338 results about "Task list" patented technology

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 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 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 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.

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 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.

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 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 discloses an automatic design method and system based on BIM software. The method comprises the following steps: initializing an adaptation layer dynamic link library, and establishing a parameter transmission channel with BIM software; preparing BIM design data, registering a basic task in BIM software based on the BIM design data, and establishing a standardized project directory; establishing a parameterized set of tasks which can be automatically modeled; serially arranging the task set into a task list according to design logic, and recording the task list to a project project; and calling the adaptation layer interface through the parameter transmission channel to drive the BIM software or a professional application module in the BIM software to complete automatic modeling. According to the method, the engineering design rule is solidified into the accurate task sequence by calling the BIM software or the interface of the professional application module in the BIM software, automatic execution of the whole process is achieved, the technical problems that manual operation efficiency is low and an existing RPA technology cannot process complex BIM design logic are solved, and design efficiency and quality are remarkably improved.

The invention discloses an online product ordering verification method, device and equipment and a storage medium, and relates to the technical field of computers, and the online product ordering verification method comprises the steps that case matching information is acquired, and the case matching information comprises a case material number of a matched target case; determining a node material number corresponding to the case material number based on a pre-constructed material number online table, wherein the node material number is a material number of a target node forming an online product with the target case; generating node matching information of the target node according to the node material number; generating a product configuration table according to the node matching information and the case matching information; and verifying the product configuration table based on a pre-constructed verification rule table, and generating a production task list of the online product under the condition of successful verification. According to the method, the selected configuration is verified in real time, it is ensured that the selected configuration meets related requirements, the technical problem that configuration verification is difficult is solved, and the technical effect of efficient and accurate material configuration is achieved.

The invention discloses an execution method and device of a cloud management task, a storage medium, equipment and a program, and relates to the field of cloud computing, and the execution method comprises the following steps: matching a text corresponding to a received user instruction with a first dictionary to determine a processing object allowed to process the user instruction, the first dictionary is associated with the business field of the cloud management task; under the condition that it is determined that the processing object comprises the large language model and the risk decision of the first intention of the user instruction is a first value, according to the text and a preset vector database, a cue word of the large language model is constructed, so that an ordered task list corresponding to the user instruction output by the large language model is obtained; and executing the cloud management tasks in the ordered task list. Through application of the method and the device, the problems that a cloud management mode in related technologies mainly depends on a graphical user interface and a command line interface to realize management, the operation is complicated and the efficiency is low are solved, and the cloud management efficiency is improved.

The invention relates to the technical field of remote measurement, in particular to a very simple protocol efficient and high-density meter reading method which comprises the following steps: acquiring data frame content, eliminating identity fields, generating a simplified format identifier, constructing a peak shifting scheduling list, detecting sequence consistency, generating a frame supplementing instruction, and dividing synchronous triggering groups to sort meter reading records. According to the method, identity field compression is replaced, frame body space proportion conversion is introduced, data structure content is simplified, a rhythm dislocation sequence is constructed in combination with time migration to achieve high-density node scheduling, frame loss behaviors are screened based on serial number jumping, a frame compensation response task list is automatically constructed, and a synchronous trigger sequence is constructed in combination with time difference judgment. The volume of transmission data is effectively compressed, the utilization rate of channel resources is improved, peak-shifting uploading between nodes is realized to reduce the probability of data collision, overhigh communication load caused by frequent handshake response and multi-level response is avoided, and the continuity, fault tolerance and rhythm collaboration of meter reading scheduling are enhanced.

The invention discloses a load-algorithm bidirectional elastic scheduling method and device for a hybrid energy storage system, and belongs to the technical field of water, wind and light stations, and the method comprises the steps: obtaining the operation data of a water, wind and light hybrid energy storage system through a collection CPU; performing wind and light ultra-short-term prediction and load evaluation through the prediction CPU, and evaluating the battery state and the water and electricity available capacity to obtain a prediction result; computing resources, priorities, resource allocation proportions and levels of energy storage algorithms of control tasks of multiple time scales are determined by scheduling a CPU according to the operation data and the prediction result, calculation-communication pre-preemption is triggered after an early warning event is generated, and a task list and a resource allocation matrix at the current moment are generated; executing a control operation by controlling the CPU according to the task list and the resource allocation matrix; adjusting a next scheduling cycle strategy or executing a thermal migration mechanism according to a result after execution control, and performing backfilling execution on selectable sub-graphs of the directed acyclic task graph in an empty window period; and bidirectional elastic adaptation of the load and the algorithm is ensured.

The invention relates to the technical field of building construction, in particular to an intelligent scheduling building construction progress optimization method, which comprises the following steps of: acquiring construction environment data in real time by utilizing a sensor network, and dynamically evaluating a resource state in combination with a historical record and a task list; generating an initial construction plan by using a multi-objective optimization algorithm, and performing real-time correction through a fuzzy logic dynamic adjustment mechanism; the resource use condition is monitored by means of Internet of Things equipment, and a data optimization strategy is fed back; and finally determining the arrangement by comparing the optimization plan with the actual progress. The method can significantly improve the accuracy and timeliness of the construction plan, cope with a complex environment, reduce human intervention, ensure that key tasks are completed preferentially, and improve the construction efficiency and the resource utilization rate.

The invention provides a task execution method and device, electronic equipment and a storage medium, and relates to the technical field of large models, in particular to the technical field of artificial intelligence such as natural language processing, image vision and deep learning. Obtaining a target sub-task list after the target task is disassembled, the target sub-task list comprising a task execution sequence of each target sub-task; an execution agent of each target subtask in the target subtask list is determined from a preset agent cluster, and a support mode of each agent in the agent cluster is at least one of multi-mode data; and based on the task execution sequence, sending a corresponding task execution instruction to each execution agent to obtain a target task execution result of the target task.

A method for task management and a system therefor are provided. The method according to some embodiments may include generating a task list by inputting user information of a user into a first model, the task list including a plurality of tasks expected to be processed by the user, generating a plurality of items related to the plurality of tasks by inputting the user information into the first model, receiving, from the user, a selection of a task to be processed by the user from among the plurality of tasks, receiving, from the user, a selection of an item related to the selected task from among the plurality of items, composing a first prompt by inputting the selected item into a second model, the first prompt requesting the selected task to be processed based on the selected item and outputting a processing result for the selected task by inputting the first prompt into a third model.

The present invention is directed to a software-based technology system for managing and executing high volumes of work on complex projects across multiple organizations involving multiple users that results in superior outcomes at significantly reduced time and cost. Litigation is the initial application of this invention; each case is handled as a project. As applied to litigation, the system of the invention empowers a novel method of end-to-end case assessment, planning, execution and management by integrating tools, processes and procedures allowing for more informed decision-making and deliberate execution of all case-related actions as well as enhanced coordination and communication among all constituents, resulting in efficient achievement of targeted outcomes. The system provides intuitive, menu-driven functionality for inputting, processing and utilizing vast amounts of information, including company and case-specific information, attorney discretionary evaluation and input, document templates and monitoring and reporting requirements. A threat algorithm cross-correlates via multivariate analysis critical data points with customized company-specific information and business logic to generate a numeric threat score as an output. The system also predicts the expected settlement range with a high degree of precision for a specific case as the output of one or a combination of multiple artificial intelligence (AI) / machine learning algorithms. The system then generates a unique case strategy (which includes an expected outcome, including projected outcome, Case Plan and estimated attorneys' fees and costs). System logic learns from thousands of examples of similar cases and directs highly effective and efficient execution of a specific Case Plan providing each attorney and staff member assigned a role in a case with a prioritized task list that is updated in real time as other users complete tasks and litigation priorities change. For each user, the system integrates assigned tasks across multiple cases and multiple client portfolios into a single, prioritized task list. The execution features become increasingly valuable given the volume and complexity of projects across multiple organizations and users, allowing users to focus on their area of expertise and avoid the wasted time and distraction of trying to determine what they should be working on or recording notes to communicate what they have been doing since the system automatically performs these functions and directs the various users' next steps while monitoring their progress against the plan. Integration of administrative functions, including automated time tracking, billing and reporting without the need to rely on users to update this information, further increases quality and efficiency of legal services. Key objectives of the system of the present invention include eliminating gaps between business objectives and litigation strategy and optimizing communication and coordination of case-related tasks and priorities in a fully automated fashion, which are all achieved by leveraging data and technology to enable a fundamentally different approach that becomes smarter and more precise via machine learning and predictive modeling.

The invention discloses a method for dynamically adjusting key distribution flow, and the method comprises the steps: a quantum key pool server receives a key distribution request sent by network element equipment connected with the quantum key pool server, generates a corresponding key distribution task according to the key distribution request, and generates a corresponding to-be-sent key file; then, distributing a key distribution task corresponding to the key distribution request to a task list of a distribution thread through the key distribution request; and the distribution thread of the quantum key pool server extracts the key distribution task with the minimum next start time Next value in the task parameters of all key distribution tasks from the task list of the distribution thread, and judges whether the next start time Next value in the key distribution task is the current time or not. According to the invention, the sending bandwidth B of the key distribution task is dynamically adjusted through the quantum key pool server, so that the distribution operation of the key file is completed under the condition that the service process of the network element equipment is not occupied.

The invention discloses a cross-system collaborative to-do task query and processing method and system based on an AI Agent architecture, and particularly relates to the technical field of artificial intelligence and information system integration. According to the method, to-be-handled task data in each heterogeneous service system is automatically collected through a distributed deployment AI Agent module, and standardized processing is carried out through a unified task modeling module; identifying and combining repeated tasks by using a semantic understanding and deduplication module based on natural language processing, and generating a global unified task view; distributing tasks according to a dynamic strategy through an intelligent task routing and distribution module; realizing cross-system operation and real-time state synchronization by means of a task execution and state synchronization module; and finally, providing an aggregated task list and an operation interface for a user through a unified interaction interface. According to the system, unified integration, intelligent processing and collaborative management of multi-source heterogeneous tasks are effectively realized.

The invention discloses an agent task decomposition method and system in the financial field and a medium, and belongs to the technical field of artificial intelligence. The method comprises the steps that financial task semantic features are extracted through a large language model, intention recognition is completed in combination with user historical preferences, and then a dynamic tree-shaped task graph is generated based on a three-level matching mechanism to enhance the complex task processing capacity; and finally, realizing dynamic reconstruction of the task tree through a confidence-driven adjustment mechanism. The content of extracting the semantic features and intention recognition comprises the steps of task input, wherein a financial task request of a user is received through a front-end dialogue type webpage; feature extraction and preference retrieval: task semantic features are extracted, and user historical preferences are retrieved in combination with user dialogue records stored in context; and structured output: arranging a task list containing task types, key parameters, constraint conditions and user preferences. According to the method, the complex task processing capability can be enhanced, the dynamic adaptability is improved, and personalized task planning is realized.

The application discloses a kind of based on plug-in AI digital person ability extension method, the method includes according to the data that user submits and interacts, extract user information feature matrix, construct user preliminary portrait;Based on user preliminary portrait, select relevant plug-in, pre-load plug-in and automatically select ability extension strategy;Initialize plug-in function, according to ability extension strategy and user dialogue system gradually enable plug-in, expand the ability of AI digital person, record enabled function information;According to enabled function information, construct AI digital person ability management system, output AI digital person ability matrix;Using the document uploaded by user, parse the task list of user, match ability matrix, customize user personalized task list.The application solves the flexibility and personalization problem of AI digital person function expansion under different user needs and scenarios, realizes the intelligent adaptation and ability improvement of AI digital person in diversified application scenarios, provides more accurate and efficient personalized service for user.

The invention provides a task scheduling method which comprises the steps that a time slice sent by a time polling device is obtained, the time slice represents time allocated to be used for executing timed tasks in a task list, the task list comprises a plurality of timed tasks registered in the form of task events, each corresponding timed task has a timestamp, and the time slice represents time allocated to be used for executing the timed tasks in the task list; the timestamps represent the time when the corresponding timed tasks are to be executed; based on the time slice, a matched task event is determined from a task list, and the matched task event represents a target timed task with a timestamp matched with the time slice; determining a node selection strategy; selecting a target node suitable for executing the target timed task from a plurality of execution nodes according to the node selection strategy; and issuing the target timed task to the target node to execute the target timed task.

The embodiment of the invention provides a media content generation method and device, equipment, a medium and a program product. The method comprises the following steps: acquiring input information; a task list is determined based on the input information through the first processing model, the task list comprises task information of multiple tasks, and the task information comprises identification information and task description information of the to-be-called object; determining a target to-be-called object based on the task list, determining an object parameter corresponding to the task description information through a second processing model associated with the target to-be-called object, and determining output content corresponding to the task based on the object parameter through the target to-be-called object; and generating the target media content based on the output content of the target to-be-called object. According to the embodiment of the invention, task arrangement and object calling are rapidly completed in combination with the first processing model and the second processing model, the time consumption of a media content generation process is shortened, and more model capabilities can be accessed, so that accurate generation and rapid presentation of a media content effect are realized.

The embodiment of the invention provides a data processing method, an online platform and a big data platform, which can continuously acquire column storage data from a storage device and convert the column storage data into row storage data, so that the row storage data is directly scanned from a local cache, and the service processing efficiency of the online platform is improved. The method comprises the steps that after an online platform generates a physical plan according to an online service request, corresponding column storage data are obtained from a storage device according to an IO task list of the physical plan, after the column storage data are converted into row storage data, the row storage data are stored in a local cache, then target row storage data are obtained from the local cache by using a scanning operator of the physical plan, and the target row storage data are stored in the storage device. And executing the online service according to the target row storage data.

The application discloses a PCBA first-piece detection method and system, electronic equipment and a storage medium, comprising: obtaining an integral board virtual assembly drawing, a PCBA physical image, and a first CAD element list; extracting an element list L4; obtaining a second CAD element list on a server, generating a task list, generating an inspection list, and assigning a current task number to at least n1 users without a task; on a user side, obtaining the current task number based on the task list, obtaining the state of the current task number, and when the state of the current task number is incomplete or complete, obtaining the inspection list updated according to the state and the inspection result in the inspection list; on the server, after the results in the inspection list are all updated, obtaining the task list after the state is closed and the current task number is cleared, generating and outputting a report of the current task number. The application can dynamically allocate detection tasks, perform first-piece detection in parallel by multiple people, set multiple re-inspections or cross-inspections, save time, and reduce the error rate of inspection.

The application provides a virtual machine processing method, device and equipment for software testing tasks, and relates to the field of virtual machines.The method comprises the following steps: obtaining a software testing task list and a template testing virtual machine; configuring a resource package of at least one software testing task on the template testing virtual machine according to a plurality of software testing tasks in the software testing task list, to obtain a plurality of virtual machines; performing dependency processing on the plurality of software testing tasks currently being tested by the plurality of virtual machines respectively; determining the running state of the current virtual machine in the dependency processing of the software testing task by the resource package of at least one software testing task after the current virtual machine is started according to a start task; and destroying the current virtual machine and starting the next virtual machine according to the running state until the plurality of software testing tasks are tested.The scheme of the application realizes the automatic management of the whole running cycle of the virtual machine, and improves the efficiency of virtual machine management and the utilization rate of network resources.

In the proxy AI, the service background and the service condition of the user are understood, and abstract instructions are autonomously decomposed into specific tasks and executed. A proxy system is provided. The present invention is provided with a dialogue unit, a resolution unit, an execution unit, and a monitoring unit, each of which can individually use a generative AI, and the dialogue unit grasps a service command from a dialogue with a user through the generative AI, stores the content of the dialogue in a short-term memory as a dialogue log, and monitors the content of the dialogue in the short-term memory. The solving part decomposes a business instruction into tasks through the generative AI and creates a task list, an execution instruction of each task is transmitted to the execution part, an execution result is prompted to a user through the dialogue part, and the execution part executes a job for a corresponding data source through the generative AI corresponding to the task related to the execution instruction and transmits the execution result to the solving part; the monitoring unit refers to the dialogue log at any time, masters the context of the dialogue through the generative AI, predicts the content corresponding to the next step, and stores the content as an abstract in a short-term memory for reference at any time.

The dialogue unit 11 grasps a business instruction by a dialogue with a user 2 by the generative AI, and stores a content of the dialogue as a dialogue log in a short-term storage 16, the solution unit 12 creates a task list by decomposing the business instruction into tasks by the generative AI and passes an execution instruction of each task to the execution unit 13, the execution unit 13 executes a work on a corresponding data source 3 by the generative AI corresponding to the task related to the execution instruction, and passes an execution result to the solution unit 12, and the monitoring unit 14 refers to the dialogue log at any time, grasps a context of the dialogue by the generative AI, predicts a content to be dealt with next, stores the content as a summary in the short-term storage 16.