56 results about "Linear relation" patented technology

The invention discloses a safety monitoring system of a liquid cooling over-charging pile, and relates to the technical field of over-charging pile monitoring, the system comprises a data acquisition module, a data processing and analysis module, a dynamic model construction module, a temperature prediction module and a safety early warning module; according to the method, the dynamic model is constructed through the long short-term memory network LSTM, the complex nonlinear relation and the time sequence dependence of the multi-dimensional data are mined by using the gating mechanism of the dynamic model, the accurate characterization of the operation state of the liquid cooling over-charging pile is realized, the actual operation state of the equipment can be accurately described, the temperature data time sequence modeling is performed through the LSTM, and the accuracy of the temperature data time sequence modeling is improved. Parameters such as multi-source temperature and cooling liquid flow are fused, real-time prediction of the temperature change trend is achieved, the defect that a traditional algorithm is insufficient in temperature time sequence dependence capture is overcome, temperature abnormity can be recognized in advance, a safety threshold value is dynamically adjusted through a fuzzy logic algorithm, and self-adaptive threshold value adjustment is achieved in combination with parameters such as charging power. The problem that a traditional fixed threshold value is poor in adaptability is solved, and the early warning accuracy is improved.

The invention relates to a sea fog quantitative forecasting method based on a period enhanced Transform, and the method comprises the steps: firstly constructing a Transform model as a forecasting model, enabling the Transform model and a self-attention mechanism to better capture the complex nonlinear relation and long-distance time dependence related to the formation of sea fog, and being superior to a conventional statistical method and an early-stage RNN / LSTM model, a data set for model training is constructed based on a time period enhancement technology and visibility standardization mapping, and the explicit time period enhancement technology enables the model to more accurately learn and forecast seasonal and daily change rules of sea fog; and a sample amplification and training set balancing strategy is executed on the data set, so that the problem of sparse fog samples is effectively solved, and the forecasting capability of the model on key low-visibility events is improved. According to the sea fog quantitative forecasting method based on the period enhanced Transform, the precision, timeliness (forecasting per hour) and spatial resolution of sea fog forecasting of a target area can be improved, and sea fog data characteristics can be effectively processed.

The invention provides a method for selecting a high-concentration VOCs adsorbent at different temperatures and application, and belongs to the technical field of waste gas governing. According to the method, through testing pore structure parameters of a model adsorption material with concentrated pore size distribution, a static adsorption isotherm and a dynamic penetration curve for VOCs at different temperatures, a high-concentration VOCs adsorbent is obtained; a linear relation equation of the critical aperture and the critical pressure is established through linear fitting, then a correlation equation of the critical aperture, the temperature and the partial pressure is established, and the appropriate aperture distribution range and the model selection standard of the high-concentration VOCs adsorbent at different temperatures are determined by combining dynamic adsorption results. According to the method, the ternary matching relation equation of the VOCs partial pressure, the temperature and the critical aperture is constructed, accurate type selection of the high-concentration VOCs adsorbent at different temperatures is achieved, the pore volume utilization rate and the adsorption capacity can be improved, meanwhile, desorption energy consumption and the VOCs pyrolysis risk are reduced, and a theoretical basis is provided for adsorbent type selection and technical optimization.

The invention discloses a bridge longitudinal and transverse displacement reconstruction method and system based on time sequence feature extraction, and relates to the technical field of bridge health monitoring. The method comprises the following steps: acquiring structural state data and environmental data of a continuous beam bridge and a cable-stayed bridge; analyzing a linear relation between the structure state data and the environment data by using a correlation coefficient analysis method; processing the related characteristics by using the bridge displacement reconstruction model to obtain three-dimensional displacement data of bridge reconstruction; and based on the obtained three-dimensional displacement data of the bridge reconstruction, screening key factors in related characteristics through a grid search algorithm, and optimizing the input of a bridge displacement reconstruction model. According to the method, the time sequence characteristics of the data are fully monitored to construct the De-SpcNet-based bridge displacement reconstruction model, so that the reconstruction precision of longitudinal and transverse displacement is improved. And then, model output is optimized through refined model parameter setting and a post-processing technology, so that the accuracy and stability of a reconstruction result are further improved.

The invention discloses a seismic oscillation parameter selection and high arch dam response prediction method based on random forest-recursive feature elimination. The method comprises the following steps: (1) constructing a high arch dam finite element model; (2) seismic oscillation input of a finite element is obtained, and nonlinear seismic response of the arch dam under the seismic oscillation input is obtained through nonlinear dynamic time-history analysis; (3) taking the output parameters as a data set to obtain an optimal feature set of the high arch dam; and (4) on the basis of the obtained optimal IM feature set of the high arch dam and the corresponding random forest model, in combination with seismic oscillation input data, predicting a response result of the high arch dam through sampling. According to the seismic oscillation parameter selection and high arch dam response prediction method based on random forest-recursive feature elimination, the complex nonlinear relation between the features can be fully captured, the interaction between the features can be better considered, the complexity of the model can be better controlled, too many or too few features are prevented from being selected, the over-fitting risk is reduced, and the method is suitable for large-scale popularization and application. The performance and the effect of feature selection are improved.

The invention relates to a UHPC-NC interface mechanical property prediction method and system considering a size effect, and the method comprises the steps: preparing a target test piece, collecting original data through a direct shear test and a pull-out test, and obtaining a shear stress-displacement curve and a tensile stress-displacement curve according to the original data; extracting shear performance parameters and a linear relation according to the curve, carrying out fitting analysis on the shear performance parameters to obtain a shear size effect law, further defining a shear macroscopic constitutive model, and carrying out fracture mechanical quantitative evaluation on the shear performance parameters to obtain a shear macroscopic constitutive model. Deriving and constructing a prediction expression of shear resistance and a shear-displacement constitutive model according to a quantitative evaluation result; extracting tensile property parameters according to the curve, performing series analysis on the tensile property parameters to obtain an interface tensile relationship, performing probability distribution analysis on the tensile property parameters to obtain a tensile size effect law, and performing derivation according to the interface tensile relationship and the tensile size effect law to obtain a tensile property prediction expression and a tensile-fracture constitutive model.

The invention discloses an object tiny deformation measurement method and system based on interference fringe analysis, and belongs to the technical field of optical measurement. The method comprises the following steps: acquiring an interference fringe image and probe position and direction data; carrying out phase demodulation and sensitivity vector calculation on the surface of a measured object; obtaining the local phase number of each probe according to the interference image; calculating sensitivity vector data through a geometrical relationship; obtaining global phase and sensitivity data, obtaining initial full-field phase data through a weighted fusion method based on a geometrical relationship between a sensitivity vector and a deformation direction, calculating a consistency error, judging whether the consistency error exceeds a threshold value or not, if so, updating a deformation direction estimation value, and executing fusion processing again; and solving and obtaining three-dimensional deformation field data through a linear relation until the error meets the requirement or reaches the maximum number of iterations. According to the invention, the reliability and early warning capability of structural safety assessment in long-term monitoring can be improved.

The invention provides a double-layer safe trainable quantum machine learning method based on polynomial dynamics Lie algebra, and the method comprises the following steps: 1, constructing core variation simulation meeting the constraint of the polynomial dynamics Lie algebra, and guaranteeing the trainability of a model; 2, performing truncated Chebyshev graph coding at an input end, and constructing a rugged loss function landscape by using graph state entanglement and a Chebyshev tower strategy to prevent a snapshot inversion attack; 3, executing dynamic local scrambling at an output end, applying time-varying random local unitary transformation before measurement, and confusing a linear relation between gradient and a snapshot to prevent recovery attack of the snapshot; and 4, measuring and calculating a loss function, and updating parameters. According to the method, an orthogonal decoupling strategy is adopted, a privacy protection mechanism is externally arranged on an input / output interface, and trainability is anchored to core configuration, so that the capability of resisting algebraic attacks is remarkably improved while model convergence is ensured.

The invention discloses a combined system error correction and target state estimation method based on multi-angle measurement, which is applied to a three-dimensional passive multi-sensor multi-target tracking system. Comprising the following steps: constructing an augmented state vector based on a target state vector, an angle measurement deviation vector, a time deviation vector and a sensor position vector, and constructing an augmented state transition equation; performing modeling by taking azimuth angle measurement, pitch angle measurement and sensor position measurement as functions of an augmented state vector, constructing an augmented measurement vector and an augmented measurement equation, and determining a nonlinear relationship between the constructed augmented measurement vector and augmented state vector; azimuth angle measurement, pitch angle measurement and sensor position measurement are obtained through actual measurement, and an augmented state vector is estimated through linearization of a non-linear relation between a constructed augmented measurement vector and an augmented state vector and sequential filtering. The method can be applied to multi-source information fusion under a passive multi-sensor system, and the tracking and positioning precision of the target is improved.

Systems and methods for quantum linear prediction include autocorrelations formed with QFTs, and a modified quantum HHL circuit that includes appropriate normalization and encoding steps for solving a linear system of equations, including normalization of the quantum autocorrelation sequence using a norm factor; measuring a probabilistic distribution associated with values of a quantum state solution vector representing a set of quantum autoregressive parameters that correlate with a linear relationship between the quantum autocorrelation matrix and the quantum autocorrelation sequence; and generating a set of quantum linear prediction coefficients by re-normalization of the quantum state solution vector using the norm factor associated with the quantum autocorrelation sequence of the preprocessed input.

The invention discloses a carbon emission intensity prediction simulation and land utilization management and control method and system, and belongs to the technical field of carbon emission calculation prediction.The method comprises the steps that firstly, multi-source space and statistical data are integrated to establish a basic database, and the carbon emission intensity of all kinds of space is calculated according to land space classification; then, constructing an extended STIRPAT model to screen a core driving factor, and constructing a feature project containing residual, lag and trend features based on the output of the extended STIRPAT model; training an XGBoost machine learning model by adopting a Bayesian optimization algorithm so as to capture a complex nonlinear relationship between the carbon emission intensity and the influence factors; and finally, inputting future scene feature data, generating a territorial space carbon emission intensity distribution map in combination with a PLUS model, and identifying a high-emission hot spot region. According to the method, the problem that the technical progress influence is difficult to quantify and the spatial explicit prediction is difficult to carry out in the prior art is solved, the prediction precision is remarkably improved, and a scientific basis and decision support are provided for regional territorial space planning and low-carbon management and control.

The invention discloses a method and a system for calculating the mixing proportion of multi-end-member water mass mixed particles. The method comprises the following steps: calculating the contribution proportion of each water mass end member of a mixing area by adopting an isotope mixing model; generating initialization information of a multi-end-member water mass mixed particle model by using the delta < 18 > O and salinity of each end member and derivative information; based on the contribution proportion and the initialization information, constructing a multi-end-member water mass mixed particle model; simulating a simulated delta < 18 > O-salinity linear relation of a mixing area under all iterative mixing proportion combinations based on a multi-end-member water mass mixed particle model; and calculating the mixing ratio of each end member by comparing the simulated delta 18O-salinity linear relationship with the actual delta 18O-salinity linear relationship. According to the method, the multi-end-member water mass mixed particle model is constructed, and iterative calculation and fluctuation simulation are combined, so that accurate quantification of a water mass mixing ratio and quantitative analysis of mixing sufficiency are realized.

The invention provides an air source heat pump frosting rate prediction method and system, and belongs to the technical field of air source heat pump defrosting control. The method comprises the steps that initial parameters needed by prediction are obtained, wherein the initial parameters comprise the outdoor air temperature and relative humidity, the outdoor heat exchanger coil pipe surface temperature and relative humidity and the outdoor heat exchanger air inlet speed; determining air state parameters at the outlet of the outdoor heat exchanger through iterative calculation on the basis of the initial parameters in combination with a linear relation of humid air state change in the enthalpy-humidity diagram; and finally, the real-time frosting rate is calculated according to the air moisture content difference, the air mass flow and the heat exchange area of the inlet and the outlet of the outdoor heat exchanger. The system comprises a corresponding parameter acquisition and calculation module. According to the method and system for predicting the frosting rate of the air source heat pump, accurate dynamic prediction of the frosting rate is achieved through theoretical modeling and iterative optimization, a reliable basis can be provided for intelligent defrosting control, and therefore the defrosting accuracy rate and the overall energy efficiency of the system are improved.

The application provides a significant wave height extraction method based on a synthetic aperture radar image, step 1: data acquisition, matching the altimeter significant wave height with the synthetic aperture radar interferometric imaging mode data to obtain a sample data set. Step 2: the data amount of the altimeter significant wave height in different incidence angle ranges is counted to obtain the numerical distribution of the altimeter significant wave height in the global near-shore range. Step 3: the normalized variance of the backscattering coefficient of the synthetic aperture radar and the altimeter significant wave height are subjected to quality control to obtain the significant wave height and normalized variance data. Step 4: a linear relationship between the significant wave height and the normalized variance is established. Step 5: the estimated value of the synthetic aperture radar significant wave height is evaluated and verified, and the significant wave height can be obtained by inputting only a single image parameter, the normalized variance. The application not only considers the synthetic aperture radar open ocean wave mode, but also relates to the near-shore research, and provides technical support for the development of related topics in the coastal area.

The invention relates to the technical field of settlement monitoring, and discloses a building accurate settlement monitoring and early warning system, which is characterized in that a measuring point module is provided with a near-side representative pile, a far-side representative pile, a pressure measuring pipe and a displacement acquisition device to respectively acquire axial force, underground water level and base displacement data; a parameter calculation module calculates a bearing share asymmetry index, an underground water level equivalent drawdown and an effective stress substitution amount, and eliminates a time trend term to obtain a base displacement residual error; the period and path analysis module divides closed periods according to the periodical characteristics of the equivalent depth reduction of the underground water level, forms parameter paths in all the periods, calculates directed areas and determines net drift amount; the mapping and prediction module establishes a linear relation between a directed area and a net drift amount based on the multi-cycle data to obtain an area drift mapping amount; the judgment module checks whether the area drift mapping amount is zero or not under a predetermined significance level, if yes, output is abnormal, and if not, output is normal.

The invention discloses a model quantification method, a model operation method, equipment, a medium and a program product. The quantification method comprises the following steps: grouping model parameters of each network layer in a to-be-quantified model; determining a quantization parameter group of any group according to a target integer digit required by the network layer to which any group belongs, and quantizing the model parameter of any group according to the quantization parameter group; and replacing the corresponding model parameters in the to-be-quantized model with the quantized model parameters of each group to obtain a quantized model. Model parameters are grouped to be quantized, so that the influence of outliers existing in the model parameters on quantization errors is reduced. When quantization is carried out according to the groups, due to the fact that the quantization parameters in the quantization parameter groups are in a non-linear relation, quantization can be completed on the model parameters in the groups in a non-linear mapping mode according to the quantization parameter groups, and the result generation capacity during model operation is ensured.

The invention discloses a dam deformation space-time prediction method and device based on Kriging interpolation, and relates to the technical field of dam safety monitoring, and the method comprises the steps: carrying out the cleaning and unified processing of dam multi-source heterogeneous deformation monitoring data, including unifying a coordinate reference and carrying out the data fusion through a linear relation, forming a data set of a unified time vector; then, fitting data of each monitoring point and predicting future moments by using regression models such as Fourier series and the like; and after the consistency of the prediction result is checked, the kriging interpolation algorithm is adopted to carry out spatial interpolation on the predicted data passing the check, the space-time continuous deformation field of the dam is finally generated, and when newly added data exist, the process is repeated to update the model, the problems of space-time discretization and difficult fusion of the deformation data in the prior art are effectively solved, and the prediction accuracy of the dam is improved. High-precision and time-space continuous dam deformation prediction can be realized, and a reliable basis is provided for dam safety analysis.

The application discloses a bridge abnormal point identification method and device based on a multi-granularity time window, and the method comprises the following steps: acquiring a plurality of sample data of a bridge by using a real-time monitoring system; constructing a sample initial feature vector representation of a sliding time window, and fusing the sample initial feature vector representation to construct a sample initial feature matrix representation; fitting a linear relationship and a nonlinear relationship of sample neighborhood features of the sliding time window; based on the linear relationship and the nonlinear relationship, fusing linear residual and nonlinear residual block features to identify an abnormal probability value of a sample, and calculating the abnormal probability value of the sample to label an abnormal sample; and identifying abnormal values in the plurality of sample data by using the labeled abnormal sample to identify a bridge abnormal point. The application predicts an abnormal probability of a sample from potential neighborhood features of the sample in a multi-granularity sliding time window, realizes identification of abnormal data points, and provides great support in bridge structure health monitoring data anomaly diagnosis.

The present invention discloses a method for determining the critical desorption pressure of a coalbed methane reservoir based on mid- to late-stage production data, comprising: assuming that the water saturation of the reservoir is stable and the desorption zone expands at a uniform rate in the mid- to late-stage coalbed methane development, linearizing the mid- to late-stage gas production equation, analyzing the linear relationship between #imgabs0# and the number of production days t using a graphical method, and obtaining a gas production data analysis equation; linearizing the mid- to late-stage water production equation, analyzing the linear relationship between #imgabs1# and the number of production days t using a graphical method, and obtaining a water production data analysis equation; and jointly solving the gas and water production data analysis equations, iteratively solving the critical desorption pressure of the coalbed methane reservoir using a bisection method. The present invention inverts the critical desorption pressure based on mid- to late-stage coalbed methane production data, providing a simple and highly adaptable production data analysis method that facilitates the formulation of coalbed methane development plans and capacity forecasting.

The invention discloses a channel flow heat transfer prediction method and device based on machine learning and a medium, and relates to the technical field of parameter prediction.Supplementary data is generated based on an existing flow heat transfer relational expression, the working condition of rare experimental data is effectively covered, and key dimensionless parameters influencing flow resistance and heat transfer coefficients are determined; therefore, the model can focus on the most critical factors influencing the flow heat transfer relation, the ability of the model to grasp the nature of the flow heat transfer phenomenon is improved, and the flow heat transfer coefficient and the frictional resistance coefficient can be predicted more accurately. A neural network model is constructed to effectively capture a complex nonlinear relationship between an input parameter and a flow heat transfer coefficient or a frictional resistance coefficient, a sparse regression method is adopted to fit experimental data, and relational prediction data and a large amount of prediction data generated by the neural network model are explicit mathematical expressions. The problems that an existing relational expression is narrow in prediction range and poor in universality can be solved, and the prediction precision of the channel flow heat transfer relation is effectively improved.

Disclosed are a video encoding method and apparatus using local illumination compensation in affine mode-based prediction. In the disclosed embodiment, a video decoding device generates a motion vector of a sub-block divided from a current block by using a shape of an affine model and a control point motion vector. A video decoding device generates a reference block of a current block by deriving a sub-reference block of a sub-block using a motion vector. When peripheral reference pixels of the current block are available, the video decoding device calculates, for an upper sub-reference block and a left sub-reference block of the reference block, positions of peripheral reference pixels of each of the upper sub-reference block and the left sub-reference block. When the peripheral reference pixels of the reference block are available at the calculated position, the video decoding device derives a parameter of a linear relationship between the peripheral reference pixels of the current block and the peripheral reference pixels of the reference block, and generates a prediction block of the current block by applying local illumination compensation to the reference block using the parameter of the linear relationship.

An existing communication optical fiber positioning method based on earthquake arrival time double difference comprises the steps that firstly, a DAS system is arranged at one end of an existing communication optical fiber and communicated, secondly, a plurality of monitoring points are evenly arranged in the length direction of the optical fiber, at least two earthquake sources are selected on the periphery, and the time when earthquake waves reach the monitoring points is collected through the DAS system, and thirdly, the time when the earthquake waves reach the monitoring points is collected through the DAS system. The method comprises the following steps: step 1, taking two adjacent monitoring points as a group of monitoring point pairs, calculating travel time difference from the same seismic source to the group of monitoring point pairs, then calculating to obtain double-difference travel time of different seismic sources, and covering all the monitoring point pairs, step 4, based on the double-difference travel time and a linear relation of relative positions of the adjacent monitoring points, establishing linear relation expressions of each group, summarizing to form an overdetermined linear equation set, and 5, solving the equation set to obtain the relative space relation of adjacent monitoring points, combining the absolute coordinates of one end of the optical fiber, and deriving the absolute coordinates of all the monitoring points through vector superposition to complete positioning. Therefore, the positioning speed is high, and the precision can be guaranteed.

A probability modeling-oriented high-parallel autoregressive scanning and masked convolution design method is provided. The method includes establishing a mathematical relationship between a number of scanning and an image resolution when the number of scanning and the image resolution exhibit a linear relationship; and constructing a specific scanning angle to meet a limit of a given number of scanning. The method also includes constructing a mask mode of masked convolution under the specific scanning angle. Compared with wavefront scanning, the number of scanning of the scanning mode according to the method is not related to the size of the convolution kernel, but related to the mask mode, so that the larger convolution kernel can be used to enhance the probability estimation performance of the model under the condition that the number of scanning is unchanged.

This invention provides an automatic measurement method and system for the elongation of stainless steel laser welds. The automatic measurement method for stainless steel laser weld elongation includes: sample preparation and image acquisition; performing DIC analysis on the acquired image sequence to obtain a full-field strain cloud map, and obtaining the full-field displacement field and longitudinal strain field of the sample during the tensile process based on the full-field strain cloud map; weld region identification and width measurement to obtain the weld width; extracting strain from multiple virtual gauge lengths to form multiple sets of data; establishing a linear relationship between the overall elongation and the proportion of weld width; performing linear fitting on the multiple sets of data based on the linear relationship to obtain a coefficient proportional to the weld elongation; and obtaining the weld elongation based on the proportional coefficient. By preparing micron-level random speckle patterns and using a sub-pixel DIC algorithm to perform high-precision full-field strain analysis on the weld region, the method achieves high-efficiency, high-precision, and automated measurement of millimeter-level ultra-narrow weld elongation.

The invention provides a three-dimensional digital core permeability calculation method based on curve fitting and electronic equipment, and belongs to the technical field of reservoir three-dimensional digital cores, and the method comprises the following steps: obtaining a three-dimensional digital core grayscale image of reservoir rock; carrying out threshold segmentation processing on the grayscale image, and segmenting the whole digital rock core into a plurality of segmentation areas; calculating the permeability of each segmented region in parallel by adopting a finite volume method; the permeability of the total segmented area is calculated, and a curvilinear relation graph is constructed according to the linear relation between the permeability of the total segmented area and different numbers of segmented areas; reversely obtaining the permeability of the whole digital rock core by adopting a curve fitting method; according to the scheme, the calculation cost of the rock core permeability can be reduced, the calculation efficiency of the rock core permeability can be improved, and high usability and practicability are achieved.

This invention discloses an accurate zero-knowledge proof method for linear relations with hidden products, which transforms linear relations with hidden products on incomplete splitting rings into linear relations on the same surface; transforms the accompanying additional conditions into component-wise product relations on the same surface; and transforms the secret evidence on the same surface into element x on the same surface using an inverse NTT transformation. i The process involves selecting a uniformly random matrix and several vectors; generating a first-stage commitment and sampling vectors from a discrete Gaussian distribution to determine the public vector; selecting first challenge information; generating new commitments, blinding elements, and redundant terms using the first challenge information; randomly sampling second challenge information c from the challenge space C; determining the vector used for blinding using c; verifying whether the vector satisfies preset conditions and checking whether a series of equations hold true. If all equations hold true, the proof is accepted; otherwise, it is rejected.

This invention provides a method and system for managing carbon reduction and efficiency improvement through biomass co-firing in power generation enterprises. The method simultaneously considers combustion efficiency and emission reduction effects, overcoming the limitations of single-objective optimization in existing technologies and achieving a win-win situation for energy utilization efficiency and environmental benefits. A dual-objective prediction model is constructed through machine learning, capable of fitting the complex nonlinear relationships in the biomass co-firing process, with prediction errors far superior to traditional empirical and linear models. A quantitative algorithm combining dynamic emission coefficients and actual operating data enables rapid assessment of emission reductions, facilitating dual control of carbon emissions. A closed-loop system of "data-model-assessment-control-quantification" is constructed, enabling real-time optimization of co-firing parameters and automatic calculation of emission reductions without manual intervention, significantly improving response speed and reducing labor costs. Model parameters and thresholds are adjusted according to the unit type, biomass type, and environmental requirements of different power generation enterprises, making it applicable to various biomass co-firing power generation scenarios.

The invention discloses an electric power system saddle node bifurcation point solving method based on a hybrid estimation-correction and secant method, and belongs to the technical field of electric power system stability analysis. The method comprises the following steps: establishing an extended power flow equation set; obtaining an effective initial value through a pre-estimation-correction method; dynamically processing a network equation for a line fault scene; judging a bifurcation point interval based on the linear relation and the sensitivity; and combining a secant method with self-adaptive step length control to realize accurate positioning of the bifurcation point. According to the method, the defect that the numerical value is unstable near the bifurcation point in the traditional method is overcome, the calculation efficiency and robustness are improved, the method is suitable for a complex power system containing faults and new energy, and effective support is provided for stability analysis.

The application provides a method for prolonging the service life of a biodegradable mulching material, and belongs to the technical field of biodegradable mulching films.The application establishes a relationship curve between environmental parameters and degradation rate, determines the optimal ratio of PBAT and PLA and the critical temperature of phase separation based on the molecular chain segment movement theory of the carrier material, optimizes the uniformity of carbon black dispersion by using the interface interaction mechanism of surface modified carbon black and dispersant to enhance the anti-degradation ability, constructs a biodegradation control optimization model to realize accurate prolongation of the degradation life, determines four adjustment intervals and a nonlinear relationship model of adjustment coefficients by the experimental establishment of the degradation rate demarcation point, calculates the optimal formula, and uses a double rotor internal mixer and a double screw extruder to prepare the material, and simultaneously dynamically adjusts the model neighbor sampling number parameter through the degradation life adjustment function, so that the technical problem of insufficient service life of the biodegradable mulching material is solved.

The invention discloses a belt type dynamic cotton conveying weighing system and method. The belt type dynamic cotton conveying weighing system comprises a belt type conveying module, a measuring module, a data processing module, a display module, a parameter configuration module and a data storage module. The filtering algorithm unit adopts a Kalman filtering algorithm based on gain adjustment, and for peak noise, the real change of flow can be quickly tracked and noise interference can be effectively inhibited in the filtering process by monitoring the variance of residual errors on line and automatically adjusting gain factors; the dynamic calibration unit adopts a dynamic calibration mechanism, establishes a linear relation between the output of the weighing sensor and the actual weight under different working conditions, stores a calibration coefficient and zero offset and calls the calibration coefficient and the zero offset in real time, so that nonlinear errors and zero offset are counteracted, and the weighing precision is improved; the parameter configuration module adopts a carrier roller configuration dynamic adjustment mechanism, and dynamically adjusts the number and spacing of weighing carrier rollers and the conveying speed of a conveying belt according to different working conditions, so as to ensure that the system can keep the optimal weighing performance under different working conditions.