54 results about "Fog computing" patented technology

The invention discloses a dynamic privacy protection closed-loop control method and system for mobile edge crowd sensing. According to the method, information related to a communication channel and data content is obtained through risk perception, and a multi-dimensional comprehensive privacy risk score is calculated; then, dynamically determining a value of a differential privacy budget epsilon according to the risk score by utilizing a reinforcement learning algorithm in a strategy decision-making stage; then, performing noise addition disturbance on crowd sensing data according to the epsilon in a privacy protection execution stage so as to realize a preset privacy protection level; then, the privacy leakage risk and the data validity of the disturbed data are evaluated in the effect verification stage, and an evaluation result is obtained; and finally, privacy control strategy parameters are adjusted according to an evaluation result in a feedback optimization stage, and feedback is applied to a risk perception and decision process of a next cycle, so that closed-loop control is formed, and a privacy protection effect is continuously optimized. According to the method, the user data privacy security can be improved, and the data availability and the energy consumption efficiency are both considered.

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 discloses a water resource fine management method and system integrated with water consumption behavior recognition, and relates to the technical field of water resource management, and the method comprises the steps: calling a water consumption event record, mining time-space water consumption characteristics, and constructing a dynamic water consumption portrait; reconstructing a fog computing topology according to the resource request condition, and establishing a resource field to determine a resource scene; deploying a user-defined scheduler according to the dynamic water consumption portrait, and executing a game to determine a water consumption strategy; and performing standardized reconstruction on a water consumption strategy, and executing scheduling and control management according to a water resource management system. The technical problems that a traditional water resource management mode does not make full use of a big data processing technology and is difficult to deal with complex water use scenes are solved, and the purposes that the big data processing technology is applied, various water use behaviors are accurately recognized, real-time dynamic resource scheduling is achieved, multi-region water use data are efficiently integrated and analyzed, the fine water resource management level is improved, and the water resource management efficiency is improved are achieved. And the technical effect of effectively dealing with complex water use scenes is achieved.

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 invention provides a palm oil plantation multi-modal decision management method, system and device and a medium, and belongs to the technical field of Internet of Things in the planting industry, and the method comprises the steps: building a heterogeneous federated learning framework for a palm oil plantation; the edge layer constructs a lightweight model, a planting industry heterogeneous terminal device is used for collecting data for local training, and asynchronous federal aggregation is performed in combination with the fog computing layer until requirements are met; the edge layer is used for extracting local features from data collected by heterogeneous terminal equipment in the planting industry; the cloud computing layer aligns local features of the edge layer by using a cross-modal space-time encoder, constructs a causal graph in combination with a Bayesian network, determines intervention measures, quantifies the influence of the intervention measures by using anti-factual reasoning, generates and executes a decision scheme, and solves layered Nash equilibrium through a distributed Actor-Critic algorithm, so as to improve the performance of the cloud computing layer. And task allocation and path planning are carried out on the planting industry heterogeneous terminal equipment. According to the invention, the utilization rate of planting management data, decision reasonability and equipment collaboration are 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.

The present application relates to a low-power communication method for fog computing based on edge-cloud integration, which is applicable to fog layer nodes. When a user washes a car, the method includes the steps of: maintaining real-time communication with the cloud layer and initiating a temporary communication request to the car wash terminal; receiving a one-time first secret key returned after passing the verification of the first secret key library of the cloud layer and sending it to the car wash terminal to wake up the terminal; performing order interaction with the awakened car wash terminal, receiving a one-time second secret key generated by the car wash terminal based on the order result, storing it and sending it to the secondary secret key library of the cloud layer for storage. Thus, except for the first car wash of a new user, any subsequent car wash uses the previous car wash as the credit basis, excluding the occurrence of malicious access to the car wash terminal by individual users, and further strengthening the protection of the car wash terminal in the offline state. The present application also relates to a communication method applicable to the cloud layer and the car wash terminal.

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 relates to the technical field of task unloading, in particular to a fog computing reconfigurable task unloading method based on industrial Internet of Things, which comprises the following steps: calculating a node response interval and screening nodes within a time limit, extracting path starting time and scheduling time to construct a difference value, and rejecting paths with frequency point difference values exceeding the interval according to a candidate unloading task list. And calculating a difference value between a task byte amount and a node cache, and performing task unloading through fog end resource scheduling to obtain an execution result. According to the method, through fine scheduling and resource management, task unloading efficiency is improved, node response is calculated in real time, an optimized path is screened, waiting time is shortened, response speed is improved, a low-delay path is selected, task binding is optimized, communication delay is reduced, system reliability is enhanced, unstable frequency points are eliminated, and signal transmission is optimized; the task path corresponding relation is dynamically adjusted, node overload is prevented, system balance is maintained, fog end resource scheduling is integrated, execution tasks are effectively distributed, and the processing efficiency and the system performance are improved.

A system for facilitating fog computing and providing resiliency against attacks. The system comprises a data center device, a network switch, and encryption retransmission devices. The encryption retransmission devices comprise an encryption unit and a communication unit. The encryption unit encrypts an egressing native packet received from the network switch and adds a connectionless header forms an egressing connectionless datagram, and decrypts an encrypted ingressing native packet of an ingressing connectionless datagram. The communication unit adds a complex header to the egressing connectionless datagram for forming an egressing packet for delivery to an external encryption retransmission device, receives an ingressing packet comprising the encrypted ingressing native packet and a complex header from the external encryption retransmission device, removes the complex header and adds a connectionless header forming the ingressing connectionless datagram. The data center device performs data center operation on the ingressing native packet to generate the egressing native packet.

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 application relates to the technical field of high earth-rock dam earthquake damage assessment, in particular to a high earth-rock dam earthquake damage rapid assessment system, which comprises a cloud computing module, a fog computing module and a twin body synchronous rendering module; the cloud computing module is used for acquiring and transmitting data of other servers and pre-processing the acquired data; the fog computing module is connected with the cloud computing module, analyzes and calculates the pre-processed data of the cloud computing module, and writes the calculation results into a database; the twin body synchronous rendering module is connected with the fog computing module, maps and processes the calculation results in the database by using a digital twin technology, and obtains a virtual detection model. Through an advanced distributed architecture, dam earthquake damage assessment is carried out by using edge, fog and cloud computing, the self-determination and online digital twin of an extra-high earth-rock dam can be realized, the digital twin of each dam can provide comprehensive and real representation by combining a physical object, a virtual model and a mapping network through the digital twin technology, and thus an effective monitoring, analyzing and decision-making process can be realized.

This invention discloses a method for constructing a high-definition map of a factory area based on edge-fog-cloud collaboration, comprising: S1, collecting environmental information of a region in a factory scene and transmitting the environmental information to a fog computing server within that region; S2, the fog computing server performing feature extraction, feature transformation, rasterization, and map post-processing on the received environmental information to obtain a high-definition map of the current region in the factory scene, and transmitting it to a cloud server via a ROS message mechanism; S3, repeating steps S1 and S2 until all locally occupied raster maps of the entire factory scene are generated; S4, the cloud server stitching all locally occupied raster maps into a unified occupied raster map; and extracting high-level information such as semantic prediction and direction prediction from the unified occupied raster map to obtain a high-definition map of the factory area. This invention improves the efficiency and accuracy of constructing high-definition factory maps.

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.