219 results about "Safety index" patented technology

The invention specifically relates to the technical field of big data analysis, and discloses a water conservancy gate multi-parameter cooperative intelligent monitoring system, which comprises a multi-parameter acquisition module, a multi-parameter processing module, a comprehensive analysis module, an intelligent decision module, an operation and maintenance early warning module and a man-machine interaction module, the multi-parameter processing module is used for calculating flood control and discharge indexes, structure safety indexes and equipment health indexes; the comprehensive analysis module is used for judging gate risk levels; the intelligent decision-making module is used for generating an optimal gate scheduling scheme; the operation and maintenance early warning module is used for constructing a multi-level early warning mechanism; according to the method, parameter coverage is comprehensive, a gate digital twinborn model and a gate opening comprehensive evaluation model are constructed, the gate risk level is evaluated, an optimization strategy is dynamically adjusted through an intelligent decision module, the accuracy of gate risk judgment is improved, and the self-adaptive capacity of the system is improved.

The invention discloses a LoRa-based ship environment multi-parameter intelligent wireless monitoring system, which is characterized in that environment state data and ship operation auxiliary data are acquired through a data acquisition module, and a data processing module is used for preprocessing and standardizing the acquired data to obtain a standardized data frame; the environment safety quantification module calculates a single parameter safety score, a comprehensive safety index and a safety situation level SL, the intelligent LoRa communication module dynamically selects a communication strategy according to the SL and the current link quality and generates and sends a safety data packet, the LoRa gateway receives the safety data packet, redundancy filtering is carried out, receiving metadata is added, a final data record is formed and uploaded, and the LoRa gateway sends the final data record to the intelligent LoRa communication module. And the data center performs visual display and data storage on the final data record, and generates a network optimization instruction based on the network performance data, so that the problem that transmission reliability, real-time performance and low power consumption are difficult to consider in ship environment monitoring is solved, and the intelligent level of ship safety management is improved.

The invention discloses a tunnel structure full life cycle damage detection and safety evaluation method, and belongs to the technical field of damage detection and safety evaluation. The method comprises the following steps: completing installation configuration of a mobile detection platform along the axial direction of a tunnel; generating a moving track and a detection point location distribution scheme of the mobile detection platform; controlling the mobile detection platform to sequentially move to each detection section position according to the detection path, and synchronously starting each detection module to perform continuous data acquisition in the moving process; generating a tunnel three-dimensional mathematical model, a lining structure thickness distribution diagram, a heat radiation anomaly distribution diagram and a crack damage distribution diagram; equally dividing the tunnel three-dimensional model into N detection segments according to a preset interval, and counting the lining thickness mean value, the number of heat radiation abnormal points, the crack length density and the damage level of each segment as safety evaluation indexes; statistical analysis and trend judgment are conducted on the safety indexes of all the segments, abnormal segments exceeding a safety threshold value are recognized, and the risk level of the abnormal segments is determined.

The invention relates to the technical field of computer application, and particularly discloses a chemical industry park safety risk assessment method based on an agent model. The method comprises the following steps: constructing a multi-agent collaborative perception framework, and fusing multi-source heterogeneous data of equipment, environment, materials and personnel to form a park panoramic view under a unified space-time reference; establishing an agent behavior model library embedded with the chemical process mechanism and the safety interlocking logic; an intelligent agent decision strategy is optimized through deep reinforcement learning, and multi-target training is carried out by taking a safety index as a reward function; executing dynamic risk deduction and accident chain simulation in a digital twin environment, and quantifying risk probability and consequences in combination with Monte Carlo sampling; and finally, a visual thermodynamic diagram and an interpretable risk assessment report are generated, weak links are identified, and intervention suggestions are provided. According to the technical scheme, dynamic, accurate and prospective evaluation of the safety risk of the chemical industry park can be realized, and the early warning timeliness and the evaluation reliability are remarkably improved.

The invention belongs to the technical field of intelligent hydropower, and discloses a hydropower station operation period dam safety classification and grading early warning system and method, through man-machine collaborative inspection of an anti-interference optical fiber sensor and an unmanned aerial vehicle, in combination with edge calculation filtering, YOLO model detection and multispectral analysis, data acquisition precision and preprocessing quality are improved; dynamic risk assessment is realized by adopting a fusion formula, a finite element and LSTM time sequence prediction, and the limitation of a fixed threshold value is broken through; according to safety index grading early warning, the Mesh ad hoc network and the GIS system are linked, and it is ensured that response is timely and accurate; self-evolution optimization is achieved through digital twin iteration and sensor and equipment health management, the false alarm rate and maintenance cost are reduced, information isolated island is eliminated, the accuracy and reliability of dam safety early warning are improved, and the problems that in the prior art, a dam early warning system is single in monitoring means and insufficient in data analysis, multi-parameter coupling risks are difficult to dynamically evaluate, and the safety early warning effect is poor are solved. And missing report or false report is easy to occur.

The invention discloses a BIM-based building construction monitoring method, and the method comprises the steps: arranging a data collection device at a construction site, and obtaining multi-source data, such as structural members, construction machinery, spatial positions of operators, construction states, safety indexes and environmental parameters; automatically mapping multi-source data to a BIM three-dimensional model through an adaptive coordinate transformation and semantic recognition algorithm, matching the multi-source data with construction plan nodes, and generating a BIM construction state model reflecting a field state in real time; the system automatically compares the model with a construction plan model, identifies a state and calculates deviations such as progress, position, size, quality and safety; the method comprises the following steps: displaying deviation parts and potential safety hazards in a BIM interface in a highlight manner, superposing rectification suggestions, issuing a construction adjustment instruction confirmed by a manager to a field terminal, and storing monitoring, comparison, early warning and processing records in a database according to a time sequence to form a digital file; and real-time intelligent monitoring and visual management of construction progress, quality and safety are realized.

The invention belongs to the technical field of hydraulic engineering, and provides a dam monitoring method based on data anomaly detection, which comprises the following steps: S1, acquiring multi-source heterogeneous data generated in a dam operation process, and performing time synchronization, data cleaning, missing value filling and normalization processing on the multi-source heterogeneous data; s2, aiming at the preprocessed multi-source heterogeneous data, extracting feature vectors of data modals respectively, and carrying out preliminary splicing and fusion on the feature vectors to form an initial multi-modal feature sequence; s3, inputting the initial multi-modal feature sequence into a mixed space-time diagram attention network, and outputting a high-dimensional space-time feature sequence containing dynamic relevance; s4, inputting the high-dimensional spatial-temporal feature sequence into a multi-view Transform decoder for prediction, and outputting a prediction time sequence of the key safety index; and S5, based on the prediction time sequence of the key safety indexes, comprehensively evaluating the future overall safety state of the dam through a multi-index decision evaluation model.

The invention belongs to the technical field of automatic driving, and particularly relates to an automatic driving decision-making method and device fusing lane dynamic evaluation and safety indexes and a medium. Real-time collision risks are calculated by collecting dynamic data of a vehicle and surrounding vehicles, multi-lane traffic flow data are collected under triggering of specific requirements, and characteristics such as density, speed and flow are extracted; and a lane basic passage score is calculated by fusing the flow and the large vehicle influence, and correction is carried out by combining the lane changing intention of surrounding vehicles to obtain a dynamic lane evaluation result. And constructing a multi-objective decision function based on the safety index and the lane evaluation result, and finally generating and selecting a trajectory with the minimum comprehensive cost value in a decision period for execution. According to the invention, collaborative evaluation of the vehicle close-range safety risk and the lane passing potential is realized, and the lane evaluation can reflect the traffic flow interaction influence. The driving efficiency and the lane adaptability are optimized, and the decision-making ability of the automatic driving system in the traffic environment is improved.

The invention relates to the technical field of traffic engineering facility monitoring, and particularly discloses a traffic engineering facility safety state monitoring system and method based on the Internet of Things, and the method comprises the steps: deploying distributed intelligent sensing nodes comprising multiple types of sensors and edge calculation units at key monitoring points of facilities; nodes collect data by adopting a dynamic self-adaptive sampling mechanism, and an edge computing unit preprocesses the data and performs preliminary abnormity early warning; the data is transmitted to a cloud platform through a hierarchical communication network; the cloud platform performs deep analysis by using a federated learning safety evaluation model, and calculates a safety index to generate an evaluation report; and when the monitoring value exceeds the threshold value, four-stage early warning is triggered corresponding to different inspection or disposal processes. According to the invention, through the whole process optimization of dynamic sampling-layered transmission-federated learning evaluation-multi-stage early warning, efficient, accurate and low-consumption monitoring of the traffic engineering facility safety state is realized, and the contradiction between data privacy protection and evaluation precision is solved at the same time.

The invention discloses a man-machine interaction safety assessment method and system for an intelligent cabin, and relates to the technical field of intelligent driving, and the method comprises the steps: obtaining scene element data which comprises environment elements, vehicle state elements and interaction task elements; generating a dynamic test scene sequence for testing the intelligent cabin man-machine interaction system based on the scene element data; operating a to-be-evaluated intelligent cabin man-machine interaction system under the dynamic test scene sequence, and obtaining multi-dimensional performance data of a user state, an interaction behavior, system performance and a vehicle environment; based on a preset causal graph model, performing causal inference analysis on the multi-dimensional performance data, and quantitatively estimating key factors influencing safety indexes; and according to the result of the causal inference analysis, outputting a safety assessment report containing risk root localization, thereby solving the static limitation of a test scene in the traditional intelligent cabin man-machine interaction safety assessment, and improving the accuracy of safety assessment.

The invention discloses an ESG index evaluation method and system based on multi-source data fusion, and relates to the technical field of medical information management, the system comprises a data acquisition module, a data analysis module, an evaluation module and a report module, the data analysis module is responsible for processing a data set acquired from the data acquisition module; the data analysis module is used for calculating a clinical medical registration missing index, a medical equipment failure rate, a medicine statistics failure rate and an environment monitoring failure rate, in addition, the module is also responsible for generating a hospital network port safety index, and the evaluation module is used for evaluating the clinical medical registration missing index by utilizing indexes provided by the data analysis module and combining evaluation of a hospital network client. All aspects of a hospital are comprehensively evaluated, powerful support and clear guidance are provided for improvement and optimization of a hospital management system, and a report module carries out detailed analysis and arrangement on information of the hospital in the aspects of clinical medical treatment, medical equipment, medicines, patients and environment according to information provided by an evaluation module, so that the information of the hospital in the aspects of clinical medical treatment, medical equipment, medicines, patients and environment is reported. And formulating a summary report containing improvement measures.

The invention discloses a truck driver safe driving evaluation method based on SEDEA, and the method comprises the steps: regarding the vehicle characteristics, behavior characteristics, road characteristics and environment characteristics of a vehicle in a driving process as external resources of a driver safe driving task, and regarding the characteristics of overspeed driving, fatigue driving, distraction driving and the like as risk results after the resources are utilized; the method comprises the following steps: taking resource utilization efficiency evaluation as a core means to measure the safe driving ability of a truck driver, and constructing a weight system containing multiple inputs and multiple outputs and a resource utilization efficiency evaluation model to calculate a safe driving score of the driver; and various safety indexes and quantitative classification management measures of the driver are given based on the model. The model is in close contact with the safe driving management situation of the truck driver, not only considers the resource input and the efficiency output of the safe travel task of the truck driver, but also sets the optimization direction between different inputs and outputs through a weight system.

The invention discloses a real-time monitoring and early warning method and system for hoisting operation of a wind generating set, and relates to the technical field of wind generating set construction, the method comprises the following steps: establishing a digital twin model of a hoisting operation site, collecting state data in real time for dynamic coupling, and constructing a virtual monitoring network; performing short-term advanced parallel simulation in the network to obtain future state trajectory clusters under different potential interferences; extracting a key safety index time sequence from the trajectory cluster, and calculating a future instability risk probability; and when the risk probability exceeds an adaptive early warning threshold, generating multi-level early warning information and triggering active intervention. The technical problem that the instability risk is difficult to recognize and intervene in time due to the fact that the hoisting operation of the existing wind generating set lacks real-time prediction of the system state in a complex environment is solved, and the technical effects of real-time early warning and effective intervention in the hoisting process are achieved by constructing the digital twinborn monitoring network and carrying out risk propagation prediction.

The invention relates to the technical field of natural gas transportation safety, in particular to a hydrogen embrittlement risk online monitoring system and method for a hydrogen-doped natural gas pipeline based on multi-parameter fusion, and the system comprises a sentry sensing unit which adopts a metal short pipe which is the same as a to-be-monitored hydrogen-doped natural gas main pipeline in property, material, process, wall thickness and service history, an internal circulating medium is kept consistent with the main pipeline; the risk sensor is provided with a multi-parameter sensor to obtain risk sensing parameters including hydrogen permeation current, pipe wall micro-strain and temperature signal parameters; the signal processing unit is connected with the sentry sensing unit, receives the risk sensing parameters and analyzes and generates hydrogen embrittlement risk indexes through a risk analysis model; and the risk warning unit is connected with the signal processing unit, and triggers a danger prompt signal when the hydrogen embrittlement risk index is greater than a preset safety index. The method has the effect of meeting the danger monitoring effectiveness of safe operation of the hydrogen-doped natural gas pipeline.

The invention relates to the technical field of hydraulic engineering safety monitoring, and discloses a dam safety monitoring system based on the Internet of Things, which comprises a sensing layer, a network layer, a platform layer and an application layer, the platform layer comprises a data management module, a model training module and an early warning decision module; by arranging the platform layer, seepage pressure, strain, displacement and external environment data can be deeply fused through the data management module, three types of parameters including the structure, the environment and the load are covered, and the problem that a single sensor monitors a blind area is solved; the dam body safety index is obtained after weight correction is introduced through the weighted Bayesian network, the uncertainty fusion problem of multi-source data is solved, the credibility of the data is described through probability distribution, noise interference of single data is effectively avoided, meanwhile, parameter contribution is quantized, and the accuracy of data fusion is improved. The relative importance of osmotic pressure, strain and displacement to dam body safety is determined through weight correction, and the defect that all parameters are treated equally is eliminated.

The invention provides a multi-parameter fusion foundation pit safety state monitoring method based on dynamic weight adjustment, which belongs to the technical field of state monitoring and comprises the following steps: collecting deformation of an enclosure structure, settlement and displacement of surrounding structures, underground water level variation and soil pressure variation in real time; performing fluctuation analysis on the change trend of each parameter, and identifying the current change state; dynamically calculating the weight of each parameter in comprehensive evaluation according to the change trend and historical stability of each parameter; multiplying each parameter by the corresponding dynamic weight, and then carrying out weighted summation to obtain a current comprehensive safety index; and comparing the comprehensive safety index with a preset threshold value, judging a current safety state and triggering a corresponding early warning level. According to the method, a dynamic weight distribution mechanism is introduced, so that the evaluation model can adapt to changes of different construction stages and geological conditions, the accuracy of safety state judgment is remarkably improved, and the occurrence of misinformation and missing report conditions is reduced.

The invention relates to the technical field of power system operation and control, in particular to a safety operation and maintenance scheduling device and method for a wind, light and storage integrated system, and the device comprises a field sensing unit which is used for collecting the real-time operation data of wind, light and storage equipment; the edge calculation and autonomous control unit is used for local data processing, short-time prediction and emergency control when communication is abnormal or a safety index is out of limit; the central optimization and safety management platform is used for fusing and standardizing the data, evaluating the safety state of the system based on a double-domain safety redundancy index, evaluating energy storage life consumption through a coupling model, generating a scheduling instruction considering economical efficiency, safety and life by adopting opportunity constraint optimization, and sending the scheduling instruction to the central optimization and safety management platform; security verification and stable execution of the instruction are guaranteed through shadow simulation, strategy progressive switching and a security instruction shielding module; and the man-machine interaction application unit is used for visual monitoring, strategy recommendation and history redisk.

The invention discloses an identification method and system for a safe power transformation operation scene based on lightweight Yolov7, and relates to the technical field of image target detection, and the method comprises the steps: based on power transformation operation scene data, extracting a key frame through the analysis of the optical flow change, constructing a labeling sample set, training an improved Yolov7 model through the labeling sample set, and obtaining a Yolov7 model; a safety supervision identification model is obtained; carrying out target detection on the real-time power transformation operation scene by using a safety supervision identification model, obtaining position information of an operator and a safety index, analyzing the position information through a preset logic judgment function, and judging a safe operation state of the operator; and generating safety early warning information according to the judgment result of the safety operation state, and displaying the safety early warning information through a human-computer interaction interface. Aiming at the defect of target identification of a Yolov7 basic model in a power transformation operation scene, the detection capability of the model on targets with different sizes can be improved, and the adaptability to a complex operation scene is enhanced.

The utility model aims to provide a pet water dispenser which mainly comprises a water storage device, a heating device for heating water stored in the water storage device, a cooling device arranged above the water storage device, a water drinking tray arranged below the cooling device and the like. Wherein the stored water is heated by the heating device to form water vapor, and when the water vapor flows upwards through the cooling device, the water vapor is cooled to form condensed water, and then the condensed water falls back into the drinking water tray. Compared with the prior art, the pet water dispenser provided by the utility model has the advantage that the safety index of drinking water is improved.

The invention discloses a decommissioned power battery safety index prediction device and a decommissioned power battery safety index prediction method, which form a'test-analysis-application 'closed loop through cross-scale parameter acquisition of a hardware module, nonlinear feature learning of a modeling module and engineering decision support of an interaction module, and solve the problem of high risk of missed judgment of traditional thermal parameter evaluation. And the safety evaluation accuracy of the retired battery is improved from 72% to 96.25%. The hardware test module can realize high-precision synchronous acquisition of microcosmic parameters such as dynamic internal resistance and charge transfer resistance and thermal parameters such as self-heating initial temperature and thermal runaway critical temperature, and ensures the accuracy and integrity of data. The data processing module can realize cross-capacity and cross-system (lithium iron phosphate / ternary) battery safety level quantitative evaluation through Z-score standardization processing in combination with a BSI formula and an SVM model of an RBF kernel function. The scheme has the advantages of nondestructive testing, shortened flow time by 60% and low cost, and a standardized tool is provided for large-scale safety evaluation of the retired battery.

The invention discloses a curve tunnel personalized lane change early warning system and method considering different speed conditions. The method comprises the following steps: collecting multi-source data; driving behavior characteristics are extracted based on the obtained data, and drivers are divided into a rash type, a stable type and a conservative type; establishing a nonlinear basic safety distance model under different speed intervals and different relative speed intervals of the two vehicles, and dynamically correcting a formula of the basic safety distance model in combination with a slope angle and a curve radius to obtain a three-dimensional safety model; generating an early warning rule in combination with the TTC threshold value and the safe distance model; and graded real-time early warning is conducted, specifically, graded early warning is triggered according to the safety indexes and early warning rules matched with the current situation, and early warning comprises visual prompt, sound warning and active braking intervention adopted according to the driving style. According to the method, driving style classification and a dynamic curvature-slope compensation algorithm are fused, and accurate lane changing early warning under different speed conditions in the curve tunnel is achieved.

The invention relates to a cyberspace security protection capability index measurement method, apparatus and device, and a medium. The method comprises the steps of calculating a static protection strength reference based on historical data and an index system; then collecting and analyzing real-time threat intelligence, and quantizing to generate an attacker intention vector; inputting the static protection intensity reference and the attacker intention vector into a non-cooperative game model for deduction, simulating attack-defense interaction and solving an equilibrium strategy, thereby identifying a protection vulnerability set and obtaining an optimal defense strategy suggestion; dynamically correcting the membership degree of each safety index according to the vulnerability point and the intention of an attacker; and fusing the dynamic membership degree and the optimal defense strategy, and performing weighted synthesis to obtain a final safety protection capability index. According to the method, the prospective and dynamic measurement of the network security protection capability is realized by introducing attacker intention analysis and game deduction.

The embodiment of the invention discloses a safety assessment method and device for a receiving station, equipment and a medium, and the method comprises the steps: determining a risk event according to a hazard source in a to-be-assessed receiving station, and enabling the risk event to represent a hazard source out-of-control event; determining a safety index of the to-be-evaluated receiving station according to historical data associated with the risk event of other receiving stations and the technological process of the to-be-evaluated receiving station; a comprehensive influence matrix is determined according to the index scores of the safety indexes, a safety evaluation graph of the to-be-evaluated receiving station is determined based on the comprehensive influence matrix, and the safety evaluation graph is used for investigation operation of risk events of the receiving station and characterization of the causal relationship and the hierarchy of the safety indexes. Based on the safety assessment diagram provided by the embodiment of the invention, accident cause factors, causal relationships and levels can be determined, the accuracy of risk disposal can be improved, and meanwhile, through the safety indexes and the safety assessment diagram, the accident occurrence mechanism can be known, the accident prevention capability can be improved, and the occurrence of accidents can be avoided.

The invention relates to a highway construction area risk situation perception method based on environmental perception, and the method specifically comprises the following steps: deploying a multi-source sensing equipment network along a highway construction area and at a key structure position to collect data, and marking the data; constructing a highway construction area risk situation awareness model, training the model, and jointly optimizing all trainable parameters of the model through back propagation to obtain a trained model; and deploying the trained model to a construction area edge computing platform, outputting a final prediction result through the trained model according to data acquired in real time, judging whether to perform early warning or not according to the prediction result, calibrating a risk position and a risk level, and continuously screening a high-confidence sample through a pseudo-tag self-enhancement module to perform fine adjustment on the model. According to the method, the model is constructed and trained, so that the construction risk can be predicted more quickly and more accurately, workers are timely guided to carry out emergency disposal, and the safety index in the construction process is improved.

The invention discloses an intelligent three-dimensional warehouse monitoring system, and relates to the technical field of intelligent storage automation, and the system comprises an early warning response module which is used for generating early warning signals of different levels according to a three-dimensional warehouse safety index, and triggering a corresponding control instruction; the control execution module is used for receiving the control instruction sent by the early warning response module and adjusting operation parameters of the environment adjusting equipment and the logistics equipment in the stereoscopic warehouse. According to the method, the specific calculation formulas of the environment risk coefficient, the equipment health coefficient and the inventory abnormity coefficient are designed, the stereoscopic warehouse safety evaluation model is constructed based on the three coefficients, the abstract stereoscopic warehouse safety state is converted into a quantifiable and comparable numerical value, a traditional subjective judgment mode is replaced, objective and accurate evaluation of the stereoscopic warehouse safety level is achieved, and the stereoscopic warehouse safety evaluation efficiency is improved. And potential safety hazards can be quickly and accurately identified.

The invention provides a small reservoir standardized operation management evaluation method and system and a storage medium, and the method comprises the steps: obtaining the historical operation data of at least one complete hydrological year of a to-be-evaluated reservoir, combining seasonal hydrological features and historical risk event records, and carrying out the clustering generation of a reference operation probability distribution model of two working conditions: a flood season and a non-flood season; calculating probability density or statistical distance to obtain a running state deviation degree characteristic value; updating a preset index sensitivity matrix, and multiplying the two matrixes to obtain the current weight of each basic evaluation index; setting respective punishment trigger thresholds according to the weights of three types of safety indexes, namely dam body seepage flow, structure displacement and inspection record abnormity; when the deviation degree characteristic values of any two types of indexes exceed the limit at two continuous time points, generating a penalty score through a function; and calculating a three-dimensional initial evaluation score of structure safety, scheduling specification and data completeness, deducting a penalty score from the structure safety initial score, and combining the penalty score with the other two types of scores to obtain a multi-dimensional operation management evaluation value.

The application discloses a kind of control methods of multiple vehicles into CAV special lane under network environment, comprising:1, calculate influence interval and judge whether the headway of adjacent target CAV car of second lane is less than influence interval;2, obtain the speed of adjacent target CAV car at initial time, calculate the speed of adjacent target CAV car at lane-changing stable time;3, calculate the speed index of CAV car into special lane;4, calculate the headway of adjacent target CAV car and its preceding vehicle at initial time and lane-changing stable time respectively;5, calculate the safety index of CAV car into special lane;6, calculate the comprehensive index of CAV car into special lane;7, take the maximum comprehensive index strategy, output lane-changing decision.The application can provide optimal merging decision through information interaction between network vehicles and network vehicles under network environment when multiple adjacent CAV cars simultaneously issue merging demand into special lane, thereby improving the efficiency of CAV car merging into special lane and improving road traffic capacity.

The invention discloses a power distribution network operation safety assessment method, system and device and a storage medium, and relates to the technical field of power distribution network safety operation and renewable energy access assessment, and the method comprises the steps: collecting the multi-source operation data, random noise and condition vectors of a power distribution network, constructing an assessment guide condition according to the multi-source operation data of the power distribution network, and carrying out the assessment of the power distribution network. Constructing an adversarial network model by evaluating guide conditions; inputting the random noise and the condition vector into the adversarial network model, and outputting a photovoltaic and load combined output sample; performing simulation calculation on the joint output sample, and generating a safety index set and an economic index set according to a simulation calculation result; and performing comprehensive risk calculation by taking the safety index set and the economic index set as input, and outputting a safety assessment report. The method provided by the invention has better effects in the aspects of multi-source data fusion processing, extreme working condition simulation generation, risk quantification and closed-loop feedback.

The invention relates to an information processing apparatus, a mobile apparatus, a method, and a program. The present invention implements a configuration in which driver information and environment information are input, and in which a safety index value representing whether a driver in autonomous driving is in a state in which the driver can perform safe manual driving or a time at which the driver can return to manual driving is estimated. The present invention includes: a driver information acquisition unit that acquires driver information of a mobile device such as an automobile; an environment information acquisition unit that acquires environment information of the mobile device; and a safety determination unit to which driver information and environment information are input, and which learns and calculates a safety index value indicating whether or not a driver in the mobile device during autonomous driving is in a state in which the driver can perform safe manual driving. The safety determination unit also estimates a time at which it is possible to return to manual driving, which is a time before a driver in the mobile device in autonomous driving is able to start safe manual driving.

This invention relates to a maintenance and testing device for an aircraft air conditioning system. An engine testing module emits sound waves, which are detected by a piping testing module. The collected signals are analyzed and processed in the main controller to obtain a diagnostic result regarding whether the air conditioning system is damaged. It can accurately determine the physical health status of the air conditioning system and accurately identify any physical damage, with high accuracy and a simple testing method. Simultaneously, it detects temperature parameters at the engine, piping, and cockpit to determine the air conditioning system's operational health index. By detecting the air composition in the cockpit during air conditioning operation, it can detect the cockpit safety index. It can effectively analyze the air conditioning's operating efficiency and the cockpit safety index, ensuring that aircraft that pass the inspection will not pose a danger during missions.