49results about How to "Capture in time" patented technology

The invention discloses a charging pile operation safety management and control system based on artificial intelligence, and belongs to the technical field of charging pile operation safety management and control. A charging pile operation safety management and control system based on artificial intelligence comprises a data acquisition module, a physical environment monitoring module, a central processing unit, a safety evaluation model, a fault detection module, a risk warning and execution module, a multi-charging pile collaborative management module and a user interface. The problems that existing charging pile data collection and environment monitoring are not comprehensive, an intelligent safety evaluation mechanism is lacked, the fault detection and response efficiency is low, and resource allocation is unreasonable are solved. Precise monitoring and safety trend prediction of the operation state of the charging pile are achieved through comprehensive data collection and the safety evaluation model, an intelligent fault detection and grading early warning mechanism can rapidly recognize and respond to potential and existing faults, resource allocation is optimized through the multi-charging-pile collaborative management module, the overall performance is improved, and the system is suitable for large-scale popularization and application. The operation safety is ensured.

The invention discloses a remote sensing data processing method and system, a product and a readable medium, and relates to the field of remote sensing data processing, and the method comprises the steps: carrying out the registration and correction of remote sensing images in different time periods, and obtaining a remote sensing image spatial-temporal index; calculating shadow ranges generated by each building in the target area at different times based on the building height data and the remote sensing image spatio-temporal index, and superposing the shadow ranges corresponding to the buildings to obtain a shadow coverage frequency map; calculating a shielding degree score of an area of each time phase in the shadow coverage frequency diagram based on a remote sensing image spatio-temporal index, and selecting an optimal time phase with the lowest shielding degree as an optimal observation time phase according to the shielding degree scores; based on the optimal observation time phase, dividing a region in the shadow coverage frequency diagram into a plurality of target sub-regions according to a shielding degree score; and repairing each target sub-region by adopting a preset repairing strategy according to the shielding degree score. By implementing the method, the accuracy of restoring the shadow-shielded region in the remote sensing image can be improved.

The invention belongs to the technical field of material preparation, and aims to solve the problems of interface separation and weak oxidation resistance under complex working conditions caused by insufficient interface bonding force of the existing carbon / carbon composite material. In order to achieve the purpose, the invention provides a carbon / carbon composite material and a preparation method thereof. The carbon / carbon composite material comprises the following components in percentage by mass: 40%-55% of polyacrylonitrile-based carbon fibers, 10%-25% of pitch-based carbon fibers, 8%-20% of nano silicon dioxide grafted carbon fibers, 8%-20% of biological pitch, 8%-15% of nano graphene modified phenolic resin, 3%-6% of silicon carbide whiskers, 2%-4% of zirconium boride nanoparticles, 2%-4% of graphitized carbon fibers and 1%-3% of a natural plant extract antioxidant. The carbon / carbon composite material provided by the invention can provide stable interface bonding force and remarkable oxidation resistance, so that the structural integrity and mechanical property stability of the material when bearing various complex stresses such as stretching, compression, bending, shearing and the like are guaranteed, and the application range of the material in the field of high-temperature oxidation resistance is widened.

The invention discloses a scale-effect-free mine dynamic disaster fine monitoring and dynamic early warning method, which comprises the following steps of: arranging a multi-point micro-seismic monitoring network on a coal mine working face, and continuously acquiring and preprocessing micro-seismic signals; an innovative analysis positioning algorithm is adopted to accurately analyze the accurate position and time information of the micro-seismic source; in combination with signal features and positioning data, a four-dimensional joint kernel density estimation and confidence interval theory is innovatively applied, a rock burst risk assessment model is constructed, and a danger level classification standard is determined; and the system calculates risk indexes in real time and dynamically issues early warning. According to the invention, the precision, timeliness and reliability of monitoring and early warning of the dynamic disasters in the mining area are obviously improved, and key technical support is provided for safe production of a coal mine.

The invention discloses a fan rotating part anomaly detection method based on artificial intelligence. The fan rotating part anomaly detection method comprises the steps that S1, a fan rotating part sound signal set is constructed; s2, generating a fan rotating part sound signal feature vector set; s3, training to obtain a deep neural network model for detecting the abnormal state of the fan rotating part; s4, a hierarchical reinforcement learning algorithm is utilized to analyze and classify the feature vector set of the fan rotating part, and a high-level strategy judges whether to activate a low-level strategy according to the operating environment and the load condition of the fan and the overall change trend of the sound signal set; according to the low-layer strategy, whether an abnormal state exists or not is further judged according to the fan rotating component feature vector set; s5, denoising and filtering parameters of the sound signal set are adjusted in real time; and S6, when the low-level strategy identifies an abnormal state through analysis of the feature vector set, the system automatically triggers an alarm mechanism. According to the method, the fan anomaly detection cost is reduced, and the detection precision and the intelligent level are improved.

The invention discloses a dynamic industry chain risk assessment method and system based on co-occurrence emotion and graph attention. The method comprises the steps of generating a co-occurrence relation graph, extracting co-occurrence emotion to obtain a co-occurrence emotion score, aggregating multi-dimensional features of neighbor nodes through a graph attention mechanism, and obtaining multi-dimensional feature embedded representation of enterprise nodes; and performing binary risk classification on industrial chain company nodes by using a neural network model in combination with the new risk embedding representation after co-occurrence emotion score adjustment, thereby realizing dynamic evaluation of industrial chain risks. According to the method, real-time insight of information such as enterprise reputation and market emotion is provided through co-occurrence sentiment analysis, and a graph attention mechanism reinforces identification of a key risk propagation path, so that the sensitivity and accuracy of risk assessment are improved. And learning expression of a co-occurrence relationship of enterprise nodes in the industrial chain, efficient calculation of co-occurrence emotion scores, timely capture of risks and dynamic assessment of industrial chain node risks are realized.

The invention provides a combustible metal dust spontaneous combustion early warning system used in an industrial dust removal facility, relates to the technical field of industrial safety monitoring, and aims to comprehensively monitor multiple parameters such as temperature, carbon monoxide concentration, hydrogen concentration, smoke concentration and the like and perform intelligent analysis by using a deep learning technology. A deep neural network model based on spatio-temporal feature fusion is adopted in the deep learning module, dynamic motion information is extracted by introducing an optical flow method, intelligent analysis and risk prediction of field images are achieved, the system can capture tiny changes in the initial stage of spontaneous combustion in time, the spontaneous combustion risk is accurately pre-judged through multi-parameter correlation analysis, and the risk prediction efficiency is improved. And alarm signals of different levels are automatically triggered according to the risk levels, so that the accuracy and timeliness of early warning are remarkably improved, and the occurrence rate of spontaneous combustion accidents of combustible metal dust is effectively reduced.

The invention discloses a house safety monitoring method and system based on machine learning, and belongs to the technical field of machine learning.The house safety monitoring method and system based on machine learning comprise the following specific steps that firstly, a plurality of sensors are deployed at key parts of a house, and the sensors comprise a strain sensor, a displacement sensor and an acceleration sensor; collecting physical parameter data of the house structure in real time, and storing the data according to a time sequence; step 2, carrying out cleaning and normalization processing on the collected data; 3, key features reflecting the health state of the house structure are mined from the preprocessed data. By means of the powerful data processing capacity of a machine learning algorithm, the system can carry out deep analysis on data from various high-precision sensors, hidden danger omission caused by human negligence or detection means limitation is effectively avoided, and omnibearing and dead-corner-free guarantee is provided for house safety.

The invention discloses a public health real information evaluation and early warning system based on an AI technology, and relates to the field of artificial intelligence technology application, and the system comprises a database module which is used for storing information collected by a collection module and providing powerful support for subsequent model construction and real-time response, and a data collection module which is used for collecting real-time information of public health. The invention discloses a public health data collection and processing method, which is used for collecting data related to public health from various sources (such as news, social media, professional forums and the like), including multi-modal data such as texts, pictures, videos and the like, and ensuring that the data sources cover data sources of a knowledge graph and data sources of a false information base. The information processing efficiency is greatly improved through a knowledge graph and advanced model construction, the system has strong adaptive capacity for novel false information, the system can rapidly learn and recognize a new false information mode through fusion of the knowledge graph and a large model, and the risk caused by the false information is effectively reduced.

The present invention provides an early warning method and system based on highway road condition monitoring, which relates to the field of radio detection technology. The method integrates multi-dimensional data collected by vehicle-mounted terminals, laser radars, acoustic wave sensors and video monitoring, calculates congestion coefficients, average delay values ​​and differences, and generates a traffic condition model; based on real-time data and historical data, a traffic state prediction model is constructed, and the indicators generated by the traffic state model are used as input to generate traffic state prediction results for future time periods; an early warning threshold is set, and when the traffic state prediction results are abnormal, an early warning signal is automatically issued. The present invention monitors the traffic conditions of highways through electromagnetic wave reflection technology, and issues early warning signals in a timely manner, thereby reducing the incidence of traffic accidents and improving road safety. The early warning method and system based on highway road condition monitoring will greatly improve the intelligent level of traffic management and contribute to the construction of a safe and smooth highway network.

The invention belongs to the field of wireless communication, and particularly relates to a wireless signal multi-dimensional array coding method and system based on dynamic rank optimization. The method comprises the following steps: detecting state information of a wireless channel, predicting state change of the channel by using a pre-trained deep learning model, and generating a dynamic optimization multiplier; adjusting a preset static optimization multiplier by using the dynamic optimization multiplier, constructing a target function for jointly optimizing the energy efficiency and the spectrum efficiency, and determining an optimal transmission rank by solving the target function; comparing the optimal transmission rank with the current transmission rank to determine a rank-up or rank-down operation; and reconstructing a transmission signal of the wireless channel according to a result of the rank increasing or decreasing operation. According to the method, the optimization target is dynamically adjusted by predicting the channel change, and the asymmetric ascending and descending rank algorithm is adopted, so that the adaptive transmission rank optimization in a complex time-varying channel environment is realized, and the communication performance is remarkably improved.

The utility model discloses a hot film anemograph which comprises a detection element, a signal converter and a signal processor. The detection element at least comprises a group of carbon nano tube films, and the carbon nano tube films are thin films made of a carbon nano tube material of which the resistance value changes along with the temperature change; the signal converter amplifies the resistance change signal of the carbon nanotube film output by the detection element to obtain an amplified signal; and the signal processor processes the amplified signal output by the signal converter. According to the hot film anemograph disclosed by the embodiment of the invention, the sensitivity of the hot film anemograph is improved by improving the hot film in the detection element, and a worker can obtain more accurate reference data for calculating the wind speed according to a processing result of an amplified signal.

The invention discloses a xanthene nitroreductase-responsive fluorescent dye compound as well as a preparation method and application thereof, and belongs to the technical field of xanthene derivatives. The fluorescent dye compound can be used for quickly and specifically recognizing overexpressed nitroreductase in tumor cells; after the fluorescent probe acts with nitroreductase, the fluorescence intensity of the fluorescent probe at 718 nm is obviously enhanced, and the fluorescent probe has excellent targeting capability on mitochondria; meanwhile, under the excitation of illumination with the specific wavelength of 660 nm, an acting product of the fluorescent dye compound and nitroreductase can efficiently generate reactive oxygen species (ROS), and then the effect of specifically killing tumor cells is achieved. The xanthene nitroreductase response type fluorescent dye compound provided by the invention can achieve the goal of integration of diagnosis and treatment of solid tumors, and has important clinical application value.

The utility model discloses online sampling and detecting equipment for water pollutants, relates to the technical field of water pollutant detection, solves the technical problem that sampling, detecting and washing cannot be automatically and repeatedly carried out in the existing technical scheme, and comprises a main vehicle body, a mounting frame is fixedly mounted on the upper wall surface of the vehicle body, and a detecting pipe is fixedly mounted on the mounting frame; the detection pipe is a vertical pipe, a detection laser transmitter is arranged at the upper end in the detection pipe, and a detection laser receiver is fixedly mounted at the lower end of the detection pipe. According to the utility model, the unique control valve is matched with a sampling and flushing structure, so that the automatic switching of water samples and the automatic flushing of the detection pipe can be realized; water sample residues and cross contamination are effectively avoided, and the accuracy and representativeness of detection data are greatly improved; the device integrates a controller and a touch screen, operation can be completed conveniently in a touch mode, multiple sampling detection modes are supported, manual guarding is not needed, and the device is suitable for long-time unmanned online monitoring in various water environments.

This invention relates to the field of nonlinear node detection technology, and particularly to a nonlinear electronic node concealment detection system based on S-band harmonic fingerprinting. The system includes: acquiring fundamental and second harmonic echo signals from multiple detection nodes within the detection area in the S-band to ensure a sufficient data source for the entire detection process; constructing harmonic excitation response features and generating a fingerprint database to improve the accuracy and targeting of subsequent node identification; dynamically capturing the node's current characteristic information by transmitting S-band frequency sweep detection signals and collecting echo harmonic components to extract real-time fingerprints, enhancing the timeliness of node detection; identifying matching concealed electronic nodes by performing multi-dimensional time-series correlation matching between real-time harmonic fingerprints and fingerprints in the fingerprint database, effectively filtering out irrelevant interference; and generating a topology map and marking abnormal nodes, enabling users to quickly grasp the overall situation and rapidly locate anomalies.

The utility model discloses a pulse damper, and relates to the technical field of pulse dampers. The pulse damper comprises a pulse damper body, one side of the top and the bottom of the pulse damper body are communicated with connecting pipelines, two fixing bases are detachably installed on the pulse damper body, an upper protective shell is detachably installed on the upper side of the pulse damper body, and a lower protective shell is detachably installed on the lower side of the pulse damper body. A lower protective shell is detachably mounted on the underground side of the pulse damper body; the fixed base comprises a fixed lantern ring, two rotating rings are rotationally matched with the fixed lantern ring, and a plurality of protruding blocks are installed on the two opposite outer sides of the two rotating rings. Through the detachable design of the fixed base, the upper protective shell and the lower protective shell, during installation, assembly of all components can be completed step by step, special tools are not needed, and the site construction difficulty is reduced; and in the operation and maintenance stage, the protection parts can be rapidly disassembled, the shutdown maintenance time is greatly shortened, and the operation and maintenance efficiency of the whole life cycle of the equipment is improved.

The utility model belongs to concrete crack detection technical field, and disclose concrete crack detection marking surveying and mapping ruler, including the upper end surface fixed of surveying and mapping ruler body is equipped with length sensor, the lower extreme of length sensor is equipped with inner groove, and surveying and mapping ruler body is fixed in inner groove inside, the upper end surface middle fixed of length sensor is equipped with length display screen, the both sides of length sensor's upper end surface are all fixedly installed and are equipped with adjusting device, through length sensor, length display screen and inner groove, utilize them can connect surveying and mapping ruler to length sensor, by length sensor control surveying and mapping ruler carries out length measurement, can improve concrete crack measurement precision, realize real -time, automatic monitoring and remote data transmission, also convenient data record analysis, can timely capture crack dynamic change, provide strong basis for structure safety evaluation and maintenance decision, reduce artificial error and workload.

This invention relates to the field of levee management technology, and more particularly to a method for identifying levee hazards based on multi-source data fusion. The method includes dividing the levee structure into multiple monitoring and control sections according to hydrogeological units, and deploying multi-level sensor arrays within each control section; real-time acquisition of spatiotemporal sequence data from each sensor array to construct a field parameter functional including displacement, strain, seepage pressure, and temperature fields; extracting the strain energy spectrum entropy value of the field parameter functional through spatiotemporal singular value decomposition to reflect the disorder of energy distribution in the levee body; calculating the coupling degree index based on the stress-seepage coupling constitutive equation to characterize the soil-water interaction intensity; and inputting the strain energy spectrum entropy value, the coupling degree index, and a pre-determined hydrodynamic load correction factor into the levee structure analysis model to obtain the levee safety and reliability. This method can improve the comprehensiveness, timeliness, and accuracy of levee hazard identification.

The application discloses a kind of low-power consumption self-organizing network water quality multi-parameter monitoring sensor systems, it is related to hydrological monitoring technical field.The system includes multi-parameter perception module (integrates 6 water quality index detection, probe covers super-hydrophobic-antibacterial coating), low-power consumption control module (using STM32L496 microcontroller, realizes periodic low-power consumption operation), adaptive communication module (support LoRaWAN / ZigBee protocol switching and Mesh multi-hop networking) and energy supplement module (solar energy+high capacity battery).By intelligent sampling scheduling, routing optimization algorithm and edge computing preprocessing, the multi-parameter real-time monitoring of endurance 6-12 months, coverage radius 1-3 kilometers is realized, and water quality anomaly can be quickly responded.The application is applicable to watershed, city water body and the like scene, greatly reduces maintenance cost, and improves monitoring timeliness and coverage range.

The present invention discloses an intelligent cultural science popularization platform management system and management method, which relates to the field of platform management technology. The system first collects diversified cultural science popularization resources from the Internet through a cultural data capture module to establish a cultural database. The combination of the user behavior acquisition module and the first analysis module can deeply analyze the personalized characteristics and behavior patterns of users. The user's knowledge acceptance Jsd is calculated and evaluated, which enables the platform to dynamically adjust the priority of pushed content according to the user's interests and behaviors. The second analysis module calculates the social activity Shy by real-time monitoring of the user's social interaction data, and corrects the push strategy in combination with the push level. The relevance of the pushed content and the user's participation are ensured, thereby improving the user's acceptance and satisfaction with the content. By analyzing the push content willingness Yyd, the push frequency and content type are adjusted, which improves the platform's responsiveness and user experience.

The application discloses an advertisement click rate prediction system, comprising a data acquisition module for real-time acquisition of user behavior data, attribute data, context data and biological characteristic signal data; a multi-modal feature extraction module for extracting multiple feature vectors; a dynamic feature fusion module for dynamically weighting and fusing multi-modal features through an adaptive attention fusion network; a graph neural network coding module for coding a heterogeneous graph structure to generate a graph embedding feature vector; an adaptive routing prediction module for dynamically selecting an expert subnetwork through a router network for prediction; a real-time feedback calibration module for online parameter updating based on real click feedback and generating a calibration signal; and a multi-objective optimization decision module for outputting an advertisement recommendation list based on multi-objective optimization. The application realizes accurate prediction and intelligent decision through adaptive routing and multi-objective optimization, and significantly improves the accuracy and scene adaptability of advertisement click rate prediction.

This invention discloses an energy metering box with an intelligent monitoring system, belonging to the technical field of energy metering boxes. It includes a box body, double doors, a mounting box, and a mounting slot. The mounting slot houses air quality detection components, temperature and humidity detection components, and control components, as well as an intelligent monitoring system. This system includes modules for air quality detection, temperature and humidity detection, signal processing, data comparison, and anomaly early warning. The data comparison module employs a dynamic calibration threshold and multi-parameter fusion comparison mechanism, combining historical data and operating conditions to determine the environmental status. The system also includes a synchronization trigger unit, a signal shielding module, and a differential transmission circuit to ensure data acquisition synchronization and anti-interference capabilities. This solution solves the problems of traditional energy metering boxes lacking effective environmental monitoring, easily affected metering accuracy, and insufficient reliability of similar monitoring solutions. It achieves accurate monitoring of the internal environment and timely early warning of anomalies, improving the operational stability and safety of the energy metering box.

The invention discloses a touch all-in-one machine interaction analysis system and method based on data analysis, and relates to the technical field of big data analysis. According to the method and the device, the touch area is divided into the plurality of touch sub-areas based on the standard size of the APP icon, so that the accurate monitoring on the user operation is remarkably improved, the consistency of the touch area is ensured, and the user experience is enhanced. Touch data can be captured in time by setting an area monitoring period, real-time analysis and rapid adjustment are supported, and the system interaction efficiency is improved. By configuring an event monitor, the system captures historical touch data of each touch sub-region in real time. And the touch event is associated with the specific area coordinates, so that the effectiveness of analyzing the touch operation is improved. By extracting the highlight object event and the mistaken touch delay value, the system dynamically adjusts the response strategy of the real-time touch data, the touch abnormity is effectively identified, the accuracy of the response of the touch operation purpose is improved, the normal operation and the mistaken touch operation are intelligently distinguished, and a finer touch management and feedback mechanism is realized.

This invention belongs to the technical field of computer-aided design and simulation, and relates to a method and system for calculating the leakage of hydraulic facilities by integrating multi-source data. The method includes: receiving raw sensor time-series signals and reconstructing a multi-dimensional time evolution trajectory in a local phase space; extracting the dynamic deviation at abrupt changes and encoding it as a hydraulic transient characteristic tensor; performing singular value decomposition on the tensor using a discrete fractured mesh model to extract the feature spectrum; when it matches the critical deformation matrix, activating the nonlinear turbulence equation to rewrite the state parameters and outputting the pressure head distribution; calculating the simulated entropy yield based on the flow field gradient characteristics and thermodynamic laws; if it is less than a compliance threshold, generating a channel mask array to shield abnormal data sources; if the threshold is met, calculating the facility leakage. This invention solves the problem that insufficient fusion depth between the physical model and multi-source monitoring data leads to limitations in the reliability and real-time response of leakage calculation under complex operating conditions.

The invention discloses a mirror operation parameter adjusting method, device and equipment, and belongs to the technical field of camera shooting. The mirror moving parameter adjusting method comprises the steps that first mirror moving parameter information of a camera is determined according to first light ray information of an object light source in a first preview image, and the object light source is a light source, corresponding to a tracked shooting object, in at least two light sources in a shooting scene; and adjusting the mirror operation parameter of the camera according to the first mirror operation parameter information.

This application relates to the technical field of slope dynamic assessment, and in particular to a slope stability dynamic assessment method based on Ward hierarchical clustering-Markov chain. The method includes obtaining a standardized data matrix, calculating objective weights, and generating a comprehensive evaluation score sequence; clustering the comprehensive evaluation score sequence into multiple stability level clusters; constructing an initial judgment interval, generating continuous boundary points, and then refining the initial judgment interval into a judgment threshold interval; mapping the comprehensive evaluation score sequence to a discrete state sequence based on the judgment threshold interval, and constructing state transition probability matrices for each state; determining the time to be predicted, extracting the state transition probabilities of each historical time step at the corresponding step size from the state transition probability matrix, performing weighted aggregation, and using the stability level corresponding to the maximum aggregation probability as the dynamic assessment prediction result of the slope. This application can overcome the early warning lag caused by the long-period smoothing effect and achieve transient prediction.

The application provides a lens module and a laser cutting head, and relates to the field of laser cutting. The lens module comprises a working lens module, a fixed support seat and a temperature field detection module; a temperature sensing surface of the temperature field detection module is arranged towards the working lens module, and the temperature field detection module is configured to monitor the temperature of the working lens. Based on the lens module provided by the application, the application further provides a laser cutting head, wherein the first protective lens module, the collimating lens module, the focusing lens module and the second protective lens module are sequentially arranged in the axial direction of the laser cutting head; and the second protective lens module is close to the nozzle of the laser cutting head. The lens module and the laser cutting head provided by the application can realize the monitoring and feedback of the lens temperature based on the reasonable arrangement of the temperature field detection module, thereby enhancing the safety and reliability of the product, improving the user experience, and assisting data analysis and optimization.

The present application relates to the technical field of finite element simulation analysis, and specifically discloses a large bearing capacity earthquake-temperature self-adaptive viscous damper and a simulation calculation method, which comprises the following steps: S1: establishing a three-dimensional solid model of a temperature deformation joint and a viscous damper with a stiffness adjusting valve; S2: obtaining a finite element model of the three-dimensional solid model; S3: obtaining a critical speed and a load working condition speed of the viscous damper, and establishing a multi-state self-adaptive stiffness adjusting model of the viscous damper based on the ratio of the load working condition speed to the critical speed; S4: obtaining a time difference value of the stiffness adjusting valve from the start of closing to the complete closing, and establishing a virtual displacement quantification model; S5: adding the multi-state self-adaptive stiffness adjusting model and the virtual displacement quantification model to the finite element model; and S6: performing mechanical simulation analysis on the finite element model. The present application can accurately simulate the complex mechanical behavior of the damper under the conditions of multi-state switching, virtual displacement effect and bidirectional earthquake reciprocating action.

The invention discloses a three-dimensional visual modeling calculation and intelligent monitoring system and method for a complex side slope. The system comprises a sensing part, a cloud server and a personal terminal. A landslide disaster risk point is obtained through a simulation analysis unit of the cloud server, a plurality of sensing pieces are embedded in a side slope in an array mode, and the sensing pieces monitor side slope geological deformation in real time; the cloud server is provided with a monitoring and analyzing unit, the monitoring and analyzing unit is connected with the sensing pieces through a first communication module and receives the side slope geological deformation information, the monitoring and analyzing unit analyzes the side slope geological deformation information and judges whether side slope landslide occurs or not, and when monitored geological deformation reaches a threshold value, the monitoring and analyzing unit sends the side slope landslide information to the cloud server. An alarm signal is sent out when the slope landslide is judged; the personal terminal is connected with the monitoring analysis unit through the second communication module, analysis information and alarm signals of the monitoring analysis unit are received in real time, the system is suitable for being used on slopes with complex stratum environments, the automation degree is high, and the monitoring accuracy and durability are improved.

The embodiment of the present invention provides a method, device and electronic device for estimating social media feature data, belonging to the field of data processing technology, the method comprising: performing data cleaning and formatting operations on a plurality of collected social media data to obtain standard data; arranging the constructed prediction data D={D1, D2, D3} in chronological order, and dividing the arranged prediction data into a training set and a test set, using the training set to train an LSTM model, minimizing the prediction error by adjusting the weight and bias terms, and after the training is completed, inputting new target feature propagation data into the LSTM model, and outputting the target feature propagation prediction value D4 within a preset time period in the future based on the learned pattern; and using a chart library to generate a trend chart of the target feature propagation, a sentiment analysis chart and a user interaction chart. The present invention improves the accuracy of social media feature data estimation.