515 results about "Swarm intelligence" patented technology

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 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 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 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 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 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 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 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.

The invention discloses a gelatin solution temperature control optimization method for donkey-hide gelatin processing, and belongs to the technical field of swarm intelligence optimization. Comprising the steps that a gelatin solution temperature closed-loop PID control system in the donkey-hide gelatin decocting process is constructed, an improved side hand spider turning optimization algorithm is configured to serve as a parameter setting core, the self-adaptive setting process of PID control parameters is executed, and a global optimal control parameter vector obtained through optimization is mapped to a donkey-hide gelatin solution temperature PID controller module. The method focuses on the PID parameter setting problem of donkey-hide gelatin liquid temperature control, introduces the improved side hand turning spider optimization algorithm as a swarm intelligence algorithm to set the donkey-hide gelatin liquid temperature PID control parameters, and can effectively overcome the defect that the side hand turning spider optimization algorithm is easy to fall into local optimum. And the temperature of the donkey-hide gelatin liquid can be controlled more excellently.

The invention provides an unmanned aerial vehicle distribution task scheduling optimization method considering an endurance state, and belongs to the field of intelligent manufacturing. The method comprises the following steps: aiming at the problems of takeoff sequence, task allocation and endurance state change of an unmanned aerial vehicle in a distribution task, constructing a power consumption model based on flight stage division; under constraint conditions of unmanned aerial vehicle electric quantity, loading capacity, safe flight height and the like, converting the scheduling scheme into a three-layer integer sequence including a take-off sequence, a task allocation sequence and an execution sequence for expression and optimization; a task interruption and battery replacement decision-making mechanism and a battery replacement station selection cost function under the condition of insufficient electric quantity are introduced, and a swarm intelligence algorithm is adopted for solving, so that the optimization target of minimizing the total task completion time, the total battery replacement frequency and the flight distance standard deviation is achieved.

The invention relates to the technical field of industrial control systems, and discloses an industrial internet intelligent control system applied to a hawthorn cake heat pump dryer. The edge processing module calculates a comprehensive energy efficiency index based on the operation parameters and identifies a drying stage conversion point to trigger a corresponding control strategy; and the industrial internet communication module realizes data interaction between an edge end and a remote central platform. Through energy efficiency index dynamic coupling and edge intelligent decision making, accurate matching of the drying process and the material state is achieved, and the drying uniformity is remarkably improved; through combination of environment disturbance dynamic compensation and an acoustic feature verification mechanism, a distributed learning network is formed based on an industrial internet architecture, and swarm intelligent optimization and process parameter adaptive adjustment of an equipment cluster are realized.

The invention provides a traffic hub large passenger flow emergency decision-making method and system based on swarm intelligent emergence and a large model, and a readable storage medium, and the system comprises a distributed sensing module which is responsible for carrying out edge intelligent processing at a data source; the group intelligent emerging module is responsible for forming a global control strategy in a self-organizing manner through local interaction among intelligent agents; the large model element agent evaluation module is responsible for carrying out macroscopic monitoring and evolution guidance on an agent group; and an emergency decision linkage execution module. The method has the beneficial effects that the mode conversion from the traditional single center decision to the distributed collaborative autonomous decision is realized, the robustness, the self-adaptability and the response real-time performance of the system in a dynamic uncertain environment are remarkably enhanced, and finally a closed-loop autonomous emergency response system integrating sensing, decision making, execution and optimization is formed; and the intelligent level and the operation safety guarantee capability of high-speed rail station large passenger flow management are comprehensively improved.

The invention discloses a multi-agent collaborative logistics distribution and scheduling system based on swarm intelligence emergence optimization and a method thereof, and relates to the technical field of swarm intelligence, multi-agent systems, intelligent logistics and distributed optimization, in particular to a multi-agent collaborative logistics distribution and scheduling system based on swarm intelligence emergence optimization and a method thereof. The system adopts a completely distributed architecture and is composed of a plurality of agents for autonomous decision making, each agent comprises a sensing module, a decision making module, a communication module and an execution module, cooperation is achieved through local sensing and neighborhood communication, and a central controller is not needed. The system performs path optimization and obstacle avoidance by using a coupling mechanism of a pheromone field and a potential field function, and supports multi-scale collaboration, distributed consensus decision and self-organization capability. The method comprises the following steps: acquiring local information by an intelligent agent, planning a path based on a pheromone field and a potential field, and realizing task allocation and conflict resolution through interaction. According to the system, the energy consumption can be effectively reduced by 28%, the efficiency is improved by 5%, and the system performance exceeds the total sum of intelligent agents by 40-50%.

The invention provides an energy storage electrical intelligent system of a self-adaptive control strategy, and relates to the technical field of electric power grids. According to the invention, multi-source operation data of a power grid, a load, new energy and an energy storage system are collected in real time through the sensing layer, the decision-making layer realizes accurate synchronization and updating of the system operation state by using a digital twin model, and distributed optimization of swarm intelligence and extreme scene testing of confrontation deduction are fused. Dynamically generating and screening out an optimal control instruction which shows robustness under various working conditions; and finally, the execution layer accurately controls the energy storage system to form a sensing-decision-execution-feedback adaptive closed loop, so that the control strategy of the energy storage system can be automatically and accurately adjusted along with the fluctuation of the running state of the power grid, the stiffness defect of a fixed strategy is overcome, the capacity of the power grid for dealing with source load fluctuation and sudden faults is enhanced, and the energy storage system is ensured to be more stable. The self-adaptive matching of the energy storage system control strategy and the power grid operation state is realized, and the dynamic stability and the operation safety of the power grid are improved.

The invention relates to the technical field of automobile dynamics control, in particular to an ambulance suspension intelligent agent based on deep reinforcement learning and an optimization method thereof, and the optimization method comprises the steps: S1, initializing an evaluation network and a control strategy network; s2, adopting a swarm intelligence algorithm to initialize a population; s3, the number M of evolution generations of the controller and the time step T of each generation are set, in each time step, the initial fitness of the controller is set to be 0, and the fitness of the controller is updated according to the reward value and the auxiliary reward of the current time step; s4, after each intergeneration is finished, updating the population according to the fitness by adopting a swarm intelligence algorithm, and training the evaluation network and the control strategy network to update network parameters; and S5, after M generations of evolution are completed, screening out the controller with the highest fitness from the controllers, and setting the network parameters corresponding to the controller in the control strategy network. The invention is at least beneficial to improving the adaptability of the ambulance to different working conditions.

The invention discloses a livestock and poultry house environment parameter self-optimization regulation and control method based on swarm intelligence, and the method comprises the steps: collecting multi-dimensional environment parameters, such as temperature and humidity, through arranging a sensor array in a livestock and poultry house, and constructing an environment parameter sensing matrix; and converting the matrix into hypergraph structure data, inputting the hypergraph structure data into an optimized hypergraph neural network, mining high-order correlation and dynamic change characteristics among parameters, and clustering environment states through a swarm intelligence optimization strategy. A multi-dimensional environmental data regulation and control model is constructed based on the information, a parameter regulation and control target interval is set in combination with livestock and poultry growth requirements, a hypergraph network search strategy is regulated and controlled after deviation is calculated, equipment is driven to adjust environmental parameters, and closed-loop feedback is formed. According to the method, the hypergraph neural network and the swarm intelligence technology are utilized, the complex relation of the environmental parameters is accurately analyzed, the growth requirements of livestock and poultry are dynamically met, efficient self-optimization regulation and control of the environmental parameters of the livestock and poultry house are achieved, and the breeding environment quality and the production benefits are improved.

The invention discloses an unmanned aerial vehicle autonomous cluster cooperative control system based on swarm intelligence, and belongs to the technical field of unmanned aerial vehicle control. The control system comprises an unmanned aerial vehicle cluster which is composed of a plurality of unmanned aerial vehicles with autonomous flight capability; each unmanned aerial vehicle is provided with a sensing unit, a navigation and flight control unit, a communication unit and a cluster cooperative control unit; a group intelligent decision module based on an ant algorithm is arranged in the cluster cooperative control unit and comprises a virtual pheromone generation and updating sub-module, a local decision sub-module and a task planning and cooperative sub-module; the system provided by the invention takes swarm intelligence as a core, realizes distributed decision-making through a virtual pheromone field, avoids communication and computing power bottlenecks of centralized control, and also can enable a cluster to autonomously emerge a global optimization behavior. Through dynamic role allocation, task decomposition and priority scheduling mechanisms, resources can be adaptively allocated along with task progress; and the collaborative behavior pheromones strengthen the efficient path, so that the execution efficiency of the cluster in the complex task is remarkably improved.

The invention relates to a swarm intelligence-driven cascade reservoir autonomous negotiation scheduling method. The method comprises the following steps: firstly, generating a scheduling basic data set; respectively packaging each reservoir node of the cascade reservoir group into an independent reservoir unit body; each reservoir unit carries out parallel computing and distributed negotiation through a group fusion negotiation strategy based on interaction data of a local scheduling basic data set and a neighborhood reservoir unit, and a preliminary scheduling scheme is generated; the group fusion negotiation strategy is based on the function types of the reservoir units, adopts a distributed negotiation mode of a contract network protocol or a bidding mechanism, takes the minimum transaction cost among the reservoir units as a core objective function, and combines flood control, power generation and ecological multi-objective weight coefficients to determine a preliminary scheduling scheme; and carrying out digital twinborn simulation verification and fine tuning to obtain a final scheme. In case of exception, preferential intra-group coordination is realized, and in case of invalidation, cross-group linkage is realized. The method improves the scheduling efficiency, guarantees the multi-target balance of flood control, power generation and the like, and enhances the anti-risk capability.

The invention discloses a power transmission and transformation project economic evaluation method, system, equipment and medium fusing adaptive fuzzy entropy weighting and a multi-target grey wolf optimization algorithm, and belongs to the technical field of power system economic analysis, and the method comprises the steps: constructing an economic index system, building a fuzzy membership matrix, and calculating an index weight through combining fuzzy entropy and information entropy; a comprehensive weight is generated by adopting a self-adaptive fusion mechanism, then a multi-target weighted evaluation model is constructed, and finally multi-target search is performed by utilizing a swarm intelligence optimization algorithm. According to the invention, by constructing a self-adaptive weighting mechanism fusing the fuzzy entropy and the information entropy and combining the global search capability of the multi-target grey wolf optimization algorithm, multi-index weight dynamic optimization and multi-target cooperative solution in the economic evaluation of the power transmission and transformation project are realized; the method effectively overcomes the limitation of a traditional method in the aspects of weight distribution subjectivity, insufficient index coupling processing and multi-target balance, and forms a closed-loop evaluation system from index processing to intelligent decision making.

The invention discloses an IDC intelligent calculation center energy efficiency optimization system and method based on an intelligent algorithm, and relates to the technical field of computer system resource collaborative management. Comprising a swarm intelligent decision center module, an ant colony optimization LSTM temperature prediction module, an ant colony collaborative task scheduling module, a path optimization cooling control module and a fault energy consumption linkage sensing module. According to the IDC intelligent calculation center energy efficiency optimization system and method based on the intelligent algorithm, a swarm intelligent decision center serves as a core, the ant colony algorithm penetrates through the whole process of temperature prediction, task scheduling and cooling control, the local optimization limitation of a single algorithm in a traditional scheme is broken through, and the optimization efficiency of the IDC intelligent calculation center is improved. According to the method, data of all modules can be integrated through a pheromone transmission mechanism, global strategy overall planning of prediction, scheduling and cooling is achieved, decision deviation caused by data splitting of an independent algorithm is avoided, and integrity and coordination of energy efficiency management are improved from the top design level of computer system resource configuration.

The invention discloses a three-dimensional space unmanned aerial vehicle path planning method based on a swarm intelligence algorithm. The method comprises the following steps: S1, defining an initial boundary and sampling in the initial boundary to generate an initial population containing a plurality of candidate paths; s2, constructing a target function according to the optimization target; fitness values of the candidate paths are calculated based on the target function and the position function, and the candidate path with the high fitness value is selected as a global optimal solution; s3, the maximum number of iterations, the maximum number of iterations and the number of iterations of dynamic updating are set, and the specific number of iterations and the target probability are preset; introducing two dynamic parameters and designing a boundary adjustment coefficient, an adaptability factor and a dynamic probability which change along with the current iteration number; according to the method, autonomous navigation and path optimization of the unmanned aerial vehicle in a complex environment are realized by simulating the self-organization and distributed decision-making mechanism of group behaviors in nature, the path planning calculation speed can be effectively improved, the distance is reduced, and the adaptive capacity to environmental changes is enhanced.

The invention discloses an education content recommendation method and system based on group intelligence, and the method comprises the steps: constructing a user dynamic cognition knowledge graph, and carrying out the dynamic attenuation of the memory intensity attribute of a knowledge point node based on a cognition forgetting model in combination with the memory stability; defining utilization pheromones on edges of the knowledge graph, and strategically injecting exploration pheromones according to forgetting states of users or knowledge breadth indexes; guiding a swarm intelligence algorithm to construct a learning path according to a rule jointly influenced by bimodal pheromones; and finally determining an optimal path and generating recommendation. According to the method, accurate intervention on a learning process and intelligent guidance on knowledge exploration are realized through deep fusion of a recognition scientific model and a swarm intelligence algorithm of bimodal pheromones, the learning stability is remarkably improved, information cocoon houses are effectively broken, and the interpretability of recommendation is enhanced through path intention analysis.

The invention discloses a business processing method and device based on an ant colony algorithm, electronic equipment and a medium. The method comprises the following steps: acquiring a business service request, and analyzing the business service request to obtain a business task; determining each agent matched with the business task; wherein the intelligent agents are used for cooperative processing of business tasks; planning the cooperation path of each agent based on an ant colony algorithm to obtain a target path; and according to the cooperation sequence of the intelligent agents in the target path, executing the business task by using the intelligent agents in sequence. According to the technical scheme, efficient collaboration between agents is achieved through the improved ant colony algorithm. According to the system, a multi-dimensional pheromone matrix is adopted to record and transmit collaborative experience among departments, a knowledge verification mechanism based on swarm intelligence is established, and dynamic optimization and rapid convergence of a cross-department business process are realized.