956 results about "Particle swarm algorithm" patented technology

The invention discloses a phase modifier unit state monitoring and load control method and system for reactive power compensation of a power grid, belongs to the technical field of automatic control of power systems, and mainly solves the problem that service life loss is aggravated or a control target is single due to the fact that the health state of equipment is not fully considered when an existing phase modifier unit responds to reactive power requirements of the power grid. The method comprises the following steps of: calculating reactive vacancy and extracting vibration frequency band energy characteristics by acquiring voltage and frequency of a power grid as well as vibration, temperature and exciting current signals of a unit in real time, and constructing a health state index HSI in combination with self-adaptive wavelet basis selection; taking minimum equipment life loss and optimal power grid voltage stability as multiple objectives, dynamically generating a load control instruction by adopting a particle swarm algorithm, realizing weight adaptive adjustment based on HSI, and introducing an online life loss model as a constraint condition; finally, reactive power output is adjusted through an excitation system, and cooperative control of power grid stability and equipment life extension is achieved.

The invention discloses a power distribution network layered optimization method oriented to source-network-load interaction, and relates to the technical field of power system automation. The method comprises the following steps: constructing a dynamic adaptive collaborative architecture, and dynamically generating a three-layer logic structure of global coordination, regional autonomy and local execution according to the real-time state of the power distribution network; operating a three-layer mixed game mechanism, and determining an optimal strategy of each main body through master-slave, cooperation and bilateral interactive games; executing an evaluation-planning-operation three-level closed-loop optimization process, and taking a key performance index fed back by an operation layer as a basis for scheme correction of a planning layer; and a mixed intelligent strategy combining multi-agent deep reinforcement learning and an improved particle swarm optimization algorithm is adopted for solving. According to the method, the problems of hierarchical solidification, benefit coordination imbalance and planning operation splitting in the prior art are effectively solved, and the adaptive capacity, the economical efficiency and the flexibility of the power distribution network in the high-permeability source-network-load interaction scene are improved.

The invention relates to the technical field of power distribution networks, in particular to an intelligent optimization method for energy storage capacity configuration in a new energy power distribution network and an application system. Comprising the following steps: data acquisition and preprocessing; building a multi-objective optimization model; and performing simulation verification and scheme output. According to the method, in a new energy power distribution network safe operation and complex working condition adaptation scene, the power grid safe operation constraint and the energy storage equipment physical characteristic constraint are integrated, so that the energy storage configuration scheme is ensured to accord with equipment physical safety and power grid operation criteria; meanwhile, a hybrid intelligent optimization method of improved particle swarm algorithm global exploration and interior point method local refinement is adopted, the solving precision is improved to avoid local optimization, multi-scene verification is carried out through a simulation environment consistent with the electrical characteristics of the actual power distribution network, the applicability of the scheme under the complex working conditions of severe fluctuation of new energy output and the like is ensured, and the method is suitable for popularization and application. The problems that a traditional scheme is insufficient in security constraint coverage and incomplete in solving and verification mechanism are solved.

The invention provides an argon flow and micro-positive pressure linkage control method for an electroslag furnace. The method comprises the steps that the oxygen content and micro-positive pressure signals in the furnace are collected; the future furnace condition is predicted through the deep belief network model; optimizing initial parameters of the fuzzy PID controller in an off-line manner by using a particle swarm algorithm; on the basis of the real-time signal and the prediction result, adaptively adjusting controller parameters on line; and finally, an argon flow and dust removal valve opening control instruction is output in a linkage mode. According to the method, future furnace conditions are predicted through the deep belief network model, the control advancement is realized, and the problem of large lag of the system is solved; offline parameter optimization is carried out on the fuzzy PID controller through a particle swarm algorithm, and excellent initial performance adapting to different working conditions is provided for the fuzzy PID controller; the parameters of the controller are adaptively adjusted through online fuzzy reasoning, so that the system has robustness for coping with a dynamic process; finally, accurate and stable linkage control of the oxygen content in the furnace and the micro-positive pressure is achieved through linkage calculation of the argon flow and a dust removal valve opening instruction.

The invention discloses a spare power automatic switching device test switch simulation test system, and belongs to the technical field of power grid simulation test. Probability distribution generated through kernel density estimation covers a fault scene of a 99% confidence interval, and the characterization capability of an extreme scene is enhanced; the time delay precision of a switch action sequence is controlled timely and reliably by combining the global search capability of quantum annealing and the local fine tuning of the particle swarm algorithm, and equipment damage caused by time sequence dislocation is avoided by combining the UWB technology and the phase compensation algorithm; through an objective function weight coefficient and a closed-loop feedback mechanism, an optimization objective can be dynamically adjusted according to a fault type, through real-time comparison of a physical device and digital twinning and through logic criterion goodness of fit analysis, control logic consistency can be guaranteed, and through loop response time difference analysis and two-dimensional index quantification, verification precision can be guaranteed. And the strict requirements of the power system on correct logic and quick action of the spare power automatic switching device are met.

The invention relates to an energy-saving control strategy for a medical building air-conditioning ventilation system based on climate change characteristics and load prediction. The energy-saving control strategy comprises the following steps of: acquiring all original data by utilizing various sensors, actuators and the like of a sensing layer; calculating, optimizing and deciding on an intelligent control layer (including building comprehensive cooling and heating load prediction, clean operation department area cooling and heating demand calculation, dynamic water supply temperature optimization, a particle swarm optimization (PSO) algorithm for domain optimization, constraint check and other algorithm modules) on a central management server; and an optimization result is issued to the execution layer and the executed equipment layer, so that energy-saving control is finally realized. The method overcomes the defects that the existing hospital building load prediction technology mostly adopts a macroscopic and rough prediction model, and the global optimization particle swarm optimization algorithm is slow in convergence and long in solving time when coping with a large medical building adopting multiple types of cold and heat source equipment. The device is suitable for large-scale hospital projects in hot-summer and warm-winter areas.

The invention provides a flight path optimization method and system based on an improved particle swarm optimization algorithm, and belongs to the technical field of intelligent optimization algorithms and aircraft flight path optimizing.The method comprises the steps that concerned performance indexes in the flight process of an aircraft are obtained to construct a cost function; the cost function is sampled, sampling points of the cost function serve as particles, the improved particle swarm optimization is adopted to optimize the cost function, and a global optimal position is obtained and serves as a final track optimization result; the improved particle swarm algorithm comprises the following steps: carrying out particle initialization by adopting a method for assigning a particle initial value, carrying out random team distribution on each particle by adopting a Monte Carlo method, carrying out first-time updating on a global optimal particle with a minimum adaptive value by using a gradient descent method, and carrying out second-time updating on a global optimal particle with a minimum adaptive value according to the optimal position of each particle in the team and the optimal position of the particle in the team. And the speed and the position of the particle are updated for the second time based on the second-order consistency theory. According to the method, the convergence speed of the algorithm is improved, and falling into a local optimal solution is avoided.

The invention belongs to the crossing field of artificial intelligence and industrial automation, particularly relates to an aeration fan frequency conversion group control energy-saving method based on an intelligent optimization algorithm, and aims to solve the problems of high energy consumption, response lag and large equipment loss of a traditional control mode. According to the method, parameters such as dissolved oxygen, flow and pressure are collected in real time, a nonlinear optimization model with minimum energy consumption, accurate oxygen supply and minimum start and stop as multiple targets is constructed, an optimal rotating speed distribution scheme is solved online by adopting an improved particle swarm algorithm, and a smooth slope instruction output and closed-loop feedback correction mechanism is combined, so that the optimal rotating speed distribution scheme is obtained. And dynamic self-adaptive adjustment of fan group control is realized. The energy-saving efficiency is remarkably improved by 18%-25%, the annual electricity is saved by more than 800000 kilowatt-hour, meanwhile, the load unevenness is reduced to below 8%, fan start and stop is reduced by 60%, the service life of equipment is prolonged by 2-3 years, hardware does not need to be newly added, the deployment period is short, investment recovery is fast, and the method is successfully applied to multiple sewage plants.

Disclosed in the present invention are a shared energy storage configuration optimization method and system for a wind farm cluster. The method comprises: constructing a shared energy storage double-layer programming model; on the basis of the output of a wind farm cluster, an electricity price in an electric energy market and a compensation price in a frequency-modulation auxiliary service market, obtaining multiple uncertainty sets, using an improved K-means clustering method to perform joint clustering on the multiple uncertainty sets so as to obtain a typical scenario, and on the basis of the annual occurrence frequency of the typical scenario, calculating a typical scenario probability; and using a distributionally robust optimization method to model the multiple uncertainty sets on a short-time scale so as to obtain an extreme uncertainty scenario, using the typical scenario probability and the extreme uncertainty scenario to update the shared energy storage double-layer programming model so as to obtain an updated shared energy storage double-layer programming model, and using a particle swarm algorithm to perform solving so as to obtain a configuration result. By means of the method, shared energy storage configuration optimization is performed by means of taking various uncertainty factors into consideration, thereby improving the accuracy of shared energy storage configuration optimization.

The invention relates to the technical field of mining engineering and geotechnical engineering support design, and discloses a roadway anchor rod group support parameter intelligent optimization method based on unbalanced force, and the method comprises the steps: building a surrounding rock finite element model, calculating a plastic stress increment through employing a minimum plastic complementary energy principle, and mapping the plastic stress increment into a node unbalanced force vector; an unbalanced force field reflecting reinforcement requirements is constructed, and an initial scheme is generated according to the unbalanced force field. Then, a virtual anchor rod unit is introduced to establish a nonlinear coupling model, an incremental balance equation is solved, and whether the unbalanced force is smaller than a threshold value or not is judged; if so, outputting an optimal parameter; otherwise, a comprehensive objective function containing safety and economy is constructed, new parameters are obtained through searching and decoding by means of the particle swarm algorithm, and the virtual anchor rod unit is re-entered for calculation to form a closed loop until convergence is achieved. According to the method, intelligent optimization is driven through a physical mechanism, the problem that a traditional design lacks a mechanical basis is solved, and accurate optimization of support parameters is achieved.

The invention relates to the field of motor structure design and optimization, and discloses a cogging torque and torque ripple optimization method for a permanent magnet motor for a vehicle. The method comprises the steps that based on an electromagnetic scheme, a geometric boundary and a term dictionary, a parameterized geometric prototype containing four symmetric semi-arc auxiliary grooves is constructed, and a design variable set structure is generated; converting the structure into a target evaluation interface structure containing a target item and a constraint item; an improved adaptive particle swarm algorithm is combined with finite element rapid check and a new iteration function to carry out optimization solution, and an updated candidate group structure is generated; and finally, optimal parameters are screened through Pareto sorting and robustness recheck, and an engineering configuration structure is generated. According to the method, through systematic parameter modeling, an efficient collaborative optimization algorithm and simulation evaluation, the optimization efficiency and reliability are remarkably improved, the cogging torque and the torque ripple of the motor are effectively reduced, and meanwhile, the process feasibility and the performance robustness of a design scheme are ensured.

The invention belongs to the technical field of electric power transaction, and particularly relates to an electric power spot transaction clearing and bidding optimization method based on computing power prediction, which comprises the following steps: collecting and preprocessing multi-source data, and screening a core feature set through mutual information entropy, Pearson's correlation coefficients and variance expansion factors; a CNN-LSTM attention model is built, and output load, power output and electricity price fluctuation interval prediction results are calibrated through error feedback; a dynamic bidding decision mathematical model is constructed, and elastic constraints are set in combination with prediction parameters; solving by adopting an improved particle swarm algorithm, and outputting an optimal bidding strategy; a node marginal electricity price mechanism is fused for accurate clearing; and verifying a clearing result in a layered manner, and performing deviation feedback iterative optimization. According to the method, a whole-process closed-loop mechanism is constructed, the prediction precision and the bidding and clearing collaboration are improved, the system operation cost is reduced, the transaction real-time requirement is met, the power grid safety and the market subject income are guaranteed, and the method has important practical value.

The invention belongs to the field of dam body stability evaluation, and provides a dam body stability evaluation method based on a multi-strategy improved particle swarm optimization algorithm, which comprises the following steps: S1, collecting and preprocessing various monitoring data for dam stability evaluation; s2, establishing a multi-physics field coupling dam model; s3, defining an objective function and constraint conditions of dam body stability evaluation; s4, based on an MSIPSO algorithm, fusing hybrid initialization, adaptive inertia weight and learning factor, dynamic disturbance and re-initialization, multi-subgroup coevolution and a multi-target constraint processing mechanism, and constructing a multi-strategy improved particle swarm optimization algorithm; s5, iteratively optimizing a to-be-evaluated parameter combination in the multi-physics coupling dam model by using the MSIPSO algorithm, inputting optimized parameters into the model for coupling analysis, and calculating a fitness value according to model output feedback so as to update a particle state of the MSIPSO algorithm; and S6, carrying out dam body stability evaluation and risk evaluation based on the optimal parameter combination obtained through optimization of an MSIPSO algorithm and the dam response.

The invention belongs to the technical field of indoor positioning, and discloses a Bluetooth path loss model parameter adaptive calibration method and system. According to the method, the historical learning mechanism and the hybrid optimization strategy are fused, so that the global search capability and the local optimization precision are considered while the dynamic updating of the path loss model parameters is realized. Compared with an existing calibration method of a fixed parameter or a single optimization algorithm, the method can automatically correct the model parameters according to the environment change, and solves the problem that the precision of a traditional model is reduced under the conditions of multipath effect, shielding interference and environment sudden change. A hybrid optimization framework of a genetic algorithm and a particle swarm algorithm is introduced, so that a parameter optimization process obtains a high-quality initial value in a global search stage, rapid convergence is realized in a local fine adjustment stage, and the calibration efficiency and precision are remarkably improved. And meanwhile, a historical learning mechanism is added, so that the model has a time memory characteristic, smooth mutation of historical parameter information can be fused, and the stability and robustness of the method in a complex dynamic environment are improved.

The present application relates to the field of power system optimization. Disclosed are a multi-objective particle swarm optimization method and system based on multi-strategy improvement. The method comprises: using a multi-strategy improved multi-objective particle swarm optimization algorithm to solve a multi-objective optimization model of a power supply of a generator state monitoring apparatus; and combining three improved strategies, i.e., adaptive adjustment of an inertia weight, coexistence of a decomposition algorithm and Pareto dominance, and introduction of a mutation factor. The present application overcomes the defects of conventional multi-objective particle swarm algorithms, and achieves a better distribution of a Pareto front, thereby obtaining the best Pareto optimal solution set. The present application solves the problems of conventional multi-objective particle swarm algorithms in solving a multi-objective optimization problem, such as premature convergence to a local non-dominated solution, and sub-optimal distribution of a Pareto front caused by an improper external archive update strategy, thereby improving the operational efficiency and reliability of a power supply system of an apparatus.

The invention provides a multi-scene urban electric vehicle charging station stochastic planning method and system based on an improved Huff-weighted Voronoi diagram, and the method comprises the steps: simulating the multi-scene travel behaviors of an electric vehicle in different typical days and urban functional areas through employing a Monte Carlo method, and generating charging demand space-time distribution considering the randomness of pile selection; establishing an improved Huff model fusing three types of attraction of power consumption, charging duration and electricity price, calculating a user station selection probability, constructing an improved weighted Voronoi diagram by taking the probability as a weight, and dividing the service range of each charging station; establishing a comprehensive expected cost minimization objective function, and applying a charging station service range constraint, a charging waiting time constraint and a charging pile number constraint; and solving the model by using an improved particle swarm algorithm combining multi-dimensional chaotic mapping and a linear decline strategy, and outputting an optimal charging station position and an optimal charging pile number. According to the invention, the randomness of user behaviors can be effectively described, and the economy and applicability of a charging station planning scheme are improved.

Disclosed in the present application are a DAB converter control method based on global minimum backflow power, a device, and a storage medium. The method comprises: acquiring the current consumed power inside a DAB converter, and determining whether the current consumed power is equal to a preset power threshold; if the current consumed power is not equal to the preset power threshold, acquiring the current input voltage and the current output voltage of the DAB converter, and then, with the objective of minimizing the deviation of the average transmitted power of the DAB converter and the output backflow power of the DAB converter, using a particle swarm optimization algorithm to optimize the current phase shift ratio combination applied by the DAB converter during triple phase shift control, so as to obtain an optimal phase shift ratio combination; and on the basis of a preset voltage reference value and the current output voltage of the DAB converter, determining an outer phase shift angle correction value, and then controlling the DAB converter by combining the outer phase shift angle correction value with the optimal phase shift ratio combination. The present application introduces the particle swarm optimization algorithm for solving a multi-objective optimization problem, such that the DAB converter can maintain a relatively high energy transmission efficiency and reduce the effect of the output backflow power after operation adjustment.

The invention discloses an optimized operation method and system for a wind-solar storage micro-grid. According to the method, a three-layer architecture of top-layer optimization, middle-layer cooperation and bottom-layer execution is adopted, and an intelligent closed loop of'prediction-planning-coordination-execution-feedback 'is formed. The top layer carries out global power flow congestion identification and dispersion based on LSTM prediction and a traffic congestion algorithm, carries out multi-target refined optimization in a feasible interval by using an improved particle swarm algorithm, and generates an optimal operation instruction; the middle layer dynamically adjusts and optimizes the target weight according to the operation scene, and decomposes an instruction into a multi-device cooperation strategy; and the bottom layer executes a customized control algorithm to realize accurate regulation and control and millisecond-level rapid protection of the equipment. The problems of micro-grid multi-target collaborative optimization, predictive control disjunction, operation risk look-ahead protection and the like are effectively solved, and the voltage stability, economical efficiency, dynamic response speed, safety and reliability of system operation are remarkably improved.

The invention relates to an excavator trajectory planning method based on an improved multi-objective particle swarm algorithm, and belongs to the technical field of engineering machinery trajectory planning. The method comprises the following steps: establishing a forward kinematics model through a D-H homogeneous coordinate transformation method; based on the forward kinematics model, establishing an inverse kinematics model through a geometric method; setting six path points in the track according to the mining track; based on an inverse kinematics model, converting the space coordinates of the six path points into space angles of joints; interpolation is carried out on the joint path points through a five-time B spline curve, and a track is generated; determining an optimization objective function according to actual application requirements, and setting constraint conditions according to a specific excavator type; and finally, the optimization problem is solved through an IADEMOPSO algorithm, an optimal Pareto solution is selected by using an average optimal evaluation formula, and an optimal track is generated. The track planning of the excavator can be realized.

The unmanned aerial vehicle path planning method based on chaos adaptive particle swarm optimization comprises the following steps: constructing a three-dimensional task scene model, setting flight performance constraint conditions, and constructing a flight path fitness objective function; initializing particle swarm parameters and population positions; calculating an adaptive value of each particle, and updating an individual historical optimal adaptive value / path and a group global optimal adaptive value / path; updating the particle speed and position; updating a current weight parameter based on a dynamic parameter self-adaptive decision-making mechanism; on the basis of a conditional trigger type population variation strategy, the positions of the particles are updated until the number of iterations reaches the maximum value, and an optimal path is output; according to the method, the optimal path is planned based on the chaos adaptive particle swarm algorithm by constructing the flight path fitness objective function, the initial solution of the algorithm is better, environment-population dual perception is realized, the convergence speed of path planning is improved, global exploration and local development are better balanced, and the method has better randomness and robustness.

The embodiment of the invention discloses a cable defect positioning and defect aging state detection method and equipment, belongs to the technical field of cable defect detection, and solves the problem that cable defects are difficult to reliably position during cable defect diagnosis in the prior art. Comprising the steps of constructing an integral transformation kernel function based on cable initial characteristic parameters; based on the integral transformation kernel function, constructing an integral transformation function corresponding to the cable head end impedance spectrum data, obtaining a cable defect diagnosis function, and determining cable defect positioning information through the cable defect diagnosis function; inputting the obtained cable defect positioning information into a cable impedance spectrum calculation model containing a local defect section, and outputting an impedance spectrum calculation value; optimizing a difference value objective function corresponding to the impedance spectrum calculation value and the impedance spectrum measurement value through a particle swarm algorithm to obtain characteristic parameters of the defect section of the cable; and determining the aging state of the local defect of the cable based on a preset cable aging characteristic parameter threshold and the characteristic parameters of the defect section of the cable.

The invention discloses an efficient refrigerating machine room energy-saving automatic control system based on reinforcement learning, and the system comprises the following modules: an operation data collection module which is used for collecting key equipment operation parameters and generating unified time sequence data; the energy consumption modeling module constructs energy consumption mathematical models of the water chilling unit, a water pump and a cooling tower and stores the energy consumption mathematical models into a database; the cooling capacity prediction module is used for predicting the future cooling capacity demand based on the historical data and the environmental parameters; the feed-forward optimization module is used for optimizing power distribution by using an adaptive particle swarm algorithm and generating control parameters; the state correction module automatically adjusts the running state according to the running efficiency deviation condition; the cooling strategy optimization module introduces an improved DSAC algorithm to optimize a cooling control strategy; and the instruction deployment module deploys the strategy to an actual system and outputs a regulation and control instruction. The energy efficiency management level of the refrigerating machine room is improved, and integration of energy conservation and intelligent control is achieved.

The embodiment of the invention discloses a production scheduling method and system applied to multi-objective optimization in waterproof roll manufacturing, and the method comprises the steps: obtaining the state constraint information of a waterproof roll production line in a preset time period according to a preset multi-objective optimization label, and enabling the information to reflect the limiting conditions which need to be satisfied by the production of the production line; based on the determined target function type and constraint condition set of the multi-target production scheduling model, performing association mapping to obtain the multi-target production scheduling model; solving the model by adopting an improved particle swarm algorithm with an adjusted particle coding mode and a fitness function, and adjusting a particle search behavior to obtain a multi-target optimization optimal solution; and generating a multi-target production scheduling strategy for the production line according to the weight distribution relationship of each target in the optimal solution and the priority ranking result, and guiding the actual production arrangement.

The invention discloses a multi-AUV global path planning method and device based on an improved GWO algorithm, and a storage medium, and belongs to the technical field of autonomous underwater vehicles. According to the method, on the basis of a grey wolf optimization algorithm (GWO), environmental constraints, distance constraints, energy consumption constraints, constraints among multiple AUVs and the like are considered, the basic grey wolf optimization algorithm in which a nonlinear convergence factor is designed is adopted to update the position, and a Levy flight strategy is introduced to update the position so as to prevent the algorithm from falling into local optimum; a method in which two fitness functions are relatively large is selected for each time of position updating, and a position updating thought of a particle swarm optimization (PSO) is introduced, so that the convergence speed of the algorithm is improved; and finally, a safe, collision-free, time-saving and energy-saving path is generated for each AUV by utilizing a polynomial trajectory generation method, and global path planning of the multiple AUVs in a complex marine environment is realized.

The invention discloses a displacement potential energy gradient oriented injection-production reconstruction optimization method between fracturing segments, and relates to the technical field of oil and gas field development. The method comprises the following steps: firstly, establishing an oil reservoir three-dimensional numerical model according to oil reservoir interpretation data, simulating in an oil reservoir numerical simulator by using the oil reservoir three-dimensional numerical model to obtain a global pressure field and a fluid flow velocity change condition in the oil reservoir, constructing a fluid potential field model, and determining potential energy gradients among injection-production units; constructing an objective function and setting constraint conditions by taking the most balanced potential energy gradient distribution among all injection and production units in the fluid potential field model as an optimization objective, solving the objective function based on a particle swarm algorithm, and optimizing the horizontal well multi-stage fracturing inter-section injection and production scheme to obtain the optimal horizontal well multi-stage fracturing inter-section injection and production scheme. The control device is used for controlling the injection-production process device between the multistage seam sections of the horizontal well, quantitative characterization and active control of an oil reservoir fluid potential field are achieved, and a basis is provided for improving the injected fluid sweep volume and the crude oil recovery rate of an oil reservoir.

The embodiment of the invention relates to the technical field of power systems and automation thereof, in particular to an alternating current and direct current hybrid power flow optimization method based on embedded direct current power flexible adjustment, and the method comprises the steps: building an embedded direct current adjustment model based on an embedded direct current power adjustment principle; constructing an AC / DC hybrid system steady-state power flow optimization model comprising a comprehensive evaluation index, a power system operation constraint and a DC operation constraint; an improved particle swarm algorithm is adopted to solve the steady-state power flow optimization model, the improved particle swarm algorithm updates a population through a chaos quasi-opposition strategy and optimizes an inertia weight and a learning factor, and a power flow optimization result is obtained; comparing different embedded direct-current power regulation modes, and verifying the effectiveness of the alternating-current and direct-current hybrid power flow optimization method; according to the method, accurate power flow optimization of the alternating-current and direct-current hybrid system in a new energy output fluctuation and load change scene is realized, and the economical efficiency, the safety and the stability of system operation are improved.

The invention relates to the technical field of cable production, in particular to a safety early warning method and system for cable production, and the method comprises the steps: responding to the change of environmental parameters at any moment in a cable production process, taking control parameters as particles, taking the sum of production quality and production efficiency as a fitness function, and obtaining a fitness function; obtaining a globally optimal solution by using a particle swarm algorithm; and generating an early warning signal in response to the situation that the variation between the globally optimal solution and the moment control parameter is greater than an allowable variation or the stability of the globally optimal solution is less than a stability threshold. Through the technical scheme of the invention, the optimal control parameter can be quickly found, early warning is realized from two aspects of the variable quantity of the control parameter and the anti-interference capability of the optimal control parameter, and the safety and controllability of the cable production process are ensured.

The invention provides a power grid energy storage system coordinated charging and discharging method considering reactive power regulation, and belongs to the technical field of power grid energy storage systems. Through collecting operation data of a power grid side and each energy storage unit, a multi-physics field coupling prediction model is used to predict a capacity attenuation rate and a temperature field evolution track of the energy storage unit; the reactive power regulation task load of each energy storage unit converter is calculated according to the reactive power vacancy of the power grid, a decoupled reactive power regulation instruction set is generated through multi-band decoupling control, a thermodynamic optimization function is established with the purpose of minimizing the total entropy increase of the system, and the non-convex constraint is converted into the second-order cone constraint through the convex relaxation technology. The optimal charging and discharging power time sequence is solved by using the improved particle swarm algorithm and is converted into a converter control instruction to be issued and executed, and the technical problem that battery health management and power grid power balance are difficult to consider when the energy storage system undertakes power grid reactive power adjustment is solved.

The invention relates to the technical field of diaphragm pump aging test, and discloses a two-parameter intelligent closed-loop diaphragm pump aging test system and method. According to the method, a temperature spectrum and a vibration spectrum are obtained through fast Fourier transform, a frequency domain coherence coefficient is calculated to identify temperature-vibration coupling characteristics, a resonance frequency band and a drift trend thereof are identified in real time by adopting a spectrum peak tracking algorithm, and a multi-objective optimization function considering aging acceleration efficiency and resonance risk is constructed. And solving the optimal temperature stress amplitude and vibration stress frequency by using a particle swarm algorithm, generating a stress loading instruction avoiding a resonance area through a self-adaptive PID controller, and dynamically updating model parameters based on a closed-loop feedback mechanism. According to the invention, the problem of resonance effect amplification caused by traditional fixed frequency stress loading is solved, and refined adaptive stress control is realized.

The invention relates to the technical field of soil engineering model tests, and discloses a test system and method for simulating water and mud bursting of a subsea tunnel under the action of vibration, and the system comprises a multi-degree-of-freedom servo feedback vibration module which is used for providing a multidirectional vibration loading platform and feeding back a motor current signal in real time; the visual rock-soil water inrush simulation and multi-field loading module is mounted above the vibration table and is used for accommodating a sample and applying confining pressure and water pressure load; the hierarchical multi-source data acquisition module is communicated with the discharge port and the observation window, and is used for acquiring the outburst mixture and monitoring physical field data; and the intelligent control and risk assessment analysis module is connected with each module to regulate and control loading parameters and invert a catastrophe state based on feedback data. According to the method, a self-adaptive optimization loading mechanism based on the improved particle swarm optimization algorithm is constructed, so that collaborative progressive regulation and control of pore water pressure and vibration frequency parameters are realized, and a critical threshold point of water inrush and mud inrush catastrophe can be accurately captured.