147 results about "Weight prediction" patented technology

The invention relates to a resource elastic scaling decision-making method, system and device and a medium. The method comprises the following steps: collecting real-time operation data of a security service node, and performing multi-dimensional security index analysis according to the real-time operation data to obtain a portrait data packet; predicting the security service weight value to obtain a prediction result, performing dynamic error compensation on the prediction result to generate a corrected weight prediction value, and generating a control instruction based on the corrected weight prediction value and the active session state; and when the instruction is a migration instruction, analyzing a session state snapshot of the instruction, calling a preset kernel state locking function to lock a memory session block of a source node, obtaining incremental state change data to generate a migration snapshot packet, and performing block verification injection operation on a target node. According to the method, by integrating multi-dimensional safety index analysis, prediction error compensation and stateful transition verification mechanisms, the accuracy and response efficiency of resource elastic scaling decision making are improved, and the continuity of stateful service transition and the consistency of safety strategies are enhanced.

The invention discloses a logistics return transportation capacity intelligent matching system and method based on journey prediction, and the method comprises the steps: carrying out the transportation capacity departure time window prediction, journey time consumption prediction, space correction prediction, sudden factor correction prediction and bearable weight prediction of the multi-source heterogeneous data of each vehicle in the current collection period; obtaining a transport capacity departure time window, an optimal return path, return time consumption and an estimated loadable weight of the target vehicle; and generating a transport capacity label based on the transport capacity departure time window, the optimal return path, the return time consumption and the estimated bearable weight of the target vehicle, performing multi-dimensional adaptation on the transport capacity label and the order information to obtain a comprehensive score of the transport capacity label, and performing order matching and state updating operation of the target vehicle based on the comprehensive score of the transport capacity label. Accurate pre-judgment of the transport capacity state is achieved, and the invalid matching rate is greatly reduced.

The invention belongs to the field of robot control, and provides a multi-view feature fusion operable part semantic segmentation method and system, and the method comprises the steps: obtaining the point cloud data of an operable part, and generating a corresponding multi-view image; carrying out target detection, extracting features for each view angle, and obtaining a two-dimensional bounding box and a semantic tag; based on the extracted two-dimensional bounding box, processing by using SAM to obtain a foreground mask; on the basis of the extracted two-dimensional bounding box, the relevance of the same semantic target under different visual angles is captured on the global scale by using the constructed global visual angle interaction module, and the feature consistency is enhanced; and processing the fused bounding box features obtained by the global view angle interaction module by using a weight prediction network, predicting the response weight of each bounding box at each super point, combining the obtained foreground mask and the predicted response weight, and back-projecting to a 3D point cloud space through view point information to obtain a final 3D semantic segmentation result. According to the invention, the segmentation accuracy is improved.

The invention relates to the field of fruit quality nondestructive testing, in particular to a winter peach cold storage shelf life classification and quality nondestructive testing method based on spectrum-image feature fusion and stacked model fusion, and the method is used for accurately classifying the winter peach cold storage shelf life and predicting the key quality of the winter peach cold storage shelf life based on hyperspectral imaging and deep learning technologies. The method comprises the following steps: acquiring a hyperspectral image in a wave band range of 400-1000nm, and extracting a region of interest through mask processing; phenotype data of the samples are measured and classified, and abnormal samples are removed; through spectrum preprocessing and characteristic wavelength screening, effective pixel size and area parameters are extracted, and a multi-task integration model is constructed. The model not only realizes 100% accurate classification of the shelf life of the winter peaches, but also predicts the sugar degree (R2 = 0.8451) and the hardness (R2 = 0.8798), and supports a pseudo-color visual distribution diagram and fruit diameter and weight prediction. The method is high in precision, good in stability and high in automation degree, and effective technical support is provided for quality management and market circulation after agricultural products are harvested.

The invention belongs to the field of animal husbandry management, and particularly relates to a livestock online weighing error analysis method based on state classification and time-frequency characteristics. According to the scheme, firstly, an original weighing signal is subjected to modal decomposition to obtain a weight prediction value, and a reference error is calculated in combination with a static weighing value; secondly, extracting time-frequency domain characteristic parameters after self-adaptive windowing processing is carried out on the signals, and exploring the relation between the time-frequency domain characteristic parameters and reference errors in different active states; and finally, establishing a state classification model and two types of error prediction models, and performing hyper-parameter optimization on the error prediction models by using a myxobacteria optimization algorithm. In practical application, the state classification model is utilized to perform state classification on the weighing object, and the specified error prediction model is called in combination with the classification result to realize weighing error prediction. According to the invention, the problems of insufficient precision and poor generalization of the existing scheme are overcome; the method can be used for compensating the error of the online weighing platform so as to promote the development of fine breeding.

The invention provides a signal transmission method, a video encoder and a computer readable storage medium. A computer-implemented signal transmission method performed by an encoder comprises the steps of: transmitting, by a processor, a bitstream comprising weight information for predicting a coding unit (CU) to a video decoder, the weight information indicating that if weighted prediction is enabled for a bidirectional prediction mode of the CU, weighted averaging for the bidirectional prediction mode is disabled, and if weighted prediction is enabled for the bidirectional prediction mode of the CU, weighted averaging for the bidirectional prediction mode of the CU is disabled; the weight information indicates that if weighted prediction is enabled for at least one of luma and chroma components of a reference picture of the CU, weighted averaging for the bidirectional prediction mode is disabled, in which the bitstream includes a flag indicating whether weighted prediction is enabled for at least one of luma and chroma components of the reference picture, and the flag includes a flag indicating whether weighted prediction is enabled for at least one of luma and chroma components of the reference picture. The mark comprises a mark lumaweighted lxflag [i] transmitted for the ith reference picture in the reference picture list Lx, and x is 0 or 1.

The present application belongs to the field of weld weight prediction, and particularly relates to a bus steel structure weld weight prediction method and system, which comprises the following steps: reading a three-dimensional model and generating an initial weld path set; filtering, continuity repairing and logical segmentation based on process avoidance features are performed on the path to obtain an effective weld information set; the information set is input into a process decision rule library, and process specification parameters are decided according to structural features, wherein at least the large-section profile specific joint is decided as intermittent welding and the conversion rate is determined; finally, the weld weight is calculated and summarized according to the process parameters and geometric features. The method realizes the full-process automation and intelligentization from the model to the weight prediction, and significantly improves the prediction accuracy and efficiency.

The invention discloses a lightweight load prediction method and system for a distribution transformer, relates to the technical field of power systems, and solves the problem that efficient and accurate lightweight prediction cannot be carried out on the load of the distribution transformer, and the method comprises the steps: analyzing the historical load of the distribution transformer in different dates in the past; binding the historical average temperature of the power utilization area in different dates in the past with the corresponding historical load; analyzing the load condition of the distribution transformer according to the date type of the power utilization area in the current date and the real-time average temperature, and analyzing to obtain a reference load interval or a reference load of the distribution transformer in the current date; predicting the load of the distribution transformer at the next time node according to the reference load interval to obtain a predicted load interval of the distribution transformer at the next time node; and analyzing the load condition of the distribution transformer at the next time node according to the predicted load interval. According to the invention, efficient lightweight prediction of the load of the distribution transformer is realized.

The embodiment of the invention provides an electric power industrial control system security detection method and device, and the method comprises the steps: obtaining network traffic and log data, extracting traffic features and log features, predicting the weight of each expert model through employing an expert weight prediction model according to the extracted features, and predicting the detection task probability through employing a task prediction model, performing enhancement processing on the weight of each expert model by using a preset attention weight to obtain an enhanced weight of each expert model, calculating an expert matching degree according to the detection task probability and a preset expert ability matrix, calculating a correction weight of each expert model according to the expert matching degree and the enhanced weight of each expert model, and obtaining a correction result of each expert model; and based on the correction weight of each expert model, selecting a predetermined number of expert models, and performing detection by using the selected expert models to obtain a detection result. According to the method, the accuracy and the dynamic adaptability of routing decision and the accuracy and the integrity of a detection result of a multi-expert system in a complex electric power industrial control scene can be improved.

An image processing method including inputting an input image to a weight prediction model to predict a weight corresponding to each lookup table of a plurality of lookup tables, mapping a pixel value of the input image to a corresponding section of each lookup table of the plurality of lookup tables, calculating an adjusted pixel value corresponding to the corresponding section with respect to each lookup table of the plurality of lookup tables, and obtaining an output image with an adjusted resolution by performing a weighted sum of the adjusted pixel values according to the weight.

The application discloses a zero-shot station behavior recognition method based on dynamic and static cooperative support set optimization, and aims to solve the problems of low sample quality and insufficient support set information mining in a video behavior recognition task. The method realizes zero-shot behavior recognition in a station scene through a key frame cascaded support set construction module and a dynamic and static cooperative weighted prediction module. The key frame cascaded support set construction module and the dynamic and static cooperative weighted prediction module constitute a zero-shot learning framework. In the key frame cascaded support set construction module, a text-to-image and image-to-video generation model is used to generate static key frame images and dynamic video support set samples for each behavior category. In the dynamic and static cooperative weighted prediction module, rich semantic information in the above samples is fully utilized, and dynamic fusion is performed on the static key frame and the dynamic video through a dynamic and static cooperative strategy, so that high-precision identification of key actions in the station scene is realized.

The application relates to a multi-level sentiment classification method for network public opinion. The method realizes real-time acquisition and processing of social media data through a distributed message queue, generates an initial topic set by using a Streaming K-means algorithm for online clustering, constructs a topic state space by using a topic state analysis model, selects a suitable classification level by using a Q-learning algorithm, and constructs an information entropy value dataset by using dependency path analysis and information entropy value calculation. Finally, a multi-level sentiment classification result is generated by using a level weight prediction model and a sentiment classification model, which is used for network public opinion monitoring. The method can adapt to changes in network public opinion in real time, and improves the accuracy and timeliness of sentiment classification.

This disclosure provides a method and apparatus for encoding / decoding image signals. The method for decoding image signals according to the present invention may include the following steps: determining whether a brightness change exists between a current image including a current block and a reference image of the current image; if a brightness change is determined to exist between the current image and the reference image, determining candidate weight prediction parameters for the current block; determining weight prediction parameters for the current block based on index information for specifying any one of the candidate weight prediction parameters; and performing prediction for the current block based on the weight prediction parameters.

The embodiment of the invention provides a weight prediction model training method and an object evaluation method, and relates to the technical field of computers. The training method of the weight prediction model comprises the following steps: extracting a target feature vector from training data; inputting the target feature vector into the initial model to obtain a first predicted value; determining a second predicted value according to the training data and the first predicted value; determining a loss value according to the first predicted value and the second predicted value; performing iterative training on the initial model according to the loss value until the model converges to obtain a weight prediction model; the weight prediction model is used for predicting the importance degree of the data of different dimensions in the evaluation process so as to evaluate the target object. The weight prediction model provided by the invention can be used in a personnel ability evaluation scene, and is used for solving the problems that traditional evaluation depends on subjective experience, the dimension is single and the accuracy is insufficient.

The present application relates to the technical field of biological breeding and artificial intelligence image recognition, and particularly relates to a poultry wing weight prediction method and device based on deep learning. Step S1: live poultry DR image acquisition and preprocessing; step S2: accurate segmentation of the poultry wing region based on an improved segmentation model; step S3: morphological feature extraction based on a segmentation mask; and step S4: poultry wing weight prediction based on a machine learning model. The present application realizes accurate, rapid and non-destructive prediction of poultry wing weight, and provides key technical support for efficient selection and breeding of poultry with high economic added value.

This disclosure generally relates to video coding / decoding, and in particular to enhancing block-adaptive weighted prediction. The method involves receiving a coded video bitstream, which includes the current block of the current frame and a first syntax element indicating the prediction mode of the current block, wherein a plurality of scaling factor lookup tables are stored and include different ranges of scaling factors. In multiple scaling factor lookup tables, the step size or precision of the scaling factor in each lookup table is the same, and A step of determining a prediction mode based on the value of a first syntax element, wherein the prediction mode is used to predict the current block based on the reference block of the reference frame. The steps include determining a scaling factor from one of several scaling factor lookup tables, The process includes the step of reconstructing the current block based on a reference block and a determined scaling factor.

This invention provides a non-contact online detection method for pig body size and weight based on 3D machine vision, belonging to the field of machine vision technology. This invention collects 3D point cloud data, RGB image data, and posture change data of pigs; constructs a point cloud perception reliability index to evaluate data quality and performs adaptive optimization when requirements are not met; further, it obtains a standard posture point cloud model through key point detection, posture alignment, surface reconstruction, and non-rigid correction, and calculates body size parameters such as body length, body height, and chest circumference; performs posture evaluation and compensation based on the posture standardization deviation index; constructs a dynamic weight prediction model to calculate weight, and verifies and corrects the results online through a weight prediction stability judgment mechanism. This method achieves high-precision, non-contact, real-time detection of pig body size and weight, improving detection accuracy and system stability.

The application provides a kind of based on user behavior perception's augmented reality content recommendation and expressive presentation method, including constructing the state representation of multiple virtual roles in augmented reality scene;Joint objective function for evaluating virtual role layout rationality is constructed;According to the attribute information of virtual role, the adaptive weight is generated by the weight prediction model constructed in advance, and the adaptive weight is used to adjust the cost item related to the importance of role in the space cost function;Based on the joint objective function, the position and orientation of all virtual roles are iteratively optimized to obtain an optimized layout;Expressive presentation is carried out in the augmented reality scene The optimized layout is realized in three-dimensional space Automatic optimization of role position and orientation, avoid visual conflict, improve the naturalness, coordination and semantic expression ability of layout, thereby significantly enhance the interactive experience and information transmission effect of AR system.

A product weight prediction device disclosed in the present document comprises: a communication interface for receiving user input data related to an order of a product; and at least one processor for extracting product information of the product from a web page on the basis of the user input data, generating preprocessed data by embedding the product information, and predicting a weight of the product by inputting the preprocessed data to a learning model, an input value of which is the preprocessed data and an output value of which is a predicted weight of the product.

This invention discloses a human pose estimation method based on adaptive multi-view feature fusion. Specifically, it involves: acquiring synchronized multi-view images and preprocessing them to form a dataset, which is then divided into a training set, a validation set, and a test set; designing a multi-view feature fusion network, including a 2D pose estimation backbone network, an appearance embedding network, a geometric embedding network, a weight prediction and learning network, and a multi-view feature weighted fusion network; training and validating the multi-view feature fusion network using the training and validation sets; inputting the test set into the trained multi-view feature fusion network for testing; and outputting a pose estimation image. This method can enhance the features of occluded views by borrowing features from visible views, while adaptively learning view weights to prevent low-quality views from contaminating global features, significantly improving the accuracy and robustness of 3D pose estimation.

The present application relates to the technical field of intelligent breeding, and particularly relates to a captive animal weight prediction method based on environment and visual feature fusion. The method comprises the following steps: synchronously collecting environment parameters, image data and weight labels, and constructing a triple data set; preprocessing the triple data set, and constructing a mapping relationship of environment parameters-single frame image-corresponding single weight of captive animals based on a timestamp and a preset spatial coordinate, and generating multi-modal training data; inputting the environment parameters in the multi-modal training data into an environment-image feature fusion module in a CNN-Transformer hybrid architecture which is pre-trained, and performing dimension matching with image features to obtain a fusion feature training model. Through constructing an environment and visual feature deep coupling, combining a time and space mapping, a multi-task constraint and a cross-scene adaptive mechanism, the present application realizes high precision, stability and generalization ability of captive animal weight prediction.

The invention belongs to the field of livestock weighing, and particularly relates to an online weighing method and an online weighing system for cattle. According to the scheme, a body weight prediction value is generated according to an original dynamic weighing signal through a variational mode decomposition method; and characteristic information related to categories is extracted from the weighing signals and input into the state classification model to obtain the active state of the cattle to be detected. And then error related feature information corresponding to the current active state is extracted from the dynamic weighing signal in combination with the active state, and the error related feature information is input into the error prediction model in the corresponding active state to predict the weighing error of the current cattle to be measured. The error prediction models comprise two types, are obtained by training related data in different active states and are used for predicting weighing errors in a specified state. And finally, taking the sum of the weight prediction value and the weighing error as a final weighing result. According to the invention, the problems of insufficient precision and poorer reliability of the existing online weighing system are solved.

The embodiment of the invention provides a multi-microphone sound mixing weight adjusting method, system and device, and the method comprises the steps: collecting audio signals of a plurality of microphone channels in real time; based on the audio signal, extracting multi-dimensional acoustic features of the plurality of microphone channels in a plurality of time windows, the multi-dimensional acoustic features including at least one of a signal-to-noise ratio, a sound source direction, reverberation time and a human voice feature; based on the multi-dimensional acoustic features, the sound mixing weight of each microphone channel is determined through a weight prediction model, and the weight prediction model is a machine learning model; and carrying out sound mixing output on the audio signals of the plurality of microphone channels based on the sound mixing weights.

Various embodiments include techniques for performing hardware-assisted weighted prediction encoding with software-generated weight prediction parameters for a media frame. The disclosed weighted prediction encoding techniques take advantage of temporal interframe correlation between adjacent media frames in a video stream. In certain examples, a media frame, or portions thereof, can exhibit a rapid luminance change, such as during a fade in from black effect, a fade out to black effect, a video depicting an explosion or blast, and / or the like. This rapid luminance change reduces interframe correlation, even in cases where the interframe correlation would be higher if not for the luminance change. The disclosed techniques remove this luminance change before encoding, resulting in higher temporal correlation between adjacent frames. The higher temporal correlation can result in reduced bit rate and improved visual quality of the resulting output video stream.

A logistics return trip capacity intelligent matching system and method based on travel prediction, including, for each vehicle current collection period of multi-source heterogeneous data, capacity departure time window prediction, travel time estimation, space correction estimation, sudden factor correction estimation and bearable weight prediction, obtaining the capacity departure time window, the optimal return path, the return time and the estimated bearable weight of the target vehicle; based on the capacity departure time window, the optimal return path, the return time and the estimated bearable weight of the target vehicle, generate the capacity label, adapt the capacity label with the order information in multiple dimensions, obtain the comprehensive score of the capacity label, based on the comprehensive score of the capacity label, the order matching and state updating operation of the target vehicle, realize the accurate prediction of the capacity state, greatly reduce the invalid matching rate.

Embodiments of the application disclose a data processing method and device, equipment and medium, which are applied to the technical field of data processing. The method comprises the following steps: obtaining a sample text pair, determining a first sample word segmentation set matched with a second sample word segmentation set from a first sample word segmentation set, and generating word weight marking information of each first sample word segmentation in the first sample word segmentation set according to the first sample word segmentation matched with the second sample word segmentation set; inputting each first sample word segmentation into an initial word segmentation processing model in sequence, performing word segmentation processing on each first sample word segmentation by the initial word segmentation processing model to obtain word weight prediction information of each first sample word segmentation; and iteratively training the initial word segmentation processing model through the word weight prediction information and the word weight marking information to obtain a target word segmentation processing model used for determining the word weight of text word segmentation. By adopting the embodiments of the application, the determination accuracy of the word weight of text word segmentation can be improved.

The present disclosure relates to determining at least one body parameter of a bovine subject in a shed environment based on visual techniques, in particular distance imaging. The body parameter may be any of a body weight of a bovine subject, a predicted slaughter (carcass) body weight, a configuration level, a predicted carcass configuration level, a fat coverage level, and a predicted carcass fat coverage level. A first embodiment relates to a method for determining at least one body parameter of a bovine subject, the method comprising the steps of acquiring at least one distance image of at least one bovine subject, processing the at least one distance image to generate at least one 3D image of a preselected bovine subject, segmenting the at least one 3D image to generate at least one point cloud representing a pre-selected bovine subject, and calculating body parameters of the pre-selected bovine subject by modeling the at least one point cloud with reference to a trained model using artificial intelligence (AI), the body parameter is selected from the group of weight, predicted slaughter (carcass) weight, predicted carcass configuration level, and predicted carcass fat coverage level.

The present application relates to the technical field of children's health assessment, in particular to a children's body fat rate adaptive prediction method and system based on trajectory feature cooperation, comprising the following steps: obtaining children's physical examination data set, analyzing height and weight change to calculate difference ratio to screen samples, extracting waist circumference change slope to evaluate stability to screen samples, calculating body sign proportion difference to adjust training weight, identifying age stage residual difference gap to adjust model parameters, combining body fat trend and body weight trend direction to adjust prediction slope to generate results. In the present application, samples are screened through height and weight trajectory, combined with the fluctuation continuity of waist circumference change slope to eliminate abnormal data, the feature weighting coefficient is set according to the proportion deviation of height and weight and muscle mass, the response coefficient is adjusted according to the distance relationship of the grouping mean and the central interval of the age stage residual, the change path is judged by the consistency of the body fat and body weight prediction trend, the non-trend prediction deviation is avoided, and the stability and sample adaptability of the model are improved.

The present application relates to the technical field of biological breeding and artificial intelligence, and particularly relates to a poultry breast muscle weight prediction method, device and electronic equipment. The method comprises the following steps: using an image enhancement network to perform enhancement processing on a digital X-ray radiography image of poultry, to obtain an enhanced digital X-ray radiography image; using an image segmentation network to perform breast muscle region segmentation on the enhanced digital X-ray radiography image, to obtain breast muscle region information; based on the breast muscle region information, extracting geometric features of the breast muscle region; inputting the geometric features into a trained machine learning model, and outputting a prediction result of the breast muscle weight of the poultry. The present application solves the defects of low efficiency and inability to realize automatic batch measurement caused by manually segmenting and weighing to obtain the breast muscle weight of poultry in the prior art, and realizes rapid and automatic prediction of the breast muscle weight of poultry.

The invention relates to a differentiatable point cloud simplification method and system based on sensitivity guidance. The method comprises the following steps: acquiring an original point cloud data set; the curvature and roughness features of the local neighborhood of each point cloud data are analyzed, and weighted fusion is carried out to obtain the sensitivity value of the point; constructing a differentiable point cloud simplified network model based on sensitivity guidance, wherein the differentiable point cloud simplified network model comprises a sensitivity weighted prediction module, a differentiable sampling module for sensitivity guidance and a geometric reconstruction constraint module; optimizing parameters of the differentiatable point cloud simplified network model based on sensitivity guidance; and inputting to-be-simplified point cloud data to the point cloud simplification network model, outputting simplified point cloud data, and evaluating the performance of the simplified point cloud data. According to the method, by introducing a sensitivity guiding mechanism and a differential sampling network, the point cloud can be flexibly simplified in proportion, complex geometric details are effectively reserved and model cavities are avoided while the data volume is remarkably reduced, and the adaptability and reconstruction quality of point cloud processing are improved.