8 results about "Minimum risk" patented technology

The invention relates to an AGV scheduling method, system and device based on a laboratory environment and a medium, and relates to the technical field of transportation scheduling, and the method comprises the steps that a plurality of elevator selection nodes and elevator reservation nodes in an optimal path planned to a sample unloading point are marked, and the elevator reservation node is the last elevator selection node of the path; when the AGV dolly arrives at one elevator selection node, calculating and obtaining a risk assessment value of each elevator based on the property of the loaded object and the real-time parameters of the elevator environment sensor, outputting a target elevator selection signal with the minimum risk assessment value, and re-planning an optimal path to the target elevator; and when the elevator arrives at the elevator reservation node, an instruction is sent to the elevator control system to lock the target elevator time period resources. The AGV can be dispatched according to the environment in the elevator in the laboratory, and the influence of the elevator environment on loaded objects is reduced.

The present invention relates to a ship navigation system that simultaneously performs real-time route change and collision avoidance. More specifically, the invention relates to a ship navigation system that simultaneously performs real-time route change and collision avoidance, capable of classifying navigable and unnavigable areas through a data classification unit, calculating the risk level of navigable areas, and providing the shortest route with the minimum risk level.

The application provides a kind of water turbine component crack operation and maintenance method, belongs to water turbine crack maintenance technical field.The method includes: obtaining water turbine component operating parameter, crack characteristics and far field stress, and determining weight distribution coefficient, stress intensity factor and the like by stress analysis.Screen out the components to be evaluated whose weight distribution coefficient, stress intensity factor and the like meet specific conditions, obtain their treatment measures and cost-benefit net present value, determine the risk level score of each measure, and select the minimum risk level score and cost-benefit net present value exceeding the threshold value as the target treatment measure.Finally, the target treatment measure is issued to the user terminal, so that the user can maintain the component.The application accurately evaluates the condition of water turbine component by obtaining relevant parameters and stress analysis, provides a scientific basis for decision-making, also screens key components according to various parameter standards, improves operation and maintenance efficiency, and can also determine the target treatment measure by comprehensively considering risk and cost-benefit, to avoid over-treatment or insufficient treatment.

This invention discloses a confidence reconstruction and minimum risk degradation method for an MCU+SoC architecture, comprising the following steps: Step 1, obtaining the original results; Step 2, acquiring independent states; Step 3, constructing deviation characterization; Step 4, generating confidence and risk envelopes; Step 5, matching degradation actions; Step 6, executing degradation and retaining observable evidence. This invention does not directly accept the original confidence output by the main SoC, but performs secondary verification through independent state vectors obtained from independent MCUs, which is more in line with functional safety and SOTIF engineering design principles. By unifying sensor health, time synchronization, bus status, actuator feedback, and physical constraints into the confidence reconstruction system, it can more accurately reflect the true risk of the system compared to single-point diagnosis or single-point confidence judgment. Through the linkage of risk envelopes and degradation mode libraries, graded degradation is performed according to functional priority, balancing driving safety and functional continuity.

The application discloses a risk management system based on offline reinforcement learning, comprising: an offline data set generation module: generating an offline data set according to a screening strategy and a four-tuple data format; a sequence data generation module: training a supervised learning model according to the offline data set, and generating sequence data according to the supervised learning model; a risk reconstruction module: calculating a risk adjustment value according to the change value of the sequence data and training data, and reconstructing a risk function according to the risk adjustment value; the training data comprises any one or a combination of multiple items of training times, training time, training completion degree; a minimum risk strategy generation module: calculating a minimum risk value according to the reconstructed risk function, inputting the minimum risk value into the supervised learning model, and obtaining a minimum risk strategy function. The application solves the problems of high policy trial and error cost, low interaction efficiency with the real environment and low policy optimization efficiency.

The application relates to the technical field of financial data auditing, and specifically provides a travel consumption risk identification method, system, device and medium based on spatial positioning, which comprises the following steps: collecting reference positioning and consumption positioning data at the two time points of travel application and reimbursement submission, and determining a consumption type; obtaining a differentiated threshold group from a dynamic rule matching engine according to the consumption type and a task city level; calculating a task distance, and triggering a first-level early warning if the task distance exceeds a basic reasonableness threshold; otherwise, calculating a minimum risk distance between consumption points and each risk geographical entity, further calculating a risk ratio of the minimum risk distance to the task distance, and triggering a second-level early warning if the ratio is lower than a threshold value. The application also realizes continuous optimization and hot updating of the risk ratio threshold value through the construction of a structured case library, an optimization model based on a detection rate and a false alarm rate, a Bayesian self-adaptive fine-tuning and policy semantic analysis. The application realizes double-layer progressive risk filtering, and significantly improves the identification accuracy and auditing efficiency of irregular travel risks.

The invention discloses a minimum risk control method of an automatic driving vehicle and the automatic driving vehicle, and relates to the field of automatic driving vehicle control. Determining a second coordinate of the target obstacle according to the distance information and a first coordinate of the distance measuring module under a preset reference coordinate system, so as to determine a second moving speed of the target obstacle in combination with a first moving speed of the autonomous vehicle; and according to the second coordinate, the second moving speed and a preset collision prediction strategy, determining predicted collision time, and finally determining a risk level of the target obstacle to the autonomous vehicle so as to control the autonomous vehicle to execute an operation corresponding to the risk level. According to the scheme, the risk level of the target obstacle for the autonomous vehicle is more accurately determined from three dimensions of the predicted collision time, the coordinates of the target obstacle and the moving speed, so that the autonomous vehicle executes the operation corresponding to the risk level, and the effectiveness and reliability of the minimum risk control scheme are improved.

The invention provides a safety prompt information obtaining method and device and a medium, and relates to the technical field of safety monitoring, and the method comprises the steps: analyzing a personnel state description text of a target object, automatically matching a corresponding target monitoring task, and generating a structured task coding vector; inputting the task coding vector into a risk value prediction model to generate a predicted risk value; combining the predicted risk value, a preset maximum risk value, a preset minimum risk value and a preset early warning threshold interval of the target monitoring task to dynamically calculate an early warning threshold corresponding to the target monitoring task and the target object; generating safety prompt information based on a comparison result of a monitoring index value output by the target monitoring task and an early warning threshold value; the dynamic threshold matching is realized, the monitoring strategy can be adaptively adjusted according to the individual difference and the overall risk state, the missing report rate of high-risk individuals and the false alarm rate of low-risk individuals are reduced, and the accuracy of the generated safety prompt information is improved.