338 results about "Micro structure" patented technology

The invention discloses an operation state monitoring method and system for AEM water electrolysis hydrogen production equipment, particularly relates to the technical field of electrolytic bath operation monitoring control, and aims to solve the problem of efficiency nonlinear fluctuation caused by polarization mode alternating dominance and microstructure degradation under a low current density working condition in the prior art. Extracting polarization fluctuation characteristics by generating a dynamic response sequence and calculating a dynamic coupling degree to identify a polarization alternating dominant mode; analyzing a local blocking probability based on frequency characteristics, positioning an active site sparse region in combination with temperature gradient spatial distribution characteristics, and generating an efficiency fluctuation evaluation coefficient; an active site sparse region and a dynamic compensation factor matrix of a cathode flow field pressure gradient are constructed to cooperatively adjust an anode current density distribution weight and a cathode pressure gradient parameter, so that an adjusting variable is accurately matched with a local blocking probability and a dynamic coupling degree; the multi-scale correlation analysis of the microcosmic degradation state and the macroscopic efficiency fluctuation of the electrolytic cell is realized, and the operation stability under the low current density is remarkably improved.

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 discloses an ARVR resin wafer process which comprises the following steps: preparing a to-be-detected resin wafer and a reference test piece, and carrying out positioning marking on a micro-nano structure area of the to-be-detected resin wafer to obtain a detection sample; performing multi-dimensional spectrum characterization on the detection sample and the reference test piece, determining a material non-uniform distribution area of the detection sample through comparative analysis, and obtaining molecular structure characterization data; carrying out stress field detection and dynamic response analysis on the material non-uniform distribution area to obtain mechanical property characterization data and high-risk defect points; carrying out microstructure and morphology analysis on the high-risk defect points to obtain defect feature data; and performing system evaluation according to the molecular structure characterization data, the mechanical property characterization data and the defect characteristic data to obtain a health degree evaluation result of the resin wafer. According to the technical scheme, multi-scale characteristic characterization can be carried out on the micro-nano structure area in the resin wafer for AR / VR, and systematic detection of the characteristics such as the molecular state, the interface stress and the phase change of the material is achieved.

The invention discloses a computer numerical simulation method, system and equipment for a composite material, particularly relates to the technical field of analog simulation, and is used for solving the problem of simulation distortion caused by neglecting the randomness of a microstructure in the prior art. The method comprises the following steps: acquiring microstructure image data of a composite material, extracting fiber arrangement direction and pore distribution parameters, and generating a fiber direction probability density function and a pore topological relation; constructing a non-uniform grid model constrained by a local coordinate system according to the fiber direction probability density function; identifying a weak connection area based on a pore topological relation and marking nodes; applying dynamic decoupling constraint to the weak connection area, wherein the dynamic decoupling constraint comprises initial rigidity and rigidity attenuation rate; applying an external load to the model and calculating a real-time stress coefficient of the weak connection area; dynamically adjusting the constraint parameter through threshold judgment and iterative correction until the stress coefficient is converged; and finally, outputting a corrected macroscopic mechanical property result.

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 discloses a breakable crop stalk discrete element simulation model construction method, which comprises the following steps: firstly, measuring the actual feature size of a crop stalk, and layering the internal structure of the crop stalk based on a microstructure; then performing an experiment to obtain intrinsic parameters and contact parameters of the stalks, constructing a multi-layer geometry three-dimensional model according to measurement characteristics, filling each layer of the geometry three-dimensional model with different types of particles, establishing an initial discrete element simulation model, introducing a contact model, and constructing an initial breakable discrete element simulation model; and finally, adjusting the mesoscopic parameters of the simulation model according to the actually measured data until the mesoscopic parameters are kept consistent. According to the method, the contact model is introduced, different structural layers are filled with particles with different material attributes, the biological characteristics and mechanical characteristics of multiple layers of real stalks from outside to inside are restored, and errors are smaller when mechanical behaviors are simulated.

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 C / C composite material ablation morphology prediction method considering a complex microstructure, and solves the technical problem that the prediction result is not accurate enough due to the fact that the influence of the material microstructure on the ablation morphology is not fully considered in the existing prediction method. A random sequence adsorption algorithm is adopted to create C / C composite material microscopic representative volume cell elements based on a three-dimensional voxel grid, and under a finite element transient heat transfer analysis framework, ablation criteria of all components of the C / C composite material are respectively established according to temperature information and chemical reaction kinetic parameter information; and finally, C / C composite material microscopic ablation morphology prediction comprehensively considering complex microstructure characteristics is 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 optical coherence elastography, in particular to a microscopic optical coherence elastography method and system.The method comprises the steps that a collection trigger signal is generated, a vibration control signal and a position control signal are output at the same time, and a target excitation system corresponding to a to-be-detected sample is determined; triggering the line scanning camera by collecting a trigger signal, collecting a line scanning image of the to-be-tested sample, and adjusting the position of the to-be-tested sample in the sample table so as to place the to-be-tested sample at a target test position; controlling a bending actuator or an ultrasonic transducer to vibrate by using the vibration control signal so as to excite low-frequency shear waves or ultrasonic waves in the sample to be detected; the to-be-detected sample is scanned through the position control signal to obtain propagation information of low-frequency shear waves or ultrasonic waves, and the pixel-level biomechanical properties of the to-be-detected sample are reconstructed by using a near-field full-wave inversion algorithm, so that the microstructure of a deeper tissue can be observed, the imaging speed is relatively high, and the detection performance of small inclusions is improved.

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 relates to the field of high-entropy alloys, in particular to a performance regulation and control method for a hole structure of a laser 3D printing high-entropy alloy stent, which comprises the following steps: step 1, component design and mechanical property research of a medical high-entropy alloy; 2, regulating and controlling the pore structure and the performance of the laser 3D printing high-entropy alloy stent; 3, preparation of the hydroxyapatite / high-entropy alloy composite material and biocompatibility evaluation.The method has the following beneficial effects that on the basis of deep understanding of physical and chemical changes designed in the laser 3D printing technology, a correlation mechanism between technological parameters and a macro / microstructure is established; precise control over the pore structure of the key metal component high-entropy alloy material is achieved, calculation is conducted in combination with the first principle, the component optimization theory of the key metal high-entropy alloy material with the human body implantation biological material as the application target is established, and on-demand preparation of the high-entropy alloy material with high strength, high hardness and low elastic modulus is achieved.

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 provides a grazing incidence small-angle scattering data fitting method and system, and the method comprises the steps: constructing a grazing incidence small-angle scattering model, and obtaining a target function; preprocessing experimental data and inputting a target function; according to the preprocessed experimental data, determining the nanoparticles by adopting a Pood fitting method; determining a target function according to a judgment result, performing Monte Carlo fitting, and outputting an optimal parameter; iteratively optimizing the optimal parameter by adopting a gradient descent method, and outputting a fitting parameter result; and post-processing the fitting parameter result, and outputting and evaluating a visual fitting result. According to the method, a multi-stage optimization process is introduced, so that the characterization deviation problems that the traditional data processing process depends on manual operation, the result is obviously influenced by subjective factors and model selection, and the stability is relatively poor are solved; the processing efficiency of GISAS experimental data and the quantitative characterization capacity of the microstructure are remarkably improved, the efficient processing requirement of multiple groups of experimental data can be met, and more reliable technical support is provided for subsequent material research.

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 provides an argon ion beam scanning electron microscope double-beam system and an in-situ sample analysis method, an electron optical lens barrel of the system generates a high-energy electron beam for scanning the surface of a sample for imaging; the ion optical lens cone generates a high-energy argon ion beam for sample surface polishing; the sample chamber is used for accommodating samples and providing a vacuum environment; the sample table is used for accurately controlling the position and angle of a sample; and the detector is used for collecting signals generated by interaction of the electron beam and the sample for imaging analysis. According to the invention, a sample transfer step is eliminated, so that the sample preparation and analysis efficiency is greatly improved; a millimeter-scale large-size sample can be treated, and a sample with a flat and smooth surface can be obtained; sample polishing and imaging are carried out in the same vacuum environment, so that sample pollution is reduced to the greatest extent, and the accuracy of an analysis result is improved; by combining argon ion beam polishing and scanning electron microscope imaging technologies, efficient, high-precision and in-situ analysis of the sample is realized, so that more comprehensive and more accurate microstructure information is obtained.

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 invention belongs to the field of geochemistry and tectonic geology research, and discloses a stress gradient analysis method based on directional change of saline inclusion in FIP. According to the method, the size, shape and distribution characteristics of the saline inclusion in the FIP in different directions are acquired and quantitatively analyzed through the system, and a stress gradient evolution model is constructed, so that the spatial change of a local tectonic stress field is revealed. Compared with a traditional stress analysis method mainly based on a macrostructure, the stress analysis method focuses on stress response behaviors under a microscopic scale, combines various precise test means such as microscopic observation, pressure measurement and cathode luminescence, and realizes a technical path of directly inverting the tectonic stress gradient from the microstructure. The method can be widely applied to geological research of structural environments such as fault zones and shear zones, provides a new technical means for crust stress field analysis, and has important research value and wide application prospects.