276 results about "Factor analysis" patented technology

The invention discloses a bolt loosening automatic acquisition and analysis method and equipment based on a 5G sensing network, and relates to the technical field of bolt state intelligent acquisition. The bolt loosening automatic acquisition and analysis method and device based on the 5G sensing network comprises the steps that S1, multi-source data in the bolt use process is acquired and preprocessed, and a standardized bolt channel state data set is constructed; s2, evaluating the state of a bolt loosening evolution stage in combination with an angular displacement change trend, and driving the sampling frequency to be dynamically switched; s3, analyzing the response intensity change of the three types of channels, evaluating the channel fusion state, and adjusting the monitoring priority; s4, comprehensively evaluating a response offset trend, and recombining monitoring resources; and S5, constructing an attribution process based on the fusion state value and the high-risk transition value, and generating an evolution link structure. The problem that a high-risk section cannot be recognized in advance due to the fact that a multi-stage multi-channel driving factor analysis and cross-time-domain expression mechanism lacks in the evolution process of bolt loosening is solved.

The invention discloses a battery carbon emission influence factor analysis method and device and a storage medium, and belongs to the technical field of data processing. Comprising the following steps: acquiring life cycle data of a battery at the current moment from a data source, constructing a knowledge fusion atlas database of the battery based on the life cycle data through an atlas construction model, generating a carbon emission prediction model according to the knowledge fusion atlas database, and predicting the carbon emission through a feature contribution degree analysis algorithm. Calculating a prediction contribution degree of each factor variable in the carbon emission prediction model, determining a target factor variable according to the prediction contribution degree, constructing a directed acyclic graph of the target factor variable based on a preset electrochemical constraint condition, and calculating conditional probability distribution of the electrochemical constraint condition in the directed acyclic graph, and forming a coupling relation model of the target factor variables, and calculating the sensitivity coefficient of the factor variables in the coupling relation model to the carbon emission. According to the method, the accuracy of the analysis result is improved by quantifying the sensitivity coefficient of each carbon emission factor under the coupling relationship.

The invention discloses a mixing parameter regulation and control method and system for an SBS-T modified asphalt mixture and a terminal. The method comprises the following steps: analyzing a modification mechanism through a scanning electron microscope and X-ray photoelectron spectroscopy combined technology, dividing three stages of rapid melting and the like, and establishing a temperature-viscosity model; constructing a multi-scale capture system by using an atomic force microscope, a Fourier transform infrared spectrum and a dynamic shear rheometer; designing a five-factor three-level test matrix by adopting a response surface method, and establishing a road performance prediction model in combination with a BP neural network; mixing parameters are optimized in a multi-objective mode based on a genetic algorithm, and dynamic correction is achieved through an Internet of Things sensor and Kalman filtering. The system comprises a mechanism analysis module, a multi-scale detection module, a terminal integrated storage unit, a processing unit and a man-machine interaction unit. According to the scheme, the limitation of traditional single-factor analysis is broken through, microscopic and macroscopic collaborative optimization and dynamic parameter regulation and control are achieved, and the construction adaptability and precision are improved.

The invention discloses a sheep abnormal behavior intelligent monitoring method and system based on multi-source data fusion and deep learning, and the method comprises the steps: collecting the multi-source data, such as the motion trail, limb joint angle, body temperature, sound and environmental parameters, of a sheep through multi-type sensing equipment, and carrying out the time-space alignment to form a feature matrix; encoding and reconstructing the feature matrix by using a variational auto-encoder to generate a reconstructed feature matrix, and calculating errors of the two feature matrixes to form an error sequence; and inputting the error sequence into a local abnormal factor analysis model to obtain an abnormal factor sequence, judging an abnormal behavior through a threshold value, and outputting related information. The system comprises a multi-dimensional parameter acquisition unit, a spatial-temporal feature fusion unit, a feature reconstruction unit, an error sequence generation unit, an abnormal factor analysis unit and an abnormal judgment output unit. The method and the system realize multi-source data fusion, improve anomaly identification accuracy, adapt to dynamic changes and are suitable for large-scale breeding.

The invention provides an investment and research transaction multi-strategy parallel execution method, and relates to the technical field of financial data analysis, and the method comprises the steps: accessing multi-source real-time data from a stock market, a future market and other financial markets, and obtaining a standardized multi-source heterogeneous data set; generating a multi-strategy transaction signal, outputting a strategy coordination instruction set, generating strategy instance operation state data, sending a transaction instruction generated by each strategy to a transaction system of a corresponding market for execution, obtaining transaction execution result data, generating risk open assessment data and a strategy consistency report, and performing risk open assessment based on an optimized strategy parameter set. And executing the steps of data access, factor analysis, strategy generation, independent execution and optimization in a loop iteration manner. Through the method and the device, the technical problems of high transaction delay and instable income caused by high coupling degree among multiple market strategies, poor execution isolation and difficulty in realizing cross-market strategy collaboration and risk unified management and control in the prior art are solved; and the transaction execution efficiency and the strategy stability are improved.

The invention relates to a building engineering green low-carbon construction evaluation and decision support system, in particular to the field of building engineering, and aims to construct a space-time atlas to comprehensively characterize a construction process by integrating an Internet of Things sensor, a building information model and meteorological data, and accurately estimate a future carbon emission trend by using a dynamic prediction mechanism. And key influence factors and contribution degrees thereof are identified through factor analysis, and finally real-time optimization suggestions are provided by virtue of a visual interface, so that scientific decision support for green low-carbon construction is realized, carbon emission is effectively reduced, the resource utilization efficiency is improved, and the environmental sustainability of constructional engineering is promoted.

The invention discloses an urban flood mapping and driving factor analysis method based on multi-source remote sensing data, and the method comprises the steps: respectively extracting an optical semi-permanent water body and an SAR semi-permanent water body based on a global surface water body data set and a Sentinel-1 image, and taking the intersection of the optical semi-permanent water body and the SAR semi-permanent water body to generate an enhanced semi-permanent water body mask; extracting a water body in a flood period by using the Sentinel-1 image to obtain flood water body distribution, and performing water body extraction precision verification in combination with the Sentinel-2 image; the precision of the SRTM DEM is improved based on a DEM error prediction model of a Transform network, and a corrected DEM is generated; and constructing a multi-dimensional spatial analysis model based on the elevation, gradient and surface coverage type of the corrected DEM, and identifying spatial distribution characteristics and a terrain driving mechanism of the flood water body. According to the method, comprehensive analysis is performed in combination with terrain factors, and the spatial precision and timeliness of flood risk assessment can be improved.

The invention provides an electrical performance prediction and influence factor analysis method and system based on machine learning, and relates to the technical field of photovoltaic cells, and the method comprises the steps: obtaining technological parameters in a photovoltaic cell production process and electrical performance indexes of corresponding production batches; extracting process statistical characteristics of the process parameters; according to the production batch number and the time window identifier, matching the process statistical characteristics with the electrical performance indexes through an automatic alignment mechanism to form a mapping relation between the process statistical characteristics and the electrical performance indexes; on the basis of the mapping relation, an enhanced process feature matrix used for representing the influence of the process on the electrical performance is constructed through a multi-modal feature extension mechanism, and an electrical performance prediction result is generated through a layered weighted regression model; analyzing the marginal influence of each process parameter on an electrical performance prediction result by using an SHAP value theory, and generating an influence matrix; and according to the influence matrix, adjusting process parameters in the production process of the photovoltaic cell so as to optimize the electrical performance of the photovoltaic cell.

The invention relates to the technical field of Bluetooth earphones, and discloses a Bluetooth earphone dynamic power consumption and transmission delay balance control system, which comprises a state acquisition module, a strategy generation module, a factor analysis module, a quantitative evaluation module, a parameter adjustment module and an execution control module. The state acquisition module obtains working current, data packet state and the like to construct an evaluation model; the strategy generation module generates an initial association strategy table; the factor analysis module analyzes historical data to identify interference factors; the quantitative evaluation module calculates a stability deviation index; the parameter adjusting module updates the strategy table according to the index; and the execution control module triggers rate switching. The system is further provided with a data packet comparison unit, a timestamp marking unit and the like to process data transmission conflicts, and the transmission quality is optimized through a three-dimensional transmission quality field model, a genetic algorithm and a three-level response mechanism. The system realizes dynamic balance of power consumption and transmission delay, and is suitable for the field of power consumption and transmission control of the Bluetooth headset.

The invention relates to the technical field of data analysis, in particular to a geological disaster analysis method and system based on cloud, and the method comprises the following steps: obtaining and coding a rainfall intensity, gradient, wind speed and humidity data source of a geological area, recognizing a disaster factor mapping structure to generate a coding group, and quantifying a gradient difference superposition humidity screening high-frequency unit. And performing connectivity analysis to extract an aggregated risk area boundary, extracting a wind speed and humidity ratio to screen out an overrun factor, outputting a high-coupling factor combination, and identifying a trend change to output a risk grading identification group. According to the method, multi-source environment data are synchronously collected and indexed according to time and space, the matching precision of disaster factors and space grids is enhanced, the high-frequency disturbance recognition stability is improved through multi-layer fusion analysis, risk boundaries are extracted through communication modeling to improve the positioning accuracy, and the coupling factor analysis capability is improved through factor ratio fusion; trend matching is combined with a response gradient to improve risk grading perspectiveness, and the overall process enhances early warning efficiency and expands data analysis depth and breadth.

The invention relates to the technical field of carbon emission prediction, and discloses a multi-source data driven automobile carbon emission characteristic space-time prediction method and system, and the method comprises the steps: collecting multi-source data, carrying out the preprocessing, and constructing a multi-factor carbon emission data set; performing factor analysis on the data set, and extracting three principal component factors; a CNN-LSTM mixed deep learning model containing an attention mechanism is constructed to predict carbon emission; calculating carbon emission intensity of different spatial scales; based on a Gaussian diffusion theory, constructing a carbon emission diffusion model under a multi-wind-speed condition, and calibrating a diffusion coefficient by adopting measured data of an air monitoring station; modeling is carried out on the fused spatio-temporal data, and a carbon emission intensity prediction value of a grid scale in a specified time period in the future is output; performing visual display on the prediction result, and triggering early warning and generating a corresponding ecological driving control strategy based on the prediction value; the problem that an existing carbon emission prediction mode is low in prediction precision is solved.

The invention discloses a shelf life kiwi fruit comprehensive quality quantitative evaluation method based on hyperspectral imaging and a convolutional neural network, and the method comprises the steps: 1, obtaining spectral images of kiwi fruits of different shelf lives through a hyperspectral imaging system, and extracting the original spectral data of a region-of-interest sample; step 2, measuring the soluble solid content, hardness and color parameters of the sample, normalizing the parameters with correlation, extracting a dominant factor combination with an accumulative contribution rate greater than or equal to 80% by using a factor analysis method, and constructing a KCQI (Kiwi berry Comprehensive Quality Index) based on factor load weight; step 3, preferably selecting a characteristic wave band related to the KCQI, and performing sample set division; 4, taking the spectral data of the characteristic wave band as input, and adopting a one-dimensional convolutional neural network to construct a quantitative prediction model of KCQI; and step 5, applying the model to a spectral image of a sample, generating a KCQI spatial distribution visual image pixel by pixel, and visually displaying the comprehensive quality change.

The invention relates to the technical field of industrial electric furnace control, and discloses a graphitization furnace power transmission power curve optimization method, which comprises the following steps: S1, data acquisition and preprocessing: acquiring historical operation data of a graphitization furnace, and carrying out cleaning and standardization processing on the data; the historical operation data comprises a power transmission power curve, in-furnace temperature and pressure data, material initial parameters and finished product quality indexes; s2, key factor analysis: determining key factors influencing the quality of a finished product from historical operation data through correlation analysis and principal component analysis methods; s3, constructing an optimization model; s4, performing model training and verification; s5, generating an optimization curve; and S6, applying and iterating. Compared with the prior art, the method has the advantages that the optimal transmission power curve is automatically generated through data driving and a machine learning model, so that the problems of unstable product quality, high energy consumption and process adjustment lagging caused by dependence on artificial experience are solved.

The invention relates to an earth pressure balance shield construction safety toughness dynamic evaluation system and method based on an extended cloud model and a network analysis method, and the system comprises an index system construction module which builds a multi-level toughness evaluation system based on a literature measurement and factor analysis method; an entropy weight TOPSIS weight calculation module objectively calculates the initial weight of the index through an information entropy theory; the ANP network weight optimization module corrects and optimizes the global weights of the indexes by constructing an inter-index nonlinear dependency network and a feedback mechanism; and the extended cloud toughness evaluation module quantifies qualitative indexes into membership degrees for different toughness levels based on expectation, entropy, hyper-entropy and other digital characteristics, comprehensively integrates weights and the membership degrees, and finally outputs soil pressure balance shield construction safety toughness levels and targeted optimization strategies. According to the method, multi-dimensional toughness quantitative evaluation and dynamic prevention and control of the construction safety risk of the earth pressure balance shield (EPB) can be realized under the complex stratum condition.

The invention belongs to the technical field of battery health degree evaluation, and particularly relates to a battery health degree evaluation method and system, electronic equipment and a storage medium. Based on the original sample data of the battery health degree evaluation indexes, establishing a standardized decision matrix to eliminate the dimensional difference between the indexes; constructing a correlation matrix according to the matrix, and evaluating index correlation; extracting common factors of a correlation matrix, calculating a core quantitative index, determining a main factor by taking standard reaching of an accumulated variance contribution rate as a standard, realizing data dimension reduction and retaining original index core information; and calculating a main factor score in combination with the original data and the rotated factor matrix, performing weighting according to a variance contribution rate, and performing weighted summation to obtain a health degree comprehensive score. And the health difference of the batteries can be judged in an auxiliary manner through clustering analysis, and data support is provided for reasonable configuration and fine management of the batteries. According to the method, dimension reduction weighting is carried out on various related indexes through a factor analysis method, a result is objectively deduced based on data features, subjective influences are reduced, and evaluation scientificity and reliability are improved.

The invention provides a closed watershed lake water level evolution prediction method based on multi-source data, and relates to the technical field of hydrological resources. The method comprises the following steps: firstly, collecting natural environment and social economic multi-source data of a closed drainage basin, and constructing a normalized multi-source feature input matrix; carrying out total element analysis on the features and extracting a nonlinear response threshold value; and meanwhile, Mantel statistical test is executed on the feature matrix to obtain a statistical correlation coefficient. And judging the characteristics which simultaneously meet the statistical significance and mechanism threshold conditions as real causal driven factors, and eliminating false related factors which are statistical significance but have no physical threshold. And finally, reconstructing a prediction model based on the screened causal-driven feature set, and outputting a water level evolution prediction value. Through'statistics-mechanism 'dual verification, interference of false correlation is effectively eliminated, the problem that a traditional black box model cannot distinguish causal and coincident trends is solved, and physical interpretability and robustness of water level prediction are remarkably improved.

The invention provides a post evaluation simulation method based on virtual reality, and relates to the technical field of virtual reality, and the method comprises the steps: calculating a real-time cognitive load pressure index and an emotion state parameter of a student; obtaining a fault evolution speed parameter and a complexity parameter used for adjusting the fault evolution process; inputting the fault evolution speed parameter and the complexity parameter into an intelligent fault chain generation system, and generating a cascade fault situation conforming to the physical law of the power system through a graph neural network model; in a high-precision simulation environment of a digital twin platform of a power system, students are guided to execute simulation operation based on a cascade fault situation, and multi-dimensional behavior data are collected and recorded; the multi-dimensional behavior data comprises operation time sequence data, operation path deviation data and decision delay data; and inputting the multi-dimensional behavior data into a multi-dimensional human factor engineering evaluation system, and processing the multi-dimensional behavior data through a human factor analysis model to obtain a post evaluation result of the student. According to the embodiment of the invention, the accuracy of post evaluation can be improved.

A method for detecting the extent of microplastic accumulation in bivalve organisms in extreme deep-sea environments is provided. This method is performed through sampling of biological communities to represent the community structure of that biological bed layer, then morphological characterization statistics are conducted to classify the age stage data of individual bivalves, multivariate factor analysis is used to obtain the individual bivalves with the greatest degree of contribution in each classified age stage, subsequently tissue-specific microplastic extraction is performed, the morphology of seafloor microplastics is restored and streamlined identification of full-size microplastics is considered, carbon-14 dating is used to trace the duration of microplastic adsorption by each individual bivalve, dynamic accumulation curves for seafloor bivalves are constructed by connecting each bivalve's survival duration, and finally this is scaled up to the entire bivalve bed to derive the microplastic accumulation rate and historical accumulation of the entire extreme ecosystem.

The invention discloses a risk control platform based on event driving, a risk control method, equipment, a medium and a program product, and relates to the technical field of risk control management, and the platform comprises a message access and event generation module which is used for obtaining and generating a risk control event set and a meta factor set corresponding to a standardized risk control information set; the element factor analysis module is used for determining a summary factor set according to the risk control event set and the element factor set; the rule and index calculation module is used for determining a risk control index set corresponding to the summary factor set and a target rule set; and the result summarization and event driving module is used for performing calculation according to the risk control index set and the risk control rule through the standardized business interface to obtain a rule calculation result, and executing a risk control task through the rule calculation result to obtain a comprehensive risk control result. According to the invention, high-performance calculation of rules in the risk control platform can be realized, and real-time risk control requirements are met.

The invention discloses a power failure risk marketing and distribution co-processing system based on RPA, and relates to the technical field of power system automation and intelligent operation and maintenance. The system comprises a data preprocessing module, a risk influence factor analysis module and an RPA co-processing flow construction module, original data can be cleaned and standardized, power failure risk factors can be identified and analyzed, an efficient co-processing flow is automatically constructed based on the RPA technology, real-time monitoring and updating of power failure risk equipment and a sensitive customer list are achieved, and the real-time monitoring and updating of the power failure risk equipment and the sensitive customer list are achieved. And marketing and distribution collaboration is enhanced through closed-loop management and control on double-active I-line and single-line. In practical application, risk identification efficiency and repair efficiency can be greatly improved, power failure time and complaint amount are reduced, an automatic and intelligent solution is provided for power failure risk management, and power supply reliability and customer satisfaction are effectively improved.

The invention discloses a power load probability prediction method and system based on a neural network quantile regression model and multiple linear regression, and belongs to the technical field of power system load prediction. The method comprises the following steps: obtaining standardized data by using a longitudinal data analysis method; identifying key influence factors of the power load in the standardized data based on a Pearson correlation analysis method, and constructing a factor analysis model to quantify influence weights of the key influence factors on the power load; constructing a neural network quantile regression model based on seasonal trend decomposition to fit the key influence factors with different influence weights to obtain a quantile prediction result; based on the quantile prediction result, estimating a continuous probability distribution curve of a load common factor by adopting a non-parametric kernel density technology to obtain an interval prediction result; and constructing a multiple linear regression model to predict the load scale change, and adjusting the interval prediction result. According to the invention, the precision and calculation efficiency of load prediction are effectively improved.

The invention relates to a measurement standard equipment operation state evaluation method and evaluation system based on verification and detection data, computer equipment and a storage medium, and solves the problems of easy deviation, low efficiency and the like in verification, calibration and testing of operation measurement standard equipment in the prior art. According to the technical scheme, related data of measurement standard equipment are obtained, and influence factors are mined; analyzing the influence factors, and constructing a running state evaluation model; comprehensively screening and constructing an evaluation system based on the operation state evaluation model; calculating a comprehensive score of the metering equipment; and performing clustering and grading evaluation. The method has the advantages that the operation state evaluation model is constructed and based on the operation state evaluation model, the data analysis technology and multiple algorithms are utilized, state evaluation influence factors are mined through historical detection data and full-period data, operation state comprehensive scoring is carried out on the measurement standard equipment, the real-time state of the equipment is evaluated, the service life is predicted, existing problems are found and checked in time, and the measurement standard equipment is evaluated. And the lean management level of standard metering equipment is improved.

The application relates to a power demand side target user identification method and device based on multi-dimensional factor analysis. The method performs field classification according to a historical user power consumption parameter sequence, determines a power consumption parameter sequence contained in a predicted field category, and further obtains a parameter mean sequence and a parameter offset sequence of the predicted field category. The parameter offset sequence is adjusted to a corrected offset sequence according to a prediction probability. A reference threshold sequence is obtained according to the parameter mean sequence and the corrected offset sequence. A target difference sequence is determined according to a target parameter sequence corresponding to a target user and the reference threshold sequence under the same field category. The target user target level is obtained according to the target difference sequence. The historical user power consumption parameter sequence can be adjusted to flexibly adapt to various scenes, the accuracy of target user identification is prevented from being reduced due to dynamic adjustment of the user and the power consumption parameter sequence, and the accuracy of target user identification is further improved based on field category classification.

The invention provides a method for on-line monitoring of hydrogen sulfide in a drilling process, and relates to the technical field of oil-gas field development, and the method comprises the following steps: preprocessing collected data from different sources to obtain original data; performing influence factor analysis processing on the original data to obtain the original data marked with influence factors; establishing a plurality of model combinations composed of machine learning models, and generating a strong classifier based on the result selection of the classifier; processing the original data marked with the influence factors by using a strong classifier to obtain a hydrogen sulfide pre-monitoring initial model; and performing parameter adjustment on the hydrogen sulfide pre-monitoring initial model by adopting a cross validation method to generate a hydrogen sulfide pre-monitoring final model. A data driving method is adopted, the hydrogen sulfide monitoring model is established by using data generated in the drilling process, hydrogen sulfide is analyzed and pre-monitored in real time, and the real-time performance of monitoring is improved.

The present disclosure provides a system for quantifying drivers of growth of niche categories. The system includes a data collection module configured to receive consumer data collected using surveys, an analysis module configured to identify consumer segments using factor analysis of the received consumer data, a market size estimation module configured to estimate market size for each identified consumer segment based on current consumption, a demographic categorization module configured to create sub-groups based on demographics, a growth potential determination module configured to determine growth potential by regressing consumption behavior on the identified consumer segments and consumer demographics using multinomial regression, and a market size prediction module configured to predict potential market size based on model weights and predicted probability of switching between consumer classes.

The invention relates to the technical field of environment monitoring, in particular to a physical and chemical factor coupled nearshore denitrification process influence factor analysis method, which comprises the following steps: S1, collecting a nearshore environment sample, and obtaining a physical and chemical factor data set; s2, on the basis of the physical and chemical factor data set, calculating the coupling strength between the physical and chemical factors, and generating a coupling strength matrix; s3, performing dimension reduction processing on the coupling strength matrix by adopting a principal component analysis method, and determining a key influence factor list; s4, analyzing the contribution degree of each dominant factor to the denitrification rate based on the key influence factor list, and generating a contribution degree analysis result; s5, according to the contribution degree analysis result, an influence factor analysis report of the nearshore denitrification process is output, and the analysis report comprises key influence factor sorting and optimization suggestions. The method provides intelligent and data-driven auxiliary decision support for coastal ecological restoration and water quality improvement, and has good popularization and application prospects.

The present application relates to the technical field of intelligent thermal management of power equipment, in particular to a temperature control intelligent regulation and control system for an electric energy metering box; comprising parameter acquisition, risk assessment, implicit factor analysis, regulation and correction and execution control modules; the system determines a comprehensive thermal runaway risk index by acquiring data such as instantaneous fluctuation gradient of junction temperature, data service flow spectrum entropy, box coating thermal light property drift coefficient and local Rayleigh number; the core is to calculate adaptive regulation and control execution amount according to the index, in combination with parasitic electromagnetic resonance index and VOC thermal barrier index determined by eddy current heating power and VOC emission index; the present application realizes the change from dependence on macro temperature feedback to multi-dimensional data forward-looking quantification, and can identify and inhibit thermal runaway risk in advance.

This invention relates to the field of intelligent product pricing technology, and discloses a method and system for generating intelligent product pricing strategies by integrating multi-factor analysis. The method involves: calculating a weight set and multiple first pricing parameters based on the historical pricing records of each tenant in the rights and benefits platform; performing a weighted operation on the multiple first pricing parameters based on the weight set to obtain the tenant's second pricing parameters; multiplying the second pricing parameters by the category benchmark price to generate M first candidate prices; verifying each of the M first candidate prices, selecting N second candidate prices that satisfy all constraints, calculating the comprehensive score of each second candidate price, and selecting the second candidate price with the highest comprehensive score as the target price and storing it in the tenant pricing matrix. This invention solves the technical problem in existing technologies where all tenants share the same pricing weights, leading to an inability to adapt to differentiated business needs, by integrating multi-factor analysis, and achieves intelligent generation of product pricing strategies.

This invention relates to the field of data processing technology, specifically to a method for evaluating the effectiveness of fish passage in an eco-friendly fishway based on multi-factor analysis. The method involves acquiring multi-dimensional data, a multi-factor evaluation calculation model, and engineering parameters of each batch of fish swimming through a test section within each monitoring period. In each period, the theoretical environmental pressure rate is calculated using the model based on water temperature and flow velocity sequences. The total time consumed is compared with a preset baseline time to determine the energy consumption index. Effective swimming weights are determined by combining the deviation of the swimming trajectory time series and engineering parameters, reducing the weight of ineffective time consumption data in the backwater rest area. Multiple indicators from all batches of fish are integrated to determine long-term evaluation deviation indicators, and effective error periods are selected. An environmental parameter constraint space is constructed, and relevant data from each batch of fish within the effective error period are used for traversal optimization to eliminate data contamination in the rest area and improve the model's adaptability and predictive accuracy to different environments and fish states.

The invention discloses a bulb tube ignition intelligent early warning algorithm based on multi-factor analysis, and the algorithm comprises the following steps: collecting data through different sensors on a bulb tube, and carrying out the feature extraction of the collected multi-factor data; constructing an early warning model based on a deep learning algorithm; performing rolling updating on real-time data through a sliding window algorithm, and dynamically adjusting model parameters; and when the model predicts that the ignition risk value exceeds a preset risk threshold, an early warning signal is triggered. According to the method, through comprehensive analysis of multi-factor data, the model can more comprehensively identify various factors related to the ignition risk, so that the early warning accuracy is remarkably improved, and the probability of false alarm and missing alarm is reduced; through dynamic self-adaptive adjustment and a sliding window algorithm, data can be processed and updated in real time, the response speed is improved, and an operator can take necessary prevention measures before problems are expanded, so that the occurrence rate of ignition faults is effectively reduced.