28 results about "Labelling algorithm" patented technology

The invention discloses a multi-unmanned aerial vehicle distribution and bus charging combined path optimization method, which aims at minimizing task total time, constructs a mixed integer programming model based on a space-time network, and comprehensively considers unmanned aerial vehicle electric quantity constraint, demand point full coverage, bus time window and charging pile number limitation. An original model is decoupled to limit a main problem and a sub-problem by adopting branch pricing and a Dantzigzag-Wolfe decomposition theory, the main problem deals with demand coverage and charging resource allocation, and modeling is a set coverage problem; for single-machine path generation, the latter is modeled as a resource-constrained and replenishable shortest path problem with a time window dependent feature. A multi-unmanned aerial vehicle initial solution is constructed through a random generation method, a dual variable is iteratively solved after a main problem is initialized, a sub-problem is solved based on a multi-label algorithm, and global optimal solution search is realized in combination with a column generation mechanism and a branch strategy. According to the invention, through joint optimization of the departure time and path planning of the unmanned aerial vehicle, the collaborative optimization problem of distribution and charging is accurately solved.

The invention provides an RDMA (Remote Direct Memory Access) out-of-order receiving and selective retransmission method, which comprises the following steps of: recording an arrival state of a data packet by adopting a Bitmap algorithm and / or a segmentation marking algorithm of a sliding window, dynamically generating ACK (Acknowledgement Character) or NAK (Negative Attached Keying) feedback information according to the algorithm, and only retransmitting a lost data packet according to the received NAK feedback information. The RDMA out-of-order receiving and selective retransmission method supports Bitmap statistics of a sliding window or a segment marking method of saving a memory, is compatible with an existing protocol and realizes an ACK mechanism of selective retransmission, and avoids the problem of redundant transmission in a traditional mechanism. The invention further discloses computer equipment, a data transmission system and a computer readable storage medium.

The invention relates to a skeleton line intersection region redundancy restoration method based on distance constraint and center substitution, and belongs to the field of image processing, and the restoration method comprises the steps: extracting a 3 * 3 neighborhood of each foreground pixel point in a skeletonized image, temporarily setting a center pixel as a background value, and detecting an intersection point through an 8-neighborhood connection marking algorithm; realizing cross point clustering through Euclidean distance calculation and a breadth-first search algorithm based on the spatial proximity relationship of the cross points; and carrying out group screening, ignoring a group only containing a single cross point, and carrying out topology reconstruction on a group containing a plurality of cross points. According to the method, the redundant pixel cluster is accurately identified, the quality of the skeleton line is improved, the redundant pixels in the diagonal line area are accurately identified and cleared through the template matching algorithm, the skeleton line is ensured to strictly meet the single-pixel width requirement, and false endpoints and line length measurement errors are avoided.

A computer-implemented method for a digital security system receives unlabeled event data associated with a computing environment, clusters via an unsupervised machine learning model the unlabeled event data into clusters of unlabeled event data where unlabeled event data in one cluster are more similar to each other than to unlabeled event data in other clusters, selects a respective subset of unlabeled event data for each cluster of unlabeled event data, translates via a large language model artificial neural network each unlabeled event datum in each respective subset of unlabeled event data into a description for the unlabeled event datum, and applies a label via a labeling algorithm to at least one unlabeled event datum in a respective cluster responsive to and representative of the respective description for the unlabeled event datum in the respective subset, thereby transforming the at least one unlabeled event datum to a labeled event datum.

The embodiment of the invention provides an industrial monitoring method and device, electronic equipment and a medium. According to the embodiment of the invention, the data model and the algorithm program of the industrial project are obtained, and the algorithm program is analyzed to generate the algorithm flow chart, so that the monitoring parameters in the data model and the program variables in the algorithm program are accurately bound based on the variable binding address. In the execution process of the industrial project, the real-time data of the monitoring parameters are dynamically mapped to the corresponding program variables in the algorithm flow chart, and the corresponding target flow chart nodes in the algorithm flow chart can be determined and marked according to the actual execution state of the industrial project, so that the operation and maintenance threshold is reduced, the automation level of industrial monitoring is improved, and the industrial monitoring efficiency is improved. Problems in the production process can be found and solved in time, and finally the stability and efficiency of industrial production are improved.

The present application discloses a blockchain-based textile industry supply chain information tamper-proof method and system, the method comprising: establishing a supply chain traceability blockchain with multiple links in the textile industry supply chain as nodes; wherein, block information is used between nodes of the traceability blockchain to transmit interactive data; block information is marked with a preset symbol marking algorithm for link relationships; it is determined whether the block information between two adjacent parent-child nodes is consistent; when the block information is inconsistent, it is determined that the parent node of the two adjacent parent-child nodes has undergone information tampering, and the link relationship mark in the block information is updated. The present invention constructs a traceability blockchain for the textile industry supply chain, realizes the transparency of supply chain data; uses a preset symbol marking algorithm to mark the link relationship of block information, and when tampering occurs in the blockchain, it can efficiently locate the location where the tampering occurred, thereby ensuring the information security of the supply chain.

The invention belongs to the field of intelligent manufacturing, and provides a multi-target flexible processing optimization method and device based on dynamic programming and an AND / OR network model for solving the problem of flexible collaborative optimization of processes, sequences and resources in multi-variety small-batch production. According to the method, an AND / OR network model integrating operation, sequence and resource flexibility is constructed, and a candidate state (process-resource tetrad) is introduced to simplify resource modeling; and establishing a time and cost dual-objective optimization function, iteratively expanding a process path by adopting a dynamic planning label algorithm, and pruning through a dominating rule to generate a non-dominating Pareto optimal solution set. According to the scheme, the limitation that a traditional MILP model is high in calculation complexity is broken through, unified modeling and global optimization of three types of flexibility are achieved, a solution set covering all the optimal schemes can be efficiently generated through example verification, the method is suitable for complex part machining scenes such as aerospace, the flexibility level of a production system is improved, and meanwhile the production efficiency is improved. Decision support is provided for dynamically balancing time and cost, and the method has remarkable technical progress and practical value.

The embodiment of the specification discloses a programmatic advertisement putting method and device based on big data, equipment and medium, which relates to the technical field of Internet, and the method comprises the following steps: receiving advertisement putting requests of a plurality of advertisement putting users through an advertisement transaction platform, collecting basic data and behavior data of a plurality of target users through the advertisement transaction platform and big data collection technology; generating attribute labels corresponding to each target user in advance according to the basic data through a user portrait generation module, and generating operation labels corresponding to each target user according to the behavior data and a preset real-time label algorithm, so as to generate a user portrait of each target user based on the attribute labels and the operation labels; determining a specified putting user among the plurality of target users according to the plurality of advertisement putting requests and the user portrait of each target user through the advertisement transaction platform, and determining a specified putting advertisement among a plurality of to-be-put advertisements, so as to put the specified putting advertisement to the specified putting user.

The application discloses a kind of based on single RGB image and realizes the fusion method of different human reconstruction model, method includes: first, the implicit expression network obtained by two different reconstruction methods is meshed by Marching Cubs algorithm, obtains the three-dimensional human reconstruction model of space and space resolution alignment, the pixel alignment depth map of each reconstruction model is obtained by rendering mode, and the thickness of space z is calculated, and the same area and different area of two models are obtained by thickness map comparison;Using the RGB image and depth image rendered by human model dataset, the depth map network of visible face and invisible face of person is trained, the predicted visible face and invisible face depth map are obtained by using RGB image input network, the z space position is determined and the z space thickness of different models is aligned using thickness scaling algorithm;Finally, the different areas of two models and the same area are interpolated and fused at boundary using edge distance marking algorithm.The application improves reconstruction accuracy.

The application discloses a truck-unmanned aerial vehicle cooperative path planning method and system. The method comprises the following steps: inputting parameter information of a vehicle path problem with an unmanned aerial vehicle considering an unmanned aerial vehicle exchange mode, calling a heuristic algorithm, and outputting an initial upper bound solution scheme; inputting the initial upper bound solution scheme, calling a column generation algorithm, solving a subproblem in the column generation algorithm by using a cooperative path label algorithm, finding a path column by using a heuristic algorithm, and outputting a linear solution scheme; inputting the linear solution scheme, calling a branching strategy and a cut constraint search strategy, adding a branch and a cut constraint after solving the column generation algorithm at each branching node, and outputting an integer solution scheme. According to the scheme, the unmanned aerial vehicle can be exchanged between different trucks without relying on an unmanned aerial vehicle parking point, and the solving efficiency of an accurate algorithm is improved.

The application discloses a large-scale multi-label text classification method based on label adaptive text representation. The application firstly explores label adaptive representation of the text to effectively process classification performance of head labels and tail labels under large-scale multi-label classification; a pre-trained language model is used to learn a representation pool for the text, so that different labels can focus on different representations to complete correlation discrimination. Considering the characteristics of deep models and long texts, text representation enhancement is proposed to ensure the difference and comprehensiveness of the representations in the pool. Therefore, the application can provide effective discriminative text features for large-scale labels to improve the prediction performance. Compared with current large-scale multi-label algorithms, the application can guarantee the overall classification performance of large-scale multi-labels on the one hand, and guarantee that tail labels can better focus on detailed text features on the other hand, and the performance is better than that of the current most advanced large-scale multi-label algorithm.

The present application relates to a skeleton line intersection region redundancy repairing method based on distance constraint and center replacement, and belongs to the field of image processing.The repairing method comprises: for each foreground pixel point in the skeleton image, extracting its 3*3 neighborhood and temporarily setting the center pixel as a background value, and detecting the intersection point through an 8-neighborhood connected marking algorithm; based on the spatial proximity relationship of the intersection point, realizing the intersection point clustering through the Euclidean distance calculation and the breadth-first search algorithm; performing group screening, ignoring the group containing only a single intersection point, and performing topological reconstruction for the group containing multiple intersection points.The present application accurately identifies the redundant pixel cluster, improves the skeleton line quality, accurately identifies and removes the redundant pixels in the diagonal region through the template matching algorithm, ensures that the skeleton line strictly meets the single-pixel width requirement, and avoids the false end point and the line length measurement error.

The application relates to a knowledge representation learning method based on group detection, wherein the method comprises the following steps: determining a target knowledge node and associated knowledge nodes, constructing a knowledge graph based on the target knowledge node and the associated knowledge nodes, processing the knowledge graph according to a preset rule to obtain group detection information corresponding to the target knowledge node, adding different negative sample labels to the associated knowledge nodes based on the group detection information, and applying different penalty weight coefficients to the associated knowledge nodes according to the negative sample labels in the process of knowledge representation learning of an algorithm model, and generating a knowledge representation vector. Through the application, the problem that the distinguishing degree of different entity vectors of the same type is low in knowledge representation learning is solved, and the distinguishing degree of the knowledge representation vector is improved.

The invention discloses a multi-target detection method based on a millimeter-wave radar, and the method comprises the steps: collecting the echo data of multiple targets in a complex scene through the millimeter-wave radar, and carrying out the preprocessing of the echo data, and obtaining the distance-Doppler two-dimensional data to be processed; based on the obtained two-dimensional data to be processed, adopting a CFAR detection algorithm to obtain a coarse estimation CFAR mark detection result; the maximum module value square of each column of pulses on the two-dimensional data to be processed is stored, and a false target interference mark is obtained through dynamic adaptive threshold estimation; and based on the obtained CFAR mark and the false target interference mark, accurate detection of the target is realized through a comprehensive mark. According to the method, a CFAR-based comprehensive marking algorithm is adopted, target detection is realized by dynamically estimating background noise and a self-adaptive threshold value, multi-dimensional information such as distance, speed and time-frequency characteristics is fused, a real target and clutter are effectively distinguished, and the target can be accurately identified and related information of the target can be extracted in a complex scene.

The present application provides a message testing method, device, electronic device, and computer-readable storage medium, which relate to the field of network communication supervision. The method includes: configuring multiple types of test data corresponding to multiple expected marking results according to a marking algorithm; marking the test data based on the marking algorithm to obtain a test marking result; determining the test result of the marking algorithm according to the expected marking result and the test marking result; wherein the test data includes at least one tested message and token bucket quantity data used to mark the tested message. The present application can mark and verify multiple types of test data under multiple expected marking results of different colors corresponding to the marking algorithm from a verification perspective based on the implementation logic of the marking algorithm, thereby effectively testing the marking accuracy of the marking algorithm, improving the purposefulness and accuracy of the test, and further improving the accuracy of the marking algorithm in marking message traffic, thereby achieving effective supervision of message traffic in the network.

The invention relates to the field of image processing, provides a micro-expression analysis method and system based on an adaptive pseudo mark and an attention mechanism, and aims to solve the problem of large pseudo mark error in an existing method by designing an adaptive pseudo mark algorithm and a micro-expression analysis network based on a fusion attention mechanism. According to the self-adaptive pseudo-marking algorithm, self-adaptive marking is carried out by combining a sliding window based on the proportion of the micro-expressions in the whole video interval, so that marking errors are reduced, the micro-expression analysis accuracy is improved, and the method can be used for solving the problem that subtle changes of the micro-expressions are difficult to accurately capture in an existing method. According to the method, a micro-expression analysis network based on a fusion attention mechanism is designed to seamlessly complete micro-expression positioning and recognition tasks, the micro-expression analysis network adopts a double-layer convolutional neural network to extract features, a double attention mechanism is introduced, channel and space information is captured respectively, significant features are focused, and the positioning accuracy of the micro-expression is improved. The accuracy of micro-expression positioning and recognition is improved, and the accuracy of micro-expression analysis is improved.

A kind of region adaptive stripe projection three-dimensional measurement method of spectral separation, comprising: the sinusoidal phase shift stripe pattern of transverse and longitudinal is projected to the surface of multiple measured objects, and the stripe sequence is collected with camera;Respectively, object contour mask and saturation mask are obtained;The white region coordinates of contour mask are converted to projector coordinates, and projection mask is obtained;Single object projection mask image is obtained using connected domain marking algorithm;Using the region marked and saturation mask, set two groups of mask region to generate longitudinal sinusoidal phase shift stripe pattern of adaptive intensity, and two groups of region stripe are projected to the surface of measured object in turn, while collecting spectral separation region stripe image;The stripe pattern of different channel color corresponding to the object is taken independently phase demodulation and phase fusion, and three-dimensional reconstruction is carried out according to the phase result after fusion;The present application significantly improves the measurement efficiency under the premise of ensuring to eliminate stripe aliasing interference.

Systems, methods, and apparatuses are described for secure transfer of tokenized data from a sender to a recipient without disclosing tokenization schemas of either party. A computing device may receive a detokenization algorithm associated with a sender and tokenized data. The computing device may generate plain data by processing the first tokenized data using the detokenization algorithm. The computing device may clear memory, receive a tokenization algorithm associated with a recipient, and generate tokenized data by processing the plain data using the tokenization algorithm. The computing device may then send the tokenized data to the recipient.

Systems, methods, and apparatuses are described for secure transfer of tokenized data from a sender to a recipient without disclosing tokenization schemas of either party. A computing device may receive a detokenization algorithm associated with a sender and tokenized data. The computing device may generate plain data by processing the first tokenized data using the detokenization algorithm. The computing device may clear memory, receive a tokenization algorithm associated with a recipient, and generate tokenized data by processing the plain data using the tokenization algorithm. The computing device may then send the tokenized data to the recipient.

The application relates to a chip detection method, device, equipment, storage medium and program product. In the case that it is determined that a chip in a to-be-detected chip image has pins, a plurality of pin binaryzation regions in the to-be-detected chip image are acquired, the number of pins is counted through a plurality of equidistant scanning lines for each pin binaryzation region, the number of connected domains of the corresponding pin binaryzation region is determined in combination with a connected domain marking algorithm, and whether the pin binaryzation region has pin adhesion is judged based on common judgment, so that the missed detection of micro-burr adhesion short circuit is effectively prevented, and the detection precision of pin adhesion is improved.

The invention discloses a shell type transformer fault diagnosis method under weak turn-to-turn short circuit and a related device, and relates to the technical field of transformer fault diagnosis, and the method comprises the steps: collecting operation parameters of a shell type transformer, and carrying out the preprocessing of the operation parameters; performing multi-scale decomposition on the preprocessed operation parameters by adopting wavelet packet decomposition to generate a two-dimensional gray feature map and generate a sample set; carrying out model training and adaptive optimization on the improved connected domain labeling algorithm to obtain an optimized improved connected domain labeling algorithm; identifying the real-time two-dimensional gray feature map to obtain an abnormal connected domain in the two-dimensional gray feature map; and extracting characteristic parameters of the abnormal connected domain, matching the characteristic parameters with a preset fault sample database to determine a fault type and a fault level, and carrying out spatial positioning. According to the invention, the problem that the fault of the shell type transformer under weak turn-to-turn short circuit cannot be accurately diagnosed due to inaccurate fault feature extraction in a complex scene in the prior art is solved.

The present invention discloses an image recognition management system and method based on data analysis, which relates to the field of image recognition technology, and comprises an initial classification module, a labeling information analysis and storage module, an effective feedback index analysis module, an optimal labeling algorithm determination module and a real-time target image storage reminder module; the initial classification module is used to perform initial classification of target images on any image data set consisting of a plurality of images; the labeling information analysis and storage module is used to store associated images in a database corresponding to identification keywords based on labeling information; the effective feedback index analysis module is used to analyze the effective feedback index of each category of target images corresponding to each category of labeling algorithms; the optimal labeling algorithm determination module is used to determine the optimal labeling algorithm for each category of target images; and the real-time target image storage reminder module is used to analyze the optimal image category of the real-time target image and perform storage reminders for the optimal labeling algorithm corresponding to the optimal image category.

The application discloses a confidence-driven pseudo-label generation method for noise labels, and belongs to the fields of deep learning and image classification; specifically, first, original labels are collected as training data, a part of which is labeled to obtain labels q containing noise labels; another part of training samples x is input into a feature extractor for feature extraction, and then is respectively input into a classifier and a linear mapping module of a deep neural network model, the classifier outputs a distribution p, and the linear mapping module outputs a confidence value conf between 0 and 1; then, pseudo labels are constructed and loss training is performed, and a loss L CDPL is obtained, gradients are returned to the feature extractor and the classifier; finally, the confidence conf is trained as a pseudo label allocation ratio, and gradients are returned to a mapping function h conf of the linear mapping module, so that a more reasonable and balanced pseudo label information allocation ratio can be constructed at each stage. The application solves the problem of unbalanced information allocation in the existing pseudo label algorithm, and greatly reduces the influence of noise.

The application discloses a kind of bad point detection method and system combined with space domain analysis and time domain analysis, belong to image processing technical field.Method includes: space domain analysis stage, edge information is extracted by Sobel operator;Image binarization is carried out, and potential cavity is filled using morphological closing operation, and bad point area is strengthened;Then all candidate bad point areas are identified using connected domain marking algorithm;Time domain analysis stage, the spatial position and area characteristics of candidate bad point area are combined, the corresponding relationship of candidate bad point area between frames is established, the continuity of same candidate bad point area on time axis is judged, only the candidate bad point area that satisfies two kinds of assumptions, and the area and contrast of bad point area exceed set threshold value, is finally determined as stable bad point and output.The application can effectively identify the abnormal bad point that exists continuously in multiple frames by analyzing the stability of bad point in time sequence, especially more stable and accurate when detecting small area, low contrast bad point.

The invention discloses an automatic data classification and grading method and system based on a label algorithm, and relates to the technical field of data processing, and the method comprises the steps: obtaining original data, carrying out the preprocessing, and forming a standard data format; performing feature extraction on the standard data, synchronously generating a metadata tag, obtaining a service context tag and a descriptive tag, and forming a multi-dimensional attribute tag set through tag fusion; constructing a classification model and a grading model based on the standard data and the corresponding label set; correspondingly training a classification model and a grading model based on a pre-collected labeled label set and historical standard data corresponding to the category result and the grade result; after obtaining new data and obtaining a corresponding label set, calling the trained classification model and grading model, and outputting an automatic classification result and a grading result; and storing data generated in the process. According to the invention, automatic classification and grading of data can be realized.

The present application belongs to the technical field of anti-seepage structure evaluation, and particularly relates to an anti-seepage structure integrity evaluation method and system based on resistivity imaging, which comprises the following steps: firstly, collecting complex admittance data of monitoring points and extracting phase loss angle features to strip the interference of underground environment humidity fluctuation through phase information; then, constructing virtual part admittance gradient features in combination with structure anisotropy correction factors to enhance the recognition degree of small damage edges; calculating seepage probability weight by using reference gradient deviation, and identifying seepage clusters based on adaptive seed point screening and eight-neighbor connected domain marking algorithm to obtain continuous seepage area; finally, calculating integrity score through a nonlinear model by introducing risk sensitivity index and area penalty weight to realize accurate early warning and positioning. The present application effectively solves the problem of poor seepage recognition accuracy under complex background interference, and significantly improves the robustness and scientificity of the evaluation result.

The application discloses a lotus phenotype identification method and device based on a pseudo-label algorithm and a MobileNetV2 network, and the method comprises the following steps: step one, a grid model is constructed, the model selects the MobileNetV2 network as a feature extraction network for lotus identification, applies an SE attention mechanism to a feature processing unit of the MobileNetV2, and simultaneously uses a pseudo-label algorithm to perform pseudo-labeling on unlabeled lotus data; step two, a model is trained, a training set in a lotus data set is used to pre-train a model to initialize the MobileNetV2 feature extraction network, then the model obtained through pre-training is used to predict the unlabeled data, the minimum entropy, i.e. the highest confidence, is selected to perform pseudo-labeling on the lotus data, and finally all the labeled data is retrained to obtain an optimal model; and step three, the model after training is used to identify lotus phenotypes. The application can improve the expression and generalization capabilities of the model, reduce the labeling amount of a large data set, and be more suitable for various unbalanced and complex data distributions.

The invention provides an intent-driven marketing activity automatic generation method and system, and the method comprises the steps: obtaining intra-bank business data and a tag library, carrying out the processing of customer data through a big data portrait and a tag algorithm model, and generating and outputting enhanced customer group data and a customer 360 portrait; based on the generated customer group data and customer 360 portraits, classifying and layering customers, establishing and managing a high-quality marketing material library through a management background, tracking and analyzing use data of materials, and outputting user demands and behavior analysis results; issuing an online marketing task to the mobile terminal of the employee, automatically tracking and collecting task completion data, and generating a precision marketing strategy for different clients; meanwhile, using data of the materials are tracked and analyzed, user requirements and behavior analysis results are output, marketing strategies are changed according to behaviors of clients, market changes can be quickly responded, and marketing efficiency is improved.