260 results about "Micro structure" patented technology

The invention relates to the technical field of computer science, in particular to a stamping prediction method based on a multi-machine learning model and an automatic compensation device.The stamping prediction method comprises the steps that firstly, a sheet microstructure and material characteristics are obtained through a transmission electron microscope and an atomic probe tomography technology, and then finite element simulation sampling is conducted; a data set is expanded by using an adaptive algorithm, multiple models are constructed and trained to obtain a final springback prediction model, an automatic compensation device is embedded to realize automatic compensation, and the automatic compensation device covers visual parameter input, embedded springback prediction, springback compensation calculation and an automatic control module. Parameters can be visually input, springback can be accurately predicted, a compensation value can be calculated, and the process can be adjusted. The method aims at solving the problems that in the high-strength plate stamping process, due to springback, the size and shape of a part deviate from the design requirement, and a traditional prediction and compensation technology is insufficient in precision and efficiency.

The invention provides a digital modeling method for enhancing connectivity of a low-porosity rock core, a medium and equipment, and relates to the field of digital modeling of rock cores, and the method comprises the following steps: reconstructing a Gaussian random field based on SAXS data to obtain an initial model of a three-dimensional digital rock core pore structure; extracting pore center points of the initial model, and constructing an initial network connecting all the pore center points; based on a minimum spanning tree algorithm, identifying mutually isolated pore clusters in the initial network, and selecting a most efficient seepage path skeleton connected with the isolated clusters; based on the seepage path skeleton, constructing a throat with fractal characteristics; and according to the total volume of the throat, performing morphological corrosion operation on an original pore area in the initial model, embedding the constructed throat into the corroded model, and then performing controllable morphological expansion operation until the volume variation of the final model is smaller than a preset error threshold. According to the method, the reconstruction precision of the digital rock core in microstructure and macroscopic connectivity is effectively improved.

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 relates to the technical field of side slope geological disaster monitoring and early warning, and discloses a Beidou-based side slope geological disaster monitoring and early warning method and system.The Beidou-based side slope geological disaster monitoring and early warning method comprises the steps that multi-scale geological signals based on Beidou system time service are acquired, the multi-scale geological signals comprise environmental noise signals, acoustic emission signals and micro-seismic signals; processing the multi-scale geological signals, and extracting environmental noise interference characteristics representing the change of a microstructure in the slope, acoustic emission signal characteristics representing the microscopic damage process of a material and micro-seismic signal characteristics representing the deformation of a macrostructure; based on the environmental noise interference features, the acoustic emission features and the micro-seismic features, constructing a slope instability risk assessment model, and outputting a risk assessment result; performing early warning according to a risk assessment result; according to the invention, high-precision, low-cost and full-coverage monitoring and early warning can be realized.

The invention provides a composite material skin isogeometric modeling method considering micro-pores, which comprises the following steps: obtaining internal characteristics of a composite material skin through an SEM scanning electron microscope, and establishing parameterized unit cell microstructure units according to the characteristics; determining a skin deformation area, constructing a constitutive matrix based on different components in the composite material, obtaining and endowing defined material attributes, and obtaining different unit mass matrixes and local stiffness matrixes in the area; and filling into the macroscopic overall matrix through an isogeometric mapping strategy, carrying out inverse mapping fitting on a skin appearance structure, and carrying out applied load calculation. According to the method provided by the invention, through an isogeometric analysis method, three-dimensional curved surface dimension reduction processing is realized, and the calculation cost is reduced; meanwhile, a parameterized microscopic unit is considered to ensure the accuracy of modeling; and the mechanical property of the composite material skin is really simulated. The method is suitable for the structure elasticoplastic deformation state that the skin bears large load, large deformation and the like, and calculation operation is easy and convenient.

A method for predicting microscopic holes of an aluminum alloy product and impact on macroscopic service properties includes: casting simulation, i.e., obtaining a casting simulation finite element mesh by dividing, and using casting simulation software to simulate a solidification process of a casting under corresponding process conditions to obtain macroshrinkages of the casting and physical information of each node on the mesh; cellular automata simulation, i.e., simulating microstructure growth by using a cellular automata model to obtain a secondary dendrite arm spacing (SDAS) value at each node of the casting simulation finite element mesh, and the morphology and size of microscopic holes including microshrinkages and microscopic blowholes; mechanical property simulation, and mapping and inputting mesh information of the casting simulation finite element mesh into the mechanical and fatigue property simulation finite element mesh to obtain a mechanical property simulation result; and fatigue property simulation.

The invention relates to the technical field of digital core modeling and artificial intelligence modeling, in particular to a weathered sandstone microscopic image generation method based on a conditional generative adversarial network, which comprises the following steps: acquiring and analyzing sandstone samples with different weathering degrees to construct a weathered sandstone multi-scale database; performing weathering geological knowledge extraction on the weathered sandstone multi-scale database to construct a sandstone feature tag set; and training the conditional generative adversarial network by using the sandstone feature tag set to obtain a weathered sandstone microscopic image generation model, and generating a weathered sandstone microstructure image with physical consistency by using the weathered sandstone microscopic image generation model. Therefore, the problems that in existing weathered sandstone physical and mechanical property modeling, the depth incidence relation between the microstructure and the macro-mechanical property is difficult to reveal, and a traditional modeling method is difficult to effectively fuse the advantages of a weathering geological mechanism and modeling generation, so that a reconstruction result lacks physical consistency and weathering response rationality are solved.

The invention relates to the field of materials, and particularly provides a composite material damage prediction method based on mesomechanics and related equipment, and the method comprises the steps: obtaining macromechanics response data and microstructure feature data of a composite material; constructing a multi-mechanism damage prediction model based on the microstructure feature data through a mesomechanics theory; performing inversion calibration on the model parameters based on macromechanics response data, and verifying the effectiveness of the multi-mechanism damage prediction model through experimental data; and based on the calibrated multi-mechanism damage prediction model, predicting the initial damage strain of the composite material under different loading conditions and microstructures. According to the method, the macromechanics response data and the microstructure feature data of the composite material are obtained, the multi-mechanism damage prediction model is constructed in combination with the micromechanics theory, and model parameters are calibrated and verified through experimental data, so that accurate prediction of the initial damage strain of the composite material under different loading conditions and microstructures is realized.

The invention discloses a natural caving method-based mining ore rock caving simulation system and method, relates to the technical field of ore rock caving simulation, and aims to solve the problems that the traditional ore rock caving simulation only focuses on acquisition of macroscopic geological data and rock mechanical parameters in the aspect of data acquisition, and the existing simulation system only focuses on acquisition of macroscopic geological data and rock mechanical parameters in the aspect of analysis methods during microstructure analysis. The algorithm is relatively single and rough, so that the simulation result cannot accurately reflect the real caving condition of the ore rock in the actual mining process. According to the invention, the data acquisition module comprehensively acquires macro and micro data and scientifically processes the macro and micro data, the microstructure analysis module accurately analyzes a microstructure by using multiple advanced algorithms and constructs a prediction model, the macro mechanical analysis module combines multiple algorithms to accurately analyze mechanical behaviors, and the data fusion calculation module ingeniously fuses the data to improve the prediction accuracy. Mining risks are effectively reduced, and safe and efficient mining operation is guaranteed.

The invention relates to the technical field of spectrum detection, and discloses a quality control method for traditional Chinese medicine capsules, which comprises the following steps: continuously scanning by using a micro light spot probe during capsule movement to obtain a spatial spectrum response sequence; performing adjacent micro-area differential operation on the sequence, and filtering a shell background and retaining a particle scattering signal by utilizing a microstructure difference between a capsule shell continuous film and a powder content discrete accumulation; according to the method, a traditional path of a shell standard model is established, through a spatial frequency domain decoupling mechanism, reference errors caused by batch drifting of shell physical attributes are avoided, and the detection sensitivity of trace component fluctuation and foreign matter mixing is improved.

The invention discloses a data-driven composite material strain field distribution prediction method, and belongs to the technical field of composite material mechanical property prediction. The method comprises the following steps: firstly, collecting a microstructure graph of the composite material and a corresponding actual strain field distribution graph, and constructing a data set after preprocessing; downsampling and feature extraction are carried out on the preprocessed image through an encoder, the encoder adopts a multi-stage residual structure, and each stage comprises a downsampling residual block and a standard residual block; obtaining potential variables from coding output sampling through a re-parameterization technique; performing up-sampling and feature reconstruction through a decoder with a symmetrical structure; and finally obtaining a normalized strain field prediction map through an output layer. And after the network training is completed, inputting the microstructure graph of the to-be-tested material to output the predicted strain field distribution. According to the method, accurate and efficient mapping from the microstructure of the material to the mechanical property is realized, and a reliable data driving solution is provided for strain analysis of the composite material.

The invention discloses a micro-positive pressure hydrogen environment in-situ stress-strain testing device and method. The device comprises a sealed reaction cavity which can be placed in a scanning electron microscope sample cabin; the loading device is arranged in the cavity and is used for fixing and applying a load; the atmosphere control system is communicated with the cavity and is used for introducing gas and maintaining a micro-positive pressure state; an electron transmission window arranged on the cavity, wherein an electronic transparent film packaged by the electron transmission window can maintain air tightness under micro-positive pressure and allows electron beams to penetrate through; and the heating module is arranged in the cavity. The method comprises the following steps: loading and sealing a sample, establishing a micro-positive pressure atmosphere, heating the sample, starting loading, and synchronously carrying out SEM real-time imaging and data recording. The problem that a traditional SEM in-situ device cannot work in a real micro-positive pressure hydrogen and high-temperature environment is solved, and synchronous visual in-situ observation of material microstructure evolution and macromechanical response under the gas-heat-force multi-physics field coupling condition is achieved.

The invention discloses a structure lightweight optimization method based on a multi-objective genetic algorithm, and belongs to the field of structure optimization. The method comprises the steps of layering of a macrostructure and optimization of geometric parameters of the microstructure. Under the constraint of the macroscopic structure configuration and the geometric parameters of the microcosmic cell elements, the geometric parameters of the microcosmic cell elements filled among different parts of the macroscopic structure are obtained through optimization, the matching of the rigidity and the relative density among different cell elements is realized, and the optimal balance between the rigidity and the quality of the optimized macroscopic structure is realized. In the optimization process, an interface of simulation software and a multi-target genetic algorithm is established, mechanical property analysis is carried out on the complex model in the simulation software, simulation data are extracted and transmitted to the multi-target genetic algorithm, and optimization of microscopic cell geometric parameters is carried out. Through coupling finite element simulation software and a multi-target genetic algorithm, collaborative optimization of macro and microstructure is realized, the problem that high-rigidity lightweight of a complex three-dimensional model is difficult is effectively solved, and the high-rigidity lightweight efficiency of a complex structure is improved.

The invention relates to the technical field of ground surface matrix monitoring, and provides a portable multi-parameter ground surface matrix tester which comprises a shell; the mobile control interface is arranged above the shell and is used for being connected with external mobile equipment and controlling the portable multi-parameter earth surface matrix tester to enter a stratum exploration hole channel in a 360-degree rotation manner; the sensor module is arranged at the lower end of the shell and used for measuring physical and chemical parameters; the image detection module is arranged at the lower end of the shell and used for collecting image information of the stratum exploration hole channel in the rotating process and generating corresponding microscopic detection information; the visual auxiliary module is connected with the shell and is used for observing the microstructure and fracture distribution of the inner wall of the stratum exploration pore channel in real time and generating auxiliary detection information; and the control module is integrated in the shell and is used for determining the insertion position and angle of the portable multi-parameter earth surface matrix tester entering the bottom of the stratum exploration pore channel and generating an earth surface matrix three-dimensional distribution diagram of the stratum exploration pore channel.

The invention relates to the technical field of formula optimization methods, and provides a formula optimization method based on AI and multi-physics field simulation, which solves the problems of multi-target dynamic optimization and quick response, and comprises the following steps: S100: inquiring whether a CCL material knowledge base has a CCL material formula or not through an interactive type selection tool according to requirements; s200, if a material formula meeting the requirement exists in the CCL material knowledge base, model selection and delivery are carried out, and the process is ended; if no material formula meeting the requirement exists in the CCL material knowledge base, new material development is carried out; s300, carrying out cross-scale modeling and multi-physical field coupling analysis on the microstructure of the material to obtain a new material formula; s400, carrying out AI agent model training and physical and data fusion on the new material formula by utilizing a physical enhanced neural network architecture; s500, judging whether the new material formula meets requirements or not, if yes, incorporating the new material formula into a CCL material knowledge base, delivering the new material formula, and ending the process; and if the new material formula does not meet the requirement, returning to the step S300.

The invention relates to the technical field of engineering material monitoring, and discloses an SHCC material full-life monitoring system for tunnel engineering and an evaluation method. According to the system, through cooperative work of an initial data acquisition module, a material state modeling module, a service environment simulation module, a damage evolution tracking module, a performance degradation evaluation module and a service life prediction module, the whole life cycle of a material is monitored. The method comprises the following steps: acquiring internal microstructure data of a material, constructing a dynamic response map, tracking a microcrack evolution path in real time by implanting a mark point in a simulated tunnel environment, and finally establishing a performance degradation evaluation matrix to complete life prediction. According to the system, internal correlation analysis from microscopic damage to macroscopic performance is realized through two core technologies of dynamic map construction and microcrack mark tracking, and the accuracy of life prediction and the reliability of engineering durability evaluation are improved.

The invention discloses a digital rock elasticity modulus calculation method based on multi-scale data fusion. The method comprises the following steps: establishing a three-dimensional digital rock model; establishing a two-dimensional digital rock model; counting pore characteristics of each phase in the three-dimensional digital rock model and the two-dimensional digital rock model; calculating the equivalent elastic modulus of each phase in the three-dimensional digital rock model and the two-dimensional digital rock model; establishing a three-dimensional digital rock grid; and calculating the elastic modulus of the three-dimensional digital rock. A three-dimensional digital rock model and a two-dimensional digital rock model are established by adopting CT scanning and SEM scanning, a nano-resolution characterization method is combined, a multi-scale pore space and complex mineral components are characterized, and the pore characteristics of each mineral phase are accurately extracted. The digital rock elastic modulus calculation method based on multi-scale data fusion has the advantages that the microstructure of pores of the rock can be reflected more accurately, and the precision of digital rock elastic modulus numerical simulation is greatly improved.

A method for determining dynamic strength of loess through average particle size and macropore ratio comprises the following steps: sampling and determining basic physical properties of a soil sample, preparing an artificial structural loess sample, determining loess n, # imgabs0 # under a microstructure, and determining the dynamic strength of the loess according to the average particle size and the macropore ratio. The method comprises the following steps: establishing a three-dimensional model of dynamic characteristics and a microstructure of loess under the action of a dynamic load by utilizing a particle analysis test and a mercury injection test, and fitting a loess sample n, # imgabs1 # formula; fitting a relation formula, establishing a model, and fitting a dynamic internal friction angle, dynamic cohesion, average particle size and macropore proportion formula through data; and finally, measuring the dynamic strength of the loess, and performing simple qualitative verification on the dynamic strength index of the loess by using the neural network according to the given data in the step 4 and adopting the Moire-Coulomb strength theory. The test precision and efficiency are improved.

The invention belongs to the technical field of organic waste treatment, and discloses a device and method for strengthening hydrothermal carbonization of organic waste through plant ash coupling granulation, organic waste is subjected to vibration fluidization through a vibration fluidization device, and the organic waste is subjected to multi-stage cutting and stirring through three layers of spiral blade paddles; the multi-stage mechanical granulation of the organic waste is realized under the combined action of the two, and the macrostructure of the organic waste is destroyed and modified, so that the organic waste becomes uniform small-particle organic waste; then, alkaline plant ash is used for promoting biomass molecules of the uniform small-particle organic waste to be subjected to alkaline deconstruction, the microstructure of the organic waste is destroyed and modified, and the hydrothermal activity is improved.

The utility model discloses sample preparation equipment, and belongs to the field of chemical sample preparation. The sample preparation equipment comprises a batching device, a pressing device, a transferring device, a calcining device, a crushing device and a detecting device. The transferring device is used for pouring the mixed powder into the material pressing mold and transferring the material pressing mold to the material pressing device, and the material pressing device is used for pressurizing the material pressing mold; the transfer device is used for transferring the first intermediate sample into the second accommodating vessel and transferring the second accommodating vessel into the calcining device, and the calcining device is used for calcining the first intermediate sample; the transfer device is used for transferring the second intermediate sample to the crushing device, and the crushing device is used for crushing the second intermediate sample to form a plurality of samples and dropping the samples into the third accommodating vessel; the transfer device is used for transferring the third accommodating vessel to the detection device, and the detection device is used for detecting the microstructure of the sample. The sample preparation equipment provided by the utility model can realize automatic preparation of the sample.

The invention provides a reverse design method of a starch hydrogel microstructure, and belongs to the technical field of computer material science. A standardized data set is constructed by collecting different starch hydrogel microstructure images and corresponding target performance index vectors. And constructing a starch hydrogel microstructure reverse generation model, and establishing nonlinear mapping between a macroscopic mechanical property index and a microscopic topological structure in combination with a multi-scale self-attention mechanism and a double-flow physical consistency identification strategy. Meanwhile, a differentiable physical performance prediction agent model and a multi-dimensional physical consistency coupling loss function are designed, adversarial iterative optimization under physical constraints is executed, and a prediction physical performance vector of a generated structure is forced to strictly approach a target performance index vector through a self-supervised feedback closed loop. According to the method, accurate anchoring of the generated structure on the mechanical property is achieved, design scheme recommendation and morphological quantitative analysis based on confidence are provided, and the intelligent level and scientificity of bio-based material design are improved.

The invention discloses a threshold judgment method for eliminating loess collapsibility through microwave irradiation, relates to the technical field of data prediction and updating, and is used for solving the problem of insufficient collapsibility control effect evaluation means when the structural change is not obvious, and the method comprises the following steps: acquiring and preprocessing a collapsible loess sample to obtain initial physical information data of the collapsible loess sample; microwave irradiation experiment conditions are set, and the collapsibility coefficient change trend of loess under the conditions is calculated. And judging the change of the internal structure according to trend evaluation whether a temperature distribution analysis method is adopted, if the change is weak, further obtaining the microstructure characteristics of the sample and the change of mineral components, and comprehensively evaluating the collapsibility reduction degree. If the reduction degree is high, void ratio parameters are introduced to carry out quantitative analysis on the collapsibility change, and a scanning electron microscope or X-ray diffraction analysis is selected to verify whether the collapsibility change reaches an expected control target or not, so that the stability and safety of loess are ensured.

The invention discloses a method and device for predicting transverse mechanical properties of a composite material, and a storage medium, and belongs to the technical field of composite materials. The method comprises the following steps: firstly, establishing a mesoscopic representative volume element model considering fiber random distribution and diameter fluctuation; constructing a transverse mechanical property database of the composite material; establishing a CNN model and an ANN model by taking the RVE microstructure image and physical structure parameters such as fiber volume fraction, clustering degree, uniformity and diameter distribution as input; analyzing and verifying the equivalent characterization capability of ANN input parameters on image features, and establishing a CNN-ANN fusion modeling strategy; and carrying out interpretability analysis on the optimized ANN model, and quantifying the contribution degree and the influence direction of each microstructure parameter to the mechanical property. According to the method, efficient and accurate performance prediction with physical interpretability can be realized under the multi-source uncertainty condition, the experiment and calculation cost can be reduced, and the reliability and engineering application value of the composite material structure design can be improved.

The invention discloses a concrete working performance monitoring method and system based on coarse aggregate slurry wrapping state recognition, and relates to a method and system for monitoring the working performance of premixed concrete based on machine vision and machine learning. The invention discloses a concrete working performance monitoring method based on coarse aggregate slurry wrapping state recognition. The concrete working performance monitoring method comprises the following steps: acquiring a video stream in a stirring process through an image acquisition device arranged in a concrete stirrer; extracting a key frame from the video stream, and identifying coarse aggregate particles in the key frame; calculating characteristic parameters reflecting the wrapping state of the slurry on the surfaces of the coarse aggregate particles; inputting the characteristic parameters into a pre-trained concrete working performance evaluation model to obtain a working performance evaluation result; and outputting the evaluation result. According to the method, direct and quantitative on-line monitoring of the microstructure state of the concrete in the stirring process is achieved, a more essential and more accurate quality evaluation means is provided by revealing the internal relation between the slurry wrapping state and the working performance, and the lagging, indirect and macroscopic defects of a traditional method are overcome.

The invention provides a reverse synthesis method of a carbon-based environmental functional material, and belongs to the technical field of computer material science. The method comprises the following steps: firstly, carrying out collaborative acquisition and joint data set construction of multi-source heterogeneous data of the carbon-based environmental functional material; building a reverse generation model of a carbon aerogel hole topological structure by means of a deep generative adversarial architecture, converting macroscopic performance indexes into high-dimensional constraint features by means of a performance coding network, and realizing directional generation of micro-pores by means of a self-adaptive modulation mechanism of a synthetic network. Meanwhile, an embedded physical attribute check and adversarial collaborative optimization strategy is implemented, a physical regression check head embedded in the authentication network is reused, and a self-supervised feedback closed loop without an agent model is constructed. And finally, completing process parameter inversion and intelligent decision recommendation based on multi-dimensional feature decoupling, and mapping the optimal microstructure features into an executable synthesis process formula through morphological quantitative verification and a process parameter inversion network.

The invention relates to the technical field of silicon carbide device packaging structure optimization, and provides a novel high-temperature-resistant silicon carbide device packaging structure optimization method and system, and the method comprises the steps: carrying out the recognition according to a physical field sensitivity threshold value, and obtaining the information of a sensitive region; computing domain grid encryption is carried out on the sensitive area, computing domain grid sparseness is kept for the non-sensitive area, and a discretization packaging structure model is constructed; obtaining batch difference and microstructure unevenness of packaging materials of the packaging structure; according to the batch difference and the microstructure unevenness, a preset number of material parameter combinations representing the intrinsic variability of the material are generated; performing association operation on the material parameter combination and the discretized packaging structure model to obtain core optimization parameter information which is used as input of a packaging structure optimization process; and optimizing the packaging structure based on the core optimization parameter information. The method has the effect of improving the stability of the silicon carbide device under the extreme working condition.

The invention provides a cross-scale model enhanced fatigue damage prediction method and system, and belongs to the field of material fatigue performance prediction. The method comprises the following steps: constructing a discrete dislocation kinetic model and a cyclic crystal plastic model, and calibrating parameters of the discrete dislocation kinetic model; determining to-be-optimized parameters of the cyclic crystal plastic model and inverting the optimal values of the to-be-optimized parameters to obtain a calibrated cross-scale mechanical model; importing the initial microstructure features of the material into a cross-scale mechanical model to obtain an extended data set containing multi-scale features and fatigue damage results; and constructing a full-connection neural network prediction model, carrying out training based on the extended data set, calculating the Shapley value of the input feature by using a model interpretability tool, and outputting the contribution degree of the multi-scale microstructure feature to the fatigue damage. According to the method, cross-scale interpretable prediction from a micromechanism to macroscopic performance is realized, and a new direction is provided for accurate evaluation of the fatigue life of the heterogeneous material and optimization design of a microstructure.

The invention discloses a wear-resistant and anti-corrosion super-hydrophobic ice-resistant coating and application thereof, and belongs to the technical field of protective coatings. The coating is formed by sequentially compounding a microcosmic framework layer and a nano functional layer, wherein the microcosmic framework layer comprises epoxy resin, a curing agent, polyaniline and a microstructure material; the nano-scale functional layer comprises epoxy resin, polydimethylsiloxane, nano particles, nano # imgabs0 # and polyaniline; and the nano-scale functional layer comprises epoxy resin, polydimethylsiloxane, nano particles, nano # imgabs0 # and polyaniline. The coating disclosed by the invention has super-hydrophobicity, ice resistance, high wear resistance and long-acting corrosion resistance, and is suitable for base material protection in high-corrosion and low-temperature ice-condensation environments of ocean engineering, industrial equipment and the like.

The invention relates to the technical field of porous material preparation process optimization, in particular to a porous material preparation process optimization method. The method comprises the steps of obtaining microscopic pore distribution characteristic data to generate preliminary design parameters, constructing a microstructure model to optimize pore sizes, adjusting key element distribution to generate an improved scheme, determining an optimal process parameter combination through simulation testing, detecting pore uniformity in real time and dynamically adjusting parameters. And monitoring the element utilization rate to generate a dynamic adjustment scheme and verifying the performance index to generate an optimization record. Intelligent control over preparation of the porous material can be achieved, the surface area, the pore uniformity and the key element utilization rate are remarkably increased, and reliable technical support is provided for application in the fields of catalysis, environmental protection and the like.

The invention relates to the technical field of ultra-high performance concrete, in particular to an ultra-high performance concrete strength prediction method considering a particle close packing effect, comprising the following steps: S1, acquiring a data set to be measured, the data set comprising a macroscopic proportioning material and a material particle gradation; s2, according to the particle size distribution of the solid particle material in the data set, calculating the volume percentage of which the cumulative volume is smaller than a preset particle size through a corrected Andersen-Andersen model; s3, calculating the stacking compactness according to the volume percentage with the accumulated volume smaller than the preset particle size; and S4, inputting the stacking compactness and the to-be-measured data set into a prediction model to obtain a predicted value of the compressive strength of the ultra-high performance concrete. And by introducing microstructure quantitative indexes such as grain composition and stacking compaction parameters as model input, the prediction precision and reliability are remarkably improved.