2942 results about "Intelligent Network" patented technology

The invention discloses a network line intelligent operation and maintenance monitoring management system and method. The method comprises the steps that an intelligent network monitoring and optimizing system regularly collects network equipment information through a protocol, and a monitoring agent collects server and machine room environment data; processing data in real time by adopting distributed storage and a stream processing framework; predicting a network fault by adopting threshold monitoring and a machine learning model, and identifying a bottleneck and scheduling resources based on flow analysis; in combination with deep packet inspection and an AI algorithm, network traffic is monitored in real time, security threats are identified, and IPS system protection is deployed; a repair operation is automatically triggered through a self-healing strategy, and backup resources are started; the AI engine analyzes historical and real-time data, generates network topology and link state visual content, and automatically generates reports, optimization and decision support; according to the intelligent operation and maintenance monitoring management system and method for the network line, a fault tree analysis and decision support system is introduced, and a plurality of fault reasons are intelligently analyzed and repair measures are automatically executed.

Disclosed in the present invention is a four-network integration architecture for an unmanned swarm system. The four-network integration architecture has the capabilities of heterogeneous platform resource pooling, intelligent dynamic computing power allocation, and timely decision planning, so as to maximize the overall benefit. The present invention focuses on abstracting and integrating independent submodules to form a mesh topology of a swarm. The present invention designs a four-network integration architecture for an unmanned swarm system, which comprises a computing power network, a perception network, a decision network and a communication network as core modules. The structure aims to achieve efficient cooperation of all parts in the swarm, thereby improving the overall performance and adaptability of the system. The system integrates environmental perception, a swarm network modeling component, a knowledge base and a resource pool, providing an intelligent environmental perception strategy and a network modeling strategy for the interior of the swarm. Therefore, the perception of environments, tasks and networks by nodes can be facilitated, thereby completing establishment of intelligent networks, so as to ensure the characteristics of the stability and flexibility of networks.

The invention discloses a power failure monitoring system, and relates to the technical field of failure monitoring. During operation of the system, multi-dimensional data acquisition is carried out on key equipment of a transformer substation and a power distribution room, the operation state of the equipment is comprehensively covered through real-time acquisition of data of various sensors, and data acquired by a data acquisition module is preprocessed; the intelligent gateway equipment is deployed, real-time data analysis and preliminary fault diagnosis are carried out locally, the running state of the equipment is monitored in real time through set rules and threshold values, potential faults are detected, preliminary alarm is given out, and the system automatically judges the severity of the faults and decides whether to take automatic control measures including load switching or equipment power failure or not. Meanwhile, alarm information is generated and sent to operation and maintenance personnel, data, fault diagnosis results and automatic control measures in system operation are fed back, and a deep learning model and an edge calculation strategy are periodically updated through online learning and model updating.

The invention relates to the technical field of vehicle control, and provides a new energy vehicle operation optimization method and system based on intelligent networking, and the method comprises the steps: collecting a first operation parameter of a vehicle at a current moment and a road parameter at a next moment through a vehicle-mounted sensor network; wherein the first operation parameter comprises a tire movement value corresponding to vehicle suspension deformation variable data based on a current pavement coefficient, and the road parameter is road feature data of a road to be driven in; according to the first operation parameters and the road feature data, a load mean value model and an acceleration safety threshold value of the current vehicle in the road to be driven in are constructed; according to the load mean value model, a damping adjusting instruction of the vehicle suspension is generated; and generating a driving torque vector distribution scheme according to the acceleration safety threshold.

The invention discloses a power equipment intelligent gateway fault detection system based on the Internet of Things, relates to the technical field of power equipment intelligent gateways, and is used for solving the problem that an existing power equipment monitoring system is inaccurate in fault detection. Comprising a power equipment access module, a data preprocessing and edge calculation module, a data storage and analysis module and a fault diagnosis and alarm module which are in signal connection. The power equipment access module is used for collecting operation state data of power equipment; the data preprocessing and edge calculation module performs cleaning, format conversion and preliminary calculation on the collected data, so that data redundancy is reduced, and the processing efficiency is improved; the data storage and analysis module stores the processed data and extracts feature information by using a big data analysis method; and the fault diagnosis and alarm module carries out fault identification based on an analysis result and triggers an alarm signal so as to realize accurate detection and efficient maintenance of the intelligent gateway of the power equipment.

The invention relates to the technical field of wireless communication networks, in particular to a vehicle ad hoc network based on intelligent networked vehicle WiFi and a V2V interaction method, a vehicle motion state and network load data are acquired through a vehicle-mounted sensor, AP / STA modes are dynamically switched by combining a deep Q network (DQN) model and an epsilon-greedy strategy, and an adaptive topological structure is constructed. And expanding OLSR protocol message embedding path delay fields, calculating and optimizing multi-hop routing selection in combination with pheromone gradient, and realizing message priority classification and differential privacy protection through a federated learning model. And dynamically adjusting an AP / STA proportion threshold based on a particle swarm algorithm, and triggering node role reconfiguration to balance network load. According to the method, the existing WiFi hardware is reused, the deployment cost is reduced, cross-layer routing optimization and a distributed decision mechanism are combined, and the network real-time performance, the expansibility and the group cooperation capability in a dynamic scene are improved.

The invention belongs to the technical field of Internet of Vehicles, and particularly provides a federated learning optimization system and method for an intelligent network connection automobile scene, which improves an asynchronous federated learning framework, divides a plurality of vehicles into different clusters due to the isomerism of computing power, and adopts a cloud-edge-end three-layer federated learning framework, so as to realize the optimization of the federated learning of the vehicles. An edge server is deployed on a roadside unit RSU, received model parameters are subjected to pseudo-asynchronous aggregation in a self-adaptive dynamic time window, the received model parameters are subjected to real-time aggregation at a cloud end, the system model training efficiency and quality are improved, meanwhile, the data security is ensured, the user privacy is protected, and the user experience is improved. And the efficiency of the federated learning optimization system and the quality of the global model in the intelligent network connection automobile scene are ensured.

The invention discloses an intelligent network transaction credit risk assessment system and method based on multi-modal data fusion and deep learning, and particularly relates to the technical field of network transaction risk assessment, and the system comprises a multi-modal data collection, fusion and deep reinforcement learning agent, and a large model interaction and risk assessment and supervision execution module. The multi-modal data acquisition module acquires various data in real time; the fusion module realizes semantic alignment and interactive learning through multiple strategies; the deep reinforcement learning agent module optimizes a monitoring and supervision strategy based on an Actor-Critic framework; the large model interaction module provides multi-aspect support by means of various technologies; and the risk assessment and supervision execution module identifies illegal behaviors and executes supervision actions. The evaluation method comprises the steps of data acquisition and preprocessing, feature vector generation, agent training, large model deployment optimization, real-time monitoring decision and the like. The system can improve the monitoring and supervision efficiency, protects the rights and interests of consumers, constructs a healthy transaction environment, and promotes the development of related technologies.

Systems, methods, and computer-readable media for autonomous agents are disclosed. The autonomous agent can include one or more processing circuits configured to receive or identify a dynamic data structure comprising one or more functions or one or more frameworks for performing a plurality of cyber resilience operations. The processing circuits can register the autonomous agent with a decentralized network, centralized network, or data source (DNCNDS). The processing circuits can receive or identify, from at least one computing system external or internal to the DNCNDS, cyber resilience data. The processing circuits can perform, in real-time, the at least one cyber resilience operation of the plurality of cyber resilience operations based at least on selecting at least one function of the one or more functions of the dynamic data structure based at least on the cyber resilience data.

The invention relates to the technical field of intelligent driving, and discloses an intelligent networked automobile multi-modal sensing and decision-making system based on deep learning, and the system comprises a multi-modal data collection module which is used for collecting image data and radar time sequence data around a vehicle through a vehicle-mounted camera and a vehicle-mounted radar sensor, the surrounding environment of the vehicle can be sensed in all directions; the image data processing module is used for processing the image data by using an improved deep learning model and extracting target object feature information in the image; the radar data processing module is used for analyzing the change trend of the radar time sequence data and making an optimal driving decision according to different traffic scenes in combination with the feature information of the target object; and the optimal decision execution module is used for executing an optimal decision action according to the optimal driving decision. According to the invention, high-precision perception and quick response to environment information in a complex traffic scene can be realized, the driving safety is improved, and the traffic efficiency is optimized.

The invention provides an intelligent network connection automobile abnormity detection system and method based on big data analysis, and the method comprises the steps: obtaining metadata in the communication between a vehicle CAN bus and a cloud, the metadata comprising a protocol type and a load feature, carrying out the analysis of the load feature according to the protocol type, and extracting a key field of a data package; constructing a load feature matrix according to the key field, and normalizing the heterogeneous data to obtain a feature data set in a uniform format; a mahalanobis distance algorithm is adopted to calculate the distance between each data point in the feature data set and a preset base line, and if the distance exceeds a preset threshold value, abnormal traffic is judged, and an abnormal traffic mark list is generated; fingerprint features of a transmission link are obtained from the 5G network slices, the fingerprint features of different slices are subjected to standardization processing, and a cross-slice consistent fingerprint feature data set is generated; therefore, security threats in a complex Internet of Vehicles environment can be effectively handled, and the accuracy and reliability of abnormal traffic detection are improved.

The invention relates to a fire prevention and control smart network method and system based on Beidou satellite navigation. The method comprises the following steps: firstly, acquiring real-time operation data of fire-fighting equipment in a building, and scanning a building structure through a three-dimensional imaging device to generate a three-dimensional digital model; secondly, fusing the real-time operation data, the three-dimensional digital model and preset environment parameters to form a fire prevention and control reference data set; thirdly, carrying out multi-dimensional data comparison on the reference data set based on a preset fire-fighting standard program, and generating an alarm signal with spatial positioning information; then, determining a fire-fighting facility failure area according to the alarm signal, and calculating a fire risk diffusion parameter; and finally, generating a grading early warning instruction according to the risk diffusion parameters, and constructing an electronic combat map containing the optimal rescue path. The method provides global and accurate basic data support for fire prevention and control, improves the timeliness and accuracy of early warning, optimizes the rescue path, and improves the fire disposal efficiency.

The invention discloses a power equipment data acquisition and processing system, which comprises a multi-protocol compatible acquisition module, an intelligent network detection module, a system service performance monitoring module and a high-concurrency processing module, the multi-protocol compatible acquisition module is responsible for adaptation and data acquisition of different power equipment protocols and normalization processing of the acquired data; the intelligent network detection module monitors a network link state, diagnoses a network fault reason and pushes fault information in real time; the system service performance monitoring module collects key performance indexes of a server, displays performance data through a visual interface, and triggers an alarm when the indexes are abnormal; the high-concurrency processing module compresses the collected data, realizes efficient pushing and priority processing of the data through a message queue, and guarantees the data processing efficiency in a high-concurrency scene; comprehensive monitoring of power equipment communication and system performance is realized, and data acquisition efficiency and system reliability are improved.

The invention discloses a network intrusion intelligent detection system based on a 5G network, and relates to the technical field of network security. Comprising a traffic anomaly detection and analysis module, a data encryption tampering detection module, an anomaly analysis and evaluation module, an attack prediction and prevention module, a dynamic isolation and protection module, a reverse tracking and tracing module and a redirection and isolation protection module. According to the system, more efficient and more accurate intrusion detection and threat defense can be realized in a 5G network environment by integrating a plurality of advanced technologies and intelligent modules. Compared with a traditional rule-based detection method, the system can adaptively learn and identify a new attack mode, and the detection capability for zero-day attacks, variant attacks and advanced persistent threats (APT) is enhanced. Besides, by combining deep learning and reinforcement learning algorithms, the system can predict potential attacks in a dynamically changing network environment and automatically optimize a defense strategy, and the response speed and accuracy of the defense system are improved.

The invention relates to the technical field of automobile network security, in particular to an intelligent networked automobile penetration test path generation method and system based on a knowledge graph, and the method comprises the steps: constructing an intelligent networked automobile intranet penetration test security knowledge graph through fusing multi-source heterogeneous data based on an intranet E / E architecture; generating a potential attack path based on the knowledge graph by using a depth-first search algorithm of dynamic pruning optimization; a multi-dimensional space-time risk assessment framework is utilized to quantify path threat intensity and execution feasibility, and quantitative assessment on the generated attack path is realized; and according to an evaluation result, selecting a high-risk and feasible attack path to carry out an actual penetration test, and discovering security vulnerabilities and weaknesses in the system. According to the method, the attack surface is fully covered, the high-value attack path is efficiently generated, the threat intensity and the execution feasibility are accurately and quantitatively evaluated, and the safety test efficiency and accuracy of the intelligent network connection automobile intranet are remarkably improved.

The invention provides a network intrusion intelligent monitoring method and system based on deep learning, relates to the field of network security, and solves the technical problem of response lag of an existing defense method. The method comprises the following steps: collecting multi-source data; preprocessing the multi-source data to generate a spatial-temporal feature map; inputting the spatial-temporal feature map into a first model and a second model constructed based on a deep learning algorithm for anomaly detection to obtain a detection result; wherein the first model is used for detecting a known attack mode, and the second model is used for detecting an unknown attack mode; and carrying out hierarchical risk level division on the detection result, and carrying out active defense according to the defense strategy of each risk level. The method is used in the network intrusion monitoring and defense process, intelligent monitoring and active defense of network intrusion are realized through multi-source data acquisition, spatial-temporal feature map generation, dual-model cooperative detection and layered defense strategy implementation, and the real-time performance and initiative of network security protection are improved.

The invention relates to the technical field of data processing, and discloses a multi-protocol fusion Internet of Things equipment intelligent gateway data conversion method and system. The method comprises the following steps: collecting a multi-protocol equipment data packet, and extracting protocol features to construct a vector library; protocol types are identified based on the vector library, data are analyzed, and a data object set containing semantic tags is constructed; semantic correlation is analyzed through an adaptive learning algorithm, and a dynamic protocol semantic mapping matrix is established; converting the data into a standard format according to the mapping matrix and recording a matching degree to form a target data pool; and extracting fusion data from the data pool, recoding according to a target protocol format, and outputting a data frame. The problem that an existing multi-protocol fusion data conversion method lacks protocol semantic understanding and self-adaptive learning ability is solved, and semantic consistency and conversion quality of data conversion among multi-protocol equipment are improved.

The invention relates to the technical field of new energy vehicle-mounted data processing, and discloses a new energy vehicle vehicle-mounted data processing method and system based on intelligent networking, and the method comprises the steps: collecting and preprocessing data through a multi-source sensor, carrying out the slicing fusion of the data based on time-space correlation, and extracting features through a lightweight convolutional neural network. Constructing an inter-vehicle communication topology model to realize collaborative feature learning, adopting a federal learning framework to train the model and protect privacy, applying a reinforcement learning algorithm to make a decision, establishing a multi-objective optimization model, deploying a dynamic priority scheduling module to manage resources, and optimizing the model based on an incremental learning algorithm. The system covers a plurality of modules such as data acquisition and preprocessing, data dynamic slicing and fusion and the like. According to the method, the data processing efficiency and accuracy are improved, intelligent decision and multi-objective optimization are realized, data privacy is guaranteed, computing resources are reasonably distributed, and the overall performance of the new energy automobile in an intelligent network connection environment is improved.

The invention relates to an intelligent network attack surface prediction method and system based on deep learning, and belongs to the technical field of network security and information, and the method comprises the steps: obtaining network asset information, vulnerability distribution information and external threat intelligence data in a target network environment, and carrying out the preprocessing to generate a standardized data set; inputting the standardized data set into a pre-trained deep learning model to extract a feature vector related to the network attack; reasoning and analyzing a potential attack link based on the feature vector and the knowledge graph, and combining an association relationship among a network asset node, a vulnerability node and a threat intelligence node in the knowledge graph; and finally, according to a reasoning analysis result, evaluating an intrusion path possibly utilized by an attacker, outputting an attack surface prediction result, and presenting the attack surface prediction result in the form of an attack path list. According to the scheme, a potential attack link can be subjected to deep reasoning analysis, an intrusion path possibly utilized by an attacker can be accurately predicted, and the effectiveness of network security protection is improved.

The invention discloses a converter station intelligent gateway image recognition system based on a YOLOv3 target detection algorithm, and the system employs a three-stage cooperative processing architecture design, and builds seamless connection of a multispectral image collection layer, an edge calculation gateway layer, and a cloud operation and maintenance management platform layer. The invention further provides an equipment defect detection method based on the system, bimodal image data are collected through the visible light camera and the thermal infrared imager, preprocessing operation is carried out, equipment positioning and defect classification are synchronously executed by utilizing the improved YOLOv3 network, a structured detection result is output, temperature field analysis is carried out on an infrared thermal image, and the equipment defect detection result is obtained. And an abnormal heating area is identified, when defects are detected, multi-level risk response early warning is generated, and a defect diagnosis report is pushed to the cloud operation and maintenance management platform layer. Real-time image analysis of converter station equipment can be realized, the method is suitable for automatic detection of typical fault defects of the equipment, and the operation and maintenance efficiency of a power grid is improved.

The invention relates to the technical field of combination of communication network management and artificial intelligence, in particular to an intelligent network traffic anomaly monitoring system fused with artificial intelligence, which comprises a data acquisition unit, a feature extraction and processing unit, an intelligent anomaly detection unit, a fault prediction and health management evaluation unit, a decision response unit and a continuous model optimization unit. Through multi-modal intelligent fusion, lightweight modeling and hybrid modeling technologies, real-time accurate identification and potential threat positioning of network flow abnormal behaviors are realized, network equipment faults and health states are predicted based on interpretable AI and physical law fusion, a transparent and prospective decision basis is provided for network operation and maintenance, and the network flow abnormal behavior prediction method has the advantages of high reliability and high reliability. The anomaly detection accuracy is greatly improved, the fault prediction advance exceeds 24 hours, and the network security and reliability are remarkably enhanced.

The invention relates to the technical field of network security, in particular to an intelligent network security detection system and method based on big data analysis, and the method comprises the steps: generating adversarial data through a historical network data set, training a network security detection model through the adversarial data, and dynamically adjusting an adversarial training objective function in training. The network security detection model can balance normal loss and adversarial loss, and the robustness and accuracy of the network security detection model are balanced; the network flow data flow is analyzed through the machine learning model, the network state index is output, the network security detection model is optimized according to the output network state index, and the detection capability of the network security detection model to the antagonistic attack is improved; according to the method, traditional network attack data is extracted from a historical network data set, the traditional network attack data and adversarial disturbance generated through adversarial training are combined, and a network security detection model is trained, so that the network security detection model can detect traditional network attacks at the same time.

The invention relates to the field of wireless communication and intelligent networks, in particular to a software defined radio-based ad hoc network dynamic slicing architecture and a management and control method, comprising a basic communication layer, an intelligent control layer and a service execution layer. The basic communication layer adopts a multi-distribution multi-center clustering architecture; the intelligent control layer is responsible for intelligent management and resource scheduling of the self-organizing network, and the intelligent control layer comprises a plurality of command and control nodes; and the service execution layer adopts a micro-service-based architecture, and encapsulates different network functions and service logics into independent micro-service modules. According to the method, the technical problems of high plane coupling, resource scheduling rigidness, insufficient survivability and high hardware dependency in the prior art are solved.

The invention discloses a method and a system for intelligently acquiring and analyzing webpage merchant information. Automatic acquisition of the merchant information is realized through five main steps of intelligent webpage capture, intelligent field identification, accurate address information analysis, intelligent merchant grouping and automatic page discovery. According to the method, a traditional rule-based extraction method is abandoned, information such as merchant names, phone numbers and addresses in webpages is automatically recognized by using context semantic analysis and mode recognition technologies, and accurate association grouping of fields is realized through a dynamic weight multi-dimensional similarity calculation method; and the merchant information can be continuously discovered and collected by using an automatic page discovery mechanism without manually specifying a collection path. The technical bottlenecks that specific rules need to be written for different websites, webpage structure changes are difficult to deal with, multi-merchant information grouping cannot be processed and the like in a traditional method are broken through, the method adapts to various webpage structure changes, and the automation degree, accuracy and efficiency of merchant information collection are greatly improved.

The invention relates to the technical field of copper wire manufacturing control, in particular to a copper wire multi-pass rolling and annealing cooperative control method and system based on an intelligent network, and the method comprises the steps: setting a process control node according to an industrial control process, building a cooperative communication network based on the process control node, and collecting a multi-mode industrial control signal; setting a data transmission processing protocol, establishing an industrial control sensing platform, integrating multi-mode industrial control signals, and converting the multi-mode industrial control signals according to process positions to generate process operation parameters; analyzing non-linear correlation between process steps, and calculating process adjustment parameters by combining the copper wire performance indexes; according to the data transmission processing protocol, the process operation parameters are transmitted to a cooperative control center and compared with the process adjustment parameters for analysis, and a parameter cooperative control instruction is generated; and the parameter cooperative control instruction is fed back to the rolling control node and the annealing temperature control node based on the cooperative communication network for parameter correction. According to the invention, the problems of low process synergy efficiency and insufficient signal processing capability in copper wire manufacturing are effectively solved.

The invention discloses a vehicle ad hoc network system and method based on deep reinforcement learning, and relates to the technical field of vehicle networking communication. The system comprises a dynamic routing module which adjusts a relay path in real time by using a deep reinforcement learning algorithm and optimizes a network load; the relay signal transmission module is used for constructing a multi-hop relay link by means of network signals of other vehicles when the signals are weak; the multimode communication module is used for intelligently switching a V2X communication protocol and a Wi-Fi 6 communication protocol according to the signal strength and the network environment; the positioning cooperation module fuses UWB and IMU sensor data to realize high-precision inter-vehicle positioning; the security verification module is used for ensuring data security and identity credibility by adopting a national cipher SM4 encryption algorithm and a block chain smart contract; and the communication control module is used for coordinating data transmission, path selection and protocol switching. According to the invention, the problems of signal attenuation, protocol switching and safety in the dynamic environment of the Internet of Vehicles are effectively solved, and the communication capability and the system stability of the intelligent networked vehicle are improved.

The invention relates to a method and a system for automatically updating an intelligent gateway and synchronizing a server. The method comprises the following steps: carrying out system self-inspection and network connection verification on the intelligent gateway to obtain network state information; obtaining version information of the intelligent gateway according to the network state information, and performing local storage management to obtain an update request data packet; establishing a secure communication connection with an OTA server based on the update request data packet to obtain a session credential; performing version comparison of multi-objective optimization by using the session voucher, and executing intelligent update decision making to obtain an update execution plan; downloading an update file from the OTA server according to the update execution plan and performing integrity verification to obtain a verified firmware image file; and deploying the verified firmware image file to the standby partition and executing update installation to complete state synchronous feedback. According to the method, the adaptability and accuracy of the update decision are improved, dynamic resource scheduling in the update process is realized, and the update is ensured to be carried out in the optimal system state.

The invention relates to the field of network security, and discloses an identity authentication unloading method and system based on an intelligent network card. The method comprises the following steps: presetting a master key in an intelligent network card; presetting a master key in the intelligent network card; receiving client request data forwarded by the application layer and extracting a service identifier; generating a private key of the corresponding service through the service identifier and the master key; acquiring a local authentication message, signing the local authentication message by using the private key, generating a server signature value, and sending the server signature value to the client through the application layer to trigger a client authentication process; receiving client certificate data forwarded by the application layer and extracting a certificate signature value; and verifying the certificate signature value by using a preset certificate issuing mechanism public key and generating a verification result, and outputting feedback information based on the verification result. According to the method, a hardware-level unloading function in an identity authentication process is realized through the intelligent network card, and the problems of high calculation overhead and poor expansibility of traditional TLS bidirectional authentication are solved.

The invention discloses an intelligent network equipment scheduling optimization method based on deep learning, and the method comprises the following steps: S1, collecting and preprocessing multi-source operation state data, and generating a scheduling input feature tensor; s2, constructing a Transform prediction model based on a multi-head self-attention mechanism, and outputting a task density and a resource pressure prediction value; s3, forming a search individual state vector by the predicted values, and initializing an individual population of the gravitational search algorithm; s4, constructing a fitness function and executing a gravitational search algorithm to generate an optimal task scheduling scheme; s5, issuing the optimal scheduling scheme to each device, executing task distribution, migration and scheduling, and collecting execution data; and S6, comparing an execution result with a predicted value, constructing a feedback data set, and jointly updating the model and the optimization mechanism. The invention aims to realize accurate prediction and global optimization of intelligent network task scheduling, improve the resource utilization rate and the system scheduling efficiency, and construct a closed-loop control mechanism with a self-learning capability.

The invention discloses a remote monitoring cooperation system based on intelligent network connection, and relates to the technical field of cooperation control, and the system comprises a protocol analysis and normalization module which analyzes an original protocol message uploaded by a heterogeneous terminal, and generates standard event metadata; the arrangement scheduling module injects an execution token into a linkage execution sequence generated based on the standard event metadata and the strategy library, issues an action instruction, and triggers a remedial sub-process along a rollback edge when feedback is abnormal; the parameter intelligent setting module is used for generating an operation portrait based on the execution track log and obtaining a strategy parameter set based on the operation portrait; the intelligent cooperative processing module executes digital signature verification, certificate rolling rotation, deterministic transmission control and video adaptive adjustment according to the strategy parameter set; according to the invention, through organic combination of strategy analysis, arrangement scheduling, parameter intelligent setting and intelligent cooperative processing modules, automatic response and efficient control during coupling of multiple types of events in an intelligent network connection remote monitoring scene are realized.