856 results about "Reduced model" patented technology

The invention discloses a strategy question-answering system and method based on a vertical domain large model, and the method comprises the steps: S1, receiving the natural language input of a user, and retrieving an open source data set to obtain an original data set with the comprehensive similarity meeting the requirements; s2, performing necessary data cleaning and preprocessing on the original data set to obtain a training set; s3, performing fine tuning on the pre-processed basic large language model by adopting the training data set, and introducing a low-rank structure to modify a weight matrix to obtain a final model; s4, the problem enters a final model for post-processing to generate prediction output; the system takes a large language basic model subjected to fine tuning training as a core, introduces a low-rank structure to perform fine tuning on model parameters, reduces parameter quantity required by training while keeping model performance, reduces model complexity, reduces an overfitting risk, improves generalization ability, supports deep fusion of a specific field knowledge base and universal field data, and has a wide application prospect. And the accuracy and correlation of questions and answers are improved.

The invention discloses a user-defined multi-dimensional analysis configuration method, system and equipment based on a constellation model and a medium, belongs to the technical field of data analysis, and aims to solve the technical problem that basic data such as dimensions, indexes and data models in multi-dimensional analysis are complicated to construct. According to the technical scheme, according to the method, the dimensionality, the index and the incidence relation between the fact table and the dimensionality table configured by the model table are imported, the mapping between the dimensionality tables is automatically imported into the correlation table, the attribute and the model relation, the model maintenance cost is reduced, and the model maintenance time is shortened; ordered model construction is realized through dimensions, indexes, fact tables, dimension tables, data models and self-defined multi-dimensional query, multi-fact-table dimensions screen application configuration scenes of reverse mapping of property of the fact tables, required indexes are flexibly selected from the fact tables through dragging operation, and according to a cascade mode of the indexes and the dimensions, the application configuration scenes of reverse mapping of property of the property of the fact tables are selected to be matched with the property of the property of the property of the property of the property of the property of the property of the property of the property of the property of the property of the property. And automatically screening out the generality dimension for display.

The invention discloses a lightweight behavior recognition method based on skeleton data. The lightweight behavior recognition method comprises the following steps: 1) preprocessing an obtained behavior data set; and 2) fusing space and time sequence information by adopting a double-branch structure, respectively putting a time sequence T and a joint point N in respective channel dimensions through a dimension conversion thought, and extracting behavior characteristics through an established efficient space-time diagram convolution model. Wherein for the efficient space-time diagram convolution model, 1 * 1 convolution and depth separable convolution are introduced, the calculation cost of parameter adjustment and model reasoning is reduced, and the difficulty of model training is reduced by introducing a residual link. After model parameters, a loss function, an optimization function and the like are set, the network is trained by a training set through model weight initialization, data loading and a model optimizer. And 3) predicting the data set by using the trained skeleton behavior recognition model, and giving a corresponding action category. According to the method, the model complexity can be effectively reduced on the basis of ensuring relatively high recognition accuracy, and the method has a wide application prospect.

The invention relates to the technical field of performance simulation analysis, in particular to a performance simulation method of a power module. The method comprises the following steps: acquiring initial physical characteristic data and initial electrical characteristic data of a power module; the initial physical characteristic data and the initial electrical characteristic data are preprocessed and simulated, and physical field distribution characteristics in the power module are obtained; performing multi-physical field simulation analysis on the power module based on physical field distribution characteristics in the power module by using an adaptive grid generation algorithm and sparse matrix solution to obtain simulation result data; performing parameter adjustment and optimization on a simulation model of the power module according to the simulation result data to generate optimized simulation model parameter data; and according to the optimization simulation model parameter data, performing simplification processing on the design of the power module to establish a simplified model. Through multi-stage simulation and optimization, the performance and the control precision of the power module can be remarkably improved, and the stability and the efficiency of the system are improved.

The embodiment of the invention provides a task processing method, a model training method and an intelligent dialogue and model training platform. The task processing method comprises the steps of obtaining initial prompt information corresponding to a target task; the execution process of the target task is disassembled based on the initial prompt information through a natural language model, multiple target sub-tasks are obtained, target prompt information corresponding to the target task is generated, and the target prompt information comprises prompt information corresponding to the multiple target sub-tasks; and determining target task models of specifications corresponding to the plurality of target sub-tasks, and correspondingly executing the plurality of target sub-tasks by using the plurality of target task models based on the prompt information corresponding to the plurality of target sub-tasks to obtain task execution results of the target tasks. By disassembling the execution process of the target task and calling the target task models of different specifications, computing resources can be optimized, the model calling cost can be reduced, and the system efficiency can be improved.

The invention discloses a design method of a coreless stator axial magnetic flux permanent magnet motor. The method specifically comprises the following steps: firstly, determining a basic structure, a permanent magnet structure and performance demand parameters of the coreless stator axial magnetic flux permanent magnet motor; determining stator and rotor structure parameters of the motor according to the performance requirement parameters of the motor; establishing a simplified model of an internal magnetic circuit of the motor, determining excitation magnetic potential of the motor, and establishing a finite element dynamic simulation model of the coreless stator axial flux permanent magnet motor based on a Maxwell equation; and finally, comparing the finite element dynamic simulation result with the motor performance demand parameter, if the finite element dynamic simulation result does not meet the requirement, correcting the motor design structure parameter and the electromagnetic parameter, and re-determining the stator and rotor structure parameter of the motor until the finite element dynamic simulation result of the motor meets the motor performance demand parameter. The design cost of the coreless stator axial magnetic flux permanent magnet motor is reduced, the design period is shortened, and the design efficiency and the design precision are improved.

The invention relates to an aquatic environment simulation monitoring method and device for evaluating the influence of pollutants, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring environmental parameters and dividing the range of a fine model based on the environmental parameters; constructing a simplified model based on the environmental parameters in the non-fine model range; constructing a fine model based on the environmental parameters in the fine model range; and outputting and displaying a corresponding simulation result based on the simplified model and the fine model. By the adoption of the method, through multi-scale coupling of the simplified model and the fine model, it can be guaranteed that the diffusion trend of pollutants can be rapidly simulated in a large range, detailed analysis can be conducted in the local key area through the high-precision model, and therefore the overall simulation efficiency and precision are improved.

The invention belongs to the technical field of artificial intelligence, and particularly relates to a spatial-temporal feature attention mechanism-based population thermodynamic data prediction method, which comprises the following steps of: acquiring a target area map, and performing node division on the target area map to generate a target area node network; obtaining population data information of each node in the target area node network; calculating the population data information of each node by using a neural network to obtain time sequence features and spatial features of each node; generating a time feature vector and a space feature vector of each node according to the time sequence features and the space features; and generating predicted population thermodynamic data information according to the time feature vector and the space feature vector, thereby solving the problem that a traditional model is insufficient in spatial and temporal feature capture capability, solving the problem of spatial and temporal feature calculation redundancy in a deep learning algorithm, reducing model parameters by introducing spatial and temporal feature attention, and improving the prediction accuracy of the population thermodynamic data information. And the calculation efficiency of large-range and big-data population thermodynamic data can be well improved.

The invention provides a method for constructing a lightweight remote sensing target instance segmentation model based on state space model knowledge distillation. The method comprises the following steps of: 1, reading data by a computer; the method comprises the following steps of: reading an unlabeled SA-1B data set and labeled remote sensing image data by using a computer, and respectively preprocessing the image data; 2, constructing a knowledge distillation network based on the state space model and a lightweight remote sensing target instance segmentation network; and step 3, training the neural network. After a network is constructed by using a Pytorch deep learning framework, a backbone model is trained by using unlabeled data, and then the complete network is trained by using labeled remote sensing data. And step 4, remote sensing image instance segmentation. And high-quality remote sensing image instance segmentation can be realized on a test set or an actual image by using the network model trained in the step 3. Compared with a traditional method, model parameters can be greatly reduced, model reasoning time is saved, and the method is more suitable for various scenes needing real-time application or quick response.

The embodiment of the invention provides a method for reducing model output illusion based on knowledge retrieval. The method comprises the following steps: constructing a knowledge base for storing knowledge entries; receiving a question text input by a user, converting the question text into vector representation, performing matching retrieval on the vector representation in the knowledge base, and retrieving and outputting candidate knowledge entries; obtaining an output answer text of the question text generated by the large model, comparing the candidate knowledge entries with the output answer text, and judging whether the candidate knowledge entries violate the output answer text or not; and performing illusion detection on the output answer text, correcting the output answer text according to an illusion detection result, and outputting an answer corresponding to the question input by the user. According to the method, real-time or near-real-time verification and intervention can be carried out on model output, and illusion is inhibited from the source or the early stage.

The positioning error of a feeding system obviously influences the machining precision, especially under the high-speed and high-load working condition. An existing positioning error compensation system often faces the problems of high calculation cost and complex sensor configuration and is difficult to be widely applied to the industry. Therefore, a simplified model needs to be established and an economical and efficient sensor integration and real-time compensation scheme needs to be developed urgently. Therefore, the invention discloses a dynamic positioning error compensation system of a numerical control machine tool feed driving system, and provides an error suppression system fusing a mechanism driving positioning error prediction model and a real-time compensation strategy. The positioning error prediction model is constructed as a hyperbolic cosine function of temperature and nut position, and the residual error-based dynamic compensation model can effectively process unmodeled errors and system dynamic changes. The dynamic positioning error compensation system shows excellent adaptability and precision under the dynamic working condition, and an extensible practical solution is provided for positioning error suppression in the high-precision manufacturing fields of aerospace, automobiles, electronics and the like.

The invention discloses an efficient Transform model reasoning accelerator deployment method based on Versaal ACAP, and belongs to the field of software and hardware collaborative acceleration numerical calculation. Firstly, load analysis is carried out on the model, and meanwhile, according to hardware performance, the types and scales of operators needing to be borne by hardware of all parts are determined; then, hardware is equipped with a data transmitter, a data receiver, a nonlinear operator and other modules; then, the modules are reasonably deployed and connected in hardware, pipeline parallelization is completed, and calculation of a multi-head attention stage and a feedforward neural network stage is achieved; and finally, integrating the two stages to realize accelerated calculation of the whole encoder / decoder layer so as to accelerate the reasoning speed of the upper layer model. Experiments prove that the throughout of the model is effectively improved, the reasoning delay is reduced, meanwhile, the accelerator has huge advantages in the aspect of batch reasoning, and the accelerator has wide application prospects in the aspects of reducing the model reasoning cost and improving the model reasoning speed.

The invention belongs to the technical field of building energy conservation and carbon reduction, discloses a building energy conservation and carbon reduction method, device and equipment and a storage medium, and constructs a high-precision building BIM model. A digital twinborn model of a Xian mansion is constructed by applying a BIM technology, and the model not only depicts the physical structure of the building in detail, but also simulates key operation details such as personnel flow and equipment operation. By strategically simplifying the model, it is ensured that the model is efficient and accurate. The comparative analysis of the corrected model and actually measured data shows that the prediction error of the model is only 0.10%, the extremely high consistency of the prediction result and the actual situation is highlighted, the high accuracy of model correction is verified, and the high standard requirement of in-depth research is met; according to the invention, balance optimization of carbon emission minimization and comfort maximization is realized. By intelligently adjusting the operation strategy of the air conditioning equipment, the method obviously reduces the carbon emission of the building while ensuring the living comfort.

The invention relates to the technical field of edge computing, distributed artificial intelligence and multi-agent collaboration, in particular to a cross-device multi-agent edge collaborative reasoning system and method, and the system comprises a cloud coordination module which is responsible for maintaining main model parameters, grading based on importance scores, and receiving edge device data to optimize a model; the edge management module communicates with the cloud, receives parameter grading data, allocates parameter subsets according to equipment resources, integrates inference experience and returns the inference experience to the cloud; the equipment execution module receives the parameter subset, executes lightweight reasoning, records experience and sends a cooperation request; the distributed knowledge distillation module realizes multi-level heterogeneous distillation, differential parameter selection and adaptive parameter compression; the collaborative reasoning module constructs a dynamic collaborative topology, allocates subtasks and integrates multi-device results; according to the system, the edge resource demand is obviously reduced, the model volume is reduced by 75-95%, 91% reasoning precision is kept, and efficient collaborative reasoning is realized.

The invention discloses a multi-hardware mixed large model training method and system and a related device, which are used for simplifying a model structure and improving model performance. The method comprises the following steps: firstly, collecting multi-modal data, constructing a multi-modal model architecture, and carrying out benchmark test on a multi-modal large model to obtain first performance indexes of multiple pieces of hardware; thirdly, decomposing the received calculation task into a plurality of sub-tasks, dividing the multi-modal large model into a plurality of sub-models, and matching the sub-models and the sub-tasks to proper hardware for training according to a first performance index; and then pruning operation is carried out on the information transmission link according to the calculated comprehensive weight value. The second performance index is evaluated, a performance evaluation report is obtained, a dynamic adjustment strategy is applied to pruning operation, subsequent pruning operation is executed, and after the pruning operation is executed, reinitialization and local fine adjustment are conducted on the sub-model. And finally, comprehensively evaluating the multi-modal large model by using the test data set.

The invention discloses a transmission tower vulnerability analysis method and system based on seismic simulation, and belongs to the technical field of electric power facility disaster prevention, and the method comprises the steps: correcting bedrock seismic oscillation parameters based on equivalent shear wave velocity, and generating a high-precision earth surface PGA distribution diagram; automatically positioning a key node layer through eigenvalue decomposition, reducing the order of the three-dimensional model of the power transmission tower into a two-dimensional series multi-degree-of-freedom model, and outputting a layer mass vector, a condensation stiffness matrix and rod piece bending stiffness; performing time-history analysis by using the corrected PGA-driven reduced-order model to generate an earthquake damage probability; the multi-source remote sensing data and the PGA are fused, the landslide space probability is output through a deep learning model, and the impact strength and the landslide kinetic energy are calculated to judge the landslide damage probability; and based on the dual-threshold condition triggering probability union set, calculating a comprehensive damage probability, and generating a three-dimensional vulnerable curved surface. According to the method, the problems of high missing report rate and low calculation efficiency of single disaster assessment are solved, and minute-level accurate early warning of the composite disaster risk of the power transmission tower is realized.

The invention discloses a sudden drought identification method and system based on a space-time double-branch fusion model. The method comprises the steps that meteorological data are acquired and preprocessed; calculating a composite sudden drought index according to the obtained data; performing data dimension reduction on the obtained data through singular value decomposition (SVD); a deep learning model is adopted to construct a time branch model, a graph attention network GAT is adopted to construct a space branch model, and the time branch model and the space branch model are dynamically fused through a cross attention mechanism to construct a space-time double-branch fusion model; according to the method, data dimensions are compressed and model complexity is reduced by fusing multi-source variables and combining singular value decomposition (SVD), meanwhile, geographic neighborhood weights are dynamically learned by adopting Transforme and based on a graph attention network (GAT), dynamic fusion of spatial-temporal characteristics is finally realized through a cross attention mechanism, a sudden drought recognition result is generated, and the method has the advantages of being high in robustness, high in accuracy and high in reliability. The limitation of a traditional method on nonlinear feature capture, space-time modeling splitting and generalization ability is broken through.

The invention is suitable for the technical field of computer vision and deep learning, and provides a multi-scale feature fusion concrete defect detection method based on improved SAM, and the method comprises the steps: obtaining a concrete defect image data set; preprocessing the image data set and dividing the image data set into a training set, a verification set and a test set; the YOLOv9 is trained to automatically detect concrete defects; constructing an improved SAM model, replacing a prompt encoder of the SAM with YOLOv9 and edge detection, and modifying a mask encoder of the SAM; the training effect of the model is evaluated through the four indexes of the accuracy rate, the recall rate, the F1 value and the intersection-to-union ratio, and the improved SAM model is optimized according to the training effect. By improving the structure of the SAM, the detection capability of the model on small defects can be enhanced, and the detection accuracy and efficiency are improved. In addition, according to the method, the complexity of the model can be reduced, and the training efficiency and generalization ability of the model are improved.

The invention discloses a high-precision electrical calibration method and system for a wedge flowmeter. The method comprises the following steps: step 1, data acquisition and preprocessing; step 2, dynamic working condition identification; 3, updating the self-adaptive model; 4, multi-source data fusion calibration is carried out; and 5, performing closed-loop verification and compensation. According to the invention, multiple types of signals are collected in real time through the multi-dimensional sensor array and de-noised to construct a data set, so that comprehensive and accurate data collection is realized, and a good basis is provided for calibration; carrying out dynamic working condition identification by utilizing a support vector machine, triggering model updating, and endowing calibration with dynamic self-adaptive capability; model parameters are updated by adopting a recursive least square method, compensation items are established, and the characterization capability of the model is enhanced; a calibration coefficient is generated by means of a data fusion algorithm, and the calibration precision is improved; through verification and compensation of a digital twinborn model, traditional defects are effectively overcome, model transplantation errors are reduced, the measurement precision of the wedge flowmeter is improved under extreme working conditions, and production stability and product quality are guaranteed.

The invention relates to a plug-in hybrid electric vehicle hierarchical energy management method based on historical data and real-time speed prediction, and belongs to the technical field of new energy automobiles, and the method comprises the following steps: S1, obtaining and preprocessing historical commuting data of a single vehicle, establishing a historical commuting data set, and predicting a future global driving condition; s2, constructing a vehicle part simplified model, and solving an optimal reference SOC track by using a dynamic programming algorithm; s3, constructing a real-time speed predictor by using a neural network to realize real-time speed prediction; and S4, optimizing a real-time speed prediction result in combination with the optimal reference SOC trajectory, and realizing energy management by using a PMP-MPC algorithm.

The invention discloses a model optimization system and method based on deep learning, the system comprises a model square module, a my model module, a model fine tuning module and a model optimization module, and the method comprises the following steps: S1, selecting an initial model; s2, applying an optimization algorithm; s21, a pruning algorithm is applied, and weights or neurons to be removed are determined; s22, a quantification algorithm is applied, and weight and activation are converted into low-precision representation from floating-point numbers; s23, knowledge distillation: selecting a teacher model, defining a student model, and training the student model by using the output of the teacher model as a guidance signal; and S3, selectively performing fine adjustment. Based on a leading edge deep learning method, the open source technology is fully utilized for improvement, and the target of low-cost deployment is achieved under the condition that performance is not lost basically; the model size is reduced, storage is saved, the method is particularly important for mobile equipment or an embedded system, and meanwhile the transmission speed and the deployment speed can be increased.

The invention discloses a BIM (Building Information Modeling) and WebGIS (Web Geographic Information System) technology-based railway line three-dimensional modeling and automatic system, which relates to the technical field of railway line planning systems, and is technically characterized in that the system generates a three-dimensional center line of a railway line through GIS data, and automatically creates a BIM model of the railway line in combination with RLIMI equipment management data; the system comprises a data input module, a three-dimensional line center line calculation module, a BIM model generation module, a multi-source data fusion module and a WebGIS platform. According to the method, the coordinate conversion problem of multi-source data is solved through a unified projection coordinate system, and seamless fusion of GIS, BIM and RLIMI data is realized; the system can display a three-dimensional railway line model on a WebGIS platform, and provides equipment query, spatial analysis and fault prediction functions. According to the method, the BIM modeling efficiency of the railway line is greatly improved, the modeling cost is reduced, real-time data updating and intelligent operation and maintenance management are supported, and the method is suitable for daily management, maintenance and planning of the railway line.

The invention discloses a machine learning model compression method and system based on federated learning and mean iteration, and belongs to the field of deep learning optimization, and the compression method comprises the following specific steps: I, a server initializes a group of basic models, and distributes the models to each client node; iI, updating model parameters by each client node by using local data, and aggregating the client parameters into a global model by the server side; according to the method, the network bandwidth burden and the risk of data privacy leakage can be reduced, effective fusion of training results of different clients can be promoted, the training quality of a global model can be improved, the overall performance and expandability of a system can be remarkably improved, meanwhile, model redundancy is reduced, and the calculation efficiency is improved; the method can further reduce the storage and calculation overhead, improve the compression efficiency, effectively maintain or even improve the precision of the compressed model, ensure the effectiveness of the model at different clients, reduce the communication overhead and calculation burden, and improve the overall efficiency of the system.

The invention requests to protect an unmanned aerial vehicle light-weight small target detection method based on a YOLO-GLL neural network, and the method comprises the following main steps: S1, constructing a YOLO-GLL target detection model based on YOLOv8s, and inputting a training set image to complete model training; s2, extracting different scale feature maps of the image through a backbone network containing a partially grouped multi-scale convolution module; s3, performing cross-level fusion on the feature map by using the lightweight multi-scale fusion feature pyramid neck network; s4, executing target classification and bounding box regression by adopting a lightweight shared detail enhancement detection head to obtain a lightweight high-precision model; and S5, inputting a to-be-detected unmanned aerial vehicle image, and realizing small target detection and identification in combination with the Soft-NMS and Shape-IoU post-processing strategies. The method has the advantages that (1) the detection precision is improved; (2) the parameter quantity of the model is reduced, and the deployment of resource-constrained equipment is adapted; and (3) a post-processing strategy is improved, the problem of missing detection of small targets in a dense shielding scene is relieved, and the robustness of a complex scene is enhanced.

The invention discloses a federated learning communication method, system and device, and the method comprises the steps: receiving a quantization model parameter and an inverse quantizer sent by each first client participating in the current round of federated aggregation in any round of federated aggregation of transverse federated learning; adopting an inverse quantizer corresponding to the target client to inversely quantize the quantization model parameter corresponding to the target client to obtain a reduced model parameter; and after the restoration model parameters corresponding to the first clients are obtained, aggregating the restoration model parameters of the first clients to obtain aggregated model parameters, and feeding back the aggregated model parameters to the first clients. Therefore, the federal learning communication efficiency is improved.

The invention discloses a retrieval method and device based on a hybrid vector model and a readable storage medium, and relates to the field of artificial intelligence, and the method comprises the steps: firstly responding to a natural language query, and processing to generate a query vector; recalling candidate results from the index library based on the query vector, and screening to obtain a refined related information set; based on the refined information set, generating and executing an SQL, obtaining result data and generating a natural language answer; and outputting answers and result basis descriptions. According to the method, the retrieval accuracy is improved through mixed vector recall, the model reasoning complexity is reduced through multi-stage processing, the robustness is guaranteed in combination with a reflection mechanism, interpretable output meets the compliance requirement, the problems that retrieval is inaccurate, generation is unreliable and a result cannot be interpreted are effectively solved, and efficient automation of complex Text2SQL query is achieved.

The invention relates to the technical field of concrete structure damage detection, in particular to a post-fire RC structure burst damage three-dimensional segmentation method, which comprises the following steps: firstly, determining the type and three-dimensional characteristics of post-fire RC structure member surface concrete burst damage, and constructing a corresponding three-dimensional point cloud data set for network training and verification; and then based on a KP-FCNN network structure, a KPConv layer is improved and optimized, so that the detection and segmentation precision is improved, the model size is reduced, the reasoning time is remarkably shortened, the optimal segmentation precision of 82.3% is achieved under the working conditions of different damage degrees, automatic damage segmentation of the burst damage of the concrete structure after the fire disaster is realized, and the automatic damage segmentation of the burst damage of the concrete structure after the fire disaster is realized. And technical support is provided for subsequent damage three-dimensional quantification and deployment to an unmanned aerial vehicle system.

The invention relates to the technical field of die design, in particular to a method for acquiring a die surface deformation compensation value of a stamping die. The method comprises the specific steps that S1, a press table top deformation measuring system is adopted, and the deformation amount of a press under a certain load is collected; s2, establishing a simulation model of a measurement system by adopting Hypermesh software, and obtaining a simplified substitution model of a press table board and a sliding block by combining the collected experimental data through size optimization; s3, cleaning and grid division are conducted on the geometric structure of the mold, and accessory structures which do not affect the strength are removed; s4, combining the simplified model with the die grid model, adding load and constraint of a press machine, and performing stamping simulation; and S5, generating a die surface deformation contour map according to a simulation result, and obtaining a die surface deformation compensation value. Compared with the prior art, by combining a high-precision measurement technology and numerical simulation analysis, technical support can be provided for design and manufacturing of the stamping die, and progress and development of the die industry are promoted.

The invention discloses a QEM algorithm optimization method based on sharp feature degree and area weighting, and belongs to the technical field of image processing, and the method comprises the steps: S1, obtaining a to-be-simplified model, carrying out the simplification through a QEM algorithm, replacing the original folding cost with the optimization folding cost during the simplification, and obtaining a first simplified model; and S2, optimizing each vertex in the first simplified model based on a Laplacian operator to obtain an optimal simplified model. According to the method, on the basis of a traditional QEM algorithm, a new constraint factor is introduced to improve folding cost and generate optimized folding cost, model folding can be considered from more aspects, especially on geometric detail feature processing, model vertex optimization is additionally introduced in model rendering except for operation simplification, and therefore the model folding efficiency is improved. Low-quality long and narrow triangles can be eliminated to the greatest extent, so that the surface of the geometric model is smoother after rendering, and the overall quality of the model is improved.

The invention provides a shale oil fracturing effect evaluation method and device, a terminal and a storage medium, and belongs to the technical field of petroleum engineering. According to the method provided by the embodiment of the invention, the fuzzy comprehensive evaluation method is utilized to perform fuzzy processing on the basic parameters such as the target reservoir parameters and the target engineering parameters, so that the values of the basic parameters are more representative and are not certain values, and therefore, an evaluation result close to the reality can be obtained, and the accuracy of a calculation result is improved; and optimizing the shale oil fracturing effect evaluation method. Moreover, by fitting the function model of the main control factors and the yield and utilizing the function model to evaluate the fracturing effect, the model construction process is simplified, and the evaluation efficiency is higher.