159 results about "Micro structure" patented technology

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 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 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 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 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.

The invention provides a control method for accurate blanking of a hopper scale, and relates to the technical field of industrial automation and process control, and the method comprises the steps: obtaining the internal density distribution data of a material in a hopper through at least one electrical capacitance tomography sensor; obtaining inter-particle stress data of the material through at least one acoustic emission sensor; taking the internal density distribution data and the inter-particle stress data as observation variables, and inputting the observation variables into a dynamic Bayesian network DBN model; utilizing a DBN model to carry out probabilistic inference on a hidden material state of the material, the hidden material state at least comprising a flow failure probability; when the flow failure probability exceeds a preset threshold value, generating a preventive control signal; a preventative control signal is applied to a flow actuator to correct the flow condition of the material prior to a macroscopic flow failure of the material. The blocking risk can be reduced to the germination state as much as possible in the early stage of formation of the pre-bridging microstructure.

The invention discloses a pulveryte lithofacies identification method based on deep learning, and relates to the technical field of deep learning and sedimentary rock lithofacies identification, and the method comprises the steps: collecting lithofacies curve data and microstructure image data, screening feature parameters, and generating a standardized multi-modal feature set through an exclusive analysis engine; calling a specific model to perform hierarchical feature extraction on the microstructure image to obtain a deep microstructure feature vector; inputting the two types of features into an exclusive network, and outputting a preliminary recognition result through cross-modal attention weight distribution; and performing inversion correction and dynamic adjustment on the preliminary result by adopting a specific algorithm, and constructing a lithofacies classification system in combination with feedback information to complete accurate division. According to the method, through multi-source feature deep fusion, cross-modal correlation enhancement and multi-link collaborative optimization, efficient and accurate identification of the lithofacies of the pulveryte rock is realized, complex lithologic features are adapted, and support is provided for technical application in related fields.

The invention belongs to the technical field of decommissioned lithium iron phosphate battery positive electrode material regeneration, and discloses a process method for preparing a high-compaction positive electrode material by decommissioned lithium iron phosphate battery black powder regeneration, which comprises the following steps: preparing a uniform spherical precursor through spray drying, then completing macroscopic densification in a low-temperature mechanical force field, and finally performing thermal treatment in an instantaneous thermal treatment stage to obtain the high-compaction positive electrode material. Fixed time is replaced by real-time monitored macroscopic conductivity evolution characteristics of the material, closed-loop adaptive control of the heating process is realized, macroscopic densification and microstructure consolidation are decoupled on a process time sequence, so that stacking density construction of the material and size control of primary particles are independently completed, and the method is suitable for large-scale industrial production. Therefore, the internal restriction that the compaction density and the rate capability trade off and trade in the prior art is solved, and the cooperative improvement of the compaction density and the rate capability is realized.

The application discloses a fine-grained sedimentary rock facies identification method based on deep learning, relates to the technical field of deep learning and sedimentary rock facies identification, and comprises the following steps: collecting facies curve data and microstructure image data and screening characteristic parameters, generating a standardized multi-modal feature set through a special analysis engine; calling a specific model to perform hierarchical feature extraction on the microstructure image to obtain a deep microstructure feature vector; inputting the two types of features into a special network to output a preliminary identification result through cross-modal attention weight distribution; and adopting a specific algorithm to perform inversion correction and dynamic adjustment on the preliminary result, combining feedback information to construct a facies classification system to complete accurate division. Through multi-source feature deep fusion, cross-modal correlation enhancement and multi-link collaborative optimization, the application realizes efficient and accurate identification of fine-grained sedimentary rock facies, adapts to complex lithological characteristics, and provides support for technical application in related fields.

This invention relates to the field of geotechnical engineering mechanics testing and non-destructive testing technology. Existing technologies suffer from high computational delays due to passive monitoring modes, the inability to perform real-time physical intervention before macroscopic brittle fracture of soil and rock masses, and the inability to effectively preserve the critical failure state of rocks for microscopic damage constitutive relationship studies. This invention discloses a system and method for analyzing acoustic emission signals from freeze-thaw damage in soil and rock masses. A signal acquisition module is used to capture acoustic emission signals in real time; a feature analysis module is used to extract frequency band energy distribution characteristics and the instantaneous rate of change of the dominant frequency drift; a state determination module is used to determine the microcrack damage evolution stage and the precursors to critical failure based on the above features; and a linkage intervention module is used to generate physical intervention commands based on the determination results, enabling real-time adaptive adjustment or state latching of mechanical loads and ambient temperature. This allows for microsecond-level closed-loop linkage intervention, suppressing sudden shear surface slippage and instability, and effectively preserving the microstructure of rocks on the eve of critical failure.

The application provides a composite gas cylinder mechanical property prediction method and system based on micro damage evolution, and relates to the technical field of composite material mechanical property analysis. The method establishes a micro representative volume element model and performs multi-axial progressive loading analysis, extracts damage initialization conditions and evolution rules, and obtains homogenization output parameters through homogenization processing. The mapping relationship between the microstructure parameters and the macro damage parameters is established by using a machine learning agent model, and the macro finite element model is implanted, and finally the predicted failure pressure and the predicted fatigue life are output. The application realizes high-precision mechanical property prediction of the composite gas cylinder from the micro damage mechanism, and has the advantages of high prediction accuracy and self-calibration.

The utility model relates to the technical field of animal tissue slicing, and discloses an adjustable animal tissue slicing device which comprises a main body shell, a driving module is arranged on the side wall of the main body shell, a conveying piece is fixedly connected to the inner side wall of the main body shell, and a transverse groove is formed in the conveying piece. On the premise of an automatic cutting mechanism, the slicing precision and uniformity can be improved, and the problem of non-uniform slicing thickness caused by human factors such as hand shaking and non-uniform force application in manual operation is effectively avoided; the thickness of each time of cutting can be accurately controlled, the consistency of the thickness of the slices is ensured, and a highly reliable and repeatable data basis is provided for subsequent histological analysis, pathological diagnosis and cell biology research, so that the accuracy and reliability of experimental results are improved, experimental errors caused by the thickness difference of the slices are reduced, and the experimental efficiency is improved. And scientific researchers can more accurately reveal the microstructure and pathological change characteristics of tissue cells.

The invention belongs to the related technical field of phase segmentation, and discloses a decoupling type phase segmentation method and system for a composite gel system microstructure, and the method comprises the steps: (1) forming a reusable particle database through particle images corresponding to each kind of target gel particles; (2) taking an artificially marked pure cement paste BSE image as a background image, inserting particles in a reusable particle database into a cement matrix area to obtain a stage 1 model training data set, and training a stage 1 universal segmentation model; (3) training the stage 2 particle classification model by adopting particles in the reusable particle database; (4) inputting the BSE image to be subjected to phase splitting into the stage 1 universal segmentation model to obtain a matrix, a pore and a particle mask; and stage 2, the classification model performs joint classification based on the particle masks and all particles in the BSE image to be subjected to phase splitting so as to obtain the specific phase category of each particle. According to the invention, the universality is improved.

The invention discloses a chip-level fault maintenance device for 10-layer and more than 10-layer PCBs, and relates to the technical field of PCB maintenance. Comprising the following steps of: 1, performing non-destructive detection on the internal structure and temperature distribution of the PCB by using a high-resolution X-ray detection system and a thermal imaging system respectively, and deducing a fault point; 2, observing the surface and microscopic internal structure of the PCB by adopting microscope equipment, obtaining the surface and internal microstructure images of the PCB, and extracting fine fault features; step 3, collecting each detection data and the extracted fine fault features; a fault point is positioned by combining an electronic probe and optical microscope equipment, and a fault element is removed by using a fusing repair mode of laser fusing or ion beam fusing; 4, monitoring and adjusting the fusing repair process in real time; and storing and analyzing maintenance process data, and providing data support for optimization and improvement.