1146 results about "Intelligent sensor" patented technology

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 building structure crack intelligent detection method, which comprises the steps of S1, constructing a multi-dimensional intelligent sensing array, and obtaining four-dimensional spatio-temporal data including vision, stress, vibration and temperature; s2, performing feature enhancement processing on the multi-modal data; s3, realizing cross-modal crack identification and positioning based on a space-time attention neural network; s4, adopting a sub-pixel edge detection and ultrasonic genetic inversion algorithm; s5, constructing a coupling dynamics prediction model; s6, establishing a dynamic threshold evaluation and multi-dimensional self-calibration mechanism; 0.05 mm micro-crack identification and 3mm depth detection precision are achieved and are improved by 50% compared with a traditional method, the detection accuracy under the complex environment is larger than or equal to 98% through the multi-modal data fusion and GAN enhancement technology, the RMSE is smaller than or equal to 0.03 mm through short-term prediction, the accuracy is larger than or equal to 85% through long-term prediction, double guarantees of a physical mechanism and data driving are established, and the detection accuracy is improved by more than or equal to 98% through the multi-modal data fusion and GAN enhancement technology. And the system has self-calibration, self-adaptive sampling and multi-modal fusion decision-making capabilities, so that the manual intervention cost is greatly reduced.

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 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 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 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 relates to the technical field of transformer detection, and discloses a nondestructive testing optimization method and system for an oil-immersed power transformer, and the method comprises the steps: building a three-dimensional dielectric response coordinate system, and generating a preliminary defect positioning map; aging-dominated and damp-dominated defects are detected and identified through spiral frequency sweep excitation; establishing a temperature gradient excitation scheme based on the defect type to generate a defect degree quantitative evaluation index; designing a sound wave modulation excitation scheme to generate a high-resolution defect characteristic spectrum; constructing a multi-mode intelligent sensor network, and combining a defect development trend prediction model to realize defect evolution prediction and generate a graded early warning signal; according to the method, the whole-process accurate detection of the insulation defect of the transformer from positioning, classification and quantitative evaluation to evolution prediction is realized, and a reliable basis is provided for operation and maintenance.

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 an electric power big data automatic reasoning platform and an electric power distribution system, and relates to the technical field of electric power systems, and the platform comprises a knowledge acquisition unit which collects and preprocesses electric power internal and external data, and converts the data into structured data through a knowledge processing unit; organizing the triple data by the dynamic knowledge graph construction unit to form an initial graph; the updating unit monitors a new dynamic state in real time and corrects or adds atlas content; the reasoning unit is based on the dynamic graph, power fault accurate reasoning and future demand trend prediction are achieved through a fault diagnosis and demand prediction algorithm, efficient integration and dynamic evolution of power knowledge are achieved through construction and real-time updating of the dynamic knowledge graph, timeliness and accuracy of the graph are ensured, and reliable data are provided for fault diagnosis; through a closed-loop architecture of data acquisition-inference analysis-scheme making-execution control, an intelligent sensor and an optimization algorithm are combined to generate a distribution scheme adaptive to a scene, and the power distribution adaptability and economy are improved.

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 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 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 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.

The invention discloses an intelligent monitoring system for full life cycle aging of equipment based on Internet of Things, and relates to the technical field of Internet of Things equipment monitoring. Original analog signals are collected through an intelligent sensor, and conditioned analog signals are output by using a built-in adaptive gain conditioning circuit; converting the conditioned analog signal into a digital sequence through an adaptive analog-to-digital converter, carrying out dynamic sliding window filtering on the digital sequence, calculating derivative features according to the filtered digital sequence, combining original features with the derivative features to obtain a real-time feature vector, dividing the whole life cycle of the equipment into a running-in period, a stable period and a decline period, and carrying out dynamic sliding window filtering on the digital sequence; and constructing a feature state matrix of each stage, obtaining an equipment aging level based on the feature state matrix and the real-time feature vector, carrying out anomaly detection from three dimensions of time, space and features in combination with the equipment aging level, and executing different early warning strategies according to an analysis result. According to the invention, the accuracy of equipment life cycle monitoring is effectively improved.

The invention relates to the technical field of cubic hydraulic press monitoring, in particular to a cubic hydraulic press health monitoring method and system based on an intelligent sensor, and the system comprises a health monitoring terminal, a data sensing module, a data processing module, a health diagnosis module, a fault management module, a diagnosis positioning module, a cooperative influence module and a rear-end execution module. According to the method, the time domain, the frequency domain and the time domain-frequency domain combined features are extracted, meanwhile, the state change slope is quantified, early warning of the equipment degradation trend is achieved, a fault type-abnormal feature-weight mapping table is constructed, a fault core component and the specific position are accurately positioned, and the fault core component is accurately determined by matching with digital twinborn visual marks. And meanwhile, the internal relation between components is quantified by constructing a component incidence matrix, the health influence coefficient of the cooperative component is calculated in combination with a fault grade coefficient, key components affected by faults are recognized in advance, a cooperative damage list is formed, and operation and maintenance personnel are assisted to intervene in advance.

The invention provides an intelligent sensor cooperative control method and system for a microbiological detection environment, and relates to the technical field of sensor control, and the method comprises the steps: carrying out the hierarchical cloud edge control architecture layout of a production workshop according to equipment distribution information and production activity information, and obtaining a cascaded hierarchical control network; the K-level cooperative control secondary center executes partition autonomous parameter mapping and outputs a K-layer distributed environment compensation strategy; the microbial environment cooperative control center carries out transition area buffer control and outputs a global cooperative control strategy; and the microbial environment cooperative control center issues a global cooperative control strategy to the environment stability maintaining equipment array based on the spatial topological relevance of the environment stability maintaining equipment, and low-fluctuation environment steady state maintenance is carried out. The technical problem that the stability of a microbiological detection environment is affected due to the fact that most microbiological detection environment adjusting systems in the prior art are based on fixed parameter setting and lack of a quick response mechanism for sudden environment changes is solved.

The invention discloses an error compensation method of a strain type six-dimensional force sensor based on multi-source sensing information fusion, and belongs to the technical field of intelligent sensors. The error compensation method comprises the following steps: data acquisition; preprocessing and fusing data; constructing an error prediction model by adopting a time sequence neural network; training data construction and model training; performing online error compensation and feedback; and verifying, adjusting and optimizing the system. Based on fusion of multiple sensors (IMU, a temperature module and a timer) and time sequence neural network modeling, combined online compensation of errors caused by gravity, temperature drift and time drift of the six-dimensional force sensor is achieved, the method has the advantages of being high in compensation precision, high in response speed and high in adaptive capacity, and the application requirements of various high-precision measurement and control systems are met.

The invention relates to the technical field of spinning quality detection, and discloses a jute spinning product quality detection method and system based on an intelligent sensor. The method comprises the following steps: acquiring real-time process parameters of a jute spinning production line, and synchronously capturing yarn surface temperature field distribution in combination with an infrared thermal imager; performing space-time alignment processing on the yarn tension fluctuation data and the temperature field distribution to generate a fused dynamic process characteristic matrix; according to the tension gradient change rate and the temperature field abnormal region coordinates in the dynamic process characteristic matrix, dividing potential occurrence sections of yarn quality defects; a microstructure image of the yarn in the potential generation section is extracted, a fiber arrangement angle deviation value is measured through a polarized light interferometer, and fiber molecular bond vibration frequency data is obtained in combination with a Raman spectrometer; and inputting the fiber arrangement angle deviation value and the molecular bond vibration frequency data into a cascade quality analysis model, and outputting a quantitative index set of the internal structure defects of the yarns.

The invention relates to the technical field of intelligent sensors, in particular to a workshop harmful gas abnormity monitoring method and system based on multi-modal data, and the method comprises the following steps: obtaining the multi-period concentration change of a node, recognizing the reverse direction, forming a bounce fragment, combining node behaviors according to the correlation of time and space, and constructing a diffusion path; and in combination with node toxicity residence and trend change and gas and time information at the convergence position, a workshop multi-modal toxicity anomaly monitoring result is obtained. According to the method, gas fluctuation characteristics are captured by recognizing the concentration change direction and the reversal amplitude, a behavior combination is constructed by combining time and space information, the linkage recognition capability between nodes is enhanced, a path sequence is constructed by depending on the trend direction and the node sequence to present a diffusion track, and extension and mutation nodes are extracted by superposing the residence state and the change trend. The dynamic monitoring range of toxic gas in multiple scenes is expanded, and correlation analysis of behavior traceability, path tracking and abnormal gathering areas is supported.

The invention relates to an internet-of-things monitoring and early warning system for lake water quality. The internet-of-things monitoring and early warning system comprises a multi-mode intelligent sensor acquisition system, a self-adaptive wireless transmission system, a distributed data storage system, an intelligent data analysis system, a visual interaction system, an intelligent early warning system and a remote intelligent control and repair system. The invention also relates to a monitoring method using the lake water quality Internet of Things monitoring and early warning system. Traditional and innovative sensors are cooperated to comprehensively collect water quality and ecological data, the biosensor accurately detects specific pollutants, the hyperspectral imaging sensor monitors algae distribution and water color change, rich and high-precision data are provided for lake water quality monitoring, the monitoring comprehensiveness and precision are improved, and the water quality monitoring system is suitable for being popularized and applied. The lake ecological protection work is powerfully supported.

The invention provides a reducer box sealing performance test method and system based on an intelligent sensor, and relates to the technical field of sealing detection, and the method comprises the steps: collecting each sealing test point; generating a primary sealing test result; building a primary sealing test platform based on the intelligent sensor, generating a primary sealing test result, carrying out factory verification based on the primary sealing test result, and calling the box simulation model and the secondary leakage variable after leaving a factory to generate a secondary leakage model; secondary leakage fitness monitoring is performed, including instantaneous fitness monitoring and cumulative fitness monitoring. The technical problem that in the prior art, due to the fact that the sealing state change of the speed reducer under the actual working condition is difficult to reflect, sealing detection is inaccurate can be solved, and intelligent detection and evolution monitoring of the sealing performance of the speed reducer in the whole process from factory leaving to service are achieved through the intelligent sensor. The technical effects of improving the sealing reliability and preventing leakage faults are achieved.

The invention discloses a metal tube float flowmeter based on an intelligent sensor, and relates to the technical field of float flowmeters, the metal tube float flowmeter comprises a tube body and a detection mechanism, an instrument is fixed on the outer side of the tube body through a bolt and used for realizing flow display, the instrument is connected with a Hall element, the detection mechanism used for flow detection is installed in the tube body, and the Hall element is connected with the detection mechanism. And the locking mechanism is used for realizing initial locking of the detection mechanism. According to the metal tube float flowmeter based on the intelligent sensor, the elastic regulation and control mechanism is arranged at the position of the liquid inlet of the tube body, peak kinetic energy of fluid is actively absorbed through elastic deformation of the first spring, and therefore the fluid entering the tube body can be buffered, pulsating flow of the external input fluid is effectively converted into stable flow, and the flow rate of the fluid is increased. Jumping of the floater body is greatly reduced, the problem that reading is difficult due to jumping of the floater body can be solved, the positive error of an average measured value caused by pulsation can be avoided, and the accuracy of detection data is guaranteed.

The invention provides a water quality abnormity early warning system and method based on multi-source data fusion, and relates to the technical field of abnormity early warning, and the method comprises the steps: recognizing a sensitive region in which the water quality is gradually changed and abnormal in a target water area through the time-space correlation characteristics of each piece of water quality multi-source data; performing sensitivity analysis on the response intensity of the water quality gradual change abnormity in the sensitive area to obtain a water quality sensitivity index of the sensitive area; determining an early warning response period when the water quality at the monitoring point is gradually changed and abnormal; performing confidence correction on a water quality abnormal fluctuation early warning threshold value at the monitoring point through the water quality sensitivity index and the early warning response period to obtain an abnormal early warning confidence coefficient of the water quality at the monitoring point; and when the abnormity early warning confidence exceeds a preset confidence threshold, sending a water quality gradual change abnormity verification instruction to an intelligent sensor at the monitoring point, and generating risk alarm information of water quality gradual change abnormity based on feedback verification data. According to the invention, risk early warning can be carried out under the condition that the water quality has gradual abnormal fluctuation.

The invention discloses a real-time adaptive temperature monitoring intelligent sensor based on a deep learning algorithm, and belongs to the technical field of environmental parameter measurement and artificial intelligence. The sensor is composed of a main temperature measuring unit, an auxiliary environment sensing unit, a data acquisition circuit, a microcontroller, a deep learning processing module, a memory, a communication module and a power supply module. Multi-modal data are obtained through the auxiliary environment sensing unit, and the multi-modal data are preprocessed and then input into the self-adaptive temperature sensing network. In the adaptive temperature sensing network, a multi-mode encoder extracts time sequence characteristics of each channel, a convolution auto-encoder reconstructs a normal temperature measurement mode and performs anomaly detection, and a fusion decoder fuses output of each sensor into a corrected temperature value based on a dynamic weight formula. The method has the advantages of high real-time performance, high measurement precision, timely anomaly detection, high self-learning capability, low power consumption and the like.

The invention discloses a building engineering construction quality progress monitoring method and system based on artificial intelligence. A BIM model is constructed according to image data and project drawings of an engineering site; according to the BIM model, intelligent sensors are arranged on site, and online construction information is collected; online construction information is extracted, three-dimensional environment scene reconstruction is carried out according to the surrounding environment, and construction progress data is obtained; calculating an environment fluctuation value according to the construction progress monitoring condition and the online construction information, and setting a construction adjustment strategy; constructing a gray scale prediction model, and predicting a progress critical value result according to the construction progress data and the construction period after construction adjustment; and the construction progress data, the three-dimensional environment scene and the progress critical value prediction result are visualized to generate a BIM simulation large screen, so that the scheduling scientization of the construction process is realized.

The invention relates to the technical field of fire safety, in particular to a fire simulation early warning system based on intelligent fire protection multi-source data fusion, which comprises a sensor health degree dynamic evaluation module, an intelligent sensor health degree dynamic evaluation module and a fire simulation early warning module, establishing a two-dimensional feature library of the state of the sensor and environmental interference; and based on the feature library data, a fuzzy comprehensive evaluation method is adopted. By filtering unreliable data of the sensor, distinguishing multi-source contradictory data, dynamically adjusting weights according to fire behavior stages, iteratively updating data credibility and optimizing parameters in a closed-loop mode, the problems of false alarm and missing alarm in the prior art are effectively solved, and the accuracy and practicability of the intelligent fire-fighting early warning system are improved.

The invention relates to the technical field of welding fault analysis, in particular to a rail welding process information intelligent collection and analysis method based on the Internet of Things technology, and the method comprises the steps: obtaining the data of each welding parameter in the rail welding process through an Internet of Things intelligent sensor group; according to the discrete degree of all modal component data in each welding parameter before each collection moment, the instantaneous interference complexity of each welding parameter at each collection moment is obtained in combination with the average level of the similarity degree between the modal components of the welding parameters, and then the credibility weight of each welding parameter at each collection moment is obtained; the method comprises the following steps: analyzing the interference removing singularity of each acquisition moment in the track welding process to obtain the interference removing singularity of each acquisition moment in the track welding process, analyzing the correlation between the interference removing singularity and each welding parameter in the track welding process to obtain the correlation fault significance of each acquisition moment in the track welding process, and judging the fault condition of the track welding equipment based on the correlation fault significance. The accuracy of intelligent analysis in the track welding process can be improved.