2466 results about "Digital conversion" patented technology

A system for AI-supported leak detection and localization in water distribution infrastructures, consisting of: a large number of distributed sensor nodes mounted along a water pipe, each sensor node comprising the following: a pressure sensor configured to measure local hydraulic pressure fluctuations within the pipeline with a resolution of at least 0.01 bar; a flow sensor configured to measure the volume flow within the pipeline with an accuracy of at least ±0.5% of the measured value; an acoustic sensor configured to detect vibration signatures caused by leaks in a frequency range between 50 Hz and 20 kHz; an embedded microcontroller with integrated analog-to-digital conversion circuitry for digitizing sensor outputs; a wireless communication module configured to transmit time-synchronized sensor data to a cloud-based processing platform; and a local energy subsystem with a rechargeable battery and an optional circuit for generating photovoltaic energy; the cloud-based processing platform includes an artificial intelligence engine that comprises the following: a data acquisition module configured to receive and decode the transmitted sensor data and to perform time alignment; a supervised learning module that is trained on historical data of flagged leaks and non-leaks to classify incoming sensor patterns; an unsupervised learning module configured to detect anomalies by modeling normal operating baselines of the pipeline; and a topology-aware localization module configured to determine leak coordinates using the topology of the pipeline network, modeling the propagation of hydraulic waves, and estimating the arrival time difference from multi-node acoustic detections; and wherein the system is configured to provide real-time leak alerts and georeferenced visualization via a remote monitoring interface.

The invention discloses a multi-dimensional information integrated transformer health state monitoring method, which relates to the technical field of power systems, and comprises the following steps: deploying a multi-source sensor, collecting various signals of a transformer, and carrying out signal conditioning and analog-to-digital conversion on analog signals collected by the sensor; performing timestamp alignment on the received multi-source heterogeneous data, and eliminating data noise by adopting a filtering algorithm; key features which are sensitive to the health state of the transformer and complement each other are selected from the extracted feature values to form a health state evaluation index set; setting a sliding time window, obtaining historical data in the window, performing normalization processing on the data, calculating the information entropy and the variation coefficient of each index, and fusing the information entropy and the variation coefficient to obtain the dynamic weight of each index; obtaining a health state index of the transformer according to the normalized value and the dynamic weight; and displaying the health state index and the change trend thereof in real time, and setting an early warning threshold value and an alarm threshold value of the health state index.

The invention relates to the technical field of fault monitoring, in particular to a power transmission line fault monitoring and positioning method, system and device and a medium. The method comprises the following steps: firstly, performing digital conversion on an original current signal to obtain time sequence data, and then identifying and classifying fault events through threshold detection and a feature extraction algorithm to obtain classification labels of fault types; then, based on the classification label, establishing a traveling wave propagation model and carrying out time difference calculation, carrying out clustering analysis on a plurality of wave head data in a preset time window, and judging whether the fault is a single-point fault or a multi-point fault through a clustering result; and finally, a positioning result is transmitted to the central server for comprehensive verification and alarm management, and a final fault response is formed. By introducing a time series data analysis and clustering judgment mechanism, the fault type can be accurately identified, single-point and multi-point fault conditions can be effectively distinguished, and meanwhile, the reliability of a positioning result is improved through comprehensive verification of the central server.

The invention relates to the technical field of radar sensing, and discloses a sensing method and system based on millimeter radar waves. The method comprises the steps that millimeter wave radar receiving signals are subjected to low-noise amplification, frequency bands are screened through a band-pass filter and then mixed with local oscillation signals to obtain distance and speed information, after analog-to-digital conversion digitization, features are extracted to distinguish a human body target in a closed / open space, and finally multi-scene adaptability analysis is executed to generate human body position state information. According to the invention, through construction of a signal processing link, from signal amplification, filtering, frequency mixing and digital processing to target identification and multi-scene adaptability analysis, comprehensive perception of a human body target is realized, a static human body and a non-human body interference source can be effectively distinguished, a processing strategy is automatically adjusted according to different space environment characteristics, and the processing efficiency is improved. And the accuracy and reliability of human body perception are greatly improved.

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 discloses a multichannel electrophysiological signal acquisition system and an acquisition method thereof, and the multichannel electrophysiological signal acquisition system comprises a power supply module electrically connected with a controller, an analog front-end chipset, a filter module and an analog-to-digital conversion module; the analog front-end chip set is used for acquiring an analog signal and preprocessing the analog signal; the pre-processed analog signal is converted into a digital signal through a filter module and an analog-to-digital conversion module, and the digital signal is transmitted to a controller; the filter module is used for filtering and denoising; the timer module connected with the controller generates a sampling frequency, transmits the sampling frequency to the analog-to-digital conversion module, and provides a time sequence signal for the analog-to-digital conversion module. The invention discloses a multi-channel electrophysiological signal acquisition system, which solves the problem of synchronous sampling time sequence control of a high-density electrode array and eliminates signal phase deviation caused by clock jitter among multiple channels under the constraint of maintaining the total power consumption of the system. And real-time power frequency interference suppression is realized.

The invention relates to the technical field of weighing equipment detection, and discloses a detection output method, device and equipment for weighing equipment and a storage medium, and the method comprises the steps: carrying out the preprocessing and analog-to-digital conversion of original output signals of a plurality of weighing sensors in the weighing equipment, and obtaining synchronous digital signal data; bayesian multi-channel signal fusion and anomaly detection are carried out based on the synchronous digital signal data, and multi-channel fusion signal data and an anomaly detection identifier are obtained; performing inter-channel correlation analysis and adaptive weight distribution according to the abnormal detection identifier and the multi-channel fusion signal data to obtain a multi-channel weighted fusion result and a calibration compensation parameter; and carrying out real-time fault isolation and multi-channel working state identification on the multi-channel weighted fusion result based on the calibration compensation parameter, and generating a weighing detection result, the weight distribution can be automatically optimized according to the real-time working state and the signal quality of each weighing sensor channel, and the accuracy and the stability of the measurement of the weighing equipment are improved.

The invention discloses a defect identification system of an ultrasonic flaw detector, and relates to the technical field of nondestructive testing, the system comprises a signal acquisition and preprocessing module, a defect feature identification module, a physical modeling analysis module, a life prediction and evaluation module and an intelligent decision visualization module; the signal acquisition and preprocessing module adopts a multi-frequency-point phased array transducer array, obtains an original signal through low-noise amplification, band-pass filtering and analog-to-digital conversion, and outputs a time domain signal matrix through adaptive noise reduction and gain compensation processing; the multi-frequency-point phased array transducer array and the advanced signal processing technology are integrated, the defect recognition precision and efficiency are remarkably improved, the system obtains high-quality original signals through low-noise amplification, band-pass filtering and analog-to-digital conversion technologies at first, then the high-quality original signals are subjected to self-adaptive noise reduction and gain compensation processing, and the defect recognition accuracy is improved. The background noise interference is effectively eliminated, and the purity of the signal is ensured.

The invention relates to a high-precision temperature sensor based on complementary dynamic element matching and a zoom analog-to-digital converter and a working method, and belongs to the technical field of sensor improvement, and the sensor comprises an analog front end, an analog-to-digital converter, a digital rear end and a control logic. An improved chopping technology is adopted, and one clock signal is adopted to control two sub operational amplifiers, so that the output stability and precision of the analog front end are improved. A complementary dynamic element matching technology is utilized, errors caused by voltage drop at the two ends of a switch are reduced, the influence of channel charge injection is counteracted, a scaling type analog-to-digital converter design is provided for an analog-to-digital conversion part, and balance among power consumption, speed and precision is achieved. According to the method, a 10-bit successive approximation register analog-to-digital converter is used for measuring a numerical value and a change trend, and the numerical value and the change trend are analyzed, so that the expression effects of a current mirror structure, a transistor, a filter and the like are evaluated, the aging of the transistor is found in advance, and a clear direction is provided for future design improvement.

The invention discloses a platinum resistor high-precision temperature measuring device capable of automatically identifying a wiring mode. The platinum resistor high-precision temperature measuring device comprises a power supply module, a signal detection module, an isolation module, a signal processing module and a cold end temperature measuring module. The power supply module supplies power to the signal detection and processing module. The signal detection module comprises a sensing probe, a wiring identification circuit, a constant current source, a measuring circuit and an analog-to-digital conversion circuit. The sensing probe measures the measured temperature, the wiring identification circuit automatically judges a two-wire system, a three-wire system or a four-wire system and feeds back a result, the constant current source provides positive and negative constant current excitation, and the measuring circuit collects and processes feedback voltage and then sends the feedback voltage to the analog-to-digital conversion circuit. The analog-to-digital conversion circuit is connected with the signal processing module through the isolation module, and the signal processing module calls a corresponding algorithm according to a wiring identification result and outputs final temperature data. According to the invention, high-precision temperature measurement at the grade of + / -0.02 DEG C can be realized in the range of-200 DEG C to 850 DEG C under the three / four-wire system and low-speed sampling conditions.

The invention discloses an ultra-weak light detection system and method based on an SPAD array and time correlation photon counting, and belongs to the technical field of ultra-weak light detection. A multi-channel SPAD array detection unit; a time-to-digital conversion module; the photon arrival time analysis unit comprises a TCSPC model, a noise modeling and suppression module and a self-adaptive convolution filter; a data compression and fitting module; a spatial denoising filter; an output unit; and a master control processing unit. Based on a multi-channel SPAD array detection unit, a time-to-digital conversion module, a TCSPC model, a noise modeling and suppression module, a self-adaptive convolution filter and the like, high-sensitivity imaging and quantitative detection of materials (such as black silicon) with extremely low reflectivity are realized. The system has the advantages of being good in real-time performance, high in time resolution, high in signal-to-noise ratio, high in on-chip processing capacity, high in integration and the like, and meanwhile the bottleneck of a traditional TCSPC method for large-scale photon data processing is solved.

The invention relates to the technical field of optical particulate matter sensing, in particular to a particulate matter particle size recognition and concentration detection method. The method comprises the following steps: laser emitted by a laser forms a laser beam which is focused and then diverged through a lens; with a laser focus as a reference, at least two photosensitive devices are distributed in the light beam propagation direction and comprise a perifocus device and a far focus device, and receiving surfaces of the near focus device and the far focus device face laser beams to collect scattered light; scattered light signals of each device are collected, and are input into a processing unit after independent current / voltage conversion, signal amplification and analog-to-digital conversion; based on the characteristics that small-particle-size scattered light is preferentially captured by perifocus high irradiance and large-particle-size scattered light is preferentially captured by far focus large beam diameter, a mapping relation between particle sizes and channels is established; the number concentration and mass concentration coefficient of each channel is calibrated through standard particles, and synchronous detection of PM1.0, PM2.5 and PM10 is realized in combination with linear processing, so that the particle size recognition precision and the concentration measurement reliability are effectively improved.

The invention discloses a magnetostriction liquid level meter temperature compensation method and system based on a support vector machine. The method comprises the steps that the currently captured original pulse number is obtained through a magnetostriction liquid level meter; the current environment temperature is detected in real time through a high-precision temperature sensor in the magnetostrictive liquid level meter; taking the original pulse number and the current environment temperature as input features, sending the input features to a pre-trained support vector machine regression model (SVM), and outputting a displacement error prediction value in the current temperature environment; and correcting the original measurement result according to the error prediction value to obtain a real pulse measurement value after temperature compensation. The system comprises a time-to-digital conversion chip, a temperature chip temperature measurement module, a support vector machine reasoning module, a pulse compensation module and a distance calculation module. According to the method, nonlinear modeling can be carried out on measurement errors caused by environment temperature changes, so that the liquid level measurement result is dynamically corrected, and the overall robustness and precision of a measurement system are improved.

Disclosed in the present invention is a high-dynamic capacitive digital microphone system based on electrostatic force feedback. The system comprises an MEMS microphone and an interface circuit chip thereof, wherein the interface circuit chip comprises a charge pump, an analog front end, an analog-to-digital conversion circuit, an amplitude detection module, an amplitude adjustment control module and a pulse density modulation module. In the present invention, the characteristic of the sensitivity being able to be adjusted by means of changing a bias voltage by a capacitive sensor is used, and a charge pump with an adjustable output voltage is used to provide electrostatic force feedback, thereby further expanding the adjustable range of a front-end gain, and also reducing requirements for the noise performance of a readout circuit. Therefore, the system in the present application has the characteristics of high dynamic range and high-precision quantization.

The invention discloses an electric power data acquisition method and equipment based on a logarithmic amplifier and a medium, and relates to the technical field of electric power monitoring signal processing, and the method comprises the steps: collecting an original analog current signal on an electric power circuit and an analog temperature signal of an environment where the logarithmic amplifier is located, and generating a mixed observation vector; based on the mixed observation vector, calculating an estimated value of a temperature characteristic correction parameter in real time through a pre-stored temperature-parameter mapping relation, and performing logarithmic compression on the original current digital signal sequence by using the estimated value of the temperature characteristic correction parameter to obtain a compressed voltage signal subjected to temperature compensation; and performing high-speed analog-to-digital conversion on the compressed voltage signal subjected to temperature compensation to obtain a discrete digital signal sequence, and storing the discrete digital signal sequence into a priority cache region. According to the invention, the data volume is effectively compressed by using the correlation between the signals, and the transmission bandwidth and the storage demand are reduced.

The invention relates to the technical field of data analysis and control, in particular to a hydraulic climbing frame safety control method based on digital monitoring, which comprises the following steps: acquiring monitoring data in real time through a sensor group arranged on a hydraulic climbing frame, preprocessing and analog-to-digital conversion are carried out on the monitoring data, then the data are formed and sent to a remote monitoring center through a wireless transmission technology, and the preprocessed monitoring data are input into a pre-stored structure safety evaluation model and a hydraulic system performance evaluation model so as to be analyzed in real time; analyzing the hydraulic climbing frame, judging a safety characterization state of the hydraulic climbing frame according to an analysis result, determining a safety risk type based on an abnormal state of the hydraulic climbing frame, sending early warning information through a user interaction platform when a risk is judged to exist, and executing a corresponding safety control instruction; automatic real-time monitoring, intelligent early warning and active control of the safety state of the hydraulic climbing frame are achieved, and construction safety and operation reliability are remarkably improved.

The invention discloses a hydraulic power plant equipment full life cycle health management system and method based on the Internet of Things, and relates to the technical field of hydraulic power plant equipment management, and the method comprises the steps: collecting operation parameters through a sensor network disposed on various types of equipment of a hydraulic power plant, and forming a data sequence after time synchronization and analog-to-digital conversion; the data sequence is subjected to noise reduction processing at the edge computing node, whether an abnormal state exists or not is judged on the basis of the data analyzed and processed by the lightweight anomaly detection model, the data is graded and marked according to a judgment result, and only statistical characteristics of abnormal data or non-abnormal data marked as high priorities are uploaded to a cloud data center; and the cloud data center identifies the type of the equipment component according to the uploaded data, calls the corresponding physical degradation evolution model, dynamically calculates the degradation degree of each component in combination with the real-time operation state, determines the weight relationship of each degradation dimension according to the service stage of the equipment, and generates a comprehensive health index.

A novel power storage system is provided. The power storage system includes a secondary battery and a subtractor. The subtractor includes a register. The subtractor has a function of converting a first voltage of the secondary battery into first voltage data by analog-digital conversion. The subtractor has a function of measuring time required for the first voltage to change only by a first voltage value. The register has a function of storing second voltage data that is higher than the first voltage data by a data value corresponding to the first voltage value. The subtractor has a function of stopping supply of power to the register until the first voltage changes by the first voltage value.

The invention relates to the technical field of bio-electricity signal acquisition, and particularly discloses a multi-channel bio-electricity signal acquisition system and a multi-channel bio-electricity signal acquisition method.The multi-channel bio-electricity signal acquisition system comprises a bio-electricity signal acquisition unit and a data processing unit; the data processing unit is used for compensating the original electrophysiological signals according to the real-time impedance value transmitted by the analog-to-digital conversion unit, performing primary screening on the original bio-electricity signals through the lowest quality threshold value, and performing primary compensation on the original bio-electricity signals according to the real-time impedance value, the standard impedance value and the breathing phase; according to the method, more flexible quality requirement setting is realized, the quality threshold setting difficulty is reduced, secondary screening is performed on the corrected bio-electricity signals through the highest quality threshold, and secondary compensation is performed on the corrected bio-electricity signals by adopting the LSTM network, so that the influence of bio-impedance fluctuation on the bio-electricity signals is reduced, and the quality of the bio-electricity signals is improved. And the acquisition quality and stability of the bio-electricity signals are improved.

The invention discloses a multi-channel parallel sampling high-dynamic response analog-to-digital conversion method, system and equipment and a storage medium, and belongs to the technical field of power electronics and signal processing, and the method comprises the steps: configuring a high-speed analog-to-digital converter channel to form a parallel sampling module, carrying out the alternate sampling of an input power grid analog signal, and generating a discrete sampling sequence group; inputting to a control unit to extract feature data, and calculating to obtain a fluctuation feature value; constructing a channel clock control vector, sending the channel clock control vector to each high-speed analog-to-digital converter channel, adjusting a sampling trigger moment, and re-executing analog signal sampling to obtain a sampling sequence after phase compensation processing; performing waveform reconstruction processing to obtain continuous reconstruction waveform data; and performing electric energy integral operation, and outputting a dynamic metering result. According to the invention, the problems of slow response, sampling distortion and large metering error of the analog-to-digital conversion system under the conditions of frequency fluctuation, unsteady disturbance and bidirectional power in the prior art are solved.

A high-linearity grid voltage bootstrap circuit relates to the technical field of analog-to-digital conversion and comprises a first charge pump boosting unit, a second charge pump boosting unit and a grid voltage bootstrap unit, working states of a sampling switch are divided into a sampling state and a holding state, an input voltage VIN is boosted to a VG through the grid voltage bootstrap unit in a sampling stage, and the sampling switch is switched on; and the holding stage sampling switch is cut off, and charges are injected to the upper polar plates of the capacitors C3 and C4 of the gate voltage bootstrap unit through the first charge pump boosting unit and the second charge pump boosting unit respectively. According to the invention, the problem that the actual gate-source voltage is reduced due to the grid parasitic capacitance of the sampling switch is solved, and the layout area occupation is greatly reduced while the bootstrap voltage is improved; the proportion of the capacitor C3 and the capacitor C4 can be flexibly set to obtain the required bootstrap voltage; and the problem that the on-resistance of the sampling switch is increased due to the reduction of the gate-source voltage can be solved, and the linearity of the sampling switch is improved.

The invention discloses a micro-water density monitoring method and system based on multi-sensor fusion compensation, and relates to the field of power equipment safety. According to the micro-water density monitoring method based on multi-sensor fusion compensation, parameters are collected in real time through a micro-water collector composed of a ceramic heater, a humidity-sensitive capacitor collection sheet, a dew point sensor, a pressure sensor and a temperature sensor; a signal conditioning circuit and an analog-to-digital conversion module are integrated by adopting a four-layer PCB (Printed Circuit Board), and the micro-water collector is heated for multiple times by a main controller; carrying out digital filtering processing, cross validation operation and operation of eliminating influence of environment temperature on the acquired parameters; the measurement precision is improved through a sensor self-calibration mechanism, finally standardized micro-water density data are output, and an early warning function is triggered based on a multi-level threshold value. According to the invention, the problems of low precision and poor reliability of the measured micro-water density in the prior art are solved. Therefore, the accuracy of micro-water density measurement under complex working conditions is effectively improved, and data support is provided for predictive maintenance of intelligent equipment.

The invention discloses a partial discharge signal denoising method based on STFT-SVD and Bayesian kurtosis threshold adaptive optimization, and the method comprises the steps: collecting an analog signal outputted by a high-frequency current transformer, carrying out the analog-to-digital conversion, obtaining a one-dimensional time domain signal sequence, carrying out the DC component removal and amplitude normalization of the signal, and obtaining a preprocessing time domain signal; performing short-time Fourier transform on the preprocessed time-domain signal to obtain a time-frequency spectrum, suppressing low-amplitude noise by adopting a soft mask method, and retaining main characteristics of partial discharge pulses; performing singular value decomposition on the time-frequency spectrum after soft masking, automatically selecting a principal component number according to a principal component, and adaptively reserving a main signal component to obtain a principal component spectrum; and performing inverse short-time Fourier transform on the principal component atlas to reconstruct a time domain signal, adaptively selecting a kurtosis threshold in combination with a Bayesian optimization algorithm, and outputting a denoised time domain signal.

The present application discloses a computing-in-memory circuit, chip and electronic device, wherein the computing-in-memory circuit comprises: a resistive random access memory array, a clamping circuit, a current mirror and an analog-to-digital conversion circuit; the clamping circuit is connected between a signal input terminal and the resistive random access memory array, configured to input clamping voltage signal to the N resistive random access memories selected of the resistive random access memory array, wherein N is an integer and N≥2; the current mirror is connected between the resistive random access memory array and the analog-to-digital conversion circuit, configured to output convergence current to the analog-to-digital conversion circuit based on the N-way currents output from the N resistive random access memories selected; the analog-to-digital conversion circuit configured to convert the convergence current to a digital signal.

The invention discloses a bedside skin flap blood flow monitoring and early warning device and computing equipment, and the device comprises a skin flap tissue perfusion monitoring module which comprises a probe housing, a microcirculation blood flow velocity sensor, a tissue oxygenation degree sensor and a flexible connecting arm; the data acquisition and transmission module is used for receiving the blood flow velocity data and the oxygenation degree data through a data line and carrying out analog-to-digital conversion and data packaging; the central processing unit comprises a data storage module, a data analysis module and an early warning module; the data analysis module comprises a baseline establishment sub-module, an anomaly detection sub-module and a trend prediction sub-module; and the early warning module is used for displaying a red alarm through a bedside display screen and sending a short message alarm containing the patient name, the bed number, the early warning type and the severity information to a medical staff mobile terminal through the wireless notification module. According to the invention, the abnormal condition of skin flap blood perfusion can be found and predicted in time, and the workload of medical staff is reduced.

The invention provides a BIM-based 3D building digital monitoring method and system, and belongs to the technical field of building engineering monitoring, and the method comprises the steps: carrying out the digital conversion and processing of a building design drawing of a target building, extracting the geometric and attribute information of the target building, and generating a standard BIM model; verifying and calibrating the basic multi-dimensional data and the standard BIM model when the target building is completed, and generating a reference BIM model; performing spatial registration and association mapping on the acquired multi-dimensional monitoring data and the reference BIM model to generate an association data set; comparing based on the associated data set to generate a comparison result; obtaining a potential risk type and a risk evolution trend of the target building through a risk prediction model based on a comparison result, historical monitoring data and a component static attribute in the reference BIM model; and generating a target monitoring strategy based on the potential risk type and the risk evolution trend. The building operation and maintenance efficiency is improved, and the service life of the building is prolonged.

The invention provides a portable fire blast risk dynamic identification and danger tracing method and terminal, and the method comprises the steps: collecting positioning data in real time through a GNSS positioning module; a high-speed analog-to-digital conversion chip ADC is used for carrying out high-frequency and synchronous processing on the gas concentration collected in real time; collecting field environment parameters in real time; performing dynamic threshold intelligent judgment to obtain a leakage judgment result, and executing a grading early warning mechanism; recording an inspection track in real time, and carrying out space gas concentration gradient analysis to obtain a gas concentration gradient; calculating a built-in space distance; judging the leakage direction according to the gas concentration gradient data, and intelligently tracing the leakage source according to the leakage judgment result; and uploading the security data to the cloud. According to the invention, the limitation of static and single-threshold alarm of an instrument is solved, and the technical problems of difficulty in spatial continuous sensing and automatic tracing positioning of gas leakage abnormity in a complex inspection environment with dynamic changes of multi-component combustible gas and environmental parameters are solved.

The invention is suitable for the technical field of semiconductor testing, and provides a data acquisition method and device based on a DDIC chip, and testing equipment, and the data acquisition method comprises the steps: receiving a test pattern, controlling the DDIC chip to output a corresponding gray scale voltage signal, and achieving the comprehensive testing of the response capability of the chip in a full gray scale range; secondly, a target acquisition link composed of functional units including impedance conversion, single-ended to differential conversion, programmable gain amplification, filtering, analog-to-digital conversion and the like is utilized, so that the signal driving capability and the anti-interference capability are effectively improved, meanwhile, high-frequency noise and nonlinear distortion are reduced, and the basic quality of sampled data is guaranteed; on the basis, sampling optimization processing is carried out on the sampling data based on the FPGA unit, and the data stability is improved. According to the scheme, through high-precision transmission of the signal link, the data acquisition precision of the DDIC chip is improved, and then the technical requirements of high-precision DDIC chip testing are met.

The invention provides a diagnosis method and system suitable for magnet current in a quasi-ring symmetrical star simulator, and relates to the technical field of star simulators, and the method comprises the steps: setting a target voltage range, and finally outputting the mutual inductance coefficient, total number of turns and sectional area parameters of a coil; utilizing an electromagnetic induction conversion algorithm to generate an original measurement signal; an original measurement signal is input into an integrator through a shielded wire to obtain a proportional voltage which is strictly proportional to the current, and the proportional voltage is transmitted to a data acquisition interface; performing analog-to-digital conversion on the proportional voltage received by the acquisition interface to generate a discrete sequence, calculating a conversion coefficient by combining a target mutual inductance coefficient and a fixed time constant, and finally restoring a digital current signal; and inputting the digital current signal to an upper computer to realize dynamic waveform display, and outputting key boundary parameters to a plasma experiment module. The method has the advantages that the method can adapt to complex changes of magnet current, and enough measurement precision and response speed can be provided in a low-parameter experiment.

The invention relates to the technical field of wireless communication, and discloses a sensing radio front-end radio frequency signal detection system, which comprises a broadband radio frequency front-end module used for receiving radio frequency signals in an environment and supporting multi-band signal capture and anti-interference preprocessing; the analog-to-digital conversion module is used for converting the radio frequency signal into a digital signal by adopting an ADC (Analog to Digital Converter) with 14-bit resolution and 2GSPS sampling rate; the signal processing module is connected to the output end of the analog-to-digital conversion module and is used for performing spectrum analysis, feature extraction and signal detection on the digital signal and outputting a detection result; and a synchronous control bus. According to the method, energy detection, cyclostationary feature analysis and a deep learning classification algorithm are fused, a time-frequency domain joint analysis framework is constructed, and the time domain transient feature extraction capability of the 1D-CNN and the frequency domain long-range dependence modeling advantage of Transform are combined, so that high-precision recognition of complex modulation signals is realized, and the detection sensitivity and the anti-interference capability are improved.