13results about How to "Avoid computational burden" patented technology

The invention relates to the field of multi-robot cooperative scheduling, and discloses a multi-robot cooperative scheduling unmanned underground mechanical garage system and method, and the system comprises a hardware subsystem which comprises a transfer robot, a parking space frame, a vertical elevator, and an entrance and exit junction area; the control system is deployed in the central control server, and the control system comprises a multi-layer dynamic data grid generation module which is used for constructing a discretized data grid corresponding to a garage physical space; and the central data calculation module is used for continuously updating the data of each data layer in the multi-layer dynamic data grid according to the real-time state of the garage. In the regional congestion degree and traffic control layer, the congestion index is calculated according to the density, the speed variance and the dispersion in the movement direction of the transfer robot, and the task gravitation modulation coefficient is generated, so that the tendency of the robot to enter the crowded region is reduced, the adaptive dispersion of the traffic flow is promoted, and the continuity of garage operation is maintained.

The invention belongs to the technical field of laser Raman gas analysis and detection, and particularly relates to a zero drift suppression and ray interference resistance method for laser Raman gas detection. According to the method, the interference of noise and cosmic rays in the laser Raman gas quantitative detection process can be reduced, so that the precision of a laser Raman gas quantitative detection result is relatively high, a data abnormal phenomenon hardly exists, and the probability of zero drift can be reduced. According to the method, the influence caused by noise and cosmic rays can be eliminated in a large probability.

This invention discloses a UAV-GPR noise suppression and imaging method and apparatus based on Hankel trajectory matrix decomposition, belonging to the field of ground-penetrating radar technology. The method first preprocesses the radar echo data and constructs a Hankel-block-Hankel trajectory matrix, achieving noise suppression through singular value decomposition and adaptive truncation and reconstruction based on a quasi-local curvature metric. Then, the derivative threshold reference point method is used to accurately extract the arrival time of surface reflections from the noise-suppressed data, thereby estimating and correcting flight altitude variations. Next, singular value decomposition based on a distance threshold is applied to suppress surface reflections in the altitude-corrected data. Finally, a time-delay summation imaging method based on refraction point approximation in a two-layer medium model is derived, and the filtered data is focused and imaged using the average flight altitude. This invention achieves accurate detection and location of underground targets.

The application provides an environment monitoring method and device based on wireless sensor network joint self-learning, and relates to the field of Internet of Things monitoring.The method comprises the following steps: acquiring an original time sequence and determining the total iteration number of each edge node;at each edge node, the original time sequence is normalized, segmented, aggregated and approximately calculated, and symbolized according to the total iteration number, to generate a discrete finite symbol sequence;the discrete finite symbol sequence is subjected to abnormal probability calculation to generate a single local anomaly detection threshold;at the convergence node, the single local anomaly detection threshold is subjected to weighted fusion to obtain a weighted threshold;the single local anomaly detection threshold is updated through the weighted threshold until the iteration ends, the local anomaly detection threshold is output, and the anomaly detection of the environment is completed.The scheme effectively reduces the storage and calculation costs, realizes the communication between nodes, balances the energy consumption of the edge nodes, and improves the detection accuracy.

The application discloses a self-adaptive gravity compensation trajectory correction method based on a lead angle and belongs to the technical field of aircraft guidance control. The application constructs a dynamic mapping relation between a trajectory deviation and a gravity compensation amount by using the real-time measured lead angle information of the aircraft, adaptively adjusts a gravity compensation coefficient to correct the trajectory, reduces the compensation amount to inhibit the trajectory lifting when attacking a short-distance target, enhances the compensation to offset the falling effect when attacking a long-distance target, improves the problem that the gravity compensation is fixed and does not affect the guidance effect in the traditional method when attacking targets with different distances, reduces the dependence on the difficult-to-measure parameters such as the remaining time in other methods, and improves the striking precision and the shooting range of the aircraft on different targets.

The invention relates to the technical field of geographic information system and seismological observation application, in particular to a data resource pool building system which comprises a data acquisition module, a non-seismic event exclusion module, a physical performance detection module and an abnormal reason tracing module. According to the method, seismic waveform original data are periodically collected, storage duration comparison is carried out, seismic observation data categories are judged based on multi-dimensional parameters, physical performance fault categories are identified through hardware, software and architecture state parameters, and the method specifically comprises the steps of extracting and analyzing data geographic positions to judge scheduling paths, identifying and clearing repeated observation contents, and determining the physical performance fault categories. Calculating waveform stacking pressure and signal writing skew, judging frequency domain sorting delay, calculating a time adjustment factor based on frequency drift and correcting a storage time threshold value; through multi-dimensional data verification and load optimization, dynamic adjustment of a data storage time threshold is realized, and data storage efficiency and system stability are improved.

The invention discloses a Mama enhanced industrial product surface defect detection method based on YOLOv11, and belongs to the technical field of defect detection.The method comprises the steps that preprocessed industrial product surface image data are input into an industrial product surface defect detection model; according to industrial product surface image data, feature vectors are extracted through a plurality of C3k2 modules in sequence, and the feature vectors are input into an SPPF module to obtain the feature vectors; according to the SPPF module, obtaining a feature vector, splicing the feature vector with a feature vector in any C3k2 module so as to obtain a feature vector through the Mama module, splicing the feature vector obtained by the Mama module with any other feature vector so as to obtain a feature vector through the Mama module, and repeating the steps; and randomly selecting the feature vectors obtained by a plurality of Mamba modules, and inputting the feature vectors into a plurality of detection heads in a one-to-one correspondence manner for detecting surface defects. According to the method, both the detection precision and the reasoning efficiency are considered, and particularly, the detection rate and the positioning accuracy of surface defects of complex, tiny and long-strip-shaped industrial products are remarkably improved.

The present application belongs to the technical field of radio frequency fingerprint identification, and aims at the problem of decline of recognition rate of existing methods in low signal-to-noise ratio environment, and proposes a radio frequency fingerprint identification method and system based on feature latent space diffusion model. The method comprises the following steps: after receiving the radio frequency signal, firstly, the system is classified and pre-trained, and the parameters of the feature extraction module and the classification module are updated; then, latent space diffusion pre-training is carried out, the noise time step prediction module and the latent space diffusion noise prediction module are trained based on the feature extraction and classification module, and the parameters are optimized in combination with feature reconstruction, time step prediction and classification loss; finally, systematic fine-tuning is carried out, and all modules are optimized as a whole. Repeat the training until the neural network converges, and output the optimal network parameters for testing. Through the combination of feature extraction and latent space noise suppression, the robustness and accuracy of radio frequency equipment identification under low signal-to-noise ratio conditions are effectively improved.

This invention discloses an online scalable configuration method and system for dynamically changing time-sensitive networks (TSNs), relating to the Internet of Things (IoT) field. The invention proposes an efficient inter-flow conflict detection mechanism based on multi-level flow grouping. By using a correlation analysis of flow period and offset, it avoids traditional conflict detection methods based on link maximal cliques and link hyperperiods, enabling rapid response to large-scale data flow conflict detection needs. It designs an online incremental method based on decoupling routing and scheduling, as well as offline pre-routing, accelerating the computation speed of routing and scheduling schemes while ensuring scheduling space. This invention reduces the computational complexity of data flow set hyperperiod sensitivity and the maximum link slot occupancy in TSNs, accelerating online scheduling and providing efficient conflict detection support for online scalable configuration.

The invention provides a spectral absorption three-dimensional imaging system and method based on parallel light beams and area array detection, relates to the technical field of optical precision measurement and tomography, and aims to solve the problems that high time and high spatial resolution, low system complexity and low cost cannot be compatible at the same time and three-dimensional field distribution cannot be quickly reconstructed with high precision in the prior art. The system comprises a laser emission and modulation module, a beam shaping module, a to-be-measured area, an area array sensor and a data processing module. The light beam shaping module, the to-be-detected area and the area array sensor are arranged on the same plane axis, the light beam shaping module comprises a first collimating mirror and an imaging module which are sequentially connected along a light path, the first collimating mirror forms parallel light beams, and the imaging module performs multi-angle rear two-dimensional imaging; and the area array sensor is electrically connected with the data processing module, is processed and controlled by the upper computer, and reconstructs a two-dimensional image into a three-dimensional field. According to the invention, problems in the prior art are solved, and rapid and high-precision three-dimensional imaging is realized.

The invention provides a digital twinning rapid modeling and simulation system oriented to a small medical scene, and relates to the technical field of image processing, a local digital twinning model is constructed based on multi-source sensing data and a local space reconstruction mechanism, and through real-time mapping of key parameters such as a patient body position, an instrument posture and propulsion depth, a digital twinning model is constructed. And rapid modeling and dynamic updating of the medical operation scene are realized. The system introduces an operation path stability evaluation mechanism, an instant operation risk response evaluation mechanism and a twinborn model effectiveness judgment mechanism, performs joint calculation on multi-dimensional parameters by adopting a weighted fusion algorithm, and realizes real-time identification and intervention on a path offset risk, an instrument tissue action risk and a model failure risk in combination with threshold judgment and strategy triggering. According to the invention, rapid construction, adaptive correction and simulation support of the digital twinborn model can be realized in a small medical scene, and the safety, stability and system applicability of interventional operation are improved.

This invention discloses a method and system for fatigue life analysis in multi-objective optimization design of pressure vessels. By constructing a parameterized model including structural parameters, material parameters, and cyclic load boundary parameters, stress response data at key locations are calculated, and a stress power spectral density function is generated. Based on the power spectral density function, spectral moment parameters and frequency domain characteristic parameters are extracted, and a probabilistic model is used to calculate the cyclic stress distribution, fatigue damage rate, and fatigue life. A multi-objective evaluation vector is constructed by combining structural design variables, stress response, and life results to uniformly evaluate structural quality, strength utilization, and fatigue life. An evolutionary optimization algorithm is used to introduce life contribution weights for iterative screening and updating of design variables. Through iterative control, model updates and optimization convergence are achieved, ultimately outputting a structural design scheme that meets multi-objective constraints and corresponding fatigue life analysis results, realizing the collaborative optimization of pressure vessel structural design and life assessment.

The invention relates to the field of logistics distribution environment perception, in particular to an intelligent logistics distribution method based on a multi-modal logistics knowledge graph. The method comprises the following steps: firstly, acquiring a road image through a visual sensor, and constructing a visual variation observation range; furthermore, a visual characteristic spectrum is selected to extract corresponding visual characteristics in a visual variation observation range, the extracted visual characteristics construct a time domain change curve of the visual characteristics, and a sensitive time domain and a non-sensitive time domain in the driving environment are dynamically captured according to the real-time change condition of the curve. And in a sensitive time domain, image abnormity is determined by comparing the change trend consistency of new energy logistics vehicle vision and radar geometric features. And in a non-sensitive time domain, whether a visual detection target has a corresponding point cloud cluster or not is determined in an interval mode. Therefore, the perception reliability of the new energy logistics vehicle in a complex high-risk period is enhanced, and the system perception reliability, the overall energy efficiency and the resource utilization rate are improved.