency non-partial discharge test transformer, the problem of phase mismatch of a power frequency signal and a high-frequency partial discharge signal in traditional multi-parameter measurement is effectively solved through a power frequency phase locked synchronous trigger mechanism and a dynamic noise suppression technology; and the positioning precision of the partial discharge source and the insulation defect diagnosis reliability are improved. The nanosecond-level time sequence error control is realized, and the power frequency coupling interference is inhibited while the details of the high-frequency pulse are kept by combining a phase correlation dynamic filtering strategy, so that the signal-to-noise ratio of the weak discharge signal is improved.

The invention discloses a multi-modal data fusion power equipment health state diagnosis method and system, and belongs to the field of power equipment state monitoring, and the method comprises the steps: S100, collecting infrared thermal imaging data, vibration signals, current harmonic data and partial discharge signals of power equipment, and carrying out the time synchronization and space registration; and S200, acquiring infrared thermal imaging data, vibration signals, current harmonic data and partial discharge signals, and inputting the infrared thermal imaging data, the vibration signals, the current harmonic data and the partial discharge signals into the dynamic weight fusion model to obtain an equipment health state score and a fault type. And S300, according to the equipment health state score and the fault type, triggering grading alarm. According to the invention, timely early warning can be carried out on potential fault hidden dangers of power equipment.

The invention relates to the technical field of new energy battery testing, and discloses a new energy battery safety testing system and method, and the system comprises a multi-physical field synchronous collection and analysis module which is used for synchronously collecting electric signals, temperature field distribution and stress-strain data in a battery testing process, and carrying out the multi-field coupling analysis; a self-adaptive test parameter regulation and control module; the thermal runaway risk prediction module is used for analyzing and predicting the risk probability and residual safety time of thermal runaway of the battery based on multi-parameter fusion; the microstructure-macroscopic performance correlation analysis module is used for acquiring microstructure information of the battery through nondestructive testing and establishing a correlation model with macroscopic safety performance, and the new energy battery safety test system realizes collaborative analysis of multiple parameters of electricity, heat and force through the multi-physics field synchronous acquisition and analysis module; the safety state of the battery can be reflected more comprehensively, and the defect that a traditional testing method is single in dimension is overcome.

Embodiments of the invention provide a pen-stylus that comprises a first antenna having a hemispherical shape at a proximal end of the pen-stylus where the pen-stylus engages with a display of a tablet device. The pen-stylus also includes a second antenna, wherein the first antenna and the second antenna communicate with the tablet device to enable a pen-stylus user to draw on the display of the tablet device. The pen-stylus additionally includes an insulating material between the first antenna and the second antenna that prevents interference between the first antenna and the second antenna. In some embodiments, the first antenna and the insulating material reside in a marker tip attached to the pen-stylus, the marker tip transmits axial forces received by engagement of the pen-stylus with the display on the tablet to a force sensor. The pen-stylus further includes a force sensor that converts axial forces received from the marker tip into an electrical signal and transmits the electrical signal to the tablet by at least one of the first antenna and the second antenna.

The invention discloses a relay state prediction and fault early warning method and system based on deep learning, and the method comprises the steps: S1, building a constraint condition of a generative adversarial network based on a relay physical model, and forming an enhanced fault waveform signal according with a physical rule through adversarial training; s2, receiving a real-time current and voltage signal and a mechanical vibration signal, and extracting an electric signal feature vector by using a time sequence convolutional network; s3, inputting the combined feature tensor into the lightweight assessment model, and outputting a health degree scoring signal; s4, responding to the meta-learning activation instruction, loading historical data of equipment to construct a parameter optimization set, performing online fine tuning on the early warning model based on a meta-learning framework, and generating a fault determination parameter; and S5, analyzing real-time signal characteristics according to the fine-tuned judgment parameters, and outputting graded early warning signals to a monitoring terminal. According to the method, the problems of early state prediction and accurate early warning of the relay under small sample fault data can be solved.

The invention relates to the technical field of gas detection, in particular to a multi-gas accurate detection method and system.The method comprises the following steps that gas to be detected flows through a miniature gas enrichment device prepared through an MEMS technology under driving of a miniature gas pump, and target gas is adsorbed and enriched through graphene and metal oxide composite sensitive materials on the surface of a spiral gas inlet flow channel in the miniature gas enrichment device; the desorption concentrated gas is heated to the multi-gas composite detector, and the sensor array reacts with the gas to generate an electric signal; the data acquisition unit performs noise reduction, amplification and analog-to-digital conversion preprocessing on the electric signal; the data processing unit extracts characteristic vectors such as a response curve peak value and a rising slope; inputting the feature vector into a pre-trained machine learning classification-regression combined model, and outputting a gas type identifier and an initial concentration; error compensation is carried out in combination with environment temperature and humidity and a cross sensitivity interference mode library, and finally accurate information of at least nine gases is output. The device has the advantages of high sensitivity, strong anti-interference performance and miniaturization.

The invention relates to the technical field of electroencephalogram signal processing, in particular to an ontology brain wave audio auditory perception synchronous feedback method, device and system and electronic equipment. The method comprises the following steps: receiving electroencephalogram signals of a collected user on line from electroencephalogram collection equipment through an upper computer, obtaining electroencephalogram signals of a specified frequency band from the electroencephalogram signals, and extracting corresponding electroencephalogram characteristics; according to the electroencephalogram features or preset rhythm parameters, the electroencephalogram signals of the specified frequency band are segmented into a plurality of electroencephalogram segments, a plurality of audio expressions corresponding to the electroencephalogram segments are generated, and feature parameters of the audio expressions are determined according to the electroencephalogram features of the corresponding electroencephalogram segments; generating brain wave audio representation data according to the audio representation corresponding to the electroencephalogram signals of the one or more designated frequency bands, and obtaining brain wave audio according to the brain wave audio representation data. Therefore, the physiological suitability of nerve regulation and control and the artistic expressivity of audio generation are met at the same time, and organic unification of nerve regulation and control and audio generation is achieved.

The invention relates to the technical field of electrical equipment defect monitoring, in particular to a high-voltage equipment defect positioning system and method based on multi-modal image fusion, and the system comprises an ultraviolet triggering module, a modal synchronization module, a space positioning module, a structure recognition module and a fusion diagnosis module. According to the method, automatic calibration of a suspected defect area can be realized by performing quantitative threshold judgment on a corona discharge signal in an ultraviolet image, the consistency of three-mode data in space and time dimensions is ensured, and the accuracy of the defect area is improved by comparing three types of coordinates of a lead hot spot, boundary geometry and a spot center in the image and performing registration offset correction. The image feature alignment precision is improved, the insulator chain light spot distribution path is extracted, the continuous distribution length is calculated and corresponds to the pollution level standard, automatic identification of the pollution level is achieved, a multi-modal image fusion feature block is constructed, and a risk sorting label is given. And the defect positioning accuracy and the comprehensive evaluation capability on the defect property and the influence degree are effectively improved.

The invention relates to a weak current cable fault positioning method and system, and the method comprises the steps: preliminarily locking a fault region through monitoring the axial stress change in real time, injecting a low-frequency pulse signal in a target section, analyzing the reflection waveform characteristics, and extracting the waveform distortion characteristics in a reflection signal; dynamically adjusting the output impedance based on the amplitude attenuation and time delay relationship, and if the dynamically adjusted output impedance cannot enable the output impedance of the injected low-frequency pulse signal to be synchronous with the impedance change trend of the cable, marking as an impedance mismatch point; after an impedance mismatch point is identified, other interferences are eliminated through multi-band signal attenuation difference analysis, and the position of a fault point is determined; the system is used for implementing the method. According to the method and system, stress monitoring and electric signal detection are combined, the detection range is remarkably shortened, the positioning precision and the detection accuracy are improved, and the method and system are suitable for weak current cable fault diagnosis in a complex environment.

The invention discloses a transformer bushing high-frequency partial discharge signal on-line monitoring method and system, and relates to the technical field of electrical equipment insulation state monitoring, and the method comprises the steps: synchronously collecting signals based on a high-frequency current sensor and an ultrahigh-frequency sensor; performing mixed noise reduction and feature extraction according to the collected signals; and carrying out discharge type identification on the processed data by constructing a lightweight convolutional neural network, and carrying out early warning and positioning in combination with a dynamic alarm threshold. The method can significantly improve the signal-to-noise ratio and enhance the weak discharge signal detection capability by combining the dual-mode sensor with a hybrid noise reduction strategy of wavelet packet decomposition and adaptive filtering, adopts the lightweight convolutional neural network, supports edge equipment to implement reasoning, realizes intelligent identification of the discharge type, and improves the detection accuracy of the weak discharge signal. The insulation state of the transformer bushing is monitored online in real time, off-line dependence is reduced, the service life of equipment is prolonged, and the intelligent level of operation and maintenance of a power system is improved.

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 discloses a multi-scale and mixed attention high-robustness motor imagery recognition method, which comprises the following steps: S1, acquiring motor imagery electroencephalogram signals, preprocessing the motor imagery electroencephalogram signals, dividing a training set and a test set, segmenting the training set, recombining the training set and expanding a training data set; s2, multi-scale feature extraction is conducted on the motor imagery electroencephalogram signals through a multi-scale convolution embedding module, and time dynamic and space cooperation features of different frequency bands are captured; s3, inputting the multi-scale features into LG-KAT, and respectively modeling a local fine-grained feature and a global time sequence dependency relationship through a local attention branch and a global attention branch; s4, features output by LG-KAT and low-layer embedded features are fused and flattened, a classification layer based on GR-KAN is input for nonlinear transformation and category mapping, model parameters are trained and optimized, and motor imagery task classification is achieved. The invention further discloses a multi-scale and mixed attention high-robustness motor imagery recognition system.

The invention provides a fault positioning method and system based on multi-mode acousto-optic electric signal collaborative diagnosis, and the method comprises the steps: firstly collecting an acoustic signal sequence, an optical signal sequence and an electric signal sequence during the operation of high-voltage electrical equipment, and then carrying out the cross-mode signal level correlation processing of a multi-mode signal, and generating a multi-mode signal correlation level chain; extracting a dynamic association feature set based on the multi-modal signal association hierarchical chain, inputting the dynamic association feature set into the fault feature evolution model to generate a fault feature identification sequence and a feature space diffusion trajectory, and finally determining a fault initial region and a fault influence boundary range of the equipment according to the fault feature identification sequence and the feature space diffusion trajectory. Therefore, the accuracy and reliability of fault positioning of the high-voltage electrical equipment can be effectively improved.

An assessment apparatus and method for objective pain assessment are provided, wherein the apparatus includes a processor comprising a frequency-domain transformation module, a frequency-band segmentation module, a first assessment submodule, and a second assessment submodule. The frequency-domain transformation module generates a global time-frequency feature matrix, which is segmented by the frequency-band segmentation module in a frequency domain into five frequency bands associated with pain perception. The first assessment submodule extracts last time step features and an adjacency matrix from the time-frequency feature matrix and generates a first feature vector representing global association patterns among electrodes used for acquiring EEG signals. The second assessment submodule generates a second feature vector with local spatiotemporal dynamic features of EEG signals, concatenates it with the first feature vector to form a fused feature vector, computes its class probability distribution, normalizes it, and generates an objective pain quantification indicator corresponding to the EEG signals.

The invention discloses a dynamic neutral position and boundary self-calibration method and system of a joystick, and belongs to the technical field of engineering machinery and mining equipment control, and the method comprises the following steps: S1, electrifying equipment, and selecting a mode; s2, calibrating a dead zone; s3, setting a threshold value; s4, data storage; and S5, exiting and applying. According to the dynamic median and boundary self-calibration method and system of the joystick, when the joystick works, a physical manipulated variable is converted into an original electric signal through a joystick sensing unit, then the original electric signal is received through a signal processing module and subjected to signal conditioning, and an analog signal is converted into a digital signal which can be processed by a microprocessor; all logical judgments are executed through the main control unit, a user issues an instruction through a user configuration interface and directly inputs the instruction to the main control unit, the main control unit and the nonvolatile memory carry out bidirectional data interaction to realize permanent storage and calling of dead zone parameters, and the main control unit generates a final control instruction to be output after processing all information. And the actuating mechanism is driven.

The invention relates to a high-precision data acquisition method and system for an MEMS sensor, and the method comprises the following steps: carrying out the multi-channel parallel sampling of an initial electric signal outputted by the MEMS sensor, and obtaining an original data flow matrix; performing adaptive denoising processing on the original data stream matrix to obtain a denoised data set; performing signal phase reconstruction on the noise reduction data set through a phase unwrapping technology to obtain a continuous phase feature sequence; performing dynamic drift compensation on the continuous phase feature sequence to obtain a calibration signal vector; performing transient characteristic extraction on the calibration signal vector to obtain a sensor response characteristic curve; and performing high-precision data resampling on the sensor response characteristic curve based on an adaptive quantization coding technology to obtain a high-precision data acquisition result, thereby solving the technical problem of how to effectively remove noise interference generated in a multi-channel parallel sampling process.

The invention provides an underwater sonar target detection system and method. The underwater sonar target detection system comprises an optical fiber hydrophone array module which comprises a plurality of optical fiber hydrophone units, each unit comprises an optical fiber Bragg grating sensor and a sound pressure signal demodulation device, and the optical fiber hydrophone array module is used for collecting underwater sound wave signals in real time and converting the underwater sound wave signals into first electric signals; the sonar transmitting-receiving array module comprises a broadband sonar transmitter, a multi-beam receiver and a signal preprocessing circuit, and is used for actively transmitting a frequency modulation continuous wave signal, receiving a target reflection echo and generating a second electric signal; and the multi-mode signal fusion processing module comprises a high-speed data acquisition card, a self-adaptive noise suppression unit and a time-frequency analysis unit. The underwater sonar target detection system and method provided by the invention have the advantages of relatively high reliability, relatively high detection performance, relatively accurate cross-modal signal fusion and capability of performing intelligent target identification and tracking.

The invention discloses a high-precision pressure sensing and self-calibration system, and relates to the technical field of pressure sensing, the system comprises a pressure sensing module, an environment monitoring module, a data processing module, a digital twinning module and a self-calibration module, the pressure sensing module collects and converts pressure signals through a high-precision sensor, and the environment monitoring module obtains environment parameters in real time; the data processing module carries out amplification, filtering, analog-to-digital conversion and timely frequency domain analysis on the initial electric signal, and outputs preprocessed data in combination with environmental parameter dynamic compensation. The digital twin module constructs a sensor virtual model, and simulates and outputs an ideal pressure predicted value; and the self-calibration module compares the predicted value with the preprocessed data, generates a final pressure value through deviation calculation, iterative learning and data fusion, and can also calculate the health index of the sensor and perform early warning maintenance. The system realizes high-precision pressure induction and real-time self-calibration, improves the measurement accuracy and prolongs the service life of the sensor.

The invention discloses a sleep state real-time monitoring method and system, and belongs to the technical field of sleep monitoring, and the method specifically comprises the steps: collecting a heart rate variability signal, an electroencephalogram signal and body movement data of a user in real time through a non-invasive sleep pad integrating a piezoelectric sensor, a flexible dry electrode and a pressure sensor; based on the physiological data, whether the user reaches an autonomous sleep state or not is judged through a first algorithm model; if not, starting an active intervention program for playing the adjustable music, and dynamically adjusting the music volume, the playing speed or the track in combination with the physiological data feedback until the user enters an autonomous sleep state; after the user falls asleep, physiological data are continuously collected, and a sober period, a light sleep period, a deep sleep period and a rapid eye movement period are divided through a second algorithm model; if the staging result is a waking period, the autonomous sleep state judgment is executed again; according to the invention, non-intrusive monitoring and personalized intervention are combined, and the monitoring comfort and accuracy are improved.

The invention discloses a partial discharge detection method based on multi-source data fusion, and particularly relates to the technical field of discharge detection. Multi-mode partial discharge signals of partial discharge target equipment are collected, unified time reference alignment and multi-mode data structure normalization processing are carried out, and multi-source partial discharge observation data are generated; constructing a cross-modal discharge event response sequence, identifying response time delays and amplitude differences among modal signals, and extracting inter-modal response coupling feature data; constructing a non-linear feature alignment mapping function, performing time domain and frequency domain joint mapping on the multi-source observation data to obtain discharge feature multi-dimensional tensor data after non-linear coupling compensation, and performing inter-modal weight reconstruction and feature redistribution to generate a partial discharge fusion feature map; accurate identification of the partial discharge type and the spatial position is realized through the classification discrimination model, and a partial discharge detection result is output; and the accuracy of partial discharge detection is effectively improved.

The invention provides a partial discharge on-line monitoring method and system based on multi-modal fusion and adaptive noise reduction, and the method comprises the steps: employing an ultrahigh frequency UHF sensor, a miniature ultrasonic sensor, and a miniature detector for gas dissolved in oil, which are disposed on a transformer; synchronously acquiring electric signals, sound signals and characteristic gas concentration data in oil generated in the partial discharge process; performing format unification, abnormal value elimination and time alignment processing on the electric signal, the sound signal and the gas concentration data in an edge calculation unit to obtain aligned multi-source original data; performing adaptive wavelet noise reduction processing on the electric signal to obtain a de-noised UHF signal; respectively extracting time domain, frequency domain and chemical features from the de-noised UHF signal, the aligned sound signal and the gas concentration data to form a multi-dimensional feature vector; and inputting the multi-dimensional feature vector into a pre-trained defect traceability model, and outputting a partial discharge defect type, severity level and development trend prediction result.

The invention relates to a related light measuring device based on a bevel mirror and a control method, and solves the problem that the existing three-dimensional detection technology is insufficient in comprehensive adaptability and cannot meet the requirements of precision detection for stability, precision and data collaboration, and the related light measuring device comprises a laser emission head, a double-bevel mirror, a light path receiving module, a zoom camera and a circuit processing unit. The first bevel mirror is provided with a through hole for laser to directly irradiate a target; the light path receiving module receives and reflects the laser-to-electric signal; the second bevel mirror reflects the target and the light spot light to the camera to obtain visual information; and the circuit processing unit controls all the components, integrates laser and visual information, and realizes three-dimensional detection of the target. The method has the following effects: through the light path collaborative design of the double-bevel mirror, precise and synchronous acquisition of laser ranging signals and visual contour / light spot information is realized, so that high-precision three-dimensional detection of the distance, the flatness and the segment difference of a target object is efficiently completed.

The invention provides a tumor electric field treatment system, an electrode slice temperature detection method and an electrode slice anomaly detection method, the system comprises an electrode slice and an adapter provided with a first controller, the electrode slice comprises a plurality of electrode units and a plurality of temperature detection units, the plurality of temperature detection units are divided into a plurality of row groups and a plurality of column groups, and the first controller is connected with the adapter. The grounding ends of the temperature detection units in each row group are jointly connected to the same grounding wire, and the signal ends of the temperature detection units in each column group are connected to the same dual-purpose signal wire after being in short circuit with the corresponding electrode units; when the dual-purpose signal line is connected to the temperature sampling point, the grounding wires are sequentially conducted, so that temperature detection signals detected by the temperature detection units are sampled based on the temperature sampling point; when the dual-purpose signal line is connected to the alternating power line, alternating electric signals are transmitted to the electrode units. Therefore, the temperature of the electrode plate can be detected by using fewer conductive traces, and whether the electrode plate is abnormal or not can be judged through temperature detection.

The invention relates to the technical field of performance testing, and discloses a multi-parameter insulating sleeve aging performance testing method and system, and the method comprises the steps: carrying out the multi-parameter detection signal collection of an insulating sleeve, and obtaining standardized signal data; inputting the standardized signal data into a coupling recognition network for feature analysis to obtain a classification recognition result; performing aging factor threshold adjustment on the rising edge slope of the discharge pulse of the insulating sleeve based on the classification identification result to obtain a dynamic separation parameter; performing feature space matching and differential identification on the partial discharge signal according to the dynamic separation parameter to obtain feature classification data; phase clustering analysis and step response characteristic analysis are carried out based on the characteristic classification data to obtain the aging state variable coefficient of the insulating sleeve, the problem that the recognition precision of a traditional fixed parameter method is reduced in different aging states is solved, and high recognition accuracy and anti-interference robustness can still be kept in a complex electromagnetic environment.

The invention relates to the technical field of stoma nursing devices, and discloses an AI visual stoma leakage early warning system, which comprises a plurality of passive piezoelectric film sensors embedded in a stoma chassis and used for capturing acoustic signals generated by adhesion interface structure instability; the edge calculation unit periodically collects an electric signal sequence and calculates an information entropy value, and through comparison with a self-adaptive resting entropy baseline, the structural instability risk is judged and early warning is generated when the entropy value continuously and abnormally rises. The risk orientation is accurately identified in combination with a multi-sensor cooperative positioning technology, body movement interference is avoided by using a dynamic compensation mechanism, and the early warning accuracy and clinical practicability are improved.

The invention provides a tumor electric field treatment system, an electrode slice temperature detection method and an electrode slice anomaly detection method, the system comprises an alternating current signal generator, an electrode slice and a controller, the electrode slice comprises a plurality of electrode units and a plurality of temperature detection units, the plurality of temperature detection units are divided into a plurality of row groups and a plurality of column groups, and the plurality of electrode units are connected with the controller. The grounding ends of the temperature detection units in each row group are jointly connected to the same grounding wire, and the signal ends of the temperature detection units in each column group are connected to the same dual-purpose signal wire after being in short circuit with the corresponding electrode units; when the dual-purpose signal line is connected to the temperature sampling point, the grounding wires are sequentially conducted, so that temperature detection signals detected by the temperature detection units are sampled based on the temperature sampling point; when the dual-purpose signal line is connected to the alternating power line, alternating electric signals are transmitted to the electrode units. Therefore, the temperature of the electrode plate can be detected by using fewer conductive traces, and whether the electrode plate is abnormal or not can be judged through temperature detection.

The invention relates to the technical field of circuit design, in particular to a digital circuit simulation design method and system. The method comprises the following steps: identifying a design demand document, performing deep semantic analysis and demand constraint mining, and constructing a demand specification constraint parameter set; performing demand complexity evaluation and multi-level division according to the demand specification constraint parameter set, and constructing a basic circuit architecture design blueprint; circuit routing path planning is carried out according to the basic circuit architecture design blueprint, and a standardized circuit topology network is constructed; performing electric signal propagation simulation on the standardized circuit topology network, and performing global circuit adaptability adjustment to obtain a circuit topology design scheme; and carrying out an extreme condition simulation test based on the circuit topology design scheme, and carrying out comprehensive performance evaluation to obtain a circuit performance evaluation report. By deeply understanding the user demand intention, the circuit layout matched with the user demand is designed, and the overall circuit performance is improved.