227 results about "PLANC" patented technology

The invention relates to the technical field of workshop scheduling, in particular to a multi-online collaborative scheduling method and system for a flexible intelligent processing production line, and the method comprises the steps: building a numerical control parameter model for processing equipment, decomposing a processing program into basic process instruction units, combining the numerical control parameter model and the real-time operation state of the equipment to analyze the adaptation degree of the basic process instruction unit and the equipment, and generating a preliminary task allocation scheme; constructing a distributed control network among the devices, generating a local task sequence of each device based on the preliminary task allocation scheme and the adaptation degree, and obtaining a final task allocation scheme and a task execution plan set based on a contract network protocol algorithm; establishing a multi-constraint collaborative framework, and generating a collaborative production scheduling scheme under the multi-constraint collaborative framework; and establishing a heterogeneous equipment motion cooperative control model, and performing multi-level adjustment through event-driven feedback control. According to the invention, flexible cooperative scheduling of heterogeneous equipment can be realized.

A computer-implemented method generates workflow definitions in workflow definition language using one or more large language models, LLMs. The method includes receiving a natural language description of an automated workflow and generating a plan generation prompt including the natural language description and plan generation instructions. The plan generation prompt is input to one of the LLMs and in response a structured plan comprising a plurality of actions are received. For each action, a corresponding segment of workflow definition language is generated to provide a plurality of segments of workflow definition language. The segments are combined to form a workflow definition corresponding to the natural language description.

The invention discloses an intelligent agent automatic arrangement method and system based on a large language model, and relates to the technical field of artificial intelligence. The method comprises the following steps: decomposing a natural language instruction of a user into a structured subtask sequence by utilizing a first large language model; based on the agent portrait library, matching and allocating agents for each sub-task to generate an initial execution plan; task execution is scheduled and monitored in real time through an event-driven architecture; when abnormity is monitored, a self-adaptive adjustment mechanism is triggered, the affected plan part is re-planned, and an updating instruction is issued. According to the invention, efficient, flexible and robust multi-agent automatic arrangement is realized.

A method of processing a compound prompt includes mapping a domain to each of several distinct machine learning models (MLMs) and decomposing the compound prompt into a plan having several steps. For each step, one of the MLMs is selected by matching the step to a corresponding mapped domain, and a language output is generated using the selected MLM. These outputs are integrated into a syntactically and semantically coherent final output using a large language model.

The invention provides an operator unloading method and system of a vectorization execution engine based on a DPU heterogeneous architecture, the DPU heterogeneous architecture comprises a CPU and a DPU network card, and the DPU heterogeneous architecture is applied to an open source distributed computing system; the open source distributed computing system performs grammar analysis on a query language input by a user and adds physical execution stage operation to obtain a physical execution plan, and the Spark plug-in intercepts the physical execution plan and converts the physical execution plan into a query plan based on a set rule; transmitting to a vectorization engine execution path and calling a vectorization execution engine to carry out data processing on a plurality of operators in the query plan; a performance function of a DPU heterogeneous calculation version in a function system of the vectorization execution engines is called, calculation resource consumption of operators capable of being processed by the multiple vectorization execution engines in the query plan is calculated, the operators higher than a preset standard are unloaded to a DPU network card from an original CPU, and the data processing performance of the query task is improved through operator unloading.

The invention discloses a cloud platform operation and maintenance method and device, equipment, a medium and a product, relates to the technical field of operation and maintenance, is applied to a server, and comprises the following steps: receiving a natural language instruction input by a user side; performing intention recognition and parameter extraction on the natural language instruction to obtain at least one atomic task, and generating an execution plan according to a target dependency relationship between the atomic tasks; screening an optimal execution path from at least two preset execution paths for each atomic task based on the task attribute of each atomic task in the execution plan; performing simulation drill analysis on the execution plan based on the optimal execution path to generate a risk pre-judgment report, and sending the risk pre-judgment report to the user side; and after execution confirmation information sent by the user side based on the risk pre-judgment report is obtained, a task executor corresponding to the optimal execution path is utilized to initiate calling to the cloud platform to execute the execution plan, and an execution result of the cloud platform is converted into a natural language response to be sent to the user side.

Methods for generating programmable logic controller (PLC) code and corresponding systems and computer-readable mediums. A method (400) includes receiving (402) at least one specification (160) by a computer system (100). The method includes generating (404) a model-based design plan (158) based on the at least one specification (160) and a large-language model (LLM) (154) and processing (406) the model-based design plan (158) using low-rank adaption techniques (162) and the LLM (154) to produce PLC code (156). The method includes performing (408) a syntax check on the PLC code (156) and outputting (414) the PLC code (156).

Embodiments of the present invention provide a code reconstruction method and system, a computing device, a storage medium and a program product wherein the code reconstruction method comprises: performing code analysis on a to-be-reconstructed initial code to generate a target reconstruction scheme wherein the target reconstruction scheme comprises a code reconstruction plan and a code test plan; the code reconstruction plan comprises a function dependency relationship of the target code; analyzing a function dependency relationship in the code reconstruction plan, and generating at least two code reconstruction tasks corresponding to the initial code; executing the at least two code reconstruction tasks in sequence to obtain each initial code snippet, and iteratively optimizing each initial code snippet through static analysis and dynamic verification until candidate code snippets conforming to a function dependency relationship are generated; and compiling a test case of each candidate code snippet based on the code test plan, and testing each candidate code snippet based on each test case to obtain a plurality of target code snippets conforming to the function dependency relationship. And the coherence and the consistency of the reconstructed codes are ensured.

Systems and methods provide for managing changes to federated systems. In accordance with some aspects, a change to a federated system is received. A historical query is accessed from a query store storing information regarding queries received by the federated system. A query plan evaluation is performed for the historical query based on the change to the federated system, and an action is caused to be performed for the change based on the query plan evaluation.

The embodiment of the invention provides a software development method based on artificial intelligence and related equipment. In the embodiment of the invention, an AI software development method and mode are provided, and a user and a technical design document generation agent cooperate to complete a technical design document of a programming task; and the programming agent autonomously performs task planning based on the technical design document, and autonomously executes the programming task based on the planned execution plan. In at least one of the technical design stage or the task execution stage of the programming task, a man-machine cooperation interface is provided, and the man-machine cooperation interface is used for a user to participate in the software development process for the programming task. Therefore, the software development efficiency and the code quality are greatly improved through division cooperation and man-machine cooperation interaction among the intelligent agents.

There is provided a system and a computerized method of remediating one or more operations linked to a given program running in an operating system, the method comprising: querying a stateful model to retrieve a group of entities related to the given program; terminating at least a sub set of the group of entities related to the given program; generating a remediation plan including one or more operations linked to the given program, the one or more operations being retrieved based on the group in the stateful model; and executing the remediation plan by undoing at least part of the one or more operations linked to the given program thereby restoring state of the operating system to a state prior to the given program being executed. There is further provided a computerized method of detecting malicious code related to a program in an operating system in a live environment.

The invention provides an application method based on a privatized large model and an AI agent, and the method comprises the steps: deploying a pre-trained large language model on an internal server of an enterprise, carrying out the adjustment of the large language model through the own data of the enterprise, and carrying out the local deployment, and obtaining a privatized large language model; constructing an AI agent framework, and defining a task execution logic, a decision rule and an interactive interface with the privatized large language model of the AI agent; the AI agent receives a task request proposed by a user through a natural language, calls the privatized large language model to analyze the task request, and generates a task execution plan; and the AI agent executes a task according to a result output by the privatized large language model, and feeds back a task execution result to the user. According to the method, a privatized large language model is deployed in an enterprise, and the autonomous decision-making capability of an AI agent is combined, so that the data security is guaranteed, and meanwhile, efficient processing and intelligent decision-making support for complex tasks are realized.

Certain aspects of the present disclosure are directed towards a method for process scheduling. The method generally includes selecting a first processing unit of a plurality of processing units to process a thread based on a use case associated with the thread and locations of the plurality of processing units within an electronic device, and allocating the thread to be processed on the first processing unit.

Disclosed herein are systems and methods for updating software object instances on a plurality of nodes. An exemplary method may comprise monitoring a software repository for an update to the software object instance installed on the plurality of nodes. In response to detecting the update, the method may comprise initiating installation of the update to the software object instance on a first subset of the plurality of nodes in accordance with an installation schedule. The method may comprise determining whether a functionality or performance issue in the software object instance or a respective node of the first subset was detected subsequent to updating the software object instance. In response to determining that the functionality or performance issue was not detected, the method may comprise updating the software object instance on a second subset of the plurality of nodes in accordance with the installation schedule.

The invention relates to the field of DevOps intelligent execution, in particular to an MCP-based DevOps task intelligent arrangement and execution method and system, and the method comprises the steps: obtaining a tool function of each MCP server to form a global tool library; receiving a natural language task instruction of a user, and inputting the natural language task instruction into the large language model; performing task decomposition on the natural language task instruction to generate a structured tool calling plan; the client side executes the structured tool calling plan and calls target tool functions on the corresponding MCP server in sequence or in parallel through the MCP according to the execution logic and the dependency relationship; monitoring execution of each tool function, determining a subsequent process or triggering a preset rollback operation according to an execution state, and feeding back a final execution result to the large language model; and generating a natural language abstract containing the overall task execution state based on the execution result, and outputting the natural language abstract to a user. And cross-environment seamless operation automation is realized.

The invention provides a penetration test system and method based on multi-agent collaboration, and the system comprises an interaction module, and a plan generation agent, a plan planning agent and a plan execution agent which comprise a large language model LLM, and the interaction module is used for receiving a to-be-solved problem inputted by a user; the plan generation agent is used for generating a penetration test task plan according to a to-be-solved problem; the plan planning agent is used for splitting the penetration test task plan to generate a plurality of sub-plans; a reinforcement learning technology is utilized to screen out an optimal adaptive API of an optimal tool corresponding to the current plan description, parameters required by the current API are extracted from the subtasks, and a final penetration test command is generated; and the plan execution agent is used for receiving the penetration test command transmitted by the plan planning agent, executing the command and recording a generated execution result. According to the method, a user only needs to give a penetration test target and purpose, a penetration test scheme can be automatically given, a penetration test task is carried out, and a result is finally given.

A method and device for hierarchically planning executions of operation sequences achieving goal states of technical system workflows and checking them through system simulation or system hardware execution is provided. To hierarchically plan executions of operation sequences achieving goal states of technical system workflows and checking them, it is proposed to implement a goal-state-specifying-process, wherein <1> for a workflow of a technical system a workflow related planning domain is given, <2> a workflow related goal state is specified, <3> a planning task is defined, and <4> by a planning approach <4a> executions of the at least one selected planning operator are checked iteratively in order to find a full plan, and <4b> when in <4a> the full plan is found, the executions of the found full plan achieving the goal state are checked iteratively by a secondary query loop until the goal state is achieved.

The invention discloses a method for generating a text system, and aims to solve the problems of long development cycle, disjunction of requirements and technology implementation and the like of traditional software and realize automatic generation from a natural language requirement to a deployable service system based on a large language model. The method is divided into two stages: in the first stage, a system is disassembled into a logic subsystem through man-machine cooperation, core business logic is extracted, a large language model is called by adopting a divide-and-conquer strategy, and a detailed specification containing a functional process and an interface form is generated; in the second stage, a large language model is called to generate a database table structure and a DDL script, an engineering framework of a basic technology stack is initialized and integrated through a predefined scaffold, CRUD codes are generated, the codes are analyzed to generate a transformation plan for structured data, and target logic codes are incrementally integrated. According to the method, control is achieved through Python, the problem of large model input limitation is solved, and generated codes are standard and extensible. The development cycle is shortened, the requirement-design-code consistency is guaranteed, the threshold is lowered, platform locking is avoided, and enterprise digital transformation is assisted.

The present disclosure is directed to monitoring internal process memory of a computer at a time with program code executes. Methods and apparatus consistent with the present disclosure monitor the operation of program code with the intent of detecting whether received program inputs may exploit vulnerabilities that may exist in the program code at runtime. By detecting suspicious activity or malicious code that may affect internal process memory at run-time, methods and apparatus described herein identify suspected malware based on suspicious actions performed as program code executes. Runtime exploit detection may detect certain anomalous activities or chain of events in a potentially vulnerable application during execution. These events may be detected using instrumentation code when a regular code execution path of an application is deviated from.

The invention discloses a dynamic operator-oriented incremental compilation optimization method and system, belongs to the technical field of deep learning, and aims to solve the technical problem of how to provide a fine-grained, reusable and extensible compilation strategy for dynamic operator-oriented incremental compilation optimization. Comprising the following steps: extracting attributes of a dynamic operator and constructing a dynamic operator hash signature according to the extracted attributes; dividing the calculation graph into a static sub-graph and a dynamic sub-graph, establishing a mapping table of a dynamic operator hash signature and a compiling kernel, and caching the mapping table in an incremental compiling cache; solving and instantiating the dynamic sub-graph through a dynamic specifying engine, generating a specialized sub-graph, querying whether a matched compiling result exists in an incremental cache or not, and updating the incremental cache; and for the static sub-graph which is not changed, directly multiplexing the last compiling result of the static sub-graph, executing cross-sub-graph fusion optimization work between the dynamic sub-graph and the static sub-graph which are subjected to specialized processing, and submitting a fusion execution plan to a runtime engine.

The embodiment of the invention provides an agent system-oriented parallel task execution method and device and electronic equipment, and is applied to the technical field of artificial intelligence. The method comprises the following steps: acquiring a structured plan, identifying a dependency relationship of the structured plan, and generating a dependency relationship table; obtaining a legal directed acyclic structure according to the obtained dependency relationship table; the task graph structure which accords with semantic logic and has concurrent scheduling and execution state monitoring capabilities is automatically generated from natural language input, so that the task execution efficiency and the system elasticity are conveniently improved; constructing a candidate node set according to the nodes of the legal directed acyclic structure, generating a to-be-executed node set, and constructing execution context information for the to-be-executed nodes; and starting the corresponding node to be executed according to the execution context information, and performing intelligent processing according to the execution result, thereby constructing a complete closed loop from a natural language task to structured scheduling, supporting efficient concurrent execution, and realizing real-time monitoring and dynamic feedback of the execution process.

The invention discloses an automatic code debugging and optimizing system and method based on an AI agent, and relates to the technical field of artificial intelligence and automatic programming, the system comprises a coordinator module, a plan generation module, a code generation module, a test verification module, a deployment operation module, a project summary module, a language model module and a context management module; the coordinator module supports to select and call an Agent for specific task execution according to the current task process, and the plan generation module is configured to automatically generate a development task plan according to the requirements of a user and transmit a task list to the code generation module. Through the exception retry mechanism and the task backtracking mechanism, the system can automatically detect errors in the code generation process and repair the errors, and the tedious process of traditional manual debugging is avoided. The system optimizes the code generation process by backtracking the historical task and adjusting the generation strategy, and the quality of the final code is ensured.

A system may include a plurality of processing nodes in communication with a storage device configured to store a plurality of data. The processing nodes may receive a query on at least a portion of the data and may generate a query plan in natural language format. The processing nodes may generate a large language model (“LLM”) input based on the natural language format of the query plan and may execute an LLM on the LLM input. The processing nodes may generate, in response to execution of the LLM, a plurality of recommended actions to perform to improve the query plan. The processing nodes may receive input to execute at least one of the plurality of recommended actions and may alter the query plan in accordance with the at least one of the plurality of recommended actions. A method and computer-readable medium are also disclosed.

A computer-implemented technique for generating program code includes receiving a natural language description of a hardware module, generating a first plan based on the natural language description, extracting first circuit information from the natural language description, generating a second plan based on the first plan and the first circuit information, and generating first program code in a hardware description language based on the second plan.

The invention discloses a full-process digital management method for manufacturing and mounting a steel structure. The method comprises digital and modular management steps of modeling, a deepened detail drawing making link, typesetting, material lifting, a material arriving link, blanking, a manufacturing link and a component delivery link. In the steps, modeling and drawing work is implemented through Tekla software; a modularized VBA table is manufactured in each link, and typesetting and blanking are achieved through combined application of Tekla software, Tekla nesting software and Actcut software. According to the method, the speed and accuracy of raw material plan reporting are improved through modularized digital technology management; the raw material arrival statistical analysis speed and accuracy are improved; the raw material utilization rate is improved; component manufacturing information is accurately tracked; matching information of component delivery is accurately tracked, so that the components are orderly displayed on site according to the on-site progress plan, the transfer volume of the components on the construction site is reduced, and the installation efficiency is improved. Finally, construction period is shortened and cost is saved.

The invention discloses a large language model multi-agent end-to-end stowage method considering outbound equipment collaborative scheduling, which comprises the following steps: acquiring description data of a stowage task, and decomposing the stowage task into sub-tasks of multiple stages based on the description data; aiming at the first subtask, based on the description data of the first subtask, constructing a first model by utilizing a modeling agent, and generating a first code for solving the first model through a model code agent; aiming at the second subtask, based on the description data of the second subtask, constructing a second model by utilizing the heuristic intelligent agent, and generating a second code for solving the second model through the heuristic code intelligent agent; matching target codes from the first codes and the second codes according to the types of the sub-tasks, and executing the target codes according to the sequence of the sub-tasks to obtain a final container stowage plan. The method can meet the reality and feasibility and completely meet all complex requirements.

The invention discloses a multi-agent parallel planning and execution-oriented dynamic task cooperation method and system. The method comprises the following steps of: constructing agents containing planning threads and execution threads which are independent and run in parallel, configuring a callable large language model and a readable and writable centralized memory system for the planning threads, and configuring a callable tool library for the execution threads; a planning thread and an execution thread of each agent establish an exclusive communication link, and a centralized memory system configures a file lock mechanism; the planning thread obtains latest environment information, team communication logs and execution history from the centralized memory system at each time step, calls a large language model to generate a formatted tool library API instruction as a next plan, independently judges whether an interrupt mechanism is triggered or not by the large language model, generates an interrupt signal if the interrupt mechanism is triggered, and sends the interrupt signal to the centralized memory system; writing the tool library API instruction into an action buffer area; the execution thread continuously obtains the latest tool library API instruction from the action buffer area and calls a tool library to execute a corresponding task; if an interrupt signal sent by the planning thread is received through the exclusive communication link, the current task is terminated immediately, and after occupied resources are released, the latest instruction of the buffer area is loaded for execution; and the centralized memory system updates contents at each time step, including an environment observation result returned by the tool library, a communication log between the intelligent agents and a task execution history of each intelligent agent, for all planning threads to access.

To assist users in artificial intelligence driven software development, techniques for agentic software development assistance leveraging application behavioral models are disclosed. A software agent receives a codebase issue and orchestrates an iteration of a refining process. To orchestrate the process, the software agent feeds the codebase issue and instructions to generate a plan into a development assistant, which automatically returns a plan to the agent. The agent feeds the plan and instructions to generate a solution to the codebase issue back to the development assistant, which automatically returns a solution to the agent. The agent feeds the solution back to the development assistant, which tests the solution in order to verify the solution's quality in responding to the codebase issue. The solution is awarded a quality score, and the quality score is returned to the agent. Based on the quality score, another iteration of the refining process can be performed.

One embodiment of a method for modifying program code includes processing, using a first trained language model, program code to identify one or more modifications to the program code; processing, using a second trained language model, the program code and the one or more modifications to generate a plan for applying the one or more modifications; and processing, using a third trained language model, the program code and the plan to generate a modified program code.

The invention discloses an AI intelligent application integration system based on a container. The system comprises a plug-in store, an application process unit and a plug-in specification, the application process unit comprises an AI large language model, a workflow engine and a container engine; the plug-in store comprises a third-party plug-in for system integration, and the plug-in specification comprises a configuration declaration corresponding to the plug-in, a tool function and a text-based input and output declaration; the AI large language model is used for querying a plug-in store based on natural language input of a user to obtain a plug-in list corresponding to a user task; generating a corresponding execution plan according to the natural language input and the plug-in list; the workflow engine is used for performing task arrangement in response to the execution plan; calling the container engine to execute the plug-in tool function in a sandbox environment to realize safe calling of the plug-in tool function; the system has the advantages of achieving fast system integration and ensuring safe and reliable system integration.