1809 results about "Complex network" patented technology

A dynamic evaluation method for an extreme rainstorm waterlogging disaster risk for disaster prevention and reduction. The method comprises: investigating and surveying urban system data and disaster prevention and reduction data, using GIS technology to divide disaster-bearing objects into refined risk units on the scale of urban buildings and road networks, and determining the spatial distribution of the disaster-bearing objects; on the basis of an extreme rainstorm waterlogging scene, simulating the disaster influence of a dynamic change process of a flood ponding depth on the disaster-bearing objects; developing refined dynamic evaluation on a waterlogging risk by combining the two methods of waterlogging process simulation and an indicator system; using a spatial complex network and a shortest path plan to calculate a traffic capacity and emergency service accessibility of a road network system; and on this basis, taking into comprehensive consideration the rational allocation of disaster prevention emergency drainage and emergency rescue services to a high-risk area, and proposing dynamic evaluation technology for a waterlogging risk that integrates a disaster evolution process and a disaster prevention response process, and ultimately realizing the dynamic evaluation of the waterlogging risk of each disaster-bearing unit during the waterlogging disaster evolution.

The invention relates to the technical field of network security situation generation, in particular to a network security situation generation method based on multi-view monitoring. The method comprises the following steps of: constructing a multi-view data acquisition system, acquiring real-time information from a multi-dimensional data source, and comprehensively capturing potential threats by utilizing an intelligent sensing technology; cleaning, noise filtering and feature extraction are performed on the acquired data based on a dynamic threshold self-adaption multi-factor cleaning method, so that the accuracy and consistency of the data are ensured; a dynamic analysis algorithm based on deep learning is adopted, the incidence relation between the security events is mined, and the development trend of potential threats is predicted; constructing a joint learning model, and comprehensively evaluating the security state of the network environment; the security situation is dynamically displayed by using a visualization technology, and a security policy is quickly generated in combination with an automatic early warning and response mechanism to resist threats. According to the method, accurate evaluation and prediction of the network security situation are realized, and support is provided for security decision-making in a complex network environment.

The invention provides a flow analysis and threat detection method and device based on machine learning, and the method comprises the steps: collecting a real-time flow data package of a target network environment, carrying out the protocol analysis and session recombination, and generating a real-time flow feature data set containing multi-dimensional flow features; loading a pre-trained multi-level threat classification model, inputting the real-time traffic feature data set into a feature extraction layer of the model, carrying out normalized coding on traffic features of corresponding dimensions through feature coding channels, generating a real-time feature vector sequence, inputting the real-time feature vector sequence into a primary classifier of the model, and classifying the real-time traffic features according to the real-time feature vector sequence; and performing abnormal probability calculation and cluster division on the real-time feature vector sequence through a mixed detection unit, outputting a primary threat tag and an abnormal confidence coefficient corresponding to each real-time feature vector, inputting the primary threat tag and the abnormal confidence coefficient into an aggregation classifier, performing dynamic weighted aggregation, and generating a comprehensive threat score so as to judge whether a threat response strategy is triggered or not. According to the invention, the accuracy and timeliness of threat detection in a complex network environment can be improved.

The invention relates to a risk management and control method and system based on real-time behavior analysis, and the method comprises the steps: carrying out the structural processing of multi-source behavior data through lightweight protocol decoding and behavior label embedding, and constructing an original behavior data set of a user and an entity; extracting multi-dimensional behavior characteristics by using a sliding window analysis and sparse representation mechanism, and constructing a user behavior graph by combining graph embedding learning; constructing a time-sensitive behavior trend model through streaming modeling and an incremental learning strategy, identifying an abnormal evolution trajectory in real time, and introducing a dynamic risk threshold regulation and control mechanism; adopting a high-throughput flow data processing and fast similarity matching algorithm to construct a fusion discrimination model, giving risk levels to abnormal behaviors and classifying the abnormal behaviors; and finally, performing closed-loop optimization in combination with a historical treatment effect. The system has the advantages of high real-time performance, high calculation efficiency, adaptability to complex network environments and the like, and the network security protection capability can be effectively improved.

The invention relates to a network information security protection method and system based on artificial intelligence dynamic defense. The method comprises the following steps: constructing a multi-level security policy library containing basic defense, scene defense and emergency response based on preprocessed network data features (including protocol features, behavior sequences and abnormal indexes); then, combining real-time threat intelligence and a network environment state, and utilizing a strategy combination optimization algorithm to generate a weight matrix reflecting strategy priorities and dependency relationships; dynamically extracting and fusing various defense strategies from a strategy library according to the weight matrix to form a customized defense scheme; and eliminating resource competition and logic contradiction between strategies through a conflict detection mechanism, and finally deploying a conflict-free optimization scheme to a target node. By adopting the method, collaborative deployment of multi-dimensional security policies can be realized, and the accuracy and real-time performance of security protection in a complex network environment are remarkably improved.

The invention relates to the technical field of network security, and discloses a DDoS attack real-time detection and traceability analysis method based on a knowledge graph. The system comprises a data acquisition module, a dynamic knowledge graph construction module, a dynamic knowledge graph updating module, an anomaly detection module, an attack chain traceability module and a visualization module. Network flow data is collected in real time, a dynamic knowledge graph with a network entity as a node and a communication relation as an edge is constructed, a dynamic model is combined to analyze the node state change rate, abnormal nodes are accurately detected, and attack paths are identified and attack sources are positioned through reverse derivation and path weight optimization technologies. Visual results generated by the system support visual display and analysis of abnormal behaviors, and meanwhile, a structured report is provided, so that a manager can make a decision quickly and conveniently. According to the method, the real-time performance of DDoS attack detection, the traceability accuracy and the operability of result analysis in the complex network are improved.

The invention relates to the technical field of integration of artificial intelligence security and network security, and discloses an AI dynamic security transmission system based on an SASE framework, which realizes security access control based on AI dynamic identity verification through an SASE integration access module, acquires and predicts network performance change in real time by using a network state sensing module, and transmits the network performance change to a network server. Equipment, environment and data content are subjected to multi-dimensional analysis by means of a security risk assessment module, a quantitative risk score is generated, and a transmission protocol, parameters and encryption strength are dynamically adjusted according to a network state and the risk score by means of a dynamic transmission optimization module and a self-adaptive encryption module; the problem that safety protection and transmission efficiency are difficult to cooperate in a traditional architecture is effectively solved, low-delay and high-reliability data transmission service can be provided for AI application in a complex network environment, meanwhile, self-adaptive dynamic protection of the whole data transmission process is achieved, and data safety is comprehensively guaranteed.

The invention discloses a network security intelligent management and control system based on big data, and relates to the field of network security management. Comprising a data acquisition processing module, an intelligence fusion analysis module, a threat dynamic detection module, an attack deduction prediction module, a virtual mapping simulation module, an edge collaborative defense module, a defense strategy optimization module, an automatic decision execution module and a threat intelligence sharing module. According to the method, the detection capability of complex network attacks is improved, the attack path in the network environment can be visually presented, the security operation and maintenance efficiency is improved, the attack path is accurately blocked, and the attack success rate is reduced; comprehensive security event priority ranking can be realized, the scientificity of defense decision is improved, meanwhile, the system can adapt to different attack scenes, the defense efficiency is improved, it is ensured that a defense strategy always adapts to the current security situation, resource waste is avoided, and the defense cost-benefit ratio is improved.

The provided are a federated reinforcement learning (FRL) end-to-end autonomous driving control system and method, as well as vehicular equipment, based on complex network cognition. An FRL algorithm framework is provided, designated as FLDPPO, for dense urban traffic. This framework combines rule-based complex network cognition with end-to-end FRL through the design of a loss function. FLDPPO employs a dynamic driving guidance system to assist agents in learning rules, thereby enabling them to navigate complex urban driving environments and dense traffic scenarios. Moreover, the provided framework utilizes a multi-agent FRL architecture, whereby models are trained through parameter aggregation to safeguard vehicle-side privacy, accelerate network convergence, reduce communication consumption, and achieve a balance between sampling efficiency and high robustness of the model.

The invention relates to the technical field of fault positioning, and is used for solving the problem that a fault event positioning system in the prior art has limitation and cannot adapt to dynamic changes. The invention provides a fault event positioning method based on topological model tracking analysis. The method comprises the following steps: S1, constructing a topological model based on system information and setting an edge weight; s2, tracking a fault propagation path by using time sequence data, and updating a path weight; s3, a fault propagation path is optimized through a shortest path algorithm, the shortest path algorithm adopts an A * algorithm, and a fault source is verified in combination with machine learning; and S4, outputting fault source nodes, related path information and confidence evaluation. The method is suitable for various complex network systems, is high in universality and expansibility, can adapt to the dynamic change of the topological structure and the running state of the system by dynamically updating the edge weight of the topological model, tracking the path and the machine learning model in real time, and always maintains high positioning accuracy and efficiency.

The invention relates to a network risk assessment method and system based on a multi-modal data pre-training model. The method comprises the following steps: firstly, collecting multi-source network security data, identifying threat event objects in the multi-source network security data, connecting the threat event objects in series into attack chains, and analyzing to obtain feature information and main attack nodes of each attack chain; risk level information of the attack chain is evaluated and generated; integrating the information, and generating and continuously recording a network security situation report; pre-constructing a network security situation knowledge base; and finally, matching and comparing the feature information of the attack chain with attack chain data in the network security situation knowledge base, predicting potential threat influence, and generating early warning information and protection strategy suggestions. Compared with the prior art, the method has the advantages that the method does not depend on predefined rules, is more suitable for complicated network scenes, and improves the reliability and accuracy of network security situation awareness.

The invention relates to a fuzzy testing method, device and equipment based on a large language model, and the method comprises the steps: analyzing and analyzing RFC standard documents of various network protocols through the large language model, and extracting protocol rules and constraint conditions; according to the protocol rule and the constraint condition, a large language model driving method is adopted to generate a test case conforming to protocol characteristics; and performing a differential test through the test case, and verifying the consistency between the actual software of the network protocol and the RFC standard document. According to the invention, the accuracy and security of implementation of the complex network protocol can be improved, and the test efficiency and coverage rate are optimized.

The invention discloses a message feature extraction method and related equipment, and relates to the technical field of feature extraction, and the method comprises the steps: obtaining original message data of target network flow and a corresponding physical layer signal waveform; analyzing the original message data based on a preset protocol layering rule, and determining a multi-layer protocol field set; determining a protocol layer feature vector based on the multi-layer protocol field set; determining a frequency domain feature vector based on the physical layer signal waveform; constructing a cross attention weight matrix based on the protocol layer feature vector and the frequency domain feature vector; and generating a target message feature based on the cross attention weight matrix. According to the method, through dual-mode deep fusion of protocol semantics and physical signals, the analysis capability of the features on encrypted traffic and protocol confusion attacks is enhanced, meanwhile, the feature robustness is improved through dynamic weight distribution and an anti-replay mechanism, and endogenous security features with high discrimination power are provided for complex network threat detection.

The invention discloses an ecological restoration quantification method based on a watershed ecological safety complex network. The method comprises the following steps: S1, obtaining multi-time-sequence watershed multi-source heterogeneous spatial data of a target area; s2, taking the grid as an evaluation unit, establishing a drainage basin ecosystem service function evaluation system, and obtaining spatial-temporal change characteristics of an ecological safety function; s3, analyzing the spatial-temporal change characteristics of the ecological land scale of the watershed; s4, constructing a drainage basin ecological resistance surface; s5, identifying an ecological corridor; s6, constructing an ecological safety complex network model by taking the ecological source as a node, the ecological corridor as an edge, the scale of the ecological source as a point weight and the proportional relation between the connection efficiency of the ecological corridor and the corridor resistance as an edge weight, and extracting spatial-temporal evolution characteristics of an ecological safety structure; s7, a coupling coordination degree model is adopted to simulate and analyze the characteristic relation among the safety function, the safety structure and the land use scale, and the coupling coordination degree is obtained; and S8, guiding and optimizing an ecological restoration implementation strategy. According to the method, ecological security network structure features on the watershed system scale are quantified.

The invention relates to a network security situation awareness method and device for multi-source data fusion, equipment and a medium, and the method comprises the steps: respectively collecting kernel logs and network flow data, and carrying out the standardization processing to generate a communication data set; constructing a dynamic process link map, defining a communication type, frequency and data volume, and updating a topological relation in real time; a sliding time window is adopted to count communication time sequence characteristics, and abnormal signals deviating from normal distribution are screened in combination with a time sequence analysis technology; training a robustness classifier model through an adversarial sample enhancement technology, fusing a graph neural network to analyze a dependency relationship between processes, and filtering a false alarm signal; and the hidden channel is accurately identified and alarm information is generated in combination with a dynamic similarity threshold and a threat intelligence gain coefficient, so that the problems of insufficient multi-source data fusion, high false alarm rate of a static rule base, failure in detection of weak signals of the hidden channel and the like in a traditional method are solved. And the real-time perception and response capability of APT attacks in a complex network environment is improved.

The invention relates to the technical field of computer network security, and discloses a computer network security access control management method based on big data. The method comprises the following steps: constructing a network security situation knowledge graph, collecting a real-time access behavior sequence through a probe, and synchronizing the real-time access behavior sequence to the knowledge graph; simulating a network entity interaction state in the knowledge graph, and predicting a threat propagation path and a potential intrusion behavior; setting a dynamic access control strategy, constructing a multi-dimensional feature matrix in combination with a real-time access behavior sequence association influence degree and a strategy execution priority constraint condition, calculating a strategy conflict risk score by using a deep learning model, comparing with a preset threshold to judge whether a conflict exists or not, and if yes, reconstructing the strategy; and automatically executing access blocking, session termination and data encryption operations according to the reconstructed strategy, recording an execution log and security feedback data, and updating the knowledge graph in real time. According to the method, the dynamic property and the security of access control are improved, and security threats in a complex network environment can be effectively handled.

The invention relates to the technical field of traffic data processing, and discloses a traffic edge computing data security sharing method and system, and the method comprises the steps: collecting traffic edge computing network multi-source heterogeneous data, and carrying out the classification standardization processing to generate a structured data set; designing a dynamic data sharing security protocol based on a multi-party security computing protocol and a homomorphic encryption algorithm; verifying the authority of a requester in a multi-level manner by using an attribute-based access control model and a zero-knowledge proof mechanism, and generating a dynamic access token; storing data by adopting a fragmentation storage and redundancy encryption strategy, and recording storage information through a hash chain; and dynamically adjusting the encryption strength and the sharing strategy according to the network threat level and the data sensitivity. The method effectively guarantees safe sharing of traffic data, accurately controls access authority, improves storage and sharing efficiency, adapts to complex network environment changes, and provides powerful support for development of an intelligent traffic system.

The invention provides an electric power monitoring system network space surveying and mapping method and device, and the method comprises the steps: initializing a plurality of agents based on the structural features and surveying and mapping demands of a target electric power monitoring system network, and distributing surveying and mapping tasks covering different network regions for each agent; based on the surveying and mapping task, each agent collects network dynamic information of different areas of the target power monitoring system network, and obtains a global dynamic network topology based on the network dynamic information of the different areas; based on the global dynamic network topology, the threat knowledge base and the attack mode base, analyzing potential safety hazards and potential threats of the target power monitoring system network through a deep learning algorithm, and generating a threat positioning result and a vulnerability repair suggestion; and formulating a linkage defense strategy based on the threat positioning result and the vulnerability repair suggestion in combination with the state data of the plurality of regional networks and a cooperation mechanism of the plurality of agents. According to the invention, the efficiency and accuracy of the power monitoring system to deal with complex network threats are improved.

The invention discloses an encrypted data transmission method and server based on a dynamic key, and relates to the field of data transmission security, and the method comprises the steps: generating a first private key through a quantum random number generator; receiving an encrypted public key data packet sent by the client, and decrypting the encrypted public key data packet based on the one-time key to obtain a client public key; performing key operation on the client public key and the first private key to generate a first temporary key; generating a confirmation message based on the one-time key encryption, and establishing an encrypted communication connection with the client based on the first temporary key after the confirmation message is sent to the client; after a preset time interval, generating a second private key through the quantum random number generator, and updating the first temporary key based on the second private key to obtain a second temporary key; and after synchronizing the second temporary key to the client, maintaining encrypted communication connection with the client based on the second temporary key. By implementing the application, the security of data transmission can be improved in a complex network environment.

The invention relates to the technical field of network security, in particular to a network security situation awareness and early warning method based on big data analysis, and the method comprises the following steps: collecting multi-modal security data, and carrying out the noise reduction and normalization processing; constructing a high-dimensional security feature vector by using feature engineering; establishing an anomaly detection model based on physical constraints and a deep residual network; a self-adaptive loss optimization strategy training model is adopted, so that the detection accuracy is improved; through cross-domain transfer learning, model adaptability is optimized, and unknown attacks can be detected; future attack trends are analyzed in combination with historical data, and defense strategies are dynamically adjusted. Through combination of deep learning and big data analysis, the accuracy, real-time performance and adaptive capability of network security situation awareness are improved, unknown attacks and variant attacks can be effectively detected, the false alarm rate is reduced, the security protection capability is enhanced, and the method is suitable for a security defense system in a complex network environment.

The embodiment of the invention provides a computer communication method and system based on the Internet of Things, and the method comprises the steps: constructing a multi-level system architecture, carrying out the preprocessing and feature extraction of an original data flow, recognizing the data characteristics through a time sequence analysis method, and constructing a dynamic data model; deploying a monitoring agent at a transmission node to collect network performance indexes in real time, and constructing a network quality evaluation model; for an incomplete sensor data flow, prior probability distribution is constructed based on a dynamic data model and a network quality grade, and an optimal estimation value of missing data is calculated by adopting a Bayesian reasoning framework and an iterative algorithm; establishing a mapping relation between a network state and an optimal parameter through reinforcement learning to realize self-adaptive adjustment and optimization; and grading the data according to reliability, extracting high-reliability data points as anchor points, designing an iterative refinement algorithm to realize information propagation, and fusing to obtain a complete sensor data stream. According to the method, the problems of poor data recovery accuracy, static parameter configuration and insufficient adaptability in a complex network environment are solved.

The invention provides a construction site safety information supervision system based on the Internet of Things, and relates to the technical field of data processing, the risk assessment accuracy is remarkably improved and the data noise interference is inhibited through a deep reinforcement learning dynamic optimization grading early warning strategy and in combination with a multi-source data compression and federated learning framework; a risk coupling model is constructed based on a complex network theory, a conduction path and a linkage effect between regions are analyzed, a global early warning scheme is optimized, and the adaptability of the system to a complex construction environment is enhanced; the model is continuously optimized through a dynamic weight updating mechanism, high adaptability is kept under the environment change of a construction site, meanwhile, a resource dynamic allocation strategy is adopted, the problem of low high-concurrency processing efficiency is effectively solved, and the overall accident early warning accuracy is improved.

The invention relates to a multilayer coupling power system risk propagation analysis method based on a space-time diagram neural network, and belongs to the technical field of power systems. According to the method, an information-electric power-traffic three-layer heterogeneous coupling network model is constructed, physical quantities in a heterogeneous coupling network are collected, and graph data are constructed based on neighborhoods and connectivity; a risk propagation prediction model is established based on a space-time multi-graph neural network, a space learning network comprises k parallel graph convolutional layers, and a time learning network is realized by combining a long short-term memory (LSTM) network and an attention mechanism; historical graph data of normal operation of the heterogeneous coupling network and historical graph data of faults are obtained and input into a prediction model for training, and a graph structure can be adaptively updated; and predicting a risk propagation path in the three-layer heterogeneous coupling network by using the trained risk propagation prediction model. According to the method, a risk propagation path in an information-electric power-traffic three-layer heterogeneous complex network structure can be realized, and a real-time space-time relationship can be reflected.

The invention relates to the technical field of network communication, and discloses an intelligent control method and system of a switch. The method comprises the following steps: receiving a heterogeneous data stream in a target network domain; performing data processing by using a preset flow feature aggregation model, and generating a flow dynamic pressure field distribution diagram and a protocol priority mapping matrix; constructing an adaptive scheduling threshold model, and generating a multi-target flow control instruction set; a cascade effect is simulated based on a network congestion chained conduction model, and collaborative routing parameters are optimized; and iteratively optimizing through a mixed integer programming framework, and outputting a network scheduling action sequence to a switch control engine. The system correspondingly comprises a data receiving module, a data processing module, an instruction generation module, a simulation optimization module and an instruction output module. According to the method, heterogeneous data can be effectively processed, congestion can be accurately predicted, flow control can be optimized, the overall performance of the network can be improved, and communication requirements in a complex network environment can be met.

The invention relates to the technical field of computer network management, and discloses an intelligent alarm preprocessing method of an adaptive rule engine, which comprises the following steps: firstly, acquiring real-time operation data of network equipment and a preset alarm baseline, and analyzing a historical alarm sequence through an association rule mining model to obtain the preset baseline; then inputting the data into an alarm decision model based on an event atlas analysis algorithm, processing the data by the model by using a multi-dimensional time window algorithm, calculating adaptive weight correction processing parameters in combination with node resource load parameters, and outputting strategy parameters; and finally, adjusting rule engine judgment logic according to parameters to realize alarm intelligent filtering and aggregation. In addition, a link emergency optimization step is provided. The method improves the accuracy and adaptive capability of alarm processing, and is suitable for a complex network environment.

The invention belongs to the technical field of power system security, discloses a load frequency control system attack detection method based on reinforcement learning, and aims to improve the recognition and defense capability of a power system on complex network attacks and overcome the defects of a traditional detection method in the aspects of attack sample generation, unknown attack recognition and system adaptability. According to the method, an attack agent based on a Markov decision process is constructed, and an improved reinforcement learning algorithm is adopted to generate a high-concealment confrontation sample; designing a bimodal detection architecture fusing LSTM supervised learning and auto-encoder unsupervised learning, and introducing an adaptive weight fusion mechanism to realize attack type identification and anomaly detection; and incremental learning and a parameter dynamic adjustment mechanism are combined, so that the detection model has continuous learning and evolution capabilities. The method can be applied to a power grid dispatching center or an intelligent micro-grid, real-time monitoring and attack defense of a load frequency control system are achieved, and the operation safety and robustness of a power system are remarkably improved.

The invention discloses a data slice recombination order-preserving method based on a Beidou short message, which relates to the technical field of data transmission and recombination optimization, and ensures the stability and low error rate of data in the transmission process by dynamically optimizing the size and quantity of slices according to channel bandwidth and delay through a self-adaptive slice adjustment mechanism. A unique serial number identifier and a first check code attached to each fragment are beneficial to identifying data under a complex channel condition, and errors and chaos in a recombination process are reduced. At a receiving end, a conflict analysis model is constructed by analyzing sequence offset and delay conditions of fragment arrival in real time, and a multi-layer correction instruction is provided according to an overall conflict rate and a time sequence consistency coefficient. The order-preserving mechanism ensures that the original order of the data can be maintained even in a complex network environment by preferentially processing the unlocked fragment and generating a retransmission instruction of the lost fragment. The combination of dynamic priority adjustment and a retransmission mechanism promotes the structure of the original data after recombination.

The invention relates to a visual safety risk early warning system for pseudo-classic architecture mixed structure construction, in particular to the field of safety risk early warning of pseudo-classic architecture construction, through multi-source data fusion and intelligent algorithm cooperation, the construction safety risk management and control capability is remarkably improved, the system collects mechanical and environmental data of key nodes of a high-support formwork body in real time, and the construction safety risk early warning capability is improved. Load distribution is dynamically updated in combination with the building information model, and a high-precision space-time label data stream is generated; predicting a short-term instability risk based on a two-channel neural network, constructing a complex network model to quantify a node failure linkage effect, and generating a visual risk propagation thermodynamic diagram; finally, a grading alarm strategy is triggered through a dynamic threshold value, augmented reality visual warning and equipment linkage control is achieved, the system breaks through the limitation of traditional manual monitoring, full-process closed-loop management of risk perception, prediction, positioning and response is achieved, the occurrence rate of safety accidents is effectively reduced, and the safety and reliability of construction of the complex structure of the pseudo-classic architecture are guaranteed.

The invention discloses an intelligent operation and maintenance method for modular data acquisition and analysis sewage treatment equipment, which comprises the following steps: designing a modular data acquisition unit, configuring an acquisition module with a standardized interface, and realizing quick access of multi-source equipment; protocol conversion, data compression and timestamp completion are completed by using an edge computing gateway, and stable data reporting under a complex network is realized by combining an MQTT protocol; a time sequence database is adopted to store and fuse OA / ERP system data, and an information island is broken; remote diagnosis, intelligent early warning and visual decision making are realized by means of a remote fault analysis interface, a user-defined rule engine and a configurable billboard; and operation and maintenance closed-loop management is achieved in cooperation with a workflow engine. The method effectively solves the problems of low data acquisition integration level, unstable communication, weak analysis capability and the like in traditional operation and maintenance, has the characteristics of modularization, intelligence, high efficiency and the like, remarkably improves the operation and maintenance management level of the sewage treatment equipment, reduces the operation and maintenance cost, and has a wide application prospect.

The invention relates to the technical field of Internet of Things data transmission, in particular to a mass data cross-security domain transmission high-low priority queue hierarchical scheduling method based on Internet of Things equipment, which comprises edge computing node classification, independent channel design, a dynamic weight distribution algorithm and a block chain encryption watermark technology. The timeliness is improved through a preemptive and compensation mechanism, and the reliability is enhanced in combination with queue degradation protection and a dynamic adjustment strategy. The method can effectively solve the problems that timeliness is insufficient and safety and efficiency are difficult to balance in a traditional method, the key data transmission efficiency and the system self-adaptive capacity are remarkably improved in a complex network environment, and the method has wide application prospects.