38 results about "Deployment algorithm" patented technology

The invention relates to a self-adaptive multi-algorithm scheduling method and system based on cloud edge collaboration, and the method comprises the steps: constructing a cloud algorithm knowledge base and a scheduling strategy model, which are used for storing a plurality of algorithms, and providing a unified scheduling rule and optimization criterion; designing an edge node real-time sensing and reporting module, dynamically monitoring the computing power state, task characteristics and operation environment of the node, and transmitting related information to the cloud in real time; a cloud intelligent scheduling decision module is constructed, a knowledge base and a scheduling strategy are combined, a received edge state is comprehensively analyzed, and an optimal algorithm selection and execution position decision is generated; an algorithm dynamic scheduling and heterogeneous execution module is deployed on the edge side, and a target algorithm is flexibly loaded and executed on a local cache or heterogeneous computing resources according to an instruction issued by the cloud; and the cloud edge collaborative closed-loop iteration and adaptive optimization module realizes adaptive iteration and continuous optimization of algorithm scheduling, so that high robustness and high efficiency of task execution in a complex and changeable scene are ensured.

An adaptive load balancing (ALB) ground user (GU) access method for an unmanned-aerial-vehicle-assisted network. By means of an unmanned aerial vehicle deployment algorithm based on a deep Q-learning network (DQN), and ALB for GU access, a GU access problem in a BS-UAV-NTN is converted into a maximization problem, and the maximization problem is converted into a Markov decision process (MDP) problem for unmanned aerial vehicle deployment in an unknown environment. The method comprises an unmanned aerial vehicle deployment algorithm based on a DQN, and an access scheme for performing priority ranking on BSs and unmanned aerial vehicles. A simulation result shows that the access scheme is superior to conventional Q-learning and random schemes in the reward aspect, and the number of access GUs.

The invention discloses an intelligent video monitoring system and method supporting AI strategy cloud side end cooperative execution. According to the system, unified management of computing power resources and dynamic deployment of an AI algorithm are realized based on an extended standardized protocol through a three-level architecture of a cloud side center platform, an edge computing node and an end side camera. The method comprises the following steps: a cloud platform acquires computing power information of edge equipment; an AI algorithm execution strategy is generated and issued according to service requirements; if the required algorithm is not deployed in the equipment, the equipment is controlled to download and automatically deploy an algorithm module; and finally, the driving equipment executes AI analysis and recovers the structured data. According to the invention, AI computing tasks are reasonably distributed through a cloud side-end cooperative computing architecture, so that the computing power construction cost of a central platform is effectively reduced; through algorithm and hardware decoupling and a standardized protocol interface, ecological binding is broken, on-demand loading and flexible scheduling of the algorithm are realized, and the economical efficiency, the flexibility and the intelligent level of the system in large-scale scenes such as smart cities and the like are remarkably improved.

The invention discloses a neighbor priority centrality-based hybrid expert model efficient deployment method, which comprises the following steps that: S1, an edge network and hybrid expert model parameters are initialized, and a hybrid expert model architecture comprises a router, an aggregator and an expert model set; s2, calculating neighbor priority centrality values of the edge network nodes in the S1, and selecting a node with the lowest neighbor priority centrality value as deployment positions of a router and an aggregator in the S1; s3, sequentially deploying the expert models in the S1 to neighbor nodes of the router and the aggregator in the S2 according to a neighbor priority centrality calculation process; and S4, outputting a final deployment scheme which comprises deployment nodes of the router and the aggregator, the deployment position of each expert model and a connection path on a physical link. The method has the beneficial effects that the deployment performance of the hybrid expert model in the edge network is remarkably improved by designing an efficient NFC index and an NFC-based router and aggregator deployment algorithm.

The invention relates to the technical field of information security, and discloses a Bootloader signature verification method based on information security, which is characterized in that a security storage module is integrated in a vehicle-mounted instrument system and is used for storing public key and session key information; a Diffie-Hellman algorithm operation environment is deployed, so that two communication parties support a key exchange protocol, an initial key pair is generated in advance, the key pair comprises a public key and a private key, the public key is stored in a secure storage area, and the private key is securely kept by a server side; configuring a firmware segmentation rule, and writing the firmware segmentation rule into a system signature verification program; and defining signature verification failure triggering conditions and response measures. According to the Bootloader signature verification method based on information security, firstly, transmission security is guaranteed through a session key, then segmented signature verification is executed to ensure the legality of firmware, and finally, the key is updated to prepare for next signature verification. Through time sequence cooperation of key dynamic and signature verification segmentation, full life cycle protection of security verification-installation-key refreshing in the vehicle-mounted instrument firmware updating process is realized, and the anti-attack ability and updating efficiency of the system are improved.

A hierarchical 5G-A network deployment method fusing resource allocation and base station position optimization comprises the following steps: step 1, establishing a system model: 1.1, constructing a network model; 1.2, constructing a path loss model; 1.3, constructing a channel model; step 2, deducing the throughput rate of the RUE as follows: 2.1, deducing a downlink data rate and an uplink data rate, and 2.1. 1, deducing a downlink data rate of the RUE u; 2.1. 2, deriving an uplink data rate of the RUE u; 2.2, deducing the throughput rate of the RUE; 2.3, an optimization problem is established, and the total deployment cost is minimized while the given QoS requirement is met; step 3, designing a solution algorithm of an optimization problem, wherein the process is as follows: 3.1, designing a resource allocation method; and 3.2, designing a base station deployment algorithm. According to the invention, the user throughput rate is improved, and the total cost of 5G-A network deployment is reduced.

The invention provides a service function chain deployment method and device for joint user access, equipment and a medium, and the method comprises the steps: determining a target deployment network based on a pre-constructed air-space-ground integrated network model for joint user access and service function chain deployment; determining a target deployment algorithm based on the importance degree of the virtualized network function called by each service function chain in the service function chain set; with minimization of the end-to-end time delay as a target, deploying the virtualized network function according to a target deployment algorithm so as to determine a target network node deployed by the virtualized network function in a target deployment network; and determining the resource quantity allocated to the virtualized network function by the target network node deployed by the virtualized network function based on the task quantity of the service function chain borne by different virtualized network functions, and completing the deployment of the service function chain set. According to the invention, the end-to-end time delay in the SDN-SAGIN can be further shortened.

The invention discloses an unmanned aerial vehicle service simulation method and system based on an Internet of Things platform. The method comprises the following steps: constructing an unmanned aerial vehicle information model; according to input from a real scene and / or a virtual scene, performing service representation on the unmanned aerial vehicle information model; independently deploying an algorithm process and a decision process for the service, and adjusting the sensing data supply of the Internet of Things platform to the unmanned aerial vehicle information model according to the processing conditions of the algorithm process and the decision process; changing the operation environment of the unmanned aerial vehicle information model in the virtual reality scene according to the sensing data supplied by the Internet of Things platform; and obtaining unmanned aerial vehicle behavior data in the operation environment, and according to the unmanned aerial vehicle behavior data, predicting an abnormal business execution condition of the unmanned aerial vehicle and feeding back the abnormal business execution condition to the Internet of Things platform. The business is represented for the simulation model in the real scene and the virtual scene, the appropriate process is deployed to respond to the simulation condition in time, the sensing data of the Internet of Things platform is utilized to generate a diversified operation environment, the business execution is effectively predicted, and the simulation credibility is improved.

The present disclosure provides a method for deploying an algorithm and related devices. The method comprises: obtaining an algorithm code file and a corresponding first configuration file; and according to the first configuration file, calling a deployment tool to perform the following steps: obtaining a second configuration file and generating a framework file for the algorithm code file based on the second configuration file, the framework file being used to convert the algorithm code file into a service; merging the framework file and the algorithm code file into a source code file of the service corresponding to the algorithm; and deploying the service corresponding to the algorithm based on the source code file.

The present invention relates to a lightweight SLAM method, belonging to the fields of SLAM and robotic motion control technology. The method comprises: acquiring a laser radar point cloud; extracting features from the laser radar point cloud based on curvature; calculating five feature parameters, namely, map coordinates, feature direction, hash value, neighboring hash value, and neighboring distance; matching nearest neighbor features using a locality-sensitive hash table based on the feature parameters; optimizing the position and orientation using the Newton iteration method based on the matching results; and updating the map based on the position and orientation and feature parameters. The method enables the deployment of SLAM algorithms on low-cost, computing-constrained embedded devices such as microprocessors.

The application discloses a general base station temperature control method and system based on reinforcement learning, and the system is composed of an information sensing system, a central processor, an intelligent controller, a base station air conditioning system and a machine room dynamic environment monitoring system. The method uses data provided by the machine room dynamic environment monitoring system as the input of the method, and can be deployed in the BBU, without requiring excessive additional hardware investment to obtain input information and deploy algorithms. The method performs local model reasoning on base stations with good data quality and rich data sources, and constructs a model library. For base stations with less data and poor data preparation, a migration model and an initial control strategy are obtained through integrated learning and migration learning technical means, so that the method can effectively learn from existing data in the case of no data accumulation and a small amount of input parameters, and the migration and generalization ability of the method for different base stations is improved. Subsequently, through the technical means of reinforcement learning, local model reasoning is performed on the collected data to optimize the control strategy.

The application discloses an information processing method and device, a storage medium and an electronic equipment. The method comprises the following steps: receiving search information; processing the search information by a target recommendation algorithm to obtain a search result, the target recommendation algorithm being obtained by iteratively training an initial recommendation algorithm by a training sample set constructed by a plurality of target data information, the plurality of target data information being obtained by screening a plurality of historical data information, the historical data information at least comprising a plurality of search result sets and behavior feedback data for search results in the plurality of search result sets, wherein the behavior feedback data is used to represent whether the search result is selected; and displaying the search result. The application solves the technical problem that the accuracy of the search result output by the iteratively updated algorithm model is relatively low after the algorithm model is deployed and the algorithm model is directly iteratively updated according to the feedback data of the user.

The invention discloses a multi-layer satellite network control node optimization deployment method based on a GCN, relates to the technical field of satellite network management and graph neural network application, and solves the problems that in an existing large-scale network, the deployment calculation complexity of a controller is high; in order to solve the problems that an MCD model lacks research on inter-layer interactive modeling in a multi-layer control plane and the like, the cooperative work of a super controller and a controller is realized by constructing a multi-layer satellite network architecture, so that the network response capability of a cross-domain service is improved. And a mathematical model which aims at reducing the network delay and balancing the load of the controller is established, and solving is carried out through an SA algorithm. Besides, a multi-controller deployment algorithm of the GCN network is provided, a solution process of a neural network learning SA algorithm is utilized, a rapid approximate optimal deployment strategy is realized, and technical support is provided for application of a large-scale satellite network. Experimental results show that the method is superior to other related schemes in the aspects of network response time delay, load balancing and expandability.

The application discloses a multi-access edge computing server deployment method and device and a storage medium, and relates to the technical field of edge computing. The method comprises the following steps: acquiring regional tasks and environmental information; establishing an associated optimization model based on a double-layer packing paradigm; using non-probabilistic interval parameters to represent interval constraints; determining the interval constraints and interval objective functions based on the center and radius weighted sum and the satisfaction threshold; obtaining edge server deployment strategies and resource allocation strategies based on a double-layer deployment algorithm of an improved grouping genetic optimizer; enabling and configuring edge servers based on the edge server deployment strategies, allocating computing, storage and multi-concurrent network bandwidth resources based on the resource allocation strategies, recording the achievement during the operation to generate operation indexes and returning the operation indexes. Through the associated optimization model and the improved solving algorithm, the application takes into account the profit and robustness, ensures the feasibility of the strategy in an uncertain scene, and improves the service response speed.

The invention provides a network slice function deployment method for a satellite-ground convergence network. The method comprises the following steps: acquiring network state information and network slice deployment requirements of the satellite-ground convergence network; based on the network state information and the network slice deployment requirement, executing a network slice function deployment algorithm to obtain a deployment result of a network slice instance; the network slice function deployment algorithm is a hybrid algorithm realized based on a multi-population particle swarm and a genetic algorithm, the network slice function deployment algorithm is composed of a plurality of sub-populations, each sub-population represents a variable of an optimization problem, and each sub-population adopts different update strategies and optimization parameters according to the variable type of the optimization problem; and sending a deployment result of the network slice instance to each network node to complete deployment. According to the network slice function deployment method of the satellite-ground fusion network, the proper satellite or ground node can be selected according to the service requirement to flexibly deploy the network slice function, so that the network slice performance is effectively improved.

The application provides a computing power network task scheduling method and system, which comprises the following steps: obtaining a to-be-deployed micro service, a deployment algorithm, to-be-deployed micro service requirements and cluster information, screening a first cluster set matched with the to-be-deployed micro service based on the to-be-deployed micro service requirements and the cluster information; determining a deployment strategy based on the first cluster set and the deployment algorithm, wherein the deployment strategy comprises the number of replicas and the name of a deployment cluster, the deployment cluster belongs to the first cluster set, and the to-be-deployed micro service is deployed based on the deployment strategy; obtaining a to-be-scheduled task, a scheduling algorithm and to-be-scheduled task requirements, screening a second cluster set from the deployment cluster set based on the to-be-scheduled task requirements; determining a scheduling strategy based on the second cluster set and the scheduling algorithm, wherein the scheduling strategy comprises the name of a scheduling cluster and the number of task requests corresponding to each scheduling cluster, the scheduling cluster belongs to the second cluster set, and the to-be-scheduled task request is scheduled based on the scheduling strategy.

The invention belongs to the field of space-air-ground integrated network resource management, and relates to a reliable multicast service chain-oriented resource collaborative deployment method, which comprises the following steps of: establishing a space-air-ground integrated network model; the air-space-ground integrated network comprises a ground network, an air network and a satellite network; constructing a multicast service request with a multicast service function chain; constructing a resource deployment optimization problem according to the space-air-ground integrated network model and the multicast service request; solving a resource deployment optimization problem by using an NCI-based deployment algorithm to obtain an optimal resource deployment decision; wherein the NCI is a node criticality index; physical nodes with high centrality and low failure rate are intelligently selected through node criticality indexes to deploy a virtual network function, step-by-step routing optimization and closed-loop reliability verification of an auxiliary network are combined, the robustness of multicast service is remarkably improved, low-delay transmission and high-reliability operation of a service function chain are ensured, and the service performance of the service function chain is improved. And the utilization efficiency of network resources is maximized.

The invention relates to the technical field of aerospace robot docking, in particular to a non-cooperative target 6D pose estimation method based on multi-spectrum feature interaction, which comprises the following steps: acquiring and preprocessing image data, and establishing a training data set; according to the training data set, respectively obtaining low-frequency features and high-frequency features, interactively constructing a 6D pose estimation model, and designing a loss function to train the 6D pose estimation model; and performing pose prediction through the trained 6D pose estimation model, and outputting a prediction result to realize pose estimation of the non-cooperative target. The 6D pose estimation model comprehensively processes high-frequency features and low-frequency features of spacecraft image data so as to realize aggregation of long-range and long-distance context features; and the 6D pose estimation model has the characteristics of lightweight deployment, efficient algorithm and the like, can improve the efficiency and precision of non-cooperative spacecraft target pose estimation, and can be applied to 6D relative pose estimation for close-range spacecrafts and assist autonomous docking of spacecrafts.

The invention relates to the technical field of micro-services, in particular to an edge native computing micro-service deployment method with low response delay, which comprises the following steps of: 1, establishing a multi-controller service grid architecture; 2, determining the number and positions of controllers based on a K-means algorithm; 3, micro-service deployment is constructed into a multi-objective optimization problem to minimize the system communication overhead and the micro-service deployment cost at the same time; and 4, obtaining an optimal micro-service deployment decision by using an RIME-ACPO micro-service deployment algorithm. According to the method, a search strategy inspired by a natural phenomenon and a self-adaptive optimization mechanism are combined, the method is suitable for the micro-service deployment problem in an edge computing scene, the communication overhead and response time can be reduced by dynamically adjusting the deployment strategy, and the system efficiency is improved.

The invention discloses a base station deployment method based on ray tracing, a storage medium, equipment and a product. The method comprises the following steps: generating an initial base station position according to a scene demand; obtaining a path propagation distance and a path loss matrix based on the initial base station position; fitting the path loss matrix and the path propagation distance based on a path loss model to obtain a channel proxy model; calculating path loss according to the channel proxy model, and updating the position of the base station according to the path loss and the channel energy efficiency; obtaining an updated channel proxy model based on the updated base station position, and if a preset condition is satisfied, determining a base station deployment position; otherwise, updating the position of the base station again until the preset condition is met. According to the invention, the complexity of the overall base station deployment algorithm is reduced, the simulation efficiency is improved, base station deployment in complex scenes such as industrial Internet of Things can be supported, and the application range of a ray tracing algorithm is further expanded.

The application provides a kind of aerospace engineering data algorithm model full life cycle management method and system, it is related to aerospace engineering algorithm model management and service technical field.The method provided by the application stores multiple algorithm model files of different categories in the model library, generates the inference flowchart corresponding to the target data analysis process in response to the editing operation and the configuration operation input by the user, generates the algorithm model service file corresponding to the target data analysis process according to the inference flowchart and sends it to the server in response to the release operation input by the user, so that the server deploys the algorithm model service according to the algorithm model service file.The method provided by the application can quickly complete the deployment of the corresponding algorithm model service based on the analysis configuration operation and the algorithm model file set by the user, and then complete the analysis and processing of data through the algorithm model service, reduce the model service construction cost, improve the efficiency of model application, and enhance the data analysis and processing capability.

The application provides a joint user access service function chain deployment method, device, equipment and medium, comprising: determining a target deployment network based on a pre-constructed space-air-ground integrated network model of joint user access and service function chain deployment; determining a target deployment algorithm based on the importance of each virtualized network function called by a service function chain in a service function chain set; deploying the virtualized network function according to the target deployment algorithm to determine the target network node of the virtualized network function in the target deployment network, with the goal of minimizing end-to-end delay; and determining the resource amount allocated to the virtualized network function based on the task amount of different virtualized network functions carrying service function chains, to complete the deployment of the service function chain set. The application can further shorten the end-to-end delay in SDN-SAGIN.

This invention discloses a multi-task-aware microservice deployment method. Using microservices as vertices and the basic rules governing their call relationships, timing relationships, and constraints as directed edges, a graph model is established. The resource indicators of each microservice represented by a vertex are mapped to the weights of its outgoing edges, forming an Area of ​​Effect (AOE) network. Microservices undertaking critical tasks are identified according to their importance, and these, combined with the resource indicators of the remaining microservices, constitute a multi-task-aware deployment model based on the AOE network. A multi-task-aware deployment algorithm calculates priority paths composed of microservices from this model. Using a greedy approach, paths are preferentially allocated to cluster physical nodes with more remaining resources according to their priority. Finally, an optimized scheme for splitting and deploying the original microservice groups is provided based on the allocation results. This invention helps users complete deployment work flexibly and efficiently, thus solving the problem of difficult deployment.

The invention provides a collaborative optimization method and system for micro-service deployment and request routing, and belongs to the technical field of deployment optimization and user request routing under a cloud computing micro-service architecture. According to a to-be-deployed micro-service instance, a deployment algorithm takes a micro-service calling relation, micro-service resource consumption characteristics and a real-time resource state of a data center server as a core basis, and an intelligent deployment strategy giving consideration to service delay control and server load balance is generated through cooperation of a communication perception grouping algorithm and reinforcement learning; after a user initiates an application request, accurate routing is realized by means of an A-star queue perception scheduler in combination with the multi-chain structure characteristics of the request and the real-time queue state of each micro-service instance, end-to-end delay is effectively reduced, and finally, low-delay and high-stability service use experience is provided for the user.

The application discloses a kind of based on target k-coverage multi-unmanned plane auxiliary charging and data acquisition method, comprising: obtaining and defining wireless chargeable sensor network area and related parameter set;Formalization based on target k-coverage unmanned plane minimization deployment problem;Using the clustering division algorithm based on coalition formation game, obtain disjoint sensor set family;Using restricted Prim algorithm, obtain candidate charging sensor set;Using the unmanned plane minimization deployment algorithm without neighborhood based on edge weight threshold, obtain the flight trajectory of all unmanned planes, supply power for the sensor of insufficient power, and collect the perception data of data concentrator.Greatly reduce the deployment cost of unmanned plane, ensure the long-term operation of wireless chargeable sensor network, while collecting important perception data for professional data analysis.

The application belongs to the technical field of edge computing, and discloses a kind of edge service autonomous deployment method for service-intensive Internet of Things scene, comprising: abstracting Internet of Things service as process component, planning data flow and service scheduling in network using various process modeling methods, and mathematically analyzing process model;Using service network model and distributed microservice data communication model to automatically monitor network data;According to the mobility of entity service node, set up appropriate service deployment algorithm to get the optimal service deployment scheme;Automatically deploy various microservices in code repository to Internet of Things service network.The application starts from process graphical modeling, autonomously completes process programming, process feasibility analysis, service network data and topology monitoring, deployment scheme calculation, and automatically deploys the whole life cycle content of running, combines the theoretical innovation of deployment algorithm with the realization of practical engineering scene application.

The invention provides a heterogeneous hardware platform-oriented automatic AI algorithm model deployment method. The method comprises the following steps of: obtaining a to-be-deployed AI algorithm model and a heterogeneous hardware platform; creating an AI algorithm model deployment task, and setting model adaptation task parameters; constructing a model lightweight algorithm library and a rule constraint engine, and establishing an exception handling mechanism; performing model lightweight and unified representation based on a rule constraint engine and an exception handling mechanism to form a recommended AI algorithm model; matching the recommended AI algorithm model with a heterogeneous hardware platform; and performing performance evaluation based on a heterogeneous hardware platform test environment. According to the automatic AI algorithm model deployment method oriented to the heterogeneous hardware platform, an AI algorithm automatic deployment process is designed and a rule constraint engine and an exception handling mechanism are constructed based on quantization, pruning, distillation and other model optimization algorithms, automatic model efficient deployment of the model is realized, and meanwhile, the performance of the deployed model is prevented from being greatly reduced.

The application discloses an enhanced practical Byzantine fault tolerance method for service function chain deployment. Firstly, a three-layer trusted network system model integrating blockchain and deep reinforcement learning is constructed, and network parameters and SFC deployment constraints are defined. Then, a VRPBFT enhanced consensus mechanism integrating a verifiable random function (VRF) and a node reputation grading model is designed to quantify node credibility and realize dynamic hierarchical division. Next, a master node fair selection method based on VRF is proposed to reduce consensus delay and improve Byzantine node detection capability. Finally, an SDRL deep reinforcement learning deployment algorithm is designed to dynamically adjust VNF and link deployment strategies in combination with node credibility, thereby optimizing resource utilization and service quality. Compared with the traditional PBFT, the consensus delay is reduced by about 30%, and the proportion of Byzantine nodes is reduced by 40% after 100 rounds of consensus. Compared with existing algorithms, the long-term average income of the SDRL algorithm is increased by 17%, the SFC request acceptance rate is increased by 14.49%, the income-cost ratio is increased by 20.35%, the CPU resource utilization rate is 42% and is increased by 27.96%, the safety of SFC deployment in a heterogeneous network is ensured, and the collaborative optimization of resource utilization and service quality is realized, so that the application is suitable for SFC trusted deployment in a heterogeneous network environment such as the Internet of Things and 5G.

The invention discloses a cost and energy consumption optimization-oriented application dynamic deployment method and device in a cloud edge environment and a medium. The method comprises the steps of S01, obtaining an initial deployment scheme through an application program deployment algorithm based on multilevel graph division; s02, for a dynamic change event of the application program, performing dynamic preprocessing on the initial deployment scheme to update the deployment scheme of the application program; s03, performing iterative optimization on the updated deployment scheme in combination with cost and energy consumption gain calculation on the basis of a label propagation algorithm on the premise of meeting the capacity limitation of the data center and the time delay requirement of an application program, so as to enable the model to converge or reach a set constraint condition; according to the scheme, in the face of dynamic scenes such as application scale expansion and task cooperation relation change, the overall deployment cost and energy consumption can be remarkably reduced, and good expandability and scheduling efficiency are achieved; according to the method, the cost and energy consumption optimization of application deployment in the cloud edge environment can be realized on the premise of meeting the capacity and time delay limitation.

The application discloses a dynamic scene binocular vision SLAM method based on target tracking and belongs to the field of simultaneous localization and mapping. In the remote end, an embedded device is used to control a follow-up holder camera installed on a moving body and collect images. In the service end, an embedded device is used to deploy an algorithm. The algorithm adopts a SLAM algorithm combined with target tracking to exclude the interference of the shielding mechanism of the moving body itself and dynamic objects in the field of view, so as to reduce the error of pose calculation and map construction. The application can not only detect and segment dynamic objects, but also segment the objects shielding the field of view. Based on a light target detection network and a distributed structure, resource consumption is reduced, so that the algorithm can be deployed on an embedded device.