6results about How to "Realize distributed deployment" patented technology

The present application relates to the technical field of machine vision, in particular to a kind of robust visual detection system of ton bag goods based on multi-cluster feature fusion and posture correction drive, the system includes non-local mean enhancement module, multi-feature clustering fusion module, geometric feature extraction module, dominant posture clustering module, posture correction transformation module and goods segmentation determination module, non-local mean filtering is carried out to the original image of ton bag goods to obtain enhanced image, the present application is equipped with non-local mean enhancement module and multi-feature clustering fusion module, can realize the efficient enhancement of ton bag goods image and multi-dimensional feature fusion, the space alignment weighted superposition of color cluster and texture cluster is generated Fusion segmentation chart, let the feature representation of ton bag goods homogeneous region be more comprehensive, accurate, effectively improve the integrity and effectiveness of geometric feature extraction, so that the feature analysis ability of system to ton bag goods image is significantly improved.

The invention provides a data processing method and device, a system, a storage medium and equipment, and the method comprises the steps: searching a processing result of a multi-modal feature from cached processing results based on the multi-modal feature after receiving the multi-modal feature processed by terminal equipment, and when the processing result of the multi-modal feature is not found, carrying out the processing of the multi-modal feature; searching attention weights associated with the multi-modal features from a cache, and if the attention weights associated with the multi-modal features are found, sending the multi-modal features processed by the terminal device and the found attention weights to a large language model backbone network deployed at a cloud end, and if the attention weights associated with the multi-modal features are found, sending the multi-modal features processed by the terminal device and the found attention weights to the large language model backbone network. A multi-modal feature processing result is obtained through large language model backbone network processing, and the problem that a multi-modal large language model has high requirement for computing power and cannot be deployed in terminal equipment with limited computing power is solved.

This application provides a security protection method and a security resource pool. The method includes: obtaining service traffic from a service switch through a traffic splitter deployed on a third server and forwarding it to an orchestrator deployed on a second server; using the orchestrator, sequentially forwarding the service traffic to corresponding security devices for security detection according to a preset security service chain, wherein the security devices are deployed on a first server; the traffic splitter, the orchestrator, and the security devices are all virtual machines; using the orchestrator, the service traffic that passes the security detection is returned to the service switch via the traffic splitter. Through a full-dimensional redundancy design from the underlying platform to virtual network elements, the single point of failure problem is completely solved, the availability of the security resource pool is improved, and continuous security protection of service traffic is achieved.

The invention relates to the technical field of machine vision, in particular to a ton bag cargo robust visual detection system based on multi-cluster feature fusion and attitude correction driving. The system comprises a non-local mean value enhancement module, a multi-feature clustering fusion module, a geometric feature extraction module, a dominant attitude clustering module, an attitude correction transformation module and a cargo segmentation judgment module, and non-local mean value filtering is carried out on an original image of the ton bag cargo to obtain an enhanced image; according to the method, the non-local mean value enhancement module and the multi-feature clustering fusion module are carried, efficient enhancement and multi-dimensional feature fusion of the ton bag cargo image can be realized, the fusion segmentation image is generated through spatial alignment weighted stacking of the color clustering cluster and the texture clustering cluster, feature representation of the homogeneous region of the ton bag cargo is more comprehensive and accurate, and the method is more suitable for mass production. The integrity and effectiveness of geometric feature extraction are effectively improved, and the feature analysis capability of the system on the ton bag cargo image is remarkably improved.

The application discloses a kind of construction method and device of large-scale virtual network.Therein, the method includes: using application container engine Docker technology to be distributed to multiple hosts on Mininet, realize the simulation of underlying physical network;By running cloud computing management platform OpenStack nova component in the mode of Docker container, the simulation of virtual network is realized.The application fuses Mininet with Docker technology, realizes the distributed deployment of Mininet, enhances business scenario test capability.In addition, in the vHost virtual host management system simulated using Docker container, a set of virtualization management program components of Nova of Openstack is run, further improves virtualization capability, and more network segments, larger-scale virtual network can be created.

The invention provides a consistency control method and device for an unmanned aerial vehicle cluster system. Each unmanned aerial vehicle in the system collects an initial state value of at least one state quantity of a target object, decomposes the initial state value into a plurality of sub-state values, and maintains a total state variable, a total quantity variable and an auxiliary variable; the method comprises the steps that each unmanned aerial vehicle judges whether the unmanned aerial vehicle meets a message sending condition or not based on a preset state non-zero constraint and a topological memory constraint; when a message sending condition is met, generating a consistency updating message carrying a part of non-zero sub-state values and auxiliary variable values, sending the consistency updating message to a neighbor, and setting the part of local non-zero sub-state values and the auxiliary variable values of the unmanned aerial vehicle to be zero; and when the unmanned aerial vehicle receives the consistency updating message, updating a local variable value based on the consistency updating message, and performing consensus judgment based on the updated variable value. According to the technical scheme, the communication efficiency, the safety and the operation reliability of the consistency control process can be improved.