27 results about "Optimal deployment" patented technology

The invention relates to an aggregation scene unmanned aerial vehicle cluster three-dimensional deployment method and device based on cyclic segmentation and multi-objective optimization. The method comprises the following steps: designing a cyclic segmentation optimization framework and a self-adaptive switching mechanism oriented to an aggregation scene; performing cyclic segmentation optimization on the unmanned aerial vehicle cluster based on a cyclic segmentation optimization framework and an adaptive switching mechanism, and updating a Pareto file; designing a comprehensive fitness function, screening out elite parent individuals based on the Pareto file, and performing cyclic segmentation optimization on the unmanned aerial vehicle cluster by using the elite parent individuals; and performing three-dimensional deployment of the unmanned aerial vehicle cluster in the aggregation scene according to a cyclic segmentation optimization result. According to the method, through multi-objective optimization based on Pareto and an innovative segmented optimization framework, and through dynamic switching of level and height optimization stages, astringency and global diversity of understanding are remarkably improved, optimal deployment of a multi-unmanned aerial vehicle communication system can be realized in a complex scene, and theoretical and method support is provided for practical engineering application.

The invention relates to the technical field of cloud computing, and discloses a multi-cloud certificate deployment intelligent arrangement engine system, which comprises a unified abstraction layer used for providing a standardized certificate deployment interface; the intelligent arrangement engine is used for analyzing a deployment task dependency relationship and generating an optimal deployment path; the distributed transaction management module is used for coordinating deployment transactions of the cross-cloud platform and ensuring the consistency of operation; and the strategy optimization and learning module is used for optimizing the deployment strategy based on the historical deployment data. According to the method, the cloud platform difference is shielded through the unified abstraction layer, the consistent deployment experience is provided, and the adaptation workload in the multi-cloud environment can be remarkably reduced; the deployment path is optimized through the intelligent arrangement engine, so that the deployment time can be remarkably shortened; through distributed transaction management, transaction consistency of cross-cloud deployment is ensured, and strong fault recovery capability is provided. Through multi-objective optimization and online learning, the system can automatically balance the cost, the performance and the reliability, so that the resource utilization rate is greatly improved.

The invention discloses an intelligent metasurface auxiliary directional charger deployment method based on a heterogeneous graph neural network and deep reinforcement learning, and belongs to the field of wireless energy supplementation of the Internet of Things. The method aims at solving the problems that at present, related work is mostly one-time static geometric deployment, the coverage range is limited, flexibility is insufficient, the optimization granularity is coarse, high-quality charging full coverage is difficult to achieve under the cost constraint, and the overall charging effectiveness of a network is difficult to maximize. According to the method, a heterogeneous network graph fusing the relation of sensor nodes, directional chargers and intelligent super surfaces (RIS) is constructed, a heterogeneous graph neural network is deployed in a base station in a centralized mode to extract the network structure and energy state characteristics, and a near-end strategy optimization algorithm is further combined. And joint optimization of a directional charger deployment position and a charging direction as well as an RIS position and reflection configuration is realized. According to the method, the charging coverage rate, the charging utility and the deployment cost are taken as a comprehensive target, the optimal deployment strategy can be intelligently generated, the charging coverage is expanded, the charging utility is improved and the node failure rate is reduced on the premise of controllable cost, so that the purpose of prolonging the service life of the network is achieved.

The invention relates to the field of network security, and particularly provides a security cooperation system and method in a hybrid cloud environment. The system comprises a unified security management module, a security solution cooperation module and a security function module. The unified security management module analyzes a self-healing algorithm through a policy graph, carries out graph modeling on a multi-cloud security policy, automatically identifies policy conflicts and drifts through graph difference analysis, and realizes continuous consistency and self-healing of a cross-cloud policy. And the security solution cooperation module is embedded into a reinforcement learning agent, dynamically decides an optimal deployment scheme of security resources according to a reward function by sensing a threat situation, a resource utilization rate and response delay, and realizes intelligent self-adaptive arrangement of protection resources. The security function module is used for micro-servitizing and containerizing security capability based on a cloud native technology, transparent injection is realized through a service grid, an automatic compliance repair and verification mechanism is built in, and a compliance closed loop for detection, repair and verification is formed. And the management efficiency and the protection accuracy of the security policy are improved.

The invention discloses a machine room resource capacity intelligent planning system and method based on multi-source data fusion, and belongs to the technical field of data center management, and the system comprises a multi-source data collection module, a resource manifold modeling module, a geodesic optimization module, a self-adaptive planning module and a verification and inspection module. The method comprises the following steps: mapping multi-dimensional resource parameters such as space, power, heat dissipation and load bearing of a machine room into a Riemannian manifold mathematical model, and constructing a resource measurement tensor representing a resource distribution density and a constraint relationship; calculating a resource allocation optimal path on the resource manifold, constructing a multi-objective function including space utilization rate, energy efficiency, heat dissipation efficiency and cost effectiveness, and generating an optimal deployment scheme of the machine room equipment; in combination with topological characteristic analysis of resource manifolds, potential resource bottlenecks are predicted, and a resource allocation scheme is dynamically adjusted; and the security and compliance of the evaluation scheme are verified through digital twinborn simulation, so that the problem of unbalanced resource allocation caused by independent planning of each system in traditional machine room planning is solved.

The invention discloses a low-voltage power line carrier communication network repeater deployment method and system, and relates to the technical field of power communication. The method comprises the following steps: (1) according to PLC network topology information, acquiring an STA set which cannot directly communicate with a CCO, and inputting model parameters; (2) calling a general optimization solver to carry out model solution to obtain a solution result; (3) if the feasible solution exists, outputting a PCO deployment mark; if not, adjusting the received signal threshold value and the transmitting power, and repeating the step (2); and (5) once a feasible solution is obtained or the model parameter reaches the set limit value, the solution is ended. According to the deployment planning model constructed by the invention, the optimal deployment position of the PCO in the network is solved by taking the minimum weighted sum of the total transmission time delay and the total cost as a target and taking the received signal strength and the single-hop relay as constraints; the problem that seamless coverage of a low-voltage power distribution network is difficult to realize by single-hop communication due to high background noise and frequency selective fading of a PLC channel is solved.

The invention provides a low-altitude unmanned system air node optimal deployment method based on communication-link-energy consumption joint optimization. The method comprises the following steps: initializing an unmanned aerial vehicle group; the system constructs a performance model according to the real-time channel information, constructs a multi-objective collaborative optimization problem, and solves the multi-objective optimization problem by adopting an MOPSO-HGD algorithm to obtain an optimal result; deploying the unmanned aerial vehicle group to the optimal position according to the optimal result to form a virtual distributed MIMO array structure; setting an unmanned aerial vehicle electric quantity threshold value, judging the size of each unmanned aerial vehicle remaining electric quantity and the unmanned aerial vehicle electric quantity threshold value, and if the unmanned aerial vehicle remaining electric quantity is smaller than the unmanned aerial vehicle electric quantity threshold value, recycling the unmanned aerial vehicle; otherwise, re-determining the position of the unmanned aerial vehicle group; through a closed-loop control mechanism of channel perception-multi-target optimization-dynamic adjustment, the unmanned aerial vehicle relay system can still keep a high signal-to-noise ratio and strong robustness in a complex low-altitude environment, is suitable for scenes such as low-altitude broadband communication, emergency rescue and urban information networks, and has a good engineering application prospect.

The invention relates to an intelligent networking planning system based on multi-objective dynamic optimization, which comprises a data input module, a data preprocessing module, a system modeling module, a multi-objective optimization module and a scheme output module, and is characterized in that the data input module is used for receiving geographic information, environmental conditions, equipment models and performance parameters input by a user; the data preprocessing module is used for carrying out standardization processing on the input geographic information and equipment parameters; the system modeling module is used for establishing a terrain and terrain model; the multi-target optimization module is used for carrying out multi-target dynamic optimization and generating an optimal deployment scheme; and the scheme output module is used for generating an equipment deployment scheme report. According to the invention, complementary coverage between devices is realized, the system cost is reduced, and the deployment efficiency is improved. The system has good expansibility and adaptability, can be flexibly adjusted according to different application scenes and requirements, improves the overall performance and adaptability of a low-altitude security system, and meets diversified low-altitude security requirements.

The method and system for joint configuration of network security functions based on the maximum satisfiability modulo theory include the following steps: (1) Problem modeling process: modeling the joint configuration problem of network security functions as a maximum satisfiability modulo theory MaxSMT problem; (2) Hard constraint generation process; (3) Soft constraint generation process; (4) MaxSMT solution process; (5) Result extraction process: extracting the joint configuration results of network security functions from the model output by the solver; The method and system of the present invention can automatically determine the optimal deployment location, configuration rules and execution order of various network security functions in the network, significantly reduce the configuration complexity and improve the intelligence level of network security configuration while strictly meeting all security requirements.

The application discloses a low-altitude defense platform deployment system and method. The system comprises: a data acquisition module for acquiring defense area related data and available platform information; a capability quantification module for uniformly quantifying the capability parameters of each platform to obtain a standardized capability description; an AI agent search module comprising a plurality of parallel working AI agents and a shared memory area; each AI agent iteratively executes the generation of a candidate deployment scheme, constraint violation degree calculation and comprehensive performance score evaluation, and updates a local optimal deployment scheme based on the evaluation results, and exchanges scheme information through the shared memory area to guide subsequent search; and a scheme determination module for determining a global optimal deployment scheme when a termination condition is met. Through capability quantification and multi-agent parallel collaborative search, the application improves the comprehensive protection performance and planning efficiency of the heterogeneous defense platform deployment scheme, and realizes automatic global optimization deployment under complex constraint conditions.

The invention discloses a base station deployment method for mine space constraints and related products, which can be applied to the technical field of mine communication network deployment, and the method comprises the following steps: constructing M to-be-selected particles; wherein each particle to be selected comprises three-dimensional position information and antenna angle information of N base stations. And determining a three-dimensional coverage rate of each to-be-selected particle for the to-be-deployed mine, and taking the to-be-selected particle with the highest three-dimensional coverage rate as a first target particle. And iteratively adjusting the first target particle based on a preset updating strategy, and determining a three-dimensional coverage rate of the first target particle to the to-be-deployed mine after each iteration adjustment. And outputting the optimal deployment scheme when the obtained three-dimensional coverage rate meets a preset iteration convergence difference value through continuous K times of iteration adjustment. Therefore, the three-dimensional space information of the mine is taken as a constraint, the three-dimensional position and the antenna angle of the base station are jointly optimized by combining the particle swarm optimization algorithm, and the three-dimensional space coverage rate of deployment of the base station in the mine environment is improved.

The application provides an APT attack active defense method based on a four-honey system, which comprises the following steps: collecting events and external threat intelligence fed back by defense components in the four-honey system to obtain security observation data, and generating an alignment subgraph representing anchored inter-tactical behavior relationships; combining the alignment subgraph with an APT knowledge graph to implement explicit relationship reasoning and implicit relationship reasoning to generate a candidate attack intent set and a confidence distribution thereof; generating an optimal deployment strategy under the constraint of system resource state, encapsulating the optimal deployment strategy into an executable work order; calling resources for deployment and generating a deployment state reply to complete new trapping environment construction; collecting attacker behavior data, evaluating strategy effectiveness according to the attacker behavior data and the deployment strategy, and updating the strategy deployment priority. The method can be applied to real-time strategy adaptation and automatic resource scheduling in response to attack behavior evolution, and can significantly improve flexibility and continuous interference capability in a complex attack and defense environment.

The embodiment of the invention provides a low-altitude communication node deployment method and system based on deep learning and reinforcement learning. The method comprises the following steps: acquiring a city three-dimensional environment map; wherein the urban three-dimensional environment map comprises the terrain, the building height and the building shielding information of the city; through a predetermined radio map prediction model, carrying out signal to interference plus noise ratio distribution prediction on the urban three-dimensional environment map under different node deployments to obtain a signal to interference plus noise ratio prediction distribution map; performing iterative optimization on the signal to interference plus noise ratio prediction distribution diagram through a predetermined reinforcement learning optimization model, and determining an optimal deployment strategy; determining the position of a target base station based on the optimal deployment strategy; wherein the target base station position is the position of the target node in the plurality of different nodes determined based on the optimal deployment strategy. According to the scheme, high-precision propagation prediction and adaptive optimization deployment are realized in a complex low-altitude environment, and the deployment efficiency and the communication performance can be improved.

The invention discloses a forecasting platform automatic deployment and load balancing control method, system, device, medium and program, and belongs to the technical field of forecasting management and control. The method comprises the following steps: acquiring original multi-target data of a forecasting platform, and preprocessing the original multi-target data to obtain multi-target data of the forecasting platform; performing optimization processing by adopting an NSGA-II algorithm model according to the multi-target data of the forecasting platform to generate an optimal deployment scheme; generating a configuration file according to the optimal deployment scheme, sending the configuration file to a server in the forecasting platform, checking the server receiving the configuration file, and generating a load distribution scheme; and monitoring the load condition of the server in real time according to the load distribution scheme, and dynamically adjusting the task distribution quantity of the server according to the load condition. According to the method, multiple conflicting targets are comprehensively considered, the optimal decision of forecasting platform deployment and load distribution is realized, platform deployment is quickly and accurately completed according to the optimal decision, and dynamic adjustment task distribution is completed in real time.

This invention belongs to the field of bank protection design technology, specifically relating to a bank protection design method and system based on ecological gabion net technology. The method includes: acquiring initial data containing information on the bank slope structure and river flow in the bank protection area; generating multiple deployment paths based on ground slope information by setting multiple offset schemes consisting of offset angles and offset distances; combining the deployment paths into multiple deployment schemes based on the matching degree between the deployment paths and the river flow direction; and determining the optimal deployment scheme and secondary deployment schemes based on the deployment balance of each deployment scheme and preset optimization conditions. This invention introduces quantitative evaluation of matching degree and deployment balance, transforming bank protection design into a data-driven process. This ensures that the gabion net deployment adapts to river information and achieves a reasonable distribution of deployment intensity. Furthermore, it utilizes secondary deployment schemes to identify dangerous deployment areas for targeted reinforcement, thereby improving the stability and overall safety assurance capabilities of the bank protection structure.

This invention discloses a serverless function workflow scheduling method, medium, and device, relating to the field of function workflow scheduling technology. The method includes: acquiring function call logs; constructing a directed acyclic graph (DAG) of the function workflow based on the function call logs, using functions as nodes and dependencies between functions as edges; merging the nodes of the DAG to obtain a simplified DAG; establishing an earliest completion time model for functions in the workflow based on the simplified DAG, and defining a communication time minimization subproblem for function pairs with dependencies; solving the communication time minimization subproblem to optimize the transmission path and size of the function workflow; and traversing the server set according to the transmission path and size of the function workflow, calculating the optimal deployment location for each function, and progressively solving for the optimal scheduling scheme of the workflow. This invention achieves transmission path segmentation, improving scheduling efficiency.

The main task sensing type container resource dynamic scheduling method provided by the invention comprises the following steps: when the number of main tasks in a current scheduling period is greater than 1, if a new main task arrives and node resources are insufficient, calculating a hash value of each container unit on each node; according to the hash value of each container unit, determining a target elimination container unit, and releasing node resources of each target elimination container unit to obtain nodes for scheduling and container units to be scheduled; combining the state of the node capable of being scheduled and the state of the container unit to be scheduled to obtain a joint state vector, and inputting the joint state vector into a pre-trained double-depth Q network to obtain an optimal deployment node; and if the available resources of the optimal deployment node are sufficient, allocating node resources to the new main task. Therefore, intelligent dynamic allocation of the container resources is realized, the response speed of task scheduling and the system operation efficiency are remarkably improved, and the utilization rate of the container resources is improved.

The invention belongs to the technical field of new energy power station monitoring, and particularly discloses a new energy station monitoring node collaborative deployment method and system, computer equipment and a storage medium. A multi-dimensional monitoring resource vector is constructed by collecting multi-source heterogeneous data such as electrical quantity, state quantity, environment quantity and resource state; feature density is calculated based on vector similarity, monitoring function partitions are automatically divided, and partition loads and fault risks are predicted; a multi-target optimization model is established, and dynamic optimal deployment of the monitoring task is realized; when the nodes are overloaded, a dynamic fragmentation mechanism is started, the tasks are split into sub-task groups and redeployed, and a scheduling system is formed. According to the method, the overall reliability and the resource utilization efficiency of the system are improved through dynamic partitioning, task optimization allocation and a real-time fragmentation mechanism.

The invention relates to the field of operation and maintenance, in particular to an automatic operation and maintenance method and system of an observable platform based on an intelligent sensor. The automatic operation and maintenance method of the observable platform based on the intelligent sensor comprises the following steps that S1, a detection module automatically collects multivariate information in the intention of deploying the observable platform by a user and sends the multivariate information to a data model; s2, the data model carries out normalization processing on multivariate information collected by the detection module to obtain a declarative configuration model and sends the declarative configuration model to a strategy engine; s3, the strategy engine is used for intelligently analyzing the declarative configuration model, dynamically generating an optimal deployment list and sending the optimal deployment list to the deployment controller; and S4, the deployment controller deploys each component of the observable platform based on the optimal deployment list. According to the automatic operation and maintenance method and system, automation, intelligence and standardization of deployment of the observable platform in a credential and credential heterogeneous environment can be realized.

The invention relates to the technical field of communication equipment management, and discloses a remote automatic deployment and configuration method and system for communication equipment, and the method comprises the steps: obtaining the signal coverage distance of the communication equipment in a deployable region through a communication database, and carrying out the analysis and calculation through combining the interference intensity index of the deployable region, thereby obtaining the optimal deployment position of the communication equipment; by constructing the three-dimensional digital twinborn model and collecting the electromagnetic spectrum data, accurate identification and positioning of the interference source are realized, and then the optimal deployment position is obtained through automatic calculation, so that the workload of manual investigation is greatly reduced, the accuracy and rationality of the deployment position are improved, and the deployment efficiency is improved. By collecting the operation data of the communication equipment and calculating the deployment effect index, the system can automatically evaluate the deployment effect, and triggers the re-optimization process when the expected target is not reached, thereby ensuring that the actual operation state of the deployed communication equipment is highly matched with the expected target.

The invention relates to the technical field of wireless communication and machine learning cross fusion, in particular to an industrial Internet of Things channel modeling method and system based on an interpretable random forest. The method comprises the following steps: constructing a channel data set containing various industrial scenes, receiving point sight distance states and three-dimensional positions, establishing a multi-output regression model by using a random forest after environment-height joint balanced sampling and standardized preprocessing, and jointly predicting root-mean-square time delay extension, azimuth angle extension and pitch angle extension by taking system parameters as input; the efficiency and precision of the model are further improved through hyper-parameter optimization with a time penalty term, a model decision-making mechanism is explained from global and local levels by means of arrangement feature importance and SHAP value analysis, and the consistency of the model decision-making mechanism and a physical propagation rule is verified. According to the method, high-precision, stable and explainable joint prediction of the key statistical characteristics of the industrial wireless channel is realized, and channel modeling, network planning and optimal deployment of an industrial Internet of Things communication system can be supported.

The application discloses a kind of microgrid group multi-source collaborative scheduling method and system based on flexible interconnection, which comprises the following steps: establishing resource-geographical coupling graph theory model, determining the optimal deployment position of flexible interconnection device using the double-target optimization function of minimizing construction cost and maximizing load accessibility, forming the physical interconnection topology structure of microgrid group;Real-time acquisition of wind, light, water and energy storage state data of each microgrid node, according to load reconstruction rule, the load of each microgrid node in physical interconnection topology structure is divided into rigid load and elastic load, the source load mismatch index and trend discriminant factor of microgrid group are calculated;Set trend discriminant factor threshold and source load mismatch index threshold, divide the operation state of microgrid group into different operation modes;According to operation mode, the corresponding energy scheduling strategy is executed;Through the method, geographical environmental factors can be considered, and flexible interconnection of key nodes can be realized with minimum cost, and the topology adaptability is strong.

This disclosure relates generally to a method and system for latency optimized heterogeneous deployment of convolutional neural network (CNN). State-of-the-art methods for optimal deployment of convolutional neural network provide a reasonable accuracy. However, for unseen networks the same level of accuracy is not attained. The disclosed method provides an automated and unified framework for the convolutional neural network (CNN) that optimally partitions the CNN and maps these partitions to hardware accelerators yielding a latency optimized deployment configuration. The method provides an optimal partitioning of the CNN for deployment on heterogeneous hardware platforms by searching network partition and hardware pair optimized for latency while including communication cost between hardware. The method employs performance model-based optimization algorithm to optimally deploy components of a deep learning pipeline across right heterogeneous hardware for high performance.

This invention discloses a method for the deployment and collaborative optimization of multi-dimensional sensor networks based on the N00 method, comprising: S1, collecting and preprocessing spatial data fused from multiple sources in the well; S2, establishing a multi-level spatial topology model based on the preprocessed data using the N00 method; S3, establishing a deployment dynamic weight function, and obtaining a deployment optimization model based on the deployment dynamic weight function and the multi-level spatial topology model; S4, obtaining the deployment optimization model based on the deployment dynamic weight function and the multi-level spatial topology model; and S5, optimizing the deployment optimization model using a deep reinforcement learning algorithm based on Transformer. This invention establishes a more accurate spatial model using multi-source data, optimizes the deployment model using a dynamic weight function to form an adaptive deployment scheme, and further optimizes the deployment optimization model to complete collaborative optimization and provide the optimal deployment scheme.

The embodiment of the application provides a low-altitude communication node deployment method and system based on deep learning and reinforcement learning, the method comprising: acquiring a city three-dimensional environment map; wherein the city three-dimensional environment map contains city terrain, building height and building shielding information; predicting, by a pre-determined radio map prediction model, signal-to-interference-and-noise ratio distribution under different node deployments for the city three-dimensional environment map to obtain a signal-to-interference-and-noise ratio prediction distribution map; iteratively optimizing, by a pre-determined reinforcement learning optimization model, the signal-to-interference-and-noise ratio prediction distribution map to determine an optimal deployment strategy; and determining a target base station position based on the optimal deployment strategy; wherein the target base station position is the position of a target node in a plurality of different nodes determined based on the optimal deployment strategy. The above scheme realizes high-precision propagation prediction and adaptive optimization deployment in a complex low-altitude environment, and can improve deployment efficiency and communication performance.

This invention relates to the field of edge computing technology, providing a method and system for deploying and routing AI services and microservices in edge scenarios. The method includes: determining a deployment and request routing model for AI services and microservices in a multi-edge collaborative network scenario based on acquired network topology data of multiple edge nodes, service characteristic data of AI services and microservices, and user request flow data; determining a joint optimization function based on the deployment and request routing model, with end-to-end latency, system energy consumption, and resource utilization as optimization objectives; training an agent to learn the optimal deployment strategy using a deep reinforcement learning algorithm based on a potential function and piecewise rewards, guided by the joint optimization function, to obtain a deployment scheme for AI service instances and microservice instances on multiple edge nodes; and calculating the routing path for user requests to reach the network based on the deployment scheme and real-time network status. The method and system provided by this invention improve service quality and resource utilization efficiency.

The invention provides a UWB positioning base station deployment optimization method based on an improved ant colony optimization algorithm. The UWB positioning base station deployment optimization method based on the improved ant colony optimization algorithm aims at achieving high-precision and high-coverage-rate UWB base station layout under the obstacle constraint condition. The method comprises the following steps: firstly, setting obstacle areas, the number of base stations and optimization parameters in a positioning environment, and generating an initial deployment scheme meeting obstacle constraints; then, a new solution is generated by adopting a pheromone archiving mechanism and a roulette strategy, evaluation is carried out in combination with a multi-target fitness function containing dispersity, uniformity and coverage rate, and a Gaussian disturbance and local search strategy is introduced to enhance the global optimization ability, so that local optimum is effectively avoided; and finally, outputting a global optimal deployment scheme after iterative convergence. Experimental results show that in a typical indoor scene, compared with traditional random layout, the method has the advantages that the positioning effective coverage rate is improved by about 15%-20%, the base station distribution uniformity is improved by about 25%, the UWB base station (Anchor) is effectively prevented from falling into an obstacle area, the UWB system positioning precision and robustness are remarkably improved, and the method is suitable for high-reliability positioning system deployment in a complex environment.