1483 results about "Performance prediction" patented technology

A transformer-based student performance prediction and reasoning intervention is disclosed. The system comprises a data repository coupled to a transformer-based prediction module that processes student data through multi-head attention mechanisms to generate performance predictions and identify potential learning shortfalls. A reasoning-enhanced large language model algorithmically generates personalized corrective action plans by applying structured decomposition of learning challenges, multi-step reasoning, and hypothesis testing. An algorithmic prompt formulation system optimizes inputs using field-specific, level-specific, and shortfall-specific templates. The system implements a workflow including shortfall detection against educational thresholds, causal factor analysis, intervention generation, and adaptive refinement based on outcomes. This approach enables early identification of academic challenges and timely implementation of personalized interventions to improve student learning outcomes.

The invention relates to the technical field of process optimization, and discloses a hot work die steel heat treatment process optimization method and system.The method comprises the steps that hot work die steel samples are collected under multiple sets of different process conditions, and a performance basic data set is obtained; constructing a multi-target coordination optimization model based on the performance basic data set; performing phase change detection on the hot work die steel sample to obtain phase change monitoring data; performing prediction in combination with the phase change monitoring data to obtain a performance prediction result; and solving an optimal process parameter combination based on the performance prediction result and the multi-target coordinated optimization model, and realizing simultaneous optimization and coordinated balance of a plurality of performance indexes in the heat treatment process of the hot work die steel by making full use of associated information among different performance indexes.

The invention relates to a multi-agent-based material performance prediction and synthesis system, and the system comprises a multi-agent data enhancement module which is configured to be used for firstly disassembling a complex problem into a plurality of subtasks, and then constructing a fine tuning data set comprising Sub-CoQ question and answer pairs by starting multi-source parallel retrieval; the multi-expert debate module is configured to be used for simulating decision conflicts of different roles in material engineering and generating a direct preference optimization DPO data set through debate; the training and verification module is configured to be used for training and verifying a large model MatMind in the field of materials by utilizing supervised fine tuning SFT and reinforcement learning RLHF based on the fine tuning data set and the DPO data set; and the material performance prediction and synthesis module is configured to be used for realizing intelligent recommendation of a material performance prediction and synthesis process by importing input parameters into the large model MatMind.

The invention provides an airfoil profile aerodynamic performance prediction method based on a large model retrieval enhancement generation framework, and the method comprises the steps: building a weakly-coupled database through fusing the geometric and aerodynamic characteristics of an airfoil profile, further building a strongly-coupled airfoil profile data distributed retrieval model, building the mutual cooperation of two stages of rough arrangement and fine arrangement, and achieving the prediction of the aerodynamic performance of the airfoil profile. And various characteristics of the airfoil profile are comprehensively considered, so that data can be retrieved more accurately. Meanwhile, fine-grained airfoil aerodynamic knowledge is generated in combination with a large-parameter language model, and the knowledge is optimized through knowledge distillation and fine tuning technologies. According to the method, explainable airfoil aerodynamic design priori knowledge can be excavated, rapid intelligent design of airfoils is powerfully supported, and the method has important significance in promoting the airfoil aerodynamic design to develop towards the intelligent and efficient direction.

The invention discloses a multi-source data fusion foam concrete construction process monitoring method and system, and relates to the technical field of concrete construction, the method comprises the following steps: obtaining a standard response data set of foam concrete, including raw material ratio parameters and performance index parameters which are stored in an associated manner; constructing and training a hybrid hierarchical performance prediction model, wherein the model comprises a shared prediction layer based on integrated learning and a plurality of independent output layers connected with the shared prediction layer; in combination with a multi-objective optimization algorithm, by taking compressive strength and cost optimization as an objective, constructing an evaluation function for global optimization, and obtaining a target mix proportion scheme; acquiring real-time process data associated with the target mix proportion scheme, and inputting the real-time process data into the mixing layering performance prediction model to obtain a prediction result; and comparing with a preset design target, and generating an adjustment instruction for adjusting the matching parameters of the subsequent stirring batches. The problem that in the prior art, the performance fluctuation of foam concrete of different batches is large due to the fact that the construction condition cannot be dynamically adjusted in real time is solved.

The invention provides a composite material performance prediction and process optimization method based on a neural network, and the method comprises the steps: firstly collecting multi-source data in the preparation and test process of a composite material, carrying out the preprocessing of the data, screening key feature variables as input variables, constructing a feedforward artificial neural network model, and predicting and outputting the performance indexes of the composite material. And training the model, performing iterative optimization on model parameters, and optimizing composite material process parameters by using the optimized model based on a reverse optimization strategy of a genetic algorithm to obtain an optimal process parameter combination. The invention provides a scientific, efficient and reliable tool for design and optimization of composite materials, and particularly has wide application prospects in high-requirement industries such as aerospace and the like.

The invention provides an aircraft aerodynamic configuration design method and device based on simulation, and relates to the technical field of aircraft aerodynamic configuration design, and the method comprises the steps: obtaining aerodynamic characteristic parameters of aircrafts of different airfoils to construct a multi-source aerodynamic database; a neural network model is established based on the database, airfoil geometric parameters serve as input, the lift coefficient, the resistance coefficient and the lift-drag ratio serve as labels for training, and an aerodynamic performance prediction model is obtained; candidate combinations are generated in an airfoil geometric parameter design space, aerodynamic characteristics of the candidate combinations are predicted through a model, a fitness function is optimized by utilizing dynamic weights, and the combination with the highest fitness is selected as an optimized airfoil parameter by adopting a genetic algorithm; and performing simulation verification on the optimization parameters, extracting a simulation result, comparing the simulation result with the output of the prediction model, judging that the optimization combination is effective when an error meets a preset requirement, and completing aerodynamic configuration design accordingly. The method improves the prediction accuracy and design efficiency of aerodynamic performance.

The invention provides an ultra-high performance concrete multi-performance prediction method based on machine learning. The ultra-high performance concrete multi-performance prediction method comprises the following steps: Step 1, establishing a data set; step 2, data preprocessing is carried out; step 3, establishing an optimal prediction model: based on the feature subset, adopting a plurality of different machine learning algorithms for training, and selecting the machine learning algorithm with the best training effect as the optimal prediction model; step 4, selecting an optimal feature subset; step 5, explaining the influence of the features on model prediction: calculating the contribution degree of each feature to a prediction result based on the optimal prediction model and the optimal feature subset, and helping to understand the decision process of the model; and Step 6, performance prediction of the ultra-high performance concrete: inputting parameters of the to-be-predicted ultra-high performance concrete into the optimal prediction model to obtain a predicted value of the performance. The technical problems that an existing UHPC performance prediction method is incomplete in data set, insufficient in consideration of data processing and feature engineering and poor in model interpretation can be solved.

The invention provides a geopolymer preparation and optimization method and system based on machine learning. The method is applied to the technical field of material science and machine learning. The method comprises the following steps: acquiring geopolymer preparation experimental data and preprocessing the data; performing nonlinear regression modeling on the geopolymer performance based on four machine learning regression algorithms, and constructing a geopolymer performance prediction model; calculating and distributing weights according to the mean square error of each machine learning model on the verification set, and performing weighted fusion to obtain a performance prediction result; receiving target performance parameters input by a user and an initial raw material ratio range, performing performance prediction by using the trained geopolymer performance prediction model, and reversely searching an optimal ratio combination meeting target performance constraints through an optimization algorithm; and preparing a geopolymer according to the optimal ratio combination to prepare the coal gangue-slag-fly ash geopolymer grouting material. According to the method, the prediction precision and the model generalization ability are effectively improved, and intelligent recommendation and accurate performance prediction of the raw material ratio are realized.

The invention discloses a multi-modal data driven commercial vehicle frame performance prediction method and system. The method comprises the following steps: uniformly coding design variables of a frame; constructing a multi-modal performance response data set based on finite element simulation and test results of mass production vehicle models, and realizing fusion of simulation and test data by adopting a maximum mean difference and related alignment algorithm; constructing a graph perception Transform multi-task prediction model fusing the structure topology and the physical position features, and predicting key performance indexes of the frame under a plurality of typical working conditions; through weighted multi-task loss function joint training, an uncertainty mechanism is introduced to dynamically adjust task weights; and after training is completed, deploying to an inference engine to realize second-level prediction and support increment fine adjustment updating. According to the method, repeated modeling and solving processes are avoided, the frame performance prediction efficiency and the adaptive capacity are remarkably improved, and the method is suitable for rapid evaluation of the frame performance of commercial vehicles of various structural configurations and material types.

The invention discloses a multi-modal measurement system and method of a three-coordinate measuring machine, relates to the cross technical field of precision measurement, computer vision and intelligent manufacturing, and is used for solving the problems of low automation degree, strong subjective dependence and lack of assembly performance prediction capability in automobile covering part characteristic line detection. According to the method, point cloud data and positioning coordinates are synchronously collected through a multi-mode sensor, and a fusion point cloud sequence is generated through time sequence synchronization; constructing a high-precision three-dimensional point cloud model through noise filtering, registration and curved surface reconstruction; automatically extracting a feature line path based on curvature analysis and generating an analysis section; calculating a sharpness index through contour fitting, and generating a quality evaluation coefficient in combination with a CAD ideal model contour tolerance deviation; and finally, defects are identified through virtual assembly analysis, a sensor remeasurement path instruction is generated, and a quality report is output in combination with a quality coefficient, so that the detection precision, the efficiency and the assembly quality prediction capability in the measurement process of the three-coordinate measuring machine are improved.

The invention discloses a machine learning assisted polyethylene reaction performance prediction model training method, prediction method and device. The method comprises the following steps: acquiring a training set; screening feature items used for model training; obtaining a gradient boosting regression model for catalytic activity, a gradient boosting regression model for molecular weight and a gradient boosting regression model for molecular weight distribution; extracting feature items for model training from the data of the training set so as to obtain feature vectors; and respectively inputting the feature vectors into each model so as to train each model, thereby respectively obtaining hyper-parameters of the trained gradient-boosted regression model for catalytic activity, hyper-parameters of the trained gradient-boosted regression model for molecular weight and hyper-parameters of the trained gradient-boosted regression model for molecular weight distribution. According to the method, a model relationship between input characteristics and polymerization results (including catalytic activity, molecular weight, molecular weight distribution and the like) is established through training set learning.

The invention belongs to the field of material performance optimization, and particularly discloses a carbon ceramic resistor formula optimization method based on a genetic algorithm and Bayesian optimization, and the method comprises the steps: receiving formula parameter combinations and corresponding performance parameters of a plurality of groups of carbon ceramic resistors; a Gaussian process regression model based on a radial basis kernel function is established to construct a mapping relation between formula parameters and performance parameters, and a performance prediction model of the carbon ceramic resistor is obtained through training by maximizing marginal likelihood optimization model hyper-parameters; and based on the performance prediction model, performing joint optimization by using a genetic algorithm and a Bayesian optimization algorithm, and determining an optimal formula combination. According to the method, global exploration and local fine convergence can be considered, the prediction efficiency can be improved, and the accuracy, comprehensiveness and reliability of a prediction result can be improved.

The invention discloses a solar cell performance prediction method and device based on a physical information fusion model, and relates to the technical field of computer data processing and semiconductor characterization. According to the technical scheme provided by the invention, the deep neural network architecture is adopted to perform feature extraction and fusion on the multi-dimensional spectral information, the prediction precision is improved by more than 35%, and the reliability is improved by more than 40%, so that the prediction performance is remarkably improved; an innovative physical information fusion loss function is adopted for training the model and comprises a data-driven prediction loss item and a constraint loss item based on a semiconductor device physical rule, and a prediction task and a physical principle are deeply fused, so that the prediction model can understand a physical causal relationship between spectral characteristics and device performance, and the prediction efficiency is improved. According to the method, more accurate performance prediction and better physical interpretability are realized, multiple physical constraints are adopted to standardize the prediction result, so that the model prediction result conforms to the physical principle of the semiconductor device, and the physical rationality and engineering application value of the prediction result are improved.

Disclosed in the present invention is an artificial-intelligence-based performance prediction processing method for a carbon-fiber carbonization process. The method comprises: preprocessing experimental data under test, so as to obtain said experimental data that has been subjected to data cleaning; then, using a sliding window processing method to slide on time series data, extracting data within a window at each position and using the extracted data as an input sample, and determining an input feature and an output variable feature of each input sample, so as to convert the time series data into a plurality of experimental data samples under test in the format of a target model input; performing random data set division on said plurality of experimental data samples, so as to obtain some training sets and some test sets; and constructing a target model, and inputting said experimental data samples into the target model. The target model can implement a relatively accurate mechanical-performance prediction for a carbon-fiber-precursor carbonization process, and the model has an optimal performance in all aspects and has a relatively good generalization capability.

The invention discloses a verification system and method for a material formula through a confidence interval, and relates to the technical field of material informatics and intelligent research and development decision. Comprising a data acquisition module used for acquiring candidate formula basic data, historical experiment basic data, environment associated data, material recessive data and equipment state data and preprocessing the acquired data; according to the method, the final error value is obtained through fusion, the adjusted final credible interval is constructed, the problems that an existing material performance prediction tool can only output a point prediction result and lacks a stable credible interval, and engineers are difficult to assess that performance reaches the standard and actually and successfully grasp are solved, the coverage rate is verified through the verification set, the error scale is scaled, and the reliability of the material performance prediction tool is improved. It is ensured that the credible interval meets the preset coverage requirement, successful mastering of performance standard reaching can be quantified, an engineer does not need to depend on experience judgment any more, and the accuracy of performance evaluation is improved.

The invention relates to a generative molecule reverse design system based on reinforcement learning, which comprises a data set construction module, a multi-target performance prediction model establishment module, a pre-training module, a reward function construction module and an optimization module, and is characterized in that the data set construction module is used for constructing and screening to obtain a molecular structure performance data set; the multi-target performance prediction model establishment module is used for establishing a multi-target performance prediction model based on the constructed molecular structure performance data set; the pre-training module is used for pre-training a molecular generation model by using the screened molecular structure data; the reward function construction module is used for constructing a layered multi-target reward function; and the optimization module is used for rapidly evaluating key indexes by using a performance prediction model by adopting a reinforcement learning method, and carrying out optimization adjustment on the molecular generation model through a layered multi-target reward function. According to the invention, efficient and systematic reverse design of lithium metal negative electrode interface self-assembly molecules can be realized.

The invention relates to the technical field of computing power intelligent scheduling, and discloses a computing power intelligent scheduling method and system for a heterogeneous computing power cluster, and the method comprises the steps: firstly analyzing task features from task description submitted by a user, and generating an internal task object; and then, constructing a resource portrait by collecting real-time resource monitoring data and static configuration information of each node in the heterogeneous computing power cluster. Based on the information, the execution performance of different tasks on each node is predicted by using a machine learning model, and a performance prediction mapping table is formed. Then, candidate resources are screened and sorted according to the mapping table, and it is ensured that the optimal node is selected to be bound with the task; therefore, not only is the matching degree of the task characteristics and the hardware attributes considered, but also the dynamic state of the hardware resources is combined, so that a more accurate task scheduling strategy is realized, and the overall operation efficiency and the resource utilization rate are effectively improved.

The invention relates to the technical field of air conditioner fan blade design optimization, and discloses an air conditioner fan blade optimization method and system based on a BP neural network and a GA algorithm. The method comprises the following steps: obtaining initial geometric parameters and performance data of a fan blade, and cleaning and standardizing the initial geometric parameters and the performance data to form a standard data set; a BP neural network is used for training to obtain a fan blade performance prediction model; a genetic algorithm is applied to optimize the prediction model, and a new design parameter population is generated through genetic operations such as selection, crossover and variation; the optimized parameters are input into a CAD system to generate a candidate fan blade model, numerical simulation is carried out, and performance indexes of the candidate fan blade model are calculated; and screening excellent individuals based on a multi-objective optimization method, iteratively executing optimization and simulation processes until convergence, and finally outputting an optimal fan blade design. According to the method, the fast prediction of the neural network and the global search capability of the genetic algorithm are combined, the dependence of traditional optimization on high-frequency numerical simulation is reduced, and the design efficiency and quality are improved.

The invention discloses an adaptive evaluation method for the health degree of an electrolytic cell, and the method comprises the following steps: collecting the multi-dimensional operation parameters of the electrolytic cell in real time, and carrying out the preprocessing of the collected time series data, so as to construct a training sample with a time window; extracting a multi-scale time sequence feature from the training sample to form a feature vector; inputting the feature vector into a weight adjustment network, and outputting a dynamic weight vector; weighting the feature vector by using the dynamic weight vector to generate a weighted feature vector; inputting the weighted feature vector into a performance prediction model, and outputting a short-term performance prediction value of the electrolytic cell at a future moment; after the corresponding real performance value is obtained, calculating a prediction error of the short-term performance prediction value, and constructing a reinforcement learning reward signal based on the prediction error; updating a strategy of the weight adjustment network through a reinforcement learning algorithm by utilizing a reward signal, thereby optimizing dynamic weight vector generation at a subsequent moment; based on the dynamic weight vector and the feature vector at the current moment, a comprehensive health degree index of the electrolytic bath is obtained through calculation; according to the method, main factors influencing the equipment health degree in different stages are intuitively revealed, and a basis is provided for operation and maintenance decision making.

The invention discloses a construction method and device of a metal material performance prediction model, equipment and a medium. The method comprises the following steps: constructing an initial graph neural network; acquiring a three-dimensional volume element structure of the specified metal material and mechanical property data corresponding to the three-dimensional volume element structure; describing the three-dimensional volume element structure as an undirected graph structure; constructing a target data set by using the undirected graph structure and the mechanical property data; and performing iterative training on the initial graph neural network through the target data set to obtain a metal material performance prediction model. Therefore, the undirected graph structure of the three-dimensional volume element structure is used as a bridge, the metal material performance prediction model between the complex three-dimensional volume element structure and the mechanical performance data is established through the graph neural network, the model can rapidly and precisely predict the macroscopic performance of the metal material, the cost is low, and the method is easy to implement. And the requirements of rapid iteration and real-time feedback of the macroscopic performance of the material in engineering design can be met.

The invention belongs to the technical field of underground engineering intelligent construction, and particularly discloses a shield tunneling performance prediction method and system based on an interpretable BO-LGBM model, and the method comprises the steps: receiving detection data in a shield tunneling process, and determining tunneling parameters, geological parameters and karst parameters based on the detection data, so as to construct an input feature set; constructing an LGBM model, performing multi-stage screening on the input feature set to obtain a comprehensive feature importance sequence, and determining key target features corresponding to model prediction influence according to the comprehensive feature importance sequence; on the basis of key target features, a Bayesian optimization algorithm is used to optimize hyper-parameters of the LGBM model, and a trained BO-LGBM prediction model is obtained; the BO-LGBM prediction model is used for predicting ground surface settlement, tunneling efficiency, specific energy and overexcavation rate of the detection data; and obtaining a performance index based on the trained prediction model so as to evaluate model performance and prediction precision. According to the invention, the model prediction accuracy of the shield performance can be improved.

The invention relates to the technical field of power grid operation and maintenance, and provides a generator set operation performance processing method and device, a generator set operation performance processing system, a readable storage medium and a program product. The method comprises the following steps: fusing a static parameter sequence, a historical operation data sequence, a real-time monitoring data sequence and an active disturbance test data sequence of the generator set to obtain a fused input vector sequence; according to the fusion input vector sequence and the generator set performance evaluation model, obtaining an operation performance prediction index value sequence; according to a set length time window and a set sliding step length, sliding on the operation performance prediction index value sequence to obtain a target operation performance prediction index value sequence; and determining a credibility first score, a drift statistic and a test statistic of each target operation performance prediction index value sequence so as to judge whether to correct or retrain the generator set performance evaluation model. By adopting the method, high-precision prediction of the operation performance of the generator set can be realized.

The invention relates to the technical field of industrial data analysis, in particular to a concrete mix proportion optimization system and method based on industrial data analysis, comprising a data acquisition and sensing module, a data storage and processing module, a core analysis module and an application output module; compared with the defects of long period, high cost and difficulty in coping with fluctuation of raw materials due to the fact that the prior art mainly depends on laboratory trial and matching and experiences of engineers, the method has the advantages that the performance prediction model based on machine learning and multi-source real-time industrial data fusion analysis are adopted, and the concrete performance can be rapidly and accurately predicted; the mixing proportion design efficiency and scientificity are remarkably improved, and fundamental conversion from experience driving to data driving is achieved.

The invention discloses an infrared film absorptivity prediction method and system based on machine learning, and relates to the technical field of optical film design and performance prediction.The infrared film absorptivity prediction method comprises the following steps that feature data of a to-be-measured film material and incident light are obtained, electromagnetic field distribution and local absorption power density are output, and according to power integration and boundary conditions, an infrared film absorptivity prediction result is obtained; obtaining initial absorptivity distribution; performing modal analysis on the initial absorptivity distribution, constructing a low-dimensional absorptivity response model, and outputting absorptivity response data after dimension reduction; constructing a physical constraint neural network by taking energy conservation and Fresnel boundary conditions as constraints; on the basis of a physical constraint neural network, absorptivity prediction is carried out on the infrared thin film under different conditions, thin film structure data are optimized through a multi-objective optimization algorithm, and an optimal structure parameter combination of the infrared thin film is obtained; according to the invention, through multi-physics field modeling and the physical constraint neural network, the problems of low infrared film absorptivity prediction precision and poor optimization efficiency are solved.

Provided in the present application is a cluster performance prediction method, the method comprising: acquiring information input by a user, the information comprising detailed information of a large-scale cluster to undergo prediction and a large task to be executed on the large-scale cluster; constructing a small-scale cluster and constructing a small task running on the small-scale cluster, wherein the number of devices in the small-scale cluster is less than the number of devices in the large-scale cluster, the interconnection mode of the devices in the small-scale cluster is the same as the interconnection mode of the devices in the large-scale cluster, and the load of each device in the small-scale cluster when running the small task is the same as the load of each device in the large-scale cluster when running the large task; when the small-scale cluster executes the small task, collecting performance data of the small-scale cluster; and using the performance data of the small-scale cluster as performance data of the large-scale cluster when executing the large task. The method can reduce the cost of performance prediction for large-scale clusters, and improve the prediction efficiency and accuracy.

The invention discloses a composite material performance prediction method based on multi-field coupling multi-scale analysis and a knowledge graph, which integrates microstructure modeling, graph neural network representation learning, cross-scale parameter coupling modeling and robust optimization analysis. The method is suitable for prediction and design of key mechanical properties such as modulus, strength and toughness of a thermosetting / thermoplastic composite material under different working conditions. The method comprises the following steps: firstly, constructing a grain-level tissue knowledge graph based on an electron backscatter diffraction image, secondly, constructing a multi-scale input system comprising a microscopic variable (such as a fiber volume fraction), a mesoscopic variable (such as a layer thickness sequence) and a macroscopic variable (such as a load condition), and finally, performing robust optimization by utilizing a multi-objective evolutionary algorithm to obtain a multi-scale input system. And outputting a material performance prediction result and a knowledge graph associated with the ji structure-process-performance. According to the method, the accuracy and interpretability of performance prediction of the composite material can be remarkably improved, and data-knowledge dual-drive support is provided for design optimization of the high-performance composite material.

The invention discloses a GFRP durability evaluation method based on a neural network, and belongs to the technical field of composite material performance evaluation. The method comprises the following steps: constructing a training data set containing working condition parameters, macroscopic performance data and microscopic mechanism data; a multi-head physical guidance neural network model is constructed, and the model is provided with a main output head for outputting a macroscopic performance data predicted value and an auxiliary output head which is connected to an internal physical mechanism characterization layer of the model and is used for outputting a microscopic mechanism data predicted value; performing multi-task cooperative training on the model through a composite loss function; and finally, synchronously outputting a macroscopic performance prediction result and a microcosmic mechanism diagnosis result by utilizing the trained model. According to the method, physical mechanism knowledge is fused into the neural network, and a traditional black box model is improved into an interpretable grey box model through middle layer supervision and multi-task cooperative training, so that the accuracy of macroscopic prediction is improved, and quantitative diagnosis of an internal degradation mechanism is realized.

The invention belongs to the technical field of education data mining and personalized learning, discloses a learner knowledge cognition level diagnosis method and system based on cross-scale learning performance dynamic modeling, and has higher accuracy in the aspects of learner cognition state prediction and knowledge point difficulty assessment. Through a selective state space modeling mechanism and cross-scale historical learning income feature engineering, cognitive change tracks of students in various learning scenes can be accurately captured; and the robustness, convergence efficiency and long sequence processing capability of the model in learner performance prediction are improved. The method can be widely applied to a personalized education platform, a self-adaptive learning system and an intelligent teaching auxiliary tool, provides accurate student learning state analysis for teachers, optimizes learning path design, and improves the teaching effect.

The invention provides an artificial intelligence chip pre-silicon verification system and method, and relates to the technical field of artificial intelligence chips, and the system comprises a model importing layer which is used for importing a current model; the model analysis layer is used for analyzing the current model to obtain a calculation graph; the distributed strategy configuration layer is used for configuring a distributed topology strategy of a hardware simulator of the artificial intelligence chip based on the distributed strategy of the current model, and adjusting a computational graph of the current model to enable the computational graph to be matched with the distributed topology strategy; the performance prediction layer is used for determining the operation performance data of the current model in the artificial intelligence chip based on the operation performance data of each operator; or determining a performance prediction result of the current model in the artificial intelligence chip based on the performance prediction result of each operator; and the operator implementation layer is used for controlling the hardware simulator to run each operator in the computational graph. According to the system and the method provided by the invention, the end-to-end whole-process verification from model input to result output is carried out on the artificial intelligence chip.