5797results about "Computational materials science" patented technology

The invention discloses an electronic material life cycle quality tracing method based on digital twinning, and relates to the technical field of industrial Internet of Things, the digital twinning of an electronic material is constructed, a material constitutive equation, a process parameter threshold library and historical quality data are integrated, and a multi-dimensional virtual model is formed; a production line real-time data stream including an equipment state, environmental parameters and material attributes is collected. According to the method, the virtual model containing the material constitutive equation and the process parameter threshold library is constructed, the real-time data flow dynamic evolution is combined, and the graph calculation and the causal reasoning algorithm are applied, so that the interaction effect of the equipment state, the environmental parameters and the material attributes can be associated, the core influence factor chain of the quality abnormality can be positioned, the single-point alarm limitation is broken through, and the quality abnormality can be accurately detected. The quality problem is deeply analyzed from the angle of multi-factor coupling, a comprehensive and systematic analysis framework is provided for accurate attribution, the source of the quality problem can be quickly and accurately found, and the efficiency and accuracy of quality tracing are improved.

The invention discloses a 3D printing path planning method based on electric arc additive anisotropy and a stress field, and the method comprises the steps: constructing a CAD three-dimensional model of a part, and obtaining the stress field of the part through finite element simulation; determining a slice plane; mapping the stress field to a slice plane to form a force flow line; according to the obtained force flow line, rotation transformation regeneration is carried out according to the anisotropy of the used electric arc additive, and a reference trajectory considering the anisotropy of the material is obtained; under the principle of alternate arc starting and extinguishing, the corresponding printing sequence in the layers and between the layers is further planned, and the reference trajectory lines are connected end to end to be planned into a continuous path; and generating a code file, and printing according to the printing sequence. The comprehensive mechanical property of a printed piece is improved.

The invention belongs to the technical field of high polymer material design and intelligent manufacturing, and discloses a machine learning driven epoxy resin based composite material thermal-mechanical property aided design method, which comprises the following steps: S1, data acquisition and feature construction; s2, performing feature screening; s3, constructing and training an interpretable prediction model; s4, carrying out reverse design and optimization; and S5, performing closed-loop verification and updating. According to the method, the quantitative relation of structure-process-performance is constructed through an interpretable machine learning model, and the contribution mechanism of each factor is revealed by means of SHAP analysis. And finally, reversely designing an optimal epoxy resin monomer structure and a matched curing process according to the performance target. The limitation of a traditional trial and error method is broken through, collaborative optimization of the material structure and the forming process can be achieved, and the development efficiency of the epoxy resin-based carbon fiber composite material is remarkably improved.

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.

Disclosed in the present invention is a physics-informed neural network-based thermo-mechanical coupling analysis method for an inertial microsystem, the method comprising: S1, configuring material parameters and boundary conditions of an inertial microsystem, and establishing a thermo-mechanical coupling analysis model; S2, performing electro-thermal coupling analysis to obtain a temperature distribution of the inertial microsystem; S3, performing thermo-mechanical coupling simulation analysis to obtain a thermal stress distribution of the microsystem; S4, predicting temperature fields of the microsystem by means of a physics-informed neural network; S5, using the temperature fields as boundary conditions for mechanical simulation of the microsystem, obtaining mechanical properties such as stress and strain of the microsystem; and S6, performing electromechanical coupling simulation analysis to analyze the impact of structural deformation on various parameters of electrical performance. The present invention improves the solution accuracy of the neural network by means of an improved adaptive weighting strategy, combines a complete polynomial basis function with the neural network, and introduces an expanded basis function to reduce the state dimensionality, thus reducing computational costs and time, achieving accurate prediction of temperature fields of microsystems at multiple moments, and allowing for computation of the performance of microsystems under electro-thermal-mechanical multi-physics coupling.

The invention discloses a stamping die health state assessment method and system based on digital twinning, and relates to the technical field of die health state assessment, and the method comprises the following steps: a physical sensor network deployed on a stamping die collects die stamping process data in real time; constructing a finite element analysis simulation FEA model to simulate the working condition of the stamping die based on the geometric structure, the material attribute and the stamping process parameters of the die; according to the invention, the data of the die stamping process are collected in real time through the physical sensor network, and real-time calculation is carried out in combination with the finite element analysis simulation model, so that transient stress field, strain field and temperature field data of the die can be output in a short time, and real-time monitoring of the health state of the die is realized; by considering the degradation of the mold material performance along with the use time and the dynamic process of quantitative damage accumulation, the damage accumulation value is accurately calculated through the dynamic material performance database and the continuous damage mechanical model, and the accuracy of the evaluation result is improved.

The invention relates to the technical field of die-casting optimization, and discloses a die-casting process parameter optimization method and system based on digital twinning, and the method comprises the steps: arranging a sensor to collect the operation parameters of die-casting equipment and the quality data of a die casting in real time, and forming multi-source die-casting production data; according to multi-source die-casting production data, a multi-physical field simulation model is established, and a digital twinborn model is constructed. And comparing the virtual prediction result with the actually measured quality data, and constructing a virtual-real difference compensation network to correct the parameters of the digital twin model. And performing a multi-target reinforcement learning method based on the compensated digital twin model to generate optimal die-casting process parameters. And applying the optimal die-casting process parameters to die-casting equipment for verification, and updating the virtual-real difference compensation network according to a verification result. Intelligent optimization and continuous self-evolution of the die-casting process parameters are achieved, and the casting forming precision, the energy efficiency utilization rate and the production stability are improved.

The invention discloses a deep learning and machine learning fused soil moisture inversion construction method, and relates to the technical field of measurement of physical properties of materials, and the method comprises the steps: capturing complementary information and spatial context of multi-source data through a multi-source heterogeneous data space-time adaptive fusion step by using a cross-modal attention mechanism and a graph neural network; through a deep learning and machine learning dual-path collaborative inversion step, advantage complementation is realized by combining data-driven nonlinear modeling and a physical constraint interpretable model; according to the method, the defects of single data source, insufficient model generalization ability and incomplete physical mechanism consideration in the prior art are overcome, the inversion precision is improved by 12%-18% under the complex earth surface condition, and the method has the advantages that the method is suitable for large-scale popularization and application. And a high-precision, strong-generalization and reliable technical means is provided for precise monitoring of soil moisture.

The present application discloses a method for calculating the risk of failure of a component on the basis of vibration load simulation, comprising: inputting a determined PSD spectrum load into predetermined target software, so as to convert the PSD spectrum load into initial time domain information; executing a signal repetition operation, obtaining target time domain information, and calculating fatigue damage information; converting the information into target frequency domain data, and generating compressed spectrum data; and inputting the data into a vibration simulation model, obtaining a model output result, and determining a risk of failure evaluation result of a target component.

The invention relates to a solidified soil proportion and strength prediction method and system based on SEM image analysis, and belongs to the technical field of solidified soil strength testing. Comprising the following steps: S1, measuring physical indexes of disturbed soil; s2, multi-working-condition sample preparation and maintenance; s3, fractal dimension calculation of the SEM image is carried out; s4, unconfined compressive strength testing; s5, establishing a microstructure inversion model and a macroscopic strength prediction model; and S6, proportion optimization and strength prediction: utilizing the microstructure inversion model and the macroscopic strength prediction model to carry out solidified soil proportion optimization or strength prediction. According to the method, the quantitative relation between the microstructure (fractal dimension) and the macroscopic strength is established through SEM image analysis, the defects in the prior art are overcome, and a brand new technical path is provided for resource utilization of disturbed soil.

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 discloses a panel vertical seam water stop structure repair effect evaluation method, which comprises the following steps of: firstly, synchronously obtaining surface crack and internal defect data through cooperative detection of infrared thermal imaging and ultrasonic flaw detection; and carrying out space alignment on point cloud data generated by three-dimensional laser scanning and an ultrasonic detection result, and establishing a BIM model containing a crack three-dimensional form. A practical engineering environment is simulated in a laboratory to carry out material accelerated aging and test key indexes such as an elastic recovery rate, a base surface state is evaluated through a temperature and humidity sensor array in a construction stage, a six-axis grouting robot is adopted to execute accurate filling, a curing process is monitored by a synchronous pre-embedded optical fiber sensor, and after aging is simulated by combining a hydraulic test with climate, a construction period is shortened. A distributed optical fiber network is used for continuously collecting deformation data, a comprehensive evaluation model is constructed based on mechanics, durability and sealing indexes, and finally material selection and process parameters are optimized through machine learning.

A method for predicting in-situ leaching effects of sandstone-type uranium mining deposits after blasting is provided, which comprises: based on a finite element analysis platform, establishing an HJC rock-blasting constitutive model, and carrying out a blasting numerical simulation; calibrating a cloud map of blasting crack damage by using a Kriging interpolation algorithm; establishing a COMSOL multiphysics model; establishing a coupling interface between the COMSOL multiphysics model and PHREEQC based on MATLAB, and optimizing a cycle duration and a time step; performing a geochemical reaction calculation and saving results, running COMSOL files, and inputting the results of geochemical reaction calculation into PHREEQC to obtain a dynamic cyclic iterative simulation; dynamically correcting migration coefficients of uranyl complexes by integrating an LSTM neural network, and stopping the cycle to output multi-scale dynamic simulation results of migration of uranium.

Disclosed in the present invention is an anti-seismic simulation method for an existing building by considering a real damage state. The method comprises: determining a quantified component damage index on the basis of an observed component damage state; determining a material damage index of a damaged component on the basis of the component damage index; determining key parameters of a constitutive law for a damaged material on the basis of the material damage index; establishing a finite element model of a damaged building on the basis of the constitutive law for the material of the damaged component; and assessing the seismic risk and anti-seismic performance of the damaged building on the basis of the finite element model. In the present invention, a constitutive law for a damaged material is incorporated into a fiber model in OpenSEES, so that rapid modeling of a damaged building can be realized, thereby effectively reproducing the real damage of an existing building; and by using a seismic vulnerability analysis method, rapid assessment of the seismic risk and anti-seismic capability of the existing building can be realized, thereby further providing support for formulating reasonable reinforcement and reconstruction strategies by related departments.

The invention provides a constructional engineering concrete compressive strength detection method and system.The method comprises the steps that multi-modal information of a to-be-detected concrete member for constructional engineering is collected, and the multi-modal information comprises rebound data, ultrasonic data, resistivity data and temperature data; constructing a hybrid prediction model; and outputting compressive strength data according to the mixed prediction model, and visually displaying the compressive strength data. According to the method and the system for detecting the compressive strength of the constructional engineering concrete, disclosed by the invention, multi-modal information such as ultrasonic, rebound, resistivity and temperature of the to-be-detected concrete member is input into the mixed prediction model for compressive strength prediction, and the model can be used for more accurately processing nonlinear and high-dimensional characteristics in data; a transfer learning mechanism is introduced, so that the model adapts to changes of different regions and materials under limited training data, and the generalization ability and accuracy of prediction are improved. And the compressive strength data is visually displayed, so that the analysis efficiency of engineers is improved.

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.

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 provides a fly ash composite material goaf filling body interface quality intelligent evaluation method, and belongs to the technical field of mining engineering and artificial intelligence detection crossing. The method comprises the steps that firstly, filling body interface quality characteristic data are collected and comprise interface sound wave signals, stress strain, coal ash composite material physical parameters and environment working condition data; secondly, constructing a multi-physical field data completion model, performing unsupervised learning on the acquired sound wave, stress, temperature and moisture content data, and generating completion data of global spatial distribution; secondly, constructing a multi-field fusion interface quality index prediction model, and inputting multi-source data into the model to obtain an interface quality index; and finally, combining the quality index to realize interface defect mode classification and grade evaluation, and generating a targeted maintenance strategy. The invention provides an intelligent evaluation method which fuses multi-source data and gives consideration to real-time performance and comprehensiveness, so as to solve the industrial pain points of interface quality evaluation lag, low precision, large destructiveness and the like.

The invention relates to the technical field of aircraft part processing, testing or inspection and the like, and provides an aircraft aluminum alloy plate aging evaluation method based on an equivalent circuit model, and the method comprises the following steps: collecting electrochemical impedance spectrums of samples with different exposure age limits, and extracting electrochemical impedance spectrum characteristics of the samples; establishing an equivalent circuit model comprising solution resistance, coating resistance, coating capacitance, Warburg impedance, anodic oxide film charge transfer resistance, interface capacitance, aluminum alloy matrix charge transfer resistance, interface electric double-layer capacitance, inductance and corresponding resistance of the inductance based on the characteristics, wherein the equivalent circuit model comprises the solution resistance, the coating resistance, the coating capacitance, the Warburg impedance, the anodic oxide film charge transfer resistance, the interface capacitance, the aluminum alloy matrix charge transfer resistance and the interface electric double-layer capacitance; and analyzing a resistance curve and a capacitance curve obtained by fitting the model to complete the evaluation of the corrosion and aging degree. According to the method, systematicness and accuracy of aging evaluation are improved, an electrochemical mechanism in the corrosion process can be disclosed, time correlation modeling of the aging process can be achieved, and the method is suitable for long-term service performance monitoring and service life prediction of the aviation aluminum alloy structure.

A dynamic adaptive learning method for mineral prediction includes: collecting a dataset including geological data and labels of the geological data; extracting features from the geological data, initializing parameters of a training model and optimizing the parameters to obtain training parameters; performing an associative training on the training model based on the training parameters and the labels in a dynamic adaptive learning framework to obtain a mineral prediction model, algorithms of the associative training including a variational expectation algorithm and a variational maximization algorithm, and the variational expectation algorithm including an unsupervised learning mode, a semi-supervised learning mode, and a fully supervised learning mode; and predicting, by using the mineral prediction model, a mineral to obtain a mineral prediction result. The method can break through limitations of the traditional machine learning technology, offering a more efficient, universal, and stable strategy for geophysical data analysis and mineral resource assessment.

The invention belongs to the technical field of additive manufacturing, and particularly relates to a multi-scale parallel optimization method for a lightweight structure based on lattice discrete optimization. According to the method, through multi-scale parallel optimization design, a macro structure and a micro lattice structure are described through geometric parameters of a movable deformation rod piece, an improved two-value coding parameterization method, called a BCP method for short, is adopted to solve discrete optimization of the micro lattice structure, and geometric parameters of an optimization result are easy to extract.

The invention relates to the technical field of computer-aided engineering, in particular to a nonlinear efficient iterative solving method for thermal strain load, which is used for solving an obtained finite element equation by using a modified Newton-Raphson method and comprises the following steps of: firstly, establishing a geometric model to be analyzed and performing grid division; the method comprises the following steps: firstly, setting a model to be analyzed, then setting boundary conditions and temperature field change conditions of the model to be analyzed, finally, inputting the conditions into an algorithm solver, solving a nonlinear equation set, iteratively inputting a load continuously according to a modified Newton-Raphson method in the solving process, calculating node displacement until convergence, and solving stress-strain conditions in the welding process according to a material constitutive relation. In the solving process, by judging the size of the two norms of the displacement increment obtained in the adjacent increment step heuristic stage, the convergence of nonlinear solving of the structure is enhanced through the calculation mode, and the solving speed of the nonlinear problem in the thermal elastic-plastic constitutive structure is increased.

The invention relates to the technical field of infrared light machine system thermal deformation prediction, in particular to a multi-material structure thermally induced stress deformation prediction method based on a graph neural network. The method comprises the steps of data set establishment, graph structure establishment, graph neural network model establishment and training and model and parameter optimization. Finite element nodes correspond to graph nodes, finite element edges correspond to graph edges, an encoder-message passing-decoder architecture model is established, and node states are updated through a three-layer physical symmetry message passing mechanism. Physical constraint loss including minimum displacement smoothness constraint and stress continuity constraint is innovatively added into a loss function. Compared with traditional finite element calculation, the method has the advantages that the speed is increased by more than 100 times, high hardware adaptability is achieved, the black box limitation of a data-driven neural network model is broken through, thermally induced stress deformation analysis caused by different material coefficients can be processed, the adaptability to geometric changes is high, and good engineering application value is achieved.

The invention relates to the technical field of intelligent manufacturing and industrial digital twinning, in particular to a latex paint packaging full-process digital twinning method and system. The method comprises the following steps: collecting multi-source data in real time through a sensor network, and preprocessing to form a standard data stream; a coupled digital twinborn model fusing material characteristics and process parameters is constructed, and real-time prediction is realized by combining physical laws and data driving residual correction and online parameter self-adaption; constructing a multi-scale abnormal index by comparing model output with actual data, identifying an abnormal mode by adopting PCA, calculating local and overall health indexes, and predicting a trend; and generating a preliminary control strategy according to the abnormal mode and the health trend, forming closed-loop adaptive control in combination with health state weighted optimization and look-ahead adjustment, and issuing execution and feeding back an update strategy. According to the invention, the quality control qualification rate and the energy-saving efficiency of the whole process of emulsion paint packaging are improved.

The invention discloses a cargo floor structural strength design optimization method and system based on load prediction, and belongs to the technical field of freight vehicle structural design. The maximum equivalent stress of each node in a floor grid area is calculated and normalized by constructing a load prediction model of the dynamic distribution state of cargoes in a cargo tank; forming a stress intensity coefficient matrix; identifying a high-risk area and constructing a reinforced target area set; obtaining an initial parameter of the bottom plate structure, establishing a finite element model, and setting an adjustable structure parameter in the target area; a candidate structure design scheme set is generated, simulation calculation is executed, the total structure mass, the maximum stress value and the first-order inherent frequency of each scheme are extracted, and a performance evaluation vector is formed; selecting an optimal design scheme through a multi-objective optimization algorithm, and outputting structural parameters of the optimal design scheme as a final design result; according to the method, the structural optimization design of the cargo tank bottom plate under the complex load working condition is achieved, and the method has high engineering adaptability and practical value.

The invention discloses a high-capacity composite material hydrogen storage cylinder layering design method considering the strength of a transition section, and relates to the technical field of hydrogen storage cylinders. Establishing a finite element model of the hydrogen storage cylinder, inserting a cohesion unit with zero thickness between adjacent layers, and performing damage evolution on the cohesion unit to obtain a hydrogen storage cylinder simulation result considering the interlayer failure in the composite material layer; according to the fiber stress of the composite material layer and the damage state of the cohesion unit, determining a weak position under the current layering design, determining to-be-optimized parameters of the weak position, and formulating a layering optimization strategy; according to the method, sensitivity analysis of layering parameters is carried out on a transition section, so that a layering optimization strategy of the transition section is formulated; and according to the layering optimization strategy, parameters to be optimized in the layering design are gradually adjusted until no fiber damage exists in the whole composite material layer. According to the invention, the layering design of the high-capacity composite hydrogen storage cylinder considering the strength of the transition section is realized.

The invention discloses a method and a device for correcting laser focusing aberration in a transparent material, and belongs to the technical field of laser processing. The method comprises the steps that a machining light path containing an object plane, an aspheric reflector and a 4F device is built in Zemax, the surface type of the aspheric reflector is optimized with the minimum focus aberration as the target, and a rise table is derived; an optical path difference and a laser phase are calculated through Matlab, and a phase diagram is generated; and an actual machining light path is built, and a phase diagram is loaded to achieve low-aberration machining. Simulation optimization and phase modulation are combined, aberration caused by refractive index difference is effectively counteracted, wavefront errors are reduced by 58%, the thickness of a machining damage layer is reduced by 70%, the method is suitable for various transparent materials, the machining precision and the material utilization rate are improved, and the method is suitable for high-precision laser machining scenes.

The invention belongs to the technical field of computer aided design and structural design, and discloses a multi-scale lattice structure and a design method thereof. The design method comprises the following steps: S1, designing a cubic unit cell with eight symmetrically distributed corner nodes according to a cubic lattice structure; s2, all the lattice unit cells are zoomed, one part of the lattice unit cells are zoomed into small lattice unit cells with the size being half of the original size, and the other part of the lattice unit cells are large lattice unit cells with the original size reserved; s3, in the macroscopic lattice structure, arranging the position relation of the small lattice unit cells and the large lattice unit cells; and S4, through a computer aided design method, adjusting the macroscopic lattice structure by adopting a large and small lattice unit cell connection mode based on rod diameter transition. A multi-scale large and small lattice unit cell arrangement mode is introduced into the lattice structure, so that the lattice structure has better mechanical performance under the same compression load.

The invention discloses a plate structure damage real-time detection method and device based on a physical information space-time diagram neural network in the technical field of structure health monitoring, and the method comprises the steps: building a three-dimensional plate structure physical grid model based on the obtained size information and material attributes of a to-be-detected plate structure; based on a predetermined sensor arrangement range, generating a sensor and excitation source position layout in the plate structure physical grid model; based on the position layout of the sensors and the excitation sources, corresponding sensors and excitation sources are arranged on the to-be-detected plate structure; the method comprises the following steps: collecting a feedback signal received by a sensor after a pulse signal is transmitted to a to-be-detected plate structure through an excitation source, sending the feedback signal to a pre-trained neural network model based on a physical information space-time diagram, and generating a position, an area, a long axis length, a short axis length, a direction angle and a severity index of each damage. According to the invention, low-cost and high-robustness structure health monitoring is realized.

The invention relates to a digital intelligent regulation and control preparation method and system for a high-solid-waste low-carbon high-performance grouting material, and solves the problems that a traditional preparation technology lacks an autonomous and controllable digital intelligent regulation and control system and is weak in adaptability to fluctuation of raw materials, and the method comprises the following steps: obtaining XRF chemical components and XRD mineral composition data of industrial solid waste raw materials; constructing a material gene database; based on the database, screening a proportioning scheme by using a performance prediction model, and predicting workability, strength development and shrinkage performance; inputting a prediction result as a fitness function into a multi-objective optimization algorithm, and outputting an optimal material gene combination; a batching scheme is generated based on the optimal combination, and a stirring process is started after technological parameters are preset; collecting data through a real-time monitoring system, and comparing the data with the digital twin model; and based on a comparison result, automatically adjusting material proportioning parameters. The high-solid-waste, low-carbon and high-performance grouting material has the advantages that accurate design and regulation of the high-solid-waste, low-carbon and high-performance grouting material are achieved, material performance is improved, and carbon emission and cost are reduced.