143 results about "Multiple experts" patented technology

The invention provides a request processing method and device based on a hybrid expert model, equipment and a medium, and relates to the technical field of artificial intelligence such as large language models, hybrid expert models, user portraits and knowledge maps. The method comprises the following steps: acquiring a target portrait of a user initiating a to-be-processed request; performing association expansion on the to-be-processed request according to the target portrait by using a preset knowledge graph to obtain a complementation demand including an expanded demand, and recording association relationships between different user entities and different behavior entities and between different preference entities in the knowledge graph; according to the intention actually expressed by the completion demand, determining a target vertical class to which the intention belongs; and sending the completion demand to a target expert sub-model corresponding to the target droop class in the current hybrid expert model for processing to obtain a processing result. According to the method, the accuracy of accurately positioning the target expert sub-model matched with the actual demand of the user in the multiple expert sub-models is improved, and then the accuracy of the request processing result is improved.

The embodiment of the invention discloses a collaborative reasoning method and device based on a multi-expert model and a medium, and relates to the technical field of collaborative reasoning, and the method comprises the steps: receiving a plurality of reasoning task requests, so as to obtain the task feature information of each reasoning task; based on the task feature information of each reasoning task and pre-acquired model registration information, performing task allocation to determine at least one allocation expert model corresponding to each reasoning task; predicting the model load of each distributed expert model, adjusting at least one distributed expert model corresponding to each reasoning task according to the predicted load rate, and determining at least one corresponding target expert model; and issuing each reasoning task to the corresponding target expert model, collecting single model reasoning data of each target expert model through an asynchronous message queue, and determining a collaborative reasoning result corresponding to each reasoning task through the single model reasoning data of each target expert model.

The invention relates to the technical field of artificial intelligence, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a signal processing method, device, equipment and medium based on mixed experts. And generating routing information based on the channel information, dynamically fusing expert output, generating a first task processing result by using the first task processing network, generating a second task processing result by using the second task processing network, jointly optimizing all network parameters, and generating a hybrid expert model for a to-be-processed signal. According to the method, the hybrid expert architecture aiming at the multi-channel characteristic is introduced, and the gating network is combined to dynamically allocate the characteristic processing path, so that the self-adaptive optimization aiming at different channel environments is realized, the accuracy of cross-channel identification and the resource utilization efficiency of the whole model are effectively improved, and the robustness of the cross-channel identification is improved. And high-precision, high-robustness and efficient signal identification can be realized in a complex and changeable environment.

The invention discloses an expert model training method and device, a storage medium and electronic equipment, and relates to the technical field of computers, and the method comprises the steps: determining a target expert network group from a plurality of expert network groups included in an initial expert model according to an estimated resource occupation condition of input data; taking a plurality of expert networks in the target expert network group as a current expert network in sequence, and determining an individual difference evaluation coefficient matched with the current expert network; determining at least one target expert network from the plurality of expert networks according to the individual difference evaluation coefficients of the plurality of expert networks in the target expert network group; and processing the input data according to the at least one target expert network, and adjusting the model parameters in the initial expert model according to the load state description information corresponding to the at least one target expert network, thereby solving the technical problem of inaccurate selection of the expert network in the prior art.

The present invention belongs to the field of image classification and designs a long-tail image data classification method based on mixed samples. The present invention proposes a new solution to the long-tail training set problem encountered in image classification research. It aims to use three experts with specialized knowledge to jointly assist the algorithm in making the final decision, avoiding problems such as excessive deviation of the model classifier weight caused by a single model. The present invention is suitable for business scenarios of image classification with long-tail data distribution. By designing multiple experts with specific field knowledge, the classification performance of the model for all frequency distribution types is improved without losing the accuracy of the head class classification. It provides a solution for the actual engineering application of image classification when the data has a long-tail distribution, alleviates problems such as data collection difficulties, improves the overfitting of the algorithm model to the head class data, and improves the learning ability of the tail class data.

The invention relates to the technical field of artificial intelligence, and particularly provides a data processing method of a large language model based on an improved hybrid expert architecture. The method comprises the following steps: processing input query information through a semantic understanding module to obtain an understanding result; based on the understanding result and a pre-stored knowledge graph, the query information is sent to a plurality of corresponding expert models, corresponding output results are obtained by the expert models, the plurality of expert models are arranged in multiple layers based on an improved hybrid expert architecture, and the expert models of different layers are obtained through division according to different expert dimensions; and carrying out fusion processing on the output results of the plurality of expert models to obtain an answer result corresponding to the query information. The problems that a large language model of a hybrid expert architecture in the related technology is low in accuracy and poor in problem solving capacity in the professional subdivision field are solved.

The invention relates to the technical field of industrial process prediction, in particular to a tundish erosion prediction method based on a hierarchical hybrid expert framework. The method comprises the following steps: collecting time sequence physical field data of the tundish, and screening features to construct a unified feature space; shunting the feature space to obtain a time sequence feature and a statistical aggregation feature; a statistical aggregation feature training classifier is utilized to generate a calibration posterior probability, and a gating network is constructed; generating an initial mode subset based on a posterior probability and training a corresponding expert model; the confidence of data to be measured is obtained by the gating network, and a single expert model is selected for prediction or multiple expert models are fused through a self-adaptive strategy for weighted prediction according to whether the confidence exceeds a threshold value or not; and finally, reconstructing the predicted value into an erosion thickness absolute value through inverse transformation. According to the method, the accuracy and adaptability of tundish erosion prediction are effectively improved.

The invention provides a laser powder bed melting method for preparing a high-toughness TC4 titanium alloy based on machine learning. The method comprises the steps that performance data of the TC4 titanium alloy prepared through different SLM process parameters are collected; constructing a hybrid expert (MoE) machine learning prediction model framework; classifying the TC4 data by adopting a K-means clustering algorithm, and taking a clustering center as an initial weight of an input layer and a first layer of the gating network; presetting a TC4 performance prediction model through the gating network and the plurality of expert networks after clustering information initialization; performing small-range iteration through large-step network search in combination with a Bayesian optimization method so as to determine an optimal model structure and complete model training; the trained performance prediction model and a genetic algorithm are used for carrying out backstepping to obtain SLM printing parameters of the high-toughness TC4 alloy; and SLM forming is carried out, and the high-toughness TC4 titanium alloy is prepared. According to the method, a calculation framework which is high in precision and suitable for exploring a high-unknown characteristic space is constructed, and far-reaching influences on titanium alloy additive manufacturing and process optimization of other alloy systems are achieved.

The invention discloses a mechanical equipment residual life prediction method based on multiple expert models, and belongs to the technical field of mechanical equipment. Comprising the steps of receiving an original mechanical vibration signal, performing preprocessing, constructing a residual life prediction model of the mechanical equipment, training the residual life prediction model of the mechanical equipment based on a preset data set, inputting a mechanical vibration signal to be detected into the trained residual life prediction model of the mechanical equipment, and performing fault category prediction of the mechanical equipment. According to the method, the accuracy, generalization and practicability of residual life prediction of mechanical equipment are remarkably improved, and through multi-scale time-frequency domain feature fusion and a double attention mechanism, the identification capability and noise immunity of the model to a complex fault mode are enhanced; the domain adaptive routing network realizes cross-device efficient knowledge migration, greatly improves the prediction performance on unseen devices, has high precision and high real-time performance, and provides reliable technical support for industrial predictive maintenance.

The invention discloses a routable distributed fingerprint embedding and migrating method and device based on hybrid experts, and belongs to the technical field of large language model intellectual property protection. The basic model is transformed based on the hybrid expert system architecture, the basic model is divided into a plurality of expert sub-modules and a gate control module, at least one expert sub-module is designated as a fingerprint expert module, and the gate control module is used for routing the trigger sample to the corresponding fingerprint expert module; the fingerprint expert module is finely adjusted based on the fingerprint data set, and model parameters embedded with fingerprint features are stored in an independent low-rank adapter; combining the fine-tuned fingerprint expert module and the low-rank adapter thereof with the expert sub-module of the corresponding level in the downstream model to realize fingerprint migration; and inputting the trigger sample into the downstream model after fingerprint migration for copyright verification and evaluation. According to the method, the model performance, the security and the verifiability are considered, and the adaptability of the fingerprint technology in an industrial large model deployment scene is improved.

The invention discloses a processing method, system and device for intelligent questionnaire survey and a medium, and the method specifically comprises the steps: obtaining questionnaire data and evaluation parameter setting inputted by a user, calling a main model to carry out universal dimension inspection on a questionnaire, and obtaining a preliminary analysis result; according to a preliminary analysis result, distributing different types of problems to corresponding expert models for deep analysis; in combination with culture feature data inquired in real time, a culture suitability score is generated to supplement an analysis result of the expert model; based on the analysis results of the plurality of expert models, performing weighted fusion processing on the score of each dimension by using a weight coefficient dynamically adjusted through a reinforcement learning mechanism to obtain a comprehensive evaluation score; the comprehensive evaluation score is returned to the user interface, feedback data are collected after the user completes interaction, and the feedback data are used for iteratively updating model parameters and setting evaluation parameters. According to the invention, efficient and accurate examination of questionnaire contents is realized.

Embodiments herein describe a system including a plurality of hardware accelerators including at least one mixture-of-experts (MoE) layer having multiple experts and a plurality of network interface cards (NICs) coupled to the plurality of hardware accelerators, wherein at least one expert of the multiple experts is offloaded from the plurality of hardware accelerators to the plurality of NICs. The plurality of hardware accelerators may be graphics processing units (GPUs). In one example, a subset of the multiple experts are selectively offloaded from the plurality of GPUs to the plurality of NICs based on memory and computational capacity available on the plurality of NICs. In another example, the multiple experts are designated as either hot experts or cold experts. The cold experts are offloaded from the plurality of GPUs to the plurality of NICs and the hot experts are duplicated for each of the plurality of GPUs.

The embodiment of the invention discloses a construction method of a cross-platform high-performance tensor program generation model and a related device. The method comprises the steps of obtaining a training data set and tensor program tuning features; training a large language model according to the training data set to obtain a base model; classifying the first hardware according to the tensor program tuning features to obtain a plurality of hardware categories; a plurality of expert layers and a knowledge aggregation layer connected with the expert layers are added in the base model, a cross-platform high-performance tensor program generation model is obtained, and each expert layer corresponds to one hardware category. And the knowledge aggregation layer is used for aggregating the relationship between the first hardware of the different hardware categories learned by the plurality of expert layers and the high-performance tensor program. By adopting the cross-platform high-performance tensor program generation model constructed in the embodiment of the invention, the efficiency and the performance of generating the high-performance tensor program on a multi-hardware platform can be remarkably improved.

The invention is suitable for the technical field of network security, and provides a network attack detection method and device, computer equipment and a program product, and the method comprises the steps: obtaining to-be-detected network traffic data; inputting the network flow data into a preset gating network to generate a routing weight value used for selecting from a plurality of expert models; processing the network flow data by using one or more of the plurality of expert models to generate an expert model processing result; wherein the plurality of expert models are arranged in parallel, and at least comprise: a first expert model configured to be used for analyzing time sequence features of the network traffic data; the second expert model is configured to be used for analyzing space-time correlation characteristics of the network flow data; and based on the routing weight value, fusing the expert model processing results to generate a network attack detection result. Therefore, diversified network attacks can be identified efficiently and accurately.

According to the intelligent question and answer method and device for the multi-agent cooperative customer service system, the storage medium and the related equipment provided by the invention, multiple expert agents are introduced, intention recognition, question processing and result generation are carried out in combination with the large language model, and flexible adaptation to complex services is realized. According to the method, the intelligent level of a customer service system is improved, the collaboration among the agents is enhanced, and the later maintenance cost is reduced, so that non-technical personnel can easily configure prompt words, processing steps, function calling and the like of expert agents through a management background; and different expert agents can solve different problems, and the agents can be increased or decreased through the management background, so that the service adaptability and expandability are effectively improved.

The application provides a miRNA-disease relationship prediction method, system and model based on a hybrid expert model and a storage medium. The method comprises the following steps: obtaining a miRNA-disease correlation matrix of multi-omics data of a miRNA-disease to be predicted, a miRNA similarity matrix and a disease similarity matrix; constructing a miRNA-disease heterogeneous graph, a miRNA homogeneous subgraph and a disease homogeneous subgraph, and fusing them into a multi-modal biological graph network; inputting the fusion network into a pre-trained gated hybrid multi-expert network model to output a correlation probability of a miRNA-disease pair to be predicted; and the gated hybrid multi-expert network model comprises multiple expert networks, a gating network and an output layer. The application adopts a gating mechanism to adjust the weights of each expert for miRNA-disease prediction, maintains high prediction accuracy on a small-scale data set, and has the advantages of portability and light weight.

The embodiment of the invention discloses a model training method and device. According to the embodiment of the invention, after an initial model and training sample sets are obtained, input of the initial model is determined according to task related information in each training sample set, and based on a multi-task structure of the initial model, embedded dense vectors corresponding to different tasks and shared embedded dense vectors are respectively calculated by utilizing a plurality of expert networks of the initial model; respectively calculating corresponding task mixing vectors through gating networks of different tasks, and then inputting the different task mixing vectors into corresponding task towers to respectively obtain an execution time length predicted value and a bearing parameter predicted value; and calculating multi-task joint loss as a training target of the initial model based on the predicted value, the task execution duration and the task bearing parameter, and training the initial model based on the multi-task joint loss to obtain a data prediction model. Therefore, the dependency of the model on the data richness can be reduced while the accuracy of task execution time length prediction can be guaranteed.

The invention provides a large language model enhancement method and system based on a dynamic adapter, and the method comprises the steps: firstly, carrying out the expansion of a backbone network of a pre-trained large language model, adding a plurality of expert adapters, and inserting a gating network into the expert adapters; the particular expert adapter is then dynamically routed and activated using the gated network according to the input token. Next, a fusion adapter switching algorithm is employed, which is designed to merge the parameters of the activated expert adapter into the original parameters of the backbone network, thereby obtaining a fused backbone network. And finally, using the fused backbone network to execute forward calculation of large language model enhancement according to an input token so as to generate a decoding result. According to the fusion adapter switching algorithm, the calculation overhead can be reduced, and the reasoning delay is remarkably reduced; and dynamic adapters are integrated, so that the performance and efficiency of the large language model are enhanced.

The invention discloses an acquisition terminal test case priority ranking method based on a fuzzy analytic hierarchy process, which comprises the following steps of: applying the fuzzy analytic hierarchy process to the field of acquisition terminal test, and quantizing fuzzy evaluation opinions of experts by triangular fuzzy through constructing a'target-criterion-scheme 'hierarchical structure; fusing a multi-expert judgment matrix by adopting a weighted geometric averaging method, and optimizing a criterion weight and performing defuzzification in combination with consistency check; and comprehensive priority scores of the test cases are calculated through weighted summation, a sorting sequence from high to low is generated, and meanwhile, dynamic adjustment during demand change is supported. According to the method, the fuzzy demand quantification accuracy can be improved by 60%, the expert dispute rate is reduced to 8% or below, the regression test error detection efficiency is improved by 40%, and the method adapts to the multi-scene test demand of the acquisition terminal.

The invention belongs to the technical field of industrial facility safety assessment, and particularly discloses an overdue service pressure pipeline risk assessment method and terminal equipment, and the method comprises the steps: constructing a fault tree with the failure of a pressure pipeline as a top event; obtaining a fuzzy number corresponding to a plurality of expert ratings of each bottom event in the fault tree, and determining a relative consistency degree of the expert ratings according to the fuzzy number; clustering the fuzzy number by using a similarity clustering method, and determining an optimal relaxation factor according to a clustering result; determining the failure probability of each bottom event according to the relative consistency degree of expert rating and the optimal relaxation factor; and determining the failure probability of the top event according to the failure probability of each bottom event. According to the method, the optimal relaxation factor is determined by combining the fuzzy number of expert rating, and the relaxation factor is improved, so that the influence of subjectivity is remarkably reduced, and the reliability of risk assessment of the overdue service pressure pipeline is greatly improved.

The application provides a kind of multimodal data-driven product crowdfunding success rate prediction method and system, it is related to deep learning technical field.The hierarchical hybrid model is constructed, and the structured data is filtered to obtain the structured features by structured branch;Text information and picture information are processed by text processing model and image processing model respectively to obtain semantic features and visual features;According to the activation probability of multiple expert networks obtained by semantic features and visual features, the expert network corresponding to the larger activation probability is activated, if the crowdfunding project focuses on aesthetic related products, the beauty expert or creativity expert will be activated, and the specialized implicit features are obtained;These specialized implicit features and structured features are input into the feature fusion model to obtain the prediction success rate.In processing unstructured data closely related to product appearance and aesthetic features, expert networks can highlight and strengthen these features, improving the prediction accuracy of aesthetic crowdfunding projects.

This invention discloses a microscopic denoising method based on multi-expert judgment, relating to the field of computer image enhancement technology. The method includes: acquiring original microscopic image data; preprocessing the image data; calculating initial expert weights through an expert weight threshold discrimination network; inputting the preprocessed data into multiple expert denoising networks for denoising; calculating result weights based on the denoising results and performing weighted fusion; and generating a denoised microscopic image. By introducing a multi-expert structure and threshold discrimination mechanism, this invention can adaptively denoise under various observation objects and imaging conditions, significantly improving the generalization ability of the denoising method. This invention, through a multi-expert mechanism, overcomes the shortcomings of current denoising methods, such as weak generalization and applicability limited to specific observation sample types or imaging conditions.

This invention relates to a method and system for detecting anomalies in wastewater treatment processes based on a hybrid expert model. The method includes: constructing a multimodal input vector for the wastewater treatment plant; inputting the multimodal input vector into a hybrid expert model, and performing anomaly inference through multiple expert networks selected by sparse gating to obtain anomaly prediction results; dynamically calculating the weight coefficients of each expert network based on the feature distribution of the multimodal input vector, and using the weight coefficients to perform weighted fusion of the anomaly prediction results to generate a preliminary comprehensive detection result; performing consistency verification on the anomaly prediction results, and triggering a thought chain inference mechanism for analysis if the verification fails, and updating the preliminary comprehensive detection result based on the analysis results; performing multi-level risk assessment and decision-making on the optimized comprehensive detection result, and generating a process anomaly report including risk level and disposal recommendations. This invention can reduce false alarms and false negatives, and improve the accuracy and efficiency of anomaly identification and decision-making in wastewater treatment plants.

The present application relates to the field of intelligent automatic driving, and particularly relates to a knowledge-driven end-to-end automatic driving method based on a sparse expert mechanism and a diffusion model. The method comprises the following steps: S1: perception information processing and state coding; S2: sparse expert module construction and multi-task training; a sparse expert module composed of multiple experts is constructed, and reusable driving skills are obtained through multi-task behavior cloning training; S3: diffusion strategy network and action sequence generation; based on the diffusion model, a future multi-step control action sequence is generated from the current state condition, and a continuous and stable driving decision is formed; and S4: continuous learning and task migration mechanism. The present application constructs a modular driving knowledge structure that is combinable and interpretable, significantly improves the strategy modeling capability; the diffusion generation mechanism effectively improves the smoothness and stability of the decision-making process; the continuous learning and task migration mechanism with structural decoupling improves the long-term adaptability and deployment efficiency of the system.

The invention relates to an industrial control system anomaly detection method based on instruction grouping, which belongs to the technical field of industrial control, and comprises the following specific steps: during the operation of an industrial control system, continuously collecting data packets with an application layer protocol as an industrial control protocol, analyzing each data packet into a single protocol field group, determining a current sample label, and sending the current sample label to the industrial control system; the protocol field group and the sample label form a training sample, and a plurality of training samples form a training data set. And obtaining a plurality of classified expert model training data sets according to the classification of the instruction type to which the sample belongs. And training each expert model by using the corresponding expert model training data set to obtain a plurality of trained expert models. And connecting the plurality of expert models in parallel. And inputting the current protocol field group into the anomaly detection model to obtain an anomaly detection result. The problem that an existing anomaly detection method based on ensemble learning does not consider the relation between different types of instructions, and the anomaly detection precision is not high is solved.

The invention discloses a leg posture analysis and rating system for a football player in a ball kicking process, and the system comprises a data collection unit which collects four-stage kinematics parameters of kicking, kicking and stretching, backward swinging, forward swinging and ball touching through a three-dimensional motion capture system and a high-speed camera; the data processing unit is used for processing parameters and extracting characteristic parameters of each stage; the expert evaluation interface is used for receiving evaluation data of multiple experts on different ball kicking technology types on the multi-score influence factors; the weight calculation unit is used for calculating an influence factor attribute weight by using a Bayesian method based on expert data; the rating decision matrix construction unit is used for constructing a comprehensive matrix in combination with a weight rough projection method; the multi-angle analysis unit is used for performing multi-angle risk rating by fusing a rough set theory and an approximate ideal solution sorting method; and the result output unit is used for outputting the posture quality grade and the training suggestion. According to the method, the actual requirements of football training on precision, standardization and comprehensiveness of posture analysis can be met.

The present disclosure relates to the field of knowledge graph technology and proposes a knowledge graph representation learning method and system based on a hybrid expert algorithm, comprising the following steps: obtaining a triple to be processed, performing global embedding to obtain a global embedding representation of the triple; obtaining an expert embedding representation corresponding to each expert model for the triple to be processed, and connecting it with the global embedding to obtain a connected embedding representation of each expert; performing routing selection based on a routing mechanism for the triple to be processed, assigning models to different relationships, and obtaining a weight for each expert model; performing weighted mixing on the obtained connected embedding representation based on the weight obtained by routing selection to generate a final triple embedding. The present disclosure uses the collaborative learning of multiple expert models, combined with the routing mechanisms of gate routing and attention routing, to assign appropriate models to different relationship types to improve the performance of representation learning.

The application provides a resource allocation method, an electronic device, a storage medium and a program product, relates to the technical field of computers, and is used for predicting the probability of passenger cancellation of resources to determine whether to allocate resources to passengers. The method comprises the following steps: determining initial features based on passenger data of a target passenger and resource data of to-be-issued resources; inputting the initial features into each expert network respectively to obtain multiple expert extraction features; each expert network corresponds to a resource allocation target; the expert network is used for extracting the correlation between the multiple initial features related to the resource allocation target to obtain the expert extraction features; weighting and fusing the multiple expert extraction features based on the weight corresponding to each resource allocation target in the multiple resource allocation targets to obtain fused features; predicting the prediction probability of the target passenger using the to-be-issued resources based on the fused features; and if the prediction probability is greater than or equal to a preset value, allocating the to-be-issued resources to the target passenger.

The invention discloses a radar intelligent interference suppression software test evaluation method, and relates to the technical field of radar test, and the method comprises the steps: S1, constructing a radar intelligent interference suppression software evaluation index system, and storing multi-dimensional indexes in the form of a knowledge graph; s2, designing a radar intelligent interference suppression software comprehensive evaluation algorithm, and quantitatively representing randomness and fuzziness in an evaluation process through effective data analysis, screening and integration of scoring opinions of multiple experts; s3, performing radar intelligent interference suppression software test evaluation; according to the method, a radar intelligent interference suppression software evaluation index system in a real application environment is constructed, the defects of a traditional index in the aspect of evaluating the operation capability and intelligence of radar software are overcome, and a comprehensive evaluation algorithm is provided; according to the method, the problem of evaluation deviation caused by large cognitive difference of people on the new concept of radar software intelligence can be solved, so that the evaluation result is more in line with subjective cognition.

This case involves a target contour uncertainty error quantification method based on conditional constrained probability generation, which is used to solve the problem that existing technologies cannot provide intuitive explanations of nodule ultrasound diagnostic results, and that existing technologies find it difficult to simultaneously learn clinical opinions from multiple experts. The method proposes a target contour uncertainty error quantification method based on conditional constrained probability generation. It captures the semantic and structural coding information in nodule ultrasound images by establishing a deep learning model, maps the coding information to the latent space distribution, and then performs sampling. Based on the sampling results, the target contour segmentation results and uncertainty quantification are obtained. The method uses the model to learn the annotation distribution from multiple experts, outputs the uncertainty quantification of its own prediction results, effectively improves the interpretability of the model, and helps doctors identify high-risk areas of nodules, thereby enhancing the transparency and reliability of the method in actual clinical applications.