11 results about "Internet of things cloud computing" patented technology

The invention belongs to the technical field of network security defense, and particularly discloses a multi-stage network security threat defense method and system based on adaptive feedback. A defense strategy is dynamically generated and optimized by performing multi-dimensional analysis on feature differences of historical security events and events to be processed and combining an adaptive feedback mechanism and an attack detection model for iterative training; when deviation or novel threats are detected, rollback and incremental training can be automatically triggered, and multi-round iterative closed-loop defense is formed. The method effectively deals with advanced persistent threats, zero-day vulnerabilities and multi-stage attacks, significantly improves threat detection accuracy and strategy response efficiency, reduces redundant feature interference, enhances expandability and adaptivity of the system, can be widely applied to environments such as the Internet of Things, cloud computing and enterprise intranets, and has wide application prospects. And the intelligence and flexibility of network protection are obviously improved.

This invention relates to the field of park management technology, specifically a smart park management platform based on park informatization. This platform integrates advanced technologies such as the Internet of Things, cloud computing, and big data to achieve informatization and intelligentization of various management tasks within the park. Specifically, it includes functions for park infrastructure management, energy management, security management, property management, and investment promotion management. By monitoring and analyzing energy usage within the park in real time, and combining big data and artificial intelligence technologies, it can propose reasonable energy allocation suggestions to achieve energy conservation and emission reduction goals, reduce park operating costs, and respond more quickly and accurately to the needs of users within the park through various management and service applications provided by the platform, such as park infrastructure management and property management, thereby improving service quality.

The invention provides a server component selection scheme obtaining method and device, electronic equipment and a readable storage medium, and relates to the technical field of artificial intelligence such as big data, cloud services, the Internet of Things and cloud computing. The method for acquiring the server component selection scheme comprises the following steps: determining first component demand information according to initial component demand information and a target inventory type of a server to be delivered; obtaining at least one piece of candidate component inventory information corresponding to the at least one candidate inventory type; and matching the first component demand information with the at least one piece of candidate component inventory information according to the priority of the at least one candidate inventory type, and obtaining a target component selection scheme of the server to be delivered according to a matching result. According to the invention, the obtaining efficiency and obtaining accuracy of the target component selection scheme can be improved.

The invention discloses a big data connection convergence and governance platform based on an intelligent adaptation engine. The platform comprises the intelligent adaptation engine, a dynamic quality evaluation module and a real-time processing engine, the intelligent adaptation engine dynamically adapts heterogeneous data source access through protocol behavior analysis; the dynamic quality evaluation module generates a quality score based on the three-dimensional features of the structure, the content and the timeliness; and the real-time processing engine executes hierarchical flow control and parallel cleaning according to the quality score. Through intelligent, dynamic and closed-loop design, the limitation of a traditional architecture is broken through, and an efficient and reliable data governance solution is provided for scenes such as industrial Internet of Things and cloud computing.

The invention provides a network fault positioning method and device, electronic equipment and a readable storage medium, and relates to the technical field of artificial intelligence such as big data, cloud services, the Internet of Things and cloud computing. The network fault positioning method comprises the following steps: after a fault positioning request is received, acquiring a plurality of positioning request packets according to target information; determining a target client and a target server according to the network fault information, and sending the plurality of positioning request packets to the target server by the target client through a cloud network; acquiring positioning request packet information of first target network equipment in the cloud network according to the target information; and in response to determining that the first receiving quantity is different from the first sending quantity, acquiring a positioning result that the first target network equipment is faulty network equipment. According to the invention, the method can achieve the precise positioning of a fault network device in a cloud network, and improves the positioning efficiency and accuracy of a network fault.

The invention provides a high-frequency network detection method and device, electronic equipment and a readable storage medium, and relates to the technical field of artificial intelligence such as big data, cloud services, the Internet of Things and cloud computing. The high-frequency network detection method applied to a source detection node comprises the following steps: taking a result of multiplying a target link number between the source detection node and a target detection node by a preset numerical value as a target numerical value; creating an original socket, and constructing a plurality of detection request packets according to the target numerical value; sending the plurality of detection request packets to a target detection node through the original sockets; receiving a detection return packet sent by the target detection node through the original socket; and obtaining the network connection state according to the plurality of detection request packets and the detection return packets. The high-frequency network detection method applied to a target detection node comprises the following steps: receiving a detection request packet sent by a source detection node; and obtaining a detection return packet according to the detection request packet, and sending the detection return packet to the source detection node.

The application discloses a kind of AI remote instruction transmission systems based on cloud native, belong to Internet of Things, cloud computing and artificial intelligence technical field.The application includes: cloud native platform, deployment in public network, provide WebSocket service and public network access address;AI module, with the cloud native platform communication, for receiving user natural language instruction, after analysis, by HTTP POST request sent to cloud native platform;Intranet equipment, located in user intranet, actively with the cloud native platform establishes WebSocket long connection, receives and analyzes instruction from AI module.The application does not need public network IP, does not need port mapping, does not need to set router, realizes the remote instruction transmission of public network to intranet equipment;Support AI automatic execution, without manual intervention;With covert security, low cost, personal can be independently completed and the like advantages.The application can be widely applied in intelligent furniture, Internet of Things, remote control and the like field, solves the technical problem that public network cannot directly control intranet equipment.

The invention provides a scheduling method and device for descriptors in a host machine, electronic equipment and a readable storage medium, and relates to the technical field of artificial intelligence such as big data, cloud services, the Internet of Things and cloud computing. The scheduling method comprises the following steps: acquiring target equipment information and target virtual machine information according to a descriptor to be scheduled in a host machine; obtaining a first descriptor storage number according to the target equipment information, and obtaining a second descriptor storage number according to the target virtual machine information; in response to determining that the storage number of the first descriptors is smaller than the first descriptor waterline and the storage number of the second descriptors is smaller than the second descriptor waterline, storing the to-be-scheduled descriptors pulled from the host machine, and performing first updating on the storage number of the descriptors; and in response to determining that the current scheduling sequence is the target scheduling sequence, scheduling the stored to-be-scheduled descriptors, and performing second update on the number of the descriptors stored. The resource storage cost can be reduced, and the descriptor scheduling efficiency can be improved.

The invention relates to the field of Internet of Things security, in particular to a multi-stage network security threat defense method and system based on AI adaptive optimization. Scene similarity calculation is carried out on multi-dimensional feature differences of historical security events and to-be-processed security events, an Adaboost algorithm is combined to carry out iterative training on a decision tree, key dimensions are screened out, a similar scene set is applied to match a strategy library, and targeted defense measures are automatically generated. The method has the beneficial effects that the multi-stage attack detection accuracy and strategy response efficiency can be remarkably improved, complex threats such as zero-day vulnerabilities and APT (Advanced Persistent Threats) can be effectively handled, redundant dimension interference is reduced, dynamic self-adaptive security decision is realized, and the method has both flexibility and expandability. Meanwhile, the method can be applied to environments such as the Internet of Things, cloud computing and an enterprise intranet, and the intelligent level of network protection is further improved.

This invention provides a remote intelligent integrated control method for smart mines, including steps such as data acquisition and integration, and 3D digital modeling. This method utilizes advanced technologies such as 3D GIS, IoT, cloud computing, and big data to achieve a comprehensive control approach for the digital transformation and upgrading of mines towards intelligence and smart technology. By integrating traditional 2D mine maps and data from various business systems, this method performs 3D digital modeling of relevant mine scenes and equipment, establishes a mine big data platform, and displays various IoT information in a 3D visualization model. Through AI models, the data is analyzed to perceive and predict various risks and hidden dangers in mine operations, thereby improving the mine's safe mining operation capabilities.

The invention discloses a cross-domain identity authentication method based on secret sharing and an SM2 algorithm, belongs to the technical field of information security, and aims at solving the problems that in an existing authentication scheme, secret keys are managed in a centralized mode, an authentication path depends on a center node, and expansibility is poor. The method comprises the following steps that: each domain control center generates and distributes secret sub-shares based on a Shamir threshold secret sharing protocol, collaboratively recovers a shared seed, and dynamically generates a group key pair according to the shared seed; the terminal equipment generates an SM2 public and private key pair and applies for a digital certificate containing identity information from the CA; the terminal equipment completes certificate legality verification through certificate issuing and signature verification; exchanging certificates and random numbers among the devices, and performing signature and signature verification by using an SM2 algorithm to complete bidirectional identity authentication; and after authentication is completed, a secure communication channel is established. The method has the advantages of decentralization, high security, high efficiency, good expansibility and the like, and is suitable for identity authentication requirements in cross-domain collaborative environments such as the Internet of Things, cloud computing and the like.