995 results about "System safety" patented technology

The invention belongs to the technical field of power system safety, and discloses a high-voltage transmission line safety assessment method based on an intelligent algorithm. A database is constructed through multi-source data acquisition and fusion processing, a deep learning model is utilized to analyze relevance between meteorological conditions and an icing formation mechanism, an evolution law of the meteorological conditions and the icing formation mechanism is mined, multi-scene simulation and sensitivity analysis are performed in combination with physical characteristics and landform information of a power transmission line, and a line icing risk partition assessment system is established. According to the method, a critical value of line mechanical strength and icing thickness is calculated based on a mechanical model, a grading early warning threshold standard is formulated, a time sequence prediction algorithm and a risk propagation model are constructed to realize intelligent early warning, and a prevention and control strategy is formed through optimal configuration of anti-icing resources and self-adaptive generation of a deicing scheme. According to the invention, the capability of resisting icing disasters of the high-voltage transmission line is effectively improved, and the safe and stable operation level of a power grid is enhanced.

A control method for fire-fighting false alarm of an energy storage system comprises the following steps of: calculating a fluctuation period, a derivative and a probability density function by using fast Fourier transform and the like by acquiring data streams such as current, voltage, power and heat energy of the energy storage system, determining a system state and predicting a related trend. And predicting power by using an autoregressive moving average algorithm and a support vector machine, and setting a threshold value according to a probability density function to judge abnormity. For heat energy data, through derivative calculation and signal analysis, states are identified and early warning is carried out in combination with models such as a deep convolutional adversarial neural network and a support vector machine. The normal range of monitoring data is determined by means of the long-short-term memory neural network, the change trend and physical quantity relation of abnormal data are analyzed and predicted through a cubic exponential smoothing method and Gaussian process regression, potential risks are judged through weighted fusion and a self-adaptive Simpson algorithm, fire-fighting false alarms are effectively reduced, fire risks are accurately recognized, and the safety of an energy storage system is guaranteed.

The invention provides a comprehensive financial IT operation and maintenance management system and method based on artificial intelligence, and the system comprises an intelligent monitoring and predictive maintenance module which is used for achieving the real-time monitoring, abnormality recognition and performance prediction of an IT system through collection of multi-dimensional time series data, and when an abnormal condition is monitored or a prediction result does not meet a preset requirement, the intelligent monitoring and predictive maintenance module carries out the operation and maintenance of the IT system; if so, triggering intelligent early warning; the AI-driven fault diagnosis and self-healing module is used for carrying out intelligent fault analysis and repair in combination with IT system logs and historical fault data based on the detected abnormal condition; the self-adaptive resource management module is used for predicting a future load trend and a future performance trend, and performing self-adaptive resource management based on the predicted future load trend and the future performance trend in combination with cost and energy consumption factors; and the security operation and maintenance automation module is used for monitoring security threats of the IT system in real time and automatically executing security strategies.

The invention relates to the technical field of industrial water system safety monitoring, in particular to an intelligent source tracing method for a process medium leaked in circulating water, which comprises the following steps of: acquiring multi-dimensional operating parameters such as conductivity, pH value, turbidity, dissolved oxygen, temperature, pressure and characteristic ion concentration; a standardized water quality parameter matrix is generated after space-time alignment and wavelet noise reduction; the method comprises the following steps: extracting an abnormal fluctuation signal by using a leakage feature recognition model based on transfer learning, simulating a diffusion process through a three-dimensional leakage diffusion model, realizing leakage source positioning by combining reverse particle tracking and kernel density estimation, associating a high-probability leakage region with upstream process equipment, extracting backtracking path features, and matching a process medium feature library, thereby realizing leakage source positioning. The leakage medium type is judged; and finally generating a structured traceability report. According to the method, high-precision identification, positioning and medium analysis of process leakage in a complex circulating water system can be realized, and the method has relatively high practicability and popularization value.

The invention relates to the technical field of power monitoring, in particular to a station power utilization system safety and energy efficiency monitoring system based on multi-source information fusion, which comprises a signal analysis module, an anomaly identification module, a risk assessment module and an emergency response module. According to the method, the fineness and the response speed of power station monitoring are improved by integrating wavelet transformation and information entropy analysis, the time-frequency characteristics of signals are deeply analyzed through wavelet transformation, the capturing capacity for short-term and long-term changes is improved, the integrity and the accuracy of data analysis are further ensured, and the accuracy of power station monitoring is improved. The information entropy is introduced to assess the stability and uncertainty of the power station, a scientific basis is provided for predicting and recognizing potential risks of the system, and by combining real-time anomaly recognition and dynamic risk assessment, equipment faults and system failures can be effectively predicted, and energy consumption is optimized, so that early warning and prevention and control are performed in advance, the downtime is remarkably shortened, and the system reliability is improved. The maintenance cost is reduced, and the level and effect of energy efficiency management are improved.

The invention discloses a cable sheath ring current fault positioning in-loop simulation method and system based on LABVIEW. The method comprises the following steps: collecting ring current data of cable sheath monitoring points in real time under the control of LABVIEW, and carrying out digital display and graphical display; decomposing circulation data to obtain a wavelet coefficient and calculating features, and inputting a classification model to obtain a fault type when the features are abnormal; calculating a compensation coefficient considering the influence of the electromagnetic field, and calculating the position of a fault point in combination with a double-end traveling wave method; the operation model switches a typical working condition simulation mode, prediction of a Bayesian reasoning correction model is carried out, and the full-life-cycle operation state of the cable is simulated; and performing fault early warning through the LSTM model and risk assessment. The method can effectively improve the cable operation and maintenance efficiency and reliability, and is widely applied to cable fault detection and guarantee of safe and stable operation of a power system.

The invention relates to the technical field of power system safety monitoring, in particular to a wire icing early warning system based on terrain analysis, which comprises a terrain analysis module, a microclimate simulation module, a meteorological data acquisition module, an icing risk assessment module, a real-time monitoring module and an early warning information generation module. Wherein the topographic analysis module is used for acquiring topographic feature data of a target area; the microclimate simulation module is used for performing microscale climate simulation on the target area to generate local meteorological parameters; the meteorological data acquisition module is used for acquiring real-time meteorological data of the target area; and the icing risk assessment module is used for constructing an icing risk assessment model. According to the method, accurate and real-time evaluation and early warning of the icing risk of the electric wire in the complex terrain area are realized by integrating the terrain features, the real-time meteorological data and the historical icing information and adopting the random forest algorithm and the microclimate simulation technology.

The invention relates to the technical field of electric power system safety early warning, and discloses a fault risk sensing early warning method for a high-low voltage switchgear assembly, and the method carries out the data preprocessing and feature extraction through collecting the state monitoring data, environmental parameters and basic attribute information of a device in historical operation. And based on the feature data sets, constructing a fault risk prediction model to predict the fault risk probability of the equipment in a period of time in the future, obtaining the operation state data and environmental parameters of the equipment in real time, and inputting the prediction model to obtain the fault risk probability. Meanwhile, the equipment is monitored in real time, original monitoring data is cleaned, abnormal values are removed, real-time monitoring data is compared with prediction data, whether the difference exceeds a threshold value or not is judged according to a preset risk threshold value, and risk points are marked. And evaluating the equipment fault risk according to the risk point and the evaluation strategy, and sending a result to related personnel to take prevention measures.

The invention belongs to the technical field of automobile information safety fuzzy testing, discloses an automobile information safety fuzzy testing method based on a large model, and develops optimization application research of LLMs in an automobile software fuzzy testing scene by utilizing the advantages of an artificial intelligence technology in the aspects of data processing, natural language interaction, generalization ability and the like. The concealment and effectiveness of the test set generated by the fuzz test are optimized, and the fuzz test process efficiency is improved. Through the implementation of the invention, related key technologies are applied to the fuzz test of the automobile, and other related products and enterprises in China are radiated by writing technical standards, so that the fuzz test system can be used as an application demonstration and sample plate of the fuzz test of the intelligent driving automobile. According to market analysis, fuzzy test calculation is carried out on 100 thousand automobiles every year according to the method, and the data safety and the system safety are improved.

The invention relates to the technical field of network information security, discloses an information security management system based on big data, and aims to solve the defects of an existing system in the aspects of data acquisition, anomaly detection, threat prediction, risk assessment and the like. The system comprises a data acquisition module for collecting various types of data in real time; the abnormal detection model is used for identifying normal and abnormal behaviors by adopting an automatic encoder AE algorithm; the threat prediction module is used for predicting a future security threat type and probability by using a recurrent neural network (RNN) algorithm; the risk assessment module comprehensively assesses the system security risk through a random forest RF algorithm; and the response decision module is used for executing corresponding safety response measures according to the evaluation result. According to the system, the big data technology is utilized, comprehensive monitoring and accurate prediction of the network security state are achieved, the efficiency and accuracy of information security management are improved, and powerful support is provided for coping with novel and complex network attacks.

The invention discloses a bird-related fault identification and early warning method based on a transmission tower gap model, and relates to the technical field of power system safety monitoring. The method comprises the following steps: firstly, constructing a three-dimensional transmission tower gap model containing a local insulated armor, and obtaining an electric field distortion feature library under various working conditions through finite element simulation; further simulating interference behaviors of the bird body, the bird nest and the excrement in the safety gap, calculating the discharge probability, and synthesizing and labeling a bird image for training a risk decision model. And in combination with a target detection algorithm integrating an attention mechanism and an SPPCSPC module, space coordinates of bird targets are extracted in real time and mapped to a high-risk area, and a discharge risk score and a breakdown trend index are output by using an LSTM network. And when the risk index exceeds a dynamically adjusted early warning threshold, generating an early warning signal and a fault coordinate. According to the invention, the precision and foresight of bird-related fault identification are improved, and the method has good engineering adaptability.

The invention relates to the technical field of Internet of Things security, and discloses an Internet of Things control system and a security early warning method, and the method comprises the steps: firstly collecting equipment basis and real-time information in an Internet of Things system, then evaluating equipment risk, network security and data encryption conditions, setting an early warning threshold according to multi-aspect evaluation results and historical security data, and finally, carrying out early warning. And finally, real-time monitoring is carried out, and early warning is triggered when the risk index exceeds a threshold value. The method has obvious advantages, equipment aging and safety risks can be accurately evaluated, equipment maintenance is planned in advance, and the maintenance cost is reduced; network attacks can be effectively identified, and network security is guaranteed; the data encryption security is enhanced, and the data leakage risk is reduced; early warning is given out in time, and the system response speed is increased; and the equipment safety state can be dynamically updated, the system safety management is continuously optimized, the safety and reliability of the Internet of Things system are comprehensively improved, and the stable operation of the Internet of Things system is promoted.

The invention relates to the technical field of pipeline safety detection, in particular to a pipeline hydrogen-doped leakage real-time monitoring and early warning method and system based on Fluent simulation, and the method comprises the steps: obtaining leakage diffusion data of gas in a pipeline detected by multiple sensors, and carrying out the preprocessing of the data; extracting data features: carrying out conjoint analysis on a time-frequency domain to obtain a time-frequency matrix; by calculating mutual information evaluation correlation of different sensor data, feature statistical correlation is realized, and features with non-significant spatial correlation are removed; the pressure data, the flow data and the concentration data are subjected to sub-channel filtering and fusion processing based on federated Kalman filtering (FKF); establishing a pipeline model based on Fluent, and simulating a pipeline hydrogen-doped leakage process by taking the extracted features as input conditions; a risk model is constructed based on real-time sensor data, simulation prediction data and environmental parameters, and a hierarchical early warning mechanism is established. Through cooperative monitoring of multiple sensors, leakage characteristic data can be accurately captured, and safety and reliability of the system are ensured.

The invention discloses a tunnel hydrogen energy vehicle leakage and explosion risk prediction and automatic ventilation emergency disposal method, and belongs to the technical field of hydrogen leakage accident risk prediction and emergency disposal. According to the method, the potential leakage point of each component is determined by obtaining hydrogen-related component information of the hydrogen fuel cell vehicle. An accident tree is used to identify a root cause that may cause hydrogen leakage in the system, and then different accident scenarios are defined. And according to the leakage scenes of different hydrogen-related parts, determining the leakage frequency and the complete fracture frequency, and determining the disaster category. And finally, according to the leakage probability, the ignition probability and the consequences of different accident situations, calculating the comprehensive probability of disaster occurrence. And in combination with a hydrogen concentration sensor array arranged in the tunnel, the longitudinal ventilation air speed and the transverse exhaust air rate are automatically analyzed and adjusted, and the optimal economical, safe and efficient combined ventilation array is obtained according to risk indexes. The method can effectively predict the disaster-pregnant risk of the hydrogen storage vehicle in different environments, provides an economic and safe emergency disposal scheme, and can provide a theoretical basis for the safety technology development of a hydrogen storage system.

The invention relates to the technical field of power system automation, and particularly discloses an intelligent fault diagnosis and analysis system and method for power secondary equipment, and the method comprises the following steps: constructing a sensor network, collecting the operation data of a power system, and extracting the characteristics related to the fault of the power secondary equipment in the operation data; constructing a fault diagnosis model of the power secondary equipment, and obtaining faults of the power secondary equipment in the monitoring time period; constructing an association period according to the fault occurrence time point, generating coordinate points according to the fault occurrence sequence in the association period, and clustering the coordinate points to obtain a cluster; and according to the density of the clustering clusters, strong correlation faults of the faults are obtained, and when the faults occur, prompt information is sent to maintain the strong correlation faults corresponding to the occurring faults in advance. According to the invention, the real-time accurate diagnosis, strong correlation early warning and interpretable visual root cause analysis of the power secondary equipment fault are realized, and the safe operation of the system is effectively guaranteed.

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 relates to the technical field of natural gas energy management and safety monitoring, and discloses a safety detection system and method suitable for natural gas hydrogen doping, and the system comprises a data collection module which is arranged at a key node of a natural gas pipeline network, and is used for collecting the spectral data, temperature, humidity and pressure parameters of a natural gas hydrogen-doped mixed gas; the edge calculation module is arranged in the nodes of the data acquisition module and is used for carrying out real-time preprocessing, compression and aggregation on the acquired data; the central data analysis module communicates with the edge calculation module and is used for receiving and analyzing the processed data and carrying out gas component calculation and anomaly detection; and the calibration module is used for adjusting sampling parameters of the sensor based on the real-time environment parameters to ensure the detection precision. According to the invention, multi-mode real-time monitoring, dynamic calibration, efficient anomaly detection and intelligent safety feedback are realized, and the detection precision, response speed and system safety under complex working conditions are improved.

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 discloses a hydrogen energy system safety assessment method based on deep learning, and the method comprises the following steps: S1, collecting and preprocessing the dynamic operation parameters of a hydrogen energy system, and generating an input feature matrix; s2, inputting the feature matrix into a multi-resolution feature sensing module to generate a high-dimensional feature map; s3, inputting the high-dimensional feature map into a dynamic time bending network to generate a parameter incidence matrix; s4, optimizing feature weight distribution based on an adaptive differential evolution algorithm; s5, abnormal distribution is captured through the dynamic graph convolutional network, and a dynamic abnormal graph is generated; s6, simulating an anomaly propagation path, and generating a cascade anomaly analysis result in combination with a dynamic traceability mechanism; s7, a cascade anomaly analysis result is optimized through a grey wolf-firefly hybrid optimization algorithm; and S8, dynamic operation parameters are adjusted based on a final cascade risk prediction result, and hydrogen energy system safety assessment is completed. According to the method, deep learning, a hybrid optimization algorithm and the like are combined, and safety evaluation of abnormal detection and risk prediction of the hydrogen energy system is realized.

The invention relates to the technical field of fault equipment detection, and discloses a power equipment fault detection method and system based on high-precision temperature measurement, and the method comprises the steps: scanning the surface temperature of power equipment through a high-precision temperature measurement device, obtaining a temperature distribution image, processing the temperature distribution image, and obtaining a temperature distribution matrix; carrying out gradient analysis and temperature difference identification on the temperature distribution matrix to obtain a hot spot candidate area; performing clustering analysis on the hot spot candidate region to obtain an initial hot spot region, and generating a static boundary by adopting a boundary tracking algorithm; correcting the static boundary according to the temperature change of each boundary point to obtain a hot spot area; and performing fault risk assessment according to the load data of the power equipment and the temperature data of the hot spot area. According to the invention, through temperature data analysis and region boundary correction, the hot spot region can be accurately identified, and in combination with a machine learning algorithm, the accuracy of power equipment fault risk assessment is improved, and safe and stable operation of a power system is ensured.

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 power system safety early warning, in particular to a power grid risk intelligent early warning and analysis method, system and equipment, and the method comprises the steps: obtaining multi-source heterogeneous data of a power grid, constructing an association relationship and an index through an association key, and constructing a directed graph knowledge base; constructing a risk knowledge graph based on the directed graph knowledge base, and setting a risk rule, so as to carry out risk event and risk level early warning when the target equipment is overhauled; and for the maintenance of the target equipment, determining a comprehensive risk representation index from the equipment risk, the user influence and the load transfer capability, and determining a risk early warning level and generating an early warning list based on the comprehensive risk representation index. Thus, through fusion of multi-source data, an intelligent risk level determination model is established, and efficient, accurate and intelligent risk early warning is realized.

The invention provides an intelligent fault diagnosis and safety protection method for a combine harvester, and the method comprises the steps: carrying out the quantitative analysis of the influence degree of a fault on the system safety and operation continuity through a risk assessment model, calculating a fault danger grade score through combining historical fault data and equipment aging state information, and carrying out the calculation of the fault danger grade score. A five-level danger classification result from slight danger to serious danger is obtained; acquiring system response data after isolation control is executed, monitoring running state changes and load redistribution conditions of residual normal parts, adjusting control parameters through a real-time feedback mechanism, and judging the effectiveness of isolation measures and the maintenance degree of system stability; a closed-loop feedback mechanism is adopted to continuously monitor the fault isolation effect and the system operation performance, key data in the processing process are collected to update fault diagnosis model parameters, the risk level evaluation accuracy and the protection range division rationality are optimized, and a continuously improved intelligent fault diagnosis and safety protection system is obtained.

The embodiment of the invention provides a hyper-parameter adjustment method based on safety reinforcement learning, and the method comprises the steps: firstly converting an optimization problem of a control strategy into a Lagrange problem with a constraint condition, defining a primary loss function and a secondary loss function, and setting a safety threshold value; secondly, combining with a Lagrange multiplier to construct an initial Lagrange function so as to minimize main loss and meet security constraints; secondly, setting an initial value of a Lagrange multiplier by solving a dual problem, and then introducing a damping factor to construct an enhanced function; and finally, after model parameters are initialized, updating the model parameters by using a gradient descent method and updating the multiplier by using a gradient ascent method in iteration, and substituting into an enhanced function to evaluate whether the strategy is optimal, namely, the main loss is minimum and the security constraint is met, until a global optimal solution is found. Therefore, the optimization of the control strategy is realized, the system performance can be improved, the system safety is guaranteed, the oscillation of the learned strategy when the constraint is satisfied is prevented, and the stable proceeding of the reinforcement learning process is ensured.

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 discloses a metering box remote control method, a storage medium and a metering box. The invention belongs to the technical field of power equipment monitoring. The method comprises the following steps: acquiring operation data and environment data of a metering box through multiple sensors, and constructing a multivariable time sequence data set; taking each metering box as a federated node, training a vector autoregression model by using local data, and performing weighted aggregation to generate a global dynamic model; calculating an environment change factor in real time, and triggering model fine tuning to adapt to a sudden change scene; and generating an abnormal confidence score based on residual mahalanobis distance and fuzzy logic fusion, and calculating a maintenance priority in combination with a topological connection relation matrix. According to the method, the problems that a traditional method depends on manpower, models are rigid and data potential safety hazards exist are solved, and the anomaly detection precision, the dynamic response capability and the system safety are remarkably improved.

The invention relates to the technical field of power system safety monitoring, in particular to an electricity utilization potential safety hazard supervision method and system based on an electricity utilization data cloud platform. Multi-dimensional electricity utilization data are collected in real time and preprocessed, and the preprocessed data are subjected to multi-source fusion in combination with a data fusion algorithm; carrying out hidden danger detection on the fused data by utilizing a multi-layer intelligent learning model; a dynamic scoring mechanism is introduced, multi-dimensional scoring is performed on a detection result, hidden danger risk levels are divided, a multi-level early warning mechanism is triggered based on the risk levels, and automatic emergency response is implemented; potential electricity utilization safety hazards can be recognized in time, early warning is achieved, and lagged detection and processing of a traditional method are avoided. By means of intelligent hidden danger detection and dynamic risk assessment, the monitoring accuracy and efficiency are improved, human intervention is reduced, and the timeliness of emergency response is improved.

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.