33 results about "Adaptive agents" patented technology

Method and agent network architecture for processing a subject message, where each agent has a view of its own domain of responsibility. An initiator agent which receives a user-input request and does not itself have a relevant interpretation policy, queries its downchain agents whether the queried agent considers such message to be in its domain of responsibility. Each queried agent recursively determines whether it has an interpretation policy of its own that applies to the request, and if not, further queries its own further downchain neighboring agents. The further agents eventually respond to such further queries, thereby allowing the first-queried agents to respond to the initiator agent. The recursive invocation of this procedure ultimately determines one or more paths through the network from the initiator agent to one more more leaf agents. The request is then transmitted down the path(s), with each agent along the way taking any local action thereon and passing the message on to the next agent in the path. In the event of a contradiction, the network is often able to resolve many of such contradictions according to predetermined algorithms. If it cannot resolve a contradiction automatically, it learns new interpretation policies necessary to interpret the subject message properly. Such learning preferably includes interaction with the user (but only to the extent necessary), and preferably localizes the learning close to the correct leaf agent in the network.

The invention relates to the field of firewalls, and aims to provide an HTTP (Hyper Text Transport Protocol) service firewall based on an adaptive agent mechanism. The HTTP service firewall based on the adaptive agent mechanism comprises an agent module, a core control and a security policy database, wherein the agent module is used for accessing HTTP access from a client, acquiring various data, transmitting the acquired data to the core control, and performing execution after receiving a judgment conclusion of the core control; the core control is used for scheduling the cooperation of each module, receiving the request of each module, and updating data records, working modes and working methods in the security policy database; and the security policy database comprises a client permission strategy, a client session state database, a start address white list and a subsequent address white list. Through adoption of the HTTP service firewall, checking work on various security aspects of accidental access, accidental parameters, session parameter check, user associated data and the like needing to be considered in a protection system can be reduced, and the security level of the system is raised.

The invention relates to an adaptive processing method oriented to the service life probability analysis of a turbine leaf disc structure. Three classical types of functions in an engineering problem are selected to serve as verification functions to carry out fitting performance analysis on seven common agent models from four aspects including a factor number, a nonlinear degree among factors, calculation precision and calculation efficiency, a calling rule of each class of agent model is formed, and the rule is integrated into a probability design system of the turbine leaf disc structure to form one set of adaptive agent models. A DOE (Design Of Experiments) method is adopted to screen the geometrical parameters of the structure, and an efficient subagent model suitable for a multifactor high-order nonlinear problem is constructed so as to screen a key geometrical parameter which obviously affects stress as a random variable. A DACE (Design And Analysis of Computer Experiments) method is adopted to construct a high-precision high-efficiency subagent model suitable for a few-factor low-order nonlinear problem, Monte Carlo sampling is carried out on the basis, and reliability data is output to finish a whole probability analysis process.

Roughly described, the interpretation phase of a system using an Adaptive Agent-Oriented Software Architecture allows queried agents to respond to queries before they have all their own responses from their own downchain agents. Alternatively or additionally, the originating agent can include a “depth-or-search” indication with each query, thereby preventing propagation of the query through more than the indicated number of agents. In the latter alternative, the originating agent can subsequently make the same query to the same downchain agents, but with an increased depth-or-search indication, if for example the originating agent is not yet satisfied with the claims it received in response to the first query.

The invention discloses a dense oil reservoir fracturing horizontal well optimization method based on a self-adaptive agent model. The method includes the following steps that S1, an optimization parameter x is determined, a target function J(x) is established, and an initial design space D is determined; S2, an initialization parameter k is 1, a sample X0 is obtained in the space D, and sample data X0 is processed to obtain a sample; S3, the variable X0 is substituted into an oil reservoir numerical simulator, an initial variable objective function value J(X0) is obtained, (X0,J(X0)) is stored in a sample point database, and according to the corresponding relationship between X0 and J(X0), a kth gaussian process agent model is established; S4, the self-adaptive process of the kth gaussianprocess agent model is carried out through point addition and an important design space; S5, iteration is stopped when a convergence criterion is satisfied, and otherwise step 6 is executed; S6, k isequal to k+1, the important design space and the sample point database are updated, and then step 4 is repeated. The integrated optimization design of horizontal well spacing and fracturing is adopted, so that accurate optimal well spacing and fracturing parameters are obtained, and the method has great significance on the development of dense oil reservoirs.

The invention provides an automobile wind resistance coefficient optimization method based on a self-adaptive agent model. The automobile wind resistance coefficient optimization method comprises thefollowing steps: conducting CFD simulation calculation on an automobile initial model; selecting a design variable and determining a change range of the design variable; constructing a parameterized model; selecting a sample point, calculating a wind resistance coefficient calculation value corresponding to the sample point, and storing the sample point into a sample point database; judging whether design variables need to be screened or not; constructing an agent model; optimizing the agent model to obtain an optimized solution and an optimized value; calculating a wind resistance coefficientcalculation value corresponding to the optimal solution; calculating the distance between the optimization solution and the sample point and the absolute value of the difference between the corresponding wind resistance coefficient calculation values, and obtaining an increase point; and calculating the error between the minimum wind resistance coefficient calculation value and the correspondingoptimization value until the precision requirement is met. The method has the beneficial effects that on the premise of ensuring the precision, the calling frequency of a complex real simulation modelis reduced, the automobile wind resistance coefficient optimization efficiency is improved, the time cost is reduced, and the research and development period is shortened.

The invention discloses an agent map-based brittleness self-organization criticality analysis method of a marine electric power system, comprising the following steps of: firstly, determining an initial state, an initial entropy value and a critical entropy value of each node by utilizing an adaptive Agent map; updating and storing the state and the entropy values of each node according to a node knowledge base of information related to a storage vertex and an IF / THEN principle; then updating a weight of the Agent map according to a weight-solving method; calculating a brittleness evaluation function of the system; increasing the charge of each load node and the capacity of each engine node of the marine electric power system; increasing the maximum transmission capacity of an open-circuit line; and finally extracting a brittleness self-organization criticality characteristic value of the system. According to the invention, an evolution rule of each adaptive main body is specified, the brittleness evaluation function of the whole system is defined, a brittleness model of the marine electric power system is established, each main body in the system and the operation state of the whole system are calculated through the modeling method to obtain a result, the brittleness degree of the system is correctly reflected, and the brittleness prevention capability of the marine electric power system is improved.

The invention discloses an adaptive agent based progressive community discovery method. By introducing at least two agent selection standards and designing at least two agent selection policies, a community that a community initiator subjectively expects to discover is realized. By introducing three agent selection standards of node activeness, inter-node social relationships and node effectiveness, and designing the multiple agent selection policies, it is ensured that the discovered community meets the subjective expectation of the community initiator, and the personal privacy of community participants is well protected.

The invention discloses a multi-energy power supply capacity configuration method based on a complex adaptive system theory, relates to the technical field of power systems, and provides a multi-energy power system planning model based on the complex adaptive system theory (CAS) in allusion to the multi-energy power supply capacity configuration problem. According to the model, the time sequence and randomness of wind power and photovoltaic power generation are considered, the multi-energy power supply capacity is reasonably configured by taking various types of power supplies as adaptive mainbodies, selecting the power supply capacity as a decision amount, taking the maximum economic benefit as a target function and depending on the behavior rule of continuously changing the main bodiesthrough the adaptive action between the main bodies and between the main bodies and the environment. An actual power system of a certain province in China is selected as an example for simulation andcompared with a Pareto solution set, new energy consumption can be remarkably improved based on power supply structure optimization configuration of a complex adaptive system theory, and the method better conforms to the operation mode of the actual power system.

The invention discloses an Agent-based heterogeneous geographical information public service platform operation and maintenance data collection method. The method comprises the steps of establishing operation and maintenance data structure standards and operation and maintenance information classification standards; according to a geographical information public service platform operation and maintenance interface, establishing an exclusive adaptive Agent, standardizing data, and providing operation and maintenance data in a REST service form; calling an adaptive Agent service to obtain the operation and maintenance data by a report Agent, adding data tags for the data, and reporting the data to an aggregate Agent; and receiving the reported operation and maintenance data by the aggregateAgent, determining operation needed to be performed for the operation and maintenance data according to the data tags, collecting the data to a database, and returning a collection result to the report Agent. For the situations of platform heterogeneity, operation and maintenance data heterogeneity, different network environments where a platform is located and the like in geographical informationpublic service platform construction, unified data structure standards are established, and unified collection of the heterogeneous geographical information public service platform operation and maintenance data is realized.

An autonomous adaptive agent which can learn verbal as well as nonverbal behavior. The primary object of the system is to optimize a primary value function over time through continuously learning how to behave in an environment (which may be physical or electronic). Inputs may include verbal advice or information from sources of varying reliability as well as direct or preprocessed environmental inputs. Desired agent behavior may include motor actions and verbal behavior which may constitute a system output (and which may also function “internally” to guide external actions. A further aspect involves an efficient “training” process by which the agent can be taught to utilize verbal advice and information along with environmental inputs.

The invention discloses an automobile passenger restraint system optimization design method based on a self-adaptive agent model. According to the method, sample points are sampled through a Latin hypercube experiment design method, meanwhile, optimal shape parameters corresponding to the sample points are solved through a radial basis function, and the solved optimal shape parameters are combinedwith the sample points to construct an agent model of the automobile passenger restraint system; in order to ensure the precision of an optimization calculation result, multiple reconstruction of theagent model is realized based on error judgment, so that a final approximate optimization design problem of the automobile passenger restraint system is obtained, and an optimization solution meetingdesign requirements is solved through an intergeneration mapping genetic algorithm, so that the safety of automobile passengers is ensured. According to the method, the protection performance of theautomobile passenger restraint system can be effectively improved, the optimized calculation efficiency and solving quality can be essentially improved, and the method has wide engineering applicationvalue in the field of automobile safety.

The invention discloses a gear transmission device multi-objective optimization design method based on a self-adaptive agent model. According to the method, sample points are sampled through a Latin hypercube experiment design method, meanwhile, optimal shape parameters corresponding to the sample points are solved through a radial basis function, and the solved optimal shape parameters are combined with the sample points to construct an agent model of the gear transmission device; in order to improve the calculation efficiency, a local encryption approximation model method is introduced intoan iterative solving process, meanwhile, the precision of the agent model and a calculation result is ensured by reconstructing the agent model for multiple times, and a non-dominated solution set meeting the structural characteristics of the gear transmission device is solved through a multi-objective optimization method; according to the method, the structural characteristics of the gear transmission device can be effectively improved, the optimized calculation efficiency and solving quality can be essentially improved, and the method has wide engineering application value in the field of gear transmission device design.

The invention discloses a connector multidisciplinary collaborative design optimization method based on a self-adaptive proxy model, which comprises the following steps: constructing a Kriging initial proxy model, introducing an EI function to upgrade and construct the Kriging proxy model into the self-adaptive proxy model, and applying the self-adaptive proxy model to multidisciplinary collaborative design optimization. And constructing a multi-disciplinary collaborative design optimization method based on a self-adaptive agent model, performing uncertainty analysis and design optimization modeling on the special lifting appliance rotary platform connecting piece in a multi-physical field mechanical property coupling state, and finally performing design optimization. According to the method, the uncertainty theory, the agent model theory and the collaborative optimization theory are combined, so that the problem that a traditional design method is difficult to qualitatively and quantitatively construct optimization problem performance functions or constraint conditions for a complex engineering model, and wide application of a collaborative design optimization method in the field of practical engineering is restricted is solved; therefore, the lightweight target requirement of a special lifting appliance rotary platform connecting piece product is met.

The invention discloses a parameter self-adaptive agent method and device suitable for power market simulation. The method comprises the steps of establishing an agent model; performing initializationprocessing on the quotation strategy set, the evaluation value and the selection probability of each agent model; carrying out initialization processing on learning parameters in each agent model; selecting a quotation strategy of each agent model according to a roulette mode; performing clearing calculation according to the selected quotation strategy and the market rule to obtain the income ofeach agent model; adaptively updating learning parameters of each agent model by using a particle swarm algorithm according to the income; updating an evaluation value and a selection probability of each quotation strategy according to the updated learning parameters; and judging whether the current simulation result meets a convergence condition or not, if so, ending simulation and outputting a final result, and if not, returning a quotation strategy for reselecting each agent model. According to the method, the parameters of each agent model can be adaptively updated, so that the simulationresult better conforms to the actual market member behaviors.

The invention discloses an adaptive Agent deployment method, the method comprises the following steps: establishing an Agent library and defining feature data of a management object; making a matchingrule for the Agent and the feature data of the management object; identifying the feature data of a newly added management object; matching a corresponding Agent in the Agent library according to theidentified feature data through the matching rule; and installing the matched Agent to the newly added management object, wherein the matched Agent can be selected automatically according to the feature data of a device, and the matched Agent can be automatically installed on a device. Further, follow-up upgrading and maintenance of the Agent can be executed in the same way.

The invention discloses a vehicle air spring engineering optimization design method based on a self-adaptive chaos polynomial-Kriging proxy model. The method comprises the steps: establishing an air spring high-fidelity nonlinear fluid-solid coupling finite element model and a parameterized model thereof according to small sample experimental data; on the basis of a test design method and a statistical regression method, establishing a self-adaptive chaos polynomial-Kriging agent model by using a small amount of simulation calculation results to approach a complex and expensive simulation model; and finally, performing parameter global optimization on the performance function of the air spring system based on the self-adaptive agent model and a multi-objective intelligent optimization method. According to the method, the air spring system optimization design is achieved with the small number of high-fidelity model simulation times, the research and development period can be greatly shortened, the research and development cost is saved, an efficient means can be provided for rapid research and development design decision making of the air spring, and the method has good engineering adaptability and application prospects.

The invention relates to an adaptive new energy ultra-short-term power prediction method and device based on reinforcement learning. The method comprises the following steps: acquiring an environment variable, an action space and a reward function for constructing an adaptive agent; wherein the environment variable is an environment variable index reflecting environment characteristics, the action space is an action function set adopted by the agent decision, and the reward function is an evaluation result of corresponding change of the environment variable after the agent action is executed; constructing a self-adaptive agent according to the environment variable and a reward function; and processing the environment variable, and training the adaptive agent by using the processed environment variable to obtain an adaptive prediction agent. The prediction result of the single-class prediction method most matched with the external environment is adaptively selected according to the environment variable, so that the accuracy of the prediction result is improved to the maximum extent. The method provided by the invention is simple in implementation process and has a relatively strong application prospect.

The invention discloses a bridge crane girder reliability calculation method based on a new adaptive agent model. Firstly, establishing a parameterized finite element model of the bridge crane; secondly, establishing a new self-adaptive agent model by using a small amount of calculation results; and finally, solving the reliability of the main beam of the bridge crane by utilizing the self-adaptive agent model. According to the method, the corresponding reliability can be calculated as long as the variables required by calculation meet the requirement that the probability distribution function is clear, so that the method has a relatively high practical value. And moreover, the calling frequency is greatly reduced by improving the reliability calculation process, and the calculation time is greatly shortened.

The invention discloses an application of a self-adaptive agent model based on sparse density and local complexity in optimization of a forearm driving connecting rod of a palletizing robot. Because high calculation cost is required for obtaining a real model response value in a complex engineering problem, the invention provides a self-adaptive agent model construction method based on sparse density and local complexity, and the self-adaptive agent model construction method is applied to the optimal design of the small arm driving connecting rod of the stacking robot. The method comprises thesteps: firstly, establishing a model of a stacking robot forearm driving connecting rod, and determining design variables and an optimization target; secondly, generating an initial sample, obtaininga real response, and constructing a sample library; and then, constructing an initial agent model according to the sample library, and constructing a high-precision agent model of the optimization target through the method provided by the invention; and finally, performing optimization design by using an agent model. The invention has a wide application prospect in the complex engineering optimization problem and under the condition that the target function is difficult to obtain.