8results about How to "Improve system robustness" patented technology

This invention discloses an AI-based data center operation and maintenance security system, belonging to the field of data center operation and maintenance security technology. It constructs an operation and maintenance intent fingerprint set, combines equipment topology relationships, power connection relationships, and airflow organization relationships to determine the disturbed closed domain, and establishes a counterfactual security twin model based on the disturbed closed domain and a reference equipment group. It performs mapping analysis on multi-source real-time data, extracts interpretable disturbance components, and separates residual abnormal sequences. It achieves anomaly propagation tracking through time-delay coupling relationships, determining the initial source point and propagation chain of the anomaly. Furthermore, it calculates the operation and maintenance intent deviation entropy based on path information entropy and the cross-domain coupling hysteresis gain coefficient based on the frequency domain coherence function, comprehensively evaluates each node within the disturbed closed domain, determines security vulnerability locations, and outputs corresponding handling instructions. This invention can effectively distinguish between operation and maintenance disturbances and abnormal disturbances, achieving accurate anomaly location and proactive intervention, thus improving the security and reliability of data center operation and maintenance.

This invention discloses a collaborative training method and system for autonomous driving planning and control, belonging to the field of autonomous driving technology. The method includes: acquiring the current state of the vehicle, the states of surrounding traffic participants, and reference road information; using a Transformer-based planning network to output a control sequence, which is then used to generate a planned trajectory via a vehicle kinematics model; inputting the planned trajectory into a model predictive control-based neural network controller for dynamic tracking optimization to obtain the executed control quantity and predicted vehicle dynamic state; constructing a control loss and backpropagating its gradient to the planning network; and, during training, layering constraints on the planning cost term, using safety and traffic rule-related terms as hard constraints and comfort-related terms as soft constraints, and employing a multi-stage cost scheduling strategy to adjust the constraint strength or weight. This invention achieves collaborative optimization of planning and control, improves the dynamic executability and safety compliance of the trajectory, and enhances training stability and closed-loop robustness.

This invention discloses an intelligent projection positioning and drilling accuracy control system for blast holes at tunnel faces, belonging to the field of tunnel drilling and blasting construction technology. It aims to solve the technical problems of inaccurate blast hole positioning and large drilling direction deviations in existing tunnel face systems, leading to low construction efficiency. The key technical points are: the control system includes a structured light projector mounted on a tunnel drilling and blasting trolley, at least two industrial cameras, a fiber optic shape demodulation module, and a controller. A fiber optic shape sensing unit is installed inside the drill rod at the end of the drill arm of the drilling and blasting trolley; the structured light projector projects structured light, the industrial cameras acquire binocular images, the fiber optic shape demodulation module calculates the curvature and torsion angle sequence of the sensing unit, and the controller controls the drill arm drive mechanism to correct the drill bit position and direction through 3D reconstruction, coordinate comparison, and real-time deviation adjustment. This invention achieves intelligent positioning and drilling accuracy control of blast holes at tunnel faces, improving the accuracy and efficiency of tunnel drilling and blasting construction.

The present application belongs to the technical field of fire detection and early warning, and particularly relates to a fire hazard intelligent early warning method based on multi-dimensional feature fusion, which comprises the following steps: first, collecting video frame sequences and multi-dimensional normalized environment features of a monitoring area, using a convolutional neural network to extract a visual feature map, and simultaneously predicting a local water vapor condensation potential index based on a temperature and humidity gradient through a BP neural network; analyzing smoke-like activation intensity and absolute smoke change rate, constructing a cross-modal consistency logic gating mechanism, and generating a spatial attention mask matrix to suppress artifact features. The present application can identify and eliminate high-concentration process water vapor interference, reduce the false alarm rate under complex industrial conditions, and improve the physical consistency and robustness of the fire hazard intelligent early warning.

This invention relates to the field of agricultural automation equipment control technology, and discloses an adaptive control system for fruit bag adsorption and gripping based on pressure feedback. The system includes a pressure sensing unit that collects the contact pressure between the vacuum suction cup and the fruit bag, as well as the air pressure in the vacuum pipeline cavity, combining them to form a two-dimensional pressure state vector. An adsorption force estimation module generates real-time effective adsorption force estimates through static mapping and dynamic correction sub-models. An adsorption force adjustment and control module uses a preset target adsorption force as a reference and outputs a control signal through a feedback control algorithm. A parameter management module adapts to different fruit bag materials, providing model parameters, gain coefficients, and control parameters. An execution and protection module drives the vacuum generator and auxiliary gripper to perform actions and generates logs. This invention uses two-dimensional pressure sensing to compensate for the shortcomings of single-dimensional air pressure sensing, effectively improving the reliability of fruit bag adsorption and gripping.

The application provides an AmBC-NOMA communication resource joint optimization method for a high-mobility V2X scene, and relates to the technical field of communication resource joint optimization. In view of the channel dynamic change caused by high-speed movement of vehicles and the influence of the channel dynamic change on short packet communication reliability and rate performance, the application combines reflective communication and non-orthogonal multiple access technology, fully considers the time-varying characteristics of the channel, and jointly optimizes the reflection coefficient, the power distribution coefficient and the block length resource allocation in the system, so that the communication rate performance and resource utilization efficiency under the requirements of short time delay and low error rate are improved. Through the method, the short packet transmission rate of the system can be significantly improved under the conditions of limited time delay and channel uncertainty, and the practical application requirements of the future vehicle networking system for efficient, low time delay, high reliable short packet communication are met.

This invention discloses an intelligent beam tracking method and apparatus for mobile environments, relating to the field of wireless network technology. The method includes: S1 constructing a beam tracking model; S2 training and optimizing the leader and follower agents; S3 acquiring MCS statistics and SSB scan results during the k-th window in real time; S4 constructing a leader state vector; S5 predicting the final leader action; S6 generating a candidate action set for followers; S7 generating follower state vectors; S8 predicting the final follower action; S9 constructing the actual transmission beam; S10 adjusting the MCS to transmit data using the transmission beam; and S11 determining whether it is time to enter the next SSB scan cycle. If yes, return to S3; otherwise, return to S9. This method significantly improves the effective user throughput and system robustness of 5G millimeter-wave systems in mobile scenarios.

The application provides a method for realizing high-precision underwater integrated navigation by fusing AUV kinetic velocity and single-beacon acoustic data, and belongs to the technical field of underwater navigation and positioning. The method comprises the following steps: estimating the speed against water in the carrier coordinate system by using the propeller rotating speed and the motor current, decomposing the speed against water to the navigation coordinate system by fusing the SINS attitude, obtaining the three-dimensional motion velocity of the AUV, and obtaining the preliminary position based on the dead reckoning; adaptively selecting the navigation strategy according to the operation time length and acoustic availability: the pure kinetic dead reckoning is adopted for the medium-short time task, and the single-beacon distance information is introduced for the long time operation, the navigation position after correction is obtained by periodically constraining the dead reckoning result through the geometric projection filtering algorithm of resetting the starting point. Compared with a single data source, the application significantly improves the navigation precision and reliability, and effectively solves the technical problem that the application of the acoustic sensor is limited in the deep sea environment.