478results about How to "Efficient solution" patented technology

The invention belongs to the technical field of image classification neural networks, and discloses a lightweight image classification neural network architecture system based on knowledge distillation. The invention provides a lightweight image classification neural network architecture system based on knowledge distillation. The lightweight image classification neural network architecture system comprises a data set preprocessing module, a teacher model preprocessing module, a subnet model construction module, a personalized training module, a real loss calculation module, a distillation loss calculation module, a total loss calculation module and a student model construction module. The invention provides a lightweight neural network architecture system based on knowledge distillation, and designs a brand new training mode by using the superiority of knowledge distillation for teacher model weight extraction: the knowledge distillation of teacher and student models is combined with a deep compression method; the system for constructing the lightweight network can be used for training various complex image classification neural networks so as to improve the network performance.

The invention discloses a wall crack detection system based on image recognition, which relates to the technical field of building monitoring and comprises an image enhancement denoising module, a geometric feature extraction module, a structure health prediction module, a crack classification rating module, a crack extension monitoring module, a repair suggestion generation module and a crack detection verification module. According to the wall crack detection system based on image recognition, through adoption of crack geometric feature extraction, a structure health prediction model based on a graph convolutional network and a crack expansion monitoring module combining time sequence analysis and an optical flow method, the precision and real-time performance of wall crack detection are remarkably improved; trend prediction and self-adaptive feedback of crack expansion are realized, an efficient and intelligent solution is provided for health management of a building structure, and potential structural failure is effectively prevented.

The invention discloses a container tracking management system and method. The method comprises the following steps: S1, carrying out dynamic encryption binding and multi-dimensional data acquisition on an RFID chip; s2, realizing real-time position data acquisition of the container under different environmental conditions through a multi-source signal fusion and error correction algorithm; s3, various sensor modules are installed inside and outside the container, and data processing is carried out through distributed edge nodes; s4, performing on-chain storage on total data generated in the transportation process through a Hash encryption algorithm, and realizing automatic data verification, state tracking and abnormity alarm in combination with an intelligent contract; s5, carrying out real-time dynamic modeling analysis on the transportation path and resource allocation, and dynamically adjusting the transportation plan by using a distributed scheduling strategy; and S6, integrating the real-time state data, the path optimization model and the transportation scheduling decision, and providing a report for a user. The method has the advantages of high positioning precision, high data security, high transportation efficiency and good real-time performance.

The invention belongs to the field of computer graphics, and discloses a fast parallel multi-scale smooth symbol distance curved surface reconstruction method and system, and the method comprises the steps: carrying out the local fitting of point cloud data through employing a moving least square method, and calculating the Gaussian curvature of a point cloud; constructing a curvature adaptive octree based on Gaussian curvature, wherein the octree is used for dynamically adjusting local resolution and capturing geometric details to improve curved surface reconstruction precision; global fitting is carried out through an implicit function, and a zero contour surface is obtained through an octree contour surface extraction algorithm; and multiple threads are introduced in the process of iteratively solving the linear system to carry out parallel computing. The adaptive octree based on Gaussian curvature can effectively capture complex geometric details, and the algorithm realizes high-precision curved surface reconstruction under different scales. In addition, the local and overall calculation efficiency is remarkably improved through parallel calculation, and the high efficiency of the algorithm in large-scale data processing is ensured. The method provided by the invention provides an efficient and accurate solution for complex curved surface reconstruction.

An RFID tag interference solving method based on a density peak value clustering algorithm reduces signal interference between tags and improves reliability and identification precision of a system by dynamically adjusting priorities and time windows of RFID tags. The method comprises the following core steps: firstly, acquiring a label response signal through an RFID reader-writer; secondly, preprocessing the data, and removing noise and abnormal values; then, calculating the local density of the label signal, and identifying a density peak value region by using a clustering algorithm; then, reducing signal collision of tags in a high-density region by dynamically adjusting priorities and time windows of the tags in the region, and preferentially processing the tags; and finally, outputting a processed tag identification result. According to the invention, the problem of signal interference in a high-density RFID tag environment is obviously solved, the identification accuracy and the operation stability of the system are improved, and the method is suitable for various high-density tag application scenes.

The invention discloses a tea garden soil improvement method based on microbial communities. The tea garden soil improvement method aims at improving soil fertility and tea tree yield by means of precisely screening beneficial microorganisms, optimizing culture conditions and the like. Firstly, soil samples are collected from a tea garden area, gene high-throughput sequencing is carried out, soil microbial community composition information is obtained, and beneficial microorganisms are screened out. Then, the breeding speed and biomass of the beneficial microorganisms are optimized by adjusting a culture medium formula. When the bio-fertilizer is prepared, besides concentrated bacterial liquid, an organic carrier, inorganic nutrient elements and humic acid serving as a stabilizer are also added, and a proper amount of trace elements and a plant growth regulator are added. In spring or autumn, according to the physicochemical property of the soil, the bio-fertilizer is uniformly spread and ploughed into the soil layer. After fertilization, soil physicochemical properties and tea tree growth conditions are regularly detected to evaluate the soil improvement effect and adjust the fertilization strategy according to the soil improvement effect. The method is beneficial for improving the tea garden soil health condition and improving the tea yield and quality.

The invention provides a subway pedestrian anomaly detection method and system based on an improved yov8 network and a clip model, and relates to the technical field of subway pedestrian detection, and the method comprises the steps: obtaining a video frame in a subway scene, and carrying out the preprocessing of the video frame; inputting the preprocessed frame data into an improved yolov8 neural network model to obtain an abnormal behavior target detection frame, and performing target candidate tracking on the target detection frame by using a target tracking algorithm to obtain a candidate image of a target area of a specific behavior; the method comprises the following steps: inputting a candidate image containing a target area into a clip model, inputting a set detection text into the clip model, mapping the detection text and the candidate image of the target area, performing text-image similarity matching, and optimizing a similarity matching threshold by adopting a Q-Learning reinforcement learning immune algorithm to obtain a similarity matching result; and the abnormal behavior detection accuracy is improved.

The invention provides a sea wave magnetic interference suppression method and device for ocean electromagnetic detection, and relates to the technical field of electromagnetic signal processing, and the method comprises the steps: synchronously collecting original electromagnetic signals through a plurality of annularly arranged magnetic sensors, generating interference parameters through employing the calculated interference signal amplitude and phase information, and carrying out the subsequent interference suppression processing. The method comprises the following steps of: calculating a covariance matrix, performing principal component analysis, extracting spatial characteristics of low-frequency magnetic interference, projecting a signal to an orthogonal subspace outside an interference subspace, removing a low-frequency magnetic interference component, retaining a target signal, performing Fourier transform to convert the signal to a frequency domain, analyzing a frequency spectrum by using a band elimination filter, and setting an interference threshold value. According to the identified interference frequency, an interference suppression signal is generated, the interference suppression signal is returned to the time domain through inverse Fourier transform and is superposed with the target signal to generate a standard electromagnetic signal, the magnetic interference in the ocean detection area can be effectively suppressed, and accurate acquisition and analysis of the standard electromagnetic signal are ensured.

The invention provides a greenhouse climate prediction control method and device, electronic equipment and a storage medium, and relates to the technical field of agriculture, and the method comprises the steps: obtaining climate data and crop data in a greenhouse, and carrying out the multi-source data fusion, and obtaining multi-source fusion data; according to the multi-source fusion data, taking a crop growth state as output, training and verifying a deep neural network, and obtaining a nonlinear dynamic model of the greenhouse climate system; through a Bayesian inference technology, taking parameters of the nonlinear dynamic model as random variables, taking climate data inside and outside a greenhouse as observation data, and establishing posterior distribution of parameter uncertainty, so as to obtain a posterior distribution model of output uncertainty; establishing an optimization problem of a multi-target random opportunity constraint MPC according to a nonlinear dynamic model of a greenhouse climate system, a posterior distribution model of parameter uncertainty and targets of a greenhouse production system; and solving to obtain control input and a control strategy of the greenhouse.

The invention provides a data backup method based on a large model, and the method comprises the steps: S1, building a data model, and carrying out the data understanding and analysis and model training and optimization; s2, making a backup strategy: performing intelligent analysis on a current to-be-backed-up data set by utilizing the trained large model; dynamically adjusting and optimizing a data backup strategy according to an analysis result of the large model, respectively adopting incremental backup or full backup or differential backup, and reasonably distributing backup resources and priorities according to importance and recovery requirements of data; s3, data backup execution: executing data backup operation according to the backup strategy formulated in S2; and S4, backup management and optimization: managing and optimizing the backup data regularly.

The invention provides a charging station capacity optimal planning method considering space-time charging scheduling of an electric vehicle. The method comprises the following steps: carrying out functional region division on a city region with a traffic-power coupling system; counting the number of nodes, node loads and branch impedance in the power distribution network, and road types, road lengths and the number of roads in the traffic network; the method comprises the following steps: acquiring data such as a typical daily road travel time index, a power load and electric vehicle behaviors, and performing ordered charging intention evaluation on electric vehicle users in a region to obtain a charging mode of each electric vehicle; and taking the charging facility capacity of the electric vehicle charging station as an optimization variable, taking the minimum comprehensive cost as a target, establishing a target function, combining the operation constraint and the time-space constraint, establishing an optimization model, and solving to obtain a planning scheme. According to the method, the influence of the charging behavior of the electric vehicle on the running state of the traffic and power system can be deeply excavated, meanwhile, the benefit requirements of multiple related subjects are considered, and remarkable economic benefits are achieved.

The invention provides a multi-modal network rumor detection method based on big language model feature enhancement, and belongs to the technical field of language processing. Comprising the following steps: acquiring text modal data and image modal data; performing enhancement processing on the text modal data and the image modal data; combining the enhanced text with a predefined prompt template to obtain an input prompt enhanced text; converting the image modal data into visual feature representation; obtaining emotion-enhanced text feature representation, fused visual feature representation and fused text feature representation based on the enhanced text; optimizing the fused visual feature representation and text feature representation, and generating a visual integrated feature vector and a text integrated feature vector; weighting the visual integration feature vector and the text integration feature vector to generate a visual cross-modal feature and a text cross-modal feature; and inputting the visual cross-modal features, the text cross-modal features, and the text information and the visual information with balanced adjustable weight coefficients into a full connection layer, and outputting a prediction result.

The invention provides an image restoration method and system based on dynamic decomposition and fusion, and the method comprises the steps: decomposing a to-be-restored image with hybrid features according to a degradation mode, and extracting degradation features of which the number is the same as the type number of the degradation mode; respectively processing each degradation feature to obtain a recovery feature corresponding to each degradation mode; and the recovery features are dynamically fused to obtain a clear recovery image with the same size as the to-be-recovered image, so that the function of performing high-quality recovery on the complex hybrid image is realized. According to the method, the recovery feature map is decomposed, processed and fused under multiple scales, the recovery feature map fusing the recovery features of the multiple scales is obtained, and the visual effect of hybrid image recovery is improved. According to the method, the symmetric U-shaped network is constructed based on the corresponding model formed by the method, and the processing sub-module corresponding to the degradation mode is constructed, so that complex and diversified degraded images in a real environment are well dealt with, and an efficient and reliable solution is provided for the field of image restoration.

The invention provides a revenue distribution method for an internal alliance cooperative game of a virtual power plant, and the method comprises the steps: forming an alliance by distributed energy resources, and constructing a multi-agent optimization scheduling model in the virtual power plant with the maximized electricity selling revenue; constructing constraint conditions of a multi-body optimization scheduling model in the virtual power plant; determining a multi-agent cooperative game model of the game participants, the strategy set and the feature function; determining the overall electricity selling income of the virtual power plant, and designing a two-stage refined income distribution scheme of a multi-factor improved shapley value method and a kernel method based on odd-even circulation; and calling a CLPEX solver of the YALMIP on a Matlab platform to solve the cooperative game scheduling model and the income distribution strategy. According to the method, cooperative complementary operation among the alliances in the virtual power plant is realized, and fairness and rationality of income distribution among multiple subjects are ensured.

The invention discloses a cross-model knowledge distillation method for a remote sensing semantic segmentation task, and relates to a cross-model knowledge distillation method. The method aims at solving the problems that an existing remote sensing image semantic segmentation model is large in parameter quantity and large in resource consumption. The method comprises the steps of selecting a student model and a teacher model which are complementary in ability, selecting a data set to train the teacher model, and using a feature map output by the trained teacher model as knowledge to be input into the student model; carrying out attention weighting on the feature map input into the student model according to channels, and calculating loss; the knowledge transmitted by the teacher model is decoupled into a target area and a non-target area, the two independent target areas are subjected to prediction and knowledge distillation, and loss is calculated; and carrying out weighted summation on the loss of the semantic segmentation of the student model and the loss of the above steps, calculating the total loss, updating the parameters of the student model through the total loss, and finally completing the training of the student model. The invention belongs to the technical field of remote sensing image processing.

The invention discloses a CAD drawing intelligent matching method and system based on multi-stage deep learning, and belongs to the field of image comparison, and the method mainly comprises the steps: S1, loading and preprocessing a CAD drawing, and constructing a triple data set through a data enhancement and sample balance technology; s2, constructing a target detection model and a target matching model, and training the target detection model and the target matching model by using the triple data set; wherein the target detection model adopts a YOLOv5 model, and the CAD drawing is detected in combination with sliding window detection and a non-maximum suppression technology; the target matching model adopts a double-end network structure; and S3, matching the CAD drawings to be identified by using the trained target detection model and target matching model. According to the invention, through a plurality of advanced technologies, intelligent matching and judgment of two or more CAD images in any different formats are realized.

The invention belongs to the technical field of fuel cells, and particularly relates to a high-dimensional multi-target multi-working-condition optimization method and system for a fuel cell centrifugal air compressor, and the method comprises the steps: constructing a fuel cell air compressor performance analysis and optimization system, a comprehensive performance test platform is built, a multi-physics field coupling CFD simulation model is built, and the simulation and optimization efficiency is improved in combination with a machine learning algorithm; the pressure ratio and isentropic efficiency are defined as optimization targets based on fuel cell system requirements, and the performance of the air compressor is evaluated through experiment and simulation data; a multi-objective multi-population optimization method, an archive sharing technology, a genetic algorithm and the like are fused to establish an MMPS algorithm, and high-dimensional multi-objective optimization is carried out on related structure parameters of an impeller, a volute and a diffuser. The technical problems that the multi-target collaborative optimization requirement of an existing fuel cell centrifugal air compressor is difficult to meet, the high-dimensional variable optimization difficulty is large, and complex flow characteristic control is difficult are solved.

The invention discloses an interactive learning optimization method and system based on brain-eye fusion and transfer learning. The method comprises the following steps that electroencephalogram data and eye movement data of different subjects are collected; preprocessing the electroencephalogram data and the eye movement data to obtain an electroencephalogram sample and an eye movement sample, and performing data pairing; automatically extracting an electroencephalogram feature vector and an eye movement feature vector in the electroencephalogram sample and the eye movement sample by using a convolutional neural network in combination with a multi-head attention structure, and fusing the electroencephalogram feature vector and the eye movement feature vector into a final feature vector through a full connection layer; aiming at data differences among different subjects, constructing a transfer learning model; and according to the pupil size and the residence time of the user in combination with the electroencephalogram data, retraining and identifying to obtain sentiment classification for different contents. On the basis of multi-modal data fusion and transfer learning, various physiological data such as electroencephalogram signals and visual signals are fused, and the limitation of traditional single signal analysis in a complex scene is overcome.

The invention relates to the technical field of numerical simulation, in particular to a grid model generation method for the fluid displacement process of well cementation engineering. The method comprises the following steps: acquiring geological data of a well cementation project area, and constructing an initial geological model; well cementation engineering design data are obtained, well cementation engineering topological structure modeling is carried out, and a well cementation engineering topological structure model is obtained; according to the initial geologic model and the well cementation engineering topological structure model, fluid dynamic simulation is carried out, and a fluid characteristic matrix is obtained; performing fluid displacement process simulation according to the fluid characteristic matrix to obtain fluid displacement process data; performing fluid displacement process optimization on the well cementation engineering design data based on the fluid displacement process data to obtain optimized fluid displacement process data, and performing resource allocation redistribution on the well cementation engineering design data according to the optimized fluid displacement process data to obtain optimized well cementation engineering design data. According to the invention, the accuracy of fluid displacement simulation can be improved.

The invention discloses a live-line measurement method and device for zero-sequence distribution parameters of a double-circuit line with double ends sharing a bus, and the method comprises the steps: measuring the power parameters of the double-circuit transmission line with double ends sharing the bus, and enabling the power parameters to be used for at least indicating the operation state of the double-circuit transmission line with double ends sharing the bus; a line impedance parameter matrix and an admittance parameter matrix are constructed based on the electric power parameters of the double-circuit power transmission line with the double ends sharing the bus, the line impedance parameter matrix is used for indicating impedance characteristics of the double-circuit power transmission line with the double ends sharing the bus, and the admittance parameter matrix is used for indicating admittance characteristics of the double-circuit power transmission line with the double ends sharing the bus; zero-sequence parameters, including at least one of zero-sequence resistance, zero-sequence inductance and zero-sequence capacitance, of the double-circuit transmission line sharing the bus at the two ends are determined based on the line impedance parameter matrix and the admittance parameter matrix. According to the invention, live-line measurement of line zero-sequence distribution parameters of double-circuit transmission lines on the same tower or different towers with double ends sharing buses can be effectively realized.

The invention discloses a power field SQL automatic generation method and system based on retrieval enhancement generation, and relates to the technical field of data processing. The problem that a traditional SQL automatic generation method is low in data processing efficiency and accuracy is solved. The method comprises the following steps of: firstly, coding a question input by a user into a vector, and matching k keys with highest similarity ranking and description information in a pre-coded vector knowledge base; and generating instruction information based on the k keys with the highest ranking and the description information corresponding to the keys, and generating an accurate SQL query statement by the SQL generation model according to the instruction information. The method is mainly applied to converting the natural language problem into the SQL query statement in the electric power field.

The invention provides a software system dependency defect tracking method and system based on a graph database, and relates to the technical field of software, and the method comprises the steps: parsing a source code into an abstract syntax tree, and extracting a dependency; constructing a dependency graph data model; establishing a defect mode feature library; performing pattern matching in the graph data model to identify potential defects; calculating a defect influence range and severity; and generating a defect tracking report, including defect priority ranking, complete call chain construction, defect detailed information recording and optimization suggestion generation. According to the method, the dependency relationship defect in a software system can be effectively identified and tracked, and the software quality is improved.

The invention relates to a source network load storage collaborative planning method and system considering a multi-agent mixed game, and the method comprises the following steps: building a power distribution network collaborative planning model considering a source network load storage agent master-slave-cooperation mixed game, and enabling the power distribution network collaborative planning model to be a double-layer model, comprising an upper-layer master-slave game sub-model of a power distribution network operator and other subjects and a lower-layer cooperative game sub-model between a source load storage side subject; and solving the power distribution network collaborative planning model by adopting a solving strategy combining a target cascade analysis method and an adaptive coefficient alternating direction multiplier method to obtain an optimal planning scheme of a new line, a distributed power supply and an energy storage device. Compared with the prior art, the method has the advantages that efficient configuration and coordinated utilization of source network load storage resources can be realized, and the planning reliability is improved.

The invention relates to an amphibious vehicle path planning method and system based on fuzzy logic rules and reinforcement learning. The planning method comprises the following steps: collecting scene parameters by using a sensor according to an application scene of the amphibious vehicle, and taking the scene parameters as input parameters; performing fuzzification processing on the input parameters, performing reasoning by using a fuzzy rule base to obtain fuzzy output, and performing output defuzzification on the fuzzy output by using a gravity center method; defining a state space and an action space, resetting an environment, executing an action and calculating a reward, and finishing the step when a turn ending condition is met; an Actor-Critic neural network is initialized, and stable learning of a strategy is realized through experience playback and an updating mechanism of a target network; and training and optimizing the Actor-Critic neural network to obtain an optimal path planning strategy. According to the amphibious vehicle path planning method based on the fuzzy logic rule in combination with reinforcement learning, efficient solution of a path planning problem in a complex environment is realized through combination of a fuzzy logic system and a reinforcement learning algorithm, and the amphibious vehicle path planning method has a wide application prospect.

The invention provides a communication perception integrated beamforming method and system for suppressing distance-angle side lobes, and the method comprises the steps: enabling a base station to communicate with a plurality of users under a digital beamforming ISAC architecture, and carrying out the detection of an air target in a self-transmitting and self-receiving mode at the same time; determining a sensing channel model and a communication channel model to obtain a communication receiving signal at each user position and a sensing receiving signal scattered back to the base station from the target; calculating to obtain the SINR of each user; a distance-angle-Doppler three-dimensional perception fuzzy function based on a digital beam forming ISAC architecture is obtained; extracting an expression of an integral sidelobe of a distance-angle dimension; according to the communication index, the sensing index and the integral sidelobe value in the preset distance-angle sidelobe area, a beam forming matrix of the ISAC system is designed in a joint optimization mode; auxiliary variables are introduced to convert the problem of optimizing the beam forming matrix into the problem of optimizing a covariance matrix of the beam forming matrix, and a feasible solution of the joint design problem is obtained.

The invention discloses an air drying system for seasoning packaging containers, comprising a conveying mechanism which is horizontally supported by a bracket; the automatic stacking mechanism is arranged above the feeding end of the conveying mechanism and is used for uniformly stacking the packaging containers on the conveying mechanism for conveying; the air shower mechanism is arranged at the bottom of the conveying mechanism and inputs positive pressure air to the conveying surface of the conveying mechanism; the conveying face of the conveying mechanism keeps ventilated, and the automatic stacking mechanism buckles the open ends of the packaging containers to the conveying face of the conveying mechanism. The air-drying system can be used for rapidly air-drying the cleaned packaging containers, the packaging containers can be automatically stacked through the automatic stacking mechanism arranged at the front end of the conveying mechanism according to the width of the conveying mechanism, and then the opening ends of the packaging containers are buckled to the conveying face of the conveying mechanism, so that drainage is facilitated; and the air shower mechanism is combined, so that the packaging container can be conveniently air-dried, and the packaging container can be conveniently filled with materials after being sterilized subsequently.

The invention provides a hovering type wireless charging control method for an electric power inspection unmanned aerial vehicle, and belongs to the technical field of unmanned aerial vehicles. According to the technical scheme, firstly, modeling is conducted on a multi-transmitting-stage magnetic coupling type wireless electric energy transmission system, then directional constant-voltage output parameters of a wireless charging system in a coil offset state are determined, and control parameters of the wireless electric energy transmission system of the unmanned aerial vehicle are determined; on the basis, modeling is carried out for unmanned aerial vehicle power inspection power consumption and the charge state, and multi-source disturbance suppression parameters of the unmanned aerial vehicle and attitude track full-time-domain preset time control parameters of the unmanned aerial vehicle are determined. According to the method, a multi-transmitting-stage MCR-WPT system is adopted, a directional constant voltage output model is constructed, meanwhile, a charging system control strategy is optimized, and a wireless charging scheme based on an LCC-S type compensation network is designed. According to the invention, the transmission efficiency and stability of the wireless charging system of the electric power inspection unmanned aerial vehicle are improved, and the hovering wireless charging control precision of the electric power inspection unmanned aerial vehicle is improved.

The invention discloses a sample position and patient information input system of a full-automatic blood coagulation tester, and particularly relates to the technical field of medical detection equipment. Analyzing the pressure fluctuation of the sample needle in the sample suction process at different positions through a residual risk judgment module, and judging whether a trace residual risk exists or not; the aerosol diffusion condition is dynamically analyzed through a cross contamination assessment module, and the cross contamination risk of the sample is assessed; the safety determination module determines the safety of the sample detection process according to the residual risk and the cross contamination risk; the positioning deviation evaluation module evaluates the influence of the positioning deviation of the sample by analyzing the temperature distribution gradient; the consistency evaluation module analyzes an information transmission error and evaluates the input consistency of a sample source and patient information; and the comprehensive adjustment module performs comprehensive analysis, evaluates the reliability of a sample detection result, and dynamically adjusts the sample site distribution and information input process, thereby providing more scientific and robust support for clinical diagnosis.

The invention discloses a preset time encircling control method for privacy protection under an event trigger mechanism. The method comprises the following steps: firstly, establishing a multi-robot system comprising a plurality of leaders and a plurality of followers, and defining a preset time encircling control problem under privacy protection constraints; designing a privacy protector based on an output mask function; a preset time privacy protection surrounding controller is further constructed, so that all the follower robots can enter the convex hulls of the leader robots on the premise of protecting the privacy of the follower robots; then, constructing an event trigger function without a sesame behavior, and designing a privacy protection preset time encircling controller based on an event trigger mechanism; according to the method, an event triggering mechanism and a finite time theory are combined, the convergence performance of privacy protection surrounding control of the multi-robot system is improved, resource consumption of algorithm calculation is reduced, privacy protection, communication broadband constraint and convergence speed are integrated, and the application scene of the algorithm is further expanded.

The invention relates to the technical field of transient heat conduction, in particular to a composite material transient heat conduction and thermal stress simulation method based on a two-dimensional arbitrary polygon hybrid finite element, which comprises the following steps: S1, determining an analysis model and material attributes, and establishing a transient heat conduction basic equation including boundary and initial conditions; s2, constructing and expanding a generalized variational functional, processing boundary conditions in combination with a Lagrange multiplier method, and discretizing a solution domain into a plurality of independent units; s3, adopting hypothetical temperature field, heat flux field and thermal stress field functions, introducing a discrete finite element format, and deducing a corresponding finite element equation from a functional; and S4, solving a transient temperature field through programming, introducing the transient temperature field into stress analysis, and finally obtaining temperature and stress distribution of the material at different time points. According to the method, temperature distribution and thermal stress of transient heat conduction are solved by hybridizing heat flux finite elements, and a new way is provided for transient heat conduction and thermal stress analysis of the composite material.