1323 results about "Signal conditioning" patented technology

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 data processing, in particular to an industrial environment data acquisition device and method, and the device comprises a sensing layer, an edge calculation layer, a cloud platform layer, and an application layer. Compared with a traditional scheme in which a single sensor is adopted to work independently, software synchronization is relied on, and signal conditioning parameters are fixed, the technical scheme of the invention realizes significant breakthrough through collaborative design of a multi-mode sensor array and an intelligent signal conditioning system, microsecond-level clock synchronization is realized by adopting a hardware-level PTP protocol, and the signal conditioning precision is improved. The program control amplifier is combined with the self-adaptive anti-aliasing filter, so that the conditioning circuit can be self-adaptive to the working condition change of equipment; meanwhile, nanosecond-level real-time processing of signals is achieved through an FPGA + ARM heterogeneous processor architecture, abnormal high-speed sampling is dynamically triggered in cooperation with an ARM, compared with a traditional scheme, the real-time performance is improved by three orders of magnitude, meanwhile, invalid data transmission is reduced by 80%, and a high-timeliness and low-redundancy intelligent monitoring solution is provided for early failure early warning of industrial equipment.

In the present invention, a location joint monitoring solution based on the state of a distributed optical fiber and based on an ultra-short baseline is used, and the solution consists of a distributed optical fiber monitoring device, an ultra-short baseline positioning device and a health monitoring host computer. The distributed optical fiber monitoring device consists of a distributed optical fiber cable (300), a distributed optical fiber demodulator (421), etc. The ultra-short baseline positioning device consists of an ultra-short baseline array (200), an underwater beacon (100), a signal processing device (411), etc. The health monitoring host computer (431) can perform signal conditioning, digital signal processing and parameter estimation by means of the signal processing device and the distributed optical fiber demodulator and by using acquired data such as key point locations, deformation and postures, and complete space curve reconstruction of an underwater riser by means of a space curve reconstruction algorithm and an existing finite element analysis software platform, thereby realizing visual outputs of an underwater state of a marine riser.

The invention relates to a water quality real-time detection system which comprises the following modules: a multi-source sensing module which is based on a multi-parameter sensing array, adopts a self-adaptive sampling strategy, realizes sensor time sequence synchronization through a state estimation algorithm, completes water body multi-dimensional parameter acquisition in combination with a micro-fluidic chip, generates a multi-modal sensing data set, and transmits the multi-modal sensing data set to a data processing module; the multi-source sensing module comprises a multi-source sensing sub-module, a signal conditioning sub-module, a time sequence synchronization sub-module and an anomaly capture sub-module. The method has the advantages that through the synergistic effect of the adaptive sampling strategy and the state estimation algorithm, the multi-sensor time sequence synchronization precision is remarkably improved, the phase deviation problem caused by traditional fixed frequency sampling is effectively eliminated, the sliding window polynomial fitting is combined with the wavelet threshold de-noising technology, and the multi-sensor time sequence synchronization precision is improved. High-frequency noise interference is greatly suppressed on the premise that effective components of the signals are reserved, and meanwhile, the abnormal value detection accuracy is improved through a dynamic threshold mechanism.

The invention discloses a high-precision current detection circuit calibration system, and belongs to the technical field of current detection circuit calibration. Comprising a shunt resistor, a Hall sensor array, a chopping amplifier, a low-noise operational amplifier, a temperature sensor, an ADC acquisition unit, an RC low-pass filter, a signal conditioning unit, a main control unit, an environmental parameter adaptive compensation module and a gain adaptive adjustment module. After an original signal is obtained from a multi-channel sensor, a dynamic error model is constructed in combination with temperature data, a long-term imbalance trend is extracted by using a moving average or median filtering algorithm, and real-time compensation is performed through a pre-calibrated temperature-drift function, so that the system maintenance period is remarkably prolonged.

The invention discloses a 5G base station power supply state intelligent monitoring system and method, and belongs to the technical field of intelligent monitoring. According to the system, operation parameters of a 5G base station power supply are acquired in real time through the data acquisition module, and after the operation parameters are processed by the signal conditioning and conversion module, the master control processing unit analyzes digital signals in real time, detects abnormal states and generates alarm information. The communication module is responsible for data transmission and instruction receiving, and the power management and safety protection module provides overvoltage, undervoltage, overcurrent and short circuit protection and triggers emergency measures. The artificial intelligence analysis module performs fault prediction and performance optimization based on historical data and real-time data, and the remote monitoring terminal realizes data visual monitoring and management. According to the invention, the real-time performance and accuracy of monitoring are improved, unified management of multiple base stations is realized, the operation and maintenance cost is reduced, and stable operation of a 5G communication network is guaranteed.

The invention discloses a dynamic neutral position and boundary self-calibration method and system of a joystick, and belongs to the technical field of engineering machinery and mining equipment control, and the method comprises the following steps: S1, electrifying equipment, and selecting a mode; s2, calibrating a dead zone; s3, setting a threshold value; s4, data storage; and S5, exiting and applying. According to the dynamic median and boundary self-calibration method and system of the joystick, when the joystick works, a physical manipulated variable is converted into an original electric signal through a joystick sensing unit, then the original electric signal is received through a signal processing module and subjected to signal conditioning, and an analog signal is converted into a digital signal which can be processed by a microprocessor; all logical judgments are executed through the main control unit, a user issues an instruction through a user configuration interface and directly inputs the instruction to the main control unit, the main control unit and the nonvolatile memory carry out bidirectional data interaction to realize permanent storage and calling of dead zone parameters, and the main control unit generates a final control instruction to be output after processing all information. And the actuating mechanism is driven.

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 an online monitoring system for partial discharge of a main transformer. The online monitoring system specifically comprises a series of modules including sensors, detection equipment, a data acquisition module, a data processing module, a data transmission module and an online monitoring platform, wherein the sensors are reasonably arranged outside the transformer by adopting multi-band combination of ultrahigh frequency, high-frequency current, ultrasonic waves and the like; the detection equipment realizes high efficiency and flexibility through a C language underlying driver and an assembly language data interface, and has various anti-interference and fault early warning technologies; the data acquisition module is used for conditioning an original signal and improving the data quality by adopting various filters; reliable and real-time data transmission is ensured by adopting a TCP / IP protocol; the data processing module comprises a time domain and frequency domain conversion sub-module and other sub-modules for auxiliary diagnosis; the online monitoring platform is based on Web, and is rich in function and extensible; the system monitors a plurality of main transformers in a transformer substation, collects signals through sensors, processes the signals through the modules and transmits the signals to a platform, meanwhile, the system is subjected to regular inspection, software and hardware upgrading optimization and the like to guarantee stable operation of the system, and support is provided for safety of an electric power system.

The invention relates to the technical field of pipeline detection, and discloses a pipeline magnetic flux leakage detection method and device, and the method comprises the steps: obtaining sensor array arrangement parameters and original magnetic field data; establishing a three-dimensional sensor network according to the sensor array arrangement parameters to obtain space coordinate information; performing adaptive signal conditioning and feature extraction according to the original magnetic field data and the space coordinate information to obtain a magnetic flux leakage defect candidate region; and comparing the magnetic flux leakage defect candidate region with a magnetic flux leakage characteristic database to obtain a defect position and a severity level. According to the method, accurate positioning and automatic grading evaluation of pipeline defects can be realized.

The invention discloses an infrared CO2 high-precision detection system and method based on a self-adaptive balance signal, and belongs to the field of gas detection. The invention discloses an infrared CO2 high-precision detection system and method based on a self-adaptive balance signal. The infrared CO2 high-precision detection system comprises a sensing module, a signal conditioning and collecting module and a self-adaptive filtering processing module. According to the method, the problems of contradiction between response speed and stability, discrete signal tracking lag and sensitivity to environmental interference in the prior art are solved, the weight of a filtering channel is adaptively and dynamically adjusted, and the advantages of a fast filtering channel and a slow filtering channel are combined, so that balance between dynamic response and steady-state precision is realized; the problem of tracking lag of discrete signals is solved by analyzing the change rate of the signals in real time and dynamically adjusting a filtering strategy; and through a hybrid filter and an environment compensation algorithm, the influence of temperature and humidity changes on a detection result is reduced, so that the long-term stability of the sensor is improved.

The invention relates to the technical field of power grid monitoring, and discloses an intelligent sensor system and method for power grid load monitoring, and the system comprises a multifunctional sensing module which is used for collecting the electrical parameters and environmental parameters of a power grid in real time, carrying out the preliminary signal processing of the collected data, and transmitting the processed data to a server; the collected data after the preliminary signal processing is output to an edge calculation and anomaly detection module; and the edge calculation and anomaly detection module is used for receiving the collected data of the multifunctional sensing module, performing fusion and compression processing on the collected data, and detecting power grid load anomaly based on rule logic and a machine learning method. Through the innovative design of the multifunctional sensing module, the system can collect electrical parameters and environmental parameters of a power grid in real time, high-precision processing of data is achieved through the signal conditioning and multi-channel fusion technology, the capacity of monitoring the running state of the power grid is remarkably improved, dynamic changes of loads can be comprehensively reflected, and a foundation is laid for precise analysis and regulation.

The invention discloses a transformer fault ultrasonic detection system, which comprises an ultrasonic sensor module, a signal conditioning module, a data acquisition and processing module, a data fusion module, a learning module, a diagnosis decision module and a human-computer interaction interface, according to the ultrasonic sensor module, 6-8 ultrasonic sensors are arranged in a transformer shell in an array mode, piezoelectric ceramic sensors with the resonant frequency of 40 kHZ are adopted as the ultrasonic sensors, the sensitivity of the ultrasonic sensors is-65 dBV / uBar, and effective capture is weak. According to the ultrasonic detection system for the transformer fault, single ultrasonic detection is easily influenced by accidental interference, the system is synchronously connected into equipment such as an infrared thermal imager and a vibration accelerometer, and a multi-physical-quantity correlation analysis model is established. When high-frequency ultrasonic pulses are detected, the system automatically calls temperature data of the corresponding position, and if the temperature gradient exceeds 2 DEG C / cm, it is confirmed that discharging heating is conducted; and if the temperature rise does not exist and the low-frequency vibration of 10-200Hz is accompanied, the mechanical looseness is judged.

The various implementations described herein include techniques and apparatuses for multi-channel biopotential signal acquisition, biopotential signal pre-processing, and adaptive signal conditioning. In one aspect, an analog frontend circuit includes an instrumentation amplifier (INA) and an analog-to-digital circuit (ADC) in a forward signal path. A digital circuit receives input from the ADC. An adaptive baseline tracking and compensation circuit tracks and compensates moving motion artifact driven changes. A power line interference (PLI) detection and compensation circuit tracks a desired number of PLI harmonics, and magnitude, phase and frequency for the PLI harmonics in real time. A digital-to-analog converter (DAC) circuit combines output of the adaptive baseline tracking and compensation circuit and the PLI detection and compensation circuit to output an analog output. A passive filter receives the analog output and drives an analog compensation signal at an input of the INA.

Method and device(s) for supporting performance of machine learning based CFR and DPD on multiple digital input signals relating to different frequency bands, respectively, in order to signal condition said signals before power amplification and subsequent transmission in said frequency bands by a wireless communication network. The device(s) obtain said multiple digital input signals as complex valued signals. The device(s) perform feature construction that takes said multiple digital input signals as input and provides constructed feature signals according to predefined constructed feature types as output. Said predefined constructed feature types relate to at least the following per complex valued sample of the obtained complex valued multiple digital input signals the real part of the sample, the imaginary part of the sample and at least one of the absolute value of the sample and the phase of the sample.

The invention discloses a VOCs gas leakage real-time monitoring system based on dual-light image fusion, which comprises a dual-light acquisition module, a preprocessing module and a fusion and identification module, and is characterized in that the dual-light acquisition module comprises a visible light lens and an infrared lens; the preprocessing module comprises a gas path dust removal unit and a signal conditioning unit; the fusion and identification module carries out dynamic registration, multi-scale fusion and intelligent noise reduction processing on the preprocessed image. According to the VOCs gas leakage real-time monitoring system based on dual-light image fusion disclosed by the invention, through linkage of the dual-light acquisition module, the preprocessing module and the fusion and identification module, high-sensitivity detection and real-time early warning of VOCs leakage are realized in a complex industrial scene, and meanwhile, the false alarm rate is reduced.

The invention relates to the technical field of fault diagnosis and intelligent manufacturing, in particular to a fusion reactor dynamic equipment collaborative fault diagnosis method based on multiple agents. According to the technical scheme, the method comprises the following steps that a multi-agent system is constructed, and a vibration agent, a temperature agent and an electromagnetic agent are deployed for fusion reactor moving equipment and used for collecting vibration data, temperature data and electromagnetic data respectively; performing signal conditioning on the acquired data by each agent and extracting universal features; based on a federated learning framework, multi-modal distributed training is carried out, and each agent independently trains a model to obtain a local gradient; each agent encrypts the local gradient and then uploads the local gradient to a cloud server, and secondary joint training of the model is carried out; and the cloud server encrypts and issues the model parameters after joint training to each agent, and through multi-agent collaboration, multi-modal data fusion and federated learning privacy protection, the fusion reactor moving equipment fault diagnosis accuracy, comprehensiveness and security are significantly improved.

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 a data acquisition and analysis system and method for laser interference measurement, and aims to solve the problem of limited measurement precision caused by asynchronous signal acquisition, imperfect environmental interference compensation and algorithm errors of the existing system. The system comprises a photoelectric conversion unit, a signal conditioning unit, a high-speed synchronous acquisition unit, a real-time processing unit and an error compensation module. The photoelectric conversion unit adopts a four-quadrant PIN photodiode array to receive interference light signals and convert the interference light signals into analog electric signals; the signal conditioning unit performs gain adjustment and bandwidth limitation on the signal; the high-speed synchronous acquisition unit realizes femtosecond-level synchronous acquisition of signals; the real-time processing unit performs phase solution and eliminates harmonic errors based on an improved arc tangent algorithm; and the error compensation module fuses temperature sensor data and accelerometer data to realize thermal drift and vibration compensation.

The invention discloses a Rogowski coil current transformer and a current measurement system, and relates to the technical field of current measurement. The Rogowski coil current transformer comprises a Rogowski coil and a signal processing circuit; the differential signal is converted into a differential constant-current signal to be output based on a signal processing circuit; the signal processing circuit comprises an integral amplification module for performing integral amplification on the differential signal based on the integral amplification module; the first direct current component removing module is used for removing a direct current component in the voltage signal after integral amplification based on the first direct current component removing module; the signal adjusting module is used for realizing amplitude in-phase amplification and phase compensation; the single-ended to differential output module is used for generating symmetrical differential voltage signals based on the single-ended to differential output module; and the conversion module is used for converting the symmetrical differential voltage signal into a differential constant current signal and outputting the differential constant current signal based on the conversion module. And through the integral amplification module and the phase compensation module, the differential signal is accurately converted into the current signal, so that the current measurement result is more accurate.

The invention discloses a rotor and stator axial clearance online measuring device and method based on optical carrier microwave interference. The measuring device comprises a broadband light source, a first polarization maintaining optical fiber, an electro-optical modulator, a second polarization maintaining optical fiber, an optical fiber circulator, a first single-mode optical fiber and a coated optical fiber probe which are connected in sequence, and the coated optical fiber probe directly faces the axial end face to be measured; a first port of the optical fiber circulator is connected with the second polarization maintaining optical fiber, a second port of the optical fiber circulator is connected with the first single-mode optical fiber, and a third port of the optical fiber circulator is sequentially connected with the second single-mode optical fiber, the photoelectric detector, the signal conditioning unit, the signal acquisition unit and the digital processing unit; the signal acquisition unit is connected with the micro-control unit, the micro-control unit is connected with the microwave frequency sweep signal generation unit, and the microwave frequency sweep signal generation unit is connected with the electro-optical modulator. According to the invention, non-contact on-line high-precision measurement of the axial clearance of the rotor and the stator of the aero-engine under the conditions of limited measurement space and complex signal leading-out path can be realized.

The invention relates to a chin electrical stimulation combined upper airway muscle group training system and method based on bioelectric feedback, and belongs to the field of upper airway muscle group training. Comprising a pressure detection module used for detecting a pressure value in an upper respiratory tract muscle group training process; the myoelectricity acquisition module is used for detecting myoelectricity numerical values in the training process of the upper respiratory tract muscle group; the electrical stimulation output module is used for stimulating partial nerve excitation and muscle contraction of the muscle group of the upper respiratory tract of the trainee; the signal conditioning module is used for carrying out preprocessing and analog-to-digital conversion on signals transmitted among the modules; and the control module is used for controlling signal interaction in the whole training process. And the display interaction terminal is used for interacting with the trainee. Based on bioelectricity feedback, a new individualized electrical stimulation and upper airway muscle group training treatment mode is newly established, accurate electrical stimulation and training intensity are formulated, and fatigue and injury are avoided.

The invention provides a system for controlling a manipulator in real time through hand electromyographic signals, which comprises a data acquisition unit, a terminal equipment unit and a tail end execution unit which are in communication connection through serial ports. The data acquisition unit comprises a data acquisition device and a signal acquisition module, and is configured to acquire a multi-channel electromyographic signal of a hand of a user in real time and perform signal conditioning and digitization so as to obtain a digital signal sequence representing a muscle activity intention of the user; the terminal equipment unit comprises a data preprocessing module, a model training module, a gesture recognition module and an instruction generation module; the tail end execution unit comprises a manipulator control unit which is in communication connection with the control output module and is configured to be used for receiving the manipulator control instruction and driving a manipulator to execute corresponding actions; in combination with a control method, the problems of insufficient real-time performance, poor hand action recognition precision and robustness, unstable control instruction, poor user adaptability and the like in the prior art are solved, and the man-machine interaction performance is improved.

The invention provides a heavy duty gas turbine control system which comprises a signal conditioning module and a plurality of parameter control modules, and a rotating speed control module, a load control module, a temperature limit control module and an acceleration control module in the parameter control modules are all used for achieving closed-loop control according to the deviation between a set value and a corresponding observation value; the starting control module is used for acquiring a starting fuel quantity instruction through a designed control strategy; the IGV and VGVs control module obtains an IGV and VGVs angle instruction according to the turbine inlet temperature set value, the turbine outlet temperature set value and the deviation maximum value of the corresponding observation value; the fuel quantity control module obtains a total fuel quantity instruction based on a full working condition range fuel quantity control strategy; the fuel gas distribution control module is used for obtaining an opening degree instruction of each fuel valve according to the total fuel quantity instruction; the adaptability, the reliability and the usability of the control system in application scenes such as autonomous design and technical improvement are improved.

The invention provides an energy-saving compressor intelligent self-adaptive control system based on multi-sensor fusion, which belongs to the technical field of industrial compressor intelligent control and comprises a data acquisition module, a signal conditioning module, a multi-sensor information fusion module, an intelligent control decision module, an execution driving module and a system power supply management module. A state display alarm and remote communication module can be additionally arranged. Multiple sensors collect operation and environment parameters of the compressor, and after conditioning, a comprehensive state evaluation result is generated through D-S evidence theory or fuzzy logic fusion; the intelligent module generates a control instruction based on a load prediction algorithm and a fuzzy PID self-tuning strategy, and the execution module drives the compressor to act; the power supply module supports solar power supply and multi-power supply switching. The system overcomes the limitation that a traditional compressor is single in sensing, rigid in control and the like, comprehensive sensing, accurate evaluation and self-adaptive energy-saving control are achieved, the operation stability is improved, and the energy consumption and the operation and maintenance cost are reduced.

The invention provides a plunger pump fault online diagnosis method based on a rotating speed frequency amplitude, which belongs to the field of plunger pump state detection and mainly comprises the steps of pressure sensor data acquisition and processing, load pressure spectrum analysis and plunger pump fault diagnosis based on the rotating speed frequency amplitude. The method comprises the specific steps that a high-frequency pressure sensor is arranged at an outlet of a plunger pump, and real-time data input is provided for fault diagnosis; a pressure signal conditioning and digital-to-analog conversion device is built, data transmission is completed, and data interfering with fault diagnosis is eliminated; fourier transform is carried out on the processed pressure signal to extract the rotating speed frequency of the plunger pump; and the specific fault type is judged based on the corresponding relation between the rotating speed frequency amplitude of the plunger pump and the specific fault. According to the method, based on the difference of the rotating speed frequency component amplitudes of load pressure signals under different plunger pump friction pair abrasion fault types, the mapping relation between the rotating speed frequency amplitudes and the plunger pump friction pair abrasion fault types is established, and the plunger pump load pressure signals serve as data input; and the specific wear fault type of the friction pair of the plunger pump is monitored and evaluated in real time. According to the invention, state monitoring and condition-based maintenance of the plunger pump are facilitated.

The present invention relates to the technical fields of human-computer interaction and brain informatics, and in particular to a closed-loop neurofeedback method and device based on haptic memory electroencephalogram signal conditioning. The method comprises: collecting electroencephalogram signals of a subject, and performing real-time online data preprocessing; designing a haptic memory stimulation task, activating the haptic memory of the subject, and recording a corresponding electroencephalogram signal response; performing feature extraction and analysis on a corresponding electroencephalogram signal to obtain an electroencephalogram feature related to haptic memory; and on the basis of a haptic memory electroencephalogram feature signal, designing a closed-loop neurofeedback system, the closed-loop neurofeedback system monitoring the electroencephalogram signals of the subject in real time, performing real-time stimulation on the basis of a preset haptic stimulation task, and recording a corresponding electroencephalogram response. In the technical solution of the present invention, the haptic memory electroencephalogram feature signal is combined with the closed-loop neurofeedback system, thus providing new ideas and methods for the diagnosis and treatment of haptic memory-related diseases.

The invention belongs to the technical field of gap measurement, and particularly relates to a high-temperature-resistant transparent shell type eddy current tip gap measuring device and method, and the device comprises a sensor probe module, a signal conditioning module, a data collection module and a data processing module. The sensor probe module is mounted on the outer wall of the casing; the sensor probe module comprises a permanent magnet excitation source, a soft magnet magnetic core and a receiving coil; the signal conditioning module is electrically connected with the receiving coil and is used for amplifying, filtering and integrating the weak induction signal; the data acquisition module is electrically connected with the signal conditioning module and converts an analog signal into a digital signal; the data processing module is used for processing the acquired digital signals and extracting blade tip clearance information.

The invention discloses a water quality monitoring device and a monitoring method thereof, belongs to the technical field of water quality monitoring, and solves the problems that trace hydrogen sulfide generated by sulfate reducing bacteria in a deposited layer at the bottom of a water tank which is not cleaned for a long time in a low-oxygen environment reacts with a platinum membrane electrode of a residual chlorine sensor to generate a platinum sulfide covering layer; and the electrode reading is continuously virtually high and drifts slowly. Comprising an optical transmitting module, an optical receiving module, a spectrum separation module, a signal conditioning module, a lock-in amplification module, a spectrum feature extraction module and a hydrogen sulfide recognition engine. Residual chlorine is monitored in a non-contact mode, deep ultraviolet pulse light is emitted to penetrate through a water body, a reflection spectrum is received, second derivative spectral characteristics are extracted through multichannel signal processing, and multivariable regression compensation is executed to calculate a real residual chlorine value after hydrogen sulfide interference characteristics are recognized. Meanwhile, the drift trend is quantified based on the double-time-constant integral, graded early warning is triggered, industrial standard signals are finally output, and the problem of electrode contact pollution is avoided in the whole process.

The invention relates to the technical field of weak signal conditioning circuits, and discloses a multi-light-source spurious noise suppression circuit based on a laser sensor and a signal conditioning method, and the circuit comprises a photoelectric conversion module which is used for converting a received optical signal into a current signal; the transimpedance conversion module is connected with the photoelectric conversion module, converts the current signal into a first voltage signal and suppresses common-mode noise; the first-stage amplification module is used for carrying out linear amplification on the first voltage signal and outputting a second voltage signal; the programmable filtering module is used for carrying out high-frequency noise suppression on the second voltage signal; the reverse phase adjusting module is used for adjusting the phase and the amplitude of the filtered signal so as to adapt to a post-stage load; and the feedback control module analyzes the frequency spectrum of the output signal in real time and dynamically adjusts the cut-off frequency of the programmable filtering module. The invention aims to solve the problems that in the prior art, a laser sensor circuit is poor in anti-interference performance in a multi-light-source environment, and measurement is inaccurate due to spurious noise.