13445results about "Program loading/initiating" patented technology

Systems and methods for building an application incorporating AI functionality include enabling access to a developer application framework associated with a SaaS platform, receiving application code for generating an application, and receiving a selection of an AI assistant add-on provided by the SaaS platform. A link is established between the selected AI assistant add-on and a SaaS platform element, permission is provided to access data from the at least one of the plurality of linked SaaS platform elements. Based on the permissions, data is transferred from the at least one of the plurality of linked SaaS platform elements, and AI integration code is received including at least one reference to the transferred data and at least one call to an AI agent. The application is published for selective use with SaaS platform elements and is configured to exchange data with the at least one of the plurality of SaaS platform elements.

Systems and methods for automatically generating and applying selected conditions to process data are provided. A system receives an application request is received, which includes one or more fields and one or more corresponding data items. The system also receives a configuration file from a configuration repository and processes the same to identify a plurality of rules. For each selected rule of the plurality of rules applicable to selected fields the one or more fields, the system evaluates the selected rules against selected data items associated with the selected fields. Based on the evaluating, the system determines a result of the application request and transmits the result for display.

Some embodiments determine machine configuration intentions from a natural language description of a target machine configuration. Intentions are refined to remove ambiguity, and mapped to pre-approved configuration functions and tasks. A machine configuration task list which invokes the pre-approved configuration functions and tasks is generated by a stabilized language model, and is executed to configure a target machine. The requested target machine is produced without requiring a user or admin to spend substantial effort and time customizing the machine and confirming its security and policy compliance.

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 provides a rules engine that evaluates, modifies, and dynamically routes inputs to AI models based on provider restrictions and management rules. Provider restrictions are stored. Based on management rules and the detected provider restrictions, the input can be modified with contextual placeholders and prompts can be injected for input into the destination model. Reversal of the modifications can occur to the outputs. The system can notify the user, an administrator, and a supervisor regarding the security evaluation and remedial actions. The evaluations can be logged for auditing purposes.

The invention provides an intelligent agent automatic generation and scheduling system based on an artificial intelligence large language model. The system comprises an intelligent agent configuration module driven by the large language model, a workflow engine and automation module and a front-end and rear-end integration module. The agent configuration module driven by the large language model realizes automatic configuration of an agent and a workflow based on the large language model, and comprises a large language model interaction layer and an agent configuration module; the workflow engine and automation module is responsible for execution logic of workflow, supports multi-task parallel processing and comprises a workflow engine module and a task scheduler; the front-end and rear-end integration module realizes cooperative work of a JavaScript front end and a Python Django rear end by designing a unified data interface, the front end is responsible for interacting with a user and displaying an execution state of a workflow, and the rear end provides service logic processing and data persistence.

The invention discloses a starting method of a server starting item, which relates to the technical field of computers, and comprises the following steps: when a server is started, under the condition of determining that starting item target starting configuration information is stored in a substrate management controller, obtaining target starting configuration information from a basic management controller, the substrate management controller manages the stored target starting configuration information based on the preset duration, so that the starting item is started according to the target priority sequence when the preset duration is exceeded, and the starting item is started according to the target priority sequence when the preset duration is exceeded. If the target priority sequence in the target starting configuration information becomes invalid, the starting item automatically recovers to the default setting, so that the technical problem of low efficiency of temporarily adjusting the starting item sequence due to the fact that the starting item sequence needs to be adjusted manually and then the default setting needs to be recovered manually in the prior art is solved; the technical effect of improving the efficiency of temporarily adjusting the starting item sequence is achieved.

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.

Embodiments of the disclosed technologies include, responsive to a first use of a first application by a first user, configuring, in a first prompt, at least one instruction based on first application context data and first user context data. The first prompt is stored in a memory that is accessible to the first application and a second application. Via the second application, first output of a generative artificial intelligence (GAI) model is presented to the first user. Based on the first output of the GAI model, at least one second use of the first application by the first user, or at least one first use of a third application by the first user, is configured.

Systems, methods, and computer program products are disclosed for running applications in a serverless environment. Running applications in a serverless environment includes configuring a serverle96ss environment by integrating a set of rules into the serverless environment, the set of rules including at least one trigger and at least one trigger-responsive action for use in conjunction with a SaaS platform; obtaining a general address for the serverless environment; using the general address for conveying code to the configured serverless environment, for combination in an immutable manner with configuration information, thereby enabling formation of an immutable image, wherein the configuration information is adapted to invoke the at least one trigger, thereby initiating the at least one trigger-responsive action; and receiving a specific address in the serverless environment for reaching an actuated instance of the immutable image for incorporation with the SaaS platform.

The invention relates to the technical field of automatic deployment of cloud computing platforms, in particular to a self-adaptive deployment method and system oriented to a credential heterogeneous environment. Aiming at three technical bottlenecks of low multi-CPU architecture adaptation efficiency, frequent software dependence conflicts and complex security baseline configuration in a localization process, a heterogeneous computing resource intelligent scheduling engine and a dynamic security policy generation mechanism are innovatively provided. The method comprises the following steps: constructing a heterogeneous resource portrait through a hardware feature automatic identification technology, and realizing component installation sequence optimization based on a DAG dependency relationship analysis algorithm and Kahn topological sorting; and creating an adaptive network security policy, and sensing the firewall state of the target system in real time through a probe. Compared with a traditional deployment mode, the method supports cross-architecture compatibility, solves the problem of dependency conflicts, improves deployment efficiency, and guarantees consistency and safety of the system.

The invention discloses an interface parameter extraction method and device based on slot filling and a medium, and relates to large model application. The method comprises the steps that semantic analysis is conducted on natural language query through a pre-trained large language model, an intermediate parameter set containing intention types and slot filling parameters is generated, and accurate conversion from natural language parameters to structured interface specifications is achieved through multi-stage standardization processing; based on a dynamic matching mechanism of parameters and interface metadata, an interface calling chain including parallel calling, transaction compensation and data conversion nodes is constructed, and a multi-source aggregation data set is generated through cross-interface data verification and space-time alignment processing; and finally, in combination with dynamic template selection and semantic generation rules, outputting a multi-modal interaction report fusing the structured visual component and natural language interpretation.

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 extension equipment function configuration method and device, computer equipment and a storage medium, and relates to the technical field of data processing. According to the method, a hardware and firmware collaborative dynamic configuration framework is constructed, and a receiving queue is adopted at an extension equipment end to trigger a central processing unit firmware processing mechanism; in combination with virtualization design of a shadow configuration space and a shadow register, dynamic reconstruction of configuration parameters and an address mapping strategy during operation is realized; a host end supports configuration space access, base address register remapping and memory data transmission interaction through a transaction generation and routing mechanism driven by an event type. Hardware solidification limitation of traditional extension equipment is broken through through software and hardware decoupling design, the problems that function extension capacity is insufficient, resource dynamic allocation is difficult and protocol updating and virtualization scenes cannot be adapted are solved, and the technical effects of improving configuration reconstruction efficiency and address space utilization rate and supporting virtual function creation are achieved. And the adaptability of the equipment in cloud computing and edge computing environments is enhanced.

The invention discloses a self-adaptive intelligent symbiotic data ecosystem and a cooperative implementation method thereof. The system comprises an intelligent agent ecosystem module, a self-adaptive domain knowledge graph module, a self-adaptive workflow engine module, a CLID configuration system module, a man-machine cooperation interface module, a data processing pipeline module and an innovation trigger module. Through cooperation of diversified intelligent agents, the dynamic knowledge graph, the self-adaptive workflow and CLID multi-dimensional configuration, high-flexibility and self-adaptive data processing and analysis capacity is achieved. The system can automatically construct a complex workflow, perform multi-modal data fusion, and integrate human judgment and AI capabilities at key points, thereby providing an innovative solution. The method is suitable for complex and dynamic big data processing and analysis scenes, and the data processing efficiency and innovation ability are remarkably improved.

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.

Methods and systems for managing data processing systems are disclosed. To manage a data processing of the data processing systems, the data processing system may be placed in a first operating state. To do so, first log in credentials may be obtained from a first user and may be used to obtain a first user persona from a server. The first user persona may be obtained via an out of band communication channel and may include a first set of configurations for the data processing system. The first set of the configurations may indicate preferences for operation of the data processing system associated with the first user. At least a portion of the first set of the configurations may be implemented by hardware resources of the data processing system and, therefore, computer-implemented services may be more likely to be provided as desired by the first user.

The invention relates to the technical field of online education, in particular to a componentized page rendering method and device for online education, equipment and a medium, and the method comprises the steps: obtaining a page rendering request, and determining component configuration information; constructing a component dependency graph; calculating component loading priority scores, and generating a loading queue; loading the execution component and managing the life cycle; establishing an inter-component communication bus; and executing a rendering process and performing performance optimization. According to the method, the user learning behavior and the component dependency relationship are intelligently analyzed, the component loading and rendering strategy is dynamically adjusted, and a dual-priority event management mechanism is introduced, so that efficient intelligent rendering of the education scene component is realized. Therefore, the problems of low online education component rendering efficiency, poor scene adaptability, insufficient learning behavior analysis, lack of performance optimization automation and the like in the prior art are solved, and the performance of an online education platform and the user experience are remarkably improved.

The invention discloses a resource allocation method and device for a large model cluster, equipment, a storage medium and a program product. Comprises: determining configuration parameters based on a configuration task selected by a user; determining at least one of a computing power demand, a bandwidth demand and a video memory demand based on the configuration task and the configuration parameters; constructing a constraint condition and a target function according to at least one of the computing power demand, the bandwidth demand and the video memory demand; and carrying out iterative optimization on the target function by utilizing a preset planning algorithm based on the constraint condition so as to solve the unknown configuration parameter and obtain a target configuration parameter. According to the resource allocation method for the large model cluster, provided by the embodiment of the invention, the optimal large model learning parameter or hardware resource allocation can be determined, and the efficiency and reliability of resource allocation of the large model cluster are improved.

The system provides a permissions engine derives permissions for responses from artificial intelligence (AI) agents. The permissions are derived based on the access permissions of chunks used to generate the response, and the response is cached for reuse with users meeting the permissions requirements. The system can also derive an access permission level for a user attempting to access the cached result or other content. This can be based on a managed access profile that includes user behavior criteria and device criteria. Likewise, the system can validate and derive sensitivity levels of documents that are ingested for use in AI agents and discriminate between chunks to use in synthesizing results based on the derived permissions of the chunks and their relationship to the user.

The invention discloses a starting item sequence adjusting method, system and device and a computer storage medium, relates to the technical field of servers, is applied to a basic input and output system, and comprises the following steps: displaying a preset key and starting item information corresponding to the preset key in a power-on self-test stage; detecting whether key triggering information is received or not; if the key triggering information is received, determining a triggered target key according to the key triggering information; determining a target starting item corresponding to the target key; calling an intelligent platform management interface command, and setting a starting mode as a target starting item; and executing a restart operation. According to the application, a user can know the preset key and the starting item information corresponding to the preset key, so that the user can select the corresponding starting item by triggering the preset key, and the operation is simple and convenient; the target starting item selected by the user is accurately set in the basic input and output system; and finally, the restarting operation is executed to ensure that the adjustment of the starting item takes effect, so that the starting item of the BIOS is simply, conveniently and accurately adjusted.

The present application discloses an image generation method and apparatus, a device, a storage medium, and a vehicle. The method comprises: obtaining a prompt word input by a user, wherein the prompt word is used for instructing to generate a target image; on the basis of a preset first correspondence, determining an image generation model and a sampler corresponding to the prompt word, wherein the first correspondence is a correspondence among the prompt word, the image generation model and the sampler, and the sampler is used for controlling a noise reduction mode of the image generation model; performing text expansion on the prompt word to obtain a final text; loading the sampler into the image generation model to update noise reduction parameters of the image generation model; and using the image generation model with updated noise reduction parameters to convert the final text into the target image. According to the embodiments of the present application, the quality of generated images can be 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.

Systems and methods are described for a maintaining consistent and reliable outputs from artificial intelligence (“AI”) based search systems that use pipelines with a dataset, AI model, and prompt. An application can send a query through a pipeline and set the result as a baseline for future results. The application can periodically resend the query through the pipeline and compare the new results to the baseline. If the new results vary from the baseline above a predetermined threshold, then corrective measures can be taken. This can include notifying an administrator or querying the pipeline for how to change the prompt so that results are more similar to the baseline.