141600results about How to "Easy to operate" patented technology

The invention relates to a method for preparing a lithium iron phosphate / carbon composite material of a lithium ion battery, which belongs to the technical field of lithium ion batteries. The method for preparing the lithium iron phosphate / carbon composite material of the lithium ion battery comprises the following steps of: 1) preparing a suspending graphene-dispersed aqueous solution system, namely, crushing graphite to 1 to 5 microns, adding the crushed graphite into distilled water or purified water, adding 0.1 to 5 percent of surfactant, heating with stirring the mixed solution to 180 to 250 DEG C in a sealing way, performing stirring for 2 to 6 hours and reducing the temperature; 2) crushing lithium iron phosphate to the particle size of 1 to 5 microns, adding the crushed lithium iron phosphate into the distilled water or the purified water, adding with stirring 0.01 to 1 percent of coupling agent, performing uniform stirring, adding the graphene-dispersed aqueous solution, and performing stirring and filtration; and 3) vacuum-drying solid powder obtained by the filtration, and calcinating the dried solid powder for 2 to 12 hours to obtain the graphene-coated lithium iron phosphate cathode material. The method has the advantages of simple process, high material performance, high conductivity, high bulk density, high compacted density and the like.

The invention discloses a self-adaptive power grid state court intelligent fusion terminal and a control method thereof, relates to the technical field of intelligent power grids, and solves the defects that an existing fusion terminal is insufficient in self-adaptive capability and limited in data processing and analysis capability. Learning power grid state change in real time through a deep power grid dynamic control algorithm and a prediction analysis engine combining LSTM and random forest, and autonomously adjusting a control strategy; the edge calculation and distributed processing module is utilized, so that the data processing flow is optimized, and the data processing speed and efficiency are greatly improved; the application of time sequence analysis and a machine learning model further excavates and understands a complex data mode; the resource configuration efficiency is improved through an intelligent scheduling algorithm, and a cross-layer coordination mechanism is established through a distributed coordination algorithm, so that rapid issuing and feedback collection of a control instruction are ensured; according to the invention, the intelligent degree of the existing fusion terminal is greatly improved.

The invention relates to the technical field of data processing, and discloses a coal and gas outburst dynamic monitoring and intelligent early warning method and device. The method comprises the following steps: collecting and preprocessing multi-source sensor data arranged in a target area to obtain a multi-dimensional data set; performing multivariable time sequence analysis on the multi-dimensional data set to obtain coal seam gas outburst risk prediction data; based on coal seam gas outburst risk prediction data, performing dynamic risk assessment and spatial interpolation processing on the multi-dimensional data set to obtain real-time risk distribution data of the target area; performing multi-dimensional threshold analysis on the real-time risk distribution data to obtain preliminary early warning data; performing multi-modal information fusion on the preliminary early warning data to obtain target early warning data; and performing intelligent decision tree analysis on the target early warning data to obtain a target early warning scheme. According to the invention, the accuracy and reliability of dynamic monitoring and intelligent early warning of coal and gas outburst are improved.

The invention relates to the technical field of data processing, and discloses a road infrastructure health monitoring and evaluation method and device, equipment and a medium. The method comprises the following steps: performing multi-mode acquisition and multi-path transmission processing on data acquired by an intelligent sensor network to obtain an original monitoring data set; cleaning, standardizing and fusing the original monitoring data set to obtain preprocessed structured data; performing multi-dimensional spatial correlation analysis on the structured data through a multi-scale time sequence analysis algorithm to obtain health state evaluation data of the road infrastructure; based on the health state evaluation data, performing dynamic threshold calculation and intelligent diagnosis on the road infrastructure to obtain early warning information and a preliminary diagnosis result; and performing expert knowledge base matching and multi-criterion decision analysis on the early warning information and the preliminary diagnosis result to obtain a maintenance decision suggestion and an optimization scheme. The road infrastructure health monitoring and evaluation efficiency and accuracy are improved.

The invention discloses an energy storage energy management system for improving a demand protection algorithm, and the system comprises a data collection module which is used for obtaining energy storage system load data, distributed energy operation data, and energy storage system operation data; the correlation feature analysis module is used for acquiring a correlation feature set between the load and the energy supply; the control strategy generation module is used for adjusting a charging and discharging threshold value and formulating a charging and discharging control strategy; the control strategy execution module is used for formulating a charging and discharging control strategy and adjusting the working mode of the energy storage converter; the cooperative execution module is used for dynamically optimizing a power distribution strategy by dispatching distributed energy and power grid cooperative work; and the dynamic power adjustment strategy adjustment module is used for adjusting the charging and discharging protection threshold value and the power adjustment strategy through a feedback mechanism. Through an intelligent control and scheduling mechanism, the charging and discharging efficiency of the energy storage system is improved, the stability and flexibility of the power grid are enhanced, and effective guarantee is provided for safe operation of the power grid.

The invention provides a fine three-dimensional modeling method and system based on air-ground multi-source data fusion, and the method comprises the steps: collecting data from three data sources: unmanned plane oblique photography, scanning point cloud data and panoramic video data, and carrying out the preprocessing of the data; according to the method, the advantages of different data sources are comprehensively utilized, the refined and full-coverage three-dimensional reconstruction of the target environment is realized, the multi-source data fusion is realized by utilizing the improved ICP algorithm to carry out precise registration, and finally, the modeling is carried out by utilizing the improved Poisson surface reconstruction algorithm. The problems of detail missing and incomplete coverage caused by a single data source in a complex building modeling process are solved; according to the invention, the multi-source data fusion technology can comprehensively capture the geometric morphology and detail features of the building in a complex scene, is suitable for complex scenes such as villages in cities and old buildings, and achieves all-around and detailed three-dimensional modeling.

The invention relates to the technical field of non-destructive testing, and discloses a non-destructive testing method for enhancing defect detection efficiency through deep learning, which comprises the following steps: step 1, acquiring multi-modal data of a target material, including data acquired through X-ray imaging, ultrasonic detection and infrared thermal imaging; 2, signal denoising processing is carried out on the obtained multi-modal data, signal denoising is carried out through wavelet transformation, and the wavelet transformation comprises the steps that multi-scale decomposition is carried out on signals, and high-frequency noise is removed through a soft threshold method; and step 3, carrying out image enhancement processing on the de-noised multi-modal data. The internal structural features of the material are captured from multiple physical dimensions by fusing multi-modal data of X-ray imaging, ultrasonic detection and infrared thermal imaging. The multi-modal fusion can make up for the deficiency of a single detection technology, so that the defect identification in complex structures and materials is more comprehensive and accurate, thereby significantly improving the detection precision.

The invention discloses a live-action three-dimensional model construction method, system and device based on a digital city, and the method comprises the steps: obtaining the multi-source sensing data of a city, and carrying out the data preprocessing, and the multi-source sensing data comprises city image data, point cloud data and GIS data; converting the preprocessed multi-source sensing data in different formats into a preset standard format, and performing registration and fusion of the multi-source sensing data to obtain fused multi-source data; performing data optimization on the fused multi-source data, and constructing a city-level three-dimensional real scene model according to the optimized fused multi-source data and a preset three-dimensional space analysis tool; the preset three-dimensional space analysis tool provides space analysis and three-dimensional space visualization functions; according to the invention, efficient and accurate real-scene three-dimensional data of a city is provided, and three-dimensional and visual space information is provided.

The invention discloses a hydraulic pressure test tube machine and a tube body clamping centering device, in the tube body clamping centering device, two guide rods and a telescopic oil cylinder are arranged on a base side by side; corresponding ends of the two guide rods are respectively provided with a first sliding block, corresponding other ends of the two guide rods are respectively provided with a second sliding block; a fixed end of the telescopic oil cylinder is fixed on one end of the base, the fixed end of the telescopic oil cylinder is arranged corresponding to the second sliding blocks; a telescopic end of the telescopic oil cylinder is connected with the first sliding blocks, and is used for driving the first sliding blocks to slide along the guide rods, one of two clamping blocks is fixed on the two first sliding block, and the other one of the two clamping blocks is fixed on the two second sliding blocks; the first sliding block on one of the two guide rods is provided with a first rack, the second sliding block on the other one of the two guide rods is provided with a second rack, both sides of a linkage gear are respectively meshed with the two racks, the first sliding blocks and the second sliding blocks are adjusted by the telescopic oil cylinder to move towards each other or depart from each other. The scheme can solve the problems of high control precision and slow regulation efficiency of a plurality of oil cylinders of the tube body clamping centering device.

The invention belongs to the technical field of data security, and discloses a data management method based on a data resource security identification level, and the method achieves the automatic and intelligent decision making: achieves the data classification and the automatic identification of the security level through a machine learning and deep learning model, remarkably reduces the manual intervention, and improves the efficiency and accuracy. And dynamic real-time performance: in combination with a risk assessment model, the system can detect abnormal behaviors in real time and dynamically adjust a security policy in a data flow process, so as to ensure that the sensitive data is properly protected in different scenes. And security and compliance: a log auditing module ensures the traceability of all data operations, improves the response capability to potential risks through an anomaly detection algorithm, and provides effective compliance guarantee for data security governance. And the system can be compatible with various data sources, supports configuration and execution of various security policies, and is suitable for data security management requirements of different industries (such as finance and medical treatment).

The invention discloses a method for in vitro slow release performance evaluation of a slow and controlled release preparation based on the overflow principle. A container with an upper outlet and a lower outlet is adopted to serve as a release tank, a to-be-detected medicine and a dissolution medium are placed in the release tank, a stirring device is adopted in the release tank to perform stirring, and the dissolution medium is added from the lower outlet of the release tank through a peristaltic pump; the dissolution medium dissolved with the medicine is taken away through the overflow from the upper outlet so as to offset medicine absorption or metabolism, and the slow and controlled release speed of the medicine, the total medicine overflow quantity and the total release amount are calculated by measuring the concentration of the medicine contained in the dissolution medium flowing out, the volume of the release tank and the overflow quantity. According to the method, the releasing degree of the slow and controlled release medicine can be evaluated accurately, the effective medicine release time of the slow and controlled release preparation is evaluated, and slow release performances of the slow and controlled release preparation are evaluated comprehensively.

The invention relates to the technical field of medical data management, in particular to a medical data document classification and marking system which comprises a document structure analysis module, a semantic information identification module, a classification and marking module and a document intelligent label module. According to the method, document visualization and retrieval efficiency is improved by analyzing document content and extracting titles, texts and appendixes, analytic performance and operability of data are enhanced by identifying key information and semantic relations and accurately extracting and labeling medical terms, medicine research data and medical record records, storage is optimized according to priorities in combination with classification and marking, and the method is high in practicability and easy to popularize and use. Clinical values and application scenes of documents are clarified, document management processes are refined, management compatible with clinical data formats is formed through structured storage of marked data and classification label generation, storage normalization and retrieval efficiency are improved, the requirement for accurate information calling in clinical tests is met, document management capacity is improved, and the document management efficiency is improved. And support is provided for improvement of clinical decision-making and research efficiency.

The invention relates to the technical field of law enforcement intelligent alarm, in particular to a multi-mode intelligent analysis method and system based on law enforcement supervision. The method comprises the following steps: acquiring a law enforcement scene video file from a law enforcement supervision recorder platform, and performing audio and video segmentation and law enforcement behavior information identification analysis to obtain law enforcement scene audio behavior information data and law enforcement scene image behavior information data; performing timestamp and spatial position extraction processing on the law enforcement scene video file, performing multi-modal time-space synchronization association analysis, and performing law enforcement behavior sequence connection processing and event behavior intention recognition analysis at the same time to obtain law enforcement scene event behavior intention information data; and obtaining a preset law enforcement event behavior knowledge graph, and carrying out behavior normative score calculation and nonstandard behavior intelligent alarm optimization analysis to generate a law enforcement scene event nonstandard behavior optimization suggestion report. According to the invention, the accuracy of law enforcement nonstandard behavior supervision alarm can be improved.

The invention discloses a method for correcting dialogue intention information of old people based on a deep neural network, and particularly relates to the technical field of dialogue optimization, and the method comprises the steps: collecting and cleaning multi-topic multi-round dialogue data, marking multi-round dialogue corpora, and unifying the format; performing feature extraction on the labeled corpus by using the multi-intention recognition sub-model to generate a multi-intention representation vector capable of representing each intention; screening context content related to the target intention in combination with a hierarchical attention mechanism of user context dynamic weight adjustment; processing context content related to the target intention through gating operation based on multiple rounds of interaction history, and reserving semantic fragments with reference value; performing parallel analysis on the reference semantic fragments based on a multi-task learning structure of a deep neural network, performing classification processing on different intentions, and calculating scores; and executing multi-intention fusion correction operation according to classification and scoring results, and outputting intention information capable of distinguishing each intention.

The invention discloses a magnetic wave ring which comprises a base, supporting legs are arranged at the bottom of the base, a machine body is installed on the base, a displayer support is installed on the top of the machine body, a displayer is installed on the displayer support, an operation interface is integrated in the displayer, a handle connector is arranged at the end, close to the displayer support, of the machine body, and a magnetic therapy handle is installed in the handle connector. A handle support is arranged on one side of the machine body, a plurality of radiators are arranged on the back of the machine body, power sockets are arranged at the bottoms of the radiators, and power switches are arranged on one sides of Compared with the prior art, the device has the advantages that painless and noninvasive electromagnetic stimulation treatment is achieved through the magnetic induction principle, the curative effect is definite, the side effect is small, and addiction is not prone to occurring. The equipment is reasonable in design, simple and convenient to operate, easy to master and suitable for medical institutions and families. Personalized treatment parameter setting is provided, and the requirements of different patients and different treatment parts are met. The device has multiple safety protection functions, and safety and effectiveness of the treatment process are ensured.

The invention provides an AR-based intelligent integrated production line special circuit control method and system, and relates to the technical field of production line control, and the method comprises the steps: collecting a real-time point cloud model of production line equipment through AR glasses, and carrying out the registration of the real-time point cloud model and a preset three-dimensional model, and obtaining the state deviation data of the equipment; and analyzing an equipment operation trend by using a time sequence convolutional network, and generating an operation state evaluation index and a control parameter correction amount. A to-be-adjusted circuit control node is projected and displayed in the AR glasses, a control instruction is generated according to eye movement tracking and gesture interaction operation of an operator, and a control model is formed by recording a corresponding relation between the instruction and an operation state evaluation index. And based on the model, an AR auxiliary control system is constructed by an edge calculation unit, a control instruction is generated through graph neural network reasoning according to a new operation state evaluation index, and operation guide information is displayed on an AR interface. And after the operator confirms, an instruction is issued to a production line controller, and an execution result is fed back to update the control model, so that closed-loop control is realized.

The invention discloses a method of preparing aluminum oxide-silicon carbide compound porous ceramic by aluminum-silicon industrial waste residues. The primary raw materials comprise industrial waste residues, carbon powder, a pore forming substance and an adhesive. The aluminum oxide-silicon carbide compound porous ceramic is prepared by the following steps of mixing, drying, forming and sintering to obtain a final product. Treatment and high additional value utilization of the industrial waste residues are achieved. The prepared porous ceramic is remarkable in performance, and can be applied to the fields of filtration, sound isolation, heat isolation, fire fighting and the like. The method is simple in process flow and convenient for industrialized promotion; and the cost is good to control and the method has extreme economical value.

Along with promotion of industry 4.0 and rising of intelligent manufacturing, an industrial decision support system plays a more and more important role in improving enterprise operation efficiency and market competitiveness. The invention provides an industrial decision support system based on big data and artificial intelligence technology, and aims to provide accurate decision support for enterprises through deep analysis and real-time processing of massive industrial data. According to the system, by integrating an advanced data acquisition technology, monitoring and data capturing of the whole process of industrial production, including but not limited to production equipment states, process parameters, quality control, supply chain information and the like, are realized. By using artificial intelligence algorithms such as machine learning and deep learning, the system can identify a mode and predict a trend from complex data and construct a corresponding decision model. In addition, a built-in operation optimization engine of the system can provide an optimal solution for production planning, inventory management and resource configuration based on a data analysis result. Through an intuitive user interaction interface, a decision maker can easily obtain key business insights and intelligent decision suggestions so as to make a quick response in a continuously changing market environment. The industrial decision support system not only can improve the production efficiency and the product quality, but also can help an enterprise to reduce the operation cost, avoid potential risks, and promote the enterprise to transform to intelligence and digitization.

The invention discloses a factor multiple optimization-based flood risk dynamic zoning method and system, and the method comprises the steps: constructing an initial flood risk assessment index set, carrying out the screening of the initial flood risk assessment index set, and calculating the frequency value of each index; constructing a comprehensive discrimination index value, and further screening to obtain a key flood risk assessment index set; constructing a risk assessment model and a key flood risk assessment index weight optimization model, and adopting a genetic-local search hybrid algorithm of simulated annealing optimization to calculate and obtain an optimal weight combination scheme; and calculating the similarity between the scene of each time point of the research area and a historical typical flood event, selecting an optimal weight combination scheme, and inputting the optimal weight combination scheme into the risk assessment model for calculation to obtain a flood risk dynamic division of the research area. According to the method, scientific and reasonable dynamic risk assessment can be provided, effective decision support can be provided for related decision makers, and the flood control toughness and the extreme environment coping capacity of the drainage basin area can be improved.

The invention discloses an algorithm for automatically extracting and identifying a photovoltaic abnormal fault, and the algorithm comprises the steps: S1, importing photovoltaic power station data, collecting and classifying power station design information data, power station historical working data and power station historical meteorological data in a database, so as to obtain first data; s2, photovoltaic power station data preprocessing is carried out, integrity and rationality checking is carried out on first data, and type conversion and normalization processing are carried out on the checked first data to obtain actual photovoltaic system operation condition data; and S3, identifying the fault of each link of the photovoltaic system. According to the algorithm for automatically extracting and identifying the photovoltaic abnormal faults, an abnormal fault sequence is rapidly and accurately extracted through data acquisition and analysis and intelligent fault prediction and diagnosis, and digital, intelligent, remote and unmanned operation and maintenance of a photovoltaic power station are achieved through accurate positioning and calculation judgment.

The invention relates to the technical field of hydrological data analysis, in particular to a dynamic hydrological coupling data analysis method and system for a complex watershed. The method comprises the following steps: acquiring historical meteorological data, historical hydrological data and historical remote sensing data, and performing hydrological cycle simulation analysis to generate a rainfall-evaporation-permeation-runoff hydrological cycle simulation model; performing drainage basin gridding processing on the rainfall-evaporation-permeation-runoff hydrological cycle simulation model to generate a hydrological cycle gridding model; obtaining actually measured meteorological data and actually measured hydrological data, and carrying out space-time simulation division and hydrological space-time correlation analysis to obtain a hydrological change space-time correlation correlation relationship among the sub-partitions of each drainage basin; and performing drainage basin space-time coupling early warning processing on the hydrological cycle gridding model to generate a hydrological change space-time coupling early warning response result corresponding to the complex drainage basin. The method can accurately analyze and predict the hydrological change and the mutual coupling relation thereof in the complex watershed.

The invention discloses an unmanned aerial vehicle intelligent dynamic inventory algorithm, and relates to the technical field of warehouse management, the unmanned aerial vehicle flight inventory route is planned according to warehouse layout, shelf distribution and material storage area information, and non-inventory areas are marked; the unmanned aerial vehicle flies to obtain radio frequency data and material image visual data on warehouse materials; the collected radio frequency data and the material image visual data are preprocessed, feature extraction is carried out, and radio frequency features and visual features are obtained; according to the unmanned aerial vehicle intelligent dynamic inventory algorithm, through cooperative application of radio frequency and visual technology, the inventory accuracy is enhanced, radio frequency identification can accurately read material label information and obtain key data of material numbers and category codes, and a visual identification system can be used for capturing material appearance characteristics from multiple angles and multiple distances and assisting in confirming material categories and states; large-area areas in a warehouse can be efficiently checked in a short time, and smooth operation of the warehouse is guaranteed.

The invention discloses a device for measuring flowability of explosive modeling powder, which comprises a step-press-loading analogue unit, a timing and weighing unit and a computer control unit, wherein the step-press-loading analogue unit directly and indirectly drives a screw rod which extends into a blanking bin to rotate and perform reciprocating movements along an axial line and drives a pusher dog in the blanking bin to perform the reciprocating movements along an arc section through the combined action of a cylinder and a pneumatic reversal valve so as to ensure that the explosive modeling powder in the blanking bin falls into the timing and weighing unit, and the process more truly imitates an explosive conveying process of an explosive step-press process. The timing and weighing unit transmits a weighing value of the explosive modeling powder which falls in the timing and weighing unit to the computer control unit in real time, and the computer control unit calculates the instantaneous flow speed of the explosive modeling powder of a north side and generates a flow speed-time curve according to sampling data. The device can measure the flowability of the explosive modeling powder so as to provide an objective basis for choosing a proper explosive step-press process for the explosive modeling powder.

The invention belongs to the field of artificial intelligence, and particularly relates to a bill information extraction method and system and a storage medium, and the method comprises the following steps: obtaining an electronic file containing a bill, and preprocessing the electronic file into an image file facilitating information extraction; extracting bill information in the image file by using a multi-modal large model and an OCR model, and outputting text information in a structured format; and analyzing, correcting and comparing text information respectively output by the multi-modal large model and the OCR model through the large language model, and performing fine tuning or cue word engineering guidance on the large language model for different tasks, so that an output result conforms to expectation. Compared with the prior art, the problems that in the prior art, OCR recognition accuracy is not high, and extracted information is incomplete are solved, the bill information processing speed is increased, the precision of the automatic processing process is greatly improved, and in financial, auditing and other related business scenes, the working efficiency and the data reliability are remarkably improved.

The invention discloses a cargo transportation state monitoring system based on the Internet of Things, and relates to the technical field of logistics management. Electromagnetic signal intensity of each frequency band in a cargo box is recorded in real time through an electromagnetic interference monitoring module, and multi-point temperature data and temperature deviation information collected by a redundant sensor module are combined; the data processing and transmission module is used for preprocessing the signals and uploading the signals to the cloud platform, and the comprehensive analysis module extracts spectrum interference intensity abnormal features and temperature difference fluctuation features and accurately evaluates the influence of electromagnetic interference on the sensor data precision; then, the interference division module divides the interference event into instantaneous interference and continuous interference, the early warning processing module delays the instantaneous interference to trigger the temperature control equipment to adjust and send low-level early warning, and the continuous interference is deeply analyzed to generate a graded early warning signal and guide a coping strategy; and the data reliability, the equipment stability and the cargo protection capability of the cold chain transportation system in a complex electromagnetic environment are effectively improved.

The invention relates to the technical field of clothing data processing, in particular to a digital design method of three-dimensional prototype clothing. The method comprises the following steps: obtaining human body three-dimensional scanning data and carrying out feature point labeling, reconstructing a three-dimensional human body model as basic data of costume design, carrying out contour looseness calculation on the human body model, generating a looseness data set, carrying out sample plate plane analysis to obtain a plane sample plate data set, and after collecting costume fabric data, carrying out data processing on the plane sample plate data set. The method comprises the following steps: performing physical characteristic mapping on a human body model and a planar template, reconstructing a three-dimensional garment model conforming to fabric characteristics, performing spatial positioning on the template, performing virtual sewing simulation, generating a shape data set of the garment, performing dynamic motion analysis on the shape data of the garment according to the human body three-dimensional model, and evaluating the comfort level of the garment. The garment form is locally adjusted to reconstruct a planar template. According to the invention, a more comprehensive and more reliable digital design method of the three-dimensional prototype clothing is realized.

The invention relates to the technical field of laser cutting, in particular to a laser cutting device and a control method thereof. The method comprises the following steps: collecting data of a laser cutting machine to obtain data of the laser cutting machine; cutting head moving speed data of the laser cutting machine data are extracted, and the cutting head moving speed data are obtained; performing power calculation on the moving speed data of the cutting head to obtain laser power data; spatial modeling is carried out based on the cutting head moving speed data and the laser power data, and a laser cutting machine three-dimensional model is generated; performing cutting path identification on the three-dimensional model of the laser cutting machine to generate cutting path data; and extracting first cutting data of the laser cutting machine data to obtain the first cutting data. Through the Internet of Things technology, the data mapping technology, the data analysis technology and the intelligent control technology, self-adaptive laser cutting control over different materials and thickness conditions is achieved, and therefore the cutting quality and efficiency are improved.

The invention relates to the technical field of environment evaluation, in particular to a comprehensive evaluation method and system for a mine geological environment. The method comprises the following steps that mine ore content data, underground water level data, soil acidity and alkalinity data and mine geological meteorological condition data are obtained and subjected to time sequence synchronization processing, and corresponding ore content data, underground water level data, soil acidity and alkalinity data and mine geological meteorological condition data in the same time sequence range are obtained; mine geologic structure exploration data are obtained, three-dimensional space structure modeling and environment risk factor monitoring of all sites are carried out, meanwhile, environment risk comprehensive evaluation analysis and environment risk management analysis are carried out on a three-dimensional space model of mine geologic structure distribution, and mine geologic environment risk management decision suggestions are generated. And executing a corresponding mine geological environment risk level management decision treatment plan. According to the invention, comprehensive dynamic monitoring of the geological environment of the mine can be realized on different time scales and spatial position dimensions.

The invention relates to the technical field of sampling, in particular to a portable sampling device for geological mineral exploration and a sampling method thereof.The portable sampling device comprises a bottom plate, guide frames, a lifting plate and the like, the two guide frames are connected to the top of the bottom plate, and the lifting plate is jointly connected to the two guide frames in a sliding mode. A rock core drill bit can be driven to move downwards through a screw rod motor, the rock core drill bit drills into soil, the soil enters a sampling pipe for sampling, a standby sampling pipe can be driven to move backwards through an electric guide rail, the standby sampling pipe is moved to the position below a mounting pipe, and the sampling pipe is spliced; the sampling pipe can be driven to move forwards through the electric guide rail, the sampling pipe can be disassembled, the sampling pipe can be automatically spliced and disassembled, operation is simpler, the burden of workers can be relieved, and therefore the sampling efficiency can be improved.

The invention relates to a construction method of a Ti-SA model for pilot situational awareness assessment, belongs to the technical field of aviation safety and situational awareness, and solves the technical problems that an existing perception model cannot assess the situational awareness ability of a pilot in real time, and selected data cannot adapt to a complex environment, so that the effectiveness is poor. Comprising the steps that S1, eye movement trace time sequence data and AOI fixation time sequence data in the simulated flight process of multiple pilots are acquired to serve as sample data, and the sample data and generated corresponding category labels form a training data set; wherein the category label is a level label of the scene awareness of the pilot; step S2, constructing a Ti-SA model used for pilot scene awareness assessment; and training the Ti-SA model by using the training data set until the trained Ti-SA model is obtained when the loss function converges. The method achieves the real-time evaluation of the situational awareness capability of the pilot through the Ti-SA model.