150 results about "Data fusion algorithms" patented technology

The invention discloses a UPA pressure automatic compensation system and method for mask substrate polishing. The UPA pressure automatic compensation system comprises a pressure execution module, a distributed sensing feedback network and an intelligent control unit. Wherein the pressure execution module is used for directly polishing a mask base plate and comprises a force application shaft, a polishing head, an air source, a UPA air bag and an electric proportional valve, the distributed sensing feedback network comprises an air path pressure sensor and a tail end contact pressure sensor, and the intelligent control unit receives a collection signal of the distributed sensing feedback network and sends the collection signal to the polishing head. On the basis of a current process formula and process parameter coupling model, a data fusion algorithm, a feed-forward compensation algorithm and a fuzzy PID algorithm are operated to obtain a feed-forward compensation amount and a fuzzy PID output amount, the feed-forward compensation amount and the fuzzy PID output amount are superposed, a total control voltage is generated and output to an electric proportional valve, and then the input air pressure of a UPA air bag is adjusted. Therefore, closed-loop control over the polishing pressure of the mask substrate is achieved. Through multi-sensor data fusion, fuzzy PID self-adaptive control and intelligent feed-forward compensation based on a process parameter coupling model, real-time, accurate and constant control of the polishing pressure is realized, so that the surface precision and yield of the mask substrate are improved.

The invention provides a method for monitoring cracks in a high-pressure vibration state of a wind tunnel in real time, and belongs to the technical field of wind tunnels. A piezoelectric ceramic sensor array is arranged on the wall surface of the wind tunnel, a vibration fatigue matrix and a structure resonance matrix are established, and a self-adaptive filtering algorithm is adopted to preprocess sensor signals; establishing a vibration coupling equation set to extract crack characteristic parameters, constructing a dynamic threshold discrimination matrix and a risk assessment matrix, utilizing a multi-sensor data fusion algorithm to accurately position crack positions and sizes, calculating risk levels according to a crack risk assessment index equation, and performing three-level safety discrimination. A complete crack real-time monitoring and risk assessment system in the high-pressure vibration environment is formed, and the technical problems that in the prior art, crack monitoring precision is low in the high-pressure vibration environment, and real-time risk assessment cannot be achieved are solved.

The invention discloses a coal type sales planning and sales time collaborative management and control method and system based on a coal enterprise, and relates to the technical field of coal sales management. Multi-source data are integrated through technologies such as a data fusion algorithm, the problems of data dispersion and different formats are solved, high-quality data support is provided for follow-up analysis, the sales trend and price fluctuation are accurately predicted by applying a long-short-term memory neural network and a regression analysis model, enterprises are assisted in optimizing sales strategies, the inventory overstock risk is reduced, and the sales quality is improved. By means of linear programming and reinforcement learning algorithms, sales tasks are dynamically adjusted in combination with real-time data, market response speed and sales efficiency are improved, scientificity and flexibility of sales management of coal enterprises are effectively improved, and market competitiveness is enhanced.

The invention relates to the technical field of energy consumption control, and discloses a computing power equipment energy consumption optimization self-adaptive control method and system and a program product, and the method comprises the steps: obtaining an initial operation environment parameter set of computing power equipment; based on the initial operation environment parameter set, calculating through a data fusion algorithm to obtain a target environment evaluation value; based on the initial operation environment parameter set and the target environment evaluation value, determining a temperature control mode and an anti-interference protection mode of the computing power equipment; controlling a temperature adjusting device to adjust the temperature of the computing power equipment by using the temperature control mode to obtain a temperature adjusting result of the computing power equipment; an anti-interference protection mode is utilized to control the protection device to adjust external disturbance of the computing power equipment, and an interference adjustment result of the computing power equipment is obtained; and controlling the energy consumption and redundancy management module to adjust the energy consumption and redundancy of the computing power equipment by using the temperature adjustment result and the interference adjustment result to obtain an energy consumption optimization self-adaptive control result of the computing power equipment, thereby realizing self-adaptive energy consumption optimization.

The invention provides an unmanned aerial vehicle high-voltage environment electromagnetic interference multi-level protection method and system, and belongs to the field of unmanned aerial vehicle anti-electromagnetic interference. Comprising the steps of collecting electromagnetic spectrum data, identifying an interference type and setting an initial anti-interference parameter; monitoring the electric field intensity in the flight process of the unmanned aerial vehicle, and dynamically adjusting an active anti-interference strategy according to the interference type; differential electromagnetic shielding and wave absorbing treatment is carried out on the unmanned aerial vehicle structure module; correcting the navigation positioning information by adopting a data fusion algorithm, and compensating the sensor data in real time based on a preset interference-error mapping model; key performance indexes of communication, positioning and sensor data are monitored in real time, and when the key performance indexes exceed a threshold value, an adjustment instruction is generated for feedback iteration, and closed-loop optimization is achieved. According to the invention, full-process collaborative protection from sensing, suppression, isolation to compensation is realized, and the communication reliability, positioning precision and data accuracy of the unmanned aerial vehicle in high-voltage strong electromagnetic environments such as a high-voltage transmission line and a transformer substation are improved.

The invention discloses an ammonia gas leakage multi-sensor fusion automatic monitoring method and system, and the method comprises the steps: deploying a plurality of gas detection sensors in a monitoring region, synchronously collecting the ammonia gas concentration and environmental parameters, and forming a multi-source data set; carrying out noise filtering and drift correction by utilizing the edge computing node, and extracting spatial-temporal characteristics to identify concentration anomaly; determining a leakage source coordinate through a data fusion algorithm, simulating a diffusion path and a trend, and constructing a risk diffusion model; and finally, an emergency trigger signal is extracted, ventilation equipment and valve control are activated, and real-time linkage response is achieved. According to the method, data layer fusion and a risk diffusion model are combined, so that the high efficiency of accurate positioning and emergency response of a leakage source is ensured, the safety and reliability of harmful gas monitoring and control are remarkably improved, and a technical guarantee is provided for gas leakage risk management in a complex environment.

The invention relates to the technical field of artificial intelligence education, and discloses a personalized English grammar learning system based on artificial intelligence. Comprising a multi-modal data acquisition module, a deep user portrait construction module, a dynamic association knowledge graph module, a multi-modal intelligent diagnosis module, a meta-cognitive guide type learning path planning module, a personalized content generation module, an emotional real-time feedback module, a meta-cognitive training module, a social cooperation module and a closed-loop optimization module. The multi-modal data acquisition module is used for acquiring multi-dimensional and multi-modal data of a learner, and covering related data such as basic information. According to the personalized English grammar learning system and method based on artificial intelligence, by setting a multi-modal data acquisition module and a deep user portrait construction module, basic information, cognitive features, meta-cognitive levels and emotional preferences of learners are captured in all directions; and constructing a dynamically updated deep user portrait by means of a multi-dimensional data fusion algorithm and a time sequence analysis algorithm.

The invention provides an on-line monitoring device and an on-line monitoring method for detecting belt slipping. The on-line monitoring device comprises a millimeter wave radar sensor, a machine vision module, an anti-jumping rocker arm speed measuring mechanism and a central processing unit. The belt speed is directly measured through the millimeter wave radar, meanwhile, the rotating speed of the roller is calculated by analyzing images of the induction strips arranged on the end face of the roller through the machine vision module, and the slip ratio between the belt and the roller is accurately calculated by fusing data of the two sensors; the high anti-interference capability of the millimeter wave radar and the visual accuracy of machine vision are utilized to form advantage complementation, real-time and high-precision online monitoring and early warning of belt slipping are achieved through a data fusion algorithm, and the problems that in the prior art, the system is prone to being affected by the environment and measurement is inaccurate are effectively solved.

The invention discloses an anti-caries early warning method and system based on oral health detection, and belongs to the technical field of data processing. The method comprises the following steps: acquiring and processing target detection data to generate a clear enamel demineralization feature image and a buffer capability score; analyzing pixel-level fluorescence intensity distribution by adopting a convolutional neural network model, determining a quantized value of the enamel demineralization degree, and further determining a comprehensive dental caries risk level category; judging a risk change trend according to the historical target detection data sequence to obtain a dynamic evaluation update value; a data fusion algorithm is adopted to obtain a final decayed tooth risk assessment index, and risk early warning information is generated according to the early warning template; by integrating fluorescence imaging, an image enhancement algorithm, a convolutional neural network, spectral analysis, k-means clustering and time sequence analysis, a complete process from enamel demineralization feature extraction to comprehensive risk assessment is constructed, the accuracy and individuation level of decayed tooth risk assessment are remarkably improved, and a scientific basis is provided for early prevention and intervention.

The invention discloses an unmanned aerial vehicle control system and method for Internet of Things communication, and relates to the technical field of unmanned aerial vehicle control, the unmanned aerial vehicle control system comprises a data acquisition module, a decision processing module and the like, a switching instruction is generated through multi-dimensional data fusion, cross-layer collaboration, dynamic spectrum scheduling and three-level link guarantee are realized, and the unmanned aerial vehicle control system and method are applied to unmanned aerial vehicle control. The system has the advantages that through a multi-dimensional data fusion algorithm and a cross-layer cooperation mechanism, link quality, flight state and task type data are acquired in real time by using the data acquisition module, and a precise network switching instruction is generated through the decision processing module; in cooperation with a pre-registered address caching mechanism of the cross-layer cooperation module and a dynamic spectrum allocation strategy of the spectrum scheduling module, the defects of heterogeneous network switching decision lag, cross-layer protocol conflict and dynamic spectrum adaptation deficiency in the prior art are effectively solved, and the scheme realizes intelligent seamless switching of the unmanned aerial vehicle between heterogeneous networks such as a cellular network and a satellite network.

The invention relates to the technical field of calcium carbide furnaces, and discloses an intelligent regulation and control system for a closed arc smelting furnace of an industrial silicon electric furnace. By integrating multiple types of sensors including electrical parameters, sound pressure frequency spectrum, infrared thermal image, feed opening vision and the like and adopting a data fusion algorithm, the application can analyze the condition in the furnace from multiple angles, especially through voiceprint recognition and thermal image analysis; state changes, which cannot be quickly sensed, of traditional electrical parameters such as a'micro-open arc ', a'local sintering' and a'material collapse precursor 'can be sensitively captured in an early stage, a data basis is provided for subsequent regulation and control, and timely regulation and control can be further facilitated. The control of each mechanism is performed based on the same intelligent decision, and the decision of the operator is recorded and fed back to the knowledge base under the situation of combined use with the man-machine collaborative intervention interface, so that the dependence on the original single fixed operation experience is gradually reduced, and the user experience is improved. And a cooperative control scheme for a plurality of control terminals is gradually improved and converted.

The invention discloses a vegetation coverage index algorithm and system based on an unmanned aerial vehicle. The algorithm comprises the following steps: S1, data acquisition and multi-source data preprocessing; s2, dynamic environment correction and multi-source data fusion; s3, red edge enhanced vegetation index calculation and terrain correction; s4, vegetation coverage intelligent prediction and precision verification; and S5, result visualization and decision support: generating a vegetation coverage spatial distribution map and a statistical report, and supporting a resource management decision. By integrating the unmanned aerial vehicle, the multispectral imaging sensor, the dynamic environment correction model and the data fusion algorithm, efficient and high-precision technical support is provided for precision agriculture, ecological resource management and disaster monitoring.

The application discloses a kind of ground and satellite-borne measurement precipitation data fusion algorithm under bayesian framework, comprising the following steps: selecting time and space matching satellite-borne measurement precipitation and ground radar measurement precipitation data, when matching, set certain space window and time window, time window is within ±6min with the time difference that ground radar starts once body scanning time that satellite-borne measurement sweeps matching space window, space window is the region that with ground radar as center, the circular region of r radius and the region that satellite-borne radar survey strip is intersected, the ground and satellite-borne measurement precipitation data fusion algorithm under bayesian framework of the application, based on hierarchical bayesian method, the fusion of ground radar and satellite-borne measurement precipitation data, improve satellite-borne measurement instantaneous precipitation precision, obtain the high-precision, high-resolution precipitation estimation result of comprehensive multi-source precipitation observation, and can quantitatively give the uncertainty size that fusion result contains, to better be applied to hydrological model.

The application is an array type electrostatic sensor, a projectile velocity measuring device and method based on the sensor. The array type electrostatic sensor is in the shape of a tube coaxially arranged behind the projectile outlet and the barrel or bore body, and a plurality of circumferentially arrayed graphite electrodes in a pleated triangle structure are arranged at the upstream and downstream cross sections along the axis of the tube. The measuring device comprises an induction unit, a signal conditioning unit and an analysis and calculation unit, obtains the electrostatic induction signal component belonging to the projectile, then according to the velocity range of different projectile models, selects the electrostatic induction signals of the electrodes at different positions in different cross sections to reconstruct the charge distribution and establish the cross correlation sensitivity matrix, and further based on the correlation focusing principle of the electrodes at different cross sections, proposes a data fusion algorithm of multiple sensor electrodes, and finally measures the muzzle projectile velocity. The application proposes to use electrostatic field sensing measurement as a new means for projectile testing in view of the complex environment of high temperature and high pressure at the muzzle.

The application relates to a safety helmet falling detection method, system and computer equipment. A mechanical low-pass filter composed of a static pressure cavity and a ventilation hole is arranged to filter out the interference of high-frequency air flow pulsation on a barometer at a physical level. A real static pressure value is obtained within a preset initial time period after weightlessness detection. After the preset initial time period is exceeded, a control unit adopts a preset data fusion algorithm to perform state estimation based on second acceleration data and the static pressure value. When the posture of the safety helmet suddenly changes and causes air pressure to be disturbed by sudden airflow, the updating weight of the static pressure value in the data fusion algorithm is actively reduced, the prediction result is effectively prevented from diverging, and finally, a relatively accurate falling judgment result is output in combination with a falling classification model, so that it can be distinguished whether a real human body falls or a false fall occurs. Therefore, the falling state classification and early warning with high accuracy can be realized under the extreme working conditions of complex airflow interference and posture mutation.

The invention provides a vehicle overload control processing method and system and a storage medium, and the method comprises the steps: obtaining multi-source time sequence data from a plurality of overload control devices, and carrying out the time-space alignment processing of the multi-source time sequence data, and obtaining aligned time sequence data; key features are extracted from the aligned time sequence data to obtain a multi-source feature set, and a data fusion algorithm is adopted to perform multi-source data fusion processing on the multi-source feature set to obtain fused vehicle information; and performing contradiction detection on the fused vehicle information to obtain a contradiction detection result and an alarm state, and generating and outputting an over-value judgment result based on the contradiction detection result and the alarm state. According to the method, the vehicle overrun treatment work can be efficiently and accurately completed.

The invention discloses a segmented sewing leak seam and wrong seam mistake proofing device, and belongs to the field of automobile safety air bag machining equipment. The device comprises a multi-dimensional sensing module, an AI decision-making module, a closed-loop control module and the like. The follow-up detection head is linked with the presser foot, and multiple physical quantity data are acquired through cooperation of vision, force-electricity coupling and the laser sensor; the AI module analyzes defect grades through a data fusion algorithm, and triggers an electromagnetic clutch, an ink-jet marker and the like to execute grading intervention. And the system also has parameter self-adaption and full-process tracing functions. According to the device, comprehensive and accurate identification of dominant and hidden defects in the sewing process of the safety air bag can be realized through multi-dimensional sensing collaborative design of vision, force-electric coupling and laser contours, defect loss is effectively controlled by means of AI intelligent decision and a real-time closed-loop intervention mechanism, and production continuity is guaranteed; and meanwhile, through technological parameter self-adaption and full-process tracing functions, the flexible production requirement is met, and the strict quality control requirement of the automobile industry is met.

The invention provides an intelligent dinner plate based on multispectral thermal imaging fusion, and belongs to the technical field of intelligent dinner plates. The multi-mode sensing module collects physical and chemical information of food in the dinner plate; the edge calculation and main control module executes real-time data processing at an equipment end; the lightweight food identification and segmentation model is used for identifying food types based on multispectral image data and segmenting areas of the food; the multi-sensor data fusion algorithm is used for fusing the food types, the area proportion information and the total weight, estimating the initial weight of each kind of food based on a pre-trained density factor, and outputting a structured list containing the food types and the initial weights; the cloud nutrition calculation engine receives the structured list and the related hyperspectral feature data, and queries and identifies standard nutritional ingredients of the food from a nutrition database; and calibrating the initial weight by using the hyperspectral characteristic data, and calculating and summarizing the total nutritional ingredients and total calorie of the whole meal based on the calibrated weight and the finely-adjusted nutritional ingredients.

The invention provides a lithium ion battery packaging reliability evaluation method and device, terminal equipment and a medium. The method comprises the following steps: constructing a multi-factor coupling acceleration model based on physical characteristics of a lithium ion battery packaging material; obtaining moisture permeation rate data of the lithium ion battery sample to be detected at multiple temperatures and humidity; fitting model parameters of the multi-factor coupling acceleration model by using a fitting algorithm; calculating equivalent test time under a preset test condition based on the fitted multi-factor coupling acceleration model; collecting battery state change data of the to-be-tested lithium ion battery sample under a preset test condition after the equivalent test time; and inputting the battery characteristic change data into a data fusion algorithm based on Kalman filtering and Bayesian reasoning, outputting the packaging failure probability of the lithium ion battery sample to be detected, and evaluating the packaging reliability of the lithium ion battery sample to be detected based on the packaging failure probability. According to the invention, the accuracy and efficiency of lithium ion battery packaging reliability evaluation can be improved.

The invention discloses a port portal crane operation state identification method, and relates to the technical field of port equipment monitoring, and the method comprises the steps: deploying a sensor group which comprises a camera, a PLC data collection module, a laser radar, a millimeter wave radar and an angle sensor at a key position of a port portal crane, and collecting the image data of portal crane operation through the camera, the PLC data acquisition module is used for acquiring operation control data of the portal crane; according to the method, the multi-source sensor is deployed, a unique data fusion algorithm is adopted, the effects of comprehensively obtaining the operation state information of the portal crane and improving the recognition instantaneity and reliability are achieved, environment parameters are collected in real time, and image preprocessing and recognition algorithm parameters are automatically adjusted; the effect of accurately identifying the operation state of the portal crane under different illumination and weather conditions is achieved, and the effects of real-time feedback of the operation state of the portal crane and timely early warning of abnormal conditions are achieved by establishing a real-time monitoring and abnormal early warning mechanism, and operation safety is guaranteed.

The invention discloses a monitoring method, system and equipment for a wind driven generator and a medium, and relates to the technical field of fan monitoring, and the method comprises the steps: carrying out the three-dimensional modeling based on the wind driven generator, and obtaining an initial three-dimensional model; acquiring operation data of the wind driven generator in real time; performing association fusion on the operation data and the initial three-dimensional model by using a preset data fusion algorithm to obtain a target three-dimensional model; and monitoring the wind driven generator based on the operation data and the target three-dimensional model, wherein the monitoring comprises data monitoring and fault monitoring. The problem that a traditional wind driven generator operation state monitoring system lacks a visual three-dimensional display function and is poor in monitoring effect on a wind driven generator is solved.

The invention relates to a perforating machine attitude sensing method, which comprises the following steps of: collecting multi-source sensor information including inertial measurement unit data and multiple pieces of distance measurement data in real time, cleaning and preprocessing the data in real time to improve the effectiveness, and further calculating the real-time attitude information of a perforating machine by adopting a data fusion algorithm. And the real-time drilling depth is calculated based on the ranging data. By comparing the real-time attitude with the preset target attitude, the attitude deviation information is generated, and visual display and operation guidance are performed in combination with the depth data, so that precise monitoring and prompting of the punching process are realized, and the punching operation precision and operation convenience are effectively improved.

The invention discloses a high-precision real-time track positioning method and system and a computer readable storage medium, and the method comprises the steps: firstly building a path digital model containing discrete high-precision azimuth angle information, carrying out dead reckoning through employing a low-cost IMU when a mobile carrier runs, and carrying out the dead reckoning through employing a low-cost IMU when the mobile carrier travels for a small fixed distance; and obtaining an accurate azimuth angle at the position from the digital model as an external observed quantity. Through a data fusion algorithm, continuous calculation of the IMU and discrete absolute azimuth information are optimally fused, and the real-time arc length position of the carrier on the path is accurately solved, and then the real-time arc length position is mapped into global coordinates. According to the method, the accumulative error of inertial navigation is effectively inhibited, continuous and high-precision track positioning which does not diverge along with time is realized at low cost, and the method is particularly suitable for scenes of water gate opening and closing, rail transit, AGV (Automatic Guided Vehicle) and the like running along a fixed path.

The invention relates to the technical field of medical equipment and home rehabilitation, and discloses an intelligent water drinking system and method for liquid management of a heart failure patient, and the system comprises a medical care end monitoring terminal, a cloud intelligent analysis platform, an intelligent water drinking terminal, a portable auxiliary collection terminal and a patient end applet; a multi-dimensional data fusion algorithm and a machine learning model are built in the cloud end, and the cloud end can be connected with a hospital diagnosis and treatment system to obtain data and generate an individualized liquid intake threshold value. According to the method, basic information is input through the medical care terminal, an initial threshold value is generated through the cloud, all the terminals synchronously collect drinking water, physical signs and other data and encrypt and upload the data, the cloud analyzes the data and then performs graded warning and terminal current limiting on abnormal triggering, the medical care terminal forms closed-loop management and control according to a report adjustment scheme, and the data safety is guaranteed through a collaborative encryption and verification mechanism in the whole process. According to the invention, intelligent and safe liquid management of the heart failure patient is realized, the management accuracy and compliance are improved, and the risk of acute attack is reduced.

The application discloses a kind of coordinate system automatic calibration method and system for ultrasonic detection terminal executor, it is related to executor calibration technical field, including replacing the nozzle of ultrasonic detection terminal executor with vector target seat, and robot is installed on pedestal;The ultrasonic detection terminal executor is driven by robot, and is moved in the measurement space of laser tracker with multiple different postures;The joint angle vector of robot and the three-dimensional coordinates of two target points under each posture are synchronously acquired;The three-dimensional coordinates of two target points are converted to robot flange coordinate system;Based on the two target point coordinates under robot flange coordinate system, the rough estimate of the pose transformation matrix of ultrasonic detection terminal executor to flange is constructed;Based on the rough estimate of pose transformation matrix under all robot postures, the accurate estimate of pose transformation matrix is calculated using data fusion algorithm;It is used to solve the problem of low precision and poor robustness of traditional calibration method.

The application discloses a kind of intelligent helmet with internal environment regulation function, it is related to helmet technical field, for the single internal microenvironment regulation function of existing helmet, low intelligent degree, fixed uncontrollable ventilation volume, including helmet body, environmental perception module, user state monitoring module, main control processing module, environment active adjustment module and wireless communication module are integrally installed in helmet body inside, environmental perception module is evenly laid out with three SHT30 temperature and humidity sensors in forehead, head, back of head area and ZE08-VOC sensor, user state monitoring module includes MAX30102 photoelectric heart rate sensor and MPU6050 motion posture sensor, main control processing module uses the built-in data fusion algorithm and adaptive control strategy of STM32F103 single-chip microcomputer, environment active adjustment module includes double air duct micro brushless DC fan, TEC1-12706 semiconductor refrigeration heating component and nanometer silver antibacterial odor removal module, the application is used for the intelligent helmet environment regulation of riding and industrial operation etc. Scene.

The invention belongs to the technical field of traffic safety risks, and discloses a traffic safety risk accurate triggering and closed-loop treatment method based on multi-modal sensing, which comprises the following steps: acquiring traffic environment data in real time through multi-modal sensing equipment such as a camera, a laser radar, an ultrasonic sensor, a meteorological sensor and an unmanned aerial vehicle; a traffic environment model is generated by adopting a data fusion algorithm, and risks are identified and early warning is dynamically triggered based on deep learning and reinforcement learning algorithms; the system automatically executes signal lamp regulation and control, path guidance, emergency scheduling, driver warning and other response measures according to the risk level; and the feedback module continuously evaluates the treatment effect and adaptively optimizes the strategy, so that accurate identification, intelligent decision and closed-loop treatment of traffic risks are realized, and the road safety and traffic efficiency are effectively improved.

The invention relates to the technical field of data analysis, in particular to a fault signal phase synchronization method and device based on a data fusion algorithm. The method comprises the following steps: preprocessing a collected bearing operation data signal; the preprocessed data signals are fused, and reliable phase offset estimation of each operation period and position is calculated; the fused data signals are processed through waveform matching and a phase correction algorithm, phase deviation caused by driving speed and load changes is eliminated, and data signals after phase synchronization are obtained; and based on the data signals after phase synchronization, bearing fault features are extracted, and a phase consistency coefficient is calculated. According to the technical scheme, the identifiability of fault signal characteristics is improved through multi-sensor data fusion and an advanced phase synchronization algorithm, and an important technical means is provided for fault diagnosis of rotating machinery.

The invention discloses a driving skill teaching method and system based on subject disassembly and dynamic adaptation, and aims to solve the problems that in the prior art, subitem recognition precision is insufficient, teaching lacks individuation, a scoring mechanism is simple, and multi-site adaptation is poor. According to the core scheme, a traditional driving subject is disassembled into single skill basic subitems, and a gradual teaching path with gradually increased difficulty is constructed; multi-dimensional vehicle operation data are collected through GNSS positioning, a sensor and the like, and integrated subject subitem dynamic recognition is achieved through track matching and a multi-dimensional data fusion algorithm after processing; combined with multi-level teaching and check point management, teaching contents and prompt levels are dynamically adjusted according to real-time operation states of students; and a multi-dimensional comprehensive scoring mechanism is adopted, and multi-site automatic adaptation is realized through standardized site data management. According to the invention, refining, intelligence and standardization of driving teaching are realized, teaching pertinence and evaluation objectivity are improved, and operation and maintenance cost is reduced.

The present application belongs to the technical field of geotechnical engineering, and specifically relates to a tunnel underpassing a site adverse geological early warning method and system. A multi-source detection network is arranged in a set region of tunnel construction, original data reflecting geological abnormal conditions are obtained and preprocessed, a multi-dimensional feature index set including wave speed, dielectric constant, rock mass integrity coefficient, etc. is obtained, a three-dimensional perspective model containing spatial distribution and physical properties of adverse geological bodies is generated through a data fusion algorithm, and a geological anomaly grade G is determined accordingly. The structure vulnerability grade V of a target historical site is obtained, and a disturbance influence coefficient S of the site by current construction operation is calculated based on construction parameters. The geological anomaly grade G, the site vulnerability grade V and the construction influence coefficient S are input into a preset risk coupling evaluation model, a comprehensive risk value R is dynamically calculated, and corresponding graded early warning is triggered according to the threshold interval of the R value.