1129 results about "Smart transducer" patented technology

The invention discloses a power semiconductor device aging on-line diagnosis method, which relates to the field of power electronics, and comprises the following steps: integrating an intelligent sensor and an aging test circuit, and establishing a multi-physics field coupling simulation model; based on the multi-physics field coupling simulation model, evaluating the aging state of the current device to obtain the health index of the device; based on the multi-dimensional data matrix, constructing an anomaly detection model, and outputting an anomaly detection result; predicting the failure probability and the remaining service life of the semiconductor device according to an abnormal detection result; generating a maintenance suggestion based on the predicted failure probability and remaining useful life of the semiconductor device; according to the method, the maintenance suggestion is generated according to the prediction result, the user is guided to take preventive maintenance measures in time, the accuracy of aging state evaluation is improved, the timeliness of fault early warning is enhanced, and economic losses and technical risks caused by sudden faults are reduced.

The invention discloses a coal mine power supply intelligent monitoring system based on the Internet of Things, belongs to the field of coal mine power supply monitoring, and aims to solve the problems that an existing coal mine power supply intelligent monitoring system is lagged in response, high in false alarm rate and large in manual dependence degree. According to the invention, through the end-side global sensing module, the data advanced analysis module, the edge data processing module, the data transmission module, the cloud data analysis and model construction module and the fault early warning and closed-loop control module, the real-time acquisition of the equipment state is realized by deploying multiple types of intelligent sensors; local data preprocessing and abnormal pre-judgment are carried out by combining edge computing nodes, an equipment health degree model is established by adopting a time sequence data association analysis algorithm, closed-loop control of overload prediction, electric leakage positioning and energy consumption optimization is realized through multi-source data fusion analysis, and finally a three-level intelligent monitoring system of end side sensing-edge computing-cloud decision is formed. The system response efficiency and accuracy are improved, and the personal labor intensity is reduced.

The invention relates to the technical field of sensor calibration, in particular to an environment adaptive multi-dimensional calibration method for an excavation type intelligent sensor, which comprises the following steps: acquiring temperature, humidity and gas data through environment monitoring, comparing with a threshold value, marking an abnormal state, evaluating the influence of each parameter on a sensor signal and analyzing fluctuation amplitude and intensity; combining sensor sensitivity and response rate to screen key factors, extracting factor fluctuation trend, optimizing signal disturbance weight and response path, correcting signals in real time, updating a calibration parameter set, monitoring signal values based on multi-dimensional parameters, and adjusting output to maintain a stable range. According to the invention, temperature and humidity and gas abnormity are dynamically identified through threshold comparison, a factor model is constructed based on sensitivity weight, a multi-dimensional calibration path is generated, a parameter set is updated in real time to decouple environment sudden change and sensor drift, signal self-adaptive convergence is realized through closed-loop control, multi-parameter interference errors are reduced, and the bottleneck of calibration delay is broken through. And the time-varying working condition data stability is ensured.

The invention discloses a property maintenance prediction system based on big data, and the system comprises the steps: monitoring equipment and environment in real time through an intelligent sensor, transmitting the data to a cloud or local storage through a wireless network, and carrying out the historical data processing and cleaning through a distributed architecture; predicting the equipment fault risk through machine learning, establishing a customized prediction model for different equipment, analyzing the life cycle and predicting the service life of the equipment; for old equipment, constructing a life extension model by using historical fault data; the system generates intelligent decision suggestions, pushes equipment health states to property management personnel, automatically formulates maintenance plans and priorities, and dispatches maintenance personnel; during fault prediction, early warning is given out in real time, related personnel are reminded through a short message or an APP, meanwhile, maintenance operation is recorded, historical data are analyzed, and future maintenance plans and budget are optimized; the system dynamically adjusts the life prediction model of the old equipment by accumulating historical data, and ensures the accuracy and efficiency of equipment health management.

The invention relates to the technical field of intelligent sensor application, and discloses a water meter flow real-time correction method and system based on an intelligent sensor, and the system comprises a flow signal collection module, a signal feature decomposition module, a frequency domain feature fusion module, an error compensation decision module and a correction instruction execution module. The method comprises the following steps: acquiring multi-dimensional vibration waveform data of a water meter pipeline through a multi-band sensor, performing time-frequency domain decomposition and energy spectrum feature extraction to obtain a local frequency domain feature tensor set, and performing dynamic noise suppression and band correlation optimization to obtain a local optimization feature tensor set; and performing global feature reconstruction based on frequency domain energy distribution to generate an error compensation decision feature map, and finally generating a real-time correction control signal. Through multi-dimensional signal acquisition and intelligent feature processing, the precision and dynamic adaptability of flow metering in a complex pipe network environment are improved, and the method is suitable for high-precision flow correction of an intelligent water meter and has the advantages of being high in anti-interference capacity, accurate in compensation and the like.

The invention discloses an operation data management system and method based on artificial intelligence, and relates to the technical field of artificial intelligence, and the method comprises the following steps: 1, collecting multi-source data in real time through an intelligent sensor and industrial equipment, achieving time synchronization, and ensuring data alignment; step 2, cleaning, labeling and standardizing the collected data, extracting feature vectors of the data, and generating multi-modal feature vectors through dynamic weighted fusion of an attention mechanism; step 3, calculating a reconstruction error based on an auto-encoder to identify an abnormal state, quantifying the contribution degree of each feature to the abnormity in combination with an SHAP algorithm, and positioning key influence factors; and step 4, dynamically adjusting the detection threshold by adopting reinforcement learning to reduce the false alarm and missing alarm rate, and evaluating the prediction confidence by quantifying the model error and the data error to realize the dynamic adjustment of the early warning priority. According to the invention, the condition that abnormal data and false alarm of the abnormal data are difficult to effectively warn in the prior art can be improved.

The invention discloses a hydraulic engineering seepage intelligent monitoring system and method, and belongs to the technical field of hydraulic engineering safety monitoring. The system comprises the following modules: a multi-source data acquisition module used for acquiring multiple types of monitoring data in real time through an intelligent sensor network; the double-window time sequence analysis module is used for constructing a quick response window and a trend analysis window to realize double identification of sudden anomalies and long-term trends; the self-learning threshold optimization module is used for automatically extracting a key quantile threshold based on the distribution characteristics of the monitoring data and continuously optimizing weight configuration and early warning threshold setting of various statistical indexes; the multi-scale fusion early warning module is used for performing multi-source information fusion, generating a comprehensive change index and a multi-stage early warning state, and outputting a seepage abnormity early warning signal and a corresponding confidence coefficient; and the visual decision support module provides visual data display, emergency response guidance and intelligent decision support.

The invention discloses a virtual power plant cooperative control intelligent scheduling system and method, and relates to the technical field of intelligent power grids. Intelligent sensors are deployed, a virtual power plant system is combined, power grid related data are collected in real time, a power grid data set S is constructed, and feature index extraction and comprehensive calculation are performed according to the power grid data set S; and the load score prediction and evaluation module predicts the comprehensive power grid load score ZHpred (t + delta t) at a future time point t + delta t through a load score prediction model in combination with historical data and real-time data, calculates a power grid load fluctuation index BD, evaluates power grid load fluctuation, and determines the power grid load score ZH when the power grid load fluctuation is abnormal. A particle swarm optimization algorithm is used to generate an optimal scheduling scheme, power output of power generation equipment and energy storage equipment is adjusted, supply-demand balance of the power grid is recovered, resource utilization efficiency is improved, stable operation of the power grid is maintained, and intelligent self-healing and efficient management of the power grid are realized.

The invention relates to a power supply quantity self-repairing method based on deep learning, and the method comprises the following steps: S1, obtaining power multi-dimensional data through an intelligent sensor and terminal equipment, and carrying out the preprocessing of the data; s2, constructing a power network graph model based on the power multi-dimensional data, and optimizing the power network graph model by introducing a graph attention mechanism and a time dynamic model to obtain power grid topology dynamic dependency information; s3, based on the processed electric power multi-dimensional data and power grid topology dynamic dependency information, using deep learning to fuse multi-modal information, detecting abnormal behaviors of power supply quantity, and determining abnormal types and grades; s4, according to key nodes identified in the power network graph model and an abnormal grading result, dynamically distributing a repair priority, and outputting a repair value; and S5, performing restoration based on the restoration value and a predetermined execution sequence, and returning the power supply curve to the power dispatching center after restoration is completed. According to the invention, rapid positioning and grading of power supply abnormity and accurate generation of a restoration scheme are realized.

The invention relates to the technical field of energy storage lithium batteries, and discloses a remote monitoring operation and maintenance management device for energy storage lithium batteries, which is characterized in that a battery temperature acquisition module, a battery voltage acquisition module, a battery current acquisition module and an environment temperature acquisition module are respectively used for acquiring the surface temperature, voltage and current of a battery cell of an energy storage lithium battery group and the environment temperature of the battery; the state of charge calculation module calculates the state of charge (SOC) of the energy storage lithium battery group; the battery fault diagnosis module performs battery abnormity detection to identify a fault battery; and the early warning module generates alarm information when the SOC is not in a preset range and detects that a fault battery exists in the energy storage lithium battery group. According to the method, more data are collected through the intelligent sensor, multiple calculation modes are established to calculate the SOC of the battery, the calculation result can accurately reflect the actual working state of the energy storage power station, real-time fault detection is carried out on the energy storage lithium battery, and the use safety of the energy storage lithium battery is guaranteed.

The invention discloses a soil environment multi-parameter monitoring system and method based on an intelligent sensor, and aims to solve the problems of single sensing dimension, shallow data analysis and lack of automatic closed loop in the prior art. The system comprises an intelligent sensor node, a data processing center and a user terminal. The nodes can synchronously acquire multi-parameter data, perform local preprocessing and adaptively switch communication protocols. And the data processing center adopts a space-time fusion analysis model to carry out deep fusion and prediction on the uploaded data. And when the user terminal receives the prediction result and meets a preset condition, the user terminal sends a control instruction to the node, and after the node receives the instruction, the external agricultural equipment is linked to execute operation. According to the invention, by constructing a collaborative architecture of intelligent perception, cloud prediction, man-machine collaboration and closed-loop control, comprehensive, accurate and automatic management of the soil environment is realized, and the resource utilization efficiency is significantly improved.

The invention discloses a smart city energy management system based on energy conservation and emission reduction, and relates to the technical field of smart city energy management. By using an intelligent sensor group, illumination related data of illumination equipment is obtained, an illumination related data set R is constructed, and a light attenuation factor Gs, a convective heat transfer index DL and a use intensity factor Qd are extracted; the method comprises the following steps: dynamically evaluating the thermal load intensity of an urban area, scientifically dividing thermal risk grades, combining performance brightness prediction and control strategy matching to realize subarea and lamp-divided refined dimming control, using a feedback self-evaluation and optimization regulation and control mechanism, carrying out power reestimation and strategy alternation on an area with a substandard control effect, and carrying out power control on the area with the substandard control effect. Redundant energy consumption is effectively reduced, heat accumulation is restrained, and the lighting efficiency and the environment comfort degree are improved.

The invention relates to the technical field of livestock and poultry epidemic disease management, and particularly discloses an intelligent livestock and poultry epidemic disease early warning and partitioned prevention and control management system and method, and the method comprises the steps: laying an integrated intelligent sensor network in a livestock and poultry farm, and collecting environment parameters, feeding management data and animal health data in real time through a low-power-consumption Internet of Things technology; transmitting the collected multi-dimensional data to a cloud platform by utilizing an edge computing technology; based on multi-dimensional data, a deep learning and multi-source data fusion analysis technology is adopted, a dynamic disease risk prediction model is constructed, and generalization ability of an adversarial generative network optimization model in a complex environment is introduced; by arranging the integrated intelligent sensor network, environmental parameters, feeding management data and animal health data in the livestock and poultry farm are comprehensively collected in real time, the early monitoring capability of epidemic diseases is remarkably improved, potential epidemic disease risks are found in time, and precious time is won for subsequent prevention and control work.

The invention relates to the technical field of pipeline corrosion detection, in particular to a detection method and analysis system applied to the corrosion problem of a Texaco gasification device, and the method comprises the steps: selecting key monitoring points to monitor the corrosion condition of a target device, installing intelligent sensors at the points, collecting operation data in real time, and transmitting the operation data to a central processing unit; analyzing the data, identifying an area possibly having a corrosion risk and an abnormal change trend, and determining a suspected corrosion area; carrying out deep detection on the areas to obtain detection data; detection data, equipment operation data and maintenance records are encrypted and stored, the authenticity and traceability of the data are ensured, and a reliable basis is provided for subsequent analysis and decision making; and in combination with an intelligent manufacturing technology, operation parameters and maintenance plans of the device are optimized according to a corrosion analysis result, and accurate analysis and effective prevention and control of corrosion problems are realized. By means of the scheme, accurate analysis and effective prevention and control of the corrosion problem of the Texaco gasification device are achieved.

The invention relates to the technical field of hydraulic engineering monitoring, and discloses a hydraulic engineering operation risk monitoring management system based on multi-sensor fusion. The system comprises a data acquisition module, a data fusion module, an early warning and instruction generation module, and an instruction distribution and verification and correction module. The data acquisition module acquires structural deformation, water flow pressure and environment temperature and humidity data in real time through multiple types of intelligent sensors; the data fusion module inputs the collected data into a dynamic weighted fusion model, and generates a comprehensive monitoring index in combination with a sensor confidence coefficient weight and a historical data deviation rate; the early warning and instruction generation module triggers multi-stage early warning and generates an equipment regulation and control instruction set according to the comprehensive monitoring index and a preset safety threshold value; the instruction distribution module distributes the instruction to an execution terminal through a distributed message queue; and the verification and correction module checks terminal feedback signals by using a back propagation verification mechanism, and corrects the confidence coefficient weight of the model.

The invention provides a multi-scene fusion perception system and method of a humanoid robot olfaction sensor, and relates to the technical field of intelligent sensors, and the system comprises an olfaction perception optimization module which is used for obtaining an olfaction perception optimization network; the first matrix module is used for monitoring a gas pipeline to obtain a first matrix; the second matrix module is used for performing interference compensation according to a real-time environment; the third matrix module is used for correcting according to the state of the sensor; the leakage detection module is used for detecting pipeline leakage; and the suppression verification module is used for carrying out false alarm suppression verification on the pipeline leakage detection result to obtain a pipeline early warning signal. The gas leakage detection method and device can solve the technical problem that the gas leakage detection precision is limited due to the fact that the gas leakage detection device is prone to environmental interference and high in false alarm rate in the prior art, and can adjust the multi-node olfaction sensing network in real time through historical data, conduct multi-level correction on monitoring data and introduce false alarm suppression verification, thereby improving the detection accuracy of gas leakage. The accuracy of gas leakage detection in a multi-scene environment is improved.

The invention discloses a system and a method for multi-parameter cooperative control in PVC steel wire hose production, and relates to the technical field of plastic hose production, the system comprises a parameter monitoring module, a central processing module, a parameter adjusting module and a quality detection module, the parameter monitoring module is a high-precision intelligent sensor and has a self-adaptive filtering function, and key parameters are measured and transmitted to the central processing module; the central processing module uses an improved multi-objective optimization algorithm to calculate an optimal cooperation value, a simulated annealing-genetic hybrid algorithm is used for iteration, the parameter adjustment module uses intelligent PID, adaptive variable structure control and model prediction control adjustment equipment, and the quality detection module uses multi-modal fusion deep learning and high-precision physical performance detection equipment. And if not, recalculating the adjustment parameters. The production quality and efficiency of the PVC steel wire hose are improved, the cost is reduced, parameters are precisely and cooperatively controlled, the hose is stable in physical performance and few in surface defect, the raw material and equipment maintenance and energy consumption cost is reduced, and the enterprise revenue and market competitiveness are 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 an online AOI detection system based on an industrial intelligent sensor, and the system comprises an image collection and preprocessing module which is used for collecting and preprocessing image data of an industrial product; the mask auto-encoder modeling module is used for constructing a mask auto-encoder model; the structure parameter optimization module is used for optimizing the mask auto-encoder model; the image reconstruction and difference extraction module is used for generating a reconstructed image, extracting an image difference region and determining a candidate defect region; the defect identification module is used for carrying out edge extraction and aggregation analysis, identifying a final defect area and acquiring spatial position information; the defect classification and labeling module is used for extracting defect area features and generating corresponding classification labels and grade labels; and the control response module is used for generating a control instruction and issuing the control instruction to the production line control device. According to the method, the high-precision automatic identification and real-time classification processing of the surface defects of the industrial product are realized by fusing the mask auto-encoder and the Tiancattle herd optimization algorithm.

The invention discloses a water pollution monitoring method and device based on an intelligent sensor, and the method comprises the steps: collecting the real-time water temperature, pollutant concentration and environmental parameter data of a water body through a sensor network, and storing the data into a distributed database to form a multi-dimensional time series data set; a feature extraction and dimension reduction module is used for obtaining key feature vectors, a potential pollution event marking module is used for screening out a preliminary event candidate set, and an event detection model is trained by means of a random forest algorithm. And when the model judges that a pollution event occurs and the confidence reaches the standard, generating an early warning signal. The water temperature abnormal trend is predicted through a long and short term memory network, Kalman filtering smoothing processing is carried out, finally, a pollution event early warning report is generated according to abnormal points in a stable water temperature change trend sequence, and efficient and accurate monitoring and early warning of water pollution are achieved.

The invention provides a full-link electricity consumption monitoring method, system and equipment based on intelligent Internet of Things, and relates to the technical field of power system management. The method comprises the following steps: acquiring operation data acquired by an intelligent sensor group deployed at a power network node in real time, and executing localized carbon flow accounting according to the operation data through an edge computing node; constructing a joint probability model based on Monte Carlo simulation and a deep belief network, and generating a node carbon flow density matrix under multiple scenes; dynamically updating an electricity-carbon conversion coefficient according to real-time energy structure data, and calculating node-level carbon emission: optimizing a power grid topology and an energy storage scheduling strategy by adopting a double-delay depth deterministic strategy gradient algorithm TD3 and taking a carbon flow density matrix as a constraint condition; and generating an intelligent report including a carbon footprint thermodynamic diagram, emission reduction potential evaluation and block chain evidence storage. According to the invention, the capability of multi-target collaborative optimization of the security and economy of the power grid can be improved.

The technical scheme of the invention discloses a photovoltaic power generation and greenhouse agriculture integrated system. According to the photovoltaic agricultural integrated technology disclosed by the invention, photovoltaic power generation and greenhouse planting are innovatively combined, and a solar panel with adjustable light transmittance of 30%-90% is mounted at the top of an agricultural facility, so that sunlight is utilized for power generation, and illumination required by crop growth is reserved. The system disclosed by the invention is provided with an intelligent sensor and an AI algorithm, environment data is monitored in real time, the angle, light supplementing intensity, temperature and humidity of the photovoltaic panel are dynamically adjusted, and different crop requirements (such as low illumination of leaf vegetables and medium and strong illumination of fruits and vegetables) are accurately matched. Residual electricity is stored or connected to a grid, photovoltaic waste heat is recycled in winter for heat preservation, sun shading and cooling are conducted in summer, and efficient circulation of energy is achieved.

The invention discloses a cable fault accurate positioning system based on an intelligent sensor system, and relates to the technical field of power fault positioning, and the cable fault accurate positioning system comprises a cloud monitoring center which is in communication connection with the following modules: a signal acquisition and processing module which is used for deploying the intelligent sensor system along a cable, operation environment data and traveling wave signals in the cable operation process are obtained, and the signals are preprocessed and stored in a cloud data warehouse. According to the invention, the front band-pass filter and the self-adaptive trap filter are combined to filter out high-frequency harmonic waves and specific-frequency interference signals, and the differential sampling technology is adopted to counteract common-mode interference, so that the signal-to-noise ratio of the signals is remarkably improved, a system can accurately capture and process weak traveling wave signals caused by a high-resistance grounding fault in a complex environment, and the fault detection accuracy is improved. Therefore, the fault positioning precision is greatly improved, the positioning error caused by signal interference is reduced, and the determination of the fault position is more reliable and accurate.

The invention relates to the technical field of laser heat treatment, and provides a calibration method of a full-automatic circuit board cutting machine. The method comprises the following steps: acquiring physical coordinates of a circuit board of a cutting device through an intelligent sensor, and constructing a reference coordinate system matrix based on the physical coordinates; determining vibration parameters of the cutting device according to coordinates of laser spots emitted by a laser and the reference coordinate system matrix, and generating a nonlinear error component according to the vibration parameters and power information of the laser; generating an error function based on an error matrix formed by the error components, and determining a calibration coefficient vector by solving the error function; closed-loop control is conducted on the laser path of the cutting device through the calibration coefficient vector, the adaptability of the cutting device to complex working conditions is improved by integrating multi-source error factors and establishing a nonlinear mapping relation, and the cutting precision and stability are ensured.

The invention relates to the technical field of industrial safety, and provides a rupture disk service life prediction method based on intelligent perception, which comprises the following steps: constructing a multi-mode intelligent sensor array, and collecting data in the running process of a rupture disk in real time; establishing a data transmission network, and transmitting data to a data processing center in real time; preprocessing the collected real-time data to form a standardized data set; and constructing a deep learning model, calculating the fatigue damage cumulant of the rupture disk according to the feature data output by the deep learning model based on a fatigue damage accumulation theory, predicting the residual life of the rupture disk, and generating life early warning information. According to the method, the running data of the rupture disk are collected in real time through the multi-mode intelligent sensor array, the life prediction model is constructed based on the fatigue damage theory, dynamic evaluation and early warning are achieved in combination with a three-level early warning mechanism, multi-source data are fused, the prediction precision is improved, the risk and cost are reduced, and it is guaranteed that industrial production is safe and reliable.

The invention discloses a settlement monitoring system based on computer vision, and relates to the technical field of infrastructure monitoring, the settlement monitoring system comprises a settlement monitoring platform, the settlement monitoring platform is in communication connection with an image acquisition module, a sensor network module, a data fusion analysis module, a settlement abnormity identification module and a settlement early warning module, the modules are in electric signal connection; and the image acquisition module is used for monitoring a foundation area of the infrastructure by using a plurality of cameras deployed in a monitoring area to obtain image data of the infrastructure. Through combination of the image acquisition module and the sensor network module, comprehensive coverage and accurate monitoring of an infrastructure foundation area are realized, a plurality of cameras are utilized to carry out omnibearing and multi-angle image acquisition, dead-corner-free monitoring is ensured, physical parameter data of a foundation are acquired in real time through multiple types of intelligent sensors, and the monitoring precision is improved. Monitoring comprehensiveness and accuracy are obviously improved, and potential safety hazards caused by monitoring blind areas are effectively avoided.

The invention relates to the technical field of medical equipment monitoring, in particular to a Picc catheter safety monitoring and alarm system and method based on the Internet of Things. The method specifically comprises the steps that multi-modal physiological parameters are collected in real time through an intelligent sensor, multi-level fusion denoising and normalization processing are carried out through an edge calculation module, and an anti-loss seal is generated; the cloud end adopts an adaptive hybrid model to analyze time series data and group patient association features, realizes unsupervised anomaly detection through comparative learning and a variational auto-encoder, and performs lightweight deployment on a complex model in combination with a knowledge distillation technology; dynamically calculating a risk level based on a Bayesian abnormal score and a weighted time sequence index, adopting a grading early warning strategy to match a differentiated alarm notification mode, synchronously generating personalized nursing suggestions, and optimizing monitoring frequency; data, risk prompt and decision support are integrated through a visual interaction interface. According to the invention, accurate identification, real-time early warning and active intervention of the abnormal state of the PICC are realized, and the false alarm rate and the medical risk are effectively reduced.

The invention provides a fault-tolerant diagnosis method adopting a dual-channel pressure sensor, and relates to the technical field of intelligent sensors, and the method comprises the steps: arranging the dual-channel pressure sensor on a target natural gas pipeline, and synchronously collecting and obtaining a first pressure signal sequence and a second pressure signal sequence; when any pressure signal reaches a preset pressure threshold value, signal deviation analysis is carried out, and a pressure signal deviation sequence is obtained; when the deviation value at any moment reaches a preset deviation threshold value, fault detection is carried out on the dual-channel pressure sensor, and a sensor detection result is obtained; when the deviation value reaching the preset deviation threshold value does not appear in the preset time interval, pipeline fault recognition and positioning are conducted, and a pipeline fault recognition result is obtained. The technical problems that in the prior art, pressure monitoring depends on a single sensor, effective fault diagnosis cannot be carried out or accurate data cannot be continuously provided when pressure abnormity occurs, pipeline faults cannot be found in time, and normal operation of a pipeline is affected are solved.

The invention provides an intelligent fault diagnosis method and system for a photovoltaic system. The method comprises the following steps: collecting multi-source heterogeneous operation parameter data of the photovoltaic system based on an intelligent sensor network and carrying out standardized preprocessing; local feature extraction is carried out on edge nodes, a frequency moment is calculated, data summary aggregation is carried out through a frequency moment optimization algorithm, and a fusion key feature vector set is constructed by using a multi-modal data fusion model; performing anomaly detection based on the normal behavior mode baseline, and generating an abnormal state alarm signal and a preliminary anomaly type indication; constructing a weighted graph model based on a system topological structure, constructing a fault association sub-graph by using an approximate spanning tree algorithm, accurately positioning a fault unit and diagnosing a fault type; and generating and executing a self-healing operation scheme according to the fault diagnosis conclusion. Through the distributed multi-modal data fusion and approximate spanning tree fault positioning technology, rapid and accurate diagnosis and autonomous recovery of the photovoltaic system fault are realized, and the system reliability and the operation efficiency are improved.

The invention discloses a man-machine interaction method and system based on an intelligent sensor, belongs to the technical field of robots, solves the problem of misoperation caused by abnormal sensor data to a certain extent, and enables the system to still run stably through quality credibility evaluation and a dynamic weight distribution mechanism when part of sensors fail. Meanwhile, based on a dynamic safety boundary strategy of scene semantic understanding, the robot can automatically adjust motion constraints according to object attributes and spatial relations, and collaborative optimization of safety and operation efficiency is achieved in complex tasks.