44results about How to "Real-time warning" patented technology

The invention belongs to the technical field of fire sensing and detection, and provides a fire sensing method and related device for a double-layer cable tunnel structure, and the method comprises the steps: respectively collecting the temperature, smoke and image data of a double-layer cable tunnel, and carrying out the double-layer data linkage; respectively constructing a temperature distribution model, a smoke diffusion model and an image target detection model according to the collected temperature, smoke and image data; and performing temperature, smoke and image multi-modal data fusion based on the temperature distribution model, the smoke diffusion model and the image target detection model, and when the fused data meets a set fire threshold, determining that a fire occurs. Through the fire sensing and verification method aiming at the characteristics of the double-layer cable tunnel, scientific and reliable decision support is provided for improving tunnel fire safety management, the safety of personnel and equipment in the tunnel is ensured, and meanwhile, the standardization process of the fire sensing technology is promoted.

The invention discloses a fumigation treatment multi-parameter comprehensive monitoring system which comprises a multi-parameter sensor monitoring module, a multi-modal data processing and analyzing module, a system intelligent monitoring and adjusting module, a fumigation environment control module, a remote maintenance and management module and a system interaction and feedback module. The multi-parameter sensor monitoring module is used for collecting parameter data. The multi-modal data processing and analyzing module is used for fusing and analyzing data. The system intelligent monitoring and adjusting module is used for intelligently monitoring and adjusting fumigation strategies. The fumigation environment control module is used for adjusting environment parameters. The remote maintenance and management module is used for system maintenance and management. And the system interaction and feedback module is used for display interaction of data and a control state. According to the fumigation treatment multi-parameter comprehensive monitoring system, a multi-modal data fusion algorithm based on information channel decomposition is provided for multi-parameter data fusion and analysis; and a multi-parameter intelligent monitoring algorithm based on a multi-layer feedforward neural network is provided to intelligently monitor the fumigation environment.

The invention discloses an internet-of-things method and system for collapsible loess foundation collapse monitoring and early warning, and relates to the technical field of foundation monitoring. The invention relates to an internet-of-things system for monitoring and early warning collapsible loess foundation collapse. The internet-of-things system comprises a loess foundation partition division module and a loess foundation monitoring and early warning module. According to the method, the minimum monitoring unit is constructed, the rainfall data, the earth surface data and the rainfall prediction evaluation model are combined, and the time-space depth fusion is performed on the rainfall data by using the CNN and the LSTM model, so that the accuracy of rainfall prediction is improved, and accurate monitoring and early warning of the collapsible loess foundation area are realized; the monitoring area can be divided into a plurality of early warning subareas through the depth clustering technology in combination with the rainfall condition and the earth surface data, the early warning accuracy and response speed are improved, disasters caused by collapsible loess foundation collapse are effectively prevented, and fine management of the loess foundation area is achieved.

The invention discloses a data processing method and device based on edge computing, equipment and a storage medium, and belongs to the technical field of big data. According to the method, firstly, features are extracted from initial data collected by a sensor through feature extraction, and the data type is identified through data classification; then, according to the data type, distributing the data to a corresponding edge server for preprocessing, and generating an edge processing result; then, in an exception handling decision library of the edge server, a target decision conforming to the processing result is searched, and the decision is executed to generate a final data processing result. And finally, uploading an edge processing result and a final data processing result to a cloud server for storage. The invention further relates to the technical field of block chains, and the initial data is stored on a block chain network. The data transmission pressure can be reduced, the data processing efficiency can be improved, and the response speed and the intelligent level of the system can be enhanced.

The invention discloses a flood prediction method based on deep learning. The method comprises the following steps: acquiring and processing geographic space data and rainfall data; inputting the obtained data into a flood numerical model to obtain a flood sample; preprocessing the obtained geographic space data, rainfall data and flood sample data obtained from the numerical model, and constructing a model training database; and taking the one-dimensional rainfall data and the two-dimensional geographic space data in the model training database as model input, and taking the flood sample as model output to train a flood prediction model. Before extreme rainfall occurs, the flood risk map of a research area can be calculated only by inputting geographic space data and rainfall sequence data of the research area into the model.

The invention discloses a real-time monitoring system and method for secondary biological pollution of pipeline direct drinking water, and relates to the technical field of water quality monitoring. Identifying the preprocessed bacteria image by adopting an edge detection algorithm, and describing the outline of a microorganism coverage area; converting the bacterium image describing the contour of the microorganism coverage area into a binary image; counting the number of pixels in the microorganism coverage area by adopting a pixel counting algorithm, and converting the number of the pixels into a coverage area based on the image resolution and the zoom coefficient of the shooting and uploading integrated equipment; the area proportion of the coverage area is obtained through proportion calculation, and the water quality pollution degree is evaluated through the area proportion. An optical microscopic device and a shooting and uploading integrated device are utilized to shoot and upload the bacterial condition on the pipe wall through a transparent window of the pipe, and then a detection method is combined to analyze the bacterial area proportion on the pipe wall, so that the secondary biological pollution condition of the direct drinking water pipe is quickly identified and judged.

The invention provides an automatic dangerous rock identification method and system based on a machine vision system, and relates to the technical field of engineering mechanics, and the method comprises the steps: obtaining dangerous rock region image data containing disturbance amplitude and frequency, obtaining disturbance stress data, and carrying out the comprehensive analysis to calculate a rockburst failure load; the method comprises the following steps: establishing a relationship among a dynamic disturbance load, a stress gradient, a rockburst failure load and rockburst strength, calculating a stress-strength ratio and establishing a relationship between the stress-strength ratio and rockburst intensity, then extracting deformation and debris granularity characteristics, comprehensively generating a dangerous rock index, setting a safety threshold, and finally comparing the dangerous rock index with the safety threshold and sending out an early warning. According to the method, the image data, the high-resolution imaging equipment, the stress sensor and the numerical simulation method are utilized, automatic dangerous rock identification is achieved, the rockburst risk is effectively predicted, and regional safety is ensured.

The invention discloses a safety helmet wearing detection system and detection method for power construction, and relates to the technical field of power construction, and the system comprises a data collection module, a hidden danger analysis module, a comparison module, a comprehensive analysis module and an early warning module. And the data acquisition module is used for acquiring various parameter information generated when the image data is analyzed based on the image recognition technology, and the parameter information comprises system response performance information and algorithm stability information. According to the invention, various parameter information based on image recognition technology analysis is collected through the data acquisition module, wherein the parameter information comprises system response performance information and algorithm stability information. The abnormal perception delay index evaluates abnormal detection delay, and the identification confidence fluctuation index evaluates identification stability. By comparing the data evaluation index with the preset threshold value, the system early warns a manager in real time, ensures that the safety helmet is worn accurately, reduces the construction safety risk, and avoids the loosening of the safety supervision of the manager.

The invention discloses a real-time intelligent disaster early warning method based on multi-modal data, and belongs to the technical field of disaster early warning, and the method comprises the steps: 1, constructing a multi-modal data platform; 2, obtaining a first training model by using a graph neural network technology, and obtaining a second training model in combination with a federated learning technology; 3, integrating the first training model and the second training model to obtain a disaster propagation model; 4, deploying a response function on edge equipment close to the data source; and 5, continuously collecting data of actual disaster occurrence, comparing the data with a model prediction result, and carrying out early warning response. According to the invention, by integrating disaster data obtained by a plurality of data sources, more data sources can be obtained, the comprehensiveness and accuracy of early warning can be improved, and the generalization ability of the model can be significantly improved by constructing the disaster recognition model by using the graph neural network and federated learning, so that the system can cope with various complex and changeable disaster scenes.

The invention discloses a steel structure digital twinborn health monitoring system and method. The system comprises a data acquisition module, a data processing module, a data storage module, a model construction module, a model integration module, a user interface and visualization module, a data analysis and optimization module and an interaction and feedback module. The model construction module constructs a steel structure digital twinborn model; the model integration module integrates the steel structure digital twin model with a corresponding entity steel structure; the data analysis and optimization module comprises a data management module, a data analysis module, a data analysis module, a model management module, a model analysis module and a monitoring and early warning module. The method solves the problems that a traditional steel structure health monitoring method wastes human resources, is low in monitoring frequency and incomplete in data acquisition, and cannot efficiently acquire practical information, and although a digital twinborn technology is applied to an individual steel structure health monitoring system, a finite element simulation process is lack of detailed research, and the working efficiency is low. And application feasibility in an actual steel structure is lacked.

The present application discloses a computer network security analysis method based on big data, which relates to the field of network security, including: collecting user behavior data and interaction data; preprocessing the collected user data set; using a federated learning architecture, taking the preprocessed user data set as a training sample, training a machine learning model locally on the user side for user status analysis; uploading the parameters of the trained machine learning model to a cloud server, performing global aggregation on the cloud service, and obtaining a global machine learning model; based on the global machine learning model and interaction data, using an unsupervised learning method based on anomaly detection to perform security analysis on the user's computer network behavior. In view of the low network security efficiency existing in the prior art, the present application performs real-time analysis of user status through a local BiLSTM neural network model on the user side, and at the same time, uses a federated learning architecture to perform global aggregation on the cloud, etc., thereby improving the efficiency of analysis.

The present invention relates to the technical field of levee hidden danger detection, and in particular to a state analysis method and system based on a data set. The method comprises the following steps: using a laser radar to detect the stratigraphic lithology of the levee, obtaining a multidimensional dataset of the levee, using GIS software to perform three-dimensional visualization processing based on the multidimensional dataset, and constructing a stratigraphic analysis map; based on the stratigraphic analysis map, using an unmanned boat and a submersible to monitor and analyze the hydrological data downstream of the levee, and generating a high-definition remote sensing image of the downstream levee; based on the high-definition remote sensing image of the downstream levee, using the sensor upstream of the levee to obtain a real-time multimodal dataset upstream of the levee, and using the real-time multimodal dataset upstream of the levee to perform cloud-based aggregation operations on the levee; based on the cloud-based dataset upstream of the levee and the downstream high-definition remote sensing image, using the levee hydrological artificial intelligence algorithm to construct a comprehensive levee decision support system; the present invention improves the efficiency of levee hidden danger investigation and analysis by processing the levee hidden danger dataset.

The application provides a railway vehicle abnormal early warning method and system based on infrared thermal imaging, first acquires an infrared thermal imaging data set of a railway vehicle, and constructs a thermal state benchmark feature set of each key component. Real-time collection of infrared thermal imaging data in operation is dynamically compared with the benchmark set to generate a thermal state deviation feature set. An infrared thermal state abnormality recognition model is called to associate and strengthen processing of the deviation feature set, and a strengthened and associated deviation feature vector is generated. The strengthened and associated deviation feature vector is matched and mapped with a preset abnormal thermal state feature library to determine an abnormal type and an associated component identifier. An early warning instruction containing component positioning information is generated based on the abnormal type and the associated component identifier, and is sent to a target monitoring terminal. The application can accurately and timely early warn thermal state abnormalities of a railway vehicle and ensure safe operation of the vehicle.

The invention provides a pollution control facility discrimination method and system based on multi-source data, and electronic equipment. The method comprises the following steps: collecting enterprise power consumption data, working condition parameters and CEMS online monitoring data; processing the data, including data cleaning, conversion and feature extraction; training and testing are carried out according to data of historical operation states, a CatBoost model is optimized by utilizing grid search and cross validation, and parameter characteristics are acquired by utilizing an optimal hyper-parameter training test model; and finally, calculating a factor contribution value by using an SHAP model, and determining an abnormal factor for early warning. According to the invention, through multi-source data fusion and an advanced model, the discrimination accuracy and interpretability of pollution control facilities are improved, and powerful support is provided for air pollution control.

This application discloses a method, apparatus, medium, and equipment for risk assessment of gas pipeline networks based on the Internet of Things (IoT). The method includes: real-time acquisition of pipeline data, including physical data, environmental data, and video data; preprocessing the physical, environmental, and video data; inputting the preprocessed physical, environmental, and video data into a pre-trained multi-source data fusion model for fusion to obtain a unified dynamic risk feature vector; calculating a risk entropy value for gas leakage based on the unified dynamic risk feature vector; and providing dynamic early warning for the gas pipeline network based on this risk entropy value. This application enables real-time, accurate, and dynamic assessment and early warning of urban gas pipeline network risks, overcoming the shortcomings of traditional methods.

A multimodal human behavior and psychological analysis platform based on generative artificial intelligence includes: a multimodal behavior data input module that receives data from multiple sources from users to establish a correlation between behavior and psychological state; a behavior and psychological analysis module that generates a psychological state scale based on the user's data from multiple sources and the correlation between behavior and psychological state; a game adaptation module that adjusts the game difficulty in real time to adapt to the correlation between the user's behavior and psychological state; an image story generation module that generates a story progression plot in real time to adapt to the correlation between the user's behavior and psychological state; an analysis and suggestion module that generates a psychological state change trend chart and psychological analysis assessment suggestions based on the user's data from multiple sources and the psychological state scale; and a report generation and progress tracking module that generates a psychological analysis assessment report for the user based on data from multiple sources, the psychological state scale, and the psychological analysis assessment suggestions, and adjusts personal goals in real time to adapt to the user's progress feedback.

The invention belongs to the technical field of structure health monitoring and safety assessment, and discloses a photovoltaic flexible support structure health assessment method and system. Firstly, acceleration signals, inclination angle data and cable force data of the photovoltaic flexible support are collected; extracting inherent frequencies, vibration modes and modal damping ratios of the first three orders of the structure based on a random subspace recognition method, analyzing inclination angle data to obtain static displacement or dynamic rotation angles of key nodes, obtaining a cable force distribution state by combining cable force data, and integrating to form a dynamic feature vector; inputting the vector into a pre-trained structure health assessment model, and outputting a damage position and degree; and when the damage degree exceeds a preset threshold value, a report containing the position of the damaged component, the damage degree and the disposal suggestion is automatically generated. Through cooperation of multi-source data fusion and an intelligent algorithm, real-time, accurate and automatic evaluation of structural health and accurate damage positioning are realized, a scientific basis is provided for preventive maintenance, safe operation of a photovoltaic power station is effectively guaranteed, and the operation and maintenance cost is reduced.

An IoT-based equipment early warning monitoring system and method, which obtains the charging pile temperature values ​​and external environment temperature values ​​at multiple predetermined time points within a predetermined time period collected by a temperature sensor; performs data preprocessing on the charging pile temperature values ​​and external environment temperature values ​​at the multiple predetermined time points to obtain a charging pile temperature relative quantity time series input vector; extracts the charging pile temperature relative quantity local change characteristics of the charging pile temperature relative quantity time series input vector to obtain a sequence of charging pile temperature relative quantity local time series feature vectors; and, based on the sequence of charging pile temperature relative quantity local time series feature vectors, determines whether the working state of the charging pile is abnormal. In this way, the charging pile can be monitored and warned in real time through the IoT technology, so that corresponding repair and maintenance measures can be taken to improve the reliability and availability of the equipment.

This invention provides a wind turbine safety control and protection method based on blade vibration signals. It uses blade acceleration sensors to acquire acceleration signals during blade operation, extracts frequency domain features and time domain features from the acceleration signals, and compares the feature values ​​with fault thresholds. When the threshold is exceeded, a warning or fault alarm is issued. By connecting the blade acceleration signals to the wind turbine control system, they directly participate in the control and protection of the unit, significantly improving the unit's risk resistance and safety.

This invention discloses a xenogeneic-free culture method for hiPSCs and its quality monitoring method. The xenogeneic-free culture method includes the following steps: hiPSC cells are seeded into the wells of a culture plate coated with VTN-N matrix gel, and cultured for the first stage using E8 complete medium containing 5-15 µM Y-27632. After 20-30 h of culture, the medium is replaced with conventional E8 complete medium without Y-27632 for the second stage of culture, until the cell confluence reaches 60%-80%. This xenogeneic-free culture method ensures efficient cell expansion under conditions with clearly defined chemical composition. This method further optimizes the seeding density and culture period of hiPSCs, controls cell passage at 60%-80% confluence, and significantly reduces the risk of spontaneous differentiation by limiting the maximum culture time and upper limit of confluence.

The invention discloses a safety wearable device and system based on biosensing and human body state monitoring, and relates to the technical field of intelligent wearable devices. In order to solve the problems that traditional safety protection measures often depend on self-perception and judgment of workers, the mode has large limitation, and potential safety risks cannot be accurately monitored and early warned in real time. Safety wearable equipment based on biosensing and human body state monitoring comprises intelligent equipment, the intelligent equipment is composed of a safety bracelet, a safety helmet, a safety waistband, safety shoes and a safety vest, vital signs of workers are monitored in real time, and construction environment data are recorded; when an abnormal condition is detected, the intelligent equipment can give an alarm in time, so that a worker and a monitoring center can take emergency measures, and a powerful guarantee is provided for the health and safety of the worker through real-time, comprehensive and intelligent data acquisition, analysis and early warning as well as efficient and stable data transmission and remote supervision control.

The invention discloses a safety early warning method and device for Internet of Things terminal equipment. The method comprises the following steps: acquiring equipment basic information and equipment operation information corresponding to target terminal equipment in the Internet of Things; determining a safety evaluation coefficient corresponding to the target terminal equipment according to the equipment basic information, and determining an operation evaluation coefficient corresponding to the target terminal equipment according to the equipment operation information; determining a credible evaluation coefficient corresponding to the target terminal equipment according to the safety evaluation coefficient and the operation evaluation coefficient corresponding to the target terminal equipment; the equipment safety level corresponding to the target terminal equipment is determined according to the credible evaluation coefficient corresponding to the target terminal equipment, equipment safety early warning is carried out based on the equipment safety level, and comprehensive and real-time safety evaluation and early warning are provided by comprehensively evaluating the equipment basic information and the equipment operation information, so that the safety of the target terminal equipment is improved. The reliability and stability of the Internet of Things are improved, and security events are effectively prevented and quickly responded.

The utility model discloses a rail transit vehicle fire early warning system. The system comprises a plurality of thermal imaging acquisition terminals which are used for acquiring thermal image data of a target in a comprehensive control cabinet of each compartment of a rail transit vehicle and judging whether the temperature of the target is at a set threshold value or not according to the thermal image data; the plurality of first communication terminals are connected with a power line in the rail transit vehicle and send out received thermal image data and judgment results through power line carrier communication; the second communication terminal receives the thermal image data and the judgment result sent by the plurality of first communication terminals based on power line carrier communication, and the monitoring host is used for displaying the thermal image and the judgment result of the target and controlling to output an early warning signal when the judgment result is that a set threshold value is exceeded. According to the utility model, early warning can be carried out on an over-high-temperature area before a fire disaster occurs; and the information of each sensor can be transmitted to the monitoring host without additional wiring, so that the maintenance efficiency of maintenance staff is improved.

The application provides a cable health degree evaluation and life prediction method, device and storage medium, including the following steps: acquiring insulation resistance, core resistance and operating temperature in the operation process of the cable; based on the operating temperature, calculating insulation degradation rate and core degradation rate according to the parameterized Arrhenius degradation model respectively; based on the insulation resistance and a preset end point threshold value thereof, calculating insulation health degree; based on the core resistance and a preset end point threshold value thereof, calculating core health degree; based on the insulation degradation rate and the insulation resistance, calculating insulation residual life; based on the core degradation rate and the core resistance, calculating core residual life; determining the smaller value between the insulation residual life and the core residual life as the overall residual life of the cable; fusing the insulation health degree and the core health degree to obtain comprehensive health degree of the cable; and outputting the health state evaluation result of the cable according to the comprehensive health degree and / or the overall residual life.

The invention discloses a flame detection control system based on an RK3588 chip, which comprises a photoelectric detection module, a signal processing module, an image acquisition module, a main control module and an early warning module, and is characterized in that the accuracy and reliability of flame detection are remarkably improved by fusing infrared and ultraviolet sensor technologies and combining image acquisition and processing. The system adopts an advanced signal processing module, signal amplification and filtering are optimized, effective amplification and stable processing of weak electric signals are ensured, and the reliability of flame detection is further improved. And in addition, the RK3588 chip has high-performance computing and image processing capabilities, so that the system can quickly identify flame characteristics, and real-time monitoring and early warning are realized. In addition, due to the self-diagnosis function and the timely alarm mechanism of the system, the stability and safety of the system are further enhanced, and powerful technical guarantee is provided for fire prevention and emergency response.

The invention, which belongs to the technical field of spatial form optimization, discloses a high and large composite slope spatial form optimization system comprising a slope basic data acquisition module, a slope geological condition analysis module and a slope three-dimensional model drawing module. The output end of the slope basic data acquisition module is connected with the input end of the slope geological condition analysis module; according to the system, the slope basic data acquisition module is arranged in the system and can acquire multi-dimensional information about the slope, the subsequent data analysis effect and accuracy can be improved through a large amount of information, and meanwhile, the slope geological condition analysis module and the slope three-dimensional model drawing module are further arranged in the system; according to the method, the geological conditions of the side slope can be analyzed, meanwhile, the three-dimensional model can be established according to a large amount of obtained information, the large amount of information about the side slope can be expressed more visually, the overall shape of the side slope can be presented more fully, and follow-up space optimization is facilitated.

This invention discloses a method and device for monitoring the temperature distribution performance of downhole circuit boards. The method includes: first, constructing a sparse dictionary using a dictionary learning algorithm and setting an observation matrix; then, deploying a sparse temperature acquisition sensor array; building an intelligent sensing model to collect and transmit real-time data of the sparse signal of the downhole circuit board temperature distribution and storing it in the cloud as historical data of the sparse signal; generating standard operating mode data features; generating a health status curve; and finally, using a binary classification method to analyze the health status curve to achieve early warning of abnormal heat sources on the downhole circuit board. This invention uses intelligent sensing technology and cloud technology to achieve block-based sparse acquisition and cloud computing of downhole circuit board performance characteristics, reducing the storage and computational load of the device. It employs a signal recovery and reconstruction algorithm to comprehensively characterize the temperature performance image of the downhole circuit board and to observe and warn of the heat source distribution of the downhole circuit in real time.

The utility model relates to an expansion side slope monitoring device which comprises an expansion side slope body and a stabilizing assembly, the stabilizing assembly is arranged on the outer side of the expansion side slope body, and the stabilizing assembly is used for achieving the stabilizing effect of the expansion side slope body. The stabilizing assembly comprises protective nets and anchor rods, and the protective nets are symmetrically arranged on the outer side of the expansion slope body through the anchor rods. The protective net can be adjusted in a self-adaptive mode according to different expansion degrees, effective protection on the expansion slope body is kept, under the action of the elastic force of the springs, the displacement ring resists expansion generated by the protective net, so that the stress is absorbed, the buffering effect is achieved, and the temperature and humidity of the expansion slope body are monitored in real time through the data acquisition device, so that the expansion slope body is protected. The expansion degree of the protective net is indirectly monitored by matching with the monitoring equipment body to monitor the expansion degree of the protective net, and the obtained data is transmitted back to the monitoring center, so that real-time data monitoring and early warning are realized.

The invention provides a network attack detection method and system based on time sequence enhancement, and relates to the field of network security, and the method comprises the steps: obtaining a network flow data flow, carrying out the time sequence feature extraction of the network flow data flow, and obtaining a feature vector set; calculating to obtain Manhattan distances among the feature vectors, and constructing a distance matrix through the Manhattan distances; clustering the feature vector set through the distance matrix to obtain a plurality of clusters; calculating to obtain an outlier score of each feature vector in each cluster; and carrying out network attack detection through each outlier score to obtain a detection result. According to the method, monitoring and early warning can be carried out on complex cross-time and cross-space attacks in real time, the network attack detection efficiency is remarkably improved, and the situations of false alarm and missing alarm are reduced. And meanwhile, deep analysis can be carried out according to the multi-dimensional time sequence features, so that the recognition capability of the detection system on unknown attacks and variant attacks is improved.

The present invention relates to a method and system for predicting risks in mine scheduling based on prediction of personnel and vehicle trajectories, comprising: an information collection unit, a communication unit, a storage unit, a risk prediction unit and an analysis and presentation unit; the information collection unit effectively collects and processes the hazardous source information in the process of personnel and vehicle scheduling and stores it in the storage unit, and transmits the data signal to the risk prediction unit through the communication unit; the risk prediction unit performs data mining analysis, algorithm operation analysis and comprehensive prediction analysis on the collected information using a risk prediction model constructed by a risk prediction index system, and draws a risk prediction conclusion, which is finally sent to the analysis and presentation unit through the communication unit for comprehensive visual early warning presentation of the prediction result. The present invention can improve production efficiency, reduce costs, and enhance safety and environmental benefits by predicting the trajectories of personnel and vehicles in production scheduling, combining task allocation and real-time data monitoring.