81 results about "Group model" patented technology

The systems and methods disclosed herein receives artifacts generated using a first set of models within a multi-model superstructure. The multi-model superstructure includes a second set of models to test the first set of models. The multi-model superstructure dynamically routes the artifacts of the first set of models to one or more models of the second set of models by (i) determining a set of dimensions of the artifacts against which to evaluate the artifacts and (ii) identifying the models in the second set used to test the particular dimension. The second set of models then assesses each artifact against a set of assessment metrics. If an artifact fails to meet one or more assessment metrics, the second set of models generates actions to align the artifact with the set of assessment metrics.

The invention relates to a multi-model-question-based group consensus large model illusion reduction method, which comprises the following steps of: screening a Top-N model from a candidate model set according to a multi-dimensional comprehensive scoring result, and executing full-combination bidirectional knowledge distillation on the Top-N model to obtain an initial model group, loading a plurality of domain knowledge bases for each model in the initial model group to carry out domain self-adaptive fine tuning, and constructing to obtain a group model set; giving a user question, triggering a plurality of fine-tuned field expert models in the group model set to perform parallel reasoning to generate an initial answer, performing iterative optimization by constructing a question set, generating a question instruction and updating the answer, and calculating the similarity of group answers by adopting a mixed kernel function in the iterative optimization process to obtain a group answer set; and when the similarity and the stability reach preset threshold values at the same time or reach the maximum number of iterations, stopping iteration and outputting a result. Compared with the prior art, the method has the advantages of high answering accuracy, high field adaptability and the like.

The invention provides a traceable federal incremental learning method based on grouping feature aggregation, and relates to the technical field of federal learning, and the method comprises the following steps: S1, carrying out end node traceable task learning; carrying out local pruning and fine tuning based on the initial sharing model, and judging whether tasks are repeated or not according to label distribution similarity; s2, constructing a cloud-end side collaborative grouping model; sending the global shared knowledge generated by the server model and the aggregation weight corresponding to the global shared knowledge to the end node; s3, at the end node, the client updates own model parameters based on the local cross entropy loss, the global shared knowledge generated by the server model of each group and the aggregation weight corresponding to the global shared knowledge, and outputs the updated model; and S4, replacing the new sub-model in the S1 with the updated model or updating the sub-model, repeating the steps S1 to S3, and stopping training until specified training times are reached. The method aims to effectively cope with challenges caused by storage limitation and task repeatability.

The invention discloses a social group simulation method and device based on multi-agent driving. The method comprises the following steps: performing user attribute sampling processing on a real e-commerce user data set, and constructing agent basic attributes based on user attributes in combination with a large language model to obtain agent basic attribute features; performing construction processing on the agent basic behaviors based on a preset memory mechanism in combination with the agent basic attribute features; according to the agent basic attribute features and the agent basic behavior features, performing construction processing on agent interaction behaviors in combination with a large language model; performing relation network construction processing based on a small-world network model on the plurality of agents; and performing simulation processing based on execution agent behaviors on the agent social group model to obtain a multi-user behavior simulation result. By constructing a multi-agent simulation system in an e-commerce scene and combining the multi-agent simulation system with a large language model, social group simulation in a complex e-commerce interaction scene is realized, and the accuracy of social group simulation in the e-commerce scene is improved.

The systems and methods disclosed herein receives artifacts generated using a first set of models within a multi-model superstructure. The multi-model superstructure includes a second set of models to test the first set of models. The multi-model superstructure dynamically routes the artifacts of the first set of models to one or more models of the second set of models by (i) determining a set of dimensions of the artifacts against which to evaluate the artifacts and (ii) identifying the models in the second set used to test the particular dimension. The second set of models then assesses each artifact against a set of assessment metrics. If an artifact fails to meet one or more assessment metrics, the second set of models generates actions to align the artifact with the set of assessment metrics.

By combining a federated learning model and a low earth orbit (LEO) satellite network, the present invention provides a general federated learning framework over an LEO satellite network (FedSN) for achieving federated learning. The FedSN is composed of two main components: a sub-structure scheme and pseudo-synchronous model aggregation. The sub-structure scheme comprises sub-structure customization, distribution and aggregation methods, and respectively solves problems such as resource limitations, training imbalance, staleness of intra-group models. According to a pseudo-synchronous model aggregation strategy, the difference between weights of models is brought into a weight function, and a buffer-based aggregation method is developed, so that the staleness of inter-group models is reduced. Extensive experimental results show that a FedSN framework is superior to a state-of-the-art baseline. The present invention shows and improves the potential of deploying a FedSN on an LEO satellite network.

A federated learning method is provided, applied to the field of artificial intelligence technologies. According to the method, when obtaining models of different network structures, an aggregation node groups models of a same network structure into a same group, and performs parameter aggregation on models in a same group, to obtain a plurality of aggregation models of different network structures. In addition, for each aggregation model, knowledge distillation training is performed on each aggregation model based on the plurality of originally obtained models, to implement experience transfer between the models of different network structures, so as to integrate knowledge and experience of models of various network structures, combine advantages of parameter aggregation and knowledge distillation in integrating model experience, implement aggregation of the models of different network structures, and ensure prediction precision of a model obtained through aggregation.

The invention discloses a cloud edge federal learning method and system and a storage medium, and belongs to the field of model training optimization. Firstly, the cloud constructs a dynamic clustering mechanism and reduces intra-group statistical heterogeneity based on model features and data distribution information uploaded by an edge terminal, and the edge terminal performs local training and intra-group model aggregation according to a cloud clustering result to improve the consistency and adaptability of a local cluster model; secondly, a decoupling knowledge transfer mechanism is adopted, the global model and the local cluster model are decoupled into a feature layer and a classification layer respectively, hierarchical knowledge alignment is carried out in the distillation process, the learning ability of the local model for intermediate feature expression and classification decision boundaries is enhanced, and the distillation efficiency is improved; therefore, the convergence speed and generalization performance of the model in the heterogeneous data environment are improved. Therefore, the technical problems of model performance reduction and weak generalization ability caused by data heterogeneity in the cloud-edge federation in the prior art are solved.

The invention provides a departure training recommendation method and device based on a grouping model, and the method comprises the steps: a departure user behavior data collection step: collecting various types of data when a user uses a departure system, and carrying out the analysis and storage of a plurality of dimensions; a departure user grouping step: according to the collected data, performing grouping by using a clustering algorithm to obtain different user group type function lists; a training content recommendation step: real-time learning progress analysis is carried out, and a personalized training recommendation list is recommended for the user; and a departure front-end display step: displaying the training function recommendation list to the user, constructing test data based on the service object characteristics, and customizing a personalized training scheme for the user. According to the method, the clustering algorithm is adopted, the operation behavior data of the users are deeply mined and analyzed, the requirements of each user can be more accurately recognized, and personalized recommendation is achieved; and an advanced stream processing technology and a real-time analysis framework are utilized to respond to a user request in real time.

The systems and methods disclosed herein receives artifacts generated using a first set of models within a multi-model superstructure. The multi-model superstructure includes a second set of models to test the first set of models. The multi-model superstructure dynamically routes the artifacts of the first set of models to one or more models of the second set of models by (i) determining a set of dimensions of the artifacts against which to evaluate the artifacts and (ii) identifying the models in the second set used to test the particular dimension. The second set of models then assesses each artifact against a set of assessment metrics. If an artifact fails to meet one or more assessment metrics, the second set of models generates actions to align the artifact with the set of assessment metrics.

This disclosure provides systems, methods, and apparatuses, including computer programs encoded on computer storage media, for accessing information associated with an organization hierarchy. In one aspect of the disclosure, a method includes transmitting, from a device to a server in which multiple group models are stored, an access request to access a first group model of the multiple group models. Each group model of the multiple group models is associated with a different organization and includes multiple group data structures, multiple group type data structures, and multiple group member data structures. Each group model is associated with group hierarchy information that indicates a hierarchy associated with the multiple group data structures associated with the group model. The method further includes receiving, at the device and based on the access request, first hierarchy information associated with a first group model. Other aspects and features are also claimed and described.

The invention discloses an aviation fleet capacity and airline network matching method and system, and belongs to the technical field of airline operation management. The method comprises the following steps: 1) determining a basic set according to an airline network, and obtaining associated parameters; 2) constructing a two-stage model: firstly generating a transport capacity distribution scene of a multi-group model combination through integer linear programming, then inputting scene data and actual case data as decision units into an SBM-DEA model for solving, calculating a corresponding matching degree, and constructing a virtual boundary and a real boundary of an actual case; calculating the distance between the virtual boundary and the actual boundary of the actual case, and determining the difference between the virtual boundary and the actual boundary so as to determine a matching degree score; and 3) dynamically optimizing fleet configuration according to the matching degree of the aviation fleet capacity and the airline network and the matching degree score obtained by the two-stage optimization model.Real-time quantification of the matching degree is realized through the two-stage optimization model, the hysteresis defect of a traditional static method is overcome, and the utilization efficiency of fleet resources is remarkably improved.

Embodiments of the present application provide a model construction system, method, electronic device and storage medium. A model grouping unit is configured to, in response to a model selection instruction for a model database, obtain a main model and a plurality of candidate models corresponding to the main model, combine the main model with each candidate model to obtain at least two model groups, a feature grouping unit is configured to obtain a first model attribute and a first model feature of the main model in each model group, a second model attribute and a second model feature of the candidate model, and group the first model feature and the second model feature according to the first model attribute and the second model attribute to obtain a corresponding feature set, the feature set including a first feature set and a second feature set, and a model construction unit is configured to fuse the first feature set and the second feature set corresponding to each model group to obtain a fused model corresponding to each model group.

The present invention relates to a multifunctional drone-assisted asynchronous cluster personalized federated learning method, which belongs to the field of wireless communication technology. The method comprises the following steps: S1: establishing a drone-assisted FL network system model; S2: establishing a drone logistics transportation and assisted FL training model; S3: establishing a communication delay, model training time, and uplink delay model between the drone and the sub-server; S4: establishing a personalized federated learning mechanism, solving the personalized model in the inner layer and the global model in the outer layer, and training the personalized model through asynchronous two-layer parallel optimization and intra-group synchronous federated averaging mechanism; S5: optimizing the drone flight path using the group model staleness as the optimization variable; S6: adjusting the optimization variable according to the relationship between the model staleness and the number of user device training times, and proposing a drone path dynamic optimization algorithm based on deep reinforcement learning. The multifunctional drone path optimization algorithm provided by the present invention significantly improves the overall communication efficiency.

The embodiment of the invention provides an account distinguishing method and device, equipment, a storage medium and a program product, and relates to the field of financial science and technology. The method comprises the following steps: acquiring account data, a device fingerprint and a relationship between the account data and the device fingerprint, wherein the device fingerprint is used for identifying a device; generating a credibility evaluation result between the account data and the equipment fingerprint according to the relationship between the account data and the equipment fingerprint by using a pre-constructed credibility evaluation model; constructing a group model according to the account data, the device fingerprints, the relationship between the account data and the device fingerprints and the credibility evaluation result; and based on the group model, according to the credibility evaluation result, determining a group type corresponding to a group to which the account data belongs. According to the method, the group type to which the account belongs is dynamically identified through the group model and the credibility evaluation result, so that the group type to which the account belongs is accurately identified, and the accuracy of determining the group type to which the account belongs is improved.

The present invention discloses a grouped training method, a server, and a client based on distributed machine learning. The grouped training method splits the training task into corresponding group consensus models for training according to the local optimization objective gradient of the client, alleviating problems such as the decline in training convergence speed, the decline in model performance, and the increase in training jitter caused by conflicts in implicit sub-optimization objectives. At the same time, during the training process, the present invention also monitors the deviation of the local training data of the client. When the local training data of the client deviates, the grouping information of the client is updated so that each client can train in the group model with the most similar optimization objective, effectively reducing the deviation problem of the machine learning model caused by unbalanced training data distribution.

Feature importance is critical to understanding how predictive models produce accurate results, and can change significantly for different models. The present invention is used to achieve a good ranking for stable feature importance. An optimized technique is presented which considers feature importance value variation within different groups of cross-trained models. Feature importance is computed for all group models with this optimized method, and then a best set of models can be selected based on classification error as well as optimized stable feature importance values.

The present invention provides a traceable federated incremental learning method based on group feature aggregation, which relates to the field of federated learning technology and includes the following steps: S1, performing traceable task learning on end nodes; performing local pruning and fine-tuning based on the initial shared model, and determining whether the task is repeated based on the similarity of label distribution; S2, building a cloud-end collaborative grouping model; sending the global shared knowledge generated by the server model and the aggregation weights corresponding to the global shared knowledge to the end nodes; S3, at the end nodes, the client updates its own model parameters based on the local cross-entropy loss and the global shared knowledge generated by the server model of each group and the aggregation weights corresponding to the global shared knowledge, and outputs the updated model; S4, replacing the new sub-model or updated sub-model in S1 with the updated model, and repeating S1 to S3 until the specified number of training times is reached and training is stopped. The present invention aims to effectively address the challenges brought by storage limitations and task repetitiveness.

The invention provides a virtual power plant element accurate aggregation scheduling method considering demand response, which comprises the following steps: performing aggregation modeling on an energy storage equipment group according to equipment charge and discharge number constraint, charge and discharge power constraint, energy constraint and periodic electric quantity balance constraint, and constructing an aggregation energy storage model; carrying out aggregation modeling on the interruptible load group according to the interruptible interval, the non-interruptible interval and the cuttable time period, and constructing an aggregation interruptible load group model; carrying out aggregation modeling on the translational load group according to the number of translation state equipment and the translational interval, and constructing an aggregation translational load group model; carrying out aggregation modeling on the transferable load group according to the number of the transfer state devices and the total number of the devices, and constructing an aggregation transferable load group model; and constructing a virtual power plant scheduling model according to the above model for solving, and generating a scheduling strategy of the virtual power plant. According to the method, the solving efficiency of the scheduling model is improved, and a more efficient and more reliable virtual power plant scheduling decision is realized.

The invention provides a longitudinal federation joint reasoning method and system based on privacy protection and a storage medium, and the method comprises the steps: an initiating terminal executes local reasoning based on a local target feature data set and reasoning model parameters, and generates an intermediate result; determining the closest classification cluster as a competition cluster, calculating similarity measurement and disclosing a cluster index; the similarity measurement and the intermediate result are processed through secret sharing, share information is generated and exchanged with other terminals; the share information is aggregated according to the public cluster index, and complete similarity measurement and reasoning output of each group of models on the corresponding cluster are obtained through reconstruction; performing weighted fusion on the reasoning ability score by utilizing similarity measurement to obtain an evaluation value, and mapping the evaluation value into a model output weight through a nonlinear amplification function; and finally, performing weighted summation on each group of model output according to the weight to obtain an overall reasoning result. The inference efficiency of the longitudinal federal model under the secret sharing mechanism can be improved.

The invention provides a load group combination regulation capability credibility evaluation method. The method comprises the steps of obtaining multi-dimensional operation data of a user group; the multi-dimensional operation data are analyzed, a load group high-precision model is constructed based on an analysis result, and the load group high-precision model is used for representing dynamic response characteristics of a load group in different scenes; utilizing the load group high-precision model to predict and obtain multi-dimensional load dynamic response characteristics of the user group; and evaluating the overall regulation capability credibility of the load group based on the load dynamic response characteristics, the load declaration value and the actual value to obtain an evaluation result. According to the invention, the comprehensiveness and accuracy of the evaluation result can be improved, and a basis is provided for fine scheduling and decision making of a power grid.

The application discloses a kind of based on function-morphology interaction's building group automatic layout method and system, including the following steps: the design boundary data of target block, road network data, ecological environment data and existing building data are collected, and three-dimensional space digital sand table is constructed;Collect the building area of several case blocks, surrounding road attribute, building storey and land function data, construct land function and space form gray comatose matrix;Factor analysis is carried out to obtain function-morphology factor loading matrix file data, generate function-morphology association model based on knowledge graph;Function-morphology dynamic interaction building group generation is carried out using deep deterministic policy gradient algorithm, and building group layout scheme is generated;Input upper planning, output 3D block building group model object set after screening in accordance with the requirements of upper planning;3D block building group model is superimposed on digital sand table by screening, and the final scheme is determined to be output.

Disclosed herein is a method for evaluating an unsupervised clustering machine learning (ML) model. The method includes generating a set of model clusters via the unsupervised clustering ML model. Further, the method includes comparing a set of test set clusters and the set of model clusters. Further, the method includes categorizing each of the set of model clusters into an assessment group based on the comparison. The categorized assessment group is at least one of a match group, a correct group, a partial group, and an incorrect group. Furthermore, the method includes assigning a similarity value to each of the set of model clusters based on the categorized assessment group. Furthermore, the method includes determining a total similarity value based on combining the assigned similarity value of each of the set of model clusters, such that the total similarity value indicates evaluation of the unsupervised clustering ML model.

The invention relates to the technical field of prediction, and provides a data prediction method and device. The method comprises the steps of performing frequency domain moving average adaptive normalization on to-be-predicted data to obtain target normalized data; performing grouping modeling on the target normalized data to obtain inter-channel dependency data inside and outside a group; inputting the inter-channel dependency data into a large language model, and performing fine tuning on the large language model to obtain prediction data output by the large language model; and carrying out reverse normalization on the prediction data to obtain target prediction data. According to the method, the problems of non-stationarity, channel dependence and insufficient data volume of operation and maintenance data can be fully solved, so that the prediction accuracy and the prediction efficiency are improved.

The application discloses a learning resource recommendation method and system based on project field knowledge and user comments, and the method comprises the following steps: collecting learner information, learning resource characteristic information and teacher information, wherein the learner information comprises learner description information and learner interaction information with the learning resource, and the learning resource characteristic information comprises learning resource description information and learning resource characteristic information; finding a teacher with the highest similarity to the learner according to the learner information, and obtaining a matching score of a target learning resource according to the teacher characteristics through a convolutional neural network; establishing a learner short-term preference model and a long-term preference model, and fusing the two models to obtain a learner personal preference model; establishing a learner group preference model, and fusing the learner personal and group models to obtain a learner preference model; and establishing a learning resource characteristic information model and a field knowledge model by using various information characteristics of the learning resource according to the learning resource characteristic information, so that the accuracy of learning resource recommendation is improved.

Disclosed in the present disclosure are a model quantization method, a model operation method, a device, a medium, and a program product. The model quantization method comprises: grouping model parameters of each network layer in a model to be quantized; determining a quantization parameter group of any group on the basis of a target integer bit number required by a network layer to which the any group belongs, and quantifying the model parameters of the any group on the basis of the quantization parameter group; and replacing the corresponding model parameters in said model with the quantized model parameters of each group to obtain a quantized model. The model parameters are grouped for quantization to reduce the influence of outliers present in the model parameters on quantization errors.

The invention provides a wind power and photovoltaic field group resource scheduling optimization method and system, and relates to the technical field of energy scheduling, and the method comprises the steps: constructing a three-dimensional field group model according to a collected equipment set which is a set of all equipment in a wind power and photovoltaic field group; based on the three-dimensional field group model, calling a predetermined clustering strategy to perform clustering analysis on the equipment set to obtain a clustering result; a first node index of first equipment is analyzed, a first initial cluster center is determined, and the first equipment refers to any equipment in a first cluster in the clustering result; performing collaborative analysis on the first resource scheduling record of the first initial cluster center to obtain first prediction information; and carrying out resource scheduling optimization on the wind power and photovoltaic field group based on the first prediction information. According to the invention, the technical problem that the scheduling strategy cannot be adaptively adjusted according to the actual scene in the prior art can be solved, and the technical effect of improving the adaptability of new energy scheduling is achieved.

The application discloses a fusion large model intelligent teaching quality evaluation improvement system and relates to the technical field of wisdom education.The application realizes automatic identification of skill response behavior by means of a disturbance group modeling module and a behavior characteristic difference set of students between original questions and candidate comparison questions.System no longer depends on subjective judgment of teachers, but analyzes answer stability of students when facing semantically equivalent and structurally stable questions through quantitative index.When it is detected that the behavior path of students deviates by a high amplitude, the system can immediately determine that there are skill dependence and concept misplacement problems.For example, after answering the disturbance question, the correct rate of the answer decreases, which indicates that there is a strategic answer instead of concept reasoning, and the objectivity and traceability of the teaching quality evaluation are further improved through the knowledge graph of the original question stem.

The invention discloses an attraction domain boundary determination method and device of a wind power system and computer equipment. The method comprises the following steps: constructing a mathematical equation set model corresponding to a wind power system; constructing a random disturbance model based on the change factors of the wind power system; adjusting the mathematical equation set model based on a random disturbance model to obtain a target mathematical equation set; solving the target mathematical equation set to obtain a phase trajectory diagram; and determining a target attraction domain boundary of the wind power system based on the phase trajectory diagram. The technical problem that the risk of system chaotic oscillation existing in the attraction domain boundary of the chaotic attractor cannot be determined due to unstable output of a wind power system caused by uncertain factors such as wind speed change is solved.

In some implementations, a model system may receive an indication of the set of models that are associated with a set of data points. Each model in the set of models may have been selected using a grid search. The model system may receive, from a user device, a query associated with a selected data point in the set of data points. The selected data point may be associated with a corresponding model in the set of models. The model system may provide information included in the query to the corresponding model in order to receive a result associated with the selected data point. The model system may transmit, to the user device, the result in response to the query.