322 results about "Safety assurance" patented technology

The invention discloses a hydraulic motor fault diagnosis method and system based on heterogeneous asynchronous data fusion, and the method comprises the steps: collecting heterogeneous asynchronous data of a sensor network, carrying out the preprocessing of the data, inputting the data into a parallel dynamic pruning residual network, extracting features, and carrying out the preliminary fusion, thereby obtaining an initial feature plane; and inputting the initial feature plane into a multi-connection neural network, fusing data, extracting features, obtaining a final feature plane, using the fused feature data as a training set, and building a feature classification model to test and evaluate the performance of the feature classification model. According to the method, the limitation of a traditional fusion model in asynchronous processing of high-frequency vibration signals and low-frequency thermodynamic data of a hydraulic system is effectively overcome, the robustness of key fault features in a strong noise environment is remarkably improved, and rapid virtual-real mapping of bench test simulation data and online monitoring data is realized; typical faults such as plunger pair abrasion and valve plate cavitation of the hydraulic motor can be accurately supported, and safety guarantee is provided for equipment life prediction and safety control.

The invention provides an online monitoring method and system based on a power transmission line, and relates to the technical field of power transmission line monitoring. According to the method, the heterogeneous sensing terminal, edge calculation, the graph neural network and Bayesian reasoning are combined, multi-source data acquisition, state identification and risk prediction are realized, the fault diagnosis accuracy and the risk early warning capability are improved, and the intelligent level and the safety guarantee capability of power transmission line operation are enhanced; the monitoring data is analyzed in real time through an edge calculation unit to generate a state label, a potential fault mode is recognized by combining graph neural network modeling space-time relevance, a risk factor library is further constructed, and a real-time fault probability graph is generated based on a Bayesian network. And dynamic identification and early warning of risk types such as wire strand breakage, icing overrun and mechanical fatigue can be realized.

The invention relates to the technical field of data processing, and discloses a coal mine area gas control and safety guarantee method and system under complex geological conditions. The method comprises the steps that geophysical prospecting, drilling and logging exploration are conducted on a coal mine area, and geological parameters are obtained; performing statistical analysis on the data, and constructing a three-dimensional gas model; identifying a high-gas area based on the model, and arranging a directional drilling network; differential extraction and anti-reflection measures are implemented according to the drilling network; fusing extraction and sensor data, and predicting a gas change trend by using a neural network; and performing intelligent diagnosis based on the early warning matrix to form a disposal scheme. Under the complex geological condition, the gas distribution rule is accurately mastered, scientific arrangement of directional drilling and optimization design of differential extraction parameters are achieved, meanwhile, a gas early warning decision support system based on artificial intelligence is established, the accuracy of gas treatment and the timeliness of early warning are improved, and therefore safe production of a coal mine is guaranteed.

The invention discloses an enamel product and an intelligent spraying system thereof, and belongs to the technical field of enamel product spraying, and the enamel product comprises a high-precision visual guidance module, an environment intelligent control module and an auxiliary function module, the intelligent environment control module is used for monitoring and dynamically regulating and controlling parameters such as temperature and humidity and dust concentration of a spraying environment in real time, ensuring stable coating quality and reducing energy consumption, and the auxiliary function module is used for realizing accurate spraying track planning and quality control through process connection, safety guarantee and data collaboration. And high efficiency and reliability of the spraying process are guaranteed. On the basis that spraying of the enamel product is achieved, the quality of the whole process is improved, and comprehensive benefits can be optimized.

The invention relates to the technical field of vehicle drive-by-wire chassis, and provides a drive-by-wire chassis safety guarantee method and device, electronic equipment and a readable storage medium, and the method comprises the steps: obtaining various types of vehicle sensor data, carrying out the data fusion, and generating fusion data; based on the fused data, multi-modal anomaly detection is carried out, a fault detection result is generated, and the fault detection result comprises a fault type and a fault confidence coefficient; determining different levels of security control strategies according to the fault detection result; and controlling a redundant actuator system of the drive-by-wire chassis according to the safety control strategy. According to the technical scheme provided by one or more embodiments, the reliability and safety of the drive-by-wire chassis in a complex scene can be effectively improved.

The invention discloses a spacecraft space environment safety guarantee system and method based on three-step calculation, and the system comprises two subsystems: a space environment data service subsystem and a three-step calculation and analysis subsystem, and the three-step calculation and analysis subsystem calls data and an algorithm model from the space environment data service subsystem. The space environment safety guarantee analysis based on the three-step calculation is executed; the space environment data service subsystem comprises a metadatabase and a comprehensive database; the metadatabase comprises space environment space-based monitoring data, space environment foundation monitoring data and data from a specified space environment mechanism; the comprehensive database comprises a space environment numerical calculation model library and a spacecraft business application rule algorithm model library; the three-step calculation and analysis subsystem comprises a sun-earth space environment analysis and calculation module, a spacecraft orbit ring layer environment analysis and calculation module and a spacecraft task influence risk assessment and calculation module, and three-step calculation is executed through the three modules.

The invention provides a digital twin intelligent pump station simulation engine design method and device, relates to the technical field of hydraulic engineering operation management and digital twin, and solves the technical problems of difficult data integration, shallow analysis, weak interaction and insufficient optimization capability in the prior art. According to the method, pump station multi-source heterogeneous data are integrated, and total-factor digital representation is obtained; constructing a multi-dimensional intelligent analysis model group, and obtaining an equipment operation comprehensive analysis result; constructing a virtual-real fusion engine, and generating a real-time data bidirectional mapping relation between the pump station and the digital twin model; actual operation parameters and comprehensive analysis results are compared in real time, a self-adaptive feedback mechanism is established, and operation deviation dynamic correction and optimal energy consumption operation strategy iterative optimization are obtained; and pump station operation, water flow, environment and four-pre business data mimic simulation effects are dynamically presented. The method is used for pump station total factor perception, accurate prediction, intelligent optimization and risk intervention, the operation efficiency is remarkably improved, management is refined, and the safety guarantee capability is achieved.

The invention discloses a virtual electronic channel design method and system based on an AIS (automatic identification system) technology, and the method comprises the steps: carrying out the precise correction of the position of a ship through multi-source data fusion in combination with AIS and GNSS (global navigation satellite system) data, generating a continuous and smooth virtual channel, and achieving the real-time dynamic correction of the channel through an environment adaptation correction term and a Kalman filtering algorithm. The influence of ocean current, wind speed, tide and other factors on the channel is dealt with. The system transmits updated virtual navigation channel data to a ship terminal and a port management center in real time in a low-delay manner through a VHF data link and a 5G edge computing technology, so that safe navigation of the ship under the condition of no physical navigation mark is ensured. In addition, the system can dynamically adjust the channel path, improves the emergency response capability of the port, and keeps the efficient operation of the port. The method is suitable for ship navigation and safety guarantee in extreme environments such as freezing and typhoon, and has a wide application prospect in sea areas which are susceptible to typhoon, such as frozen ports in northern China, East China Sea and the like.

The invention relates to the technical field of tower crane safety monitoring, and discloses a tower crane construction safety guarantee method and system.The method comprises the steps that a multi-tower crane cross operation area is dynamically recognized based on tower crane parameter data and real-time operation state data, and a dynamic distribution diagram of the cross operation area is obtained; based on the tower crane load data and the environment parameter data, tower crane operation stability evaluation is conducted, and an operation stability evaluation value is obtained; performing dynamic collision risk analysis based on the dynamic distribution map and the operation stability evaluation value to obtain a dynamic collision risk index; setting an adaptive safety distance threshold based on the dynamic collision risk index; and grading early warning and intervention control are carried out based on a self-adaptive safety distance threshold. Through collection and fusion analysis of multi-dimensional data, comprehensive perception and dynamic monitoring of a multi-tower crane operation environment are realized, and dispersed tower crane safety monitoring can be integrated into a cooperative system, so that the safety management level of a construction site is remarkably enhanced.

The invention discloses an automatic foreign matter selecting system based on cooperation of vision and a manipulator, and belongs to the technical field of automatic control. Pickled vegetable products are conveyed to the unfolding platform through the lifting buckets, and a plurality of non-overlapped pickled vegetable product areas are formed; preliminary image acquisition is carried out in the primary visual area, grid division is carried out, and foreign matter confidence is calculated; a foreign matter confidence coefficient threshold value is preset to recognize a risky foreign matter pickled vegetable product area grid, and the risky foreign matter pickled vegetable product area grid is transmitted to a primary removal area; after primary foreign matter removal is completed, the pickled vegetable products are turned over and conveyed to a secondary visual area, and the secondary visual area is used for secondarily judging whether foreign matter exists in the turned pickled vegetable products or not. An intelligent judgment mechanism of visual recognition and an accurate physical execution mechanism of the mechanical arm are fused, the stability and the automation degree of system operation are improved, and the quality safety guarantee level in the food processing process is remarkably improved.

The invention provides a traffic control decision-making method, device and equipment based on data analysis, and aims to solve the technical problems of high subjectivity, insufficient data value mining, disjunction of strategy and practical application and lack of continuous learning optimization capability in the traditional traffic control decision-making. Through standardized fusion processing of multi-source traffic data and reverse optimization configuration of a traffic feature library, in combination with a progressive effect evaluation and reverse deduction verification mechanism, a multi-time scale effect tracking and strategy evolution trajectory acquisition system is innovatively established, and a decision cycle mechanism with autonomous learning and dynamic adjustment capabilities is constructed. The association relationship between the traffic state evolution rule and the control strategy is systematically analyzed, and finally an intelligent traffic control decision framework based on operation state data driving is formed; scientific and reliable decision support and technical basis are provided for application scenes such as urban traffic fine management, intelligent traffic system optimization control, traffic jam treatment and traffic safety guarantee.

Typical autonomous systems implement black-box models for tasks such as motion detection and triaging failure events, and as a result are unable to provide an explanation for its input features. An explainable framework may utilize one or more explainable white-box architectures. Explainable models allow for a new set of capabilities in industrial, commercial, and non-commercial applications, such as behavioral prediction and boundary settings, and therefore may provide additional safety mechanisms to be a part of the control loop of automated machinery, apparatus, and systems. An embodiment may provide a practical solution for the safe operation of automated machinery and systems based on the anticipation and prediction of consequences. The ability to guarantee a safe mode of operation in an autonomous system which may include machinery and robots which interact with human beings is a major unresolved problem which may be solved by an exemplary explainable framework.

The invention provides a civil aircraft flight control system dynamic reconstruction method fusing uncertainty quantification and toughness decision, and belongs to the field of aviation electromechanical product reliability engineering, and the method comprises the steps: S1, multi-source uncertainty modeling and optimization; s2, running state monitoring and risk prediction; s3, evaluating the toughness margin of the flight control system; S4, generating a candidate toughness response strategy; s5, multi-attribute utility evaluation based on online model prediction; s6, selecting an optimal robust toughness decision; s7, executing an optimal robust toughness decision; and S8, empirical learning and model adaptive optimization are carried out. The invention provides a novel operation safety guarantee theory and method which deeply integrates uncertainty quantification, a toughness engineering principle and a multi-criterion robust decision-making aiming at severe uncertainty faced by a dynamic reconfigurable civil aircraft flight control system in a complex operation environment and challenge on flight safety. And the safety decision quality and the overall operation toughness of the system under uncertainty can be obviously improved.

The invention discloses an intelligent temperature control method and system, and belongs to the technical field of temperature control, and the method specifically comprises the steps: collecting the related data of a target region in real time; according to the relevant data of the target area, a four-field coupling dynamic prediction model is constructed, the temperature of the target area is predicted, the four-field coupling dynamic prediction model is constructed based on a four-field coupling model and the relevant data, collected in real time, of the target area, and the four-field coupling model is obtained based on simultaneous coupling of a basic field variable model; determining a cooling water pipe flow control strategy according to the predicted temperature of the target area; cooling the target area until the target temperature is reached; the temperature monitoring precision, the prediction accuracy and the control intelligence level of the mass concrete structure in the construction process are remarkably improved, the crack risk is effectively reduced, and the construction quality and the safety guarantee capacity are improved.

Proposed is a calibration method for a camera and a lidar using a calibration board having a specific pattern. The method may include extracting one frame for an image captured by a camera and point cloud data acquired by a lidar, by a data generator, identifying a feature point for a calibration board included in one extracted frame for the image and the lidar, by the data generator, and performing calibration for the camera and the lidar based on the identified feature point, by the data generator. The present method is a technology developed with support from the Ministry of Trade, Industry and Energy / Korea Planning and Evaluation Institute of Industrial Technology (Project No. 20022003 / Project name-Automotive industry technology development project / Project name-Development of industrial autonomous driving and safety assurance technology based on longitudinal and lateral interconnection).

The invention relates to the technical field of closed-loop drift control, and particularly discloses a closed-loop drift control method and system for an autonomous vehicle in an uncertain environment, and the method comprises the steps: building a vehicle kinematic model based on a Frenet coordinate system, and constructing an MPC path tracking controller with a preview mechanism; establishing a three-degree-of-freedom vehicle dynamics model with additional yawing moment; the method comprises the following steps: establishing a UniTile-Ctrl tire dynamic model; designing a robust drift controller by combining LQR and integral sliding mode control on the basis of the whole vehicle dynamic model and the tire dynamic model, designing a torque distribution module by taking minimization of distribution error and tire load utilization rate as targets on the basis of ideal rear wheel rotating speed and additional yawing moment output by the robust controller, and outputting a torque distribution model; according to the drifting vehicle tracking control method, closed-loop drifting control can be achieved, the drifting vehicle tracking control performance and robustness are improved, and safety guarantee is provided for limit control over the uncertain road surface of the vehicle.

The invention provides a campus security risk prevention and control cloud service platform, and relates to the technical field of cloud platforms, and the platform comprises a data processing and transmission module which is used for carrying out data storage, processing and secure transmission based on a cloud platform architecture, and supporting the automatic reconnection and concurrency control of data transmission; the safety guarantee module is connected with the data processing and transmission module and is used for carrying out digital management on civil defense, object defense and technical guarantee information of the campus; the safety risk prevention and control module is connected with the data processing and transmission module and the safety guarantee module and is used for carrying out informatization processing and linkage on campus safety management, safety education and safety emergency processes; and the joint defense and joint control module is connected with the safety risk prevention and control module and is used for realizing data linkage and emergency response command between the multi-level education management department and the school based on a processing result of the safety risk prevention and control module. By constructing the campus safety risk prevention and control cloud service platform, the intelligence and collaboration level of campus safety management is remarkably improved.

The invention discloses an unmanned aerial vehicle-based power grid safety guarantee device and method for live stripping of a lead sheath, the device comprises a stripper main body and a chuck, the lower end of the stripper main body is slidably connected with the upper end of a bottom plate through a guide rail pair, a vertical forward and reverse lead screw is arranged on a support frame arranged on one side of the bottom plate, and the lower end of the stripper main body is connected with the chuck. The two chucks are arranged on the forward and reverse lead screw, a lead screw motor is arranged at the lower end of the supporting frame, the bottom plate is connected with the lower end of a hanging frame, the upper end of the hanging frame is connected with an unmanned aerial vehicle through a connecting rod, and a controller is arranged on the supporting frame. And the controller receives and sends an instruction to enable the lead screw motor and the peeler main body to move. Through deep intelligent integration of the unmanned aerial vehicle platform and the automatic stripping device, remote, automatic and accurate stripping of the insulated wire is realized, the safety of a large-scale power grid can be guaranteed, the labor intensity and the safety risk of operators can be remarkably reduced, and the operation and maintenance level of a power distribution network is improved.

The invention discloses a building construction safety monitoring system based on the Internet of Things technology, and the system comprises the following steps: collecting various types of data of construction personnel, equipment and environment, constructing a unified data sequence, extracting feature information through employing an incremental principal component analysis method, and further employing an elliptical envelope line algorithm by the system, the method comprises the following steps: establishing an anomaly discrimination model in a plurality of local feature spaces, realizing the recognition of abnormal behaviors, integrating noise suppression processing and a dynamic parameter updating mechanism by a system, improving the monitoring accuracy and adaptive ability, combining the states of personnel, equipment and environment on a construction site, generating a corresponding risk level and an early warning control instruction by the system, and improving the early warning efficiency. Real-time risk identification and response linkage is realized, and the safety guarantee level of a construction site is improved. The method has the key characteristics of multi-source data fusion, boundary adaptive updating, risk response linkage and the like, and intelligent monitoring and dynamic intervention of construction site risks are realized.

The invention belongs to the technical field of marine environment monitoring and numerical simulation, and relates to a three-dimensional marine element field reconstruction method based on multivariable empirical orthogonal function decomposition. Comprising the steps of S1, multi-source data collaborative preprocessing; S2, MEOF joint modal extraction; S3, vertical projection operator optimization construction; S4, three-dimensional element field dynamic reconstruction; according to the method, through organic combination of multivariable modal decomposition and physical constraint projection, limitation of precision, efficiency and adaptability in three-dimensional field reconstruction in the prior art is broken through, key technical support is provided for real-time monitoring of the ocean dynamic environment, and the method can be widely applied to the fields of ocean disaster early warning, underwater engineering safety guarantee and the like.

The invention relates to the technical field of aviation safety data analysis and pilot training optimization, provides an aviation safety data and training data integrated analysis method and system, electronic equipment and a storage medium, and solves the problems of poor pertinence of pilot training and poor foresight of safety guarantee. The method comprises the following steps: automatically extracting threats, errors and unexpected aircraft states in aviation unsafe events from multi-source heterogeneous data, and judging a management state; based on the aviation safety knowledge graph, positive and negative competency and behavior indexes corresponding to the managed and unmanaged states are identified through graph calculation; the statistical frequency is analyzed, and a priority list of the security data side is generated; carrying out weighted fusion to obtain a target training list by combining a training data statistical analysis result of the same period; and in combination with the threat priority, generating a training scene configuration scheme containing threat elements and capability items, and forming a course content, evaluation standard and resource optimization configuration scheme. According to the invention, the pertinence of pilot training is improved.

The invention discloses a limited space gas monitoring and emergency response method and system based on multi-modal sensing and intelligent decision, and a storage medium, and aims to solve the problems of one-sided monitoring, low recognition precision and the like in the prior art. The method comprises the steps of system initialization and parameter configuration, multi-modal data real-time acquisition and transmission, fusion analysis and risk identification, leakage source positioning and concentration field modeling, multi-stage dynamic emergency response, emergency process adjustment interaction and system self-learning optimization. The system comprises a multi-mode sensing assembly, an intelligent decision-making module and the like, and can integrate a personnel positioning system and an access control system. According to the method, the risk identification precision is improved through multi-source data fusion, the response pertinence is enhanced through a dynamic emergency strategy, the adaptability is guaranteed through a self-learning and linkage mechanism, and comprehensive safety guarantee is provided for limited space operation.

To provide a safety design device, a safety design method, and a safety design program capable of deriving a security parameter whose safety is guaranteed in encryption design based on a Reused-A-LWE problem using a discrete Gaussian distribution as an error distribution.SOLUTION: A safety design device 1 includes: an inputting portion 11 that receives input of parameters n, m, q, s1, s2 of a Reused -A-LWE problem with a discrete Gaussian distribution as an error distribution; a calculation portion 12 that calculates a parameter sb of the LWE problem with the discrete Gaussian distribution as the error distribution from the parameter s1 and a constant C; an acquiring portion 13 that acquires a security parameter λ of the LWE problem by a calculation formula that guarantees predetermined safety; and an outputting portion 14 that outputs the acquired security parameter λ as a security parameter of the Reused -A-LWE problem.SELECTED DRAWING: Figure 1

The invention discloses a method for comprehensively scoring vehicle driving based on multiple factors, and relates to the technical field of vehicle insurance, and the system comprises the following components: a data collection module, a risk scoring model module, a scoring system module, a technical framework support module and a safety guarantee module. By integrating a plurality of data collection modules, including an intelligent sensor and a multi-source data fusion technology, driving records, driving experience, basic information, vehicle types and value data of drivers can be comprehensively collected, and meanwhile, the risk scoring model module applies an improved multi-factor weighted risk assessment algorithm, comprehensively considers a plurality of factors, and improves the risk assessment efficiency. And the psychological and health condition data of the driver is introduced, so that the risk assessment is more accurate and comprehensive, and the insurance company and the traffic management department can identify the high-risk driver and vehicle more accurately, thereby taking corresponding risk management measures.

Disclosed in the present invention are a non-technical source hierarchical rule base system and risk assessment method for critical information infrastructure. The present solution comprises: first, constructing a rule base of first-level index attributes and second-level rule attributes, so as to form a hierarchical rule base model; second, further classifying and analyzing non-technical source data, and using a confidence rule base model to assess non-technical source rule indexes and safety of a critical information infrastructure system; and finally, with regard to the safety risk situations of critical information infrastructure institutions, fusing and analyzing technical source information and non-technical source information, so as to provide a more accurate risk assessment result. On the basis of a critical information infrastructure safety assurance system, the present solution classifies rules into different levels, so as to improve the detection efficiency and accurate assessment of rule bases.

The invention relates to the technical field of flow safety guarantee of offshore oil and gas fields, and discloses a control method for a throttle valve of a gathering and transportation riser system of an offshore oil and gas platform, which comprises the following steps: acquiring the average gas phase flow and the average liquid phase flow of a riser in the gathering and transportation riser system of the offshore oil and gas platform during a serious slug flow period; target pressure drop values of the two ends of the throttling valve of the stand pipe under the standard working condition are obtained; according to the average gas phase flow and the average liquid phase flow, the valve local resistance coefficient when the pressure drop at the two ends of the throttling valve is controlled to reach the target pressure drop value during the serious slug flow period of the vertical pipe is determined, and the valve flow coefficient corresponding to the valve local resistance coefficient is determined; wherein the valve local resistance coefficient is used for indicating the valve resistance when the throttle valve is controlled, and the valve flow coefficient is used for indicating the flow capacity of the throttle valve; and the target opening degree of the throttling valve is determined through the valve flow coefficient, and the throttling valve is controlled to be adjusted to the target opening degree so as to eliminate the serious slug flow.

The invention discloses a road traffic safety intelligent early warning method and device based on environment characteristics, and relates to the technical field of municipal roads, and the early warning steps are as follows: collecting road environment data based on a sensor, including road surface condition data and motor vehicle traffic flow data; performing size judgment on the traffic flow data, establishing a congestion threshold value, dividing and marking road sections with traffic flows greater than the congestion threshold value, and uploading the road sections to an unmanned aerial vehicle system; the unmanned aerial vehicle carries a meteorological sensor, constructs a meteorological monitoring network, carries out flight path planning based on the divided and marked congested road sections, collects meteorological environment characteristic data of the congested road sections, and carries out traffic safety early warning on related vehicles. According to the method, in the aspects of traffic management and safety assurance, the congested road sections are marked to assist a traffic department in planning and dispersion in advance, the road passing efficiency is improved, early warning is conducted in time to remind vehicle owners of prevention, accident risks are reduced, personnel and property losses are reduced, and long-term effective operation is achieved. And the road traffic safety management level is comprehensively improved.

According to the zipper tooth detection device, a zipper head driving assembly comprises a movable frame, a connecting frame and a linear driver, the movable frame is in transmission connection with the linear driver through the connecting frame and is in sliding connection with the connecting frame, the movable frame and the zipper fixing direction are arranged in a crossed mode, and a zipper head clamping mechanism is arranged at the tail end of the movable frame; the fixed part is fixedly connected with a zipper head of a to-be-detected zipper; a first spring is further arranged between the movable frame and the connecting frame, and when the movable frame and the zipper head are fixed, the first spring can drive the movable frame to be pre-tightened in the direction of the front face or the back face of the zipper head. According to the zipper tooth detection device, when the movable frame is driven to move back and forth to execute the opening and closing action of the zipper, the first spring drives the movable frame to pull the zipper head outwards, the simulation effect on the actual use action of the zipper is improved, the test is more suitable for the actual application scene, and the test efficiency is improved. And the guiding capability of the test result in the aspects of zipper quality control and safety guarantee of related products is improved.

Embodiments disclosed herein provide an organic matter processing apparatus and method for the use thereof to convert organic matter into a ground and desiccated product. This can be accomplished using a bucket assembly that can grind, paddle, and heat organic matter contained therein. An air treatment system is provided to treat the air interacting with the organic matter. The processing apparatus is outfitted with sensors and switches that provide feedback data to a processing unit and a safety monitor. The feedback data is used to monitor the operating conditions and the status of various components, as well as control the operation of the processing apparatus. In addition, the feedback data is used to enforce a safety protocol.

The invention provides a mining disturbance surrounding rock bearing structure analysis method and a mining disturbance surrounding rock bearing structure analysis system, and relates to the technical field of surrounding rock bearing structure analysis. Through intelligent risk prediction of deep learning methods such as LSTM and the like, whole-process refined management and control of the surrounding rock bearing structure under roadway mining disturbance are realized; in combination with fault zone distribution, rock mass damage evolution and disturbance working conditions, the scheme can identify potential damage surfaces in real time and dynamically correct key geomechanical parameters, and it is ensured that the supporting structure design is highly matched with the actual risk; through hierarchical risk early warning and multi-objective optimization measures, the timeliness and accuracy of disaster early warning are improved, and accurate configuration of support resources and collaborative minimization of economic loss are effectively realized, so that the safety guarantee and management efficiency in a complex underground engineering scene is enhanced.