83 results about "Fog computing" patented technology

The invention relates to the field of Internet of Things data defense, in particular to an Internet of Things data security collaborative defense system based on edge computing. The technical problem to be solved by the invention is to provide an Internet of Things data security collaborative defense system capable of fusing lightweight cryptography, dynamic trust evaluation and cross-layer collaborative decision, and ensuring edge real-time response and global security situation awareness. The invention discloses an Internet of Things data security collaborative defense system based on edge computing. The system comprises a terminal equipment layer, an edge computing layer, a fog computing layer and a cloud collaborative layer which work cooperatively, the terminal device layer comprises a plurality of Internet of Things devices, and a lightweight security module is arranged in each device. According to the method, the effects of fusing lightweight cryptography, dynamic trust evaluation and cross-layer collaborative decision, and ensuring edge real-time response and global security situation awareness are achieved.

The invention provides a controllable SCADA (supervisory control and data acquisition) industrial production control platform based on an industrial internet architecture. The controllable SCADA industrial production control platform comprises a field layer, an edge layer, a communication layer, a cloud / fog computing layer and a data management layer, wherein the field layer is composed of a sensor array, an execution mechanism, a PLC controller and an RTU terminal; the sensor array comprises a temperature sensor, a pressure sensor, a flow sensor and a position sensor, and adopts 4-20mA current loop signal output; the execution mechanism comprises an electric valve, a servo motor and a variable frequency driver, and communicates with the PLC through a Modbus RTU protocol; the PLC controller is provided with a dual-core processor and is provided with 16 digital quantity input / output channels and 8 analog quantity input channels; the RTU terminal is internally provided with a LoRa wireless communication module; the edge layer comprises an edge computing node and a protocol conversion gateway; the edge computing node is provided with an FPGA coprocessor and supports an edge reasoning framework; and multi-protocol conversion is realized through the protocol conversion gateway.

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 GIS equipment partial discharge detection system based on deep learning, and the system comprises an edge layer which is used for collecting sensing data collected by each sensor node, extracting a sensing feature vector, and transmitting a compressed waveform fragment; the fog calculation layer is used for determining defect positions by using a time difference of arrival algorithm according to the sensing data uploaded by the edge layer, and obtaining defect types and confidence coefficients according to the compressed waveform fragments and the sensing feature vectors in combination with a double-flow deformable attention convolutional network model; and the cloud platform is used for updating parameters of the double-flow deformable attention convolutional network model by adopting a meta-learning method according to the defect position, the defect type and the confidence which are uploaded by the fog computing layer in combination with virtual training data generated by the digital twin simulation model, and sending the updated parameters to the fog computing layer. The GIS equipment partial discharge detection system provided by the invention is light in weight and low in cost.

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.

A system for scheduling vehicle tasks for vehicles is described. The system includes a plurality of vehicles having the advanced driver assistance systems and cellular vehicle-to-everything communication, and a plurality of road side unit (RSU) nodes arranged at predetermined locations to execute driver assist tasks. The system further includes a central base station configured to receive and dynamically maintain a record of task data rates between the vehicles and the RSUs and delay time to return an executed task, and schedule offloading of the tasks for the advanced driver assistance systems. The RSUs are fog nodes for computing the tasks in accordance with the schedule, and the tasks are control tasks for the advanced driver assistance systems.

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 discloses a factory high-definition map construction method based on edge-fog-cloud cooperation, and the method comprises the steps: S1, collecting the environment information of a region in a factory scene, and transmitting the environment information to a fog calculation server in the region; s2, the fog computing server performs feature extraction, feature conversion, rasterization processing and map post-processing on the received environment information to obtain a high-definition map of a current area in the factory scene, and transmits the high-definition map to a cloud server through an ROS message mechanism; s3, repeatedly executing the steps S1 and S2 until all local occupied grid maps of the whole factory scene are generated; s4, the cloud server splices all the local occupation grid maps into an integral occupation grid map; and extracting high-level information of semantic prediction and direction prediction from the overall occupied grid map to obtain a factory high-definition map. According to the method, the efficiency of building the factory high-definition map is improved, and the precision of the factory high-definition map is 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 invention provides an intelligent interaction method and system for a smart central control screen based on a large model, which is applied to the field of interactive data processing. By constructing a local computing architecture in the smart central control screen, the present invention significantly reduces the dependence on the cloud, improves the real-time performance of interaction, and ensures data security. It dynamically allocates computing tasks using the main computing node and data support node within the local area network to perform calculations locally, reducing the need to upload data to the cloud and improving the response speed. At the same time, it adopts various local computing modes such as edge computing, fog computing, and end-side AI computing to adaptively adjust the task processing method, further reducing the dependence on the cloud and optimizing the real-time interaction experience. And by classifying the interactive perception data into sensitive categories (such as public, restricted, sensitive data), combined with homomorphic encryption and secret sharing technologies, it ensures that the data remains secure during the calculation process among multiple nodes and prevents data leakage.

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 embodiment of the invention provides a data processing system, method and device. The data processing system comprises at least one terminal device, a fog service platform and a cloud service platform. Each terminal device is configured to establish a communication connection with the fog service platform and send information of each terminal device to the fog service platform based on the communication connection; the fog service platform is configured to receive the terminal equipment information corresponding to the terminal equipment, process the terminal equipment information based on the fog service business request, generate fog computing processing results, and send the terminal equipment information and the fog computing processing results to the cloud service platform; and the cloud service platform is configured to receive the information of each terminal device and each fog computing processing result sent by the fog service platform, and process the information of each terminal device and the information of each aggregated terminal device according to the cloud service business request. By adding the fog service platform in the data processing system, high-availability, high-performance and low-delay fog service calculation is provided, the cost is reduced, and the deployment period is shortened.

The present application discloses a safety monitoring and management method and system for an intelligent box with an integrated lock, which is used to enhance the safety protection ability of the intelligent box and eliminate potential safety hazards. The method of the present application includes: the edge computing unit obtains multi-modal sensor data of the box in real time; the edge computing unit performs real-time analysis on the multi-modal sensor data according to a preset abnormal threshold to detect and respond to single-point abnormal events of the box; the edge computing unit performs local primary feature extraction on the multi-modal sensor data and uploads the extracted primary feature vectors to the fog computing node; the fog computing node performs high-level feature extraction and spatio-temporal correlation analysis on the primary feature vectors uploaded by all boxes in the regional network to identify whether there are group safety risk events; if so, the fog computing node dynamically adjusts the safety policy level of all boxes in the regional network to the highest level.

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 discloses an efficient PLC process data high-speed acquisition method, and belongs to the field of industrial automation control. Aiming at the problems of insufficient stability, poor real-time performance, low processing efficiency and the like of the existing PLC data acquisition communication, an array queue and a time variable are created in the PLC, a data transmission structure is improved, a data acquisition subprogram is arranged in a high-speed operation cycle program block, and 10ms interval data acquisition is realized by matching with a 10ms timer. Edge equipment and fog computing are utilized to cooperate, a machine learning algorithm is introduced to evaluate data quality, and an upper computer adopts a self-adaptive communication strategy to encrypt key data. In the implementation process, a Siemens 1500 series PLC is selected and matched with TIA Portal software and CMS upper computer software, and through the steps of hardware and software preparation, PLC configuration, upper computer software CMS configuration and the like, 10 ms data high-speed collection is achieved, data processing efficiency is improved, real-time performance is enhanced, data integrity is guaranteed, and powerful support is provided for industrial automatic production.

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 present invention discloses a resource allocation method in a heterogeneous cloud and fog collaborative network based on an improved genetic algorithm, including: collecting the number of computing tasks to be completed by terminal devices; based on the constructed heterogeneous network computing migration problem model, obtaining the optimal computing resources allocated to the terminal devices for completing the computing tasks, the optimal uplink transmission power required for the terminal devices to migrate the computing tasks, and the task offloading strategy of the optimal terminal devices. The present invention collects three parameters, namely, the number of computing tasks to be completed by terminal devices, the amount of computing required to complete the computing tasks, and the maximum delay acceptable for completing the tasks, at the fog end, and realizes the joint optimization of the offloading decision, allocated computing resources, and transmission power of the terminal devices through the improved genetic algorithm, obtaining a resource allocation scheme based on cloud and fog collaborative computing in a heterogeneous network, solving the resource allocation problem of coexistence and collaboration of fog computing and cloud computing in a heterogeneous network, and reducing the amount of computation.

The invention discloses an efficient fine-grained access control method for fog computing, and relates to the technical field of access control. According to the method, in a cloud-fog cooperative computing architecture, a data owner entrusts ciphertext storage to a cloud storage platform, enjoys elastic storage service at low cost, and outsources partial decryption operation to a fog node, so that an authorized data acquirer can obtain data access service through lightweight operation. Therefore, a CP-ABE mechanism supporting double-stage decryption is innovatively designed, and a private key sk of an original CP-ABE is divided into two parts, namely a conversion key and a decryption key, based on a key splitting idea. The most resource-intensive strategy matching operation is executed by the fog node, and the data acquirer terminal only needs to carry out lightweight final decryption operation. The whole access control process comprises seven stages of system initialization, key generation, data outsourcing, request initiation, data retrieval, primary decryption and secondary decryption. According to the method, the problem of coarse-grained and high-cost data access privacy protection when cloud computing faces massive ciphertext sharing scenes and diversified retrieval requirements of users in the existing access control technology is effectively solved, the dependence on high computing and decryption overhead is avoided, and safe outsourcing part decryption and fine-grained access control are 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.

The invention relates to the technical field of cloud computing and fog computing security, and discloses a traceable and revocable attribute-based encryption method suitable for cloud computing, which comprises the following steps of: initializing system parameters, and generating a system public key, a system main private key, an attribute manager public key and an attribute manager main private key; according to a user identity, a system public key, an attribute manager public key, a system main private key and a data user attribute set, a user private key, an initial attribute group key, a conversion key and an attribute group key of a user are generated, encryption is carried out according to the public key, an access strategy and the like, and encryption is carried out with the help of fog equipment in the encryption process; the data user carries out decryption and requests outsourcing decryption help from the fog equipment; and tracking and positioning the data, further updating the user attribute group key, and updating the ciphertext according to the ciphertext information and the revoked attribute. Compared with the prior art, the method can achieve the tracking of the user key and the revocation of the attribute in the cloud storage.