108 results about "Curve analysis" patented technology

The invention provides an oil seal press-fitting abnormity real-time detection method based on pressure displacement curve analysis, and relates to the technical field of oil seal assembly control. Pressure displacement data acquisition is executed and a real-time pressure displacement curve is fitted when press-fitting equipment is started; based on displacement progress threshold triggering stage switching of the time sequence displacement data, an association stage characteristic curve is called from the oil seal press-fitting reference template; comparing the characteristic curve in the association stage with the real-time pressure displacement curve to solve a real-time curve deviation vector; and according to the real-time curve deviation vector mapping physical failure abnormal mode, self-adaptive adjustment of oil seal press-fitting process parameters is triggered. The technical problems that in the prior art, a continuous press-fitting process is adopted for oil seal assembly, rubber stress concentration is caused, hydraulic locking is formed due to lubricant retention, and the press-fitting depth is out of control are solved. Physical failure prevention in the oil seal assembly process is achieved, it is guaranteed that the press-fitting depth meets the technological specification tolerance in the whole process, oil seal assembly defects are restrained, and the sealing reliability of an engine is improved.

The invention belongs to the technical field of transformer operation loss monitoring, and particularly relates to a transformer operation loss condition optimization monitoring method, which comprises the following steps of: calculating an anomaly degree based on oil temperature and vibration characteristics, generating a real-time working condition in combination with load intensity, and associating accumulated operation duration to analyze a real-time operation loss anomaly index; the method comprises the following steps: realizing dynamic correlation between loss and the actual state of equipment, analyzing and extracting fluctuation parameters through a load characteristic change curve, calculating a comprehensive load fluctuation degree, correcting an operation loss abnormal index to eliminate load fluctuation interference, finally determining a dynamic reference threshold according to a historical loss curve, judging continuous overrun through double rules, and triggering early warning. Meanwhile, guide factors such as overload and internal heating abnormity are accurately recognized, corresponding adjustment instructions are output, loss monitoring accuracy and scene adaptability are improved, operation and maintenance closed loop of early warning, guide factors and disposal is achieved, and the method is suitable for transformer full-life-cycle loss monitoring.

The invention provides an electromagnetic safety test system and method for switch integrated equipment, and relates to the technical field of power system switch equipment. The system is used for solving the problems that multi-parameter dynamic coupling analysis of a traditional test is insufficient, transient and gradual interference is difficult to capture by a fixed window, and comprehensive quantitative evaluation is lacked, and comprises a test module, an interference analysis module and a test evaluation module, calculating a total coupling interference variable; the interference analysis module draws a change curve based on the total coupling interference variable, analyzes a threshold over-area, a threshold duration and a peak value, and generates an interference risk index; and the test evaluation module calculates a total evaluation value and a threshold super-window proportion by combining the risk indexes of the windows, and divides safety levels. According to the invention, multi-parameter dynamic coupling simulation is realized, different types of interferences are accurately captured, a full-period dynamic evaluation system is constructed, and the accuracy of electromagnetic safety performance judgment of equipment is improved.

The invention relates to the technical field of oil and gas field development, in particular to a fracture parameter inversion method based on a Bayesian neural network, which comprises the following steps: establishing a bottom hole net pressure conversion model based on an actual construction curve, and drawing a bottom hole net pressure curve; calculating a bottom hole net pressure index sequence and a corresponding time sequence; establishing a shaft bottom crack extension mode judgment criterion on the basis of a classic double logarithmic curve analysis method; taking the net pressure index sequence and the time sequence obtained in the previous step as input data, combining actual physical parameter constraints, and establishing an inversion fracture parameter model based on a Bayesian neural network; and inputting the pressure index sequence to be inverted into the Bayesian neural network model to obtain a specific fracture parameter inversion result. According to the technical scheme, the confidence interval of the prediction result can be given, the prediction result and uncertainty quantification capability can be synchronously provided, and the reliability and decision support value of the inversion result are greatly improved.

The invention discloses an AI-based press-fitting curve intelligent analysis and standard curve generation method and system, and relates to the field of press-fitting curve analysis, and the method comprises the steps: constructing an AI classification model and a training set; training an AI classification model by using the training set to obtain a trained AI classification model; performing quality classification on the training set by using the trained AI classification model to obtain a quality classification result; performing standard curve fitting on the press-fitting curve samples with normal quality classification results to obtain a standard curve; and performing quality classification on the to-be-analyzed press fitting curve by using the trained AI classification model to obtain a quality classification result of the to-be-analyzed press fitting curve. The AI intelligent classification diagnosis of the press-fitting curve to be analyzed is automatically realized, manual intervention is reduced, the judgment consistency is improved, and the classification accuracy is improved.

The invention relates to the field of rail transit, and discloses an online monitoring system adapted to a SiC power device recycling scene, and the system comprises the steps: collecting key operation parameters of a SiC power device in real time, and when the collected parameters exceed a preset warning threshold value, judging whether the subsequent failure trend prediction needs to be carried out or not; constructing a device performance change curve based on the collected operation parameters, analyzing the curve type and the fluctuation trend, judging whether the device has a potential failure risk or not, and judging whether key monitoring or load regulation and control need to be performed on a specific module or not by combining the parallel operation state of the device and a historical abnormal fluctuation event; the influence degree of the potential failure trend is determined by analyzing the relevance among on-resistance sudden change, junction temperature fluctuation, welding layer cavity expansion and bonding interface aging, and according to the influence degree of the potential failure trend and a warning threshold value, an online early warning signal is output and a regulation and control instruction is executed. The method has the advantage of finding the potential failure trend in advance.

The invention discloses a numerical control machine tool positioning error dynamic real-time compensation system which is specially used for aluminum shaft machining and comprises a machining parameter acquisition component, a precision curve analysis component and a cutting control component. The machining parameter obtaining component inputs a parameter a needing to be machined, the precision curve analysis component obtains cutting parameters of rough machining and finish machining according to the first machining compensation model and the second machining compensation curve, and the cutting control component conducts rough machining and finish machining according to the parameters. In the rough machining stage, the system completes 80% of machining amount in a uniform cutting mode, stress data in the cutting process are obtained through a stress sensor, and a first machining compensation model is constructed and used for dynamically adjusting follow-up cutting parameters. And in the finish machining stage, the system ensures the stability of the cutting process by using the second machining compensation curve. According to the system, through the dynamic real-time compensation technology, the machining precision and efficiency are remarkably improved, cutting force fluctuation is reduced, and the system is particularly suitable for machining of materials such as aluminum shafts.

The invention relates to the technical field of intelligent paying-off, in particular to an intelligent high-precision paying-off power construction method, which comprises the following steps of: acquiring multi-source observation data and analyzing disturbance distribution, generating an ultralimit section identifier, calculating a three-dimensional coordinate and safety margin to generate a position optimization scheme, and monitoring tension and track deviation for adjustment. According to the method, the wind speed and the vehicle wake flow in different height intervals are collected, vector analysis is carried out, disturbance distribution can be rapidly constructed, the risk area is locked, dynamic calculation is carried out in combination with the space position and the safety boundary distance, the anchoring position is adjusted in time, interference is avoided, and the method has the advantages of being high in practicability and easy to popularize. Real-time tension difference value monitoring is used for achieving automatic adjustment of the inhaul cable, real-time traction force correction is executed in cooperation with three-dimensional track continuous comparison, deviation is positioned in sag curve analysis, targeted adjustment is carried out, and a wire keeps track stability and spatial form consistency under multi-source disturbance.

An information processing apparatus according to one embodiment comprises an acquisition unit for acquiring a first S-P table and a second S-P table that indicate respondents which are arranged in a prescribed order of scores and questions which are arranged in a prescribed order of the number of correct answers, a conversion processing unit for reordering the respondents in the second S-P table in accordance with the arrangement of the respondents in the first S-P table, drawing a first S curve and a first P curve of the first S-P table, and converting the first S curve and the first P curve according to the horizontal width of the second S-P table, an analysis processing unit for evaluating learning of the respondents and the questions in the second S-P tables on the basis of the converted first S curve and first P curve and the reordered second S-P table, and an output control unit for outputting the result of the evaluation.

The invention relates to the technical field of modular design, in particular to a parameter matching and debugging method and system for modular transformation of a large-scale wire barrow, and the method comprises the following steps: collecting operation data of the wire barrow, generating a real-time data set, calculating the offset of a part, analyzing a residual error, generating a comparison table, and calculating response time and inertia characteristics; the method comprises the following steps of: acquiring and synchronously storing data in real time, analyzing correction quantity and sequencing through an optimization algorithm, correcting parts and generating a response curve, analyzing errors and generating a feedback graph, calculating offset, and re-correcting if the error exceeds a threshold value. In the method, through real-time data acquisition and synchronous storage, monitoring the running state of the equipment, capturing the dynamic change of the parts, calculating the offset and the deviation, and evaluating the stability of the equipment; based on an offset value comparison table and a particle swarm optimization algorithm, components are corrected, the priority is reasonably sorted and adjusted, the correction time and cost are reduced, optimization is continued until an expected state is reached, the debugging precision and efficiency are improved, and long-term stable operation of equipment is ensured.

The invention discloses an electric reactor multi-field coupling vibration fatigue simulation method and system based on an S-N curve, and belongs to the technical field of electric power equipment fatigue simulation, and the method comprises the steps: obtaining structure parameters and material parameters of a target electric reactor, and building a parameterized physical model comprising an iron core, a winding and an insulation assembly; based on the parameterized physical model of the target reactor, simulating a time-varying mechanical force in the target reactor under the excitation of rated power frequency current; according to the time-varying mechanical force, calculating the vibration response of each part of the target reactor, and extracting the alternating stress distribution of the target reactor under the steady-state operation condition; and based on the material S-N curve of each component of the target reactor, analyzing the alternating stress distribution by adopting a linear cumulative damage theory, and calculating the failure cycle index and the fatigue life of each component under the preset cycle load. According to the method, through multi-field coupling simulation and S-N curve analysis, the fatigue life of the electric reactor is predicted, and a basis is provided for preventive maintenance.

The application belongs to the technical field of image processing, and particularly relates to a seedling root system disc identification method and system based on image processing, which comprises the following steps: collecting an original image and preprocessing, calculating an optimal segmentation threshold, extracting a root system region of interest, obtaining a root system skeleton through thinning processing, and calculating a support radius of each point of the skeleton and a discrete curvature of each skeleton point to obtain an average root system curvature by averaging; calculating a root system fractal dimension; constructing a seedling root system sample library, normalizing the average root system curvature and the fractal dimension, calculating two types of feature weight coefficients based on information entropy theory, and obtaining a disc root degree evaluation index through weighted summation; determining mild and severe disc root threshold values by combining sample library statistical characteristics and ROC curve analysis, mapping a classification judgment function to a grading signal, introducing a confidence degree checking mechanism to process boundary samples, and finally completing seedling automatic sorting and manual review triggering. The application improves the accuracy of seedling root system disc identification.

The invention relates to the technical field of intelligent security check, and discloses a video analysis and prediction system applied to intelligent scheduling management of a security check channel, and the system comprises a data processing module which obtains flight information from a flight information system of an airport, fuses real-time passenger flow data collected by video monitoring, and constructs a multi-dimensional data set; the multi-curve analysis module is used for generating a full-load curve, an actual attendance curve, a historical prediction curve and a crew curve based on the multi-dimensional data set; and the intelligent scheduling recommendation module is used for calculating the number of security check channels required at the current time point according to the real-time number of people obtained by superposing the full load curve, the actual attendance curve, the historical prediction curve and the crew curve in combination with a preset security check speed. By means of the scheme, minute-level data processing, multi-dimensional prediction analysis and intelligent scheduling can be achieved, so that the security check efficiency and the resource utilization rate are improved, and efficient and safe operation of air transportation is guaranteed.

The invention discloses a tight gas well unstable open-flow capacity evaluation method based on production dynamic data, and belongs to the technical field of gas reservoir engineering. The method comprises the following steps: firstly, collecting tight gas well production dynamic data and basic parameters and preprocessing; then, a deconvolution algorithm and a Duhamel principle are introduced, and variable-yield variable-pressure data are standardized into constant-pressure variable-yield data; then, in combination with a gas reservoir seepage differential equation considering natural gas compressibility, a'bottom hole flowing pressure-yield-time 'relation model is established; a yield-time function is reversely deduced, the bottom hole flowing pressure is made to be 0, and the unstable open-flow capacity is calculated; and finally, drawing a change curve, analyzing a dynamic rule and a decline mechanism, and providing reference for development countermeasures. The method realizes efficient utilization of production dynamic data, accurately captures an open-flow capacity decline rule, improves evaluation precision and timeliness, reduces cost, is suitable for unconventional gas reservoirs such as tight gas reservoirs, and provides support for efficient development of the gas reservoirs.

The present application belongs to the technical field of transformer operation loss monitoring, and in particular relates to a transformer operation loss condition optimization monitoring method. The present application calculates abnormality degree based on oil temperature and vibration characteristics, generates real-time working conditions in combination with load intensity, analyzes real-time operation loss abnormality index in association with cumulative operation duration, realizes dynamic association of loss and actual state of equipment, extracts fluctuation parameters through load characteristic change curve analysis, calculates comprehensive load fluctuation degree, corrects operation loss abnormality index to eliminate load fluctuation interference, finally determines dynamic reference threshold value according to historical loss curve, determines sustained over-limiting through double rules and triggers early warning, simultaneously accurately identifies causes such as load overload and internal heating abnormality, and outputs corresponding adjustment instructions, which improves loss monitoring accuracy and scene adaptability, realizes operation and maintenance closed loop of early warning, cause and disposal, and is suitable for transformer full life cycle loss monitoring.

The invention discloses an industrial edge rolling technology unknown fault early warning method and system based on unsupervised learning. The method comprises the steps that multi-dimensional time sequence track data in the operation process of an edge rolling robot is acquired; dividing the data into independent track segments; extracting a group of statistical features covering a centralized trend, a dispersion degree and a distribution pattern from each track segment to construct a high-dimensional feature vector; a sorting method based on feature importance is adopted, a key feature subset with the most discriminative power is screened out from all the features, and therefore optimal feature representation is constructed; training a deep auto-encoder model only by using sample features in a normal state, so that the deep auto-encoder model learns the accurate reconstruction capability of normal feature vectors; during state judgment, a reconstruction error between a new sample feature vector and a model reconstruction result is calculated to serve as an abnormal score; and a decision threshold is scientifically determined through receiver operation characteristic curve analysis and a Youden index maximization criterion, and the abnormal score is compared with the threshold, so that real-time and automatic judgment of the process state is realized.

The embodiment of the invention provides a space bounding box calculation method for a three-dimensional model. The method comprises the steps that continuous parameterized expression of the surface of a target three-dimensional model on a parameter domain is determined; according to the continuous parameterized expression, analyzing a plurality of profile curves obtained by projecting the target three-dimensional model on a plurality of mutually orthogonal coordinate planes in a three-dimensional coordinate system; according to the parameterized expressions of the plurality of profile curves, determining two-dimensional bounding boxes respectively corresponding to the plurality of profile curves on the corresponding coordinate planes; and performing extreme value selection operation on the boundary value of the two-dimensional bounding box in the coordinate axis direction to generate a three-dimensional space bounding box of the target three-dimensional model. A projection profile curve is directly analyzed through continuous parameterization expression, the geometric boundary of the model is locked in mathematics essence, the defect that extreme points are missed in discrete sampling is avoided, and the calculation precision is improved; and three-dimensional extremum search is converted into two-dimensional profile curve analysis and two-dimensional bounding box construction on a plurality of orthogonal coordinate planes, so that the calculation complexity is reduced.

This invention belongs to the fields of computer technology and on-demand delivery technology, and provides a method, device, and electronic device for analyzing rider learning curves based on causal inference. The method includes: sending a data request carrying rider identification to an on-demand delivery platform via a data interface, and receiving multiple completed order data for riders corresponding to the rider identification; constructing a theoretical rider learning curve model, wherein the theoretical rider learning curve model uses the total delivery time in the completed order data as the dependent variable, the cumulative number of orders as the independent variable, and at least one of seven factors—delivery distance, order price, restaurant waiting time, temperature, humidity, visibility, and wind speed—as control variables; solving for the model parameters of the theoretical rider learning curve model using multiple completed order data and storing them in a database in association with the rider identification; calculating and outputting the rider's learning rate based on the model parameters. The introduction of a learning curve effectively depicts the growth trajectory of the rider's learning process.

The application discloses a kind of urban low-altitude unmanned aerial vehicle logistics transport route evaluation method and system, it is related to electric digital data processing technical field.The urban low-altitude unmanned aerial vehicle logistics transport route evaluation method includes the following steps: initial double-target curve analysis;Dynamic environment correction evaluation;Hovering energy consumption evaluation.The interference degree of initial double-target curve is evaluated by the first environment data obtained, whether the first double-target curve is obtained is judged, if yes, then based on the second environment data obtained, dynamic environment correction evaluation is carried out, whether the second double-target curve is obtained is judged, if yes, then based on the third environment data obtained, hovering energy consumption evaluation is carried out, whether the third double-target curve is obtained is judged, reach the effect of improving the accuracy of urban low-altitude unmanned aerial vehicle logistics transport route evaluation, solve the problem that the balance between transport stability and dynamic risk of urban low-altitude unmanned aerial vehicle logistics on transport route is not fully considered in prior art.

The invention provides a new energy power system frequency stability characteristic analysis method and device, equipment and a storage medium. The invention relates to the technical field of high-proportion new energy power system frequency stabilization. According to the method, a response characteristic curve based on key frequency characteristic quantity and frequency can be drawn in a new frequency characteristic distribution scene, firstly, a set of inertia center frequency and related characteristic quantity of a system is constructed, and after time sequence characteristic quantity is extracted, a thermodynamic diagram is drawn through Pearson correlation analysis; a hierarchical clustering graph is drawn through clustering analysis, so that the hierarchical clustering graph and the hierarchical clustering graph jointly present characteristic quantities with high correlation degree with the frequency; drawing a response characteristic curve of the system center frequency and the characteristic quantity with high correlation degree to form a criterion, wherein the response characteristic curve converges when the system frequency is stable; and the curve is divergent during instability. In addition, the influence of different new energy proportions on the frequency characteristic can be analyzed through the response characteristic curve.

The invention belongs to the field of sensor detection, relates to a data analysis technology, is used for solving the problem that comprehensive analysis cannot be carried out by combining multiple curve parameters in a detection process in the prior art, and particularly relates to a current sensor reliability detection and analysis system based on data processing. Comprising a standard detection module, a parameter configuration module, a curve analysis module, a standard evaluation module and a reliability evaluation module. According to the invention, standardized detection is carried out on the current sensor, curve analysis is carried out on detection data in combination with a curve analysis module to obtain multiple curve parameters, and multiple basic performances of a detection object running under standard parameters are fed back through the curve parameters.

The invention discloses a user load curve analysis method and system based on Bayesian nonparametric clustering and a time covariance structure, and relates to the technical field of power system data analysis, and the method comprises the steps: collecting intelligent electric meter data, and carrying out the preprocessing of the original intelligent electric meter data; calculating a covariance matrix of the preprocessed data; calculating a mahalanobis distance between the load curves based on the covariance matrix; and constructing a distance dependent Chinese restaurant process model based on mahalanobis distance, performing link allocation and clustering parameter inference in combination with Gibbs sampling, and performing adaptive clustering to obtain a user load curve analysis result. According to the method, the problem of singularity of a covariance matrix under a high-dimensional small sample is solved by adopting Leidoit-Wolf shrinkage estimation, complete Bayesian inference is realized, the clustering number is adaptively determined, and manual intervention is not needed; while high clustering precision is maintained, calculation complexity is significantly reduced, and the method is suitable for large-scale actual data.

The application provides a power distribution network operation state analysis method and system based on graph data, comprising: constructing a power distribution network graph database by converting the obtained technical parameters, historical and real-time operation data, user historical power consumption data, historical distributed power generation data, corresponding weather data and weather forecast data of the power distribution network into graph data by using a graph database construction method; based on the power distribution network graph database, using a power distribution network parallel power flow analysis method, a power supply path analysis method, a user power consumption curve analysis method and a distributed power generation prediction data analysis method to analyze the operation state of the power distribution network, and obtaining the analysis result of the operation state of the power distribution network. The method provided by the application can comprehensively and accurately analyze the operation state of the power distribution network at each time period.

This invention relates to the field of photovoltaic power plant operation and maintenance, specifically to an intelligent operation and maintenance scheduling method and system for distributed photovoltaic power plants. An intelligent operation and maintenance scheduling system for distributed photovoltaic power plants includes: a task generation module, a task aggregation module, and a dynamic scheduling and execution module. This invention overcomes the technical challenges of inaccurate fault location and difficulty in monitoring latent degradation in distributed photovoltaic power plants by establishing a collaborative diagnostic mechanism that integrates string-level IV curve analysis and UAV image recognition. This method accurately correlates and cross-validates electrical characteristics and visual features in the spatiotemporal dimension, enabling precise identification of the smallest fault unit and clear distinction of fault types. Simultaneously, it effectively isolates environmental noise, achieving early warning of slow component performance degradation, thereby upgrading the operation and maintenance mode from overall extensive monitoring to component-level refined management.

The invention relates to the technical field of power distribution cabinet intelligent configuration, in particular to a power distribution cabinet intelligent configuration method and device based on power utilization curve analysis, and the method comprises the steps: collecting basic power data, environment data and equipment state data, and generating a historical power utilization curve and a real-time power utilization curve; screening out a plurality of historical power consumption curves highly related to the real-time power consumption curve through a dynamic time warping method; for the plurality of screened historical power consumption curves, obtaining a load average value according to corresponding moments, obtaining a reference prediction value of each moment, constructing a reference prediction curve, and matching a corresponding prediction model; and obtaining a deviation prediction value in a future prediction period through the matched prediction model according to the deviation value between the actual load data in the historical period and the prediction data of the corresponding reference prediction curve, obtaining a final load prediction value according to the deviation prediction value and the reference prediction value, and adjusting key parameters in the power distribution cabinet. The operation efficiency of the power distribution cabinet can be improved.

The invention provides an elution curve real-time analysis method and system for an At-211 purification process, and relates to the technical field of signal analys.The elution curve real-time analysis method comprises the steps that real-time sampling is conducted on continuous detection signals output by a chromatographic separation device, elution curve data are generated, and the radioactive half-life period characteristic and the allowable activity loss requirement of At-211 are combined, so that the elution curve data are obtained. Setting a time constraint of elution judgment and dividing a plurality of judgment time windows; a reference elution baseline is constructed and updated in the elution process, candidate elution state judgment, effective elution interval judgment and elution end judgment are completed in sequence by analyzing the difference change of an elution curve relative to the reference elution baseline, then the collection starting time and the collection end time of the eluent are determined, and segmented collection control over the At-211 eluent is achieved; according to the method, the elution state can be continuously judged within a controlled time range, and the method is suitable for the purification process of radionuclides.

The invention relates to the field of health behavior prediction and rehabilitation management, and discloses construction and application of a stroke patient home functional exercise compliance risk prediction model. The method comprises the following steps: collecting demographic and clinical data and scale evaluation data of a stroke patient, and carrying out missing value processing and preprocessing; identifying a compliance potential category by adopting potential profile analysis, and determining an optimal cutoff value of a total score of a scale through subject working characteristic curve analysis to form a dichotomy result; key predictive factors are screened in a training set by applying LASSO regression, and a'non-good compliance 'predictive model is established by incorporating the key predictive factors into multivariate Logistic regression; an online dynamic column graph webpage calculator is developed based on the Shiny technology, and a user is supported to input variables in real time and output a prediction probability and a confidence interval; and the discrimination degree, the calibration degree and the clinical net income are analyzed and evaluated through five-fold cross validation, Hosmer-Lemeshop test and decision curve analysis. According to the invention, rapid identification and hierarchical management of high-risk patients can be realized, and a basis is provided for follow-up visit and individualized intervention.

The invention relates to the technical field of equipment intelligent control, and discloses an intelligent control method and system based on cooling equipment, and the method comprises the steps: carrying out the real-time monitoring of the cooling equipment, collecting temperature time sequence data and current time sequence data, carrying out the phase-space reconstruction of the current time sequence data, obtaining an attractor track, and extracting a rheological state parameter; rotating speed disturbance is applied, a hysteresis loop curve is constructed, main shaft rotating direction characteristics are extracted, and a two-dimensional rheological phase plane is constructed; constructing a feasible region by using the hysteresis loop curve and the attractor trajectory, calculating an evolution vector direction of the current state point, and calculating a target stirring speed and a target cooling rate by taking the feasible region as a constraint condition; performing temperature return curve analysis based on the temperature time sequence data to generate a thermal field abnormal signal; and the cooling rate and the stirring speed are adjusted by combining thermal field abnormal signals. The Qian-flavor hotpot condiment online monitoring system realizes online monitoring of the Qian-flavor hotpot condiment, can ensure the product texture under normal conditions, and ensures food safety when abnormality is detected.

This invention relates to the field of curve analysis technology, and more particularly to a method, apparatus, device, and storage medium for generating smooth curves. The method involves obtaining first and second variable data from a preset constraint interval sequence; constructing a curve parametric equation using the first and second variable data according to a preset curve fitting method; solving for the first and second variable data using a preset curvature formula to obtain a curvature value sequence; solving for the curvature value sequence using a preset global optimization algorithm and the curve parametric equation to obtain a curve point sequence; and constructing the curve point sequence using a preset engineering drawing method to obtain a smooth curve. By using the first and second variable data and curve fitting, the curve parametric equation is obtained, quantifying the curvature values ​​to support smoothness assessment. By converting the curve point sequence into an industry standard curve, it is adapted to engineering software, improving efficiency and reducing costs.

The invention discloses a method for monitoring the average transmitting power of an optical module based on deep learning, and the method comprises the steps: 1, collecting the operation data and environment data of the optical module, and carrying out the timestamp marking of the collected data; 2, constructing a network model based on deep learning based on the data in the step 1, and training the network model; 3, generating and analyzing a model prediction curve based on the step 2; step 4, curve analysis and decision support; 5, carrying out model iteration and process improvement; and step 6, embedding the trained LSTM model into the optical module monitoring system. The reliability and the maintenance efficiency of the optical communication system can be improved.