878 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.

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 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 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 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 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 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 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.

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 discloses a power transmission line icing galloping prediction method and system based on an improved time sequence large model, equipment and a storage medium, and relates to the technical field of power system safety monitoring, and the method comprises the steps: obtaining line parameters, and meteorological data and power transmission line icing galloping data in an abnormal accumulation time period; constructing a data feature project by using the line parameters, the meteorological data in the abnormal accumulation time period and the icing galloping data of the power transmission line; performing data synthesis on the meteorological data and the icing galloping data of the power transmission line in the abnormal accumulation time period through time alignment to obtain a comprehensive time sequence; and taking the comprehensive time sequence as input, and obtaining the icing galloping probability and the galloping amplitude of the power transmission line based on the output of the improved time sequence large model. According to the invention, the icing galloping risk early warning of the power transmission line can be rapidly and accurately output, and reliable guarantee is provided for safe operation of a power grid.

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 discloses a security boundary consistency control method for a multi-agent spatio-temporal dynamic system. The method specifically comprises the following technical steps: firstly, constructing a leader-follower spatio-temporal dynamic mathematical model of the multi-agent system based on a partial differential equation; then establishing an abnormal working condition mathematical model containing an actuator fault and a network spoofing attack; deducing to obtain an error dynamic system equation by defining a system consistency control target; constructing an energy function by using a Lyapunov stability theory, and deducing to obtain a system stability criterion; finally, a security boundary consistency control method is designed, and multi-agent space-time dynamic consistency control is achieved. The method effectively solves the problem of high cost caused by arrangement of actuators in a whole spatial domain in a traditional scheme and the problem that a multi-agent system is easily interfered by network attacks in a communication process, and has remarkable application value in engineering practice.

The invention provides a vehicle auxiliary driving test scene generation method and device, equipment and a medium, and relates to the technical field of vehicles, and the method comprises the steps: obtaining the real-time state data and environment data of a vehicle; calculating a dynamic security threshold based on the real-time state data and the environmental data; the dynamic safety threshold value represents the safety risk of vehicle auxiliary driving in a vehicle real-time state dynamic change scene; under the condition that the dynamic safety threshold triggers and adjusts the complexity of the vehicle aided driving scene, guiding a preset test scene generation model to generate a test scene of vehicle aided driving based on the dynamic safety threshold; the test scene is matched with the safety risk. According to the invention, the safety risk can be fed back in real time based on the dynamic safety threshold so as to reduce the braking decision error, thereby improving the accuracy and comprehensiveness of verifying the safety of the auxiliary driving system.

The invention discloses an iced conductor galloping amplitude prediction method and system and a storage medium, and belongs to the technical field of power system safety monitoring and prediction.The method comprises the steps that environmental meteorological data of a power transmission conductor are collected in real time and preprocessed; inputting the preprocessed environmental meteorological data into a pre-constructed conductor dynamic response prediction model under the normal working condition to obtain a galloping amplitude prediction value of the transmission conductor under the normal working condition; inputting the preprocessed environmental meteorological data into a pre-constructed icing thickness prediction model to obtain an icing thickness prediction value; determining a lift coefficient and a resistance coefficient based on the preprocessed wind speed data and the icing thickness prediction value; and calculating an equivalent aerodynamic coefficient according to the lift coefficient and the resistance coefficient, converting the galloping amplitude predicted value of the transmission conductor under the normal working condition in combination with a pre-constructed nonlinear conversion formula, obtaining the galloping amplitude predicted value of the transmission conductor under the current icing condition, and transmitting the galloping amplitude predicted value to a monitoring center in real time.

The invention provides an explosion-proof safety cabinet intelligent control method and system based on deep learning, and relates to the technical field of explosion-proof safety cabinet control, and the method comprises the steps: collecting multi-dimensional state data and environment image data in a cabinet, carrying out the state prediction through employing a multi-layer causal convolutional network of dynamic discrete wavelet transform and a segmented recursive structure, and obtaining a state prediction result; abnormal target detection is carried out by adopting a twin neural network and a local similarity region growing algorithm, and a control track is extracted from an expert strategy library in combination with a Gaussian kernel function and is optimized, so that intelligent control of the explosion-proof safety cabinet is realized. According to the method, the state prediction accuracy, the anomaly detection sensitivity and the control strategy adaptability are improved, and the system safety is enhanced.

The invention discloses a gas-liquid low-temperature two-phase flow precise monitoring and metering test system, which comprises a segmented sensing acquisition module, a high-speed signal analysis module, a pulsation resonance identification module, an active damping control module, a phase change intelligent identification module and a monitoring metering output module, a plurality of groups of separated fluid dynamic response sensors are arranged in a low-temperature two-phase flow pipeline and are respectively positioned at a two-phase mixing inlet section, a middle stable section and an outlet section, so that segmented data acquisition is realized. The method has the advantages that the resonance risk caused by gas-liquid phase change can be accurately identified, a damping structure is regulated and controlled in real time, and system resonance and sensor damage are effectively avoided; meanwhile, through multi-point flow velocity fusion and gas phase molar fraction dynamic correction, high-precision measurement of the two-phase fluid volume flow is achieved, the measurement stability and the system safety are improved, and the device is widely suitable for low-temperature gas-liquid mixed transportation scenes.

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.