74results about How to "Improve modeling accuracy" patented technology

The invention discloses a short-term traffic flow prediction method based on a dynamic multi-dimensional global perception graph convolutional network, and belongs to the field of traffic flow prediction. The method comprises the following steps: acquiring an input dynamic graph feature sequence, and generating space-time element parameters; carrying out feature enhancement on the dynamic graph feature sequence, fusing the dynamic graph feature sequence with the space-time element parameters, and then respectively carrying out space-time feature coding; fusing the spatio-temporal characteristics obtained by coding the spatio-temporal characteristics; and carrying out space-time trend alignment decoding on the fused space-time features. According to the method, the defects of limited modeling precision, insufficient dynamic adaptability, incomplete spatial-temporal feature capture and the like in short-term traffic flow prediction are overcome, and the accuracy and the applicability of short-term traffic flow prediction are improved.

The invention relates to the technical field of industrial big data, in particular to an Internet of Things sensing and data monitoring integrated system, which comprises an Internet of Things sensing module used for acquiring a sensing parameter set of a distributed simulation unit of an intelligent power plant and a historical performance evolution trend reflecting a dynamic evolution rule; the twin mapping module is used for realizing signal space-time synchronization by utilizing a time service protocol, calling a multi-physics field evolution model to calculate thermal stress offset so as to execute numerical compensation, and generating a high-fidelity physical feature vector; the collaborative decision-making module shares observation information by using a multi-agent distributed collaborative architecture, identifies an electromagnetic-dynamic coupling response mode through a joint reward function and outputs a collaborative optimization strategy; and the execution module is used for generating an evaluation result by calculating the numerical deviation between the physical feature vector and the design envelope, and adjusting the excitation control variable according to a strategy until the system converges to a target equilibrium value. According to the method, the steady state monitoring of the power system is realized through twin mapping and collaborative decision.

The invention discloses a novel information system reliability determination method for a multi-input dependent starting scene, and belongs to the technical field of system reliability. Comprising the following steps: drawing a system structure chart containing a multi-input dependency starting module, and dividing a three-stage system structure of a core module-a support signal module-an auxiliary guarantee module; a GO graph is drawn, the multi-input dependent starting module is uniquely identified as a type 29 operator, and the number of input ports of the operator is in one-to-one correspondence with the number of support signals of the corresponding core module; and determining the signal effective probability, the correlation coefficient and the element reliability according to a standardized process, calculating the module reliability by combining a type 29 operator quantitative model, and finally determining the system reliability. According to the method, the problem that an existing GO method operator cannot adapt to a multi-input dependent starting scene is solved, the blank of a special tool for GO method modeling in the scene is filled up, and the application range of the GO method in a novel information system is expanded.

This invention discloses a modeling method, electronic device, and storage medium for hydrogen energy systems. The method includes: establishing a hydrogen production mass flow model for an electrolyzer, and using a multi-condition linearization method to handle the nonlinear operating efficiency of the electrolyzer; establishing a storage mass flow model for a hydrogen storage tank, dynamically characterizing its effective capacity based on the tank's physical volume, operating pressure, and ambient temperature parameters; constructing a hydrogen mass flow bus, linking the electrolyzer's hydrogen production variable and the storage tank's hydrogen filling variable to this bus, and establishing hydrogen mass flow balance constraints among the models to characterize that at any given time, the total amount of hydrogen flowing into the bus equals the total amount flowing out. This invention solves the problem of insufficient accuracy in traditional models, achieving a balance between high accuracy of the equipment model and high solution efficiency for the optimization problem, providing a reliable modeling foundation for the optimal scheduling of hydrogen-containing integrated energy systems.

The invention relates to the technical field of power equipment fault diagnosis, in particular to a fault detection method and system for an electric energy metering box, and aims at solving the problems that in the prior art, fault detection of the electric energy metering box faces intermittent fault missing detection for a long time, root positioning is fuzzy, and environmental adaptability is insufficient. According to the method, multi-modal time sequence data and environment characteristics are fused, dynamic parameter aggregation is realized by adopting an attention enhancement federation VAR model, intermittent faults are captured in combination with a dual-condition triggering mechanism, and bidirectional reasoning traceability is realized by constructing a multi-modal knowledge graph. The problems that a traditional method is insufficient in dynamic fault detection capability, deficient in root analysis means and poor in environmental adaptability are effectively solved, and the method has the advantages of improving fault detection sensitivity and positioning precision.

The invention discloses a segmented quantitative analysis method for uplift pressure of a gravity dam. The uplift pressure of the gravity dam is influenced by various factors, and safety monitoring data of the gravity dam often have the problems of multiple collinearity and small samples. The method comprises the following steps: introducing a BFAST time sequence decomposition method, decomposing original uplift pressure monitoring data into a trend component and a seasonal component, and obtaining trend segment points and seasonal segment points; a Bayesian model average modeling method integrating Bayesian model selection is introduced, and a gravity dam uplift pressure statistical model is established in a segmented mode; and finally, upstream and downstream water pressure components of the uplift pressure are separated according to the average multiple models, and quantitative analysis of the uplift pressure of the gravity dam is achieved. According to the method, more accurate fitting and component separation effects can be obtained, and accurate quantitative analysis of the uplift pressure of the gravity dam can be realized.

The invention provides a three-dimensional organ reconstruction registration fusion method based on a multi-modal medical image, and belongs to the technical field of medical images. Comprising the following steps: S1, collecting a multi-modal medical image of an ith organ of a target patient in real time, and constructing a preoperative data set; s2, after the first data set is constructed, continuously constructing an intra-operative data set through the intra-operative ultrasonic image; s3, constructing an AI model by using a convolutional neural network; s4, based on the preoperative data set and the intraoperative data set, constructing a stability coefficient and a crowding degree coefficient of the ith organ; and S5, associating the stability coefficient of the ith organ with the crowding degree coefficient of the ith organ, and constructing a comprehensive dynamic risk coefficient. According to the method, CT, MRI and intraoperative ultrasonic images are collected at the same time, the preoperative data set is constructed, the organ volume change rate and the body position adjustment displacement are updated in real time in the operation, the optimal operation path can be automatically predicted, and the subjectivity of depending on doctor experience in traditional operation path planning is reduced.

This invention provides a physical information neural network modeling method for the hysteresis characteristics of piezoelectric fast-acting servo systems, belonging to the technical field of hysteresis characteristic modeling for piezoelectric fast-acting servo systems. The method includes the following steps: based on the input voltage of the piezoelectric fast-acting servo system at multiple sampling times up to the current time, an expert feature vector for the current time is constructed through an expert feature layer based on a memory path; static and dynamic representations are extracted through static and dynamic branches respectively; the static and dynamic representations are fused through a feature representation layer to obtain a fused representation for the current time; and the fused representation for the current time is mapped to a scalar displacement prediction of the piezoelectric fast-acting servo system through a regression head. The advantage of this invention is that it improves the accuracy and robustness of modeling the hysteresis characteristics of piezoelectric actuators.

The invention discloses an object-level semantic SLAM (Simultaneous Localization and Mapping) construction method and device based on NeRF. The method comprises the following steps: acquiring image data and camera parameter data; preprocessing the image data and the camera parameter data to obtain image preprocessing data and camera calibration parameter data; and utilizing an object-level semantic SLAM modeling model to process the image preprocessing data and the camera calibration parameter data to obtain camera pose data, object scene modeling data and scene object semantic segmentation data. According to the method, the NeRF technology is used for object modeling, and the problem that traditional object-level semantic SLAM object modeling precision is poor is solved; object semantic recognition is carried out by using an image segmentation large model SEEM, and the problem that a traditional object-level semantic SLAM cannot recognize objects except prior information is solved.

According to the heterogeneous resource-oriented virtual power plant hierarchical clustering-cooperative game aggregation and trusted interaction system provided by the invention, refined and structured representation of a strong heterogeneous resource set is realized by constructing a dynamic digital twinborn archive and adaptive clustering of resources, and a hybrid prediction model fusing physical rules and data driving is adopted, so that the reliability of the system is improved, and the reliability of the system is improved. Physical rationality of behavior prediction and quantitative capture of uncertainty are ensured; through a hierarchical distributed optimization architecture of a master-slave-alliance collaborative game, a global complex optimization problem is efficiently decoupled, and real-time collaborative optimal scheduling under large-scale resource access is realized while privacy and autonomy of a resource main body are protected; through an online rolling credibility regulation capability interval evaluation mechanism, a diversified capability vector with a confidence level is output, so that the virtual power plant becomes a reliable transaction object which is transparent to a power grid and has a risk known, and the interaction credibility and credit rating of the virtual power plant in the power market are fundamentally enhanced.

The application relates to the technical field of semantic analysis, in particular to a customer group evolution prediction method based on large model semantic analysis, which comprises the following steps: obtaining historical unstructured data of users of a streaming media platform, performing semantic analysis on multiple time points by a large model to generate semantic vectors; processing the semantic vectors by a loop module to generate a historical semantic sequence; training a semantic evolution model based on the sequence; in an offline batch processing stage, calculating future semantic vectors of all users in advance by using the evolution model, and constructing a semantic index; when a seed customer group is received online, calculating a customer group centroid vector representing a future trend for the seed customer group; performing approximate neighbor search on the centroid vector in the predictive semantic index, and outputting a predicted evolved customer group. The application improves prediction accuracy through future-to-future matching, and ensures low delay and feasibility of the evolution prediction function through an offline precalculation architecture.

The application discloses a power distribution network power supply scheme optimization management method and system based on dynamic sensing, and belongs to the technical field of power supply management. The system comprises a dynamic sensing module, a power distribution analysis module, a power supply optimization module and an execution management module; the dynamic sensing module is used for collecting GIS maps and power consumption data, and operation parameters and environmental parameters of all power supply objects; a three-dimensional scene is built and dynamically mapped; the power distribution analysis module divides and identifies domains according to the relative positions of the power supply objects in the three-dimensional scene, constructs three-dimensional air flow fields for the respective domains and predicts future wind field data, and fits power relationship formulas for the power supply objects; the power supply optimization module fits power consumption curves through the power consumption data, constructs power generation reference curves in combination with the future wind field data and the power relationship formulas; different power generation strategies are established and schemes are randomly built, the stability indexes of the schemes are calculated to set the power supply scheme; and the execution management module adjusts according to the power supply scheme and records the operation parameters and the environmental parameters.

The invention discloses a deep learning-based intelligent recognition method for a motion mode of an unmanned surface vehicle. The method comprises the following steps: acquiring original sensing data in real time through a multi-source sensor system mounted on the unmanned surface vehicle, wherein the multi-source sensor system at least comprises an inertial measurement unit (IMU), a global navigation satellite system (GNSS) receiver, a laser radar and a visible light camera; performing time synchronization and coordinate system alignment processing on the original sensing data to generate a fused sensing data stream with a unified timestamp and a spatial reference system; dividing the fused sensing data stream into time window segments with fixed lengths, wherein each time window segment corresponds to one to-be-identified motion state sample; the IMU, the GNSS, the laser radar and the visible light / infrared camera are synchronously integrated, quadruple sensing dimensions are formed, the IMU captures instantaneous acceleration and angular velocity changes and reflects control instruction execution, the reliability of single-point observation is improved, and the system can still maintain the basic recognition capability when part of sensors fail.

The invention discloses a soft measurement method for the content of ferrous oxide in sintered ore and a related device. The method comprises the following steps: acquiring time sequence data and image data of different sinter samples under different sintering stage conditions, constructing a labeled data set and a non-labeled data set, dividing the labeled data set into a training set, a verification set and a test set according to a preset proportion, and adding the non-labeled data set as extension into the training set; performing iterative training and evaluation on the MTFIF-STMM model based on the training set, the verification set and the test set to obtain a final MTFIF-STMM model; and inputting multi-source data, collected in real time, of the sinter into the final MTFIF-STMM model for soft measurement to obtain a real-time evaluation result of the ferrous oxide content of the sinter. According to the method, real-time soft measurement of the ferrous oxide content in the ore in the sintering process is realized, and the modeling precision is improved.

This invention relates to a method for modeling the capillary evolution of humidity-related adhesion forces under micrometer-scale probes. This method establishes a physical relationship between ambient relative humidity and Kelvin radius, and calculates the Kelvin radius in conjunction with the initial volume of the liquid bridge, thereby constructing a parameterized model of capillary forces that characterizes the change in liquid bridge volume with ambient humidity. The influence of the liquid film on the solid surface is considered during model construction, enabling the model to more realistically reflect the formation and evolution of liquid bridges in actual micro / nano interfaces. As the two surfaces gradually move away, the relationship between the curvature of the liquid bridge interface and the Kelvin radius is established under the condition that the liquid bridge volume remains constant, thus achieving a continuous description of the evolution of capillary and adhesion forces during the separation process between the micrometer-scale probe and the sample interface. Furthermore, experimental measurements and model verification of micrometer-scale interface forces are conducted using a self-developed fiber Bragg grating probe.

The application discloses a kind of based on finite element simulation mobile mould frame construction monitoring method and system, it is related to bridge construction monitoring and structural health monitoring field;Solve the problem of model fixed in traditional mobile mould frame construction monitoring, early warning lag, difficult to dynamically reflect the real state of structure.The present application establishes digital structure module including calibratable parameter, carries out working condition simulation to identify theoretical high-risk area and optimizes sensor layout;Carry out preposition simulation output predicted value before each process, synchronously collect measured data and calculate standardized deviation sequence during construction;Based on time domain feature extraction and multi-index fusion, dynamic safety evaluation and early warning are carried out;Using historical deviation data, the model parameters are calibrated and updated online by bayesian variational inference, realize the closed loop of simulation, monitoring, early warning, model optimization, significantly improve the safety monitoring precision and real-time of mobile mould frame construction process.

The application relates to an environment sensing and data back transmission device, which comprises a data acquisition module for acquiring accurate images and point cloud data of a disaster area of infrastructure; a multi-path transmission module for transmitting the data collected at a low altitude back to a ground server; a post-disaster evaluation and reconstruction module for generating disaster evaluation, disaster risk heat map and three-dimensional model of a disaster area, and planning emergency treatment measures and material allocation. The three-dimensional reconstruction technology and multi-link transmission technology are used to realize that a UAV quickly and accurately acquires disaster area data and generates a three-dimensional model, and promote the intellectualization of disaster detection.

This invention discloses a method for 3D modeling of small celestial bodies based on SFS (Self-Focused Spectroscopic Method), belonging to the field of intelligent 3D modeling and image reconstruction technology for small celestial bodies. To improve the accuracy of 3D modeling of small celestial bodies, this invention performs modeling under the condition that the camera's extrinsic parameters are determined. This includes setting the positions of the camera and the small celestial body in a simulation environment and setting the camera's motion trajectory to obtain multiple frames of small celestial body observation images; extracting the contours from the small celestial body observation images; transforming the 3D modeling problem of small celestial bodies into a pseudo-Boolean function minimization problem; applying local minimization and hierarchical minimization algorithms to solve for the minimum value of the pseudo-Boolean function to obtain a 3D voxel model of the small celestial body. For modeling under the condition that the camera's extrinsic parameters are uncertain, this includes converting the 3D voxel model of the small celestial body into a polygon model; selecting 3D model points; matching the 3D model points with 2D image points; and applying the LM algorithm to optimize the matching error between the 3D model points and 2D image points. This patented method is applied to the process of small celestial body detection.

This invention relates to the fields of shipbuilding and ocean engineering and fluid mechanics, specifically to a multi-objective optimization design method for the bulbous bow of a large ship based on parametric modeling, comprising the following steps: S1: Dividing the geometry of the bulbous bow into multiple sections along the transverse and longitudinal directions, and fitting the shape of each section with a high-order polynomial; S2: Defining minimizing wave-making resistance as the first objective function. The second objective function is to maximize the seakeeping performance of the ship. The collaborative optimization objective is as follows: S3: Solve using a decomposition-based multi-objective evolutionary algorithm; S4: Verify the rationality of the Pareto optimal solution set obtained in step S3 through CFD simulation. This invention integrates parametric modeling, surrogate model acceleration, multi-objective evolutionary algorithm, and high-fidelity verification into a complete closed-loop system, reducing reliance on designer experience and shifting the bulbous bow design process from traditional manual trial and error to systematic and automated numerical optimization.

The invention provides a method for generating a three-dimensional solid geologic model of a mine. The method comprises the following steps of: 1, acquiring and preprocessing multi-source mine geologic data; 2, constructing a preliminary mine three-dimensional entity geologic model: based on the multi-source mine geologic data preprocessed in the step 1, performing discretization processing on the geologic data by adopting a three-dimensional gridding method, generating a basic mine three-dimensional entity geologic model of the mine by using modeling software, identifying special structures of cracks and faults in the geologic data, and establishing a preliminary mine three-dimensional entity geologic model; carrying out special structure modeling to obtain a fault model; the fault model is adopted to refine the basic mine three-dimensional entity geologic model, and a preliminary mine three-dimensional entity geologic model is obtained; and step 3, carrying out iterative optimization and uncertainty quantification on the model precision. And 4, dynamically adjusting the model in real time. The model generated by the method has the characteristics of low data processing complexity, high model precision and comprehensive and definite geological information, and the performance of the mine three-dimensional entity geological model is integrally improved.

This application discloses a method, apparatus, device, and medium for processing data related to neuropsychiatric diseases, relating to the field of data processing technology. The method includes: acquiring gut microbial species characteristic data of a target sample, including relative abundance characteristics or species presence / absence status characteristics; acquiring the target disease and its corresponding decision tree binary classification model, which is trained using the CatBoost algorithm, and the model's output categories include a target category and a null category; inputting the gut microbial species characteristic data into the model, and outputting a probability value indicating that the target sample belongs to the target category; and generating a data processing result corresponding to the target sample based on the probability value. Using the neuropsychiatric disease data processing method of this application can improve the accuracy and cross-disease generalization ability of neuropsychiatric disease data processing models.

This invention discloses a light projector and a sensing system used in conjunction with the light projector. The light projector includes a light source and a diffraction optical element. The light source has a first light-emitting area and a second light-emitting area, and the diffraction optical element is disposed on the light source. The diffraction optical element has a first diffraction structure and a second diffraction structure, which overlap the first light-emitting area and the second light-emitting area, respectively. The first diffraction structure is different from the second diffraction structure.

The invention provides a three-dimensional reconstruction method and device, a medium and electronic equipment. The method comprises the following steps: firstly, acquiring an original two-dimensional image containing a to-be-reconstructed main body, then identifying the original two-dimensional image to determine a pixel position corresponding to the to-be-reconstructed main body contained in the original two-dimensional image, and further performing differential coding processing on the original two-dimensional image based on the pixel position to obtain a coded image, a pixel region corresponding to a to-be-reconstructed main body is coded by adopting a first coding parameter with higher coding quality, and other pixel regions are coded by adopting a second coding parameter with lower coding quality, so that the image quality of the to-be-reconstructed main body is preferentially ensured on the premise of controlling the occupation of computing resources and network transmission bandwidth of an acquisition end, and the image quality of the to-be-reconstructed main body is improved. And finally, the encoded image is sent to a server, and the server completes three-dimensional reconstruction of the to-be-reconstructed main body according to the encoded image, so that the overall efficiency and precision of three-dimensional reconstruction are effectively improved.

The invention discloses an intelligent building structure construction method based on point cloud data and digital twinning, and the method comprises the steps: obtaining a building point cloud through three-dimensional laser scanning, carrying out the high-precision registration and preprocessing, carrying out the component feature extraction and parameterized reverse modeling through an RANSAC algorithm, and generating a high-fidelity BIM model; then, the model is deeply fused with GIS, finite element and Internet of Things data under a unified space-time reference, and a dynamically updated digital twinning scene is constructed. Finally, a virtual simulation platform is developed based on the scene, and real-time simulation, visualization and intelligent decision support of the construction progress and mechanical response are achieved. According to the method, the problems of insufficient precision and difficulty in multi-source data fusion of a traditional modeling method are solved, accurate mapping and interaction of a building structure from a physical entity to a digital virtual body are realized, and the intelligent level and decision scientificity of a construction process are remarkably improved.

The invention relates to the technical field of optical element precision manufacturing, in particular to an optical element three-dimensional modeling method based on normal projection inverse transformation. Comprising the steps of establishing an ideal curved surface parameterization vector expression according to optical design parameters, and determining a curved surface body coordinate system and a vector height function; solving the gradient of the rise function to obtain gradient information of each point of the curved surface and calculating a unit normal vector; projecting the three-dimensional curved surface points to a preset reference plane along the normal direction of the curved surface, establishing a forward geometric mapping relation from the three-dimensional coordinates of the curved surface body coordinate system to the measurement coordinates of the interferometer, and obtaining an explicit expression of the measurement coordinates; two-dimensional normal error distribution data of the optical element are collected, errors are overlaid in the normal direction to construct an actual curved surface expression containing manufacturing errors, and the corresponding relation between three-dimensional normal errors and measurement data is established; and on the basis of forward mapping relation numerical value inverse transformation, the measurement coordinates are inversely calculated into curved surface body coordinates, and the curved surface body coordinates are substituted into an actual curved surface expression to obtain a complete three-dimensional curved surface model containing manufacturing errors.

The application provides a positive and negative sequence four-port impedance modeling method and device of a mixed frequency power transmission system, and relates to the technical field of impedance modeling. The method comprises the following steps: obtaining a positive and negative sequence voltage coupling matrix according to a power frequency side PCC steady-state q-axis voltage, a frequency division side PCC steady-state q-axis voltage, a power frequency side PLL transfer function and a frequency division side PLL transfer function; obtaining the positive and negative sequence four-port impedance according to a pre-obtained power frequency side current inner loop controller matrix, a frequency division side current inner loop controller matrix, a power frequency side voltage outer loop controller matrix, a frequency division side voltage outer loop controller matrix, a power frequency side decoupling matrix, a frequency division side decoupling matrix, a voltage calculation matrix, a current calculation matrix and the positive and negative sequence voltage coupling matrix. The method and device provided in the application embodiment can improve the modeling accuracy of the positive and negative sequence four-port impedance of the mixed frequency power transmission system.

The application relates to the technical field of radar target scattering characteristic modeling, and discloses a target optical area electromagnetic scattering characteristic modeling method and device, which comprises the following steps: acquiring a multi-view wideband electromagnetic scattering frequency response of a target; calculating one-dimensional scattering center parameter estimators of the target at each view angle by using the multi-view wideband electromagnetic scattering frequency response; acquiring a scattering intensity field of the target in a three-dimensional space by using a RANSAC method and a Parzen window estimation method according to the one-dimensional scattering center parameter estimators; obtaining a rough position of a three-dimensional scattering center in the scattering intensity field by using a three-dimensional spectrum peak search method; and calculating the accurate position of the three-dimensional scattering center by projection, correlation and relocation. In this way, the three-dimensional scattering center of the target can be stably obtained based on the RANSAC and three-dimensional spectrum peak search ideas, and has good consistency with the target structure and scattering principle, so that the electromagnetic scattering characteristics of the target can be accurately reconstructed by using fewer three-dimensional scattering centers.

The application discloses a method for intelligently modeling rock mechanics properties of a tight sandstone reservoir, comprising the following steps: obtaining three-dimensional seismic interpretation data, well logging data and rock mechanics experiment data; establishing a three-dimensional geological structure model of the tight sandstone reservoir which fuses a fault structure; calculating a quantitative fitting relationship between static rock mechanics parameters and a sand ratio; extracting a lithology factor data body from the three-dimensional seismic interpretation data, discriminating the lithology factor data body, generating a three-dimensional spatial distribution of sandstone and mudstone, and thus obtaining a three-dimensional sand ratio distribution model; and based on the three-dimensional sand ratio distribution model and the quantitative fitting relationship, calculating initial values of rock mechanics parameters of each grid unit, and assigning values to the three-dimensional geological structure model and correcting the fault, so as to obtain a final three-dimensional rock mechanics property model. The application realizes multi-source data fusion and quantitative modeling of fault disturbance, and solves the problems of insufficient description of reservoir heterogeneity, lack of physical basis of the model and low reliability of modeling of a fracture zone in the prior art.

The invention relates to the technical field of image processing, and discloses an operation scene real-time dynamic reconstruction method and system based on three-dimensional Gaussian sputtering. The method comprises the following steps: extracting spatial-temporal characteristics of a soft tissue deformation Gaussian subset through a HexPlane spatial-temporal encoder; fusing the decoded feature vectors to generate a soft tissue deformation parameter set; a deformation field Jacobian matrix of Gaussian elements in the soft tissue deformation Gaussian subset is calculated, a local volume strain rate is obtained through determinant operation, when the strain rate exceeds a preset threshold value, self-adaptive densification operation is executed, and new Gaussian elements inherit parent Gaussian element deformation parameters and are updated to a corresponding set; and combining the updated parameters to carry out probabilistic boundary allocation and transmissivity perception rendering to obtain a reconstruction result. The accuracy, the physical authenticity and the stability of real-time dynamic reconstruction of the operation scene are improved.

The invention provides a mainboard assembly stress simulation modeling method, electronic equipment and a program product, which can be used in the technical field of simulation modeling. The method comprises the steps of selecting a target component of a mainboard three-dimensional model in response to a component selection operation of a user; wherein the target component comprises a mainboard, a chip and a bonding pad silk-screen line; chip soldering tin is determined based on the bonding pad silk-screen lines, grid division is carried out on the chip soldering tin, and chip soldering tin grids are obtained; performing grid division on the mainboard to obtain a mainboard grid; determining a chip main body of the chip based on the soldering tin thickness of the chip soldering tin, and performing grid division on the chip main body to obtain a chip grid; and based on the constraint relationship among the target components, establishing a binding connection relationship among the grids of the target components, and obtaining a stress simulation model. According to the method, the accuracy and modeling efficiency of stress simulation modeling of the mainboard assembly are remarkably improved.