202 results about "Task analysis" patented technology

The invention belongs to the field of unmanned aerial vehicle control, and particularly relates to a complex task-oriented unmanned aerial vehicle modular integrated control platform, which comprises an acquisition module for acquiring and processing information such as a real-time flight state of an unmanned aerial vehicle group, a complex task text, meteorological data and the like; the task analysis module decomposes a complex task into a single subtask set and an incidence matrix, and evaluates the task complexity for resource allocation; the atmospheric analysis module is used for inverting a real-time flight association adjustment graph based on the corrected meteorological data, flight states and the like in combination with a target optimization algorithm and an optimization function taking resource quantity minimization and task completion probability maximization as targets; the record analysis module generates a performance transfer matrix by using a hidden Markov algorithm; and the flight control module responds to the adjustment graph to correct the flight state, and ensures that the task completion probability meets a preset safety threshold. According to the platform, efficient cooperative control and resource optimization allocation of the unmanned aerial vehicle group under complex tasks are realized through modular integration.

The invention belongs to the technical field of crossing of artificial intelligence and computer system architecture, particularly relates to a computing power center equipment power supply intelligent system of a knowledge graph, and aims to solve the problem of low energy efficiency caused by extensive power supply management, unbalanced energy consumption and lagged load response. The system comprises a task analysis module, a dynamic load prediction module, a multi-source power supply scheduling module, an equipment-level power supply control module and a feedback optimization module, through load prediction and multi-source power supply cooperative scheduling driven by task semantics, voltage and current dynamic adjustment and sleep wake-up control are achieved, and a strategy is optimized online based on operation feedback. The scheme improves energy efficiency, equipment life and system stability.

The invention provides a medical data-oriented efficient privacy computing fusion engine method and system, and the method comprises the steps: constructing a medical data encryption and decryption system as a basis, and carrying out the standardized packaging of federated learning, homomorphic encryption, secure multi-party computing and other technologies, a unified technical framework comprising a data encryption interface, a ciphertext calculation interface and a result decryption interface is defined; an intelligent task analysis mechanism is adopted, high-level medical analysis requirements can be automatically decomposed into privacy calculation subtask sequences, and multi-technology collaboration is achieved through an execution engine; meanwhile, the functions of resource monitoring and performance analysis are achieved, technology switching and parameter adjustment and optimization during operation are supported, and a self-optimization execution environment is formed. And finally, displaying through a friendly application interface and visualization. According to the method, the problems of single technical route, low calculation efficiency, poor resource utilization rate, serious memory limitation and the like in medical data privacy protection are solved.

The invention discloses a medical business system processing method based on multi-agent cooperation, a medium and equipment, and belongs to the technical field of medical informatization. In the method, a task analysis agent receives a medical service request, calls a large language model to analyze a user intention, and generates a standardized task description; the routing scheduling agent decomposes the task and distributes the task to the corresponding service execution agent according to the task type and the service rule; the plurality of service execution agents process respective responsible subtasks in parallel, and call corresponding medical information system interfaces to complete specific service operation; and the result summarizing agent collects the processing results of the execution agents, and the processing results are integrated to generate a final service response. According to the invention, through specialized division and cooperation of multiple agents, automatic processing of complex medical services is realized, and the processing efficiency and the intelligent level of a medical information system can be significantly improved.

The invention is suitable for the technical field of computers, and provides a CPU and GPU cooperative work heterogeneous computing method and platform, and the method comprises the following steps: S1, carrying out the meta-task analysis of an input computing task, extracting the computing feature metadata of the computing task, and predicting the performance of the computing task based on a pre-trained performance prediction model; dynamically deciding execution path planning of the task between the CPU and the GPU; s2, according to the execution path planning, carrying out adaptive resource collaborative configuration; and S3, on the basis of the calculation feature metadata and the current hardware state, through a parameterized kernel template or a just-in-time compilation technology, heterogeneous perception optimized kernel codes are generated. The method effectively solves the problems that a task scheduling strategy is rigid, the bottleneck of memory and data transmission is prominent, calculation kernel optimization is insufficient and is lack of adaptability, and a system is lack of self-evolution and learning ability.

The invention discloses a multi-body system dynamics intelligent modeling calculation system and method based on a natural language, and belongs to the field of overall design of spacecrafts. The problems that in the prior art, when the simulation requirement related to extreme working conditions and strong nonlinear coupling is met, the period is long, manual experience is relied on, and the maintenance cost is high are solved. The system comprises a text segmentation embedding unit used for carrying out text segmentation embedding processing on a multi-body dynamics knowledge base and constructing a vectorized dynamics knowledge context; the large language model unit is used for receiving natural language task description input by a user, performing semantic understanding through LLM (Logistics Language Model) and generating a task analysis result according to dynamic knowledge context learning; and the task processing unit is used for classifying the tasks into inquiry tasks, command and control tasks or construction tasks according to the task analysis result, and calling corresponding modules according to LLM and MCP technologies. The method is used in the field of multi-body system dynamics modeling of spacecrafts.

The invention discloses a multi-delivery robot path collaborative optimization system and a scheduling method, and aims to solve the problems of non-uniform scheduling load, unsmooth path, weak conflict avoidance capability and poor dynamic adaptability of existing multi-robot scheduling. The system comprises a central scheduling module, a distribution robot terminal, an environment sensing module and a map management module, and realizes task analysis, state monitoring, path planning and conflict coordination. According to the method, region division and task balanced distribution are completed by improving a DARP algorithm, path coverage efficiency and smoothness are optimized by fusing a BA * algorithm, a cattle farming method and a TEB algorithm, multi-type path conflicts are solved by adopting a time window reservation and distributed negotiation strategy, and dynamic adjustment under new task access, robot faults and environment sudden change is supported. According to the method, the load balance, the path optimization effect and the conflict avoidance capability of multi-robot collaborative distribution can be remarkably improved, the efficient and stable execution of the distribution task is ensured, and the method is suitable for terminal distribution scenes such as communities and office buildings.

The invention discloses a GPU cluster task scheduling method and device, equipment and a medium. The method comprises the following steps: constructing a task dependency graph in response to a to-be-scheduled task; obtaining a matched scheduling strategy according to the task characteristics of the to-be-scheduled task, marking a node type for each computing node in the task dependency graph, and determining an available GPU resource pool according to the scheduling strategy and the node type; generating all candidate GPU allocation schemes, and calculating an end-to-end execution delay of each candidate GPU allocation scheme by using a preset delay calculation model; and according to each end-to-end execution time delay and the scheduling strategy, selecting a target GPU allocation scheme from all the candidate GPU allocation schemes, so that a cluster resource manager schedules a target GPU cluster to execute the to-be-scheduled task according to the target GPU allocation scheme. According to the method, a closed loop from task analysis, resource screening to scheme optimization is formed, and the accuracy, efficiency and adaptability of GPU cluster task scheduling are improved.

The invention discloses a development data query management method and system based on data security, and relates to the technical field of computers, the method comprises the following steps: obtaining a query task configured by a user at a client based on a target demand; performing access permission pre-verification from the dimensions of a user, a role and a data resource through a three-level permission model, and performing security processing on a query task and associated data after verification succeeds to generate a standardized query task with a security identifier; extracting multi-dimensional features of request execution time, task emergency degree, data scale and data type; generating a storage node priority sequence; a plurality of mainstream databases are adapted through a unified adaptation interface, and task analysis, conversion and execution are completed; monitoring a task execution state in real time; according to the method, access permission preprocessing can be carried out from multiple dimensions of users, roles and data resources through a three-level permission model and security processing, and the risks of unauthorized access and data leakage are avoided.

The invention relates to the technical field of grinding path optimization, and discloses an abrasive paper grinding path optimization system based on reinforcement learning. According to the system, a grinding task is decomposed through a task analysis module, and a state space is defined by fusing a current position, a historical track and real-time surface roughness through a state construction module. The strategy learning module utilizes a deep reinforcement learning network to carry out iterative training by exploring actions and collecting reward experience in simulation so as to learn an optimization strategy. And the trained network inferes local optimization paths for the sub-regions, and a global grinding path is formed after splicing and smoothing. According to the method, self-adaptive path planning is achieved through autonomous decision-making of reinforcement learning, closed-loop quality control is formed by means of real-time roughness feedback, and the intelligent level of grinding machining and the consistency of workpiece surface quality are improved.

An object of a system according to an embodiment is to propose an optimal processing method or route for a task registered by a user.SOLUTION: A system according to an embodiment includes a task registration unit, an analysis unit, and a proposal unit. The task registration unit allows a user to register a task. The analysis unit analyzes the task registered by the task registration unit. The proposal unit proposes an optimal processing method or route based on the result analyzed by the analysis unit.SELECTED DRAWING: Figure 1

The invention discloses a task processing device and a computing device, and relates to the technical field of computers.According to the task processing device, a task management module is newly added behind each task execution unit, and a task analysis module sends a task mask to the task management module, so that the task management module can perform task processing according to the task masks. After each sub-task execution module executes the sub-tasks, execution results of the sub-tasks can be sent to the task management module for caching, then the task management module monitors whether all the sub-tasks of the to-be-calculated task are executed or not based on the task masks, and under the condition that all the sub-tasks of the to-be-calculated task are executed, the sub-tasks of the to-be-calculated task are executed. And outputting a task execution result. Therefore, the sub-tasks can be prevented from being retained in the sub-task execution units, so that the processing efficiency of the system is improved.

The application discloses an mRNA vaccine sequence design system and method based on a large language model intelligent agent, and the mRNA vaccine sequence design system based on the large language model intelligent agent comprises a user interaction and task analysis module, a task planning and intelligent agent scheduling module, an RNA design skill library and skill description module, a multi-target evaluation and closed-loop optimization module and a data and model management module. The application integrates a large language model and various mRNA sequence analysis and optimization tools, automatically completes mRNA vaccine sequence generation, evaluation and optimization for a target antigen in a computer environment, is suitable for candidate sequence design and screening of a preventive or therapeutic mRNA vaccine, improves design efficiency, and reduces the degree of dependence on manual experience and manual script arrangement.

The embodiment of the invention provides a task early warning method, device, system and equipment for animal physiological statuses, and relates to the technical field of data processing, the method comprises the following steps: responding to a condition for triggering task analysis, and aiming at each monitoring period in a plurality of continuous monitoring periods, performing task analysis according to the condition for triggering task analysis; determining each multi-dimensional feature of the target animal in the monitoring period; for each monitoring period, based on each multi-dimensional feature in the monitoring period, constructing a target time sequence feature sequence corresponding to the monitoring period; and inputting each target time sequence feature sequence into a prediction model to obtain a prediction result about whether a task set for the specified physiological state of the target animal needs to be executed or not in the candidate task execution time period to which the current time belongs. According to the scheme, effective early warning can be carried out on the task set according to the specified physiological state of the animal.

The invention relates to the technical field of multi-intelligent task analysis, and discloses a power distribution network real field multi-influence coupling and interference configuration test method, system and device and a medium, and the method comprises the steps: enabling a complex multi-influence scene design to be rapidly completed without programming through a scene sequence control unit, and reducing the configuration difficulty and human errors; a multi-influence-quantity physical simulation unit and a communication interference simulation unit are combined to establish a basic operation condition which is completely consistent with an actual topology, and result distortion caused by a traditional isolated test is avoided; a plurality of controllable aging simulations are accurately superposed on a real working condition, an equipment degradation process is accelerated and reproduced, evolutionary faults are injected according to a preset time sequence, communication interference can be synchronously superposed, and extreme scenes of field multi-factor linkage, fault development and communication abnormity coupling are truly reproduced. The problem of deep maloperation or refusal operation which cannot be exposed in the traditional single test can be fully shown; the problems of incomplete and inaccurate real-model field measurement and low efficiency in the prior art can be solved.

The invention provides a hair detection and hair density calculation method based on image input and multi-model analysis. The method comprises the following steps: acquiring a head image by using common imaging equipment; preprocessing the image; inputting the image into a plurality of analysis models for independent analysis; hair detection and hair quantity density calculation precision is optimized through a self-research fusion algorithm and high-order multi-task analysis; outputting the hair quantity estimation, the partitioned hair density, the scalp coverage rate, the health score, the thermodynamic diagram and the trend curve, and generating personalized nursing suggestions. Further, according to the method, images and related data are periodically acquired and stored as historical data, and the historical data are periodically analyzed to generate a hair health change trend; the system can access an additional analysis model or external sensor data through an expansion interface to realize long-term tracking and comprehensive evaluation, so that the evaluation precision and the use value of a user are improved.

The invention discloses an intelligent drilling path planning method and system for a multi-layer thin plate, and relates to the field of intelligent path planning. Firstly, task analysis is conducted on hole sites according to material attributes and drilling parameters, and single-hole generation heat and a temperature attenuation function are calculated through thermophysical model parameterization; and thus, a minimum cooling interval time matrix of the hole site set is obtained. On the basis, the tool moving speed and the drilling time consumption are comprehensively considered, a time sequence diagram fused with the cooling constraint is constructed, and global path searching and optimization are conducted through a space-time A * algorithm. According to the method, a traditional static planning problem which purely pursues the shortest geometric distance is converted into a spatio-temporal dynamic optimization problem which meets a thermal safety threshold value, cooling waiting time and moving travel time are automatically balanced through an algorithm, the problem of material performance degradation caused by local heat accumulation in the prior art is effectively solved, and the service life of the material is prolonged. The drilling quality and efficiency of the multi-layer thin plate are both improved.

The invention relates to the technical field of combinatorial optimization, and discloses a logistics vehicle path planning method and system for a supply chain system, a terminal and a storage medium, and the method comprises the steps: obtaining a vehicle scheduling task of a logistics end, carrying out the analysis and processing of the task, obtaining a task analysis result, and carrying out the information extraction, and obtaining scheduling information; constructing a path planning expert collaboration framework, performing strategy selection on the scheduling information to obtain a plurality of path planning strategies, and performing hybrid processing to obtain a hybrid expert strategy; and performing symmetric self-distillation processing on the hybrid expert strategy to obtain a target path planning strategy, and performing path planning on the scheduling information to obtain a target planning path. According to the invention, through a hybrid expert collaborative decision-making mechanism based on dynamic weight distribution and introduction of a two-stage knowledge fusion strategy, the vehicle path planning efficiency is improved, and the optimal vehicle planning path can be obtained.

The invention provides a household appliance control method and system, and relates to the technical field of intelligent household appliances. According to the method, a user instruction is subjected to localization analysis by introducing an intention complexity classifier, and a processing path is dynamically allocated according to an analysis result: a simple instruction is directly executed by a user terminal and feedback is generated, so that low-delay response and offline availability are realized, and the interaction fluency is improved; complex instructions are uploaded to the cloud service platform to be processed, multi-step task analysis and equipment cooperative control are completed through the powerful computing power of the cloud service platform, and the intelligent level in a complex scene is guaranteed. Through cooperative processing of local real-time response and cloud deep planning, dependence on cloud resources and sensitive data transmission are remarkably reduced, efficient, safe and consistent user interaction experience is supported while network loads and computing overhead are reduced, and performance, privacy and cost benefits are considered.

This invention relates to the field of inventory management technology, and proposes a method and system for real-time monitoring of material inventory based on Internet of Things (IoT) sensing. The method includes: distributing personalized data collection strategies to IoT sensing nodes via a communication network of a central monitoring platform; performing differentiated processing on initial inventory sensing data based on these strategies to generate inventory status records; dynamically adjusting the reporting frequency of status priority tag sequences and sending the adjustment process and records to the central monitoring platform in real time; correlating and analyzing the inventory status records with real-time sensing signals to construct a multi-dimensional dynamic inventory ledger; comparing the real-time inventory quantity in the ledger with a safety stock threshold, and performing task analysis in conjunction with an anomaly triggering mechanism to obtain an adaptive task list. This invention can improve the real-time performance, accuracy, and adaptive efficiency of material inventory monitoring.

The application discloses a kind of unmanned aerial vehicle real-time semantic perception systems and methods based on flight state and task adaptation.The system includes flight state perception module, task analysis module, adaptive scheduler and configurable perception engine.The core is, according to unmanned aerial vehicle real-time flight state (such as attitude, maneuver mode) and preset task stage (such as search, crossing, landing), dynamically scheduling sensor configuration, selecting algorithm model and adjusting semantic output granularity.The application solves the problem that the resource consumption of existing unmanned aerial vehicle perception system is fixed and the environmental adaptability is poor, realizes the optimal matching of perception ability and task demand, significantly improves the efficiency, safety and overall system robustness of unmanned aerial vehicle autonomous flight in complex dynamic environment.

The invention discloses a data collaboration system, device and method, electronic equipment and a medium, and relates to the technical field of cloud computing. The system comprises a task analysis module used for determining task configuration information of a data collaboration task in response to an obtained data collaboration task; the evaluation module is used for determining the score of each computing power device according to the computing power index value of each computing power device corresponding to the source storage address and the weight corresponding to the computing power index; the distribution module is used for dividing the data collaboration task into a plurality of data collaboration sub-tasks based on the score of each computing power device, and distributing the plurality of data collaboration sub-tasks to the plurality of computing power devices; each data collaboration sub-task indicates to transmit part of files in the file set of the source storage address to the target storage address, and the size of the file indicated by the distributed data collaboration sub-task of each computing power device is in positive correlation with the score of each computing power device. According to the system, the plurality of computing power devices transmit data in parallel, so that the transmission efficiency is improved.

The invention relates to the technical field of logistics storage intelligent scheduling, and discloses a logistics storage intelligent scheduling method and system. The method comprises the following steps: constructing a virtual storage model and injecting a real-time operation data stream to form a storage dynamic twinborn body; implanting a self-adaptive scheduling logic core in the twin body, and performing multi-dimensional task analysis through the core to generate a task decomposition atlas; resource matching operation is carried out in the logic partition based on the atlas, and a resource matching scheme is output; generating a control instruction sequence containing time sequence constraint according to the scheme and issuing the control instruction sequence to a physical execution terminal; and collecting a feedback data stream to correct the twinborn state. Through real-time data flow driving, synchronous sensing and instantaneous response of the storage state are realized, and the scheduling deviation caused by data updating delay of a traditional system is solved. Through the task decomposition atlas, deep structured analysis and multi-resource collaborative matching of complex tasks are realized, and the overall utilization efficiency of resources and the task execution fluency are improved.

The present application relates to the technical field of automation plate processing and carrying control, and particularly relates to a plate automatic feeding and discharging control system and a control method thereof, which comprises: a task analysis module, which receives an external production task and calculates the external production task into processing procedure parameters containing plate geometric size, thickness and material density characteristics; and a gravity calculation module, which calculates plate mass distribution based on the processing procedure parameters and outputs gravity matrix data containing target gravity coordinates, suction cup matrix gating strategy and plate specification characteristics. In the present application, through automatic detection and calculation of plate geometric characteristics and density information, on-demand dynamic gating of the suction cup array and adaptive calculation of the vacuum negative pressure threshold are realized, frequent parameter adjustment caused by plate specification changes in the traditional manual teaching mode is avoided, and the automatic adaptation capability of the feeding process to multi-specification plates is improved.

The embodiment of the invention provides a passive equipment state monitoring method, device and equipment, a medium and a product, which are applied to the technical field of optical communication. The method comprises the following steps: acquiring multi-source monitoring data of a target passive optical network based on a preset acquisition frequency; wherein the multi-source monitoring data comprises passive equipment state data, network performance data, service operation data and environment associated data in the target passive optical network; performing feature fusion processing based on the multi-source monitoring data to obtain a multi-dimensional feature vector; and performing multi-task analysis on the target passive optical network based on the multi-dimensional feature vector to obtain a passive equipment state reasoning set, a passive equipment fault root cause set and a passive equipment predictive maintenance set of the target passive optical network. The method achieves the technical effect of improving the fault management efficiency of the passive optical network.

The invention provides an integrated control method and system based on a whole satellite control macro instruction, and the method comprises the steps: generating a macro instruction in a unified format, and the macro instruction comprises a header, segment information and an auxiliary package; uploading the macro instruction to a satellite integrated electronic system; analyzing the macro instruction, and dynamically scheduling a task queue according to task analysis time in the header; when the task is executed, cooperative operation of the associated subsystems is automatically triggered, and the cooperative operation comprises attitude and orbit control pointing, load startup and shutdown, solid memory recording or playback and link establishment; task conflicts are processed through a task queue conflict detection mechanism, the planned tasks cover the planned tasks of the same time window, and insertion of new tasks is shielded by the protection time window for the tasks in execution. According to the method, collaborative management and task scheduling of multiple subsystems such as satellite attitude and orbit control, load, solid storage and link are realized, the instruction complexity is remarkably reduced, and the whole satellite control efficiency and robustness are improved.

The application relates to a robot safe operation method and system based on a double-system architecture, and belongs to the field of robot operation. The method comprises the following steps: task analysis is performed on a natural voice instruction, and a task in the natural voice instruction is decomposed into multiple subtasks; for each subtask, target object point cloud and observation scene point cloud are determined according to robot vision information; an optimal grabbing position and an optimal grabbing posture are determined based on a grabbing posture generation network according to the target object point cloud and the observation scene point cloud; a path map, a posture map and a gripper state map are constructed based on the optimal grabbing position and the optimal grabbing posture; path planning is performed according to the path map, and posture planning is performed according to the posture map, so that a final trajectory is obtained; and the robot is controlled according to the final trajectory and the gripper state map to complete the subtask. The application combines the semantic understanding ability with the execution robustness of a traditional planning control method, and realizes task-driven operation without samples and training.

The application discloses a spectrum detection method based on a large language model, applied to the technical field of spectrum detection, and combines field instruction fine-tuning and retrieval enhancement generation technology, and introduces a parameter efficient fine-tuning method such as LoRA, effectively solves the problems of insufficient understanding of scene problems, model illusion and high training cost of general models in professional fields. With the physical constraint spectrum data augmentation algorithm and the specially designed double task analysis network, the feature extraction and quantitative analysis ability of small sample and low concentration spectrum data is improved. The finally constructed model of the application has the advantages of professional question and answer accuracy, reliable result and light deployment, and only needs a single consumer-grade GPU to complete training and deployment, providing an efficient, reliable and low-cost intelligent auxiliary analysis solution for spectrum detection.

The invention discloses a power grid dispatching control method and device based on artificial intelligence, a terminal and a medium. According to the scheme, the method comprises the steps that when scheduling control triggering information is received, triggering scene information is extracted through a language text recognition sub-model in a preset AI scheduling model; matching a target scene model according to the triggering scene information; performing scene task operation by combining the multi-source data in the data base, and outputting a scene task analysis result; and finally generating and issuing a power grid dispatching control instruction. According to the scheme, the AI regulation and control integrated control architecture with the AI dispatching model as the core is constructed, automatic operation of the power grid dispatching control method based on artificial intelligence is achieved, the technical problems of manual dispatching response lag and inaccurate decision are solved, and the method has the advantages that dispatching control trigger information can be automatically processed, and the dispatching efficiency is improved. An accurate dispatching control instruction is quickly generated, and the response speed and the decision accuracy of power grid dispatching are remarkably improved.