279 results about "Weight analysis" patented technology

The invention discloses an industrial odor online monitoring system based on big data. The system comprises a data acquisition module, a preprocessing module, a feature fusion module, a multi-source sensing weight analysis module, a dynamic risk assessment module, a self-adaptive correction module and a real-time feedback module. Data interaction among the modules adopts a block chain encryption transmission protocol, and a feature evaluation mapping chain with a timestamp is established in a distributed database. Through the innovative design of heterogeneous sensor network collaborative perception, cross-modal feature organic fusion, pollution diffusion three-dimensional dynamic deduction and a credible data governance architecture, a new industrial bad smell monitoring normal form with environment intelligent adaptability is constructed. The four technical effects form complementary support from four dimensions of data acquisition credibility, feature modeling scientificity, risk assessment accuracy and system decision robustness.

The invention discloses an agricultural whole industrial chain traceability method and system based on big data, particularly relates to the technical field of agricultural Internet of Things and intelligent decision making, and is used for solving the problems of cause and effect chain breakage, damage-causing factor positioning deviation and insufficient traceability reliability caused by neglecting irreversibility of biological growth in the existing agricultural traceability system. The method comprises the following steps: dividing time sequence data fragments based on an agricultural growth stage, restoring a time sequence rule of crop growth, and dynamically marking a time window of a key abnormal event so as to lock a damage-causing action interval; quality inspection and transportation parameters are mapped into a system trajectory by combining a phase-space reconstruction technology, and a target growth stage associated with a biological dynamic rule is screened; through contribution weight analysis and stress resistance data fusion, identifying a main cause damage factor and a synergistic effect relationship thereof; and finally, the tracing path is dynamically corrected based on a historical verification result, and the threshold value and the weight of the agricultural rule base are optimized, so that the accuracy and the credibility of agricultural whole industry chain tracing are remarkably improved.

The embodiment of the invention provides a method and system for generating medical suggestions based on multi-modal data fusion, and the method comprises the steps: integrating a medical image, a physical examination report and dynamic physiological parameters of a patient through a multi-source data fusion module, generating a multi-modal data set, and synchronously inputting the multi-modal data set into a hybrid reasoning module and a dynamic knowledge graph engine. And the dynamic knowledge graph engine accurately recall a target diagnosis and treatment guide associated with the current multi-modal data set. The rule reasoning sub-module generates a first diagnosis suggestion containing a diagnosis conclusion, a treatment scheme and an evidence level based on a guide structured rule, and meanwhile, the neural network reasoning sub-module analyzes a multi-modal data set by relying on a triple topological structure and an edge weight; and generating a second diagnosis suggestion comprising the disease risk probability, the differentiated treatment suggestion and the evidence source. And finally, the interactive output module fuses the two suggestions to generate a medical suggestion report covering the diagnosis basis, the evidence level and the treatment scheme, so that the diagnosis and treatment precision of chronic disease management and health risk assessment is remarkably improved.

The invention belongs to the technical field of data processing, and particularly relates to a hidden ore body evaluation and positioning method based on multi-source data processing. The method mainly aims at the problems of incompleteness and isomerism of multi-source geological data in acquisition, fusion and modeling. Comprising the following steps: acquiring hyperspectral, geochemical and magnetic anomaly multi-source data of an evaluation area; intelligently complementing missing modal data by using a generative adversarial network based on geological constraints and modal outburst to form a complete multi-source data set; an unsupervised clustering algorithm combining geological correlation and entropy weight analysis is adopted to construct high-confidence-coefficient pseudo-label data, and knowledge mining of unlabeled samples is achieved; feature purification and dimension reduction are carried out through multi-modal feature fusion and hierarchical principal component analysis, and key feature vectors representing the existence of the ore body are extracted; and finally realizing space prediction of the concealed ore body by utilizing the classification model. According to the method, a high-quality data basis and a unified processing framework are provided for intelligent recognition of the hidden ore body, and efficient and accurate positioning of the hidden ore body is achieved.

The invention relates to the technical field of sewage treatment, in particular to a sewage treatment effluent quality real-time detection method and system.The method is applied to a sewage treatment system.The method comprises the steps that a discharge path of the sewage treatment system is divided into at least two effluent monitoring nodes, presetting a weight rule base of water quality characteristic parameters for each effluent monitoring node; collecting a water quality characteristic parameter set of each effluent monitoring node, an operation state data set of the sewage treatment system and an environment parameter data set in real time; determining the current operation condition of the sewage treatment system according to the operation state data, and matching corresponding parameter weight configuration in a weight rule base; performing weight analysis on the water quality characteristic parameter data and the parameter weight configuration to generate a water quality comprehensive abnormal index corresponding to the effluent monitoring node; the abnormal process position of sewage treatment can be accurately traced, and the accuracy of abnormal positioning is improved.

The invention discloses a financial anomaly detection and risk early warning system based on Bayesian deep learning, and relates to the technical field of deep learning, and the system comprises an information contribution weight analysis unit which is used for selecting financial indexes used for measuring financial robustness from historical data of a rolling window formed by a plurality of recent continuous report periods, and outputting the selected financial indexes to a database; for each financial index, normalizing the value of the mutual information into a weight; the pressure score construction unit is used for estimating the probability of occurrence of abnormity at the current period by using a logistic regression model which introduces nonlinear logic Student-t likelihood in the same rolling window; the risk early warning unit is used for calculating potential loss of monetization; and if the monetization potential loss exceeds a set loss threshold value, the system sends out early warning information. According to the method, accurate modeling and uncertainty quantification of the abnormal probability are realized, default opening and loss rate parameters are introduced, and the risk prediction result is monetized into potential loss for dynamic early warning triggering.

The invention relates to the field of computer industry, and discloses an integrated computer software and hardware combined sales optimization system and method, and the method comprises the steps: extracting software and hardware sales records, customer transaction behaviors and version updating tracks, and constructing a fusion data set of heterogeneous sales data; performing association weight analysis on the fused data set, and extracting strategy sensitive points in software and hardware linkage transaction in combination with a customer behavior intention model and product life cycle characteristics; constructing a product combination feasible region in a specific sales window by adopting a combination optimization graph search algorithm based on the tag nested structure; mapping the candidate combined product list to a historical sales behavior map, and extracting dynamic feedback modes of a combined strategy under different sales paths by introducing a price elastic response model and market disturbance fluctuation characteristics; and carrying out iterative adjustment and node intervention on the strategy path based on the high-sensitivity area. The method has the advantage of improving the scientificity of sales strategy formulation.

The invention discloses a cable insulation aging state evaluation method, which realizes aging state evaluation of a running cable system by fusing data acquired by multiple sensors and combining an intelligent algorithm. The method comprises the following steps: 1, collecting cable operation data of different years, and constructing a preliminary data set; 2, generating simulation data to supplement the insufficiency of actually acquired data, and constructing a complete data set; 3, performing feature extraction on the signals in the complete data set; 4, carrying out weight analysis on the extracted features; 5, arranging a sensor, and collecting multi-source data in real time; 6, an alarm threshold value is set, early warning is carried out on the cable section with the aging score exceeding the threshold value, and the remaining life is evaluated; 7, synchronizing the early warning information to a maintenance management platform, and generating a detailed early warning report; and 8, checking an alarmed section, evaluating the alarm accuracy, and if the alarm is unreasonable, correcting the weight coefficient and the scoring criterion in time. By fusing data acquired by multiple sensors and combining an intelligent algorithm, the aging state of the operating system is evaluated.

The invention discloses a building pollution reduction and carbon reduction collaborative evaluation method based on LCA and dynamic weight analysis, and relates to the field of building pollution reduction and carbon reduction collaborative evaluation. According to the method, the influence parameters of the design, construction and operation stages of the whole life cycle of the building are systematically identified and quantified, and the cooperative and unified evaluation of pollution reduction and carbon reduction is realized through dynamic weight reconstruction analysis and a function coupling LCA model, so that the environmental performance of the building in the whole life cycle can be integrally and accurately reflected, and meanwhile, the environmental performance of the building in the whole life cycle can be accurately evaluated. According to the method, the interaction between indexes and the time sensitivity of inter-stage energy consumption and pollutant transfer and emission can be considered at the same time, the complex multi-index and multi-stage environmental influence is converted into a comprehensive synergistic effect value, and an operable environmental performance grade is formed through grading judgment.

The invention provides a wind and light equipment predictive maintenance system and method based on big data and hierarchical weight. The system comprises a data acquisition and edge processing layer, a data management and storage layer, a hierarchical weight analysis and model construction layer and a fault prediction and intelligent decision-making layer. Multi-source data are collected by deploying various sensors, and the multi-source data are transmitted to a cloud for deep cleaning and storage after being preprocessed by edge computing. The index weight of each level is determined by using an improved algorithm, and a fault prediction model is constructed by fusing a plurality of intelligent algorithms. The model combines real-time data and hierarchical weights, predicts the fault probability, type and remaining service life of equipment components, and gives an alarm when a threshold value is exceeded. Meanwhile, a multi-objective optimization model is constructed according to a fault prediction result to formulate a maintenance strategy, and the maintenance strategy is executed after digital twinborn rehearsal, so that the operation and maintenance efficiency is improved. According to the method, multi-source data fusion, accurate fault prediction and intelligent maintenance decision making are realized, the operation and maintenance cost can be effectively reduced, and the equipment reliability and the power generation efficiency are improved.

The invention discloses an information management and analysis system and method based on image recognition and machine learning. The system analyzes image data into a structured text through the image recognition module, performs text semantic analysis by using the natural language processing module, calculates quantitative indexes such as sensitive word scores and abnormal indexes in combination with sensitive word weight analysis, anomaly detection, a risk conduction map and a co-occurrence relation prediction model of the machine learning module, and performs semantic analysis on the structured text. And a risk index RI and an opportunity index OI are generated through a comprehensive decision module, and decision suggestions such as careful cooperation and preferential investment are output according to a preset rule. The model can dynamically optimize weight parameters based on historical data, and multi-modal data automatic processing and risk opportunity quantitative evaluation are achieved. According to the scheme, the decision making efficiency and scientificity are improved, the method is suitable for financial risk control, enterprise compliance and other scenes, and a traceable scientific basis is provided for commercial decision making.

The invention discloses a reservoir dam safety monitoring method and system based on video monitoring, and the method comprises the steps: S100, constructing a dynamic reference monitoring grid, dividing a dam into a plurality of monitoring points, and adjusting the density of the monitoring grid according to the stress distribution of the dam; s200, collecting video streams of the dam, recording environment data at the same time, and collecting monitoring data; s300, performing physical constraint feature decoupling on the data obtained in the step S200 to obtain pure deformation features and environmental noise features; s400, performing differential consistency data fusion on the pure deformation features and the environmental noise features to obtain a fusion coefficient and a fusion deformation field; s500, performing seepage type anomaly propagation prediction in the reference grid monitoring grid to obtain a risk area set and a propagation path probability graph of the dam; s600, establishing a topology perception model, performing weight analysis on the risk areas in the risk area set, and performing key monitoring on the key areas; and S700, carrying out multi-dimensional risk assessment on the key area, and effectively capturing the deformation condition of the dam.

The invention discloses an intelligent auxiliary method, a medium and equipment for constructional engineering title review. According to the method, key feature information of unstructured text data in a declaration material is extracted by using a natural language processing technology, image data features are analyzed by using a ResNet-50 deep learning model, and a comprehensive quantitative scoring mechanism of a construction engineering performance material is established. And integrating education background, working experience, scientific research achievements and multi-modal performance data, constructing a constructional engineering title review rule base, setting a standard score threshold based on historical declaration data, and generating a title simulation review score and a review prediction result. And finally, aiming at the model, designing a feature weight analysis layer, and automatically outputting improvement suggestions of the declaration material defects according to the simulation review score after being associated with the associated review rule base. According to the method, automatic feature extraction and quantitative evaluation of the unstructured material are realized, the problems of low manual auditing efficiency and fuzzy standard are solved, and the objectivity of title evaluation is improved through the rule base and the evaluation prediction model.

The invention relates to the technical field of artificial intelligence, in particular to a gynecological traditional Chinese medicine data processing method and system based on four diagnosis data weight analysis. The method comprises the following steps: acquiring four diagnosis data; map mapping is carried out according to the four-diagnosis data to obtain four-diagnosis map mapping data; obtaining expert experience data and historical case data according to the four-diagnosis map mapping data; performing decision tree construction according to the expert experience data to obtain an expert experience judgment tree model; performing hierarchical weighted regression according to the historical case data to obtain historical case hierarchical weighted data; performing four-diagnosis feature weighting processing according to the expert experience judgment tree model and the historical case level weighting data to obtain four-diagnosis data weight analysis data. According to the method, accurate butt joint of four diagnosis features and a traditional Chinese medicine knowledge system is realized, and a complete link from four diagnosis original feature collection, semantic mapping and rule data collaborative modeling to context adaptive reasoning is constructed.

The invention relates to a large silo coal blending analysis system, and belongs to the technical field of coking. The system comprises a silo coal resource access statistics module, a switching analysis module, a coal blending ratio difference analysis module, a coal blending and coke quality prediction module and an information display module. Firstly, the silo coal seam distribution condition is established, then the coal seam resource switching condition is analyzed and judged according to the discharging weight, the accumulated coal blending execution deviation rate is calculated, and the coal blending amount and the switching condition of the next period are estimated; the blended coal and coke quality prediction module predicts the quality and analyzes the change condition according to the actual coal blending ratio and the like; and the information display module stores and visually displays the result. The system has strong analysis and prediction capabilities, and can comprehensively and deeply analyze yesterday coal blending conditions, scientifically predict coke quality, intelligently plan today coal blending by means of accumulated coal blending execution deviation rates, simply, conveniently and quickly grasp dynamics and trends, guide coal blending and quality control, improve product quality, reduce cost and enhance competitiveness.

The invention relates to the technical field of drilling parameter optimization control, and provides a petroleum drilling parameter optimization method and system and a storage medium. Comprising the steps that torque fluctuation amplitude and bit pressure response delay data are collected in real time, an original data sequence is constructed, and parameter trend data are obtained; analyzing the parameter trend data to identify a stratum change mode, extracting trend feature information, and when the trend feature information meets a preset condition, determining a corresponding abnormal mode identifier; generating a parameter adjustment direction of drilling control according to the abnormal mode identifier, and optimizing the parameter adjustment direction in combination with parameter weight analysis and a real-time feedback mechanism to obtain final optimization guidance data for guiding drilling operation; and mapping the final optimization guidance data to a control interface, and generating a dynamic adjustment instruction for drilling operation. The problems that in the existing petroleum drilling process, parameter adjustment lags behind, and stratum changes are difficult to adapt are solved, and intelligent optimization and dynamic control over drilling parameters are achieved.

The invention discloses a drainage pipe network data cleaning and intelligent repairing method fusing multiple models. The method comprises the following steps that S1, original monitoring data flow is collected; s2, carrying out the preliminary detection and elimination of the abnormity based on IQR; s3, performing upstream and downstream multi-source feature extraction and XGBoost weight analysis; step S4, multivariable depth autoregression prediction based on PatchTST is carried out; s5, generating and publishing continuous high-quality data; according to the method, the key technical problems of various abnormal types, incomplete abnormal detection, low cleaning and repairing precision, poor data continuity and trend consistency, high system integration and real-time processing difficulty and the like commonly existing in the actual collection and transmission process of the online monitoring data of the drainage pipe network are solved.

The invention relates to the technical field of anti-seismic performance evaluation of ECC jacketed combination columns, and particularly provides a quantitative evaluation method for anti-seismic performance indexes of an ECC jacketed combination column, which comprises the following steps of: calculating key anti-seismic performance indexes such as bearing capacity, accumulated energy dissipation, ductility coefficient and limit value of repairable interval, and combining weight analysis and importance coefficient adjustment to evaluate the anti-seismic performance indexes of the ECC jacketed combination column. A scientific and comprehensive anti-seismic performance evaluation system is established; a method for determining the damage point of the ECC jacket combination column is provided, and the damage state is accurately determined; reparability interval evaluation is particularly introduced, repair grades are divided based on residual deformation and Park-Ang damage indexes, and a basis is provided for post-earthquake maintenance decision making; the provided anti-seismic performance index evaluation index comprehensively considers different loading working conditions, and a clear engineering applicable threshold value is set, so that engineering personnel can quickly evaluate and optimally design; the method is of great significance to promotion of engineering application of the ECC jacket combination column.

The invention discloses a shale oil carbon dioxide huff and puff graded pressure drop mining experiment method. The method comprises the following steps that S1, the crude oil recovery degrees under different huff and puff experiment requirements are obtained through experiments; the method is suitable for the technical field of oil and gas field development, the CO2 utilization rate and the recovery percentage of each stage in the shale oil carbon dioxide huff and puff graded pressure drop exploitation process can be accurately measured, a database is established in combination with influence factors of different injection conditions, and the reliability of the shale oil carbon dioxide huff and puff graded pressure drop exploitation process is improved. And on the basis of experimental results, weight analysis of shale oil carbon dioxide huff and puff graded pressure drop mining is carried out on each factor, so that main influence factors of shale oil carbon dioxide huff and puff graded pressure drop mining can be screened out, which is of great significance to subsequent shale oil carbon dioxide huff and puff mining adjustment.

The invention discloses a data real-time analysis and fault early warning method and system based on a digital electric port box transformer substation, and belongs to the technical field of power equipment monitoring and control systems. The method comprises the following steps: acquiring environmental parameters of each component of the box transformer substation in real time through a monitoring network, and performing data smooth denoising to obtain a parameter sequence; constructing a dynamic storage framework based on the sequence in combination with space-time attributes; extracting historical records of the target component to perform multi-dimensional sorting and change rate calculation, and identifying potential anomalies; when the change rate exceeds the limit and the distribution boundary breaks through, generating an alarm sequence and determining a preliminary abnormal mark; comparing the mark with the historical record, fusing the influence weight analysis verification accuracy, and obtaining a final early warning grading result; and parameter summary information is pushed to a control system according to a grading result, response feedback is obtained, and closed-loop control of the operation state is formed. According to the invention, the problem of control lag caused by data noise and delay is solved, and the timeliness, reliability and automation level of box transformer substation control are improved.

The invention provides a data grading method and device, electronic equipment, a medium and a program product, and can be applied to the technical field of big data, the technical field of artificial intelligence and the field of financial science and technology. The method comprises the following steps: acquiring to-be-graded data, and processing the to-be-graded data to generate a standard data stream; extracting multi-modal features based on the standard data stream, and fusing the multi-modal features to obtain semantic representation information; acquiring real-time risk information, and performing dynamic risk weight analysis on the real-time risk information to generate risk weight information; and performing classification and grading decision based on the semantic representation information and the risk weight information to obtain a data grading result, and performing data grading on the to-be-graded data based on the data grading result.

The invention relates to the technical field of vegetation remote sensing observation, in particular to a chlorophyll fluorescence observation system based on laser radar, which comprises a target evaluation module used for responding to an observation evaluation condition to determine an observation analysis strategy of each observation target; the observation analysis module is used for determining the dominant direction of the mirror surface of the observation target and determining an inversion execution strategy of the observation target in response to the inversion execution condition; the dominant inversion module is used for determining a pre-estimation execution wave band set and determining observation pre-estimation data based on the polarization reflection parameters of the pre-estimation execution wave bands; the multi-parameter inversion module is used for determining an inversion weight coefficient of each observation wave band and determining whether to adjust the inversion weight index of each observation wave band in response to a weight analysis condition; the auxiliary analysis module is used for responding to the auxiliary analysis condition to determine the auxiliary analysis mode of the observation target, and the reliability of the fluorescence observation result of the observation target is improved.

The invention discloses a permanent magnet brushless direct current motor and a parameter optimization method thereof. A rotor adopts a regular hexagon structure, and permanent magnets are alternately embedded into the rotor. Seven parameters such as the maximum thickness of a permanent magnet, a pole-arc coefficient and the width of a stator notch are selected for optimization, air gap flux density, no-load back electromotive force and no-load cogging torque are taken as optimization targets, a response surface model of a multi-objective function is constructed, parameter weights of the multi-objective function are determined in combination with a CRITIC weight analysis method, the motor parameters are optimized by adopting an improved MOEA / D algorithm, and the optimal parameters of the multi-objective function are obtained. And carrying out crossover variation on the population by directional variation guided by electromagnetic characteristics, and designing a weight vector optimization Chebyshev aggregation method to find an optimal iteration population. According to the weight in the Chebyshev aggregation function designed by the invention, the problems that the traditional Chebyshev aggregation method is not smooth enough for the continuous MOP problem and the obtained solution is not uniform are solved, and the distribution uniformity of the Pareto solution set is optimized. The method has obvious advantages in the aspects of convergence speed, solution set distribution and engineering feasibility.

The invention relates to the field, and particularly discloses a construction method of an operating vehicle energy consumption estimation model based on big data analysis, and the method comprises the steps: monitoring an Internet of Vehicles system, analyzing the operating state of an operating vehicle, and estimating the energy consumption of the operating vehicle. The real-time performance and integrity of data transmission are directly reflected, the problems of network delay, signal interruption and the like can be found in time through comparison with a preset defined value, energy consumption data distortion caused by system fluctuation is avoided, the abnormal main cause is rapidly judged through weight analysis of the environment influence index and the driving behavior influence index, blind troubleshooting is avoided, and the system reliability is improved. Through high-frequency acquisition of environment and driving behavior data, an energy consumption estimation model can respond to scene changes in real time, a real-time early warning mechanism of energy consumption abnormity is established, the operation cost is reduced, a high-energy-consumption state is intervened in time, and synergistic interaction of energy consumption management and safe operation and maintenance is realized.

The invention belongs to the technical field of power dispatching automation, and particularly relates to a power data asset blood relationship analysis method and system. The method comprises the following steps: acquiring power data, and judging whether the type of the acquired power data is structured model data or unstructured operation data; performing blood relationship analysis on the unstructured operation data to obtain a data blood relationship; and inputting a data blood relationship obtained by blood relationship analysis of the unstructured operation data into a pre-trained self-trained recurrent neural network to obtain a data blood relationship influence factor under the data blood relationship of the unstructured operation data. According to the method, the data blood relationship can be automatically established for the unstructured operation data; by establishing a data blood relationship between structured data and unstructured operation data, dynamic tracking of a data relationship is realized, and upstream and downstream links of the data are updated in real time; the weight analysis of the change influence among the data is realized by acquiring the data blood relationship influence factor.

The invention discloses an overhead transmission line X-ray intelligent flaw detection method based on a six-rotor unmanned aerial vehicle, and relates to the technical field of X-ray intelligent flaw detection, and the method comprises the following steps: collecting flaw detection data including flight attitude data, X-ray imaging data, overhead transmission line data and positioning data, carrying out preprocessing and feature extraction on the collected data; adjusting the hovering position of the six-rotor unmanned aerial vehicle based on the preprocessed positioning data; and based on an output result of the intelligent flaw detection model, analyzing the severity of internal defects of the overhead transmission line, and further generating an X-ray intelligent flaw detection report of the overhead transmission line. According to the method, a multi-sensor data acquisition technology, a data preprocessing technology, a feature extraction technology, a weight analysis technology, a flight attitude adjustment technology, a multiple linear regression modeling technology and a neural network modeling technology are closely combined with a modern information technology; accurate detection and high-precision positioning of internal defects of the overhead transmission line are achieved.

The invention provides an indoor environment comprehensive control method and system, and the method comprises the steps: carrying out the subjective and objective comprehensive weight analysis of indoor environment indexes which do not accord with an indoor environment judgment standard, and taking the indoor environment index with the highest comprehensive weight as an optimization object; determining a passive optimization measure for the optimization object; and when the collected outdoor environment indexes do not meet the outdoor environment judgment standard, active optimization measures are adopted. According to the method, subjective and objective comprehensive weight analysis is conducted on environment indexes such as indoor light, heat humidity and air quality through the analytic hierarchy process and the change rate method, appropriate indoor environment control measures are provided on the basis of the passive priority principle and in combination with the outdoor environment indexes, and the purposes of energy conservation, greenness, health and comfort can be achieved.

The invention belongs to the technical field of building design, and provides an optimization method and system based on the mix proportion of a heavy slag water permeable brick, and the method comprises the following steps: determining the mix proportion parameter of the heavy slag water permeable brick; establishing a performance data set of the heavy slag water permeable brick, and constructing a nonlinear mapping relation function between the mix proportion parameters of the heavy slag water permeable brick and the performance data set of the heavy slag water permeable brick; constructing a multi-objective function based on the nonlinear mapping relation function, establishing a multi-objective constraint condition, and constructing a multi-objective optimization data set according to the objective function and the multi-objective constraint condition; and performing weight analysis on each performance by adopting entropy weight TOPSIS, and substituting a weight value into the multi-objective optimization data set to determine an optimal mix proportion. According to the method, the RSM is combined with the NSGA-II and the entropy weight TOPSIS, so that the mix proportion is more accurate, and the adaptability is higher.

The invention belongs to the technical field of agricultural information, and particularly discloses and provides a smart field intelligent scale system based on statistical analysis, which comprises a horizontal calibration module, a gravity center calibration module, a weight analysis module, a weight correction module, a yield analysis module and a yield confirmation module. According to the method, multi-dimensional environmental parameters such as crop stacking images, wind speeds and wind direction angles are fused, the influence of wind interference and stacking forms on weighing is dynamically analyzed, accurate quantification of wind interference in the field environment is achieved, and meanwhile, the crop weight is accurately measured based on the corrected weight of crops during weighing and the position coordinates of a harvesting area. According to the method, the actual yield of each harvested farmland area is accurately calculated in combination with the area of each farmland area, then the predicted yield of the target farmland is reversely deduced, the balance and scientificity of field yield prediction are improved, and through cooperative work of horizontal calibration and gravity center calibration, the problem of measurement deviation caused by uneven ground and uneven gravity center in a field environment is solved.

The invention relates to the technical field of data processing, and discloses a fault analysis method and system based on a label propagation algorithm. The method comprises the following steps: acquiring operation state data through a monitoring equipment network, extracting the data to obtain a system topology data set, performing label propagation fault analysis on the system topology data set to obtain node fault information, and performing fault influence weight analysis on each node based on the node fault information to obtain a maintenance priority, and inputting the influence weight data into the graph neural network for coping strategy analysis to obtain a target processing strategy. According to the invention, the efficiency of fault propagation analysis and the accuracy of decision making are improved.