392 results about "Configuration generation" patented technology

The invention discloses a low-code platform based on a component library and a form engine, and relates to the field of data processing. The platform comprises a design system, an application system and a service system. The design system is provided with a form designer and a template manager, the form designer can configure a form component to generate a description file, and the template manager supports separate storage of a page template; the application system comprises a form analysis engine, a UI rendering engine and a reusable resource management component, the analysis engine analyzes configuration generation logic, the rendering engine loads a resource rendering page, and the reusable resource management component stores resources and generates configuration; the service system comprises a metadata analysis engine and an SQL assembler, the metadata analysis engine analyzes business rules to generate SQL and filtering strategies, and the SQL assembler generates operation statements adaptive to multiple data sources. Through the modular architecture design and the dynamic analysis mechanism, the problem that a low-code platform is insufficient in flexibility and usability is solved.

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 an enhanced decision method, device, equipment and medium based on thinking chain annotation, which comprises the following steps: extracting core information features to generate a structured data set, loading a basic language model and executing supervision fine tuning to generate a fine-tuned model, fusing multi-modal input to generate fusion features, constructing a state observation space to receive the fusion features as input, generating reward signals based on a double reward mechanism and optimizing model parameters to generate an optimized model, deploying a monitoring module to dynamically adjust parameter configuration to generate an adaptive decision model, and outputting a decision response result. According to the method, multi-source data extraction, structured expression, multi-modal fusion, reinforcement learning optimization and dynamic adaptive mechanism fusion are carried out, so that the understanding ability of the model to complex data, reasoning transparency and the adaptive ability of the model to coping with environmental changes are remarkably improved.

The present application provides a shopping guide method and system based on a large language model, and the method is applied to the technical field of artificial intelligence. The method comprises: acquiring a training shopping guide requirement and a carried content enhancement configuration (S201); on the basis of the content enhancement configuration, generating a training prompt describing the training shopping guide requirement (S202); using a large language model to be trained to perform semantic analysis on the training prompt so as to determine a plurality of application interfaces required for responding to the training shopping guide requirement (S203); on the basis of a preset database, performing screening and summarization processing on returned results of the plurality of application interfaces to obtain predicted shopping guide information (S204); on the basis of the predicted shopping guide information and the training shopping guide requirement, performing parameter adjustment on the large language model to obtain a trained large language model (S205); and, when shopping guiding is conducted, on the basis of a prompt corresponding to a shopping guide requirement inputted by a user end, guiding the trained large language model to generate and output target shopping guide information. According to the method, target shopping guide information that is more in line with a user's natural language can be provided for the user end.

The invention relates to the technical field of network management, and discloses an intent-driven network management method and system based on a large language model (LLM). The method comprises the following steps: receiving a natural language intention of a network administrator, analyzing the intention by utilizing a pre-trained large language model, extracting a key target and constraint, generating a configuration scheme of a TCP / IP or QUIC protocol based on an analysis result and a network protocol knowledge base, and ensuring the effectiveness and safety of configuration through a network constraint verification module. And the configuration passing the verification is applied to the network system. The system comprises an intention input module, an LLM analysis module, a configuration generation module, a verification module, an application module and the like. According to the invention, through conversion from automation intention to configuration, the network management process is simplified, the management efficiency and flexibility are improved, the method is especially suitable for multi-parameter optimization scenes of protocols such as TCP / IP and QUIC, and intelligent network management is realized.

Techniques for simulating multi-agent interactions within a virtual world in response to generated hypotheses using dynamically retrieved external data are disclosed herein. A computing system can receive instructions including a user input with a seed phrase and provide the user input to a configuration generation model to generate agent traits associated with agents in a virtual world and / or data-source identifiers associated with external data sources. The system can retrieve external data from the external data sources and provide the agent traits and external data to a hypothesis generation model to generate a hypothesis that includes a constraint associated with the external data. The system can instantiate agents that have the agent traits and execute a simulation session consistent with the constraint (e.g., by causing the agents to generate an output set using the hypothesis and question). The system can generate a representation of the output set for analysis.

The invention discloses an internal and external network security service passing method and system based on edge computing, and belongs to the technical field of network security, and the method comprises the steps: obtaining multi-dimensional attribute information of an edge node, generating a security policy basic data set, generating a traffic feature fingerprint and a self-adaptive environment adaptive fingerprint, and calculating a fusion matching degree; edge security entity nodes are generated in combination with service scene adaptive threshold clustering, and then a security policy meta-model with a security policy blueprint as a core is constructed; analyzing the security policy meta-model through a multi-modal semantic analysis engine, generating a dynamic security enhancement model, and mapping the dynamic security enhancement model to a hierarchical security control model; based on the hierarchical model, outputting edge node internal and external network safety passage configuration and a corresponding safety passage ledger through a scenarized configuration generation algorithm; according to the method, adaptive generation, dynamic optimization and accurate execution of the security policy are realized, and the security, the automation level and the operation and maintenance efficiency of internal and external network passing in the edge computing environment are effectively improved.

Systems and methods are described for recommending a proposed hardware configuration to a first user in response to determining that a performance aspect of the first user having a first hardware configuration playing a multiplayer game is worse than a performance aspect of a second user having a second hardware configuration jointly playing the multiplayer game. The proposed hardware configuration is generated based on the first hardware configuration and the second hardware configuration and caused to be displayed on a device of the first user.

The invention discloses a method and device for generating mass database table test data based on configuration, and the method comprises the steps: obtaining table building information, extracting table metadata from the table building information, and carrying out the standardization processing of the table metadata, and obtaining the standardized table metadata; obtaining a data generation template based on the standardized table metadata; modifying the data generation template according to a target demand to generate a target data generation template; extracting global variable information from a global configuration node of the target data generation template; analyzing a data generation rule corresponding to each field based on the global variable information, and generating test data based on the field generation rule; and based on the test data, generating an insertion statement, and writing the insertion statement into a preset file to obtain a final test data file. According to the embodiment of the invention, full-process automation from table metadata extraction to test data files can be realized, real association of data is guaranteed, and test effectiveness is improved.

The invention discloses a method and a system for automatically generating a business APP of a fusion terminal, which are applied to the technical field of smart power grids, and the method comprises the following steps: accessing an external storage device to a target fusion terminal; the management APP of the target fusion terminal obtains basic information of a service APP needing to be generated, and sends the basic information to the packaging APP and the guarding APP; the packaging APP selects a corresponding adaptive template from a template library according to the adaptive data, and performs parameter configuration on the selected adaptive template to form a service APP needing to be generated; the management APP is locally deployed and operated in the target fusion terminal; and the guarding APP maintains the running service APP according to the configuration data. According to the method, code development is not needed, and the service APP is automatically configured and generated; meanwhile, automatic operation and maintenance of the generated service APPs are achieved, the service APPs which are not needed temporarily are directly deleted from the fusion terminal, and storage resources of the fusion terminal are effectively saved.

The invention provides a BIM-combined construction process digital management method and system, and the method comprises the steps: firstly collecting a real-time three-dimensional model data set of a construction site, including geometric attributes and spatial topology association information of construction components, then analyzing the logic execution relation between different construction components, generating a progress scheduling rule set, and carrying out the construction process digital management. The actual construction progress data is dynamically matched with the set to obtain a construction process anomaly detection strategy and a resource allocation optimization strategy, then component parameter configuration of the real-time three-dimensional model data set is adjusted according to the anomaly detection strategy, updated three-dimensional model verification data is generated, and the updated three-dimensional model verification data is stored in a database. And finally, a construction progress adjustment instruction is generated according to the resource allocation optimization strategy and the updated three-dimensional model verification data, and is synchronized to the construction management terminal, so that digital and intelligent management of the construction process is realized, and the construction efficiency and the project management level are effectively improved.

The invention provides a simulation deduction method and system based on distributed parallel scheduling and a storage medium, and is applied to the technical field of data processing.The method comprises the steps that proxy services are deployed at a plurality of preset computing nodes, resource state information of all the nodes is dynamically collected, and a computing resource pool is obtained; arranging a pre-registered plug-in based on the data dependency relationship to obtain a simulation task process; in response to simulation scene configuration submitted by a user, generating a distributed scheduling task containing a fragmentation strategy; determining a plurality of sub-tasks corresponding to the distributed scheduling task based on the simulation task process; and based on the real-time load of the computing resource pool and the task priorities of the plurality of sub-tasks, respectively distributing the plurality of sub-tasks to a plurality of target computing nodes for task execution, and obtaining task execution result information. According to the method and the device, the time delay performance of distributed scheduling and the high availability performance of scheduling simulation tasks can be improved.

The invention relates to a source network load storage capacity planning method and system based on a multi-agent game, and the method comprises the steps: constructing a multi-agent game behavior data set, analyzing the dynamic interaction of a strategy through employing a game theory model, and obtaining the strategy preference and interaction mode of each agent; constructing a source network load storage collaborative planning model, and determining an initial capacity configuration scheme of each main body; extracting a benefit distribution proportion of each main body from the initial capacity configuration scheme, and adjusting capacity configuration by adopting a multi-objective optimization algorithm to obtain an optimized capacity planning scheme; analyzing the influence of peak demand fluctuation and transmission capacity limitation on the scheme by adopting a Monte Carlo simulation method to obtain a dynamic adaptability index of the scheme; and adjusting game theory model parameters to obtain updated strategy preferences and interaction modes, adjusting capacity configuration through iterative calculation, and generating a new capacity planning scheme. Compared with the prior art, the method has the advantages of comprehensively considering multi-main-body strategy dynamic interaction, improving the feasibility of the planning scheme and the like.

The invention provides a multivariate data-based printing machine top project approval design method, which relates to the technical field of printing equipment, and comprises the following steps of: acquiring printing machine operation data comprising printing quality parameters, equipment working condition parameters and environment parameters; performing feature analysis and parameter extraction through an adaptive fuzzy clustering algorithm, dynamically adjusting a sample classification membership degree, identifying a key parameter combination influencing the printing quality, and constructing a correlation matrix; constructing a dynamic Bayesian probability network model based on a parameter mapping relation, constructing a core network structure through an information entropy value and information gain, and establishing probability association between a printing quality parameter and an equipment working condition parameter; and determining the optimal parameter configuration of the function modules of the printing machine by utilizing a bidirectional constraint dynamic programming algorithm and combining the coupling weight and the state transition equation among the function modules, and generating a top design scheme.

A method of grasp generation for a robot includes searching within a configuration space of a robot hand model for robot hand configurations to engage an object model with a grasp type. The method includes generating a set of candidate grasps based on the robot hand configurations. Grasping of the object model with the robot hand model is simulated in a physics engine using simulated grasps generated based on a given candidate grasp. A simulated grasp is assigned a score based on a response of the object model to an applied wrench disturbance when the object is engaged with the simulated grasp. The method includes generating a set of feasible grasps for the given candidate grasp based on the respective simulated grasps having a score above a score threshold at a target wrench disturbance.

Described are systems and methods for providing AI and ML-assisted workflows, and more specifically, to improved systems and methods for creating user interfaces for software applications that interact with AI models. A computer-implemented system and method for generating an AI-assisted user interface comprises providing, at a memory, a page element database comprising a plurality of page element configurations; receiving, at a network device, a page generation request from a user at a user device; generating, at a processor in communication with the memory and the network device, an AI-assisted user interface comprising a plurality of page elements, each of the plurality of page elements generated based on a corresponding page element configuration in the plurality of page element configurations; outputting, at a display device in communication with the processor, the AI-assisted user interface; and wherein the plurality of page element configurations comprises at least one AI-based page element configuration.

The present application relates to the technical field of workflow management, and discloses a cross-system digital workflow integration system, and a workflow management method and system. The method comprises: acquiring a customized workflow configuration table, and analyzing the customized workflow configuration table to determine logic control information; and on the basis of logic control information, performing management configuration on a default cyclic approval workflow template to generate a digital workflow. The present application can effectively improve the approval efficiency and reduce the error rate, so that the approval requirements of different service scenarios are met.

The invention belongs to the technical field of data processing, and particularly discloses a computer vision model service scenarized configuration generation method, which comprises the following steps: constructing a basic information database comprising a plurality of computer vision models, the database being used for storing model identification information and feature parameters; extracting structured features of the scene according to a scene analysis engine, wherein the structured features are used for describing operation conditions of the scene; calculating a model matching score according to the structured features and the feature parameters, and obtaining a recommendation model through a multi-condition weight matching function; executing a model selection strategy based on the matching score, and obtaining a model matched with the scene; and generating a configuration file conforming to a predetermined specification according to the recommendation model. The objective of the invention is to solve the problems of low efficiency and error proneness caused by the fact that a computer vision model in the prior art depends on a manual selection model and cannot quickly select and adapt to an optimal model in diversified scenes.

The invention relates to an LLM-based comprehensive modular avionics system resource allocation method and system, and the method comprises the steps: removing a universal semantic module through model pruning, taking an IMA configuration description material as a training corpus, forming a lightweight IMA exclusive large language model which is finely adjusted by the training corpus, collecting a user natural language, analyzing a configuration interaction instruction of the user, and carrying out the resource allocation of the user. And then the configuration interpretation model / configuration generation model is called to execute interpretation and change tasks, so that natural language interaction of resource configuration of the integrated modular avionics system is realized on the basis of ensuring functional integrity, and the change cost of resource configuration is obviously reduced. Compared with the prior art, the method has the advantages that the lightweight LLM is trained and deployed in combination with avionics field knowledge, efficient deployment of the model is realized, and the accuracy of semantic understanding of the model in professional scenes in the avionics field is enhanced.

The invention discloses a verification feedback-based large model reinforcement learning network configuration generation method, which comprises the following steps of: converting input network phenomena and state contents into a semantic and action sequence through network configuration semantic analysis; the method comprises the following steps: generating a configuration framework and parameters meeting requirements through a hybrid action space strategy generation and neural symbol collaborative reinforcement learning model, verifying and feeding back the generated network configuration information in a digital twin system, simulating a real network scene through formalized verification and performance simulation in a virtual environment, and establishing a network configuration model. According to the method, the correctness and the performance of configuration are comprehensively evaluated, then a feedback signal containing multi-dimensional information is generated, a strategy model is corrected and optimized according to digital twinborn verification, and finally, an intelligent agent is guided to adjust a high-entropy configuration item through a reward mechanism, and configuration is optimized to generate a large model.

The invention discloses a UVM automatic verification method for loading an external memory function on a chip, and belongs to the technical field of integrated circuit design and verification. According to the method, test excitation is divided into configuration information filled by a user and control information automatically generated through a formula to generate a configuration file, the configuration file is read by means of a memory model, data is configured to a specified register after a chip is powered on, and cross-channel data verification is achieved through an analysis module, a driving module and a data checking module. And randomly selecting one node from broadcast nodes for a register configured by the broadcast, and reading back data for comparison. According to the method, the limitation of single-link verification is broken through, a closed loop of configuration generation-simulation execution-cross-channel verification is formed, end-to-end automatic verification is realized, verification efficiency and reliability are improved, manual configuration errors are reduced, abnormity is accurately positioned in real time, and verification completeness and efficiency are balanced.

Systems and methods for tuning hyperparameters for a machine learning model using a challenger champion model are described. A set of challenger configurations are generated based on a hyperparameter for tuning and a subset of the set of challenger configurations are scheduled for evaluation based on a loss function. A loss value derived from the loss function for the challenger configurations is compared to a loss value derived from the loss function for a champion configuration, and the champion configuration is replaced with the challenger configuration based on the comparison of the loss value derived from the loss function for the challenger configuration and the loss value derived from the loss function for the champion configuration. When the champion is replaced, a new set of challenger configurations is generated based on the new champion configuration.

The invention discloses a large model mixed load-oriented self-adaptive low-delay reasoning configuration generation method and device, computer equipment and a storage medium, and the method comprises the steps: determining a first token generation delay and an adjacent token delay interval which are historically configured on requests with different reasoning configurations, and obtaining tuples to form a configuration performance database, generating a delay prediction model by combining a least square method with the configuration performance database; dynamically dividing the historical request into a plurality of buckets according to the input length and the output length through a self-adaptive bucket dividing strategy; a configuration generator generates reasoning configuration for each bucket according to the input length and the output length of the historical request of each bucket; under the real mixed load, the problems of remarkable resource contention, queue head blockage, KV Cache switching overhead increase and the like are avoided in concurrent execution of long and short requests, and meanwhile, the problem of tail delay amplification is avoided, so that a reasoning system gives consideration to low delay and high throughput among different requests.

The invention relates to an automatic test system for an energy storage system. The system comprises a test management module, a fault injection module, an automatic configuration module, a strategy verification module and a test report module. The test management module is used for creating a test task for the energy storage system; the fault injection module is used for simulating a preset fault scene corresponding to the test task and generating a fault test instruction corresponding to the preset fault scene; the automatic configuration module is used for performing parameter configuration on a target device associated with the test task in the energy storage system according to a preset system parameter configuration file, and generating a target configuration parameter of the target device; the strategy verification module is used for collecting actual response data of the energy storage system in the test process of the fault test instruction, and performing feature matching on the actual response data and expected response data in a preset strategy library to generate a strategy matching result; and the test report module is used for generating a test report of the test task according to the parameters. The efficiency and accuracy of the energy storage system test can be improved.

The invention relates to the technical field of supervision information processing, in particular to an intelligent supervision method and system based on new energy charging infrastructure service. The method comprises the steps of obtaining supervision target domain data and strategy version records, and generating multi-source standardized data through term dictionary registration and protocol session configuration; performing incremental compensation parameter registration, data hash comparison and missing marking, time anchor point alignment and time window index generation to obtain an alarm event packet; based on the alarm event packet, performing alarm duplication removal, rule matching, work order assembly and operation and maintenance resource loading, and generating a work order distribution record; and carrying out evidence storage load packaging, on-chain verification rule matching, dynamic gridding arrangement and strategy version updating, and finally generating a supervision report. According to the invention, the problems of difficult data fusion, weak transmission integrity guarantee and imperfect supervision closed loop of the multi-source heterogeneous charging equipment are effectively solved, and the accuracy, reliability and automation level of supervision are improved.

The invention discloses a building block report zero-code visualization dynamic design multi-terminal adaptation system and method, and relates to the technical field of data visualization and report generation. The method comprises the following steps: component templated multiplexing: generating a component template with a unique identifier by extracting complete configuration of a configured component, separating basic parameters from instance parameters by adopting a double-layer parameter structure, and maintaining a directed acyclic graph structure through a dependency tracking engine to realize batch synchronous updating; performing multi-terminal intelligent adaptation, constructing a terminal characteristic library to store different terminal parameters, setting adaptive rules for various components, and automatically adjusting the component size, layout and interaction mode by identifying an access terminal; and carrying out visual binding on the business rules, constructing business rule logic through a dragging operator, and establishing rule-component association mapping. According to the method, the component reuse rate is increased by 80%, the report development period is shortened by 70%, and an efficient, intelligent and easy-to-use complete solution is provided for enterprise-level zero code report development.

The invention belongs to the technical field of artificial intelligence, and relates to a component configuration method which comprises the following steps: judging whether a component configuration request triggered by a user and corresponding to a target system is received or not; if yes, displaying a preset visual configuration page; obtaining an initial component selected by a user in the visual configuration interface, and receiving configuration information of the initial component input by the user; performing corresponding configuration processing on the initial component based on the configuration information to obtain a configured target component; based on a preset component auditing strategy, auditing the target component; and if the target component passes the audit, deploying the target component into the target system. The invention further provides a component configuration device, computer equipment and a storage medium. In addition, the target component can be stored in the block chain. The method can be applied to component generation scenes in the financial field and the medical field, the processing efficiency and accuracy of component development are effectively improved, and the quality of the configured and generated component is ensured.

The invention discloses an adaptive rendering decision-making system and method based on an AI large model, a storage medium and equipment, and the system comprises the steps: a rendering decision-making manager is internally provided with a rendering decision-making library which is used for storing rendering decisions, requesting an experiment rendering decision from an experiment manager according to the type of to-be-rendered equipment, and issuing the rendering decision; an experiment pool is arranged in the experiment manager and is used for storing an experiment rendering decision and scheduling the experiment generator to create an experiment of the to-be-rendered equipment; the rendering configuration generator generates a plurality of experiment rendering decisions according to the created experiment, stores the experiment rendering decisions in an experiment pool, and sequentially issues the experiment rendering decisions to the to-be-rendered equipment for rendering to generate experiment rendering feedback; and the rendering decision scoring device generates experimental rendering decision scores according to the experimental rendering feedback, sorts the experimental rendering decisions according to the sequence of the experimental rendering decision scores from high to low, and stores the first m experimental rendering decisions in a rendering decision library as rendering decisions. According to the method, the rendering decision accuracy is improved, and manual dependence is avoided.

The present application discloses a network configuration method and apparatus. The method comprises: acquiring a configuration generation request, wherein the configuration generation request is information that is written in natural language and comprises an intent; on the basis of the configuration generation request and a language model, obtaining a configuration result that matches the information comprising the intent, wherein the configuration result is a configuration that is expressed in a preset language and corresponds to the information comprising the intent; and outputting the configuration result. In the present application, on the basis of a configuration generation request, a network configuration apparatus generates a configuration result matching information that is in the configuration generation request and comprises an intent, without the need for users to manually configure network configuration parameters, thereby improving network configuration efficiency.

The invention relates to the technical field of AI intelligent calculation, in particular to a broadcast content real-time analysis interaction method and system based on an AI multi-modal large model, and the method comprises the steps: achieving the precise event recognition and structural description through the efficient modeling of single-modal real-time data, and providing the standardized input for the subsequent content generation; then, broadcast texts meeting object features and scene requirements are generated based on event information and global static audience feature configuration, and quantified semantic priorities are calculated to support strategy decision; on the basis, performing deep intention recognition on the generated content by utilizing multi-modal large model reasoning, introducing targeted parameters such as historical broadcast diversity punishment and interference sensitivity adjustment, and generating an executable scheduling strategy; and finally, through area matching and terminal capability verification, mapping the strategy parameter into a specific equipment control instruction, and issuing and executing the specific equipment control instruction to form a closed loop from content generation to precise broadcasting.

The invention provides an electricity market bidding strategy optimization method, system and device and a medium, and belongs to the technical field of electricity market transaction. The method comprises the following steps: acquiring public data of a power market, performing structured processing, extracting key information, and generating power market data; establishing a data optimization prediction model, and predicting the electricity price, the power demand load, the supply load, the enterprise power consumption cost and the power consumption at the future moment based on the power market data; receiving and configuring risk preference parameters input by a user, and generating an enterprise feature configuration file; sequentially calculating a market supply and demand influence factor, a real-time transaction price deviation degree, a reference income fluctuation value and a risk constraint term, and generating a final premium proportion based on a calculation result; on the basis of the final premium proportion, determining a bidding price at a future moment, constructing an objective function to optimize the bidding cost, solving an optimal premium proportion sequence and generating a bidding plan; and monitoring the income fluctuation rate in real time, triggering a feedback mechanism, and updating data.