15 results about "Multiple classification" patented technology

The application discloses a kind of material handling control method and system with classified goods allocation function, it is related to the process control technical field based on big data analysis, based on high priority attribute subset, fusion path distance calculation generates optimization path sequence;On the basis of extracting path node conflict, temperature cross influence and time window overlap and other mutual restraint relations, iteratively adjust storage occupancy and handling order, get balanced order list;Finally, combined with material scheduling demand parameters and cargo classification dimension determines target handling instruction set, effectively eliminates the core contradiction of emergency material delay, temperature-sensitive material deterioration and low handling efficiency, realizes when receiving a batch of goods allocation instruction containing multiple attribute constraints, according to current specific circumstances automatically determine the relative importance of each dimension, and generate a set of reasonable handling instruction that meets multiple classification requirements simultaneously.

The application relates to a method and system for mining and analyzing the correlation of clinical comorbidity of discharged patients. The method comprises the following steps: constructing an individual diagnosis and treatment narrative graph for each patient according to the discharge medical record text, generating a time sequence transaction sequence corresponding to each patient, and constructing a sequence database; based on the database, the original time sequence frequent pattern set is constructed by analyzing through an improved generalized sequence pattern algorithm; each pattern in the set is classified to obtain multiple classification clusters, and the original time sequence frequent pattern in each classification cluster is processed through multi-sequence alignment to construct a generalized clinical path graph, and the information in the generalized clinical path graph is extracted to generate a natural language abstract. The method improves the time sequence logic, knowledge abstraction degree and clinical interpretability of the clinical comorbidity correlation mining by constructing a diagnosis and treatment narrative graph, mining a time sequence frequent pattern, and constructing a generalized clinical path graph through clustering and multi-sequence alignment.

The utility model discloses a kind of waste storage box for cell culture, belong to cell culture technical field, including outer frame, the inside of the outer frame is provided with storage box, the side of storage box is provided with square groove, the inside of square groove is provided with classification mechanism, the side of outer frame side and classification mechanism is adjacent and is fixedly connected with pull rod, the top of outer frame is provided with top cover, the bottom of outer frame is provided with support mechanism, the side of outer frame bottom close to support mechanism is provided with universal wheel, the top of storage box is provided with discharge chute, the utility model is provided with classification mechanism, and storage box can be inserted multiple classification boxes by square groove, hold classification box, simultaneously, the slider of classification box bottom is inserted into sliding slot, multiple classification boxes can be fixed below discharge chute, each classification box corresponds to a kind of waste, push the plug into jack, and fixed in fixed groove, classification box can be closed, prevent waste overflow, the classification storage of waste through classification box can improve the rigor of experiment.

This application discloses a wafer defect classification method and related apparatus. By introducing a multi-branch classification tree, decision nodes can have at least three branching scenarios, resulting in multiple classification results corresponding to multiple leaf nodes. The target classification result is then determined based on these multiple results. Since the multi-branch classification tree allows multiple leaf nodes to point to the same classification result, the same defect can obtain the same classification result through different decision paths. This allows the classification result to be verified through multi-path logic, thereby improving the fault tolerance of the defect classification process, reducing the probability of misclassification due to errors in single feature judgments, and improving the accuracy and stability of wafer defect classification results.

The application discloses a multi-agent-based data classification and grading method and device, electronic equipment and computer program product. The method comprises the following steps: obtaining data to be classified and graded; using multiple decision agents to classify and grade the data to be classified and graded respectively to obtain multiple classification and grading results; determining the voting results of the multiple decision agents according to the multiple classification and grading results, wherein the voting results represent the number of classification and grading results that are the same through the number of votes; in the case that the voting results have the same number of votes, using a coordination agent to divide the multiple decision agents into multiple target debate groups for multi-agent debate to obtain a target classification and grading result, wherein the classification and grading results obtained by the decision agents in the same target debate group are different. The application solves the technical problem of low classification and grading accuracy of the traditional data classification and grading method.

The application relates to a clothing design step-by-step auxiliary optimization method, system, device and medium. The method first introduces a knowledge graph, converts general optimization intentions into specific design categories and types by analyzing the correspondence between multiple classification tags of clothing in a clothing design drawing and multiple optimization intention keywords corresponding to optimization information, and effectively avoids design direction deviation. Secondly, a double sorting mechanism is adopted to comprehensively consider the correlation with the classification tags and the optimization intention keywords, accurately sort the design categories and the design types, intuitively display the modification content most consistent with the optimization intention, and greatly improve the clothing design decision efficiency. Finally, the step-by-step user self-optimization experience is realized by sequentially pushing the drawing optimization page, the user can independently adjust the categories, and each step is based on the previous results, ensuring the coherence of the modification. The step-by-step guidance mode reduces the design threshold, solves the problems of blindness and black box in the optimization process, and effectively improves the design quality and user experience.

This invention discloses a modality balancing training method and system for heterogeneous multimodal domain adaptation. The method acquires labeled source domain multimodal samples and unlabeled target domain multimodal samples. For each modality, a modality encoder and classifier are constructed to extract unimodal representations and generate multimodal fusion representations. Constrained training is performed on the modality representations based on the source domain samples. Pseudo-labels are generated for the target domain samples using the consistency among multiple classification results. Distribution alignment loss between source domain and target domain representations is calculated for each modality. Modality balancing weights are dynamically determined based on the state of each modality alignment loss during training. Model parameters are updated based on the source domain supervision loss, the target domain pseudo-label loss, and the distribution alignment loss adjusted by the balancing weights. This invention can alleviate the training imbalance problem caused by inconsistent modality offsets in heterogeneous multimodal domain adaptation, and improve the model's prediction performance and generalization ability in the target domain.

This application provides a training method for a classification model, a text classification method, and related equipment to address the problem of low classification flexibility in classification models. The method includes at least the following steps: based on preset multiple feature dimensions, extracting corresponding multidimensional text features, multidimensional associated label features, and at least one multidimensional other label feature for selected sample text, associated classification labels, and at least one other classification label from among multiple classification labels; determining the negative sample similarity between each of the at least one other classification label feature and the multidimensional text feature based on the hierarchical distance between each of the at least one other classification label and the associated classification label; and adjusting model parameters based on the obtained positive sample similarity and at least one negative sample similarity between the multidimensional text feature and the multidimensional associated label feature. This enables a target classification model to have feature extraction capabilities of different granularities, improving classification flexibility.

This application provides a device wake-up method, apparatus, computer device, and storage medium, belonging to the field of speech recognition technology. The method includes: classifying multiple speech frames in an acquired speech signal to obtain multiple classification information, the classification information indicating the probability that each syllable, character, or word from a target phrase is included in the speech frame; determining the end point of the target phrase based on the multiple classification information, the end point indicating the time when the playback of the target phrase in the speech signal ends; and waking up the target device based on the end point of the target phrase. This technical solution can determine the time when the playback of the target phrase ends in the speech signal and finally wake up the target device at the time when the playback of the target phrase ends, ensuring that the target device is only woken up when the target phrase is completely detected, avoiding false wake-ups and improving the accuracy of wake-up.

The application discloses a kind of multi-phase coupling reservoir sweet spot determination method, device, equipment and medium, comprising: obtaining the original reservoir data corresponding to target research reservoir, and determining target reservoir data according to original reservoir data;According to target reservoir data, determine the reservoir classification basis corresponding to target research reservoir;Based on target reservoir data, establish the target classification identification model corresponding to reservoir classification basis;Based on target classification identification model, the logging data of target well is processed to obtain the reservoir sweet spot prediction result corresponding to target well.Based on the above technical solution, the target classification identification model of multiple classification basis is established by fusing logging data and logging data, and the reservoir sweet spot prediction result of target well is determined according to the target classification identification model, the continuous identification of low porosity and permeability reservoir diagenetic facies is realized, reservoir classification evaluation and accurate prediction of favorable reservoir sweet spot, which provides reliable technical support for exploration and development of low porosity and permeability oil and gas reservoir.

The application provides a training method and device of a mental attention tendency hierarchical emotion classification model, relates to the technical field of natural language processing, and aims to solve the problems of low efficiency, insufficient generalization and weak interpretability of traditional mental attention tendency analysis methods. The method comprises the following steps: obtaining parent-child dialogue text carrying hierarchical labels, connecting multiple classification heads to be trained according to the hierarchical relationship represented by the hierarchical labels, and constructing a hierarchical task classification module to be trained; processing the parent-child dialogue text through a pre-trained large language model and the hierarchical task classification module to be trained to obtain hierarchical task classification prediction results of the parent-child dialogue text; updating the model parameters of the hierarchical task classification module to be trained according to the hierarchical task classification prediction results of the parent-child dialogue text and the hierarchical labels carried thereby, and obtaining the trained hierarchical task classification module.

The application discloses a primer design method, system and computer storage medium based on minimum degeneracy, the method comprising: obtaining nucleotide sequences; dividing the nucleotide sequences into multiple classification units; selecting a representative sequence from each classification unit; designing a corresponding primer pair set for the representative sequence based on the base frequency in the representative sequence; determining the coverage of the primer pair set relative to all nucleotide sequences, and selecting multiple primer pairs in each primer pair set as a candidate primer pair set according to the coverage of each primer pair set; and selecting multiple candidate primers in the candidate primer pair set to establish a primer pool. The application considers the mismatch between primer pairs and the coverage in the primer design process, thereby allowing the primer pairs to have extremely high coverage and lower degeneracy.

The present application discloses a kind of automatic performance classification equipment of uniform temperature plate, including rotary mechanism, carrier is set on rotary mechanism;Feeding mechanism, set in the feeding station of rotary mechanism, for transferring the product to be handled to carrier;Information acquisition mechanism, along the conveying path of rotary mechanism and set downstream of feeding mechanism, for obtaining the performance parameter identification information of product;Multiple classification mechanisms, along the conveying path of rotary mechanism and interval distribution downstream of information acquisition mechanism, each classification mechanism corresponds to the performance parameter category of pre-set respectively;Control unit, respectively with information acquisition mechanism and each classification mechanism are electrically connected, and control unit is used to control the classification mechanism corresponding to product from carrier according to the performance parameter identification information read by information acquisition mechanism.The present application is used to realize the automatic classification of uniform temperature plate, improve work efficiency, reduce process time consumption, avoid the situation of wrong classification and damaged product, save cost.

This invention provides an SNS article search support device and SNS article search support method that can help users view SNS search results from diverse perspectives. [Solution] The article search support server includes a search query input module, an SNS search module that performs an SNS search and retrieves multiple SNS articles as search results, a classification axis query module that generates multiple classification axes for analyzing multiple SNS articles using a generation AI and retrieves the generated multiple classification axes from the generation AI, an article score query module that assigns scores to SNS articles according to the classification axes using a generation AI and retrieves the SNS articles to which scores have been assigned from the generation AI, and an article layout module that uses a classification axis selected from the multiple classification axes to create graph information in which nodes representing multiple SNS articles are placed at positions corresponding to the scores.

The utility model discloses a kind of packing boxes with classification structure, including box body and cover plate, the box body upper end is set to open, and cover plate is buckled on the box body upper end, the box body both sides inner wall is all set to have sliding slot, and extend to the box body both sides outer wall, two groups of plate body are slidably connected in the sliding slot, multiple classification grooves are equidistantly set in the plate body end face, multiple groups of partition plate are fixedly connected in the box body bottom, multiple groups of partition plate divide the bottom of box body into multiple storage areas with same surface area, there is certain gap between two groups of plate body, sliding mechanism is installed on the cover plate outer wall.