219 results about "Model configuration" patented technology

The present invention discloses a system and method for performing artificial intelligence (AI) safety red-teaming with integrated policy fuzzing and adversarial prompting to systematically identify, characterize, and mitigate unsafe or non-compliant behaviors in AI models. The disclosed invention automates the process of generating, executing, and analyzing adversarial test cases through coordinated functional units comprising a policy fuzzing unit, an adversarial prompting unit, an execution sandbox, a telemetry processing unit, a scoring and triage processor, and a cryptographic provenance processor. The system applies grammar-driven and reinforcement-based fuzzing techniques to vary policy descriptors, model configuration parameters, and instruction hierarchies, while a learned adversarial prompt generator synthesizes contextually coherent adversarial prompts optimized for maximum policy violation likelihood. The generated prompts and policy vectors are executed in an isolated, instrumented sandbox that records input-output interactions, timing characteristics, and intermediate representations.

The invention discloses an engineering field-oriented AI agent construction and management and control system. The system comprises a data acquisition module, a data preprocessing module, a vector knowledge base module, a large model module, a model configuration strategy module and an agent construction module. The data acquisition module is used for acquiring related data of an engineering project; the data preprocessing module is used for processing original engineering data; the vector knowledge base module is used for performing semantic analysis and feature extraction on the processed data; the large model module is used for deep understanding, semantic analysis and intelligent reasoning of engineering project data; the model configuration strategy module is used for screening out a large model most suitable for a certain task; and the agent construction module is used for constructing an agent. The AI capacity can be managed in a unified mode, model selection and data processing are optimized, repeated construction is effectively avoided, the safety risk is reduced, and the intelligent agent performance and the application effect are improved.

The invention discloses a medical ethical analysis report generation method based on a multi-model game, and the method comprises the steps: analyzing a medical ethical case, and constructing a structured element set and a role model configuration set; implementing a multi-model game, dynamically evolving the demonstration intensity through an excited state attenuation mechanism, and obtaining an evolution demonstration intensity matrix, game track data and a resonance suppression record; analyzing the evolution argumentation intensity matrix and game trajectory data, mining viewpoint fusion and crystallizing structural divergence, and obtaining a fusion viewpoint set, a crystallization divergence set and an ethical tension map; and integrating and generating a medical ethical analysis report. According to the invention, the dynamic authenticity of the medical ethical analysis process and the objectivity of the result are improved.

The invention discloses a differential chlorophyll fluorescence inversion method, system and device and a storage medium, and is applied to the field of vegetation remote sensing and physiological ecological monitoring, and the method comprises the steps: obtaining vegetation state monitoring data of a target region; on the basis of vegetation state monitoring data, asynchronous leaf-changing type evergreen arbors and synchronous deciduous type arbors are identified; carrying out SIF simulation on the asynchronous leaf-changing type evergreen arbors and the synchronous deciduous type arbors by adopting corresponding models respectively; according to the phenological phase function and the stress response function, generating correction factors corresponding to the asynchronous leaf-changing evergreen arbors and the synchronous deciduous arbors; the obtained initial SIF value is optimized through a corresponding correction factor; fusing the optimized SIF values to generate an SIF time sequence product; according to the method, differential model configuration and physical mechanism driven online correction are coupled, so that the problems of model mismatching and physiological mechanism deficiency in heterogeneous vegetation SIF inversion are solved, and an SIF time sequence product which is high in precision and clear in physiological and ecological significance is generated.

According to the large language model reasoning optimization method and device, the storage medium and the computer equipment provided by the invention, the multi-dimensional monitoring data is acquired, and then the multi-dimensional monitoring data is input into the pre-trained environment decision network to obtain the optimal model configuration. The current resource state is evaluated according to the multi-dimensional monitoring data, and the optimal execution path is determined according to the resource state and the optimal model configuration. And finally, adjusting the target model based on the optimal model configuration, and performing model reasoning according to the optimal execution path in the reasoning process of the target model. In the process, the resource state is evaluated and the optimal model configuration is determined by collecting the multi-dimensional detection data, and then the optimal execution path is dynamically determined, so that the model structure and the calculation strategy can be dynamically adjusted according to the state during actual operation, and the response delay is effectively reduced and the resource utilization efficiency is improved while the reasoning precision is guaranteed.

The invention provides a multi-dimensional and dynamic digital assessment system and assessment method. Comprising a support module (comprising an index knowledge base, an algorithm operation base and an evaluation model base), a multi-source index fusion center (for constructing a three-dimensional data model and integrating a multi-dimensional data resource pool), a dynamic rule configuration engine (for realizing flexible configuration through a visual interface, a model adaptation mechanism and double-track verification) and a core business module (for managing assessment indexes, evaluating indexes and evaluating indexes). And evaluating the model and plan). The support module lays a basic resource layer, a fusion center constructs a data base, a configuration engine realizes flexible verification, the business module drives full-process execution, and full-process digital management from index creation, model configuration to examination execution is realized. The problems of iteration lag, multi-scene adaptation redundancy, weak data verification and the like of a traditional system are solved, index legality is guaranteed through static grammar detection and dynamic simulation operation, data traceability is achieved through a temporary database and full-link recording, and the dynamic response capacity, scene compatibility and data reliability of the assessment system are improved.

The invention relates to a production line vehicle type configuration transmission and verification method, and the method comprises the steps: obtaining vehicle type configuration data corresponding to a work-in-process when an intelligent transportation trolley enters an initial station of a production line; the vehicle model configuration data are written into the flexible conveying bracket provided with the work-in-process product; the intelligent transportation trolley is controlled to carry the flexible conveying bracket to enter a plurality of target stations matched with the vehicle type configuration data; when the intelligent transportation trolley arrives at any target station, vehicle type configuration data are read from the flexible transportation bracket; verifying the configuration state of the current station based on the vehicle type configuration data; if verification is not passed, the target station is controlled to execute configuration switching operation, and verification is executed again after configuration switching is completed; and if the verification is passed, controlling the target station to execute the corresponding production operation. By adopting the method and the device, the consistency of the vehicle type configuration data acquired by the station and the actual vehicle type configuration data of the product in process can be improved, and the processing abnormity caused by wrong station configuration is reduced.

The invention relates to a causal structure discovery method based on evolutionary neural architecture search and reinforcement learning, and the method comprises the steps: obtaining to-be-detected observation data, inputting the to-be-detected observation data into a causal structure detection model, and obtaining a directed acyclic graph composed of highest confidence coefficient edges; the causal structure detection model is obtained by training an Actor-Critic model by using a training set; the Actor-Critic model comprises an actor network model and a commentator network model; in the training process, the model configuration of the Actor-Critic model is optimized based on an evolutionary algorithm, and the optimal configuration is obtained; and inputting the observation data in the training set into the actor network model under the optimal configuration to obtain a candidate directed acyclic graph, and evaluating the candidate directed acyclic graph by the commentator network model under the optimal configuration to assist in strategy optimization to obtain a candidate directed acyclic graph. And strengthening the actor network model under the optimization strategy through a reinforcement learning algorithm to tend to output an optimal causal graph structure.

The invention discloses an asynchronous intelligent control method for a real-time simulation system. The asynchronous intelligent control method is based on a simulation environment construction module, an asynchronous feedback acquisition module and an intelligent control decision module. The simulation environment construction module is used for loading nodes, channels and model configuration files and defining simulation operation parameters; the module supports node starting, stopping and parameter adjusting operation, and an executable interface is provided for subsequent intelligent control; the asynchronous feedback acquisition module is used for periodically acquiring node states, link quality, throughput rate and time delay indexes through a probe module in a simulation running process, and writing the node states, the link quality, the throughput rate and the time delay indexes into a state cache in an asynchronous mode; the intelligent control decision module is used for carrying out analysis and strategy reasoning on the asynchronously acquired state by utilizing a reinforcement learning or deep reinforcement learning algorithm; the intelligent agent generates an action decision according to the current state, and outputs a node parameter adjustment instruction, an interface control instruction, a power configuration instruction or a flow scheduling control instruction to the environment construction module; the decision-making process can be continuously carried out in simulation operation, and dynamic self-adaptive optimization is achieved.

Various aspects of the present disclosure relate to quasi co-location (QCL) indication for AI / ML-based model configuration. An apparatus, such as a UE, receives, from a network entity, an artificial intelligence (AI)-based configuration corresponding to signal transmission and / or signal reception by the UE, where the AI-based configuration is associated with one or more AI models, and an AI model is associated with a dataset that is configured with a set of condition parameters. The UE measures a set of report parameters responsive to a signal received from the network entity, and the set of report parameters are associated with the set of condition parameters of the dataset of the AI model. The UE transmits, to the network entity, one or more feedback parameters based at least in part on the set of report parameters, and the one or more feedback parameters usable by the network entity to select the AI model.

The invention provides an embedded operating environment-oriented AI model cross-platform deployment method and system. The method comprises the following steps: generating a model configuration file conforming to a heterogeneous model configuration information standard format to realize model decoupling; the running state of the heterogeneous processor is collected in real time and abstracted as a reasoning resource node, and static or dynamic deployment is executed according to the priority and the running state for efficient resource scheduling; bottom layer differences are shielded by calling a unified packaged standardized inference engine service interface, and model inference is completed; and starting an AI model file dynamic updating process in application software operation, and dynamically switching the AI model file under the condition that the system does not need to be restarted through legality, content change and check value consistency check. According to the method, the cross-platform portability, the heterogeneous resource utilization efficiency and the system reliability of the AI model application software are remarkably improved.

The invention discloses a supply chain demand prediction system based on federated learning, and belongs to the field of federated learning, and the system comprises the steps: constructing a local data processing module in each node based on a federated learning method, carrying out the preprocessing of original data through employing an encryption technology, and carrying out the operation of a local model training process on each node according to a data feature set, analyzing the feature set through a preset training parameter to obtain a parameter update value of the local model; collecting a parameter update value from each node through a global coordination mechanism, and fusing parameters of multiple nodes by adopting an aggregation algorithm to obtain a preliminary update result of a global model; according to the corrected global parameter set, an updating instruction is distributed to each node, the parameter set is adapted through a local module, and local model configuration of each node is obtained; and for local model configuration, executing a prediction task at each node, and processing real-time input of market environment data to obtain a local demand prediction result of each node.

The invention discloses a direct-current cable terminal structure optimization method and device based on a double-layer integrated stacking optimization proxy model and a medium. The method comprises the following steps: (1) carrying out trapezoid-chamfer parametric modeling on an outer side curve of an epoxy sleeve, and constructing a training sample by Latin hypercube sampling; (2) constructing a'base learner-meta learner 'double-layer integrated stacking agent model, and synchronously optimizing model configuration and hyper-parameters by using an improved sparrow search algorithm; (3) driving ISSA (International Standard Standard Architecture) to quickly optimize by using the trained proxy model to obtain an optimal geometric curve; and (4) through finite element-stream theory-thermal shock joint verification, it is confirmed that the maximum electric field intensity of the optimized terminal is reduced, the initial discharge voltage is increased, and the mechanical reliability meets the long-term operation requirement. The method solves the problem that a traditional agent model is insufficient in precision and insufficient in insulation margin of a gas-solid interface of a 550kV direct-current cable terminal, and is widely applied to localization design of + / -550kV GIS cable terminals.

The invention discloses a throat mucosa intelligent state evaluation and inflammation early warning management system and method. The method comprises the following steps: synchronously acquiring a throat mucosa image, breath sound / cough sound / sounding signals, airflow data and body temperature or environment relative humidity data, evaluating image definition, shielding degree and coverage rate, and outputting acquisition guide superposition information and triggering re-acquisition when the image does not reach the standard; reliability is calculated for the multi-modal features, weight fusion output state vectors and confidence coefficients are determined in combination with quality gating, and cross-modal consistency verification is executed to trigger re-sampling or strategy adjustment; generating a risk index based on the change of the sliding time window, and outputting an early warning level by adopting hysteretic classification; and writing the threshold, the weight and the sampling and guiding strategy into a parameter library and a fusion model configuration area to form a closed loop. According to the system, improper electroencephalogram fusion and multi-terminal collaborative optimization can be selected, and daily scene evaluation stability and early warning reliability are improved.

Methods and systems for determining a recommended ensemble model configuration is disclosed. Method performed by server system includes access a validation dataset and generating one or more ensemble model configurations. Each ensemble model configuration includes a subset of base models. Operations are performed iteratively for each ensemble model configuration till predefined criteria are met. Operations include determining, by the subset of base models, a set of predictions, computing, one or more prediction losses, computing a pairwise diversity loss metric for the subset of base models, and fine-tuning the subset of base models on backpropagating the one or more prediction losses and the pairwise diversity loss metric. Method includes determining the recommended ensemble model configuration based on each ensemble model configuration including the subset of fine-tuned base models.

The invention provides a large model fine tuning method and system for power intelligent question answering, and relates to the technical field of natural language processing, and the method comprises the steps: obtaining power industry multi-source business data, carrying out the data preprocessing based on a preset script, and generating a structured training sample set; loading the pre-training language model, and configuring hyper-parameters; performing iterative training on the pre-training language model based on the training sample set and the hyper-parameters to obtain a fine tuning model, and storing model weight and configuration information; performing performance evaluation on the fine tuning model through the test set, wherein the performance evaluation comprises accuracy, response speed and robustness; and applying the fine tuning model to the intelligent question-answering system, judging an effect improvement condition and continuously performing fine tuning. According to the method, data preprocessing, efficient fine tuning and multi-dimensional performance evaluation are constructed, so that the adaptation efficiency and the application reliability of a large model in a professional scene are remarkably improved.

The invention relates to the technical field of semiconductor manufacturing, in particular to a semiconductor scheduling algorithm model configuration system, method and equipment and a medium. The system comprises a data configuration module, an algorithm configuration module, a parameter analysis module, a task scheduling module, a data acquisition module, a model execution module and a result processing module. The data configuration module receives data model configuration parameters input by a user; the algorithm configuration module receives algorithm model configuration parameters. The parameter analysis module analyzes parameters and generates a data preprocessing rule and an algorithm model instance, the task scheduling module creates a scheduling task and triggers a process, the data acquisition module acquires original data from a source data table and preprocesses the original data, the model execution module executes a scheduling algorithm based on the algorithm instance, and the result processing module outputs standard data. According to the invention, decoupling of the data and the algorithm is realized, and the adaptability and expandability of the system are improved.

The invention relates to a Go-pos scene self-adaptive remote sensing image super-resolution reconstruction system and method, relates to the technical field of space optics, and solves the technical problems that when a traditional algorithm faces a remote sensing image with a complex landform, super-resolution reconstruction fails, the image edge reconstruction effect is poor, initial frame construction is incomplete, and subsequent iteration cannot be supported. The system comprises an input image loading module, a scene recognition module, a degradation model configuration module, an initial reconstruction NEDI module, a POCS iteration reconstruction module, an edge enhancement module and an output and storage module. According to the Go-pos scene adaptive remote sensing image super-resolution reconstruction system and method provided by the invention, a set of remote sensing image super-resolution reconstruction scheme with strong robustness, wide adaptability and excellent detail reconstruction effect is constructed through combination of scene recognition driven adaptive degradation modeling and an edge-oriented reconstruction strategy; and particularly, the method shows higher universality and reconstruction quality in diversified landform scenes.

The invention provides a CAN signal acquisition method applied to an Internet of Vehicles terminal, and belongs to the technical field of Internet of Vehicles, and the method comprises the steps: determining a configuration file corresponding to the vehicle type configuration information of a target vehicle, the configuration file being a database table type file or a text format file, collection and analysis rule information of target CAN signals corresponding to a plurality of different target parameters is recorded in the configuration file, the collection and analysis rule information comprises signal collection information and signal analysis information, and the signal collection information is used for defining a collection rule for the vehicle network terminal to collect the corresponding target CAN signals from the CAN bus; the signal analysis information is used for defining the vehicle network terminal to analyze the obtained target CAN signal to obtain an analysis rule corresponding to the target parameter; analyzing the configuration file to obtain collection and analysis rule information of each target parameter; and acquiring and analyzing the corresponding target CAN signals according to the acquisition and analysis rule information.

The application provides a tunnel surrounding rock inherent frequency determination method and system based on numerical sample, relates to the technical field of tunnel engineering, and comprises the following steps: obtaining geological survey data of a target deep buried joint rock mass tunnel engineering; according to the geological survey data, combining a three-dimensional numerical inversion method to establish a parameter model and set parameters, obtaining a parameterized model configuration; according to the parameterized model configuration, carrying out numerical model construction processing to generate an inherent frequency numerical sample data set; according to the inherent frequency numerical sample data set, carrying out sensitivity analysis to obtain a main control factor screening result; according to the main control factor screening result, establishing a prediction model to obtain an inherent frequency prediction model; inputting real-time main control factor data obtained through field monitoring into the inherent frequency prediction model for prediction processing to obtain inherent frequency time-frequency characteristics and main frequency information of the target deep buried joint rock mass tunnel. The application significantly improves construction safety and engineering decision efficiency.

A method of wireless communication by a base station includes receiving a user equipment (UE) radio capability and a UE machine learning capability. The method also includes determining a neural network function (NNF) based on the UE radio capability. The method includes determining a neural network model. The neural network model includes a model structure and a parameter set, based on the NNF, the UE machine learning capability, and a capability of a network entity. The method also includes configuring the network entity with the neural network model.

The invention relates to the technical field of order management, in particular to a mobile charging order processing system and method based on a workflow engine. The interaction module is used for realizing visual configuration and workflow monitoring; the system comprises a state machine engine and a rule engine, the rule engine and the state machine engine cooperatively work, the rule engine is used for processing complex logic judgment in services, and when encountering nodes needing decision making, the state machine engine can call the rule engine to obtain decision making results and then drive state circulation according to the results. The event driving layer comprises an event dispatcher and an event coordinator, the event dispatcher is responsible for efficiently and reliably transmitting events, and the event coordinator is responsible for processing cross-service complex business processes and realizing thorough decoupling among business components; and the data support layer provides data support for the interaction layer, the core engine layer and the event driving layer. And order processing is converted from a code writing normal form to a model configuration normal form, so that unprecedented flexibility and maintainability are realized.

The invention relates to the technical field of meteorological data assimilation, discloses an assimilation method suitable for a nonlinear problem, and aims to solve the problem that an incremental variation assimilation method has no trust region constraint and may not converge in practical application. A scheme for solving convergence and calculation efficiency of non-linear cloud radiance assimilation in theoretical and practical application is discussed. According to the method, the comparison of the total variation assimilation method and the increment variation assimilation method is realized under the same framework, the comparison of the multi-grid method and the multi-external circulation method is realized under the same framework, the problem that direct comparison is difficult to realize in different platforms due to complex parameters in different methods is solved, and the system operation complexity is greatly simplified through unified model configuration. Experiments show that by adopting the linear search total variation assimilation method under the multi-grid framework, the accuracy of an analysis field is stably improved, and the minimization process of a nonlinear total variation objective function is accelerated.

The invention discloses a full-scene AI intelligent agent decision-making method based on a reasoning-action-reflection normal form, lightweight ontology description and a small model, and belongs to the technical field of full-scene AI intelligent agents. The method comprises the following steps: firstly, constructing a lightweight ontology description module (S1) based on JSON-LD; then, designing and executing a scene-based flexible scheduling reasoning-action-reflection (R-A-R) normal form (S2); the normal form comprises a reasoning link for adaptively selecting a reasoning path according to a scene type, an action link for executing a decision based on a scene-resource mapping table, and a reflection link for bidirectionally driving ontology knowledge and small model configuration to update according to an evaluation result; then, scene reasoning optimization is carried out on the small model (S3); and finally, circularly executing the decision closed loop (S4) formed by the steps. Through the innovative R-A-R normal form, the lightweight ontology and the scenarized small model are deeply coordinated, the problems that an existing AI agent decision normal form is rigid, the knowledge representation is bloated and the resource configuration is single are solved, cross-scene high compatibility, lightweight efficient iteration of knowledge and accurate energy-saving adaptation of resources are achieved, and the method is suitable for popularization and application. The method has significant advantages in all-scene application such as community pension and industrial inspection.

In some embodiments, techniques described herein relate to a method including: receiving a user instruction at an application executed by an electronic device; generating, at a framework executed by a server, one or more feature groups based on the profile data; generating, at the framework, a data configuration file; generating, at the framework, a model configuration file; specifying, at the framework, a sequence of functionality steps for execution; generating, at the framework, a prediction score based on the sequence of functionality steps; saving and / or executing, at the framework, the sequence of functionality steps; and generating, at the electronic device, one or more outputs including, for example, a prediction based on the sequence of functionality steps.

Systems, computer program products, and methods are described herein for automatic adjustment of network device configurations. The present disclosure is configured to receive a network device comprising a set of adjustable settings configured to adapt to a network assembly, wherein the network assembly comprises a plurality of network devices, wherein the set of adjustable settings within the network device can be adjusted to the network assembly and components within the network assembly; designate a function of the network device with respect to the network assembly; match the function of the network device with a model configuration plan associated with the network assembly via a machine learning model (MLM); configure the set of adjustable settings within the network device using the model configuration plan via the MLM to operably connect with the plurality of network devices within the network assembly.

A method includes: partitioning a dataset into data groups; assigning the data groups to a set of workers; generating a first set of workloads including a first workload for training a first model configuration according to the set of data groups; allocating subclusters of resources of the set of workers to the first set of workloads for a first epoch; scheduling concurrent execution of the first set of workloads at the set of workers for the first epoch; calculating a first accuracy value for the first model configuration for the first epoch; in response to the first accuracy value failing to exceed a threshold accuracy value, generating a second set of workloads excluding the first workload; allocating subclusters of resources to the second set of workloads for a second epoch; and scheduling concurrent execution of the second set of workloads at the set of workers for the second epoch.

The invention provides an audit information extraction method and system based on a large language model, and belongs to the technical field of intelligent information processing. Accurate extraction of unstructured documents is achieved through dynamic matching of an intelligent agent, the ultra-long text processing bottleneck is broken through in combination with natural segment slices and knowledge graph association, and document content clustering analysis is completed by adopting a preprocessing-semantic representation-dimension reduction-clustering process; meanwhile, extracted content self-definition is supported, and a complex auditing scene can be adapted without improving model configuration; the system constructed based on the method covers the functions of structured task management, agent scheduling, batch processing, result query and the like, the pain points of low efficiency and error proneness when auditing personnel manually process unstructured data are effectively solved, the labor cost is remarkably reduced in the whole process, the auditing information extraction efficiency and accuracy are improved, and the auditing efficiency is improved. And a powerful support is provided for intelligent auditing work.

The application relates to a river closure date prediction method based on historical similar year performance driving, which comprises the following steps: using a plurality of feature selection algorithms and leave-one-out cross-validation to screen target predictor sets respectively matched with selected machine learning models and statistical models; based on parameter sensitivity analysis and a Bayesian optimization algorithm, sensitive hyperparameters of the machine learning models are optimized to obtain optimized machine learning models; the statistical models and the optimized machine learning models are configured as candidate river closure date prediction models; a K-neighbor algorithm is used to search a set of similar historical years in a historical observation data set according to current observation data, and a target river closure date prediction model is dynamically optimized according to the comprehensive prediction error of the candidate river closure date prediction models on the set of similar historical years, and a prediction result is output, so that the advantages of multiple models are effectively fused, the generalization limitation of a single model in a complex non-stationary environment is avoided, and the accuracy and robustness of the prediction result are significantly improved.