9 results about "Clonal selection algorithm" patented technology

The invention belongs to the technical field of software development and testing, and discloses an artificial intelligence-based software development automatic test case generation system, which is characterized in that an immune heuristic case self-repairing module is adopted, defects are regarded as antigens, antibody cases capable of being self-updated are generated by using a clone selection algorithm, and a gene rearrangement mechanism is automatically triggered when an interface is changed, so that the test efficiency is improved. The details of the use case are adjusted while the core detection logic is reserved; compared with a traditional method, the mechanism can realize use case dynamic adaptation without manual intervention, the maintenance workload is remarkably reduced, and the method is particularly suitable for a complex software system with frequent iteration; the space-time coupling test scene generation engine fuses dynamic scenes such as interaction and state transition of a module and short-time operation after precise coverage login by using a space-time convolutional network; the cross-dimension holographic use case synthesis module integrates multi-source data such as codes, hardware and user behaviors through tensor decomposition to generate a composite use case; functions and performance of software in a complex scene can be comprehensively verified, and test blind areas are remarkably reduced.

The invention discloses an urban rail transit security check system, and the system comprises a passenger flow dynamics prediction module which constructs a modified Navier-Stokes equation passenger flow model based on the passenger flow pressure distribution and a motion vector field, and outputs a congestion entropy value and a risk particle flow; the adaptive millimeter wave scanning array switches a mobile focusing scanning mode or a wide-area screening mode according to double thresholds of risk particle flow intensity and congestion entropy, and the wide-area screening mode generates a basic threat index and marks a to-be-rechecked target; the feature analysis module generates a real-time threat level through contour feature comparison in a mobile focusing mode, and starts lightweight analysis on a to-be-rechecked target in a wide-domain mode to generate a threat assessment report; the immune decision-making center fuses multi-source data and dynamically generates a decision-making antibody through a clone selection algorithm, and a channel topology instruction contained in the decision-making antibody is executed according to a rule. According to the invention, through dynamic scanning decision and intelligent channel regulation and control, collaborative optimization of security check efficiency and security of a large passenger flow scene is realized.

The invention discloses a robot cluster optical network communication method and system based on optical communication data, and relates to the field of robot communication, and the method comprises the steps: carrying out the logic partitioning of a robot based on a coupling communication link and a partitioning instruction of a central control module; the robots establish an optical communication network between the robots in each logic partition according to the logic partitions and an autonomous posture adjustment positioning mechanism; carrying out local ordering on each logic partition based on a partition two-way bubble merging sorting algorithm; determining a minimum robot, a maximum robot and boundary information of each logic partition according to a fuzzy immune clone selection algorithm; data merging and boundary ordering are carried out between the logic partitions; and performing global broadcasting according to the distributed negotiation model. According to the method, the problems of low efficiency, wireless communication bottleneck and consistency of existing distributed sorting are effectively solved through an integrated process of robot partition efficient sorting, boundary collaborative merging, distributed broadcast negotiation and the like.

The present invention provides a method, device and electronic device for image processing, wherein the method includes: obtaining a first image and a second image to be spliced; generating an antigen based on a first feature point in the first image and generating an antibody based on a second feature point in the second image; cyclically executing multiple rounds of clonal selection operations for antigen variation until the iteration ends and the global optimal antibody is determined; the first feature point of the antigen corresponding to the global optimal antibody and the second feature point corresponding to the global optimal antibody are used as alignment point pairs for alignment. The image processing method, device and electronic device provided by the embodiments of the present invention use a clonal selection algorithm to determine the alignment point pairs without the need for secondary alignment, and have good adaptability and robustness; the antigen is mutated, and the local optimal antibody determined in each round is memorized through multiple rounds of clonal selection operations, which has better adaptability and can more accurately determine the alignment point pairs to prevent local optimality.

This invention discloses a method for ultra-dense wireless audio terminal networking and multi-source data fusion, belonging to the field of ultra-dense networking and audio processing technology. The method involves the terminal collecting sound field temporal characteristics, wireless channel status, and location information; performing distributed time-slot collaborative scheduling based on sound field spatial correlation, allocating orthogonal short time slots to suppress co-channel interference; implementing unsupervised clustering of multi-source data and generating fusion weights through a Dirichlet process variational autoencoder; dynamically adjusting point density and transmission power using the Navier-Stokes equation, and optimizing relay links by combining small-world networks and quantum heuristics; compensating for high-frequency audio gaps using a conditional generative adversarial network, and uploading the data after joint encoding of the source and channel; and employing a clonal selection algorithm to achieve self-healing of network anomalies. This invention can reduce interference and redundant transmission, improving audio transmission quality and system robustness in ultra-dense scenarios.

This invention belongs to the interdisciplinary field of artificial intelligence and distributed computing, specifically a method for high-dimensional data feature selection in computing power networks based on immune federated learning. This method includes: constructing a federated immune feature space; designing a dynamic clonal selection algorithm incorporating a spatiotemporal decay factor to achieve the co-evolution and optimization of feature affinity; using a federated graph attention network to quantize and extract feature embeddings; establishing a three-level immune memory bank to enhance adaptability; designing a computing power-aware antibody diffusion scheduling mechanism to control the diffusion range based on node computing power and privacy constraints; and dynamically adjusting the immune response threshold using reinforcement learning. Ultimately, it achieves collaborative and green optimization of high-dimensional features and computing resources under privacy protection. This invention effectively solves the problems of local optima, high communication overhead, and low computing power utilization in cross-institutional high-dimensional data feature selection, significantly improving the performance and generalization ability of federated models while reducing system energy consumption.

The present application relates to the field of intelligent coal mine gas sequence prediction, in particular to a coal and gas outburst risk identification method based on PSO-CSA, which is as follows: the present application uses clonal selection algorithm (PSO) to identify the risk of coal and gas outburst, uses particle swarm optimization algorithm (PSO) to improve the demand of CSA for the identification of coal and gas outburst risk, accelerates the convergence speed, improves the global search ability, eliminates the shock in the later stage of operation, improves the success rate of identification, and effectively identifies the outburst risk. The PSO is introduced into the mutation process of CSA, so that the mutation is no longer dependent on a large number of calculations of binary encoding and decoding, and the antibodies generated in the mutation process can also achieve the purpose of showing high affinity. A coal and gas outburst risk identification method based on PSO-CSA is established, which effectively identifies the risk of coal and gas outburst, has beneficial effects, and can be used to guide coal mine engineering practice.

The invention provides a green computing power network resource demand prediction method based on an immune dot space-time model, and the method comprises the steps: constructing a six-dimensional feature space containing space coordinates (x, y) of computing power nodes, time information, a resource entropy value and task affinity; inputting the six-dimensional feature space into a space-time diagram convolutional network, and performing quantitative extraction and prediction on space-time features of node computing power requirements; adopting a dynamic clone selection algorithm to calculate the initialized antibody concentration of the node, updating the antibody concentration, and iteratively optimizing the calculation power demand result; simulating effective propagation of the computing power demand among the nodes in the computing power network based on a resource scheduling model to migrate resources among the nodes; a Q-learning strategy in reinforcement learning is used for dynamically judging nodes needing resource scheduling. The method combines an immune mechanism, space-time deep learning and reinforcement learning, improves the accuracy of computing power demand prediction and the scheduling efficiency of network resources, and achieves the intelligent optimization management of the green computing power network.

The invention relates to a computing power network scheduling method based on graph clone selection algorithm optimization, and the method comprises the steps: obtaining the node information and task information of a target computing power network, constructing a graph model according to the information, and enabling the time minimization of a maximized task to serve as an optimization target; generating a plurality of initial antibody populations based on the uniformity of the nodes and the randomness of the tasks; determining a fitness function based on the calculation time of each node and the average calculation time; based on Pareto selection, a plurality of antibodies with the highest fitness are selected in proportion through fitness function values for cloning; based on the adaptive mutation probability, mutating the cloned antibody to obtain a plurality of variation populations; and iterating the plurality of variation populations through an elitism strategy until convergence or iteration times reach a threshold value, and obtaining an optimal task allocation diagram corresponding to the optimization target. According to the method, efficient search is carried out in the graph space through the clone selection algorithm, and low cost, low time delay and high reliability in the dynamic computing power network are achieved.