3107 results about "Analog to 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 relates to the technical field of Internet of Things, in particular to an electric energy meter with a wireless communication module, which comprises a parameter acquisition module, a communication optimization module, an interference analysis module, a fluctuation evaluation response module and a load classification module. According to the invention, a mutual inductor captures a single-phase circuit signal, analog-to-digital conversion is combined to realize parameter quantization, interference is eliminated based on an active power identification algorithm, a structured parameter set is integrated, a wavelet packet decomposition parameter and a quantization threshold are dynamically adjusted according to a load rate, and spread spectrum factor configuration is adopted to improve channel efficiency. The method comprises the following steps: extracting key features by using wavelet packet characteristics; continuously monitoring voltage fluctuation by a sliding window; detecting and identifying an instantaneous interference event by a break variable; inhibiting noise by Kalman filtering and predicting a trend; and the performances of the system in the aspects of acquisition precision, transmission efficiency, interference immunity and detection sensitivity are enhanced.

The invention relates to the technical field of industrial automation control, in particular to a container liquid level measurement method, system and equipment of a pressure sensor and a medium, and provides a method for eliminating gas phase pressure influence based on a double-sensor differential measurement mechanism and dynamically correcting liquid density and sensor drift errors in combination with a temperature compensation model. The temperature data are linked in real time through a three-dimensional compensation function, density calculation and drift correction are synchronously adjusted, and multi-parameter dynamic decoupling and self-adaptive compensation are achieved; a collaborative measurement mechanism is adopted to integrate bottom and top sensor data, and a built-in temperature probe and a preset threshold are combined to complete multi-source signal fusion and compensation calculation; a dynamic calibration method is designed, and system errors are eliminated through reference pressure point calibration and structure coefficient correction; constructing a closed-loop signal processing flow, and integrating high-precision analog-to-digital conversion, digital filtering and real-time algorithm to output a compensated liquid level value; the method improves the measurement precision and stability, and is suitable for temperature change, gas phase pressure fluctuation and multi-medium scenes.

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 signal acquisition, and discloses a signal acquisition method and system for an MEMS gas sensor array, and the method comprises the steps: carrying out the analog-to-digital conversion and temperature and humidity compensation of resistance value signals synchronously acquired by a plurality of sensing units in the MEMS gas sensor array, and obtaining a compensated response matrix; performing fast Fourier transform and dynamic feature extraction to obtain a frequency domain feature matrix of each sensing unit; performing sensing unit crosstalk coefficient calculation and response component decoupling to obtain a decoupling feature vector; carrying out Hilbert transform and adsorption-desorption characteristic extraction to obtain a gas sensitive characteristic sequence; inputting a self-adaptive power spectrum analysis model to carry out cross sensitivity identification processing to obtain a target gas response component; according to the method, the accuracy of target gas detection and concentration quantitative analysis is improved.

The invention relates to the technical field of multifunctional detection, in particular to a multifunctional metering detection system which comprises a time alignment module, an analog-to-digital conversion module, a data coupling module, a vector construction module and an event packaging module. According to the invention, the time alignment is carried out on the measurement data in multiple channels, the synchronization consistency of heterogeneous data is improved, the dynamic analysis of amplitude fluctuation characteristics is combined, the real-time adjustment of the analog-to-digital conversion resolution is realized, the response sensitivity of the system to unsteady signals is enhanced, and the reliability of the system is improved. A composite parameter sequence is constructed by using a cross-channel amplitude and trend relationship, the coupling expression capability among multiple parameters is optimized, the recognition precision of a multi-node linkage trend is improved through state vector modeling and direction difference judgment, an abnormal event path is reconstructed by adopting a time sequence, and a process record is packaged. And the detection data is converted into structured event chain data from original sampling, so that the organizability and traceability of the anomaly detection result are enhanced.

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 belongs to the technical field of signal acquisition and processing, and discloses a low-noise biopotential signal acquisition system and a low-noise biopotential signal processing method. An original bioelectricity signal of a human body is collected through an electrode array, and analog-to-digital conversion is carried out after the signal is processed by a multi-stage self-adaptive filtering and amplifying circuit. A digital signal is sequentially subjected to adaptive wavelet transform denoising and empirical mode decomposition to obtain a multi-level intrinsic mode function set, an intrinsic mode function of a characteristic frequency band is extracted from the multi-level intrinsic mode function set, and the signal is reconstructed. Introducing a dual noise reduction mechanism combining adaptive wavelet transform and empirical mode decomposition; a signal quality real-time evaluation system is established, and system parameters including the gain of a variable gain amplifier and the bandwidth of a dynamic band-pass filter are dynamically adjusted through closed-loop feedback. The self-adaptive optimization of the whole biopotential signal acquisition process is realized, various interferences are effectively inhibited, the signal characteristics are reserved, and the signal-to-noise ratio is remarkably 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 discloses a frequency hopping signal real-time detection method and system based on FPGA + DSP. The system comprises a time frequency analysis module, a data communication module and a frequency hopping signal detection module. The method comprises the following steps: a time frequency analysis module performs analog-to-digital conversion and digital down-conversion on an intermediate frequency signal received from a radio frequency end, and performs short-time Fourier transform on a zero intermediate frequency signal by adopting a four-pipeline processing mode; the data communication module transmits the frequency spectrum information from the FPGA to the DSP through an SRIO protocol; the frequency hopping signal detection module calculates a dynamic threshold of a current frame signal according to the received frequency spectrum information, and completes preliminary detection of the signal according to a threshold value; and carrying out binarization processing and connected domain marking on the detected frequency spectrum, screening non-frequency hopping signals according to time-frequency information of a connected domain, and packaging and uploading parameter information of the detected frequency hopping signals to an upper computer. The method is simple in algorithm, low in hardware resource requirement and high in detection efficiency, and the feasibility of engineering implementation is improved.

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 embodiment of the invention provides a signal processing circuit, a chip and electronic equipment. The signal processing circuit comprises an analog-to-digital conversion module and a signal amplification module, the first chopping switch module is used for modulating the differential input signal to high frequency under the control of a preset clock signal and inputting the signal to the signal amplification module; the second chopping switch module is used for modulating the differential output signal output by the signal amplification module to a low frequency under the control of a preset control signal and inputting the low frequency to the analog-to-digital conversion module; and the second chopping switch module also enables the analog-to-digital conversion module to establish sampling under the control of a preset control signal. According to the application, the second chopping switch module is multiplexed as a sampling switch of the analog-to-digital conversion module, so that the problem that on-resistance is increased due to the fact that a chopping demodulation switch and the sampling switch are arranged at the same time can be avoided.

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 the technical field of image enhancement, and discloses an image enhancement method based on an ISP image signal processing visual sensor, and the method comprises the steps: carrying out the collection of multiple frames of images of the same scene, carrying out the analog-to-digital conversion of the collected multiple frames of images, generating an RGB three-channel digital matrix, and obtaining N frames of original image sequences; dark current correction and lens shadow correction are executed, an affine transformation matrix from the N-1 frame to the first frame of image is calculated based on SIFT feature point extraction and matching, and an image sequence after space alignment is obtained; performing spatial feature extraction to obtain a multi-scale feature map sequence, and performing residual feature connection operation to obtain a time sequence correlation feature map sequence; according to the method, attention calculation of the channel dimension and the space dimension is executed, meanwhile, structural layer edge enhancement and texture layer denoising processing are executed, a target enhanced image is obtained, various kinds of distortion and noise interference in the image acquisition process are reduced, and the definition and the detail fidelity of the enhanced image are ensured.

The invention discloses an intelligent underwater sound signal processing system and method based on multi-sensor data fusion, and belongs to the field of underwater signal processing. A plurality of sensors are used for collecting underwater sound signals, linear acceleration, angular velocity and other information of an underwater target and outputting continuous analog signals; then the differential signals are transmitted to a signal conditioning module, gain is dynamically adjusted according to target intensity, amplified differential signals are output to an analog-to-digital conversion module, high-precision digitalization is carried out through a 24-bit delta-sigma ADC, digital signals are output to a main control unit, and the main control unit carries out multi-sensor data fusion and target orientation calculation; and finally, the main control unit uploads the underwater acoustic signal and the equipment attitude information data to an upper computer through an RS485 bus for man-machine interaction. The scheme is based on multi-sensor data fusion, can realize high-precision target detection and positioning in a dynamic underwater environment, and has wide popularization and application values.

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

The invention 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 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 an intelligent identification system and method based on time-frequency feature fusion and adaptive classification, and relates to the technical field of power equipment state monitoring. Comprising an ultrahigh frequency signal processing unit, a filter amplification unit, an analog-to-digital conversion unit, a digital signal preprocessing unit, a multi-dimensional feature extraction unit, a discharge phase analysis unit, a classification model optimization unit, an interference elimination unit, a defect type identification and diagnosis unit and a discharge source positioning unit. The ultrahigh frequency signal processing unit is used for collecting and transmitting partial discharge weak electromagnetic signals, and the filtering and amplifying unit is used for filtering, eliminating interference and amplifying signal amplitude. Through the ultrahigh frequency signal processing unit, the filtering and amplifying unit and the analog-to-digital conversion unit, weak electromagnetic signals are accurately collected, interference is efficiently filtered out, the signals are amplified and converted, the accuracy and effectiveness of the detection process are improved, and the misjudgment risk is reduced through pulse segmentation and normalization, multi-dimensional feature extraction and classification model establishment and optimization.

The invention discloses a multi-channel analog-to-digital converter for multi-channel frequency division multiplexing and a conversion circuit and method thereof, and the frequency division multiplexing multi-channel analog-to-digital conversion circuit comprises an analog frequency conversion module which is used for receiving N paths of modulation clock signals, and generating N paths of modulation clock signals according to the modulation clock signals, n paths of input signals are modulated to preset frequencies of the input signals respectively, and N paths of modulation signals are obtained; wherein N is greater than or equal to 2, and N is a positive integer; the frequency bands where the N paths of modulation signals are located are not overlapped; the analog adder is used for combining the N paths of modulation signals to obtain one path of analog signal; the input end of the analog-digital conversion module is connected with the analog-digital conversion module; the digital logic control module is used for receiving the N paths of modulation clock signals and separating the digital signals into N paths of digital signals which are output in parallel. Multi-channel analog-to-digital conversion can be realized by adopting one analog-to-digital converter, the hardware volume, cost and power consumption can be reduced, and the analog-to-digital conversion rate and precision can be improved.

The invention provides a highway bridge joint vibration and deformation condition monitoring method, and particularly relates to the technical field of bridge structure health monitoring, and the method comprises the steps: laying a fiber bragg grating sensor array at a bridge joint, and the array comprises a plurality of fiber bragg grating sensors which are distributed at intervals along the length direction of the joint; and each sensor is connected with the joint filling body contact surface through a pre-embedded fixing structure. The sensor array collects strain signals and vibration signals in the joint in real time, analog-to-digital conversion and caching are completed through the data collection unit, digital sensing signals are generated and stored, and a historical monitoring data set is constructed. A noise suppression algorithm is adopted to filter the signal, and ambient temperature and humidity and electromagnetic noise interference are eliminated. Features are extracted based on a data fusion algorithm, and crack feature parameters reflecting the seam fine crack expansion trend are generated. A historical monitoring data set and the parameters are combined, a supervised learning model is used for calculating a dynamic adjustment threshold value, a real-time monitoring early warning signal is generated through comparison, and early-stage high-sensitivity monitoring of the seam is achieved.

The invention relates to a flexible multi-channel multi-mode sensor data acquisition system. The system comprises a signal collector for collecting multi-channel signals, a pin header interface for providing multi-channel signal access, a plurality of analog switches for realizing switching and collection of the multi-channel signals and improving the flexibility and expandability of signal collection, a sensor part meeting multi-dimensional data collection requirements, and an analog-to-digital conversion chip, the control unit adopts an FPGA (Field Programmable Gate Array) to control switching of an analog switch and time sequence management, and the wireless transmission module is provided with a Wi-Fi chip; one end of the pin header interface is connected with the signal collector and the other end is connected with the analog switch; the sensor part is connected to the analog-to-digital conversion chip; the analog switch, the analog-to-digital conversion chip and the wireless transmission module are all connected to the controller. The system is a modularized multi-sensor data acquisition system with high precision and high bandwidth, and can realize dynamic sampling, efficient processing and wireless transmission of multi-sensor signals in a limited size.

A method of operating a time-interleaved analog-to-digital converter (TIADC). The TIADC converts an input signal to generate a digital output signal and includes multiple asynchronous sub-ADCs. The asynchronous sub-ADCs each convert the input signal at a different time point to generate a digital output code, and each of the asynchronous sub-ADCs generates a completion signal upon completion of an analog-to-digital conversion operation. The method includes the following steps: generating an indication signal according to the completion signals, wherein the indication signal indicates multiple candidate sub-ADCs; selecting one of the candidate sub-ADCs as a target sub-ADC according to the indication signal; and correcting the digital output code of the target sub-ADC to generate the digital output signal.

The invention discloses a substation environment monitoring system and method based on an electric power internet of things, relates to the technical field of environment monitoring, and is used for solving the problem of inaccurate substation environment detection. Comprising a data acquisition module, a data transmission module, a data analysis module, a future prediction module and a data storage module which work cooperatively through signal connection. The data acquisition module acquires environmental data of each area of the transformer substation and performs signal smoothing, analog-to-digital conversion and normalization processing. And the data transmission module adopts an MQTT protocol and a ZigBee protocol to carry out data convergence and long-distance transmission. The data analysis module calculates the overall environment coefficient and the equipment state coefficient of the transformer substation according to the environment data, compares the coefficients with the early warning value, and evaluates the overall operation condition of the transformer substation. The future prediction module performs modeling and prediction on historical data by using an ARIMA model, and early warns possible environment and equipment risks in advance. Through the system, the safety, the stability and the operation efficiency of the transformer substation can be improved.

The embodiment of the utility model provides a resistor calibration circuit, a resistor calibration device and an electronic device. The resistor calibration circuit comprises a current source module used for outputting a reference current; the standard resistor module is connected with the current source module to receive the reference current and generate a reference voltage; in the working process of the resistor calibration circuit, a resistor to be calibrated is connected with the current source module to receive the reference current and generate a voltage to be measured, and the analog-to-digital converter is connected with the standard resistor module to perform analog-to-digital conversion on the voltage to be measured based on the reference voltage. According to the resistance calibration circuit, the analog-to-digital converter in the module to be measured is multiplexed for resistance calibration, an additional high-precision measurement calibration system is not needed, and the resistance can be calibrated only by providing the current source module and the standard resistance module, so that the resistance calibration cost is reduced.

The invention relates to the technical field of digital control switching power supplies, and particularly discloses a switching power supply dynamic response adjusting system based on digital control, which comprises a digital controller, a multi-channel analog-to-digital conversion module, a parameter identification module, a self-adaptive control law adjusting module, a pulse width modulation driving module and a stability monitoring module. On-line identification of key parameters of a system is realized by constructing a nonlinear state space model and combining an extended Kalman filtering algorithm, dynamic compensation is carried out for non-minimum phase characteristics such as a right half plane zero point, and a self-adaptive control law adjustment module optimizes a control strategy in real time according to an updated model. A Lyapunov stability criterion and a predictive control mechanism are introduced, the robustness and response speed of the system are improved, and a stability monitoring module identifies an abnormal state through sliding window variance analysis and peak detection and triggers a fault-tolerant mechanism to guarantee safe operation of the system.

The invention relates to the technical field of wireless communication networks, and provides a low-channel electroencephalogram amplification circuit and brain-computer interface equipment oriented to an after-ear electroencephalogram signal, and the low-channel electroencephalogram amplification circuit comprises a dry electrode sensing module which is used for collecting an electroencephalogram signal; the front active amplification module comprises an operational amplifier circuit with high input impedance; the anti-aliasing filtering module is formed by cascading a multi-order passive filtering network and an active filtering circuit, is connected with the output end of the front active amplification module and is used for filtering high-frequency interference signals; the right leg driving feedback module comprises a programmable impedance adjusting circuit and a current limiting protection circuit, the right leg driving feedback module and the front active amplification module form a closed-loop feedback loop, and the output end of the right leg driving feedback module and the human body form a current loop through a protection resistor; the analog-to-digital conversion module adopts an integrated low-power-consumption analog front-end chip; a wireless transmission module; and the power supply management module provides independent power supply for the analog front-end chip, the operational amplifier circuit and the wireless transmission module.