217 results about "Micro structure" patented technology

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 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 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 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 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.

The invention discloses a multi-scale modeling and simulation prediction method and device for a two-phase composite material, and the method comprises the following steps: S1, carrying out the molecular dynamics simulation of the penetration process of a solution in the composite material based on molecular dynamics, extracting the microscopic information in the simulation process, and obtaining a microstructure model of the composite material; s2, mapping the microstructure model to a finite element grid, and reserving structures between particles and interfaces to obtain a finite element model; s3, inserting a cohesive zone unit into an interface of the finite element model, and modeling an interface adhesion and separation phenomenon between a matrix and particles of the composite material based on the cohesive zone unit; and S4, carrying out analogue simulation on the whole damage evolution process based on the finite element model to realize mechanical simulation analysis and prediction. According to the method, composite material modeling is carried out based on a real microstructure, and more accurate mechanical simulation analysis is carried out based on the modeling.

The invention discloses an algorithm-based detritus dolomite sedimentary microfacies identification method, and relates to the technical field of rock sedimentary microfacies identification, and the method comprises the following steps: collecting a detritus dolomite sample, and carrying out analysis to obtain the structure and characteristics of the detritus dolomite sample; extracting features in the structure and the characteristics; selecting a machine learning algorithm; using known detritus dolomite sedimentary microfacies data as a training set, and training a machine learning algorithm to obtain a microfacies recognition model; and then the microfacies identification model is applied to actual detritus dolomite sedimentary microfacies identification, the method can obviously reduce the labor force required by detritus dolomite sedimentary microfacies identification, so that the laboratory analysis is time-saving and labor-saving, and when the microfacies identification model is used for identifying detritus dolomite, the identification result is more objective, effective, accurate and reliable; and the microscopic characteristics or microstructure of the rock stratum can also be captured.

The present application relates to a kind of in-situ testing methods of metal 3D printing sample microstructure evolution, comprising: cutting two test samples from the sample to be tested;Two test samples are respectively processed into tensile specimen and embedded clamp;The tensile specimen and embedded clamp are placed in the corresponding position of in-situ mechanical testing equipment, and tensile test is carried out;The change of the microstructure of the tensile specimen in the tensile process and the creep process is observed.In the present application, the tensile specimen and embedded clamp are prepared from the sample to be tested, and the tensile specimen is clamped by the embedded clamp due to the small size of the tensile specimen, which can ensure the clamping stability and constraint reliability of the tensile specimen, avoid the clamping of the standard clamp on the tensile specimen, and avoid the clamping of the standard clamp on the tensile specimen, and avoid the clamping of the standard clamp on the tensile specimen, and avoid the clamping of the standard clamp on the tensile specimen, and avoid the clamping of the standard clamp on the tensile specimen, and avoid the clamping of the standard clamp on the tensile specimen, and avoid the clamping of the standard clamp on the tensile specimen, and avoid the clamping of the standard clamp on the tensile specimen, and avoid the clamping of the standard clamp on the tensile specimen, and avoid the clamping of the standard clamp on the tensile specimen, and avoid the clamping of the standard clamp on the tensile specimen, and avoid the clamping of the standard clamp on the tensile specimen, and avoid the clamping of the standard clamp on the tensile specimen, and avoid the clamping of the standard clamp on the tensile specimen, and avoid the clamping of the standard clamp on the tensile specimen, and avoid the clamping of the standard clamp on the tensile specimen, and avoid the clamping of the standard clamp on the tensile specimen, and avoid the clamping of the standard clamp on the tensile specimen, and avoid the clamping of the standard clamp on the tensile specimen, and avoid the clamping of the standard clamp on the tensile specimen, and avoid the clamping of the standard clamp on the tensile specimen, and avoid the clamping of the standard clamp on the tensile specimen, and avoid the clamping of the standard clamp on the tensile specimen, and avoid the clamping of the standard clamp on the tensile specimen, and avoid the

The invention provides a copper-based composite material performance prediction method based on a space-time attention mechanism, and the method comprises the steps: obtaining a microstructure diagram and stress-strain data through molecular dynamics simulation, extracting topological features through a diagram attention network, and dynamically adjusting the parameters of a time sequence convolution network; a cross-space-time attention module is constructed, and bidirectional feedback fusion of space feature screening and time sequence feature optimization is realized; and finally, carrying out dimensionality reduction and nonlinear transformation on the fused features through a full-connection layer, and outputting performance prediction values such as yield strength and Young modulus. According to the method, the precision and reliability of performance prediction of the copper-based composite material can be improved, and efficient and accurate theoretical support is provided for material design and performance optimization.

The invention belongs to the technical field of reliability analysis of devices, and particularly relates to a quantitative analysis method for migration characteristics of an indium component in an epitaxial wafer. Comprising the following steps: providing epitaxial wafers with consistent initial characteristics, and carrying out uniform cleavage; taking one small block as a reference sample, and performing external disturbance treatment on other small blocks; the method comprises the following steps: respectively carrying out non-destructive structure characterization treatment on a reference sample and a sample subjected to external disturbance treatment, including high-resolution X-ray diffraction test, photoluminescence and cathode fluorescence combined test and time-resolved photoluminescence spectrum measurement; comprising high-resolution transmission electron microscope and EDS energy spectrum combined imaging and secondary ion mass spectrum; finally, FP lasers are prepared respectively, and electrical characterization processing is carried out. And quantitatively analyzing the influence of indium component migration on the electrical properties of the FP laser according to the result. The method has the advantages that the migration of indium atoms is analyzed according to a method for changing the microstructure in the quantum well and changing the dynamic behavior of a carrier.

The present application relates to the technical field of spectral detection, and discloses a quality control method for traditional Chinese medicine capsules, comprising: acquiring a spatial spectrum response sequence by continuously scanning with a micro-spot probe in capsule movement; performing adjacent micro-area difference operation on the sequence, filtering out the shell background and retaining the particle scattering signal by using the microstructure difference between the continuous film of the capsule shell and the discrete accumulation of the powder content; and calculating the statistical dispersion of the difference sequence to construct a fluctuation fingerprint feature and perform qualification determination. The present application establishes a traditional path of the shell standard model, avoids the reference error caused by the batch drift of the physical properties of the shell through a spatial frequency domain decoupling mechanism, and improves the detection sensitivity of the fluctuation of trace components and the mixing of foreign matters.

The utility model provides a perfusion device and biological sample microstructure observation system, including device main part, device main part is provided with liquid inlet channel, liquid outlet channel and sample storehouse, sample storehouse only bottom is open, liquid inlet channel communicates device main part outside with sample storehouse, liquid outlet channel communicates device main part outside with sample storehouse. The position, corresponding to the sample bin, of the bottom of the device body is used for being connected with the cover glass, the top edge of the cover glass is detachably connected with the bottom face of the device body, and after connection, the cover glass completely seals the sample bin. According to the perfusion device, the liquid medicine of the preset type or the preset concentration can be conveyed into the sample bin through the liquid inlet channel, then the effect of rapidly replacing the type of the liquid medicine or accurately regulating and controlling the concentration of the liquid medicine is achieved, when the biological samples need to be replaced, only the cover glass loaded with different biological samples needs to be replaced, and operation is convenient. The in-situ observation effect of the biological sample can be realized.

The application discloses modified nanocellulose as well as a preparation method and application thereof. The modified nanocellulose is esterification modified on the surface of nanocellulose, and the structural formula of the surface after modification is shown as formula I: wherein n is 150-1300, R1 is selected from C 3～12 alkyl, and R2 is -CH2OH or -CHO. The modified nanocellulose provided by the application retains the original microstructure and morphology of nanocellulose, hybridizes the boron element with green friction activity, and simultaneously enhances the surface hydrophobic property of nanocellulose.

The present application relates to the technical field of electrochemical hydrogen production, and specifically provides a health state prediction method for a water electrolysis hydrogen production device, which comprises collecting operation data of the water electrolysis hydrogen production device, wherein the operation data at least includes voltage data, current data and operation time; decomposing the voltage data into an overpotential component, calculating an electrochemical performance score based on a maximum voltage decay threshold and the overpotential component; calculating a cumulative damage amount of a microstructure damage parameter based on the current data and the operation time, and converting the cumulative damage amount into a physical structure integrity score through a mapping function; and weighting and fusing the electrochemical performance score and the physical structure integrity score to obtain the health state of the electrochemical hydrogen production device. The present application can provide early warning for fatal damage of microstructure when macroscopic voltage has not yet deteriorated significantly, so as to realize accurate prediction and active maintenance of sudden failure of the water electrolysis hydrogen production device under different working conditions.

This invention belongs to the field of materials property simulation technology and discloses a multi-scale coupled simulation method for irradiation damage of energetic materials. This method integrates first-principles calculations, Monte Carlo simulations, molecular dynamics, and accelerated molecular dynamics calculations. By cascading and coupling these simulation techniques at different scales and utilizing parallel computing technology, it overcomes the limitations of single-scale simulations in existing technologies, enabling long-term, large-scale simulations of energetic materials under irradiation conditions, from atomic-scale defect generation to microstructure characterization. This invention solves the limitation of single-scale simulations in existing technologies for irradiation damage simulation of energetic materials and is applicable to irradiation damage simulation of energetic materials.