2913results about How to "Avoid failure" patented technology

The invention relates to the technical field of unmanned aerial vehicles, in particular to a mooring unmanned aerial vehicle safety management system which comprises a multi-source data acquisition module, a data fusion and analysis module, an intelligent risk assessment module, a control instruction generation module and an execution mechanism module. The multi-source data acquisition module acquires flight and mooring cable data of the unmanned aerial vehicle by using various sensors, and transmits the flight and mooring cable data to the data fusion and analysis module through a time division multiple access protocol with low packet loss rate and low delay; the intelligent risk assessment module judges a risk level by combining a risk assessment model based on a support vector machine and a Bayesian network algorithm according to the information, and generates prompt information if the risk level exceeds a preset range; the control instruction generation module generates instructions according to a risk prompt and a preset strategy, ranks the instructions and sends the instructions to the execution mechanism module; a flight control system in the execution mechanism module cooperates with a mooring cable electric take-up and pay-off device and the like, real-time adjustment and control over the flight attitude of the unmanned aerial vehicle and the cable state are achieved, and it is ensured that tasks are safely and stably executed in the complex environment.

The invention relates to the technical field of machine learning, in particular to a heating fault prediction method and system for a high-voltage circuit breaker and an isolating switch of the high-voltage circuit breaker. The method comprises the following steps: carrying out multi-source data acquisition on a high-voltage isolation switch and an associated high-voltage circuit breaker through an SCADA system, and carrying out data preprocessing to obtain a preprocessed data set; performing operation feature correlation analysis on the preprocessed data set to obtain a correlation operation feature set; performing dynamic association network construction on the association operation feature set to obtain a dynamic association network graph; acquiring historical fault data; performing propagation risk path search according to the historical fault data and the dynamic association network diagram to obtain a candidate risk path set; and performing path propagation probability calculation on the candidate risk path set to obtain a path probability data set. According to the method, the influence of the associated high-voltage circuit breaker on the heating risk of the isolating switch is effectively identified, so that the accuracy of fault prediction and the timeliness of early warning are improved, and a more reliable guarantee is provided for safe and stable operation of a power system.

The invention relates to the technical field of industrial automation, and discloses a method for detecting abnormal operation of industrial control equipment based on industrial control protocol analysis, which comprises the following steps of: 1, acquiring operation data of the industrial control equipment through at least one industrial control protocol which is a Modbus protocol, an OPC UA protocol or a PROFIBUS protocol, the operation data comprises sensor data, register data and equipment state data of the equipment; and 2, analyzing the collected data packet by using a protocol analyzer, extracting key information related to the equipment operation state, the key information comprising parameters such as equipment working frequency, load, vibration, temperature and pressure, and converting the key information into a uniform data format. By collecting, analyzing and analyzing the operation data of the industrial control equipment in real time, abnormal conditions in operation of the equipment can be found in time, spreading of equipment faults is avoided, and stable operation of the equipment is guaranteed.

The invention discloses a construction site digital-to-analog conversion and collaborative management method based on a distributed computing architecture. The method comprises the following steps: S1, collecting microscopic level data of a construction site; s2, obtaining mid-level data of the construction site; s3, acquiring macroscopic hierarchical data, and constructing a multi-scale spatio-temporal data set; s4, decomposing and processing the multi-scale spatio-temporal data set by using a spatio-temporal diagram convolutional network, and constructing a dynamic digital twin model; s5, designing a competition and cooperation mechanism between agents by using a game theory model, solving resource allocation conflicts through Nash equilibrium, and dynamically optimizing resource scheduling; s6, optimizing a scheduling strategy of the intelligent agent through a distributed algorithm based on near-end strategy optimization; s7, detecting errors in digital-to-analog conversion and scheduling processes by using a belief propagation algorithm; and S8, optimizing a scheduling strategy through a distributed adaptive feedback control mechanism. According to the invention, a distributed computing architecture, space-time diagram convolution, an intelligent scheduling technology and the like are adopted, and digital-to-analog conversion and resource management of the construction site are optimized.

The invention discloses an exhibition hall intelligent control system and method based on the Internet of Things technology, and the method comprises the following steps: collecting environment data in an exhibition hall in real time through a self-adaptive multi-mode sensor network, and dynamically adjusting the sampling frequency of a sensor through a multi-dimensional data sudden change monitoring algorithm by the system, the central processing module is combined with historical and real-time data, time sequence multi-dimensional regression analysis is adopted, an environment change prediction model is generated, then equipment control parameters are generated, the system dynamically adjusts the operation states of temperature equipment, illumination equipment and the like in the exhibition hall according to behavior data of visitors and the environment parameters, the experience of the visitors is optimized, and the experience of the visitors is improved. The intelligent anomaly detection module monitors the operation state of equipment in real time and identifies and isolates fault equipment in time, and the system preferentially guarantees equipment operation and reduces energy consumption when visitors are dense through a long-term energy consumption optimization learning algorithm, so that energy-saving management and long-term self-optimization of the exhibition hall are realized.

The invention discloses a numerical control machine tool motor operation monitoring method and system based on data analysis, which are used in the field of motor monitoring and fault diagnosis, and the method comprises the steps: collecting working condition data corresponding to the operation process of a numerical control machine tool motor; establishing a temperature change trend model, and predicting temperature change conditions of the motor of the numerical control machine tool under different working conditions; combining the extracted frequency and amplitude with the prediction result of the temperature trend model, and judging the current operation state of the numerical control machine tool motor; and setting a threshold value of the working condition data, comparing a classification analysis result with a preset threshold value, and automatically triggering a multi-stage early warning mechanism when monitoring that the working condition data of the motor of the numerical control machine tool exceeds the threshold value. According to the method, preventative maintenance is realized, sudden faults are avoided, the motor is ensured to operate under efficient and stable conditions by determining the optimal electrical working range and real-time monitoring data, energy consumption is reduced, the service life of equipment is prolonged, and intelligent motor state analysis and prediction are realized.

The invention provides an intelligent refrigeration system efficiency optimization method, system and device based on deep learning, and relates to the technical field of intelligent refrigeration system efficiency optimization. The method comprises the following steps: firstly, collecting historical operation parameters and environmental data of the refrigeration system, preprocessing, and integrating into a sample set according to a timestamp; and based on the hybrid model of the convolutional neural network and the long-short term memory network, constructing a state prediction model for predicting the system operation state. And in combination with the state prediction model, an optimization objective function is defined for data prediction at the next future moment, and an optimal adjustment strategy for system operation is formulated by using a deep Q network model. And finally, monitoring current operation parameters and environment data in real time, calculating a comprehensive safety score, comparing the comprehensive safety score with a safety threshold value, and deciding whether to continue to optimize the efficiency or preferentially carry out troubleshooting. According to the method, through combination of deep learning and physical modeling, efficient and safe refrigeration system operation optimization is realized.

The invention discloses a natural gas station intelligent distribution method based on the Internet of Things, and relates to the technical field of natural gas station distribution design. Flow, pressure and valve state data are obtained in real time through an Internet of Things sensor, and a weighted average method and Kalman filtering are used for data fusion and optimization; meanwhile, the relative position relation between the flow meter and the adjusting valve is judged in real time, branch configuration changes are dynamically recognized, self-adaptive branch matching logic is triggered, and therefore control logic is adjusted; meanwhile, the opening degree of the valve is controlled through PID, self-adaptive dead zone compensation is conducted in combination with historical operation data of the valve and the current state, valve response delay is eliminated, the adjusting precision is improved, and flow fluctuation is reduced; in addition, in the feedback adjusting stage, the adjusting result is continuously monitored, the actual flow and the pressure value are compared with the target value, the opening degree of the valve is dynamically adjusted through real-time error calculation, and the output is kept consistent with the target.

The invention discloses an automatic single-yarn strength tester and a test method, and relates to the technical field of textile detection equipment, the automatic single-yarn strength tester comprises a single-yarn strength tester used for testing breaking strength, breaking elongation and breaking strength indexes of a single yarn and fiber pure spinning or blended yarn; and the translation mechanism is used for translating a plurality of distributed yarns and carrying out continuous high-efficiency test operation. According to the automatic single-yarn strength tester, a connecting piece is pulled through a tension spring, then a fastening piece and a limiting block are driven to conduct limiting installation on a limiting groove in a rubber pad, the rubber pad can be rapidly replaced after being abraded, the yarn clamping stability is ensured, and the testing accuracy is ensured; the good friction force of the clamping face can be recovered by replacing the rubber pad, it is ensured that the single yarn is tightly fixed in the clamping mechanism, the single yarn cannot move due to unstable clamping in the tensile test process, it is ensured that the test can be conducted smoothly, and the result is accurate.

The invention discloses a mahjong machine and a tile conveying device thereof, the tile conveying device comprises a tile conveying seat and a tile conveying motor, the tile conveying seat is provided with a tile conveying channel used for conveying mahjong tiles, a tile inlet of the tile conveying channel is provided with a tile suction wheel used for picking up the mahjong tiles, and the tile conveying seat is provided with a tile conveying side blocking part; the tile feeding side blocking part is located outside the tile outlet of the tile feeding channel and blocks and limits the mahjong tiles fed out by the tile feeding device from the outer side, and the tile feeding side blocking part and the tile feeding base are fixedly connected or integrally formed. According to the scheme, the tile feeding side blocking part is arranged on the tile feeding seat to block and limit the outer side of the space outside the tile outlet of the tile feeding device, so that the mahjong tiles are prevented from falling into the space outside the tile bearing block to cause faults.

The invention relates to the technical field of instrument calibration, and particularly discloses a gas detection instrument prediction calibration system and method. The analysis module is used for determining a detection method of the zero drift phenomenon of the electrochemical gas sensor to be calibrated based on the use environment type; the calibration module is used for determining real-time calibration or periodic calibration of the electrochemical gas sensor in response to the occurrence of the zero drift phenomenon and the abnormal opening state of the electrochemical gas sensor; the evaluation module is used for determining the long-term zero point stability degree or the real-time signal fluctuation degree to evaluate the calibration precision according to the calibration method; the adjusting module is used for determining whether to update the environment compensation coefficient and adjust the calibration interval duration for the next calibration according to the evaluation result of the evaluation module; according to the invention, the accuracy of analyzing the use environment of the electrochemical gas sensor is improved, so that the accuracy of analyzing the zero drift phenomenon is improved, and accurate calibration is realized.

The invention relates to the technical field of sickbed control, in particular to a control system of an intelligent bed and a control method thereof. The method comprises the following steps: acquiring structural data of the intelligent bed; carrying out human body fitting area gravity sensor deployment on the intelligent bed structure data to obtain sickbed sensor deployment data; performing gravity sensor data acquisition on the sickbed sensor deployment data to obtain an initial gravity sensor data set; carrying out data matrix processing on the gravity sensor data set to generate a multi-dimensional pressure sensing matrix; acquiring user pain part data and bed board motor initial position data; and performing pressure distribution mode recognition on the multi-dimensional pressure sensing matrix to obtain a uniform distribution mode and a concentrated distribution mode. According to the invention, through the personalized support regulation, global attitude optimization and real-time feedback mechanism of the intelligent bed, the control accuracy and intelligence of the intelligent bed are improved.

The invention discloses a high-voltage cable distributed continuous monitoring big data analysis processing method, and relates to the technical field of high-voltage cable operation monitoring and fault early warning. Calculating a partial discharge pulse average value, a current harmonic component and a voltage fluctuation amplitude in sequence by combining a temperature change rate and the like; calculating a predicted temperature influence index according to the temperature change rate, the temperature data and the operation state data, sequentially calculating a predicted partial discharge influence index, a predicted current influence index and a predicted voltage influence index in combination with the predicted temperature influence index, and calculating an operation basic index according to the basic parameter data; and finally, calculating a cable operation risk index according to the operation basic index and the predicted voltage influence index, setting a risk index threshold set, judging whether to trigger early warning or not according to a comparison result, and generating an adjustment instruction and a risk assessment report so as to guarantee safe operation of the high-voltage cable.

The invention relates to the technical field of power distribution cabinets, and discloses a power distribution cabinet with a self-adaptive environment adjusting mechanism, and the power distribution cabinet comprises a cabinet body which is used for fixing and supporting internal electrical elements; the automatic dehumidification assembly is mounted on the outer side of the cabinet body and is used for dehumidifying the cabinet body when detecting that the moisture content in the cabinet body is too high; the cleaning assembly is installed on one side of the cabinet body and used for conducting anti-blocking treatment on a filter screen arranged outside the cabinet body, and the driving assembly is installed outside the cleaning assembly and used for driving the cleaning assembly. By adding the automatic dehumidification assembly, the humidity in the power distribution cabinet can be effectively reduced, and the risk of electrical short circuit caused by a humid environment is reduced. By discharging moisture, safe operation of electrical equipment is guaranteed, and fire and electric shock accidents caused by moisture are avoided. And meanwhile, corrosion and aging processes can be slowed down, the service lives of the power distribution cabinet and internal components are remarkably prolonged, and the maintenance and replacement cost is reduced.

The invention belongs to the field of time sequence anomaly detection, and discloses a time sequence anomaly detection method and system based on frequency enhanced convolution Transform, and the method comprises the steps: carrying out the sequence-level preprocessing of an input sequence, including normalization and block generation; encoding the input sequence by adopting a multi-head self-attention mechanism; extracting interrelationships in the sequence by a full convolution module having a plurality of stacked 1D convolution layers of different kernel sizes; s2 and S3 are repeated, and multi-layer coding is carried out on the sequence; converting the encoded embedding into a frequency signal by using a Fourier transform method; the KL divergence and the absolute error are respectively used for calculating the consistency loss of the time domain and the frequency domain, calculating the point-by-point anomaly score, and identifying whether the point is abnormal or not. Wide experiments on five common data sets show that the method is superior to an existing method in the aspect of abnormal recognition.

The invention discloses a communication network power distribution cabinet convenient for heat dissipation, and relates to the technical field of power distribution cabinets, and the power distribution cabinet comprises a composite mechanism. According to the communication network power distribution cabinet convenient to dissipate heat, through the design of a composite mechanism, the box body shell is used for storing electronic components, after long-time operation, the temperature in the box body shell rises, hot gas rises, and then the hot gas flows to the processing mechanism from the circular groove, so that the heat dissipation effect is achieved, and the internal temperature of equipment is reduced; the ventilation mechanisms are arranged on the two sides of the box body shell, so that the convection effect is achieved, the cold air flowing effect is improved, the cold air is located at the low position with the high density, hot air is squeezed upwards after entering the box body shell, the discharging speed is increased, and the heat dissipation efficiency is improved. The box body shell is supported through the supporting assembly, so that the effect of reducing deformation of the box body is achieved, and internal electronic elements are protected.

The invention relates to the technical field of microcosmic damage mechanism recognition of refractory materials, and discloses a refractory material damage mode recognition method based on an acoustic emission technology, which comprises the following steps of: acquiring acoustic emission signals of a refractory material under the action of static load through a three-point bending experiment, determining the number and characteristic parameters of data set categories of the acoustic emission signals, and determining the number of the data set categories of the acoustic emission signals; and performing clustering analysis on the acoustic emission signals by adopting a K-means clustering algorithm, dividing the acoustic emission signals into three classes, analyzing by combining an acoustic emission parameter association diagram, determining acoustic emission characteristics corresponding to different damage mechanisms, and determining a damage mode corresponding to each class of signals. According to the method for identifying the damage mode of the refractory material based on the acoustic emission technology, a method combining clustering analysis and acoustic emission parameter association diagram analysis is adopted, accurate judgment on the microscopic damage process of the refractory material can be effectively achieved, and theoretical support is provided for further research on mechanical behaviors and the microscopic damage mechanism of the refractory material.

The invention relates to the technical field of variable-frequency water pump control, in particular to a variable-frequency water pump self-adaptive control method and system.The method comprises the steps that motor parameters of a variable-frequency water pump are collected, and whether the motor parameters meet preset starting threshold conditions at the same time or not is judged; under the condition that the starting condition of the motor is met, sensing information of all sensors on the variable frequency water pump is collected; the collected motor parameters and sensing information are preprocessed, filtered and denoised, and key information is extracted; calculating a motor state analysis result based on the processed motor parameters and sensing information, and performing fault diagnosis and prediction to obtain a motor state analysis result; and according to the motor state analysis result, the output voltage, frequency and phase of the frequency converter are dynamically adjusted to optimize the operation efficiency and stability of the water pump. The operation state of the water pump motor is monitored and analyzed in real time, accurate calculation and evaluation are combined, the output parameters of the frequency converter are dynamically adjusted, and efficient and stable operation of the water pump is achieved.

The embodiment of the invention provides a self-adaptive detection and detection system for low-altitude flight inspection, and aims to improve the efficiency and safety of unmanned aerial vehicle inspection operation. Firstly, a plurality of unmanned aerial vehicle description information sets, unmanned aerial vehicle cluster description information and flight inspection task description information are acquired; the information covers performance parameters and task requirements of the unmanned aerial vehicles in different inspection scenes, then the information is loaded into a target adaptive inspection decision model for knowledge learning in advance, and target estimation confidence is generated by analyzing the information so as to evaluate the success probability of the unmanned aerial vehicle cluster in executing a specific inspection task. And when the target estimation confidence meets the set requirement, the corresponding unmanned aerial vehicle cluster is intelligently scheduled to execute the inspection task, so that efficient completion of the task is ensured, and the adaptability and flexibility of the unmanned aerial vehicle inspection operation are effectively improved through intelligent evaluation and dynamic scheduling.

The invention relates to the technical field of wind power generation, and discloses a wind turbine generator set with a vibration reduction function and a construction method thereof.According to the wind turbine generator set with the vibration reduction function, a vibration reduction structure is additionally arranged, and the two elastic ends, arranged in the axial direction, of an elastic buffering piece are connected with the outer wall of a tower drum and the inner wall of a sleeve correspondingly; the elastic buffering piece can effectively absorb and relieve vibration of the sleeve, vibration impact borne by the tower drum is reduced, the overall stability of the wind turbine generator is improved, and safe and stable movement of the wind turbine generator is guaranteed. Compared with a conventional scheme that the diameter or the wall thickness of the tower drum is increased, the damping structure is simple in structure, convenient to install and maintain and low in cost.

The invention relates to the technical field of electric power facility fault detection, and discloses an electric power facility multispectral remote sensing inspection and fault diagnosis method, and the method comprises the steps: carrying a multispectral remote sensing device through an unmanned plane platform to obtain multispectral image data of an electric power facility, and carrying out the preprocessing to extract spectral features; and constructing a spectral feature analysis model to evaluate the health condition of the electric power facility, constructing a fault diagnosis model in combination with historical fault data, and determining the fault type and position. In order to optimize the fault diagnosis process, fault diagnosis is modeled as a Markov decision process, and the accuracy and efficiency of fault diagnosis are improved by defining a state space, an action space, a reward function and an output strategy and continuously optimizing decision actions by using a reinforcement learning algorithm. According to the invention, intelligent and automatic inspection and fault diagnosis of the electric power facility are realized, powerful technical support is provided for safe and stable operation of the electric power facility, and the method has wide application prospect and important practical value.

The invention discloses a business process optimization method and system, relates to the technical field of computers, and provides a preventive business process optimization method which can identify and solve potential problems in advance so as to ensure stable operation of a preset business process. According to the main technical scheme, the method comprises the steps of obtaining a preset business process, wherein the preset business process comprises business nodes, a logic operation sequence between the business nodes and monitoring index information corresponding to the business nodes; executing a preset business process according to the logic operation sequence among the business nodes by utilizing a process execution engine, and outputting a monitoring index value corresponding to the monitoring index information in the execution process of the preset business process; judging whether each service node in the preset service process has a fault risk or not by using the monitoring index value; and if yes, adjusting each service node according to a preset adjustment method to generate an optimized service process. The method is used for optimizing the preset business process.

The invention relates to the technical field of Internet of Things gateways, in particular to an Internet of Things gateway and an operation method thereof. The method comprises the following steps: acquiring power system data, and constructing a power grid node topological structure diagram, so as to form a power system Internet of Things gateway; based on an Internet of Things gateway, power grid transmission demand prediction is carried out, an abnormal load state is analyzed, abnormal fluctuation of voltage and frequency is detected, and the stability of a power system is ensured; according to the frequency abnormity and voltage fluctuation data, the aging trend of power grid equipment is estimated, power resource loss estimation is carried out, abnormity early warning is carried out on power resource loss and equipment damage prediction data by utilizing an Internet of Things gateway, safe operation of a power grid is ensured, and early warning information is fed back to a terminal in time. According to the method, the intelligent level of the power system is effectively improved, and efficient resource configuration and fault management can be realized. According to the method, the Internet of Things gateway early warning is optimized, so that the Internet of Things gateway prediction is more efficient and accurate.

The invention discloses a vehicle-mounted tire pressure sensor rapid programming and automatic positioning method and related equipment, and the method comprises the following steps: respectively transmitting low-frequency programming signals to a preset number of tire pressure sensors; controlling a tire pressure sensor to transmit a high-frequency response signal to a tire pressure receiver; writing the high-frequency response signal into a program code corresponding to the tire pressure sensor, and generating a one-to-one corresponding unique identity; respectively transmitting low-frequency positioning signals to a preset number of tire pressure sensors; generating status words in one-to-one correspondence according to a preset corresponding relation and the unique identity; a preset number of tire pressure sensors are controlled to send corresponding status words to a tire pressure receiver; controlling the tire pressure sensor to identify the status word to obtain corresponding real-time pressure data, and determining position information corresponding to the tire pressure sensor according to the unique identity; and displaying the real-time pressure data and the position information corresponding to the preset number of tire pressure sensors in a preset display screen. The method is used for improving the programming efficiency and the positioning accuracy of the tire pressure sensor.

The invention discloses a method for researching diffusion stress of a lithium ion battery by constructing a high-fidelity microstructure model. The method comprises the following steps: firstly, performing tomography on an electrode material by using a synchrotron radiation X-ray tomography microscope, identifying active particles in the electrode material, calculating geometric parameters such as spatial distribution and volume radius of the particles, and establishing a three-dimensional geometric model of the lithium ion battery; based on mathematical models such as a reaction mechanism, a kinetic equation and a mechanical equilibrium equation of the lithium ion battery, a three-dimensional heterogeneous electrochemical-force coupling model is constructed, initial and boundary conditions are set for the model, grids are divided, and finite element calculation is carried out. According to the invention, the distribution of diffusion stress and the relationship between the distribution of diffusion stress and lithium ion concentration can be analyzed, and meanwhile, the electrode characteristics such as porosity, curvature, anisotropy and uniformity of the electrode can be analyzed. And battery failure phenomena such as particle breakage and particle stripping can be avoided, and theoretical guidance can be provided for preventing battery failure and capacity fading.

The invention relates to a medical instrument storage information management method and system, and relates to the technical field of information management. The method comprises the following steps: automatically acquiring medical instrument information of a plurality of medical instruments by using an image recognition technology and a multi-dimensional sensor, and preprocessing and standardizing the acquired medical instrument information to obtain preprocessed data; according to the preprocessed data, determining the current optimal inventory of the medical apparatus and instruments; calculating a preferential use index of each medical device according to the preprocessed data; monitoring the current storage environment of the medical instrument in real time, and calculating a corresponding environment risk index; automatically generating a current replenishment suggestion for the medical apparatus based on the historical data of replenishment of the medical apparatus and the prediction model; and according to the optimal stock amount, the preferential use index, the environmental risk index and the replenishment suggestion, optimizing a warehouse management strategy of the medical apparatus and instruments. According to the invention, the efficiency and accuracy of medical instrument warehouse management can be improved.

The invention discloses an anti-interference linear motor module motion control method, and relates to the technical field of linear motor module control. The method comprises the following steps: monitoring and analyzing multiple parameters of displacement error # imgabs0 #, acceleration fluctuation value # imgabs1 #, motor load capacity # imgabs2 #, motor working current I, motor real-time voltage value U, dragging force # imgabs3 # and guide rail friction force # imgabs4 # in the operation process of a motor module in real time, and interference of external environmental factors (such as airflow velocity # imgabs5 #, airflow pressure # imgabs6 #, deposition thickness # imgabs7 # and electrostatic effect coefficient # imgabs8 #); according to the method, the motion state of the motor module can be comprehensively evaluated, so that the deviation of the motion track is effectively reduced, and the motion stability of the motor module is remarkably improved. An interference model is established by collecting external environment interference parameters (airflow interference, deposition thickness, environment vibration and the like) and combining a neural network technology, a motion control strategy of a motor module can be dynamically adjusted, and different types of interference factors are compensated in real time.

The invention relates to the field of robot technology, machine vision, deep learning, computer graphics and digital twinning, in particular to a mechanical arm action recognition and control method based on the machine vision and the digital twinning technology. The motion state and environment information of the mechanical arm are captured in real time through the machine vision technology, high-precision recognition of the motion of the mechanical arm can be achieved in combination with an image processing algorithm, and a control strategy is adjusted in real time according to the recognition result; based on the digital twin model, the state change of the mechanical arm can be monitored in real time, and the mechanical arm and the virtual model are synchronously driven, so that the action of the mechanical arm is ensured to be consistent with the expected action; through intelligent identification and automatic adjustment, manual operation errors are reduced, the working efficiency and the production precision are improved, and the complexity of system maintenance is reduced; real-time optimization of the operation process of the mechanical arm can be achieved through combination of digital twinning and machine vision, potential faults are avoided through simulation and prediction, and stable operation of a production line is ensured.

The invention discloses an air curtain laser failure prevention device for an aluminum-lithium alloy, and relates to the technical field of aluminum-lithium alloy ingots. The air curtain laser failure prevention device comprises a glass sheet located right in front of the laser range finder, a glass upper cover plate arranged on the side face, close to the laser range finder, of the glass sheet, a glass lower cover plate arranged on the side face, away from the laser range finder, of the glass sheet and a ventilation pipeline arranged on the side portion of the glass lower cover plate, and the ventilation pipeline is used for outputting inert gas. The side face, away from the laser range finder, of the glass sheet is purged, and an inert gas protection layer is formed. The inert gas is introduced into the ventilation pipeline at one side of the glass lower cover plate below the laser irradiation glass, and the lower part of the glass sheet is continuously purged, so that an inert gas protection air curtain is formed below the glass sheet, dust and the like can be effectively prevented from being attached to the upper glass sheet, the glass is kept clean, the laser failure is prevented, and the service life of the glass sheet is prolonged. And meanwhile, aluminum alloy oxidation of the launder can be reduced or even avoided through introduction of inert gas, and the ingot casting efficiency is improved.

The invention discloses a new energy station network security assessment method and system, and relates to the technical field of new energy stations, and the new energy station network security assessment method comprises the following steps: obtaining network operation feature data; identifying the change trend of the network stability; identifying key security risk factors influencing the network stability; and adjusting the network protection strategy of the new energy station based on the evaluation result. According to the method, the network behavior recognition algorithm is used for carrying out stability analysis on the network operation characteristic data, the stability change trend is recognized, potential unstable behaviors or hidden dangers in the new energy station network can be found in time, the network state change can be pre-judged in advance, sufficient time and basis are provided for risk control, and the risk control efficiency is improved. According to the method, the key security risk causing network instability is accurately identified, generalization or fuzzy risk assessment is avoided, a basis is provided for formulating an accurate network protection strategy, and the risk prevention and control cost is reduced.