2932 results about "Performance prediction" patented technology

The invention provides an intelligent operation and maintenance method and system for an air conditioning system based on the Internet of Things technology, and the method comprises the steps: processing operation data through employing an intelligent sensor and an edge calculation node, and generating a comprehensive operation information packet through combining with environment parameters; next, simulation analysis is carried out through a cloud digital twin model, performance prediction and fault point evaluation are generated, the fault probability is evaluated by using a Bayesian network algorithm, and personalized operation and maintenance suggestions are generated; secondly, optimizing operation parameters by adopting a genetic algorithm, and smoothly adjusting the setting through a self-adaptive control theory; and when an abnormality is detected, the fault severity is judged by using a multi-level threshold value and fuzzy logic, an early warning mechanism is triggered in time, and an emergency response scheme is pushed. And finally, recording a maintenance process and user feedback, updating the digital twin model, forming a closed-loop operation and maintenance system, and ensuring efficient and stable operation and service quality improvement.

The invention discloses a building structure monitoring and management method, system and equipment and a storage medium, and relates to the field of civil engineering. The method comprises the following steps: acquiring three-dimensional data of a building structure, and constructing a digital twinborn model of the building structure according to the three-dimensional data; acquiring real-time state data of the building structure from a preset sensor network, and comparing the real-time state data with reference state data in the digital twin model; and when the difference between the real-time state data and the reference state data in the digital twin model is smaller than a first threshold value, performing performance prediction and life evaluation on the building structure according to the real-time state data in combination with influence factors. By implementing the technical scheme provided by the invention, informatization and intelligent management of the whole life cycle of the project is realized.

The invention relates to the technical field of concrete detection, in particular to a dynamic detection and analysis method and system for early performance of ultra-high performance concrete. According to the technical scheme, the method comprises the steps of establishing a raw material characteristic spectrum library based on multi-physics field coupling, constructing a dynamic detection array based on a time sequence, developing a multi-modal data fusion algorithm, establishing a nonlinear coupling effect decomposition model and implementing dynamic parameter inversion. According to the method, a technical chain of four-dimensional monitoring network-multi-physics field coupling analysis-cross-scale prediction-adaptive regulation and control is constructed, so that dynamic accurate detection and intelligent regulation and control of the early performance of the ultra-high performance concrete are realized, and the technical bottlenecks of a traditional method in the aspects of parameter coupling, cross-scale association and real-time regulation and control are solved; the early performance prediction precision and the maintenance efficiency are remarkably improved, the cracking risk is reduced, and reliable technical support is provided for application of the ultra-high performance concrete in major engineering.

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 provides an intelligent analysis and execution method and system for database cascading operation, and relates to the technical field of database management.The intelligent analysis and execution method comprises the steps that a target table and an association table are extracted through a recursive query algorithm, field mapping analysis is carried out to generate a dependency weight matrix, cyclic dependency is recognized based on the matrix and decomposed into directed acyclic subgraphs, and the directed acyclic subgraphs are analyzed; node depth values and breadth values are calculated for topological sorting to generate an execution sequence; performing node classification on the execution sequence, organizing the execution sequence into a batch processing task group, calculating an optimal execution path through an adaptive path optimization algorithm, generating a distributed transaction control instruction, and allocating the task group to an execution thread pool for execution by using a consistent Hash algorithm; monitoring an execution process, dynamically adjusting thread pool resources according to a performance prediction value, executing cascade rollback when an exception occurs, and reconstructing a transaction execution path; according to the method, the execution efficiency and stability of the database cascade operation can be effectively improved, and the system resource consumption is reduced.

The invention discloses a dynamic scheduling method for a cloud computing resource pool in the technical field of cloud computing. Comprising the following steps: S1, analyzing resource demands; s2, performing performance prediction on the resource pool through load prediction; s3, predicting task demand resources; s4, resource allocation decision making; s5, task scheduling; and S6, resource monitoring and feedback. According to the invention, load prediction is carried out through the long-short-term memory network, an adaptive mechanism is introduced, and a load prediction model is updated along with continuous change of data, so that the accuracy is improved; the resource demand of the task is predicted through a machine learning algorithm, a reasonable resource allocation decision is made according to the state of the current resource pool and the resource demand predicted by the task, the utilization rate of the resource is improved, stable operation calculation of the task is guaranteed, efficient task allocation and scheduling are achieved, resource utilization maximization is guaranteed, and the resource utilization rate is increased. And the change of the system load is effectively responded.

The invention discloses a communication network performance prediction method and system based on a large model, and relates to a communication network and artificial intelligence. The method comprises the following steps: acquiring performance time series data from a historical database of a network management system, preprocessing the data in an offline training stage, and generating data description according to a prompt structure; the preprocessed performance time sequence data are respectively input into a time sequence large model TimeGPT for model fine tuning and a patch reprogramming module to generate a group of new vector representations; converting the data description into a time sequence prompt prefix by using a pre-trained large language model, and splicing the time sequence prompt prefix with a vector output by a patch reprogramming module to form a new time sequence to represent the understanding ability of the pre-trained large language model training model to the time sequence data; in the real-time stage, the TimeGPT carries out anomaly and performance prediction according to input performance time sequence data, detection and prediction results are input into a pre-trained large language model, and efficient and accurate detection results, anomaly description, prediction results and potential fault description are output.

The invention relates to the technical field of chip aging tests, and discloses a chip aging test fixture control method and device and a storage medium, and the method comprises the steps: carrying out the parameter collection of a test fixture, and obtaining original test data; performing control signal analysis and signal fusion, and inputting a fused temperature control instruction into a temperature control unit of the test fixture to obtain steady-state test data; performing multi-dimensional analysis and data screening to obtain effective test data; performing performance feature extraction to obtain performance feature data; grouping is carried out, the weight coefficient of each test channel is calculated, weighting processing is carried out in combination with a failure mode standard, and aging characteristic data is obtained; and inputting the aging characteristic data into the life prediction model to calculate a performance prediction value and a life prediction value to obtain a chip aging test result, the chip performance degradation characteristics can be accurately captured, the performance prediction value and the life prediction value are output at the same time, and a comprehensive aging evaluation result is provided.

The invention discloses a valve performance simulation and optimization method and system based on mixed deep learning, and the method comprises the steps: S1, carrying out the finite element simulation analysis of a valve, and solving through the thermosetting coupling of a multi-physics field, stress, deformation and temperature distribution data of the valve under different working conditions and maximum thermal stress and maximum thermal deformation displacement of key parts of the valve are obtained; s2, after data processing, taking stress, deformation and temperature distribution data of the valve under different working conditions as input parameters, taking maximum thermal stress and maximum thermal deformation displacement as output parameters, training a neural network model, and establishing a thermal-structural performance prediction model; s3, on the basis of a prediction result, a reference point-based non-dominated genetic algorithm is adopted, a reference point generation and self-adaptive agent model is combined, and multi-objective optimization is carried out to output an optimal parameter combination so as to adjust valve design; according to the invention, deep learning and multi-objective optimization are carried out on the finite element simulation result by using the neural network, and the valve performance prediction precision and optimization efficiency are improved.

The invention relates to the technical field of brushless motor testing, and discloses a test adjusting method and system for an intelligent brushless motor, and the method comprises the steps: carrying out the real-time monitoring of the current, rotating speed and load characteristics of the brushless motor, obtaining the operation data of the motor, and obtaining a motor working mode recognition result; constructing a motor parameter hierarchical optimization scheme; performing dynamic optimization to obtain a motor adaptive control parameter set; performing rolling optimization solution to obtain a motor prediction control instruction sequence; performing multi-objective optimization calculation to obtain a motor multi-performance balance adjustment scheme; a motor multi-performance balance adjustment scheme is integrated into a digital twin test platform, simulation verification is carried out on the performance of the brushless motor under different working conditions, a motor test adjustment strategy is obtained, the working mode of the motor can be recognized in real time, parameter optimization and performance prediction can be carried out based on historical data and the current state, and the stability of the motor test adjustment strategy is improved. Therefore, the optimal operation state of the motor under different working conditions is realized.

The invention relates to the technical field of electrical design and intelligent optimization calculation, and discloses a capacitor structure design optimization method and system and a storage medium. The method comprises the following steps: carrying out modeling processing on capacitor structure parameters, and dividing a parameter space to obtain an initial model set; obtaining a multi-physical field simulation model according to the geometric model and the material attributes; obtaining performance indexes such as capacitance value, heat distribution and stress based on the simulation model; setting a target function and constraint conditions, and operating an optimization algorithm to obtain an optimal solution set; performing feedback control processing on an optimization result, and analyzing a convergence path to obtain a structure parameter; according to the invention, the execution efficiency of capacitor structure design optimization is improved, and the consistency and stability of the optimization process and the performance prediction result are improved.

The invention relates to the technical field of equipment state monitoring and control, and discloses an equipment state monitoring distributed control system and method, and the method comprises the steps: collecting the operation parameters of all equipment, and forming a first historical operation data set and a first real-time operation data set; performing cleaning, anomaly detection and feature extraction on the data to generate a feature data set; constructing an equipment parameter association network model; key features related to boiler performance are screened, a historical feature data set is generated, and a boiler performance prediction model is trained; inputting the real-time characteristic data set into a prediction model to obtain a boiler performance parameter prediction value, and comparing the boiler performance parameter prediction value with a preset performance threshold value; when the predicted value is lower than a threshold value, triggering a cooperative control process, calculating optimal operation parameters of desulfurization and denitrification equipment, a fan and an air compressor, and generating an adjustment parameter set; and generating a cooperative control strategy set according to the adjustment parameter set and issuing the cooperative control strategy set to each device for regulation and control. According to the invention, through multi-device collaborative optimization control, the energy efficiency and the operation stability of the system are improved.

The invention discloses a sugarcane yield prediction method and system based on machine learning, and relates to the technical field of intelligent agriculture. Through comprehensive application of technical means of data acquisition, preprocessing, feature extraction, model construction, training, prediction, decision support and the like, accurate prediction of the sugarcane yield is realized. According to the method, multi-source data is combined, key features are extracted by using an advanced machine learning algorithm, and a high-performance prediction model is constructed. Through historical data training and model parameter optimization, prediction accuracy and stability are improved. Meanwhile, the system provides a user-friendly interactive interface, and collects user feedback to continuously improve the model. The method is suitable for the field of agriculture, especially for sugarcane yield prediction, solves the problem that traditional methods such as experience estimation and statistical model prediction are inaccurate, realizes high-precision prediction through technologies such as machine learning, and has important significance for promoting agricultural sustainable development.

The invention discloses a comprehensive performance prediction and structural parameter optimization method for a hemispherical resonator, and belongs to the technical field of aerospace inertia. According to the method, a high-precision finite element simulation model of the ideal hemispherical harmonic oscillator is established based on a multi-region grid uniform division method; aiming at the condition that each structure parameter corresponds to different structure sizes, calculating through a finite element model of a control variable to obtain a data set of different performance parameters of the hemispherical resonator; based on a BP neural network algorithm optimized by a particle swarm optimization algorithm, after the data set is divided into a training set and a test set, a comprehensive performance parameter prediction model of the hemispherical harmonic oscillator is established; and taking the PSO-BP neural network model as the input of a second-generation non-dominated sorting genetic algorithm, and performing multi-objective optimization on the structural parameters of the hemispherical harmonic oscillator by taking higher thermoelastic damping and larger minimum frequency difference between the interference modal and the PSO-BP neural network model as optimization objectives. The use performance of the hemispherical resonator is improved, and the performance requirement of the aerospace technology field on the high precision of the hemispherical resonator gyroscope is met.

The invention provides a comprehensive financial IT operation and maintenance management system and method based on artificial intelligence, and the system comprises an intelligent monitoring and predictive maintenance module which is used for achieving the real-time monitoring, abnormality recognition and performance prediction of an IT system through collection of multi-dimensional time series data, and when an abnormal condition is monitored or a prediction result does not meet a preset requirement, the intelligent monitoring and predictive maintenance module carries out the operation and maintenance of the IT system; if so, triggering intelligent early warning; the AI-driven fault diagnosis and self-healing module is used for carrying out intelligent fault analysis and repair in combination with IT system logs and historical fault data based on the detected abnormal condition; the self-adaptive resource management module is used for predicting a future load trend and a future performance trend, and performing self-adaptive resource management based on the predicted future load trend and the future performance trend in combination with cost and energy consumption factors; and the security operation and maintenance automation module is used for monitoring security threats of the IT system in real time and automatically executing security strategies.

The invention relates to the technical field of building simulation, and discloses a BIM-based special-shaped hyperboloid building site model generation method and system, and the method comprises the steps: collecting the multi-source heterogeneous point cloud data of a special-shaped hyperboloid building site to construct a three-dimensional feature tensor model, and recognizing the key curvature mutation point of the tensor model to construct a space reference topology network; based on the digital skeleton model, geometric contour features are analyzed, hyperboloid fitting is carried out, and an initial BIM model is obtained; performing multi-physics field coupling simulation on the initial model to obtain a target BIM model; extracting curvature distribution and structural stress parameters of the target BIM model to obtain basic model parameters, and constructing a building performance prediction model in combination with historical performance reference data; and inquiring a bearing capacity standard and a deformation threshold value, and analyzing the optimal curved surface parameter combination and a prestress applying strategy to obtain optimized simulation parameters, so as to simulate the special-shaped hyperboloid building by utilizing target model simulation. According to the method, the simulation precision of the special-shaped hyperboloid building can be improved.

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 the technical field of steam extraction check valves, and discloses a design method and device for quickly responding to a steam extraction check valve under a high-parameter working condition, and the method comprises the following steps: constructing a valve working condition database and a valve body database; based on the valve working condition database and the valve body database, a valve performance prediction model is constructed, and a valve state quantity prediction value is obtained through performance influence factors collected in real time; correcting the predicted value to obtain a valve state quantity corrected value; a valve control environment based on reinforcement learning is established, a valve optimization control strategy model is constructed, a real-time optimal control action is generated and executed based on a valve state quantity correction value, and efficient optimization control over the valve is achieved; according to the method, through combination of data-driven performance prediction and reinforcement learning optimization control, rapid response and efficient operation of the steam extraction check valve under the high-parameter working condition are achieved, and the reliability and the energy-saving effect of the system are improved.

The invention provides an AlGaN solar blind APD performance prediction and optimization method and system, and relates to the technical field of photoelectric devices, and the method comprises the steps: obtaining the experimental data information of an AlGaN solar blind APD; based on a deep learning algorithm, performing model training by taking the process parameters as input and the device performance parameters as output, and constructing a performance prediction model; obtaining a performance prediction result of the target device through training, and generating an optimization scheme including material component adjustment parameters, device structure improvement parameters and process condition optimization parameters; the optimization scheme is subjected to experimental verification, the optimization effect is verified, an evaluation result is obtained, and after the evaluation is qualified, the optimization scheme is fed back to the performance prediction model for model updating, so that the technical problems that the performance optimization of the AlGaN solar blind APD is low in efficiency, not accurate enough and difficult to adapt to complex process conditions are solved; the technical effects of fast and accurate performance prediction and automatic optimization, and improvement of optimization efficiency and working condition adaptability are achieved.

The invention relates to a hybrid architecture mechanical property prediction method and system for an additive manufacturing lattice structure, and the method comprises the steps: converting a lattice structure node three-dimensional coordinate into a normalized distance from a lattice center, introducing a self-attention mechanism, and constructing a feature prediction model in combination with residual connection and layer normalization; constructing a generative adversarial network comprising a generator and a discriminator; a strategy network based on prediction error rewards is constructed, and a depth deterministic strategy gradient algorithm is adopted for optimization; jointly training the models, and constructing a performance prediction feedback model; finally, the node coordinates of the target structure serve as input, and mechanical property prediction output is achieved. According to the method, the generative adversarial neural network, reinforcement learning and an attention mechanism are mutually combined to generate a hybrid architecture, the problems of gradient disappearance and mode collapse of a traditional GAN can be effectively relieved, the model can accurately recognize implicit association between node positions and mechanical properties, and then the mechanical properties of target lattice structure parameters are rapidly predicted.

The invention discloses a valve vibration impact test method and system based on digital twinning, and belongs to the technical field of industrial equipment test and evaluation. The method comprises the following steps: firstly, deploying a multi-source sensor network, constructing a physical entity layer of a valve vibration impact test, collecting and preprocessing various physical monitoring data, and constructing a digital twinborn body layer; secondly, real-time data assimilation with a physical test bed is realized through parameter calibration and updating of a digital twinborn model based on various physical monitoring data and the digital twinborn layer; and finally, executing a virtual vibration impact test, realizing prediction and evaluation of global response and key part information of the physical entity according to a test result, and finally realizing interaction and performance prediction. And the valve vibration impact test is realized through the valve vibration impact test system. According to the method, the fault diagnosis capability, the early damage identification capability and the valve state prediction and evaluation capability can be remarkably improved, and the defect of insufficient field monitoring information is overcome.

The invention discloses a twin network performance prediction method and device based on a graph neural network and a storage medium, and belongs to the technical field of communication networks, and the method comprises the steps: carrying out the preprocessing of the obtained historical information of a physical communication network, and obtaining a dependency graph data set; initializing the state of each node in the dependency graph data set to obtain the initial hidden state of each node; the initial hidden state is used to train a pre-constructed network performance prediction model to obtain a trained network performance prediction model, and the network performance prediction model obtains iteratively updated hidden state information and adjusts model parameters through cooperative work of an attention mechanism, a message passing neural network and a gating circulation unit; and inputting the obtained real-time information of the physical communication network into the trained network performance prediction model to obtain a performance prediction result, thereby improving the stability, accuracy and efficiency of performance prediction of the digital twin communication network.

The invention provides a refrigeration machine room equipment performance model health degree evaluation method and system, a medium and a terminal, and the method comprises the steps: monitoring the real-time operation data, historical operation data and equipment performance prediction data generated in the operation process of a refrigeration machine room, carrying out the prediction accuracy of an equipment performance model, and employing a multi-mode fusion technology, and obtaining the health degree of the refrigeration machine room. The weights of different input dimensions in the health degree calculation model are dynamically adjusted in combination with the current operation condition, the environmental parameters and the equipment aging condition, and self-adaptive health degree evaluation of the equipment performance model is realized, so that the health degree of the model can be evaluated more comprehensively and accurately, errors of a single data source are effectively reduced, and the user experience is improved. And the comprehensive health degree score of the most representative equipment performance model is calculated. And when the comprehensive health degree score of the equipment performance model is lower than the preset health degree threshold value, model retraining is automatically triggered, so that adaptive adjustment of the model is realized to ensure that the equipment performance model is always in an optimal state.

The invention discloses a subway tunnel structure service performance analysis method and device, and the method comprises the steps: building a prediction model database based on physical experiments and numerical simulation, and determining main factors which affect the service process of a tunnel structure through an interpretable machine learning method; through coupling with a physical equation, the service performance of the tunnel structure is accurately evaluated under the small sample condition, and the characteristic that in an existing machine learning method, the prediction precision is not high under the small sample condition, and a black box is avoided; according to the tunnel structure service performance prediction model constructed by the invention, the subway service performance (safety performance) can be qualitatively evaluated objectively, so that the service state of the lining structure is monitored, evaluated and pre-warned, and the service performance of the tunnel structure can be predicted under the evaluation framework of monitoring-analysis-evaluation-pre-warning-disposal-decision making. Problems existing in a current structure are identified and processed, and a solid technical guarantee is provided for tunnel safety and continuous service.

The invention provides a high-efficiency intelligent teaching resource scheduling algorithm and an implementation method thereof, and the method comprises the steps: constructing a judgment matrix according to a learner capability difference evaluation result, cooperation efficiency analysis data, knowledge graph coverage rate statistics and learning style matching degree score, carrying out the calculation to obtain a group heterogeneity index, solving the weight of each index through a feature value method, and carrying out the calculation of the group heterogeneity index. Carrying out weighted summation to obtain a group complementarity comprehensive index; acquiring a historical learning data sequence of a learner, extracting time sequence features, combining a current individual progress deviation monitoring result, individual resource demand evaluation and individual learning content suitability analysis, constructing an individual development trajectory prediction model, and predicting a future individual learning performance curve; and aiming at the group complementarity comprehensive index and a future individual learning performance prediction result, constructing a multi-objective optimization model of resource allocation, setting a group complementarity objective function and an individual satisfaction objective function, and solving to obtain a resource allocation scheme for balancing group and individual benefits.

The invention provides a photocuring material performance prediction method based on deep learning, and relates to the technical field of photocuring 3D printing, and the method comprises the steps: collecting the multi-modal data of a photocuring material in real time; performing data cleaning, de-noising and standardization processing on the acquired multi-modal data, fusing the processed multi-modal data, and extracting key index values and printing process parameters of a material formula; constructing an initial deep learning neural network model by using key index values in the extracted multi-modal data; printing process parameters are used for training the initial neural network model, gradient descent is adopted for optimizing the model weight, performance evaluation and optimization are conducted on the model through cross validation, and a material performance prediction model is obtained after verification; and predicting the performance of a to-be-predicted photocuring material based on the material performance prediction model. By comprehensively analyzing the formula, illumination conditions and printing parameters of the photocuring material, the performance of the material under different conditions is accurately predicted.

The invention relates to the technical field of metal materials, and provides a medium-and-high-entropy aluminum alloy performance prediction method, system and method and a medium, and the method comprises the following steps: obtaining a characteristic data set for medium-and-high-entropy aluminum alloy performance; constructing an MLP prediction model, and obtaining an optimal hyper-parameter combination of the MLP prediction model through ESOA; training the MLP prediction model according to the feature data set and the optimal hyper-parameter combination; and predicting the performance of the medium-high entropy aluminum alloy according to the trained MLP prediction model. According to the method, the advantages of the ESOA and the MLP prediction model are combined, the accuracy of performance prediction of the medium-high entropy aluminum alloy can be effectively improved, the time and experiment cost can be reduced, the prediction cost and the calculation amount can be reduced through the powerful calculation capacity of the MLP prediction model, the prediction precision and the generalization capacity of the model are enhanced, and the method is suitable for popularization and application. The method is suitable for performance prediction of the medium-high-entropy aluminum alloy material.

The invention is suitable for the technical field of performance detection, and provides a method for detecting the ablation resistance of a burning-resistant nozzle and an arc contact, and the method comprises the steps: collecting a temperature field matrix, an arc image sequence, an electrical parameter time sequence and a surface displacement curve of an ablation region, and generating a multi-modal data set; based on the data set, extracting spatial features of the ablation core region through a cavity convolution layer and a channel attention mechanism; inputting a preset bidirectional LSTM network, fusing thermal stress data of the COMSOL simulation model to drive gating weight update, and extracting time sequence evolution characteristics; generating a space-time fusion feature vector containing the temperature field gradient, the arc form change rate and the surface roughness evolution feature; inputting the space-time fusion feature vector, the thermal stress distribution data and the material phase change threshold parameter into a constructed ablation prediction model for training; the ablation prediction model after training is completed is optimized; and a performance prediction result is generated in real time by using the optimized ablation prediction model, so that the detection accuracy is effectively improved.

The invention provides an AI-enhanced large mine water-doped concrete material performance prediction and mix proportion design integrated system, aiming at the key problem of large mine water-doped concrete material temperature control, the importance of different influence factors such as raw material-structure-environment and the like is comprehensively and quantitatively evaluated; aiming at a complex raw material system of the large mine water-doped industrial concrete, main features of a performance prediction model such as feature dimension reduction and weight calculation are optimized, and meanwhile, a machine learning algorithm combined with a self-attention mechanism is constructed so as to reduce interference of features weakly related to target performance, and the performance regulation and control capability of the large mine water-doped industrial concrete is enhanced. Furthermore, a multi / super multi-target optimization algorithm containing a constraint system is developed and integrated aiming at targets such as material key performance, carbon emission and cost in the preparation process, and a comprehensive and flexible data-driven solution is provided for temperature control, influence factor evaluation, performance regulation and control and proportion optimization of the large-mine water-doped concrete material. Therefore, the overall decision-making and design process is optimized.

The invention discloses an insulating material performance evaluation and formula optimization method and device and a medium, and the method comprises the steps: extracting multi-dimensional data features according to the multi-physical field data of an insulating material, carrying out the feature fusion, and obtaining multi-modal fusion features; constructing a material performance prediction model based on the multi-modal fusion features, and responding to real-time multi-physical field data to obtain a residual life prediction result of the insulating material; determining a key factor influencing the life based on the residual life prediction result, and mapping data characteristics of the key factor to an insulation material failure mechanism to determine a failure factor weight; based on the failure factor weight, establishing a formula optimization objective function by adopting a multi-objective reinforcement learning algorithm; solving the formula optimization objective function based on constraint conditions to obtain a material formula optimization result; the accuracy of the residual life prediction result of the insulating material is improved, and meanwhile, the reliability and pertinence of the formula optimization result of the insulating material are considered.