307 results about "Configuration generation" patented technology

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 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.

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 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.

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 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.

A network function configuration and test generation framework are configured to generate a test script indicative of a configuration and test cases for verifying a virtual function implemented in a virtualized computing environment. A test input is received that encodes information for verifying a network function. The information includes identification of a test tool, a target testbed for testing the network function, and network information including parameters for network conditions to be operational during testing of the network function. A data parser translates and formats the test input. The formatted test input in input to a topology discoverer configured to identify a network topology of the testbed and which network functions deployed on the testbed are emulated and which network functions deployed on the testbed are real. A configuration generator generates a configuration file usable to configure applicable network functions and their functionalities.

The invention belongs to the technical field of message decoding, and particularly relates to an efficient RRC message decoding system and method based on dynamic configuration, and the system comprises a scene sensing module which collects the operation state and communication environment information of communication equipment in real time, processes the collected information, and abstracts a current communication scene into a group of feature vectors; the configuration generation module is used for combining the feature vector with a pre-stored RRC protocol rule base to generate a decoding configuration file for the current scene; the resource management module is used for distributing and dynamically managing computing resources and memory resources of the system according to the decoding configuration file; the decoding execution module is used for decoding the RRC message according to the decoding configuration file and the resources distributed by the resource management module; and the self-adaptive learning module is used for realizing decoding by analyzing and learning a new rule when the decoding operation cannot correctly decode so as to improve the compatibility of the system. According to the invention, compatibility and decoding efficiency can be improved.

A network function configuration and test generation framework are configured to generate a test script indicative of a configuration and test cases for verifying a virtual function implemented in a virtualized computing environment. A test input is received that encodes information for verifying a network function. The information includes identification of a test tool, a target testbed for testing the network function, and network information including parameters for network conditions to be operational during testing of the network function. A data parser translates and formats the test input. The formatted test input in input to a topology discoverer configured to identify a network topology of the testbed and which network functions deployed on the testbed are emulated and which network functions deployed on the testbed are real. A configuration generator generates a configuration file usable to configure applicable network functions and their functionalities.

Techniques for providing a language model with dynamic configuration data for network devices in a network that is then used by the language model to generate network-contextual responses to prompts received at the language model. A network controller may collect configuration data from the network devices where the configuration data includes device configurations of the network devices. The network controller may further receive a prompt from a network operator to which the language model is to respond and identify a relevant device configuration that is contextually relevant to the prompt. Further, the network controller may provide the prompt and the relevant device configuration to the language model, generate, by the language model and using the relevant device configuration, a response to the prompt, and provide the network operator with an indication of the response to the prompt.

The invention relates to the technical field of computers, particularly provides a service building method based on a low-code-free platform, and aims at solving the technical problems that in an existing low-code platform, the coupling degree of a service process engine, a form engine and a rule engine is high, troubleshooting is difficult and the like. The method comprises the following steps: generating a unified metadata model through visual interface configuration, and defining a business process, a form, a business rule instance and an association relationship thereof in a structured mode; analyzing the model and converting the model into deployment configuration information which can be loaded by each engine according to a grammar mapping rule; a coordination rule for coordinating a multi-engine execution sequence and a data interface is automatically generated based on the incidence relation between the instances, and the coordination rule is integrated with deployment configuration information; and driving the corresponding engine to execute the service. According to the method, business and engine decoupling is realized through the unified metadata model, and pluggable replacement of the engine is supported; a collaboration rule is automatically generated through visualization, and the development threshold is reduced; and through unified time axis debugging, the operation and maintenance efficiency is improved.

The invention discloses a structure configuration generation method, device and equipment and a readable storage medium, and is applied to the technical field of computers, and the method comprises the steps: carrying out the decoupling processing of a global grid model, and obtaining an order reduction model and a structure optimization model; applying an external load and a boundary condition to the reduced-order model, and solving to obtain a displacement load; and inputting the displacement load, the design constraint and the manufacturing constraint into a pre-trained structure configuration optimization model to obtain a generated optimal structure configuration. According to the method, the calculation scale of the structure configuration optimization model is greatly reduced, the calculation efficiency is improved, and the grid resolution in the to-be-optimized target structure area is improved, so that the high-fidelity structure configuration is generated; unnecessary materials can be reduced on the premise that the safety of a train is not influenced; on the premise of keeping the geometric features of the target structure region to be optimized similar, a high-fidelity structure configuration can be generated for any size, external load and boundary conditions, and the universality in a global parameter space is realized.

Systems and methods are described herein for generating, training, and federating machine learning models to detect anomalous or rare-event activity in sensitive electronic data, such as financial transactions. In some implementations, configuration information for a machine learning script (MLS) is obtained. The MLS is generated by a server based on the configuration. Data representing the MLS is provided to a user device. The user device is caused to perform operations when executing the MLS. An instance of a machine learning model is trained based on the MLS. One or more parameters associated with the instance of the machine learning model are identified. Data representing the one or more parameters associated with the instance of the machine learning model are received by the server from the user. A federated model is generated based at least in part on the received data representing the identified parameters and provided for output.

The invention discloses a computer network management system, which comprises a user interface layer for performing three-level strategy control through a hierarchical strategy manager integrated with a Drools rule engine and a temporary rule engine based on a timed automaton; according to the intelligent analysis layer, a demand level analysis module carries out single-sample incremental learning, version variation real-time capture and network demand label dynamic adjustment, and an adaptive optimization decision module carries out smooth processing on a CV / r index through EWMA, predicts network load change through an ARIMA model, carries out logistic mapping on a nonlinear adjustment function, and completes time sequence perception optimization adjustment. According to the execution control layer, a constraint feedback regulator ensures that weight updating is within a constraint range and supports automatic rollback during exception, and a basic configuration generator achieves configuration issuing of hardware acceleration and basic network parameter generation; and the network entity layer is used for receiving configuration data through an NETCONF protocol, reporting an equipment fault by an SNMP Trap, and cooperating with the execution control layer to complete closed-loop management from a user demand to network configuration.

The invention provides a honey array transformation configuration generation method and apparatus, a medium and an electronic device. The method comprises the steps of obtaining TTP information and matching CVE to obtain CVE information; obtaining a CVSS score according to the CVE information, and selecting M mirror images associated with the CVE information with the highest score to generate a corresponding CVE mirror image configuration section; extracting scene feature information according to the TTP information to generate a feature mirror image configuration section; generating honey point configuration segments of newly added honey points and service features according to the TTP information; and mixing the CVE mirror image configuration section, the feature mirror image configuration section and the honey point configuration section to generate a honey array transformation configuration file for transforming trapping scene configuration. According to the method, TTP scene information highly associated with attacking behaviors of an attacker and various threat modeling scales are designed and introduced, mapping from the attacker behaviors to vulnerabilities and services is completed, honey point mirror images interested by the attacker are selected, and a honey array transformation configuration file is generated in a highly self-adaptive mode in combination with the scene information.

The invention discloses a service component generation system and method in an intelligent low-code development platform. The method comprises the following steps: S1, generating a service semantic vector through semantic coding; s2, inputting the semantic vector into a constructive sparse transformation network, and extracting semantic feature representation; s3, matching is performed based on the semantic features and the component semantic vectors, and a candidate component set is generated; s4, if the matching is incomplete, constructing a component structure tree and generating a new component structure description; s5, completing field binding, control flow construction and interface configuration, and generating a component template set; s6, optimizing a model structure and parameters by adopting a sparse singular value adaptive ant nest optimization algorithm; s7, deploying the component template to the low-code platform; and S8, collecting component operation data, updating a model path and a weight, and continuously optimizing. According to the method, intelligent generation and structure optimization of the low-code component are realized, and the development efficiency and the semantic matching precision are remarkably improved.

The invention relates to the technical field of computer hardware configuration intelligent generation, and discloses a machine learning-based complete machine configuration intelligent generation system, which comprises a demand acquisition unit, a feature analysis unit and a configuration generation unit. The demand acquisition unit acquires a user configuration demand through the interactive interface and performs format standardization processing; the feature analysis unit extracts key demand features by using a natural language understanding model; and the configuration generation unit generates a candidate configuration scheme according to the key feature calling model. The system also evaluates the reasonability of the scheme through a random forest model, diagnoses the problem type by using a sequence neural model, and dynamically optimizes configuration parameters. According to the invention, intelligent generation and optimization of the configuration scheme are realized, and the accuracy and user satisfaction of the configuration scheme are improved.