370results about "Chaos models" patented technology

The invention relates to the technical field of agricultural intelligent irrigation, and discloses a high-standard farmland intelligent irrigation system based on Internet of Things and data analysis, and the system comprises a soil moisture content sensing module which collects data through a multi-source sensor to construct a three-dimensional soil moisture content distribution model, and generates a soil moisture content characteristic spectrum; the soil moisture content prediction module generates water demand prediction data based on the soil moisture content characteristic spectrum, the meteorological data and the crop growth stage; the irrigation strategy module fuses terrain elevation and pipe network pressure parameters to generate an irrigation control map; the equipment state monitoring module collects water pump current waveform and other data to generate equipment health degree parameters; the pipe network optimization module optimizes pipe network topology and generates an adjusting instruction; the multi-source data fusion module generates fusion evaluation indexes by using an evidence theory, an entropy weight method and the like; and the intelligent execution module generates an execution control instruction accordingly. All the modules cooperate to achieve precise irrigation, and the utilization efficiency of water resources and the intelligent level of farmland management are improved.

The invention discloses an intelligent power prediction method considering dynamic load change, and relates to the technical field of power grid load prediction, and the method comprises the steps: collecting original load data, carrying out the preprocessing, constructing a VMD constraint optimization model, carrying out the four-stage improvement of optimization parameters through employing an improved dung beetle optimization algorithm, so as to generate IMF components, reconstructing the IMF component by calculating a sample entropy to obtain a low-frequency component and a high-frequency component; establishing a Kalman filtering state space model based on the low-frequency component, and decomposing the low-frequency component into a residual component and a pseudo trend component through a Kalman filtering recursive algorithm; external features are obtained, the high-frequency component, the residual component and the pseudo trend component are aligned and spliced with the external features, multi-component collaborative prediction is carried out through a local-global interactive attention mechanism, and a final load prediction result is obtained; and generating a power demand visualization chart based on the final load prediction result. And reliable decision support is provided for power dispatching and energy management.

The invention discloses an ultrasonic guided wave signal noise reduction method based on HHO-SVMD and singular spectrum analysis, and the method comprises the steps: obtaining a to-be-processed ultrasonic guided wave signal, optimizing a penalty factor of variation mode decomposition (SVMD) through employing an improved Harris eagle algorithm, initializing a population through Circle chaotic mapping, introducing chaotic disturbance and weight, and carrying out the noise reduction of the to-be-processed ultrasonic guided wave signal. Determining an optimal alpha value and decomposing the signal into an optimal modal component; calculating a kurtosis value of each modal component, and screening effective components containing damage information according to a threshold value; singular spectrum analysis denoising is carried out on the effective components, and a self-adaptive window mechanism is introduced to dynamically adjust the window length and the truncation strength; reconstructing the de-noised effective component to obtain a de-noised signal; according to the method, the parameter optimization precision and efficiency are improved, damage characteristics and noise are effectively separated, different dominant frequency signals are adapted, the damage characteristics can still be reserved in a low-signal-to-noise-ratio environment, the noise reduction effect of ultrasonic guided wave signals and the damage detection reliability are improved, and the method is suitable for nondestructive detection of components such as ultra-long small-diameter heat absorption pipes.

The invention discloses a multi-modal large model training data acquisition method and system, and relates to the technical field of data acquisition optimization, and the method comprises the steps: building a causal graph adjacency matrix based on a cleaning alignment data set, carrying out the anti-fact intervention after recognizing a prejudice variable, and generating an anti-fact sample set; combining the anti-fact sample set and the cleaning alignment data set into an enhanced data set; performing cross-modal anti-long-tail compensation on the enhanced data set to obtain a balanced data set; based on the image data and the text data in the balance data set, depth separable convolution feature extraction and BERT semantic coding are carried out respectively, cross-modal fusion is carried out, and fusion features are output; through the steps of depth separable convolution, BERT coding, quantum latent variable evolution and cross-modal semantic verification, the generalization ability, robustness and social adaptability of the multi-modal large model in a complex and real scene are significantly improved.

The invention relates to the field of state monitoring and fault diagnosis of power equipment, in particular to a transformer fault diagnosis method based on a chaos evolutionary optimization algorithm, which comprises the following steps of: 1, acquiring a magnetic flux leakage signal during operation of a transformer; 2, optimizing a parameter modal number K and a penalty factor alpha of variational modal decomposition by using a chaos evolutionary optimization algorithm; 3, performing variational mode decomposition on the magnetic flux leakage signal to obtain an intrinsic mode function component; 4, calculating the envelope entropy of the intrinsic mode function component, and obtaining an effective intrinsic mode function component through screening; 5, extracting the energy entropy and the sample entropy of the effective intrinsic mode function component to form a feature vector; and 6, inputting the feature vector into a pre-trained support vector machine classifier, and outputting a fault type diagnosis result of the transformer. According to the method, the CEO algorithm is combined with the ergodicity of chaotic mapping and the global search capability of the evolutionary algorithm, and the problems that VMD parameters K and alpha are sensitive and depend on experience, and a traditional optimization algorithm is prone to local optimum are effectively solved.

The invention discloses a cable-stayed bridge cross brace optimization system based on system reliability and an intelligent algorithm, which relates to the technical field of bridge engineering, and comprises a multi-source parameter input module used for collecting and processing data such as a main beam section, stay cable parameters, a strain sequence, a vibration sequence, a satellite cloud picture, a traffic monitoring flow, laser point cloud and the like; and outputting the structured design data, the monitoring data flow, the dynamic load spectrum, the probability distribution model and the construction error correction parameters. According to the invention, all-dimensional data such as design, monitoring, load, materials and construction errors are integrated through the multi-source parameter input module, and multi-level and dynamic reliability evaluation is carried out on the component, the subsystem and the system level by using the system reliability analysis module, so that the component failure probability, the system reliability index time sequence and the like can be output; therefore, the real reliability level of the cross brace of the cable-stayed bridge in the whole life cycle is reflected more accurately, and the one-sidedness of a traditional method is avoided.

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

The invention relates to the field of artificial intelligence, and discloses a multi-base cooperative detection method based on an improved marine predator algorithm. The method comprises the following steps: adopting a Tent chaotic sequence to guide initialization so as to improve the global exploration capability; dynamically dividing a three-stage search process according to the effective dimension, and adaptively prolonging the global exploration time; constructing Levy disturbance vector enhanced directivity search of chaotic modulation; designing a composite fitness function containing key area weighting and spacing penalty; and an FADs effect and an elite solution library linkage mechanism are introduced to break through convergence stagnation. The system comprises a parameter input module, an environment loading module, a probability calculation module, a chaos generation module, a stage control module, a disturbance modulation module, a fitness evaluation module and the like. The convergence speed and the key sea area detection efficiency of the multi-base sonar layout scheme are improved, and experiments show that the convergence speed is improved, and the key area detection probability is improved.

The invention discloses a power distribution network traveling wave fault positioning method and a related device, and relates to the technical field of power distribution networks, and the method comprises the steps: collecting a transient signal after a fault at a preset key monitoring point of a power distribution network, extracting an initial traveling wave head from the transient signal, and measuring the moment when the initial traveling wave head reaches other preset monitoring points in the power distribution network, obtaining traveling wave arrival time between the key monitoring point and each monitoring point; the time difference between the traveling wave arrival time is used as the actually measured traveling wave arrival time difference to construct a fault positioning optimization model with the purpose of minimizing the error sum of squares of the theoretical traveling wave arrival time difference and the actually measured traveling wave arrival time difference; and solving the fault positioning optimization model through a pre-constructed chaos particle swarm optimization algorithm to obtain a traveling wave fault positioning result of the power distribution network. The method solves the problems that the positioning effect is not ideal due to the fact that the prior art is prone to falling into local optimum and is poor in adaptability to a complex topological structure, and the convergence speed cannot meet the requirement for rapid power supply recovery.

The invention discloses a grinding wheel state recognition method and system based on a multi-strategy improved DBO algorithm. The method comprises the steps that multi-sensor data of a numerical control gear grinding machine tool are collected, preprocessed and marked with abrasion labels; performing efficient global optimization on hyper-parameters of the CNN-BiLSTM-Attention model by adopting a multi-strategy improved dung beetle optimization algorithm fusing Logistic chaotic mapping initialization, a spiral search strategy optimization breeding stage and a Levy flight strategy optimization stealing stage; and finally, precise prediction and evaluation of the abrasion state of the grinding wheel are achieved through the optimized model. The system comprises a multi-source data preprocessing module, an intelligent hyper-parameter optimization module, a depth prediction model training module and a state prediction and performance evaluation module. According to the method, the problems that a traditional hyper-parameter optimization method is low in efficiency and prone to falling into local optimum are effectively solved, and the precision, convergence speed and working condition adaptability of a state recognition model are remarkably improved.

The invention discloses a photovoltaic power prediction method based on improved empirical mode decomposition and an optimized long short-term memory network, and the method comprises the steps: firstly carrying out the preprocessing of abnormal value elimination, missing value filling, normalization and the like of photovoltaic power and related meteorological data, and improving the data quality; then, an improved empirical mode decomposition (EE-ANEMD) algorithm is adopted to decompose the preprocessed power sequence into a multi-scale intrinsic mode function component and a residual term, and high-frequency noise, intermediate-frequency fluctuation and a low-frequency trend are effectively separated; global optimization is carried out on the hidden layer unit number, the initial learning rate and the maximum number of training times of the LSTM network through an improved sparrow search algorithm (ISSA), finally, the optimized LSTM is utilized to carry out training prediction on each component, and results are fused and subjected to reverse normalization to obtain a final value. Experiments show that the test set RMSE of the method is reduced compared with that of a single LSTM, the mid-term prediction precision is remarkably improved, and reliable technical support is provided for power system dispatching, new energy consumption planning and photovoltaic power station operation and maintenance.

A power load prediction method, system and device based on GHOA-CNN-iTransform-LSTM, and the method comprises the steps: firstly obtaining the multi-source data of a power load and analyzing the causal relationship, then constructing an analysis model based on scene injection, constructing an improved hiking optimization algorithm GHOA based on a hierarchical strategy evolution framework, optimizing the hyper-parameters of the analysis model, and finally obtaining the multi-source data of the power load. And finally, after the analysis model is trained, power load prediction of the smart power grid is realized. By acquiring the multi-source data, analyzing the causal relationship and adaptively transforming the kernel of the analysis model, the analysis model can integrate the time and the causal logic for dynamic allocation, accurate perception of external events is realized, a stable deduction result is obtained from complex data, and the accuracy of the deduction result is improved. According to the method, the self-adaptive optimization of the optimal parameters is realized through the hierarchical strategy evolution framework, the search strategy is ensured to dynamically adapt to the high dimension and complexity of the parameter space, and the precision and adaptability of power load prediction are ensured.

The invention relates to the technical field of coal processing and utilization, and discloses a clean coal yield prediction method based on a support vector machine, which comprises a data acquisition module used for analyzing factors influencing the clean coal yield, acquiring related data and integrating the data into a data set, and a data preprocessing module connected with the data acquisition module and used for preprocessing the data. The data preprocessing module is used for randomly dividing a data set according to a 70% training set and a 30% test set and carrying out standardization and normalization preprocessing, the parameter optimization module is connected with the data preprocessing module and optimizes hyper-parameters of a support vector machine through an improved grey wolf algorithm, and the model building module is connected with the parameter optimization module and is used for building a model. The model building module is used for building and training a support vector regression model based on the optimized hyper-parameters, and the model verification module is connected with the model building module and uses a test set to verify the performance of the model. According to the method, the hyper-parameters of the support vector machine are optimized through the improved grey wolf algorithm, the problem that a traditional optimization method is prone to falling into local optimum is effectively avoided, and the precision of clean coal yield prediction is remarkably improved.

The invention provides an endoscope image real-time dynamic three-dimensional reconstruction method and system, and the method comprises the steps: obtaining initial monocular endoscope image data, constructing an endoscope image training data set through a Gaussian splash algorithm and a chaos algorithm, constructing a 3D reconstruction constraint network, and carrying out the real-time dynamic three-dimensional reconstruction of an endoscope image. The 3D reconstruction constraint network is pre-trained in combination with the endoscope image training data set and a preset physical loss function, real-time monocular endoscope image data is obtained, a dynamic 3D Gaussian scene is generated, scene rendering is conducted on the dynamic 3D Gaussian scene of the endoscope based on endoscope visual parameters corresponding to the real-time monocular endoscope image data, and the dynamic 3D Gaussian scene of the endoscope is obtained. The method comprises the steps of generating ideal monocular endoscope image data, obtaining a discrimination update parameter group by adopting a rendering discrimination update mechanism, and performing feedback optimization on a 3D reconstruction constraint network based on the discrimination update parameter group. The accuracy, the speed and the robustness of three-dimensional reconstruction of the endoscope are improved.

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

The invention discloses a method and a system for arranging and constructing an industrial chain based on semantics. The method comprises the following steps: collecting enterprise, supply, demand, market and policy data, cleaning and standardizing to generate a semantic map and embedding representation; calculating semantic signal vectors of the candidate links or node clusters; selecting chaotic mapping and setting disturbance intensity according to the signal vector, and generating an initial candidate solution set; coding the chromosomes with topology maintenance, and checking and repairing non-compliant individuals to obtain a feasible solution set; evolutionary optimization is carried out by using a chaos sequence, and an elite solution set is screened according to fitness; updating a semantic coverage file based on the elite solution set; if drifting or stagnation is detected, self-adaptive adjustment and restart are executed, and finally the industry chain topology and the alternative path are output and written back to the atlas. According to the method, semantic map modeling is combined with chaotic mapping and evolutionary optimization, so that intelligent generation, compliance repair and dynamic adaptive optimization of the industry chain topology are realized.

The invention discloses an on-load tap-changer fault diagnosis method based on TVFEMD noise reduction-chaotic feature fusion-NGBoost, relates to the technical field of on-load tap-changer fault diagnosis, and is used for solving the problems that complex vibration signals are difficult to effectively analyze and single feature diagnosis precision is insufficient. Self-adaptive positioning of a vibration signal at the action moment of a change-over switch is realized by adopting a short-time energy-entropy ratio algorithm; signal noise reduction is carried out on the vibration signal by utilizing time-varying filtering empirical mode decomposition TVFEMD; feature extraction is carried out based on chaotic characteristics; delay time is determined through a mutual information method; and inputting the vibration signal into an NGBoost multi-classification fault diagnosis model for type identification. According to the method, the problems of instability, nonlinearity and high noise of OLTC vibration signals can be effectively solved, feature extraction is stable and reliable, and the fault diagnosis accuracy is high.

The invention discloses a task allocation method and system based on an improved whale optimization algorithm framework, relates to the technical field, and is used for optimizing order type adaptive cross-domain traffic control network task allocation and improving the key task response capability of a time-sensitive traffic system. ICWOA initializes a population through Chebyshev mapping, and introduces Levy flight disturbance to enhance the optimization ability; dynamically balancing local and global search by means of adaptive parameters; relieving population diversity attenuation through randomness retention, diversity maintenance and boundary constraint; dimension type pinhole imaging reverse learning is fused to reduce high-dimensional optimization dimension interference. According to the algorithm, the convergence speed and the solving precision are better, sub-second calculation time is kept under different task scales, the task distribution efficiency is improved, and the time-sensitive scene task success rate is remarkably improved. The method solves the problems that an existing algorithm is insufficient in adaptive order type'order application-order sending 'structure and prone to falling into local optimum, population diversity attenuation and calculation speed.

The invention discloses a lithium ion battery health state prediction method for optimizing a B-PINNs model based on a PO algorithm. Comprising the following steps: selecting lithium ion battery capacity to quantitatively describe current lithium ion SOH; carrying out a battery charge and discharge characteristic test experiment to obtain change data of voltage, current and temperature in the lithium ion battery aging process; extracting health factors capable of representing battery degradation from the acquired data; correlation analysis is carried out on the health factors and SOH, and the health factors highly representing battery aging are selected; preprocessing the data; a PO algorithm is used as a parameter optimization algorithm of a B-PINNs model, and the PO algorithm is improved to obtain an optimal B-PINNs estimation model.

The invention discloses a variational mode decomposition high-voltage switchgear signal noise reduction method and system. The method comprises the following steps: firstly, collecting a mechanical signal of a high-voltage switchgear; performing adaptive global optimization on key parameters of variational mode decomposition by using an improved sparrow search algorithm, wherein the improved strategy comprises chaotic disturbance reverse learning initialization, dynamic step size control and a Levy flight mechanism; performing variational mode decomposition on the signal by using the optimized parameters to obtain a series of intrinsic mode function components; effective components are screened by calculating Pearson correlation coefficients of each component and an original signal; and finally, reconstructing the effective component to obtain a denoised signal. According to the method, the problems of blind parameter setting, mode aliasing and serious noise residual of a traditional method are solved, high-precision noise reduction of signals can be adaptively realized, the signal-to-noise ratio and the waveform fidelity are remarkably improved, fault features are effectively reserved, and a reliable data basis is provided for accurate fault diagnosis of the high-voltage switch equipment.

A grid management platform is disclosed for predicting and mitigating peak electricity demand events using probabilistic modeling and cost-optimized control. The system includes a likelihood model that evaluates the probability of each remaining day within an evaluation interval being the peak load day. This is achieved through a Monte Carlo simulation engine that generates multiple stochastic realizations of future load trajectories based on historical data, forecast data, and forecast error profiles derived from back testing. The system computes likelihood scores and classifies days using optimized bin thresholds that minimize operational cost, considering demand charges, available distributed energy resources, and forecast uncertainty. Based on the predicted likelihoods, the platform generates actionable insights and transmits control signals to grid-connected assets such as batteries, HVAC systems, and electric vehicle chargers to shift or reduce load during predicted peak periods.

The invention relates to the technical field of slope engineering stability evaluation, in particular to a strip mine slope fatigue instability dynamic evaluation method and system based on a quantitative model. The method comprises the following steps: acquiring physical field parameter data of a strip mine slope; preprocessing the physical field parameter data; establishing a fatigue damage dynamic evaluation model; quantitative characterization parameters of the slope damage state are obtained; constructing a chaotic phase change enhancement model, a path connectivity enhancement model and a health state analysis model; and obtaining analysis results of mechanism early warning, path prediction and probability forecasting. According to the method, a standard field theory and a quantum entanglement enhancement algorithm are introduced, a dynamic evaluation system driven by a physical rule is constructed, multi-physical field perception and a fractional order chaos model are fused, and 97% high-precision identification and 63-day advanced early warning of a damage path are realized; by creating a mechanism-path-probability three-dimensional intelligent evaluation system, a major span from passive monitoring to active regulation and control and full life cycle decision making is realized.

The invention provides a Bayesian-based aviation equipment residual life uncertainty prediction method and system. The method comprises the following steps: S1, constructing an observation function equation and a state parameter description equation of residual life of key or important systems, modules and parts of aviation equipment; s2, constructing an agent model; s3, solving joint posterior probability distribution and performing residual life prediction; and S4, according to the prediction probability distribution of the remaining life of the aviation equipment, generating an optimization control instruction or a maintenance instruction for the aviation equipment, and acting the optimization control instruction or the maintenance instruction on the aviation equipment. The method provided by the invention not only provides a theoretical basis for solving the problem of prediction, analysis and prediction of the service life of the aviation equipment, but also clearly defines how information and uncertainty thereof flow and convert between a physical world and a digital world, and provides a brand new thought for prediction of the uncertainty of the residual service life of the aviation equipment.

The invention discloses a flexible interconnected power distribution network fault recovery method and a related device. The method comprises the following steps: constructing a loss model of an energy storage type intelligent soft switch; generating a typical photovoltaic output scene in combination with a principal component analysis method and a K-means clustering algorithm; establishing a target function with minimum weight of recovery load and system loss as a target, and constructing constraint conditions to obtain a fault recovery model of the flexible interconnected power distribution network; and in combination with second-order cone programming and an improved sparrow search algorithm, solving the fault recovery model of the flexible interconnected power distribution network to obtain a power supply scheme which is used for fault recovery of the flexible interconnected power distribution network. The method has a good power supply recovery effect, the influence of the DG permeability on power distribution network fault recovery is considered, and reliable technical support is provided for power distribution network planning and operation.

The present invention provides a process to implement and control localized air- conditioning of occupied regions in large confined space to reduce HVAC energy consumption comprising delineating the space into multiple micro-zones based on a first level of protocol and static parameters thereof including planned activities, occupancy patterns, diffuser characteristics, and furniture layout and controlling airflow through diffusers in the occupied and unoccupied micro-zones to achieve setpoint temperature or thermal comfort only in the occupied regions using minimum HVAC energy through implementing a second level of protocol with dynamic parameters relating to the micro-zones including real-time occupancy, thermal comfort preferences, ambient conditions, and inter-micro-zonal thermal interactions.

The application discloses a variational mode decomposition high-voltage switchgear signal denoising method and system, which comprises the following steps: firstly, collecting the mechanical signal of the high-voltage switchgear; then, performing self-adaptive global optimization on the key parameters of the variational mode decomposition by using an improved sparrow search algorithm, wherein the improved strategy comprises chaotic disturbance reverse learning initialization, dynamic step control and Levy flight mechanism; then, performing variational mode decomposition on the signal by using the optimized parameters to obtain a series of intrinsic mode function components; then, screening the effective components by calculating the Pearson correlation coefficients of the components and the original signal; finally, reconstructing the effective components to obtain the denoised signal. The application overcomes the problems of blind parameter setting, mode aliasing and serious noise residue in the traditional method, and can adaptively realize high-precision signal denoising, significantly improves the signal-to-noise ratio and waveform fidelity, effectively retains the fault characteristics, and provides a reliable data basis for accurate fault diagnosis of the high-voltage switchgear.

Method and system for DC power distribution for charging stations based on a chaotic topology. The invention relates to a method for DC power distribution for charging stations based on a chaotic topology, comprising: according to user requirements, establishing an initial chaotic topology distribution network for charging station power based on a matrix topology network (S1); using a Gaussian kernel function to extend one dimension of the initial distribution network to a high-dimensional surface to obtain a distribution network with multiple charging nodes (S2); based on the distribution network, the required energy of each charging point, and an energy distribution strategy, controlling the opening and closing of contactor switching units to direct the output energy from power conversion units (S3). Figure 1

The invention discloses a probability static voltage stability analysis method and system based on a multi-fidelity agent model and a storage medium thereof, and relates to the field of power system safety and stability analysis, and the method comprises the following steps: generating a low-precision sample set and a high-precision sample set of power system source load uncertainty parameters; based on the low-precision sample set, a low-fidelity model used for estimating the static voltage stability margin is constructed through a sparse polynomial chaos expansion method; constructing a correction function based on an output difference value of the high-precision sample set and the low-fidelity model under the same input; combining a low-fidelity model with the correction function to construct a multi-fidelity agent model; and calculating and evaluating the probability static voltage stability margin of the power system by using the multi-fidelity agent model. According to the method, through cooperative use of high-precision and low-precision samples and multi-fidelity modeling, the calculation efficiency is remarkably improved while the calculation precision is ensured.

The invention discloses a diabetes feature selection method and system based on an optimized doliolaria algorithm. The method comprises the following steps: initializing an obtained high-dimensional diabetes medical feature data set for diagnosing diabetes by adopting a Logistic chaotic mapping mechanism to obtain a first-generation candidate diabetes medical feature data group; and in each iteration, respectively introducing a self-adaptive mechanism and a leader position updating formula and a follower position updating formula of a preset disturbance item, updating leader feature data and follower feature data in the current candidate diabetes medical feature data group, and obtaining a next-generation candidate diabetes medical feature data group. And repeating the process until a preset number of iterations is reached, and finally determining the leader feature data in the last-generation candidate diabetes medical feature data group as the target diabetes medical feature data. According to the application, by introducing chaotic mapping and an adaptive mechanism, the efficiency and precision of screening key features of diabetes mellitus by the doliolaria algorithm can be improved.

The invention discloses a method for judging the grouting effect of a coal mine tunnel bottom plate, and belongs to the technical field of coal mine tunnel safety control. The technical problem that in the prior art, the grouting effect of a coal mine roadway bottom plate is not accurate is solved. The method comprises the following steps: determining an evaluation index according to a main control factor of a coal mine tunnel floor grouting effect; standardizing the plurality of groups of evaluation index data by using MATLAB (Matrix Laboratory); a CRITIC method is used for giving weights to the evaluation indexes, a CRITIC-CEO-KMeans grouting effect prediction model is constructed, an evaluation database of the coal mine tunnel floor aquifer grouting effect is constructed, evaluation index data of a to-be-evaluated sample is input into the evaluation database, a prediction result is output, and grouting effect grades are divided. According to the method, the prediction accuracy of the coal mine tunnel floor grouting effect can be improved, and technical guidance is provided for improving the mine deep mining safety.