265 results about "Hybrid algorithm" patented technology

The invention discloses a high-precision static aeroelastic model optimization design method based on a model correction technology, and relates to the technical field of aircraft design, and the method comprises the following steps: S1, firstly constructing an initial model, and carrying out statics pre-analysis to verify integrity; s2, executing SOL 101 statics analysis based on the initial model and outputting a physical field result; s3, carrying out consistency analysis in combination with test data and generating a correction decision; s4, screening high-priority correction parameters through local or global sensitivity analysis; s5, correcting model parameters by adopting a mixed algorithm of a gradient method and an agent model, and verifying precision and generalization ability; s6, the corrected model is output as a Nastran file and a reduced-order model in a standardized mode, and a parameter change log is recorded; s7, executing static aeroelastic coupling and flutter analysis, and feeding back a result to drive optimization iteration; s8, constructing a multidisciplinary coupling optimization model in combination with aeroelastic and flutter results to realize collaborative optimization; and S9, finally performing engineering standardization packaging on the optimization model and outputting a verification report.

According to the large power grid reactive power optimization method and device, the storage medium and the computer equipment provided by the invention, the advantages of the two algorithms are fully exerted through the hybrid chaos quantum particle swarm optimization algorithm and the dimension-by-dimension convex space search algorithm. According to the chaotic quantum particle swarm algorithm, the global search capability and the capability of jumping out of local optimum of a particle swarm are enhanced by utilizing the characteristics of quantum behaviors and chaotic mapping, and the problem of premature convergence of a traditional heuristic intelligent algorithm is avoided. And according to the dimension-by-dimension convex space search algorithm, fine search is carried out on each excellent particle in different dimensions, a local optimal solution is determined, and the search precision and efficiency are further improved. According to the design of the hybrid algorithm, special optimization is carried out aiming at the characteristics of a reactive power optimization problem model, such as variable property difference, constraint complexity and the like, and the technical defects of poor optimization effect and optimization efficiency of an optimization solution algorithm in the prior art are effectively overcome.

The invention relates to the technical field of coal washing equipment, in particular to a lifting type peripheral transmission rake thickener operation system for coal washing, which comprises a working condition sensing module used for collecting slime water concentration, rake frame torque, slag layer thickness and equipment operation temperature data; the intelligent decision-making module is used for constructing a slime water sedimentation prediction model through an LSTM-GRU hybrid algorithm according to the data, and generating an equipment operation regulation and control instruction; the lifting transmission regulation and control module is used for adjusting the vertical height of a rake frame according to a regulation and control instruction to adapt to different slag layer thickness treatment requirements, and meanwhile, combining real-time feedback of the torque of the rake frame, dynamically adjusting the rotating speed of a peripheral transmission motor and optimizing coal slime and water separation conditions; and the safety early warning module is used for monitoring parameter mutation through a sliding window algorithm, and triggering corresponding strategies of different levels when the harrow torque and the slime water concentration exceed multiple groups of adaptability thresholds. Therefore, the problems that in the prior art, the height of the rake frame is fixed, regulation and control depend on manpower, and an early warning mechanism is extensive are solved.

The invention provides a load prediction and intelligent real-time control system based on hybrid algorithm fusion. Relates to the field of energy storage systems and energy scheduling, and comprises a data acquisition and processing module used for acquiring and preprocessing multi-source heterogeneous data of the energy storage system in multiple scenes to generate a structured time sequence feature set; the multi-source heterogeneous data comprises historical load data, time variables, environmental data, economic data and scene exclusive data; the power load prediction module outputs a load prediction result by integrating two different types of prediction models, and the two prediction models comprise a first prediction model based on historical sequence similarity and a second prediction model based on time decomposition; and the intelligent real-time control module is used for dynamically generating an energy storage system charging and discharging instruction and an energy scheduling strategy through a rolling optimization strategy based on a load prediction result and a preset target function, so that multi-target energy management is realized. According to the invention, the load response capability and prediction control precision of the energy storage system in multiple scenes are improved.

The invention discloses a virtual power plant source load interaction optimization scheduling model based on low-carbon response and a solving algorithm, and belongs to the technical field of power system optimization scheduling. A low-carbon scheduling framework containing a distributed power supply, energy storage, a flexible load and a carbon transaction mechanism is constructed, the carbon emission intensity of each link is quantified to form a carbon flow scheduling signal, and a dynamic carbon emission factor and energy cost are coupled. A multi-objective optimization model is established, a complex function is processed by piecewise linearization, and a hybrid algorithm of an improved genetic algorithm and a commercial solver is designed to improve the solving efficiency. The prediction error is dynamically corrected through a'prediction-optimization-feedback 'closed loop, and the strategy is adjusted. According to the scheme, low-carbon and economic collaborative optimization is realized, renewable energy consumption and system stability are enhanced, user satisfaction and real-time scheduling are considered, and a solution is provided for low-carbon intelligent operation of the power distribution network.

The invention relates to the technical field of traffic engineering management, and particularly discloses a traffic engineering quality safety dynamic supervision and management system and method, and the method comprises the steps: constructing a multi-dimensional sensing network which comprises an entity, an environment and a behavior sensing layer, and collecting data; a three-dimensional model is built based on the BIM technology, and standardized data and model millisecond mapping are fused and visualized; an LSTM and random forest hybrid algorithm is adopted to establish a three-level early warning model, and risks are identified and pushed in a graded manner; matching the disposal flow according to the early warning level, tracking the progress by the mobile terminal, and checking the closed loop in a grading manner; and key node data are stored through a multi-level block structure, an alliance chain permission, a consensus mechanism and an intelligent contract, so that reverse tracing is realized. According to the invention, real-time acquisition, dynamic modeling, intelligent early warning, co-processing and credible tracing of quality safety data in the traffic engineering construction process are realized, the quality safety risk is reduced, and the management efficiency and supervision transparency are improved.

The invention relates to the technical field of tunnel engineering, in particular to a tunnel lining long-term safety assessment method based on a PSO-SA hybrid algorithm, and the method comprises the steps: determining a surrounding rock creep constitutive model, and obtaining initial surrounding rock parameters through an inversion algorithm; the influence of a supporting structure on surrounding rock deformation is introduced, and corrected surrounding rock parameters are obtained to improve authenticity; based on the time-varying factors, the surrounding rock parameters and the lining parameters are iteratively updated, and long-term safety dynamic evaluation is achieved; inputting the iteratively updated surrounding rock parameters and lining parameters into numerical analysis software, and obtaining stress deformation characteristics of the lining structure through the numerical analysis software; according to the method, the long-term safety of the tunnel lining structure is evaluated according to the stress deformation characteristics and related specifications, a dynamic evaluation model which truly reflects the time-varying characteristics of the tunnel structure is constructed, qualitative leap in evaluation precision, reliability and foresight is brought, and a powerful technical guarantee is provided for long-term safety of an operation tunnel.

The invention discloses a hub motor vector control method based on an improved grey wolf particle swarm algorithm, and the method comprises the steps: building a mathematical model of a hub permanent magnet synchronous motor, building a double-closed-loop vector control simulation model containing a speed loop and a current loop in Simulink based on the mathematical model, and selecting a speed loop PID controller as a to-be-set object; improving the original grey wolf particle swarm algorithm to obtain an improved grey wolf particle swarm hybrid algorithm; and performing iterative optimization on the three parameters Kp, Ki and Kd of the speed loop PID controller through the improved grey wolf particle swarm hybrid algorithm, and outputting the finally obtained global optimal solution q as the optimal values of the three parameters Kp, Ki and Kd of the speed loop PID controller to complete the hub permanent magnet synchronous motor vector control.

The invention discloses a photovoltaic cleaning robot path optimization method based on a hybrid algorithm. The method is used for improving the operation performance of a robot in a multi-region environment. The method comprises the following steps: S1, constructing a plurality of target optimization functions based on a scene; s2, constructing an initialized population based on a plurality of target optimization functions, and constructing a total target function value matrix X; s3, calculating fitness values of all individuals; s4, performing non-dominated sorting on all individuals in the total objective function value matrix X according to the fitness values of all the individuals, dividing the individuals into a plurality of hierarchies, and enabling the individuals in each same hierarchy not to be dominated; s5, crowding degree calculation: selecting an individual with the largest crowding degree distance in the non-dominated hierarchy; and S6, performing particle swarm optimization and speed updating, and outputting a current optimal solution. The invention has the advantages of high efficiency and energy conservation.

The invention provides a parking method and device, electronic equipment, a storage medium and a vehicle. The method comprises the following steps: acquiring vehicle characteristic parameters of a vehicle in an automatic parking process; inputting the vehicle characteristic parameters into a reinforcement learning model to obtain a parking path output by the reinforcement learning model; wherein the reinforcement learning model is obtained by training a path obtained by neutralizing a path obtained by a mixed A * algorithm and a pedestrian driving path; and controlling the vehicle to execute automatic parking according to the parking path. The path planning speed can be increased, and the planning time is shortened.

The invention relates to the technical field of pipeline layout, in particular to a single-pipeline layout method based on an intelligent optimization hybrid algorithm. On the basis of a pipeline layout mathematical model, oil and gas treatment system pipeline layout engineering constraint conditions are considered, and an intelligent optimization hybrid algorithm fusing an A * algorithm and an artificial bee colony is provided for the defects that an ant colony algorithm is low in convergence speed, premature and prone to falling into local optimum. The ant colony is divided into four kinds of ant types of different working mechanisms: A * ant, leading ant, following ant and investigation ant, the A * ant improves the ant colony search speed, the leading ant and the following ant improve the ant colony search breadth, and the investigation ant further enhances the ant colony search depth.

The invention discloses a communication power supply load measurement and calculation and air switch control method and system based on service priority, and relates to the technical field of communication power supply intelligent management. Comprising the following steps: initializing and acquiring a load service priority list and a time threshold; monitoring an AC input state, switching to a standby power supply mode when the AC input state fails, and recording a power-off moment; a hybrid algorithm of ampere-hour integration and dynamic voltage calibration is adopted to measure the residual capacity of the storage battery in real time and with high precision, and residual power supply time is dynamically predicted; when the waiting time exceeds a first threshold value and the remaining power supply time is smaller than a second safety threshold value, a turn-off instruction is generated; and dynamically generating a turn-off sequence from low to high according to the service priority list, and iteratively executing turn-off and state re-evaluation until the endurance safety requirement of the core service is met. According to the invention, the problems of low control precision, incapability of distinguishing service priorities and low resource utilization rate in the prior art are solved, and accurate, intelligent and service-aware power management is realized.

A computer-implemented method and an apparatus for automatic modulation recognition that enables the detection and identification of modulation schemes in received raw signals with a signal receiving unit (70) without prior information about the raw signal detail, characterized by; comprising at least one computing unit (10) configured to transforming received raw signals from time domain to frequency domain including the noise in the signal with segmenting the signal and computing its modulation in multiple image with the spectrogram extraction process in order to capture temporal dependencies and sequential information by treating the raw signals received from signal receiving unit (70) as images; augmentation of the data for increasing the dataset size for increasing the accuracy with the limited data; training the data for enabling the network to learn spatiotemporal relationships based on a predefined or a precalculated signal-to-noise ratio (SNR) level; applying the data to one algorithm of two, which are convolutional neural network (CNN) and convolutional neural network (CNN) long short-term memory network (LSTM) hybrid algorithm.

The invention relates to the technical field of logistics management systems, and particularly discloses an artificial intelligence-based supply chain logistics information management system, which comprises a data acquisition module, a demand analysis module, a logistics decision module, a dynamic sensing module and a supply chain monitoring module. Through multi-source data acquisition and standardized preprocessing, the problem of traditional system data is solved, and a foundation is laid for analysis; on the basis of an improved LSTM model and a dynamic threshold value, demands are accurately predicted, risks are early warned, and inventory and stockout losses are reduced; a hybrid algorithm is adopted to collaboratively optimize paths, assess inventory and dynamically adjust environmental interference, and balance logistics efficiency and cost; a toughness index is generated by means of an Attention-LSTM model, and risk quantitative traceability and rapid disposal are realized in combination with hierarchical response and visualization; the modules are in real-time cooperative closed loop, cooperate with parameter self-optimization and threshold updating, adapt to industry and environment changes, and provide stable and intelligent support for supply chain management.

The invention provides an unmanned aerial vehicle target tracking method and system based on a hybrid algorithm, and the method comprises the steps: carrying out the ID number matching of an unmanned aerial vehicle target based on a byterack algorithm of Yolov5 when a to-be-tracked unmanned aerial vehicle in a current frame image can be recognized through a Yolov5 algorithm, and continuously tracking the to-be-tracked unmanned aerial vehicle target; when the Yolov5 algorithm cannot identify the to-be-tracked unmanned aerial vehicle in the current frame image, the byterack algorithm is switched to the kcf algorithm, and the unmanned aerial vehicle target is predicted and tracked based on the kcf algorithm; when the kcf algorithm is switched to the Byterack algorithm, the target motion track information accumulated by the kcf algorithm is used as initialization data to be fused into the Byterack algorithm, so that the initialization time and errors of the Byterack algorithm after switching are reduced, and the target tracking reliability of the unmanned aerial vehicle is improved.

The invention provides a wide-area tomography calculation astronomical imaging system and device, and the system comprises a snapshot spectral imaging module which is used for capturing full-view three-dimensional hyperspectral data of an observation view field; the red shift resolving module is used for processing the three-dimensional hyperspectral data through a hybrid algorithm based on the three-dimensional hyperspectral data, and inverting a target red shift value, three-dimensional space coordinates and physical parameters of the target celestial body; the chromatography data visualization module is used for generating a three-dimensional chromatography map through pseudo-color labeling and a three-dimensional modeling method based on the target red shift value, the three-dimensional space coordinates and the physical parameters; and the control module is used for configuring the snapshot spectral imaging module based on the observation target parameters. According to the method, high-precision cosmic chromatography of a wide-area sky area is realized, three-dimensional hyperspectral data is converted into a visual and reliable three-dimensional space distribution model and a physical parameter map, and reliable technical support is provided for frontier research of cosmic large-scale structures, galaxy evolution and dark matter distribution.

The invention provides a power grid voltage stability risk assessment index construction method based on multiple power devices, and belongs to the technical field of power grid voltage stability risk assessment. The method comprises the following steps: collecting full-dimensional original data to generate a comprehensive data set; an equivalent virtual inertia quantification model is constructed, an inertia-voltage fluctuation suppression ratio correlation simulation platform is established, and a virtual inertia parameter table is generated; analyzing a vulnerability mechanism and a fault rule of multiple power devices, and constructing a dynamically updated extreme scene-device vulnerability association library; constructing a power grid complex network topology model, and performing fault chain simulation by adopting a hybrid algorithm; constructing a voltage stability risk assessment index, positioning a new energy inertia weak node, and dynamically adjusting a compensation threshold and a regulation and control parameter; and designing a grading early warning mechanism, and generating an emergency linkage strategy. According to the method, the voltage stability risk assessment error is reduced in an extreme scene, the fault cascading path prediction accuracy is improved, the emergency disposal response time is shortened, and the high risk value is reduced.

The invention discloses an underwater robot battery temperature rise health state prediction method based on a PO-LSSVM hybrid algorithm, and relates to the field of underwater robot battery health state prediction, and the method comprises the steps: collecting the battery working condition data in the operation process of an underwater robot battery; performing adaptive optimization on the hyper-parameters of the LSSVM by adopting an improved parrot optimization algorithm to obtain optimized hyper-parameters, and training an LSSVM model based on a kernel function to obtain a PO-LSSVM hybrid prediction model; using a PO-LSSVM hybrid prediction model to obtain an initial prediction value of the battery health state; inputting the preliminary prediction value into a constructed temperature-pressure-salinity compensation system for secondary correction to obtain a battery health state accurate estimation value suitable for the underwater complex environment; the degradation rule of the battery performance in the temperature rise process can be effectively captured, high-precision migration prediction is achieved, and the generalization ability of the model under the unknown temperature working condition is remarkably improved.

The invention belongs to the technical field of offshore wind power fan arrangement and submarine cable path planning, and provides an offshore wind power fan arrangement and submarine cable path planning method and system based on a hybrid algorithm, and the method comprises the steps: carrying out the continuous space coding and searching of a fan layout scheme through employing an artificial travel mouse algorithm; performing non-dominated sorting and crowding degree calculation by taking a high-quality solution set obtained by an artificial travel mouse algorithm as an initial population; and adopting an A * algorithm integrated with a dynamic risk updating mechanism, and planning a submarine cable path connected with a fan by taking the minimization of accumulated risk cost and heuristic estimation cost as a target. A mixed strategy of an artificial squirrel algorithm and an A algorithm is adopted, and the artificial squirrel algorithm is used as an efficient global searcher to quickly position a high-quality solution area; according to the algorithm A, under the fixed fan layout, cable path planning with cost and risk comprehensive optimization is achieved, a complete optimization chain of macroscopic layout, multi-target tradeoff and microscopic path is formed, and the limitation of a single algorithm is systematically solved.

The invention discloses an automatic detection method for dynamic balance parameters of an automobile hub, and the method comprises the steps: employing a piezoelectric ceramic array self-sensing main shaft technology, enabling detection reference establishment and signal collection to be integrated in a main shaft body, and carrying out the fusion of a hub installation process and a detection reference establishment process; synchronous acquisition of multi-dimensional vibration signals is carried out; an original mixed signal is decomposed into modal components in a self-adaptive mode, and environmental vibration interference is eliminated; a FastICA-RLS hybrid algorithm is adopted to separate an independent source signal from the preliminarily purified signal, and non-stationary interference is tracked and compensated in real time; constructing a flexible rotor model based on modal parameter identification, and calculating an unbalance amount; self-adaptive Kalman filtering error compensation and prediction are carried out, and the amount of unbalance after compensation is output; and an optimal counterweight scheme is automatically calculated based on the compensated unbalance amount, and visual display and guidance are performed through a human-computer interface, so that the detection precision and the detection efficiency are improved.

The invention relates to the technical field of big data processing, and discloses a cloud computing and edge computing-based big data co-processing method and a cloud server, which are applied to a co-processing system consisting of an edge node cluster and the cloud server. And obtaining target data through a preprocessing strategy consisting of data filtering, format conversion, increment extraction and lightweight compression. According to the method, the compression ratio larger than or equal to 62% is achieved in combination with the improved LZ4 algorithm, only differential data with service value is transmitted, the invalid data transmission amount is greatly reduced, and the network bandwidth occupation pressure is remarkably reduced; and a dynamic task scheduling link, which is fused with a genetic-reinforcement learning hybrid algorithm of bidirectional LSTM load prediction, can accurately predict edge node computing power fluctuation, dynamically divide task types, match optimal processing nodes, control real-time task response delay within a reasonable range, and realize maximization of computing power resource utilization rate at the same time.

The invention provides a geotechnical engineering geological disaster prediction method and system for mining exploitation, and relates to the technical field of geotechnical engineering, and the method comprises the steps: S1, obtaining multi-dimensional data from different regions of a mining area in real time through a plurality of data collection devices, S2, carrying out the denoising, abnormal value correction, space-time alignment and missing value supplementation of the multi-dimensional data obtained in S1, and obtaining a prediction result; and the dynamic time window technology is adopted to process the time sequence data, so that the data under different time scales can be effectively synchronized, and accurate alignment and dynamic updating of the data are ensured. According to the geotechnical engineering geological disaster prediction method and system for mining exploitation, the fusion problem of multi-source heterogeneous data is effectively solved by combining the advantages of fuzzy logic and a neural network through an adaptive neural network-fuzzy hybrid algorithm. The fuzzy logic model carries out modeling on spatial and temporal changes of different data sources, and the influence degree of each data source on disaster prediction is accurately evaluated by dynamically adjusting parameters of a membership function.

This application discloses a path planning method, apparatus, medium, and product for an aircraft traction system in confined spaces, relating to the field of aircraft path planning technology. The method includes: establishing a kinematic model of the aircraft traction system located in a hangar; constructing a path planning mathematical model based on the kinematic model; employing an improved hybrid algorithm to perform path search on the aircraft traction system based on the path planning mathematical model to obtain the target path of the aircraft traction system; the improved hybrid algorithm is derived from a hybrid algorithm, and the improvements include: using RS curves for bidirectional node expansion; setting the cost function to include the cumulative historical cost from the starting node to the current node and the heuristic cost from the current node to the target node; and establishing a closed table distinguishing between forward and backward directions. This application realizes path planning for an aircraft traction system in confined spaces.

This invention relates to the field of power system operation and planning technology, specifically to a short-term wind power segment probability prediction method. It utilizes deep learning to mine implicit information in data and nonlinear features in wind power sequences to generate prediction probability intervals. Simultaneously, a nonlinear weighting method is selected to improve the optimization performance of the particle swarm optimization algorithm (IPSO), addressing some problems of traditional algorithms and improving convergence speed. Furthermore, a hybrid artificial intelligence algorithm, CNN-LSTM, is selected to construct a prediction model based on a combination of SVM and quantile regression (IPSO-CNN-LSTM). After training, short-term wind power probability prediction is completed. The CNN network can extract latent features from sample data using convolutional kernels, while the Long Short-Term Memory (LSTM) network can capture long-term components, avoiding gradient vanishing and exploding phenomena found in some existing algorithms, thus improving the efficiency of wind power probability prediction.

The invention discloses an official vehicle scheduling and optimizing method and system for mixed type tasks, and belongs to the field of official vehicle intelligent scheduling. The method comprises the following steps: acquiring basic information of official business tasks, drivers and vehicles, constructing a task distance matrix, then establishing a scheduling model which takes the minimum total fuel consumption as a target and contains constraints such as grade matching and passenger-carrying upper limit, and setting genetic and simulated annealing algorithm parameters; constructing an initial scheme population through chromosome codes matched with task levels, and calculating the fitness value of each scheme; and taking the chromosome as a simulated annealing initial solution to complete local optimization, transmitting the solution into a genetic algorithm to carry out crossover and mutation iteration, and finally outputting an optimal scheduling scheme. According to the method, a hybrid algorithm framework is adopted, global search and local optimization capabilities are considered, the compliance, low oil consumption and high resource utilization rate of a scheduling scheme are realized, the problems of level mismatching, resource waste and algorithm limitation of traditional scheduling are solved, and the method is suitable for official car scheduling scenes of various organizations, enterprises and public institutions.

This invention discloses a photovoltaic power prediction method based on a GRU-DDPG hybrid algorithm, comprising: replacing the original fully connected layers in the DDPG algorithm with GRU layers to construct a GRU-DDPG photovoltaic power prediction model, the GRU-DDPG photovoltaic power prediction model including a policy network and a comment network, the policy network including a main policy network and a target policy network, and the comment network including a main comment network and a target comment network; training the GRU-DDPG photovoltaic power prediction model based on historical climate feature data and corresponding historical photovoltaic power data; and using the trained photovoltaic power prediction model, predicting the photovoltaic power value for the next time step based on the climate feature data and the actual photovoltaic power value at the current time step. This invention can accurately capture temporal features while retaining the decision-making ability of deep reinforcement learning algorithms, significantly improving the model's adaptability to complex meteorological conditions and sudden changes in output, thereby improving the model's prediction accuracy and more accurately predicting photovoltaic power generation.

The application discloses a lightweight design method for a DD layup composite material rotary body based on a MIGA-SQP hybrid algorithm, relates to the technical field of composite material structure optimization, and comprises the following steps: dividing a composite material rotary body structure into a plurality of design regions, establishing continuous design variables based on a DD layup mode, and establishing a parameterized finite element model template file; establishing a two-stage hybrid optimization strategy; iteratively optimizing the parameterized finite element model template file and the two-stage hybrid optimization strategy; substituting an optimal design scheme into a finite element model for verification; and after verification, generating a manufacturing guidance file according to optimal layup parameters; and the method is used for solving the problems of low optimization efficiency and insufficient utilization of the continuous variable characteristics of the DD layup.

The invention relates to the technical field of unmanned aerial vehicle logistics and path planning, in particular to an improved grey wolf optimization method for unmanned aerial vehicle logistics distribution planning. The method comprises the following steps: firstly, constructing a three-dimensional simulation environment containing an obstacle and setting a path constraint condition; a comprehensive objective function is established, and four sub-objectives of path length, collision penalty, smooth penalty and height violation penalty are integrated through weighted summation; in the algorithm level, Kent chaotic mapping is adopted to generate an initial population to improve diversity, control parameters are changed into a nonlinear adaptive updating strategy to balance global exploration and local development, and a particle swarm optimization algorithm idea is fused to introduce an adaptive inertia coefficient and a dynamic weight coefficient to improve a grey wolf position updating rule; and finally, performing iterative optimization through an improved hybrid algorithm, and outputting an optimal path. The method can effectively improve the quality and efficiency of path planning, and enables the generated path to be economical, safe and smooth.

The invention discloses an alarm processing method, device and equipment and a computer readable storage medium, and is applied to the field of network security, and the method comprises the steps: carrying out the merging of original alarms through employing an algorithm model based on a dynamic time threshold, and obtaining a merged alarm result; performing association analysis on the merged alarm result by using a hybrid algorithm model to obtain an analyzed alarm result; the hybrid algorithm model is a model based on combination of a time sequence prediction algorithm and a neural network algorithm; and carrying out disposal priority evaluation on the analyzed alarm result, and carrying out disposal according to an evaluation result and a matched disposal strategy. According to the method, an information island in a water conservancy system is broken, the number of alarm events is reduced to the greatest extent on the premise of ensuring that the alarm information is not lost, and a large number of identical and complicated alarm events are prevented from being handled repeatedly; in combination with priority evaluation and strategy matching, high-risk alarms can be responded preferentially, and core safety support is provided for stable operation of the intelligent water conservancy and digital twinborn drainage basin.

The invention relates to a production plan Pareto optimization method considering carbon emission, punishment and balance, and belongs to the technical field of computational intelligence and production plan optimization. The method comprises the following steps: constructing a mathematical model of a heterogeneous parallel machine production plan problem considering carbon emission, rolling punishment and load balancing; and a non-dominated sorting hybrid algorithm based on Gaussian learning is proposed to optimize three conflicting targets, i.e., minimization of carbon emission, minimization of task rolling penalty and minimization of load imbalance, in the model. According to the method, a non-dominated sorting hybrid algorithm based on Gaussian learning is extracted and designed from an actual production scene, meanwhile, a Gaussian mixture model training strategy based on a disturbance solution set is designed to be combined with traditional genetic manipulation to generate new offspring individuals, and improvement of algorithm convergence and exploration ability is facilitated. The invention provides a group of high-quality non-dominated solutions for balancing carbon emission, rolling punishment and load balancing, and provides important management enlightenment for making an actual production plan of an enterprise.