181 results about "Strategy selection" patented technology

Various embodiments of a method, system and article of manufacture to discover relationships among a first set of elements and a second set of elements are provided. At least one metric algorithm is identified based on a metric selection parameter. A raw result is determined based on the at least one metric algorithm, a first specified structural description of the first set of elements and a second specified structural description of the second set of elements. The raw result comprises a plurality of relationship measurements and the raw result is ordered. In some embodiments, a balanced result is produced based on the raw result and a matching strategy algorithm. In other embodiments, the matching strategy algorithm is identified based on a matching strategy selection parameter.

The invention relates to a method and a device of integrated data disaster recovery based on cloud platform, belonging to the field of data processing technology. The method mainly comprises three steps: establishing a cloud environment and deploying virtual machines and applications in the cloud environment; configuring data disaster recovery strategies for various applications deployed on the cloud platform and monitoring running status of the various applications on the cloud platform; and selecting appropriate disaster recovery strategies to carry out recoveries for abnormal applications according to monitoring results. The device comprises a data disaster recovery strategy selection module, a data disaster recovery response module, a data disaster recovery scheduling module, and a data disaster recovery storage module. The invention provides a safe, reliable, and strategy-flexible disaster recovery platform for data disaster recovery and a highly reliable storage system for data backup fault tolerance, which can be applied to data disaster recovery on the cloud platform.

The invention relates to a network defense strategy selection method and apparatus based on Markov evolutionary game. The method comprises the following steps: constructing a multi-stage Markov attack and defense evolutionary game model according to dynamic attack and defense game in a network attack and defense process, wherein the model contains a plurality of sub game stages; solving and outputting optimal defense strategies of the stages of the attack and defense game by using an optimal defense strategy selection algorithm for the multi-stage Markov attack and defense evolutionary game model. According to the network defense strategy selection method and apparatus provided by the invention, a dynamic evolution process of network attack and defense in view of the multi-stage Markov attack and defense evolutionary game model, the state skip of the evolutionary stages is described as a random process from the angle of attack and defense confrontation, and the multi-stage Markov evolutionary game is constructed on the basis of the Markov process; the total discount gain of the game is used as a target function, a discount factor eta is imported to perform discount processing on the game gains of different stages, a network security analysis method and a defense technology system are researched and searched, and thus the network defense strategy selection method and apparatus have important practical significance.

The present invention relates to method, terminal device and network device for providing redundancy parameters for an automatic repeat request processing at a terminal device. At least one sequence of redundancy parameters is selected from a predetermined set of sequences and is signaled to the terminal device by using a strategy information indicating the selected parameter sequence. Thereby, the network operator is able to select the redundancy version strategy to be used by the terminal device, while little signaling is required between the network and the UE. Moreover, a flexible strategy can be provided where individual redundancy version strategies can be allocated to the connections.

The invention discloses a method for establishing an automatic driving lane changing decision model in a hybrid driving environment, a multi-step dynamic game lane changing model of an automatic driving vehicle LV and a human driving vehicle RV in the hybrid driving environment are established, and a set of nested game algorithm for the automatic driving vehicle is designed. The method comprises the following steps: firstly, establishing a multi-step dynamic game framework; enabling the vehicles of the two parties to decide the following action according to the strategy selection of the otherparty; defining potential conflict points of the lane changing vehicles and the rear vehicles of the target lane; formulating starting and ending condition criteria of the game according to the initial information of the vehicle, the strategy and the game step number; and then enabling the two vehicles to use respective strategy and acceleration selection methods to carry out dynamic gaming untilthe lane changing termination condition is met, so that under the condition that the automatic driving vehicle does not meet the condition of direct lane changing, the lane changing space can also bemanufactured through the game playing with the human driving vehicle, and safe lane changing is achieved.

The invention belongs to the technical field of wireless communication, and relates to an anti-interference method for communication based on deep deterministic gradient reinforced learning. The method provided by the invention comprises the steps of firstly building an interference environment model according to the quantity of interference sources and a wireless channel model; building a utilityfunction according to a legal user communication quality index, using the utility function as a return in learning; forming spectrum information sampled at different time slots into a spectrum time slot matrix, and describing an interference environment state by using the matrix; and then building a convolutional neural network according to a deep deterministic gradient reinforced learning mechanism, and when an anti-interference decision is made, an environment state matrix achieves anti-interference strategy selection of the corresponding state in a continuous space via a target actor convolutional neural network. According to the method provided by the invention, the continuous anti-interference strategy selection in communication is completed based on the deep deterministic gradient strategy reinforced learning mechanism. The quantization error caused by quantized discrete processing on the policy space is overcome, the quantity of cells output by the neural network and the network complexity are reduced, and the anti-interference algorithm performance is improved.

Systems and methods are disclosed that assist in making decisions, valuing a choice or action, promoting creative solutions, and reducing risks. Among other situations, the systems and methods consider situations where the user or users are evaluating a possible single choice or several alternative choices. The criteria to evaluate the decisions are input into a grid, and the user or users rate the one or more alternatives against the different criteria. Given several alternative choices or options, the system and method provides the probability each alternative is the right selection. In one embodiment, the system implements a Bayesian approach. The systems and methods may estimate the value of an option or choice, where the value might be financial or other metric, and may estimate the value of a risk, opportunity or factor that might influence the result. The systems and methods may estimate the amount the chance of being surprised or missing some relevant consideration has been reduced by the analysis. The possibility of black or gray swans can also be estimated, in the phraseology of Nassim Taleb. Systems and methods so disclosed may be employed in financial analyses, project or strategy selection, betting, resolution of disagreements, intelligence analysis, and so on.

The invention discloses a near-distance air combat automatic decision-making method based on single-step prediction matrix gaming. The method comprises the following steps: step 1, erecting a six-degree-of-freedom nonlinear unmanned combat air vehicle control law structure; step 2, initializing a matrix gaming chessboard; step 3, according to the gaming chessboard, carrying out single-step prediction calculation; step 4, calculating a payment function matrix; step 5, carrying out strategy selection through a minimax algorithm; step 6, updating a six-degree-of-freedom plane kinetic and dynamic equation; and step 7, determining whether an air combat termination condition is reached. The method has the following advantages: compared to a three-degree-of-freedom particle model, the actual application value is higher. At the same time, a conventional matrix gaming method based on a maneuver library is changed to be based on a maneuver library of an instruction model, what is needed is only prediction of a step length of a single step, the decision-making time is effectively reduced, the requirement for real-time performance of air verification is satisfied, the method can be better adapted to complex dynamic baffle field environment change, and the combat capability of an unmanned combat air vehicle in a near-distance combat is improved.

The invention discloses a denormalization strategy selection method on a data set and particularly relates to a system for reasonably managing establishment, allocation and withdrawing of the software licenses in the enterprise internal network range. The software license management system comprises a main server, agent servers and clients. The software license management system has the advantages that through the mechanism that a part of clients share a small part of software licenses, the demanded quantity of software licenses of an enterprise is effectively reduced, the agent servers are arranged in all departments to manage the issuing of the software licenses of the departments, meanwhile, the main server correspondingly controls the number of the software licenses on each agent server, reasonable allocation of the software licenses in the enterprise is achieved, tasks of allocating the software licenses are distributed to all the agent servers through a layering idea, therefore, the load of the main server can be effectively reduced, meanwhile, flow of the enterprise internal network is saved, and excessive system communication information is prevented from flowing into an enterprise backbone network.

The invention relates to a greenhouse environment and irrigation control system with group control of multi-temperature regions, which mainly comprises a greenhouse control software system run in a centralized monitoring computer, a communication network system based on a CAN field bus and unit controllers distributed in various temperature regions. The unit controller comprises a microcontroller, a CAN field bus communication module, an input/output interface module for digital quantity and analog quantity, an input module for collecting sensing signals from environmental sensors and plant physiological sensor in a greenhouse and performing signal processing, and an output module for driving and controlling the operation of various equipments in the greenhouse. The greenhouse control software in the centralized monitoring computer is used for processing environmental and plant physiological information of the multi-temperature regions, performing control algorithm and strategy selection and algorithmic calculation, and real-time transmitting a control implantation action obtained based on calculation results to each unit controller to control the operation of greenhouse equipments. The greenhouse environment and irrigation control system with group control of the multi-temperature regions adopts a development method of configuration software, so that the overall system can perform software configuration setting on the unit controller, the sensors, the control equipments, the display interface and the greenhouse control strategy according to specific conditions of the greenhouse, so as to adapt to different requirements of different greenhouse agricultural production managements on the control system.

The invention discloses a method capable of meeting requirements of different people applied to an intelligent conversation robot. The method solves the problem that the conversation of the intelligent conversation robot is not flexible enough. According to the technical scheme, a verbal trick of conversation with a rule library is set, besides the rule library, the verbal trick of the conversation also comprises an answer sentence template, a conversation process and a conversation strategy, and the rule library comprise a conversation strategy selection rule, an answer selection rule, a variable evaluation rule, an audio selection rule and the like. The rules in the rule library are used so that the conversation robot can flexibly deal with various users, and can adjust the conversationstrategy in real time according to the content, mood or emotion of the user when the user speaks, so that the conversation is more close to real human-human conversation.

The invention provides an optimal protection policy analysis system and method based on an improved ant colony algorithm. The system comprises a network risk assessment subsystem, an attack path prediction subsystem and a security policy management subsystem. The method comprises the following steps: 1) importing a Bayesian probabilistic model to describe the complex relationship between multiple steps of atomic attacks, and constructing an attack diagram by using the Bayesian belief network to evaluate a network security risk; 2) simulating a decision making process of an attacker in combination with the quantitative indexes of the protection cost and the gain, and predicting a possible attack path; and 3) using the improved ant colony algorithm capable of calculating the quasi-optimal solution within a polynomial time to solve the security policy selection problem on the attack path. By adoption of the optimal protection policy analysis system and method, a risk solution can be provided for a network security administrator in time, the attack path prediction and protection strategy selection problems in network security protection are solved, and the security of the whole network under the condition of limited protection resources is effectively protected.

The invention discloses a connecting strategy selection method and a connecting strategy selection terminal. The method comprises the following steps: a local computer acquires a preset connecting strategy and data interaction types, wherein the preset connecting strategy is a user preferential connection mode; the data interaction types comprise data sharing and network access; the local computer selects one of the connection modes to perform data interaction according to the preset connecting strategy and the data interaction types. With adoption of the scheme, the wireless connection mode can be quickly and intelligently selected according to comprehensive customer requirement, data transmission, transmission distance and other factors, so that the data interaction can be performed quickly, and the user experience can be improved.

The invention discloses a denormalization strategy selection method based on the frequent item set mining algorithm and particularly relates to a denormalization strategy selection method for mass data sets based on the frequent item set mining algorithm. The frequent pattern mining method is applied to guiding database denormalization for the first time; based on the frequent pattern mining algorithm of a concise tree, a brand-new process of establishing the concise tree and a correct counting method, serving for database denormalization selection, are provided. The denormalization strategy selection method based on the frequent item set mining algorithm has the advantages that through the frequent item set mining algorithm of association rules, important association or relation of item sets in mass data is discovered to guide DBA and the like to select and build denormalization strategies of databases, and the problem of performance bottleneck caused by mass table joins in the mass data is solved.

The invention belongs to the technical field of network security and particularly relates to a network optimal defense strategy selection method and device based on a bounded rational game theory. Themethod comprises the following steps: constructing an offensive and defensive game model in a bounded rational situation according to the bounded rationality of the actual offensive and defensive sides; a random optimal reaction dynamic differential equation of a participant selection strategy is established by setting a participant rationality degree parameter in combination with an offensive and defensive game strategy set and according to historical data; the two simultaneous offensive and defensive parties are treated as the random optimal reaction dynamic differential equation of the participant; and an optimal defense strategy is determined and output by solving and screening a stable equilibrium point. Compared with the method of only giving an equilibrium solution and not analyzing the Nash equilibrium formation process, the network optimal defense strategy selection method and device based on bounded rational game theory in the present invention analyze the dynamic evolutionprocess of the bounded rational evolution game equilibrium, and the utility and the guiding significance are stronger when being applied to network defense decision; the dynamic analysis efficiency ofstrategy selection can be improved; and the network security performance is further improved.

The invention discloses a large-scale wind turbine optimal control system provided with a multi-level hierarchical structure, which comprises a data analysis and strategy selection unit on a wind power field grade, wherein, the unit is in charge of processing the function of large information amount demand and complex calculation task, and meanwhile, coordinates the coordination between each wind turbine, leading the whole efficacy of the wind power field to be the best; a coordinating control and fault diagnosis unit on a turbine grade, which is in charge of handling various transaction in the turbine, and meanwhile, coordinates the coordination between each loop in the turbine, leading the whole efficacy of the turbine to be the best and to reasonably and efficiently complete a task assigned to the wind turbine by the data analysis and strategy selection unit on the wind power field grade; and an execution mechanism control unit on a loop grade, which makes specific action request for each loop and guarantees the accurate and exact operation of various execution mechanisms, such as a motor, a pump, a valve, a switch, and the like. The system solves the problem of inadequacy hardware resource utilization, lower information resource utilization ratio, and low coordination degree between each function in a large-scale wind turbine control system.

The invention relates to a trajectory optimization method for a spraying robot based on reinforcement learning. The trajectory optimization method determines the optimal spraying trajectory through image acquisition, image processing, graphic three-dimensional reconstruction, graphic discretization, initial point selection and optimal trajectory strategy selection, wherein the image processing includes camera calibration, image correction and stereo matching. The advantages of the invention lie in that the trajectory optimization method for the spraying robot based on reinforcement learning can plan a feasible path according to different states in different environments, is short in decision-making time and high in success rate and can meet the real-time requirements of online planning, thereby overcoming the shortcomings of poor real-time performance and great calculation amount of the traditional manipulator path planning method.

The invention discloses a cascading failure multi-stage dynamic game defense method applied to a power system. The method is characterized by comprising the following steps: firstly setting the total stage number of a game process and initial values of stages of a failure party and a defense party; then generating a deterministic strategy set and a risky strategy set by calculating the probability, the consequence and the risk factor of each action adopted at the stage of a bounded rationality failure party in sequence; stipulating a strategy selection sequence of the bounded rationality failure party; judging whether line overload or power angle instability is caused by the action adopted by the bounded rationality failure party, wherein direct current tide sensitivity-based overload control or extended equal area criterion-based stability control can be selectively adopted by a perfect rationality defense party according to a judgment result; judging whether an ending condition of the game process is met; finally calculating revenue functions of the bounded rationality failure party and the perfect rationality defense party; ending the multi-stage dynamic game defense process. According to the method, the blockage of cascading failure is realized, and power blackout accidents are prevented.

The invention relates to a column-storage-oriented data compression method. The column-storage-oriented data compression method is characterized by comprising a step 1, dividing data stored according to columns into a plurality of areas; a step 2, defining a group of statistic information for the data in the areas; a step 3, sequentially defining a group of statistic quantities for each area by the aid of the statistic information in the step 2, and realizing quantitative estimation according to characteristics of distribution of the data in each area; a step 4, computing a similarity factor between each two adjacent area according to the learned statistic quantities of the two adjacent areas; a step 5, sequentially computing a value of each statistic quantity for the first area in a column, and selecting the values by a compression method according to the statistic values by the aid of a and computing a stepwise selection method; a step 6, computing a similarity factor between each two adjacent areas according to the statistic values of the remaining i area, directly applying compression strategy of the adjacent previous area if the two adjacent areas are similar, selecting a mode according to the compression method in the step 5 and selecting the compression method again; and a step 7, compressing the current area according to the obtained compression method. The compression method is based on column storage, compression is carried out according to the areas, a high-efficiency compression strategy selection method is designed, and column-storage-oriented massive data management can be effectively supported.