2617 results about "Data loss" patented technology

The invention belongs to the technical field of earthquake disaster scene recognition, and discloses an earthquake disaster scene recognition method based on deep learning. The method comprises the following specific steps: S1, data acquisition and preprocessing; S1.1, multi-source heterogeneous data acquisition and establishment of a comprehensive database containing seismic waveform data, surface deformation data, building structure data, geographic information data and historical disaster record data; through fusion of a 3D convolutional network, a graph attention mechanism, a space-time LSTM and an adaptive cross-modal attention fusion technology, combined modeling of a seismic waveform space-time evolution law, an earth surface deformation space distribution characteristic, a building group topology vulnerability and disaster chain time sequence association is realized, the characterization capability of a complex nonlinear disaster mode is effectively improved, and the method has the advantages of high adaptability and high reliability. And disaster assessment response time is shortened to a sub-second level through mixed precision quantification and edge computing deployment, and high recognition accuracy is still kept in a scene with strong noise and data missing in combination with a multi-task classifier and a physical constraint verification mechanism.

Image series transformation for optimal compressibility is performed with neural upsampling and error resilience. A novel correlation network composed of convolutional layers for feature extraction that extract multi-dimensional features from the image and a channel-wise transformer with attention to capture complex inter-channel dependencies. An angle optimizer enhances compressibility of an image and an error resilience subsystem improves robustness against transmission errors and data loss. The error resilience subsystem applies forward error correction coding, data partitioning based on importance, and embeds error concealment hints. This hybrid approach addresses both local and global features, mitigates compression artifacts, improves image quality, and enhances data integrity during transmission. The correlation network incorporates error correction and concealment techniques during decoding. The model's outputs enable effective image reconstruction, achieving advanced compression while preserving information for accurate analysis.

The invention discloses a solar radiation space-time prediction method and system based on physical information constraint and a neural network, and the method comprises the steps: collecting multi-dimensional time sequence meteorological data, extracting high-dimensional time sequence dynamic features, converting geographic space data into a fuzzy set, and carrying out the defuzzification of the fuzzy set through an inference rule, thereby obtaining geographic space features; and a gating mechanism is adopted to realize deep fusion of the space-time features to generate high-dimensional space-time fusion features. In a model training stage, an energy conservation equation is introduced into an optimization process, a physical residual error is constructed by calculating a time derivative and a space derivative of a predicted value, a physical constraint total loss function is formed in combination with a data loss item, and model parameters are updated by using a gradient descent method. According to the method, the accuracy and reliability of a prediction result are remarkably improved while the calculation efficiency is ensured, and the method is particularly suitable for solar radiation prediction under complex meteorological conditions; according to the method, abnormal prediction caused by data noise can be effectively corrected, and a solution with physical rationality and data adaptability is provided for the fields of solar resource evaluation, photovoltaic power generation power prediction and the like.

The invention relates to the technical field of crop yield analysis, in particular to a crop yield prediction method based on a time sequence remote sensing image, and the method comprises the steps: obtaining an optical remote sensing image and a synthetic aperture radar image in a crop growth period, and constructing a time sequence image through complementary information fusion and under-cloud information recovery; the problems of cloud interference and data missing are solved, a multi-scale time regulation and control convolution module is used for processing a time sequence image, multi-scale time sequence features are extracted, feature fusion is carried out in combination with attention difference jump connection, and a rice distribution map is obtained; determining a rice planting area based on the rice distribution diagram, selecting an optimal assimilation time period in the rice planting area, and assimilating the leaf area index subjected to remote sensing inversion and a crop growth model simulation result to generate a simulated yield after assimilation; based on the multi-scale time sequence characteristics, a parameter dynamic response matrix related to crop growth model parameters is established, and residual error correction is performed on assimilated simulation yield, so that the rice yield prediction precision is improved.

The invention discloses an electronic lead seal automatic detection method applied to logistics tracking, and relates to the technical field of Internet of Things safety monitoring. The problems of packaging data loss, tampering risk increase and real-time monitoring failure caused by wireless communication signal interference, transmission delay and state updating lag of an existing electronic lead seal are solved. The method comprises the following steps: fusing multi-physical field sensing data through a space-time correlation sampling algorithm, and constructing an anti-interference characteristic matrix; evaluating communication quality based on the dynamic probability network model, and triggering a multi-path fragmentation concurrent transmission strategy to avoid signal attenuation; a hybrid reasoning model is deployed at an edge end to screen key event data, and the transmission efficiency is optimized in combination with differential coding and an IEEE 1588 clock synchronization mechanism; the cloud end adopts a space-time diagram fusion analysis model to carry out cross-modal abnormal association scoring, corrects misjudgment and updates an edge model; according to the invention, the communication reliability, the real-time transmission efficiency and the anomaly detection precision of the lead sealing state data in a complex environment are obviously improved.

Systems and methods are provided for dynamically selecting, displaying, and synchronizing electronic forms within a conversational AI interface. A chat-based assistant determines the most contextually relevant form or form section using machine learning models and updates the graphical user interface in a split-screen layout. The system supports bidirectional synchronization, such that changes in form fields generate corresponding chat messages, and chat inputs populate structured form fields in real time. Users may pause and resume form workflows across sessions without data loss. The system further supports folder-based organization of structured inputs and document uploads, enabling hierarchical navigation and predictive workflow continuation. These improvements enhance form completion accuracy, user experience, and operational efficiency.

The invention discloses an injection mold part feature recognition method based on improved PointNet + +, and the method comprises the steps: obtaining standard injection molding part models of a plurality of injection mold parts, and carrying out the uniform sampling of point clouds, and constructing a point cloud set; performing label labeling based on RGB information on each point of the point cloud set according to different processing feature types, and then performing normalization, random scaling and random translation; constructing and training a classification label prediction model based on an improved PointNet + + network according to the processed point cloud set; the entity model of the injection mold part needing feature recognition is converted into point cloud data, the point cloud data are input into the trained classification label prediction model, semantic segmentation output of different machining features is obtained, the semantic segmentation output is matched with the entity model of the injection mold part needing feature recognition in a mapping mode, and feature recognition is achieved. According to the method, data loss and precision reduction are avoided, and the feature recognition efficiency is improved.

The invention relates to the field of excavation equipment obstacle avoidance based on image analysis, in particular to a coal mine excavation equipment obstacle avoidance method and system based on machine vision, and the method comprises the steps: obtaining the frequency domain characteristic parameters of the current electromagnetic noise during the operation of underground electromechanical equipment, and synchronously collecting the current original image frame output by a visual sensor; after a current de-noised image frame is obtained after dynamic de-noising processing, an obstacle contour feature point set is extracted through multi-scale edge detection, and an obstacle position confidence map is obtained according to space-time consistency analysis; obtaining point cloud coordinates of an image data loss area caused by electromagnetic interference, and mapping the point cloud coordinates to an image coordinate system to obtain a three-dimensional geometric complemented image; and constructing a dynamic occupation grid map of the underground environment according to the three-dimensional geometric complementation image and the obstacle position confidence map, and planning an obstacle avoidance path of the mining equipment according to the dynamic occupation grid map. And the obstacle avoidance capability of the mining equipment is improved, and safe and efficient coal mining operation is guaranteed.

The invention discloses a time sequence data management method of an edge computing gateway, which relates to the technical field of edge computing and industrial Internet of Things, and comprises the following steps of: respectively recording a communication bandwidth occupancy rate, a buffer area residual rate and a scheduling thread occupancy rate of the edge computing gateway in a preset fixed time period; and constructing a resource use original data matrix covering all time points in the fixed time period. According to the method, by periodically monitoring the resource use state and fusing the high-priority task scheduling performance, the scheduling resource abnormal occupancy index is dynamically generated, and intelligent sensing and scheduling optimization of the edge computing gateway on the resource pressure are achieved. When the abnormal index is increased, the system automatically triggers buffer area redistribution and low-optimal task data compression, data writing and scheduling real-time performance of key tasks are guaranteed preferentially, the problems of task starvation and data loss are effectively avoided, and the stability and the response capability of the system in a high-pressure environment are improved.

The invention provides a protocol-based Internet of Things central control host data communication method and system, and is applied to the field of communication data processing. By means of data updating timestamp detection, equipment updating frequency division, local data buffering and the like, the real-time data acquisition capability of the central control host on the multi-protocol Internet of Things equipment is improved, a layered acquisition strategy is adopted, the polling period is optimized for equipment with different frequencies, unnecessary requests are reduced, and the efficiency is improved. Meanwhile, data missing caused by long-time no response of low-frequency updating equipment is avoided, data updating abnormity can be intelligently detected, a data refreshing request is triggered, the polling period is shortened, acquisition of latest data is ensured, finally, data compensation is conducted through historical data trend analysis and missing estimation value calculation, and data integrity and continuity are improved.

Systems and methods for Data Loss Protection (DLP) utilizing distilled models include receiving a plurality of general data predictions from a teacher model; determining one or more strengths of the teacher model based on the received general data predictions; generating a synthetic dataset based on the one or more strengths of the teacher model; providing the synthetic dataset to the teacher model and receiving a plurality of synthetic data predictions from the teacher model based thereon; and performing knowledge distillation on a student model based on the synthetic data predictions received from the teacher model to produce a distilled model. The distilled model is then used in production for classifying inputs to a DLP system.

The invention provides an electromagnetic field prediction method and device and electronic equipment, and relates to the technical field of electromagnetic field solving. The method comprises the following steps: acquiring historical electromagnetic original data in a field-line coupling scene, preprocessing the historical electromagnetic original data, inputting the preprocessed historical electromagnetic original data into an LSTM-PINN model, and outputting a physical field quantity mapping result; wherein the physical field quantity comprises an electric field component and a magnetic field component; setting a weighted loss function, and performing optimization training on the LSTM-PINN model based on a physical field quantity mapping result and an error of a real physical field quantity corresponding to historical electromagnetic original data to obtain a trained electromagnetic field prediction model; wherein the weighted loss function comprises a data loss function, a physical residual loss function, an initial condition loss function and a boundary condition loss function; and inputting real-time electromagnetic original data into the trained electromagnetic field prediction model to obtain a spatio-temporal distribution electromagnetic field prediction result. The method can effectively extract the spatial distribution features and the time sequence features at the same time, and is suitable for a complex field-line coupling problem.

The invention discloses an industry return on investment real-time acquisition method based on multi-source heterogeneous data, and relates to the technical field of data analysis and processing, and the method comprises the following steps: normalizing a multi-source timestamp and retaining metadata information based on a UTC reference, an adjustable gain and a power exponent; in the streaming processing, reordering of out-of-order data and marking of expired data are realized by setting a buffer queue and a watermark threshold value; when null value fields or cross-source conflicts are detected, interpolation and conflict processing are carried out, and complete data subjected to consistency correction are output; then, an external high-precision reference or a cross-correlation function is used for further fine tuning the timestamp at a millisecond level, and if the adjustment amplitude exceeds a safety boundary, the timestamp is marked as suspicious; and finally, the corrected data is distributed to multiple nodes according to a fragment mapping strategy, and fault-tolerant consensus and difference repair are triggered under fault duration judgment, so that high-precision time sequence alignment and high availability in a massive concurrent scene are kept, data loss or precision attenuation caused by time sequence inconsistency and node faults is avoided, and the method can be widely applied to high-frequency analysis.

The invention relates to the technical field of information security, in particular to a computing method and system for information security. The method comprises the following steps of: partitioning and fragmenting input data, and distributing the data to a plurality of storage nodes through randomization, distributed storage and Hash mapping to ensure the safety of data storage and the distribution flexibility; establishing an index table for all the fragments; regularly triggering a data confusion mechanism, and adjusting the storage position of data fragments through a pseudo-random function; the integrity of the data is detected by generating a check code and an erasure code, it is ensured that the data is not tampered or damaged in the transmission or storage process, and the data is recovered through a redundant copy when the data is lost; the identity verification and the data integrity when the user accesses the data are ensured through authority authentication; and monitoring storage nodes and data access behaviors in real time, detecting potential threats and taking dynamic adjustment measures. According to the invention, through a multi-level security policy, the security and fault-tolerant capability of data processing are effectively improved.

The invention discloses a multi-scale fatigue crack propagation prediction method and system based on a physical information neural network, and aims to improve the prediction precision of a crack propagation rate. The method comprises the following steps: S1, acquiring crack lengths and cycle cycles of a metal material under different scales by using a microscope; s2, calculating an initial small crack propagation rate based on the acquired data, performing fitting by adopting a Newman-Wu model, constructing an initial crack propagation physical model, further combining experimental data to identify physical characteristics influencing propagation behaviors, introducing a PSC-LC turning point to divide small crack and long crack stages, and constructing a crack propagation rate model fusing multiple characteristics; s3, preprocessing the expansion rate data; and S4, constructing a physical loss function in combination with a crack propagation rule in an experiment, fusing the physical loss function with data loss to form a total loss function, and training the neural network model to realize accurate prediction of the multi-scale fatigue crack propagation behavior of the metal material.

The invention discloses a low-altitude three-dimensional wind field inversion method and system based on a single wind measurement laser radar, relates to the technical field of radar detection, and solves the technical problems that a traditional wind measurement laser radar cannot directly obtain a velocity vector, data missing of a key area is caused by a blocking effect, and detection nodes are not uniform. The method specifically comprises the following steps: S1, executing a multi-elevation-angle body scanning detection mode and a wind profile detection mode by using a wind measurement laser radar, and obtaining radial wind speed data of a target area; s2, quality control is carried out on the radial wind speed, isolated abnormal points are eliminated, and isolated data missing points are filled; s3, carrying out blocking correction on a single scanning surface by using a smooth spline interpolation method; s4, after the radial wind speed is corrected based on the wind profile, an initial background three-dimensional wind field is obtained; and high-precision wind field inversion of a wind field in a three-dimensional space can be realized by using the radial wind speed of multi-layer body scanning of a single wind measurement laser radar.

The invention discloses a solid-state storage hard disk data recovery method and system, and relates to the technical field of data processing, the method comprises the following steps: carrying out difference analysis on historical log state characteristics and health state characteristics stored in a solid-state hard disk, and identifying a data loss event of the solid-state hard disk based on a difference analysis result; constructing a data recovery mechanism based on the data loss abnormal event, and performing recovery processing on lost data of the solid state disk based on the data recovery mechanism; and verifying and evaluating the recovered data, and optimizing the data recovery mechanism according to a verification and evaluation result. According to the method, differentiated technical means such as metadata restoration, backtracking reasoning and data engraving are combined, classification positioning, cause recognition and structured restoration of lost data are achieved, and the pertinence, accuracy and automation degree of data restoration are effectively improved.

The invention discloses a pipeline flow field remodeling method based on LAAF-PINN, and the method comprises the steps: collecting the flow field data of a pipeline measurement point; dividing the measuring points into monitoring points and testing points, and preprocessing the data sequence; constructing an LAAF-PINN, randomly selecting a matching point along a pipeline, and inputting a time-space sequence of the monitoring point and the matching point to obtain model output; calculating a data loss item according to the model output of the monitoring point, calculating a residual error according to the model output of the collocation point to obtain a partial differential equation loss item, and combining the two items to obtain total loss; after multiple rounds of iteration updating, a trained PINN model is obtained, a time-space sequence of a test set is input, and corresponding flow field information can be quickly and accurately reconstructed. According to the method, the LAAF-PINN is applied to pipeline hydraulic transient research, an existing flow field remodeling method is expanded, good robustness is achieved for data containing uncertain factors, and meanwhile the problem that the calculation precision is not high possibly existing in forward simulation is solved.

The invention relates to the technical field of satellite orbit prediction, and discloses a satellite orbit prediction method based on a physical information neural network, a terminal and a medium. The method comprises the following steps: constructing a sample set based on satellite position data, sun position data and moon position data in the same time period; constructing a physical information neural network; the network takes satellite historical position data as input to predict a satellite orbit position at a future moment; training a physical information neural network by using the sample set, and executing a satellite orbit prediction task by using the trained physical information neural network; the trained loss function comprises data loss, physical loss and smoothness loss; the data loss is used for measuring the difference between network prediction output and actual observation data; the physical loss is jointly calculated by the earth aspheric perturbation, the three-body gravitational perturbation and the solar light pressure physical perturbation borne by the satellite; the smoothness loss is used for constraining the smoothness of the predicted satellite orbit. According to the invention, the prediction precision and robustness of the satellite orbit are improved.

The embodiment of the invention discloses a hard disk fault prediction method and device, electronic equipment and a storage medium, and relates to the technical field of reliability of data storage systems.The method comprises the steps that hard disk data are obtained, data cleaning and feature selection are conducted on the hard disk data to obtain feature data, and the feature data are stored in a database; performing standardization and normalization on the feature data to obtain a data set, using a long-short term memory network as a deep learning model, designing a model structure, introducing a regularization technology and a dropout layer to construct an initial model, training and optimizing the initial model by using the data set to obtain a trained prediction model, and performing prediction on the trained prediction model. And performing state monitoring, fault early warning and maintenance on the hard disk by using the prediction model. According to the method, efficient and accurate hard disk state monitoring, fault early warning and maintenance are achieved through data preprocessing and the LSTM network deep learning model, and the problems that in the prior art, changes of the running state of the hard disk cannot be accurately captured, and data loss and system crash cannot be prevented are solved.

The invention relates to a mass concrete raft foundation three-dimensional temperature field reconstruction method, and relates to the technical field of concrete raft foundation structure three-dimensional temperature field reconstruction. The method comprises the following steps: arranging temperature sensing optical fibers in a plurality of temperature measurement areas of a concrete raft foundation to be measured; constructing an optical fiber temperature measurement monitoring system, and obtaining space-time temperature data; a temperature field prediction model is constructed, the temperature field prediction model is based on a physical information neural network, and a data loss function, a physical loss function and an initial condition loss function are fused; performing multi-objective loss function joint optimization training on the temperature field prediction model based on the space-time temperature data; utilizing the trained temperature field prediction model to predict the temperature value of the concrete raft foundation to be measured at any time and space point coordinates; and reconstructing a three-dimensional temperature field based on a prediction result output by the temperature field prediction model. By adopting the method, high-precision reconstruction of the three-dimensional temperature field of the mass concrete raft foundation can be realized.

The invention relates to the technical field of database migration verification, in particular to a cross-data-platform database migration verification method and system applied to autonomous controllable transformation. The method comprises the following steps: configuring a source database and a target database in a data source management module; newly establishing a comparison task in the task management module; a comparison database is connected through a dynamic data source assembly, different databases are connected through JDBC, and comparison verification is executed after database connection parameters are configured; database comparison is carried out in the task management module, wherein the database comparison comprises data volume verification and data consistency verification; a comparison report is generated and stored in a built-in mode. The problem of data integrity in the cross-platform data migration process is solved, the system fully considers the difference between the MySQL database and the local database in character set, function and technical characteristics, data verification and comparison work after migration of the MySQL database source and various local target databases is supported, consistency verification before and after data migration is achieved, and the verification efficiency is improved. And the risk of data loss caused by data migration is avoided.

The invention relates to the technical field of geophysical exploration, in particular to a multichannel deep learning magnetotelluric inversion method based on physical information constraint. The method comprises the following steps: generating a synthetic data set containing a geoelectric model and forward modeling response thereof, and adding a noise simulation actual observation condition; constructing a hybrid network architecture combining Transform and U-Net, taking apparent resistivity and impedance phase as dual-channel input, extracting global features by using an encoder, gradually recovering spatial resolution through a decoder, and outputting an underground resistivity model; network training adopts a composite loss function fusing model loss and data loss, and an inversion process is constrained by introducing a magnetotelluric forward modeling physical rule, so that a result is ensured to fit observation data and conform to a physical mechanism; after training is completed, preprocessed actual measurement data are input into the model, and a resistivity image can be directly obtained. The method is used for geological structure identification and reservoir interpretation, and the inversion precision and reliability are effectively improved.

The invention relates to the technical field of image analysis, and discloses a multi-modal emotion recognition method and system based on hypergraph diffusion and evidence fusion, a terminal and a storage medium, and the method comprises the steps: carrying out the random shielding of a data set through a randomly generated mask, thereby simulating the random data missing condition, and carrying out inverse sampling on the preprocessed simulation data by using a trained conditional diffusion model to obtain a training set in which missing modals are complemented so as to train an emotion classification network, and finally carrying out emotion recognition. According to the method, through dual-channel evidence fusion, uncertainty is estimated at a feature source level and a discrimination level at the same time, so that adaptive evidence fusion is realized, the condition of performance reduction caused by modal loss is reduced, potential features of the lost modal are explicitly recovered in a feature space, and the accuracy of final emotion recognition is improved.

The invention discloses a data processing method and system for a dental three-dimensional model, and relates to the technical field of dental three-dimensional modeling, and the method comprises the steps: obtaining initial data of oral cavity scanning, recognizing a tooth type in the initial data, and determining a corresponding tooth surface reflection parameter according to the tooth type; generating scanning parameter combinations for different tooth positions according to the tooth surface reflection parameters and the tooth anatomical structure; tooth point cloud data are collected, and a tooth enamel reflective area and a shadow shielding area are identified; performing light reflection removal processing on the enamel light reflection area, performing data completion on the shadow shielding area, and generating corrected tooth point cloud data; and performing registration fusion on the corrected tooth point cloud data to generate a dental three-dimensional model containing a complete dentition. According to the method, the problem of data missing caused by enamel light reflection and adjacent tooth shielding is solved through a differential scanning strategy and regional data processing aiming at specific optical characteristics of teeth, and automatic construction of a high-quality dental three-dimensional model is realized.

A method for implementing data loss prevention (DLP) includes: generating an asset lineage map from file system metadata; identifying, based on the asset lineage map, an input feature linked to the asset, a type of the asset, and a plurality of activities linked to the asset; obtaining a sensitivity score for the asset based on the input feature and the type of the asset; obtaining, based on the plurality of activities, a malicious score and a data loss score for the asset; determining a user level of a user; and initiating implementation of a first DLP policy for the user based on the user level, the malicious score, the data loss score, and the sensitivity score.

The invention discloses an electric energy metering device operation state on-line evaluation system and method, and the system comprises a data collection device, a data transmission device, and a data receiving and management device, and the data collection device is used for collecting electric energy metering data of an electric energy metering device and outputting the electric energy metering data to the data receiving and management device through the data transmission device. The data transmission device is used for transmitting an electric energy metering data packet in a dynamic load balancing mode; the data receiving and managing device is used for processing the electric energy metering data packet and evaluating the running state of the electric energy metering device; data are transmitted in a dynamic task distribution and load balancing mode, the response speed and the processing capacity of the system are improved, the stability and the reliability of data transmission are improved, the risk of data loss is reduced, relevant processing is carried out based on the received electric energy metering data, the state of the electric energy metering device is evaluated, and the reliability of the system is improved. And the real-time performance of the operation state evaluation of the electric energy metering device is improved.

A video data loss prevention (vDLP) system that uses machine-learning for protection against data exfiltration of sensitive content across multiple tenants in a cloud-based network. The vDLP system consists of tenants that include end-user devices and a vDLP server. The vDLP server is configured to intercept traffic at an application layer of the cloud-based network and receive a video file from traffic. A viewer for the video file is remote from the cloud-based network. The vDLP server further recognizes text of audio and frames extracted from the video file using a machine-learning engine. The vDLP server then analyzes the frames and text using machine-learning classifiers, enforces policies against machine-learning classifiers for protection against data exfiltration of sensitive content in the video file, and sends a notification away from the cloud-based network upon detection of violation of a policy.

The invention discloses a method for predicting settlement deformation of a foundation pit pipeline based on a heterogeneous spatio-temporal data source. The method comprises the following steps: S1, preprocessing data; s2, spatial feature extraction: introducing a graph attention network, and extracting mutual influence weights among the monitoring points by using a distribution graph of the monitoring points around the foundation pit and a pipeline distribution graph to obtain the mutual influence weights among the monitoring points; s3, spatio-temporal feature fusion; s4, heterogeneous data processing; s5, time sequence prediction; by fusing the spatial features and the time sequence features of the foundation pit monitoring points, the prediction accuracy is improved in predicting the settlement deformation problem of the foundation pit pipeline; missing data is repaired by adopting a diffusion model, so that the utilization rate of the whole data set is improved; the blank of data missing is made up, the original information of the foundation pit monitoring data is reserved to the maximum extent, and the robustness and reliability of the deformation prediction model are further enhanced; the limitation of pipeline data can be effectively supplemented by utilizing multi-source heterogeneous data, and the overall prediction effect is improved.

The invention provides an online monitoring method for product quality in a solid waste recycling process, and belongs to the technical field of solid waste recycling. A parameter acquisition system is constructed to synchronously acquire a multi-parameter matrix, a sampling rate determination function is applied to realize dynamic adjustment of a sampling frequency, and a solid waste component migration equation is utilized to analyze a substance conversion rule; a spectrum fusion optimization function is used for processing various spectrum data, an FWM model is used for achieving product quality multi-dimensional evaluation, the heavy metal content is monitored in combination with an electrochemical sensor network, a comprehensive quality evaluation result is finally formed, an updating sampling rate is output through the FWM model, a function weight coefficient is determined, online monitoring closed-loop control is formed, and the comprehensive quality evaluation result is obtained. Real-time, accurate and comprehensive monitoring of the quality of solid waste recycling products is realized, the problem of key process transition point data missing caused by fixed frequency sampling is effectively solved, and reliable technical support is provided for optimization of the solid waste recycling process.