92 results about "Decision points" patented technology

The invention discloses an electric power information operation violation risk supervision system based on a knowledge graph, which relates to the field of violation risk supervision and comprises a dynamic graph construction module, a causal analysis module, a strategy analysis module, a strategy modeling module and a supervision decision module. The method comprises the following steps: obtaining multi-type electric power operation field sensing data and information system data, and carrying out data processing and knowledge graph dynamic construction to obtain a dynamic knowledge graph; performing risk situation quantification and risk decision point inference based on the dynamic knowledge graph to obtain a key causal decision point set; based on the key causal decision point set, through strategy logic analysis, obtaining a logic rule set which can be directly deployed and executed; according to the method, a logic rule set which can be directly deployed and executed is subjected to dynamic strategy evolution of a stochastic differential equation to obtain a dynamic strategy model, and the dynamic strategy model is subjected to decision optimal screening of measurement transformation to obtain an optimal supervision decision set, so that a risk value can be accurately calculated, and the supervision response speed can be increased.

The invention discloses an HCPS workshop system-oriented scheduling method based on multi-agent deep reinforcement learning, and the method comprises the steps: taking the minimization of completion time as a target, and constructing a target optimization model; independently establishing a worker efficiency fluctuation model for each worker; the method comprises the following steps: modeling an HCPS workshop system as a Markov decision process, and designing decision points; designing a state space; and designing an action space: designing a reward function based on an ARSI training mechanism. According to the method, the multi-agent deep reinforcement learning is introduced, so that the accuracy and efficiency of workshop scheduling are remarkably improved, and particularly, the production process is effectively optimized by independently modeling each worker and considering factors such as fatigue and skills of the worker.

The invention relates to the technical field of computers, and discloses a data control method and system, electronic equipment and a storage medium, and the method comprises the steps: receiving a data control strategy defined by a data owner, and the data control strategy is constructed based on a quintuple framework and comprises a subject, an object, an environment condition, an operation behavior and an effect; compiling the data control strategy into an intelligent contract code, and generating a source code based on an intelligent contract template; deploying the smart contract code to a block chain network to form a decentralized strategy decision point; intercepting the data access request, triggering the smart contract to verify the access request, and generating an access credential when the verification is passed; and allowing data access based on the access credential, and monitoring a data use behavior to ensure consistency with the data control strategy. According to the method, the fine granularity and the whole-process coverage of data control can be improved, the auditing credibility can be improved, the automation level and the efficiency of a control system can be improved, and the end-to-end safety guarantee capability is enhanced.

The invention relates to the technical field of artificial intelligence, and discloses an information security model auxiliary decision-making method and system based on an intelligent knowledge graph, and the method comprises the steps: constructing a multi-modal security data pool of a to-be-decided scene, training a joint extraction model of the to-be-decided scene, and extracting data examples and example relationships of the multi-modal security data pool; constructing a security knowledge graph of the scene to be decided; constructing an attack tactics-technology-process ontology layer of the scene to be decided to calculate a potential attack path of the scene to be decided, and calculating an attack path occurrence probability and an attack influence range of the potential attack path; marking a strategy decision point of the security knowledge graph, and analyzing a Top-K strategy of the strategy decision point by using a preset PPO algorithm; and constructing an attack chain analysis interface of the Top-K policy in the security knowledge graph to generate a policy optimization parameter of the Top-K policy, and executing the information security model aided decision of the scene to be decided based on the policy optimization parameter. According to the invention, the efficiency of security decision-making of to-be-decided scene information can be improved.

The invention provides a data processing method based on request interception, which comprises the following steps of: intercepting a calling request for marking a service method through a request interception mechanism, preposing a decision point of service logic, and selecting whether the service method is executed or not according to a request context. According to the method and the device, the business rules are stored in the configuration center, external rule configuration is performed, the business rule expressions are pre-stored in the configuration center, dynamic acquisition is performed based on the rule identifiers, decoupling of the business rules and the codes is realized, the business rules can be configured independent of the codes, and flexible business processing can be realized only by modifying the expressions of the corresponding rules in the configuration center. The problems of code expansion and difficult maintenance caused by business rule change in a multi-channel environment are effectively solved, so that the flexibility and maintainability of a business data processing system are improved. The method can be applied to a business processing system in the financial field or the medical field, so that the flexibility of the business processing system in the financial field or the medical field is improved.

The invention discloses a visual process management method and device and a storage medium, and the method comprises the steps: obtaining the input data of task state change, and carrying out the model initialization processing, and obtaining a task network model; according to the task network model, performing path search by adopting a graph traversal algorithm to obtain an influence path set; according to the influence path set, carrying out influence matrix construction operation to obtain an influence degree matrix; according to the influence degree matrix and the task network model, performing dynamic influence path mapping to obtain a dynamic propagation path diagram; key nodes are extracted according to the influence degree matrix and the dynamic propagation path diagram, and a decision point set is obtained; performing priority calculation and visual identification processing according to the decision point set to obtain a deviation node identification result; and performing model optimization processing according to the deviation node identification result to obtain an optimization task network model. According to the method, real-time dynamic layout adjustment of process management can be realized.

The invention relates to the technical field of artificial intelligence, and discloses a military simulation agent design method based on a knowledge graph, and the method comprises the steps: taking a synchronous matrix, an execution matrix and decision support information formed in the military planning and war game deduction process as the basis, and uniformly representing units, stages, combat functions, decision points and condition elements in the knowledge graph, and a computable high-dimensional plan state expression is formed. On the basis, a plan updating model reflecting stage propulsion, function collaboration and decision point triggering relations is constructed and used for describing dynamic evolution of plan states in the simulation process. Through systematic description of a transient amplification phenomenon in a plan evolution process, clear association is established between the transient amplification phenomenon and decision points and condition elements in a knowledge graph, so that a simulation agent has stable and reproducible behavior performance in decision generation and action execution processes.

The invention belongs to the technical field of heat exchangers, and discloses a step-by-step iteration multi-objective optimization design method and system for a heat exchanger, numerical calculation verification is carried out on performance indexes of decision points, the maximum error of a result obtained through first optimization design is 25.3%, and the maximum error of a result obtained through second optimization design is only 0.45%. Calculation shows that the step-by-step iteration optimization strategy effectively screens out key optimization variables and gradually reduces the optimization range, and multi-parameter, wide-range, low-cost, high-precision and multi-objective optimization design is achieved. From the three performance indexes of the unit volume heat exchange amount, the average Nusselt number and the average friction coefficient, the performance of the large flow and the small fins is better; the heat exchange amount of unit volume can be improved by reducing the transverse spacing of the fins, and the friction coefficient can be reduced by increasing the transverse spacing of the fins. The method and the conclusion provide a new design calculation method and theoretical support for design and optimization of the heat exchanger.

A system includes processor(s) configured to: receive natural language text describing software requirements for software program; analyze natural language text describing software requirements to identify relationships between different software requirements at least in part by: analyzing how data flows between different software requirements; analyzing how different software requirements influence path and decision points to achieve functionality identified by software requirements; and identifying dependencies between different software requirements; establish sequence for different software requirements based on relationships identified between different software requirements; group plurality of different software requirements together into logical group(s) of software requirements based on sequence for different software requirements and relationships between different software requirements; generate test cases based on logical group(s) of software requirements; execute software program using test cases; and analyze results of execution of software program using test cases to identify any defects in software program.

The present invention relates to a platform management method and system based on low-code development, the method comprising: obtaining multiple heterogeneous data sources, extracting first data from the multiple heterogeneous data sources, and preprocessing the first data to obtain standard data for constructing a knowledge graph. A plurality of different entities and entity relationships between different entities are obtained from the standard data, and a knowledge graph is constructed based on the plurality of different entities and entity relationships between different entities. The entity nodes in the knowledge graph and the entity relationships between different entity nodes are mapped to a low-dimensional space for vector representation through a graph embedding model to extract global knowledge including entity relationships between different entity nodes and network topology structures. A predefined business process and a plurality of decision points corresponding to the execution of the business process are obtained, and the execution results of the business process are evaluated by executing global knowledge and current business process variables to obtain corresponding guiding decisions.

The invention discloses a distributed intelligent deduction system and method for multi-source data fusion and dynamic scheduling, and relates to the technical field of deduction simulation. The deduction preprocessing module is used for carrying out task stage division on military scenarios and setting decision points, generating branch tasks according to tactical rules and carrying out model classification; the deduction management module is used for promoting deduction from an initial state based on a simulation engine, generating branch tasks at a decision point according to a real-time state, distributing initial weight coefficients by combining historical efficiency parameters and model types, and realizing parallel deduction and dynamic model switching; the deduction optimization module continuously updates a weight coefficient through a branch efficiency parameter, screens an optimal deduction path and iterates a state snapshot in a closed loop; the resource regulation and control module collects data in real time to support efficiency evaluation, resource distribution is dynamically optimized according to task loads, the deduction efficiency and accuracy are remarkably improved, and intelligent resource scheduling and system self-adaptive optimization are achieved.

The invention belongs to the field of workshop scheduling, and particularly discloses a product process planning and workshop collaborative online scheduling method and system considering random arrival of workpieces, and the method comprises the steps: converting a dynamic integrated process planning and workshop scheduling problem into a Markov decision process, and converting workpiece process information into an OR description matrix; performing reinforcement learning training on the deep neural network of the intelligent agent based on a Markov decision process; during training, at each decision point, if there is a random arrival workpiece, the process information of the workpiece is converted into an OR description matrix and workshop information is updated; secondly, determining a workshop feature vector according to the workshop information, making a scheduling decision by an intelligent agent according to the workshop feature vector, optimizing deep neural network parameters according to a reward function, and updating the workshop information; and realizing product process planning and workshop collaborative online scheduling based on the trained intelligent agent. Real-time and efficient operation of the production scheduling system can be guaranteed, so that the production line keeps continuous and stable operation, and the production efficiency is improved.

The invention discloses an LCD display screen production multi-workshop collaborative management method based on AI prediction, and particularly relates to the technical field of production management. The method is used for solving the decision difficulty that an existing production management system is difficult to quantitatively evaluate process interruption loss and order delay loss when dynamic demands change. The method comprises the following steps: acquiring production data, order information and material commitment data of each workshop, respectively predicting first loss of interrupting a current irreversible process and second loss of delaying a new order by using an AI prediction model, correcting the first loss in combination with the idle cost of downstream key resources, generating an advantage and disadvantage balancing index based on the corrected loss value, and calculating the defect of the new order according to the advantage and disadvantage balancing index. And constructing a multi-workshop topology network model to simulate disturbance propagation and identify an optimal decision point, and finally generating an adjusted production plan and outputting the adjusted production plan to a production management system to realize multi-workshop collaborative optimization management.

Intent-based automation that discovers automatable tasks and / or determines task variants in data is disclosed. Task capture data may be utilized to determine task variants in task mining data. Semantic understanding of user actions by artificial intelligence (AI) / machine learning (ML) model(s), for example, may be applied to determine the intent of the user rather than only focusing on what actions the user is performing on the computing system. Application logs and semantic understanding may be used to facilitate a more accurate determination of what the user actually intends to do. Task capture for individual user flows may be performed. Once these are captured, task capture algorithms and AI / ML models are used to determine which parts of the flows are similar and / or match and which parts are unique. The path through these flows can then be followed to build a process graph that includes decision points representing the unique flows.

The invention provides a termination strategy optimization method considering a staged task system, and relates to the field of task termination strategies. Firstly, for each stage of a staged task, it is defined that the degradation state of a system obeys a Wiener process, and the number of decision points of each stage is obtained; secondly, converting the degradation state of each stage into a discrete state model to solve the state transition probability of the system; secondly, solving the task termination cost and the task continuation cost of each decision point in different system states, and selecting a smaller value as a corresponding expected cost; and finally, selecting task termination or task continuation action based on the current decision point and the expected cost in the system state by adopting a reverse dynamic programming algorithm to obtain a task termination strategy set. According to the optimization method provided by the invention, the blank that a staged task termination strategy is not considered in related technologies is filled up, and meanwhile, the optimization problem is expressed by adopting a random dynamic programming framework, so that the cost is effectively reduced compared with a fixed threshold strategy.

The invention provides a bus dynamic multi-objective optimization scheduling method based on reinforcement learning. The method comprises the following steps: firstly, initializing a motorcade scale and a one-way set, and segmenting an operation time period into a plurality of rescheduling stages; constructing a reinforcement learning model of the current rescheduling stage; sequentially carrying out decision making on each decision point in the current rescheduling stage by utilizing a reinforcement learning model to form an optimal solution of the current rescheduling stage; repeating the decision-making process of the current rescheduling stage for multiple times to form an optimal solution set of the current stage; and sequentially processing each rescheduling stage until all rescheduling stages are processed. Through the scheme provided by the invention, the vehicle scheduling scheme can be more suitable for a real operation environment, conflicts between targets are fully considered, and the operation level and the service quality of buses are improved.

Example embodiments of the present disclosure relate to dynamic authorization. According to embodiments of the present disclosure, a solution for dynamic access control to data is proposed. On receiving data registration from a data source, a first device checks the data types to be produced by the data source and adds policies for the data or updates existing policies for the data according to its property. It also serves as access control decision point to determine consumers' access rights based on centrally managed policies. Authorization for data access is granted / denied according to local attributes / policies. In this way, it achieves a dynamic, context-aware and risk-intelligent access control to different kind of data from various data sources (i.e., service producers).

A method for requesting predicted routes from a backend server by means of a vehicle includes receiving a predicted route and a quantity of decision points for the predicted route from the backend server by the vehicle. The method also includes determining a traversal of a decision point from the quantity of decision points by the vehicle. A request is submitted to provide a further predicted route and a further quantity of decision points from the vehicle to the backend server after the vehicle has traversed the decision point. The method further includes receiving the further predicted route and the further quantity of decision points from the backend server by the vehicle.

The application provides a heterogeneous computing power automatic adaptation method and system for an intelligent chip, relates to the technical field of heterogeneous computing adaptation of the intelligent chip, and acquires computing graph structure and operator feature data and hardware state data of a to-be-deployed model; an execution knowledge base is constructed, a scheme list is generated in combination with real-time data and the execution knowledge base; execution code is distributed according to the decision weight of the scheme list; the current execution state is evaluated at a predefined execution decision point to determine whether to switch the adaptive path; the actual execution effect and environment data generated after each decision are summarized, execution track records are formed, and the execution knowledge base is updated to periodically optimize the basis for scheme generation. The application can realize automatic perception, decision, optimization and online learning of a computing task in a heterogeneous computing power environment for an intelligent chip, significantly reduces the adaptation cost and improves the resource utilization efficiency.

The invention belongs to the field of aircraft landing, and particularly relates to an aircraft automatic landing flight quality evaluation method, which divides a landing process and analyzes the influence of different landing stages on arresting landing, determines evaluation indexes of each stage, a decision point and a touch plate point, and then establishes a comprehensive evaluation system of the landing process. The method comprises the following steps of: firstly, obtaining a weight factor of an evaluation index system by utilizing an expert scoring method, and introducing different membership functions to score an evaluation index; and finally, obtaining a comprehensive evaluation score of the automatic landing process of the aircraft by combining the evaluation score of the index in each stage and the weight factor of each level. By evaluating the automatic landing process and constructing a comprehensive evaluation system, the systematicness and logicality of the evaluation method are improved; a weight factor is obtained by utilizing an expert scoring method, so that the trueness of an evaluation result is improved; and engineering realization is easy.

The invention discloses an AI-driven full-scene work transaction intelligent recording and sorting method. The invention discloses a full-scene work affair intelligent recording and sorting method based on artificial intelligence (AI), and aims to solve the problems of information omission, cross-platform data dispersion and low efficiency of manual sorting work caused by multi-task parallelism of a work scene. According to the technical scheme, the method comprises the following steps: data acquisition: monitoring computer operation behaviors (such as window switching and file modification) and voice input (such as conference recording) of a user in real time; and key transaction identification: extracting tasks, decision points and persons in charge from documents and conference contents by using a natural language processing (NLP) technology, and identifying key transactions in combination with a user behavior pattern. And structured arrangement: generating a list to be handled and extracting the conference abstract. And outputting and reminding: automatically generating a work log and pushing a task reminder.

A dynamic task scheduling method for heterogeneous multi-machine systems based on multi-agent reinforcement learning includes the following steps: 1. Inputting task information and decomposing all tasks into multiple atomic tasks with different capability requirements that can be executed by a single robot; 2. Establishing an adhesion utility evaluation model and heterogeneous priorities to create a mathematical optimization model; 3. Establishing a local perception domain model and a Markov decision model based on the sequential decision-making process of scheduling decision points; 4. Building a heterogeneous multi-machine system scheduling environment and combining multiple advanced training strategies to learn a deep reinforcement learning solver for heterogeneous multi-machine task scheduling through the interaction between the environment and the agents; 5. Using the deep reinforcement learning solver, sequentially outputting actions for each scheduling decision point for the scheduling instance to obtain a dynamic scheduling solution. This method can handle the uncertainty in the task execution process by inserting dynamic scheduling decision points for dynamic events, thereby achieving fast and robust dynamic task scheduling.

The application discloses a kind of based on blockchain's pedigree data dynamic permission access control system and method, rely on attribute-based access control paradigm, and combine pedigree data access constraint to carry out dynamic access control.First, user sends access request to policy decision point;Decision point according to the policy loaded from policy management point, request relevant information to blockchain, and call user historical behavior verification module based on pedigree data, the legality of current access request is verified using dependency relationship and pedigree data graph.System administrator records access request and verification result to blockchain, to support the fast verification of same query, reduce query overhead.In addition, the system passes access information such as query user, time, result, operation content to management node, for subsequent user access tracking and management.The method uses the anonymity and non-tamperability of blockchain, provides strong evidence for user supervision, realizes efficient management and reasoning to source information.

The application discloses a task scheduling method, electronic equipment, readable storage medium and program product, and relates to the technical field of computers, which comprises the following steps: adopting non-cooperative game theory, multiple iterations of game, determining the final task scheduling strategy according to respective regret values corresponding to respective iterations, determining respective regret values of each game participant at respective decision points by sampling and simulating the game process of a task scheduling game instance based on the theory of counterfactual regret minimization, and then determining the task scheduling strategy according to respective regret values corresponding to respective iterations. The application is also applicable to new types of task scenarios, solves the technical problems of poor real-time performance of task scheduling, low accuracy of task scheduling and low system resource utilization, and achieves the technical effects of improving the real-time performance of task scheduling, improving the accuracy of task scheduling and improving the system resource utilization.

Systems and methods in which a training dataset of a trained tree-based model is embedded as an array of vectors, where each dimension represents a decision point in the model; a distance between historical sample points in a time series is defined as a cosine similarity function between two of these vectors; the array of vectors is processed through a Hierarchical Navigable Small World index, thereby producing an approximate view of similar vectors; for a new prediction, there is a search for a number of most similar vectors; and a discrete probability distribution is created.

The invention discloses a traceable access control method, a traceable access control system and a traceable access control product based on provenance data, firstly, a user sends an access request to a strategy execution point, and then a strategy decision point loads a related access strategy from a strategy management point. And then the strategy decision point obtains related attribute information of the request data through the strategy information point, and the provenance data strategy decision point evaluates whether the access request accords with the allowable purpose according to the loaded strategy and data source information. And if a plurality of strategies are involved, the strategy decision point merges the target set by using strategy algebra, and the strategy decision point makes an access decision according to an evaluation result. Then, the strategy decision point sends a decision result to the strategy execution point, the strategy execution point executes an access control decision and allows or rejects a user request, and finally, the block chain records the access request and result of the user, so that whole-course traceability of an access behavior is realized.

Systems and methods for implementation of a multi-agent reinforcement learning based decision system for a materials handling system, including initializing a simulation environment comprising decision points for dispatching materials and attributes of the materials handling system, the simulation environment configured to request a decision for materials dispatch at the decision points to the multi-agent reinforcement learning based decision system; initializing the reinforcement learning agents representative of the decision points for the multi-agent reinforcement learning based decision system; initializing domain expert heuristics for the decision points for the multi-agent reinforcement learning based decision system; and iteratively training the reinforcement learning based decision system with the initialized domain expert heuristics on the simulation environment.

The application provides a man-machine cooperative cognitive decision space construction and on-the-spot decision method, that is, in the action planning process, on the basis of the main line action plan designed based on the static task purpose, a person estimates possible variables in action execution in advance, refines situation cognitive characteristics of key decision points, and proposes countermeasures, the system explores diversified possible action processes, analyzes and excavates key variables which have greater influence on action results, explores the best action options under the guidance of countermeasures and trains a dynamic planning intelligent agent, finally, the cognitive characteristics and countermeasure options, the planning intelligent agent are added to the main line action plan, and a cognitive-decision space is constructed; in the action execution process, the cognitive-decision space is used to guide the real-time monitoring of the change of situation characteristics and the dynamic triggering of key decision points, the online optimization of the best countermeasure and the dynamic generation of subsequent action planning.

The invention provides a dynamic self-adaptive DNN collaborative reasoning method supporting a backspacing mechanism, relates to the technical field of image processing, and designs an elastic two-way task flow from equipment to edge to equipment, so that the elastic two-way task flow becomes an intelligent scheduling decision point. And when the edge server is overloaded, the task can be actively returned to the source mobile equipment to be executed, so that the bottleneck of queuing delay caused by edge resource competition is fundamentally avoided. A rollback decision is introduced, so that an unloading strategy is upgraded from'static 'or'semi-static' to real'dynamic self-adaption ', the change of edge resources can be directly responded, and a decision target is expanded from optimizing a single task to optimizing a global system load. Three key dimensions, namely a backspacing decision, resource allocation and a model segmentation point, are arranged in a unified framework for joint optimization. Through an integrated reinforcement learning model, the three decisions are output at the same time, so that the three decisions can cooperate with each other and have a synergistic effect.

The embodiment of the invention discloses a quality evaluation model training method, which comprises the following steps of: dividing a thinking chain into a plurality of reasoning nodes, regarding each reasoning node as a decision point, evaluating the risk of the reasoning node by simulating the future track of the reasoning node, and quantifying the inherent potential of successful guidance of the reasoning node. And constructing a fine-grained label of the whole thinking chain according to an evaluation result of the reasoning node. A quality evaluation model is trained by utilizing thinking chain sample data with a fine-grained label, so that the quality evaluation model not only can pay attention to a final result, but also can learn the ability of deeply insight into the quality, risk and potential of a thinking chain reasoning process. The quality evaluation model training device, the quality evaluation method, the reply generation method and the reply generation device in the embodiment of the specification also have the above beneficial effects.