2585 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 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 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 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 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.

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 detection, in particular to a three-dimensional multi-parameter real-time monitoring system and method for a floating roof of a crude oil storage tank. The multi-mode sensor array is arranged on the surface of a floating roof and the inner wall of a storage tank and used for collecting displacement, deformation, pressure and environmental parameters of the floating roof in real time; the data acquisition and preprocessing module is connected with the sensor array and is used for signal conditioning, analog-to-digital conversion and noise suppression; the edge calculation unit is deployed on a storage tank site and used for processing sensor data in real time and executing initial analysis; the three-dimensional dynamic modeling module is used for constructing a floating roof three-dimensional attitude model based on multi-source sensor data; the cloud analysis platform is connected with the edge computing unit through the industrial Internet of Things and is used for deep data analysis and early warning decision making; according to the scheme, the technical problems of insufficient parameter coverage, difficult data fusion, one-sided state evaluation and the like can be solved.

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 invention discloses a sensor array acquisition system for detecting gas, and relates to the field of gas sensors. The sensor array acquisition system for detecting gas comprises a gas sensor array, a signal conditioning circuit, an analog-to-digital conversion module, a control module and a power supply module. According to the sensor array acquisition system for detecting the gas, the voltage follower circuit with high input impedance is adopted, so that the problem of attenuation during signal acquisition of a high-impedance sensor is effectively solved, and the signal acquisition precision is improved. Through the adjustable direct-current bias circuit and the differential amplification circuit, the dynamic adjustment of the measurement range is realized, the gas detection requirements of different concentration ranges can be met, and hardware does not need to be replaced. A constant-current driving chip is adopted to control a sensor heating circuit, heating current can be accurately set by adjusting external resistance, it is ensured that the sensor works at the optimal temperature, and the stability and response speed of the sensor are improved.

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.