27 results about "Dynamic capabilities" patented technology

The invention discloses an agent task scheduling planning method, and relates to the technical field of agent scheduling. The method comprises the steps of analyzing a task instruction to generate atomic tasks capable of being independently executed, constructing a subtask dependency graph, and defining association constraints between the tasks; determining the real-time resource occupancy state of the intelligent agent to obtain a resource state tensor, completing resource-task association anchoring and dependency priority ranking in combination with the sub-task dependency graph, and generating a task priority sequence with resource constraint; performing dynamic capability matching and predictive load balancing calculation on the sequence through a task-agent adaptation model, and determining a target execution agent of each atomic task; and based on the target execution agent and the subtask dependency graph, performing time sequence scheduling arrangement and conflict resolution, and generating a collaborative execution scheme. The method improves the reasonability and efficiency of agent task scheduling, reduces resource conflicts and execution timeout risks, and is suitable for agent cluster collaborative scheduling in a complex scene.

The invention provides an operation and maintenance process optimization method and system applied to operation and maintenance technical services, and relates to the technical field of operation and maintenance technical services. Firstly, scene features, execution association relationships and resource demand attributes in an operation and maintenance task demand document are collected to form an operation and maintenance task information set; and meanwhile, collecting real-time operation state data and capability dimension information of the service nodes to construct dynamic capability portraits of the service nodes. And then, based on the scene features and the scene adaptation capability, executing bidirectional adaptation processing to generate a preliminary adaptation result, identifying and resolving resource supply conflicts in the preliminary adaptation result, and generating a final adaptation scheme. And finally, integrating an operation and maintenance task execution sequence and service node resource supply arrangement according to the final adaptation scheme to form an operation and maintenance process optimization scheme, and dynamically evolving and calibrating the scheme content according to real-time data in the execution process. The operation and maintenance task can be accurately allocated, the flexibility and adaptability of the operation and maintenance process are improved, and the operation and maintenance service quality and efficiency are improved.

The invention discloses a human resource production and teaching fusion supply and demand matching system based on a dynamic ability portrait, and relates to the field of human resource management. Comprising a capability analysis module, a structure modeling module, an occupational mapping module, a selection monitoring module, a path generation module, a behavior analysis module, a task matching module, a supply and demand evaluation module, a guide iteration module and a recruitment screening module. According to the method, capability portraits do not depend on subjective resumes any more, the accuracy and credibility are remarkably improved, the problem that a static label model cannot reflect a real growth track is solved, the competency and potential rising space of talents for posts can be more accurately judged, the problem that traditional HR cannot process cross-domain task matching is solved, and the method is suitable for popularization and application. And real-time closed-loop alignment of supply and demand of production and education is realized.

The application provides a robot dynamic matching and cooperation method and system based on capability discovery. The method comprises the following steps: obtaining physical capability parameter data and real-time running state parameter data by constructing an ecological integrated cooperative interface set, generating a dynamic capability evaluation graph, analyzing and processing task demand instructions to generate a task demand vector, intelligently matching and combining a preset robot digital twin model for analysis to obtain a dynamic resource scheduling strategy, controlling the robot to perform an action, obtaining execution state detection data, further processing to obtain an execution state abnormal evaluation parameter, and finally determining the dynamic matching and cooperation state of the robot through threshold comparison. The application constructs a multi-layer interface abstraction architecture, relies on a dynamic capability discovery mechanism, constructs a dynamic capability graph and a task demand vector, intelligently matches a dynamic resource scheduling strategy, and forms a closed loop processing by evaluating and feeding back the execution to realize the robot dynamic matching and cooperation based on capability discovery.

The invention relates to the technical field of post recommendation, and particularly discloses a vocational ability dynamic portrait and post recommendation method and system. The method comprises the steps of firstly collecting static data (work resumes, assessment scores and the like) and dynamic behavior data (project tasks, skill training and the like) of a user, laying a comprehensive data source foundation, then disassembling the work resumes, extracting and clustering multiple types of entities, screening and determining a core capability field label in combination with time, secondly quantifying core capability scores and a performance stability coefficient, and finally obtaining a core capability model. Then analyzing dynamic behavior data, extracting evolution characteristics, constructing dynamic capability evolution vectors, fusing the dynamic capability evolution vectors with the static vectors to form vocational capability dynamic portraits, analyzing post demand data, extracting capability requirements and environment characteristics, quantifying the post demand data and the capability requirements and environment characteristics, and carrying out model processing to generate post portrait vectors; and finally, comprehensive suitability is calculated through the cosine similarity and the growth trend matching degree, candidate posts are sorted and recommended, and the integrating degree of the posts and talents is effectively improved.

This invention discloses an automatic task allocation system and method based on workload and capacity balance monitoring. The system includes: a dynamic capacity modeling module for constructing ground station capacity models and measurement, control, and operations (TT&C) task models; a real-time workload assessment module for dynamically assessing ground station workload; an adaptive allocation decision module for adaptively balancing and allocating optimal ground station resources; and a task execution monitoring module for employing conflict resolution strategies to eliminate abnormal ground station states. By introducing real-time workload assessment and capacity margin calculation mechanisms, this invention effectively solves the problem of uneven resource utilization caused by traditional static allocation modes. The adaptive allocation decision module can dynamically adjust task allocation strategies and automatically trigger task migration mechanisms, effectively avoiding resource waste caused by some stations being overloaded while others are idle, achieving dynamic load balancing, and significantly improving system resource utilization.

The application discloses a media creation task distribution method and system based on multi-dimensional feature matching, belonging to the field of data processing. The method first receives a multi-modal task requirement containing a text report, a reference material set and a structured constraint, and generates a multi-dimensional structured task feature vector group through joint semantic understanding and feature decomposition. Relying on the extraction of features from the author's historical works and the calculation of the historical originality index, a static capability portrait is constructed. Through the release of style transfer innovation challenge tasks, the creation output is evaluated and the innovation index is calculated to generate a dynamic capability vector. Combined with the task feature vector, the author's static portrait and dynamic vector, the original adaptation score is obtained through the prediction network, and the risk is corrected according to the two types of original innovation indexes to generate an author ranking recommendation list. In the task execution, the similarity of the creation content, the reference material and the copyright library features is compared in real time, and a hierarchical copyright guidance prompt is triggered, which can improve the efficiency of media creation task distribution and control the copyright risk.

The invention discloses a tourism internship and employment system based on big data sharing, and aims to solve the technical problems that talent training and industrial requirements in the current tourism industry are disjointed, internship and employment information is asymmetric, and vocational development lacks data support. According to the system, multi-source data of colleges, enterprises, students and government / industry associations are integrated by constructing a'government-enterprise-school-bank 'multi-dimensional data sharing and fusion architecture; high-precision bidirectional recommendation of students and posts is realized by utilizing an intelligent matching engine driven by dynamic ability portraits and deep learning; a full life cycle tracking and feedback closed loop throughout'practice-employment-occupational development 'is established, and industry talent demand prediction and personalized occupational planning are provided based on big data analysis. The method effectively breaks the information island, improves the man-post matching efficiency, meets the production and study gap, enables the personalized development of students, provides data support for talent strategy decision-making in the tourism industry, and is suitable for talent cultivation and human resource allocation scenes in the tourism industry.

The invention relates to a network equipment self-adaptive management method based on a reference template and a dynamic capability value, and the method is applied to the technical field of network management, and comprises the steps: obtaining the dynamic capability value after equipment is on line, matching a corresponding template in a reference function template library, carrying out the dynamic operation of the template according to the difference between the template and the dynamic capability value, and obtaining the dynamic capability value. According to the method, a global capability view is obtained to serve as a basis for generating a front-end equipment management interface, and the method covers the certainty of'pre-definition 'of a reference function template and the flexibility of'dynamic transmission'. The reference function template ensures the stability of the system architecture and the predictability of front-end rendering; and the dynamic capability value endows the system with the flexibility of coping with equipment diversity and function iteration. Through the method, real-time, dynamic and automatic adaptation of the network management front end to multiple types of equipment is realized, so that developers are liberated from endless adaptation work, the development and maintenance costs are remarkably reduced, and the agility of network management services is greatly improved.

The invention discloses an intelligent decision engine adaptive scheduling method and system based on multi-objective optimization, relates to the technical field of artificial intelligence and intelligent scheduling, and discloses the intelligent decision engine adaptive scheduling method and system based on multi-objective optimization. By receiving the decision request, constructing the dynamic capability portrait, inputting the pre-training model to generate the candidate scheme and selecting the optimal scheduling, the adaptive decision scheduling is realized, the optimal decision branch can be dynamically matched according to the task characteristics and the system state, the dynamic service environment is adapted, the resource utilization efficiency is improved, and the stable decision quality is ensured.

A method in a producer node configured to communicate with a management node and a consumer node is described. The method includes determining a data type state corresponding to a data type. The data type is associated with data that a producer node is configurable to produce to a consumer node. The data type status indicates the ability of the producer node to produce data of the data type. The method further includes sending a first request to the management node, the first request requesting the management node to register the data type. The first request comprises a corresponding data type state.

This invention discloses a heterogeneous extension component concurrent loading and dependency coordination architecture. Through concurrent acquisition of heterogeneous sources (remote repositories, temporary injection, pre-embedded components, dynamic inlining), declarative pattern verification of extension component descriptors, multiple heterogeneous source type adapters, offline resource discovery, dependency coordination and degradation, runtime configuration optimization, hierarchical exception management, adaptive dependency resolution, intelligent hot updates, sandbox isolation, dynamic capability negotiation, and predictive preloading, it achieves an efficient, reliable, and secure extension system. The system supports trusted source authentication, access control decisions, and functional unit extraction, and is suitable for command-line tools, integrated development environment extension systems, and extensible application frameworks. This invention effectively solves the shortcomings of existing extension component loading mechanisms in terms of heterogeneous source support, dependency coordination, security isolation, and loading efficiency, and can be widely applied in plug-in systems, modular applications, and microservice architectures.

The invention is suitable for the technical field of collection training, and provides a self-adaptive collection learning path planning method and system, and the method comprises the steps: constructing a three-dimensional procurement capability model containing compliance operation, a negotiation strategy and risk management and control, decomposing the procurement capability model into capability units, and setting a quantitative index; collecting operation, decision and assessment data in the simulation scene in real time, and generating a dynamic capability assessment matrix; constructing a three-dimensional space model based on the matrix, calculating the association strength and the lifting priority of the power unit, and outputting a personalized learning path; recombining the knowledge module according to the evaluation value change of the capability unit; dynamically adjusting model parameters in combination with policies and industries and re-planning a path; a cross-dimension learning path is generated when the plurality of capability units reach the standard. According to the method, dynamic evaluation, personalized path adjustment and cross-dimension capability integration can be realized, the problems of one-sided evaluation, fixed path and insufficient adaptability in the prior art are solved, and the accuracy and efficiency of auditory collection training are improved.

The application discloses a kind of human in loop multi-agent microservice system AgentInn design and construction method, including human in loop control module, multi-agent cooperation module, dynamic capability expansion module and task arrangement module.Human in loop control module realizes the control, participation and observation of human to agent by starting confirmation, running intervention, abnormal recovery, human agent registration, cooperation routing and experience feedback;Multi-agent cooperation module realizes cooperation arrangement and load balancing by cooperation space, role load balancing and agent discovery;Dynamic capability expansion module supports on-demand creation and hierarchical exposure of tool service and intelligent service;Task arrangement module manages progress using Cycle-Sprint-Phase three-layer time abstraction, and realizes task flow by combining flow decision and context transmission.The application solves the problems of lack of human participation in multi-agent cooperation, high complexity of cooperation arrangement and limited capability expansion, and realizes multi-agent microservice cooperation.

The application discloses a dynamic control method of a bionic multi-joint system and related equipment, and relates to the technical field of automatic control. The method comprises the following steps: acquiring dynamic characteristics of the bionic multi-joint system under different motion tasks in combination with a graph neural network model; establishing a kinematic constraint model for the bionic multi-joint system according to physical boundaries; establishing a dynamics constraint model of a turnover task according to the law of conservation of angular momentum and in combination with the mass distribution of the bionic multi-joint system and a turnover target; solving the kinematic constraint model and the dynamics constraint model based on the dynamic characteristics, the real-time state of the bionic multi-joint system and the turnover task target by using an MPC solver to obtain expected control parameters; and dynamically controlling the bionic multi-joint system according to the expected control parameters. The application can fully utilize the dynamic capability of the bionic multi-joint system and improve the adaptability of the bionic multi-joint system to multiple tasks in combination with the graph neural network model and the MPC solver.

The invention discloses an evaluation method and system based on a dynamic ability portrait and thinking chain reasoning, and relates to the technical field of computer-aided education, and the method comprises the steps: obtaining multi-modal teaching resources of a photovoltaic power station, generating a knowledge base, and constructing an expert thinking chain template library based on the knowledge base in a test question generation stage; generating personalized question setting strategies for the employees based on the dynamic ability portraits of the employees, calling latest data from a knowledge base to generate test questions, and generating a standard thinking chain of each question in the test questions according to an expert thinking chain template library; in the test question review stage, multi-modal answer data of employees for test questions are obtained, standardized to-be-analyzed process data are generated through preprocessing, and an employee answer thinking chain of each question in the test questions is generated by using a large language model; and comparing the employee answering thinking chain with the standard thinking chain to generate a review conclusion, and updating the dynamic ability portrait of the employee. According to the invention, the quality and efficiency of enterprise training can be practically improved.

The invention discloses a personalized capability access control method oriented to a large language model, and belongs to the technical field of artificial intelligence and information security. According to the method, the problems that a defense mechanism is easily bypassed in a local deployment scene, the dynamic capability permission cannot be flexibly managed, and the overhead is huge due to the fact that multiple model versions are maintained for different permission combinations in an existing method are solved. According to the method, accurate positioning of the internal capability of the model is achieved by extracting task specific neurons; neuron access permissions and user attributes can be dynamically bound by using a mixed key management architecture based on a policy tree; on-demand activation of the model capability can be realized through efficient symmetric encryption and selective decryption based on attributes; and an adaptive pruning mechanism is introduced to effectively inhibit interference of unauthorized neurons in reasoning, so that reliable, flexible and authenticated model capability abuse protection is realized in an untrusted local environment.

The application discloses a training resource adaptive recommendation and balanced scheduling method based on dynamic capability portrait, relates to the technical field of data processing and resource scheduling, and successfully solves the problem that individual capability demand and resource supply pressure cannot be considered simultaneously in a traditional training recommendation system by adopting technical means such as capability evidence event sequence, training capability knowledge graph and dynamic capability portrait. The method introduces graph reasoning and dual variable balanced scheduling technology, can not only accurately recommend adaptive training resources, but also reasonably allocates resources and optimizes scheduling under the condition of limited resources, and guarantees the efficiency and fairness of the training process. The design significantly improves the utilization rate of training resources, alleviates the imbalance between resource supply and learning demand, and improves the adaptive capability and intelligent level of the training system.

This invention discloses a data engineering task allocation method and system based on multi-dimensional dynamic capability prediction, belonging to the field of intelligent task management and scheduling technology. The method includes: collecting historical task records of all data engineers, modeling explicit and implicit skills and constructing a database; constructing a semantic field for the data engineering tasks to be allocated, extracting explicit requirements and quantifying uncertainties; screening candidates through neural semantic coupling matching, and then optimizing the screening based on workload to determine the final task allocator, achieving scientific allocation. This invention solves the technical problem that traditional data engineering task allocation struggles to fully grasp the actual capabilities of personnel and the core requirements of tasks, resulting in inaccurate allocation and inefficient resource utilization. It achieves precise matching of data engineering tasks with suitable engineers, balances team workload, and improves resource utilization efficiency and project execution quality.

The invention discloses a personalized vocational skill training system based on a dynamic ability map, and relates to the technical field of vocational skill training, and the system comprises an ability data collection module, a mapping rule definition module, a knowledge map construction module, an ability gap reasoning module and a personalized path customization module. The method comprises the following steps of: constructing association mapping rules of four dimensions of skills, knowledge, attainment and experience, constructing a cross-dimension knowledge graph, performing reverse tracing along an association path of the cross-dimension knowledge graph by taking a dominant capability short plate as a starting point based on a user capability data set, defining a substandard upstream node as a recessive root factor, and then calculating an association weight and an influence coefficient. A root problem list is generated according to the influence degree, the process depends on a four-dimensional associated cross-dimension knowledge graph structure, the internal relation of the capability of each dimension is established, dominant problems are cut into and traced to a hidden root layer by layer, accurate positioning of a user capability gap is realized, and a scientific basis is provided for formulating a training path.

The invention discloses a self-adaptive training method and system based on workflow situation triggering, belongs to the field of enterprise intelligent management, and aims to solve the problems that existing training is disjointed with a working situation and is lack of instantaneity. The method comprises the following steps: acquiring workflow data of a target employee to generate a dynamic ability portrait; workflow data is monitored in real time, and when a situation trigger event is monitored, learning intervention is carried out in combination with dynamic capability portrait determination; providing personalized learning resources or self-adaptive learning paths; and obtaining the intervened workflow data to evaluate the business effect, and iteratively updating the dynamic capability portrait, the artificial intelligence model and the learning resources according to the evaluation result. According to the invention, by constructing a data closed loop driven by a working situation and fed back by effect evaluation, deep fusion and accurate intervention of training and business are realized, the training effect can be quantified, and a self-optimized intelligent learning system is formed.

The invention discloses an IT talent skill matching method based on a dynamic ability evaluation model, and relates to the technical field of IT talent skill matching, which comprises the following steps: in an embedding space training stage of constructing the dynamic ability evaluation model, establishing a multi-scale gradient orthogonal mapping baseline, performing real-time decomposition and re-parameterization on the gradient direction of each skill feature in an embedding space, and establishing a multi-scale gradient orthogonal mapping baseline; a plurality of mutually independent gradient subspaces are generated through high-dimensional tensor rotation, and a corresponding independent gradient support plane is established for a non-dominant skill in each independent gradient subspace. Through multi-scale gradient orthogonal mapping, dynamic coupling suppression and an energy redistribution mechanism, direction independence and energy balance of multi-dimensional skill features are realized, feature collapse is prevented, and composite skill expression is strengthened; and through a multi-layer adaptive projection and time sequence correction model, dynamic stable evolution and topological integrity of an embedded space are maintained, and robustness of the model in long-term training and IT talent skill matching accuracy are effectively improved.

The application discloses a multi-target optimization-based intelligent decision engine adaptive scheduling method and system, relates to the technical field of artificial intelligence and intelligent scheduling, and discloses a multi-target optimization-based intelligent decision engine adaptive scheduling method and system, which realizes adaptive decision scheduling by receiving a decision request, constructing a dynamic capability portrait, inputting a pre-training model to generate a candidate scheme and selecting an optimal scheduling, can dynamically match an optimal decision branch according to task characteristics and system states, adapts to a dynamic business environment, improves resource utilization efficiency, and ensures stable decision quality.

The application relates to the technical field of power distribution networks and discloses a hybrid power distribution network architecture matching method suitable for high-proportion new energy access, which comprises the following steps: constructing a probability demand vector set; extracting multi-dimensional capability indexes to construct a dynamic capability vector set and recording dynamic response parameters according to the probability demand vector set; obtaining a cumulative robust matching degree and a dynamic response capability index of a candidate architecture based on the probability demand vector set and the dynamic capability vector set; obtaining a comprehensive score of the candidate architecture according to the cumulative robust matching degree and the dynamic response capability index, and sorting the candidate architecture according to the comprehensive score to obtain an optimal architecture scheme. The comprehensive score of the candidate architecture is calculated by combining the cumulative matching degree of the candidate architecture and the dynamic response capability index constructed according to the dynamic response parameters, so that the optimal architecture has long-term comprehensive adaptability and the ability to respond to high-proportion new energy access and rapid power fluctuation.

The invention discloses a game entrusting task management method and device and a medium, and relates to the technical field of computers.The method comprises the steps that data structural processing is conducted on a game entrusting task to obtain game task structural data; based on the historical performance data of the service providers, performing dynamic capability analysis on the service providers to determine capability portraits of the current service providers; according to the game task structure data and the capability portrait, task allocation data corresponding to the game entrusting task is obtained through task adaptation degree analysis and current service load analysis of a service provider; performing user interaction security configuration on the task allocation data to determine session context data of user security communication; and performing real-time risk assessment on the session context data to obtain a user security risk assessment report. According to the method, the technical problems that in the prior art, the safety of user information in the game entrusting task is insufficient, and the distribution efficiency of the game entrusting task is low are solved.

The invention relates to the technical field of edge computing and resource scheduling, in particular to an edge node dynamic capability evaluation and intelligent scheduling method and system based on multi-dimensional real-time perception, and the method comprises the steps: S1, carrying out the lightweight multi-dimensional collection and reliable reporting of the computing, network and environment data of an edge node through an eBPF technology and a hardware sensor; s2, constructing a dynamic capability evaluation model based on the collected data, calculating a computing power score, a network score and an environment score of a node, and generating a service adaptive comprehensive score according to a task portrait; s3, synchronizing the node portrait to a scheduler in real time through a high-performance API service; and S4, the scheduler selects an optimal node according to the task portrait, and intelligent scheduling is completed through multi-constraint condition filtering. According to the method, real-time accurate evaluation of the edge node capability and intelligent scheduling of service driving are realized, and the resource utilization rate and the task execution success rate are remarkably improved.

Methods and apparatus relating to dynamic capability discovery and enforcement for accelerators and devices in multi-tenant systems are described. In an embodiment, a hardware accelerator device advertises one or more available operations and / or capabilities of the hardware accelerator device to one or more tenants. Logic circuitry controls access to the one or more available operations and / or capabilities of the one or more work queues on a per-tenant basis. Other embodiments are also disclosed and claimed.