255 results about "Correlation index" patented technology

The invention belongs to the technical field of wind-solar power generation power monitoring, and particularly relates to a new energy wind-solar power data monitoring system and method. According to the method, power fluctuation characteristics and equipment state correlation indexes are extracted through abnormal value detection and time sequence analysis, so that the precision and efficiency of data processing are improved, and in the aspect of prediction, dynamic comparison between a historical operation data set and the current power fluctuation characteristics is utilized, and environmental parameter change trends are combined. A power prediction curve and a confidence interval are generated, so that the system can recognize potential abnormal operation in advance, decision support time is provided for operation and maintenance personnel, in addition, the system has corresponding fault diagnosis and optimization capabilities, when the prediction deviation degree exceeds a preset threshold value, an early warning mechanism is automatically triggered, a fault source is positioned through a fault diagnosis process, and the fault diagnosis efficiency is improved. And finally, according to a fault diagnosis result, a power prediction curve is corrected and optimized to form a closed-loop optimization mechanism, and the prediction precision and the operation efficiency of the system are continuously improved.

The invention relates to the technical field of legal text generation, in particular to an intelligent case generation method based on a judicial knowledge domain atlas, which comprises the following steps: S1, constructing the judicial knowledge domain atlas; s2, extracting case facts and judgment reason parts of the document; s3, classifying and splitting case facts of the document; s4, carrying out deep learning to generate an abstract; s5, correlation comparison of the content is carried out, and integrity and accuracy are verified; s6, optimizing a judgment key through a convolutional neural network model; s7, automatically generating correlation indexes with other historical cases according to correlation analysis in the judicial knowledge domain graph; s8, summarizing the results, and splicing the results into a complete case document; according to the method, by combining the judicial knowledge domain graph and the deep learning technology, the legal document is automatically generated, the integrity of document content and the accuracy of legal logic are ensured, historical cases are efficiently associated, and the efficiency and quality of judgment document generation are remarkably improved.

The present invention is applicable to the technical field of carbon emission monitoring. Provided are a carbon emission prediction method and a related apparatus. The method comprises: acquiring total carbon emission and total power consumption of each industry; calculating a correlation index of the total carbon emission and the total power consumption, so as to screen a target monitored industry; acquiring carbon emission and power consumption of the target monitored industry; on the basis of the carbon emission and the power consumption, calculating an electricity-to-carbon emission transfer coefficient based on the target monitored industry; on the basis of the target monitored industry, matching a target monitored enterprise, and on the basis of randomness, periodicity and climate factors, constructing a power consumption prediction model of the target monitored enterprise; acquiring actual power consumption of the target monitored enterprise and inputting same into the power consumption prediction model for training; matching an outputted predicted power consumption value with the electricity-to-carbon emission transfer coefficient; and generating a predicted carbon emission value. On the basis of the linear relationship between carbon emission and power consumption, the present invention uses power consumption prediction to estimate predicted carbon emission values, thus effectively improving the efficiency and accuracy of carbon emission prediction.

The invention discloses a box-type substation monitoring data processing method and system, and relates to the technical field of monitoring data processing, and the method comprises the steps: determining the type of monitoring data, constructing a monitoring data set and an abnormal fault set, and determining the correlation between the monitoring data set and the abnormal fault set; the method comprises the following steps: calculating a correlation index between monitoring data and an abnormal type, and a weight of each associated monitoring data in the abnormal type; a periodic monitoring strategy is adopted, data changes are captured in real time, abnormal values are screened out, and data fluctuation interference is eliminated through analysis; aiming at the quantity of initial monitoring data, adjusting and executing an anomaly detection strategy: comprehensively considering all anomaly types associated with the data when the data is single, and predicting a future anomaly risk by utilizing buffer time; when multiple data exist, completely matched anomaly judgment is preferentially carried out, and then whether anomaly judgment of single data needs to be carried out or not is judged; comprehensive and real-time monitoring and efficient abnormity early warning of the operation state of the box-type substation are realized, and the safe and stable operation capability of the substation is improved.

The invention relates to the technical field of geological data processing, in particular to a prospecting method and system based on geological text data, and the method comprises the following steps: obtaining exploration drilling data, extracting lithologic description, mineral composition and fracture filling conditions of a rock core, calculating the correlation index of drilling depth and mineralization thickness, screening the change trend of ore grade, and obtaining the geological text data. And obtaining mining area borehole lithology distribution data. According to the method, by deeply analyzing drilling data and lithologic characteristics, accurately recognizing a mineralized fluid accumulation area and improving the prediction precision of an ore body occurrence position, boundary control of a mineralized zone is optimized through measurement and calculation of spatial homogeneity, the extension precision of the mineralized zone is enhanced, scientific adjustment of a prospecting range is ensured through detailed analysis of altered mineral characteristics, and the method is suitable for large-scale popularization and application. The reasonability of demarcation of the prospecting target area is improved, multi-dimensional data and parameter analysis are integrated, a complete prospecting logic chain is constructed, and mineral product prediction precision and resource exploration efficiency are remarkably improved.

The invention relates to the field of data processing, in particular to a server fault diagnosis method and system for a server group, and the method comprises the steps: obtaining and preprocessing server operation state data; constructing a running state isolated tree by using an isolated forest algorithm, analyzing over-limit and low-limit conditions of data of each dimension, determining a neighbor monitoring sequence and constructing a threshold offset index; based on the index, analyzing association characteristics among the dimension data to construct a fault association index; further analyzing data anomaly differences between leaf nodes and child nodes in the isolated tree, and constructing an intra-layer isolated index; and calculating an abnormal isolated weight and a significant abnormal weight, improving an isolated forest algorithm, and realizing accurate diagnosis of server faults. According to the method, the significant abnormal weight is introduced to improve the isolated forest algorithm, and the threshold offset index and the fault correlation index are constructed, so that the server fault diagnosis precision is significantly improved, false alarm and missing alarm are reduced, operation and maintenance personnel are helped to accurately position faults, and stable operation of the server is guaranteed.

The invention discloses an anti-counterfeiting verification tracking method and system based on an RFID technology, and relates to the technical field of anti-counterfeiting tracking. All unexpected nodes in a second sequence are identified, historical data of the unexpected nodes are analyzed, then a confidence score of each unexpected node is obtained, an influence weight of each unexpected node is obtained according to a correlation index, and the confidence score of each unexpected node is obtained; calculating confidence scores and influence weights of all unexpected nodes through a fusion algorithm, generating a verification coefficient for a product, generating a corresponding management strategy for the product according to a comparison result of the verification coefficient and a verification threshold value, and regularly obtaining an unexpected node identification number of all products in the same series. And analyzing whether the same series of products need to be managed in advance. The tracking system recognizes all unexpected nodes through sequence comparison and performs historical data analysis on the unexpected nodes, so that path abnormity can be effectively found, the recognition capability of the system on abnormal circulation is enhanced, and the tracking precision is improved.

The invention relates to the technical field of anomaly detection, in particular to a pantograph arcing anomaly detection and early warning method based on fusion of ultraviolet light and infrared imaging, and the method comprises the steps: collecting an ultraviolet spectrum signal of a pantograph region in real time, and extracting arcing feature data; acquiring infrared temperature distribution images of the pantograph and the overhead line system, and extracting temperature abnormal region data; timestamp synchronization and space coordinate mapping are carried out on the arcing characteristic data and the temperature anomaly region data, and a fusion data set with time-space alignment is generated; on the basis of the fused data set, calculating the space-time similarity between the arcing feature data and the temperature anomaly region data, and outputting an arcing-temperature correlation index; constructing a Gaussian mixture model, and dynamically judging an abnormal level; and when the abnormal grade exceeds a preset threshold value, a grading early warning signal is triggered, and a fault positioning report including an arcing position and a temperature abnormal range is generated. The method has high engineering practicability, and has important significance for guaranteeing safe and stable operation of a rail transit power supply system.

A disease marker structure evolution characteristic change point determination method belongs to the field of disease markers, and comprises the steps of obtaining and preprocessing time sequence structure characteristic data of a disease marker, constructing a characteristic transformation image and calculating characteristic intensity distribution, establishing a structure characteristic fitting model and calculating a time evolution coefficient and an intensity evolution coefficient, determining a structural feature evolution trajectory and calculating a correlation index; establishing a structural feature piecewise function and identifying a feature mutation point; calculating a structural feature contribution value and generating a feature evolution matrix; calculating an evolution stability index and determining a structural feature change point; hierarchical clustering is carried out, main structural feature change points and secondary structural feature change points are determined, a feature weight distribution diagram is constructed, a change point time sequence table is generated, a time sequence corresponding relation is established, and a structural feature change point determination result is output; and fine analysis and accurate description of structural evolution characteristics of complex disease markers are realized.

The invention relates to the technical field of cloth rolling machine control, in particular to a cloth rolling machine control method and system for automobile interior fabric production, and the method comprises the steps that correlation indexes are determined by analyzing the correlation between the average level of all rotating speed data in each rotating speed data window in each time period and a fluctuation abnormal value; respectively analyzing the difference between the rotating speed data and the correlation index and the corresponding fitting value, and constructing a first difference degree and a second difference degree to determine an abnormal coefficient at the current moment; and determining the tension complexity by analyzing the complexity of all tension data from the beginning of the fabric winding process to the current moment, and determining a tension abnormal value by combining the abnormal coefficient so as to control the rotating speed of a cloth roller in the fabric winding process at the current moment. Interference of mechanical abrasion of the cloth rolling machine in the fabric rolling process on rotation speed adjustment is reduced, and the tension adjusting precision in the fabric rolling process is improved.

The invention discloses a chemical plant safety early warning and multi-dimensional correlation analysis method and device and a storage medium. By collecting equipment operation parameters, personnel operation and environment data, a reference model library is constructed to realize dynamic threshold early warning; after the early warning is triggered, calculating a first correlation index, a second correlation index and a third correlation index respectively by adopting time sequence similarity analysis, historical statistical analysis and process knowledge graph reasoning; and positioning the core inducement through multi-index weighted fusion. The problems that a traditional method is poor in early warning adaptability, weak in system collaboration and insufficient in tracing depth are solved, and accurate early warning, deep tracing and closed-loop management are achieved.

The invention provides a trade whole industry chain data intelligent matching and resource docking method, and relates to the technical field of international trade data analysis and geographic information systems.The method comprises the steps that customs declaration data, logistics tracking data, enterprise supply chain data and market public opinion data are collected in real time through a standardized interface; constructing a multi-source heterogeneous data fusion system of block chain evidence storage; dynamically modeling a whole industry chain relation network based on the knowledge graph, and quantifying the supply chain dependency, the trade flow direction and the policy influence weight; calculating an industrial chain correlation index ICRI and a resource matching priority coefficient RMPC, optimizing a logistics path in combination with a geographic space analysis algorithm, and avoiding a policy restriction area in real time; and rendering a matching result through a three-dimensional visualization platform, and iteratively updating model parameters based on user interaction feedback. According to the method, the limitation of a traditional data island is broken through, dynamic quantitative analysis and accurate resource matching are realized, and the trade data integration efficiency is remarkably improved through intelligent path planning and interactive decision support.

The invention relates to the field of image processing, in particular to an oilstone static pressure forming quality detection method based on image processing, and the method comprises the steps: obtaining an infrared image set of an oilstone sintering and cooling stage, carrying out the superpixel segmentation to obtain a superpixel region of each frame of image, matching the superpixel regions of adjacent frames, and building a corresponding relation, and calculating a temperature anomaly characteristic value of each region. And analyzing the temperature change trend of each region, calculating a correlation coefficient, and introducing distance and temperature anomaly weight to carry out weighted fusion to obtain a comprehensive correlation index. And in combination with the temperature anomaly characteristic value and the comprehensive correlation index, evaluating the possibility value of occurrence of pore anomaly in each superpixel region, and judging whether pore distribution anomaly exists in the oilstone or not, thereby completing quality detection. According to the method, through superpixel segmentation and matching, in combination with temperature anomaly characteristics and comprehensive correlation analysis, precise detection of oilstone internal pore anomaly is realized, and the reliability of static pressure forming quality detection is improved.

The invention discloses a traditional Chinese medicine diagnosis and treatment aided decision-making method and system, and relates to the technical field of traditional Chinese medicine intelligent diagnosis and treatment aided decision-making, and the method comprises the steps: carrying out the wavelet transformation analysis of pulse condition waveform time series data, extracting a pulse condition dynamic change feature sequence, inputting a tongue image color change rate and a pulse condition feature change rate into a space-time convolution network, and obtaining a time-space convolution network; generating a tongue pulse dynamic association index and outputting a preliminary syndrome classification; fourier transform analysis is carried out on the spleen channel acupoint impedance spectrum, false-true state judgment is carried out in combination with tongue pulse dynamic correlation indexes, preliminary syndrome classification is corrected, and a false-true state quantification result and corrected syndrome classification are output; and matching the corrected syndrome classification with the traditional Chinese medicine knowledge map, adjusting the dosage according to a virtual-real state quantification result, reversely verifying the drug-syndrome specificity through a curative effect database, screening an optimal treatment scheme, and outputting a personalized diagnosis and treatment scheme. According to the method, the syndrome classification precision is improved, and an objective basis is provided for personalized dose adjustment.

The invention belongs to the technical field of fault detection, and particularly relates to a wire drawing machine fault detection method based on the Internet of Things. Historical vibration and temperature and real-time data of operation of the wire drawing machine are collected through an Internet of Things sensor and are transmitted to a server through an Internet of Things protocol. After data are subjected to preprocessing denoising such as variational mode decomposition and a FastICA algorithm, a vibration anomaly judgment index (including effective anomaly time period judgment, vibration mode matching degree calculation and index synthesis) and a temperature anomaly judgment index (including correlation index calculation, heat conduction model establishment and index synthesis) are calculated, and then the two indexes are fused to obtain an anomaly index. A prediction model is constructed by using a neural network algorithm, after optimization of a genetic algorithm, a fault probability is output in combination with real-time data, and finally, a probability value and an abnormal index are normalized and averaged to obtain a fault score, so that fault detection and evaluation are realized. The method improves the fault detection accuracy and the equipment operation reliability, and reduces the maintenance cost.

The invention provides a mechanical and electrical product cross-domain semantic matching method and system, and the method comprises the steps: obtaining a sample set, the sample set comprises sub-samples related to mechanical and electrical products in a plurality of domains, and the sub-samples comprise labeled samples and unlabeled samples, the annotation sample comprises a query statement, text data and a correlation index between the query statement and the text data; performing incremental pre-training on the pre-training model according to the unlabeled samples to obtain an intermediate model; performing data enhancement on the labeled sample by utilizing a large language model to obtain an enhanced sample; and performing supervision fine tuning on the intermediate model according to the labeled sample and the enhanced sample to obtain a cross-domain semantic matching model. According to the method, the problem of poor generalization of a mechanical and electrical product cross-domain semantic matching model can be improved.

An exchange computer system configured to continuously, securely, dynamically, and efficiently generate and disseminate implied correlation index values is described. The exchange computer system may include at least one communication interface that is configured to receive, via a computer network, data related to a stock market index option, data related to a plurality of stocks, and data related to a plurality of stock options. The exchange computer system may further include at least one non-transitory computer-readable medium configured to store data received over particular periods of time, and an index engine including at least one hardware processor that is configured to continuously generate and disseminate implied correlation index values for display on a plurality of user computing devices that are connected via the computer network, and that are configured to facilitate trading of financial instruments related to the implied correlation index via a graphical user interface.

The invention discloses a document reordering method and device and electronic equipment, and belongs to the technical field of artificial intelligence. The method comprises the steps that in response to received query information, at least one candidate document matched with the query information is obtained, and the candidate document comprises at least one candidate sentence; dividing the query information into a plurality of query elements, and for each query element, based on the similarity between each candidate sentence in the at least one candidate document and the query element, obtaining k first candidate sentences of which the similarity with the query element is greater than a preset value in all candidate sentences in the at least one candidate document; based on the number of the first candidate sentences included in each candidate document, obtaining a correlation index of each candidate document and the query information; and according to the correlation index of each candidate document, reordering each candidate document. Through the method, the calculation complexity of document reordering can be reduced, and the efficiency can be improved.

The invention discloses an FME-based adjacent pattern spot fusion method and system, and relates to the technical field of adjacent pattern spot fusion, and the method comprises the steps of data processing, parameter setting and rule configuration, adjacent pattern spot fusion execution, result verification and output and feedback and optimization, fusion standard and rule determination, index system unified evaluation, and spatial relationship optimization processing. Manual intervention is reduced through the automatic working process, and the processing efficiency is improved; the invention further provides an adjacent pattern spot fusion system based on FME, the adjacent pattern spot fusion system comprises a data preprocessing module, a data input module, a parameter setting and rule configuration module, a comprehensive correlation index calculation module, a pattern spot fusion module and a result verification and output module, and the adjacent pattern spot fusion method based on FME is executed.

The invention relates to a monitoring and early warning method and device for coal mine rock burst display. The method comprises the following steps: acquiring ground sound frequency data and ground sound energy data of a target monitoring area in a coal mine in real time; storing the ground sound frequency data into a frequency data set, and storing the ground sound energy data into an energy data set; based on the frequency data set and the energy data set, determining a first relevance index value between the rock sound frequency and the rock sound energy interval in the current rock burst period; based on the energy data set and a plurality of preset energy intervals, determining a second relevance index value between the rock noise energy and the energy intervals in the current rock burst period; according to the first relevance index value and the second relevance index value, the danger degree of rock burst in the target monitoring area is determined, and early warning is carried out based on the danger degree. According to the scheme, the accuracy and timeliness of rock burst early warning are improved.

The invention discloses an automatic conversion analysis method and system based on an implicit common cause fault model, and relates to the technical field of power system fault analysis, and the method comprises the steps: obtaining and marking power equipment, forming a plurality of equipment sets, setting a monitoring period for operation monitoring, and storing data into an operation record library; extracting feature data of the equipment, calculating a correlation index between the equipment according to the category of the equipment, and calculating the correlation between the same category of equipment and different categories of equipment by adopting different strategies; utilizing an implicit common cause fault model to calculate the probability that the reference equipment and other equipment are abnormal at the same time under each abnormal cause, including an independent failure probability, a common cause failure probability and a comprehensive probability; and according to the comparison of the comprehensive probability and a preset threshold value, the monitoring period of the equipment exceeding the threshold value is adjusted, and according to the abnormal reason, the monitoring period of all the associated equipment is also adjusted, so that the effective monitoring and fault prevention of the power system are realized.

The invention relates to the technical field of anti-seismic performance detection, in particular to an anti-seismic performance detection method and system for an assembled steel-wood composite structure, and the method comprises the steps: arranging a multidirectional acoustic emission sensor in a steel-wood structure node area, applying simulated seismic vibration, and synchronously collecting acoustic emission signals; the method comprises the following steps: preprocessing collected acoustic emission signals, dividing the acoustic emission signals into signals corresponding to microcracks and contact slippage, respectively calculating an energy attenuation rate and a phase offset, and constructing a matrix; micro-impact is applied to the steel-wood node through an electromagnetic excitation device, a response signal is collected, resonant frequency is extracted, the drift distance between the resonant frequency and a reference value is calculated, and the severity of interface damage is determined according to the value of the drift distance; the damage correlation index calculation takes the characteristic value of the interface wave propagation characteristic matrix and the resonance frequency drift distance as input, the damage correlation index is calculated through the correlation coefficient, and the damage grade is divided according to the correlation coefficient. And differential characterization of different damage types is realized through the interface wave propagation characteristic matrix.

The invention relates to the technical field of hydraulic ring geology monitoring, in particular to a hydraulic ring geology monitoring system and a monitoring method thereof.The hydraulic ring geology monitoring method comprises the steps that a microseismic sensing array and a distributed optical fiber temperature sensing network are arranged, and three-dimensional vibration waveform data and axial temperature gradient data of a target area are collected; and feature parameters such as micro-seismic event space positioning, vibration energy release and temperature anomaly gradient are extracted through space-time registration processing. And calculating a seepage mutation correlation index based on the characteristic parameters, inputting the seepage mutation correlation index into a pre-constructed three-dimensional seepage-stress coupling model, generating a formation pore pressure distribution cloud picture and a rock mass stability coefficient K, and improving the seepage mutation monitoring precision by dynamically adjusting a weight coefficient of the seepage mutation correlation index and updating boundary conditions of the coupling model. According to the invention, the detection capability of seepage mutation is improved, a real-time and accurate prediction result can be provided for a complex geological environment, and effective support is provided for safety monitoring in the fields of mines, underground engineering and the like.

The invention provides an energy storage equipment service life prediction method and system, electronic equipment and a storage medium, and belongs to the technical field of equipment health prediction, and the method comprises the steps: obtaining operation data corresponding to target energy storage equipment; a target correlation index is obtained, the target correlation index is a correlation index between the multiple factors and the service life of the energy storage equipment, and the target correlation index is obtained through calculation based on historical operation data of the multiple pieces of energy storage equipment; target nonlinear change features are obtained, the target nonlinear change features are nonlinear change features between a multi-factor association vector extracted based on a causal index and the service life of the energy storage equipment, and the multi-factor association vector is constructed based on data corresponding to multiple factors; and inputting the target correlation index, the target nonlinear change feature and the operation data corresponding to the target energy storage equipment in the target time period into an energy storage equipment life prediction model for equipment life prediction to obtain a life prediction result. The accuracy of predicting the service life of the energy storage equipment can be improved.

The invention discloses an image classification method and device based on a pruning neural network, equipment and a medium, and the method comprises the steps: traversing a convolution layer of an original image classification model, and constructing a Gaussian mixture model based on the weight data of the convolution layer; calculating a zero activation rate of a convolution kernel in each convolution layer; based on the Gaussian mixture model and the zero activation rate of the convolution kernels, calculating the composite convolution correlation index of each convolution kernel, selecting the convolution layer with the highest average value of the composite convolution correlation indexes as a target convolution layer, removing the convolution kernels with the composite convolution correlation indexes greater than a preset threshold in the target convolution layer, and obtaining the target convolution layer. A pruned image classification model is obtained; and inputting image data to be processed into the pruned image classification model to obtain image classification data. According to the method provided by the invention, the image classification model can be compressed while the model precision is ensured, so that the feasibility of deploying the image classification model on lightweight equipment such as edge side nodes is improved.

The invention provides a low-voltage arc fault detection method and apparatus, and an electronic device. The method comprises the steps of obtaining m current data of a target low-voltage power distribution network within a preset time period; m is a positive integer greater than or equal to 1, feature extraction is performed on the m current data based on a preset feature extraction method, m first feature sets are obtained, and each first feature set comprises n first features; n is a positive integer greater than or equal to 24, performing correlation calculation on n first features in the target first feature set to obtain a correlation index scores, and performing feature selection on the target first feature set according to the a correlation index scores by adopting a preset feature selection method to obtain a target second feature set; and inputting the target second feature set into a preset arc detection model to obtain a target arc detection result. When arc fault detection is carried out, the accuracy and the stability of fault detection can be improved.

The invention discloses a chemical process anomaly type identification method based on a multi-dimensional causal association index, and belongs to the technical field of chemical process monitoring and fault diagnosis. The method comprises the following steps: firstly, carrying out offline modeling: acquiring normal working condition data, constructing a monitoring model by utilizing a trend slow feature analysis algorithm, and determining a statistic control limit; during online application, statistics of real-time data are calculated through a model, and an alarm is given and a type identification process is triggered if the statistics exceed a limit. A multi-dimensional causal association index (MCAI) is constructed by analyzing whether a quality variable deviates or not and further performing comprehensive analysis on a root variable causing an anomaly from four dimensions of a position (CPI) of the root variable in a process, an attribute (CAI) of the root variable in a causal network and an influence propagation capability (CPD) of the root variable. The problem that a traditional anomaly detection method cannot distinguish process faults from new working conditions is solved, false alarm can be effectively avoided, and a decision basis is provided for accurate maintenance.

The embodiment of the invention provides a sitting posture human spine stability evaluation method and device in a vibration environment. The method comprises the steps of obtaining current human spine sitting posture data; performing curve calculation on the human spine sitting posture data to obtain a corresponding score; and generating a corresponding human spine report based on the score. According to the method, the curve graph corresponding to the spine sitting posture data of the human body is analyzed and calculated, the corresponding human body spine report can be generated in combination with the correlation index between the trunk and the pelvic bone, and the sitting posture human body spine is evaluated according to the report. And the stability of the human body sitting posture spine is calculated and evaluated, so that the detection efficiency and accuracy of the human body spine movement mode and data are improved.

The invention relates to the technical field of circuit board analysis, in particular to an integrated circuit defect analysis method and system, and the method comprises the steps: extracting and screening E-test data of a target product from a big data platform for integrated circuit manufacturing; performing defect classification modeling processing on the screened E-test test data; calculating a defect correlation index according to the classification accuracy and the correlation metric value of each candidate E-test item; determining a defect judgment threshold value and a numerical value distribution direction related to the defect based on a logic regression classifier result of the key test item, and generating a binary defect distribution diagram corresponding to each jointed board; calculating the image similarity between the bad jointed board graph and each good jointed board graph; judging whether the image similarity is higher than a preset threshold value or not, and adding the jointed boards of the good jointed board graph into a virtual bad jointed board list; and based on the virtual bad jointed board list, analyzing and comparing the manufacturing process flow data of the virtual bad jointed board and the original defective jointed board, and determining the potential root cause causing the defect. According to the method, the problems that in the E-test scene of the circuit board, the number of defect samples is extremely small, categories are extremely unbalanced, an experience threshold value is difficult to set, and root causes are difficult to trace can be solved.

The invention discloses a GIS disconnecting switch full-state quantity edge intelligent analysis system based on the Internet of Things, particularly relates to the field of online monitoring of high-voltage power equipment, and is used for solving the problem of false correlation interference caused by lack of stage anchor points and causal constraints in an existing power sequence overall synchronization method. Therefore, the problems of the jitter of the opening and closing position criterion and the inconsistency of multi-mode diagnosis can be solved. According to the method, event anchor points are extracted from a power sequence, an auxiliary contact sequence and a travel sequence on the edge side, a unified time axis and a stage tag sequence are constructed, and a multi-modal fragment stream is segmented; generating a causal mask according to the stage label, calculating a masking mutual information matrix, and outputting a peak sequence and a peak sequence index; calculating a mutual information arc length ratio and a peak sequence accumulative homomorphism in the window, assigning a trust mark based on a prototype facet area coefficient, and outputting a position sequence in the stage; and splicing the position track and generating an evidence association index to realize full state quantity intelligent analysis.