105results about How to "Avoid premature convergence" patented technology

The invention discloses an air conditioner energy-saving control method and system based on multi-objective optimization, a medium and a product, and relates to the field of data processing. The method comprises the steps that optimization targets for conducting energy-saving control on the air conditioner and corresponding constraint conditions are determined, and the optimization targets comprise target air conditioner energy consumption and target comfort degree; acquiring current environment data of the air conditioner; the equipment operation parameter combination of the air conditioner is used as particles in the multi-target particle swarm algorithm; determining an optimal solution through a multi-target particle swarm algorithm; the air conditioner is controlled according to the parameter combination corresponding to the optimal solution; the actual air conditioner energy consumption and the actual comfort degree are obtained; based on the target air conditioner energy consumption, the target comfort level, the actual air conditioner energy consumption and the actual comfort level, the equipment operation parameters of the air conditioner are adjusted through a PID control algorithm, and final equipment operation parameters are obtained; and controlling the air conditioner to work according to the final equipment operation parameters. According to the method, both the energy-saving effect and the comfort level of the air conditioner are balanced.

The invention discloses an electrical automatic control method and system. The method comprises the following steps: acquiring temperature data and load current data in an electrical cabinet; denoising the temperature data by adopting a wavelet transform algorithm; filtering the load current data by adopting a Kalman filtering algorithm; establishing a temperature rise compensation model based on a long short-term memory (LSTM) neural network, wherein the output of the temperature rise compensation model is a temperature rise predicted value in the electrical cabinet; setting a multi-objective function of temperature rise control in the electrical cabinet; solving by adopting a multi-target ant colony algorithm to obtain an optimal control strategy of the cooling fan; and starting, stopping and speed adjustment of the cooling fan are controlled according to the optimal control strategy of the cooling fan. Aiming at frequent starting and stopping of a cooling fan of an electrical cabinet in the prior art, a temperature rise compensation model is established based on a long short-term memory (LSTM) neural network, a multi-objective optimization function including temperature rise deviation and starting and stopping times of the cooling fan is set, an improved multi-objective ant colony algorithm is adopted for solving, and starting and stopping of the cooling fan are reduced.

The invention provides a valve leakage diagnosis method and system based on an improved Hereum algorithm optimized SVM (Support Vector Machine), and relates to the technical field of valve leakage diagnosis. Decomposing the collected original signal to obtain a limited number of IMF components; calculating a fuzzy entropy, and constructing a feature vector; taking the feature vectors as data samples, and dividing the data samples into a training set, a verification set and a test set; using the training set to train the SVM model, and according to the classification error rate of the verification set on the trained SVM model, performing iterative optimization on the parameters of the SVM model based on an improved He-Ma algorithm to obtain an optimized SVM model; inputting the test set into the optimized SVM model to obtain a valve leakage diagnosis model; and acquiring a new acoustic emission signal, and inputting the new acoustic emission signal into the valve leakage diagnosis model to obtain a diagnosis result. The method not only can improve the classification accuracy and generalization ability of the diagnosis model, but also can enhance the stability of the model under different working conditions and environmental noise.

The invention discloses a path planning and cooperative control method for a heterogeneous unmanned cluster system in a dynamic environment. The method comprises the following steps: firstly, constructing a distributed position observer of predefined time, and estimating initial and final formation target positions of each follower unmanned aerial vehicle by utilizing local information interaction between the unmanned aerial vehicles; constructing a global path planner based on an adaptive variable solution space RRT algorithm, and planning a safe global path when each unmanned aerial vehicle passes through the dense obstacle area; a local path planner based on a predefined time prediction filter is constructed, and the potential collision risk between the unmanned aerial vehicle and a dynamic obstacle in formation tracking is avoided; and finally, performing motion control on the unmanned aerial vehicle by adopting a sliding mode control method according to the safe global path and the local path planning result, thereby realizing fixed-time formation tracking control of the unmanned aerial vehicle cluster. By using the method, the multi-unmanned aerial vehicle system can realize safe flight in a complex dynamic environment, and the control performance when a formation tracking flight task is realized is ensured.

The invention relates to an air quality prediction method suitable for an underground building space, and belongs to the field of data prediction. Comprising the following steps: S1, collecting multi-source data related to air quality in an underground building space, selecting the multi-source data to obtain a high characteristic variable, and constructing a data set by using the high characteristic variable; s2, modeling time series data by using a long short-term memory network based on an LSTM prediction model, and dividing the time series data in a data set into a test set and a training set according to a certain proportion; s3, inputting the training set into a prediction model of the LSTM, setting the learning rate and the hidden unit number of the prediction model of the LSTM by using an improved individual and collective learning balance strategy, and training the prediction model of the LSTM to obtain a high-performance air quality prediction model; and S4, inputting the input high characteristic variables in the test set into the high-performance air quality prediction model, outputting accurate target pollutant concentration, and obtaining the air quality of the underground building space.

The invention discloses a multi-agent experience exploration cooperation method based on a curiosity mechanism. The method comprises the following steps: constructing a multi-agent decision framework; a curiosity mechanism suitable for a multi-agent decision-making framework is constructed; constructing an experience exploration component suitable for the multi-agent decision-making architecture; constructing an experience playback pool and a state playback pool; a multi-agent cooperative confrontation environment is adapted; training the obtained deep neural network formed by the multi-agent experience exploration decision architecture based on the curiosity mechanism to obtain a multi-agent experience exploration collaborative strategy model; and in the multi-agent experience exploration collaborative strategy model, solving the multi-agent collaborative confrontation strategy in the current confrontation scene. Through the above mode, the method can balance the exploration and utilization process of a multi-agent algorithm during training, alleviates the influence caused by reward sparsity in a multi-agent confrontation environment, helps the multi-agent to carry out experiential exploration, searches an optimal strategy for confrontation, and improves the learning efficiency at the same time.

The invention discloses a PID (Proportion Integration Differentiation) parameter optimization method based on an improved graffiti search algorithm, and relates to the technical field of PID. The invention aims to solve the problems of insufficient local search capability, slow convergence speed and poor system stability in the prior art, thereby realizing a better PID parameter optimization effect. Specifically, the PID parameter optimization method based on the improved graffiti search algorithm comprises the following steps: constructing a PID controller model; a step of replacing a fixed perception probability and a step length in the model with an adaptive perception probability and a Levy flight step length; a step of adding a crow misleading prevention factor into the model; and acquiring preset parameters in the PID controller model based on a particle swarm algorithm. The preset parameters comprise proportional (Kp), integral (Ki) and differential (Kd) parameters. Parameters of the Levy flight step length are dynamically adjusted through a Bayesian optimization algorithm. The method is suitable for being applied to PID parameter optimization work.

The invention provides a grey wolf optimization feature selection method, device and equipment based on an adaptive mechanism, and relates to the technical field of data processing. The method comprises the following steps: acquiring a data set to be subjected to feature selection, and initializing a grey wolf optimization algorithm; the fitness value of each grey wolf is calculated with the purpose of obtaining the current optimal solution, and three current optimal wolves are obtained; then taking the current optimal three wolves as search guidance, combining a nonlinear parameter control strategy, adopting an improved grey wolf step length updating calculation mode, adaptively adjusting variation intensity in the search process, updating and calculating related parameters, positions and fitness values of each grey wolf, and continuously converging the distance between the prey and the grey wolf in the search process; and calculating the score of each grey wolf, and selecting the solution candidate with the highest score as the optimal solution in the current population until the maximum number of iterations is reached, thereby obtaining a global optimal solution. The method is used for processing feature selection in high-dimensional and complex classification, and is high in convergence speed and high in accuracy.

The invention discloses an indoor positioning method for optimizing a neural network based on an improved black wing plinary algorithm, and belongs to the technical field of indoor positioning. According to the method, the algorithm performance is improved by utilizing the characteristics of high evolutionary ability and high search speed of a black-wing plinary optimization algorithm, the black-wing plinary algorithm is further improved, uniformly distributed initial individuals are generated by adopting Tent chaotic mapping, and the diversity of populations is improved, so that the algorithm is prevented from being converged to a locally optimal solution too early, and the global search ability is enhanced; through a lens imaging reverse learning strategy, the stability and robustness of the algorithm are improved; a golden sine strategy is introduced in an attack and migration stage, a solution space is cut according to a golden section coefficient, and the position of a black wing plenum individual is updated. Compared with the prior art, the black-wing supporting optimization algorithm is more excellent in performance, the RSSI ranging model based on the BP neural network is constructed through the black-wing supporting optimization algorithm, the weight and the threshold value of the neural network are optimized, and high-precision positioning in the complex indoor environment is achieved.

The invention provides a cross section optimization method for a large-diameter offshore wind power hollow interlayer steel pipe concrete combined single pile foundation, which comprises the following steps of: digitally coding cross section parameters of the steel-concrete combined single pile foundation, setting an optimization target and a constraint condition, selecting a design variable range and generating an initial population; each individual represents a different section combination scheme; then, the fitness value of each section combination scheme of the steel-concrete combined single pile foundation is calculated, and multi-target Pareto layered screening is adopted for evaluation; finally, carrying out crossover operation on the selected steel-concrete combined single pile foundation section combination scheme to generate a new section combination scheme, and carrying out tiny random variation on section parameters of part of individuals through a set variation probability; and fitness evaluation, crossing and mutation operations are repeated, and through multi-generation evolution, a steel-concrete combined single-pile foundation section combination scheme conforming to the bending resistance and the total quality evaluation index of the inner and outer steel pipes is finally obtained.

The invention provides a sports student development core accomplishment comprehensive management method and system based on big data, and the method comprises the steps: receiving and integrating multi-channel sports student dynamic data streams, and constructing a personal growth trajectory map; analyzing nodes and edges in the atlas by using an association rule mining algorithm, identifying key factors and turning points influencing growth, and performing optimal matching on educational resources and training plans in combination with a core literacy framework and a particle swarm optimization algorithm to generate a matching result; personalized learning materials and training suggestions are accurately pushed to form a preliminary development path plan; monitoring the execution progress in real time through a reinforcement learning algorithm, and dynamically adjusting the preliminary plan to obtain a target development path plan; and establishing a multi-dimensional feedback mechanism, collecting feedback information of coaches, sports students and third-party reviews, performing analysis processing to obtain an evaluation result, and finally generating an optimized final target development path plan. According to the invention, the management effect of core accomplishment of sports students is improved.

The invention discloses an oxygen-enriched bottom blowing copper matte grade prediction method based on a GA-BP neural network, which improves optimization efficiency and model prediction precision by improving a core mechanism of a genetic algorithm. The method comprises the following steps: collecting actual production data in a stable production period of a copper smelting plant; an existing GA-BP neural network is improved by adding a self-adaptive crossover probability adjustment mechanism and a self-adaptive mutation probability adjustment mechanism in a genetic algorithm, and the self-adaptive crossover probability adjustment mechanism not only depends on individual fitness, but also is combined with global and local information to be used for enhancing global search capability and local optimization capability. The adaptive mutation probability adjustment mechanism not only combines individual fitness, but also introduces global entropy and dynamic noise influence, and meanwhile, allows the mutation probability to be increased randomly in the later stage of evolution so as to jump out of local optimum; and by utilizing the improved GA-BP neural network, taking actual production data as input, predicting the oxygen-enriched bottom blowing copper matte grade and outputting a copper matte grade value.

The invention discloses a micro-grid optimal scheduling method based on a self-adaptive scheduling participation mutation particle swarm algorithm, and aims to improve the operation efficiency and environmental protection performance of a micro-grid. According to the method, a self-adaptive parameter adjustment method and a variation mechanism are innovatively provided, the problems that a traditional particle swarm algorithm is prone to premature and lacks population diversity are solved, and the global search capability and the optimization effect are remarkably enhanced. Constructing a mathematical model in a microgrid grid-connected operation mode by taking the operation cost and the environmental protection cost as optimization targets; scheduling optimization is carried out by using a self-adaptive scheduling participation mutation particle swarm algorithm; through comparison simulation experiments, the superiority of the method under different load requirements and resource distribution conditions is verified. The method is suitable for micro-grid optimization scheduling scenes including distributed power supplies, energy storage systems and the like, and maximization of economic and environmental protection benefits can be realized while power supply reliability is ensured.

The invention relates to the technical field of energy optimization scheduling, in particular to a multi-source microgrid optimization scheduling method and system. The method comprises the following steps: acquiring a mathematical model of each distributed power supply and an energy conversion device of a micro-grid in a to-be-optimized region, and constructing a target function by taking weighted minimization of fuel cost and operation maintenance cost of the micro-grid, pollutant discharge treatment cost of the micro-grid and load control compensation cost of the micro-grid as a target; setting a power balance constraint, an energy storage charge state constraint, a generation power constraint, each distributed power supply output constraint and a ramp rate constraint as constraint conditions, and constructing a day-ahead optimization model; the day-ahead optimization model is solved through the whale-bat algorithm, and the whale-bat algorithm replaces the search stage of the whale algorithm with the improved bat algorithm to solve the day-ahead optimization scheduling strategy. The optimal scheduling strategy obtained by the invention can respond to dynamic changes more quickly while ensuring high precision.

The invention discloses an unmanned aerial vehicle path planning method and device based on an improved frost ice optimization algorithm, and the method comprises the steps: constructing a flight space according to the starting point and the terminal point of an unmanned aerial vehicle, and planning a flight path of the unmanned aerial vehicle according to an obstacle in the flight space and a comprehensive cost function of the path planning of the unmanned aerial vehicle; substituting the coordinate value of the rime particle position obtained through a multi-strategy improved frost ice optimization algorithm into the comprehensive cost function to calculate and output an optimal path; and adjusting the flight path of the unmanned aerial vehicle according to the optimal path. According to the invention, unmanned aerial vehicle path planning is realized based on the improved frost ice optimization algorithm, the length of the optimized path, the flight time, the energy consumption and other aspects are obviously improved, the path with low cost, high safety and short arrival time can be quickly found, and the task execution efficiency of the unmanned aerial vehicle is improved.

The invention belongs to the technical field of industrial scheduling, and discloses a flexible job shop lean production self-organization optimization method for a peak load shifting strategy, and the method comprises the steps: building a problem model, and determining an optimization target and a constraint condition; encoding and decoding chromosomes, and initializing a population; sorting the individuals, executing selection, crossover and variation, and forming a primary filial generation population by using an equipment efficiency cooperative operator and an energy efficiency cooperative operator; combining the parent population and the filial population, performing sorting, selection, crossover and variation again, forming a new filial population by using an equipment efficiency cooperative operator and an energy efficiency cooperative operator, and taking the previous filial population as a new parent population; and repeatedly executing the iteration step, and outputting an optimal scheduling scheme after a termination condition is reached. According to the method, the productivity and the energy efficiency can be effectively coordinated, the actual technical support is provided for the flexible job shop to realize peak clipping and valley filling and reduce the peak-valley electricity proportion, and the method system of the shop scheduling problem is enriched.

The invention provides a distributed flexible workshop scheduling method and system based on a multi-target grey wolf algorithm, and belongs to the technical field of workshop scheduling. The method comprises the following steps: initializing algorithm parameters, and defining a coding and decoding mechanism by adopting three-layer coding including a process sequence, machine selection and factory selection; initializing the initial wolf pack by adopting a hybrid strategy based on three-layer coding; dividing non-dominated levels of the initial wolf pack through non-dominated sorting and sorting population crowding degree; determining the position of a wolf pack leader based on the number of non-dominated hierarchies and crowding degree sorting; performing crossover variation and wolf pack combination on the individual positions of the wolf pack, and updating the combined wolf pack to obtain a non-dominated elite solution set; setting a destruction operator and a repair operator, dynamically adjusting the selection probability of the destruction operator and the repair operator, introducing a simulated annealing algorithm to expand a search solution space, and optimizing an elite solution set to obtain a final solution. The scheduling efficiency of the distributed flexible job shop is improved, local optimum is avoided, and the production benefit and quality are guaranteed.

The invention relates to a multi-parameter collaborative optimization solid-state battery life prediction method, which comprehensively considers main parameters influencing the life of a solid-state battery, including ionic conductivity, an electrode material, working temperature, charge-discharge rate and the like, and the key of life evaluation of the solid-state battery is capacity attenuation and internal resistance increase of the solid-state battery. By establishing a multi-parameter collaborative optimization mathematical model, the accuracy and reliability of solid-state battery life prediction are improved, the optimization algorithm is adopted to optimize the model parameters, the optimal parameter combination can be quickly found, and the prediction efficiency is improved. The prediction method provided by the invention has relatively strong generalization ability, can be suitable for solid-state batteries of different types and specifications, and provides powerful support for design, manufacturing and application of the solid-state batteries.

The invention relates to the technical field of information management, and discloses a human resource information digital management method and system based on big data. Firstly, human resource data index scores in the original human resource data matrix are calculated by using a principal component analysis method, and executive ability values of employees are calculated after principal components are screened; secondly, classifying the employee execution capability values by using an improved clustering algorithm, and introducing a particle swarm optimization algorithm to search an optimal initial clustering center so as to complete position distribution; training a BP neural network to obtain a BP neural network model, predicting employee performance, and outputting an employee performance prediction value; and finally, performing employee performance assessment and evaluation according to the employee performance predicted value, thereby realizing digital management of human resource information. By analyzing and processing the human resource data, the purpose of human resource information digital management is achieved, and the method is objective and accurate.

The invention discloses a cold-chain logistics vehicle planning system. The system comprises the following steps of initial population creation, explosion and spark generation, evaluation and selection, genetic manipulation, pheromone updating, termination judgment and optimal solution output. According to the method, a dynamic pheromone updating and path reward and punishment mechanism is introduced through improvement of pheromone updating, so that the convergence speed of the algorithm is accelerated, and the quality of a solution is improved; the exploration depth of a solution space is increased by improving the crossover operation and considering the length and quality of an introduced path; improvement of mutation operation depends on a self-adaptive mutation strategy, the exploration capability of the algorithm is enhanced, multi-objective optimization is introduced into path planning through the improved firework algorithm to consider transportation cost, temperature control and energy consumption management at the same time, and a dynamic adjustment mechanism is combined to cope with frequently changing terrain and climate conditions in the karst landform, so that the development of the karst landform is facilitated. Therefore, the adaptability of the algorithm in a complex environment is improved.

The invention discloses a data acquisition method based on big data, and relates to the technical field of big data, and the data acquisition method comprises the following steps: obtaining to-be-acquired data; obtaining target collection data; marking target acquisition abnormal data; evaluating the influence degree of the target acquisition abnormal data; and judging whether the target acquisition abnormal data meets a preset data acquisition standard or not. Based on the anomaly recognition algorithm, anomaly detection and marking can be quickly performed on the target data, accurate positioning of potential problem data is realized, the data anomaly processing efficiency can be improved, the abnormal data is compared with the existing database through the evaluation algorithm, the influence degree of the abnormal data can be accurately evaluated, and the accuracy of data anomaly processing is improved. The method shortens the manual judgment time, judges whether the data meets the collection standard or not according to the evaluation result, can effectively eliminate irrelevant or low-value data, guarantees the high correlation and application value of the collected data, and reduces the processing cost of invalid data.

The application relates to the technical field of human resource management, in particular to a medical staff scheduling system based on medical staff skills, which comprises a standard skill input and classification device, which is used for inputting the working skills of each department, dividing the same working skills of each department into general skills, and dividing the special working skills of each department into special skills; a department patient input device, which obtains the treatment information of patients in each department ward, extracts the predicted workload of each patient in the next scheduling cycle, and counts the predicted workload of each department; the predicted workload comprises the execution times of each general skill and the execution times of each special skill; the system realizes cross-department dynamic scheduling by constructing a hospital-level skill resource pool and a demand prediction mechanism, standardizes the general skills into shared resources, and enables medical staff to flexibly support departments with high demand peaks.

The application belongs to the field of workshop scheduling, and particularly discloses a semiconductor wafer probe test scheduling method and system based on an improved genetic algorithm, which adopts double-layer process-machine coding, and combines auxiliary resource taboo coding as an individual, effectively converts a complex scheduling problem into a coding form through an innovative coding and decoding strategy, and avoids traversal search on all selectable auxiliary resources; global-local hybrid search is carried out through the improved genetic algorithm, on the basis of machine level exchange with the traditional critical path-key block, search on the auxiliary resource immediately preceding work is increased, and neighborhood exploration is carried out based on a load balancing strategy, so that the search efficiency and solution quality are improved. The application can quickly and efficiently solve the semiconductor wafer probe test scheduling scheme, effectively improve the equipment utilization, speed up the scheduling efficiency, and break through the bottleneck of traditional scheduling.

This invention discloses an optimization method and system for smoke screen chaff deployment based on a view cone masking model, belonging to the field of signal jamming technology. The method includes the following steps: S1, constructing an adversarial kinematic model of the missile, target, and chaff; S2, establishing a view cone masking quantitative evaluation model with masking completeness as the core; S3, defining a fitness function with the maximum effective masking time as the objective; and S4, using an adaptive genetic algorithm to solve for the optimal deployment strategy that satisfies spatiotemporal constraints. This invention solves two major bottlenecks in existing technologies: the disconnect between effect evaluation and decision control, and the excessive reliance of advanced decision models on preset conditions. It achieves accurate, direct, and decision-driven quantitative evaluation of masking effects from the first-person perspective of the missile seeker.

The invention discloses a fractional order ultra-precision platform identification method based on genetic optimization and data filtering, and solves the technical problem that the control precision is reduced due to the nonlinear characteristic of a system in high-precision motion control. Comprising the following steps: firstly, establishing an ultra-precise nonlinear platform system model comprising a fractional order linear dynamic part and a static nonlinear part according to GL definition; then, data filtering processing is carried out on input and output of the system, and complex colored noise is converted into simple moving average noise to improve the signal-to-noise ratio; and then, improving a crossover rule, dynamically adjusting the crossover probability, enhancing the global search capability and convergence rate of the algorithm, and improving the precision and robustness of parameter estimation. The method has high precision and good anti-interference capability in processing the parameter estimation problem of a fractional order nonlinear system, and is suitable for modeling and control of an ultra-precision platform complex industrial system.

The present application discloses a method and system for temperature compensation of an optical fiber gyroscope, and relates to the field of temperature compensation. The method comprises: introducing chaotic disturbance and Gaussian variation mechanism into a sand cat swarm optimization algorithm to obtain an improved sand cat swarm optimization algorithm; optimizing a BP neural network using the improved sand cat swarm optimization algorithm to obtain a BP neural network optimization model; constructing a temperature error model based on the BP neural network optimization model; obtaining the temperature, temperature change rate and temperature-varying zero bias measurement value of an optical fiber gyroscope under variable temperature conditions; inputting the temperature and temperature change rate of the optical fiber gyroscope into a temperature error model to obtain a temperature error prediction value; and obtaining a temperature-compensated optical fiber gyroscope zero bias based on the temperature-varying zero bias measurement value and the temperature error prediction value. The present application can improve the accuracy of temperature compensation of an optical fiber gyroscope.

The present application provides a comprehensive management method and system for the core literacy of sports students based on big data, wherein the dynamic data streams of sports students from multiple channels are received and integrated to construct a personal growth trajectory map; the nodes and edges in the map are analyzed using an association rule mining algorithm to identify key factors and turning points that affect growth, and the core literacy framework and particle swarm optimization algorithm are combined to optimize the matching of educational resources and training plans to generate matching results; personalized learning materials and training suggestions are accurately pushed to form a preliminary development path plan; the execution progress is monitored in real time through a reinforcement learning algorithm, and the preliminary plan is dynamically adjusted to obtain a target development path plan; a multi-dimensional feedback mechanism is established to collect feedback information from coaches, sports students themselves, and third-party reviews, and the evaluation results are obtained after parsing and processing, and finally the optimized final target development path plan is generated. The present application improves the management effect of the core literacy of sports students.

The present application relates to a transformer fault diagnosis method based on improved sparrow search algorithm optimization SVM, through the pretreatment and feature engineering of transformer oil chromatographic data; according to the data of pretreatment and feature engineering, the model based on oil chromatographic data is constructed, and the model parameters are optimized and trained; the input transformer oil chromatographic data is carried out transformer fault diagnosis based on the optimized and trained model.The present application improves the sparrow search algorithm by introducing the best point set strategy, the golden regular update rule, the differential mutation disturbance and the reverse learning mechanism, so as to comprehensively improve the parameter optimization process of support vector machine, the global exploration ability of the algorithm is enhanced, the risk of falling into local optimum is avoided, and the problem of premature convergence is effectively avoided.In addition, the improved algorithm pays attention to multi-link optimization, and the parameter setting is simple, the algorithm is low in use difficulty, so that the optimized support vector machine (SVM) can effectively improve the transformer fault prediction precision.

This invention discloses a method for monitoring the state trends of hydropower units based on two-stage signal decomposition and the IBiLSTM model. The method involves collecting vibration signals from the hydropower units for preprocessing; constructing the ITGCOA optimization algorithm and designing a fitness function; using the ITGCOA optimization algorithm to adaptively optimize the SVMD and BAACMD models, achieving initial decomposition of the preprocessed signal and secondary decomposition of the sub-mode component with the highest center frequency obtained from the initial decomposition; calculating the fuzzy entropy values ​​of the remaining sub-mode components for reconstruction; and fusing the high-frequency feature sub-sequences obtained from the secondary decomposition with the reconstructed feature sub-sequences to construct the input sequence of the prediction model. This input sequence is then used to construct the IBiLSTM prediction model for monitoring the state trends of hydropower units, achieving high-precision prediction. Compared with existing technologies, this invention improves the efficiency and accuracy of hydropower unit state trend prediction, accurately warns of abnormal unit operating conditions, ensures the safe and stable operation of the units, and improves the overall efficiency of the power plant.

This invention discloses an AI-based low-code runtime anomaly identification system, comprising a data acquisition module, a node runtime anomaly identification module, a process runtime anomaly identification module, and a low-code runtime anomaly management module. This invention relates to the field of data processing technology, specifically to an AI-based low-code runtime anomaly identification system. This solution innovatively proposes performing full-dimensional anomaly prediction before low-code deployment and operation, and conducting three-level progressive runtime anomaly identification, thereby improving the stability of low-code process operation. It introduces TT tensor decomposition technology to construct a Laplace matrix, and uses a dynamic correlation matrix matching the current node component type as the correlation weight for graph convolution operations, improving the accuracy of the low-code node anomaly identification output. It employs a sine and cosine periodic perturbation chaotic mapping strategy and introduces a dual adaptive step-size mechanism to improve the optimization algorithm, thereby enhancing the stability and identification accuracy of the process runtime anomaly identification model.