230 results about "Quantitative assessment" patented technology

The invention relates to the technical field of monitoring and early warning, in particular to a fire hydrant monitoring intelligent early warning system based on an anomaly analysis technology, which comprises a flow velocity anomaly identification module, a node collaborative pressure difference detection module, a pressure difference trend independence judgment module, a node degradation feature extraction module and a risk level generation module. According to the method, through correlation judgment of flow velocity deviation and control signals, no-signal recognition of abnormal water taking behaviors and a cooperative analysis mechanism of pressure change of adjacent nodes, a hydraulic disturbance area under non-manual control can be accurately recognized, and through analysis of spatial independence of a pressure response trend, a water flow disturbance area under non-manual control can be accurately recognized. The function degradation level of the device is extracted according to historical data of on-off time delay and response performance, the quantitative evaluation capability of the node function state is enhanced, the capability of finely dividing the risk level is achieved when risk early warning is given out, the risk identification accuracy is improved, and the active discovery capability of early fault hidden dangers is enhanced.

The invention relates to the technical field of slope protection, in particular to a slope protection intelligent detection system based on deep learning. According to the technical scheme, the system comprises a multi-source heterogeneous data sensing module, a data fusion and feature extraction module, a slope state intelligent diagnosis and early warning module, an edge-cloud collaborative computing architecture and a system optimization module. Registration and feature complementation of multi-source heterogeneous data are realized through a multi-modal detection network, an overfitting phenomenon is effectively inhibited through a physical information neural network architecture, risk quantitative evaluation is realized through construction of a dynamic risk evaluation model, early warning response time is shortened in cooperation with a four-level early warning strategy, the false alarm rate is reduced, and the early warning efficiency is improved. Besides, the detection precision of the system in an extreme scene is improved through a physical constraint adversarial training method, so that the environmental adaptability of the system is improved, continuous updating and evolution of the model are realized through an online incremental learning module, and the problem of performance degradation of a traditional system caused by change of geological conditions is solved.

The invention relates to the technical field of automobile network security, in particular to an intelligent networked automobile penetration test path generation method and system based on a knowledge graph, and the method comprises the steps: constructing an intelligent networked automobile intranet penetration test security knowledge graph through fusing multi-source heterogeneous data based on an intranet E / E architecture; generating a potential attack path based on the knowledge graph by using a depth-first search algorithm of dynamic pruning optimization; a multi-dimensional space-time risk assessment framework is utilized to quantify path threat intensity and execution feasibility, and quantitative assessment on the generated attack path is realized; and according to an evaluation result, selecting a high-risk and feasible attack path to carry out an actual penetration test, and discovering security vulnerabilities and weaknesses in the system. According to the method, the attack surface is fully covered, the high-value attack path is efficiently generated, the threat intensity and the execution feasibility are accurately and quantitatively evaluated, and the safety test efficiency and accuracy of the intelligent network connection automobile intranet are remarkably improved.

The invention discloses an embedded hydraulic engineering risk intelligent regulation and control system based on a knowledge graph, and belongs to the technical field of hydraulic engineering. The system comprises a multi-level knowledge graph construction module for constructing a multi-level knowledge graph and forming a dynamically updated knowledge network; the atlas embedding expression module is used for encoding entity attributes, relation characteristics and time sequence evolution characteristics thereof in real time; the risk identification and evaluation module is used for integrating atlas embedded features and expert knowledge rules and analyzing hydraulic engineering risk features and spatial propagation modes thereof so as to realize accurate identification and quantitative evaluation of risks; the reinforcement learning regulation and control decision module encodes the risk state and the regulation and control target into a state-action space based on a deep reinforcement learning framework; the dynamic feedback optimization module is used for automatically updating the entity state and the relationship strength so as to form a closed loop of continuous optimization and knowledge accumulation; and the visual interaction module is used for visually displaying the risk situation, the regulation and control effect and the propagation path of the water conservancy project.

The invention discloses a road network abnormal event identification and risk evaluation early warning method based on vibration perception, and relates to the technical field of urban road safety monitoring. Comprising the steps of collecting road vibration data of a road network through a sensor; preprocessing the road vibration data through Kalman filtering and multi-dimensional feature extraction; abnormal event recognition is carried out on the processed road vibration data through a differentiation algorithm, wherein abnormal events comprise vehicle overload, illegal construction and road structure damage; and carrying out risk scoring on the abnormal events through dynamic weight distribution, dividing the risk scores according to the risk levels, and carrying out graded response. According to the invention, through differential algorithm design, a mixed model architecture and a hierarchical response mechanism are adopted, abnormal event identification, risk level quantitative evaluation and differential early warning processing are realized, the safety state of an urban road network is monitored in real time, and early warning information is provided.

The invention discloses a data marking system and method based on database intelligent management, and relates to the technical field of artificial intelligence, and the system comprises a database multi-source data collection module, a log image feature extraction module, a marking prediction module, a scoring early warning module and a visual feedback module. The data acquisition module acquires a user operation log and an equipment image, the feature extraction module extracts an operation frequency and a red channel pixel proportion, the mark prediction module outputs a user behavior embedding vector and an equipment abnormal probability, and the score early warning module calculates a comprehensive risk score, sets a mark logic and triggers early warning. And the visual feedback module carries out visual display and dynamically updates the node weight of the knowledge graph. Through multi-source heterogeneous data fusion and knowledge graph dynamic modeling, user-equipment behavior association analysis and double risk quantitative evaluation are realized, the marking accuracy and real-time performance are improved, and through visual feedback closed-loop optimization, the manual maintenance cost is reduced, and a self-adaptive intelligent marking system is formed.

The embodiment of the invention discloses a landslide disaster evolution analysis method and system based on a coupling model, and the method comprises the steps: carrying out the multi-source data integration of geological structure monitoring data, landform distribution data and hydrological environment monitoring data of a target seismic region, and generating a comprehensive monitoring data set; based on the data set, establishing an interaction association relationship of the target equation set, and constructing a geology-landform-hydrology coupling model; performing dynamic simulation and quantitative evaluation on the instability probability and the landslide scale of the potential landslide mass by using the coupling model according to a preset scene parameter combination, and generating an image-text annotation quantitative evaluation report; and finally, carrying out vulnerability analysis and risk assessment on the disaster-bearing body based on the report, generating a comprehensive guidance report containing a disaster-bearing body vulnerability spatial distribution diagram, landslide risk prevention and control measure suggestions and a regional space planning optimization scheme, and providing a scientific basis for landslide disaster prevention and control and regional planning.

The invention provides a building fire early warning and protection system based on an internet platform and deep visual identification, and relates to the technical field of internet fire alarm. Through real-time monitoring and quantitative evaluation of a fatal structure failure risk in a fire, an evacuation decision is established on the basis of building structure safety for the first time; personnel are fundamentally prevented from entering a dangerous area about to collapse; on the basis, a principal component analysis method is adopted to replace traditional fixed weights, objective and dynamic fusion of multi-dimensional risks such as structures, environments and behaviors is achieved, and therefore the really scientific and self-adaptive optimal survival path is planned. And finally, by generating a rescue priority index and adaptively adjusting the guidance intensity, the complex risk data is converted into accurate decision suggestions for firemen and efficient evacuation instructions for the public, intelligent and differentiated linkage control is realized, and the overall rescue and evacuation efficiency is remarkably improved.

The invention discloses a high geological disaster chain risk quantitative assessment method adapted to a high-cold and high-altitude environment, and relates to the technical field of geological disaster monitoring and prevention and control, and the method comprises the following steps: S100, building an irradiation and reflection combined observation layer, collecting the dynamic intensity of direct light and reflected light, generating an exposure time sequence fingerprint baseline, and obtaining an exposure time sequence fingerprint baseline; and determining a critical threshold value of the direct light intensity according to the base line. The method comprises the following steps: constructing an irradiation and reflection combined observation layer to extract light intensity characteristics, and generating an exposure time sequence fingerprint baseline; a trigger point boundary is extracted by adopting coherent phase decomposition and anti-fact playback, and a continuous track is reconstructed; the overexposure risk is predicted based on the time-frequency score, and spectrum gating and polarization rotation are linked to regulate and control the exposure rhythm; and reverse phase traction and phase conjugate intervention are implemented in a steady-state window, so that boundary information is maintained and the risk identification precision is improved.

The invention belongs to the technical field of power system risk assessment, and particularly relates to a new energy station broadband oscillation risk redisk assessment method based on multivariate data, which comprises the following steps: performing data acquisition and data preprocessing on a new energy station-power grid coupling system to obtain a historical data set; extracting oscillation characteristic indexes to form index vectors; carrying out oscillation frequency source positioning analysis, calculating a time-frequency spectrum energy function to obtain an oscillation energy spectrum, carrying out main frequency energy track extraction, and introducing a frequency-power cross analysis mechanism to realize accurate identification of an oscillation source device; constructing a source-network coupled transfer function, performing stability margin calculation, and judging the stable state of the coupled system; and based on the index vector and the stability margin, carrying out oscillation risk grade division, and combining a source positioning result to generate an oscillation redisk map. According to the invention, panoramic recovery and risk quantitative evaluation of the broadband oscillation problem of the new energy field station can be realized.

The present invention discloses a full - probability risk quantitative assessment method for the landslide of a muck yard considering the three - dimensional spatial variability of muck, which includes the following steps: conducting borehole exploration of the muck yard site to obtain muck yard data; obtaining random field parameters characterizing the spatial variability of muck; simulating the three - dimensional spatial variation characteristics of muck; based on the limit equilibrium method, conducting the three - dimensional slope stability assessment of the muck yard and estimating the potential landslide volume; simulating the possible migration ranges under various landslide volumes; statistically collecting the life and property data of downstream disaster - bearing bodies, and based on the vulnerability assessment formulas for buildings and personnel, estimating the vulnerability of disaster - bearing bodies according to the landslide intensity; establishing a full - probability risk assessment formula for the muck yard landslide, and conducting quantitative risk assessment and risk rating. The present invention considers the unique spatial variation characteristics of muck, analyzes the stability, potential sliding position and volume from a three - dimensional perspective, and combines with the quantitative vulnerability assessment method to achieve accurate risk assessment.

The invention discloses a building outer wall falling risk detection method and system based on deep learning, and the method comprises the steps: obtaining an original surface image, extracting vertical face geometric features, constructing a homography matrix based on vanishing points, carrying out the geometric correction, and reconstructing an orthographic projection image; performing enhancement processing on the orthographic projection image, extracting an effective detection area and intercepting a detection image; inputting the detection image into a deep learning segmentation model into which edge weight constraint is introduced, and performing semantic segmentation, binaryzation and optimization to obtain a falling region; a mapping relation is established according to the actual physical size of the building outer wall, and pixel information of the falling area is converted into multi-dimensional parameters such as the actual physical area, the elevation, the width and the height; and combining the defect intensity input quantification risk assessment model to calculate a comprehensive risk index, determining a risk level and outputting a result. Automatic identification and quantitative evaluation can be realized, distortion and background interference are eliminated, accurate conversion from pixels to a multi-dimensional physical space is realized, and a scientific basis is provided for safety investigation.

The invention discloses an intelligent pulmonary tuberculosis recognition method and system based on image data, and relates to the technical field of image recognition, and the method comprises the steps: collecting and preprocessing conventional inspection data from different medical devices, and forming a standardized data set; extracting features from the standardized data set by using a deep learning model, and positioning a suspected focus area; based on the positioned suspected focus area, constructing a personalized risk assessment model, and generating a risk score; performing focus quantitative analysis based on the risk score, and generating a diagnosis report; the diagnosis report is applied to clinical practice, and a personalized diagnosis and treatment plan is made; according to the method, the personalized risk assessment model is constructed based on the positioned suspected focus area, the risk score is generated, and quantitative assessment of the health condition of the patient is realized.

The invention discloses a quantitative evaluation system based on a contract document dynamic risk map model, and relates to the field of contract document evaluation. The system comprises a data acquisition and preprocessing module, a dynamic risk map construction module and a quantitative evaluation module. And the data acquisition and preprocessing module is used for performing character recognition on the scanned contract PDF through an optical character recognition (OCR) engine. According to the method, the risk map can dynamically evolve along with changes of internal and external environments by defining the risk entities capable of being updated in real time and the incidence relation and combining a triple updating mechanism of time triggering, event triggering and rule triggering, and compared with a traditional static rule engine, the mechanism can respond to law and regulation updating, industry policy change and enterprise business adjustment in real time, and the efficiency is improved. And the problem that the risk assessment model lags behind the actual risk change is solved.

The invention provides a spartina alterniflora invasion risk grade evaluation method, medium and system, and belongs to the technical field of spartina alterniflora invasion risk evaluation.Intertidal zone environment monitoring equipment is established to obtain multi-dimensional environment data in a tide period, and a tide inundation influence matrix and a tide emerging growth matrix are constructed; a matrix multivariate decomposition technology is combined with a minimum spanning tree algorithm to extract key environment factors and growth factors, a multi-layer neural network is utilized to construct a risk assessment model integrated with a gating weight adjustment mechanism, network parameters are dynamically adjusted through a tidal change influence coefficient, accurate quantitative assessment of the spartina alterniflora invasion risk is realized, and the risk assessment accuracy is improved. A risk early warning mechanism of differential monitoring frequencies is established, continuous improvement of a risk assessment model is realized based on iterative optimization of monitoring frequency adjustment coefficients, and the technical problem of insufficient accuracy of spartina alterniflora intrusion risk assessment in a tidal environment is solved.

The invention discloses a drought disaster risk quantitative assessment method fusing an irrigation disaster reduction effect, and belongs to the field of disaster risk assessment, and the method comprises the steps: obtaining different meteorological data of a target region in a three-dimensional space grid scale, and calculating a drought index; a drought event is identified by using an ST-KMeans clustering method; drought events are screened and combined, the space overlapping degree of the clustered drought region and the historical disaster region is calculated, and risk loss data docking is carried out; generating a drought event characteristic dynamic data set, and measuring the drought event; and in combination with the land coverage and utilization data of the target area and the farmland irrigation area data, extracting irrigation response information serving as a drought adaptability measure, and carrying out irrigation disaster reduction capability quantification and risk assessment. According to the method, irrigation information can be integrated on the basis of drought event identification, the drought slow release effect of irrigation in different time and space scales is quantitatively evaluated, and a scientific basis is provided for drought disaster risk management and agricultural adaptability policies.

The invention discloses an automatic driving risk assessment method and system based on a dynamic Bayesian network, and the method comprises the steps: 1, recognizing a hazard event which may be caused by function missing through an HAZOP method according to each function on which an automatic driving system depends, and constructing a hazard graph model G = (V, E): enabling a root node and a child node of a node set V to represent a trigger node, the leaf node represents a hazardous event node, the trigger node causes occurrence of the hazardous event node, and the directed edge of the E comprises a directed edge of the trigger node pointing to the hazardous event node; 2, constructing a dynamic Bayesian network, and predicting the individual occurrence probability of each node in the hazard graph model in a future time period, the occurrence probability of child nodes on the basis of the occurrence of a father node, and the occurrence probability of the nodes between adjacent moments through the dynamic Bayesian network; and step 3, obtaining the risk value of the automatic driving system at the prediction moment. According to the invention, multi-dimensional quantitative evaluation of risks can be realized.

The invention discloses an AI-driven transport layer security protocol adaptive optimization system, which relates to the technical field of traffic data access and comprises a data access module, a state sensing module, a risk assessment module, an optimization transmission module and an adaptive encryption module. The data access module is used for capturing an original data flow from a network and a host in real time; the state sensing module is used for receiving the original data stream and setting a feature engineering state index; the risk assessment module is used for quantitatively assessing the safety score of the current state according to the current traffic environment state; the self-adaptive encryption module is used for constructing a password strategy library and generating an encryption configuration strategy according to a current traffic state; and the optimization transmission module is used for constructing a parameter intelligent optimization model and controlling algorithm interaction to generate performance scheduling optimization actions.

The invention relates to the technical field of medical message management, in particular to an HPV detection information management method and system based on privacy protection, and the method comprises the steps: obtaining HPV detection information of a target subject, carrying out the feature extraction of the HPV detection information, and obtaining personal basic information and detection sample information; performing encryption processing on the detection sample information to obtain HPV detection encryption data, and performing privacy feature recognition on the HPV detection encryption data to obtain a privacy feature set; performing privacy risk assessment on the privacy feature set to obtain a privacy risk index; acquiring a whole-process information security parameter of the HPV detection information of the target subject; based on the whole-process information security parameters, performing privacy leakage influence quantitative evaluation on the privacy risk index to obtain a privacy leakage risk evaluation index; calling a corresponding privacy leakage risk threshold value based on the personal basic information, and performing leakage risk early warning judgment on the privacy leakage risk assessment index according to the corresponding privacy leakage risk threshold value; the threat identification precision can be improved, and risk early warning is realized.

The invention relates to the technical field of computer systems, and particularly discloses a data resource migration risk prediction method and system, terminal equipment and a storage medium, and the method comprises the steps: respectively constructing explicit dependency features and implicit dependency features of various resources; comprehensive dependency characteristics of various types of resources are obtained, and then the association strength between the resources is calculated; constructing a dynamic semantic association graph based on the association strength among the resources, and updating the affected sub-graphs in response to resource change and operation log update; on the basis of the topological features of the dynamic semantic association graph and the risk features of the resources, in combination with the association strength between the resources, obtaining neighborhood aggregation features of the resources; and calculating a risk score of data resource migration by using the neighborhood aggregation feature after feature enhancement. According to the method, map topological features and resource features are deeply fused, so that risk assessment is changed from qualitative judgment to quantitative prediction, and quantitative assessment of dependency intensity and risks is realized.

The invention relates to the technical field of medical image processing, in particular to an intraoperative risk assessment system based on multi-modal operation image fusion. The method comprises the following steps: acquiring a multi-modal operation grayscale image; determining difference images of the same type; acquiring a change area on the difference image, and performing image projection to obtain a modal change sequence; determining an abnormal coefficient according to the consistency performance of the modal change sequence; performing dynamic time warping matching on the modal change sequence, and determining a midpoint offset coefficient according to the position difference between a central point and a point matched with corresponding warping processing in the sequence; and in combination with the abnormal coefficient and the midpoint offset coefficient, determining an operation influence degree, and in combination with the operation influence degrees of all types of operation grayscale images in the sampling period, realizing risk assessment. According to the method, operation data of different modes are fused, operation scenes which are complex in operation and fine are compressed into simple multi-mode sequences for processing, the quantitative evaluation capability of risks in the operation is enhanced, and the risk evaluation accuracy and timeliness are improved.

The invention relates to the technical field of enterprise license management. The invention relates to an enterprise license data intelligent management and early warning platform. The system comprises a certificate obtaining unit, a historical use collection unit, a behavior tracking unit, a certificate encryption unit and a certificate verification unit. The certificate obtaining unit obtains standard certificate data of an enterprise certificate and obtains historical use records of the enterprise certificate at the same time. Behavior data of historical use requirements are collected and subjected to standard analysis through the data tracking module, risk use requirements are judged after comparison with preset behavior standards, then a risk database is established through the risk assessment module, a risk value of a user is calculated, quantitative assessment and hierarchical management of the license use risk are achieved, and the user experience is improved. Meanwhile, the real-time use requirement, the risk database and the user risk numerical value are combined, the use duration and the encryption frequency are dynamically adjusted, accurate control is achieved, and the potential safety hazard of high-risk behaviors is reduced.

The invention relates to the technical field of warehouse environment risk monitoring, in particular to a warehouse environment risk monitoring method and system, and the method comprises the following steps: obtaining a scheduling table and current operation time of forklift drivers or inspectors in a target warehouse, collecting a task load factor of a current task type and the temperature and noise level of an operation area, and obtaining a task load factor of a current task type; and calculating to obtain a scene fatigue risk value. According to the invention, by analyzing the scheduling data and real-time task load and environmental pressure indexes of the forklift driver and the inspector, the comprehensive influence of the personnel operation state and the environmental condition on the fatigue risk is quantitatively evaluated, so that the risk evaluation basis is more comprehensive and specific, and the oneness of risk judgment is avoided; according to the method, a warehouse layout map and cargo distribution information are obtained in real time, and multi-source risk factors are associated into specific warehouse grid units in combination with readings of fire smoke and toxic gas sensors in the warehouse, so that fusion of finer space granularity and more comprehensive risk information is realized.

The invention relates to the technical field of human resource management, in particular to a human resource management system and method based on a block chain. The method comprises the following steps: firstly, dividing personnel into a plurality of talent unit groups based on functions, professions and levels, performing quantitative evaluation from two dimensions of talent loss risk and post influence degree, and setting evaluation factors such as work satisfaction, occupational development opportunities, work pressure indexes and team relationship scores, so as to obtain evaluation results of the talent loss risk and the post influence degree; calculating a demission tendency index in combination with an analytic hierarchy process; generating an early warning signal based on the evaluation result; and finally, performing credible storage on the whole-process data by utilizing a block chain technology. According to the method, the conversion from passive statistics to active early warning is realized, the evaluation granularity is optimized by introducing the talent unit group, the accuracy of risk identification is improved, and the credibility of data is ensured by using the block chain technology.

The invention discloses a CEMI-based maize drought and flood and high-temperature composite stress risk assessment method, which comprises the following steps: step 1, data identification and quantification: based on daily value data of a meteorological station, constructing maize drought and flood and high-temperature indexes, and identifying and quantifying maize drought, waterlogging and high temperature; step 2, constructing a composite event vibration level index: by fitting marginal distribution of drought, waterlogging and high-temperature strength values, constructing the composite event vibration level index CEMI, including a composite drought high-temperature vibration level index CDHMI and a composite waterlogging high-temperature vibration level index CWHMI; according to the method, agricultural meteorological disaster research is promoted to be converted from a single disaster seed to multiple disaster seeds, occurrence laws of CDHEs and CWHEs are identified by fitting marginal distribution and constructing CEMI, and potential influences of the occurrence laws on corn production are analyzed. Therefore, the multi-disaster coupling stress effect and the danger formation mechanism thereof are disclosed.

The invention discloses a railway hidden danger road section early warning system and method based on edge calculation, and relates to the technical field of railway safety, and the method comprises the steps: collecting railway section information of a railway road section monitored in real time; importing the railway section information into a pre-constructed early warning identification model, and identifying a plurality of early warning type events; wherein the early warning identification model comprises a first analysis unit for processing railway section images, a second analysis unit for processing railway section environment data, and a third analysis unit for processing train operation data and determining risk grade types; a pre-configured judgment and analysis mechanism triggers corresponding color early warning after receiving the risk level type; according to the technical key points, a plurality of prompt signals are generated by accurately capturing hidden danger feature groups in railway section images, and a curve associated with a risk coefficient is also established, so that quantitative evaluation of floating objects is realized, subsequent risk prediction is facilitated, and active early warning and accurate prevention and control of hidden dangers are realized.

The invention relates to the technical field of faults of the Internet of Things, in particular to a work order closed-loop management method and system for automatically handling faults of fire-fighting Internet of Things equipment. The method comprises the following steps: obtaining the equipment type, the located area code and the linkage fire-fighting equipment number, and calculating a fault risk index; the partial derivatives of the weight coefficients are solved according to the objective function, the gradient is formed according to the partial derivatives, and the weight coefficients are updated according to the partial derivatives in the reverse direction of the gradient through the principle of the gradient descent method. And the updated weight coefficient is combined with the maximum accommodation number of people corresponding to the region, the maximum linkage number of single equipment, the equipment type weight corresponding to the equipment type, the real-time number of people corresponding to the region and the linkage fire-fighting equipment number to recalculate the fault risk index, and after the weight is dynamically adjusted, influence factors are comprehensively considered, so that the fault risk index is obtained. More reasonable risk assessment is provided for each fault, risk misjudgment caused by unreasonable weight is avoided, and the accuracy of quantitative assessment of fault influence is improved.

The invention discloses an intelligent construction site real-time monitoring method and system, and relates to the technical field of artificial intelligence, and the method comprises the steps: collecting video data and three-dimensional coordinate data of construction site workers, obtaining posture features and position features, inputting the posture features into a posture recognition model based on a long-short-term memory network, and obtaining a posture recognition result; and outputting the instantaneous attitude category probability of the worker, inputting the position features into a regional risk assessment model based on a support vector machine, outputting the risk level of the position where the worker is located, performing weighted summation on the instantaneous attitude category probability and the quantitative value of the risk level, outputting a comprehensive risk score, and generating an early warning instruction. And marking the position of the corresponding worker on the three-dimensional electronic map of the construction site, and calling the corresponding real-time video data. According to the method, quantitative risk assessment is carried out by combining the attitude features and the position features, accurate judgment of the risk of the worker and quantitative assessment of the scene danger level are realized, and the early warning accuracy and the system reliability are remarkably improved.

The invention discloses a low-altitude fusion airspace collision risk assessment method and system, and relates to the technical field of aircraft operation safety assessment, and the method comprises the steps: carrying out the assessment of a risk assessment index before the operation of an aircraft, and obtaining the operation risk and safety target of the aircraft in a to-be-operated region; according to the operation risk and the safety target of the aircraft in the to-be-operated area, a double-layer protection area collision risk assessment model is constructed, risk analysis is carried out on the aircraft in operation, and the conflict resolution probability is obtained; according to the conflict resolution probability, the collision risk of the aircraft is evaluated, and the minimum safety interval standard and the airspace capacity under different error conditions are obtained. According to the invention, through systematic risk assessment, double-layer protection area modeling and Monte Carlo simulation, refined collision risk quantitative assessment in a complex low-altitude fusion scene is realized.

The invention discloses a high-resolution standardized microarray compound eye lens module and a light field regulation and control system, particularly relates to the technical field of light field regulation and control, and realizes high-precision standardized definition of key parameters of a microlens unit and optical axis micro-angle deviation calibration through a microlens array standardized module. A standardized micro-lens array is used for completing efficient acquisition of multi-view and multi-focus high-resolution light field data of a target three-dimensional scene, and non-consistent temperature control focus drift information and visual axis offset accumulation out-of-control information between micro-arrays are synchronously captured in real time; a light field reconstruction imbalance risk assessment model is cooperatively constructed based on a non-uniform temperature control focus drift coefficient and a visual axis offset accumulation out-of-control coefficient between microarrays, a light field reconstruction imbalance risk assessment index is output in real time, quantitative assessment and dynamic early warning of the imbalance risk degree in the light field reconstruction process are achieved, and an intelligent decision basis is provided for reconstruction regulation and control.