102 results about "Exponential smoothing" patented technology

The invention discloses a backup task scheduling method based on load prediction and an improved SAC algorithm, and relates to the technical field of data backup and intelligent scheduling. Aiming at the problems of response lagging, insufficient key task guarantee and low strategy exploration efficiency of a traditional scheduling method under a dynamic load, a task weight monitoring matrix is constructed, server performance, a network state and data importance are fused, and a task risk coefficient is calculated in combination with an exponential smoothing algorithm; carrying out sequence decomposition on a service load by adopting an Autoformer model, identifying a periodic mode through a weighted autocorrelation mechanism, and predicting a future load change trend; on this basis, an improved SAC scheduling framework is designed, a task weight guidance and temperature coefficient adaptive adjustment mechanism is introduced, and the task starting opportunity and execution duration are optimized in combination with an elastic time window. The invention provides an intelligent scheduling method with strong adaptive capability, which can autonomously optimize the task scheduling strategy according to the dynamic change of the system and effectively reduce the peak load of the server and the task backlog risk.

The invention relates to the technical field of data communication, and discloses an intelligent agent high-speed bus implementation method and system based on an FPGA and a dynamic smoothing technology. The method comprises the following steps: constructing a single handshake message through a lightweight protocol, adjusting network indexes by adopting an exponential smoothing method, constructing a pipeline processing unit in an FPGA card by utilizing a VHDL, realizing key updating and encryption processing, constructing a three-layer adaptive structure connection subsystem, and optimizing algorithm parameters and resource allocation according to a running state. And a high-performance bus communication system is formed. On the premise of ensuring the communication security and reliability, the network transmission delay is remarkably reduced, and the data throughput is improved, so that the communication system can meet the requirements of emergency linkage and large data volume transmission in a scene (such as a rail transit station-level system) with a high real-time requirement.

The invention relates to the technical field of industrial automation control, and discloses a chemical production-oriented multi-stage cooperative intelligent control and equipment operation and maintenance data management platform, which comprises a multi-source data acquisition module for acquiring multi-source heterogeneous data and performing time alignment processing and quality verification; the working condition identification and feature extraction module is used for obtaining equipment operation state data by adopting weighted fusion and principal component analysis; the equipment health assessment module is used for calculating an equipment health degree score and predicting a degradation trend through exponential smoothing; the self-adaptive control module is used for designing a multivariable coordination controller and dynamically generating operation constraints of the multivariable coordination controller according to the equipment health degree score; the layered optimization control module is used for establishing an interlayer feedback mechanism; the collaborative decision-making module generates a collaborative decision-making trigger signal according to the equipment health degree information and generates a collaborative decision-making scheme; according to the invention, by establishing a collaborative decision-making mechanism, a decision-making scheme considering the production task completion degree, the equipment health degree and the maintenance cost is generated.

The invention belongs to the technical field of data processing, and particularly relates to a power supply load prediction method and system for a new energy vehicle, and the method comprises the steps: obtaining real-time operation parameters and a historical power supply load data set of the new energy vehicle; for any moment, determining the load fluctuation sensitivity according to the offset of the power supply load relative to the historical power supply load in the same period and the difference of the power supply load relative to the previous moment; determining the load demand potential by combining the difference between the residual electric quantity of the on-line controlled vehicle and the environment temperature; and determining a prediction weight based on the two indexes, extracting a long or short period data set in combination with a magnitude relationship between the prediction weight and a preset weight threshold, and realizing prediction by using an exponential smoothing method. According to the method, the problems of response lag and insufficient precision of a traditional prediction algorithm when a charging scene is highly dynamic are effectively solved, and high-precision real-time tracking of unsteady state load characteristics is realized.

The invention discloses a maximum correlation entropy adaptive dynamic estimation method containing a distributed photovoltaic power distribution network, and belongs to the technical field of power systems. The invention discloses a maximum correlation entropy adaptive dynamic estimation method for a distributed photovoltaic power distribution network, and the method comprises the steps: building a two-stage three-phase distributed photovoltaic physical model containing a feedback control link, and forming a photovoltaic system state equation containing a differential link; establishing a power distribution network state equation based on a Holt's two-parameter exponential smoothing method; the kernel width parameter of the maximum correlation entropy is adjusted in real time according to the measurement error statistical characteristics; a maximum correlation entropy criterion is adopted as a target function, and a robust Kalman gain updating formula is obtained in combination with a Gaussian kernel function and an adaptive weight matrix; and integrating the robust Kalman gain updating formula into the integral framework of the volume Kalman filtering to complete the dynamic state estimation of the power distribution network. By adopting the maximum correlation entropy adaptive dynamic estimation method containing the distributed photovoltaic power distribution network, the problem that an existing dynamic state estimation method is insufficient in estimation precision in the distributed photovoltaic and non-Gaussian noise power distribution network can be solved.

The invention provides an optical access network PON port fault prediction and operation and maintenance support method and system, and the method comprises the steps: collecting real-time multi-index sequences of optical power, bit error rate, temperature, frame loss rate, business load and the like, automatically extracting a long-term trend based on variational mode decomposition, constructing a residual sequence, generating a state transition map through employing a Markov transition field algorithm, and carrying out the fault prediction and operation and maintenance support of the PON port. And future operation state distribution prediction is realized through the LSTM neural network. And after an abnormal threshold is dynamically adjusted in combination with quantile regression, a time sequence prediction result and a threshold response are fused to output a comprehensive fault probability score, and an early warning signal is triggered through exponential smoothing and continuity check, so that the progressive fault detection accuracy and advancement can be improved, and an efficient and steady prediction and early warning basis is provided for intelligent operation and maintenance of the PON port.

The invention provides a multi-branch sequence recommendation method based on dynamic channel fusion. The method aims at solving the problems that in sequence recommendation, data have a large amount of noise and are sparse, and existing model prediction is smooth. The method comprises the following steps: on one hand, enabling a user sequence to pass through an embedding layer, introducing position information by utilizing RoPE rotation position coding, and extracting long-term dependency of a user in the sequence through a multi-feature channel feature network to obtain a new embedded Eglobal, a short-term interest and long-term dependency interactive embedded Ecross, an embedded Eema after exponential smoothing filtering and an embedded Efreq after frequency domain modulation; and on the other hand, in order to fully fuse the characteristics of different channels, a traditional hyper-parameter fusion mode is abandoned, and a pooling network layered adaptive fusion mode is adopted. Firstly, four channel features are divided into two groups including a self-attention group and a trend group, features of different channels are learned through Squeeze average pooling, learned parameters are activated through Excitation by adopting sigmoid to obtain channel weights, and feature representations of different groups are sent into a gating network for weighted summation loss calculation.

The invention relates to an asynchronous federated learning method and system, and the method comprises the steps: firstly, obtaining the prediction training time of a client through employing an exponential smoothing method in combination with anomaly detection and mutation detection according to the federated learning reality conditions of data isomerism, system isomerism and the like; secondly, utilizing a dynamic threshold segmentation algorithm and Monte Carlo simulation to obtain a two-stage waiting time threshold; and finally, the server selects a local model uploaded by the client to perform parameter aggregation in combination with the training time predicted by the client and a two-stage waiting time threshold, and finally forms a self-adaptive federated learning resource scheduling strategy. According to the method, client node resources can be used to the maximum extent at low cost, the global model training efficiency and precision are improved, and the multi-scene adaptability of federal learning is effectively enhanced. Experimental results show that compared with a classical method, the method has higher accuracy.

The invention belongs to the technical field of audio recognition, and particularly relates to a string music tremolo automatic detection method which comprises the following steps: S1, preprocessing and framing an input audio, and extracting time sequence features; s2, obtaining an emotion intensity score through an emotion intensity evaluation function; s3, mapping the emotion intensity score into an expected tremor speed, and obtaining a stable tremor speed through exponential smoothing and amplitude limiting; s4, generating a dynamic threshold value changing along with time; and S5, the main tremolo detector calculates the criterion in parallel, compares the criterion with a dynamic threshold value on a synchronization time axis to determine frame-level tremolo, connects adjacent frames to obtain a tremolo segment, and drives threshold value self-adaption by emotional intensity, so that the detection criterion is synchronized with a playing situation, wrong division and over-division are reduced, and the boundary consistency and the stability and accuracy of tremolo estimation are improved.

The invention discloses an anti-fogging endoscope lens temperature control method and system, and relates to the technical field of temperature control, and the method comprises the steps: arranging a temperature and humidity sensor on an endoscope, collecting temperature and humidity data, carrying out the noise suppression, and calculating the temperature and humidity of an endoscope lens according to the temperature and humidity data; performing stable clamping on the humidity, calculating a normal pressure correction coefficient, calculating saturation water vapor pressure of an endoscope lens so as to obtain actual water vapor pressure, performing inverse solution through the normal pressure correction coefficient so as to obtain an instantaneous dew point, and smoothly outputting a final dew point by using a first-order index; calculating an instant dew point margin according to the lens temperature and the final dew point, calculating a negative margin correction item, predicting a future margin, calculating a logistic risk index, and finally comprehensively obtaining a lens adaptive margin; and calculating the total error of PID control, outputting a control command by adopting a PID control algorithm, and executing the control command. According to the invention, the fogging probability and the definition fluctuation are obviously reduced, and the temperature overshoot and fluctuation are reduced.

The present application relates to the technical field of data processing, in particular to a multi-source data integration management method and system for pollution source monitoring, which comprises the following steps: determining an abnormal mutation point in the electrical data sequence of a desulfurization wastewater purification device in a historical time period; obtaining the power supply abnormality significance feature of the desulfurization wastewater purification device according to the difference between the electrical data before and after the abnormal mutation point; determining the pollutant concentration growth trend after the starting point of the rising trend of the pollutant concentration in the current time period; adjusting the smoothing coefficient in the cubic exponential smoothing algorithm according to the power supply abnormality significance feature, the pollutant concentration growth trend and the change correlation degree of the electrical data and the pollutant concentration, so as to improve the accuracy of the smoothing coefficient; and predicting the pollutant concentration by using the cubic exponential smoothing algorithm according to the smoothing coefficient, so as to improve the accuracy of the pollutant concentration prediction and realize reliable and accurate management of industrial desulfurization wastewater.

The application relates to the technical field of intelligent sensor calibration and characteristic parameter quantification, and discloses a sensitivity coefficient quantitative calibration method of an intelligent sensor. Original output voltages are collected in sequence at different inclination positions and direct current bias is removed, a multilayer exponential smoothing kernel is adaptively constructed based on the maximum span and the minimum adjacent difference of the voltage sequence, and the signal is subjected to multiscale smoothing; the gradient of each layer of the smoothed voltage sequence is extracted by adopting central difference and end point difference respectively, and the maximum gradient and the corresponding inclination value are extracted in each layer as the local sensitivity; the sensitivities of the layers are arithmetically averaged to obtain the comprehensive sensitivity coefficient; finally, the prediction output and the real inclination residual are calculated by combining the verification data, and whether the calibration is qualified is judged according to the preset root mean square error threshold value; when the calibration is unqualified, sample points are supplemented or the smoothing parameter is adjusted to perform iteration.

The invention discloses a multi-agent workflow automatic optimization generation system and method based on prediction driving, and relates to the technical field of artificial intelligence, in particular to an automatic workflow generation method based on a data processing flow and an algorithm model. Through a combination mechanism of lightweight quality prediction, uncertainty estimation and candidate selection based on an upper confidence boundary, efficient optimization search of the multi-agent workflow is realized. Different from an existing AFlow method which completely depends on real evaluation, the method introduces a recursive least square method quality prediction model and a residual exponential smoothing uncertainty estimation mechanism into automatic workflow generation for the first time, so that the evaluation cost is remarkably reduced, and the convergence speed is increased.

The invention relates to the technical field of converters, in particular to a power dispatching system based on a multi-level direct current chopping technology, which comprises a flux linkage trend analysis module, a duty ratio constraint module, a standby unit evaluation module and a path synchronization coordination module. According to the method, the power integral curve is constructed in real time, the flux linkage increment trend is analyzed, the relevance between the flux linkage direction and the current change is dynamically judged, the duty ratio amplitude limiting range is adjusted in combination with the exponential smoothing prediction model, and phase compensation correction is executed, so that the dynamic response capability of the system is remarkably improved; a sliding standard deviation algorithm is adopted to screen a standby unit group and analyze a current fluctuation coefficient, linear regression is combined to process a temperature change trend, the anti-interference capability of the system is enhanced, a dynamic time warping algorithm is adopted to calculate a phase offset and construct a voltage difference vector, accurate synchronous control of a multi-level architecture is realized, and the stability of the system is improved. Harmonic content and energy loss are effectively reduced, electric energy conversion efficiency and equipment operation stability are improved, and the service life of a power device is prolonged.

This invention belongs to the field of medical image processing technology, specifically relating to a method for filtering and enhancing endoscopic video images. This invention achieves reliable restoration of tissue texture beneath specular highlight-occluded areas in endoscopic videos through five processing stages. The first stage uses a three-factor joint decision to adaptively detect highlight regions in video frames and generate a time-varying highlight mask, effectively distinguishing between moving highlights and fixed bright spots. The second stage uses hierarchical sparse Lucas-Kanade optical flow tracing and RANSAC affine transformation estimation to accurately map highlight-occluded pixels to corresponding non-highlight tissue locations in historical frames, constructing a candidate texture set. The third stage uses a two-color reflectance model to extract tissue intrinsic colors and perform illumination normalization migration on the candidate textures, eliminating inter-frame illumination differences. The fourth stage constructs confidence scores using the product of three-dimensional weights and obtains the restored color through weighted median aggregation. The fifth stage applies guided filtering and inter-frame exponential smoothing to output a temporally consistent enhanced video.

This invention proposes a product cost prediction method, system, and electronic device using a multi-mode interpolation strategy, relating to the field of data processing technology. The method includes acquiring product cost data to be predicted; determining whether missing values ​​exist in the product cost data; if missing values ​​exist, judging the significance of the trend and seasonality of the cost data based on set rules, and dynamically interpolating for each case to obtain the interpolation result corresponding to the missing value; and inserting the interpolation result into the corresponding position in the cost data to be predicted, thereby achieving product cost prediction. This invention performs qualitative analysis of the trend and seasonality of the cost data to be predicted, classifies cases according to the significance of the trend and seasonality, and automatically assigns different interpolation models to form interpolated data. This ensures that the exponential smoothing algorithm remains usable even when the time series is interrupted, and guarantees prediction accuracy.

The application relates to the technical field of resource scheduling and discloses an intelligent resource scheduling supply method and system in a heterogeneous computing environment, which comprises the following steps: acquiring core heterogeneous data, performing exponential smoothing denoising to obtain a smooth state value, combining an HAT extended tuple, and analyzing to obtain a comprehensive capability score; automatically adjusting contribution degree by similarity of static characteristics and dynamic characteristics; constructing a coupling correction function, calculating a hybrid precision matching degree, combining an exponential product to reflect nonlinear correlation between targets; constructing a dynamic penalty function, optimizing a quantum genetic algorithm fitness function, and performing multi-target collaborative optimization; constructing a complexity coupling and dynamic adaptation mechanism to dynamically adjust a task segmentation ratio; designing a frequency and reconfiguration time index ratio to optimize FPGA bit stream preloading; constructing a synchronous delay coupling model to perform state synchronization optimization; balancing multidimensional feedback through a combined reward function; and improving explainability by quantifying the influence of decision factors through contribution degree entropy.

The application provides a time-varying coherence estimation method of ground motion based on Bayesian collaborative optimization. The method constructs double target constraints covering physical consistency and spectrum graph distinguishability through generalized S transform and time-frequency bidirectional exponential smoothing method, cooperatively determines the optimal combination of generalized S transform kernel parameters and exponential smoothing operator by using a Bayesian optimization algorithm, and outputs an optimal parameter set and a corresponding time-varying coherence spectrum graph. Therefore, more real data reflecting the spatial variation characteristics of ground motion can be obtained, and the design requirements under the limit adverse state can be met.

The invention provides a multi-level dynamic adaptive trajectory optimization method and device, a medium and a program product, and the method comprises the steps: obtaining a trajectory point set of a target vehicle, the trajectory point set comprising a plurality of trajectory points and first steering angles of the trajectory points; detecting noise points in the track points by using a median filtering method, performing de-noising processing, and adjusting a first steering angle of the noise points to obtain a second steering angle of the track points; smoothing the track point in the local area by using a weight average method to obtain a third steering angle of the locally smoothed track point; fitting a global trajectory trend of the trajectory points by using a curve fitting method to obtain a fourth steering angle of the trajectory points after global optimization; and dynamically updating the fourth steering angle of the track point by using an exponential smoothing method on the basis of a global track trend fitting result in combination with the previous track point to obtain a final steering angle of the track point after real-time optimization. According to the method, trajectory optimization is carried out through multi-method combination of hierarchical processing and dynamic adaptation, and the driving trajectory and state of the vehicle are reflected more truly.

A method for quickly discovering a hotspot problem SQL based on big data analysis comprises the following steps: 1) constructing a unified data analysis platform, and collecting and fusing global data; 2) performing association analysis on the data acquired in the step 1): monitoring SQL performance indexes by integrating with an SQL engine, and associating SQL performance with a data underlying object and an execution plan change at the same time; 3) establishing a dynamic baseline for the performance index of each SQL by adopting a cubic exponential smoothing time sequence fitting algorithm; 4, actual execution data of the SQL and the dynamic base line obtained in the step 3 are compared in real time, and an SQL list related to an application release event is marked. According to the method for rapidly discovering the SQL with the hot spot problem based on big data analysis, multi-dimensional data are fused through an intelligent algorithm, and active, accurate and automatic SQL discovering of the hot spot problem is achieved.

The present application relates to the technical field of intelligent packaging detection, and more particularly to a carton sealing abnormality detection method and system based on reflection sensing, which comprises constructing an adjustable physical detection channel, using a reflection type photoelectric sensing array to collect carton contour signals, and identifying sealing abnormality through adaptive threshold segmentation and an improved DTW algorithm; a confidence assessment model is constructed in combination with multi-modal data, and weighted exponential smoothing filtering is used to confirm effective abnormality; a PLC controller accurately separates abnormal cartons based on a predictive trajectory mapping algorithm, and device fault early warning is realized through edge computing cluster analysis. The present application can improve detection accuracy, reduce false positive rate and support intelligent maintenance.

The invention discloses a large-section surrounding rock deformation prediction method and device, a storage medium and electronic equipment, relates to the technical field of mine or tunnel safety monitoring, and mainly aims at solving the problems that an existing surrounding rock deformation prediction method is large in prediction deviation, and a prediction model cannot sense and respond to sudden change situations such as deformation rate and trend. The method comprises the following steps: performing decomposition processing on large-section surrounding rock deformation data by adopting a Hodry-Precott filtering method to obtain periodic term deformation data and trend term deformation data; constructing a trend term deformation prediction model by adopting a double-exponential smoothing method based on the trend term deformation data; based on the periodic term deformation data, constructing a periodic term deformation prediction model by adopting a sparrow search optimized gating cycle unit algorithm SAA-GRU; a trend term deformation prediction model is adopted to predict and obtain a trend term deformation prediction result; a periodic term deformation prediction model is adopted to predict and obtain a periodic term deformation prediction result; and integrating the two prediction results to obtain a total deformation prediction value of the large-section surrounding rock.

The invention relates to a metering asset intelligent distribution method and system, and belongs to the field of power metering asset supply chain management. Comprising the following steps: setting three grades of initial inventory limits according to the storage capacity of a three-stage warehouse-dividing intelligent turnover cabinet, updating the three grades of initial inventory limits through an exponential smoothing method, and comparing the updated alarm limits with the current inventory to obtain the scattered requirements of the metering assets; for a flat warehouse area, setting three grades of initial inventory limits according to a special task list, updating according to rules, comparing alarm limits with special task requirements of the current inventory, and adding the alarm limits and the special task requirements to obtain a total distribution requirement; demand pre-auditing is carried out by a second-level regional warehouse division administrator in combination with the inventory and a preset rule; after the verification is passed, the total delivery demand is converted into the number of standard turnover boxes, based on the delivery number, an optimal delivery route is generated through a TSP algorithm, an optimal loading scheme is planned in combination with a CVRP mileage saving method and vehicle specifications, an optimal delivery scheme is formed through integration, and a second-level regional sub-warehouse administrator confirms and adjusts the scheme; and the distribution task is issued after the distribution scheme information is confirmed. According to the invention, accurate measurement and calculation and intelligent distribution of measurement asset requirements are realized, the distribution efficiency is improved, and the inventory pressure and the labor cost are reduced.

The invention relates to the technical field of carbon emission prediction, in particular to a regional carbon emission prediction method, system, equipment and medium, and the method comprises the steps: measuring and calculating power utilization carbon emission data of a provincial level and a prefecture and municipal level through a ridge regression and time sequence algorithm; based on the power utilization carbon emission data, total energy carbon emission data are measured and calculated by using a BP neural network algorithm; based on the total energy carbon emission data over the years, predicting the atmosphere carbon emission trend by using a time sequence exponential smoothing method; constructing a carbon emission prediction model through the predicted atmospheric carbon emission trend, and analyzing the carbon emission trends of the whole province and each city; and summarizing all measurement and calculation data, predicting the total amount and intensity of annual carbon emission through deep learning in combination with various driving factors, and generating an overall prediction analysis report. The method has the beneficial effects that flexible adaptation of multiple levels and multiple regions is supported through model design, multi-source heterogeneous data is integrated, recognition of a complex emission mode is enhanced, and the modeling effect of the model on a nonlinear relation is greatly improved by utilizing the BP neural network and deep learning.

The application discloses a battery replacement cabinet intelligent operation monitoring method and system, the method comprises the following steps: based on the 24-hour sliding statistical data of the voltage and temperature of a single battery compartment in the battery replacement cabinet, a thermal-electric imbalance index is obtained through standardized deviation calculation and exponential smoothing; based on the multi-physical information of the threshold-exceeding compartment of the thermal-electric imbalance index, a multi-dimensional topology node set is constructed, and a thermal-electric coupling spread potential is calculated in combination with a minimum spanning tree structure; based on the thermal-electric coupling spread potential and the length of the waiting queue, an exponential adjustment of the time period adaptive weight and a tail risk penalty are fused to obtain a service resilience index; based on the service resilience index and the current number of idle compartments, a dynamic redundancy compression and a safety lower limit protection are performed to obtain an available safety margin; and based on the available safety margin of the battery replacement cabinet and the difference between the service resilience indexes of adjacent cabinets, a standard deviation discrimination and an exponential decay mapping are performed to obtain a regional collaborative guidance intensity. The application improves the intelligent level of the battery replacement cabinet in the aspects of safety risk perception and service capacity adjustment.

The invention discloses an energy storage capacity measuring and calculating method based on electric power and electric quantity balance, and the method comprises the steps: predicting all kinds of new energy total addition and regional total load data in a target year through employing an exponential smoothing method in a time sequence based on all kinds of new energy total addition and regional total load data in a current year, and constructing an electric power and electric quantity balance model through the data in the target year, adding constraint conditions to the model, constructing an electric power and electric quantity balance index, confirming an electric power gap value, an electric power surplus value, an electric quantity gap value and an electric quantity surplus value, carrying out energy storage capacity measurement and calculation based on the electric power and electric quantity balance index in combination with the constraint conditions, and finally verifying the energy storage power. The purposes of ensuring the accuracy and rationality of an energy storage capacity measurement result based on electric power and electric quantity balance, promoting energy storage to be implemented as soon as possible, better meeting the power supply demand and ensuring the economy and safety of a power grid are achieved.

The invention relates to the technical field of asphalt pavement rut depth prediction, in particular to an asphalt pavement rut local accumulation grey prediction method based on sine driving periodicity, which comprises the following steps of: 1, defining a sequence; 2, introducing a sine driving item and a periodic correction item; step 3, constructing a seasonal weakening buffer operator SAWBO; 4, optimizing the parameters in the preprocessing by adopting a fruit fly optimization algorithm FOA; 5, constructing a dynamic local accumulation generation sequence; step 6, solving a local accumulated number; step 7, constructing an SAWBO-DPDGSM (1, 1), and estimating a parameter vector; and step 8, case application. The method is not only superior to a traditional statistical model and an exponential smoothing method, but also has obvious precision advantages and stability advantages in comparison with a machine learning model, and is more suitable for track depth sequence prediction with remarkable trend evolution and seasonal fluctuation characteristics.

The invention discloses a distribution network tail end low voltage comprehensive treatment method and system, and relates to the technical field of power electronics, precise treatment is realized through six steps: firstly, collecting various operating parameters such as three-phase voltage, load power and the like, and adopting a combined model fusing LSTM and grey prediction to adaptively adjust an output proportion and improve parameter prediction precision through a dynamic weight; a three-dimensional fuzzy evaluation matrix is constructed based on the voltage deviation, the load fluctuation coefficient and the DG output randomness, and risk grading is realized; then constructing a multi-objective optimization model with a dynamic weight, and synchronously setting multiple constraint conditions; solving an optimal treatment parameter by adopting an improved particle swarm algorithm introducing chaotic disturbance; governance is executed according to the parameters, and the prediction model is corrected through an exponential smoothing method; and finally, calculating a comprehensive benefit index, and dynamically adjusting and optimizing the weight of the model. The system comprises nine modules including a data acquisition module, a combined prediction module, a risk grading module and the like, and closed-loop optimization is formed. The method and system can significantly improve the voltage qualified rate, reduce the network loss and compensation cost, adapt to a complex distribution network scene, and provide double guarantees for the reliability and economy of power supply.

The application provides a hoisting equipment health assessment and life prediction method and system based on an exponential smoothing method, and the method comprises the following steps: acquiring operation data of the hoisting equipment indicating the health state, wherein the operation data comprises the average hoisting weight of each working cycle of the main force structure, the cumulative braking times of the brake, the cumulative action times of the contactor, and the starting times of the motor; confirming the health state of each component of the hoisting equipment based on the operation data of each component of the hoisting equipment and the preset rated life limit value of each component of the hoisting equipment; assessing the overall health state of the hoisting equipment based on the health state of each component of the hoisting equipment; and applying the exponential smoothing method to predict the life of each component of the hoisting equipment based on the health state of each component of the hoisting equipment. The method can collect the operation data of the hoisting equipment, import the operation data into a health assessment model, obtain the health state assessment result of the hoisting equipment, and realize the residual life prediction of the hoisting equipment through the exponential smoothing method.