48results about How to "Balance accuracy" patented technology

The invention discloses a scene perception information fusion decision-making method for industrial field multi-source heterogeneous data, which comprises an edge layer, a cloud layer and an interaction layer which form a three-level collaborative architecture, and belongs to the technical field of industrial field data processing. The method comprises the specific steps that S1, an edge layer collects industrial field multi-source heterogeneous original data and carries out self-adaptive preprocessing to obtain cleaned multi-source feature data; s2, the edge layer processes the multi-source feature data through a lightweight scene perception model, and outputs a preliminary scene judgment result; s3, the cloud layer constructs and updates a causal relationship graph based on industrial business knowledge and incremental data of the edge layer, processes data uploaded by the edge layer through a causal reasoning fusion model, outputs a decision result and issues the decision result through the interaction layer; and S4, the cloud layer optimizes parameters of the causal reasoning fusion model and the lightweight scene perception model based on a decision execution result fed back by the edge layer, and issues the optimized parameters to the edge layer through the interaction layer to realize dynamic iteration.

The invention discloses a global neural drawing method and system oriented to hybrid representation optimization, and belongs to the technical field of computer graphics, and the method comprises the steps: dividing a screen into a plurality of tiles, and obtaining a first workload index on each tile in a first drawing stage and a second workload index on each tile in a second drawing stage; generating a cross-tile and cross-stage unified scheduling queue based on the first workload index and the second workload index, and executing the drawing tasks on different tiles in parallel according to the unified scheduling queue; and triggering a subsequent feature fusion and neural global illumination drawing stage when the inter-stage dependency condition is met so as to generate an output image with a global illumination effect. According to the method, the end-to-end frame rate oriented to the modern AI accelerator / AI-GPU can be remarkably improved and the tail delay can be reduced on the premise of ensuring the visual quality and the time sequence stability, and the method is suitable for application scenes such as real-time global illumination, large-scale complex scene drawing and interactive neural rendering.

The application discloses a kind of federal positioning method based on individualized patch and dynamic fusion, belong to indoor positioning and federal learning technical field.When applying federal learning to indoor Wi-Fi fingerprint positioning of Internet of Things, global model is often difficult to capture local data feature preference, local training cost is high and poor adaptability alone, the present application is by setting individualized patch and dynamic parameter fusion mechanism, reduce the influence of above-mentioned problem on positioning performance.The algorithm configures private position decoder as individualized patch for each local client, extracts local data features in training and uploads to server, server calculates the similarity of data distribution between clients to generate combination weight, for local clients with similar data distribution, individualized patch can selectively linearly combine with the decoder of the local model with higher relevance, avoid local model overfitting, improve the positioning performance of global model in local scene.

The present application relates to the field of artificial intelligence technology and early warning analysis, in particular to an engineering machinery identification and safety risk early warning method based on continuous learning, comprising: collecting optical cable line video, environmental parameters and positioning data for frame synchronization alignment to build an initial identification model; the edge side preliminarily screens suspected targets and uploads to the cloud; the cloud accurately identifies the mechanical category, working state and confidence, combines the positioning data to build multi-modal risk judgment features; based on compliance verification and distance determination to determine the risk level, generate graded early warning and work order; based on operation and maintenance feedback to screen difficult samples, update the model through incremental training and synchronize to the edge side, realize the continuous iteration of the identification model, balance real-time and accuracy, reduce the false positive rate, and improve the automation level of optical cable inspection.

The application relates to the field of 5G communication technology, and discloses a 5G network slice security isolation method and system.The 5G physical network is divided into multiple independent slices, each slice contains an access node, a core node and an output node, the access node is used for security authentication, the terminal matching index is dynamically calculated based on terminal state parameters and authentication matching times; the core node is used for a security management module, the flow security index is obtained based on the weighted comparison of the current flow and a reference value and historical flow; the output node is used for security evaluation, the output security index is obtained by the weighted comparison of the current communication times and a reference value and historical times; the access, flow and output indexes are compared with corresponding threshold values; if any one is abnormal, the current slice is directly isolated; the deviation of each security index and the threshold value is weighted calculated and compared with the threshold value; if greater than or equal to the corresponding threshold value, the current slice is isolated, multi-dimensional security isolation of the 5G network slice is realized, and the balance between precision and calculation efficiency is achieved.

The invention discloses a lithium ion battery health state evaluation method and equipment. The method comprises the following steps: presetting a cell attenuation rate matrix in a battery management system, wherein the matrix defines single-circle capacity attenuation rates under different temperature and discharge depth combinations; acquiring the duration time and the corresponding discharge depth of the target battery in different temperature intervals in the complete time period; calculating cumulative capacity attenuation according to actual operation data and the matrix; and determining the current SOH according to the initial capacity sum. According to the method, simulation and actual measurement are combined to construct a high-precision matrix, a dynamic look-up table accumulation algorithm is utilized, high-precision and low-cost online evaluation of the SOH of the battery under complex variable working conditions is achieved, and the problems that a traditional method is insufficient in precision and high in calculation complexity in a dynamic scene are effectively solved.

The invention relates to the technical field of cultural relic identification and visual large model fusion, in particular to a ceramic cultural relic full-dimensional identification system and method based on multi-modal visual large model and microscopic feature fusion. After high-quality applicable data are screened, other data are calibrated with the optimal mode as the reference, cross-mode deviation is eliminated, and a data foundation is built firmly; the core features are converted into topological nodes, association strength is calculated, feature association edges with guidance are established, a traceable and explainable identification guidance link is generated, and the strong ceramic identification logic requirement is met; the optimal link is screened to be combined with the pre-training model, and the identification precision and the careful property are balanced by depending on a confidence hierarchical judgment mechanism, so that the black box pain point of the traditional model is effectively solved, and the credibility of the identification result is improved.

This application discloses a method, apparatus, storage medium, and electronic device for updating the mesh scale, relating to the field of smart home technology. The method includes: upon obtaining the contour cross-sectional data of the object to be modeled, processing the curve path corresponding to the contour cross-sectional data using preset modeling control parameters and a first formula to obtain multiple sampling points; calculating the tangent direction of each sampling point on the path, and determining all two-dimensional vertices constituting the preset contour cross-sectional shape based on the contour cross-sectional data; projecting each two-dimensional vertex along the tangent direction of the corresponding sampling point to obtain a three-dimensional contour surface consistent with the position and direction of the sampling point; adjusting the mesh subdivision density of the three-dimensional contour surface, and updating the mesh scale of the object to be modeled during three-dimensional modeling based on the adjusted mesh subdivision density. This method solves the problems of insufficient accuracy and low efficiency in traditional modeling methods when processing complex curve paths.

This invention discloses an AI-based physical education exam scoring method, belonging to the field of exam scoring methods, including the following steps: (1) activating the cameras deployed in the exam room to collect motion images and facial expression images of the examinee during pull-ups; (2) analyzing the collected facial expression images using AI image recognition technology to divide the exam process into three stages; (3) for each stage, analyzing the examinee's motion images using AI technology to determine whether each pull-up motion is a qualified motion; (4) counting the number of qualified motions in each stage and scoring them separately, adding the scores of the three stages to obtain the examinee's final exam score. This invention can divide the exam into three stages based on facial expression recognition, perform differentiated analysis on the motion characteristics of each stage, better fit the examinee's physical fitness change patterns, and make the evaluation results more targeted.

The application discloses a kind of based on image data's track area abnormal detection method and device of machine tool, it is related to image recognition technical field.The method includes: using image recognition algorithm, introduce multi-objective optimization algorithm, build machine tool target detection model, and connect to candidate region segmentation engine and space-time correlation filter engine;Video stream of track area is collected, and pretreatment is carried out, and the image data of continuous frame is obtained;The image data of continuous frame is input to candidate region segmentation engine, and the ROI region of each frame of image data is obtained;ROI region is input to machine tool target detection model, and the detection frame of machine tool target is obtained;The detection frame of machine tool target is input to space-time correlation filter engine, identifies stationary effective target, and carries out abnormal detection to stationary effective target.The application solves the problems of low detection accuracy, difficult model deployment and poor anti-interference ability in the prior art.

The application discloses a Chinese human phenotype ontology recognition method and related equipment, which can be applied to the technical field of text information processing. The application can effectively improve the recall rate by expanding the human phenotype ontology concept synonym set through a large language model, generating an expanded dictionary with the initial dictionary, significantly enhancing the discrimination ability of the recognition model for semantically ambiguous concepts through two-stage comparative learning, balancing the precision and recall rate by constructing an initial dictionary based on the human phenotype ontology official database and the Chinese human phenotype ontology official data, merging and disambiguating the current input text after accurate matching and semantic similarity matching to obtain a fused candidate result list, and finally obtaining the structured recognition result corresponding to each current input text by post-processing each candidate fragment in the fused candidate result list, which can provide effective data support for disease diagnosis.

This application relates to the field of data verification technology, specifically to a data verification method and apparatus for a chip programming system. The method includes: for a multi-channel parallel chip programming system, statistically analyzing the clock cycles of each channel within each time window; determining the clock phase offset of each channel; evaluating the deviation between each channel and a second reference channel in the current clock cycle and the deviation of the clock phase offset, obtaining the timing deviation of each channel; and dynamically determining the verification delay time of each channel using the timing deviation and clock cycle, generating the verification trigger time for each channel for data verification. This balances verification accuracy and overall efficiency.

The invention discloses an infant paper diaper defect detection method. The method comprises the following steps: firstly, collecting baby diaper pictures and constructing a training data set; thirdly, constructing an infant paper diaper surface defect detection model, and training the infant paper diaper surface defect detection model by utilizing the training data set to obtain a trained infant paper diaper surface defect detection model; the infant paper diaper surface defect detection model is obtained by improving a yo11n model. And finally, inputting a to-be-detected baby paper diaper picture into the trained baby paper diaper surface defect detection model, and outputting a defect detection result by the model. According to the infant paper diaper surface defect detection model provided by the invention, on the premise that the model parameter quantity and the calculation quantity are remarkably reduced, the average precision can still be kept at the same level as that of an existing reference model, and good balance of precision and efficiency is realized.

The invention discloses a wireless federated learning energy consumption optimization method and a medium, and belongs to the technical field of wireless communication, and the method comprises the steps: building a bidirectional model compression frame based on a wireless federated learning system model, and building a joint optimization problem with the minimization of the total energy consumption of the system as a target and a model convergence condition as a constraint; solving the joint optimization problem by adopting an alternating iterative optimization algorithm, and solving a sub-problem of optimal bandwidth allocation and a sub-problem of optimal bidirectional sparseness parameters; and executing compressed global model issuing and local model uploading between the central server and the client according to the solved optimal bandwidth allocation and the optimal bidirectional sparseness parameter. According to the method, the minimization of the total energy consumption of the system is realized on the premise of ensuring the model precision by constructing the bidirectional model compression framework and the bandwidth joint optimization, the method is particularly suitable for large-scale deployment, the communication overhead can be reduced, the model performance of federal learning can be maintained, and the collaborative optimization of the energy efficiency and the precision can be realized.

The application provides a method and system for generating forward simulation data, the method comprising: constructing a three-dimensional voxel model corresponding to a simulation scene, dividing a binning region according to the numerical characteristics of electromagnetic parameters corresponding to each voxel contained in the three-dimensional voxel model, and the binning region describing the value range of the numerical characteristics of the electromagnetic parameters; mapping and converting the electromagnetic parameters with different numerical characteristics corresponding to multiple voxels in one binning region into electromagnetic parameters with the same numerical characteristics; determining a target connected domain in the three-dimensional voxel model, wherein the voxels contained in the target connected domain correspond to electromagnetic parameters with the same numerical characteristics and the voxels are connected in space in the three-dimensional voxel model; selecting a local region of interest from the three-dimensional voxel model, generating geometric commands corresponding to the target connected domain included in the local region of interest; and executing the geometric commands to generate forward simulation data. The technical problem of excessive number of geometric commands and low efficiency of simulation data generation is solved.

This invention discloses a heuristic dynamic memory weighting and recall reordering method, comprising six steps: initial impression value calculation, Ebbinghaus forgetting curve modeling, recall depth parameterization, multi-dimensional independent sorting, multi-dimensional result fusion, and recall-triggered impression value inversely proportional increment. This invention simulates the natural decay of memory through an 8-stage Ebbinghaus forgetting curve, achieves a recall-as-reinforcement effect through an inverse impression value increment mechanism, comprehensively considers semantic similarity, temporal freshness, and memory importance through multi-dimensional reordering, and flexibly adjusts the breadth and depth of recall through recall depth parameterization. This invention provides two forgetting curve implementation methods: discrete-stage lookup table method and continuous curve calculation method (including exponential decay type and power-law decay type), suitable for different application scenarios. This invention significantly improves the intelligence level and anthropomorphism of AI long-term memory systems, enabling AI to perform natural memory recall like humans.

The invention relates to the technical field of airfoil profile optimization design, and particularly discloses an airfoil profile geometric parameterization method and program with self-adaptive orders, equipment and a storage medium. The method aims to solve the problem that precision and efficiency are difficult to balance due to the fact that the order of a Bernstein polynomial depends on manual presetting in an existing CST parameterization technology. According to the core scheme, the method comprises the steps of obtaining airfoil profile coordinate data and performing normalization processing; determining a class function according to the airfoil profile type; automatically searching and determining the optimal order of the Bernstein polynomial by combining a genetic optimization algorithm and taking the relative change rate of the fitting error as a judgment criterion; and constructing a parameterized model based on the optimal order and outputting geometric parameters. The method has the beneficial effects that the self-adaptive determination of the order is realized, overfitting and calculation redundancy are effectively avoided, the design efficiency is improved while the precision is ensured, and the method has good universality and is suitable for parameterization design of various airfoils.

The embodiment of the invention discloses a voice data processing method and device. According to the method, voice activity detection is adopted at the equipment terminal to screen out the first audio data stream only containing effective voice segments, and the invalid audio transmission quantity is reduced. According to the method, the problem that the voice frames processed by an equipment terminal are possibly discontinuous due to limited computing power of the equipment terminal is solved, and in order to solve the problem, the method adopts a mode of calculating the mute duration at a server side to determine the truncation point between effective voice segments, so that the completeness of sentences in audio data is ensured, and the processing precision of subsequent services is improved. Through the method, the contradiction between the transmission bandwidth and the processing precision can be effectively balanced. The voice data processing device provided by the embodiment of the invention also has the above beneficial effects.

The application provides a visual-based wind blade damage defect detection method and system, and relates to the field of image recognition.The method comprises the following steps: constructing a damage correlation knowledge graph based on the working environment characteristics and damage defects of a plurality of sample wind blades; constructing and training an improved YOLOv11 detection network, wherein the improved YOLOv11 detection network comprises an adaptive input unit, and the adaptive input unit is used for adjusting the resolution of an image based on the image features of the image and the damage correlation knowledge graph; constructing damage detection paths corresponding to various working environment types based on the damage correlation knowledge graph and the improved YOLOv11 detection network; determining a damage detection path based on the working environment characteristics of a wind blade to be detected and the damage detection paths corresponding to various working environment types; and performing damage defect detection on the wind blade to be detected based on the damage detection path and the improved YOLOv11 detection network, thereby achieving the advantage of automatic detection of wind blade damage.

An aircraft equipment cabin visual field blind area augmented reality auxiliary assembly method comprises a depth camera self-positioning based on point cloud registration, an assembly object pose estimation based on deep learning, an assembly object pose estimation based on an edge region, a pose estimation method adaptive switching and an auxiliary assembly guide visualization; the adaptive switching method is used to fuse the pose estimation method based on deep learning and the pose estimation method of traditional image processing, so that the robustness, real-time performance and accuracy of the pose estimation are balanced; meanwhile, based on the pose estimation result, the visual field blind area of the assembly process can be visualized by using the augmented reality technology, and the assembly result is detected, which is beneficial to the user to quickly locate the assembly object and complete the assembly task, so as to improve the consistency, real-time performance and immersion of the aircraft assembly operation guide, and further improve the artificial assembly efficiency and quality.

This invention belongs to the field of data retrieval and discloses an efficient spatial dataset query method based on density grayscale images. It includes two stages: a data processing stage, which processes a given spatial database, constructs a global rectangular region, divides it into grids, and generates a density grayscale image for each spatial dataset based on the density distribution information of location points within the corresponding grid; and a query execution stage, which generates a sample grayscale image based on a given query sample dataset, compares the sample grayscale image with the grayscale images of each dataset, identifies overlapping candidate datasets, calculates the similarity between the candidate datasets and the query sample datasets based on the generated candidate dataset grayscale images and the sample grayscale images, and selects the k datasets with the highest similarity as the query results. This invention can effectively reduce the computational load and complexity of queries, providing more accurate and comprehensive query results, thereby balancing query accuracy and efficiency.

The application discloses a gas turbine air system analysis method and system, and relates to the technical field of gas turbine air system simulation. The method comprises the following steps: constructing a three-dimensional CFD benchmark model of a non-standard element of an air system; establishing a parameterized CFD analysis process, generating a parameter combination matrix covering the working range of the non-standard element; performing a multi-working condition three-dimensional CFD steady-state calculation to generate a data set; constructing and verifying a reduced-order model of the non-standard element based on the data set until the prediction accuracy of the reduced-order model meets preset engineering requirements; encapsulating the verified reduced-order model as a functional model unit; and replacing the original element with the functional model unit in a one-dimensional fluid network modeling environment, and calling the functional model unit to perform system-level simulation iteration. The application realizes seamless integration with the one-dimensional fluid network environment, decouples high-precision three-dimensional modeling from system-level iteration solving, significantly reduces the calculation cost, avoids the convergence risk caused by the three-dimensional CFD embedded iteration cycle, and realizes efficient and stable analysis of the overall performance of the air system.

The application provides a construction worker unsafe behavior intelligent identification method and early warning system, the method comprises the following steps: obtaining a to-be-detected image of a construction site; inputting the to-be-detected image into a construction worker unsafe behavior identification model to obtain an unsafe behavior identification result of a construction worker; wherein the unsafe behavior at least includes an unworn state, a wrong wearing state or a blocked state of personal protective equipment; and the construction worker unsafe behavior identification model is a neural network model for target detection. The detection accuracy is significantly improved, and the fine-grained identification capability is enhanced. Since a learnable position coding component is introduced into the backbone network, the model can explicitly model the spatial position relationship of the feature map, enhance the spatial position perception capability of the personnel (especially small targets and blocked targets) in the construction scene under complex background interference, solve the problem that the traditional convolutional network is not sensitive to absolute position information, and improve the feature extraction accuracy in a complex background.

The utility model provides an automatic alignment laminating machine, which relates to the technical field of laminating machines and comprises a working plate, square holes are symmetrically formed in the top of the working plate, two first L-shaped moving plates are arranged in the square holes in a sliding manner, first linear motors are mounted in the first L-shaped moving plates, and the first linear motors are arranged in the first L-shaped moving plates. And one end of the first linear motor is fixedly connected with the inner wall of the square hole, square plates are symmetrically and fixedly connected to the top of the first L-shaped moving plate, and convex grooves are formed in the tops of the square plates. The first L-shaped moving plate is accurately driven by the first linear motor, and the second L-shaped moving plate is driven by the second linear motor, so that the automatic process of conveying components from two ends and transferring the components from stations to accurate lamination is realized. Motor driving replaces manual operation, personal errors are reduced, micron-order alignment precision is guaranteed, all links are connected in order, production efficiency is improved, precision and efficiency are balanced, and high-yield and large-scale production is assisted.

This invention discloses a method, system, and electronic device for detecting road defects in ground-penetrating radar (GPR) images, belonging to the field of computer vision and road defect detection technology. The method includes: acquiring and preprocessing GPR images; constructing a defect detection model, which includes a multi-scale vertical / horizontal strip convolution module, an adaptive gated routing module, and a bidirectional attention fusion module, used for extracting directional features, dynamically fusing multi-scale features, and adaptively fusing directional features, respectively; training and using the model for defect identification and localization. This invention reduces computational complexity through strip convolution and achieves adaptive feature fusion through gating and attention mechanisms, significantly improving the detection accuracy and efficiency of multi-scale, directional defects in GPR images.

The invention discloses a 5G network construction quality monitoring method based on big data analysis, and the method comprises the steps: carrying out the multi-modal data collection of a 5G base station process through a mobile terminal collection device, carrying out the offline image recognition based on a lightweight model, and generating a supervision data package; constructing a hierarchical supervision rule knowledge base including a specification standard layer, a historical experience layer and a manufacturer guide layer, and establishing a probability association model of construction parameter deviation and acceptance results in combination with an improved weighted time sequence association mining algorithm; a double-threshold risk assessment mechanism is adopted, when a rule threshold and a probability risk threshold are triggered at the same time, a risk level is assessed through a multi-dimensional risk scoring model, and a graded early warning report is generated; a structured rectification notice is generated for the quality problem, a rectification process time sequence verification chain is constructed based on a timestamp and a digital signature, and closed-loop management is realized through comparison between design parameters and an actual state; and establishing a decision feedback optimization ring, and continuously optimizing probability correlation model parameters and a supervision rule knowledge base.

The present application relates to the field of text processing, in particular to a text generation method based on a large model, an electronic device and a storage medium. The method first acquires an event topic input by a user or composite information containing reference text, retrieves associated text from a text library based on the event topic; when the input only contains the event topic, the associated text is directly used as the model guidance text; if the reference text is contained, the associated text and the reference text are integrated as the guidance text, and three types of feature values representing the text scale, the number of entities and the number of entity relationships are extracted; the extracted feature values are compared with the preset scale threshold, entity quantity threshold and entity relationship threshold, and the text generation mode is dynamically selected accordingly. The present application can realize intelligent regulation and control of the processing load of the large model, ensure the integrity of the generated text semantics, and avoid the efficiency decline caused by the input scale exceeding the processing capacity of the model.

The present application relates to the technical field of three-dimensional model adaptive voxelization, and particularly relates to a three-dimensional model adaptive voxelization and splitting method and system for complex structures. The method comprises the following steps: obtaining original three-dimensional model data, performing multi-dimensional feature analysis based on the original three-dimensional model data to obtain adaptive voxelization strategy data; performing multi-resolution voxelization on the original three-dimensional model data based on the adaptive voxelization strategy data to obtain initial voxel set data; performing voxel block evaluation based on the initial voxel set data to obtain to-be-split voxel block set data; and performing optimization based on the to-be-split voxel block set data to output split voxel model data. The present application improves the accuracy, efficiency and operability of three-dimensional model processing through three-dimensional model adaptive voxelization and splitting.

The invention provides a multi-modal data annotation method and device combining overlapped frame annotation and single frame annotation, and is applied to the technical field of data processing. According to the method, core source data such as a 2D image, a 3D / 4D point cloud, a target object motion state and a double-coordinate system are firstly obtained, dynamic and static attributes of a target object are recognized by means of a double-coordinate system real-time conversion algorithm in combination with a displacement threshold value, and a labeling basic data set matched in a classified mode is generated. The method comprises the following steps: firstly, constructing a multi-dimensional label association node by pressing static classification, generating a bidirectional mapping association graph through 2D-3D / 4D bidirectional projection, and mining space consistency through a multi-coordinate system verification model to obtain a label space verification feature vector; the method comprises the following steps of: selecting a multi-modal labeling model, importing the multi-modal labeling model into an optimization engine, constructing a multi-modal labeling basic model in combination with static cross-frame propagation, dynamic track calibration and a manual correction mechanism, and finally fusing real-time motion data and deviation correction signal dynamic labeling, balancing precision and efficiency to generate multi-modal labeling information which is matched with stacked frame and single frame combination of a complex scene.

The invention discloses an AI-driven 3D NAND flash memory read voltage self-adaptive optimization method and system, and relates to the technical field of data storage, and the method comprises the steps: a feature perception and prediction stage: collecting a multi-dimensional feature vector of a 3D NAND flash memory storage unit, inputting a lightweight AI prediction model, and outputting a prediction voltage interval, an offset direction probability and a prediction confidence; a self-adaptive decision and optimization stage: selecting a corresponding search strategy according to a comparison result of the prediction confidence coefficient and a preset threshold value, executing scanning in a prediction voltage interval or the periphery of a reference voltage, and determining an optimal read voltage; and a hierarchical knowledge construction and real-time fine tuning stage: acquiring multi-source voltage data, performing dynamic weighted fusion to obtain an initial voltage, executing local voltage scanning by taking the initial voltage as a starting point to finish fine tuning, and starting a progressive safety backspacing mechanism if read reliability gain is insufficient after fine tuning. According to the invention, extremely high calibration precision of read voltage is realized, and delay and power consumption are reduced at the same time.