16 results about "Immune genetic algorithm" patented technology

The invention relates to a direct current furnace machine-furnace coordination system dynamic modeling method suitable for a wet condition. Based on the law of mass conservation and energy conservation, a lumped parameter method is adopted to construct a dynamic model comprising a coal pulverizing system, a coal economizer-water wall system, a water storage tank system, a superheater system and a steam turbine system; identifying parameters are optimized step by step by only utilizing unit variable load dynamic operation data and combining with the mechanism constraint that the heat absorption capacity of a working medium is monotonically increased along with the coal quantity as fired: static parameters are determined through dynamic data regression, nonlinear parameters are optimized based on a preset water-cooled wall outlet flow and dryness function structure, and the heat absorption capacity of the working medium is calculated. And finally, identifying dynamic parameters step by step by adopting an immune genetic algorithm. The dynamic characteristics and the physical consistency of the established model are verified through open-loop simulation, and the technical problems that dynamic description of the water level of the water storage tank is missing and the modeling precision is low under the wet working condition are solved.

The invention provides a method for quantitatively identifying contribution of multi-source phosphorus pollution, relates to the field of water environment management, and aims to accurately analyze sources of multi-source phosphorus pollution of river and lake water bodies. The method comprises the following steps: firstly, determining the pollution source type of a research area, collecting river and lake water and pollution source water samples, synchronously retaining subsamples, determining the phosphorus form concentration, and establishing an associated ledger; then measuring a three-dimensional fluorescence EEM spectrum of a water sample, preprocessing the three-dimensional fluorescence EEM spectrum, extracting a fluorescence component by using a PARAFAC model, calculating a Fmax value, constructing fluorescence component-Fmax-phosphorus concentration associated data, optimizing a projection direction through an immune genetic algorithm, constructing a projection pursuit regression model, and supplementing phosphorus pollution associated characteristic values to form two-dimensional correction data; and finally, inputting correction data by using a MixSIAR model, operating MCMC to simulate and estimate a pollution source contribution proportion, verifying model convergence, and analyzing and visualizing sample point pollution contribution and spatial distribution characteristics. According to the method, the phosphorus pollution traceability accuracy and reliability are improved, a scientific basis is provided for water environment treatment, and drainage basin management and ecological civilization construction are assisted.

The application discloses a soft soil foundation settlement prediction method based on a physical information neural network, and relates to the technical field of geotechnical engineering monitoring. Multi-source data containing settlement, displacement and pore water pressure are collected, wavelet denoising and feature screening are performed, and a high-quality data set is constructed; a forward mapping model based on finite element FEM is constructed, an improved immune genetic algorithm IGA is used to minimize errors, and accurate inversion of key mechanical parameters is realized; the mechanical parameters are embedded into a long short-term memory network LSTM, a hybrid loss function containing data fitting loss and physical limit constraint loss is constructed, and a physical information neural network PINN-LSTM is formed; the future deformation trend is predicted through the network, the safety factor is calculated in combination with the limit equilibrium theory, and hierarchical early warning and disposal suggestion pushing are realized according to a dynamic early warning threshold. The method significantly improves the physical interpretability and precision of soft soil foundation settlement prediction, and has wide application value.

The invention relates to the technical field of feature screening, in particular to a multi-label data feature screening method and system based on an immune genetic algorithm. Each label population is constructed by single-label prior features, and recognition of complex information between features-features, features-labels and labels is realized through multi-population parallel operation, immunization, elite retention, concentration inhibition and global feature migration strategies. Therefore, the optimal feature subset screened out by each label has a good prediction effect on the corresponding label result, and the accuracy of multi-label medical disease prediction is improved. By constructing classification and survival multi-label models and calculating chromosome fitness values, the method can be simultaneously suitable for feature screening of medical multi-label classification and survival data.

The embodiment of the invention provides a method for gas-water alternate development of an oil and gas field, a control device and a storage medium, and belongs to the technical field of oil and gas field development. The method comprises the steps that main control factors influencing the accumulated oil production of a target oil and gas field are determined, wherein the main control factors comprise the gas-water slug ratio and at least one piece of basic feature information; based on the main control factors, an initial scheme for gas-water alternate development of the target oil and gas field is constructed; and based on an immune genetic algorithm, through the initial scheme and a preset cumulative oil production prediction model, determining an optimal scheme for gas-water alternate development of the target oil and gas field. According to the embodiment of the invention, in oil reservoir yield prediction of the target oil and gas field, the immune genetic algorithm is combined with numerical simulation, and the optimal scheme and the production strategy for gas-water alternate development of the target oil and gas field are determined by utilizing engineering constraints to improve the recovery efficiency to the maximum extent, reduce the gas-oil ratio and reduce gas channeling.

The invention discloses a WESP feedforward control method and system considering frequency conversion and starting number of slurry pumps of a desulfurizing tower, and belongs to the technical field of automatic control of environmental protection islands of thermal generator sets. According to the method, modeling is carried out on slurry pump frequency and number step test data, a characteristic model between the slurry pump frequency and the concentration of smoke at an outlet of a desulfurizing tower is established, and gain, inertia time and lag time parameters are identified by adopting an immune genetic algorithm. And constructing a frequency feed-forward channel and a number feed-forward channel based on Pade approximation and a low-pass filter, and superposing the feed-forward compensation quantity and the PID feedback control quantity of the wet electric dust remover to generate the operation control quantity of the wet electric dust remover power supply. The control system comprises a slurry pump working condition acquisition module, a smoke dust concentration acquisition module, a model storage and calling module, a feed-forward and feedback operation module and a control quantity synthesis execution module, rapid response to the operation working condition of the slurry pump of the desulfurization tower is achieved, the auxiliary dust removal capacity of the desulfurization tower is fully utilized, and automatic, efficient and energy-saving control over the wet-type electric dust remover is achieved.

This invention discloses a hydraulic pump fault detection method based on signal processing, comprising: acquiring vibration signals during the operation of the hydraulic pump in real time using a vibration sensor, and extracting features from the vibration signals to obtain a feature vector of the vibration signals; wherein, is a first feature factor, is a second feature factor, is a third feature factor, is a fourth feature factor, is a fifth feature factor, is a sixth feature factor, and is a seventh feature factor; optimizing the connection weights of the SOM network model using an immune genetic algorithm to obtain an optimized SOM network model; using the feature vector of the vibration signals as input to the optimized SOM network model, finding the neuron in the competitive layer of the SOM network model that best matches the feature vector of the vibration signals, and using the output of the neuron that best matches the feature vector of the vibration signals as the detection result of the hydraulic pump fault.

The application discloses a user behavior prediction method based on immune genetic algorithm optimization log audit, including S1, information collection: the behavior characteristics of the user are collected from the log file of the system, and the behavior characteristics include basic information data, operation behavior data and expenditure level data of the user; S2, a prediction model is constructed: a user behavior prediction model is constructed through an immune genetic algorithm; S3, data identification: the log file of the target user is analyzed, and the target user behavior data is identified.The application collects the behavior characteristics of the user from the log file of the system, constructs a user behavior prediction model through an immune genetic algorithm, identifies the behavior data of the target user, predicts whether the user behavior is normal, predicts the user behavior abnormality through setting three to five unqualified items, can reduce the influence of some occasional factors on the user behavior, leads to the user behavior abnormality alarm, and thus the false positive rate of the user abnormal behavior alarm is reduced.

This invention discloses a wave power generation control system based on an immune genetic algorithm. The outer loop control loop includes: an immune memory bank, a nonlinear error feedback control law, a primary dual-buoy object, a secondary dual-buoy object, and a mirror image of the secondary buoy object. The inner loop control loop includes: a vertical velocity control module and a process model. The output of the mirror image of the secondary buoy object is connected to the input of the nonlinear feedback control law. The output of the nonlinear error feedback control law is connected to the input of the vertical velocity control module. The output of the vertical velocity control module is connected to the input of the process model, the input of the secondary dual-buoy object, and the input of the mirror image of the secondary dual-buoy object. The output of the secondary dual-buoy object is connected to the input of the primary dual-buoy object. The output of the process model is connected to the input of the vertical velocity control module. This invention solves the problem of interference from unmeasurable state variables, optimizes the wavelength and wave height of the waves, and improves the stability of the system.

The invention belongs to the technical field of ship motion, and discloses a ship LAAV motion model parameter identification method based on a dynamic immune genetic algorithm, which comprises the following steps of: constructing an identification model fitness criterion function by using ship real sea test data, setting a hyper-parameter adaptive adjustment mechanism, and performing several-generation selection, intersection, variation and immune operation on an initial population to identify ship LAAV motion model parameters. And finally determining model parameters. According to experimental verification, unknown parameters in a ship LAAV motion model are optimized by utilizing a DIGA framework in combination with sea test data, and an optimal solution is substituted into the LAAV model to generate AVP (Attribute, Velocity, Position) data in a simulation manner. The test result shows that the DIGA method has the advantages of high precision and high convergence speed in the LAAV model parameter identification problem, and the similarity between the generated AVP data and the sea test data is more than 90%.

The application relates to a short code optimization method of intra-pulse-inter-pulse joint constraint, and belongs to the field of radar signal processing. According to current distributed radar transmission system and system requirements, long code and short code mathematical representations of a two-phase code waveform signal model are established; based on the long code and short code mathematical representations, radar transmission waveform target optimization functions of intra-pulse-inter-pulse joint constraint are established from intra-pulse and inter-pulse two-dimensional orthogonality; based on the radar transmission waveform target optimization functions, an immune genetic algorithm is adopted to sequentially perform initialization population, diversity evaluation, immune selection, cross and mutation and population merging on the transmission waveform; according to optimal excitation degree and average fitness as a judgment condition, a set of waveforms of intra-pulse-inter-pulse two-dimensional orthogonality optimization is designed as the transmission waveform. By adding intra-pulse-inter-pulse two-dimensional joint orthogonal constraint orthogonality, the orthogonality of the short code waveform is enhanced, and the purpose of improving the distance resolution and positioning accuracy under the detection mode of multiple radars is achieved.

The embodiment of the invention discloses a traffic evacuation path optimization method based on a cellular transmission model, and the method comprises the steps: building an emergency traffic flow evacuation model based on a cellular transmission model according to the random and dynamic characteristics of an evacuation traffic flow; taking a bus and a car as evacuation vehicles, and optimizing the emergency traffic flow evacuation model by adopting a vehicle path problem; on the basis of an immune genetic algorithm, a feasible optimal solution is searched for the emergency traffic flow evacuation model; and selecting an urban regional road network to carry out evacuation efficiency evaluation on the emergency traffic flow evacuation model. The method has the advantages that buses and cars are combined to serve as evacuation vehicles, the emergency traffic evacuation model with multiple starting points and multiple terminal points is established, and the emergency traffic evacuation model is more consistent with the actual situation; a reverse lane setting method is adopted, a regional road network is utilized to the maximum extent by temporarily changing a current road section organization mode, and the road traffic capacity in the evacuation direction is improved.

The invention discloses a multimodal transport method and system in an extremely cold environment. The method comprises the following steps: constructing a multimodal transport low-temperature adaptive transport capacity grid model based on regional nodes and road sections; determining a conditional hypothesis of the model; constructing an optimization model taking the minimum total cost as a target; the total cost comprises basic transportation cost, transfer cost, loss / goods damage cost, energy consumption / refrigeration cost, equipment failure and maintenance cost and time window punishment and retention cost; establishing a mixed integer programming model, and determining constraint conditions; a hybrid particle swarm-immune dual optimization method is adopted to complete the above model solution iteration output optimal combined transport scheme; particles are constructed through three-section hybrid coding, and a double-optimization mechanism is established based on particle swarm local optimization continuous quantity and immune genetic global exploration discrete decision. And efficient, continuous and economical operation of multimodal transport in the extremely cold environment is guaranteed.

The invention discloses a capacitor energy storage intelligent management method, and relates to the technical field of capacitor energy storage management, and the method comprises the following steps: dividing the charging and discharging process of a vehicle-mounted capacitor into a plurality of dynamic charging intervals, formulating a management strategy for each dynamic charging interval through iterative calculation, the method comprises the following steps: obtaining a capacitor charging and discharging optimal threshold sequence corresponding to each dynamic charging interval, identifying the dynamic charging interval in the running process of the electric vehicle, performing dynamic regulation and control management on the charging and discharging behavior of the vehicle-mounted capacitor according to the capacitor charging and discharging optimal threshold sequence, and performing dynamic regulation and control management on the charging and discharging behavior of the vehicle-mounted capacitor by dividing the dynamic charging intervals. A capacitor health state, a battery cycle index and an environment temperature correction interval boundary are combined, an energy recovery efficiency data optimization management strategy in a braking process is used, an optimal charging and discharging threshold value is solved by an immune genetic algorithm, a motor low-efficiency interval is discharged preferentially, and a battery charge state closed loop is matched to prevent a battery from being overcharged or overdischarged. The performance is optimized cooperatively, and the stability and economical efficiency of the power system are improved.

The invention discloses a wet-type electric precipitation feedforward control method considering frequency conversion of a desulfurization slurry pump, which comprises the following steps of: establishing a mathematical model for dust removal of the slurry pump of a desulfurization tower, including a mathematical relationship between the frequency of a variable-frequency slurry pump and dust removal and a mathematical relationship between the number of started constant-speed slurry pumps and dust removal; designing a desulfurization tower slurry pump dust removal characteristic experiment scheme, and extracting data through a desulfurization tower slurry pump dust removal characteristic test; fitting the provided slurry pump dust removal model by adopting an immune genetic algorithm, and establishing a dust removal dynamic characteristic model of the desulfurization tower slurry pump; a feed-forward channel considering slurry pump frequency conversion is designed, a wet-type electric precipitator Smith coupling control method comprising the feed-forward channel is established, and a control system is developed through experimental verification. According to the invention, automatic, efficient and energy-saving control of the wet-type electric dust remover is realized by rapid reaction of the operation condition of the slurry pump of the desulfurization tower and full utilization of the auxiliary dust removal capability of the desulfurization tower.

This invention discloses an industrial hazardous waste reduction and treatment system, including a pretreatment module, an activation module, a fermentation reaction module, a tailings packaging module, a tail gas treatment module, a wastewater treatment module, a monitoring and control module, and a parameter control module. The parameter control module is used to adjust various parameters during the fermentation and degradation process. The fermentation and degradation reaction temperature is adjusted based on an immune genetic algorithm. A balanced treatment unit dynamically assists a PID control unit to adjust each parameter to a preset value, and then takes corresponding measures to achieve optimal reaction conditions for the fermentation and degradation process. This invention uses a PID controller based on an immune genetic algorithm to adjust the fermentation and degradation reaction temperature. By combining the hazardous waste reduction rate with real-time and precise adjustment of the pH value of the fermentation and degradation reaction, it effectively controls the reaction conditions for the biodegradation of hazardous waste and improves the processing speed of hazardous waste fermentation and degradation.