1254 results about "Swarm algorithms" patented technology

The invention discloses an optimization method of a network variable structure with a distributed type power supply distribution system. The optimization method includes that basic static data of electric power network structure parameters, distributed type power supply distribution, load capacity, faulty lines and the like are extracted, under the condition of present line fault, the basic static data are utilized to perform islanding scheme calculation and correction, survivability indexes constructed by the method are combined to perform an online evaluation for islanding network safe operation performances and implement immediate control measures, finally, on account of residual network structure data after final islanding electric power network division is performed, by means of an ant colony algorithm, a reconfiguration optimal computation is performed for residual network with minimum network loss as a target, and finally the electric power network structure with high safe operation level is obtained. On the basis of the developed algorism and function modules, the invention further provides a large-scale power distribution network intelligent optimization decision system based on multi-stage computation correction and survivability evaluation, according to the technical scheme, the division and reconstruction of the network with the generalized model distributed type power supply distribution system are achieved, and accurate reference of network structure adjustment, schedule and operation can be excellently provided for regional electric power network schedule staffs.

The invention provides an ant colony optimization computing resource distribution method based on a cloud computing environment. The computing resource distribution method is based on ant colony optimization and characteristics of the cloud computing environment. The cloud computing resource distribution method comprises the steps of predicting computing quality of potential available nodes, analyzing influence of factors such as network bandwidth occupation, quality of a track, responding time, task cost and reliability on resource distribution according to characteristics of a cloud computing service mode, and then obtaining a set of optimized computing resources by means of the ant colony algorithm. According to the algorithm, shorter responding time and better running quality can be acquired compared with other distribution algorithms which aim at the network on the premise that cloud computing environment requirements are met, and therefore the ant colony optimization computing resource distribution method based on the cloud computing environment is more suitable for the cloud environment.

The invention discloses a joint optimized scheduling method for multiple types of generating sets of a self-supply power plant of an iron and steel enterprise, and belongs to the technical field of energy optimized scheduling of the iron and steel enterprise. Influence of fuel types and gas mixed burning amount on energy consumption of the sets is taken into consideration in construction of a set energy consumption characteristic model, fitting is performed under different gas mixed burning, and the accuracy and representativeness of the model are improved; and influence of the fuel cost, time-of-use power price and surplus gas dynamic change on the generating cost is considered comprehensively in construction of an optimized scheduling model, meanwhile, various constraint conditions including power balance constraint, generating set self-running constraint, purchased power quantity constraint, gas supply constraint, variable load rate limit and the like are considered, and the performability of a generation schedule is guaranteed. Optimization solution is performed on the models by adopting the adaptive particle swarm optimization algorithm, the problems of high dimensionality, nonconvexity, nonlinearity and multiple constraints of the power generation scheduling of the self-supply power plant can be well solved, power production optimization and purchasing rationalization are realized, surplus gas is sufficiently used, and the power supply cost is reduced to the greatest extent.

The invention belongs to the computer field, and discloses a method for scheduling a flow shop based on a multi-swarm hybrid particle swarm algorithm, which solves the problems that the flow shop scheduling method based on the hybrid particle swarm algorithm is easy to result in premature convergence and local optimum. The method comprises the following steps of: setting parameters and generating S sub-swarms; judging whether the terminal condition is satisfied, if so, outputting a current optimum scheduling scheme, otherwise, updating positions of particles in each sub-swarm with the particle swarm algorithm, carrying out a local search on odd and even sub-swarms respectively by using searching operators 1 and 2 to obtain an optimum scheduling sequence of each sub-swarm; sharing information of the obtained optimum scheduling sequence by using a statistics-based probability model; and optimizing an optimum working sequence with a simulated annealing algorithm. In the invention, multiple swarms are added, the local search is carried out by using different searching operators, a good flow shop scheduling scheme is obtained, the production time is shortened, and the method can be used for the selection of the job shop scheduling scheme.

The invention relates to a wind electric power prediction method and a device thereof. The method comprises the following steps of: step one: extracting data from SCADA (Supervisory Control and Data Acquisition) relative to a numerical weather prediciton system or a power system, and carrying out smoothing processing on the extracted data; step two: determining input and output of training samples of a least squares support vector machine according to the processed data; step three: initializing relevant parameters of a smallest squares support vector machine and an improved self-adaptive particle swarm algorithm; step four: optimizing model parameters according to an optimization process; step five: acquiring a model of the smallest squares support vector machine according to the optimized parameters; and step six: carrying out prediction according to the model of the smallest squares support vector machine. According to the wind electric power prediction method disclosed by the invention, a modelling process is simple and practical, wind electric power can be rapidly and effectively predicted, and the wind electric power prediction method has an important significance on safety and stability, and scheduling and running of the electric power system, and therefore, the wind electric power prediction method has wide popularization and application values.

The invention belongs to the technical field of robot navigation, automatic control and pattern recognition, and discloses an application method of adaptive ant colony algorithm (AACO) in mobile robotpath planning. The method comprises steps of constructing a point-to-point adaptive path selection strategy to select the best path point by the transfer optimization mode in the segmentation combination state; using an obstacle avoidance planning strategy to identify the nature of the obstacle, and selecting different local obstacle avoidance points to avoid obstacles; and applying a mixed deadlock processing strategy in the deadlock environment, and counting deadlock point and its fallback path point distribution information to guide ants to jump out of the deadlock environment. The data inthe embodiment shows that the AACO described in the present invention has better optimization ability than the basic ant colony algorithm (ACO), has better overall performance than the ACO, and can be effectively applied to the global process of robot path planning.

The invention provides an intelligent scheduling system and method rapidly recovering the on-schedule operation of a high-speed rail, and relates to the technical field of high-speed rail dynamic scheduling. The system includes an application server, a communication server, a database server, an interface server, a data collector, a running map workstation, a central control workstation, a plurality of station workstations, a converter and an intelligent optimizer. According to method using the system for scheduling, initial parameters of a certain section of a train are obtained from a staticdatabase of the database server, and the dynamic parameters related to the train operation are collected in real time through the data collector; and in response to train delay caused by unexpected events, a train adjustment model is established through the intelligent optimizer, and the particle swarm optimization is used to adjust a train actual performance map according to a train planned running chart and the basic information of the line to obtain a train stage plan so as to carry out intelligent scheduling of the train. The system and method of the present invention reduces the number of adjustments to the plan by people and increases the efficiency of the adjustment.

The invention discloses a method for parallel execution of particle swarm optimization algorithm on multiple computers. The method comprises the initialization step, the evaluation and adjustment step, the step of judging termination conditions and the termination and output step, wherein the evaluation and adjustment is the part for realizing parallel computation through parallel programming of MPI plus OpenMP. The method carries out parallelization on the operations of updating particles and evaluating particles in the particle swarm optimization algorithm by combining with the existing MPI plus OpenMP multi-core programming method according to the independence before and after updating the particle swarm optimization algorithm. The invention adopts a master-slave parallel programming mode for solving the problem of too slow speed of running the particle swarm optimization algorithm on the single computer in the past and accelerating the speed of the particle swarm optimization algorithm, thereby greatly expanding the application value and the application field of the particle swarm optimization algorithm.

A method of acquiring a part-processing optimal scheduling scheme based on an improved chicken flock algorithm comprises the following steps: step 1, determining an evaluation index of an optimization object for a multi-objective flexible workshop scheduling problem; step 2, establishing an optimization object function; step 3, determining a constraint condition of a scheduling optimization process; step 4, designing Pareto improved chicken flock algorithm; step 5, carrying out iterative operation, outputting a Pareto non-dominated solution, selecting an optimal solution according with an enterprise need, carrying out decoding on the optimal solution and taking the solution as a final scheduling scheme. In the invention, under the condition of satisfying a resource constraint, an operation constraint and the like, time of completion, a maximum load of a single machine and a total load of all the machines are taken as an integration optimization object, the improved chicken flock algorithm is used so that an optimal scheduling scheme of part processing can be rapidly acquired. In a chicken position updating formula, a cock learning portion in the group where the chicken belongs is added. A algorithm convergence speed is guaranteed and simultaneously solution quality is greatly increased.

The invention relates to an electric power system net rack reconstruction optimization method capable of considering a microgrid as a black-start power supply. The method comprises the following stepsthat: inputting electric power system parameters; taking the maximum recovery of the power generation capacity of the system and the minimization of a load loss amount in the microgrid as targets toconstruct a net rack reconstruction optimization model capable of considering the microgrid as the black-start power supply; determining the constraint condition of the net rack reconstruction optimization model capable of considering the microgrid as the black-start power supply; on the basis of a node importance degree and path recovery time, constructing a synthesis path evaluation index to optimize the recovery path of a unit to be recovered; and jointly combining a bee colony algorithm with a quantum theory to solve the model. A net rack reconstruction strategy which adopts the microgridwhich contains high-ratio renewable energy sources as the black-start power supply has certain feasibility and effectiveness. Under a situation that the conventional black-start resources of the system are insufficient, the microgrid can be taken as the black-start power supply to provide starting power for non-black-start units, and the non-black-start units can be quickly recovered to achieve apurpose of net rack reconstruction.

The invention relates to a combined method for predicting short-term wind speed. The method comprises the following steps: 1, extracting historical wind speed data from a related data acquisition and monitoring control system; 2, performing sequence analysis on the extracted wind speed data by adopting clustering empirical mode decomposition; 3, respectively establishing a least squares support vector machine model for each subsequence obtained through the clustering empirical mode decomposition, and comprehensively selecting three parameters which influence the prediction effect of the least squares support vector machine by adopting an adaptive disturbance particle swarm algorithm and learning effect feedback; 4, predicting by selecting the optimal parameters according to the learning effect of the least squares support vector machine; 5, superposing the prediction result of each subsequence, and obtaining a wind speed prediction result; and 6, performing error analysis on the wind speed prediction result. The modeling process is simple and practical, and the wind speed can be rapidly and effectively predicted, so that the wind power is effectively predicted, and the method has significance on safety, stability and scheduled operation of the power system and has wide popularization and application values.

The invention relates to a credible security route of a wireless sensor network on the basis of a quantum ant colony algorithm. A method for determining the route specifically includes steps of 1), setting initial information elements; 2), determining a credibility function; 3), selecting paths; 4), recording and updating the optimal solution; 5), updating the information elements; 6), jumping to the step 3) to repeatedly implement the steps until iteration is terminated; and 7), outputting the optimal solution. As shown by analysis, the method for determining the route is superior to the traditional ant colony algorithm in the aspects of convergence rate and global optimization, energy consumption of network nodes can be globally balanced, the network is prevented from being divided into a plurality of islands due to premature death of certain critical nodes, and the credible security route can effectively resist energy black hole attack such as Wormholes attack which is typical for the wireless sensor network, and is beneficial to building a credible network environment.

The invention belongs to the technical field of automatic ship anti-collision route planning and mainly relates to an automatic ship anti-collision method optimized by a wolf colony search algorithm. The method disclosed by the invention comprises the following steps: establishing a simulation interface needed by a ship simulation test, and determining ship parameters used for collision avoidance; and determining ship encounter situation and analyzing collision risk index, namely adopting the across encounter situation of two ships, analyzing the collision risk index shown in the specification of the ship and a target ship, wherein UtT is the time-based collision risk index, UdT is the space collision risk index, and only when UtT and UdT are not zero, ship collision risk exists. According to the method disclosed by the invention, the defect that the rate of convergence is reduced because the wolf colony algorithm crosses the border in the migrating, moving and surrounding processes when exceeding the search space is overcome, the improved algorithm is applied to automatic ship anti-collision route planning, and an automatic ship anti-collision method optimized by the wolf colony search algorithm is generated through establishment of a ship motion model and generation of the objective function.

The invention discloses an inner and outer layer nesting ECMS (equivalent fuel consumption minimization strategy) multi-objective double-layer optimization method. The inner and outer layer nesting ECMS multi-objective double-layer optimization method includes steps of building multi-objective optimization models of plug-in hybrid electric vehicles; solving the multi-objective optimization modelsby the aid of inner and outer layer nesting multi-objective particle swarm algorithms to obtain multi-objective optimized Pareto solution set front edges; weighting equivalent fuel consumption per hundred kilometers and variation ranges of deviation of SOC (state of charge) final values and target values, building total evaluation functions related to the equivalent fuel consumption per hundred kilometers and SOC deviation and selecting the optimal charge and discharge equivalent factors and engine and motor power distribution modes corresponding to the optimal charge and discharge equivalentfactors. The inner and outer layer nesting ECMS multi-objective double-layer optimization method has the advantages that output power of engines and motors of the plug-in hybrid electric vehicles canbe reasonably distributed at CS (charge sustaining) stages, so that fuel consumption can be reduced as much as possible, battery SOC balance still can be effectively kept, and the fuel economy of theintegral vehicles can be improved.

The invention discloses a UAV route planning method based on a mixed ant colony algorithm. On the basis of the existing UAV route planning research, the present invention adopts the method of combining the ant colony algorithm and the artificial potential field method to carry out the route planning respectively. Global route planning and local route re-planning. Using the mixed ant colony algorithm, the drone first performs global route planning, and needs to make local path re-planning for some corners that cannot fly, so that the drone can successfully bypass obstacles while avoiding its own constraints, improving the efficiency of unmanned aerial vehicles. The operational efficiency and survival probability of the aircraft.

The invention discloses an adaptive PID controller optimization design and setting method based on binary ant colony algorithm. The method can automatically optimize the design of the PID controller structure according to the set performance index in the open-loop and closed-loop states and optimize the corresponding control parameters online. In the present invention, a maximum-minimum binary ant colony optimization algorithm is proposed to realize object system identification, control structure design and parameter optimization. In the specific implementation, parameters such as the encoding length of the binary ant colony algorithm are adaptively set according to the parameter accuracy and range. The proposed method of judging re-initialization based on the maximum and minimum limit probability of pheromone can further improve the global optimization performance of the algorithm and improve the optimal control quality of the controller. The PID controller optimization design and parameter tuning method has universal applicability and flexibility, simple application, and can be widely used in the optimization design tuning of PID controllers in industrial control.

The invention discloses a UUV (unmanned underwater vehicle) dynamic planning method based on an LSTM-RNN (long short term memory-recurrent neural network), and belongs to the field of unmanned underwater vehicles. The UUV dynamic planning method includes the steps: (1) selecting a geometric model to build an obstacle environment model; (2) building a UUV dynamic planner for acquiring a data set byan ant colony algorithm; (3) designing an LSTM-RNN model for dynamic planning; (4) acquiring the data set; (5) training the LSTM-RNN by data of a training set in the data set to obtain the dynamic planner based on the LSTM-RNN; (6) inputting sonar detection information and target point information to the dynamic planner based on the LSTM-RNN to obtain the navigational direction and the navigational speed of a UUV at a next time. The method has strong learning capacity and further has quite strong generalization capacity, so that the implemented dynamic planner is applicable to complex environments. The requirement of real-time performance is met, and planned routes conform to movement characteristics of the UUV.

The invention provides an environmental economy power generation dispatching method. The environmental economy power generation dispatching method comprises the steps of establishing a multi-target scheduling model taking power output stability of a thermal power generating unit and a hydroelectric generating set into consideration, wherein the multi-target scheduling model comprises an objective function and a constraint condition; inputting preset a real-time system expected loss of a load; solving the Pareto optimality leading edge of environmental economy scheduling based on the multi-target particle swarm optimization to obtain the historical Pareto optimality solution set; calculating the satisfaction degree of Pareto optimality solutions and selecting the optimality solution with the highest satisfaction degree as the needed environmental economy power generation dispatching result. The environmental economy power generation dispatching method can provide a wind-fire-water coordinated optimized scheme containing operational risks, and is reasonable and practical.

The invention provides a robot path planning method integrating an artificial potential field and a logarithm ant colony algorithm. The method comprises the following steps: S1, initializing; S2, establishing a grid map containing obstacle information; S3, establishing a movable grid table of the ants according to the current positions of the ants; S4, calculating an attractive force and a repulsive force received by the position of the current ant in the artificial potential field, establishing an influence function q (t) of the artificial potential field, and calculating a minimum included angle between a resultant force borne by the ant in the artificial potential field and an adjacent grid direction; S5, improving an ant colony algorithm heuristic function eta ij and a pheromone updating strategy; S6, calculating the transition probability density of the improved ant colony algorithm, and updating the tabu table; S7, judging whether path planning exploration is completed or not, ifnot, entering S3, and if yes, entering S8; and S8, performing re-iteration or ending according to the judgment condition. According to the method, the convergence speed of the ant colony algorithm inpath planning is effectively improved, and the situation that the artificial potential field algorithm is prone to falling into local optimum is reduced to a great extent.

The invention discloses a three-level cooperative integrative optimization method for a combined cooling heating and power system. Optimization variables are determined; optimization targets are determined; a first-level optimization target is an annual energy utilization rate, a second-level optimization target is an annual CO2 emission amount, and a third-level optimization target is an annual operating cost; a type number of to-be-selected equipment and cooling heating power loads of the combined cooling heating and power system are determined; first-level optimization is carried out, and discrete particle swarm optimization algorithm is adopted for acquiring an optimal equipment selection type; second-level optimization is carried out, and general particle swarm optimization algorithm is adopted for acquiring the optimal capacity; third-level optimization is carried out, three-level cooperative integrative optimization constraint conditions are determined; particle swarm optimization algorithm is used for optimizing optimal operating parameters; whether to meet the maximum iteration number is checked, if yes, the eighth step is returned, and if not, the fourth step is returned; and a three-level cooperative integrative optimization result of the combined cooling heating and power system is obtained. The problem of optimization of the combined cooling heating and power system which has the characteristics of multiple inputs, multiple outputs, multiple pieces of equipment, and complicated coupling can be solved.

The invention discloses a multi-satellite imaging task planning method, which comprises the following steps of: establishing a task model, and representing all point target tasks by using the task model; taking the orbital circles as a reference, taking tasks in the orbital circles as nodes of the clustering graph model, and constructing undirected edges among the nodes in the clustering graph model based on clustering constraint conditions to obtain an imaging task clustering graph model; aggregating the point target tasks meeting the clustering constraint condition into clustering tasks based on a heuristic rule, and calculating side swing angles of the clustering tasks based on a median theorem; constructing and utilizing constraint conditions and an objective function of task planningto construct a task planning directed acyclic graph model corresponding to the imaging task clustering graph model; and based on the task planning directed acyclic graph model, performing task planning by adopting a maximum and minimum ant colony algorithm to obtain a multi-satellite imaging task planning scheme. Under different data scales, a satisfactory task planning result can be obtained, andthe method has good stability.

The invention discloses a PEPSO-basedsiting and sizing method optimization method of a distribution type power supplyand belongs to the field of electric systems. The method is rapid, precise, effective and proper and is mainly provided for planning optimization of a power distribution network including a distribution type power supply. The objective is to determine an optimal access position and the capacity of the distribution type power supply. The technological means include capacity expanding of a transformer substation, circuit transformation upgrading and new accessing of the distribution power supply. The method is advantageous in that a complete multi-target optimization teaching model is established; the model covers multiple aspects of actors such as economic performance, reliability and environment protection; optimization seeking calculation is carried out based on a novel improved multi-target particle group algorithm PEPSO; a construction planning scheme is determined via the fuzzy decision technology; and power grid auxiliary analysis programs are compiles and planned based on the method, and the optimization seeking calculation is carried out, so a feasible scheme set is formed and an optimal scheme is determined.

The invention relates to a four-rotor UAV (unmanned aerial vehicle) attitude control parameter tuning method based on an improved fish swarm algorithm. Based on a standard artificial fish swarm algorithm, the invention provides a strategy for dynamically adjusting an artificial fish moving step length, an elitist preservation and reproductive behavior and an external fishing behavior are introduced, and defects of series reduction of a later convergence speed, low convergence accuracy and easy falling into local optimum of a standard artificial fish swarm algorithm in the prior art are overcome. The method can be used for solving problems of aircraft design, flight control parameter tuning and path planning and has good problem solving accuracy and efficiency.

The invention discloses a fabricated type building intelligent hoisting method and a system based on machine vision. The fabricated type building intelligent hoisting system based on the machine vision comprises an image gathering module, an image processing and decision module and a device control module, the image processing and decision module and the image gathering module are communicated, and the device control module and the image processing and decision module are communicated. According to the fabricated type building intelligent hoisting method and the system based on the machine vision, the machine vision replaces the reliance on human vision in the hoisting process of the fabricated type building. Obstacle recognition is conducted in a complex fabricated scene through a deep learning model of a convolutional neural network and a type-II fuzzy neural network by using the machine vision, prefabrication hoisting path planning is conducted by using ant colony algorithm, and consequently, the movement of equipment on the scene is controlled according to the decision result. The fabricated type building intelligent hoisting method and the system based on the machine vision analyze and obtain the best scheme from the complex scene by using the camera instead of the human eye, can better plan the prefabrication hoisting path, greatly improve the hoisting efficiency and accuracy of the fabricated type building, realize the intelligent hoisting of the fabricated type building, and improve the shortcomings of artificial hoisting.

The invention discloses a non-invasive load monitoring method based on event detection. The method comprises the steps of selecting an original measurement power signal in a public REDD data set for denoising processing, carrying out event detection on the processed data by using a generalized likelihood ratio detection method, and identifying a load switch and a state change time node by detecting an active or reactive power sequence of a load; switching a detected electric device into an event, extracting a steady-state current before and after switching, carrying out fast Fourier transformto extract current the harmonic characteristics, combining the active power, establishing a load characteristic library through an affinity propagation clustering algorithm, fitting the actual electric appliance data characteristics and a load characteristic set through a dynamic adaptive discrete particle swarm algorithm, and determining the operation state of the household electric appliance. According to the method, users can conveniently carry out household energy-saving management and make demand response measures for a power grid, the real-time bidirectional interaction of the intelligent power grid is realized, and the asset utilization rate and the energy utilization efficiency are effectively improved.

The invention relates to a lane changing model parameter optimization method based on a mixed Gaussian-hidden Markov model. Operation states of a vehicle and surrounding vehicles are analyzed, and a driving lane changing intention recognition method based on the hidden Markov model is established, that is, prediction of an implicit driver lane changing intention is achieved by using lane changingcharacterization parameter observation states capable of being observed; a highway vehicle database with concentrated NGSIM data serves as the basis, a lane changing characterization parameter sampleis extracted, training and verification are carried out on the hidden Markov model by using an HMM tool box programming algorithm in MATLAB, and the number of mixed Gaussian models and the number of states are optimized by using a particle swarm algorithm. The number of the mixed Gaussian models and the number of the states are optimized by using the particle swarm algorithm, so that the accuracyof driver lane changing intention recognition is improved; better accuracy is finally obtained for the prediction result of the driver lane change intention.

The invention discloses an embedded software test data generating method based on a fuzzy-genetic algorithm and relates to a test data generating method. The problem that a test dataset generated with an existing test data generating method is large in scale, so that generating time is long is solved. A genetic algorithm is improved, a fuzzy control method is used, through population entropy and the disperse degree, selecting of a genetic operator in a genetic process is controlled in a self-adaptation mode, when population diversity becomes poor, crossover probability and mutation probability are enlarged, so that population is evolved in a global-optimum direction, and the scale of test data is decreased. Then, an ant colony algorithm is used for sorting the generated combination test data according to the large disperse degree, so that the distance between adjacent test data values is enlarged, and test data sorting with the large disperse degree are selected from the optimum path sorting of all combination test data and is used as final embedded software test data for outputting. The embedded software test data generating method is suitable for embedded software test data generating.

The invention discloses a source-load-storage coordinated scheduling method for improving new energy consumption. The method comprises the steps of S1, aggregating a thermal power plant, a new energypower plant, user loads and energy storage equipment into a source-load-storage scheduling system, and obtaining a new energy output curve, a heat utilization load curve and a power utilization load curve of the system; S2, establishing a source-load-storage coordinated scheduling model with the objective of maximizing new energy consumption quantity and minimizing the system operation cost; and S3, solving the source-load-storage coordinated scheduling model through utilization of improved multi-objective particle swarm optimization, and carrying out computing to obtain the source-load-storage coordinated scheduling method. The method has the advantages of simple realization method and flexible application. Schedulable resources such as power equipment, the user loads and the energy storage equipment in the system can be utilized rationally. The new energy consumption is facilitated, and moreover, operation cost of the system is reduced.

The invention relates to a wireless optical fiber sensor network deployment method based on particle swarm optimization. The method comprises the following steps of: establishing a bottom layer network deployment optimization model specific to the aim of minimizing the energy consumption of an optical fiber sensor node and bottom layer network cost, and solving by adopting multi-target particle swarm optimization based on a discrete binary system to obtain a bottom layer network deployment scheme and realize distributed sensing of a monitored area; and establishing an upper layer network deployment optimization model on the basic of the bottom layer network deployment scheme specific to the aim of minimizing upper layer network cost under the constraining action of upper layer network full communication, designing a fitness function for increasing particle difference, and solving by adopting particle swarm optimization based on simulated annealing to obtain an upper layer network deployment scheme and realize multiple hop transmission of sensing data. Due to the adoption of the method, the energy consumption of the optical fiber sensor node can be lowered, the energy consumption of management nodes is balanced, the network life is prolonged, the quantity of deployed routing nodes and management nodes can be reduced on the premise of ensuring full network communication, and network cost is reduced.

The invention relates to a joint optimization method for task unloading and resource allocation in a multi-cell scene, and belongs to the field of mobile edge computing. The method comprises the following steps: firstly, establishing an MEC task unloading model in a multi-cell scene, and designing a system total overhead function; then, optimizing an unloading decision of the user by adopting a chaotic variation binary particle swarm algorithm; under the condition that an unloading decision of a user is obtained, decomposing an original problem into two sub-problems of MEC computing resource allocation and uplink sub-channel allocation; adopting a Lagrange multiplier method to carry out MEC calculation resource allocation on the unloading users, and adopting an improved Kuhn-Munkres algorithm to carry out uplink sub-channel allocation on the unloading users under the constraint conditions of meeting the minimum rate and the maximum tolerable interference of the users. According to theinvention, the total system overhead for a user to execute tasks can be reduced, and the system performance is effectively improved.