212 results about "Signal transformation" patented technology

The invention relates to the technical field of optical current transformer detection, and provides an optical current transformer real-time calibration and health diagnosis method and system, and the method comprises the steps: carrying out the harmonic analysis of an initial digital signal sequence obtained through the conversion of a continuous modulation optical signal of a target optical current transformer, and obtaining harmonic component data; physical state evaluation is carried out according to the higher harmonic component data and preset health state baseline data to generate a physical state evaluation result, nonlinear disturbance compensation is carried out according to an error control signal generated based on the higher harmonic component data, and after a linear working point is reached, nonlinear disturbance compensation is carried out; and calculating an electrical measurement value according to the output target fundamental component data, and performing comprehensive analysis according to the physical state evaluation result and the electrical measurement value to generate a diagnosis report. According to the invention, through a measurement calibration and state diagnosis mechanism based on harmonic analysis, while the electrical performance measurement precision is effectively improved, reliable quantitative diagnosis and accurate traceability of different physical faults in the equipment are realized.

The invention relates to the technical field of precision shell contour metering, and discloses an online metering method for a precision shell contour, and the method comprises the steps: obtaining an original sampling point cloud and a feature point group of a to-be-measured workpiece in a conveying state; resolving a real-time pose matrix of the to-be-measured workpiece relative to a preset measurement reference by using a spatial topology constraint relationship of the feature point groups; carrying out differential processing on a real-time pose matrix in the sampling sequence, and synthesizing an instantaneous motion vector corresponding to a sampling moment; in combination with a signal response time delay constant of the sampling system, phase compensation is carried out on a motion displacement deviator induced by signal conversion delay, and a corrected sampling point cloud is generated; according to the method, the geometric distortion of the point cloud generated by response delay of the sensor is solved, the problem of instantaneous drift of the measurement reference in an unsteady state conveying state is solved, and the contour reduction precision in a complex working condition is improved.

The invention discloses a natural gas pipeline leakage detection method based on novel wavelet basis transformation and singular value decomposition in a two-dimensional convolutional neural network. Firstly, a sound signal collected by a sound wave sensor is converted into a digital signal; secondly, in the data preprocessing stage, singular value decomposition is carried out on the digital signals to effectively eliminate background noise interference, and then batch normalization is carried out on the processed data; then, converting the one-dimensional time sequence signal into a two-dimensional time-frequency image by adopting a self-defined Morlet wavelet basis function; and finally, based on the time-frequency images, constructing and training a 2D-CNN model for fault classification, and presenting a diagnosis result through a confusion matrix and a comparison graph. According to the method, 97.55% of fault recognition accuracy is obtained in a public data set, and compared with other competitive methods, the method shows more excellent noise robustness and classification performance, and has higher accuracy and wide application prospects in pipeline leakage diagnosis in a complex noise environment.

The invention discloses a video intelligent monitoring system and method based on cross-modal time-frequency fusion, and relates to the technical field of new energy power monitoring and safety management, and the method comprises the following steps: obtaining multi-modal physical data; detecting a waveform characteristic of the electrical signal, and when the waveform characteristic of the electrical signal meets a preset electrical abnormal condition, performing high frame rate sampling and frame rate compensation on the visible light video to obtain video data with enhanced time sequence continuity; converting the video data, the thermal infrared image, the acoustic signal and the electrical signal to a time-frequency domain to obtain a multi-modal video feature, and inputting the multi-modal video feature to a cross-modal fusion network to obtain a fusion feature vector; and performing anomaly type prediction on the fusion feature vector based on a long-short term memory network in combination with a semantic map model to obtain an anomaly type recognition result and a risk level judgment result. According to the method, the problem that the multi-modal data lacks a unified time reference and fusion mechanism can be solved.

The invention discloses an electric energy meter operation state inspection and evaluation method and system, and relates to the technical field of electric power metering safety, and the method comprises the steps: collecting and preprocessing the electrical working data of an electric energy meter, obtaining the reference current and working current of the electric energy meter, extracting error features, and generating a current error signal; carrying out voiceprint collection on the electric energy meter, and capturing a voiceprint signal of the ammeter; converting the voiceprint signal into a discrete pulse sequence through a coding algorithm, and obtaining a standardized pulse sequence through dimension conversion and optimization compression; performing feature extraction on the standardized pulse sequence by applying a pulse neural network, and obtaining fault probability distribution through synaptic weight calculation and neuron state updating after obtaining pulse sequence features; the feedback control signal and the electric energy meter evaluation report are generated based on the fault probability distribution, and the problem of poor real-time performance of fault mode recognition is solved.

The invention discloses a satellite-ground enhanced signal conversion method and a satellite-ground enhanced signal conversion model based on PPP-RTK, which are used for solving the problems that conventional RTK in a ground-free network area fails, and satellite-based PPP-RTK depends on a special terminal and is slow in convergence. Generating an RTCM-OSR correction number by analyzing the SSR correction number, fusing the orbital clock correction, ionosphere and troposphere correction and performing weighted interpolation; the ambiguity is solved in combination with the observation value of the base station, dual-path verification and weighted fusion are carried out in combination with M-W and LAMBDA algorithms, and the wide lane ambiguity fixing reliability is improved; and when the ground-based signal is interrupted for more than 1 second, inheriting and maintaining Kalman filtering continuity by using the state vector and generating a switching identifier. The model integrates a signal processing module, a correction generation module, an ambiguity verification module and a switching control module, so that a conventional RTK terminal obtains centimeter-level positioning (level 2.3 cm and elevation 4.1 cm) without a public network, the interruption recovery is less than or equal to 1.5 seconds, and the scene upgrading cost of power inspection, power transmission monitoring, wind power plant monitoring and the like is reduced.

The invention relates to the technical field of industrial automation, and particularly provides an equipment operation state intelligent diagnosis method based on current component detection. The method comprises the steps that a current sensor and a data acquisition card are adopted, non-intrusive sampling is carried out at an equipment power supply loop, and a high-frequency current signal is acquired; preprocessing the collected high-frequency current signal to obtain a preprocessed time-domain current signal; converting the preprocessed time domain current signal into a frequency domain signal by adopting fast Fourier transform (FFT), calculating the amplitude and phase of each harmonic wave from a fundamental wave to a higher harmonic wave, carrying out feature engineering, and constructing a feature vector representing the state of the equipment; comparing the feature vector with historical harmonic data to obtain an operation state result; according to the operation state result, a dynamic health baseline model is constructed, online monitoring and abnormity diagnosis are carried out, the method can adaptively monitor the operation state of the equipment, the accuracy and reliability of state diagnosis are improved, and the manual intervention degree is reduced.

A signal generation system includes a first computing device configured to generate a trajectory signal representing a planned trajectory of a vehicle; and a second computing device configured to generate an output word signal representing word information to be given to an occupant of the vehicle, by inputting an input word signal representing an utterance of the occupant in natural language format into a trained language model. At least one of the first computing device and the second computing device converts at least one of the sensor signal and a signal generated based on the sensor signal to a trajectory data signal represented in a format that can be inputted into the trained language model. The second computing device generates a trajectory word signal related to the planned trajectory as the output word signal, based on the trajectory data signal.

The invention discloses a pneumatic control valve fault diagnosis method and device based on microwave vibration measurement, and relates to the technical field of industrial process control. The method comprises the steps that a non-contact control valve rod vibration signal is collected; constructing a multi-scale time-frequency domain dual-channel feature fusion network structure, and extracting time sequence dynamic features of a valve rod vibration signal in a time domain branch through combination of multi-scale one-dimensional convolution and a bidirectional gating circulation unit; the frequency domain branch converts the one-dimensional signal into a two-dimensional time-frequency image, and uses a multi-scale two-dimensional convolutional network to extract spectrum texture features; introducing a channel attention mechanism to adaptively learn feature importance weight, and adopting a cross attention mechanism to deeply fuse time-frequency domain features; and based on a preset control valve fault category, performing fault classification on the fused control valve time-frequency features to obtain a control valve classification result. According to the method, complementary information of different modes is fully excavated, fault features of the control valve are fully extracted, and therefore accurate diagnosis of the faults of the control valve is achieved.

The application discloses an AI sound online analysis and identification method and system based on optical fiber sensing, which comprises the following steps: capturing sound signals of a target area by a distributed optical fiber sensing unit and converting the sound signals into optical fiber transmission optical signals, and completing photoelectric signal conversion to form an acoustic electric signal stream through a sound signal conversion transmission unit. Hierarchical voiceprint feature extraction is sequentially performed through an optical fiber voiceprint feature self-encoding model, feature refinement and redundancy elimination are realized by using a voiceprint feature distillation optimization algorithm, time sequence feature correlation is constructed through a sound signal time sequence prediction identification model, and finally online analysis and category determination are completed by a sound signal AI real-time identification engine. The application fully adapts to the characteristics of optical fiber sensing signals, strengthens the synergy of feature processing and time sequence modeling, improves the identification response speed and accuracy, effectively solves the problems of insufficient feature synergy and time sequence adaptation in the prior art, and can be widely applied to online analysis of sound signals in complex scenes such as industrial monitoring and security warning.

The invention discloses a borrowing type successive approximation analog-to-digital converter adopting a back propagation neural network for calibration. The borrowing type successive approximation analog-to-digital converter comprises a system composed of a positive and negative capacitance digital-to-analog converter adopting a three-stage split bridge type capacitor array, a bootstrap sampling switch, an automatic return-to-zero comparator, an SAR logic control unit, an original-to-binary module and a BPNN calibration engine. Bootstrap sampling switches are respectively arranged at the tail ends of the positive and negative capacitance digital-to-analog converters, a comparator is connected with the output ends of the two, and the output is connected with an SAR logic control unit; the original-to-binary module converts the code generated by the SAR logic control unit into a binary code; and a BPNN calibration engine receives the code and performs calibration calculation by using a trained back propagation neural network model. By implementing the converter disclosed by the invention, hardware implementation can be simplified while high performance is ensured, so that the ADC realizes efficient and accurate signal conversion under a 180-nanometer BCD process, and power consumption and cost are reduced.

Embodiments of the present application disclose a kind of integrated circuit digital back-end physical optimization method, equipment, medium and product.The method comprises: the pin node in circuit diagram and the interconnection between nodes in the circuit diagram are obtained by pre-processing to be optimized circuit diagram;According to pin node and interconnection, the wiring estimation of circuit diagram is carried out;According to the pre-processing result and the wiring estimation result of circuit diagram, the layout optimization index and timing index in circuit diagram are determined;Layout optimization index includes at least one of the following: power consumption parameter, gate circuit area parameter, buffer area parameter and half perimeter line length;Timing index includes at least one of the following: load violation parameter, signal conversion violation parameter and timing margin parameter;According to layout optimization index and timing index, the target optimization function of circuit optimization is determined, the circuit layout optimization in integrated circuit optimization, gate size adjustment and buffer layout adjustment are carried out, and optimization result is obtained, and circuit layout, gate size adjustment and buffer layout can be optimized.

The invention discloses a method and a system for unvarnished transmission of logic signals on a two-wire power supply circuit, and the system comprises a power supply, a power isolator, the two-wire power supply circuit, a load isolator, an impedance matcher, at least one transmitting node and at least one receiving node. One of the sending nodes sends the logic signal, encodes the sending logic signal into a first narrow pulse code signal, and then converts the first narrow pulse code signal into a second narrow pulse code signal on the two-wire power supply line for transmission; and the receiving node receives the logic signal, converts a second narrow pulse code signal on the two-wire power supply line into a third narrow pulse code signal, and decodes the third narrow pulse code signal into a receiving logic signal. The method has the advantages of high transmission rate, strong real-time performance, high reliability, low cost, wide application range and the like.

The invention relates to the technical field of ground type identification, and provides a ground type sensing method based on wheel force signal bimodal fusion, and the method comprises the steps: obtaining a vertical force borne by a wheel in the running process of a vehicle through a wheel force sensor installed on the wheel, segmenting a wheel force signal by adopting a sliding window technology, and performing data enhancement by adopting a time warping technology; converting the one-dimensional wheel force signal into a homologous two-dimensional image by using a recurrence plot conversion method; performing multi-scale feature extraction on the wheel force one-dimensional time sequence signal and the homologous two-dimensional image, and performing multi-modal feature fusion by adopting a weighted interactive attention mechanism; and finally, a ground type identification classifier is trained and constructed by using a simple classifier. According to the method, more discriminative information in the homologous bimodal features can be adaptively concerned, so that a better classification effect is achieved.

The application discloses a fault location method and system based on decomposition noise reduction and a novel convolutional neural network, and belongs to the technical field of power systems. The method comprises the following steps: collecting three-phase current signals of a line as original current signals; performing decomposition noise reduction processing on the collected original current signals; calculating the entropy values of each IMF component, screening out the IMF components with the entropy values greater than a threshold value, and reconstructing the remaining IMF components to obtain a denoised signal; converting the denoised signal into a two-dimensional feature image by using a Gram angle field algorithm, and performing data enhancement processing on the obtained two-dimensional feature image; inputting the generated two-dimensional feature image dataset into a novel convolutional neural network model to obtain a location result. The system comprises a computer readable storage medium and a processor, and the processor is used for reading executable instructions stored in the computer readable storage medium to execute the steps of the above method. The application combines a noise reduction method and a novel convolutional neural network to improve the accuracy and reliability of fault location.

The application discloses a sleep apnea detection method based on a millimeter wave radar, and relates to the technical fields of radar signal processing and vital sign detection.The method comprises the following steps: transmitting a detection signal to a target through a millimeter wave radar to obtain an intermediate frequency signal and a signal transformation matrix; calculating a dynamic energy change value based on the preprocessed signal transformation matrix, and determining a target distance bin of each frame according to a statistical result of the dynamic energy change value; determining whether the target is in a position stable state according to the discrete degree of the target distance bin of each frame in a preset time window; extracting a phase signal at the target distance bin of each frame in the window in the position stable state, obtaining a breathing signal waveform curve based on the phase signal, and detecting a sleep apnea event.The application can realize high-precision detection of the sleep apnea event under various complex conditions, and effectively solves the problems of unstable target locking, large noise interference and lack of distance adaptability in state determination.

The invention provides a tool magazine cam time sequence angle detection method and system based on a sensor, and relates to the technical field of tool magazine cam time sequence angle detection. The method comprises the following steps: periodically acquiring a sensor signal to obtain a background signal in a non-detection period of the tool magazine cam; updating a sensor state reference according to the background signal; during tool magazine cam angle measurement, a sensor probe signal is collected to obtain a detection signal; obtaining a first compensation detection signal according to the detection signal and a sensor state reference; and converting the first compensation detection signal into a time sequence angle value of the tool magazine cam according to a preset conversion function. According to the method, the production efficiency is improved, the equipment operation risk is reduced, the service life of the equipment is prolonged, and the technical problem that stable and reliable data are difficult to provide under complex working conditions through a traditional method is solved.

The utility model discloses a lossless wire-breaking-free automobile CAN bus testing device, and the device is characterized in that an induction signal interface is connected with a conductive clamp, and is used for obtaining a surface induction signal of a CAN signal wire; the CAN signal extraction module is connected with the induction signal interface and is used for converting a surface induction signal of a CAN signal line into a logic signal; the CAN signal conversion module is connected with the CAN signal extraction module, the main control module and the CAN signal interface and used for converting logic signals from the CAN signal extraction module or the main control module into CAN signals and sending the CAN signals to the CAN signal interface and converting the CAN signals received from the CAN signal interface into logic signals and sending the logic signals to the main control module, and the display module is connected with the main control module. And the display module is used for displaying CAN signal identification data information. By using the CAN bus signal acquisition device, a user can conveniently acquire a CAN bus signal without damaging a CAN communication line or additionally arranging a terminal during automobile maintenance, so that the maintenance efficiency can be improved.

The invention discloses a chip detection system and method. The method comprises the following steps: acquiring an image distortion data set of each detection point on the surface of a chip; performing pattern recognition on the data set to extract a defect deviation feature cluster, and determining a key fault area through association matching; constructing a pixel correction identification network in a fault area to capture a luminosity interference signal, converting the luminosity interference signal into an identification enhancement signal to form an interference correction graph, and performing time sequence correlation analysis to generate a main deviation source; performing parallel processing on the main deviation source to generate an identification queue, performing focusing processing on the queue to form a feature enhancement source, and performing delay analysis to generate a space transmission time difference; converting the space transmission time difference into a probability transmission chain, implementing multi-layer probability accumulation processing, and determining, identifying and judging nodes through probability fusion analysis; and carrying out probability weight distribution on the identification judgment nodes to construct a probability distribution network, and determining a hierarchical defect identification result based on the network and time window extension parameters, thereby realizing high-precision intelligent detection of chip surface defects.

The invention provides an oil and gas pipeline leakage detection method and system fusing multi-modal characteristics, and belongs to the field of pipeline leakage detection. The invention aims to solve the problems that in a signal conversion link during pipeline leakage detection, a traditional Grubrum angle field does not consider local time sequence correlation sufficiently to restrict feature representation, single-mode processing has information one-sided, multi-mode fusion lacks deep interaction, and information redundancy is difficult to effectively reduce. The method comprises the following steps of: performing conversion from a one-dimensional time sequence signal to a two-dimensional image on a pipeline signal by adopting a wavelet threshold filtering and angle calculation weighting improvement-based Gramb angle difference field method; extracting the spatial characteristics of the two-dimensional GADF image through a Swin Transform, and obtaining the time sequence characteristics of the one-dimensional time sequence signal through a gating circulation unit; and finally, carrying out weighted fusion on the spatial features and the time sequence features by utilizing a weighted cross attention mechanism module, capturing cross-modal relevance, carrying out classification by utilizing fused features, and identifying different working conditions of the pipeline.

This invention belongs to the field of measurement and control while drilling (MWD) technology, specifically relating to a main control function testing system for rotary steerable drilling systems. It aims to address the shortcomings in the testing and debugging of the main control function of rotary steerable drilling systems in existing technologies. The main control function testing system for rotary steerable drilling systems provided in this application is used to test and debug the main control program of a rotary steerable drilling system. The system includes a signal conversion module, a control module, a main control module, a decoding and display module, and several MWD tool simulators. The signal conversion module receives the steerable control commands input by the system and converts them into pulse signals. The control module includes a bypass-type downlink command device simulator, a wellbore-surface circulation system hydraulic simulator, and a downhole tool generator simulator, all interconnected. This system can provide a simulation system for testing the main control unit of a rotary steerable drilling system, testing various functions of the main control unit, and facilitating rapid testing and debugging of the main control program.

The invention relates to the technical field of high-voltage circuit breaker fault identification, and discloses a high-voltage circuit breaker fault identification method, system and device and a medium, and the method comprises the steps: converting a multi-state vibration signal of a high-voltage circuit breaker into a multi-state signal time-frequency diagram; performing data expansion on the multi-state signal time-frequency diagram, and dividing the multi-state signal time-frequency diagram into a training set and a test set; extracting a time-frequency graph feature vector of the training set, and performing parameter optimization on the fault classifier according to the time-frequency graph feature vector to obtain an optimized classification model; calculating verification model parameters according to the optimization classification model; and constructing a target fault prediction model according to the verification model parameters, performing fault prediction on the to-be-diagnosed high-voltage circuit breaker signal by using a target fault diagnosis model, and calculating a corresponding feature importance thermodynamic diagram and a target fault evaluation factor. According to the method, the accuracy of target fault category prediction can be improved, the fault reason is directly positioned through the feature importance thermodynamic diagram, and accurate identification of high-voltage circuit breaker fault detection is realized.

The invention discloses a double-domain non-convex joint optimization equipment state signal denoising method and system, and the method comprises the steps: carrying out the modeling of a partial discharge signal containing aliasing noise, obtaining a one-dimensional observation signal, and converting the one-dimensional observation signal into a two-dimensional observation matrix; based on the two-dimensional observation matrix, obtaining a double-domain collaborative regular model through double-domain decomposition, and optimizing the double-domain collaborative regular model by adopting a non-convex penalty function to obtain a non-convex optimized regular model; and carrying out iterative solution on the non-convex optimization regular model by adopting an improved collaborative alternating direction multiplier algorithm, obtaining an updated two-dimensional observation matrix of the partial discharge signal in combination with an original residual threshold and a dual residual threshold, and carrying out inverse conversion on the two-dimensional observation matrix of the partial discharge signal to obtain a denoised partial discharge signal. Cooperative iteration of double-domain variables is realized based on an improved cooperative alternating direction multiplier algorithm, double-domain residual index dynamic judgment convergence is designed, and redundant iteration is reduced while denoising precision is guaranteed.

The embodiment of the utility model discloses a synchronous rectification control system and a power supply system, which are applied to the technical field of electronic information processing, and the synchronous rectification control system comprises a control module and a first side time sequence control module, and can be correspondingly used for performing time sequence control on a switching tube in a synchronous rectification system of a switching power supply. The first side time sequence control module can generate time sequence control signals according to midpoint voltage signals of two first bridge arms in the synchronous rectification system so as to drive corresponding full-bridge switch tubes, so that signal conversion for driving the full-bridge switch tubes is consistent with conversion of the midpoint voltage signals, and therefore, frequency conversion of the full-bridge switch tubes along with frequency conversion of input voltage cannot occur. The condition that the phase of the input voltage and the phase of the current are increased is avoided, so that the optimal turn-on time of synchronous rectification is realized, and the efficiency is maximized.

This application discloses an LVDS adaptive frame synchronization system, comprising: a transmitting end: a clock generation module providing multi-rate clocks; a data parallel-to-serial conversion module converting parallel data into a high-speed serial bit stream under clock control; a multi-functional auxiliary channel parallel-to-serial conversion module interleaving and encoding auxiliary control information and synchronization information into a composite bit stream; and an LVDS driver group converting signals into differential signals for transmission. The receiving end: an LVDS receiver group converts differential signals into single-ended signals, including multiple data signals, one auxiliary channel signal, and one half-rate clock signal. A serial-to-parallel conversion and synchronization control module uses a half-rate clock to perform DDR sampling of the data and auxiliary channel signals. Synchronization information is extracted by sampling on the falling edge of the auxiliary channel signal, and auxiliary control information is extracted by sampling on the rising edge. A synchronization detection unit obtains the capture sequence from the synchronization information, and a numerically controlled delay unit calculates the delay correction amount based on the mapping relationship between the capture sequence and the correction signal to delay the data, thereby achieving adaptive frame synchronization.

The invention relates to the field of power monitoring and management, in particular to a power distribution room equipment internet of things monitoring system. The system comprises a distributed sensing node network, an edge computing gateway, a data center platform and a multi-stage early warning module, the distributed sensing node network comprises a temperature and humidity sensor, a Hall effect current sensor and a voltage sampling circuit, and a signal processing unit is arranged in the distributed sensing node network; the edge computing gateway is connected with the distributed sensing nodes through a communication bus; the data center platform constructs a digital twin model of the power distribution equipment, stores time sequence data and configures a waveform analysis engine; the multi-stage early warning module is configured with a three-stage threshold interval, establishes a communication link with the linkage control device, and is configured with an alarm signal conversion unit. According to the invention, the technical effects of effective monitoring of the distribution room, processing of sensing signals, dynamic rendering display, linkage instruction generation, waveform analysis and diagnosis, and the communication bus having a protocol conversion function are achieved.

The present disclosure discloses a method for monitoring power consumption of a server. The method is applied to a system for monitoring power consumption of the server, which includes a computer power supply unit, a first one-time programmable memory, a second one-time programmable memory, a first server plug-in and a second server plug-in, the method including: obtaining a power supply voltage signal of the computer power supply unit; performing sampling voltage signal conversion to obtain a first sampling voltage signal; obtaining a first analog output voltage signal of the first one-time programmable memory; performing sampling current signal conversion according to the first analog output voltage signal to obtain a first sampling current signal; generating server power consumption according to the first sampling current signal and the first sampling voltage signal; performing server power consumption monitoring according to the server power consumption to obtain a server power consumption monitoring result.

The invention provides a test control multi-protocol interface circuit, a test control device and test equipment, and is suitable for the technical field of semiconductor test, the interface circuit comprises at least one bus line, one bus line is connected between a main control unit and one communication terminal used for being connected with an external circuit, and the other bus line is connected with the external circuit. The main control unit is used for outputting a sending signal and inputting a receiving signal and generating a switch control signal according to the sending signal, and the bus line comprises a first voltage conversion circuit used for converting the sending signal to a first level to be output; the open-drain control circuit is used for providing another sending signal path; and the switching circuit is connected with the main control unit, the first voltage conversion circuit, the open-drain control circuit and the communication terminal, is used for transmitting the output of the first voltage conversion circuit or the open-drain control circuit to the communication terminal according to the switching control signal, can be compatible with I2C and I3C, supports a wide voltage range, and meets the system integration test requirements.