187results about How to "Improve predictability" patented technology

The invention provides a rail transit platform door multilayer architecture intelligent operation and maintenance and predictive maintenance method and system, and relates to the technical field of intelligent operation and maintenance, and the method comprises the steps: obtaining platform door operation parameters through a multilayer data collection system, and generating health state feature vectors; inputting the feature vectors into a deep neural network, and constructing a fault prediction model; based on the prediction result, a decision tree algorithm is adopted to evaluate the maintenance priority, and an intelligent maintenance scheme is generated; and pushing an instruction to maintenance personnel through the mobile terminal, and feeding back an optimization model by using new data to realize self-adaptive adjustment of a maintenance strategy.

The dynamic hot runner temperature control method suitable for the mold comprises the steps that S1, a temperature sensor is installed in a mold hot runner to collect flow characteristics, heat distribution and temperature original data in the material solidification process, and the temperature original data are transmitted to a temperature control system; s2, the required power adjustment amount Pc is calculated through a PID controller in the temperature control system according to the deviation value delta T between the actual temperature Ta and the set target temperature Ts, and the power adjustment amount Pc is sent to a heater for adjustment; s3, constructing a temperature prediction model M, and predicting the temperature change trend of each section of the mold hot runner through the temperature prediction model M; and S4, a multi-objective optimization function Ft is created, an optimal combination solution St is searched through a particle swarm optimization algorithm PSO, and the optimal set value combination of the hot runner temperature is maintained according to an optimization objective. According to the invention, the energy consumption is reduced while uniform temperature distribution is ensured, the temperature control precision and the response speed are obviously improved, and the requirement of human intervention is reduced.

The invention discloses a multi-warehouse intelligent distribution and scheduling method for raw material inventory. The method comprises the following steps: collecting raw material inventory data of each warehouse, and monitoring the inventory data of the warehouses in real time; based on the real-time inventory data and the historical inventory data, the inventory state of each warehouse is analyzed to determine the inventory balance state of each warehouse, and the warehouses with excess or short inventory are identified; and calculating an optimal inventory distribution scheme by adopting a dynamic adjustment algorithm based on an inventory state analysis result. According to the invention, by collecting the real-time inventory data and the historical inventory data, the inventory state of each warehouse is deeply analyzed, the inventory balance state is determined, and the warehouse with excess or short inventory is identified. Through the real-time data monitoring and analysis, the system can provide accurate inventory early warning, help managers to take measures in time, avoid production interruption and resource waste, and improve the accuracy and predictability of inventory management.

The invention belongs to the technical field of unmanned aerial vehicle clusters, and particularly relates to an unmanned aerial vehicle cluster hierarchical behavior decision-making method oriented to ground hunting, which comprises the following steps of: 1, determining various constraint conditions and income targets in a ground hunting task so as to establish a random game model oriented to the ground hunting task; the second step of considering the prediction problem of the motion trail of the surrounding target and the optimization problem of the multi-agent decision algorithm, and establishing an unmanned aerial vehicle cluster behavior decision network based on the improved MASAC algorithm, and the third step of online decision making. According to the method, the random game model oriented to the ground surrounding task can be established, then the position information of the surrounding target at the next moment can be predicted, the predicted trajectory data is input into the bottom layer MASAC decision network, and finally the optimal unmanned aerial vehicle cluster behavior decision strategy oriented to the ground surrounding task is obtained through offline training. And the ground surrounding of the target is completed by adopting an offline trained unmanned aerial vehicle cluster behavior decision-making method.

The invention relates to a high-voltage cable intelligent inspection method and system based on multimode identification, and the method comprises the steps: deploying an intelligent identifier and cable monitoring equipment according to the equipment characteristics and environmental conditions in a high-voltage cable line; reading identification data of the intelligent identification through a double-frequency reading device, and collecting cable state monitoring data through a cable monitoring device; the information is uploaded to the Internet of Things cloud platform; carrying out fusion analysis on the received data in the cloud platform, evaluating the health condition of the cable in combination with a historical record and a fault recognition model, and generating an inspection result; and displaying the inspection result through the intelligent monitoring center, calculating the risk score of each device according to the identification data, the cable state monitoring data and the fault risk prediction result, and dynamically adjusting the optimal inspection route and the inspection frequency. Compared with the prior art, unique identification and multi-dimensional information storage of the cable and accessory equipment of the cable are achieved, and the fineness and comprehensiveness of cable operation state monitoring are improved.

The invention discloses a data processing and quality evaluation method for efficiency test data of an electric drive system. The method comprises the following steps: acquiring an original electric drive efficiency test data set; performing noise reduction on the original electric driving efficiency test data set through a probability distribution-based data noise reduction method to obtain a noise-reduced electric driving efficiency test data set; carrying out the first abnormity detection and processing of the noise reduction electric drive efficiency test data set through a DBSCAN model, and obtaining a preliminary screening electric drive efficiency test data set; carrying out the second anomaly detection and processing of the preliminary screening electric drive efficiency test data set through an LOF algorithm model and an iForest algorithm model, and obtaining a preprocessed electric drive efficiency test data set; and performing quality evaluation on the preprocessed electric drive efficiency test data set through the constructed six-tuple data quality model. According to the invention, effective noise reduction and abnormity detection can be carried out on the efficiency test data of the electric drive system, so that the data quality is improved.

The invention relates to the technical field of breach image processing, in particular to a breach rapid situation awareness method and system based on multi-source data. The method comprises the following steps: acquiring dam monitoring station data; performing hydrological sensor network setting on the dam monitoring station data to obtain dam hydrological sensor network data; performing ultrasonic water level change trend analysis on the dam hydrological sensor network data to generate an ultrasonic water level change trend chart; performing breach node sensing on the dam hydrological sensor network data according to the ultrasonic water level change trend chart to generate breach node data and breach prediction node data; and according to the breach node data, macroscopic disaster risk analysis is carried out on the breach area, and a breach area macroscopic disaster risk schematic diagram is generated. According to the invention, through real-time data integration, comprehensive risk assessment, unmanned aerial vehicle monitoring, dynamic prediction model and multi-party cooperative response, the accuracy and response speed of breach rapid situation awareness are improved.

The invention discloses an intelligent tension control datamation method and system, and relates to the technical field of tension control. An intelligent tension control datamation system comprises a target equipment tension acquisition module, a target equipment tension prediction module and a target equipment tension adjustment module. The real-time tension information and the parameter information of the target equipment are acquired at preset time intervals, so that the state of the equipment is monitored and analyzed in real time; the tension data in the next preset time interval can be predicted by using the target equipment tension prediction model, so that the potential tension deviation problem can be found in advance; by comparing the to-be-analyzed tension data with the predicted tension data, whether the target equipment needs to be adjusted or not can be actively judged, instead of passively responding only after the tension exceeds a preset range; and the target equipment parameter control model can output an optimal parameter control scheme according to the prediction information and the current parameter information, and the equipment performance is optimized.

The invention provides an ink quality online monitoring method and system based on multi-sensor fusion, and relates to the technical field of printers, and the method comprises the steps: obtaining the temperature, humidity, pH value, viscosity, particle thickness, flow velocity and other data collected by a plurality of sensors in the production process of water-based ink, and employing a multi-sensor data fusion algorithm of adaptive weight, calculating a sensing weight corresponding to the sensor data, and combining the sensor data to obtain fused water-based ink quality characteristic data; establishing a water-based ink quality prediction model by adopting a support vector machine algorithm, and inputting the fused water-based ink quality feature data into the trained water-based ink quality prediction model to obtain a water-based ink quality prediction result; and if the water-based ink quality prediction result does not meet the preset condition, judging that the water-based ink quality is unqualified, sending a feedback signal in a preset format to water-based ink production equipment, and adjusting water-based ink production parameters through a PID controller of the water-based ink production equipment. According to the method, the related parameters of the ink can be monitored and reflected in real time, and the possible problem of artificial false description is solved.

The invention discloses a real-time regulation and control test method and system for a power frequency magnetic field of an electric energy meter. The method comprises the steps of monitoring power frequency magnetic field intensity and distribution in a laboratory in real time, performing magnetic field data preprocessing, capturing frequency characteristics of magnetic field signals through a frequency domain multi-scale decomposition algorithm, and adjusting output of a magnetic field generator in real time by using a multi-layer nested adaptive feedback control algorithm on the basis of frequency domain analysis. Accurate magnetic field regulation and control are realized; and uploading test data to a cloud platform, dynamically adjusting a test scheme by using a multi-dimensional fuzzy logic algorithm, generating hidden variables through an enhanced time sequence prediction algorithm, performing time sequence prediction, evaluating performance parameters of the electric energy meter in a power frequency magnetic field environment, and providing support for real-time regulation and control test of the electric energy meter. By analyzing the prediction result, the performance parameters of the electric energy meter in the power frequency magnetic field environment are evaluated, support is provided for real-time regulation and control testing of the electric energy meter, and optimal regulation and control of the magnetic field can be achieved.

The invention relates to the technical field of rolling of road rollers, in particular to an identification method and system for a rolling operation track of a road roller. The method comprises the following steps: acquiring a target rolling area; performing geographic space data acquisition on the target rolling area to obtain geographic environment data of the operation area; positioning the road roller to obtain position coordinates of the road roller; sensing the working environment of the road roller in real time to obtain a working environment three-dimensional model; performing topographic feature recognition on the three-dimensional model of the working environment to obtain environmental topographic feature mapping data; performing geographic topology analysis on the target rolling area based on the environmental topographic feature mapping data to obtain an operation area topology model; and carrying out matching analysis on the position coordinates of the road roller and the working area topology model to obtain a road roller working path candidate set. According to the invention, the workload that an operator needs to frequently stop to check and adjust the position of the road roller is reduced, so that the operation process is smoother.

The invention discloses a charger communication test method and system, a storage medium and a program product, and the method comprises the steps: obtaining a state parameter of a to-be-tested charger; collecting a communication message of the charger to be tested, and constructing a message time sequence queue; generating a message interval sequence reflecting the time sequence characteristics of the message; calculating a discrete degree coefficient of a message time sequence according to the message interval sequence; recording record data at an abnormal moment when the communication abnormality occurs, and inputting the record data into a preset state transition matrix to obtain a corresponding relationship; calculating probability distribution of communication abnormity under different state parameters to obtain a correlation model; generating a message sequence for testing based on the association model, wherein the time sequence characteristic of the message sequence for testing is the same as the state parameter and the dispersion degree coefficient corresponding to the high-probability abnormal scene of which the probability is greater than a preset probability threshold in the association model; and sending the test message sequence to the to-be-tested charger to detect response data of the to-be-tested charger. And the accuracy and efficiency of the communication test of the charger are improved.

The present invention relates to the field of real-time data processing technology, specifically a distributed real-time data stream processing system and method, the system includes a state mapping modeling module, a load dynamic calculation module, a node task adjustment module, and a resource scheduling execution module. In the present invention, by analyzing the data flow change sequence, identifying the frequency and trend of state switching, realizing dynamic identification and classification of flow states, improving the monitoring and understanding capabilities of real-time data, accurately quantifying node resource pressure in resource occupancy and load range analysis, enhancing the predictability of load balancing, providing a scientific basis for resource scheduling, combining task priority sorting and dynamic task adjustment, optimizing node computing resource utilization and task processing response speed, realizing flexible configuration of memory and computing resources, alleviating peak load pressure, improving data processing efficiency and system stability, and improving the system's adaptability and reliability in processing large-scale data flows.

The invention discloses an individualized pain prediction method and system based on a mixing effect. The method comprises the following steps: acquiring pain scores and corresponding central nervous system data when a plurality of healthy individuals receive pain stimulation; performing data processing on the obtained central nervous system data to obtain central nervous system physiological signals of pain stimulation received by corresponding individuals; combining the obtained physiological signals of the central nervous system and the corresponding pain scores to construct a mixed effect model; predicting the pain state of the individual by using the constructed mixed effect model; the system comprises a pain stimulation device, a central nervous detection system, a data acquisition module, a data analysis module, a prediction model construction module and a pain recognition module. The constructed mixed effect model gives consideration to the characteristics of an individualized model and a group model, not only can new individuals be migrated, but also individual differences are considered, the hierarchical characteristics of data are fully utilized, and the pain score prediction effectiveness can be effectively improved.

The invention relates to the technical field of maritime ship target beyond visual range monitoring, in particular to a compact type ground wave radar maritime ship track prediction method and system. AIS track data are obtained and converted into distance, speed and azimuth angles under a radar coordinate system to serve as characteristic parameters; the multi-dimensional target features which are segmented into n historical time steps through preprocessing and a sliding window serve as input of a prediction model, and the model outputs a prediction result of a future single time step; in the prediction model, each encoder layer extracts time sequence features of different depths from input features, and modeling is performed on the time sequence features by using a plurality of different attention mechanisms; and the output of the last layer of encoder passes through a linear mapping layer, and the high-dimensional time sequence representation is converted into the output prediction characteristics of the future time step. The multi-head self-attention mechanism is utilized to dynamically distribute the weights of the multi-dimensional target features, the complex association among different features is captured, the relationship among different time steps is analyzed from multiple angles, and the anti-interference capability is enhanced.

The invention discloses a dance formation self-organization control method based on a robot group. The method comprises the following steps: S1, obtaining dynamic changes of light frequency, color and intensity through a spectrum camera; s2, judging an effective light source and an invalid light source, and removing the invalid light source to form a target light signal; s3, converting the optical signal into a digital signal through a photoelectric converter and a digital signal processor, and extracting the characteristics of the digital signal to form a characteristic instruction; s4, comparing the feature instruction with a predefined action matching database in real time, and outputting a robot action instruction; s5, analyzing the characteristics of the received digital signal, predicting an action instruction at the next moment, and generating the next action instruction in advance; s6, generating a robot action track by using a path planning algorithm; and S7, adopting a distributed control strategy, dynamically adjusting the motion track, and completing dance arrangement. According to the invention, dance arrangement of the robot group is controlled through light, and diversity of dance of the robot group is realized.

The invention provides a negative plate and a battery, the negative plate comprises a current collector and an active material layer located on at least one side surface of the current collector, and the active material layer comprises a first coating close to the surface of the current collector and a second coating far away from the surface of the current collector; the first coating comprises first graphite particles and second graphite particles; the second coating comprises third graphite particles and silicon carbon particles; and a plurality of concave parts are arranged on the surface of the active material layer. According to the negative electrode plate, the expansion problem and the lithium separation problem of the silicon-based material doped graphite negative electrode can be inhibited, so that the cycle performance and the rate capability of the lithium ion battery are remarkably improved.

The invention relates to a heat supply prediction method and system based on spatial-temporal feature fusion deep learning, and the method comprises the following steps: S1, carrying out the collection and fusion of multi-source heterogeneous data, and constructing an integrated data set; s2, preprocessing the data; s3, constructing a graph structure model of the heat supply system, and constructing a weighted undirected graph; s4, constructing and executing forward calculation of the space-time double-flow deep network; s5, designing a composite loss function including mean square error loss and physical constraint loss, and performing joint optimization training on the space-time double-flow deep network; and S6, performing multi-step heat supply load prediction by using the trained model, outputting a heat supply load curve of each heat exchange station in a specified time period in the future, and integrating a prediction result with a heat supply scheduling system. The method has the advantages that the prediction precision is improved compared with that of a traditional machine learning model by capturing the spatial-temporal characteristics at the same time, and the advantages are more remarkable in extreme weather.

The invention discloses a multi-mode self-adaptive switching ultra-short-term wind power prediction method and system, and belongs to the technical field of industrial control, and the method comprises the steps: obtaining the meteorological data of a to-be-predicted time and the parameter data of a wind driven generator; marking timestamps based on the meteorological data and the parameter data of the wind driven generator, and performing data alignment to obtain an initial prediction data set; performing abnormal value detection on the data by using a clustering algorithm, performing sorting according to timestamps after detecting and eliminating abnormal data, performing normalization operation on an initial data set, and taking meteorological data after normalization operation and parameter data of the wind driven generator as input features; and according to the change of the wind speed measured in real time, performing multi-mode adaptive switching, inputting the input characteristics into a pre-trained high-fluctuation wind speed-wind power prediction model or a pre-trained low-fluctuation wind speed-wind power prediction model, and predicting to obtain the output power of the fan. The method can improve the prediction accuracy.

The invention discloses a safety management system for a water conservancy and hydropower construction enterprise, and relates to the technical field of water conservancy and hydropower construction management.When the system operates, position information and behavior data of workers are obtained in real time through a personnel behavior collection module, behavior specifications of the workers are quantified, and the safety of the workers is evaluated through a safety behavior scoring index S and a risk staying factor T; and a safety risk assessment coefficient R is further fitted, and when the safety risk assessment coefficient R exceeds a preset threshold value Rth, a safety early warning notification is triggered to inform related personnel of potential risks in time, so that the possibility of accidents is effectively reduced. And specific construction operation safety suggestions are provided, and workers are timely informed of execution, so that the workers can take proper safety measures according to real-time feedback, and the problems that the behaviors of the workers cannot be comprehensively monitored, timely early warning cannot be performed and effective intervention measures cannot be formulated in a traditional safety management mode are solved. And the management frequency of high-risk areas and the intervention frequency of high-risk workers are improved.

The invention discloses a novel power system network security protection and verification method and system. The method comprises the following steps: monitoring network traffic and log data, forming a network space behavior analysis rule by using a machine learning algorithm or a path search algorithm, and generating an attack path from an attacker perspective; constructing a network attack behavior event association model oriented to an attack and defense scene of the power system based on an analysis result of the attack path, and automatically identifying a network threat; aiming at the identified network threats, integrating power network security domain knowledge, a power monitoring system network plan and an on-site disposal scheme by utilizing a knowledge graph technology, and automatically generating a protection strategy; comprehensively evaluating the protection effect of the protection strategy through a pre-established evaluation system, and verifying and optimizing the protection effect by simulating a real attack and defense scene. According to the scheme of the invention, the accuracy and timeliness of threat identification are improved, and the verification process of a protection strategy is simplified.

The invention discloses a production scheduling method based on wooden furniture process standards and product BOM, and relates to the technical field of wooden furniture production scheduling. The production scheduling method based on the wooden furniture process standard and the product BOM comprises the following steps: S101, establishing a standard process model according to a wooden furniture production process; s102, obtaining information to be produced; s103, carrying out productivity evaluation on each process node; s104, making a corresponding scheduling adjustment measure according to a scheduling detection result; s105, outputting a preliminary scheduling detection result, and calculating a scheduling result adjustment value according to the scheduling parameters; s106, productivity evaluation and scheduling detection are carried out again according to the scheduling result adjustment value; and S107, the adjusted scheduling parameter information is acquired, and the scheduling parameters are analyzed. The problem of how to dynamically and efficiently adjust the production schedule to adapt to variable production requirements and complex production processes is solved.

The invention discloses a meteorological sensitive load prediction method, device and equipment and a medium, and relates to the technical field of electric power prediction, and the method comprises the steps: obtaining original sequence data; the method comprises the following steps: decomposing original sequence data by adopting a CEEMDAN decomposition method to obtain a plurality of IMF components with different frequencies and scales; calculating sample entropies of the plurality of IMF components with different frequencies and scales, and aggregating the plurality of IMF components with different frequencies and scales into a first high-frequency sub-sequence, an intermediate-frequency sub-sequence and a low-frequency sub-sequence based on the sample entropies; performing secondary decomposition on the first high-frequency sub-sequence by adopting a variational mode decomposition method to obtain a second high-frequency sub-sequence; respectively inputting the second high-frequency sub-sequence, the intermediate-frequency sub-sequence and the low-frequency sub-sequence into the LSTM neural network for prediction to obtain respective prediction results of the second high-frequency sub-sequence, the intermediate-frequency sub-sequence and the low-frequency sub-sequence, and carrying out weighted fusion on the respective prediction results of the second high-frequency sub-sequence, the intermediate-frequency sub-sequence and the low-frequency sub-sequence to obtain a prediction result of the second high-frequency sub-sequence; and obtaining a prediction result of the meteorological sensitive load.

The invention discloses a geological disaster multi-scale environment control factor identification system based on machine learning, and relates to the technical field of geological disaster identification, and the system comprises a multi-scale environment factor collection module which obtains historical geological disaster data of a target area, extracts geological disaster multi-scale environment features, and constructs a geological disaster-environment factor correlation feature set; the quantitative evaluation module is used for establishing a geological disaster risk probability prediction model, quantitatively evaluating the marginal contribution degree of each environmental factor on different spatial scales, identifying the environmental factors dominating geological disaster development in multiple scales of a region, a drainage basin and a slope, and establishing a geological disaster risk probability curved surface of a target region and an environmental factor contribution spatial distribution diagram; and the prevention and control module introduces a human activity intensity index and an ecological system state index in a target area, establishes an environmental factor dynamic coupling risk propagation model and a real-time multi-scale risk transmission path, and generates a dynamic risk grading early warning map. According to the invention, the accuracy of geological disaster risk assessment is improved.

The invention discloses a method and a system for identifying years of dried orange peel by a multi-dimensional atlas model and a storage medium. The method comprises the following steps: obtaining the composition and content of flavonoid components, amino acids, organic acids and volatile components of dried orange peel in different known years; taking the composition and content of flavonoid components, amino acids, organic acids and volatile components of the dried orange peel as samples in the data set; training an orthogonal partial least square discriminant analysis model by using the data set to obtain a year OPLS-DA standard model of the dried orange peel; and inputting the composition and content of the flavonoid components, the amino acids, the organic acids and the volatile components of the to-be-identified pericarpium citri reticulatae into the pericarpium citri reticulatae year OPLS-DA standard model to obtain the year of the to-be-identified pericarpium citri reticulatae. According to the method, the fingerprint spectrum is constructed by utilizing data of flavonoid components, amino acids, organic acids and volatile components of the pericarpium citri reticulatae, and the aging year of the to-be-identified pericarpium citri reticulatae can be accurately predicted in combination with the orthogonal partial least squares discriminant analysis model.

The invention discloses a telecommunication industry cloud resource pool resource management system and method. The system comprises a multi-cloud management platform, a heterogeneous resource pool, a uniform resource arrangement module, a capacity planning module, a uniform monitoring module, an intelligent operation and maintenance module and a security audit module. The multi-cloud management platform realizes unified management of public cloud, private cloud and heterogeneous resources; the heterogeneous resource pool is used for providing calculation and storage resources; the uniform resource arrangement module realizes automatic arrangement and deployment of cloud resources; the capacity planning module plans the capacity of the cloud resource pool; the unified monitoring module monitors the cloud resource pool; the intelligent operation and maintenance module analyzes and finds potential faults and risk points, and carries out early warning and disposal; and the security auditing module provides an auditing function for the use condition of the cloud resources. According to the method, the resource management process can be simplified, the management efficiency is improved, the labor cost and the operation and maintenance cost are reduced, and the resource scheduling flexibility and the operation and maintenance efficiency are improved.

This invention discloses a method for enhancing and controlling the radiation of an LPP-EUV light source based on dual-pulse spatiotemporal co-constraint, belonging to the field of semiconductor manufacturing technology. This invention obtains a spatiotemporal reference by real-time monitoring of a liquid metal droplet, uses a pre-pulse laser to induce target material deformation into a fogged target cloud, and combines high-speed imaging and a hydrodynamic model to predict the time and coordinates of the target cloud's energy absorption peak. It dynamically adjusts the delay time and optical axis direction of the main pulse laser to achieve dual-pulse spatiotemporal co-constraint, and constructs a closed-loop control system through a recursive least squares algorithm with a dynamic forgetting factor. This invention effectively improves the extreme ultraviolet light conversion efficiency, controls the power fluctuation of the light source within ±0.5%, and extends the maintenance cycle of optical components by 2 times. It can be widely applied to next-generation high-power extreme ultraviolet lithography light source systems, meeting the application requirements of semiconductor miniaturization processes.

The application discloses a lining cloth dyeing control method and system based on artificial intelligence, and is used for the control field, and the method comprises the following steps: real-time monitoring of multi-source data in the lining cloth dye vat, capturing the video stream inside the lining cloth dye vat; using a Gaussian mixture variational autoencoder model to extract features from the multi-source data, obtaining multi-source feature data; obtaining the changing moment of the fabric state in the video stream, and extracting the fabric state change feature data according to the observed fabric color change and the heterogeneity of the dye distribution in the video stream; fusion of multi-source feature data and fabric state change feature data; introducing a model predictive control layer, predicting the result to adjust the dyeing parameters in real time; establishing an intelligent feedback mechanism. The Gaussian mixture variational autoencoder model is trained and optimized through the small-batch stochastic gradient descent and back propagation algorithm, important features are learned and captured from the data.

The invention relates to a low-fault-energy micro-twin synergistic deformation cobalt-based alloy and a preparation method thereof, and belongs to the field of cobalt-based high-temperature alloys. According to the alloy, on the basis of Co-Al-W, Ti and Ta which are smaller than or equal to 4 wt% are added according to calculation of a first principle, and the stacking fault energy is controlled to be-95 mJ / m < 2 > to-89 mJ / m < 2 > (0K). Aiming at the defect of insufficient high-temperature plasticity of the gamma '-phase strengthened cobalt-based alloy, an electronic structure is accurately regulated and controlled through a first principle, intrinsic stacking fault energy is designed to reach an optimal negative value interval, a micro-twin mechanism is activated, dislocation slippage and micro-twin coordination is realized, and the problem of sudden plasticity drop caused by single deformation mechanism (dislocation slippage leading) and grain boundary weakening of a traditional alloy is solved; the high-temperature strength and the plasticity are synchronously improved.

The utility model discloses an automatic plastic proportioning and feeding device for an aluminum-plastic panel. The automatic plastic proportioning and feeding device comprises a pulse tank, a vacuum pump, a central controller and a plurality of raw material bins, the pulse tank is provided with a discharge port, an air suction port and a discharge valve; the suction opening is connected to the vacuum pump, and the pulse tank is supported and arranged on the rack in a real-time weighing manner through an electronic weighing structure; a discharge port of the raw material bin is communicated with the pulse tank through a conveying pipeline, and a feeding valve is arranged on each conveying pipeline; the central controller is connected with the electronic weighing structure, the vacuum pump and the plurality of feeding valves; the central controller starts the vacuum pump and sequentially opens the feeding valves of the corresponding conveying pipelines according to a specified batching table to suck the types and weights of the raw materials. According to the automatic plastic batching and feeding device for the aluminum-plastic panel, industrial automation of the whole material taking, batching and feeding process is achieved, the production efficiency and the production quality are improved, manpower is saved, and the environment is improved.