675 results about "System safety" patented technology

The invention discloses a 10KV station electric control system of a convertor station adopting selecting 2 from 3 logic. Two PS830 boards are added, three PS830 boards are utilized to concurrently process and jointly realize a spare power automatic switching function and an operation interlocking logic function; the selecting 2 from 3 logic is added in PS853 programs of an ASI host machine and a switching value output board, i.e. the electric control system can be exported when at least two of three integrated circuit boards sends out same instructions. The damage of any one of the three PS830 boards can not lead to switch fault trip or refusing trip, the 10KV control system can be ensured to run safely and stably without being influenced by the faults of single element. The electric control system has the advantages of convenient use, safety and the like.

The invention discloses a substation switching misoperation risk intelligent control method and system, and relates to the technical field of power system safety production, and the method comprises the steps: collecting multi-dimensional real-time risk factor data, building a dynamic probability dependency relationship model based on the risk factor data, quantifying the correlation influence between different dimensions, and updating the model parameters, feature information of a current operation scene is converted into a standardized vector, similarity matching is carried out on the standardized vector and a scene template, an adaptive basic strategy is screened, the basic strategy is dynamically adjusted according to a real-time risk assessment result, and a scenarized risk management and control strategy is generated; and implementing a scenarized risk management and control strategy, collecting multi-dimensional feedback data in an execution process, generating a new generation of optimization strategy based on the feedback data, and updating the strategy library. The risk management and control strategy is generated and adjusted according to the actual situation, the strategy is continuously optimized, the safety and reliability of switching operation of the transformer substation are improved, accidents caused by misoperation are reduced, and stable operation of a power grid is guaranteed.

The invention discloses an electric power system safety early warning method and system based on multi-modal cooperation, and relates to the technical field of electric power system safety early warning, and the method comprises the steps: collecting multi-source operation data, carrying out the preprocessing, carrying out the multi-modal feature extraction and fusion based on the preprocessed data, and carrying out the multi-modal feature extraction and fusion. Inputting an edge detection model and outputting an abnormal confidence score in combination with an attention mechanism; and performing alarm grading according to the abnormal confidence score, constructing a causal diagram for alarms with high risk levels in combination with associated security events, and performing future attack path prediction by adopting a time sequence diagram neural network. According to the method, multi-scale convolution and a channel attention mechanism are fused, the extraction capability of the multi-source data time sequence features of the power system is enhanced, the anomaly detection precision is improved, dynamic attack path prediction is realized in combination with RMTPP and causal atlas topological constraints, sequence modeling is enhanced through self-attention and position coding, and the detection accuracy is improved. And the perspectiveness and the reliability of the safety early warning of the power system are obviously enhanced.

The invention relates to the technical field of injection molding, and discloses a plastic part injection mold regulation and control system and method based on a complex curved surface and a thin-wall structure, and the plastic part injection mold regulation and control system based on the complex curved surface and the thin-wall structure comprises a mold modeling module, a temperature modeling module, a control modeling module, a control optimization module and a control execution module; the plastic part injection mold regulation and control method based on the complex curved surface and the thin-wall structure comprises the steps that a mold three-dimensional model and thermal control area mapping are built, a heat conduction model is built, modal dimensionality reduction is conducted, a target temperature field and a performance function are combined, optimal temperature regulation and control are achieved through a predictive control algorithm, real-time feedback is matched, and a heating and cooling unit is driven. And dynamic closed-loop control of the mold thermal field is realized. According to the method, thermal control precision and energy consumption efficiency are optimized through Galerkin modal dimension reduction, model predictive control and a weighted performance function, global closed-loop temperature control is achieved by combining mapping of a three-dimensional area and a heating and cooling unit of the mold, and system safety and stability are enhanced.

The invention relates to the technical field of large-scale power grid safety guarantee, in particular to an optimal decentralized coordination control method for a micro-grid in a new energy power system, which comprises the following steps of: performing virtual disconnection verification on key power transmission lines one by one based on online acquired power grid topology and real-time line power flow data, and establishing a whole-grid cascade risk quantitative index. According to the method, power grid topology and line power flow data are acquired in real time, virtual disconnection verification of key power transmission lines is implemented one by one, simulation and analysis of a line fault scene are realized, whole-network cascade risk quantitative indexes are established on the basis, and an initial fault line with the maximum threat to system safety is identified by comparing the indexes with a safety threshold value in real time, so that the safety of the system is improved. A fragile line risk contribution list is generated, and high-risk lines are positioned and prevented in advance; and further directly calculating the power adjustment amount required by the micro-grid group according to the risk contribution list, and forming a forward-looking tide dredging instruction set.

The invention relates to the field of control systems, and discloses a vent valve control system with a pressure regulation function for a generator set, which is used for breaking through the limitation of static state, single target and passive response of a traditional vent valve control method. Comprising the following steps: acquiring pressure, flow velocity and valve opening data of a ventilation system in real time, generating a dimensionless parameter set for dynamic calibration, integrating parameter uncertainty disturbance terms to form a hybrid prediction model, solving an optimal valve opening instruction sequence by adopting a multi-target robust optimization algorithm, and designing a layered safety control mechanism. The system safety is guaranteed through dual-threshold monitoring and emergency intervention, redundant sensor data fusion and parameter reverse optimization are introduced, and fault tolerance and model self-adaption are achieved. According to the method, through collaborative innovation of dynamic modeling, robust optimization and intelligent fault tolerance, the control precision, the anti-interference capability, the safety and the reliability of the ventilation system of the generator set are remarkably improved, and the method is suitable for complex and changeable industrial operation scenes.

The invention provides an intelligent prediction method and system applied to system log security audit, and the method comprises the steps: firstly obtaining a historical log data set of a power monitoring system, carrying out the event correlation modeling, generating a log event correlation model, generating a security event prediction rule library based on the log event correlation model through a rule mining algorithm, and carrying out the prediction of the security event. And then obtaining a real-time log data stream, inputting the log event association model to obtain a real-time event association result, matching the real-time event association result with the rule base to generate an abnormal event prediction result, finally generating a security audit report according to the abnormal event prediction result, sending the security audit report to the power monitoring terminal, and updating rule base parameters according to feedback information. Therefore, the abnormal event in the power monitoring system can be predicted in advance, and the intelligent level and the safety guarantee capability of system safety auditing are improved.

The invention discloses a deep and far sea wind power foundation platform and load integrated system under extreme environment conditions, and belongs to the technical field of offshore wind power platforms. The system comprises a basic platform body, a multi-dimensional monitoring subsystem, an environment-load coupling analysis subsystem, an intelligent regulation and control execution subsystem, a safety early warning decision subsystem and a shore-based collaborative management subsystem. Through synchronous acquisition of extreme marine environment, basic platform structure load and operation state data, distributed multi-type sensor layout and high-frequency sampling, data acquisition comprehensiveness and timeliness are realized, monitoring data are fused, a coupling model is constructed, 24-hour load change is predicted, and the real-time performance of the system is improved. According to the method, the attitude of the platform is dynamically adjusted, bearing configuration is optimized, emergency protection is started, the limitation of traditional separation analysis and fixed regulation and control is broken through, and the risk disposal and operation and maintenance efficiency is improved through risk quantification and grading early warning, remote monitoring and intelligent scheduling of a shore-based collaborative management subsystem and four-level early warning accurate matching coping strategies.

The invention provides an unmanned system security monitoring method based on multi-modal perception and autonomous decision discrimination, belongs to the technical field of artificial intelligence and industrial automation crossing, and aims to solve the problem that a machine learning decision security monitoring system of an existing unmanned system is difficult to quickly perceive environment change and make accurate response. The method solves the problems that in a high-dynamic scene, recognition of sudden risks is lagged, decision adjustment is slow, and the safety and stability of task execution are affected in the prior art, and comprises the steps that S1, key parameters of an unmanned autonomous system are screened; s2, modeling a parameter coupling relation; s3, time sequence driven key parameter extreme value boundary modeling and statistical analysis are carried out; s4, discovering danger cognition; s5, building a real-time prediction framework; s6, constructing a risk assessment model; and S7, determining an optimal safety switching mechanism. The method is suitable for real-time safety control of unmanned equipment such as an unmanned aerial vehicle, an automatic driving vehicle and an industrial robot.

The invention discloses a system safety assessment method and system for an energy storage power station containing a network-building type energy storage converter, and the method comprises the following steps: S1, collecting converter control parameters, power grid dynamic data and lithium battery mechanism data through multi-source heterogeneous data fusion, and carrying out the preprocessing through noise elimination and a normalization algorithm; s2, constructing a network construction characteristic quantitative evaluation system including indexes such as dynamic stability and voltage supporting capability; s3, establishing a three-layer evaluation system of an equipment layer, a control layer and a system layer, and calculating a total risk value of the system by using a weight distribution algorithm; s4, verifying system adaptability and optimizing parameters through time domain simulation in scenes such as a strong power grid and a weak power grid; and S5, generating an evaluation report containing parameter optimization suggestions, triggering graded early warning based on an index threshold value, and automatically generating a closed-loop control instruction. According to the invention, multi-dimensional safety assessment of the energy storage power station is realized, and systematic technical support is provided for safe operation of the network-forming type energy storage power station.

The invention relates to the technical field of power system safety analysis, in particular to a source-grid-load-storage integrated n-1 scene voltage safety analysis method, which comprises the following steps of: acquiring and preprocessing n-1 scene data; constructing a static voltage safety analysis model based on the gradient boosting regression tree; constructing a dynamic voltage safety analysis model based on a long short-term memory network; analyzing safety collaboration of the static voltage and the dynamic voltage; and verifying and optimizing the static voltage safety analysis model and the dynamic voltage safety analysis model. According to the method, the static voltage stability margin and the dynamic response characteristic are fused, so that the comprehensive risk identification accuracy is improved; a gradient boosting regression tree and a long-short-term memory network are adopted to capture nonlinear characteristics, and the voltage prediction error of an n-1 scene is remarkably reduced; characteristics and weights are customized for different source network load storage integrated project types such as industrial and incremental power distribution networks, and the pertinence and engineering practicability of analysis are improved.

The invention discloses an end-to-end automatic driving method based on hierarchical uncertainty estimation and dynamic safety response, and belongs to the technical field of intelligent driving. The method comprises the steps of environment observation data acquisition and preprocessing, perception information enhancement, adaptive space-time attention fusion and trajectory prediction, hierarchical uncertainty estimation and dynamic safety response, experience pool storage and end-to-end deep learning optimization. By dynamically integrating the multi-modal features of the RGB image and the LiDAR point cloud, a space-time dependency relationship is captured, and high-precision trajectory prediction is realized; meanwhile, layering quantification cognition and random and time sequence uncertainty are carried out, and a five-level safety response strategy is triggered in combination with an environment self-adaptive threshold value; and carrying out reinforcement learning by adopting priority experience playback and a multi-task loss function. According to the method, the limitation of traditional fixed weight fusion is overcome, the prediction accuracy and the system safety are improved, the average displacement error (ADE) can be reduced in a complex traffic scene, the uncertainty calibration degree is improved, and the method is suitable for real-time decision making of an automatic driving vehicle.

The invention discloses a power load prediction method, and the method comprises the steps: firstly solving an extreme event data sparsity problem through a generative adversarial network, and constructing an event time sequence library through a time sequence anomaly detection algorithm; then analyzing the causal relationship between the event and the load by applying a causal discovery algorithm, and converting prediction output into probability distribution by adopting a Bayesian neural network to quantify uncertainty; constructing a prediction model triggered by an event, and generating a multi-time scale probability prediction interval; and finally, generating a multi-scene prediction result through Monte Carlo simulation, quantifying the system recovery capability in combination with a toughness index, and integrating the system recovery capability to a decision support system to generate a risk response scheme. According to the method, the accuracy and robustness of load prediction under the extreme climate are remarkably improved, full-chain risk insight from early warning to recovery is realized, and prospective decision support is provided for safe operation of a power system.

The invention discloses an electric power system transient stability evaluation sample enhancement method, system and device based on a timing constraint generative adversarial network and a medium, and belongs to the technical field of electric power system safety analysis, and the method comprises the steps: constructing a sample enhancement model, generating transient time sequence data based on random noise and system feature tags by a generator, the discriminator outputs a sample authenticity score and a time sequence consistency score through a time sequence neural network, the physical constraint module calculates physical constraint loss of the generated data, the model is alternately trained in combination with multi-dimensional loss, the trained generator is utilized to synthesize samples, and qualified samples are screened through physical verification. According to the invention, by introducing the LSTM time sequence constraint, the WGAN-GP stable training mechanism and the multi-dimensional physical consistency constraint, three core problems of poor time sequence coherence, modal collapse and physical law violation in the existing sample enhancement method are cooperatively solved, and high-quality transient scarce samples can be generated.

The invention provides a software monitoring method, system and device based on byte code replacement and risk awareness and a medium, and belongs to the technical field of software security. The method comprises the steps that a special annotation mark in a source code is scanned, a test code entry point is recognized, and metadata information is extracted; performing encryption processing on the metadata and generating a registry file with a digital signature; environment safety data are collected through four monitoring dimensions, and a comprehensive risk score is calculated; generating a grading fusing instruction according to the score; and finally executing the fusing operation of byte code replacement or thread sandbox isolation. According to the invention, accurate identification and real-time protection of the leakage risk of the test code are realized; through a multi-dimensional monitoring and hierarchical fusing mechanism, the system security is ensured, and the influence on the service performance is minimized; and byte code replacement and thread isolation technologies are adopted, so that security protection can be realized without restarting the application, and the availability and the operation and maintenance efficiency of the system are improved.

The invention discloses an API (Application Program Interface) monitoring security audit model based on a government affair system, which relates to the technical field of network security, and comprises the following steps: collecting and converging network traffic of each collection point; analyzing the distribution condition of APIs in the collected flow, identifying and displaying key information, meanwhile, realizing data real-time processing, data stream splitting, data reading and writing and offline data analysis, and detecting risk behaviors by utilizing a rule strategy library; aPI asset weaknesses are identified and marked, state management is supported, and an attacker portrait, security study and judgment and attack traceability model is constructed; and pushing and displaying risk model early warning, realizing multi-dimensional data source real-time association analysis based on a big data framework, automatically converging alarms to form an event file, and linking with automatic arrangement to complete event response and report generation. According to the method, the problem of insufficient security and stability of the government affair system is solved, and complex attacks and advanced persistent threats can be identified more accurately.

The invention relates to a power transmission line fault risk grading evaluation method based on multistage early warning, and belongs to the field of power system safety. Comprising the following steps: acquiring an insulator surface pollutant data generation matrix; extracting spatial distribution characteristics of pollutants; leakage current waveforms under different humidity are calculated; extracting a leakage current harmonic component and pulse characteristics; dividing pollution flashover risk grades; matching the maintenance strategy and generating an equipment scheduling instruction; and performing fault risk grading evaluation to generate a report. According to the method, through multi-source data fusion and dynamic modeling, accurate evaluation and preventive maintenance of the pollution flashover risk of the insulator are realized, and the safety and reliability of a power system are improved.

The invention relates to the technical field of power systems and automation thereof, in particular to a generative adversarial network-based source-load joint scene generation method, which comprises the following steps of: constructing a multi-source time sequence database and extracting weather, time, space and historical state driving factors; establishing a joint probability distribution model based on a vine connection function; taking a numerical weather forecast path and a date type as conditional input, constructing a generative adversarial network embedded with a physical constraint microloss function of the power system, and forming a physical information generator; performing dependent structure fidelity verification on the generated scene by using the joint probability distribution model; generator parameters are fixed, potential space vectors are optimized through a gradient ascending method to maximize power grid risk indexes, and a high-risk source-load joint scene set is generated. According to the technical scheme, accurate generation of the source-load joint scene which is physically feasible and reasonable in statistics and focuses on the high-risk working condition is realized, and the safe operation toughness and the risk early warning capability of the novel power system are remarkably improved.

The invention relates to the field of fluid pipeline system safety and intelligent control, and provides a water hammer pressure prediction and active suppression method and system. The method comprises the following steps: acquiring a multi-dimensional digital signal flow, and generating a window data matrix sequence through sliding window segmentation and dimensionless; image coding is carried out to form a spatiotemporal feature sequence, the spatiotemporal feature sequence is processed by a double-branch spatiotemporal prediction network, and a prediction feature spectrum sequence is output and decoded to form a prediction waveform matrix; future risk indexes and morphological characteristics are extracted and spliced with the multi-dimensional digital signal flow to form a prediction enhancement state vector, and the prediction enhancement state vector is input into a strategy network to generate valve action control parameters; and a target valve opening curve is decoded, a composite control signal is obtained through feedforward-feedback composite control, actual response data flow and historical data packaging are collected, and a prediction network and a strategy network are updated online through mixed priority screening. According to the method, prospective and self-adaptive water hammer prediction and active suppression are realized, the pressure evolution trend of a pipeline system is informed, and decision support is provided for safe operation of a fluid pipe network.

The invention relates to the technical field of power system safe operation safety and intelligent monitoring, and discloses a vegetation intrusion risk assessment method and system for a power transmission corridor and electronic equipment, and the method comprises the steps: obtaining multi-source remote sensing data and meteorological data of vegetation in a preset range of a target power transmission corridor; performing data fusion on the multi-source remote sensing data to obtain fused data; extracting a vegetation height change sequence based on the fused data, and performing prediction based on the vegetation height change sequence and the meteorological data to obtain a future time sequence height change curve; obtaining engineering parameters of a target power transmission corridor, and calculating a vegetation invasion risk value based on the engineering parameters and a future time sequence height change curve; and performing risk assessment based on the vegetation invasion risk value. The method breaks through the limitation of traditional static monitoring, integrates InSAR time sequence analysis and a machine learning algorithm, realizes vegetation growth dynamic modeling and future trend prediction, realizes risk level quantitative evaluation in combination with power transmission line structure parameters, and can provide technical support for power grid risk early warning.

The invention discloses a structure construction deviation automatic identification and safety evaluation method, which comprises the following steps: acquiring multi-modal original signals such as space measurement, environment, equipment operation and images from multiple positions of a structure construction site, and establishing a trend drift feature distribution model through time sequence alignment, normalization, noise reduction and feature extraction; then through combination with generative artificial intelligence, virtual samples of historical and extreme working conditions are expanded, and construction of a mixed working condition data pool is realized; through distribution drift analysis and multi-target parameter optimization, a deviation detection threshold value is dynamically and adaptively adjusted, parameters and a judgment result are pushed to a digital twinning and safety control platform in real time, and meanwhile, whole-process log and performance archiving are carried out, so that the sensitivity, adaptability and system safety of structure construction deviation detection are improved, and the construction safety is improved. And the method has relatively high data fusion efficiency and algorithm generalization ability.

The invention relates to the technical field of power system safety and stability control, and discloses a new energy field station broadband oscillation risk early warning method based on voltage dynamic indications, which comprises the following steps: acquiring instantaneous value data in a new energy electric field station to obtain electric quantity time sequence data; processing the electrical quantity time sequence data according to the target sliding window and outputting risk characteristic quantity; performing nonlinear normalization processing on the risk characteristic quantity by using a weighted fusion technology, and generating a single broadband oscillation risk index reflecting the broadband oscillation level of the new energy station according to a normalization processing result; and carrying out difference comparison on the single broadband oscillation risk index and a dynamic risk early warning threshold, and triggering an oscillation risk early warning signal and implementing oscillation risk early warning when a difference result meets a target warning requirement. According to the method, the defense gateway is moved forward by capturing the dynamic symptoms before oscillation occurs, so that the fundamental conversion from post-event to beforehand is realized, and a precious decision window period is won for operation adjustment.

The invention discloses an equipment operation safety detection method and system of an energy storage system, and relates to the technical field of energy storage system safety detection.The method comprises the steps that multichannel original data are collected and preprocessed, and multichannel signals are obtained; extracting an instantaneous frequency through adaptive decomposition and Hilbert transformation based on the multi-channel signal, performing modal reconstruction based on the instantaneous frequency, extracting a switch frequency band energy feature, an EMI feature, a DC ripple and a coherent feature, and constructing a fingerprint vector; collecting health state historical data, calculating a mean vector and a covariance matrix, and constructing a health template; based on the health template and the real-time fingerprint vector, analyzing a safety state through dual statistical judgment, and outputting a safety statistical magnitude; and judging abnormity based on the current safety statistics, performing LASSO sparse reconstruction by using a predefined typical fault template, and outputting a fault type. Therefore, efficient and accurate fault diagnosis and automatic alarm are realized.

The invention relates to the technical field of distributed system security and intelligent operation and maintenance, in particular to an API full-life-cycle intelligent treatment method and system based on multi-modal data perception and a blood relationship atlas, and the method comprises the steps: S1, collecting network traffic in a non-intrusive mode, automatically discovering an API, extracting metadata features, and generating an API change event; s2, performing semantic analysis on the API metadata based on a natural language processing technology, calculating a sensitivity score, and performing marking alarm on a high-risk interface; s3, constructing an API blood relationship map, and carrying out change influence analysis and risk assessment based on the map; s4, risk simulation is carried out on API changes based on flow playback, and plan changes are tested in a layered sandbox environment; and S5, automatically generating a document conforming to an open API specification based on the API metadata, and synchronously updating in real time. All links are connected in series to form a closed-loop governance system through the event-driven engine, automatic and intelligent governance of API assets is achieved, and the safety, stability and operation and maintenance efficiency of the system are improved.

The invention discloses a power-traffic coupling network reconstruction method and system. The method at least comprises the following steps: step 1, establishing a two-stage optimization model of a power-traffic coupling network; wherein in the first stage, power grid reconstruction decision making is carried out with the purpose of minimizing the power grid reconstruction cost, and in the second stage, electric vehicle dispatching optimization is carried out based on the power grid emergency power supply requirement in the first stage; 2, constructing a layered reinforcement learning coupling network real-time double-layer linkage decision model; 3, solving the decision model by adopting a rainbow-based improved DQN algorithm; and 4, based on the decision model and a rainbow improved DQN algorithm, establishing a power-traffic coupling network reconstruction system. According to the method, the problems of low reconstruction efficiency, high model over-estimation risk and insufficient system safety after the power-traffic coupling network fails are solved, and the toughness and the operation safety of the coupling network are improved.

The invention discloses a method for evaluating the security performance of an information system. The method comprises the following steps: S1, matching an asset security baseline and compliance; s2, a dynamic security situation awareness step; s3, a multi-agent collaborative evaluation step; according to the information system safety performance assessment method, through real-time perception and trend prediction, the crossing from static assessment to dynamic early warning is realized, the adaptability to dynamic risks is improved, meanwhile, a multi-dimensional assessment system integrating asset compliance, dynamic situation and service toughness is established, and the assessment efficiency is improved. According to the method, firstly, multi-agent collaboration and topological correlation analysis are adopted, system key paths and deep correlation risks are accurately recognized, the risk recognition depth is improved, finally, evaluation full-process automation and model self-learning optimization are achieved, the evaluation efficiency is greatly improved, and the method is suitable for large-scale popularization and application. And the continuous advancement of the method is ensured.

The invention discloses a micro-patch dynamic protection method, system, device and medium for a security partition of a power monitoring system, and belongs to the technical field of dynamic protection, and the method comprises the steps: achieving the secure transmission of a micro-patch through TPM offline signature and segmented verification, and completing atomization instruction replacement in TEE to achieve uninterrupted deployment; implementing multi-dimensional operation attitude measurement and dynamic threshold analysis by utilizing a hardware one-way channel synchronization strategy beacon and combining a hardware trust root; and meanwhile, a double-chain auditing system is deployed to realize automatic compliance evaluation. The method has the beneficial effects that patch integrity verification, credible loading, measurement during operation and closed-loop evidence obtaining are deeply embedded into a real-time link of the power monitoring system; the method has the following core beneficial effects that the vulnerability opening time is shortened, the contradiction between compliance and real-time performance is broken, the cross-regional defense cooperation efficiency is improved, the integrity protection during operation is enhanced, a legal-level credible auditing chain is constructed, meanwhile, heterogeneous equipment is compatible, and the hard real-time stability is kept.

The invention discloses a transformer substation safety risk prediction and alarm system based on edge calculation, and belongs to the technical field of power system safety monitoring. The system comprises a sensing layer equipment group, an edge computing device, a cloud edge collaborative management platform and an intelligent alarm linkage system. The sensing layer equipment group acquires multi-source data in real time through a broadband current transformer, an ultrahigh frequency partial discharge detection array and a micro-meteorological monitoring module; the edge computing device performs local risk prediction by adopting a lightweight model generated by knowledge distillation, and realizes millisecond response in combination with FPGA hardware acceleration; the cloud edge collaboration platform realizes model dynamic optimization through federated learning and incremental updating; and the intelligent alarm system triggers a light stripe coding alarm and priority instruction sequence based on the multi-source fusion confidence coefficient. The problems that a traditional system is lagged in response, slow in model updating, difficult in multi-source data fusion, weak in privacy protection and the like are solved, and real-time prediction and accurate disposal of the safety risk of the transformer substation are achieved.

The invention provides a power monitoring system safety operation and maintenance service and inspection management optimization method and system, and relates to the field of power system safety operation and maintenance, and the method comprises the steps: collecting equipment operation information to construct a feature vector, generating an operation track, analyzing an abnormal interval, and calculating the equipment state correlation degree to form a fault propagation link diagram; recognizing a fault source node, determining a sweep device, and establishing a routing inspection sequence and path; constructing a security isolation domain and a communication matrix deployment protection strategy; polling is executed, and feature matching is carried out to position potential safety hazards. According to the invention, accurate positioning and prevention of faults of the power monitoring system are realized, and the operation and maintenance efficiency and the system safety are improved.

The invention discloses a coal blocking prediction and control method and system for a coal conveying and falling pipe based on artificial intelligence, and belongs to the technical field of power plant coal conveying system safety monitoring. Multi-source sensor data such as coal flow density, sound wave frequency spectrum and pressure gradient are fused, and are input into a neural network model after time alignment and feature extraction; a hierarchical risk label is output in combination with an integrated learning algorithm, and the coal conveying equipment is linked to execute hierarchical control including low-risk continuous monitoring, medium-risk speed reduction and intermittent excitation, high-risk emergency jump stop and maximum power rapping; the problems that a traditional single sensor is high in false alarm rate and manual response lags behind are solved, equipment adjustment can be accurately triggered in the early stage of coal blockage, non-planned shutdown is avoided, industrial protocol data frames are packaged through a PLC interface to ensure reliable execution, and the safety and operation continuity of a coal conveying system are remarkably improved.