11 results about "Average path length" patented technology

This invention discloses a method and system for three-dimensional optimization of pedestrian networks with the goal of improving traffic efficiency. The method includes: acquiring image data of the road system; preprocessing and simplifying the image data to construct a road network dataset containing multiple road nodes; establishing an optimization objective function based on the average path length of the three-dimensional pedestrian passage and the number of turns in the underpass; performing simulation optimization on the objective function according to preset constraints and the road network dataset to obtain the optimal solution composed of road nodes; optimizing the pedestrian network based on the optimal solution, calculating the optimized traffic efficiency index, and optimizing the simulation optimization process based on the traffic efficiency index. This invention not only improves the accuracy and flexibility of three-dimensional pedestrian network optimization but also enhances user comfort and satisfaction, providing a more scientific and flexible solution for urban planning and construction.

The embodiment of the invention provides a social network feature-based team communication complexity analysis method and system, and the method comprises the steps: constructing a command and control social network model, carrying out the fuzzy evaluation of a task unit, determining the overall fuzziness of a task, carrying out the multi-dimensional evaluation of a communication medium, determining a comprehensive value of the richness of the medium, and carrying out the analysis of the richness of the medium. Constructing a task fuzziness-communication medium matching matrix, coupling the task overall fuzziness with a medium richness comprehensive value, determining a medium communication efficiency parameter, and performing complexity analysis on a command and control social network model according to the medium communication efficiency parameter to obtain a command and control social network model; the average path length complexity feature, the network diameter complexity feature and the network density complexity feature of the network are obtained, weighted optimization is carried out on the network edge according to the network complexity feature, an optimized command and control social network model is obtained, and the efficiency of team communication and task execution can be improved.

The invention provides an intelligent identification method and system for abnormal auditing data of a hospital. The intelligent identification method comprises the steps of obtaining a multi-dimensional data set of the hospital, calculating a local outlier factor LOF of each data record, distributing a differential privacy budget and generating a privacy data set; constructing a weighted K nearest neighbor graph based on the privacy data set and the LOF value, and performing community division on the graph; calculating weighted intermediary centrality, community connection strength and average path length of each node, fusing the weighted intermediary centrality, the community connection strength and the average path length into a structural anomaly score, and screening data records of which the scores are higher than a first threshold value as first-level candidate anomaly data; for each piece of first-level candidate abnormal data, mapping the data structure abnormal score into a time window, extracting a time sequence track, and calculating an average time sequence track of the community as a prototype track; outputting a time sequence similarity distance; constructing a confirmation threshold value, wherein the threshold value is adjusted based on community structure characteristics, data differential privacy budget and a structure anomaly score; and when the time sequence similarity distance is greater than the threshold value, identifying the data as abnormal data.

This invention belongs to the field of machine tool cutting state monitoring and intelligent control technology, specifically relating to a method for controlling the cutting state of a tool during the boring process of a space frame member. The method includes: real-time acquisition and preprocessing of multi-dimensional cutting data during the boring process to obtain a basic feature sequence; calculation of the ratio of the average effective cutting power of the sliding window to the instantaneous material removal rate, and obtaining a steady-state cutting energy index after deducting the initial cutting energy benchmark; calculation of the time-dependent decay weight of historical samples using an exponential decay function, and then obtaining the drift compensation boundary probability through root mean square fusion to dynamically correct the isolation forest model's decision boundary; further fusion of the average path length and normalization constant, and obtaining an anti-baseline drift correction anomaly score through the product of the exponential term and the logarithmic suppression term; setting a safety threshold, and performing anti-chipping closed-loop control based on the comparison result between the anomaly score and the threshold. This invention improves the accuracy of tool control during the boring process of space frame members.

The invention discloses a deep reinforcement learning path planning method based on local environment driving, belongs to the technical field of mobile robots, and is used for autonomous navigation and obstacle avoidance of a mobile robot in a complex environment. The method comprises the following steps: firstly, constructing an obstacle contour model according to obstacle point information in a local environment; then constructing a local environment graph structure between the obstacle and the robot by adopting an undirected graph, and extracting spatial feature and deep feature information in a local environment by utilizing a graph attention network; acquiring an optimal local target point by using a local target driving mechanism; and finally, fusing deep reinforcement learning to train an optimal path planning strategy. According to the method, key features in a local environment are learned through local target point guidance and graph attention network reinforcement, the local environment perception ability of the robot is improved, the path planning ability of the robot in a complex environment is enhanced, the average path length can be remarkably shortened while the navigation success rate is improved, and the track smoothness is enhanced.

The application relates to the technical field of distributed computing, and discloses an information collaborative processing method and system in a distributed environment, which comprises the following steps: acquiring node connection relations to form a global connection dataset, calculating node connection degrees, average path lengths and comprehensive load scores; screening high-stability nodes as candidate backbones, and selecting a minimum backbone set to cover all non-backbones; periodically collecting local state characteristic vectors of the non-backbones, collecting global state information, and redundantly storing the global state information in the non-backbones after encoding by using a Vandermonde matrix; monitoring backbone abnormalities to trigger reconstruction, and acquiring encoded blocks from the non-backbones by a new backbone node to recover the global state information through error correction code decoding; and publishing an updated task list and routing instruction according to the recovered information to complete collaborative relationship reconstruction. The method can realize reliable storage and rapid recovery of global state information, and significantly improves the self-adaptability, fault tolerance and collaborative efficiency of a distributed system.

The application discloses a kind of local environment driving-based deep reinforcement learning path planning method, belong to mobile robot technical field, for the autonomous navigation and obstacle avoidance of mobile robot in complex environment.The method first constructs obstacle profile model according to the obstacle point information in local environment;Then local environment graph structure between obstacle and robot is constructed using undirected graph, spatial features and deep feature information in local environment are extracted using graph attention network;Then the optimal local target point is obtained using local target driving mechanism;Finally, the optimal path planning strategy is trained by fusing deep reinforcement learning.The application guides local target point and enhances the key features in local environment by graph attention network reinforcement learning, improves the local environment perception ability of robot, enhances the path planning ability of robot in complex environment, can significantly shorten the average path length while improving the navigation success rate, enhances trajectory smoothness.

The invention discloses a steel production modeling method based on a complex network, and relates to the technical field of industrial production process modeling. The method comprises the following steps: deconstructing the whole process of a steel production system, and abstracting three types of units including production processing, energy / medium conversion and storage and material input / output / buffer into network nodes; extracting a device physical connection and production flow transmission relation, and defining a directed edge; a directed complex network model is constructed and visualized; and calculating degree distribution, a global average path length, an improved average path length and a clustering coefficient, and verifying scale-free and small-world characteristics. According to the method, through a'basic theory layer-engineering application layer 'double-layer index system, steel production whole-process global topological representation and system characteristic quantification are achieved, model support is provided for whole-process optimization, academic preciseness and engineering practicability are achieved, and the method can be popularized to cement, thermal power and other process industrial systems.

The invention provides a multi-AGV path planning algorithm based on dynamic adaptive exploration and course learning, and relates to the technical field of automation and intelligent logistics. According to the method, although a traditional deep reinforcement learning method is preliminarily applied to multi-AGV system path planning, the limitations of low efficiency, poor dynamic adaptability, insufficient cooperative competition relation processing and the like still exist, and the specific expressions are low exploration efficiency, insufficient sample utilization, slow convergence speed and even non-convergence. For this purpose, a multi-agent depth deterministic strategy gradient algorithm (AECL-MADDPG) based on adaptive exploration and course learning is designed, and centralized training is adopted. A distributed execution framework is adopted, the obstacle avoidance capability and the implicit cooperation efficiency of the AGV in a high-density environment are enhanced by sensing the environment congestion degree and decision uncertainty in real time to dynamically adjust the exploration strength, meanwhile, a course learning-based priority experience playback mechanism is constructed, a training normal form from easy to difficult is combined with key experience priority sampling, and the accuracy and the robustness of the AGV are improved. Model convergence is remarkably accelerated; and the robustness of a final strategy is improved. The algorithm established and designed under the actual operation condition in the automation and intelligent logistics field shows significant advantages in key indexes such as convergence speed, task success rate, average path length and the like, and an efficient and reliable solution is provided for the multi-agent path planning problem in a complex dynamic environment.

The invention discloses a combat system vulnerability multi-dimensional evaluation method, and belongs to the technical field of complex system safety evaluation. According to the method, a combat system is decomposed into a physical layer, a logic layer and a cross-layer coupling layer for multi-dimensional modeling; the physical layer calculates topological indexes such as degree centrality and betweenness centrality based on a complex network theory; the logic layer searches a killing chain through a Ullmann algorithm based on a killing chain theory and quantifies indexes such as the chain number and the average path length; and the cross-layer coupling layer analyzes the element failure cascade effect and the capability recovery degree. And integrating multi-dimensional evaluation results through intersection and union set operation to form a vulnerability interval based on the average path length of the killing chain. According to the method, the problems of one-sided single-dimensional evaluation and lack of cross-layer coupling analysis in the prior art are solved, the multi-dimensional quantitative evaluation of the combat system vulnerability is realized, and a scientific basis is provided for system destroy-resistant optimization and key node protection.

The application provides a wind direction sensor abnormal data detection method, comprising the following steps: acquiring Gray code data of a wind direction sensor; selecting one bit value, dividing the one bit value into data units with the same data according to time sequence, and counting the number of data in each data unit as a data point, and storing the data point into a to-be-detected data set; then, a non-replacement sampling is performed to generate a sub-sample, a plurality of isolated trees are constructed until all data points are sampled, and the skewness of the sub-sample is calculated; a split point is generated according to the skewness, the data of the sub-sample is recursively divided into two subsets until a stop condition is met; the average path length of each data point in the isolated tree is calculated to determine whether the data point is an abnormal data point; the foregoing steps are repeated until each bit value of the Gray code data is selected, the skewness is used to dynamically adjust the split strategy, the split point is closer to the abnormal data dense area, and the abnormal data elimination speed and accuracy are improved.