557 results about "Task list" patented technology

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 provides a project management method, system and device and a medium, and belongs to the field of project management. The method comprises the following steps: collecting project related data, and sorting and cleaning the project related data to obtain a structured data set; adopting an entity identification and relation extraction algorithm to carry out entity labeling and relation extraction processing on the structured data set so as to construct a knowledge graph related to the project; the method comprises the following steps: performing task decomposition by adopting a graph traversal algorithm on the basis of a knowledge graph, generating a task list and a priority sequence thereof, and monitoring task progress and resource utilization rate in real time on the basis of a dynamic optimization algorithm so as to dynamically adjust task priority and a resource configuration strategy. The task module is automatically decomposed based on the knowledge graph, the task decomposition efficiency and accuracy are improved, intelligent resource allocation and task priority adjustment are achieved in combination with dynamic planning and an optimization algorithm, the team cooperation efficiency is improved, and the flexibility and adaptability of a project are improved.

The invention discloses a computing task scheduling method and device in a multivariate heterogeneous environment and a medium, and relates to the technical field of parallel computing. The method comprises the following steps: monitoring resource states of various computing resources in a multivariate heterogeneous environment in real time, and quantifying the resource states to obtain corresponding resource information; under the condition that the to-be-processed calculation task is generated in the scheduling task list, performing demand analysis on the to-be-processed calculation task to determine a task demand report; calculating the task demand report and the resource information by using a preset scheduling strategy generation model to generate an intelligent scheduling strategy; according to the intelligent scheduling strategy, various computing resources are called to execute the to-be-processed computing task; and continuously monitoring the scheduling task list and the resource state so as to update the intelligent scheduling strategy corresponding to the to-be-processed calculation task based on the scheduling strategy strategy optimization logic.

The invention discloses an intelligent auxiliary bid evaluation method and system for an electric power bid invitation specialty, and the method comprises the steps: obtaining uploaded purchase information, and creating purchase batches based on configured basic parameters and purchase review rules, so as to generate a review task list with a unique batch number; and based on the review task list and the configured basic parameters, extracting context information of each review task to construct context feature vectors of the review tasks. And matching the context feature vector with a preset negative rule to obtain a negative suggestion list corresponding to each review task in the review task list. And binding the negative suggestion list with the corresponding bidding task, and pushing the bidding task bound with the negative suggestion list to the user terminal. According to the technical scheme, the user terminal receives the operation of the user on each negative suggestion so as to generate the final negative suggestion, and the final negative suggestion is stored and archived, so that the negative suggestion generation efficiency is improved, the negative expression is standardized, and the work intensity of experts is reduced.

The invention provides an intelligent production scheduling system and method for a coal industry scene, and the method comprises the steps: fusing multi-source production information (including equipment state information, coal quality index information and safety detection information) through a server, constructing a fault prediction model, and carrying out the fault prediction of the real-time production information; and generating a task list based on the fault prediction result, the real-time production information and the historical production information, matching a workflow and an optimal executor for each task in the task list, then sending the task to the PC terminal for execution, monitoring the execution progress of each task in real time, and urging the overdue task. And generating a corresponding report for each executed task based on the fused information, the fault prediction result, the real-time production information, the historical production information and the execution process information of each task, and sending the report to a display device for display. By adopting the method, the efficiency and the accuracy of production scheduling in a coal industry scene can be improved, the real-time performance of production scheduling can be ensured, and the statistics and the checking of the whole process link can be facilitated.

The invention belongs to the technical field of AGVs, and provides an AGV transportation method and a robot, and the method comprises the steps: obtaining a task list and the initial position of each AGV; the task execution sequence is optimized through tabu search so as to minimize the time consumption of final task completion, the average completion time and the weighted cost of the number of pauses; according to the optimization sequence, constructing a space-time diagram of node representation time and space positions for each AGV; planning the path of each AGV on the space-time diagram by adopting a time enhancement algorithm, regarding the paths of other AGVs as time obstacles, and adopting a path cost function; in the path planning process, the AGVs are allowed to wait in situ to avoid collision, and the paths of the AGVs are updated in real time; performing Bezier curve smoothing on the planned discrete path for three times to generate a continuous and controllable driving track; and finally, each AGV is controlled to execute the task along the track. According to the invention, the problems of low task scheduling efficiency and frequent path conflicts of a plurality of AGVs in the prior art can be solved.

The invention provides an artificial intelligence routing method. The method comprises the following steps: S100) an AI routing agent receives and analyzes at least one piece of prompt information; s200) the AI routing agent analyzes at least one request intention of the user according to the prompt information, decomposes and splits the request intention into a plurality of sub-tasks, and plans an interdependence relationship among the sub-tasks to form a task list; s300) the AI routing agent selects an AI model agent interface registered on an AI gateway according to the characteristics of an artificial intelligence model so as to match the requirements of the task list, and the selected AI model matching the requirements of the task list is the selected AI model; s400) calling and executing each selected AI model, and returning a result to the AI routing agent; and S500) integrating the prediction results of all the selected AI models by the AI routing agent, and generating a response.

The invention discloses a path planning method and system based on an automatic three-dimensional warehouse four-way shuttle vehicle, and the method comprises the steps: building a storage environment model through a grid method, making traffic rules according to a grid map, and building a map table; based on the map table, generating a task list by using a data memorization processing technology, and sorting according to task priorities; the distributor performs task distribution on the shuttle vehicle according to the task list, performs path planning by using an improved A * algorithm, divides an obtained path into a plurality of road sections, generates time window list information of the road sections, checks whether a time window conflicts with a time window of a planned path node or not, shields the road section corresponding to the time window if the time window conflicts with the time window of the planned path node, and does not shield the path corresponding to the time window if the time window conflicts with the time window of the planned path node. Re-planning the path; otherwise, time window dynamic adjustment is carried out, and the path is executed. According to the invention, conflict judgment is carried out through the time window table of the shuttle vehicle, the possibility of conflict occurrence is further reduced, and the operation efficiency is improved.

The present disclosure is directed toward systems, methods, and non-transitory computer readable media for utilizing a catalyst application to analyze digital content of another computer application to generate or extract tasks. The catalyst application can accompany and operate in conjunction with another computer application to extract data from the application and generate tasks. For example, the disclosed systems can scan displayed (or otherwise presented) digital content in an application and can generate a prompt for causing a large language model to identify or extract tasks from the digital content. Through the catalyst application, the disclosed systems can thus implement a large language model to generate a task list from the digital content of another computer application. The disclosed systems can further utilize the catalyst application to automatically execute or perform extracted tasks.

The invention provides a computing power center resource centralized self-adaptive control scheduling system and method, and the method comprises the steps: S1, a user submits a computing power training task through an API, and sends the task to a DAG task composer, and forms a task list to be scheduled; s2, the intelligent monitoring analysis module is used for continuously collecting node data and calculating real-time CS and HS scores of each node; s3, analyzing the dependency relationship type data by a task composer, and calling a dynamic programming algorithm in an adaptive scheduler to make a decision; s4, the adaptive scheduler performs dynamic task distribution according to the task list of the DAG task orchestrator and the intelligent monitoring analysis module, and S5, the distributed runtime and environment management module is used for uploading the prepared Docker mirror image to the distributed storage; and S6, through the unified communication adaptation layer, the mirror image is pulled from the distributed storage on the target node and the container is started, the user does not need to concern the difference of the operating system in the whole process, and efficient and intelligent adaptive scheduling is realized.

The invention provides an intelligent customer service system, which comprises an information acquisition module used for identifying a customer identity according to a user login account and obtaining historical data under the account; the grading module is used for establishing a customer portrait model according to the historical data and classifying customer types; the matching module is used for automatically matching a service strategy model version corresponding to the account according to the customer type obtained by the grading module; the knowledge graph response module is used for carrying out deep analysis on client input contents based on the natural language understanding capability of a large language model in combination with a context, a client portrait and historical data after the version of the service strategy model is determined, automatically generating a task list and a semantic tag tree for a dialogue, and continuously optimizing the task list and the semantic tag tree; starting from a problem node of the task list, analyzing a corresponding semantic tag along an optimized and trained path in the knowledge graph, and matching a service processing action; and triggering a back-end system interface associated with the node according to needs, transferring real data, and solving client problems.

The embodiment of the invention provides a distributed task execution method and system under GPU (Graphics Processing Unit) mixing, which are applied to a platform comprising a plurality of GPUs, a first class of tasks and a second class of tasks are deployed, and the priority of the second class of tasks is lower than that of the first class of tasks. The task execution method comprises the steps that a target application calls a set communication library and requests to execute a target communication operation between GPUs, and the target application belongs to a second type of tasks. Then, the set communication library determines a sub-task list involved in the target communication operation, and the sub-task list comprises a plurality of sub-tasks distributed in different GPUs; and enabling the plurality of subtasks to synchronously submit communication kernel l to a GPU driving environment through an inter-process synchronous operation. And under the condition that the GPU driving environment determines that the communication kernel l starts synchronous submission, the communication kernel l is submitted to the GPU immediately.

The invention relates to a cache data processing method and device, computer equipment, a computer readable storage medium and a computer program product. The method is applied to a distributed storage system adopting a three-layer architecture design, and the three-layer architecture comprises a service application layer, a data processing layer and a persistence layer. Comprising the following steps: acquiring access request monitoring information through a service application layer to obtain an access feature data set, and performing pattern classification identification on the access feature data set to obtain a predicted access pattern; generating a prefetching task list and prefetching data according to the predicted access mode, and storing the prefetching data to a data processing layer for caching processing; and executing each prefetching task according to the sequence of the prefetching task list, and reading the prefetching data from the mechanical hard disk and writing the prefetching data into a cache space in a solid state disk resource pool under the condition that the prefetching data does not exist in the solid state disk resource pool of the persistence layer. By adopting the method, the cache performance can be improved.

The invention discloses a multi-task-oriented sensor scheduling method and system, and belongs to the technical field of sensor scheduling. In the scheme of the invention, an alternate agent collects all tasks to obtain a first task list, and formulates a sensor scheduling scheme according to the requirements of all the tasks on a sensor; other agents collect all the tasks to obtain a second task list, and if the number of the tasks in the second task list is larger than that in the first task list, a reverse ticket is put; if not, sponsoring to form a ticket; then counting whether the whole ticket is approved by the round value agent, if so, carrying out sensor scheduling based on a sensor scheduling scheme, and selecting a new round value agent to enter the next round value period; otherwise, the agent which puts the reverse ticket publishes the second task list, and selects a new round agent to enter the next round period. According to the scheme, multi-task-oriented multi-sensor resource collaborative scheduling is realized.

The invention discloses a surgical instrument automatic scheduling method, system and terminal based on pre-operation scheduling, and the method comprises the steps: obtaining pre-operation scheduling information which comprises an operation style, an operator, a time period and inter-operation scheduling; a standard instrument set template is obtained according to the operation mode, historical instrument use behavior data of the operator is obtained, and the historical use behavior data is used for dynamically correcting the standard instrument set template to obtain a personalized template; acquiring instrument inventory information in the time period, and intelligently scoring the standard instrument package template and the personalized template according to the instrument inventory information and the recommendation model to obtain a target template; obtaining a scheduling task list according to the target template, the operation style, the time period and the inter-operation arrangement; and inputting the scheduling task list into an automatic transfer system, generating a route task package, and packaging, delivering and delivering the instruments according to the route task package and the scheduling instruction. According to the method, the instrument set is automatically generated and optimized, and the surgical instrument configuration efficiency is remarkably improved.

A data processing system implements receiving a first request to collaboratively author a mixed reality experience with a vision-language model planner, the mixed reality experience comprising an interactive guide for performing a task involving a complex multipart object; obtaining 3D object geometry information for the complex multipart object; obtaining a description of the task to be performed including a plurality of subtasks each associated with a user action to be performed on a respective part of the complex multipart object; constructing a prompt to the model using a prompt construction unit, the prompt instructing the model to generate a task list based on the geometry information and the description of the task to be performed; providing the prompt as an input to the model to obtain the task list; and generating content for the mixed reality experience using the task list in response to a second request to execute the mixed reality experience.

The invention relates to the technical field of computers, in particular to an AGV task allocation method and system based on multi-objective optimization, and the method comprises the steps: obtaining a task demand set and a state information set of AGV equipment in a current operation scene, and the task demand set comprises a plurality of task units; performing feature extraction processing on the task demand set and the state information set to obtain task feature description of each task unit and capability feature description of each AGV device; performing joint optimization matching processing according to the task feature description and the capability feature description to generate a matching association relationship set of the task and the AGV equipment, wherein the matching association relationship set comprises a corresponding relationship between the task unit and the AGV equipment and a task execution sequence relationship; and generating a task allocation scheme based on the matching association relationship set, wherein the task allocation scheme comprises a to-be-executed task list of each AGV device and execution time sequence arrangement information of each task. Therefore, task requirements and equipment states can be accurately grasped, and efficient and matched innovative task distribution processing is realized.

The embodiment of the invention discloses a picking task list building method and device and a storage medium, and the method comprises the steps: carrying out the grouping of a batch of positioning results in any wave according to a preset grouping rule, and obtaining a plurality of groups of positioning results; the preset grouping rule is a grouping rule comprising commodity codes, storage positions and commodity picking area information; in the multiple groups of positioning results, splitting and / or combining the multiple positioning results in any group of positioning results according to a preset task order picking specification to obtain any group of task order information until multiple groups of task order information is obtained according to the multiple groups of positioning results; the multiple groups of positioning results are in one-to-one correspondence with the multiple groups of task list information; and building a picking task list according to the multiple groups of task list information.

The invention discloses a business data query reconstruction method and device, equipment and a storage medium. Comprising the steps of obtaining a query task list constructed by a user, and performing permission verification on the query task list; after the verification is passed, reconstructing the query task list to generate a standard query command; executing the standard query command to obtain original query data, desensitizing the original query data to generate safe query data, and displaying the safe query data to a user. The user permission is verified through the permission verification process, the unauthorized query is intercepted from the source, and illegal access to the production database is avoided. By generating the standard query command, the row-level permission condition can be embedded into the query logic, so that the generated query command is ensured to be compliant, permission bypassing is avoided, errors and performance risks of manual writing are avoided, and meanwhile, the development efficiency is improved. By dynamically desensitizing the original query data, on the premise of guaranteeing the service continuity, the data can be available and invisible, the compliance requirement is accurately met, and the data leakage risk is reduced.

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 provides a task processing method and device based on multiple agents, equipment and a medium, and relates to the technical field of artificial intelligence such as deep learning, natural language processing and large models. When it is monitored that at least one root task to be processed is contained in a root task list in the information pool, threads with the same number as the root tasks to be processed are started, the threads corresponding to the root tasks are used for disassembling the root tasks, and subtask sequences corresponding to the root tasks are determined; according to the function of each second agent in the agent set and each subtask type in the subtask sequence, determining a second agent for executing each subtask, sending the subtask to the corresponding second agent, and when it is monitored that any second agent returns a first execution result for any subtask, executing the subtask according to the first execution result. And sending an instruction for releasing resources occupied by any subtask to a second agent, and releasing a thread corresponding to any root task under the condition that a second execution result corresponding to any root task is determined.

The invention discloses a robot and an intelligent interaction method thereof, and relates to the technical field of robots, and the robot comprises an instruction obtaining module which is used for converting external instruction information into text information; the artificial intelligence module is used for performing vectorization processing and similarity comparison on the text information and keywords in a preset task list through a large model to obtain a conventional target task and an unconventional target task; the task decomposition module is used for decomposing the conventional target task or the unconventional target task into a plurality of sub-tasks and determining solutions matched with the sub-tasks according to the types of the sub-tasks; the task execution module is used for supervising and controlling execution of each subtask; the memory module is used for storing key data and a knowledge graph database in the execution process of each subtask; and the planning decision module is used for controlling the operation of the modules. According to the invention, man-machine interaction and autonomous planning and intelligent control of the robot body can be realized.

The invention relates to an expired video cleaning method for large-scale video storage, which comprises the following steps of: creating a task list in Redis, and putting all storage index table names into the task list; the OSSScheduler periodically issues a plurality of cleaning tasks according to a database table; more than one OSS preempts the cleaning task; and more than one OSS executes a cleaning task at the same time, each task is fixedly queried, and the expired video in one database table is cleaned. According to the method, through regular task release of the OSSScheduler and a task preemption mechanism of the OSS, each cleaning task is clearly designated to be responsible for cleaning the expired video in a certain database table, so that the uniqueness and the accuracy of the cleaning process are ensured, and the repeated cleaning phenomenon is completely eradicated. On the premise that database nodes do not need to be added (namely extra hardware cost input is not needed), the key problems of insufficient video cleaning speed, repeated cleaning and the like in a large-scale video monitoring platform are successfully solved, and the overall performance and the operation efficiency of the system are remarkably improved.

The invention provides a rule-based approval method and system, which is applied to a server, and comprises the following steps: obtaining a government affair approval item, the government affair approval item comprising business basic data and business characteristic data; reading service approval parameters according to government affair approval items; judging whether a workflow engine is adopted or not according to the service approval parameters; if yes, calling a workflow engine, creating an approval process, starting the approval process, and generating a to-do task list according to the process permission; if not, calling an automatic examination and approval process, and executing the automatic examination and approval process; after the workflow engine or the automatic approval process is finished, an approval completion result is stored in a historical approval result database; intelligent management and automatic execution of the approval process are realized, and the approval efficiency and the user experience are improved.

The invention relates to the technical field of multi-machine clone cross-regional data transmission, in particular to a cloud mobile phone multi-machine clone cross-regional data transmission method and system. Firstly, a user submits a cloning task request, a system generates a task list according to the request, calculates required resources and allocates the required resources to a target area, establishes a task framework diagram, then performs logic division on instance data, marks difference data blocks, and authenticates a link relationship between instances through a dual link protocol; then, the difference data blocks are transmitted in combination with a multi-thread technology, meanwhile, thread task allocation is dynamically balanced to optimize efficiency, in the transmission process, a system conducts consistency verification on data, detects and retransmits wrong data blocks, infinite retransmission is avoided by adopting a timeout and frequency upper limit mechanism, and finally a target instance completes data reconstruction by combining the difference data blocks. And the data consistency is verified through the hash value. According to the method, efficient and safe data cloning is ensured based on task management, data fragmentation, link authentication and check repair.

The invention relates to the technical field of loading of computer programs, in particular to a task unloading method and system based on task similarity and a storage medium, and the method comprises the following steps: initializing an unmatched edge server set and an unmatched equipment set, and generating a similarity score between equipment based on a task list of the equipment; the edge server and the equipment respectively establish respective preference lists according to respective preference functions; matching the equipment with an edge server; calculating the shortest distance between every two clusters based on the equipment task type, and outputting a result cluster; selecting a cluster head from the result cluster, and sending a task main body code and an input parameter to an edge server by the cluster head; other devices in the result cluster send task input parameters to the edge server; channel allocation is carried out on the devices in the result cluster based on the size proportion of the device unloading tasks in the cluster; and carrying out power optimization on the equipment based on an equipment matching result and a sub-channel allocation result. According to the method, the unloading task can be completed with low energy consumption.

The invention relates to a Gantt chart management method and device, a storage medium and electronic equipment, and relates to the technical field of Gantt charts, and the method comprises the steps: obtaining a newly-added instruction of a task node in a target progress bar in a target Gantt chart; determining a first time range and a second time range corresponding to a newly added task node according to the newly added instruction and a click position corresponding to a mouse click operation; according to a target task list corresponding to the target progress bar, the first time range and the second time range, adjusting the temporary time range to obtain a final time range of the newly added task node; and adding the final time range to the target task list to obtain an updated target task list, and generating the newly added task node in the target progress bar according to the updated target task list. The method and the device have the effect of improving the flexibility when the task nodes are newly added.

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.

The invention provides a computing power priority scheduling method and device for an intelligent computing center, and relates to the technical field of intelligent computing centers, intelligent computing centers and computing power infrastructure, and the method comprises the following steps: S1, based on the task type of each to-be-scheduled computing task in a plurality of to-be-scheduled computing tasks on the intelligent computing center, determining the computing power priority of each to-be-scheduled computing task; determining a scheduling strategy of each to-be-scheduled calculation task; s2, determining a task scheduling queue of the intelligent computing center according to the scheduling strategy of each to-be-scheduled computing task and a preset target task list; the scheduling priority of each calculation task in the target task list is higher than the scheduling priorities of the plurality of calculation tasks to be scheduled; and S3, scheduling a plurality of to-be-scheduled computing tasks on the intelligent computing center according to the task scheduling queue. According to the method, scheduling of tasks of different task types in the intelligent computing center can be optimized, and reasonable resource utilization and efficient task scheduling are achieved.

The invention relates to the field of computers, and provides a deployment method for realizing distributed training on k8s by using a heterogeneous GPU (Graphics Processing Unit), which comprises the following steps of: compiling a distributed training task list file CR1 and submitting the file CR1 to the k8s; the first controller monitors the creation of the CR1, creates a plurality of CR2 according to the configuration of the CR1, and allocates training task parameters to each CR2; the controller II monitors the creation of each CR2, creates a plurality of Pods according to the configuration of each CR2, and allocates training task parameters to each Pod; the second controller judges whether current distributed training is elastic training or not, and if yes, an Hpa resource is created for each CR2; and after the current distributed training is completed, the controller I deletes all CR2 of the current distributed training task so as to delete all Pods. The technical problem that distributed training cannot be accelerated by simultaneously using multiple GPUs in one training task in the prior art is solved.