7972 results about "Voltage" patented technology

An apparatus is dedicated to controlling the voltage of a battery comprising at least two modules connected in series and each comprising at least one electrochemical cell, and each coupled to voltage balancing means. The apparatus comprises measurement means for determining first voltages across the terminals of each of the modules, calculation means for measuring a second voltage across the terminals of the battery and for determining a mean voltage per module representative of the second voltage divided by the number of modules, and processor means for comparing each measured first voltage with the mean voltage per module and for delivering to the balancing means signals representative of the result of the comparison whenever the first voltage of a module is greater than the mean voltage, such that the balancing means reduce the voltage across the terminals of the module.

The invention relates to the technical field of electrical fault diagnosis, and discloses an electrical system fault diagnosis method based on multiple parameters. According to the method, multi-dimensional operation parameters of an electrical system are collected, wherein the multi-dimensional operation parameters comprise voltage waveform data, current waveform data and temperature distribution data; time-frequency conjoint analysis is carried out on the multi-dimensional operation parameters, and multi-scale electrical features are extracted and comprise steady-state feature components and transient feature components; based on a historical fault case library, performing mode matching on the multi-scale electrical characteristics to generate an initial fault type set; performing confidence evaluation on the fault types in the initial fault type set by using a dynamic weight distribution algorithm, and screening out high-confidence fault types; and according to the high-confidence fault type, a fault evolution path model is constructed, and the fault evolution path model is used for describing a time sequence of fault features.

The invention relates to the technical field of energy storage battery diagnosis, and discloses an energy storage battery fault diagnosis method and system based on a data fusion algorithm. The method comprises the following steps: acquiring historical operation data and real-time operation data of an energy storage battery in a preset operation period, and generating a historical fault data set according to the historical operation data; performing multi-source feature fusion processing on the historical fault data set to generate a fusion feature parameter set integrating voltage, current, temperature and impedance parameter joint change features; a multi-dimensional fault space is constructed based on the parameter set, coordinate axes of the multi-dimensional fault space correspond to different parameter dimensions, and spatial position coordinates represent parameter change characteristic values; calculating the fault correlation degree of the historical fault event in the multi-dimensional fault space, and determining a fault early warning index set; and extracting real-time characteristic parameters based on the real-time operation data to form a state vector, carrying out space mapping correlation calculation on the state vector and the fault early warning index set in a multi-dimensional fault space, and outputting a real-time fault correlation factor.

The invention provides a power equipment intelligent inspection abnormity identification method based on multi-dimensional data fusion, and the method comprises the steps: obtaining historical operation data and real-time monitoring data, fusing the historical operation data and the real-time monitoring data through a time sequence alignment method, generating a unified data set containing voltage, current, temperature and load information, and obtaining multi-dimensional equipment operation features; aiming at the outlier feature vector, adopting an adaptive neural network to adjust a weight coefficient of the health state evaluation model, generating an optimized weight parameter, and determining an optimized model structure; and performing health state evaluation on the unified data set through the optimized model structure, generating a health index scoring result of the equipment in combination with load over-limit period counting and a temperature abnormal fluctuation range, and determining an evaluation basis for guiding local maintenance of the miniature equipment.

The invention discloses an energy storage power supply residual life prediction method and system based on dynamic weight distribution, and belongs to the technical field of energy storage power supply health management. The method comprises the following steps: acquiring voltage, current and temperature time sequence signals of energy storage power supply operation through a multi-source sensor, and constructing a degradation characteristic sequence by adopting a sliding window method; a degradation inflection point division stage is detected and identified by using a curvature extreme value, a historical degradation mode is matched based on a dynamic time warping algorithm, and an optimal model group is selected; respectively carrying out dynamic weight distribution on the time step length and the feature dimension by adopting a dual-channel attention network, and fusing a learnable coefficient with space-time attention output to generate a life prediction result; residual errors and feature drift are monitored and predicted in combination with an incremental learning mechanism, and model parameters are updated and a degradation knowledge base is expanded by adopting an elastic weight consolidation algorithm. According to the method, the key information capturing capability is enhanced through a space-time attention mechanism, the model adaptability is optimized in combination with incremental learning, and the energy storage power supply life prediction precision and the working condition generalization performance are remarkably improved.

The invention relates to the technical field of electric energy metering and electric power system safety, and discloses an electricity larceny prevention method and system for multi-source data fusion of an electric energy meter, and the method comprises the steps: obtaining and preprocessing current and voltage waveform data and user electricity consumption behavior data; generating multi-dimensional power consumption characteristic parameters, performing screening to form a characteristic vector set, and constructing an abnormal power consumption detection reference set; dynamically verifying power utilization data based on the reference set, and analyzing a phase relation and a mismatch state of current and voltage signals; and generating an electricity consumption behavior deviation index to judge an electricity stealing behavior. The system comprises a multi-source data acquisition module, a feature modeling analysis module, an electrical relation verification module and an electricity stealing judgment module. According to the invention, through multi-source data fusion and multi-dimensional feature analysis, accurate detection of electricity larceny behaviors is realized, accuracy and reliability of electricity larceny prevention are improved, and the method is suitable for power utilization safety management of a smart power grid.

The invention relates to the technical field of electric energy meter metering, and discloses an electric energy meter metering anomaly analysis method and system, and the method comprises the steps: collecting data, such as voltage waveforms, to generate a metering feature vector set, and constructing an anomaly detection rule base; setting a scene parameter type set, and establishing an abnormal association judgment model; dynamically correcting the threshold value by combining the model, and generating an optimized error threshold value set and an abnormal triggering condition set; and updating a metering analysis strategy to generate an abnormal judgment scheme, and correcting data verification sequential logic. The system comprises a data acquisition module, an abnormal rule base construction module, a scene parameter configuration module, a correlation model training module, a dynamic threshold optimization module, a strategy updating module and a time sequence correction module. Through multi-dimensional data modeling, scene-based threshold configuration and dynamic time sequence calibration, the accuracy and adaptability of electric energy meter measurement anomaly detection are improved, and the method is suitable for measurement anomaly analysis of diversified power consumption scenes in a smart power grid.

The invention discloses an intelligent electric equipment monitoring and optimizing method, and relates to the technical field of electric power, and the method comprises the steps: collecting high-dimensional voltage-current time sequence data and transient event marks, calculating topological invariant features, inputting the topological invariant features to a lightweight neural network model, and recognizing the features of all electric equipment; performing abnormal attribution and anti-fact energy efficiency prediction by using causal reasoning and dynamic regularization regression based on the identified characteristics of each electric device, and constructing a multi-objective optimization function through the abnormal attribution and anti-fact energy efficiency prediction; and based on the multi-objective optimization function, generating an equipment operation scheduling strategy through a deep reinforcement learning agent, based on the operation scheduling strategy, sending a control instruction to the electric equipment, and collecting an operation result feedback in real time for optimization and updating. According to the method, through fusion of topological features, causal reasoning and safety reinforcement learning, the precision, robustness and safety of monitoring and optimization of the intelligent electric equipment are improved.

The invention discloses a power equipment fault prediction and early warning method based on edge calculation, and the method comprises the following steps: S1, collecting voltage, current, temperature, frequency and other parameters, carrying out the data cleaning and normalization, and generating standardized time series data; s2, dividing the time sequence data into a plurality of subsequences through a sliding window; s3, extracting trend characteristics of each sub-sequence, and calculating a disturbance value reflecting trend change; s4, constructing a cutting model with a priority division strategy in combination with the disturbance value, and obtaining an abnormal degree score of each sub-sequence; s5, performing weighted fusion on the trend disturbance value and the abnormal score, and outputting a prediction score; s6, according to a comparison result of the prediction score and a threshold value, generating early warning information containing time, a serial number and an abnormal level; and S7, packing and packaging the early warning information on the equipment site, and sending the early warning information to a superior system or a local terminal. According to the invention, on-site rapid prediction and graded early warning of potential faults of power equipment can be realized.

The invention relates to the technical field of electronic timer calibration, in particular to an ammeter clock intelligent calibration method and system based on multi-modal fusion, and the method comprises the steps: firstly obtaining ammeter clock voltage and environment data, carrying out the preprocessing, and generating an instantaneous disturbance sequence and a steady-state voltage time sequence; respectively calculating a voltage chronic influence factor, a voltage acute influence factor and an environment acceleration factor; determining the influence coefficient of each factor through historical data regression of similar ammeters, carrying out fusion calculation on total life loss, and carrying out iteration to generate a dynamic correction period adaptive to individual damage; and finally, constructing a same-batch and same-environment electric meter reference group, screening similar clusters through clustering, comparing corrected time consistency, judging unconventional abnormity, and outputting a targeted calibration strategy. According to the method, the multi-dimensional error factors are quantified through multi-modal fusion, the differentiation model is constructed, the dynamic correction period is generated through iteration, the traditional fixed period calibration defect is overcome, and the long-term timing precision stability of the electric meter is improved.

The invention relates to the field of load monitoring, in particular to an electric energy meter time-sharing load monitoring method and system based on multi-dimensional electricity utilization characteristics, and the method comprises the steps: collecting and preprocessing the electrical time sequence data of a user electric energy meter; extracting multi-dimensional statistical characteristics of current and voltage of the users under a plurality of analysis windows, and clustering to obtain a cluster and membership degree of each user; for the target user, respectively calculating a first anomaly degree of the target user and a historical sequence thereof and a second anomaly degree of the target user and a sequence of other users in the same period in the cluster to which the target user belongs; according to the membership degree of the user under each analysis window, carrying out weighted fusion on the second anomaly degree to obtain a third anomaly degree; and calculating a comprehensive abnormality based on the first abnormality and the third abnormality, and determining whether the load is abnormal. According to the invention, through dual reference of individual history and a dynamic group and multi-scale credible fusion, the accuracy, the adaptive ability and the reliability of load anomaly monitoring are significantly improved.

The invention discloses a digital calibration system and method for dynamic response parameters of a surge protection circuit, relates to the technical field of power electronic protection and test calibration, and is used for solving the problem that an existing surge protection circuit calibration method cannot reflect device degradation and dynamic response mismatch. Surge event voltage and current waveforms are collected through a synchronous trigger mechanism, non-stationary signal features are extracted through combined time-frequency analysis, and a voltage-current phase trajectory diagram is constructed to generate a response feature matrix; performing phase-space reconstruction on the matrix, calculating Kolmogorov entropy quantification system stability, and establishing a material degradation transfer function in combination with an energy absorption sequence to generate a grain boundary degradation index; key parameters are screened through L1 regularization regression, and a quantitative relation between a grain boundary degradation index and a protection threshold value is established to realize adaptive threshold value calculation; and executing strategy switching from calibration to predictive maintenance according to the degradation trend and threshold fluctuation change, and outputting a maintenance signal to complete dynamic accurate calibration and reliable operation of the surge protection circuit.

The invention discloses a pulse stable output control method and system for an ns-level high repetition frequency fast pulse power supply, and relates to the technical field of automatic control, the system is composed of a plurality of functional modules, and the system comprises a self-adaptive absorption module, a control module and an output control module, and the self-adaptive absorption module obtains power supply parameters through real-time sampling, and the power supply parameters comprise load impedance and switch tube parasitic parameters; dynamically optimizing R and C parameters of the RCD absorption circuit and the active clamping circuit based on power supply parameters; the feed-forward prediction module is used for extracting derivative state information according to the power supply parameters, acquiring load current feed-forward and resonant circuit states based on the derivative state information, modeling based on the load current feed-forward and resonant circuit states, and predicting the drain-source voltage valley bottom moment of the switch tube; and the multi-section variable impedance transmission module adopts a three-section variable impedance structure, performs broadband impedance matching based on R and C parameters of the optimized RCD absorption circuit and the active clamping circuit, inhibits pulse leading edge reflection distortion, and outputs undistorted pulse signals.

The invention belongs to the crossing field of artificial intelligence and process control, particularly relates to an intelligent coupling control strategy for chlorine production through seawater electrolysis based on digital twinning, and aims to solve the problems of response lag, weak disturbance suppression and lack of electrode aging early warning in a traditional control method. According to the strategy, a high-fidelity digital twin fusing electrochemistry, fluid and thermodynamics is constructed, characteristics of current, temperature, impedance spectroscopy and the like are collected and fused at high frequency through a multi-source sensor, a state space model driven by a deep recurrent neural network is established, and current efficiency and electrode aging coupling evolution recognition is achieved; when salinization or flow mutation is detected, the twinborn body predicts and dynamically calibrates the current density and the voltage set value in advance, and the safety process window is synchronously scaled. According to the strategy, the chlorine gas yield fluctuation is reduced by 60%, the energy consumption standard deviation is reduced by 45%, the fault early warning is advanced by 2-5 hours, the fault-free operation time of the system is prolonged by 50%, and high-precision, self-adaptive and preventive intelligent control of the electrolysis process is realized.

The invention relates to the technical field of high-voltage circuit breaker fault diagnosis, and discloses a multi-feature optimization fusion high-voltage circuit breaker fault diagnosis method. The method comprises the following steps: adaptively optimizing variational mode decomposition parameters by adopting a particle swarm optimization algorithm, and accurately decomposing an original vibration signal; performing noise dominant and fault feature dominant classification on the intrinsic mode function based on permutation entropy; aiming at the two types of modes, respectively taking signal-to-noise ratio maximization and kurtosis maximization as targets, and implementing differential wavelet threshold denoising; after reconstructing the signal, extracting an energy entropy, a singular value entropy and a power spectrum entropy to form a multi-dimensional feature vector; and inputting the data into a support vector machine classifier subjected to particle swarm optimization hyper-parameter for state diagnosis. According to the invention, through full-chain collaborative optimization, the accuracy and robustness of fault diagnosis in a strong noise environment are significantly improved.

The invention discloses a method for establishing a high-voltage circuit breaker fault detection model, and the method comprises the following steps: collecting current, voltage, mechanical response, temperature and other multi-dimensional signals of a circuit breaker under different working conditions, and unifying the signals into standardized time sequence data; a nonlinear dynamic sparse identification method is utilized to establish a dynamic model for describing equipment state evolution, and sparse coefficients reflecting physical change rules are extracted from the dynamic model to serve as health features. And the features are fused with current monitoring data to generate a joint feature input vector, and a health prediction model based on a TabPFN architecture is introduced for training and discrimination. And finally, accurate prediction of the current health state or the potential fault of the circuit breaker is realized, and the model self-adaptive updating capability is realized. According to the method, physical modeling and data analysis are combined, so that the accuracy and interpretability of fault prediction are improved.

The invention, which relates to the technical field of energy storage and discharge, provides an energy storage and discharge system comprising a power supply input interface, a slow power-on unit, a slow power-on control unit, an energy storage unit, a start-stop control unit, a sampling unit, a detection unit, a main loop switch unit and an output interface. The power supply input interface is connected with the input end of the main loop switch unit and the input end of the slow power-on unit, the output end of the slow power-on unit is connected with the first port of the slow power-on control unit, and the output end of the main loop switch unit and the second port of the slow power-on control unit are connected with the port of the energy storage unit. The sampling unit is connected with the input end of the detection unit, and the output end of the detection unit is connected with the control end of the main loop switch unit. According to the invention, the problem of'maloperation 'protection of the power supply due to increase of the switching voltage difference in the process of switching the main loop can be avoided, and the reliability of the energy storage discharge system is improved.

The invention discloses an electricity meter adaptive error compensation method, system and device based on multi-physical coupling and hidden variable modeling and a storage medium, and relates to the technical field of electric energy metering and detection, and the method comprises the steps: constructing a multi-physical coupling basis vector containing a single variable and a cross variable based on a sensing output signal; hidden variable joint modeling is carried out based on the multi-physical coupling basis vector and parasitic capacitance change, magnetic conductivity drift and shunt resistance offset, and a compensation mapping relation of electromagnetic, thermal and stress coupling is formed; generating a compensation coefficient by adopting the compensation mapping relation, and applying the compensation coefficient in an ammeter metering link to complete an error compensation process; according to the method, voltage, current and phase compensation coefficients are generated through multi-physical coupling basis vector and hidden variable modeling, dynamic description and self-adaptive correction of error sources are achieved, dynamic accurate correction of voltage, current and phase is achieved, and it is ensured that the ammeter keeps high-precision metering for a long time under the complex environment and the device drift condition.

A method and system for predicting an effective remaining electrical life of a high-voltage circuit breaker. A circuit breaker electrical life test is carried out and associated data sets of single arcing times and single arcing energy are constructed; a degree of wear of a contact is replaced with accumulated arcing energy to characterize whether a circuit breaker fails; and function fitting is used to construct the relationship between the single arcing energy and the single arcing times; and on this basis, the method for predicting an effective remaining electrical life of a high-voltage circuit breaker is formed. Therefore, the rapid and accurate prediction of a remaining service life of a circuit breaker can be realized only by measuring simple data, thereby providing guarantee for the reliable operation of a power grid.

The invention discloses a power grid fault characteristic determination and positioning method, system, device and storage medium, and relates to the field of power grid fault processing, and the method comprises the steps: synchronously collecting the voltage, current, frequency and other electrical quantities and temperature, humidity and other environmental quantities of a power grid node through a multi-mode sensor, and forming an original time sequence data set; separating electrical quantity frequency domain fault features by using improved variational mode decomposition, and analyzing the correlation between the environmental quantity and the electrical quantity through a grey correlation degree to suppress noise; mapping the dynamic deviation of each order of derivative of the voltage / current into a spatial-temporal characteristic matrix, and constructing a distribution map in combination with power grid topology; based on the dynamic graph neural network, inputting node features and edge features, and establishing a fault-environment association model through feature propagation and a graph attention mechanism; and carrying out fine trimming on a positioning result by combining double-end traveling wave coarse positioning with an environment correlation degree correction coefficient, and verifying connectivity of a fault point and a power grid topology.

The invention discloses an asynchronous motor energy-saving monitoring and dynamic early warning method based on situation awareness, and relates to the technical field of asynchronous motor energy-saving monitoring. The method comprises the steps that S1, multi-source heterogeneous data are synchronously collected, power supply parameters and operation parameters are obtained in real time, and a frequency conversion working condition self-adaptive deviation value and a current stability coefficient are obtained; s2, generating four-dimensional situation factors including an overtemperature danger value, a current stability coefficient, a voltage stability coefficient and a frequency deviation value; s3, constructing a fourth-order transmission network, and generating a primary early warning signal based on the path mark combination; s4, calculating the motor efficiency in real time, triggering an efficiency abnormity mark, fusing the efficiency abnormity mark with the primary early warning signal, and reconstructing a final early warning instruction; and S5, executing hierarchical control according to the final early warning instruction. Through multi-parameter coupling and dynamic early warning transmission, the early-stage capture capability of the composite fault is improved, the early warning response delay is shortened, energy-saving optimization and fault protection of the motor are realized, and the method has a remarkable practical value.

The invention relates to the technical field of wireless charging, and provides a foreign matter detection method and system for a wireless charging system and a storage medium. The method comprises the following steps: firstly, calibrating at multiple temperature points and establishing a temperature-parameter comparison table under the condition of no foreign matters; in a no-load standby state, the inversion module sends out detection drive and collects voltage, current and temperature; calculating a real-time quality factor and resonant frequency according to sampling, obtaining a compensation reference according to a temperature look-up table, and solving the variation of the quality factor and the resonant frequency; and performing fusion judgment by adopting dual-condition locking or weighted scoring, and triggering fault protection when a foreign matter is confirmed. Due to the fact that equivalent inductance and resistance changes are caused by metal foreign matters, quality factors are remarkably reduced along with resonant frequency deviation, temperature excursion and foreign matter effects are distinguished through temperature compensation, and dual-condition locking or weighted scoring fusion judgment is combined, the interference immunity and accuracy of detection are improved, false alarms and missing alarms are remarkably reduced, and the detection accuracy is improved. The method is suitable for high-power wireless charging scenes.

The invention discloses a printed circuit board withstand voltage detection and evaluation method, which comprises the following steps: in the whole withstand voltage detection process, aiming at different process batches and environments, acquiring electrical signals, process parameters, environment variables and multi-modal visual data, and carrying out time sequence synchronization marking and standardized preprocessing; extracting multi-source features by using a feature coding algorithm, and establishing a multi-mode distribution model covering signal mutation, visual defects and working condition anomalies; through cross-modal feature fusion and a time sequence causal inference model, an abnormal event causal relationship graph is automatically constructed, and a voltage withstanding failure root cause chain is identified and positioned; according to the method, the efficient structured management of the detection data, the accurate traceability of the abnormal reasons and the cross-batch analysis are realized, and the capability of diagnosing and preventing the withstand voltage abnormality of the printed circuit board is remarkably improved.

The present disclosure provides a fault measurement impedance calculation method and device for a hybrid grid-following / grid-forming wind power system. The method comprises: acquiring characteristics of a grid-following converter and a grid-forming converter, and corresponding low-voltage ride-through control strategies; establishing an equivalent circuit corresponding to a fault state; performing simplified analysis on a hybrid wind power system to obtain an equivalent network diagram; obtaining a branch circuit diagram under individual action of each power excitation; obtaining short-circuit currents and grid-connected point voltages on two sides of the system corresponding to the individual action of each power excitation; performing superimposition to obtain a total short-circuit current and a total grid-connected point voltage at the two sides of the hybrid wind power system; and obtaining a measurement impedance at a protection installation point on a new energy side. In the method, a measurement impedance value at a protection installation point of a hybrid wind power system after a fault occurs can be accurately calculated, so that the setting calculation of distance protection is ensured, so as to accurately reflect fault characteristic information of a hybrid AC system, thereby providing a basis for the distance protection applicability research of the hybrid wind power system, and thus ensuring the safe and stable operation of new energy systems.

The invention provides a high-power high-reliability voltage retainer used for an automobile starting and stopping system. The high-power high-reliability voltage retainer is higher in power and reliability and smaller in size and ripple. The high-power high-reliability voltage retainer comprises a through circuit module (1), a booster circuit module (2), a reversed polarity circuit module (3), a CAN transceiver (4), a CPU (5), a DSP (digital signal processor) (6) and a battery. A battery input port and a battery output port are formed in the through circuit module (1), a battery input port and a battery output port are formed in the booster circuit module (2), and the reversed polarity circuit module (3) is electrically connected with the booster circuit module (2); the CAN transceiver (4), the CPU (5) and the DSP (6) are sequentially and electrically connected; the CPU (5) is electrically connected with the through circuit module (1), and the DSP (6) is electrically connected with the through circuit module (1); the DSP (6) is electrically connected with the booster circuit module (2).

The present invention relates to the technical field of energy meter electrical parameter measurement. Disclosed is an intelligent anti-interference system for a power line carrier energy meter. The system comprises: collecting voltage and current signals of a power line, and constructing a sample database; performing time-frequency feature classification by means of a preset model, and establishing feature and scenario mapping by synchronizing scenario recognition results; building a scenario interference recognition network on the basis of the mapping result; collecting real-time data of N communication channels of a power line carrier energy meter, and performing channel coordination decision and anti-interference enhancement; performing communication data evaluation on the basis of a comprehensive scoring function; and performing anti-interference management on the basis of channel access decision, anti-interference enhancement, and a parallel access result. The present invention solves the technical problem of poor accuracy and reliability of metering data of existing power line carrier energy meters caused by interference signals and noise when the power line carrier energy meters are in complex electromagnetic environments, and achieves the technical effect of improving the anti-interference capability and the reliability of the power line carrier energy meters.

The invention provides a sheath grounding ring current online monitoring and fault source positioning diagnosis method and system, and relates to the technical field of power system fault diagnosis, and the method comprises the steps: obtaining multi-physical field time domain signals, extracting features, constructing a sheath electrical connectivity topological graph, carrying out the partitioning, building the energy dissipation space-time field distribution, and recognizing a suspected fault region; and analyzing a voltage-circulating current response relationship, and finally judging a fault type and positioning a fault source through a Lyapunov index. The cable sheath grounding circulation state can be accurately monitored, accurate positioning and type identification of a fault source are realized, and the cable operation reliability is improved.

The invention relates to the technical field of electrical measurement, and discloses a high-voltage switch cabinet contact wear on-line diagnosis method, which comprises the following steps: extracting multi-dimensional time-frequency domain characteristics of a high-voltage switch cabinet contact based on vibration and current signals of time sequence alignment to obtain contact motion characteristics; wear state matching is carried out on the features to obtain the wear degree of the contact, and then a preliminary wear diagnosis result is generated through wear label association; performing multi-factor coupling analysis in combination with historical operation times and environment temperature and humidity, evaluating a wear accumulation effect and an oxidation influence degree, and further generating a target wear diagnosis result; and finally, performing communication protocol packaging on the result to form a wear diagnosis data message, and outputting a maintenance suggestion instruction through instruction mapping. The reliability of contact diagnosis of the high-voltage switch cabinet can be improved.

The invention discloses a non-contact electric power parameter measurement method, system and device, and a storage medium. The method comprises the steps of obtaining a first output voltage signal and a second output voltage signal generated by induction of a space electric field around a to-be-measured wire; establishing an equation set of a to-be-measured voltage phasor based on the first output voltage signal and the second output voltage signal, and solving the equation set of the to-be-measured voltage phasor to obtain a parameter of the to-be-measured voltage; acquiring a magnetic field measurement signal generated by a space magnetic field around the to-be-measured wire; and establishing an equation set of the current to be measured according to the magnetic field measurement signal, and solving the equation set of the current to be measured to obtain parameters of the current to be measured, thereby effectively solving the problem that the gain and the phase shift are unknown after the probe is connected.

An inspection method according to one aspect of the present disclosure is for inspecting a wiring board P by applying an inspection voltage or an inspection current to an inspection target X. The inspection target X is a part of the wiring board P that comprises an insulator and a conductor and on which an electric component is mounted. The inspection target is subject to a plurality of inspections, and the inspections include a first inspection for inspecting the conduction state of the conductor or the insulator, and a second inspection for inspecting time-series data of the conductive state of the conductor or the insulator. The result of the first inspection and the result of the second inspection are recorded in a recording device. A third inspection is further included, which is carried out after the first inspection and the second inspection, in a state where processing is performed and an electric component is mounted in a subsequent process. The result of the third inspection is recorded in association with the result of the first inspection and the result of the second inspection.