175 results about "Tabu search" patented technology

The invention provides an intelligent logistics optimization method, device and equipment and a storage medium, and the method comprises the steps: carrying out the multi-source fusion processing of the Internet of Things sensor data and historical business data of a multimodal transportation reloading node in a logistics process, and obtaining a spatial-temporal feature vector; performing operation adaptability evaluation on the reloading nodes by using a P median model according to the spatial-temporal feature vectors to obtain a comprehensive score reflecting the node operation condition; a Tabu search algorithm is adopted to carry out combinational optimization on operation parameters of the reloading nodes based on the spatial-temporal feature vectors and the comprehensive scores, and an initial reloading scheme is obtained; and according to the initial reloading scheme, performing multi-agent cooperative scheduling on the reloading nodes on the digital twin platform to obtain a logistics resource optimal allocation scheme. Through combination of multi-source fusion and an efficient optimization algorithm, dynamic operation analysis and collaborative scheduling of multimodal transport reloading nodes are realized, logistics timeliness and resource utilization rate are improved, and the method is suitable for a complex logistics network and a changeable market environment.

The invention provides a multi-stage embedded control equipment state sensing and energy cascade scheduling system. Comprising a master control decision center module, a distributed edge embedded node module, an equipment full-dimension state sensing module, an energy dynamic optimization scheduling module, a fault prediction and self-healing control module, a cross-protocol communication interconnection module and a man-machine cooperative command module. According to the invention, through constructing a three-layer time domain control chain of edge node nanosecond-level signal processing, cloud second-level optimization scheduling and equipment hour-level strategy presetting, seamless cooperation of turbine bearing pedestal micro-vibration monitoring and a power grid peak regulation strategy is realized, and real-time wavelet noise reduction preprocessing of embedded nodes is combined with cloud LSTM life prediction. A taboo search algorithm is driven to dynamically reconstruct a power supply scheme, and the pain point of control response lag in a high-fluctuation scene is solved.

The invention relates to the technical field of heat management of energy storage systems, and discloses a heat management method of an intelligent distributed liquid cooling energy storage system. The method comprises the following steps: acquiring operation state parameters of a plurality of single batteries and optimizing timestamp synchronization; and processing the temperature data by adopting small-batch time series decomposition, and extracting change trend characteristics and abnormal hot spot positions. And based on the information, optimizing a heat exchange control rule base parameter set through a non-dominated sorting strategy, and generating optimized control parameters and priority rules. And in combination with inlet temperature and flow parameters of the liquid cooling system, a tabu search mechanism is utilized to decide a cooling liquid flow distribution scheme, and a branch control instruction is output. And finally, a flow constraint boundary is dynamically adjusted by adopting an approximate gradient projection technology, and a branch and bound strategy is fused to discretize and optimize a distribution path, so that the branch flow is finely regulated and controlled, and the thermal management precision and reliability of the energy storage system are improved. According to the method, the accuracy, adaptability and stability of thermal management of the liquid cooling energy storage system are improved.

The invention discloses an unmanned aerial vehicle path planning method based on global search and meta-heuristic optimization, and the method comprises the steps: firstly building an unmanned aerial vehicle path planning model under complex constraints, determining a fitness function and a constraint condition thereof, setting algorithm initialization parameters, carrying out the chaotic mapping of an initialization population position, and carrying out the optimization of the algorithm; and performing loop iteration solution by using a meta-heuristic optimization algorithm, outputting a global optimal path, finally realizing the global optimal path on the unmanned aerial vehicle, and calculating a flight distance corresponding to the optimal path to complete unmanned aerial vehicle path planning. According to the method, a meta-heuristic algorithm, namely a crown porcupine algorithm, is combined with tabu search, the global search capability is enhanced, and meanwhile, population initialization is optimized in combination with chaotic mapping, so that the randomness and unpredictability are increased, the algorithm can converge more quickly, and the robustness of the algorithm is improved. The method has obvious advantages in solving the problem of path planning with complex environment and complex constraint, and solves the problem of path planning of the unmanned aerial vehicle in the three-dimensional complex mountain environment.

The invention discloses an industrial and commercial energy storage EMS scheduling system based on multi-objective optimization, and relates to the field of intelligent control of energy storage systems. The method is used for solving the problems of battery life attenuation, electricity price fluctuation and insufficient load abrupt change adaptability in energy storage scheduling. The service life management module analyzes the ion migration state to generate a dynamic power safety envelope line as a layered optimization hard constraint; the feature extraction module constructs an electricity price association network, predicts photovoltaic / load in combination with quantile regression and extracts net load abrupt change features; the hierarchical optimization module compresses the discharge depth and activates load regulation in demand risk, and synchronously optimizes economy, life loss and power grid constraint through taboo search multi-objective evolution; the execution arbitration module monitors the distance between the power and the envelope line in real time, and when the distance is lower than a threshold value, rolling optimization is started to improve the life weight and feed back the lithium precipitation characteristic to drive the envelope line to update, and closed-loop protection is formed.

A neural network on-chip mapping method and apparatus based on a tabu search algorithm are provided. The method includes: constructing a tabu search table and using a heuristic-based iterative search process to select local computing cores of a network-on-chip as candidates, establishing an integer programming model and solving an optimal solution, continuously reducing an objective cost function of a mapping solution by loop iteration, and finally obtaining an approximately optimal deployment scheme.

The invention discloses a production scheduling method, a production scheduling system and electronic equipment, and belongs to the technical field of production management and scheduling. The production scheduling method comprises the steps that according to production requirements and a preset production correlation model, a fitness function and an initial production scheduling scheme set are determined, and the fitness function comprises at least two of the following production scheduling strategies: total delay time minimization, resource utilization rate maximization, production line load balancing and intermediate product minimization; at least two preset algorithms are fused, the initial production scheduling scheme set is optimized based on the fitness function, the optimal production scheduling scheme is obtained, and the preset algorithms comprise any one of a genetic algorithm, a simulated annealing algorithm and a tabu search algorithm. The method can solve the problems of insufficient processing of resource constraints, low production scheduling efficiency and incapability of effectively finding the optimal production scheduling scheme in related technologies.

The invention discloses a BIM (Building Information Modeling) and measurement robot cooperative intelligent measurement method, system, equipment and medium, and relates to the technical field of robots, and the method comprises the steps that a robot receives BIM data and collects real-time data of a sensor, and the real-time data and the BIM data are preprocessed; fusing the real-time data and the BIM data, taking the fused data as input, and constructing a navigation model through a genetic algorithm and tabu search; and the navigation model outputs an optimal path, and the robot moves to a target position along the optimal path to measure the target. According to the method, the BIM data and the real-time data of the sensor are fused, so that the measurement precision is remarkably improved, errors are reduced, and the accuracy of a result is ensured. The system has automatic navigation and measurement functions, manual operation is reduced, and efficiency is improved; and through fault detection and recovery functions, the robustness and stability of the system are enhanced. The application of the genetic algorithm and the tabu search optimizes the path planning and improves the navigation efficiency.

The invention belongs to the technical field of AGVs, and provides an AGV transportation method and a robot, and the method comprises the steps: obtaining a task list and the initial position of each AGV; the task execution sequence is optimized through tabu search so as to minimize the time consumption of final task completion, the average completion time and the weighted cost of the number of pauses; according to the optimization sequence, constructing a space-time diagram of node representation time and space positions for each AGV; planning the path of each AGV on the space-time diagram by adopting a time enhancement algorithm, regarding the paths of other AGVs as time obstacles, and adopting a path cost function; in the path planning process, the AGVs are allowed to wait in situ to avoid collision, and the paths of the AGVs are updated in real time; performing Bezier curve smoothing on the planned discrete path for three times to generate a continuous and controllable driving track; and finally, each AGV is controlled to execute the task along the track. According to the invention, the problems of low task scheduling efficiency and frequent path conflicts of a plurality of AGVs in the prior art can be solved.

The invention provides a large-scale constellation earth observation task planning method and device, and belongs to the technical field of satellite task planning. According to the embodiment of the invention, a large-scale constellation earth observation task problem is divided into a multi-satellite-cluster cluster allocation problem and a single-satellite-cluster cluster autonomous task planning problem, and the improved adaptive large neighborhood search algorithm fused with tabu search is adopted to allocate the large-scale constellation earth observation tasks to different satellite clusters. And then, multi-satellite task planning in a satellite cluster is realized by adopting a satellite cluster autonomous task planning algorithm based on an extended contract network mechanism. Therefore, the dimension reduction of the problem scale can be realized, the search space can be greatly reduced, the solving efficiency can be improved, the cooperative capability between satellites in the satellite cluster can be improved, and especially in the scene of satellite damage and interference, the anti-interference capability and cooperative completion efficiency of the task can be improved, and the robustness and reliability of task planning can be ensured.

The invention relates to the technical field of logistics, and provides a logistics scheduling decision optimization method and device, electronic equipment and a storage medium. Multi-source heterogeneous data are acquired in real time, space-time tensor fusion processing is performed on the multi-source heterogeneous data to obtain a dynamic space-time feature tensor, and dynamic service capability evaluation is performed on logistics nodes according to the dynamic space-time feature tensor to obtain a node contribution degree matrix. Constructing a multi-target constraint optimization model according to the node contribution degree matrix to obtain an initial scheduling strategy, performing dynamic tabu search optimization processing on the initial scheduling strategy to obtain a candidate solution set, and performing dynamic simulation verification on the candidate solution set through a digital twin model to obtain a conflict resolution strategy, and finally, adjusting the scheduling scheme in real time according to the conflict solving strategy and the optimization mode to obtain a logistics scheduling strategy optimization instruction. Through multi-source data fusion, optimization modeling and simulation verification, the logistics scheduling decision is optimized to improve the logistics scheduling optimization efficiency.

The invention discloses a departure peak period-oriented flight driving and taking-off collaborative optimization method, which comprises the following steps of: firstly, accurately identifying an optimal release control rate based on historical flight data, secondly, improving the sliding time prediction accuracy by utilizing integrated learning, and then, establishing a departure flight collaborative scheduling model according to an actual operation rule so as to realize the departure flight collaborative scheduling. And establishing a conflict mechanism to set flight priorities. A solving algorithm adopts a hybrid algorithm combining a genetic algorithm and tabu search, effective combination of global search and local search is ensured, and falling into a local optimal trap is avoided. The scheduling scheme covers key indexes such as runway throughput and average taxiing time, the airport scene congestion condition is relieved, the operation efficiency and safety of an airport in the departure peak period are improved, flight delay is reduced, and the utilization rate of runway time slot resources is increased.

The invention relates to the technical field of unmanned aerial vehicle path planning, and provides a double-layer optimized unmanned aerial vehicle path planning method and related equipment. The method comprises the following steps: acquiring a base station position of a target unmanned aerial vehicle and positions of a plurality of inspection target points; constructing a path optimization function of the target unmanned aerial vehicle based on the position of the base station and the positions of all the inspection target points; solving the path optimization function by using a genetic algorithm and a tabu search algorithm to obtain an optimal path planning scheme of the target unmanned aerial vehicle; and controlling the target unmanned aerial vehicle to inspect all the inspection target points according to the optimal path planning scheme. The method provided by the invention can improve the precision of unmanned aerial vehicle path planning.

The invention discloses a power module multi-objective optimization method and device, and the method comprises the steps: determining a key structure parameter and a constraint range of a to-be-optimized power module, and constructing a multi-dimensional parameter space based on the key structure parameter and the constraint range; performing global uniform sampling on the multi-dimensional parameter space based on a Sobo sequence to generate a sample point set; performing electromagnetic-thermal coupling simulation based on the sample point set to obtain a target performance index, and constructing a training data set based on the target performance index and the sample point set; constructing an agent model of the MLP, and training the agent model based on the training data set to obtain a target optimization model; and constructing a multi-target optimization model based on the target performance index, solving the multi-target optimization model based on a tabu search algorithm, a fast non-dominated sorting genetic fusion algorithm and the target optimization model, and obtaining optimal key parameters of the to-be-optimized power module.

The invention discloses a self-adaptive micro-service deployment resource optimization method for cloud edge-end cooperation. The method comprises the following steps: 1) modeling a system model; 2) modeling a communication model; 3) modeling a user demand processing delay module; 4) deploying resource consumption according to a micro-service type required by user demand processing, and modeling a micro-service deployment resource consumption model and a user demand processing resource consumption model; 5) performing formulation definition on the micro-service deployment resource optimization objective function to complete heterogeneous constraint high-quality characterization; 6) aiming at the difference dimension of the multi-objective optimization function, finishing normalization processing of different dimensions; and 7) solving a multi-objective optimization problem by adopting a tabu search method to realize effective utilization of resources in a deployment process.The invention belongs to the technical field of system resource optimization of the Internet of Things, and solves the problem that the Internet of Things in the prior art has defects in the aspects of application space demand coupling, decentralized coordination and service difference.

The invention discloses a retail cabinet inventory path decision optimization method and equipment, and the method comprises the following steps: (1), building an inventory path optimization model of a retail cabinet, and enabling the inventory path optimization model to take the minimum sum of the inventory cost, the stockout cost and the vehicle path cost at the current planning period as an objective function, taking a replenishment constraint, a maximum inventory level constraint, a vehicle capacity constraint, a retail cabinet flow balance constraint, a vehicle closed-loop constraint and an elimination sub-loop constraint as constraint conditions; (2) obtaining inventory path related information; and (3) solving the inventory path optimization model by adopting a tabu search method according to the inventory path related information to obtain an optimized inventory decision and path decision. According to the invention, the replenishment decision and the path planning decision are considered at the same time during decision making, and the efficiency is higher.

The invention relates to a power distribution network reconstruction method, system and device based on an improved PSO algorithm and a medium, and the method comprises the steps: carrying out the double-layer coding processing of a switching state and a particle swarm parameter, and generating an initial particle swarm meeting the radial constraint; performing classification processing in combination with real-time current data and historical fluctuation characteristics, dividing the power distribution network into a high-load key area, a medium-load transition area and a low-load elastic area, constructing a differential tabu search strategy, and dynamically adjusting tabu period parameters through regional importance weights and load rates; a dynamic inertia weight adjustment mechanism and differential tabu parameter collaborative optimization particle swarm optimization algorithm are adopted, and candidate reconstruction schemes meeting power grid operation constraints are screened; according to the method, the network loss, the voltage deviation and the load balancing indexes are quantified, an optimal scheme is output, a network topology reconstruction instruction is generated based on priority ranking, power distribution automation accurate regulation and control are achieved, and therefore the real-time performance, the economical efficiency and the operation reliability of power distribution network reconstruction are improved.

The invention belongs to the technical field of information, and particularly relates to a taboo search algorithm-based waste plastic regeneration production process energy-saving method, which comprises the following steps of: firstly, carrying out detailed modeling and energy consumption analysis on a production process, covering links such as raw material pretreatment, melt extrusion and the like, and constructing an energy consumption model; secondly, designing a tabu search algorithm, representing an optimization scheme by a solution vector, and searching an optimal solution by taking the minimum energy consumption as a target function through the steps of initial solution generation, neighborhood search, tabu table operation and the like; and finally, making an optimization scheme according to the optimal solution and implementing, monitoring and evaluating in real time, and adjusting and perfecting according to a result. The scheme aims at improving the energy utilization efficiency and reducing the cost and environmental pollution.

The invention relates to the technical field of aircraft simulation training, and discloses a multi-mode man-machine interaction aircraft simulation training system. A multi-modal feature extraction module of the system generates a multi-modal feature map including a flight environment situation map and an equipment operation health map through a hierarchical feature extraction network; the incremental analysis module divides an incremental data set, updates flight state prediction model parameters through an incremental clustering algorithm and outputs an incremental state prediction result; the parameter optimization layer initializes a tabu search population according to a prediction result, and iteratively optimizes parameters of an operation rule base through a dynamic efficiency evaluation matrix; the decision generation module fuses the optimized rule base parameters and the real-time multi-modal feature map, and generates a flight operation instruction sequence based on dynamic confidence score; and the execution optimization layer analyzes the instruction sequence, satisfies constraint conditions through a constraint projection algorithm, and completes instruction sequence integer programming in combination with the operation portrait deviation. The system improves the interaction accuracy and scene adaptability of simulation training.

The invention discloses a distribution network engineering transformer area site selection planning method, system and device and a medium, and the method comprises the steps: processing the obtained power equipment information, load information and topological structure information around a target distribution network transformer area through a geographic information system technology, and generating visual transformer area information; acquiring all fire falling point data in the visual transformer area information, and processing the fire falling point data by using a gravity center algorithm to obtain transformer area load center coordinates corresponding to the visual transformer area information; inputting the load center coordinates into the trained region screening model to obtain a preliminary available plot region; inputting a knowledge graph constructed by map information in the preliminary available plot region into a graph neural network model to obtain a recommended available plot region; using an improved taboo search algorithm to determine a target recommended plot area from the recommended available plot areas to perform site selection and planning of the courts; according to the method, the distribution network planning area site selection efficiency can be remarkably improved.

The invention discloses an intelligent scheduling system for sports event organization and management, particularly relates to the technical field of sports event management, and solves the problems of resource mismatching and event interruption caused by rule stiffness, single conflict resolution dimension and emergency response lag of an existing scheduling system. Dynamic priority evaluation is driven through multi-source data real-time acquisition, a Pareto optimal solution set is generated in combination with three-dimensional conflict detection and audience influence factor quantitative analysis, efficient scheduling is realized by adopting a genetic-tabu search hybrid engine, and a hierarchical emergency response and block chain evidence storage mechanism is established. And the resource adaptability, the scheduling credibility and the emergency response efficiency are improved.

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

The invention discloses an in-port container truck scheduling optimization system and method based on dynamic time matrix prediction, and the method comprises the steps: building a dynamic time matrix which introduces a bridge crane rhythm and a lane impedance factor through collection of TOS and vehicle monitoring data, and carrying out the feedback correction of the matrix through a task result; self-adaptive adjustment of a container truck resource pool threshold value is realized based on a task density function, and multi-target scheduling optimization is carried out by adopting a tabu search and discrete particle swarm interaction algorithm; and when the storage yard is congested or rhythm deviates, task priorities and key road marks of operation roads are considered, task sequence rearrangement is triggered, and algorithm parameters are automatically updated according to execution results. According to the system, a real-time closed loop of prediction-scheduling-feedback is realized, and the port operation efficiency and the resource utilization rate are improved.

The invention relates to the field of data prediction, and provides a method and a system for acquiring a cutting sequence of panel-type customized furniture based on a hybrid strategy. The method comprises the following steps: grouping furniture plates in a plurality of orders to be processed according to plate designs and colors to obtain a plurality of design and color groups; based on the number of the to-be-processed orders associated with the pattern groups and the occurrence frequency of the cross-pattern to-be-processed orders, performing initial arrangement on each pattern group to obtain an initial pattern group sequence; in each design and color group, according to the order coverage rate and the position sensitivity of each furniture plate, performing initial arrangement on each furniture plate to obtain a plate sequence in an initial group; dynamically optimizing the position of each flower color group in the initial flower color group sequence by adopting an adjacency exchange method, and performing secondary optimization on an adjacency exchange result; dynamically optimizing the position of each furniture plate in the plate sequence in the initial group on the basis of an adjacent exchange method through a tabu search algorithm; and building a furniture plate cutting sequence, and starting a production process of the plurality of orders to be processed.

The invention discloses a thermoelectric coordination regulation system for a new and old battery mixing station, and relates to the technical field of thermoelectric coordination regulation, and the system comprises a data collection module which collects the surface contact temperature data of a battery, the instantaneous fluctuation data of the terminal voltage of the battery and the state of charge in the new and old battery mixing station in real time; the early warning analysis module is used for dynamically analyzing the battery surface contact temperature data and the battery end voltage instantaneous fluctuation data in the continuous sliding time window to obtain a fractal dimension standard exceeding early warning signal; the risk modeling module is used for constructing a three-dimensional target vector by adopting a multi-target cooperative computing framework based on the fractal dimension standard exceeding early warning signal, the battery surface contact temperature data, the battery end voltage instantaneous fluctuation data and the state of charge; the decision regulation and control module is used for performing multi-target decision on the three-dimensional target vector through a tabu search algorithm to generate three types of regulation and control instruction sets; the taboo search mechanism breaks through the dimension coupling constraint of the multi-objective decision, and achieves the dynamic optimization of the thermal-electric regulation and control strategy.

Wireless communication assisted by a reconfigurable intelligent surface under near-field propagation faces the problem of array gain loss, and the increase of system capacity is limited. In order to further improve the communication system capacity of a large-scale intelligent reflecting surface (XL-RIS) in a near field and compensate for serious path loss, the invention provides a multi-user wireless communication system assisted by optimally deploying the large-scale intelligent reflecting surface, so that the near field range is further expanded, and the system capacity is improved. And the XL-RIS topological structure and the beam design are jointly optimized by taking the minimization of the beam gain error expected by the user as a target. In order to solve the non-convex problem, a self-adaptive tabu search algorithm, a Major-Minimization (MM) algorithm and the like are used for converting the sub-problems, and then alternate iterative optimization is carried out on the sub-problems. Particularly, on the premise that hardware cost is not increased, extra spatial freedom is obtained through selection of element positions, the user sum rate is effectively improved, and beam pointing is closer to the direction expected by the user. A simulation result verifies the effectiveness of the method, and a new solution thought is provided for the application of the XL-RIS in a near field communication system.

The invention discloses a technological parameter optimization method for electric pulse rock breaking. The method comprises the steps that electric tree characteristic parameters under different technological parameters are obtained; establishing a process parameter prediction model by taking the process parameters as input and the electrical tree characteristic parameters as output; based on a process parameter prediction model, an improved NSGA-2 multi-objective optimization algorithm is utilized to optimize process parameters, and the process comprises the following steps: providing a selection, intersection and variation process fusing a tabu search thought, namely, in a filial generation population generation process, a process of generating a filial generation population is provided; a solution with better fitness than a parent and a suboptimal solution within the number limit of flouting criteria serve as offspring solutions and are added into a taboo table, repeated acquisition is avoided, the local search ability of the algorithm is enhanced, a crowding degree value calculation method is improved based on the super-lattice thought to complete offspring population selection and construction, and the global search ability of the algorithm is improved. According to the method, the technological parameters are optimized through the improved NSGA-2 multi-objective optimization algorithm, the optimal technological parameter combination can be obtained, and the rock breaking efficiency is improved.

The invention relates to the technical field of power distribution network fault recovery, in particular to a power distribution network local load segmented transfer method based on line bearing capacity evaluation and a parallel tabu search algorithm. Obtaining a power distribution network coefficient, distributed photovoltaic and energy storage equipment output, a load demand and fault position information; calculating the power flow distribution of the power distribution network after the fault occurs and before transfer by adopting a forward-backward substitution method, defining a safety margin index, and screening out a transferable safety line set; a power distribution network load transfer model fusing high voltage priority, important user priority and loss load priority is constructed, and based on a transferable safety line set, a parallel tabu search algorithm is adopted to perform optimization in a safety solution space to obtain an optimal transfer scheme; and calculating a fault rate based on the load rate of the line after transfer, and marking a high-risk line exceeding a threshold value. According to the invention, an effective load transfer scheme can be provided immediately after the power distribution network has a fault, so that the power failure loss after the fault occurs is reduced, and the user satisfaction is improved.

The invention relates to the technical field of substation monitoring, and discloses an indoor substation high-voltage live display state identification method and system. The system comprises a data acquisition module for acquiring multi-modal state data of a display and optimizing an acquisition process; the state feature extraction module is used for performing incremental feature learning analysis on the real-time data; the identification model optimization module is used for optimizing model parameters and structures through a multi-target particle swarm; the state reasoning module is used for performing deep reasoning by using taboo search in combination with newly added data and an optimization model and converting the data into identification signals; the control output module is used for adjusting control constraints by using an approximate gradient method and optimizing output by combining a potential field method so as to generate a control instruction; and the state verification module verifies the state of the equipment through adaptive filtering and feeds parameters back to the related module so as to adjust the identification strategy. The system can comprehensively and accurately identify the state of the display, improves the adaptability and operation and maintenance safety, and adapts to the dynamic operation environment of a transformer substation.

The invention discloses an improved particle swarm optimization algorithm-based unequal-area facility layout method with interference. The method belongs to the field of intelligent optimization algorithms, and comprises the following main steps: S1, constructing a mathematical model with an interference unequal area facility layout problem; s2, initializing a population, and calculating an objective function value; s3, selecting a global optimal particle by using a target space division method; s4, executing heuristic mutation operation by using an adaptive gradient method to improve the search precision; and S5, using a tabu search rule and a neighborhood transformation rule to prevent the algorithm from being caught in a local optimal solution trap too early. Compared with the existing method, the method has the following main advantages: (1) the influence caused by a physical interference object is considered in the unequal-area facility layout problem, so that the applicability of the problem is enhanced, and the method can be more suitable for actual application scenes such as factory workshop transportation scenes; and (2) a tabu search rule is introduced into the multi-target particle swarm optimization algorithm, so that the convergence speed and the global search capability of the algorithm are improved.