573 results about "Genetic algorithm" patented technology

The invention relates to a power equipment fault prediction method based on multi-source heterogeneous data and a twinborn model, and the method comprises the following steps: S1, obtaining multi-modal data related to the operation of power equipment, constructing a joint feature encoder through an attention mechanism in deep learning, and carrying out the joint modeling of multi-modal information, and obtaining a joint feature representation; s2, based on joint feature representation, adopting a dynamic twin model of self-supervised learning and fusion reinforcement learning, and dynamically updating the equipment health state; s3, constructing a complete-cycle prediction framework based on Transform, capturing long-term dependency and dynamic change characteristics in equipment operation, and identifying trigger factors of faults in combination with causal relationship analysis; and S4, based on the equipment health state and the fault prediction result, optimizing equipment operation parameters by adopting an intelligent operation and maintenance optimization engine, and dynamically generating an optimal operation and maintenance plan in combination with a genetic algorithm. The reliability and the operation efficiency of the power equipment are remarkably improved.

The invention discloses a multi-modal visual arrangement recommendation method, belongs to the technical field of artificial intelligence and data visualization crossing, and realizes visual arrangement recommendation based on multi-modal input analysis, a dynamic mixed recommendation model and an intelligent optimization algorithm. Comprising the following steps: multi-modal intention analysis: realizing intelligent analysis of multi-modal input through combined use of a base model and a fine tuning model, realizing high-precision intention classification in combination with a pre-training language model and a domain adaptation fine tuning technology, and triggering dynamic prompt word recommendation; performing intelligent layout generation: performing global optimization of component space allocation by adopting a genetic algorithm, performing business rule adaptation by combining a constraint solver, and modeling an interaction relationship between components by utilizing a graph neural network; and dynamic mixed recommendation: constructing a three-level recommendation architecture including collaborative filtering, content matching and reinforcement learning. According to the method, a closed-loop recommendation process of user intention-intelligent recommendation-feedback optimization is realized, and the intelligent level of visual arrangement and the user experience are remarkably improved.

A system and method for AI-based dynamic pricing, mapping, and emissions control to optimize inner-city mobility. The system acquires real-time nitrogen dioxide data across a geographic area from sources including satellites, feeds this data into a decision support system along with road-specific weight matrices from authorities, and updates a dynamic penalty matrix for roads in the area. Automatic number plate recognition tracks vehicle movement, queries vehicle databases for emissions profiles, and updates the platform. Users input journey parameters and the system, using AI tools including a genetic algorithm economic and environmental dispatch (GA-EED) optimizer, generates an optimized route based on user-selected criteria such as time, emissions, penalty, and cost. The system enables dynamic, environmentally-aware route optimization for inner-city mobility.

The invention provides a coal mining digital twinning method based on multi-level dynamic deviation correction and AI optimization. The method comprises the steps of monitoring data acquisition and dynamic preprocessing, and performing denoising and space consistency detection on data; constructing and pre-processing the coal mine digital twinborn body; generating a nonlinear deviation field by adopting a high nonlinear variable interpolation and model mapping method; performing Kalman filtering error analysis and correction under the continuous analysis step, and marking an optimization model state in combination with a space mutation step; performing staged sliding window long-time-sequence fluctuation analysis, and classifying and marking regions according to an absolute value of an error and fluctuation; performing multi-level weighted error optimization based on a genetic algorithm, and adjusting material parameters and boundary conditions; carrying out material constitutive rectification based on dynamic Bayesian, and gradually enhancing the adaptability and accuracy of the model; and constructing a hybrid network structure AI optimization prediction model based on deep data driving, capturing the dynamic change of the material constitutive and the characteristic difference of the model constitutive, and realizing the precise digital twinborn simulation of the whole coal mining process.

The invention discloses a power grid real-time optimization scheduling system and method based on digital twinning, and relates to the technical field of power grid scheduling. A sensor is deployed to collect environmental parameters of key nodes in real time, a dynamic environmental condition coefficient is constructed, a power grid state is analyzed in combination with frequency stability and a relative strength index, and a multi-model fusion prediction mechanism is established, so that space-time two-dimensional accurate prediction of load and power generation is realized. A multi-objective optimization model is adopted to take'maximization of new energy consumption + minimization of scheduling cost 'as a core objective, a genetic algorithm is introduced to solve an optimal scheduling scheme, and a feasible solution is screened in combination with forward simulation of a digital twin model. Through abnormal early warning triggering, environment correlation analysis and model iterative optimization, a scheduling strategy is dynamically adjusted, and the power supply efficiency and the emergency response capability in an extreme scene are improved. According to the method, multi-source heterogeneous data are effectively fused, and real-time sensing of a power grid operation state, collaborative optimization of multiple energy resources and adaptive iteration of a scheduling model are realized.

The invention discloses an intelligent shield muck improvement method and system based on machine learning, and the method and system achieve the intellectualization and precision of muck improvement in the tunneling process of a shield tunneling machine through integrating muck image classification, improvement parameter prediction and model optimization. Firstly, a YOLOV8 network is utilized to classify muck images in real time, classification randomness is corrected in combination with an XGBOOST model, and classification accuracy and stability are improved. Secondly, performing time sequence prediction on the muck improvement parameters based on an LSTM network, and introducing a linear regression and genetic algorithm to improve the prediction precision; and finally, optimal solution is carried out on the muck improvement parameters through a particle swarm optimization (PSO) algorithm, and the stability and efficiency of shield tunneling are remarkably improved. The method solves the problems of randomness and hysteresis caused by dependence on manual adjustment in the prior art, has the advantages of high efficiency and accuracy, and is suitable for automatic application scenes of shield engineering.

The invention discloses a BIM-based full-period management method and system for tunnel construction organization optimization, and belongs to the technical field of construction organization and cost management. The management method comprises the following steps: establishing a three-dimensional geometric model and a parameterized model of a tunnel based on a BIM technology; performing work decomposition on a construction task, and calculating a construction activity time parameter; calculating the cost of materials, machinery and manpower of each construction unit, and summarizing the total cost of the project; optimizing a construction period and resource allocation under a multi-constraint condition by adopting a genetic algorithm; predicting future process time consumption and construction progress based on an LSTM time sequence prediction model; and integrating dynamic data of the construction site, and updating the BIM model in real time. Through combination with dynamic data, efficient organization and scientific management of the whole tunnel construction process are realized, the construction efficiency can be improved, the engineering cost can be reduced, and the scientificity and rationality of project decision making are remarkably improved.

The invention discloses a building robot multi-machine collaborative operation optimizing and monitoring method and system based on digital twinning, and belongs to the technical field of building construction intellectualization. The method comprises the following steps: constructing a digital twinborn body comprising an environment perception model and a kinematics / dynamics model based on a building information model and robot physical parameters; performing multi-machine task allocation and path planning containing static / dynamic obstacle avoidance in the virtual environment; synchronizing physical environment data in real time through a multi-mode sensor; dynamically optimizing operation parameters by adopting a genetic algorithm or a particle swarm algorithm and realizing closed-loop control; and generating a safety early warning and emergency scheme based on machine learning. According to the method, Markov decision path planning of reinforcement learning and multi-agent game task allocation are creatively fused, laser radar-vision-inertial navigation multi-source data fusion is adopted, the technical problems that in a traditional method, digital twinning precision is insufficient, and dynamic cooperation efficiency is low are solved, and the method is suitable for large-scale popularization and application. And the construction efficiency, the safety and the man-machine interaction experience are remarkably improved.

The invention relates to an artificial intelligence-based cargo transportation path planning method and system for automatic warehouse logistics. The method comprises the steps of performing weighted scoring calculation based on customer historical transaction data and cargo attributes, and generating service label data for distinguishing a high-value hierarchy and a common hierarchy; a differentiation algorithm strategy is called according to the service label, an exclusive transportation path is distributed to the high-value goods, obstacle avoidance is optimized in real time, and adjacent orders are matched for common goods to be merged and distributed; in combination with real-time road conditions and storage scheduling data, through path weight dynamic calculation and genetic algorithm iterative optimization, generating a global transportation scheme considering both time efficiency and cost; customer preference parameters are updated in a closed loop mode based on execution feedback data, and system self-adaptive learning is achieved. According to the method, the problems that differentiated services, dynamic response to environment changes and low-efficiency resource utilization cannot be achieved in a traditional method are solved, the punctuality rate of high-value orders and common orders can be increased, and meanwhile the transportation cost of the common orders can be reduced.

The invention provides an intelligent optimization method and system for tunnel smooth blasting blast holes in layered rock mass, and belongs to the technical field of tunnel engineering and blasting. The method comprises the steps of collecting stratified rock geological data, evaluating and extracting features, constructing a multi-objective optimization function, solving by an intelligent optimization algorithm, performing construction feedback and dynamic optimization and the like. The method comprises the following steps of: acquiring data by using various geological exploration equipment, performing evaluation processing on the data by using a support vector machine model, constructing an optimization function considering multiple indexes, solving an optimal blast hole parameter combination by combining a genetic algorithm and a particle swarm optimization algorithm, and finally feeding back a blasting effect to dynamically adjust an optimization scheme. According to the intelligent optimization method and system for the smooth blasting blast holes of the tunnel in the layered rock mass, the blasting effect can be improved, over-break and under-break and concrete consumption can be reduced, the construction cost can be reduced, the construction safety and efficiency can be enhanced, and remarkable economic and social benefits can be achieved for layered rock mass tunnel engineering construction.

The invention discloses an adaptive game-driven network defense method and system, and relates to the technical field of network security. The method comprises the following steps: generating a multi-modal bait according to network context information, and screening an optimal bait through credibility evaluation; constructing a time sequence feature tensor according to the network event sequence information of the bait, and obtaining predicted attack information by adopting a pre-trained attack prediction model; combining the network event sequence information of the bait and the predicted attack information to construct a defense income matrix, and carrying out iterative equilibrium solution to obtain an optimal strategy candidate pool; and taking the defense hybrid strategy of the optimal strategy candidate pool as an initial population, performing multi-objective optimization through a non-dominated sorting genetic algorithm to obtain a Pareto optimal strategy set, and performing screening to obtain an execution strategy set for dynamic defense decision making. According to the invention, the dynamic property, intelligence and self-adaptability of network defense are realized, and the ability of a network system to cope with complex attacks is effectively improved.

The invention discloses an AI-based network security event tracing method, system and device and a medium, and the method specifically comprises the steps: constructing a network entity association graph based on a multi-modal data set, mining the implicit association between entities through a graph convolutional network, recognizing an APT attack chain, and obtaining graph feature data; based on the multi-modal data set, an LSTM-Transform hybrid model is adopted to analyze time sequence characteristics of network traffic, slow penetration and low-frequency detection behaviors are detected, and time sequence characteristic data are obtained; based on the graph feature data and the time sequence feature data, high-value features are screened through a genetic algorithm, and cross-modal combination features are generated by using a depth auto-encoder; based on cross-modal combination features, a network environment digital twin is constructed, an attack diffusion path is simulated, and a service influence range is quantified. According to the method, accurate tracing of the network security event is realized, and the detection and tracking capabilities of complex network attacks and the intelligent level of a response strategy are comprehensively improved.

A wellbore temperature optimization and predication method integrating numerical models and machine learning includes the following steps: establishing a wellbore-formation transient heat transfer model, obtaining an initial data set composed of relevant parameters, normalizing the initial data set, training a wellbore temperature prediction model by using a random forest algorithm, then optimizing the hyperparameters of the random forest algorithm by using a genetic algorithm, performing global optimization by using an annealing algorithm to obtain the optimized wellbore temperature and related parameters, calculating the wellbore temperature by substituting the optimized parameters into the wellbore-formation transient heat transfer model, and performing comparative verification on the optimized wellbore temperature and the calculated wellbore temperature.

The present invention relates to the technical field of artificial intelligence for antenna array deployment, in particular to an intelligent arrangement method for array antenna elements and a clustering epigenetic optimization method. The intelligent arrangement method for array antenna elements comprises: performing encoding for a J_K array, and initializing an epigenetic algorithm so as to generate J+K genes. The present invention uses a clustering algorithm in machine learning to divide a population into sub-populations having different characteristics. Adaptive learning is performed on a portion of individuals in each type, and an epigenetics-based inheritance operation is performed on the basis of the fitness probability of population individuals. Then, minor random mutation operations are performed to generate a new sub-population. Algorithm hyperparameters such as population size and mutation probability participate in the evolution of individual genes, and adaptively change on the basis of evolutionary states. The invention solves the problems of low convergence speed and poor obtained results in optimization processes of current L-shaped array antenna system arrangement algorithms.

The invention provides an RIES multi-objective optimization planning method considering the uncertainty of source load prediction, and the method comprises the steps: building an RIES basic structure, generating an uncertainty scene set of a source load prediction error through a Latin hypercube sampling method, carrying out the reduction of the uncertainty scene set through a clustering technology, and carrying out the reduction of the uncertainty scene set. According to the method, total cost, carbon dioxide emission and system reliability are taken as objective functions, an RIES multi-objective optimization planning model considering source load prediction uncertainty is constructed, a multi-objective optimization function is solved by adopting a non-dominated sorting genetic algorithm NSGA-III, a Pareto leading set is obtained, and through a strategy of combining a fuzzy entropy theory and TOPSIS, a Pareto leading set is obtained. And selecting the optimal solution of the Pareto leading edge set to obtain a multi-target optimization planning result of each typical scene. The method can comprehensively consider the influence of uncertain source load prediction on planning, improves the reliability of the system and the utilization rate of renewable energy sources, reduces the investment and operation and maintenance cost of the system, and reduces the emission of carbon dioxide at the same time.

The invention relates to the technical field of structural design, and discloses a topology and size joint optimization design method and system for a hierarchical composite structure, and the method comprises the following steps: dividing a structural domain into a plurality of hierarchical sub-domains, defining the size constraint range of each sub-domain, and distributing a multi-phase material for each sub-domain; grid discretization processing is carried out on the structural domain, a topological design variable is given to each discrete unit, acoustic unit modeling is carried out on the sound field domain, sound pressure boundary conditions are defined, and coupling boundary conditions are applied to the interface of the sound field domain and the structural domain; solving an acoustic-structure coupling equation based on a finite element method, calculating an objective function and constraint conditions, analyzing the sensitivity of the objective function to topological variables by adopting an adjoint method, and iteratively updating the topological design variables through an optimization algorithm until a convergence criterion is met; and on the basis of a topological optimization result, constructing a neural network agent model of an input layer, a hidden layer and an output layer, and carrying out global optimization on sub-domain size parameters by adopting a genetic algorithm so as to minimize a target function.

The invention provides an HC-MOGA-based agent task collaborative scheduling method in an industrial internet, and aims to solve the key problems of low scheduling efficiency, poor system robustness and the like caused by heterogeneous types of equipment (subsequent modeling is an agent), complex task dependence and non-uniform resource distribution in an industrial site. According to the method, a heterogeneous agent scheduling model with task identification, resource matching and dependent modeling capabilities is constructed for a multi-stage operation task cooperatively completed by a data acquisition agent and an operation type agent in an industrial internet environment. An HC-MOGA (heterogeneous constraint multi-objective genetic algorithm) is provided, multi-dimensional chromosome coding, a layered mapping mechanism, a dual-stage variation strategy and a dynamic constraint repair mechanism are introduced, and a dual-objective optimization model fusing work completion income and comprehensive resource consumption and potential loss is established. The method is suitable for industrial internet typical application scenes such as intelligent manufacturing, automatic production lines and multi-agent task collaborative management.

The invention relates to the technical field of engineering cost dynamic measurement and calculation, in particular to an intelligent engineering cost dynamic measurement and calculation and cost control method and system. Comprehensive influence factors of nodes can be extracted from a complex construction task incidence relation through a graph convolutional network; comprising resource intensity, progress sequence and potential resource conflict problems, so that accurate identification of key construction paths is improved, the real-time feedback capability of project progress and cost is enhanced, dependence of a traditional budget model on static factors is effectively avoided, project decision is optimized according to real-time changes, and the construction efficiency is improved. The efficient distribution of budget and resources is ensured, through the application of the non-dominated sorting genetic algorithm, and according to the residual budget, the construction period and the resource value, comprehensive evaluation and node screening optimization are carried out, so that project management can flexibly adjust the budget and the construction period based on the actual situation, the situation of unreasonable fund scheduling or resource waste is avoided, and the project management efficiency is improved. And the accuracy and flexibility of engineering project management are improved.

The invention discloses an oil reservoir carbon dioxide geological sequestration process parameter multi-objective optimization method, and relates to the technical field of oil exploitation, and the method comprises the steps: employing an oil reservoir carbon dioxide geological sequestration effect prediction data set to train a variational automatic coding-deep belief neural network model, a reservoir carbon dioxide geological sequestration effect prediction agent model is obtained; constructing an oil reservoir carbon dioxide geological sequestration effect optimization model according to the safety risk coefficient, the effective sequestration coefficient and the benefit optimization coefficient; the reservoir carbon dioxide geological sequestration effect prediction agent model is combined, a non-dominated sorting genetic algorithm is utilized, and a reservoir carbon dioxide geological sequestration process parameter multi-objective optimization mathematical model is solved. The constructed reservoir carbon dioxide geological sequestration process parameter multi-objective optimization mathematical model aims at sequestration safety, potential and economy; and the optimization precision and efficiency of the oil reservoir carbon dioxide geological sequestration process parameters are improved.

The invention relates to a Markov game-based satellite cluster observation resource allocation method and system. Through a two-stage decomposition strategy, a multi-target balance problem of a task integrity rate, a resource utilization rate and a cost-efficiency ratio is effectively solved. In the first stage, a genetic algorithm (GA) is adopted to complete satellite-task-time window three-dimensional matching, and optimal distribution of limited visible windows is achieved. And in the second stage, a distributed decision-making mechanism is implemented based on an improved Improved-MADDPG framework, and after an intelligent agent adopts a random game strategy to execute exploration in a distributed manner, optimal dynamic configuration of observation resources is achieved through global information sharing and local strategy iteration, and key calculation indexes such as an average reward value and the like are remarkably improved.

The invention discloses an intelligent electric meter box operation and maintenance management method and system, and relates to the field of electric power operation and maintenance management. The intelligent electric meter box operation and maintenance management method comprises the steps of multi-source data acquisition and preprocessing, equipment health state dynamic evaluation, fault root cause positioning and prediction, maintenance task dynamic scheduling, adaptive maintenance execution and closed-loop optimization, feedback of a maintenance result to a health evaluation model, updating of a dynamic weight coefficient, and monthly generation of an equipment health white book. And continuously optimizing the operation and maintenance strategy. According to the intelligent electric meter box operation and maintenance management method, the fault prediction model is established based on the LSTM neural network, the typical fault mode is matched in combination with the knowledge graph, the fault prediction accuracy is improved, and non-planned power failure is reduced. The improved genetic algorithm improves the utilization rate of maintenance resources and shortens the emergency response time. Predictive maintenance replaces regular maintenance, invalid inspection is reduced, and the spare part inventory turnover rate is increased. Human errors are reduced through AR assistance; and the maintenance operation accuracy is improved.

The invention discloses an automobile lengthened longitudinal beam stamping springback prediction and compensation method, which comprises the following steps: constructing an initial data set containing an initial prediction value according to the influence of preset process parameters on stamping springback; constructing a springback prediction model by adopting an incremental plasticity theory, training the springback prediction model by adopting a gradient boosting decision tree, and adjusting process parameters by adopting a Bayesian optimization method; according to the optimal process parameters, finite element simulation is adopted to manufacture a longitudinal beam, simulation stamping springback data is generated, and a genetic algorithm and a variational auto-encoder are adopted to optimize a die compensation shape; and calculating compensation data of auxiliary stamping according to the die compensation shape, generating longitudinal beam processing data, and taking the longitudinal beam processing data as real stamping process parameters of the longitudinal beam. According to the stamping springback prediction and compensation method for the lengthened longitudinal beam of the automobile, the springback prediction accuracy can be improved, and the forming quality of the longitudinal beam is improved based on the optimized mold compensation shape and the auxiliary stamping compensation data.

The invention provides an optimal design method of an ultralow-temperature multi-load heat insulation support, a support assembly and an experimental research method, and relates to the field of large superfluid helium low-temperature distribution systems. Comprising the following steps: selecting thickness parameters of a casing pipe in each stage of temperature zone of a supporting assembly and a supporting panel as optimization design parameters to carry out parametric modeling, and introducing a stress maximum value and a heat leakage value as target values; and based on the thickness parameters of the cold shield sleeve and the supporting panel of each stage of temperature zone, taking a genetic algorithm as an optimization function, performing iterative calculation in combination with simulation software, obtaining the thickness parameters of the cold shield sleeve and the supporting panel under the optimal boundary, determining the heat leakage value and the maximum stress value of the supporting assembly according to the optimal boundary, and then processing the supporting assembly. By combining various simulation technical means and experimental research, the problems of poor performance of the support assembly, lack of process verification and the like caused by the adoption of a classic model are solved, and the rigidity and robustness of the large-scale superfluid helium transmission and distribution system are effectively optimized and improved.

The invention discloses an MT-InSAR deformation monitoring method in combination with reservoir bank landslide deformation spatial-temporal characteristics. The method comprises the following steps: S1, selecting SAR data; step S2, constructing small baseline set InSAR data, including SAR image registration, interferogram combination, differential interference processing, phase filtering, coherence estimation and interferogram optimization combination; s3, based on the dual-polarized SAR intensity image, performing earth surface classification in combination with random forest classification, gradient and direction constraint and a deep neural network; then adaptive homogeneous filtering is carried out on the interferometric phase based on a secondary classification result; s4, combining a plurality of Sigmoid functions and a linear creep and periodic function to obtain an adaptive deformation model of the reservoir bank landslide; s5, performing deformation parameter nonlinear search by using a genetic algorithm to determine an optimal parameter set; on the basis of low-frequency deformation, atmospheric delay and decoherence noise are separated through low-pass filtering of a space domain and high-pass filtering of a time domain, and finally a deformation time sequence and an average deformation rate are recovered.

The invention provides a distribution network lightning arrester transient temperature distribution improvement method based on insertion of an aluminum gasket, and belongs to the technical field of electrical equipment, and the method comprises the steps: constructing a lightning arrester three-dimensional model in simulation software; constructing the lightning arrester conductivity of the coupling temperature; constructing a lightning stroke simulation model of the lightning arrester; lightning stroke transient temperature distribution of the lightning arrester is obtained, and a lightning stroke comparison result before and after addition of the aluminum gasket is obtained; constructing an aluminum gasket thickness-position set; and obtaining the position-thickness optimization relation of the aluminum gasket, constructing a genetic algorithm model according to the aluminum gasket thickness-position set, and obtaining the optimal aluminum gasket thickness based on the position. The method has the advantages that the temperature change of the ZnO valve plate of the lightning arrester under the lightning impulse is obtained, and the transient temperature distribution of the lightning arrester during the lightning impulse is obtained by using the temperature distribution coefficient; comparing the transient temperature distribution of the lightning arrester before and after adding the aluminum gasket to confirm that the temperature distribution of the lightning arrester can be optimized by adding the aluminum gasket; therefore, the method for introducing the aluminum gasket between the lightning arrester valve plates is provided.

The invention provides a pneumatic down-the-hole hammer automatic control system based on airflow dynamic adjustment, comprising: a sensor module for collecting monitoring data; the actuator module is used for controlling the flow and pressure of compressed air and adjusting the propelling speed and pressure of the drill rod; the control and processing module is used for carrying out data processing and intelligent control by utilizing an embedded controller and a control algorithm; the airflow dynamic adjusting module is used for constructing a geological recognition model, recognizing rock hardness and structure changes and automatically adjusting impact energy and drilling efficiency; the fault prediction module is used for constructing a fault prediction model and carrying out fault prediction and alarm; the energy consumption optimization module is used for constructing an energy consumption model, presetting an optimization target, designing an optimization genetic algorithm and calculating an optimal airflow parameter; and the man-machine interaction module is used for data display, state alarm and parameter adjustment. The drilling process can be intelligently controlled, the drilling efficiency is improved, the equipment failure rate is reduced, energy can be saved, and the system adapts to complex geological conditions.

The invention discloses a complex geological weak surrounding rock tunnel micro-disturbance green blasting construction method which comprises the following steps: acquiring multi-source data through a sensor and geological survey equipment, and extracting a standardized feature set; and constructing a blasting parameter and surrounding rock response prediction model by using a random forest algorithm, inputting explosive load and hole net parameters, and outputting predicted surrounding rock deformation and vibration values. And if the vibration value exceeds the environment-friendly threshold value, adjusting the parameter by adopting a gradient descent algorithm until the requirement is met. And in combination with real-time monitoring data, the surrounding rock response is dynamically updated by using a Kalman filtering algorithm. If the overexcavation probability exceeds the threshold value, the parameters are further optimized through a genetic algorithm. And finally, optimizing resource allocation through a linear programming algorithm, and determining a final blasting scheme. Intelligent regulation and control of blasting parameters of the weak surrounding rock tunnel are achieved, vibration and deformation are effectively controlled, the overexcavation risk is reduced, the construction efficiency and quality are improved, and a new technical scheme is provided for similar projects.

The invention relates to the technical field of atmospheric dust pollution prevention and environmental protection, and discloses a multi-objective optimization layout method for dust monitoring points of a bulk cargo port, which comprises the following steps: processing historical meteorological data and port geographic information, and calculating the comprehensive evaluation concentration of each candidate grid point in combination with a pollutant diffusion model; constructing a multi-target nonlinear mixed integer programming model taking maximization of environment sensitive point coverage and minimization of concentration interpolation deviation as target functions, wherein the model comprehensively considers multiple reality constraints; solving the model by adopting an improved genetic algorithm to obtain an optimal solution set capable of forming tradeoff among different targets; and carrying out quantitative evaluation on the solution set through preset monitoring area coverage rate and monitoring accuracy indexes, and determining an optimal monitoring point position layout scheme. According to the invention, a scientific and quantitative optimized layout scheme can be provided for bulk cargo port dust monitoring, and the reliability and spatial representativeness of a monitoring network are improved.

The invention discloses a photovoltaic power generation power prediction method based on Bi-LSTM, and relates to the technical field of photovoltaic power generation power prediction methods. The method comprises the following steps: constructing a Bi-LSTM recurrent neural network model; historical data of photovoltaic power generation power and related meteorological data are obtained to serve as model data and are aligned into a time sequence; preprocessing the model data and training a Bi-LSTM recurrent neural network model; and optimizing hyper-parameters of the Bi-LSTM recurrent neural network model by using a genetic algorithm to obtain an optimal Bi-LSTM recurrent neural network model to predict photovoltaic power generation power. According to the method, the Bi-LSTM model is utilized, and past and future information in the time sequence data can be captured at the same time, so that the accuracy of photovoltaic power generation power prediction is improved; meanwhile, effective features can be screened and utilized more effectively, the defects that a traditional prediction model is poor in feature screening capacity and prone to noise interference are overcome, and then the prediction precision of the photovoltaic power generation power is remarkably improved.

The invention discloses a machine learning-based membrane pollution pretreatment optimization control method and system, and the method comprises the steps: constructing a high-dimensional feature data set for Fenton pretreatment prevention and control of membrane pollution, and carrying out the comprehensive pretreatment of data; training and optimizing the candidate machine learning model based on the decision tree; evaluating the trained machine learning model, and selecting the model with the most generalization ability as a Fenton pretreatment control membrane pollution prediction model; based on the prediction model, reversely searching the optimal reagent dosage under a preset environment variable by adopting a genetic algorithm; and carrying out membrane pollution Fenton pretreatment under the corresponding environment variable condition by utilizing the optimal reagent adding amount. According to the technical scheme, the reagent adding amount of Fenton pretreatment with the optimal membrane pollution retarding capacity of different pollutants under different membrane filtration conditions can be optimized and directionally controlled, and the Fenton pretreatment device can be suitable for different types of pollutants and membrane assemblies and has the characteristics of low cost, high efficiency, accuracy, reliability and the like.