757 results about "Cloud management" patented technology

The invention provides a distributed collaboration-based computing network intelligent agent system and a resource dynamic scheduling method thereof, and belongs to the technical field of computing network integration. The system comprises an edge agent used for sensing local computing power, network bandwidth and task load in real time, predicting task demand fluctuation by using a lightweight neural network, adjusting resource allocation weight in real time in combination with network topology change, and executing a preliminary task scheduling decision; the regional collaborative agent is used for aggregating multiple edge node states based on federated learning, generating a cross-node resource scheduling strategy, verifying the credibility of a computing power transaction smart contract and determining a cross-domain resource allocation scheme; and the cloud management agent is used for constructing a global resource portrait model according to the information provided by the edge agent and the regional collaborative agent, performing long-term strategy optimization, issuing global strategy information, and constructing and updating a computing power transaction smart contract based on a preset computing power transaction smart contract template. According to the invention, multi-level refined scheduling of computing network resources is realized.

The embodiment of the invention provides a cloud side-end cooperative control method and system based on remote communication. The method comprises the following steps: generating a task scheduling input data set according to real-time operation state data generated by terminal equipment in a remote communication process, current computing resource information of an edge computing node and global optimization target parameters issued by a cloud management system; calling a preset task scheduling decision model to carry out structured splitting on the cloud side-end cooperative control task to obtain original task structure information; task structure labeling information is generated for sub-task labeling priorities in combination with multi-aspect information, and cyclic matching deduction processing is performed on the task structure labeling information to obtain a hierarchical resource allocation result; and distributing the sub-tasks to corresponding cloud edge end nodes (including a first node corresponding to the terminal device, a second node corresponding to the edge computing node and a third node corresponding to the cloud management system) for execution, and configuring a task execution process adjustment mechanism based on real-time resource change to adapt to resource fluctuation.

The invention discloses a jewelry flaw detection and grading system based on machine vision and a manipulator. The system comprises a multi-mode imaging module; the image processing module is used for carrying out preprocessing operation on the original image; the edge processing module comprises a GPU acceleration processor and is used for deploying a flaw detection model to detect flaws; training data are automatically generated through a semi-supervised learning algorithm; the mechanical execution module comprises a six-axis mechanical arm, a path planning algorithm and a collision detection model for a bounding box and a directed bounding box of the six-axis mechanical arm; the cloud management module is used for storing detection data based on a block chain technology and supporting third-party verification; and dynamically updating the expert knowledge base. According to the jewelry sorting method and system, the types and grades of the jewelry are divided through the DINOv2 defect detection model, meanwhile, the PnP algorithm is adopted to be fused with the depth information, the jewelry is sorted according to grade division, meanwhile, consumers can rapidly and accurately obtain relevant information (such as materials, authenticity and value evaluation) of the jewelry, and the shopping experience is greatly improved.

The invention relates to the technical field of electrical equipment insulation performance detection, in particular to an insulating oil dielectric loss test system based on temperature compensation, which comprises a temperature acquisition module for acquiring insulating oil temperature data and environment temperature data; the dielectric loss measurement module measures a dielectric loss value and a dielectric loss angle through a self-adaptive anti-interference algorithm, and synchronously records voltage and frequency; the intelligent compensation module matches temperature characteristic parameters for different temperature intervals, and compensates a dielectric loss detection value through a segmented temperature compensation model; the state analysis module judges the aging degree and state of the insulating oil according to the compensated dielectric loss value and acid value, generates a multi-stage early warning signal and a diagnosis report by combining the breakdown voltage and the moisture content, and dynamically adjusts the test frequency according to the oil sample type and the operating age limit; and the cloud management platform stores historical data and compensation parameters, establishes a performance degradation model, analyzes a dielectric loss trend and generates maintenance suggestions. Therefore, the problems of insufficient test precision, poor environmental adaptability and the like in the prior art are solved.

The invention discloses an intelligent building multi-dimensional monitoring system supporting edge computing and cross-terminal collaborative management, and relates to the technical field of intelligent buildings, the intelligent building multi-dimensional monitoring system comprises a multi-area edge monitoring node and an asynchronous communication cloud management platform, the edge node is provided with a lightweight dynamic collaborative engine, a node database and equipment management and control module, and a management and control terminal device; data are collected and standardized, simple / complex events are identified, risk levels are divided, and autonomous networking is carried out to form a temporary collaborative cluster when the risk is high; the data interaction module adopts UDP lightweight protocol low-delay transmission and local retransmission to guarantee data integrity, the cloud platform stores event processing abstracts, analyzes historical data optimization strategies and rules and silently issues in idle periods, the visual module assists monitoring and decision making, the system realizes distributed sensing and collaborative management and control, cloud dependence is reduced, and the real-time performance of the system is improved. The event response efficiency and the resource utilization rate are improved.

The invention discloses a cloud management and intelligent distribution method for classroom teaching resources, and relates to the technical field of cloud computing software. The classroom teaching resource cloud management and intelligent distribution method comprises the steps that S1, multi-source teaching resource data and auxiliary distribution data are acquired and preprocessed, and a knowledge content database is constructed; s2, performing semantic fusion analysis through text semantic association, structural consistency and professional term coverage data; s3, carrying out dependency evaluation by fusing pre-association, knowledge co-occurrence, sequence association and semantic fusion data; s4, performing resource adaptability distribution analysis through the maximum knowledge dependence value in combination with the learning state data and the historical feedback data; and S5, evaluating the teaching resource quality by collecting classroom feedback, teacher evaluation and resource use data, and implementing content optimization. The problems of semantic distortion, path breakage, distribution mismatching and resource updating inconsistency in the teaching resource data fusion and intelligent resource distribution process are solved.

The invention provides a resource recommendation method and related equipment. The method is applied to a cloud management platform. The method comprises the following steps: receiving first indication information of a tenant; determining at least one first resource recommendation scheme according to available resources in the at least one computing unit cluster and model information of a first AI model, the cost performance of the at least one first resource recommendation scheme is superior to that of candidate resource recommendation schemes except the at least one first resource recommendation scheme in a first candidate resource recommendation scheme set; and providing the at least one first resource recommendation scheme for the tenant. According to the method, at least one first resource recommendation scheme with relatively high cost performance can be provided for the tenant according to the model information of the AI model to be operated by the tenant and the remaining available resources in the computing unit cluster, so that the tenant can purchase proper resources, and the resource utilization rate in the computing unit cluster is improved.

The invention discloses a distributed RID receiving system based on edge multi-source fusion, which relates to the technical field of low-altitude supervision, and is characterized in that a distributed RID receiving node cluster adopts a fixed node and maneuvering node collaborative grid deployment scheme to capture and preliminarily analyze low-altitude aircraft RID signals in real time, and node data is output after the low-altitude aircraft RID signals are processed by a primary fusion algorithm; the edge multi-source fusion processing unit is deployed in each management and control partition, converges node data in the area, completes calibration, de-duplication and noise reduction through a secondary fusion algorithm, and outputs structured RID data; the cloud management and control platform realizes global data overall planning, situation visualization, intelligent decision making and instruction issuing through a three-level fusion algorithm, and is linked with the urban low-altitude scheduling system to form closed-loop management and control; the clock synchronization module provides nanosecond time reference for the whole system. According to the method, the problems of incomplete coverage and insufficient data processing precision of a traditional RID receiving scheme are solved, and global sensing, accurate data processing and closed-loop management and control of the low-altitude aircraft are realized.

The invention discloses a cloud-edge collaborative intelligent control edge computing terminal, and particularly relates to the technical field of edge computing, the terminal comprises an acquisition management module, a topology analysis module, a rule management module and a disconnection recovery module; the acquisition management module establishes a unified time base, performs event packaging on acquired data and control actions, and implements local sequential caching and uploading scheduling in a disconnection scene; the topology analysis module extracts consistency features and identifies topology change doubtful points; the rule management module realizes topology version configuration, versioning management of a linkage rule set and hardware resource mapping verification; and the disconnection recovery module completes disconnection detection, cache playback and progressive control recovery in the link interruption and recovery process. Therefore, under the condition that the network is unstable, the control state is kept consistent with the event time axis, synchronous reconciliation with the cloud management platform is carried out after the link is recovered, and the method is suitable for collection, in-situ linkage control and cooperative operation and maintenance of distributed sites such as water service pipe networks and agricultural trunk canals.

The invention provides an SDN (Software Defined Network) dynamic control system based on a cloud native architecture, and belongs to the technical field of SDN and cloud native. The system adopts a three-layer decoupling architecture of a service layer, a control layer and a data layer, wherein the service layer consists of a core network, a cloud platform and edge nodes and is used for issuing service requirements and executing a joint resource scheduling strategy; the control layer comprises four core modules, namely a micro-servitization SDN control module, a cross-domain collaborative engine module, an end-to-end service quality guarantee module and an edge collaborative optimization module, and is responsible for unified analysis, decision and scheduling to realize dynamic updating of a network strategy and multi-cluster collaborative optimization; the data layer is composed of an SDN switch and a router, and is used for executing a flow table rule and collecting network situation data. Through deep fusion of the SDN and the cloud native technology, the problems of upgrade interruption, low cross-domain collaboration efficiency and edge resource limitation of a traditional network architecture are solved, and the method can be widely applied to hybrid cloud management, industrial Internet of Things and intelligent city network scenes.

The invention discloses a non-sensitive management control system and method for a shared office. The system comprises a mobile APP, a cloud management platform, an intelligent access control terminal, environment sensing equipment and an electrical control unit, through the management system integrating the mobile APP, the cloud management platform, the intelligent access control terminal, the environment sensing equipment and the electrical control unit, non-sensitive management of the shared office can be realized. According to the system, a tedious and low-efficiency application process is simplified, so that the employees can quickly complete office reservation, and the consumption of office time is remarkably reduced. Meanwhile, the system can dynamically adjust the electrical control unit according to real-time data provided by the environment sensing device, it is ensured that device operation is automatically closed or reduced in the unmanned period, energy waste is effectively avoided, and the operation cost is reduced. In addition, the resource utilization efficiency is improved through the fine management mode, administrative expenses caused by idle or overuse of resources are reduced, and therefore the operation efficiency of an enterprise is overall improved.

The invention relates to the technical field of network intercommunication, and provides a method and equipment for realizing a bare metal gateway based on an OpenFlow flow table, and the method comprises the steps: enabling a bare metal server to be accessed to a VPC network, and creating and binding a network port to a specified bare metal gateway node in a specified VPC subnet through a cloud management platform; a logic flow table is generated through the OVN controller according to the network ports and the binding relation of the network ports, and the logic flow table is issued to the bare metal net nodes bound to the network ports; the bare metal net node translates the received logic flow table into an OpenFlow flow table and issues the OpenFlow flow table to a local OVS virtual switch; and the OVS virtual switch performs two-layer forwarding processing on the message transmitted between the bare metal server and the virtual machine in the VPC according to the received OpenFlow flow table. The bare metal gateway is realized in a pure software mode, the dependence on special hardware is avoided, and the cost is remarkably reduced. The efficient conversion between the VXLAN and the VLAN is realized based on the OpenFlow flow table, the flexibility, the expandability and the operation and maintenance automation degree are improved, and the high-performance two-layer intercommunication is ensured.

The invention relates to the technical field of industrial robot data acquisition and transmission, in particular to a welding robot behavior recording system integrating edge acquisition and cloud management, which comprises the following modules: an edge end data acquisition module for acquiring the state of a welding robot and sensor data in real time, and performing time alignment and validity processing, generating edge end data; the data preprocessing module is used for optimizing the edge end data and unifying the data format to form preprocessed data; and the local storage management module is used for carrying out hierarchical storage and index management on the preprocessed data according to the data access frequency and importance. According to the invention, through a breakpoint continuous transmission mechanism of cooperation of the edge end and the cloud end, fragmentation and recording of the transmission progress in the data transmission process are realized, continuous transmission is only performed on the uncompleted fragmentation after the network is interrupted, meanwhile, sorting and integrity verification are performed on the received fragmentation at the cloud end, and reliable uploading of large-capacity data in a complex network environment is realized.

Disclosed in the embodiments of the present application is a cloud service-based design code generation method, the method being applied to a cloud management platform. The method can not only acquire from a configuration interface an explicit design intention input by a user, but also query a knowledge graph for target design knowledge associated with the current explicit design intention of the user so as to capture an implicit design intention of a target design object. Thus, the explicit design intention and the target design knowledge from the knowledge graph can be combined to guide a large language model in efficiently and accurately generating target design code that meets a design requirement of the user, thereby intelligently generating the target design code so as to accurately and efficiently implement a design task on the basis of the target design code to obtain the target design object.

The invention provides a cloud resource pricing method based on cost sharing, a medium and equipment, and belongs to the technical field of cloud computing. Comprising the steps of inputting all physical equipment information of a data center; calculating and generating a cloud resource total amount table according to the input physical equipment information; inputting each operation cost of a specified year; configuring a resource allocation proportion, and determining a bottleneck resource of the current pricing cycle in combination with a cloud resource gross table; calculating the total cost of the cloud product according to each operation cost of the specified year, and further calculating the unit price of the bottleneck resource charging item; and according to the unit price of the bottleneck resource charging item, automatically generating or updating a price strategy in the cloud management platform for automatically charging the cloud resource use of the user. According to the invention, the cost of a single instance can be derived more accurately, and a foundation is laid for a cost-related module of operation analysis.

A method for configuring a network address translation NAT gateway based on a public cloud service including: a cloud management platform obtains NAT gateway creation information that is input by a tenant; The cloud management platform creates the NAT gateway in the first VPC based on the NAT gateway creation information; The cloud management platform obtains configuration information that is input by the tenant and applied to the NAT gateway; The cloud management platform sets, based on the identifier of the second VPC, the NAT gateway to be connected to the second VPC, and sends the first NAT rule to the NAT gateway, where the first NAT rule is used to indicate the NAT gateway to: bind a first network segment in the first VPC to a first elastic IP address EIP; and bind the first network segment in the first VPC to a first transit private IP address.

The invention discloses a wireless authentication and privacy infrastructure (WAPI) intelligent air switch, an end-network-cloud security measurement and control method and system thereof and a medium, and relates to the field of power internet of things security. A closed-loop system consisting of terminal equipment, a WAPI wireless access network, an authentication server and a cloud management platform is constructed; the authentication server verifies a negotiation session key and establishes an encrypted communication link, the terminal collects electric power parameters, encrypts and uploads the electric power parameters to the cloud, the cloud analyzes the electric power parameters and then issues a control signal, the terminal verifies and analyzes the electric power parameters through a trusted execution environment and drives an execution mechanism to operate, meanwhile, a power consumption mode is dynamically switched, and the cloud stores logs in a hash chain mode. According to the invention, through the technologies of hardware encryption, isolation execution and the like, the communication security and the equipment reliability are improved, and the requirements of high security, low power consumption and traceability of an electric power scene are met.

The invention relates to a management method and system of an intelligent battery replacement cabinet, and solves the problem that the safety efficiency and large-scale development of battery replacement service are restricted due to the fact that the intelligent battery replacement cabinet is difficult to adapt to the complex scene and diversified requirements of outdoor battery replacement of an electric vehicle. Matching authentication is completed through the electric signal detection assembly, and a connection health degree score is generated in combination with the physical contact state of the battery connector and the electric signal conduction quality; when the adaptation is passed and the score is not lower than a preset abnormal threshold value, triggering face authentication, and comparing an authorized face database to obtain an identity authentication result; and generating a battery replacement authorization, auxiliary authentication or connection exception instruction based on collaborative decision-making of the two, and recording and synchronizing whole-process data to the cloud management platform after executing corresponding operation. The method has the following effects that the safety of the battery replacement connection core is guaranteed, and the safety and the energy complementing efficiency are cooperatively balanced through the battery and identity authentication.

The invention discloses a multi-fusion intelligent Internet of Things cloud management system, which relates to the technical field of Internet of Things, is constructed based on a digital life entity concept, and comprises an autonomous cell equipment layer, a digital body fluid network layer, a cloud evolution control layer and an application interaction layer. According to the invention, equipment self-discovery and flexible access are realized, manual configuration is not needed, and equipment compatibility and deployment efficiency are improved; routing transmission adapts to multi-dimensional factors, signal transmission delay is reduced, and transmission reliability and resource utilization rate are improved; through global evolutionary optimization and a closed-loop operation mechanism, the system autonomously adapts to a dynamic scene, and the operation performance is continuously improved; a global conflict arbitration mechanism guarantees multi-device cooperation consistency, and operation disorder caused by instruction conflicts is avoided; user target preference dynamic adaptation is supported, personalized optimization is achieved, and the application scene of the system is widened; the distributed architecture and the multi-fusion technology improve the robustness of the system, and avoid the influence of a single-point fault on the overall operation.

The invention relates to the technical field of satellite-borne edge computing and simulation, and provides a satellite-borne edge computing task unloading semi-physical simulation system. Comprising a physical satellite-borne edge computing device, a software virtual satellite-borne edge computing device, a ground user device, a space environment simulator, a container and cloud management base, a resource data distribution module and a task unloading scheduling module. The physical satellite-borne edge computing device and the software virtual satellite-borne edge computing device jointly construct a cloud side end cooperative computing cluster, the ground user device is used for initiating a computing task request, the space environment simulator is used for simulating spatial network characteristics, and the container and the cloud management base are used for achieving all-node unified management and control. The resource data distribution module is used for issuing and subscribing node resources and environment information, and the task unloading scheduling module is used for executing a task unloading decision. The problems that in the prior art, on-orbit verification cost is high, a pure software simulation environment is distorted, algorithm credibility is insufficient, and a satellite-borne data set is scarce are solved.

The invention discloses a server management system, a server management method and electronic equipment, and relates to the technical field of computers.In the method, on one hand, edge nodes are arranged in a plurality of different areas respectively, and each edge node is connected with a server in the area where the edge node is located through a first communication line; and connecting a cloud management platform through a second communication line. The edge node can perform autonomous management on the server in the area based on the management information issued by the cloud management platform. The server and the edge node are located in the same area, and the communication delay is short, so that when the server fails, the edge node can timely issue an instruction to perform fault processing, and the fault recovery duration of the server can be shortened. And on the other hand, the theoretical communication rate of the first communication line is higher than the theoretical communication rate of the second communication line, so that the data transmission rate between the server and the edge node can be improved, and the fault recovery duration of the server is further shortened.

The invention belongs to the technical field of relay protection practical training and evaluation of a power system, and particularly relates to a relay protection operation coach and evaluation system and method based on AR glasses, and the system comprises an AR intelligent glasses terminal which is worn by an operator and is used for collecting the depth visual flow data of a field environment in real time; an edge computing server which is in communication connection with the AR intelligent glasses terminal and is used for obtaining the visual flow information collected by the AR intelligent glasses terminal, constructing a state sensing model based on a power equipment topological relation and realizing direct semantic level identification of screen cabinet elements; meanwhile, generating a three-dimensional visual operation indicator, a dynamic safety early warning signal and real-time evaluation feedback information, and superposing the three-dimensional visual operation indicator, the dynamic safety early warning signal and the real-time evaluation feedback information in the visual field of the AR intelligent glasses terminal; the cloud management platform is in communication connection with the edge computing server and is used for performing user management, storage and updating of an operation instruction knowledge graph, storage and analysis of historical evaluation data and unified allocation of system resources; the defects of a traditional training and evaluation system are overcome.

The invention discloses a shared charging pile authorization payment system and an implementation method, and belongs to the technical field of shared charging piles, and the system comprises a plurality of user terminals, a cloud management platform, a charging pile control terminal and a plurality of shared charging piles. The user side receives an operation request of a user; the cloud management platform generates a cloud instruction based on the operation request; the charging pile control end controls the shared charging pile according to the cloud instruction; the shared charging pile comprises a temperature detection module, an electric energy metering module and an MCU. The temperature detection module detects the temperature of a data processing chip of the shared charging pile or the temperature of the internal environment of the shared charging pile; the electric energy metering module collects state parameters of the shared charging pile, and a metering chip in the electric energy metering module sends the state parameters to the MCU. Each charging process is metered through the metering chip, the utilization rate of the charging pile is improved, the installation number and installation cost of private charging piles and the number of erected wires are reduced, and the probability of safety fire protection risks is reduced.

The invention discloses a hypertension nursing system based on a remote AI monitoring technology, and particularly relates to the technical field of telemedicine, the hypertension nursing system comprises a data acquisition module, a data fusion module, a risk assessment module, a dynamic intervention module and a cloud management module, and physiological data, behavior data and environmental data are acquired through heterogeneous sensors; the method comprises the following steps: preprocessing the data, transmitting the preprocessed data to a data fusion module and a cloud management module, carrying out alignment and feature extraction on heterogeneous data from different sources to obtain comprehensive risk assessment features, carrying out blood pressure fluctuation risk prediction through an LSTM model, aggregating multi-center cases through cloud federated learning, and generating personalized long-term risk scores. Performing risk level judgment according to the long-term health risk score of the patient, outputting a customized nursing scheme in combination with historical data of the patient, performing privacy protection and storage on the collected data, supporting consultation between the patient and a doctor, automatically generating a dialogue abstract, and updating the dialogue abstract to an electronic health file.

The invention discloses an execution method and device of a cloud management task, a storage medium, equipment and a program, and relates to the field of cloud computing, and the execution method comprises the following steps: matching a text corresponding to a received user instruction with a first dictionary to determine a processing object allowed to process the user instruction, the first dictionary is associated with the business field of the cloud management task; under the condition that it is determined that the processing object comprises the large language model and the risk decision of the first intention of the user instruction is a first value, according to the text and a preset vector database, a cue word of the large language model is constructed, so that an ordered task list corresponding to the user instruction output by the large language model is obtained; and executing the cloud management tasks in the ordered task list. Through application of the method and the device, the problems that a cloud management mode in related technologies mainly depends on a graphical user interface and a command line interface to realize management, the operation is complicated and the efficiency is low are solved, and the cloud management efficiency is improved.

The invention discloses a truck frame robot welding self-adaptive deviation rectifying method and system based on machine vision, and relates to the technical field of robot welding intelligent control. The method comprises the steps that the self-adaptive deviation rectifying system is constructed, and a robot is controlled to move to a preset photographing position in front of a welding seam; by arranging the machine vision sensing module, the robot control module, the self-adaptive deviation rectifying port and the welding database, the track smoothness and stability under high-speed welding are guaranteed, the system not only completes instant deviation rectifying, but also records historical deviation data and optimized welding parameters through the welding database, and the welding precision is improved. When a similar welding seam type or a deviation mode is encountered, the system can intelligently recommend or directly call optimized parameters, the stability of the overall welding quality is improved, welding data and corresponding analysis results are managed, visualized and stored, welding management can be achieved through cloud management and control of the Internet of Things, and the intelligent level of welding management is improved.

The invention discloses a vehicle-mounted charging and discharging system and method based on a V2G technology, and relates to the technical field of electric vehicle charging and discharging. The system comprises a power grid interaction module used for performing power grid connection confirmation, overcurrent protection, leakage protection and grid-connected synchronous detection; the man-machine interaction module is used for displaying a charging and discharging setting interface and battery state information and supporting remote operation of a user; the communication module is used for realizing information interaction with an external power grid dispatching center and a cloud management platform; the vehicle-mounted charging and discharging module is used for adjusting charging and discharging voltage and current of a vehicle power battery; the battery management module is used for collecting state parameters of a vehicle power battery and controlling and protecting the charging and discharging process. The control center module is used for monitoring battery parameters, receiving a user instruction and an external power grid dispatching signal, predicting the power of a vehicle power battery and generating a charging and discharging plan. The method can improve the flexibility and economy of the electric vehicle participating in the power grid interaction, guarantees the safety of the battery, and prolongs the service life of the battery.

Provided in the present application are a resource recommendation method and a related device. The method is applied to a cloud management platform. The method comprises: receiving first indication information of a tenant; determining at least one first resource recommendation scheme on the basis of available resources in at least one computing unit cluster and model information of a first AI model, wherein the cost-performance ratio of the at least one first resource recommendation scheme is superior to the cost-performance ratios of candidate resource recommendation schemes in a first candidate resource recommendation scheme set other than the at least one first resource recommendation scheme; and providing the at least one first resource recommendation scheme to the tenant. In the method, on the basis of model information of an AI model to be run by a tenant and the remaining available resources in a computing unit cluster, at least one first resource recommendation scheme having a relatively high cost-performance ratio can be provided to the tenant, thereby enabling the tenant to purchase appropriate resources and improving the resource utilization rate within the computing unit cluster.

The invention relates to the technical field of wind power generation, and particularly discloses a comprehensive supervision method and system for a wind power generation tower drum and a communication base station. The system comprises a data fusion preprocessing module, a collaborative health assessment module, a co-location fault reasoning module, a collaborative monitoring control module, an intelligent decision-making and planning module, a maintenance execution and feedback module and a cloud supervision and analysis platform. According to the method, data fusion, collaborative evaluation, fault positioning and accurate monitoring are converted into intelligent decision making, comprehensive supervision of a co-location system of the wind power generation tower and the communication base station is achieved, the defects of a traditional independent operation and maintenance mode are overcome, chain communication faults caused by the tower structure problem can be actively recognized, and the communication efficiency is improved. By making the collaborative maintenance plan, the tower climbing frequency of operation and maintenance personnel is remarkably reduced, the operation and maintenance efficiency and safety are improved, and meanwhile, the health management level of the system can be continuously improved by utilizing encryption uploading and file updating.

The invention discloses a digital electric power operation safety cloud management and control system based on an AI deep learning technology, and belongs to the field of electric power operation safety management and control. The system comprises a cloud end, a power operation management system and an edge end, a front end sensing module of the edge end obtains power operation video streams and power operation environment data, and the data are encrypted and transmitted to the cloud end through a data transmission module; a data processing module of the cloud preprocesses the power operation video stream and the power operation environment data to obtain a video frame sequence and an environment data JSON sequence; the AI intelligent analysis module calls the electric power operation business data according to the data, and performs safety analysis including authority verification, protection check, operation analysis and environment suitability judgment on the video frame sequence and the environment data JSON sequence through a sub-module to obtain an electric power operation safety analysis result; the early warning response module gives an alarm based on the electric power operation safety analysis result. According to the invention, intelligent management and control of electric power operation safety are realized through AI deep learning, and the operation safety and normalization are improved.