9833 results about "Electric signal" patented technology

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.

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 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 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.

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 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 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.

The invention relates to a low-voltage circuit breaker state detection method, which comprises the following steps of: synchronously acquiring multi-source state data of a low-voltage circuit breaker in an operation process through a plurality of sensors, the multi-source state data at least comprising a vibration signal, an opening and closing coil current signal, a moving contact displacement signal, a temperature signal and a partial discharge signal; the collected signals are preprocessed, and feature vectors related to the health state of the circuit breaker are extracted; inputting the feature vector into an improved weighted D-S evidence theory fusion diagnosis model; the improved weighted D-S evidence theory fusion diagnosis model is used for fusing evidences from a plurality of sensors in a mode of distributing weights for evidences of different sensors and calculating weighted average evidences; and determining the comprehensive health state grade of the low-voltage circuit breaker according to an output result of the fusion diagnosis model. According to the invention, comprehensive evaluation of the multi-dimensional working condition of the circuit breaker is realized, and the method has the advantages of comprehensively reflecting the multi-dimensional working condition of the circuit breaker, reducing the risk of misjudgment and missed judgment, and improving the performance degradation pre-judgment capability of equipment.

The invention relates to the technical field of capacitance compensation matching, in particular to a dynamic tuning capacitance compensation matching method and system for a transformer simulation test. The method comprises the following steps: before a test is started, establishing a digital twin model based on parameters of a transformer to be tested, identifying a systematic measurement error source caused by inherent characteristics of a system through simulation, and generating error source correction parameters; in the test period, voltage and current original waveforms containing electrical disturbance in a test loop are obtained in real time through high-frequency synchronous sampling; a mixed disturbance separation model is used, the collected original waveform and the generated error source correction parameters are used as input, and random measurement disturbance components caused by load dynamic changes are calculated and separated in real time; and based on the separated disturbance component, driving a dynamic capacitance compensation module to generate a compensatory electric signal which is equal to the disturbance component in size and opposite to the disturbance component in phase, and injecting the compensatory electric signal into a test loop so as to realize active disturbance cancellation. According to the invention, the measurement precision of the transformer simulation test can be improved.

The invention relates to the technical field of data processing, in particular to an induction cooker data acquisition and processing system which comprises a power jump identification module, a response section correction module, a trend sampling analysis module, a temperature control abnormity judgment module and a working condition balance data calibration module. According to the method, the rate change value between the power sampling points in the operation period is calculated, the sections with obvious abrupt change characteristics are screened, and the power lag response is corrected in combination with the voltage change slope, so that the time offset between the control response and the electric signal change is effectively corrected; the temperature fluctuation trend and the temperature control deviation direction are analyzed on the basis of the correction sequence, a temperature control abnormal state label set is formed by matching current waveform symmetry characteristics, a basis is provided for output power adjustment according to the temperature rise rate, the adaptation capacity of data analysis to actual working conditions and the execution precision of a control strategy are improved, and the accuracy of data analysis is improved. And the stable operation capability of the electromagnetic heating equipment under the complex thermal load change is enhanced.

The invention provides an intelligent monitoring and early warning method based on multi-source information fusion, and belongs to the technical field of intelligent diagnosis and early warning, and the method comprises the steps: synchronously collecting the multi-modal data of a voltage transformer, the multi-modal data comprising voltage / current waveform, partial discharge signal, temperature and vibration data; preprocessing the multi-modal data, wherein a preprocessing method comprises waveform segmentation, spectrogram generation and scalar normalization; constructing a dynamic weight distribution mechanism: calculating a weight matrix based on the relevance between the time sequence features and the spatial features, and dynamically adjusting scalar weights by combining the physical coupling relationship between the temperature and the vibration to generate feature fusion weights; iteratively updating the weight matrix according to the real-time prediction error; fusing the time sequence features, the spatial features and the scalar features through the dynamic weight distribution mechanism to generate a comprehensive feature vector; and synchronously outputting a fault type classification result and an equipment health score based on the comprehensive feature vector, and calculating a real-time prediction error.

The invention discloses an insulation state fusion detection method, system, medium and equipment under transient impact in a cable operation process, and the method comprises the steps: synchronously collecting a transient impact signal and a partial discharge signal of a cable under the transient impact effect; performing time-frequency domain separation on the acquired transient impact signal and the partial discharge signal to obtain a transient impact component and a partial discharge component after separation; when it is detected that the amplitude of the transient impact signal exceeds a preset threshold value, a partial discharge detection window is started, and whether a partial discharge signal exists or not is detected in the window period; if a partial discharge signal is detected, multi-dimensional characteristic parameters of the partial discharge signal are extracted, a state vector is constructed in combination with environmental parameters, the state vector is input into a pre-trained machine learning model, and a cable insulation state grade is output; if the partial discharge signal is not detected, performing inversion calculation on the transient impact signal based on a transmission line model, and reconstructing an overvoltage waveform; and extracting propagation characteristic parameters according to the reconstructed overvoltage waveform, and judging the insulation defect type according to the propagation characteristic parameters.

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 provides a preparation method of an inertial sensor and the sensor. The preparation method comprises the following steps: sequentially forming a first cavity and a first oxide layer on a first silicon wafer; bonding the first silicon wafer and a second silicon wafer, wherein the second silicon wafer is a low-resistance silicon wafer; forming an inertia sensitive structure in the second silicon wafer; correspondingly forming a second cavity on a third silicon wafer, wherein the third silicon wafer is a low-resistance silicon wafer; bonding the third silicon wafer and the second silicon wafer; an isolation groove penetrating through the third silicon wafer is formed through etching, a conductive silicon column and an electric signal isolation ring are formed, and the conductive silicon column is connected with the inertia sensitive structure; and forming a sealing insulating layer on the surface of the third silicon wafer, and forming a signal lead-out pin in the sealing insulating layer. According to the scheme, thermal stress caused by thermal expansion coefficient mismatch among the three layers of wafers is reduced through an all-silicon wafer level packaging process, and the full-temperature characteristic of the device is greatly enhanced; and the preparation process flow is optimized, the process control difficulty is reduced, and the preparation cost is reduced.

The invention relates to the technical field of gas detection, in particular to a dual-wavelength-based adaptive methane detection method and system, and the method comprises the following steps: collecting environmental interference characteristics of a to-be-detected gas environment in real time; synchronously emitting a first laser beam and a second laser beam to a to-be-detected gas environment according to the tunable laser source; receiving optical signals of the first laser beam and the second laser beam and converting the optical signals into electric signals; calculating a light intensity difference value of the first laser beam and the second laser beam based on the electric signal to generate a differential absorption signal; performing optical differential compensation on the differential absorption signal, outputting a methane concentration value, and performing environmental drift compensation on the methane concentration value based on environmental interference characteristics; and performing real-time threshold value judgment on the methane concentration value, and if the methane concentration value exceeds a preset threshold value, triggering a local early warning signal. According to the invention, the problem that the detection precision is reduced due to the fact that methane absorption signal common-mode noise and absorption coefficient drift cannot be synchronously inhibited due to dust concentration and temperature and humidity interference in a dynamic complex environment under a mine is effectively solved.

The invention belongs to the technical field of power equipment state monitoring, and discloses a cable branch box operation state intelligent evaluation system based on multi-sensor fusion, which comprises a state acquisition module used for acquiring a partial discharge signal, a voltage fluctuation signal, a terminal temperature signal and a terminal contact resistance signal of a cable branch box in an operation process, constructing a multi-source operation state parameter set of the cable branch box through time synchronization and physical quantity normalization processing; the mutual coupling analysis module is used for performing transient correlation deduction on the partial discharge signal and the corresponding voltage fluctuation signal based on the multi-source operation state parameter set to obtain a partial discharge-voltage coupling amount; when the partial discharge-voltage coupling amount exceeds a preset partial discharge-voltage coupling amount threshold value, determining that partial discharge abnormity caused by insulation degradation exists in the cable branch box; comprehensive and accurate evaluation of the operation state of the cable branch box is realized, and reliable support is provided for safe operation and maintenance of equipment.

The invention relates to the technical field of integrated circuit board manufacturing and packaging, and discloses an integrated circuit board manufacturing and packaging method and system based on laser measurement, and the method comprises the steps: sensing arrangement: employing a focusing lens to focus a laser beam to the surface of a substrate, arranging a reflector and a spectroscope to guide the laser beam to the surface of the substrate, meanwhile, a sensor is arranged in the packaging equipment for monitoring; signal conversion: receiving a reflected light signal by adopting a silicon photodiode, and converting the reflected light signal into an electric signal; data acquisition: acquiring electric signal data, environment data and substrate information data; data calculation: calculating a warping prediction index and an offset prediction index; data evaluation: evaluating the packaging effect index; and comprehensive judgment: according to the packaging result index, judging whether automatic adjustment and compensation need to be carried out, and under the condition that automatic adjustment and compensation need to be carried out, calculating a compensation coefficient to carry out automatic compensation, so that active suppression of the substrate warping offset defect and intelligent improvement of the process efficiency are realized.

The invention provides a sensing system fusing optical fiber vibration and acoustic emission, a monitoring method and a device, and relates to the technical field of optical fiber sensing. The device comprises a light source module, a sensing optical fiber, a detection module and a controller, the light source module is used for respectively emitting modulation pulse light for vibration monitoring and continuous wave laser for acoustic emission monitoring; the sensing optical fiber is arranged along a monitored structure or area, and at least one optical fiber acoustic emission sensing head is connected to the optical fiber in series. The detection module collects Rayleigh scattering light and beat frequency signals at all positions in the sensing optical fiber through a circulator and a coherent light receiver, and optical signals are converted into electric signals; wherein the electric signals comprise vibration electric signals and acoustic emission electric signals; the signal processing unit carries out phase demodulation and envelope analysis processing on the collected vibration electric signals and acoustic emission electric signals so as to obtain vibration waveforms and acoustic emission event space-time information along all points of the optical fiber. According to the invention, a monitoring task fusing vibration and acoustic emission can be accurately completed.

The invention provides a chip assembly and a consumable box set, and relates to the technical field of printing imaging. The consumable box set is used for being in contact with contact pins of printing equipment and receiving printing signals, the printing equipment is provided with at least one first contact pin set and at least one second contact pin set, the first contact pin set and the second contact pin set are not overlapped, a first consumable box of the consumable box set is matched with the first contact pin set, the first consumable box is provided with a first electronic module, and the second consumable box is provided with a second electronic module. The first electronic module is electrically connected with at least one first contact part, and the first contact part is in contact with the first contact pin group to receive a first electric signal; the second consumable box is matched with the second contact pin group and is provided with a second electronic module, the second electronic module is electrically connected with the first electronic module, and the second electronic module receives the first electric signal through the first contact part. According to the invention, the risk of short circuit can be reduced, and normal use of the chip assembly is ensured.

The invention provides a method for extracting electrical characteristic signals of electrical equipment in a complex working condition environment, and belongs to the technical field of electrical equipment detection.The method comprises the steps that a multi-channel ultrasonic sensor array is arranged to collect partial discharge signals, background noise is eliminated through adaptive noise cancellation processing, and an ultra-sparse frequency band energy distribution vector is constructed; the method comprises the following steps: calling a self-adaptive time-frequency analysis model to extract instantaneous frequency, amplitude and phase parameters to form a micro-hour-frequency characteristic matrix, separating independent source signals through independent component analysis, calculating a kurtosis value and a skewness value, fusing multi-domain characteristics to construct a transient stationary comprehensive characteristic vector, matching with a standard discharge characteristic vector library to identify the discharge type and intensity, and calculating the discharge intensity. A corresponding prediction algorithm is selected according to the discharge mode, multi-parameter coupling optimization adjustment is started under a certain condition, an electrical characteristic signal description vector is finally constructed, and the technical problem that the partial discharge signal of the power equipment is difficult to accurately extract under a complex working condition environment is solved.

The invention discloses an excitation load grounding fault detection method, system and device based on EEMD and Hilbert spectrum analysis and a medium, and belongs to the technical field of power system energy storage equipment, and the method comprises the steps: collecting and preprocessing an electric signal, and constructing a discretization time domain data sequence; performing iterative decomposition on the sequence to obtain multiple groups of components; time domain structure analysis is carried out, and target components containing fault mutation features are screened; frequency domain transformation is carried out on the target component to construct spectrum distribution, and normalization processing is carried out; and then time-frequency joint transformation is carried out, energy distribution and energy entropy indexes are calculated, and diagnosis is completed. According to the method, modal aliasing is suppressed by using EEMD adaptive noise injection and a set average strategy, accurate separation of high-frequency transient disturbance and low-frequency harmonic waves is realized, multi-dimensional feature combined diagnosis is formed by combining frequency domain screening and time-frequency analysis, and the fault detection precision and reliability are improved.