9 results about "Flooding algorithm" patented technology

The invention discloses an intelligent main plan scheduling system oriented to the chemical industry, and the system comprises a data center layer which is used for aggregating order, equipment, materials, inventory and process constraint data; the MPS optimizer operates the tabu search module, the step-counting hill-climbing module and the flood algorithm module in sequence or in parallel in the same optimization period, and automatically switches algorithms under the following conditions: when the continuous iteration improvement rate is lower than a set threshold value, the tabu search is switched to step-counting hill-climbing, and the step-counting hill-climbing is switched to step-counting hill-climbing; when the local search step number is not improved, starting the flood algorithm to re-initialize the candidate solution; and the process scheduler is used for splitting the coarse-grained plan output by the MPS optimizer into minute-level process schedules, and transmitting the execution deviation back to the data center layer in real time through a bidirectional feedback loop so as to trigger incremental rearrangement. According to the intelligent main plan scheduling system for the chemical industry, through a hybrid algorithm dynamic switching mode, the thousand-level order rearrangement time is greatly shortened, and the plan response speed is remarkably improved.

The present application relates to a smoke fire detection method based on feature matching and multi-frame time sequence fusion, which is suitable for highway fire detection scene. The method first screens out foreground change pictures based on the steady-state entropy change of video frame pixels. Then, the color and texture features of smoke fire are matched using the fast flooding algorithm to screen out candidate pictures from the foreground pictures. Next, a cache queue is established to store the candidate pictures and their timestamps, and the multi-frame time sequence image data in the queue is sent to the detection model based on the preset trigger condition. Finally, the multi-frame time sequence fusion smoke fire detection algorithm model extracts features and analyzes the time sequence fusion of the input multi-frame images, and outputs the final fire and smoke detection results. The present application realizes efficient detection through low-power cameras, reduces false alarm rate, improves complex environment monitoring distance, and has lower power cost.

The invention provides an automatic ridge line identification method and system based on hydrological analysis and density clustering, and the method comprises the steps: fusing the hydrological analysis with spatial clustering, elevation screening, morphological processing and other multi-step optimization, carrying out the automatic primary selection of ridge points through positive topography and zero convergence accumulation, and effectively removing noise and short branches through the combination of DBSCAN clustering and elevation quantile purification. A set of complete technical scheme of'preprocessing-positive terrain recognition-confluence analysis-clustering denoising-elevation screening-continuous optimization 'is constructed by utilizing a priority flood algorithm to efficiently fill pits and utilizing a skeletonization algorithm to optimize continuity, so that the adaptivity, geometric accuracy and processing efficiency of ridge line extraction are remarkably improved; a more reliable terrain skeleton data basis is provided for wind resource evaluation and fan site selection and layout, and finally the scientificity of wind power generation project planning and power generation capacity estimation is enhanced.

The invention discloses a method and a system for generating a complex and variable-oriented adaptive network. The method comprises the following steps of: constructing a cross-layer routing architecture consisting of a routing layer and an MAC (Media Access Control) layer; after the MAC layer obtains a first-hop neighbor and a second-hop neighbor, respective backbone nodes are selected in a distributed mode, flooding is conducted to the whole network, and declarations of the flooding backbone nodes are grouped and loaded into beacons; the routing layer starts to run on the backbone nodes, performs secondary relay selection on the backbone nodes, notifies the positions of the relay nodes of the whole virtual backbone network, and establishes a local topology table; meanwhile, a zero-interval triggering type cross-layer cooperation flooding algorithm and a constraint flooding algorithm based on fuzzy vision are provided; and finally, selecting an optimal routing path by taking the media occupancy time as a scale for measuring the advantages and disadvantages of the path. According to the invention, the network topology structure can be adaptively, rapidly and automatically adjusted according to the task and node state, the network delay and overhead are reduced, and rapid routing selection and maintenance can be realized on a large scale.

The application relates to a micro-power wireless hybrid routing networking method based on AI decision, which comprises the following steps: step S1, gateway beacon frame flooding issuing and node repeated packet discrimination; step S2, neighbor table collection and node state recording; step S3, AI-assisted path generation; step S4, fixed routing path registration and distribution; step S5, hybrid forwarding mechanism; step S6, AI-assisted path switching decision; and step S7, low power consumption and self-adaptive adjustment. The technical problems of the existing full flooding algorithm, such as serious broadcast storm, high node energy consumption, path redundancy, path failure in a dynamic environment, high maintenance cost and lack of intelligent switching, are solved.

The invention discloses an air quality index prediction method based on BP optimized by an improved dung beetle algorithm. Aiming at the problems that a BP neural network is difficult in parameter adjustment and poor in prediction model precision, a multi-strategy improved dung beetle optimization algorithm FLADBO is provided to optimize a prediction model of the BP neural network, Logistic chaotic mapping is introduced to solve the problem that initial population distribution is uneven, and an elite reverse strategy is introduced to improve population diversity; in the small dung beetle foraging stage, dynamic weights are used, and the optimization capacity of foraging dung beetles is improved; in order to balance the global search capability and the local search capability of the algorithm, the flood algorithm FLA is fused, and the parameter R is nonlinearly adjusted. And the global search capability and the local search capability of the algorithm are improved, so that the prediction precision and the stability of the algorithm are improved.

The present invention discloses an application expansion method, device and equipment for a CFD immersed boundary geometry capture algorithm, comprising the following steps: S1, establishing a solid boundary grid and a fluid grid; S2, dividing each quadrilateral grid unit in the solid boundary grid into two triangles; S3, constructing a first envelope block and a second envelope block; S4, determining a first projection point and a second projection point based on the second envelope block intersecting with the first envelope block; S5, determining the vector of the center point of the grid unit in the fluid grid and the algebraic distance from the first projection point to the center point of the grid unit in the corresponding fluid grid; S6, determining the position attributes of the grid unit in the fluid grid corresponding to each first projection point based on each algebraic distance, thereby judging whether it is in the actual fluid; S7, calculating the position attributes of the remaining grid units in the fluid grid using a flood algorithm; S8, determining the pierced triangular grid unit and the coordinates of the piercing point of the triangular grid unit.

Embodiments of the present disclosure relate to seamless support and node-by-node migration of multiple distributed flooding algorithms in the same network. A first network device of a first layer of the network may receive a modified link state protocol (LSP) data unit, and may determine, based on the modified LSP data unit, whether an origin of the modified LSP data unit is executing the same algorithm as the first network device. The first network device may compute a shortest path spanning tree (SPT) and may set a metric to 1 for links associated with the first network device and the plurality of other network devices based on a determination that the origin of the modified LSP data unit is executing a different algorithm than the first network device. The first network device may truncate the SPT to determine a flooding group, may select a member of the flooding group to flood the modified LSP data unit, and may cause the member to provide the modified LSP data unit to the second layer network device.

The invention relates to a smog fire detection method based on representation matching and multi-frame time sequence fusion, which is suitable for a highway fire detection scene. According to the method, firstly, foreground change pictures are screened out based on steady-state entropy change of video frame pixels; then, matching the color and texture features of the smoke fire disaster by using a fast flooding algorithm, and screening out candidate pictures from the foreground pictures; then, establishing a cache queue to store the candidate pictures and timestamps thereof, and sending multi-frame time sequence image data in the queue to a detection model based on a preset triggering condition; and finally, performing feature extraction and time sequence fusion analysis on the input multi-frame image through a multi-frame time sequence fusion smoke fire detection algorithm model, and outputting a final fire and smoke detection result. According to the invention, efficient detection is realized through the low-computing-power camera, the false alarm rate is reduced, the complex environment monitoring distance is increased, and the computing power cost is lower.