654 results about "Swarm intelligence" patented technology

The invention discloses a bionic group intelligent low-altitude logistics unmanned aerial vehicle cluster anti-wind interference cooperation method, and the method comprises the steps: collecting the historical flight data and three-dimensional wind field data of an unmanned aerial vehicle cluster, and generating a bionic formation feature set with a wind field label; inputting the bionic formation feature set into a swarm intelligence model fused with fluid mechanics, and generating a dynamic formation topology instruction; according to the dynamic formation topology instruction, adjusting the relative position and attitude angle of each unmanned aerial vehicle through a distributed cooperative control algorithm, and generating an anti-wind disturbance cooperative flight state; and continuously monitoring the deviation between the three-dimensional wind field change and the cooperative flight state, dynamically correcting the weight of the formation density-anti-wind disturbance intensity mapping relation through a reinforcement learning algorithm, updating a dynamic formation topology instruction, and realizing adaptive control of bionic group anti-wind disturbance cooperation. According to the embodiment of the invention, high-disturbance-rejection cooperative flight of the unmanned aerial vehicle cluster in the dynamic wind field can be realized, the formation energy consumption is reduced, and the obstacle avoidance capability under the sudden wind condition is improved.

The invention discloses a harbor flexible resource distributed cooperative scheduling method based on swarm intelligence, and the method comprises the steps: obtaining harbor flexible resource basic data and real-time operation data, and carrying out the data preprocessing and fusion; a hierarchical prediction architecture is adopted to predict ship arrival, load demands and renewable energy output, and prediction confidence is calculated; a multi-objective optimization model is constructed based on the prediction result, and a scheduling strategy is solved and generated; and decomposing the scheduling strategy into edge execution instructions and carrying out real-time monitoring and evaluation. Through the multi-layer data processing, prediction and optimization framework, the utilization efficiency of flexible resources in the harbor district and the operation reliability of the system are improved, and the operation cost is reduced.

The invention provides an on-line monitoring method and system for crack propagation of silicon-based new material equipment in a high-temperature environment, and relates to the technical field of crack detection, and the method comprises the steps: collecting stress distribution data through arranging a stress sensor, and generating a stress field distribution diagram by using an attention mechanism deep neural network model; identifying a stress concentration region based on a region growing algorithm, when a stress value exceeds a preset threshold value, acquiring temperature distribution data, extracting temperature distribution characteristics through a deep mixed probability model, performing multi-modal data fusion in combination with a stress field distribution diagram, and establishing a crack propagation prediction model by using a graph structure neural network. The crack propagation rate and direction are solved through a swarm intelligence optimization algorithm, when the crack propagation rate exceeds a preset threshold value, a control system adjusts equipment operation parameters, the sampling frequency of a sensor is adjusted according to the crack propagation direction, and model parameters are updated through an incremental learning method to achieve real-time monitoring.

The invention relates to the technical field of power adapter temperature management, and discloses a gallium nitride power adapter temperature management system comprising a dynamic thermal model module used for accurately predicting the temperature change trend of each key component; the multi-level temperature monitoring network module is used for forming a complete temperature distribution diagram; the multi-device collaborative sensing module is used for generating a sensing result of the whole heat dissipation environment; the self-adaptive air duct structure module is used for optimizing an air flow path; the heat dissipation path module based on swarm intelligence is used for calculating and generating an optimal multi-device collaborative heat dissipation path by applying a swarm intelligence algorithm; the self-adaptive heat dissipation control strategy module is used for realizing multi-target balance optimization of temperature, noise and energy consumption; the problem that heat dissipation is difficult when multiple adapters are used in a centralized mode is solved, heat dissipation efficiency is improved, energy consumption is reduced, and noise is reduced.

The invention relates to the technical field of simulation, and particularly discloses a real-time simulation method for detecting photoelectric tracking equipment, which comprises the following steps of: performing feature decoupling processing on photoelectric signals through a dual-channel adaptive neural network architecture, and constructing an equipment mathematical model by adopting a dynamic gating fusion mechanism based on a processing result; the method comprises the following steps: establishing a multi-physics field coupling simulation environment based on a heterogeneous computing architecture, introducing an equipment mathematical model, performing three-field co-evolution through light transmission modeling, electromagnetic field distribution calculation and target motion prediction, and generating a dynamic test scene containing space-time relevance; according to the method, a mode of combining mixed feature analysis and fuzzy reasoning is adopted, multi-dimensional performance indexes are extracted from simulation data, and multi-target dynamic optimization is carried out through a strategy of combining a quantum evolution algorithm and swarm intelligence optimization; by means of a digital twin platform and a hardware-in-loop interface, real-time verification and closed-loop optimization are achieved, and the response speed and tracking precision of photoelectric tracking equipment in a complex environment are greatly improved.

The invention provides a sequential network traffic prediction method and system based on swarm intelligence parameter optimization, and relates to the technical field of network traffic prediction. The method comprises the following steps: acquiring indexes such as throughput packet loss rate and round-trip delay of a target link by using a network probe, and performing deletion filling normalization and multi-scale decomposition to obtain a standardized traffic sequence; calculating information entropy, constructing a traffic complexity feature vector, and dividing a training set and a verification set; constructing a hybrid depth prediction model composed of a one-dimensional convolutional network and a gating cycle unit, and establishing a hyper-parameter search space; using particle swarm optimization and entropy-driven inertia weight adjustment and mutation probability mapping to reconstruct a speed and position updating strategy, and iteratively outputting a global optimal hyper-parameter; and generating a benchmark prediction result according to full-amount training, extracting a residual error, training a nonlinear residual error compensation model to carry out superposition correction and reverse normalization, obtaining a final flow prediction result, and improving prediction precision and generalization ability.

The invention provides an intelligent inspection and diagnosis system, method and device for a photovoltaic power station unmanned aerial vehicle and a medium, and the system comprises a heterogeneous perception fusion module which is used for constructing digital mirror image mapping of a power station global physical field through multi-mode sensor space-time coding and electromagnetic fingerprint matching; the group intelligent decision module is used for realizing autonomous task negotiation and anti-fragile path evolution of a multi-unmanned aerial vehicle cluster based on a dynamic Bayesian game model; the embedded diagnostic kernel module is used for running a quantization feature extraction algorithm at an edge computing node to realize component-level defect subsurface-level diagnostic reasoning; the superbody self-evolution module is used for continuously reconstructing a diagnostic knowledge graph by means of a genetic programming framework to realize Darwin asymptotic optimization of a system cognitive architecture; the problems that in unmanned aerial vehicle inspection, fault type judgment and accurate geographic positioning cannot be conducted online, photovoltaic array arrangement changes and sudden shielding scenes cannot be self-adapted, and transmission bandwidth limitation and analysis lag are likely to happen in a 5G weak coverage area are solved.

The invention discloses an on-load tap-changer vibration fault diagnosis algorithm based on tensor feature and adaptive weighted Stacking integration, relates to the technical field of on-load tap-changer fault diagnosis, and is used for improving the fault diagnosis precision. Comprising the following steps: S1, data acquisition; s2, feature extraction; the method comprises the following steps: extracting multi-scale time-frequency characteristics of an on-load tap-changer vibration signal by using wavelet scattering transform WST, and realizing low-rank decomposition and dimensionality reduction characterization of high-dimensional characteristics by combining a non-negative tensor decomposition model NTF; s3, fault diagnosis; a multi-base learner Stacking integration framework is adopted, and a prediction matrix is generated through K-fold cross validation; through a swarm intelligent optimization algorithm SRA, hyper-parameters and fusion weights of all base learners are adjusted, L2 regularization suppression over-fitting is introduced, and finally fault classification is realized by adopting a logic regression element learner with Softmax cross entropy. According to the invention, through fault diagnosis of multi-model adaptive fusion and optimization, the fault identification precision, stability and on-line monitoring capability are improved.

The invention discloses a sewage plant total phosphorus concentration prediction method based on multiple machine learning models and a control system, and belongs to the field of environmental monitoring and treatment. The method comprises the following steps: automatically collecting detection data of a sewage plant, and generating a time sequence data set through intelligent preprocessing; according to the method, a total phosphorus concentration prediction model is constructed by adopting multiple machine learning algorithms, a reference prediction model is automatically selected through evaluation indexes, hyper-parameter tuning is performed by utilizing a swarm intelligence optimization method, and an optimized high-performance prediction model is obtained. The model is deployed to a real-time monitoring system, and through integration with a PLC and monitoring hardware, high-frequency prediction and dynamic regulation and control closed loop are realized; and continuously optimizing model parameters and a regulation and control strategy by returning deviation information to form a'prediction-control-optimization 'closed loop. According to the method, the effluent total phosphorus concentration prediction precision and regulation efficiency are remarkably improved, the agent adding cost is reduced, and the intelligent level of a sewage treatment system is improved.

The invention relates to the field of intelligent agricultural robots, in particular to a multi-robot collaborative harvesting method and system for woody oil plants based on swarm intelligence, and provides efficient harvesting of the woody oil plants by constructing a distributed sensing network and a semantic enhanced digital twinborn model, and the system adopts a three-layer decision architecture to formulate a harvesting strategy. The working efficiency is improved through self-adaptive task allocation and collaborative harvesting, multiple robots share experience through federated learning, group collaborative learning is achieved, harvesting performance is optimized, the reliability of the system is improved by 85%, single-point fault risks do not exist, the recognition accuracy is improved by 50% through a multi-modal sensing system, and the system is suitable for popularization and application. And an innovative harvesting solution is provided for the field of intelligent agricultural robots.

The invention discloses a flexible control method for new energy grid connection, and relates to the technical field of new energy grid connection control. The method comprises the steps of obtaining grid-connected point electrical parameters through a self-measurement mode or a communication interface mode, performing harmonic feature extraction and compensation based on a deep learning network, and adopting a state estimation method to fuse multi-source data to estimate power grid parameters; and constructing a multi-objective optimization function including voltage quality, a power factor and harmonic suppression, dynamically adjusting a weight coefficient of the optimization function by using a swarm intelligence algorithm, and realizing inverter switching sequence optimization through multi-objective model prediction control. Through a dual-mode communication redundancy mechanism and local intelligent control, while a communication function of a traditional main link is reserved, a standby wireless communication link is introduced to realize real-time backup transmission of a control instruction, and through a local autonomous decision-making capability, the problem of control failure caused by a communication problem in a traditional scheme is fundamentally solved.

The invention discloses a fishery pond water temperature control optimization method based on a swarm intelligence algorithm, and belongs to the technical field of control optimization, and the method comprises the following steps: S1, constructing a fishery pond water temperature PID control system introducing an improved single-corner whale optimization algorithm module; s2, introducing an improved single-corner whale optimization algorithm, and constructing an improved single-corner whale optimization algorithm module; s3, setting and optimizing the pond water temperature PID control parameters by using an improved single-corner whale optimization algorithm to obtain optimal control parameters; and S4, setting an optimal control parameter obtained through optimization by using the improved single-corner whale optimization algorithm as a parameter of a pool water temperature PID controller, and optimizing a pool water temperature regulation control effect. According to the method, a swarm intelligent optimization algorithm, namely an improved unicorn whale optimization algorithm, is introduced, high-performance control over the pond water temperature is achieved, and reliable control technical support is provided for efficient, stable and safe operation of aquaculture.

The invention provides a multiphase sewage heat exchanger performance prediction and structure optimization method and system based on an improved particle swarm algorithm, and relates to the technical field of waste heat resource recovery. The method comprises the steps that a performance database with working medium parameters and structure parameters as input and heat exchange and resistance performance as output is constructed based on multi-physics field numerical simulation; training a BP neural network by using the database to construct an initial prediction model, and globally optimizing model parameters by introducing a particle swarm algorithm of a dynamic inertia weight and an adaptive variation mechanism to obtain an optimized prediction model; and further calling the optimization model to carry out multi-objective optimization on the structural parameters by taking a Nusselt number and a Nanning friction factor as a dual-objective function, and outputting an optimal structural combination. According to the method, intelligent prediction and swarm intelligent optimization are fused, and the prediction precision and optimization efficiency of the design of the multiphase heat exchanger are improved.

The invention relates to the technical field of industrial equipment intelligent operation and maintenance, and discloses a self-adaptive predictive maintenance system and method for industrial equipment. A physical mechanism and neural residual parallel mixed digital twinborn model is constructed, predictive deviation is explicitly modeled into a residual, and through a meta-learning technology, the prediction deviation and the residual are subjected to prediction prediction; a twin mother set with universal knowledge is trained offline from historical data of an equipment group, for new equipment, a small amount of initial data can be used for rapid fine adjustment based on the mother set, personalized twin is efficiently generated, a residual sequence is continuously monitored in an online diagnosis stage and a system, probability modeling is performed on health residual distribution, and the probability modeling result is obtained. Sensitive detection of early and unknown faults is achieved, and after individual twins obtain new knowledge through self-adaptive learning, the knowledge is shared through a federated network in a model updating quantity mode and used for iteratively optimizing a global twinborn mother set, and intelligent population evolution between devices is achieved.

The invention discloses an intelligent cavity operation navigation method and system based on multi-mode perception fusion, and relates to the technical field of surgical medical treatment. Firstly, bionic instrument posture data and cavity environment data are obtained; then, based on the bionic instrument posture data and the orifice environment data, the target curvature of each section is calculated through a bionic kinematics model, and the magnetorheological fluid pressure is dynamically adjusted to control the local rigidity; meanwhile, the cavity point cloud topological graph is converted into an obstacle density graph, and path weight matrixes of different paths are quantified based on a swarm intelligence algorithm; and finally, according to the target curvature, the local rigidity and the path weight matrix, driving a bionic instrument to execute compliant motion and cooperative obstacle avoidance operation. According to the method, multi-modal data (curvature, rigidity and contact force) are fused through a bionic kinematics model, navigation deviation correction is achieved in combination with a curvature-force nonlinear mapping model and three-dimensional Euclidean distance monitoring, and the navigation precision is improved in combination with a path re-planning mechanism.

The invention relates to a copper-clad plate production line collaborative regulation and control method based on digital twinning, and the method comprises the steps: constructing a multi-level data set of dynamic virtual-real mapping through full-process digital twinning modeling, process data normalization and multi-level feature tagging, achieving the high consistency of a physical production line working condition and a simulation system, and achieving the collaborative regulation and control of a copper-clad plate production line in combination with an autonomous scheduling intelligent agent. Real-time perception and joint feature mining of multi-dimensional data such as equipment load, health degree and energy consumption are realized, the real-time adaptive scheduling capability of a production line to states such as sudden load and equipment aging is improved, scheduling target weights are periodically and adaptively generated, optimal instant balance of multi-target income is realized, and the optimal real-time scheduling capability of the production line is realized by adopting an evolutionary game and swarm intelligent optimization. A cross-device optimal resource scheduling scheme under the multi-target constraint is obtained, an evolution model is continuously fed back through real-time production data, a scheduling-execution-calibration-rescheduling closed-loop mechanism is formed, and the self-learning and long-period stability capabilities of a scheduling system are remarkably improved.

The present invention discloses a distributed heuristic unmanned cluster brain-like swarm intelligence fusion search and capture method, comprising: step 1: completing single target detection on the basis of a Yolov5 network, and predicting the position of a next frame of target on the basis of a DeepSort algorithm; step 2, on the basis of the distance difference and the area difference, determining, by mapping, navigation end point coordinates formula (1) of an intelligent agent in a physical space; step 3, constructing a diffusion-contraction coordinated chaotic mapping algorithm, and mapping the update of the navigation end point coordinates into a uniformly distributed chaotic space; step 4: on the basis of target detection confidence decision information and regional position information of all intelligent agents, fusing and generating global search information formula (3) of a search space formula (2); step 5: on the basis of the intelligent agent global search information, fusing various sensing data in a multi-agent system, so as to estimate target position information; and step 6: performing measurement on the basis of a real-time position of the intelligent agent and an obstacle position. The present invention has high capture efficiency and can accurately update a capture end point of the intelligent agent to complete a task of encircling a moving target.

The invention provides a distributed energy storage device group intelligent cooperative control optimization method and system, and relates to the technical field of group intelligence, and the method comprises the steps: obtaining source load fluctuation data, constructing a dynamic defense topological graph with energy storage devices as nodes and electrical coupling relations as edges, recognizing a disturbance propagation path and a node disturbance arrival time sequence through graph convolution operation, and obtaining a distributed energy storage device group intelligent cooperative control optimization model. Obtaining a node disturbance sensitivity quantized value; dividing the energy storage equipment group into a front defense domain and a back-up defense domain based on the quantized value and a preset layering threshold value; reversely deducing a power regulation sequence aiming at the front defense domain to generate an active defense instruction, and generating a following type scheduling instruction aiming at the back-up defense domain; issuing and executing the instruction, collecting topological response data, and jointly updating the edge weight and the propagation coefficient based on the deviation. According to the method, disturbance pre-compensation control is realized through dynamic topology modeling and a layered defense strategy, and the cooperative response efficiency of the energy storage equipment group and the system stability are improved.

The invention discloses a medium-short wave signal transmission interference suppression method and system based on wireless radio and television, and the method comprises the following steps: S1, collecting and preprocessing medium-short wave frequency band data, and constructing a frequency spectrum state data set; s2, constructing a spectrum evolution cognitive map model based on the spectrum state data set; s3, performing clustering analysis by using the spectrum evolution cognitive map model to generate a broadcast interference popularity map; s4, inputting the spectrum evolution cognitive map model and the broadcast interference heat map into an improved badger interference avoidance algorithm, and outputting a frequency hopping avoidance path; s5, the broadcast transmitting system executes frequency hopping scheduling control according to the frequency hopping avoiding path, and it is guaranteed that frequency point switching is completed under the condition that broadcast service is not interrupted; and S6, the broadcast receiving terminal collects signal quality parameters and transmits the signal quality parameters back to the central system, and the node state of the spectrum evolution cognitive graph model is updated in real time. According to the method, active avoidance of medium-short wave broadcast interference and stable frequency hopping scheduling are realized by combining frequency spectrum cognitive modeling and a swarm intelligent optimization algorithm.

The invention relates to the technical field of intelligent airport logistics, in particular to an airport luggage flow group intelligent AI-driven digital twinning low-carbon scheduling system, which comprises a physical sensing layer, a digital twinning engine layer, a group intelligent decision-making layer, a block chain evidence storage layer and a dynamic scheduling execution layer, and forms a'sensing-modeling-decision-evidence storage-execution 'closed loop; the physical sensing layer collects equipment and luggage state data; the digital twinborn engine layer constructs a total-factor twinborn body to realize synchronous mapping and carbon accounting of a physical system; the group intelligent decision-making layer adopts an improved carbon sensitive ant colony algorithm to generate a multi-objective optimization strategy of total energy consumption, residence time and load balance; the block chain evidence storage layer ensures credibility and traceability of the carbon data; and the dynamic scheduling execution layer converts the decision into a control signal and corrects simulation and actual deviation. According to the method, low-carbon, high-efficiency and reliable luggage scheduling is realized, and the method is suitable for green operation of a smart airport.

The invention discloses an intelligent fireproof electrical control cabinet operation method, and the method comprises the steps: building a multi-source data set in which a physical position is strongly bound with a network topology label through the distributed collection of multiple types of sensors, and achieving the precise normalization, noise suppression and unified feature calibration of original data; and an abnormal resonance identification algorithm and causal inference are further adopted to extract an equipment transaction association mode, a dynamic equipment trust map is constructed based on Bayesian updating and an attention mechanism, and a map neural network is utilized to intelligently predict a risk diffusion path and high-risk node distribution. The system automatically triggers and issues a graded alarm and protection plan according to a risk cascade threshold value to realize active prevention and control of physical isolation, load degradation and the like, and meanwhile, periodically corrects a risk relationship and model parameters by means of an execution feedback mechanism. High-precision, whole-process dynamic optimization and self-adaptive evolution of power distribution network risk identification can be realized, and the group intelligent protection level and the system stability are effectively improved.

The invention relates to the technical field of multi-agent cooperation, provides a multi-agent cooperative unmanned aerial vehicle cluster simulation training method, system and device, and a storage medium, and solves the problems of low penetration success rate and poor formation cooperation efficiency of an unmanned aerial vehicle cluster. The method comprises the following steps: collecting pigeon flock biological motion data, cluster trajectory data when an unmanned aerial vehicle cluster executes a historical penetration task, and magnetic field gradient data of a simulation environment; constructing a three-dimensional path library, and calculating geomagnetic course angle deviation; on the basis of the geomagnetic course angle deviation and the three-dimensional path library, a multi-target collaborative path is generated in combination with a swarm intelligent optimization model; generating a flight control instruction based on the multi-target cooperation path in combination with the real-time state data of the unmanned aerial vehicle cluster; and loading the flight control instruction to a simulation model of the unmanned aerial vehicle cluster, and driving the unmanned aerial vehicle cluster to carry out cooperative defense penetration training in a simulation environment. According to the invention, the penetration success rate and formation cooperation efficiency of the unmanned aerial vehicle cluster in a complex electromagnetic environment are improved.

The invention provides a multi-bucket wheel machine collaborative operation scheduling system and method based on a swarm intelligence algorithm, and relates to the technical field of electronic information. Inputting the equipment information, the stacking information and the environment information into a first model to generate constraint conditions; performing global search based on an artificial bee colony algorithm introducing harmony search, and screening solutions according to constraint conditions in each iteration to obtain a group of feasible solutions; performing local search in each feasible solution neighborhood by adopting mixed integer programming to obtain a globally optimal solution; and generating working parameters of the equipment according to the globally optimal solution, and performing job scheduling. The artificial bee colony algorithm is improved by introducing a harmony search mechanism, the global search capability is enhanced, and premature convergence is avoided; and meanwhile, local optimization is carried out in combination with mixed integer programming, so that the solution accuracy is improved, the algorithm can adapt to yard scheduling requirements of different scales and different constraint conditions, and the overall operation efficiency is improved.

The invention discloses an assembly equipment health management system based on industrial Internet of Things, and particularly relates to the field of intelligent maintenance of industrial equipment, comprising a distributed sensing module, a cross-production-line transfer learning module, a dynamic maintenance strategy optimization module, a multi-modal man-machine cooperation module and an enhanced execution terminal module, multi-source sensor clusters and intelligent edge nodes are deployed, multi-dimensional operation data are collected and preprocessed at high frequency, an encrypted global fault feature library is constructed by using a federal transfer learning algorithm, cross-production-line group intelligent advanced early warning is realized, and the equipment health degree, the production plan and the resource state are comprehensively considered based on a deep reinforcement learning decision-making agent. A priority maintenance strategy is output, a digital twinning and AR technology is fused to generate an enhanced maintenance guidance package, real-time operation verification and health re-evaluation are realized through an execution terminal, accurate and efficient closed-loop health management is formed, and the equipment reliability and the maintenance intelligence level are remarkably improved.

The invention discloses an unmanned autonomous cluster flight control method based on a bionic alert mechanism, which is applied to the field of unmanned autonomous cluster control, and is characterized in that a W-MSR algorithm and a dynamic weighted bionic alert mechanism are fused, the W-MSR filters and eliminates extreme values of neighbor individuals, the dynamic weighted bionic alert mechanism strengthens input from informed individuals, and the dynamic weighted bionic alert mechanism is used for improving the robustness of the unmanned autonomous cluster flight control. And meanwhile, the influence of suspicious individuals is inhibited, so that the local information aggregation degree is effectively improved, and group splitting can be prevented. Different from a scheme depending on explicit attacker recognition, the method can improve the motion accuracy and connectivity of the unmanned autonomous cluster in a confrontation environment by probabilistically suppressing the influence of suspicious individuals and amplifying a consistent signal, provides a new idea for improving the security of a swarm intelligence system, and has a wide application prospect. Damage of malicious individuals to the cluster is effectively defended, and robustness and recovery capability of the cluster in a complex environment are improved.

The invention relates to a multi-mobile-robot path planning method based on swarm intelligence, and belongs to the technical field of robot path planning. The method comprises the following steps: setting the size of a map, starting and target positions and colors of a robot, creating a preset grid map, and initializing an ant colony algorithm, a genetic algorithm and a pheromone system; iteratively searching paths for the mobile robots with different starting points at the same time through an ant colony algorithm; a genetic algorithm is used for optimizing the path, conflict detection and processing are carried out after the path is optimized, and it is ensured that the final path is free of conflicts; and when the maximum number of iterations is reached, outputting the shortest path that each robot arrives at the target node and no collision exists between the robots. According to the path planning method, unnecessary turning can be reduced, the convergence speed of path searching is obviously improved, robot conflicts can be avoided, and the actual requirements of multi-robot path planning are met.

The invention discloses an analysis method for applying a dense model to big data, and the method comprises the following steps: S1, collecting and preprocessing heterogeneous original data, and generating a standardized data set; s2, performing feature extraction and feature splicing on different types of fields in the standardized data set to form a preliminary feature vector matrix; s3, inputting the initial feature vector matrix into a heterogeneous feature fusion and completion algorithm to generate a fused feature vector matrix; s4, inputting the fusion feature vector matrix into a deep dense residual network model, and outputting deep semantic feature representation; s5, inputting the deep semantic feature representation into a full-connection output layer to generate an analysis result vector; s6, constructing a loss function, and executing back propagation to optimize weight parameters in the deep dense residual network model; and S7, repeatedly executing the steps S1 to S6 on the newly added data. According to the method, deep residual network modeling and a swarm intelligence anomaly recognition mechanism are fused, and unified modeling, deletion completion and high-dimensional feature expression analysis of big data multi-source fields are realized.

The invention relates to the technical field of virtual power plant collaborative optimization, and discloses a virtual power plant multi-target carbon economy optimization method based on swarm intelligence, which comprises the following steps: S1, constructing a digital twinborn body of a virtual power plant, and synchronizing distributed energy output, load demand and carbon emission data of a physical VPP (virtual power plant) in real time; s2, establishing an energy-carbon economy dual-objective optimization model; s3, solving the dual-objective optimization model through an improved swarm intelligence algorithm, wherein the algorithm dynamically adjusts the weight of the economical efficiency and the weight of the carbon emission objective; s4, real-time data based on the digital twinborn body; and S5, outputting a Pareto optimal solution set. The total operation cost and the full life cycle carbon emission in the operation cycle of the virtual power plant are taken as double optimization targets, the target weight is adjusted through the dynamic carbon price sensitivity coefficient, carbon market signals are coupled, the decision-making deviation of high implicit carbon and operation pseudo-low carbon of equipment is avoided, and the multi-target decision-making scientificity of the virtual power plant under the carbon constraint is improved.

The invention discloses a customer demand analysis method based on AI artificial intelligence CRM system data, and the method comprises the following steps: S1, collecting multi-source heterogeneous customer data, executing the preprocessing, and carrying out the fusion; s2, customer behavior features are extracted and fused to form feature vector representation, feature importance evaluation is carried out, and high-correlation feature subsets are screened; s3, inputting the high-correlation feature subset into a swarm intelligence optimization module, and outputting initial network configuration parameters; s4, constructing and initializing a customer demand prediction model according to the initial network configuration parameters; s5, training the customer demand prediction model, and dynamically adjusting the structure of the customer demand prediction model; s6, continuing to iteratively train the customer demand prediction model; and S7, generating a customer demand prediction result. According to the method, intelligent swarm optimization and deep learning methods are fused, accurate prediction of customer demands is realized, and the method has the advantages of high adaptability, accurate prediction and fast response.

The invention relates to the field of emergency management, in particular to an emergency cooperative scheduling strategy generation method based on swarm intelligence. The method comprises the following steps: acquiring multi-source data of each agent, preprocessing the multi-source data, and feeding back the preprocessed multi-source data to the corresponding agent; the intelligent agent generates a preliminary scheduling strategy according to the received multi-source data, and updates and maintains a strategy distribution snapshot of an edge node in the preliminary scheduling strategy; and obtaining an evolution path of the secondary disaster, inputting the evolution path of the secondary disaster, the preprocessed multi-source data and the strategy distribution snapshot of the edge node into the federal depth Q network model, and generating a collaborative scheduling strategy. In this way, the technical problems that structural obstacles exist in data integration and sharing facing emergency scenes, allocation and scheduling of computing resources are difficult to meet dynamic and high-timeliness requirements of emergency responses, and the overall toughness and cooperative capacity of a system are insufficient are solved.