8497results about "Program loading/initiating" patented technology

The invention belongs to the technical field of electrical digital data processing and resource allocation, and provides an agent lightweight deployment method and a computing power elastic allocation method, lightweight deployment realizes one-time development of multi-platform deployment by constructing a plug-in container packaging tool and a multi-architecture compiling engine; static analysis is used for stripping redundant dependence of the model, and the model is dynamically cut in combination with edge resources; developing an intelligent resource description language, integrating a control group and a filtering technology to realize container-level resource monitoring, constructing a 12-dimensional dynamic state space fusing a node state, task characteristics and a network environment through computing power elastic distribution, and introducing a time delay, cost and reliability three-dimensional weighted reward model to quantify distribution earnings; an edge cloud cooperative training framework and an edge execution strategy are designed, experience is collected, a cloud end trains a Q network through federal learning, an optimization decision is played back in combination with priority experience, the agent deployment efficiency and the resource utilization rate are improved, and data privacy is guaranteed.

The invention relates to the technical field of data processing, in particular to a modular task management system based on dynamic process configuration, which comprises a dynamic process configuration module, a task state circulation engine, a visual interaction interface module, a historical track tracing module and a modular expansion interface. The dynamic process configuration module dynamically binds enterprise configuration data with regional process original data through a process template ID to generate an enterprise exclusive process topological structure comprising a regional identifier, a node association relationship and a process classification label; and the task state circulation engine updates the task state data based on an event-driven and atomized transaction lock mechanism, and synchronizes the task state data to the change management general table. According to the method, flexible configuration of process logic, visual display of task states and cross-enterprise rapid adaptation are realized, and system expansibility and operation efficiency are improved.

The invention provides a multi-agent cooperation method, system and device and a storage medium, and relates to the technical field of multi-agent collaboration.The method comprises the steps that initial role allocation is conducted on multiple agents, one agent is an observer, the other agent is a coordinator, and the other agents are all executors; a strategy network based on deep reinforcement learning is introduced according to the running state of the multiple agents to dynamically adjust role allocation of the multiple agents, and a role allocation strategy is dynamically adjusted according to task completion rewards, role conflict punishment and resource conflict rewards; the coordinator constructs a task priority and a dependency relationship based on the task graph or the task dependency tree, and dynamically allocates tasks according to the state, the capability vector and the task adaptation degree of the executor; conflicts are recognized through resource contention detection, task overlapping detection and behavior conflict detection, and the conflicts are coordinated. According to the invention, multi-agent responsibilities are layered, and the task completion efficiency is improved through task allocation and conflict detection and coordination.

The invention discloses an automatic control code generation and verification method and device, equipment and a storage medium, and relates to the technical field of automatic control, the method is applied to a large language model, and a natural language command is received; performing matching retrieval on the vector database according to the natural language command to obtain an example code snippet; obtaining API structured information corresponding to the example code snippets from a knowledge graph database; generating an initial control code based on the example code snippet and the API structured information; and performing multi-stage virtual operation verification on the initial control code in the software motion control system, and generating a target control code according to a multi-stage verification result confirmed by a user for multiple times and the initial control code. According to the method, the initial control code is automatically generated through double-database retrieval based on the large language model, then multi-stage code verification is performed through the software motion control system, the target control code is generated in combination with verification results confirmed by a user for multiple times, and the code generation speed and reliability are improved.

The invention provides a large model lightweight reasoning deployment method under limited hardware resources, and the method comprises the steps: quantifying the weight importance of a large model through a composite index of gradient sensitivity and activation frequency, and carrying out pruning operation in combination with an improved index weighted moving average strategy, thereby obtaining a structured sparse model; the sparse model is divided into sub-networks by adopting double rules, a routing decision network is trained, and an adaptive feature shunting architecture model is constructed; a multi-precision weight set is generated through a nested quantization technology, quantization bit width is dynamically adjusted, and edge equipment hardware parameters are adapted to complete reasoning environment initialization; after a reasoning request is received, an optimal sub-network is selected based on the trained routing decision network, corresponding weights are loaded in parallel, and a reasoning result is fused and output; and converting a reasoning result format, and dynamically optimizing a scheduling strategy based on a system real-time monitoring index. The method is compatible with a mainstream large model and a hardware platform, and an efficient and universal deployment scheme is provided for end-side AI engineering landing.

Systems and methods are described for building artificial intelligence (“AI”) agents. A server can provide a user interface (UI) that includes options to select and connect various agent objects. The agent objects can include prompt objects, dataset objects, model objects, and one or more code objects. A subset of the agent objects can be identified as available for selection based on evaluation of at least one management policy associated with an administrative user. A manifest file is generated based on the connected agent objects, and the manifest is validated against dependency rules to ensure that the stages of the agent meet prerequisites for the stages. Then, the server performs a simulated execution of an agent that corresponds to the validated manifest file, including an identification of at least one execution metric associated with the simulated execution.

The invention provides a PCIe resource dynamic configuration method and device, and relates to the technical field of computer hardware resource management. The method comprises the following steps of: establishing communication connection with the pluggable equipment through an expansion interface, and reading equipment identification information pre-stored in a characteristic value storage module of the pluggable equipment by utilizing an I2C (Inter-Integrated Circuit) interface; identifying mainboard PCIe resource allocation parameters and the equipment identification information; displaying configurable PCIe resource parameters on a system configuration interface, and providing configuration options which can be operated by a user; according to the device identification information and a configuration option selected by a user, a pre-stored PCIe configuration mapping table is inquired, a corresponding PCIe resource configuration instruction is generated, and the PCIe configuration mapping table comprises the device identification information and the corresponding relation between the configuration option and the PCIe resource configuration instruction; and configuring PCIe resource allocation of the mainboard according to the PCIe resource allocation instruction.

The invention belongs to the technical field of data processing, relates to an aircraft multi-mode instruction conflict resolution method and system, and aims to solve the problems of high conflict detection time delay and priority strategy staticization of a traditional method. The method comprises the steps that after a multi-mode instruction is received, semantic feature extraction and time sequence alignment are completed through a three-layer attention mechanism and a dynamic time warping algorithm, and a structured instruction set is generated; evaluating the priority of each instruction by combining a four-dimensional dynamic weight system with reinforcement learning; inputting the instruction set with the priority into a six-tuple model to identify a conflict type and an instruction set; and a three-level progressive arbitration method is adopted to resolve conflicts, an optimal execution sequence is generated, and the four-dimensional dynamic weight system is fed back and optimized according to an execution result. According to the method, the real-time performance and the dynamic adaptability of multi-mode instruction conflict resolution are remarkably improved, and reliable guarantee is provided for aviation instruction interaction safety.

The invention provides an online map tile fast loading and caching optimization method, medium and system, and belongs to the technical field of map processing.The method includes the steps that a tile index database and an exchange priority matrix are established, a viewport moving trend vector is calculated according to user sliding operation, a future viewport range is predicted, and a preloaded tile list is screened; the method comprises the following steps: performing cache validity check on tiles, establishing a small change detection vector to judge whether updating is needed, establishing a tile hit rate statistical matrix, fusing a time decay factor and a spatial correlation factor to calculate a popularity score, and gathering high-popularity tiles in a quick access area by adopting a thermodynamics-imitating entropy reduction algorithm to realize cache ordering. And the next tile demand of the user is predicted, so that the technical problem that the overall loading response speed is reduced due to an overlong high-value tile access path caused by disordered distribution of cache resources in the online map tile loading process is solved.

The invention discloses an automatic resource planning method, system and device for hybrid clouds and a medium, and the method specifically comprises the steps: determining a business change trend through employing a time sequence analysis algorithm, and obtaining a demand prediction model; obtaining dynamic resource demand parameters from the demand prediction model, judging a resource allocation proportion of the private cloud and the public cloud, and determining an initial configuration scheme of a cross-cloud resource pool; based on the initial configuration scheme, a reinforcement learning algorithm is adopted to optimize resource scheduling dynamics, and a real-time scheduling plan is obtained; specific parameters of resource allocation are extracted from the real-time scheduling plan, whether the requirements for safety and flexibility are met or not is judged, and a final scheduling instruction is determined; and based on the final scheduling instruction, generating a configuration script and an automatic deployment file for the cross-cloud environment by adopting a preset template engine. According to the method, the hybrid cloud resource delivery efficiency and the resource utilization rate are improved, and deepening and breakthrough of the hybrid cloud technology in enterprise application are promoted.

The present application is directed towards systems and methods for cluster-based code analysis and transformation. Cluster-based analysis may group code objects based on their similarity across functional areas, such as where a code object is cloned in multiple areas (e.g. sort functions that are duplicated across areas, or reports or tables that are identical). In some implementations, objects may be grouped into clusters by type, or based on reading from or writing to a common table. In some implementations, clustering at different layers may be possible.

The invention provides a large model autonomous evaluation method, device and equipment based on a multi-agent technology, and a storage medium. The method comprises the following steps: generating an evaluation task set covering multiple scenes and grading multiple difficulties by setting dimension weights of basic indexes and extension indexes; a multi-agent evaluation system of a questioner, an evaluator and a reference agent is constructed based on the task set, a parallel interaction mechanism is adopted to simultaneously initiate tasks to a to-be-evaluated model and the reference agent, the evaluator agent compares the to-be-evaluated model and the reference agent according to a multi-dimensional scoring standard and outputs the two agents, and task instances, output results and scores are constructed into a structured data set; the subsequent task set is dynamically optimized through analysis of the data set, comprehensive and iterative evaluation of the capacity of the large model is achieved, and the problem that an existing evaluation mode cannot comprehensively reflect the real capacity level of the large model is solved.

The invention relates to the technical field of network set top boxes, and discloses a firmware segmented loading method and device in the starting process of a network set top box. The method comprises the following steps: performing firmware division on the network set top box to obtain a firmware segment set and an inter-segment dependency relationship matrix; determining a real-time state vector of the starting state based on hardware sensor data of the network set top box; performing starting stage identification on the network set top box according to the real-time state vector to obtain a current starting stage identifier and a time sequence optimization condition parameter; carrying out loading decision and Markov state transition analysis on the firmware segment set based on the current starting stage identifier and the inter-segment dependency relationship matrix to obtain a loading action sequence; and carrying out target path loading on the firmware segment set according to the loading action sequence and the time sequence optimization condition parameters to obtain a loading execution result. According to the method, the firmware segments can be flexibly combined and dynamically scheduled according to actual requirements, and the loading efficiency and stability under different network conditions are ensured.

The invention provides an automatic visual screen configuration method, and belongs to the technical field of data processing, and the method comprises the steps: obtaining initial data from a plurality of data sources, carrying out the analysis processing of the types of the data sources through a dynamic protocol adaptation layer, and carrying out the data cleaning and standardization of the analyzed data, and obtaining a standard data set; extracting feature information based on the standard data set, and matching the feature information through a pre-configured rule base to obtain a visual display component; and obtaining a target screen physical parameter, determining the priority of the visual display component, and obtaining a component layout coordinate scheme through an adaptive layout algorithm, the target screen physical parameter and the priority. A visual component template is automatically matched by analyzing a data source type, and an optimal layout scheme is generated by utilizing an algorithm, so that full-process automation from data access to visual display is realized, and the problems of low efficiency and high cost of traditional manual configuration are avoided.

According to the urban traffic automatic simulation plug-in based on the large language model and the implementation method, after a user inputs a request for a road network and a request for vehicle routing by using a natural language, the request is transmitted into a full-process automatic simulation module, and the full-process automatic simulation module carries out simulation; the input analysis agent extracts a key value from a natural language to generate a character string in a Json format, then the character string is spliced, a prompt input simulation input construction agent containing detailed simulation information is generated, the simulation input construction agent calls a packaged tool function, a vehicle route xml file and a road network xml file are generated respectively, and the vehicle route xml file and the road network xml file are connected with the simulation input construction agent. And the simulation execution intelligent body opens the SUMO-gu i to start simulation, and returns a result through a graphical interface. According to the method, the technical complexity of traditional traffic simulation is effectively simplified, a simulation tool is promoted to be transformed from professional modeling to intelligent decision support, and an innovative technical path is provided for optimization and management of a dynamic traffic system.

Systems and methods are described for dynamic execution of artificial intelligence (“AI”) agents. A server can receive, from a client device, an input associated with an AI agent. Based on a manifest file or user profile, the server can identify a management policy that applies to the AI agent. The server then dynamically configures access to the agent objects based on applying the management policy. The management policy is applied to a device status of the client device, a user profile of a user of the client device, and / or a network configuration of the client device. The server then executes a modified workflow based on the dynamically configured access, wherein the modified workflow bypasses or changes operation of at least one of the agent objects. Based on the modified workflow, the server transmits an output to the client device.

Systems and methods are described for building artificial intelligence (“AI”) agents. A server can provide a user interface (UI) that includes options to select and connect various agent objects. The agent objects can include prompt objects, dataset objects, model objects, and one or more code objects. A subset of the agent objects can be identified as available for selection based on evaluation of at least one management policy associated with an administrative user. A manifest file is generated based on the connected agent objects, and the manifest is validated against dependency rules to ensure that the stages of the agent meet prerequisites for the stages. Then, the server performs a simulated execution of an agent that corresponds to the validated manifest file, including an identification of at least one execution metric associated with the simulated execution.

The invention relates to the technical field of automatic deployment, in particular to a cloud code deployment system which comprises a resource monitoring module, a task scheduling module, a path construction module, a log analysis module and a container repair module. According to the method, by collecting the node load and dynamically collecting the node load, the bandwidth and the storage capacity, accurate comparison of the resource request and the surplus is achieved, the high-matching-degree task node relation is established, the resource allocation scientificity is improved, the scheduling priority and the mirror image dependency sequence are extracted, and the deployment sequence is optimized in combination with conflict task sorting; the scheduling adaptability in a multi-task environment is enhanced, the port and environment configuration is compared in a path construction link, the deployment compatibility is improved, the dependency relationship between deployment log analysis and fusion timestamp and exception identifier verification is improved, the diagnosis precision is improved, re-deployment is carried out based on state and frequency linkage in an exception processing stage, accurate node positioning is replaced, a closed-loop mechanism is formed, and the reliability of the system is improved. And the system stability and the self-adjusting capability are enhanced.

The invention discloses a biological hazard big data analysis and monitoring early warning system, and relates to the technical field of public health safety, and an analysis subsystem in the system comprises a core logic module comprising a quality control unit, an error correction unit, an assembly unit and a box separation unit; the unit analysis module comprises a pathogen analysis unit, a resistance gene unit, a virulence evaluation unit and an evolution development unit; the flora integrated analysis module comprises a traceability analysis unit, a mutation characteristic unit, a propagation evolution unit and a transformation management and control unit; the early warning subsystem comprises a risk assessment and early warning system design unit, a dynamic research and propagation analysis unit, an assessment model construction unit, a pathogen evolution and function research unit, a toxicity and propagation risk comprehensive prediction unit, a pathogen risk monitoring network unit and an unknown pathogen and potential risk identification unit. According to the method, the biological hazard data can be comprehensively, efficiently and accurately analyzed.

The invention provides a write-after-read conflict prediction method, device and equipment, which are applied to the technical field of data processing, and the method comprises the following steps: reading a to-be-transmitted loading instruction; the loading instruction is located in a loading queue of the out-of-order execution processor. Determining an index value of the loading instruction; the index value is used for quickly positioning a conflict record matched with the loading instruction in a historical conflict table of the out-of-order execution processor. The conflict record comprises a first storage instruction corresponding to the loading instruction. And when it is determined that the conflict record corresponding to the index value exists in the historical conflict table, setting the loading instruction to be in a suspended state. And after the execution of the first storage instruction is finished, executing the loading instruction. The problem that the execution efficiency of a processor is affected due to pipeline emptying and pause caused by write conflicts after reading can be solved.

The invention provides a front-end resource dynamic preloading method and system based on user behavior intention prediction, and relates to the technical field of computers, and the method comprises the steps: collecting continuous behavior data of a user in real time in a client browser, carrying out the feature extraction of the collected behavior data, and generating a behavior feature vector; on the basis of an intention prediction model deployed at a client, according to the behavior feature vector, performing real-time prediction on the click intention probability of each interactive element in the page; when the predicted click intention probability exceeds a preset threshold value, triggering a resource preloading instruction; and according to the resource preloading instruction, dynamically creating a browser preloading label, and starting background downloading of a target resource so as to perform dynamic preloading of a front-end resource. According to the method and the device, the resource utilization rate and the page loading efficiency are improved, so that the waste of a server and network resources is reduced while the user experience is improved.

The invention discloses a low-code platform and Wasm high-performance computing integration system and method, and relates to the technical field of front-end development. In order to solve the problems that an existing low-code platform is limited in calculation performance and poor in security isolation performance, the scheme adopted by the invention comprises five modules: a description and registration module establishes a metadata structure for a Wasm module; the dynamic loading and asynchronous compiling module realizes loading as required and compiling during operation, and supports caching, version verification and hot replacement; the authority sandbox and resource isolation module constructs a security environment based on authority configuration to prevent illegal operation; the parameter binding and safety bridging module encapsulates a uniform interface to realize standardized interaction; the packaging and life cycle management module packages the module into a visual component, and supports full life cycle management. According to the method, high-performance, high-safety and visual integration of the Wasm module in a low-code platform is realized.

The invention relates to the technical field of data processing, can be applied to business scenes of financial science and technology, medical health and the like, and discloses a data processing and analysis method, device, equipment and medium, and the method comprises the steps: obtaining a data source set, and registering a source label and field mapping relation; executing extraction, cleaning, conversion and loading in the data processing assembly line based on the data source set, the source label and the field mapping relation to generate a processing table set; performing quality verification and exception handling on the processing table set to generate a verified processing table set; generating an index cloud based on the verified processing table set and the configuration strategy; the index cloud is pushed to the analysis tool set through the interface transmission channel, and the analysis tool set generates analysis output according to the index cloud. According to the method, through unified data registration, standardized processing and quality verification, the index cloud is generated in combination with the configuration strategy and pushed to the analysis tool set, the analysis result is generated and transmitted, the data consistency is ensured, and the real-time performance is improved.

Disclosed herein are system, method, and computer program product embodiments for detecting anomalies during software testing. The methods include generating a plurality of test reports for the software program by executing one or more test cases on a plurality of versions of the software program, generating a control chart based on the plurality of test reports, generating an alert when at least one testing characteristic includes an anomaly over the plurality of versions of the software program as determined based on the control chart. The control chart includes a plot associated with at least one testing characteristic of the software program, and a historical context associated with execution of the one or more test cases on the plurality of versions of the software program.

The invention discloses an automatic compiling and adapting method for an RISC-V extension instruction, and belongs to the technical field of compilers. According to the method, a dynamic DSL (Digital Subscriber Line) for registering a custom instruction, adding register use constraints and defining a specific code mode is designed. The LLVM plug-in is used for automatically integrating the registered custom instruction in the compiling process. The invention relates to a self-defined instruction rapid adaptation mechanism which is specially provided for a continuously provided RISC-V self-defined instruction expansion scene, and by automatically generating correct assembly codes of registered self-defined instructions, correct registers are automatically distributed and reserved according to register use constraints; the method comprises the following steps of: searching a defined specific code mode and semantics, and automatically generating and inserting a registered custom instruction in a program, so as to realize the rapid adaptation of a compiler to an RISC-V custom extension instruction and the automatic use of a program code to the custom instruction, and the whole process does not need a developer to manually modify the compiler or a program source code.

The invention discloses a software and hardware combined data interaction method and system based on a Modbus communication component. According to the method and the system, different types of Modbus slave devices are managed through a unified communication configuration file and a point table configuration file, so that compatible access to heterogeneous devices is realized; through an optimized point table analysis mechanism, efficient and self-adaptive data acquisition of discontinuous register addresses is supported, and fine processing and conversion are performed on different data types; a graphical configuration management tool is provided, and the equipment configuration and point table management process is simplified; and a standardized command model is used, so that unified control and data interaction of an upper-layer application on Modbus equipment are facilitated. According to the invention, the flexibility, usability and data interaction efficiency of Modbus communication can be remarkably improved, the system integration and operation and maintenance cost is reduced, and an efficient and reliable Modbus communication solution is provided for the field of industrial automation.

The invention relates to the technical field of audio control, and discloses a low-delay audio input switching method and system, a storage medium and equipment, and the method comprises the steps: carrying out the parallel pre-initialization of a plurality of pieces of audio input equipment, and building an independent parallel audio data cache for each piece of equipment; monitoring the state of each audio input device and the audio stream quality in real time, and judging whether switching is triggered or not based on a multi-factor decision model; after the switching decision is triggered, seamless audio data stream switching is executed, and format unification processing and cross fade-in and fade-out transition are included; a unified equipment operation interface is provided through the hardware abstraction layer, and system resources are optimized and managed; a delay sensing closed-loop control mechanism is constructed, processing delay of each link is monitored in real time, a caching strategy, a processing algorithm and resource allocation parameters are dynamically adjusted, self-adaptive balance of low delay and high tone quality is achieved, and through the method, quick and smooth switching of audio input equipment is achieved, delay is remarkably reduced, and real-time audio experience is improved.

Systems and methods are described for dynamic execution of artificial intelligence (“AI”) agents. A server can receive, from a client device, an input associated with an AI agent. Based on a manifest file or user profile, the server can identify a management policy that applies to the AI agent. The server then dynamically configures access to the agent objects based on applying the management policy. The management policy is applied to a device status of the client device, a user profile of a user of the client device, and / or a network configuration of the client device. The server then executes a modified workflow based on the dynamically configured access, wherein the modified workflow bypasses or changes operation of at least one of the agent objects. Based on the modified workflow, the server transmits an output to the client device.

The invention relates to an Internet of Vehicles edge computing multi-target unloading method and system fusing dynamic environment modeling and improved SARSA, and belongs to the field of intelligent traffic and edge computing fusion. The method and the system comprise MEC environment perception and multi-dimensional state construction, dynamic reward feedback oriented to multi-dimensional performance indexes, intelligent decision model construction and learning based on improved SARSA, and antagonism training oriented to real disturbance. A high-fidelity environment model is constructed through a space-time attention mechanism, an SARSA algorithm is improved to realize hierarchical qualification trace attenuation and collaborative Q table updating, a multi-target hierarchical reward engine is combined to implement differential optimization on an emergency task and a conventional task, and an adversarial training mechanism is introduced to improve robustness. The core problems of high mobility, task diversity, resource limitation and the like in the Internet of Vehicles are effectively solved, the comprehensive performance is optimal in multiple dimensions of delay, energy consumption, resource utilization rate and the like, and the actual landing of the edge computing technology of the Internet of Vehicles is promoted.

The invention discloses a high-precision multi-modal twin model assembly method based on a large language model and a multiple docking optimization mechanism. The whole system framework is composed of three modules: a database, an interaction system and human factors. Wherein the database module integrates a model library module, a user interaction data recording module and a temporary data recording module, and is used for storing and managing various data information required for constructing a virtual scene; the interactive system module is used as a core part, integrates a model dynamic loading module, a model accurate positioning module and a model adaptive assembly module, realizes instruction analysis and model generation by relying on a large language model, and combines a multi-docking optimization mechanism of eye movement tracking, gesture recognition, automatic docking and logic rule binding to realize multi-docking. Efficient and accurate interaction between the user and the database is effectively guaranteed; the method not only provides a solid foundation for virtual experiments and virtual-real symbiosis, but also significantly improves the construction efficiency and precision of digital twin model assembly.