1789 results about "Signal conditioning" patented technology

The invention discloses an intelligent adjusting system of an adaptive multi-environment ship electric control valve actuator, which relates to the technical field of control valve adjustment and comprises an environment judgment module, a real-time monitoring module, an adaptive adjusting module and a feedback optimization module. The environment judgment module collects environment data through various sensors distributed on an electric control valve and around the electric control valve, and the environment data are processed through a signal conditioning and microprocessor application algorithm. According to the intelligent adjusting system of the self-adaptive multi-environment ship electric control valve actuator, the adaptability and the stability of the ship electric control valve actuator in variable environments are remarkably improved; the electric control valve actuator can monitor and respond to environment changes such as temperature, humidity, vibration and electromagnetic interference in real time, it is ensured that the electric control valve actuator can be accurately regulated and controlled under the extreme working condition, and therefore the reliability and safety of a ship system are improved.

The invention proposes a GIS equipment partial discharge mode recognition method based on deep learning, and relates to the technical field of GIS partial discharge recognition, and the method comprises the steps: collecting a partial discharge signal of GIS equipment through an ultrahigh frequency sensor, and carrying out the signal conditioning; performing digital sampling and processing on the conditioned partial discharge signal to generate a phase-resolved pulse sequence (PRPS) map, and performing intelligent noise reduction processing on the PRPS map, the PRPS map including three-dimensional information of phase, amplitude and frequency; inputting the PRPS atlas into a preset neural network model, and extracting atlas features; and identifying and classifying the partial discharge type based on the extracted map features, and outputting an identification result. According to the method, automatic acquisition, intelligent processing and accurate identification of the partial discharge signals are realized, and compared with a traditional manual identification method, the identification efficiency and accuracy are greatly improved, and reliable technical support is provided for state monitoring and fault diagnosis of GIS equipment.

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 provides a multi-channel signal acquisition and analysis system for safe navigation of a ship, and relates to the technical field of signal acquisition, and the system comprises a signal acquisition layer which comprises signal acquisition equipment and a signal conditioning module which comprises a programmable filter, a gain amplifier and an analog-to-digital converter; the signal processing layer comprises a logic control module, and the logic control module adopts an FPGA (Field Programmable Gate Array) and a reconfigurable data flow pipeline to carry out synchronous signal processing; the data analysis layer comprises an edge processing unit, an analysis algorithm library and a real-time decision unit; and the control and communication layer comprises a system control unit, a data transmission module and a storage management unit, the system control unit performs working mode management according to an instruction of the upper computer and is provided with a resource scheduler and a power consumption manager, and the data transmission module is provided with a multiple access transmission controller, a data compression unit and an encryption transmission module. According to the invention, comprehensive integration from signal acquisition to data analysis is realized, and the safe navigation capability of the ship is greatly improved.

The invention discloses a high-precision laser ranging system and method based on multi-frequency modulation, and relates to the field of laser ranging, and the system comprises a multi-frequency laser transmitting module which comprises generators of different frequency bands and can be independently regulated, rapidly switched and matched with a high-precision frequency modulation unit; the laser receiving and signal conditioning module is used for processing signals by using an avalanche photodiode, a low-noise preamplifier and the like; the signal processing and analyzing module is used for calculating the distance by means of a high-speed digital signal processor in combination with a multi-frequency distance measurement algorithm; a display and control module and a temperature compensation module are further included, the method comprises the steps of multi-frequency laser emission, receiving conditioning, signal processing, display control and temperature compensation, and high-precision distance measurement is achieved through fine operation according to environment and target characteristics. The method is high in distance measurement precision, and error compensation in a complex environment exceeds 90%; the adaptability is high, and automatic adjustment is achieved in different distances and environments; the real-time performance is good, and a result can be obtained within 10 milliseconds; operation is convenient, wireless and data management is supported, and laser ranging innovation is promoted.

The invention relates to the technical field of PWM control, and discloses a high-precision PWM dead zone effect compensation method and system. The method comprises the following steps: carrying out differential sampling and signal conditioning on PWM output current to obtain a current sampling value; the driving signal is input into an FPGA state judgment module for state conversion analysis, and switch state information is obtained; performing dead zone characteristic quantity extraction based on the current sampling value and the switch state information to obtain a dead zone effect influence factor; according to the dead zone effect influence factor, compensation coefficient calculation is carried out in a piecewise linear mode, a real-time compensation value and a predicted compensation value are obtained, the real-time compensation value and the predicted compensation value are used for compensating dead zone position voltage drop in a PWM output voltage waveform, the real-time compensation value is used for current period compensation, and the predicted compensation value is used for next period compensation presetting. And high-precision PWM dead zone effect compensation is realized. According to the method, accurate calculation and dynamic prediction of the compensation value are realized, the compensation adaptability is improved, and finer compensation control is realized.

The invention belongs to the field of power equipment monitoring, and discloses an electrical signal acquisition system based on intelligent dynamic sampling, which comprises a signal acquisition module, a signal conditioning module, an abrupt change detection module, an impedance calculation module and an upper computer, and is characterized in that the signal acquisition module comprises a first ADC module, a second ADC module, a first MCU module and a second MCU module. Short-circuit impedance real-time calculation and waveform abrupt change detection are adopted for cooperative judgment, and when equipment operates stably, the sampling rate of the system is kept low; when impedance changes or waveform changes suddenly, the sampling rate is improved automatically, important signal changes are recorded accurately, and waveform abrupt change detection is carried out by adopting an edge detection algorithm improved based on an FIR (Finite Impulse Response) filter. The system ensures the time sequence consistency of data under different sampling frequencies through multi-channel data synchronization and timestamp management, and meanwhile, smooth transition is performed by applying an interpolation algorithm during sampling switching, so that the continuity of a data sequence is ensured. The data redundancy is reduced; and the integrity of key information is ensured.

The invention discloses a transformer oil conservator oil level online monitoring method and system based on an ultrasonic technology, and belongs to the field of power equipment monitoring. The oil level of the transformer is related to equipment safety, a traditional monitoring means has defects, and false oil level is prone to misleading operation and maintenance. With the ultrasonic technology as the core, the multi-band ultrasonic sensor transmits and receives ultrasonic waves to the oil conservator, obtains oil level signals and collects oil temperature and load data at the same time. The oil level is calculated by means of ultrasonic propagation time and a multi-band weighted average algorithm, and the false oil level is discriminated according to oil temperature compensation and load correlation. Through signal conditioning and acquisition, microprocessor operation and wireless communication transmission, data is stored and analyzed in a background monitoring system. The innovative system architecture and algorithm realize high-precision oil level measurement, strong interference resistance, intelligent false oil level discrimination and real-time remote monitoring. On-site tests show that the system stably and accurately monitors complex working conditions, effectively prevents oil level abnormal faults, improves the operation and maintenance efficiency of the transformer and the power supply reliability of a power grid, and firmly guarantees the reliable operation of a power system.

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 belongs to the technical field of hardware-in-loop simulation testing, and particularly relates to a hardware-in-loop interface board card. According to the hardware-in-the-loop interface board card disclosed by the invention, mutual conversion between a simulation machine input / output low-voltage signal and a controller output / input high-voltage signal is realized through a simulation machine signal photoelectric conversion sending circuit and a controller signal photoelectric conversion sending circuit in the signal conditioning module; the low-voltage signal output by the simulation machine interface can indirectly control the high-voltage signal input into the controller interface, and the high-voltage signal output by the controller interface can be converted into the low-voltage signal matched with the simulation machine interface, so that the problems of specification difference and signal matching difference between the IO interface and the controller interface are solved; the low-voltage simulator system is electrically isolated from the high-voltage controller system, so that the influence of the high-voltage controller on the simulator is avoided; and in the aspect of structural design, the hardware-in-the-loop interface board card is divided into an upper layer board and a lower layer board, so that the occupied area is reduced.

The invention discloses a non-contact mud flowmeter signal processing system, which comprises a sensor unit, a signal amplification unit, a signal processing unit, a control unit and a flow calculation and display unit, and is characterized in that the sensor unit is used for acquiring a pressure pulsation signal of fluid in a pipeline; the signal amplification unit is used for performing signal pre-amplification and filtering; the signal processing unit carries out signal conditioning and conversion. The control unit is used for storing the digital signal and simultaneously carrying out time sequence control; and the flow calculation and display unit calculates and displays the flow velocity and the flow of the pipeline. Through non-contact flow measurement, the problems of abrasion, water leakage and regular overturning of a detection sensor of a traditional dredger electromagnetic flow meter are effectively avoided, the service life is long, and meanwhile the precision is higher than that of an ultrasonic flow meter.

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 invention discloses a firmware processing method, a retiming card, a computer system and a readable storage medium, and the method comprises the steps: obtaining the level information of two single-ended signals of a high-speed expansion slot through a wire connected with the high-speed expansion slot; determining a target bandwidth of the external device based on the level information according to a mapping relationship between the level and the bandwidth; determining a target memory to be connected with the retiming component based on the target bandwidth according to a mapping relationship between the bandwidth and firmware; wherein firmware with different bandwidths is stored in different memories; and sending a control signal for connecting the target memory to the selection switch, so that the retiming component performs signal adjustment based on the target firmware. The method and the device have the technical effects that the firmware switching work of the retiming card can be completed without starting up without depending on a substrate management controller or depending on bandwidth information stored in other equipment, and non-inductive operation is realized.

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.

The invention discloses an intelligent electronic stethoscope system based on wireless transmission and a data processing method thereof, and belongs to the technical field of electronic stethoscopes, the intelligent electronic stethoscope system comprises a heart sound acquisition module, a signal conditioning module, a power supply module, a wireless transmission module, an upper computer module and a database module; the microprocessor module serves as a core control unit, and the system can collect heart sound signals of a patient in real time and conduct amplification, filtering and spectrum analysis processing on the heart sound signals. The processed heart sound data is transmitted to a mobile device or a computer in real time through a wireless transmission module, so that doctors and users can check heart sound waveforms and related data conveniently; the system also supports the uploading of the data to a cloud database for remote diagnosis and query; the method not only can meet the requirements of modern medical treatment for high efficiency and convenience, but also can play an important role in improving the diagnosis precision and the remote diagnosis capability and reducing the infection risk.

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 GIS partial discharge acoustic-optical-electric combined detection composite sensor comprises a composite sensing device installed on a GIS, and the composite sensing device comprises a sealing plate, a dielectric layer plate, an upper polar plate, an optical fiber connector, a fluorescent optical fiber, a feed rod and a limiting device; the limiting device comprises a fixed ring seat, a rotating mechanism, a synchronizing mechanism and an adjustable limiting mechanism; the fixed ring seat is fixedly mounted on the lower surface of the sealing plate; the rotating mechanism is rotationally arranged at the bottom of the fixed ring seat; the synchronizing mechanism is rotationally arranged in the fixed ring seat; the adjustable limiting mechanism is arranged on the edge of the fixing ring base in a sliding and penetrating mode and meshed with the synchronizing mechanism. Through the adjustable limiting mechanism, the position of the sliding limiting seat is reasonably adjusted according to the thickness of the head of the bolt, so that it is guaranteed that the adjusted sliding limiting seat can make contact with and press the head of the bolt, using is more flexible, the signal conditioning amplifier and the photoelectric converter are matched, and the problem of false alarm of partial discharge is solved.

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.