15 results about "Autoload" patented technology

The invention provides a link tracking implementation method and device of an application program, electronic equipment and a medium, and relates to the technical field of computers. The method comprises the steps that when an application program is started, a core module in a tracking dependency package is loaded to a currently running virtual machine process; loading a preset agent program into a virtual machine process by executing initialization logic in the core module; modifying the target class file through a preset agent program and a function plug-in module in the tracking dependency package so as to realize a link tracking function of the application program; the target class file comprises a class file related to cross-thread task processing and a class file related to request calling. By executing the initialization logic of the core module in the tracking dependency package, the agent program is automatically loaded when the application program is started, so that loading of the agent program and deployment of the link tracking function can be completed without manually configuring JVM starting parameters by a user, the deployment complexity is reduced, and the user experience is improved.

The invention relates to the technical field of simulation, in particular to an intelligent multi-target parameter optimization method and system for analog machine equipment. The method comprises the following steps of: constructing a decoupling architecture of a component model algorithm and a debugging algorithm of a simulation platform, and respectively compiling the component model algorithm and the debugging algorithm into a dynamic link library; a standardized debugging parameter input framework is established, and flexible input of multi-working-condition debugging parameters is achieved; automatically loading a corresponding dynamic link library file, and converting multiple groups of working condition data configured by a user into a memory structure body; the structural body transmits to the currently loaded debugging algorithm for execution; the debugging algorithm is operated, and monitoring and feedback are implemented in the debugging algorithm execution process; when the debugging algorithm is terminated, the simulation platform receives a state code returned by the debugging algorithm, and converts the state code into error information readable by a user through a predefined error code mapping table; and the simulation platform extracts the optimized parameter result from the debugging algorithm output, and the result data is verified and automatically updated to the corresponding equipment parameters. According to the invention, multi-working-condition automatic debugging of the simulator equipment is realized, and the debugging efficiency is improved.

The application discloses a unified adaptation and standardized evaluation method and system for a heterogeneous RAG framework. The method constructs a three-layer decoupling architecture of the arrangement layer, the adaptation layer and the evaluation layer. First, the ability characteristics of the target framework are automatically loaded through the framework capability perception matrix; then, an independent API adapter is assigned to encapsulate the heterogeneous framework into a unified operation interface, and an intelligent uploading strategy is executed and the heterogeneous response format is parsed into a unified four-tuple structure; then, six mutually orthogonal evaluation dimensions are established, and automatic evaluation is carried out based on a large language model evaluator; finally, combined with the framework capability, a differentiated evaluation standard is applied, different failure modes are mapped to specific dimension combinations to realize accurate performance attribution, and a comparative analysis report is output. The application effectively solves the problems that the heterogeneous framework cannot be uniformly evaluated and a single index is difficult to locate the bottleneck, and significantly improves the scientificity, reproducibility and fairness of cross-framework comparison of the evaluation.

A new apparatus and method for securely distributing an application to the processors of a processing unit. The processing unit is formed as part of an integrated circuit and includes a plurality of processors (referred to as tiles), each having their own execution unit and storage for storing application data and additional executable instructions. The integrated circuit includes a hardware module (referred to herein as an autoloader) configured to distribute a set of bootloader instructions (referred to herein as a secondary bootloader) to each of at least some of the tiles. Each of the tiles then executes the instructions of the received secondary bootloader, which causes each tile to issue a read request to read a set of executable application instructions from a memory external to the integrated circuit. Each tile then executes operations using the received set of executable application instructions in order to execute the application using the processing unit.

The application provides a large model inference optimization method, system, medium, terminal and program product for Ascend processors. The method comprises: comparing the automatically detected version with a built-in compatibility table; if the versions do not match, automatically loading a runtime patch or calling a backup library; if the versions match, processing a user request based on a preset hybrid scheduling mechanism; dynamically selecting a MindIE inference framework or a vLLM inference framework based on a runtime adaptation layer according to the user request parameters; converting large model weights into low-bit weights and compensating based on an error compensation matrix; identifying subgraphs and fusing operators for a model graph according to the computing characteristics of the Ascend processor, and rearranging intermediate tensors according to the 512B memory access alignment rule of the Ascend processor; performing pipelined overlap processing on the matrix multiplication calculation of the current token and the full aggregation operation based on an asynchronous execution flow; and performing inference and generating an inference result according to the user request. The application can improve the efficiency and reliability of large model inference.

The invention belongs to the technical field of computer information processing, and provides a natural language-driven simulation scene full-automatic generation method and system and application, and the method comprises the steps: converting a natural language instruction input by a user into a structured parameter, and outputting a standardized parameter table; road network decision making is carried out, and structured parameters after decision making are converted into standard static environment files; the method comprises the following steps: hierarchically analyzing key parameters of a fixed hierarchy, generating a behavior of a dynamic entity through template replacement, generating a standard dynamic scene file, automatically loading the standard dynamic scene file into simulation software to run, synchronously analyzing a running log, and monitoring a deployment state so as to successfully load and start a simulation scene; and constructing a three-stage guarantee system of pre-verification, verification during operation and manual verification, performing operation verification on the simulation scene, and executing a correction instruction input by a user when a problem is found in verification to complete feedback and closed-loop optimization. According to the method, the technical threshold of VTD simulation scene construction can be reduced, and non-professional users can also quickly create complex scenes.

The application provides a configuration-driven DL4NLP model automatic training and deployment method, which comprises the following steps: parsing a YAML configuration file and verifying parameters to generate a unified process configuration; automatically loading data and performing preprocessing according to the configuration to generate a model input tensor; instantiating model components through a component factory and assembling the model components into a complete network model; automatically configuring a training environment to train and evaluate the model to obtain an optimal model; exporting the model into a TorchScript format binary file; and automatically generating a standardized deployment package based on a template engine. The application abstracts the whole process of model development and deployment into a configuration-driven standardized pipeline by parsing the YAML configuration file, automatically performs data loading and preprocessing, model component instantiation and assembly, training optimization and deployment package generation, and solves the problems of low development efficiency, long cycle and technical gap between training and deployment caused by repeated code writing and manual conversion and deployment for different tasks in traditional model training and deployment.

The present invention relates to a method for loading an application program in an embedded system, and belongs to the fields of communication technology and storage technology. When any main processor board in an embedded system fails, another normal main processor board is installed in the failed slot of a computer chassis. Without the need for manual parameter modification, the parameters in the main processor board in the slot can be dynamically modified and the application program in the board can be loaded automatically and quickly. The method for loading an application program in an embedded system proposed by the present invention perfectly solves the problem of "complex operation of replacing the main processor board and error-prone parameter configuration", realizes the rapid adaptive dynamic modification of the parameters of the main processor board in the computer chassis of the embedded system and the automatic loading of the application program, and improves the reliability of replacing the main processor board in the computer chassis.

Method for operating a working machine, wherein the working machine has at least one machine unit (2) with at least one drive device (22) and each of these machine units (2) has at least one control device (4) for controlling the machine unit (2) and this control device (4) has a storage device in which programs required for the operation of the respective machine units (2) are stored, and wherein interfaces (8) for data exchange are assigned to each of the machine units (2), wherein, in order to change a machine configuration of the working machine (1), the programs required for changing or commissioning the machine configuration are automatically loaded in all machine units (2) and uniform interfaces (8) are used for this purpose and the programs are provided by a platform (20) independent of the working machine (1), characterized in that When the machine configuration is changed in each machine unit (2), at least one basic program is retained, wherein the programs required to change or commission the machine configuration are loaded from the at least one basic program.

The application provides a method and system for batch exporting Allegro file packaging based on Python, which comprises the following steps: configuring an Allegro executable file path and a pre-recorded Skill script path; selecting a target export directory through a graphical interface and updating an export path parameter in the Skill script; selecting Allegro PCB files to be processed in batch through the graphical interface; verifying a file list and an export path, and starting batch processing after confirmation; traversing the file list, and performing the following operations on each Allegro PCB file to be processed: calling an Allegro command line tool to open the PCB file in batch processing mode; automatically loading the pre-recorded Skill script to perform packaging and export operations; and capturing and feeding back a command line execution state. The application can improve efficiency, support automatic batch processing of multiple files, and reduce manual operations; through a path automatic configuration and an exception capturing mechanism, human errors can be avoided; the application is interactive, simple in operation steps, and low in operation threshold.

The application provides a model weight file distribution method and device, a computer program product, a host and a storage medium, relates to the technical field of software, and the host monitors the working state of a target container that executes a model running task. The working state includes a starting state. The target container can access a host directory of the host in the starting state. In response to a first query request for a target model weight file sent by the target container in the starting state, the target file address of the target model weight file in the host directory is queried. The target file address is returned to the target container, so that the target container loads the target model weight file in the host directory based on the target file address. The application can not only release the binding of the model weight file and the container image, and the disk occupation when multiple containers deploy the same model, but also can realize the automatic loading of the container to the model weight file in the host directory, reduce the workload when deploying multiple hosts, and improve the security.

The invention provides an intelligent test generation method oriented to an operating system test task. The intelligent test generation method comprises the following steps: step 1, automatically loading a user personalized configuration file when a local client side main program is started; 2, constructing a test code knowledge base; 3, inputting a personalized test requirement in an input console constructed by a main program, wherein the main program is connected with the selected large model through api configuration of a configuration file; the large model generates a test list and verifies the test list based on the knowledge base configured in the step 2 and user requirements; the test list is related to the knowledge base provided in the step 2, and the LLM intelligently analyzes a test target corresponding to a test scene and generates formatted json data; 4, based on the verified test list, the main program generates a test code according to the determined json file; and 5, storing the formatted test result, and allowing the result to be returned to the main program for automatic analysis and natural language analysis.

The invention discloses a Windows handle adaptive program silent automatic loading method and system, and relates to the technical field of program loading.The method comprises the steps that a target process is obtained, and window handles of the target process are enumerated to form a candidate handle set; collecting multi-source rectangular observation of the candidate handles to form historical buffering, and screening, determining and analyzing the handles; the cross-screen mixing degree and the time jitter degree of the handle are calculated and analyzed, and the geometric state of the handle is judged to be a resident state, a half-cross state or a transition state according to a preset threshold value; counting and generating a gating result in the gating time window; when gating is passed, determining a rectangle used in the round according to a geometric state: if the rectangle is in a resident state, adopting a main source mode for selection, and if the rectangle is in a half-span state or a transition state, adopting a fusion mode; and determining a final binding handle in the candidate handle set based on the current round of use of a rectangle, and updating a coordinate mapping relation of silent automatic loading. The method can adapt to multi-display, window cross-screen and jitter scenes, and the accuracy and robustness of automatic loading are improved.

The application provides a system automatic repair and fault environment preservation method, which is characterized by comprising the following steps: S100, system power-on and starting a boot program; S200, detecting whether an abnormal mark exists in an information partition; if the abnormal mark exists, entering step S300; if the abnormal mark does not exist, entering step S400; S300, automatically loading a system image in a repair partition, judging a fault cause, and performing corresponding recovery and saving a fault scene according to the fault cause; S400, setting the abnormal mark, automatically loading a start image in a regular partition and starting; and S500, clearing the abnormal mark. By detecting whether the abnormal mark exists in the information partition, whether the system has a fault can be automatically judged, and recovery can be automatically performed without manual operation, and automatic recovery can be realized in an unattended deployment scene.

This invention discloses a metadata-driven method and system for constructing a large language model intelligent agent through bootstrapping, relating to the field of artificial intelligence technology. The method includes the following steps: collecting natural language requirements generated from business scenarios and organizing these requirements into a unified expression format; generating readable metadata description content within this unified expression format; performing field splitting and role attribution around the metadata description content; constructing data type definition metadata within the unified expression format; and embedding prompt words into the data type definition metadata to generate content. This invention achieves progressive generation from natural language to data types, rules, and intelligent agent definitions through unified metadata expression, supporting dynamic construction and automatic loading at runtime, reducing manual intervention, and improving automation. Simultaneously, new metadata is generated during runtime and fed back into the generation chain, forming a continuous bootstrapping closed loop, enhancing structural consistency and version evolution controllability.