102 results about "Molecular interactions" patented technology

The present invention relates to the technical field of molecular interaction analysis, and specifically provides a method for analyzing intermolecular interactions. The method comprises the following steps: using a rare earth material with a ligand molecule modified on its surface as an upconversion material, contacting the upconversion material with a target molecule in a solution system, irradiating the solution system with excitation light that matches the rare earth material, and continuously changing the intensity of the excitation light actually received by the upconversion material as the target molecule continuously binds and dissociates with the ligand molecule of the upconversion material in a dynamic process, thereby changing the intensity of the emitted light. By recording the curve of the emission light intensity changing with time, the molecular interaction kinetic constants, such as the binding rate constant (K on ), dissociation rate constant (K off ) and affinity (K D ), etc., which solves the technical problems of low accuracy of existing BLI technology and high cost of SPR technology. Based on the above principles, the present invention has also developed a series of experimental consumables, kits, sensors and analytical devices for molecular interaction analysis.

The invention relates to the technical field of electrical engineering, in particular to a composite material dielectric breakdown multi-scale simulation method and device, and the method comprises the steps: calculating the electric field intensity of a grid based on a preset geometric structure of a nano composite material, and calculating the electric field intensity of the grid based on material parameters and structure parameters of the nano composite material; calculating the molecular interaction energy of the interface between the nanofiller and the matrix; calculating a target breakdown probability of the grid by utilizing the electric field intensity and molecular interaction energy, generating a target state parameter of the grid based on the target breakdown probability, determining a target breakdown strength value meeting a condition by utilizing the target state parameter, and performing Weber distribution fitting processing on the target breakdown strength value to obtain a target breakdown strength value; and determining a simulation result of the breakdown strength of the nano composite material. Therefore, the problems that most of breakdown simulation methods in the prior art are single-scale models, the interfacial effect is not fully considered, errors in the aspect of quantitative prediction of the breakdown strength are large, and the accuracy of breakdown simulation is reduced are solved.

The utility model relates to the technical field of emulsifying devices, and discloses a collagen emulsifying device which comprises an emulsifying barrel, a barrel cover is mounted at the top of the emulsifying barrel, a stirring shaft is rotatably mounted on the barrel cover through a bearing embedded in the middle of the top of the barrel cover, and a stirring shaft is mounted on the stirring shaft. Stirring pieces are mounted at the bottom end and the outer wall surface of the stirring shaft, and an ultrasonic driving assembly is arranged at the top end of the stirring shaft. According to the collagen emulsifying device, the vibrating rod is in an ultrasonic vibration state, ultrasonic waves can generate violent vibration at the molecular level, the activity of emulsifier molecules can be improved, the emulsifier molecules can interact with grease molecules and water molecules more easily, the emulsifying effect is improved, meanwhile, the ultrasonic waves can generate tiny bubbles and cavities, and the emulsifying effect is improved. The tiny bubbles and cavities can increase the area of an emulsification interface, so that grease and water are easier to mix and emulsify, and the emulsification efficiency is improved.

The invention relates to a UCNPs probe-based in-situ dynamic monitoring molecular interaction method, which comprises: S1, providing a UCNPs probe, the core layer of the UCNPs probe being a rare earth up-conversion nanocrystal, the shell layer of the UCNPs probe being an anti-quenching inert material, and the anti-quenching inert material coating the surface of the rare earth up-conversion nanocrystal; the surface of the anti-quenching inert material is modified with ligand molecules, and the ligand molecules are used for being specifically combined with target molecules in a to-be-detected sample; s2, mixing or contacting the UCNPs probe with a to-be-detected sample containing target molecules to construct a to-be-analyzed molecular interaction system; s3, irradiating the molecular interaction system by adopting near-infrared pulse laser, and collecting the change of the fluorescence lifetime tau of the UCNPs along with the time t and the change of the fluorescence intensity I along with the time t in real time to obtain a tau-t curve and an I-t curve; and S4, according to the tau-t curve and the I-t curve, realizing in-situ dynamic monitoring on the molecular interaction state. According to the method, a molecular binding / dissociation event is directly converted into a quantifiable and anti-interference optical signal, and the technical defect that in-situ dynamic monitoring of molecular interaction cannot be achieved in a traditional technology is overcome.

The present invention provides a method for detecting two target molecules in a sample that are independent molecules and that are adjacent to or interact with each other. The method involves performing three separate assay reactions to detect a first target molecule and a second target molecule and their interactions using a proximity probe common among the three assay reactions and a nucleic acid reagent that interacts with the probe. In particular, the methods use a unique nucleic acid substrate molecule, such as a padlock probe, to detect the probes bound to their targets and determine when the probes for the two targets are in close proximity indicative of the interaction between the targets.

The invention belongs to the technical field of perovskite solar cells, and particularly discloses a self-assembled monomolecular layer organic semiconductor material based on indene [2, 1-b] carbazole and a preparation method and application thereof.The indene [2, 1-b] carbazole and derivatives thereof are used as functional groups in semiconductor material molecules, and the semiconductor material molecules have a large conjugate plane structure, so that the structure of the semiconductor material is improved; according to the invention, phosphoric acid is taken as an anchoring group, strong intermolecular interaction is induced, molecules are promoted to form more ordered and compact SAM on an ITO substrate, two methyl groups on the SAM generate a spatial effect, aggregation and blockage at a crystal boundary in a film forming process are prevented, a recombination center is generated, and phosphoric acid is taken as the anchoring group, so that firm self-assembly of a self-assembly monomolecular layer on the surface of the ITO substrate can be realized. The self-assembled monomolecular layer organic semiconductor material has the advantages of good stability, expandability, no dopant, simple preparation and low cost. When the material is applied to a perovskite solar cell, the photoelectric conversion efficiency exceeding 24% can be obtained, and the material is an organic semiconductor material with a very good prospect.

Disclosed is a novel strategy for engineering tissue adhesives in which molecular building blocks are manipulated to allow for precise control and optimization of the various aforementioned properties without any tradeoffs. To introduce tunable mechanical properties and robust tissue adhesion, the hydrogel network presents different modes of covalent and noncovalent interactions using N-hydroxysuccinimide ester (NHS) conjugated alginate (Alg-NHS), poly (ethylene glycol) diacrylate (PEGDA), tannic acid (TA), and Fe3+ ions. Through combining and tuning different molecular interactions and a variety of crosslinking mechanisms, we were able to design an extremely elastic (924%) and tough (4697 kJ / m3) multifunctional hydrogel that could quickly adhere to wet tissue surfaces within 5 sec of gentle pressing and deform to support physiological tissue function over time under wet conditions.

The invention belongs to the technical field of sympathetic nerve mimetic pharmaceutical preparations, and particularly relates to a stable oxymetazoline hydrochloride preparation and a preparation method thereof. The oxymetazoline hydrochloride preparation is prepared from oxymetazoline hydrochloride, rutin, a pH regulator, a preservative, an isotonic agent and water. According to the invention, the intermolecular interaction of oxymetazoline hydrochloride and rutin is utilized to form a compound system, and the preparation method of the oxymetazoline hydrochloride preparation is optimized, so that the stability of the oxymetazoline hydrochloride preparation is effectively improved.

Disclosed herein is a composite material comprising:a substrate; anda patterned hydrogel disposed on the substrate, wherein:the patterned hydrogel comprises stimulus-responsive constitutional units and constitutional units comprising one or more target molecule recognition moieties;the stimulus-responsive constitutional units are responsive to a stimulus and are configured to produce a stress value S1 to the patterned hydrogel when the stimulus is applied;the target molecule recognition moieties are responsive to a target molecule and are configured to impart a stress value S2 to the patterned hydrogel upon interaction with the target molecule;the patterned hydrogel is configured to reversibly buckle and / or reversibly swell when a threshold stress level T of the patterned hydrogel is crossed.

The invention belongs to the technical field of biomolecular interaction detection, and particularly relates to a method for synergistically analyzing a molecular interaction state based on multimodal optical parameters of a rare earth up-conversion nano material. The method comprises the following steps: S1, providing a ligand coated rare earth up-conversion nano material UCNPs probe; s2, enabling the rare earth up-conversion nano material UCNPs probe to be in contact with a solution sample to be detected; s3, exciting the rare earth up-conversion nano material UCNPs probe by using near-infrared pulse laser, and collecting the following optical parameters: luminous intensity change, molecular layer thickness / mass change delta D, luminous life change delta tau and emission peak displacement delta lambda; and S4, based on at least two of the parameters, cooperatively analyzing and judging the interaction state of the ligand on the surface of the probe and the molecules in the solution sample to be detected. The molecular interaction state is synergistically analyzed based on changes of multiple optical parameters, and the technical problem that an existing molecular interaction analysis technology cannot be applied to complex biological samples (serum and cell lysis buffer) is solved.

The invention relates to the technical field of food processing, in particular to 3D printing flaxseed gum fish-source protein gel adaptive to elderly swallowing and bionic digestion response as well as a preparation method and application of the 3D printing flaxseed gum fish-source protein gel. According to the present invention, by constructing the lycopene-loaded flaxseed gum-myofibrillar protein composite gel system, the multi-scale structure regulation and the lycopene precise delivery are achieved, and the function adaptation mechanism of the lycopene in the 3D printing swallowing food is systematically clarified; the invention focuses on a molecular mechanism that flaxseed gum molecule acting force rearrangement drives myofibrillar protein secondary structure transformation, and combines rheological recovery characteristics of LFG gel under a dynamic shearing condition and a cross-scale regulation effect of swallowing kinetics, and further discloses a lycopene release behavior of the LFG gel in a process of simulating dynamic digestion of old people. From the synergetic perspective of molecular interaction, rheological response and digestive behavior regulation, reference is provided for the development of elderly special dietary food with swallowing suitability, accurate nutrition delivery and sensory palatability.

The invention discloses a multi-mode chemical reaction yield prediction method based on adaptive data screening, and belongs to the technical field of chemical synthesis and machine learning crossing. According to the method, a multi-modal input system containing one-dimensional chemical attribute data, a two-dimensional molecular structure map and a three-dimensional spatial configuration is constructed, so that multi-dimensional analysis of molecular interaction is realized; and meanwhile, a multi-stage training process is adopted to simulate a human cognitive rule, so that the model progressively learns layer by layer from a basic reaction feature to a complex reaction mechanism. According to the method, the screening threshold is dynamically adjusted, noise interference is effectively suppressed, and the adaptability to long-tail distribution data is improved. Compared with a traditional single-mode prediction model, the method has the advantages that the prediction accuracy on a noisy data set is improved, the trial and error cost of chemical synthesis experiments is remarkably reduced, and reliable technical support is provided for efficiently screening reaction conditions and accelerating research and development of new compounds.

The present invention discloses a dense hydrogen bond-enhanced bio-based supramolecular adhesive and a preparation method and application thereof. The adhesive is prepared by mixing tannic acid (TA) and citric acid (CA) or malic acid (MA) to prepare a solution. As the solvent water continues to volatilize, the solution gradually becomes viscous to form a supramolecular adhesive (BAS), combining renewable molecules with innovative supramolecular interactions. Through hydrogen bonding, these adhesives exhibit strong adhesion. Since BAS (CA) has a greater hydrogen bond density than BAS (MA), BAS (CA) exhibits stronger adhesion performance, with a maximum of 6.0 MPa, while BAS (MA) is relatively weak, with a maximum of 4.0 MPa. In addition, the adhesive prepared by the present invention exhibits superior adhesion strength, toughness and resistance to environmental factors.

The invention discloses a molecular interaction analyzer with a liquid level monitoring function. Relates to the technical field of molecular interaction analyzers. The analysis assembly comprises a detector, a signal processor and a display screen; the reagent conveying assembly comprises a base, a placement frame, reagent bottles and a distribution pump, the base is connected with the side wall of the box body, a plurality of placement grooves are formed in the placement frame, the reagent bottles are placed in the placement grooves, the distribution pump is connected with the detector through a sample feeding hose, and the reagent bottles are connected with the distribution pump through a sampling hose; the liquid level monitoring assembly comprises a capacitive sensor and an electrode, the capacitive sensor is electrically connected with the signal processor, the electrode is connected with the capacitive sensor through an electric wire, an adhesion layer is arranged on the electrode, and the electrode is tightly attached to the side wall of the reagent bottle. By arranging the liquid level monitoring assembly, accurate measurement of a reagent can be ensured, overflow or insufficiency is prevented, and therefore the monitoring reliability and the automation degree are improved.

The invention belongs to the technical field of dynamic simulation, and discloses a construction method of a molecular dynamic model for different bubble contents of ice and methane hydrate, and the method comprises the following specific steps: 1, selecting a crystal structure of the ice and methane hydrate as a model basis for initial structure construction; by constructing a molecular dynamic model of an ice and methane hydrate coexistence system, the problem of extensive quantitative regulation and control of bubble content is solved, accurate association of microscopic molecule removal and macroscopic volume fraction is realized, force field parameter suitability of a cross-phase interface is optimized, interaction of a hydrogen bond network and molecules at an ice and methane hydrate interface is accurately described, and the method has the advantages of simple operation and low cost. The simulation precision of the molecular diffusion behavior and the structural stability of the interface area is improved, meanwhile, the bubble form diversity and the temperature and pressure coupling environment are comprehensively considered, a multi-dimensional experimental verification system is established, and the influences of different bubble contents on the thermodynamic property, the dynamic behavior and the mechanical property of the system can be systematically evaluated.

The invention discloses a molecular structure prediction system of a multimodal fine-grained molecule pre-training model based on prompt learning, and relates to the field of molecular structure prediction, in particular to a molecular structure prediction system. The objective of the invention is to solve the problems of low accuracy and efficiency and low inter-molecular interaction prediction accuracy when complex molecular data is processed due to data scarcity and insufficient task applicability in the existing method. The system comprises a data acquisition module used for acquiring sample data in a multi-modal molecule pre-training data set and sample data in a downstream task data set; the processing module is used for establishing a multi-modal fine-grained molecule pre-training model based on prompt learning and obtaining a trained multi-modal fine-grained molecule pre-training model based on prompt learning; and the prediction module is used for performing attribute and drug interaction relationship prediction on a to-be-detected molecular structure based on the trained prompt learning multi-mode fine-grained molecule pre-training model to obtain a prediction result.

The invention discloses a method for quantitatively characterizing an ionic liquid structure at an interface, which comprises the following steps of: regulating a self-assembly structure of ionic liquid on the surface of an electrode through voltage, measuring data such as Young modulus, film thickness, indentation depth and effective contact area of an ionic liquid film under different voltage conditions, and combining the data with reciprocals (1 / F0) of the interaction of quantitative molecules to obtain the ionic liquid structure at the interface. According to the method, an ionic liquid structure model (EMSF) is constructed, the rigidity and the stability of different ionic liquid membranes can be quantitatively analyzed according to the EMSF model by measuring interaction, and a new theory and method support is provided for design of an ionic liquid-electrode interface. The method has a wide application prospect, and an optimized interface design scheme can be provided for the fields of electrochemical batteries, lubrication and the like.

This invention discloses a ferroptosis target identification system and method based on an attention mechanism, belonging to the field of artificial intelligence technology. The system includes modules for extracting multi-source features of ferroptosis regulation, labeling weak signal targets, calculating attention potential energy mapping, and ranking ferroptosis targets. It extracts multimodal features from transcriptome expression, molecular interaction networks, and ferroptosis pathway annotations. Targets with expression intensities below a preset threshold that participate in the ferroptosis pathway are labeled with weak signals. Weak signal characteristics are injected, and a potential energy bias is constructed to complete attention potential energy mapping. Then, through pathway mapping and cross-pathway linkage identification and fusion weights, the target linkage degree is calculated and ranked. This system solves the problem of traditional methods easily neglecting weak signal targets, and can identify targets with insignificant expression changes in the ferroptosis regulatory network. Through attention and pathway-level linkage modeling, the comprehensiveness of target identification is improved, and the obtained ranking results have stability and discriminative power, making it suitable for the identification and research of ferroptosis-related targets.

The invention discloses a method and system for predicting the effect of repairing lung injury by isoliquiritigenin based on machine learning, and relates to the field of drug effect prediction.The method comprises the steps that biomarker data and historical experiment repairing effect data are obtained; on the basis of molecular structure data and biomarker data, calculating a molecular interaction force index and a cell permeability index of the isoliquiritigenin, and analyzing through a multiple regression analyzer in combination with historical experiment repair effect data to obtain a repair effect coefficient; according to the repair effect coefficient, configuring an effect confidence interval, and performing range optimization on the molecular interaction force index and the cell permeability index to obtain an optimized biological activity index; and constructing an effect prediction model based on a neural network, inputting the optimized biological activity indexes into the effect prediction model, and outputting to obtain a lung injury repair effect prediction value. The problems that the prediction precision is insufficient and the prediction is too one-sided due to the lack of comprehensive analysis on a molecular mechanism and a biomarker are solved.

The invention relates to a postoperative acute pain prediction method, belongs to the technical field of medical data analysis and processing, and solves the problem of insufficient postoperative acute pain prediction precision in the prior art. The method comprises the following steps: acquiring multi-omics data and clinical data of a to-be-predicted patient; constructing a primary network of each omics based on the anchor point molecules of each omics; the second-level network and each later-level network are obtained by expanding the previous-level network based on the molecular interaction relationship; taking each level of network as a multi-level molecular interaction network of the to-be-predicted patient; adopting a trained deep learning model to extract high-dimensional biological characteristics of a to-be-predicted patient based on the multistage molecular interaction network; and predicting the pain type of the to-be-predicted patient based on the high-dimensional biological characteristics, the multi-omics data and the clinical data. And the accuracy of postoperative acute pain prediction is improved.

The present disclosure provides an affinity prediction method, related devices and media. The method comprises: determining a first molecular structure graph and an atomic structure graph of a first biomolecule, and a second molecular structure graph of a second biomolecule; intercepting a binding region graph from the second molecular structure graph; extracting a first graph feature of the first molecular structure graph, a second graph feature of the atomic structure graph, a third graph feature of the second molecular structure graph, and a fourth graph feature of the binding region graph based on a feature extraction network of a preset affinity prediction model; determining a molecular interaction feature based on the first graph feature, the second graph feature, the third graph feature, and the fourth graph feature; and performing affinity prediction on the first biomolecule and the second biomolecule based on the molecular interaction feature to obtain a binding affinity prediction result. The present disclosure can improve the prediction accuracy of the binding affinity between biomolecules. The present disclosure can be applied to various scenarios such as big data, artificial intelligence, cloud technology, etc.

The invention relates to a heptafluoroisobutyronitrile discharge decomposition gas detection method and a heptafluoroisobutyronitrile discharge decomposition gas detection device. The method comprises the following steps: calculating the stability of a pre-constructed transition metal doped single-layer AlN model by using binding energy parameters, and determining a most stable transition metal-AlN single-layer structure; the adsorption energy, obtained in advance, of heptafluoroisobutyronitrile decomposition gas molecules on the most stable transition metal-AlN single-layer structure is calculated; calculating the work function change of the most stable transition metal-AlN single-layer structure after interaction with heptafluoroisobutyronitrile decomposition gas molecules; wherein the adsorption energy and the work function change are used for determining the detection result of the heptafluoroisobutyronitrile decomposition gas molecules. Compared with the prior art, the method has the advantages that efficient and directional detection of the C4F7N discharge decomposition gas is realized, and the like.

The present invention relates to the field of biomedical technology, and in particular to a modeling and analysis system for the interaction mechanism between walnut peptide and SIRT2. The system includes a structural energy minimum optimization module, a molecular docking search module, a molecular interaction dynamics simulation module, and an interaction mechanism prediction modeling module. The system can obtain the walnut peptide amino acid sequence and the SIRT2 structure and perform structural energy minimum optimization processing and pre-docking preparation to generate the active site of the docking region corresponding to the walnut peptide and the SIRT2 structure; based on the docking active site of the action region, molecular docking search and screening and molecular interaction dynamics simulation are performed to generate a molecular docking interaction dynamics simulation field; the molecular docking interaction dynamics simulation field is subjected to molecular interaction feature analysis and interaction mechanism prediction modeling to generate a walnut peptide and SIRT2 interaction mechanism prediction model, and output the interaction activity. The present invention can deeply analyze the interaction mechanism between walnut peptide and SIRT2.

The invention discloses a difunctional aqueous zinc ion battery high-voltage electrolyte and a preparation method and application thereof, and belongs to the technical field of aqueous zinc ion batteries. The electrolyte is prepared from the additive with the structural general formula that R groups are hydroxyl, carboxyl, acetyl and methoxyl, and the additive contains oxygen atom-containing functional groups capable of serving as hydrogen bond acceptors, so that the water-phase solubility of molecules can be remarkably improved, the electrolyte ion concentration can be adjusted through specific interaction with solvent molecules, and the service life of the electrolyte is prolonged. The solvation sheath structure of zinc ions is regulated and controlled by reducing the number of solvent molecules. Meanwhile, the functional group containing the oxygen atom serving as the hydrogen bond acceptor is a strong electron withdrawing group and can interact with water molecules to form intermolecular hydrogen bonds, the oxygen atom can serve as an electron donor, and the hydrogen atom of water serves as a proton donor to destroy the original hydrogen bonds and tetrahedral structures of the water molecules, so that the water molecules can be absorbed. And the formation of a hydrogen bond network of water molecules is blocked, so that the corrosion side reaction and hydrogen evolution behavior caused by water are effectively inhibited.

A contrast agent for magnetic resonance imaging (MRI) is proposed, comprising a polarizable agent (tracer) possessing a nuclear spin, wherein tracer molecules can be brought into a polarized state by polarizing their nuclear spins, and a retarder, wherein a molecular interaction of retarder molecules with the tracer molecules results in a prolongation of the T1 relaxation time of the tracer. Furthermore, a method for the production of a hyperpolarized contrast agent, a retarder for use in a contrast agent for MRI, a contrast agent for use in a diagnostic procedure in vivo and / or in vitro, and the use of a retarder in a contrast agent for MRI to prolong the T1 relaxation time of the tracer are proposed.

Disclosed is a method for calculating an eigenstate of a Hamiltonian, comprising the steps of providing at least one classical computing unit and at least one quantum computing unit comprising a plurality of qubits, providing a plurality of physical properties of at least one molecule, initializing an initial state on the at least one quantum computing unit to model the properties of the provided physical properties of the at least one molecule, selecting a plurality of operators from a pool of operators using the at least one quantum computing unit performing measurement operations on the diagonal of the Hamiltonian to calculate the eigenstate of the Hamiltonian describing the at least one molecule, estimating an energy of the eigenstate of the Hamiltonian using the selected plurality of operators, and repeating the previous steps until a convergence criterion is met.

The present invention relates to a method of detecting intermolecular interaction kinetics, the method comprising a first molecule and a second molecule to be analyzed, dispersing the first molecule in a solution as a detection solution, attaching the second molecule to a biosensor and being freely translatable over the surface of the sensor; the biosensor comprises a tethered bilayer phospholipid membrane and a solid conductive surface, a lower layer membrane of the tethered bilayer phospholipid membrane is fixed, and an upper layer membrane of the tethered bilayer phospholipid membrane can freely translate; the phospholipid membrane contains ion channel protein; and contacting the detection solution with the biosensor, detecting the conductivity change of the tethered bilayer phospholipid membrane, and analyzing the interaction kinetics of the first molecule and the second molecule. The method for detecting intermolecular interaction kinetics based on tethered bilayer phospholipid membrane and ion channel switch technology development can realize accurate quantitative detection in a wide concentration range, does not need sample flow, and is suitable for the fields of antibody screening, drug screening and life science.

Provided herein are computer-implemented methods for scoring a binding affinity between molecular subsystems. The methods include representing an interaction structure of the molecular subsystems, calculating the molecular interactions between the molecular subsystems, and generating an interaction energy score between the molecular subsystems.

The embodiment of the invention provides an object molecule sequence data processing method and a model training method. The object molecule sequence data processing method comprises the following steps: determining at least two object molecule sequence data; at least two pieces of object molecule sequence data are input into the feature extraction model, predicted molecule sequence features of each piece of object molecule sequence data in the at least two pieces of object molecule sequence data are obtained, the predicted molecule sequence features comprise element vectors of molecule elements, and the object molecule sequence data are formed according to the molecule elements; inputting the predicted molecule sequence feature of each object molecule sequence data into a molecule interaction prediction model to obtain an interaction result between the at least two object molecule sequence data, and according to the correlation between element vectors contained in the predicted molecule sequence feature of each object molecule sequence data, obtaining the interaction result between the at least two object molecule sequence data. And outputting an interaction result between the at least two object molecule sequence data. And molecular interaction prediction based on artificial intelligence is realized.

The invention discloses a Leidenfrost effect three-dimensional numerical simulation method based on a double-distribution function lattice Boltzmann method coupled with a finite difference method. The method comprises the following steps: 1, constructing a three-dimensional computational domain and defining lattice point types; 2, initializing a lattice point type, a fluid domain density field, a velocity field, a temperature field and a distribution function; 3, calculating fluid domain density, pressure and thermal diffusion coefficient according to the distribution function; 4, calculating resultant force of molecular interaction force and volume force among different phase states based on a pseudo-potential model; 5, calculating a fluid domain collision process; 6, calculating a migration process; 7, treating wall surface boundary conditions; 8, updating the fluid domain temperature field by using the temperature distribution function, and updating the solid domain temperature field by using a finite difference method; and 9, carrying out loop iteration on the steps 3-8 until the preset simulation time is reached. According to the invention, the whole process of the Leidenfrost phenomenon generated when the liquid drops impact the high-temperature wall surface can be stably and accurately simulated, and an accurate and reliable numerical simulation tool is provided for the design of efficient heat dissipation schemes such as spray cooling and the like.