352 results about "Sensitivity coefficient" patented technology

The invention relates to the technical field of electrical fault monitoring, in particular to an intelligent mobile substation electrical fault monitoring method, which comprises the following steps of: synchronously acquiring an electrical monitoring signal, a multi-dimensional environment noise signal and a dynamic working condition parameter of a mobile substation through a multi-source sensor; the method comprises the following steps: constructing an environmental noise floor vector group by adopting multi-scale spectrum mode decomposition, filtering an environmental noise interference component from an electrical monitoring signal through orthogonal projection filtering, and outputting a baseline correction signal; a working condition disturbance response field matrix is constructed based on time domain and frequency domain correlation analysis of dynamic working condition parameters, gradient sensitivity coefficients are calculated, and components strongly related to working condition disturbance and residual components weakly related to equipment faults are separated out; reconstructing the residual component into a pure fault feature vector; and finally, fault type diagnosis and risk early warning are carried out on the basis. The method effectively solves the problem of fault feature annihilation caused by noise pollution in a complex environment and the problem of false alarm and missing alarm caused by confusion of working condition disturbance and real fault signals.

The invention provides a tunnel deformation monitoring system and method based on a distributed optical fiber sensing technology, and relates to the technical field of tunnel monitoring. A spiral winding type optical fiber is arranged in the longitudinal axis direction of the surface of a tunnel lining, a transverse optical fiber is arranged on a key section, and the data acquisition frequency is set to be 1Hz. Dividing the optical fiber into a plurality of data acquisition points at equal intervals, obtaining the real-time temperature and Brillouin frequency shift change of each point, measuring the temperature and strain sensitivity coefficient, and calculating the total strain; calculating long-term temperature strain compensation based on the long-term temperature component, generating an instantaneous temperature difference through the real-time temperature and the long-term temperature component, further calculating instantaneous temperature strain compensation, subtracting the two kinds of strain compensation from the total strain to obtain stress strain, and evaluating the deformation level of the tunnel based on the stress strain. And the accuracy of evaluation is judged through clustering analysis.

The invention provides a decoupling and Kalman filtering temperature strain demodulation method and system for double fiber gratings, and relates to the technical field of optical fiber sensing. The method comprises the following steps: firstly, designing an FBG sensing module for measuring environment temperature change and strain information; the FBG sensing module comprises a two-channel sensing system composed of an FBG1 sensor and an FBG2 sensor, and is used for measuring environment temperature change and strain information at the same time. Then, aiming at optical signals which are acquired by the two FBG sensors and contain physical quantity information, a wavelength demodulation module is used for acquiring the central wavelength drift distance of reflection spectrums of the two FBG sensors; temperature sensitivity coefficients and strain sensitivity coefficients of the two FBG sensors are obtained, and a linear equation set is constructed for independent variation of temperature and strain to perform matrix decoupling so as to distinguish the influence of the temperature and the strain on the change of the central wavelength; and performing optimization estimation on the decoupled temperature and strain by using the prior information of the temperature and the strain and combining a real-time measurement value by adopting a Kalman filtering method.

The invention discloses a focusing control method and related equipment, and the method comprises the steps: synchronously collecting the data of a multi-modal sensor, extracting features and parameters, inputting a multi-modal fusion model subjected to joint optimization to generate a motor control instruction, and achieving the focusing through the hierarchical control of rough adjustment, fine adjustment and dynamic tracking. The dynamic weight fusion network deploys the weight of each modal data according to the environmental sensitivity coefficient, and combines the historical memory and trajectory pre-judgment of the alignment trajectory prediction network to reduce invalid calculation and repeated search and greatly improve the focusing speed; the multi-mode sensor works cooperatively to replace a special hardware function, so that the cost and the power consumption are reduced; the dynamic weight fusion network can adjust the modal confidence according to the environment and enhance the anti-interference capability in the complex light environment; the alignment trajectory prediction network and the fuzzy self-adaptive PID controller are in hierarchical cooperation, the problem of erroneous judgment of a complex scene is solved, and the micro-distance and dynamic tracking precision is improved; the model is subjected to pruning, distillation and quantitative optimization, light weight is achieved, and low-cost equipment is adapted.

The invention relates to the technical field of industrial equipment monitoring, and discloses an industrial equipment state monitoring and fault early warning method and system based on big data, and the system comprises a data collection and fusion module, an intelligent diagnosis module and an early warning execution module. Through innovative design of a spatio-temporal feature fusion engine, the problem of spatio-temporal mismatch of vibration, temperature, current and other signals is solved, dynamic characteristics of millisecond-level vibration signals are captured through a long-short-term memory network, a spatial topological relation of equipment is modeled through a graph convolutional network, cross-scale alignment of multi-physical-quantity data is achieved, the limitation of physical significance isolation in a traditional method is broken through, and the method has a good application prospect. The composite fault recognition capability is improved, the diagnostic value of unstructured data such as sound emission is released, a full-life-cycle self-adaptive mechanism is constructed through a dynamic threshold algorithm, the working condition fluctuation tolerance is intelligently adjusted through a sensitivity coefficient, an early warning model continuously optimized along with the operation time is formed, the stiffness defect of a static threshold is changed, and the early warning effect is improved. And an accurate intelligent early warning system is established.

The invention provides a myopia occurrence risk assessment method based on multi-source data, and relates to the technical field of myopia prevention and control, and the method comprises the steps: collecting user physiological parameters and dynamic behavior data through multiple sensors, and carrying out the preprocessing and labeling; detecting a behavior short-term trend by applying a sliding window algorithm, generating a myopia risk index change process, identifying a false risk reduction interval and a delay window period, and marking a myopia risk index rebound event and an accumulated rebound risk peak value; when the behavior continuously reaches the standard, a physiological parameter verification process is started, a closed-loop intervention suggestion is generated based on the rebound early warning point, and intervention feedback is collected; comparing the score change with the actually measured physiological data direction, suspending the process when a conflict event is triggered, and generating a fusing log; and extracting prevention and control compliance abnormal data to update the causal atlas, non-linearly adjusting the model weight and the sensitivity coefficient according to the comprehensive feedback index, iteratively optimizing the model until early warning is removed, and outputting a report.

The invention relates to the technical field of data processing, and provides a resistance line-associated underwater bench blasting numerical simulation error correction method, which is characterized in that land and underwater measured data are matched through the same resistance line and similar material parameters, and the resistance line and the water depth are used as basic input of an error prediction model; a reference basis is provided for error correction of subsequent numerical simulation; calculating a target result in combination with a prediction error after initial simulation, comparing a simulation result with the target result during iterative simulation to generate an iterative error, and analyzing and determining sensitivity coefficients of different error classifications in a resistance line and the iterative error so as to adjust material parameters to reduce an invalid iterative process; simulation results are processed according to stress areas in a zoning mode through an equidistant slicing method, adjustment precision is verified in combination with the root rate and the damage area, and the reliability of the simulation results is improved; and meanwhile, parameter data passing verification are input into a back propagation neural network, a correction parameter mapping library is constructed, and the design efficiency of an underwater blasting scheme is improved.

The invention provides an intelligent optical cable performance prediction method and system for a data center, and relates to the technical field of data center performance analys.The intelligent optical cable performance prediction method comprises the steps that firstly, an optical cable microstructure disturbance quantization model is constructed, and an optical cable is divided into a plurality of virtual quantization units in the longitudinal direction; each unit packages a real-time stress-strain state parameter and a response sensitivity coefficient of the basic structure unit, inputting a Brillouin scattering spectrum frequency shift data flow collected in real time into the model for disturbance source analysis, generating a physical excitation decomposition sequence, driving a virtual quantization unit to perform state evolution iterative operation according to the physical excitation decomposition sequence, and obtaining a Brillouin scattering spectrum frequency shift data flow model; a stress-strain state evolution track set is obtained, a time-space correlation network of optical cable link disturbance propagation is constructed according to the stress-strain state evolution track set, network flow characteristic analysis is conducted on the time-space correlation network, an early warning node set is extracted, the future evolution trend of the early warning node set is calculated, and serialized optical cable performance abnormity early warning information is generated. The optical cable performance abnormity can be predicted in advance, and stable operation of the data center optical cable is guaranteed.

The invention discloses a face gear worm grinding machine error compensation method based on sensitivity analysis and a multi-source error mapping model, which comprises the following steps of: 1, constructing a multi-source error-tooth surface error mapping model based on a vector decomposition and truncation function method, the truncation function method is used for quantitatively describing propagation mechanisms of geometric errors and thermal errors in a machining chain and influences of the propagation mechanisms on tooth surface deviation, and high-order error terms are eliminated to improve calculation efficiency; 2, responding to the output of the mapping model, applying an improved Morris sensitivity analysis method, distributing a weighting coefficient higher than a geometric error component for a thermal error component, and calculating a weighting sensitivity coefficient so as to identify a key error component which has the greatest influence on the tooth surface precision; and 3, based on the key error component, a motion axis compensation value is generated through an error compensation model, and the compensation value is input into a machine tool control system to adjust the relative position of the worm grinding wheel and the workpiece, so that the tooth surface error is reduced.

The invention relates to the technical field of earthquake monitoring, and discloses a comprehensive earthquake prediction method, which comprises the following steps of: acquiring a continuous micro-motion data sequence to reconstruct an empirical green function sequence, and dividing the empirical green function sequence into a compression phase subset and a stretching phase subset by utilizing a synchronously acquired theoretical earth tide stress phase; respectively extracting a causal branch waveform corresponding to the positive time axis and a non-causal branch waveform corresponding to the negative time axis, and determining a weight index for inhibiting the non-structural interference by calculating a reciprocity residual error between a causal wave velocity offset and a non-causal wave velocity offset; correcting a wave velocity offset mean value by using the weight index to obtain a stress sensitivity coefficient reflecting the stress state of the earth crust medium; according to the method, a physical criterion is established by using an elastic wave propagation reciprocity principle, decoupling of medium attribute change and noise source spatial drift is realized, and visual wave velocity offset induced by environmental interference is eliminated.

The invention discloses an HPC curtain wall safety intelligent monitoring method and system, and the method comprises the steps: obtaining curtain wall multi-domain monitoring data and a cable climbing robot scanning record, recognizing a vibration abnormal component, generating a defect sensitivity coefficient, and constructing a damage response field domain; scanning and generating a modal capacity contour surface, setting a resonance early warning trigger node, extracting frequency response distribution to determine a resonance dangerous area, and generating a high-sensitivity monitoring area and a sensor control strategy; an environment load sequence is collected and projected to a damage response field domain to form an effective load bandwidth, and a safety control range is determined by combining structure bearing requirements; performing path search by applying a layered monitoring and adjusting scheme, identifying a stress concentration position through keel deformation modulation, and generating a structure control anchor; analyzing a structure control anchor to generate a fatigue accumulation rate, forming a damage development speed in combination with a scanning record, and extracting collaborative deformation parameters; and comparing the cooperative deformation parameter with a preset value to form a control deviation signal, and generating a self-adaptive control sequence to realize intelligent monitoring and active adjustment of structure safety.

The invention discloses a side plate type heat exchanger performance testing system and method, and relates to the technical field of heat exchanger testing, and the system comprises a computer which is used for constructing a digital twinborn model, generating multiple groups of dynamic working condition data, inputting the multiple groups of dynamic working condition data into the digital twinborn model to obtain dynamic performance data, screening according to the sensitivity coefficient of each piece of dynamic performance data to obtain sensitive working condition data; the test equipment executes the sensitive working condition data and collects actual measurement state data of the side plate type heat exchanger; and the computer judges whether the performance of the side plate heat exchanger meets the design requirement according to the actually measured state data. Physical modeling is carried out on the heat exchanger in a digital twinning mode, heat exchange simulation is carried out on the heat exchanger, invalid test working conditions are deleted from a parameter set, only sensitive working conditions are reserved, and time, manpower and energy cost needed by testing is greatly saved.

The invention provides a medical insurance fund health monitoring method and system based on a neural network, and a medium. Comprising the steps that multi-source heterogeneous medical insurance data are collected and input into a two-channel neural network model in parallel after space-time normalization processing, a time sequence prediction channel predicts a fund sustainability index through a long and short-term memory network in combination with an attention mechanism, an anomaly detection channel calculates a medical fund abuse risk probability and a regional circulation balance degree through a graph neural network, and the medical fund abuse risk probability and the regional circulation balance degree are calculated. And finally, fusing the indexes to generate a health index, comparing the health index with a dynamically adjusted threshold value to realize three-level early warning, constructing a medical risk propagation network, generating a sensitivity coefficient based on a clustering coefficient, betweenness centrality and historical risk intensity, dynamically optimizing an early warning threshold value, and enabling the system to support anti-factual causal analysis and generate policy intervention suggestions. According to the method, the problems of insufficient medical insurance fund space-time heterogeneity modeling, neglect of a risk conduction mechanism and poor static threshold adaptability of a traditional method are solved, and accurate monitoring and active prevention and control of fund health are realized.

The invention relates to the technical field of power system signal measurement, in particular to a mutual inductor digital sampling method and system. The method comprises the following steps: acquiring a signal output by a secondary side of a mutual inductor for a power system in real time; determining the load fluctuation degree at the current moment; determining a dynamic sensitivity coefficient at the current moment; obtaining a dynamic reference mean value, a dynamic relaxation parameter and a dynamic decision threshold value used in the CUSUM algorithm at the current moment; determining the cumulative sum between the signal and the dynamic reference value in the CUSUM algorithm at the current moment; and determining the sampling frequency of the mutual inductor according to the cumulative sum between the signal and the dynamic reference value in the CUSUM algorithm at the current moment and the size of the dynamic decision threshold so as to realize digital sampling of the mutual inductor. According to the invention, through adaptive adjustment of the reference mean value, the relaxation parameter and the decision threshold value, the rate of missing report and false report is reduced, and the identification capability of initial faults such as mutual inductor turn-to-turn short circuit and the like is improved.

The invention relates to a proportion optimization method for a cement stabilized macadam base of recycled aggregate. Taking an interface failure sensitivity coefficient IFSI as a quantitative index; the method comprises the following steps: performing microstructure reconstruction on recycled aggregate particles through SEM scanning, resistivity testing and CT scanning, extracting two indexes of defect density and surface adsorption strength, and constructing a degradation prediction model ITZ; the ITZ result is used as a basic parameter for adjusting the water cement ratio and the fine aggregate content, so that a mechanism from aggregate defects to interface failure is connected; establishing unit volume interface energy consumption minimization as an optimization target based on an IFSI value output by the interface failure model; the influence of the water cement ratio, the grading difference and the fine particle wrapping degree on the interface bonding energy consumption is analyzed, and a matching point under the specific recycled aggregate condition is found out through a multivariable function coupling model.

The invention discloses an intelligent decision-making system for dynamic stress sensitive area identification and efficiency compensation of a hydroelectric generating set, and belongs to the field of intelligent monitoring and optimal control of hydroelectric equipment. Running state data, environment data and task instruction data of key parts of the hydroelectric generating set are acquired in real time through a data acquisition module; the sensitive area identification and analysis module identifies a dynamic stress sensitive area by utilizing time-frequency domain analysis and sensitive coefficient calculation; the risk division module judges operation risk intervals under different working condition parameter combinations based on clustering analysis and outlier detection; the execution decision module screens an optimal working condition parameter group in combination with the working condition risk value and the power generation efficiency; the efficiency compensation module generates a compensation priority sequence according to the sensitivity coefficient, and monitors and triggers a compensation mechanism in real time; according to the invention, the intelligent operation management of the hydroelectric generating set is realized, the safety, the power generation efficiency and the economic benefit of the equipment are obviously improved, and a new thought and method are provided for the technical progress and sustainable development of the hydroelectric industry.

The present invention relates to the technical field of electric fault monitoring, and specifically to an intelligent mobile substation electric fault monitoring method. The method synchronously collects electrical monitoring signals, multi-dimensional environmental noise signals and dynamic operating parameters of the mobile substation through multi-source sensors; adopts multi-scale spectral modal decomposition to construct an environmental noise basis vector group, filters out environmental noise interference components from the electrical monitoring signal through orthogonal projection filtering, and outputs a baseline correction signal; constructs an operating condition disturbance response field matrix based on time domain and frequency domain correlation analysis of dynamic operating condition parameters, calculates gradient sensitivity coefficients, separates components strongly correlated with operating condition disturbances and residual components weakly correlated with equipment faults; reconstructs the residual components into pure fault feature vectors; and finally performs fault type diagnosis and risk warning based on the pure fault feature vectors. The method effectively solves the problem of fault feature annihilation caused by noise pollution in complex environments, and the problem of false alarms and missed alarms caused by confusion between operating condition disturbances and real fault signals.

The invention discloses a machine learning-based kernel data verification method, which comprises the following steps of: firstly, obtaining simulated effective proliferation factors and actually measured effective proliferation factors of a plurality of kernel experiment references, and calculating to obtain a difference value; 2, calculating nuclear data sensitivity characteristics of each experimental reference, wherein the nuclear data sensitivity characteristics comprise a sensitivity coefficient for a nuclear reaction cross section and experimental metadata; thirdly, constructing a machine learning model based on a random forest, and performing training by taking the kernel data sensitivity characteristics as input and the effective multiplication factor difference value as output; and finally, carrying out model feature importance analysis, and identifying kernel data sensitivity features which have significant influence on effective proliferation factor differences. The nuclear data verification method based on machine learning is established for the first time, and is used for identifying potential defects in nuclear reaction data or deviation of experimental references.

The invention relates to the technical field of artificial intelligence, in particular to a pavement quality detection method and device based on artificial intelligence, and the method comprises the following steps: obtaining pavement images and load data, extracting crack boundary coordinates through edge detection, calculating a displacement difference to obtain crack distribution, calculating an extension rate in combination with time, and carrying out the weighted analysis of a direction difference. The method comprises the following steps: extracting crack boundary displacement difference, identifying defect evolution parameters, calculating stability and environmental sensitivity coefficients through cross coupling, judging degradation types in combination with load fluctuation, calculating defect grades through classification comparison threshold values of a support vector machine, and outputting a pavement overall quality result. Angle difference variation recognizes evolution direction difference, coupling extension rate and direction consistency depicts stability, environmental sensitivity and load fluctuation linkage calculation is introduced to present external action association, and grading is completed based on parameter and threshold difference to realize dynamic grading recognition of defect development rules.

The invention is suitable for the technical field of model prediction, and provides a method and system for constructing a pulmonary infection risk prediction model based on machine learning, and the method comprises the following steps: collecting data information, and carrying out the standardization processing of the data information, the data information comprising structured data, image data, time series data and environment data; performing multi-modal feature extraction based on the data information to obtain an image feature F1, a physiological time sequence feature F2 and an environment feature F3; performing multi-modal feature fusion, determining weights of the image features, the physiological time sequence features and the environment features, and adaptively adjusting the weights of the environment features based on seasonal factors; processing class imbalance through focus loss, determining a time decay weight according to infection latency characteristics, and determining a total loss function; and carrying out model training by adopting a multi-layer perceptron to obtain a pulmonary infection risk prediction model. According to the invention, by introducing the seasonal factor and the seasonal sensitivity coefficient, adaptive adjustment of the environmental feature weight is realized.

The invention relates to the technical field of digital twinning, in particular to a smart factory data management system based on digital twinning. The device comprises an abnormal lagging unit, a window changing unit and a window updating unit. According to the method, a window module is defined to obtain a change speed sensitive coefficient through historical data, and then a data change speed value and a digital twin model update frequency are combined with the change speed sensitive coefficient to calculate an update frequency weight coefficient; the size of the sliding window of the digital twin model is defined by using the data change speed value, the update frequency of the digital twin model, the change speed sensitivity coefficient and the update frequency weight coefficient, and the size of the sliding window is adjusted, so that newest data can be quickly included, model parameters can be updated in time, update lag caused by interference of old data is avoided, and the update efficiency is improved. According to the method, the data utilization efficiency is improved, and when the data change speed is high or low, fine changes can be captured in time while the sliding window is properly enlarged, so that model parameters are updated more accurately.

The invention discloses a shaft system alignment key process identification and optimization method based on an error flow model, and the method comprises the steps: constructing an error source set in a shaft system assembly process, and building a related space coordinate system based on an assembly topological relation; establishing a nominal pose transfer equation based on an infinitesimal rigid body coordinate transformation principle; constructing a joint surface contact error vector by combining the diameter of the flange on the basis of a flange opening value and a dislocation value measured on site; performing first-order linearization processing on the nominal pose transfer equation based on the joint surface contact error vector, and obtaining a sensitivity matrix; constructing a state equation for describing error accumulation according to the sensitivity matrix and the error source set; a sensitivity coefficient is constructed based on a state equation and is compared with a judgment threshold, key control characteristics are identified, accurate errors can be predicted by quantifying a transmission mechanism of each error source, and key procedures are accurately identified in combination with the sensitivity coefficient, so that a reasonable strategy is adopted for optimization, and the product quality is ensured to be qualified.

The present invention relates to the field of data processing technology, and in particular to a low-altitude, economical drone scheduling method and system based on big data. The method comprises the following steps: determining a collision risk assessment function between the starting node and the destination node based on the visibility value at each moment from the starting node to the destination node; determining the drone's load sensitivity coefficient based on the drone's load offset and critical load offset; using the product of the drone's wind direction potential energy value and the load sensitivity coefficient at each moment as a resistance risk assessment function between the starting node and the destination node at each moment; and determining the selection probability of the starting node and destination node paths in an ant colony algorithm based on the distance between the starting node and the destination node, the resistance risk assessment function at each moment, and the collision risk assessment function, thereby achieving drone scheduling and effectively improving the accuracy of drone scheduling.

The invention relates to the field of energy storage and reactive compensation capacity optimization configuration, in particular to a new energy pooling station energy storage and reactive compensation configuration method based on big data analysis. The method comprises the following steps: firstly, acquiring data, respectively obtaining a sensitivity coefficient of active power and a sensitivity coefficient of reactive power through a disturbance response sampling linearization identification method, and introducing a dynamic active-reactive decoupling voltage improvement evaluation algorithm based on sensitivity weighting to obtain a voltage improvement amplitude of a node; then, based on the voltage improvement amplitude of the node, constructing a target function; and carrying out iterative solution on the constructed objective function, and outputting the optimal active power regulation quantity of each energy storage unit and the optimal reactive power regulation quantity of each reactive compensation device. The technical problems that the sensitivity coefficient is difficult to update in real time, the calculation scale is huge, the response speed of real-time optimization scheduling, unnecessary active loss and battery life loss are seriously influenced, and the physical balance of the voltage improvement amplitude between the nodes is ignored are solved.

The present application belongs to the technical field of determining the load of loading nodes in a static test of a component, and specifically relates to a method for determining the load of loading nodes in a static test of a component, comprising: determining the loading nodes in the static test of the component; determining the initial distribution of the loading load of each loading node; constructing a finite element model of the static test of the component, performing finite element analysis, obtaining the sensitivity coefficient of the assessment target to each loading node, and then constructing a sensitivity coefficient matrix; constructing a finite element model of a theoretical state of the component, performing finite element analysis, obtaining the assessment target response error under the static test and theoretical state finite element model of the component, and then constructing a response error matrix; using the sensitivity coefficient matrix and the response error matrix, calculating the increment of the loading load of each loading node, updating the loading load of each loading node, until the assessment target response error under the static test and theoretical state finite element model of the component meets the requirements, and obtaining the loading load of each loading node.

The invention discloses a resistance strain gauge sensitivity coefficient measuring device, which belongs to the technical field of resistance strain measurement and comprises a support, a displacement generation mechanism, a displacement detection mechanism and a steel ruler. The displacement generation mechanism comprises a swing arm and a mounting seat, the swing arm is mounted on the support and forms a lever structure, two ends of the straight steel ruler are detachably connected with the short arm end of the swing arm and the support respectively, and a strain gauge pasting area is arranged on the surface of the straight steel ruler; the displacement detection mechanism comprises at least one displacement sensor. The upper end of the steel ruler can be driven to generate axial displacement of a corresponding distance through the lever action of the swing arm, so that the strain gauges synchronously generate telescopic deformation, the real strain of the steel ruler and the strain gauges can be directly measured through the displacement sensor, and finally the sensitivity coefficient can be obtained according to the resistance change rate of the strain gauges. And the reliability and the effectiveness of a measurement result are improved.

The invention relates to a port area illumination energy saving and visual performance comprehensive evaluation method based on multi-objective optimization, and the method specifically comprises the following steps: deploying an illumination sensor and an intelligent electric meter at a key position of a port area, and collecting illumination and energy consumption parameters to form a training data set containing a performance label; defining a sensitivity coefficient and a normalization index for each data feature and completing normalization calculation; constructing a comprehensive evaluation model, inputting a normalized value, and then realizing classification prediction through a non-linear interaction kernel illumination-energy consumption feature fusion enhancement module and a double-branch attention feature extraction deep neural network backbone module; fusing multiple constraints to construct a composite multi-objective loss function to calculate model loss; iteratively optimizing the model by adopting a gradient descent algorithm in combination with the training data set until a stop condition is met; and normalizing new data, inputting the normalized new data into the trained model, and selecting the highest probability category as an evaluation result. According to the invention, the energy-saving and visual performance of harbor lighting can be accurately balanced, and effective guidance is provided for optimization of a harbor lighting system.

The invention provides a steel plate internal defect identification method and device, and relates to the technical field of steel plate defect identification, and the specific steps are as follows: synchronously acquiring ultrasonic and electromagnetic signals through integrated detection equipment, and aligning to obtain bimodal original data; collaborative denoising and numerical calibration are carried out on the data, and preprocessing is completed; extracting acoustic features and electromagnetic features, determining a feature sensitivity coefficient based on a historical defect sample, and performing weighted fusion according to the feature sensitivity coefficient to generate fused feature data; and finally, inputting the fused feature data into a pre-trained feature-defect mapping model, and outputting the specific type, the position coordinate and the risk level of the defect, thereby realizing accurate and automatic identification and evaluation of the internal defect of the steel plate.

The invention provides a curing process online monitoring method for a variable-thickness composite material component, and relates to the technical field of structural health monitoring of composite materials, and the method comprises the steps: collecting original monitoring data of each optical fiber sensing point in the variable-thickness composite material component; based on a preset temperature sensitivity coefficient and a preset strain sensitivity coefficient, performing temperature-strain double-parameter decoupling on the original monitoring data to obtain chemical shrinkage strain; according to the chemical shrinkage strain and the change rate, a gel point, a maximum heat release rate point and a glass transition interval in the resin curing process are obtained, and curing state characteristics are constructed; and determining a curing stage corresponding to the variable-thickness composite material component based on the curing state characteristics, calculating a curing degree gradient, and when the curing degree gradient is greater than or equal to a preset process safety threshold value, sending a closed-loop regulation and control instruction to curing equipment so as to adjust the curing process of the variable-thickness composite material component, according to the invention, the curing monitoring precision and stability can be improved.

The embodiment of the invention discloses a sub-basin division method of a target area and related equipment, and the method comprises the steps: fusing the elevation model data of the target area with a multispectral remote sensing image according to a preset terrain sensitivity coefficient, a preset river elevation constraint weight and a preset dynamic river reference elevation; the fused data can effectively reflect the topographic structure characteristics of the target area; historical meteorological raster data, future predicted meteorological raster data and fusion data serve as a data set to be input into an ST-Mama model for water system extraction, so that the ST-Mama model can fully combine topographic features and meteorological conditions to optimize the water system recognition effect, and the probability value of each raster output by the ST-Mama model belonging to the water system is more accurate. Therefore, an accurate and accurate water system graph is obtained when the probability value is used for water system division, so that the accuracy of the obtained sub-basins is improved.