5064 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 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 broadband weak current signal detection system comprises a signal acquisition mother board and an upper computer system, the signal acquisition mother board is provided with a signal input interface, a channel switching circuit, a transimpedance amplification circuit, an analog-to-digital conversion circuit, a control core board and the upper computer system, and a three-coaxial BNC interface is adopted. Electromagnetic noise interference in the environment is shielded by a metal noise shielding structure; detecting the switching of a wide measuring range and the switching of an alternating current and direct current coupling mode; weak current signals are converted into measurable voltage signals through a trans-impedance amplifier, and the voltage signals obtained after conditioning are converted into digital quantized values through an analog-to-digital converter (ADC); after being processed by a digital noise reduction algorithm, the data are stored; therefore, the real-time measurement of the broadband weak current signal is realized. The broadband weak current signal detection system has the characteristics of high resolution, good repeatability, high measurement accuracy and good real-time performance, and has good detection capability on weak current signals.

The present disclosure provides a magnetic resonance signal processing device. The device may include: a signal receiving unit configured to obtain a received magnetic resonance signal, wherein at least part of the magnetic resonance signal is generated by exciting one or more specific nuclides; an analog-to-digital converter configured to perform an analog-to-digital conversion on the magnetic resonance signal; and a control unit configured to obtain a digital output signal of the magnetic resonance signal by processing the magnetic resonance signal after the analog-to-digital conversion.

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 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 hydraulic fracturing, in particular to a method for evaluating effectiveness of fracture parameters of artificial fracturing of a horizontal well. The method comprises the following steps that information collection based on ground process parameters and underground dynamic parameters is conducted on the horizontal well artificial fracturing construction process, and fracturing working condition data are obtained; according to the fracturing working condition data, the fracture expansion of the horizontal well is monitored in real time, digital conversion processing is conducted, and real-time fracture monitoring data is generated; performing micro-seismic signal analysis on the real-time crack monitoring data, and performing crack network space distribution calculation to generate crack network distribution data; performing reservoir stress field dynamic evolution analysis according to the fracture network distribution data to generate stress field distribution data; and carrying out propping agent migration trajectory tracking according to the stress field distribution data to generate propping agent distribution data. According to the method, the reservoir stress field dynamic evolution analysis and the proppant migration mechanical model are combined, and the effectiveness of the fracture parameters can be evaluated more accurately.

The invention relates to the technical field of air pressure intensity adjustment, and discloses a pressure control method and device for accurately adjusting air pressure intensity, and the method comprises the steps: carrying out the digital conversion and temperature and humidity compensation of an original pressure signal collected by a pressure sensing array, and obtaining a pressure data matrix and a pressure gradient matrix; calculating a pressure distribution mass center and a pressure fluctuation frequency spectrum to obtain a pressure space distribution characteristic matrix and a pressure time domain characteristic matrix; inputting a three-state Markov model for dynamic state identification to obtain a pressure state transition probability matrix and a pressure response prediction matrix; constructing an objective function containing a pressure tracking error term and an organization safety constraint term, and solving to obtain a PID control parameter matrix and a pressure safety limit matrix; and constructing a three-stage control system comprising a basic controller, an interference compensator and a safety protector, and outputting an air pressure regulation driving signal. According to the method, high-precision pressure sensing control is realized.

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 discloses a SiC MOSFET (Metal-Oxide-Semiconductor Field Effect Transistor) driving circuit and a control method thereof, the SiC MOSFET driving circuit comprises an active current source driving circuit, a SiC MOSFET, a load current detection circuit, a drain-source inter-electrode voltage overshoot detection circuit, an analog-to-digital conversion module and an FPGA (Field Programmable Gate Array) module, and a self-adaptive adjustment algorithm is built in the FPGA module. The load current detection circuit and the drain-source inter-electrode voltage overshoot detection circuit have low requirements for real-time performance, use of high-speed simulation devices is avoided, hardware design cost is reduced, a self-adaptive adjustment algorithm can adjust SiC MOSFET gate drive parameters in real time according to output of the load current detection circuit and the drain-source inter-electrode voltage overshoot detection circuit, and the self-adaptive adjustment algorithm can adjust the gate drive parameters of the SiC MOSFET in real time according to the output of the drain-source inter-electrode voltage overshoot detection circuit. Therefore, overshoot and switching loss are self-adaptively balanced, and safe operation of the SiC MOSFET is ensured. The technical problems that a SiC MOSFET driving circuit driven by an active grid is high in hardware cost and is difficult to balance voltage and current overshoot and switching loss are solved.

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 relates to the technical field of exercise rehabilitation, and discloses a percutaneous spinal nerve electrical stimulation system and method, and the system comprises a power module which is used for converting an external alternating current into a stabilized power supply; the MCU module controls the pulse signal generation module; the pulse signal generation module is used for generating an electrical stimulation waveform; the DAC module receives the digital signal; the modulation module is used for receiving analog signals; the amplification module is used for receiving modulation waveforms; the multi-channel gating module is used for distributing stimulation current to a plurality of channels; the current detection module monitors the magnitude of current output by each channel in real time; and the ADC module performs digital conversion on the current analog signal output by the electrical stimulator. According to the invention, multi-channel and real-time modulation electrical stimulation signals can be output through modulation of two paths of stimulation signals, spinal cord sensory nerves are activated through the non-invasive epidermal electrode, amputation hand feeling is induced, the defect that electromyographic signals are interfered by the tail end of a stump in the prior art is improved, and the accuracy of an electromyographic signal decoding algorithm is also improved.

The invention discloses an intelligent digital conversion method for an electric power drawing. The method comprises the following steps: acquiring various types of electric power design drawings; converting the format of each electric power design drawing into a first fixed format, reading the file content of each electric power design drawing after format conversion, and converting each file content into a corresponding image; adopting an image recognition step to obtain a recognition result of the image; the method comprises the following steps of: matching pre-stored ledger data with drawing elements in an image by adopting an image association step to establish an association relationship between different electric power design drawings containing the same drawing element, and generating an association link based on the association relationship to realize jump between the different electric power design drawings and determine a plurality of binding association drawings; and based on the binding association drawing, determining a target association drawing and skipping to the target association drawing. According to the method, the accuracy of digital conversion of the paper electric power design drawings can be effectively improved, and the efficiency of digital storage of the electric power design drawings is improved.

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 provides an underwater ultrasonic Doppler real-time analysis method and device based on a digital transducer, and relates to the technical field of data analys.The method comprises the steps that 1, multiple beams of directional ultrasonic waves are emitted into drilling fluid through a digital transducer array on the outer wall of a marine riser, echo signals reflected by gas-liquid two-phase flow are received, and the digital transducer array is used for receiving the echo signals; performing real-time digital conversion on the echo signal to generate an original digital signal containing phase and amplitude information; 2, Doppler frequency shift feature extraction is conducted on the original digital signals, and the real-time movement speed and direction of bubbles in the drilling fluid are determined by calculating the frequency deviation of reflected ultrasonic waves and transmitted ultrasonic waves. Through the digital transducer array and the ultrasonic Doppler technology, accurate measurement of motion parameters of bubbles in drilling fluid, real-time calculation of local gas content and early warning and grading response of gas cut are achieved, and the safety and the intelligent level of drilling operation are improved.

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 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.

Signal processing is applied to a digital input audio signal. An analog audio output signal is provided based on the digital input audio signal via a switching converter circuit driven by a PWM signal. The analog audio output signal is sensed to generate an analog feedback signal. The applied signal processing includes: producing a digital error signal indicative of a difference between the digital input audio signal and a digital word signal; applying digital-to-analog conversion to the digital error signal to produce an analog replica of the digital error signal; producing an analog difference signal indicative of a difference between the analog replica of the digital error signal and the analog feedback signal; applying analog-to-digital conversion to the analog difference signal to produce the digital word signal; applying digital filtering to the digital word signal to produce a filtered digital word signal that generates the PWM signal.

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.

A system for managing digital transformation projects, consisting of: • a processing unit configured to execute a variety of instructions in real time during the lifecycle of the digital transformation project to perform risk assessment, quality control, and dynamic compromise optimization; • a storage unit coupled to the processing unit, wherein the storage unit stores project data, risk categories, predefined quality metrics and resilience indicators; • a user interface module configured to display project performance metrics, including risk levels, quality status and resilience indicators, and to allow user interactions for real-time adjustment of project parameters; • A communication module configured to synchronize project data with external systems, databases and cloud-based platforms, ensuring seamless data exchange and real-time reporting between multiple project management tools and stakeholders.