478 results about "Machine models" patented technology

The invention discloses a multi-modal wind turbine generator electromechanical transient modeling method based on artificial intelligence, and relates to the technical field of new energy power generation modeling, and the method comprises the steps: carrying out the causal association mining and dependence path recognition of a standardized spatio-temporal data cube through a causal discovery algorithm, constructing a causal topological graph, and carrying out the modeling of the new energy power generation. Performing cross-modal feature fusion through a space-time diagram attention network to generate a high-order feature tensor; dividing the high-order feature tensor into meta-learning task pools according to different models and environmental conditions, and training a cross-model general parameterization framework by adopting a double-layer optimization strategy; constructing a composite working condition generator based on the trained cross-model general parameterization framework, and performing constraint through a causal regularization loss function to form an extended test working condition set; and performing control parameter optimization on the extended test working condition set by adopting a multi-modal deep reinforcement learning algorithm to obtain optimized control parameters. According to the method, a foundation is laid for realizing electromechanical transient modeling with high robustness and high generalization ability through accurate causal modeling and cross-modal fusion.

The invention provides an Alzheimer disease auxiliary accurate identification method and system based on a multi-omics marker network and an adaptive support vector machine. Accurate identification of Alzheimer's disease and other types of dementia is realized through a marker network based on five modal data and an adaptive support vector machine model. According to the method, multi-source heterogeneous data such as blood, urine, neuroimaging, electrophysiology and clinical evaluation are fused, specific markers are analyzed and screened by adopting a weighted gene co-expression network, and cross-modal feature interactive learning is realized through a self-attention mechanism. An online learning mechanism is introduced to enable the model to adapt to new data distribution, and the contribution degree of each marker to diagnosis is output in combination with an interpretability module. Finally, multi-center data synchronization and model optimization are realized by means of a cloud platform, the accuracy, specificity and early diagnosis capability of AD diagnosis and identification and the adaptability of the model are remarkably improved, the limitation of the prior art is overcome, and doctors are assisted to diagnose and treat the Alzheimer's disease.

The invention discloses a psychological counseling intelligent recommendation method and system based on artificial intelligence, and relates to the field of psychological counseling cross technology, and the method comprises the steps: calculating a three-dimensional emotion vector according to an emotion axis space and a syntactic structure, constructing a psychological semantic tensor by using a tensor outer product, generating a user intention tensor by using a prototype weight, and carrying out a flattening operation. Obtaining a final intention vector; combining the structural potential energy deviation and the spectral distance to construct a matching factor tensor; constructing a factorization machine model, predicting a matching score, generating a prediction score matrix, screening by using a Top-K selection method, generating recommended content, and constructing a visual interface to display the recommended content. According to the method, the fine granularity and accuracy of emotional representation are improved through emotional axis space and syntactic dependency analysis, the matching precision of intention and resources is improved through comprehensive calculation of semantic gravitation intensity and spectral distance, and the expression ability of matching factor tensor is enhanced through fusion of structural potential energy deviation and spectral distance.

The invention provides a construction method and system of a settlement landscape evolution database based on multivariate spatial-temporal characteristics. According to the method, historical literatures, aerial photography, satellite images and landscape data are integrated to form a multivariate data set; constructing a dynamically updated database by using MySQL, and realizing data source associated storage by using a relational model; establishing a settlement landscape state machine model, and extracting spatio-temporal data to identify typical states and conversion conditions; constructing a state transition matrix, and analyzing the influence of social economic and environmental changes on evolution; the database is updated through field investigation, and a visual tool is developed to display the evolution process. According to the method, full-process dynamic analysis of settlement landscape evolution is realized, and efficient and accurate support is provided for decision making.

The invention relates to a flexible combined machine tool control system. The system comprises a multi-channel user interface module, a servo parameter monitoring module, a workpiece coordinate system management module and the like. The multi-channel user interface module is associated with a work station by means of a dynamic binding technology, automatically generates an interactive interface according to configuration parameters and visually configures an enabling state. And the servo parameter monitoring module displays and adjusts the shaft motion parameters in real time and adjusts the shaft motion parameters on line, the deviation is corrected through a self-adaptive algorithm, coordinate synchronization of multiple workstations is achieved through the algorithm and a batch writing function, and mixed analysis of an NC program and a user-defined instruction is supported. The hardware abstraction layer module encapsulates a heterogeneous control card interface, dynamically allocates hardware resources, and coordinates time sequence linkage of each part by using a state machine model. The system also combines visual inspection to correct cutter compensation parameters, and double safety protection is set. According to the invention, the problem of low equipment cooperation efficiency in multi-station flexible processing is effectively solved, and the system compatibility and the processing precision are improved.

The invention discloses a precise air conditioner energy-saving control optimization method based on reinforcement learning, which comprises the following steps of: acquiring environmental parameters such as temperature, humidity, thermal load, electric energy consumption and running state of an air conditioner system, generating a vector reflecting the current environmental condition, scoring a plurality of control actions through an improved Banditron perceptron model, and calculating the control parameters of the air conditioner system according to the scored control actions. Selecting an optimal control action through a Thompson sampling mechanism, executing the selected control action, collecting environment feedback information after execution, forming a feedback parameter set, dynamically adjusting parameter values of exploration behaviors through data analysis of a continuous control period, and finally jointly inputting a current environment state and the control action into a decision model. And generating a parameter set for setting operation of the air conditioner. Intelligent optimization and energy-saving regulation and control of the air conditioner control process are achieved.

The invention provides a heterogeneous database structure dynamic synchronization and fault-tolerant migration method, system and device, and belongs to the technical field of database migration. The method comprises the following steps: detecting a structural difference through combination of active detection and passive triggering; realizing DDL statement conversion of different databases by utilizing AST analysis through a cross-platform DDL converter; defining a migration process by adopting a migration state machine model so as to ensure orderly migration; and a breakpoint resume and consistency guarantee mechanism is utilized to ensure that migration fault tolerance is consistent with data by means of a vernier triple, an intelligent recovery strategy and a bidirectional check protocol. According to the method, the problems of structure synchronization and migration fault tolerance of the heterogeneous database in an environment without native change log support are effectively solved, the automation, high efficiency, stability and reliability of database migration are improved, and the method has wide application value.

The invention relates to the field of impact failure full-process prediction of complex-structure die castings, and discloses a data-driven impact failure full-process prediction method for complex-structure die castings, which comprises the following steps: step 1, performing mold flow simulation on a target part to obtain the solidification time and porosity of any position of the target part; 2, the solidification time and porosity of any position of the target part are input into a multi-layer perceptron model, and the multi-layer perceptron model outputs mechanical parameters of any position of the target part; the multi-layer perceptron model is trained based on data of actually measured mechanical properties; 3, acquiring a collision simulation grid model of the part, mapping the mechanical parameters to each grid unit of the collision simulation grid model, and generating an input file for collision simulation; and 4, performing collision simulation based on the input file in the step 3.

The invention relates to a wind power transmission fault early warning method and device based on single machine-cluster model interaction. The method comprises the steps that historical operation data of wind turbine generators of the same type in the same wind power plant are collected; aggregating to obtain a first data set corresponding to a single wind turbine generator set and aggregating to obtain a second data set of the same type of wind turbine generator sets; performing model training according to the first data set to obtain a single-machine early warning model, and performing model training according to the second data set to obtain a cluster early warning model; interactively correcting an output result by using the single-machine early warning model and the cluster early warning model; and adopting a voting fusion strategy to obtain a final prediction result. Through interaction and fusion of the single machine-cluster model, personalized features of the single machine model and a common rule of the cluster model are fully utilized, result misjudgment and missed judgment are avoided, and the accuracy of fault prediction of the wind turbine generator transmission system is remarkably improved. The method can better adapt to complex and changeable operation conditions of the wind turbine generator, and the reliability of fault early warning is enhanced.

The invention provides a cooperative control method and device for low-voltage power consumption of a whole vehicle and electronic equipment, and relates to the technical field of automotive electronics, and the method comprises the steps: obtaining multi-modal original data (including vehicle state information, environment information and user behavior information), and carrying out the preprocessing; performing feature extraction and fusion processing on the preprocessed multi-modal original data to generate a high-level semantic event used for representing the vehicle leaving intention of the user or the parking scene; matching a global power consumption management strategy of a high-level semantic event through a hierarchical finite-state machine model; and according to the global power consumption management strategy, executing hierarchical power supply control on the low-voltage electricity load of the whole vehicle, and in the hierarchical power supply control process, dynamically adjusting control parameters in the global power consumption management strategy based on a comparison result of the actual power consumption monitoring data and a preset baseline model. Depth power shortage of the storage battery caused by redundant power consumption is effectively avoided, the risks that the vehicle cannot be started and equipment is abnormal are reduced, and the reliability of the whole vehicle and the user experience are improved.

The invention relates to the technical field of data processing, and discloses an intelligent arrangement, storage, tracking, management and control method and system for sulfur hexafluoride gas cylinders. The method comprises the following steps: performing leakage risk assessment through a sulfur hexafluoride concentration sensor and a gas cylinder pressure monitoring device to obtain an environmental risk data set; performing multi-dimensional state sensing on the gas cylinder according to the intelligent label and the inclination angle sensor to obtain a dynamic state information set of the gas cylinder; performing path optimization through a multi-target search algorithm to obtain a multi-target path cost matrix; performing Pareto optimal solution screening through a leading edge set maintenance mechanism to obtain an optimal scheduling path set; and tracking the life cycle of the gas cylinder based on the state machine model to obtain full life cycle tracking data. The problems of intelligent scheduling optimization and full-life-cycle tracking management and control under the multi-target constraint condition in sulfur hexafluoride gas cylinder storage management are solved.

The invention discloses a pouring planning method and device of a material distributing machine, electronic equipment and a storage medium, and the method comprises the steps: obtaining a BIM model of a pouring site, a material distributing machine model and obstacle information of the pouring site, and obtaining a feasible region of the material distributing machine corresponding to the material distributing machine model in the pouring site according to the BIM model, the material distributing machine model and the obstacle information; according to the pouring attributes of the components contained in the BIM model, the components contained in the pouring site are divided into pouring components and poured components, communication processing is conducted according to the adjacency of the pouring components, and the area where all the pouring components are located is determined as a pouring area; according to the BIM model of the pouring area and the feasible region, based on concrete flow collapse and component connectivity, a collapse statistical algorithm is used for calculation, and a pouring planning result of the material distributing machine is generated; and according to the pouring planning result, a material distributing machine is controlled to move in the pouring site so as to conduct pouring treatment on all the areas in the pouring area. And the pouring safety and the pouring efficiency are improved.

The invention relates to a multi-Agent-based hierarchical progressive RFC state machine model extraction method, belongs to the technical field of network protocol extraction, and solves the problems that RFC document information is difficult to refine when a state machine model is extracted at present, the large model used for extraction is easily influenced by illusion due to the limitation of the large model, and the accuracy and recall rate are difficult to guarantee. Comprising the steps of obtaining an RFC document of a to-be-extracted state machine model and performing structured processing to obtain each RFC document block taking a title as a demarcation point; based on a preset protocol state machine term, recalling a core document block, a supplementary document block and a long tail document block from each RFC document block; extracting an initial state machine model from the core document block, performing review, and optimizing the initial state machine model based on a review result to obtain a reviewed state machine model; and on the basis of the supplementary document block and the long tail document block, performing supplementary optimization on the reviewed state machine model to obtain an RFC state machine model.

The invention discloses a spherical roller bearing entity simulation method, device and equipment and a medium, and relates to the technical field of wind power generation. According to the scheme, when the finite element mesh model of the spherical roller bearing is constructed, the rollers are connected to the bearing inner ring and the bearing outer ring through the spring units, that is, traditional finite element constraint is replaced with the mode that the rollers are constrained through the spring units, and the conventional constraint limitation in finite element simulation is broken through; accurate analysis of bearing roller contact in a complete machine model is realized; after a complete machine finite element mesh model is generated based on a model of the spherical roller bearing, simulation parameters and loading parameters of the complete machine finite element mesh model are set, the simulation parameters at least comprise a working clearance between a roller and a raceway, that is, contact offset is added to simulate the existence of the radial clearance of the bearing, and the loading parameters of the complete machine finite element mesh model are set. The problem of simulation of play contact of the SRB main bearing under the working condition of high axial force is solved, and three-dimensional balance of physical authenticity, calculation efficiency and engineering applicability is achieved.

A method for designing proteins using multi-objective reinforcement learning can include generating, by one or more processors using a machine model, based on an initial protein sequence data structure, a plurality of protein sequences, the machine learning model configured based on reinforcement learning from a plurality of reward metrics including at least one reward metric associated with experimental data regarding example sequence data, scoring, by the one or more processors, using a plurality of scoring functions, the plurality of protein sequences, to select a subset of protein sequences of the plurality of protein sequences, and outputting one or more selected protein sequences of the subset of selected protein sequences.

The invention discloses a concrete vibration sufficiency judgment method and system based on multi-sensor fusion. The method comprises the steps that first information of a vibration rod in concrete and second information in the concrete are obtained; the first information is motion state information; the second information is hydration reaction state information and comprises temperature change in the concrete; fusing the first information and the second information to obtain a fusion result; vibration sufficiency judgment is conducted according to the fusion result, and a vibration sufficiency judgment result is obtained; according to vibration sufficiency judgment, weighted decision fusion is carried out on the first information and the second information according to a preset weight, and judgment is carried out in combination with a self-adaptive threshold value and a state machine model; and generating operation guidance information based on the global region or the local sub-region according to the vibration sufficiency judgment result. According to the method, intelligent and accurate judgment on the concrete vibration sufficiency is achieved, the vibration effect can be evaluated more comprehensively and objectively, under-vibration or over-vibration is effectively avoided, and the concrete engineering quality is improved.

The invention discloses a multi-dimensional attribution analysis method and system for a cross-media advertisement putting effect, relates to the technical field of data analysis and advertisement, and is used for solving the problems that the advertisement conversion path recognition accuracy is reduced and the putting strategy optimization strategy of an attribution result is low. The method comprises the following steps: collecting contacts, time sequences and advertisement creative information of a user in a multimedia channel through a point burying technology, classifying according to a channel mapping rule, calculating the conversion contribution degree and propagation efficiency of each channel in combination with historical conversion data, obtaining an influence weight, constructing a user cross-channel access path diagram, and counting node switching frequency. And a multi-layer perceptron model is adopted to calculate a key node coupling coefficient, extract a nonlinear interaction relationship, fuse the coupling coefficient, a time sequence and advertisement originality, extract an attribution variable based on principal component analysis, generate a structured attribution result, and realize refined optimization of an advertisement strategy.

The invention relates to a real-time video stream quality analysis and adaptive control method based on AI driving, and belongs to the technical field of artificial intelligence and video stream transmission. The method comprises the following steps: receiving a real-time video stream from a server through client equipment, and the real-time video stream comprises a dynamic adaptive streaming media and a live broadcast stream; analyzing the video stream and network conditions according to the video stream in combination with a network monitoring tool to extract video features; inputting the extracted features into a machine model based on a neural network to generate quality evaluation including network conditions, video parameters and annotations; determining whether to switch to video representation forms with different bit rates or not by analyzing a quality evaluation result; and when the switching is determined, a request for updating the playing representation form is sent to the server, so that the artificial intelligence technology is realized to optimize the video quality, reduce buffering and improve the user experience, and the method is suitable for a real-time streaming media scene.

The invention discloses a distributed power supply access and control method in an intelligent circuit breaker, and particularly relates to the technical field of intelligent circuit breakers. Determining a preliminary synchronization state of the distributed power supply based on the voltage, the frequency and the phase difference; after the synchronization condition is met, collecting the frequency change rate, the voltage amplitude and the phase angle change of each node, and constructing an electrical diagram structure with a dynamic weight; performing weighted aggregation on the node information by using a graph attention network, and outputting a virtual inertia estimated value; inverting node output power based on a synchronous machine model, and estimating a system load disturbance rate in combination with time difference; inputting the inertia and the disturbance rate into a synchronous risk prediction model to generate a synchronous risk score value; when the score value is higher than a threshold, determining that a false synchronization risk exists, and prohibiting grid connection; if the score value is lower than the threshold value, the circuit breaker is controlled to be closed, slope type power injection is executed, smooth grid connection is achieved, the dynamic judgment capacity of the distributed power supply before grid connection is effectively improved, and the grid connection instability risk is reduced.

The invention provides a highway soft rock slope three-dimensional intelligent monitoring and disaster early-stage identification early-warning method, and belongs to the technical field of slope monitoring, and the method comprises the following steps: shooting a highway roadbed by using remote sensing equipment, obtaining corresponding remote sensing image data, and identifying a remote sensing image; the image is input into a machine model for feature comparison learning, hidden danger point screening and disaster body recognition are carried out, a soft rock area is sampled, a soft rock mass three-dimensional space model is established, data in the soft rock mass are collected in real time by arranging a collecting device, the collected data serve as dynamic data of the soft rock mass three-dimensional space model, and the dynamic data are stored in a storage device. Remote sensing data and real-time collected data are input into an on-line early warning system, whether instability occurs in a soft rock area or not is judged, early warning information is sent to an area where instability or landslide possibly occurs, and management personnel are notified to check on site and take protective measures.

The invention discloses a meta-learning-based small sample pharmacokinetic property prediction method and related equipment, and relates to the field of pharmacokinetics. The method comprises the following steps: acquiring multi-dimensional feature data of candidate compounds, wherein the multi-dimensional feature data comprises atomic-scale features, molecular descriptors and molecular structure features; inputting the multi-dimensional feature data into a preset pharmacokinetic property prediction model to obtain a preliminary prediction result, the pharmacokinetic property prediction model being pre-trained through meta-learning based on small samples, the model comprising a first machine learning model and a second machine learning model, the preliminary prediction result comprises a first prediction result output by the first machine learning model and a second prediction result output by the second machine learning model; and determining a pharmacokinetic property prediction result of the candidate compound according to the first prediction result and the second prediction result. According to the method, the problem of low accuracy of a pharmacokinetic property prediction result can be relieved in a small sample scene of early drug research and development.

A method can include receiving property data for a simulation model; analyzing the property data with respect to geometry of the simulation model to select a matrix preconditioner scheme using a machine model; and executing a computational simulator that implements parallel processing based on application of the matrix preconditioner scheme to a matrix representing the simulation model to generate simulation results based on the property data.

The invention relates to an RRC (Radio Resource Control) layer enhanced mobile model simulation method and system based on a 3GPP (3rd Generation Partnership Project) NTN (Network Temporary Network) standard. The method comprises the following steps: S1, constructing a simulation environment conforming to the 3GPP NTN standard; s2, constructing an RRC state machine in the NTN scene, wherein the RRC state machine comprises a user RRC state machine and a satellite RRC state machine; s3, configuring a dynamic conversion condition of the RRC state machine and a corresponding behavior logic; s4, scheduling events and injecting NTN parameters, and performing NTN propagation characteristic simulation based on the simulation environment; and S5, counting performance indexes generated in the simulation process, and outputting an NTN scene simulation report. According to the method, the simulation environment conforming to the 3GPP NTN standard is constructed, the RRC state machine in the NTN scene is constructed, the characteristics of long delay, high mobility and orbit prediction of the satellite are adapted, and the problem that a state machine model of a traditional simulation method is not applicable is solved.

The invention provides a virtual camera projection method and system, electronic equipment and a medium, and belongs to the technical field of target detection, and the method comprises the steps: constructing a training data set according to sampling points in a three-dimensional space; modeling projection and back projection between the real camera view and the virtual camera view to obtain a two-stage multi-layer perceptron model; training a two-stage multi-layer perceptron model according to the training data set to obtain a trained model; and obtaining a pixel mapping table between the virtual camera view and the real camera view based on the trained model. According to the method, the reversible pixel mapping relation between the real camera and the virtual camera is constructed by adopting the two-stage multi-layer perceptron model, and the pictures under any group of camera parameters are uniformly converted to be under the virtual camera parameters, so that high-quality label data are efficiently generated through the automatic labeling model; the data utilization rate and the generalization ability of the automatic labeling model are remarkably improved, so that the model can directly process multi-source heterogeneous data, and the model adaptation cost is reduced.

The invention belongs to the technical field of crowd sensing, and discloses a trust-based crowd sensing distributed privacy protection method and system, and the method comprises the steps: constructing a detailed reputation evaluation system, covering the calculation of a reputation value and the setting of a reputation threshold value of a participating user, and integrating a game model in an evaluation stage, the user is guided to avoid malicious behaviors through an income incentive mechanism so as to obtain higher income; a dobby machine model is introduced to dynamically balance the decision-making process of'exploring 'new users and'utilizing' high-reputation users, so that the cold start problem of insufficient reputation of the new users is effectively solved; and finally, verifying the security of the scheme through theoretical derivation of the security attribute of the scheme, verifying the validity of the scheme in the aspects of data credibility, privacy protection effect and the like in combination with experimental analysis, and providing support for credible operation and privacy protection of the crowd sensing system.

The invention provides a three-coordinate measuring machine adaptive dynamic error compensation method, and relates to the field of three-coordinate measuring machines, and the method comprises the steps: obtaining multi-source state data of a three-coordinate measuring machine, and calculating a real-time dynamic error, the state data comprising a motion state, a dynamic response and environment disturbance data; based on the real-time dynamic error, using a recurrent neural network to construct a virtual measuring machine model, and performing offline training to obtain an initial error prediction model; and acquiring real-time error feedback data, and performing fine adjustment on the initial error prediction model by using an incremental learning algorithm and a sliding time window mechanism to obtain a prediction model capable of dynamically evolving and aging adaptive parameters. The method is used for overcoming the defect that in the prior art, a linear model or a fixed compensation parameter is difficult to accurately describe and compensate all dynamic errors sometimes.

The invention belongs to the field of building environment control, and provides a thermal comfort dynamic adaptive control method based on multi-modal wave analysis and AI driving. According to the method, environmental parameters, user physiological signals (HRV) and subjective thermal comfort data are synchronously collected in real time, time-frequency characteristics of the physiological signals are extracted through continuous Morlet wavelet transform, high-order dynamic characteristics are constructed, the thermal comfort state is predicted in combination with deep learning, a deep reinforcement learning strategy is optimized through an NSGA-II algorithm, a multi-target optimal strategy set is generated, and the optimal thermal comfort state is obtained. And finally, a multi-arm bandit model with Bayesian inference is utilized to realize strategy online adaptive updating. By applying the method, the thermal comfort prediction precision can be expected to be greater than or equal to 90%, the net energy conservation is greater than or equal to 32%, the limitation of the existing thermal comfort regulation and control method in a dynamic situation is effectively solved, and the dynamic balance of personalized thermal comfort and system energy conservation is realized.

The invention provides a system behavior simulation and logic verification method and system based on a state machine model. The method comprises the following steps: constructing a state machine model of system behaviors; generating an intermediate representation code embedded with a time constraint check function, wherein the time constraint calculates a state transition time upper limit based on a Lyapunov stability model; compiling the executable code framework; receiving real-time operation data through a human-in-the-loop interface, mapping the real-time operation data into an input event, driving simulation operation, and dynamically rendering a state conversion process by adopting a virtual reality technology; verifying the behavior correctness based on the state coverage and a logic closed-loop index, wherein the logic closed-loop detects a deadlock risk through an improved banker algorithm; and if the verification is not passed, the recent valid state is traced back, the model is adjusted, and the verification is executed again. According to the method, the problems of inaccurate time sequence verification, insufficient real-time interaction, single verification index and the like in a traditional method are solved, and the accuracy and reliability of system simulation are remarkably improved.

The invention discloses a design method for an on-orbit accompanying measurement and control communication machine state machine model, and the method comprises the steps: constructing a data model of a measurement and control communication machine state machine, and enabling the data model to be used for calling data externally; constructing a context model of the state machine of the measurement and control communication machine based on the data model, and constructing a logic architecture of the state machine of the measurement and control communication machine based on scene service data; based on the logic architecture, the context model obtains an executable extended state machine and updates the feature attributes; obtaining a simulation model through periodic continuation based on the state machine capable of executing expansion and the context model, wherein the simulation model is used for processing uplink and downlink remote control and telemetering, voice and distance measurement service data of satellite communication, responding to a communication machine instruction set of the state machine of the measurement and control communication machine and outputting communication machine product telemetering; universal configuration is implemented based on the communicator instruction set and the communicator product telemetry.

The invention relates to a machine learning and physical priori knowledge constraint-based double perovskite halide material layer-by-layer screening method, equipment and a medium, and aims at solving the problems of low efficiency, large candidate space and insufficient multi-performance balance of a traditional data-driven screening framework, and constructing a cascade screening method taking spectral maximum efficiency (SLME) as a guide. Discovery of a high-precision photovoltaic material is realized through a progressive decision chain strictly following the sequence of space group symmetry, SLME, band gap characteristics and thermodynamic stability. A candidate material space is constructed based on an element coordination environment and a charge neutrality principle, the structural stability is primarily screened by using a new tolerance factor, then layer-by-layer screening is performed sequentially through a machine model, and finally a high-performance material is screened out. Compared with the prior art, the invention provides a layer-by-layer screening method which takes SLME as a core and is constrained by physical priori knowledge, and multi-dimensional efficient screening from structure, optics, electricity to stability is realized.