996 results about "Genetic algorithm optimization" patented technology

The invention relates to the technical field of devices for adjusting, controlling or stabilizing power or frequency in a power grid, in particular to a rapid frequency modulation method for a wind turbine generator based on disturbance adaptive compensation, which comprises the following steps of: constructing a dynamic disturbance sensing data set by collecting frequency deviation, a frequency deviation change rate and fan state parameters; a window length and a weight coefficient of a short-time window sliding mean algorithm are dynamically optimized by adopting a genetic algorithm, background noise interference of a power grid is suppressed, and high-precision disturbance characteristics are extracted. And reducing frequency interference of inertia identification by using a reverse test signal. And updating boundary layer parameters of the sliding mode control model through an online incremental learning algorithm, dynamically adjusting the output priority based on the disturbance energy entropy, and eliminating power conflicts. A multi-island genetic algorithm is introduced to optimize a mode switching threshold value, and frequency modulation safe exit is realized in combination with adaptive power ramp rate limitation. According to the method, the frequency response speed and the multi-source cooperation efficiency in the dynamic multi-disturbance and inertia time-varying scene are remarkably improved, and the frequency secondary drop risk is reduced.

The invention relates to the technical field of computing power scheduling, and discloses a heterogeneous resource computing power intelligent scheduling method and system. According to the method, real-time state monitoring is conducted on heterogeneous computing resources, and resource state parameters such as the computing unit utilization rate and the memory occupancy rate are obtained; task attributes and user request parameters of the task queue are collected, historical task data are processed based on the genetic algorithm optimization model to execute task demand prediction, and predicted demand parameters are generated. A dependency graph containing resource unit nodes and communication link roadsides is constructed through a resource topology analysis tool, predicted demand parameters are input into a scheduling priority classifier trained by a graph neural network, and an actual scheduling priority is identified. And executing resource conflict prediction based on the priority, inputting task feature vectors into a conflict resolution module of a fuzzy logic decision maker, outputting actual conflict resolution parameters, and finally integrating to generate a scheduling scheme containing a resource allocation sequence and an execution time table.

The invention relates to the technical field of abalone peptide production, and discloses an abalone peptide production process optimization and intelligent decision-making method and system, and the method comprises the steps: collecting production data through a multi-source sensor, and extracting features through a convolutional neural network; the genetic algorithm optimization model is combined with an adaptive mechanism and a multi-target fitness function to generate a process parameter optimization scheme; and the multi-objective production optimization model outputs optimal production process parameters by means of an improved particle swarm algorithm. In the hierarchical intelligent decision-making model, the planning layer realizes global resource allocation, the scheduling layer adjusts a process in real time based on a Bayesian network and the like, and the operation layer controls equipment through fuzzy logic and constructs quality closed-loop control. According to the method, the problems that parameter control depends on experience, data processing is difficult, optimization is one-sided, decision making is not intelligent and the like in traditional production are solved, data can be accurately processed, the process is optimized, decision making is intelligent, production efficiency, the raw material utilization rate and product quality are effectively improved, cost is reduced, and the application prospect is wide.

The invention provides a brushless motor attitude detection and balance control method and system based on a gyroscope, and relates to the technical field of control, and the method comprises the steps: collecting angular velocity and acceleration data through a six-axis gyroscope, carrying out the noise reduction through wavelet transform, and carrying out the attitude calculation through the combination of an extended Kalman filter and a quaternion algorithm. A rotor position signal is obtained through a magnetic encoder, nonlinear compensation is carried out, and rotating speed data are calculated. A motor state is modeled by adopting a long-short-term memory network, a double-layer adaptive fuzzy neural network controller is constructed, and attitude error compensation and rotation speed fluctuation suppression are realized. A controller model is optimized through particle swarm optimization and a genetic algorithm, a compensation current vector is corrected in real time, and the control precision and stability of the brushless motor are improved. According to the method, the operation efficiency and the dynamic response capability of the brushless motor are effectively improved.

The invention provides an intelligent operation and maintenance method and system for an air conditioning system based on the Internet of Things technology, and the method comprises the steps: processing operation data through employing an intelligent sensor and an edge calculation node, and generating a comprehensive operation information packet through combining with environment parameters; next, simulation analysis is carried out through a cloud digital twin model, performance prediction and fault point evaluation are generated, the fault probability is evaluated by using a Bayesian network algorithm, and personalized operation and maintenance suggestions are generated; secondly, optimizing operation parameters by adopting a genetic algorithm, and smoothly adjusting the setting through a self-adaptive control theory; and when an abnormality is detected, the fault severity is judged by using a multi-level threshold value and fuzzy logic, an early warning mechanism is triggered in time, and an emergency response scheme is pushed. And finally, recording a maintenance process and user feedback, updating the digital twin model, forming a closed-loop operation and maintenance system, and ensuring efficient and stable operation and service quality improvement.

The invention relates to the technical field of temperature regulation and control, in particular to a liquid cooling control method and system of a battery control device, in the liquid cooling control method and system, through real-time collection and area screening of temperature and power data, accurate recognition of a high-temperature area is achieved, a clear cooling requirement partition is provided, and the cooling requirement of a battery is met. A dynamic adjustment strategy of pressure and flow parameters of the liquid cooling pump is optimized through a genetic algorithm, the cooling response speed and control precision are improved while energy consumption and cooling efficiency are effectively balanced, the pump speed and the valve opening degree are adjusted in real time, temperature changes are rapidly responded through automatic control, gradual implementation of a cooling target is ensured, and the cooling efficiency is improved. The hysteresis in the heat management process is reduced, the temperature control effect is continuously evaluated through a fuzzy control algorithm, the cooling intensity is dynamically adjusted according to the monitoring result, efficient cooling is ensured, meanwhile, equalization of temperature distribution is achieved, and a systematic heat management framework is established through long-term data monitoring and continuous optimization of cooling operation. And efficient and stable operation of temperature management is realized.

Disclosed in the present invention are a railway vehicle-track-bridge system dynamic response prediction method and system, and a medium. The method comprises: inputting vehicle speed samples and track irregularity samples into a vehicle-track-bridge system coupled random distribution physical model to obtain corresponding bridge dynamic responses and effective loads of the vehicle-track-bridge system, and extracting a global stiffness matrix of the vehicle-track-bridge system; constructing a training sample set; constructing a fitness function considering the effective loads, and then on the basis of the training sample set, using a genetic algorithm to optimize parameters of a BP neural network prediction model and training same to obtain a bridge dynamic response prediction model; and using the bridge dynamic response prediction model to carry out bridge dynamic response prediction. By introducing effective loads in a vehicle-track-bridge system into a fitness function in a genetic algorithm, a neural network model and a vehicle-track-bridge physical model are organically combined, thereby improving the prediction precision.

A system and method for integrated optimization of autonomous electric vehicle fleets across transportation and electricity networks which employs artificial intelligence to dynamically allocate autonomous electric vehicles between mobility services and electricity grid services based on real-time conditions. The platform acquires data including energy mix forecasts, earth observation measurements, vehicle owner schedules, and emission-based route penalties to generate coordinated allocation decisions. Vehicle owners specify availability through a scheduling interface. The system optimizes vehicle utilization through a hierarchical optimization approach implementing mobility demand-side flexibility and electricity demand-side flexibility simultaneously. Multi-objective genetic algorithm optimization balances revenue generation, energy costs, emissions reduction, and battery health. The integrated approach maximizes value creation across both transportation and energy domains, reducing urban emissions while enhancing grid stability through coordinated management of distributed energy resources in autonomous electric vehicle fleets.

The invention discloses a dynamic leakage prediction method and system for an old oil and gas pipeline, and the method comprises the steps: collecting pressure, flow and temperature parameters in real time through a multi-source sensor, and recognizing abnormal fluctuation through the combination of time sequence analysis and frequency domain feature extraction; calculating a pipeline state evaluation result based on the material degradation model; establishing a leakage prediction model fusing a wall thickness degradation kinetic equation and an LSTM neural network, calculating a leakage probability by adopting a Monte Carlo method, and generating a diffusion velocity and a concentration gradient through CFD numerical simulation; when the diffusion prediction exceeds a safety threshold value, a control strategy is optimized through fuzzy logic and a genetic algorithm; the verification model is fed back after real-time adjustment, and online learning is carried out through Bayesian optimization; and finally, calibrating the model by using experimental data, and deploying and generating risk early warning. The system comprises a multi-source sensor array, a data processing platform and other modules, and full-chain closed-loop control from sensing to early warning is achieved.

The invention discloses an intelligent film-drawing and unwinding control method and system based on real-time tension, and relates to the field of automatic control and intelligent manufacturing, and the method comprises the following steps: ensuring normal starting of equipment and system states through equipment self-inspection and technological parameter input; tension and speed data are collected in real time and preprocessed, and support is provided for fuzzy PID control; the parameters of the fuzzy PID controller are dynamically adjusted by calculating the tension error and the change rate of the tension error so as to adapt to different membrane material characteristics and unwinding working conditions; an improved genetic algorithm is used for optimizing parameters of a PID controller, overshoot is minimized, the adjusting time is shortened, and the robustness and the anti-interference capability of the system are improved; a closed-loop control system is constructed, and the stability of unwinding tension is ensured through real-time feedback signals; and the system state is monitored in real time in combination with an anomaly detection algorithm, and a control strategy is automatically adjusted or fault protection is performed. The control method disclosed by the invention can be widely applied to the fields of film unwinding and automatic production lines, and has a relatively high intelligent level.

The invention discloses a network traceability data processing method, system and device and a medium, and the method comprises the steps: collecting network data, carrying out the preprocessing of the network data, and generating the preprocessed network data; extracting behavior characteristics from the preprocessed network data to obtain network behavior characteristics; constructing an attack graph according to the obtained network behavior characteristics, and forming a dynamic graph representing an attack path; and carrying out traceability decision on the dynamic atlas, and positioning an attack source and an attack path through the traceability decision. Through the technical means of six-dimensional feature vector construction, quantum derivation genetic algorithm optimization detection and attack atlas construction through the tense graph convolutional network, the problems of data dimension missing, correlation analysis lagging and insufficient traceability precision are effectively solved.

The invention, which relates to the technical field of customer relationship management, discloses a multi-channel interactive customer relationship management system comprising a dynamic routing decision module, a multi-modal data fusion module and an intelligent feedback optimization module. The dynamic routing decision module evaluates channel load through a deep neural network, dynamically allocates client requests to an optimal node by utilizing reinforcement learning, and realizes load balancing and service continuity; the multi-modal data fusion module integrates text, voice and image data, constructs a space-time correlation graph, identifies a cross-channel behavior mode, and ensures data consistency through multi-dimensional verification; the intelligent feedback optimization module combines customer satisfaction evaluation and multi-modal sentiment analysis, optimizes a service strategy by using a genetic algorithm, and synchronizes the service strategy to a cross-channel knowledge graph to realize adaptive iteration; according to the method, the problems of unreasonable multi-channel load distribution, insufficient data fusion and consistency verification and inaccurate service strategy optimization are effectively solved, and the customer service quality and experience are improved.

The invention relates to the technical field of image processing, and discloses an image recognition and analysis system based on AI. The system comprises a data acquisition module, a feature extraction module, a model training module, a multi-modal fusion module, a dynamic optimization module and the like. The method comprises the steps of collecting real-time image data by a multi-source sensor, extracting features by a cascade convolutional neural network, generating an adversarial network training model, integrating multi-source data by multi-modal fusion, optimizing feature vectors by an improved genetic algorithm, and constructing a classification decision tree. In addition, an anomaly detection module, a real-time reasoning module, a data enhancement module and a visualization module are further arranged. The system can accurately identify and analyze images, improve the model performance and generalization ability, meet the real-time requirement of edge computing equipment, generate an interpretable report to assist decision making, and have wide application prospects in the fields of security, medical treatment, automatic driving and the like.

The invention relates to the technical field of system control data processing, in particular to a waste heat recovery heat supply system based on an injection type heat pump, which comprises a multi-source data acquisition module, a genetic algorithm optimization module, a dynamic mode switching module, a thermal stress suppression module and a self-learning updating module. The multi-source data collection module collects industrial waste heat temperature, power grid load and heat supply network backwater flow data through a temperature sensor array and a flow metering device, and feature vectors are extracted through sliding window standardization and principal component analysis. And heat transfer end difference fluctuation is restrained through closed-loop feedback. And the self-learning updating module constructs a multi-parameter incidence matrix to dynamically adjust the weight of a fitness function, and realizes parameter iterative optimization in combination with a historical optimal solution caching mechanism. The problems that the industrial waste heat recovery efficiency is low, thermoelectric coupling adjustment is delayed, and thermal stress of equipment is accumulated are solved.

The invention relates to the technical field of cable nondestructive testing, in particular to a cable defect detection system. The method mainly solves the problems that in the prior art, early-stage tiny defects in a cable are insufficient in recognition sensitivity, the omission ratio is high, and accurate classification cannot be achieved. According to the system, signals are synchronously collected through a multi-physics field composite sensing array, excitation parameters are optimized through a genetic algorithm, a three-dimensional defect probability graph is generated through fusion, a double-current Transform model is adopted to deeply analyze features and automatically recognize defect types, and finally, maintenance is guided through AR visualization, so that non-intrusive online accurate diagnosis and intelligent operation and maintenance of cable defects are achieved.

The invention provides a natural gas pipeline leakage probability calculation method and system based on a genetic algorithm, and relates to the technical field of probability calculation, and the method comprises the steps: collecting pipeline operation parameters, building a pipeline structure dynamic stress model, a material fatigue model and a corrosion damage model, carrying out the coupling analysis of the three models, and obtaining a risk assessment parameter matrix; a leakage risk assessment initial model is constructed as a fitness function, and a leakage probability calculation model is obtained through genetic algorithm optimization. According to the method, the pipeline leakage risk can be accurately predicted, accurate evaluation and early warning of pipeline safety are realized, and the pipeline operation safety is improved.

The invention provides a sewage treatment carbon source intelligent adding method, which comprises the following steps: collecting multi-parameter feed-forward and feedback signals of water inlet and an anoxic tank, constructing a dynamic model containing feed-forward compensation, model prediction control and feedback compensation, calculating the theoretical adding amount of a carbon source, inputting a predicted value and feedback parameters into an LSTM network for correction, and optimizing a network structure by a genetic algorithm. The adding amount is controlled in a closed-loop mode through a variable frequency pump, the LSTM weight is updated on the basis that the error is larger than 5%, and finally a control strategy is optimized by using an NSGA-II algorithm and integrating carbon source consumption, effluent total nitrogen and energy consumption. The dynamic self-adaptive carbon source adding method is constructed by fusing feedforward perception, LSTM prediction, feedback regulation and multi-objective optimization, so that quick response and accurate control on water quality fluctuation are realized, the denitrification efficiency and the carbon source utilization rate are improved, and the method has excellent engineering adaptability and popularization value.

The invention discloses a life prediction method based on health index construction and neural network fusion, and belongs to the technical field of equipment state monitoring and predictive maintenance. According to the method, through multi-source degradation feature extraction, common dynamic principal component analysis (CDPCA) dimensionality reduction, health index construction and normalization, deep learning multi-model modeling, integrated learning fusion and Bayesian optimization hyper-parameter optimization, online health assessment and residual life prediction of the equipment part degradation process are realized. Specifically, the method comprises the following steps: firstly, extracting time domain, frequency domain and time-frequency domain features from a sensor acquisition signal, and performing dimension reduction through CDPCA to obtain effective degradation characterization; then, weighting the main features to construct a health index (HI) curve, optimizing the weight through a genetic algorithm, and then performing normalization; a plurality of neural network models such as CNN, Bi-GRU, Bi-RNN, Bi-LSTM and SRNN are constructed based on the normalized HI sequence, and degradation trend modeling is realized; inputting the output results of the neural networks into an integrated learning module for fusion optimization; and finally, carrying out automatic optimization on the key hyper-parameters of the model by utilizing Bayesian optimization. In the equipment operation process, a normalized HI curve can be calculated in real time and input into the fusion model, and the residual life estimation value of the part is dynamically output. According to the method, high-precision, high-robustness and online life prediction can be provided under complex working conditions, the safety and reliability of equipment operation and maintenance are improved, and the method has wide engineering application value.

The invention relates to the technical field of mold management, in particular to a mold abnormity management system and method based on machine vision, and the system comprises a mold abnormity detection module, a fault prediction and health management module, an intelligent repair and maintenance scheduling module, and a data analysis and visualization module. The mold anomaly detection module collects data by using a high-definition camera and a thermal infrared imager, and identifies surface defects through a convolutional neural network; the fault prediction and health management module predicts a fault risk and performs early warning by means of time sequence analysis and a machine learning technology; the intelligent repair and maintenance scheduling module optimizes a repair strategy and task allocation by using deep reinforcement learning and a genetic algorithm; and the data analysis and visualization module displays data by means of Grafana and Kibana and realizes intelligent alarm and decision support. According to the invention, accurate detection of mold abnormity, effective prediction of faults, optimization of maintenance scheduling and efficient processing and visual display of data are realized, and the intelligent level of mold management is improved.

The invention relates to the technical field of unmanned aerial vehicles. The invention discloses a task allocation and endurance optimization method for a multi-unmanned aerial vehicle system. The method comprises the steps of S1, task modeling, S2, initial population generation, S3, fitness function design, S4, genetic algorithm task optimization, S5, iterative optimization, S6, path planning and S7, data collection, prediction and adjustment. The multi-unmanned aerial vehicle system task allocation and endurance optimization method can process temporary tasks in real time, flexibly adjust a task allocation scheme, dynamically adjust flight parameters according to real-time flight conditions and task requirements, comprehensively consider various factors such as task priorities, flight paths, energy consumption and the like, and optimize the task allocation and endurance optimization of the multi-unmanned aerial vehicle system on the basis of actual flight data. And an energy consumption prediction model is established, and accurate energy consumption management and path planning are realized. According to the method, temporary tasks can be processed, task allocation and path planning are dynamically adjusted, and task execution efficiency and safety are ensured.

The invention provides a multi-mechanical-arm cooperative control method which comprises the following steps: modeling initial states and virtual constraints of mechanical arms, constructing an overall description of a multi-mechanical-arm cooperative task, decomposing the task into sub-tasks of the mechanical arms based on the description, and optimizing load distribution; the initial state is represented by a quintuple and comprises the position, posture, linear velocity, angular velocity and load information of the tail end of the mechanical arm; the virtual constraints comprise relative positions, relative postures, synchronous speeds, load balancing and power optimization constraints and are used for limiting kinematics and dynamics behaviors of the multiple mechanical arms; on the basis of the initial state and the virtual constraint, load distribution is optimized by utilizing a genetic algorithm, and a smooth trajectory meeting the collaborative task requirement is generated by adopting a quintic polynomial interpolation method; and the pose deviation of the multiple mechanical arms is corrected based on a virtual spring damper by combining trajectory tracking control and collaborative error compensation. According to the method, the movement efficiency and the task completion quality of the multi-mechanical-arm cooperative task are improved.

The invention relates to the technical field of power monitoring, in particular to a moisture-proof environment-friendly ring main unit online monitoring system with continuous fault indication and a ring main unit, and the system comprises an electric parameter dynamic analysis module, an environment collaborative verification module, a closed-loop control execution module and a behavior path optimization module. According to the method, the current fluctuation and the power difference value are matched through dynamic time warping, the temperature rise rate and the condensation index are calculated through linear regression, multi-dimensional verification is formed through fuzzy logic judgment, operation parameters are adjusted in real time through PID control, strategy weight is optimized through a genetic algorithm, feature extraction is reversely corrected, and the fault recognition precision and the response speed are improved; closed-loop feedback is established to continuously optimize the system state, environment and electrical parameter coupling analysis is enhanced, the misjudgment probability of a single threshold value is reduced, parameters are dynamically corrected, the adjustment real-time performance and accuracy are improved, the adaptive capacity under the complex working condition is enhanced, a complete closed loop of collection, analysis and feedback is established, and the insulation degradation judgment reliability is improved.

The invention discloses a power proxy service system and method based on artificial intelligence, and the method comprises the steps: collecting power data in real time through an intelligent electric meter and a sensor, and uploading the power data to a cloud platform through the Internet of Things for preprocessing and marking; predicting the power load by using time sequence analysis, regression, SVM and LSTM methods in combination with weather, season and holiday factors; power grid load dispatching is optimized through reinforcement learning or a genetic algorithm, and automatic allocation is achieved; the sensor monitors the state of the power equipment in real time, predicts the fault risk and gives an alarm in advance; the system provides energy-saving suggestions according to historical power consumption data of a user, and adjusts an equipment operation mode in real time; the intelligent customer service server pushes personalized electricity consumption suggestions based on natural language processing; an encryption technology and multiple identity authentication are adopted to guarantee data security, regular security audit and vulnerability repair; according to the electric power proxy service system and method based on artificial intelligence, integration with an AI system and construction and optimization of an intelligent power grid and renewable energy technology are promoted.

The invention relates to the technical field of medical diagnosis, and discloses a DCM early noninvasive analysis method based on multi-radiomics and serum markers. Collecting image data through a multi-modal medical imaging device, and collecting serum marker data through a blood detection device; respectively generating a radiomics feature set and a serum marker time sequence feature set by using a multi-scale feature extraction algorithm and a time sequence analysis model; fusing the features by adopting a dynamic weighted fusion strategy to generate a joint feature matrix; inputting the model into a pre-trained multi-task deep learning model to predict a DCM risk probability; and finally, based on a genetic algorithm, optimizing the diagnosis decision tree and outputting an early DCM diagnosis result. The method is noninvasive and accurate, and can effectively improve the early diagnosis accuracy of DCM.

The invention provides a double-pulse high-frequency switching power supply control method and system for precise electroplating, and relates to the field of electroplating, and the method comprises the steps: collecting area surface data through a three-dimensional scanner, and obtaining a three-dimensional model; extracting a high-curvature area grid according to the three-dimensional model, and calculating to obtain a surface concave-convex degree index; performing current field simulation, and calculating current density vector distribution; when the deviation of the current density vector distribution exceeds a preset deviation threshold value, pulse parameters are optimized through a genetic algorithm; selecting a pulse parameter which is most matched with the surface concave-convex degree index, and carrying out iterative calculation to obtain updated current density vector distribution; based on the difference between the updated current density vector distribution and the initial distribution, predicting a coating thickness value, and determining a preliminary evaluation index; and optimizing pulse parameters to obtain a final control scheme of uniform current distribution. According to the method, pulse parameters can be dynamically optimized according to the geometrical shape of the workpiece, and uniform current distribution and stable plating quality are realized.

A genetic algorithm optimization-based fuzzy PID control method and apparatus, and a device. The method comprises: S1, acquiring a preset standard electrode plate quality, a measured electrode plate quality and a system default ray intensity, acquiring an ambient temperature parameter, retrieving a monitoring index, and monitoring the stability of a surface density measurement system according to the monitoring index; S2, on the basis of a preset system parameter of the surface density measurement system, setting a data sampling interval; S3, on the basis of the data sampling interval, continuously sampling a number N of pieces of data using a median average filtering algorithm, removing extreme values, calculating the arithmetic mean value of the N-2 pieces of data, and eliminating sampling value deviation of the arithmetic mean value, to obtain surface density sampling data; S4, performing fuzzification processing on the surface density sampling data, determining a fuzzy rule, and performing optimization in real time according to the fuzzy rule and by means of fuzzy reasoning, to obtain applicable PID parameters; S5, on the basis of the applicable PID parameters, using a genetic algorithm to control a servo motor of the surface density measurement system. Thus, the technical problems of parameter tuning being difficult and the robustness of closed-loop control being low are solved.

The invention discloses an automatic dovetail welding seam grinding control method based on robot visual positioning, and relates to the technical field of visual positioning. The method comprises the steps that after welding is completed, a dovetail welding seam image is collected and converted into a three-dimensional coordinate through a vision algorithm, and a three-dimensional point cloud is generated; smooth interpolation is performed on the three-dimensional point cloud through a B spline curve method to obtain a parameterized curve, and a control point set is optimized through a genetic algorithm to generate a global optimal polishing path; the polishing robot executes a task according to a path, a tail end sensor collects a real-time path and calculates a deviation value with a global path, and when the deviation value exceeds a preset threshold value, inverse kinematics is triggered to solve and correct the path; the hardness of the dovetail weld is measured through laser-induced breakdown spectroscopy, a comprehensive hardness value is obtained in combination with a matrix hardness database, meanwhile, a visual sensor collects the surface state, and polishing process parameters are dynamically adjusted according to the surface state; after the task is completed, the welding seam angle and flatness are detected. According to the method, the optimal path is generated through visual positioning, and automatic grinding of the dovetail welding seam is achieved.

The invention provides an inertia optimization method for electric heat gas energy transport channel coordination, and relates to the technical field of energy transport channels, and the method comprises the steps: building a fluid flow state and vortex intensity mapping relation model, which is used for predicting the possible vortex conditions under different working conditions; and based on a prediction result, a genetic algorithm is adopted to optimize a sensor arrangement scheme, and the most comprehensive flow state information is obtained by using the least sensors. According to the method, pipeline vibration safety is considered, a vibration monitoring and valve adjustment limiting mechanism is set, in addition, a tradeoff model of energy loss and transportation efficiency is further constructed, an optimal adjustment strategy is solved by applying a multi-objective optimization algorithm, finally, an eddy current suppression effect evaluation model is established, quantitative evaluation and strategy optimization of a control effect are achieved, and the control effect is optimized. Through the comprehensive application of a series of innovative methods, the precise inhibition of the vortex in the fluid transportation process and the remarkable improvement of the transportation efficiency are realized, and meanwhile, the safe operation of a pipeline system is ensured.

The invention relates to the technical field of logistics order delivery management, in particular to a cross-border e-commerce logistics order management system based on big data, which comprises a data acquisition processing unit, an order clustering delivery unit and a delivery execution feedback unit, the data acquisition and processing unit collects orders, geographic information and real-time traffic data from a cross-border e-commerce platform and performs cleaning preprocessing, repeated orders are removed by using a Hash algorithm, the order clustering and delivery unit clusters the orders by using a density peak clustering algorithm, and the clustering effect is optimized by combining the order emergency degree and the customer loyalty, so that the service quality of the cross-border e-commerce platform is improved. A genetic algorithm is adopted to optimize the delivery sequence, the crossover and mutation probability is dynamically adjusted, a fitness function is calculated by considering the delivery distance, time cost and regional limitation penalty factors, a delivery execution feedback unit sends an optimized route to a delivery vehicle, client feedback evaluation data is collected, a delivery strategy is dynamically adjusted by means of an upper confidence bound algorithm, and the delivery efficiency is improved. And the delivery efficiency and the customer satisfaction are improved.

The invention provides a sewage treatment progress monitoring method and system, and the method comprises the steps: collecting and processing a first water quality parameter value of a key node of a sewage treatment facility in a historical time period, constructing a dynamic model of a sewage treatment process through a deep neural network, and introducing a genetic algorithm to optimize the structure and parameters of the dynamic model. An optimized dynamic model is obtained, the optimized dynamic model is used for predicting the sewage treatment effect, and a prediction result and operation data of the sewage treatment facility are obtained; using a fuzzy logic control algorithm to adjust the operation parameter setting value of the sewage treatment system, and using a support vector machine algorithm to classify and identify the fault mode of the sewage treatment system to obtain a fault classification result; based on the prediction result, the operation data, the optimized operation parameter setting value and the fault classification result, a comprehensive monitoring management system based on the cloud platform is constructed; according to the invention, comprehensive monitoring and fault identification of the sewage treatment process are realized, and efficient, stable and reliable operation of sewage treatment is ensured.