39 results about "Interactive modeling" patented technology

The embodiment of the invention provides a digital human interaction system and method based on multi-modal emotion recognition, and belongs to the technical field of digital human interaction. The system comprises a multi-modal sensing module used for collecting multi-modal data and preprocessing the multi-modal data to generate a standardized data stream; the cross-modal fusion and emotion recognition module is used for carrying out interactive modeling on the multi-modal features and outputting a current emotion label and emotion intensity; the reaction planning module is used for generating a composite reaction strategy; and the digital human rendering module is used for mapping the composite reaction strategy into control signals corresponding to the voice, the facial expression and the action respectively, and driving a digital human to execute corresponding voice output, facial expression change and limb action through the control signals so as to realize interaction. According to the method, multi-modal data are deeply fused through the cross-modal graph neural network and comparative learning, the weight is dynamically adjusted in combination with the modal confidence, and the emotion recognition accuracy and robustness are improved.

The invention relates to an implicit voxel Boolean subtraction optimization method in five-axis machining simulation. According to the method, an implicit tool geometric expression model based on a symbolic distance function is constructed, and a dynamic voxel modeling technology with a layered structure is combined, so that efficient interactive modeling of a material removal process between a tool and a workpiece is realized. The whole process of the method comprises four key stages of spatial positioning, vertex interference detection, projection correction and grid reconstruction, and can cope with geometric modeling challenges caused by continuous motion of a multi-degree-of-freedom and multi-attitude cutter. A dynamic block loading mechanism and a local state synchronization strategy are introduced, so that the real-time response capability of Boolean subtraction operation under complex working conditions is effectively improved, and meanwhile, the continuity and integrity of machining geometric boundaries are kept. The method has good expandability and robustness, obviously reduces space-time complexity of calculation on the basis of keeping geometric surface continuity and topological correctness, realizes millisecond-level response time in an experiment, and has a good application prospect. The method is widely applied to virtual machining simulation and intelligent process verification systems of complex free-form surface parts such as aero-engine blades and automobile structural parts.

The invention discloses a multi-modal image fusion model construction method, and particularly relates to the technical field of image fusion model construction. Interactive modeling of a modal contribution imbalance coefficient and a confidence coefficient estimation anomaly coefficient is introduced in a multi-modal image fusion process; a causal association between confidence prediction fluctuation and modal weight extreme is converted into a quantifiable cross-modal complementarity degeneration index, and the cross-modal complementarity degeneration index is used as a core adjustment mechanism to dynamically optimize a fusion weight and training regularization, so that the problem that a single mode is excessively amplified or mistakenly weakened is effectively inhibited on a fusion strategy level; according to the method, the structure retention capability of the model in edge details and texture regions is remarkably improved, and artifacts and information loss caused by modal complementarity degradation are avoided.

The invention belongs to the technical field of intelligent emotion recognition, and discloses an aspect-level multi-modal emotion analysis method, system and device and a medium, and the method comprises the steps: respectively obtaining image features and text features based on an image and a text, and selecting aspect word features through a pooling operation; modeling the image-text words and the image-aspect words through an image convolutional neural network to obtain image-text interaction features and image-aspect interaction features; image-text interaction features are used as query vectors, image-aspect interaction features are used as key vectors and value vectors, residual connection and normalization are introduced, interaction features are obtained and fused with aspect word features, and sentiment classification is performed based on the fused features. According to the method, from multi-modal feature extraction, interactive modeling to fusion optimization, a series of operations are progressive layer by layer, comprehensive, accurate and high-quality feature support is provided for sentiment classification, and finally, the accuracy and reliability of aspect-level multi-modal sentiment classification are effectively improved.

The invention discloses a multi-person collaborative human factor risk monitoring method and device based on multi-modal perception, and relates to the technical field of human factor engineering, intelligent perception and group decision behavior modeling. The cognitive state, emotion change and physiological load of participants are sensed in real time by fusing EEG, motion state, positioning track, heart rate, blood pressure and other multi-source information, and the collaborative decision behavior and cognitive consistency of a group in a high-pressure task are evaluated through interactive modeling. The method is suitable for multi-personnel, multi-role and task-intensive cooperation scenes such as emergency management, military drilling, industrial safety, complex traffic guidance and the like.

The invention discloses a staring estimation method based on multi-modal three-dimensional feature fusion. The method comprises the following steps: firstly, extracting multi-modal three-dimensional features such as human body three-dimensional posture features, view direction priori and scene object three-dimensional position features from an RGB image and a depth image; then the extracted three-dimensional features are converted from a camera coordinate system to a self-center space with the head as the original point; then, direction and distance separation coding is carried out on the transformed three-dimensional features; and finally, interactive modeling is carried out on the coded three-dimensional features of the human body and the object through a Transform network, so that a final three-dimensional staring direction is predicted. According to the method, multi-modal three-dimensional information is fused and normalized interactive modeling is carried out, so that the dependence of a traditional method on a clear and close-range facial image is effectively overcome, and the robustness and accuracy of three-dimensional staring estimation in unconstrained scenes such as long-distance and shielding scenes are remarkably improved.

The invention provides a task unloading decision-making method based on multi-agent reinforcement learning. The method comprises an execution stage and a training stage. In the training stage, the centralized critic network can obtain global information of all agents so as to guide optimization of strategies of all the agents. In the execution stage, each agent only needs to realize the distributed unloading decision of each wireless device through a local actor network according to the local observation of the agent. According to the method, the global state and the joint action are evaluated by adopting the centralized value evaluation network, and the resource competition and coupling relationship between the intelligent agents is explicitly learned through the interactive modeling structure, so that the value estimation deviation caused by a simple splicing structure is reduced, the joint value evaluation precision is improved, and a more accurate updating direction is provided for each actor.

The invention relates to a VR-based multi-scene learning state modeling and learning effect evaluation method and system, and the method comprises the steps: defining a student learning state as a dynamic interaction modeling object and a static modeling object according to the VR modeling requirements and the demands of multi-scene learning effect evaluation for modeling, and carrying out the decomposition; drawing a multi-scene learning effect evaluation modeling network diagram; a teaching scene modeling topological structure is extracted from the network diagram, multi-scene learning state modeling parameter quantification is carried out, and a student multi-scene learning state model is constructed through a virtual reality modeling technology; and based on a multi-scene learning target, constructing a learning effect comprehensive evaluation parameter, carrying out multi-scene learning effect comprehensive evaluation and each scene process learning effect evaluation, automatically generating an evaluation report, and synchronizing the evaluation report to a teacher end and student multi-scene learning state model. According to the method, scene coverage is more comprehensive, data association is tighter, evaluation precision is improved, unified modeling of multi-scene data is achieved, and the visualization effect is better.

The invention discloses a Web service causal depolarization recommendation method and a Web service causal depolarization recommendation system based on attention enhancement contrast learning. The method comprises the following steps: 1) randomly sampling a negative sample for each positive sample in user-project interaction, and dividing into two mutually exclusive training sets, namely, a more popular positive sample set and a more popular positive sample set, according to project popularity; 2) based on the two mutually exclusive training sets, executing a graph convolutional network and an Attention attention mechanism, and generating an interest representation matrix and a consistency representation matrix; 3) performing regularization constraint on the embedding space of the user and the project by introducing a triple loss function on the basis of a comparative learning representation distribution optimization framework; 4) performing joint optimization on a plurality of losses of interest modeling, conformity modeling, interactive modeling, independence constraint and comparative learning, and optimizing the model through a curriculum-type training strategy for gradually tightening constraint boundaries, and 5) calculating a comprehensive preference score based on the final interest representation matrix and the consistency representation matrix, and outputting a debiased recommendation result.

The invention discloses an interactive modeling system based on a large model, and the system comprises a data preprocessing module which is used for carrying out the preprocessing of modeling data based on an input first interaction instruction, and obtaining the preprocessed modeling data; the feature engineering module is used for performing feature engineering processing on the preprocessed modeling data based on an input second interaction instruction to obtain modeling data after feature engineering processing; the model construction module is used for performing model construction on the modeling data subjected to feature engineering processing based on an input third interaction instruction to obtain a model corresponding to the modeling data; the intelligent agent is used for receiving and analyzing an input modeling starting instruction, obtaining a modeling step according to input modeling data and feeding back the modeling step to a user; and respectively calling the data preprocessing module, the feature engineering module and the model building module in sequence based on the modeling step. Through the above mode, conversational interactive modeling can be realized, the modeling threshold is reduced, and the modeling efficiency and accuracy are improved through intelligent recommendation.

The invention discloses an interactive generation method for a three-dimensional scene, and the method comprises the steps: carrying out the interactive image segmentation based on SAM: extracting a key primitive taking a mountain as a main part in a picture, and generating a segmentation silhouette of the corresponding primitive; performing three-distance perspective analysis on the landscape painting image based on LLM: outputting probability distribution and interpretable analysis results of high-distance, deep-distance and flat-distance perspective modes, and recommending an optimal perspective mode; according to the three-dimensional scene generation based on three-distance perspective, SAM-based primitive segmentation, LLM-based perspective analysis and a three-dimensional perspective conversion algorithm can be fused into a whole, and a'primitive segmentation-perspective analysis-three-dimensional generation 'three-stage interactive modeling system is constructed. Through interpretable three-distance perspective recommendation and real-time slider parameter adjustment, the system helps a user to quickly grasp three perspective layout points of high distance, deep distance and flat distance, and solves the pain points of difficult spatial restoration and long adjustment time under landscape painting scatter perspective.

The invention is applied to the technical field of new energy power generation prediction, and discloses a wind power prediction method based on non-overlapping multi-scale division and interactive modeling, and the method comprises the specific steps: firstly, carrying out the space-time embedding of historical wind power data, and obtaining the space information; secondly, a multi-scale token length set is set, non-overlapping multi-scale partitioning is carried out on the embedded sequence, and tokens of different scales are generated so as to reduce redundancy caused by overlapping; then, constructing a hierarchical interactive encoder, extracting features layer by layer from a small scale to a large scale, and embedding the small-scale potential representation of the previous layer into the input of the current layer in the large-scale encoding process to realize the guidance of the large scale by the small-scale features; and finally, fusing and mapping each scale feature output to obtain a future wind power prediction value, and training and optimizing the model by adopting mean square error loss. Verification on actual wind power plant data shows that the method can effectively improve the accuracy of wind power prediction.

This application relates to the interdisciplinary field of artificial intelligence and reliability systems engineering, and provides a method and apparatus for intelligent generation of equipment fault behavior logic models based on LLM (Limited Linear Modeling). This method combines a vector database to achieve enhanced retrieval, constructs an agent-based framework for generating equipment fault behavior logic models, and then generates structural elements in the agent for each business component involved in the equipment fault behavior logic. Based on the model structure, the results are integrated and merged, ultimately achieving a complete and runnable equipment fault behavior logic simulation model. This method integrates the interactive modeling capabilities of agents with the semantic understanding and logical reasoning capabilities of large language models, enabling intelligent generation of fault behavior models for complex equipment and effectively improving the generation efficiency of equipment fault behavior models.

The invention relates to the technical field of natural language processing, and particularly discloses a few-sample relation classification method and system based on multi-scale hierarchical prototype learning, and the method comprises the steps: constructing a few-sample relation classification task, and generating a support set and a query set; encoding the support set sample, the query set sample and the relationship description information by using a pre-training language model to generate semantic vector representation; multi-scale hierarchical prototype representation is constructed through three levels of cross-set collaboration, intra-class collaboration and inter-class collaboration; calculating the distance between the query sample and each type of prototype, and outputting the classification probability through softmax; the cross entropy loss and the distribution alignment loss are jointly optimized, the contribution of each support sample to prototype construction is dynamically adjustable by introducing an intra-class collaborative modeling mechanism, the recognition ability of the model to the fine-grained relationship is improved by introducing inter-class interactive modeling and relationship description information, and the recognition ability of the model to the fine-grained relationship is improved by introducing a distribution alignment module between a support set and a query set. And the spatial offset between the prototype and the to-be-classified sample is obviously reduced.

The invention discloses a method and a system for enhancing multi-scale and multi-modal intelligent interactive modeling, and a medium. The method comprises the following steps: generating vertexes and 3D human body grids by using an SMPL model; performing feature extraction on the sketch by using a double-branch encoder, wherein the double-branch encoder comprises a segmentation encoder of a pre-trained human body sketch and a sketch local feature encoder of a sketch based on ResNet34; enhancing the ability of the model to capture a key region in the feature map by using a spatial feature enhancement module; using a multi-scale context fusion module to extract a multi-scale feature map from an intermediate layer of the double-branch encoder; and the output is converted into a 3D human body grid through a multi-branch regression device and an SMPL model. According to the invention, key spatial details are reserved through the multi-scale context fusion module, so that the model can focus key information on feature maps of different levels and scales. According to the invention, higher precision can be realized in 3D human body reconstruction, and optimal performance can be achieved.

The invention discloses a common preference enhanced collaborative recommendation method and device, and the method comprises the steps: obtaining user implicit interaction data in a target domain and a source domain, and constructing corresponding user features and commodity feature representations; carrying out fusion coding on the target domain user features, the source domain user features and the target domain commodity features, and generating cross-domain migration features facing target domain recommendation tasks; performing fusion iteration de-noising processing on the cross-domain migration features, gradually inhibiting source domain noise interference and strengthening migratable collaborative preference information through multiple rounds of feature fusion and weight screening operation, and generating de-noised cross-domain user feature representation; after denoising is completed, user structure constraint is introduced, and consistency constraint is carried out on the similarity relation between the target domain users before and after feature enhancement, so that the overall distribution stability of the target domain user interest structure is kept; and finally, performing interactive modeling on the final user representation and the target domain commodity features to obtain a matching relationship between the user and the commodity. The device comprises a processor and a memory. Through a collaborative modeling mode of fusing iterative denoising and structural constraint, the problems of noise interference in cross-domain recommendation and user similarity structure change before and after feature migration can be relieved, and the stability and accuracy of a recommendation result are improved.

The invention relates to the technical field of trajectory prediction and planning, in particular to a parallel iterative joint prediction planning method considering bidirectional interaction characteristics, and the method comprises the steps: constructing a bidirectional interaction modeling mechanism considering future trajectory interaction; iteratively executing interactive trajectory prediction and interactive trajectory planning based on a bidirectional interactive modeling mechanism in parallel until a preset condition is met, so as to respectively obtain a final planning code and a final prediction code; and decoding the final planning code and the final prediction code to obtain a multi-modal prediction trajectory and a planning trajectory of a planning time domain, and obtaining a final prediction trajectory and a planning trajectory according to the confidence coefficient. Therefore, the problems that the coupling relation between prediction and planning is difficult to fully reflect and the vehicle track cannot be timely adjusted according to the traffic environment change due to the fact that only the one-way influence of prediction on planning is established in the related technology are solved; therefore, the problems of conservative planning, influence on road traffic efficiency, even traffic accidents and the like easily occur in a complex dynamic scene.

The invention discloses a technology debt elimination opportunity prediction method and system based on multi-modal data fusion. The method comprises the following steps: firstly, extracting technical debt features including numerical features, text semantic features and files related to Git submission records, and constructing a file covariant graph based on the submission records; secondly, performing multi-modal feature coding on the numerical features, the text semantic features and the graph structure features based on the file covariant graph; then, based on a cross-modal attention mechanism, interactive modeling is carried out on the coded numerical value features, text semantic features and graph structure features, and three paths of interactive representations are generated respectively and spliced into a joint representation; and finally, inputting the joint representation into a multi-layer perceptron regression network, and predicting a numerical value of the residual life of the technology debt through layer-by-layer calculation. According to the method, static code measurement, text semantic information and graph structure features can be fused, key factors are dynamically weighted through attention, and by means of a deep classification model, high-precision prediction of the technical debt remaining life is achieved.

The invention discloses an interactive perception trajectory prediction method for heterogeneous agents in a shared space, and the method comprises the steps: S1, encoder-historical trajectory and interactive feature extraction, applying Transform-based spatial modeling at each time step, processing historical trajectory data, and extracting and fusing the motion features of each agent and the spatial interactive features of a historical period; and S2, decoder-future trajectory iterative prediction and interactive modeling: iteratively predicting a future trajectory based on an encoder output state, and dynamically and sparsely updating interaction between the intelligent agents in the prediction process to simulate future interaction evolution. According to the method, heterogeneous characteristics can be accurately perceived, future interaction can be foreseen, and the calculation efficiency is high.

The invention relates to the field of turbomachinery, and discloses a blade reverse-forward interactive modeling method based on geometric parameters of a mean camber line, and the method comprises the steps: extracting parameters, carrying out the geometric definition based on an inscribed circle of a source blade, solving to obtain the mean camber line, and extracting beta angle distribution and inscribed circle radius distribution; a forward mean camber line is generated, wherein the forward mean camber line is generated according to the extracted parameter integral; generating a forward blade molded line: constructing an inscribed circle along the forward mean camber line and solving an envelope line to generate the blade molded line; verification parameter extraction: performing parameter extraction on the positive blade profile; and deviation determination: comparing the original and verification parameters to determine the reproduction precision. The system comprises a parameter extraction module used for executing a parameter extraction function; the forward modeling module is used for executing a forward mean camber line generation function and a forward blade molded line generation function; and an interactive verification module for performing a deviation determination function. According to the method, through accurate geometric definition and a closed-loop verification mechanism, the blade design and manufacturing efficiency and precision are improved.

This application relates to the interdisciplinary field of artificial intelligence and reliability systems engineering, and provides a method and apparatus for intelligent generation of equipment maintenance and operation support models based on LLM (Limited Linear Modeling). This method combines a vector database to achieve enhanced retrieval and constructs an agent-based framework for generating equipment maintenance and operation support models. Then, for each business component involved in the equipment maintenance and operation support model, structural elements in the agent are generated one by one. Based on the model structure, the results are integrated and merged, ultimately achieving a complete and operational equipment maintenance and operation support model. This method integrates the interactive modeling capabilities of agents with the semantic understanding and logical reasoning capabilities of large language models, realizing the intelligent generation of maintenance and operation support models for complex equipment, and effectively improving the generation efficiency of equipment maintenance and support models.

This invention relates to a method for force feedback modeling of intravenous puncture in virtual training for intravenous therapy nurses. The method comprises two parts: hierarchical interactive object construction and force feedback mapping, and hierarchical tactile model establishment. By performing hierarchical interactive modeling of the arm and blood vessel models, a positional mapping relationship between the force feedback device and the virtual needle is established, and the contact state is dynamically updated through interaction between the needle tip collider and the arm and blood vessel layers. Damped tactile feedback models and puncture-feeling tactile feedback models are established for the arm and blood vessel layers respectively, and force feedback signals are calculated and output in real time at a preset refresh frequency. Simultaneously, stiffness, damping, static friction, and dynamic friction parameters are visualized and adjusted, and stress feedback parameters are configured according to different body types. This method can improve the realism and adaptability of force feedback in virtual training for intravenous puncture.

The invention discloses a multi-modal simulation method and system based on a real-time data component, and relates to the technical field of power system simulation. Through construction of a multi-modal simulation application framework, data-driven cross-modal interactive modeling is realized, full-process configuration operation from visual design to lightweight deployment is supported, collaboration with an embedded development system of a unified research and development platform is ensured, a real-time collection and fusion system of multi-source heterogeneous data is established, and the real-time collection and fusion system of the multi-source heterogeneous data is realized. Filtering conversion and interactive visualization of data streams are achieved, balance of data real-time performance and display performance is ensured, state collaboration and self-adaptive adjustment of multi-modal components are achieved through linkage collaboration of a hardware simulation test environment and components of the platform, a linkage rule system conforming to power grid service logic is formed, equipment configuration service of the platform is achieved, and the service performance of the platform is improved. A cross-platform simulation operation environment is constructed, lightweight deployment and continuous optimization of applications are realized, the consistency of a simulation result and physical equipment is ensured, and the simulation efficiency and reliability are greatly improved.

The invention discloses an emergency braking decision model generation method, an emergency braking decision method and an emergency braking decision device, and relates to the technical field of computers. A specific embodiment of the training method of the information recommendation prediction model comprises the steps of determining motion state information of a target based on point cloud data and image data, and constructing a target feature sequence according to the motion state information; constructing a main vehicle feature sequence based on the key point information of the main vehicle; and performing interactive modeling on the target feature sequence and the main vehicle feature sequence, and generating an emergency braking decision model. According to the embodiment of the invention, the interaction and decision-making of the main vehicle information and the target information can be realized without manual module splitting, and the coupling and decision-making effect are improved. A specific embodiment of the emergency braking decision-making method comprises the steps that to-be-decided point cloud data and to-be-decided image data of a user are input into an emergency braking decision-making model, and the emergency braking decision-making model outputs an emergency braking decision-making result and feeds back the emergency braking decision-making result to the user. According to the embodiment, the feature mining capability is higher, and the decision-making effect is better.

The invention relates to the technical field of artificial intelligence and reliability system engineering crossing, and provides an LLM-based equipment fault behavior logic model intelligent generation method and device. The method combines a vector database to realize enhanced retrieval, constructs an equipment fault behavior logic model generation framework based on an intelligent agent, then generates structural elements in the intelligent agent one by one for business components involved in equipment fault behavior logic, and completes integration and combination of results based on a model structure. And finally, a complete and operable equipment fault behavior logic simulation model is realized. According to the method, the interactive modeling ability of the intelligent agent and the semantic understanding and logical reasoning ability of the large language model are fused, intelligent generation of the fault behavior model of the complex equipment is realized, and the generation efficiency of the equipment fault behavior model is effectively improved.

The invention relates to the technical field of geological exploration, and discloses a mine modeling method, device, equipment and medium, and the method comprises the steps: obtaining the topographic data of a goaf; constructing a fault model by using an interpolation method based on the topographic data; based on topographic data, constructing a thin-layer ore bar model by utilizing spectral characteristics of thin-layer ore bars; and fusing the fault model and the thin-layer ore bar model, and determining a mine model corresponding to the goaf. According to the scheme, the terrain data and the interpolation method are utilized, the fault model is constructed, the fault constraint surface is formed, and the thin-layer ore bar model is constructed by utilizing the spectral characteristics of the thin-layer ore bars to enhance the boundary recognition precision of the thin-layer ore bars, so that the fault space distribution and thin-layer ore bar distribution rules are recognized on the basis, traditional manual interactive modeling is replaced, and the recognition precision of the boundary of the thin-layer ore bars is improved. And constructing a self-adaptive three-dimensional grid model.

The invention discloses an offline double-path interactive modeling method and system for a complex game scene, and the method comprises the steps: building a distributed simulation-training closed loop for a data-sufficient scene through large-sample deep reinforcement learning, and achieving the online evolution of an unmanned cluster game strategy; for a data scarce or high-confidentiality scene, small sample machine learning is adopted to complete fast reasoning of a target behavior type; the system is internally provided with an interactive modeling auxiliary platform driven by a local off-line large model, an integrated data warehouse, an algorithm library, an intelligent modeling assistant and a model management and visual evaluation module. According to the method, intelligent jump of complex game scene modeling from data driving to knowledge guiding is achieved, the method is suitable for intelligent decision scenes such as behavior recognition, strategy evolution and task planning, and the method has high adaptability, expansibility and practicability.

The invention relates to the field of new energy centralized control, and discloses a new energy centralized control scene-oriented AI model cloud edge collaborative management system, and the system comprises a model development unit which is used for selecting at least one modeling mode selected from automatic modeling, visual modeling and interactive modeling in combination with the business scene demands of new energy centralized control to construct an AI model; the model training and optimizing unit is used for training, verifying and evaluating the AI model output by the model developing unit in sequence; the AI asset management unit is used for performing version management on the trained AI model, and performing layered packaging on the AI model according to an application layer, a model layer, a configuration layer and an environment dependence layer according to a cloud edge collaborative deployment requirement; and the cloud edge collaborative management unit is used for issuing and deploying the target model package to a specified area side or station side edge node. According to the invention, by constructing a hierarchical progressive intelligent decision-making process, automation and optimization of modeling mode selection are realized.

The invention provides a meteorological and pollutant interactive modeling and intelligent analysis system and method, and relates to the technical field of air pollution abatement.The meteorological and pollutant interactive modeling and intelligent analysis system comprises a front-end interactive layer, a rear-end service cluster, a machine learning model engine, a data storage layer and a communication interface layer. The method comprises the following steps: carrying out intelligent data cleaning on collected multi-source heterogeneous data, constructing a plurality of meteorological and pollutant intelligent analysis models, training the models, evaluating and comparing the trained models, selecting a final model, calculating SHAP based on the final model, carrying out feature contribution analysis, and outputting a visual result according to an analysis result. Through a Java front and back end separation architecture and micro-service design, automatic collection, intelligent cleaning, multi-model comparison training and multi-scale SHAP analysis of meteorological and pollutant data are realized, and a high-expandability and high-safety scheme is provided for environmental governance.

The invention provides a parameterized origami modeling method for obtaining a Miura crease graph from a folding form curve, which comprises the following steps of: S1, drawing a folding form two-dimensional curve in interactive modeling software, and averagely segmenting the curve to obtain a target polyline similar to the curve in shape; s2, for the generated target polyline, calculating a mathematical transformation relation between the polyline and a line segment of the crease graph by adopting a mathematical method, and generating a crease strip graph; s3, carrying out simulation folding on the generated crease graph; and S4, observing the model structure obtained by simulation folding, adjusting the parameter values of the crease graph, ensuring the reasonability of the folding structure in mathematics and the aesthetic property of the folding structure in design, and obtaining a printable and directly-foldable crease graph drawing. According to the method, efficient conversion from the polyline form to the crease graph can be achieved, and the operation threshold of non-professional designers and paper folding beginners is remarkably lowered.