40 results about "Fog computing" patented technology

The invention relates to the technical field of data transmission of the Internet of Things, in particular to a self-adaptive data compression and transmission method of a low-power-consumption wide-area Internet of Things. According to the method, a three-layer collaborative architecture comprising an equipment layer, a fog computing layer and a cloud computing layer is constructed, data preprocessing and feature analysis are performed on the equipment layer, and data types, entropy values and repeatability information are extracted; the fog calculation layer selects an optimal compression algorithm based on a multi-dimensional decision engine, and reduces redundancy through spatial-temporal correlation analysis and data aggregation; the cloud computing layer collects compression performance data, adopts reinforcement learning and federal learning to train a global optimization model, and dynamically issues strategy parameters; and the fog node adaptively adjusts a compression strategy in combination with the system state to realize the optimal balance between the compression ratio and the reconstruction precision. The method is suitable for field deployment environments with limited electric quantity and network, has the advantages of low power consumption, high efficiency, strong adaptability and the like, and can be widely applied to Internet of Things scenes such as remote monitoring, smart energy and the like.

The invention provides a multi-dimensional information fusion processing system for a photovoltaic power station. Processing of mass operation data of the photovoltaic power station is realized through a collaborative architecture of an edge computing layer, a fog computing layer and a cloud platform layer; by constructing a dynamic carbon intensity model, accurate real-time accounting of power station full life cycle carbon emission is achieved, indirect influences such as indirect power grid replacement emission are taken into consideration, and therefore a reliable basis is provided for carbon transaction income calculation; a digital twinborn body is constructed through a cloud platform layer, a randomness optimization model is operated, economic values and carbon footprints of different operation and maintenance strategies in long-term operation can be evaluated in a simulation environment, and the comprehensive influence of multiple uncertainty factors in the future is fully considered; and finally, through a multi-attribute value function model, comprehensive balance and fusion decision making are carried out in multiple dimensions of economy, risk, environmental protection and the like, a comprehensive optimal operation and maintenance strategy is output, and collaborative optimization of economic benefits, operation safety and carbon emission reduction benefits is realized.

The invention discloses an automatic alarm system of an air cushion type belt conveyor. The automatic alarm system comprises a sensing layer, a control layer and an execution layer. The sensing layer constructs a multi-dimensional sensing network through an air film thickness sensor, an air pressure sensor, a belt state sensor, an air chamber airflow audio analysis module, a motor power fluctuation detection module and a distributed optical fiber sensing module. The control layer adopts an edge-cloud computing collaborative architecture, an edge computing node is responsible for real-time data preprocessing, the fog computing layer runs a digital twin model to complete data fusion and state simulation, and a cloud computing center stores historical data and optimizes an algorithm and is provided with a three-level early warning and adaptive sensing mechanism. The execution layer executes parameter adjustment, sound-light alarm or emergency shutdown according to instructions of the control layer. Comprehensive monitoring, intelligent diagnosis and automatic regulation and control of the operation state of the air cushion type belt conveyor are achieved, and the operation reliability and the maintenance efficiency of equipment are remarkably improved.

The invention relates to the technical field of remote monitoring, and discloses a security and protection monitoring data security management method and system based on the Internet of Things, and the method comprises the steps: extracting a dynamic object key frame and a dimension reduction audio feature of block multi-mode security and protection data; dividing the block multi-mode security and protection data into sensitive biological characteristic data and conventional monitoring metadata; storing the sensitive biological characteristic data to a private chain to obtain private chain sensitive data, storing the conventional monitoring metadata to a public chain to obtain public chain conventional data, and establishing a three-level threat detection network of the private chain sensitive data and the public chain conventional data; identifying an abnormal behavior mode of the security and protection monitoring scene by using the edge layer so as to analyze global abnormal model parameters of the security and protection monitoring scene by using the fog computing layer; and analyzing the abnormal state of the security and protection monitoring scene by using the global enhancement layer, and generating a hierarchical response strategy of the security and protection monitoring scene according to the abnormal state. According to the invention, the recognition speed and accuracy of the key threats of the security and protection monitoring scene can be improved.

The present application relates to a kind of DAG structure block chain empowerment data sharing incentive method in fog computing, belong to the field of blockchain.First, the blockchain based on DAG structure is integrated into fog network, an improved Conflux consensus mechanism is proposed, which can improve network security and blockchain throughput.Second, to better utilize network resources, reduce the impact of resource consumption due to proof of work (PoW) on the deployment of blockchain in fog network, an incentive mechanism for device and fog node collaboration is proposed, a two-stage stanley colberg game model is established, and the objective is to maximize the benefit function of fog node and block device, to optimize the benefits of device and fog node.Collectively, the resource configuration between fog nodes and pricing problem is modeled as a mixed integer linear programming problem, and a resource allocation and pricing algorithm GIRP based on greedy search and iteration is proposed to reduce the resource sharing cost of fog nodes and improve the benefits.

The application discloses a computing power distribution method and device, and belongs to the technical field of Internet of Things. The computing power distribution method is applied to a first fog computing gateway, the first fog computing gateway is connected with N devices, the first fog computing gateway is connected with M fog computing nodes, the M fog computing nodes are used for storing idle time computing power of the N devices, M and N are positive integers, and the computing power distribution method comprises the following steps: in the case that a running task from a first device in the N devices is received, a required computing power value of the running task is acquired; in the case that the required computing power value is greater than a first computing power value of the first device, the running task is split into P subtasks; and the P subtasks are distributed to Q fog computing nodes for processing, wherein Q is less than or equal to M.

The invention provides a fog computing unloading method and system, and relates to the technical field of communication, and the method comprises the steps: based on a received computing task, an access point determines a computing task view according to the resource state information of a fog node; on the basis of service quality evaluation of the fog node in the view of the calculation task, it is determined that the target node is unloaded to execute the calculation task, and service quality evaluation comprises a reputation evaluation angle; after completion, generating a task completion record and broadcasting the task completion record to the whole network transaction pool; on the basis of the released proof-of-work calculation target, after the fog node collects transaction assembly candidate blocks from the whole-network transaction pool, proof-of-work calculation is carried out; the fog node which is fastest in completion broadcasts the candidate block containing the calculation result to the whole network; when all the nodes pass verification, adding the candidate blocks to a local block chain account book; and the access point updates an evaluation result of the fog node in the aspect of reputation evaluation based on the reputation evaluation data in the local block chain account book. The task allocation and execution efficiency and the allocation accuracy are improved.

The invention relates to the field of traffic control, in particular to a regional traffic situation perception system based on fog computing, which comprises a perception acquisition module, an edge analysis module, a regional cognition module and a situation service module. The system acquires data through the sensing acquisition module; the edge analysis module constructs a digital twinborn model, and outputs a structured scene risk signal by using a multi-modal dynamic attention generative adversarial network and an intention reasoning model; the regional cognition module performs parallel simulation in the digital twin sand table and preferentially generates a collaborative strategy; and the system finally realizes closed-loop optimization of the internal model through a complete scene information packet formed by feedback. According to the invention, by constructing a cloud-edge collaborative digital twinning and multi-agent decision framework, intelligent optimization transformation of traffic management from passive response and local control to active pre-judgment and regional collaboration is realized.

The application discloses a kind of based on attribute-based encryption and trust model's fog computing access control method, comprising: constructing fog node trust model: through direct trust, audit trust and peer entity trust three dimensions, the credit of fog node and the trust value of user terminal to fog node are calculated;Establish semantic optimization attribute-based encryption method: based on semantic reasoning attribute mapping, attribute-based encryption method is optimized, and access policy tree and user's character attribute mapping are converted into semantic attribute mapping;Access control in fog computing is carried out based on fog node trust model and semantic optimization attribute-based encryption method: data owner sets access control policy tree and privacy threshold, selects proxy fog node by weight load balancing algorithm based on trust, and outsourcing encryption and ciphertext upload are carried out by proxy fog node;Data accesser obtains the privacy threshold of target information, calls proxy fog node to carry out outsourcing decryption and obtains intermediate ciphertext, and the required plaintext is obtained by decrypting intermediate ciphertext.

The application discloses a kind of based on lightweight fog computing's Internet of Vehicles data sharing method, including system initialization, vehicle registration, multiple authority attribute organization generates part attribute key car to vehicle, information decryption and outsourcing request;Fog vehicle executes outsourcing task and carries out information part decryption and information complete decryption.The application adopts lightweight CP-ABE algorithm to the information in Internet of Vehicles is encrypted, effectively protects the privacy of vehicle and reduces the computing overhead in communication process.The application can realize the safe privacy protection communication information in Internet of Vehicles, carries out outsourcing calculation by fog car with computing resource, to greatly reduce the computing overhead of vehicle data acquisition, and adopts multiple authority organization access control scheme, prevents the single attribute organization possibly appears single point performance bottleneck, to cause the problem of low system efficiency.

The invention relates to the technical field of Internet of Vehicles and mobile edge computing, in particular to an Internet of Vehicles-oriented fog computing node dynamic scheduling method and system. The method comprises the following steps that: a vehicle models a task into a multi-element group containing priority and reports a motion state; the regional fog controller predicts a vehicle trajectory, and pre-distributes task data to a candidate fog node set to be accessed by a vehicle in the future; a model based on combined auction is adopted for resource allocation, vehicles bid according to task priorities, and a controller allocates tasks to an optimal fog node with maximization of the total benefit of a system as a target under the condition that node computing power and task delay constraints are met; and the vehicle is immediately triggered to execute after entering the bid-winning node range. Through a pre-distribution and auction distribution mechanism, the problem of unstable connection caused by high mobility of vehicles is effectively solved, low-delay processing of high-priority tasks is ensured, and the reliability of Internet of Vehicles services and the system efficiency are remarkably improved.

A method for implementing fine-grained data access sharing in 6G network based on fog computing and cloud computing environment includes: (1) a security parameter is given for initialization; (2) each user is registered as a user with the said attribute authority AA; (3) a corresponding user private key is generated given a set of attributes S of a user; (4) an access policy is given by the owner of the data and data encryption is performed; (5) the user performs the data decryption process according to the embedded policy for the set of owned attributes in different environments; (6) the update key generation process is performed; (7) after receiving the private key update key from the attribute authority AA, the user updates the private key component associated with the revoked attribute in order to complete the private key update process; (8) upon receiving the ciphertext update key from the attribute authority AA, the cloud server updates the ciphertext component associated with the revocation attribute to complete the ciphertext update process.

The application discloses a vehicle fog computing method based on deep reinforcement learning and contract matching, on the basis of vehicle fog computing, deep reinforcement learning is used to reasonably allocate and optimize vehicle resources, and a task offloading framework based on vehicle fog computing is constructed. The framework adopts the mode of signing a contract, in the case of vehicle mobility, task priority and vehicle service availability, the reasonable allocation of resources is maximized. Vehicles are roughly divided into two categories: task vehicles and service vehicles. First, the task vehicle generates a task and sends it to the roadside processing unit RSU, and the SAC algorithm is used to establish a preference list of service vehicles according to the priority of the task, the availability of the vehicle service and the shared computing capacity provided by the service vehicle; secondly, the RSU selects the corresponding service vehicle to sign a contract and make a reward, and finally, a task offloading strategy is executed, which maximizes the reasonable allocation of resources on the premise that the task can be completed.

The invention discloses a generator set full life cycle real-time monitoring system based on fog computing, and relates to the field of cycle monitoring, and the method comprises the steps: obtaining the monitoring network information of a monitoring network corresponding to a target generator set, carrying out the node screening based on the monitoring network information, and determining the potential node topology and the monitoring node topology; calculating the node health entropy of each monitoring node and each potential node, and obtaining a bidirectional entropy set; based on a preset optimization algorithm, performing a dynamic monitoring decision according to the bidirectional entropy set, the potential node topology and the monitoring node topology, and generating a dynamic monitoring scheme; and applying the dynamic monitoring scheme to a monitoring network of the target generator set, and executing real-time monitoring. According to the invention, the problems of resource waste, low reliability and poor maintenance effect of a traditional static monitoring system are solved.

The invention relates to the technical field of intelligent medical treatment and privacy computing, in particular to a human health data processing method and system based on federated learning and fog computing. The system adopts a three-layer architecture of a personal fog node, a community fog node and a cloud center; the personal fog node locally stores health data and trains a federated learning model, and the privacy during parameter uploading is protected by adding differential privacy noise; the community fog node aggregates personal node parameters to form a community model; the cloud center carries out global aggregation; the method comprises the steps that a model is trained cooperatively through hierarchical federal learning; multi-modal data fusion analysis is carried out on personal nodes, high-credibility early warning alarms are generated only under the evidence of multiple evidences, and the alarms do not contain original data; continuously optimizing the model by using medical care feedback; according to the method, efficient value mining under privacy protection of the health data is realized, data islands are broken, accurate and credible health risk early warning is provided, and the method is particularly suitable for distributed health monitoring scenes such as families and communities.

The invention provides an encryption deduplication method in a fog computing environment, belongs to the field of encryption deduplication, and particularly relates to a random key encryption deduplication method supporting data updating. The problem of single-point failure caused by auxiliary duplicate removal of a third-party independent server can be solved. According to the method, the random key or the convergence key is adopted to encrypt the file according to the number of the users uploading the same file, so that off-line / on-line violent attacks can be resisted, and the generation efficiency of the key is improved. And secondly, the method adapts to the current fog computing network structure, and the network flow load is relieved. Due to the fact that the random number is introduced into the secret key and the frequency of accessing the fog node by the user is limited, off-line and on-line violent attacks are effectively resisted. And secondly, potential side channel attacks can be resisted by adjusting the threshold. Finally, in combination with a fog computing network structure, the load of the central network is further relieved.

The present application relates to the technical field of virtual power plant control, and discloses a plant control method and system for a virtual power, and a transaction system. The method comprises: sending a network formation completion instruction to a fog computing layer, so that the fog computing layer sends a data reporting instruction to a corresponding networking device on the basis of the network formation completion instruction, and then provides feedback; acquiring target data fed back by the fog computing layer, the target data being calculated on the basis of reporting data submitted by the networking device; sending the target data to a virtual power plant management center, and receiving a scheduling instruction generated by the virtual power plant management center on the basis of the target data; and performing device control according to the scheduling instruction, thereby quickly processing, by means of the fog computing layer, an instruction sent by a cloud computing center, and therefore addressing the problems of high network latency and high bandwidth overhead in current mainstream cloud computing models based on distributed control frameworks.

The application is suitable for the computer technology field, and provides a user selection method and device considering privacy protection in hybrid crowd sensing, including: obtaining reference utility data, the reference utility data including fuzzy utility corresponding to users participating in historical sensing tasks, the fuzzy utility being utility after privacy processing based on a privacy budget of the user; adjusting a current allocation weight of each user according to the reference utility data to obtain an adjusted allocation weight, the allocation weight reflecting the ability of the user to execute a sensing task; determining a target user from each user based on the adjusted allocation weight; and allocating a current sensing task to the target user to enable the target user to execute the current sensing task. The application can effectively guarantee user privacy security.

The application provides a global asset dynamic perception and intelligent management method and system based on fog computing, relates to the technical field of asset management, and comprises the following steps: collecting asset states and space parameters through a distributed perception unit, performing feature fusion and space correlation calculation, generating a state atlas, and generating a management instruction according to resource demand ordering, so that cross-regional collaborative management of assets in physically isolated regions can be realized, asset monitoring precision is improved, resource allocation efficiency is optimized, and management cost is reduced.

The invention belongs to the technical field of virtual power plant control, and discloses a virtual power plant control method and system and a transaction system, and the method comprises the steps: transmitting a networking completion instruction to a fog computing layer, so that the fog computing layer transmits a data reporting instruction to corresponding networking equipment based on the networking completion instruction, and feeds back the data reporting instruction; obtaining target data fed back by the fog computing layer, wherein the target data is obtained by calculating reported data reported by the networking equipment; sending the target data to a virtual power plant management center, and receiving a scheduling instruction generated by the virtual power plant management center based on the target data; and equipment control is performed according to the scheduling instruction, and the instruction sent by the cloud computing center is quickly processed through the fog computing layer, so that the problems of large network delay and high bandwidth overhead in a current mainstream distributed control framework cloud computing mode are solved.

The application discloses a water resource fine management method and system integrated with water behavior identification, relates to the technical field of water resource management, and comprises the following steps: calling water use event records, mining space-time water use characteristics to construct a dynamic water image; reconstructing a fog computing topology according to resource request conditions, establishing a resource field to determine a resource scene; deploying a self-defined scheduler according to the dynamic water image, executing a game to determine a water use strategy; and standardizing the water use strategy reconstruction, executing scheduling and control management according to a water resource management system. The application solves the technical problem that the traditional water resource management mode does not fully utilize big data processing technology and is difficult to cope with complex water use scenes, achieves the technical effects of utilizing big data processing technology, accurately identifying various water use behaviors, realizing resource real-time dynamic scheduling, efficiently integrating and analyzing multi-region water use data, improving the water resource fine management level, and effectively coping with complex water use scenes.

The application provides a kind of multimodal community security sensing data convergence and early warning system, it is related to Internet of Things and intelligent security technology field.The system includes data acquisition layer, edge computing layer, fog computing convergence early warning layer and blockchain storage module.Data acquisition layer deploys multiple heterogeneous sensors;Edge computing layer carries out local preprocessing and feature extraction;Fog computing convergence early warning layer integrates multimodal features through adaptive weighted fusion algorithm, uses risk situation map to carry out dynamic analysis and generates graded warning;The blockchain storage module synchronously stores the reliable evidence of the early warning event.The application realizes the efficient fusion and intelligent analysis of multi-source security data through end-edge-fog-chain collaborative architecture, significantly improves the accuracy of community risk early warning, response timeliness and the reliability of post-event evidence.

This application provides an IoT intrusion detection method and apparatus based on an improved GAN model, relating to the field of network security technology. It is implemented in a fog computing environment. The specific implementation scheme is as follows: acquiring network traffic data to be detected from an edge device; using the network traffic data to be detected as input, determining a first anomaly score corresponding to the network traffic data based on a pre-trained intrusion detection model, wherein the intrusion detection model is trained based on an improved GAN model; and determining the network traffic data to be detected as intrusion data in response to the first anomaly score exceeding a preset anomaly score threshold. This application can detect IoT anomalies, thereby improving the security and reliability of IoT application services.

The application provides a data encryption transmission method, device and system, the method comprises the following steps: obtaining a random disturbance value sent by a fog computing component, and performing disturbance processing based on the random disturbance value to obtain disturbance data; performing noise adding processing on user data based on the disturbance data to obtain noise-added user data; sending the noise-added user data to the fog computing component, and performing primary operation on the noise-added user data based on a preset processing logic to obtain a primary operation result; and performing secondary operation on the primary operation result based on the preset processing logic to obtain target operation data. The data encryption transmission method, device and system provided by the application ensure the encryption transmission of data and effectively avoid the information leakage problem in the data transmission process.

The invention relates to the technical field of smart text travel, and discloses a man-machine-object ternary cooperative sensing and resource scheduling method for a smart text travel scene, and the method comprises the following steps: S1, an edge device deployed in the smart text travel scene; s2, the edge devices mutually broadcast the unified state vector through a local network, and construct and maintain a dynamic situation field containing adjacent device state information in each local; s3, converging state information reported by the edge equipment in the jurisdiction area of the fog computing node by the fog computing node; s4, the fog computing node obtains a comparison result according to the value of the situation field entropy and a preset threshold value; and S5, executing a corresponding resource scheduling operation according to the determined scheduling mode. By constructing an edge-fog collaborative architecture, the decision-making right dynamically drifts between an edge layer and a fog layer according to the situation stability, so that low-delay collaborative awareness, objective quantification of the situation and adaptive balance of resource scheduling between efficiency and robustness are realized.

The invention discloses a cloud-fog-edge-end cooperation fused intelligent management system for a lead bonding machine, and the system comprises an equipment layer which comprises a multi-source sensor array integrated with or carried by the lead bonding machine, a physical execution unit, and a microsecond closed-loop controller. The multi-source sensor array is used for collecting real-time multi-physical field data in a bonding process, the physical execution unit is used for executing a bonding operation, and the microsecond closed-loop controller is used for responding to a control instruction and driving the physical execution unit; the edge calculation layer is deployed on a lead bonding machine and performs microsecond-level communication with the equipment layer; the fog computing layer is deployed in a local network and is used for performing plant-level collaboration on data and models of the at least two edge computing layers; and the cloud computing layer is deployed in a remote data center and is used for globally aggregating and analyzing the data and the model of the at least one fog computing layer and providing cross-domain knowledge service and full-life-cycle management, so that a high-reliability and self-adaptive bonding environment is favorably provided for semiconductor packaging.

The application belongs to the technical field of Internet of Things, and discloses a mutual authentication and secure communication method and system between Internet of Things devices; in an initialization stage, a system administrator performs initialization, and generates and distributes a master key and registers a fog node; in a registration stage, registration of all Internet of Things devices is performed, and all Internet of Things devices are registered at a nearest fog node, and identity information after registration is attached to a block chain in the form of a transaction; in an authentication stage, a secure channel is established and information sharing is performed after identity authentication when two Internet of Things devices communicate. With the aid of block chain technology and fog computing technology, a distributed trust mechanism is adopted to replace a central trust mechanism, so that authentication between devices is simpler and safer; the block chain technology is fused with fog computing to solve a data delay problem in access control; authentication and communication between devices are lightweight, have anonymity and controllability, and can resist various attacks.

A kind of group fog monitoring method based on edge computing, the method comprises: (one), using data mining technology to train group fog early warning model;(two), train group fog monitoring model;(three), group fog early warning model and group fog monitoring model are evaluated, update and joint, form real-time group fog computing model;(four), the environmental data of monitored area are collected, the environmental data of monitored area are substituted into real-time group fog computing model, and current group fog monitoring result is calculated by real-time group fog computing model;A kind of group fog monitoring system based on edge computing, the system includes sensing end, edge device, data transmission module, server end;Wherein, the signal output end of sensing end is connected with the signal input end of edge device, the signal output end of edge device is connected with the signal input end of data transmission module, the signal output end of data transmission module is connected with the signal input end of server end.

The invention discloses an electrocardiogram monitoring and early warning system based on multi-modal perception and federated learning. The electrocardiogram monitoring and early warning system comprises a plurality of edge layers, a fog computing layer and a user interaction layer, the edge layer collects and preprocesses user data through a multi-modal hardware sensing module, and performs arrhythmia classification and real-time early warning by using a lightweight time sequence diagram neural network model; the fog computing layer aggregates classification and early warning information of a plurality of edge nodes, and generates collective health risk assessment and family health risk reminding of family members through a comprehensive risk assessment model; the user interaction layer integrates various early warning information, displays the information through an AR visual interface and triggers multi-modal feedback. The system realizes efficient and real-time electrocardiogram monitoring, accurate early warning and privacy protection, and is suitable for family health monitoring scenes.

The invention relates to the field of unmanned aerial vehicles, and discloses an unmanned aerial vehicle-ground station-cloud three-level linkage power transmission line inspection system. Comprising a sensing acquisition node, a fog computing hub, a cloud decision node, a dual-mode communication link, a three-level computing power scheduling module, a closed-loop acquisition control module, a multi-mode enhancement module, a layered transmission module and a twinborn pre-judgment scheduling module. The sensing acquisition node acquires multi-dimensional data of a power transmission line, outputs the multi-dimensional data to the fog computing hub after being subjected to lightweight processing, and feeds back own state data at the same time; the fog computing hub receives the multi-dimensional data of the sensing acquisition nodes, and the multi-dimensional data is subjected to multi-modal fusion and defect preliminary screening. According to the invention, cross-node task reasonable allocation is realized through the three-level computing power scheduling module, tasks such as simple defect preliminary screening and complex defect research and judgment are respectively adapted to computing power characteristics of a fog computing hub and a cloud decision node, and meanwhile, the collection frequency of the unmanned aerial vehicle is dynamically adjusted according to the health state of a line, so that computing power waste and invalid collection are avoided.