224 results about "Industrial data processing" patented technology

The invention relates to the technical field of intelligent control of test equipment, and discloses a semiconductor device test equipment control system and method based on industrial data processing, and the method comprises the steps: collecting multi-source heterogeneous data of semiconductor device test equipment, and carrying out the preprocessing; constructing a structural causal model, and performing root cause identification through anti-fact reasoning by using the structural causal model; constructing an abnormal test fingerprint and a knowledge base; generating an intervention scheme, evaluating the generated intervention scheme, and selecting an optimal intervention scheme; processing the detected anomaly, and generating and implementing a preventive control strategy based on historical anomaly data and a causal analysis result; according to the invention, by introducing innovative technologies such as causal inference, anti-factual inference, abnormal test fingerprint identification and Monte Carlo tree search, intelligent control of semiconductor test equipment is realized.

The invention relates to the technical field of industrial data processing, in particular to a suspension spring multi-station production line collaborative scheduling system based on digital twinning, which comprises a station control programming module, a multi-station autonomous collaborative module, a quality pre-control module, a manufacturing process tracing module and a multi-machine execution safety module, according to the system, through real-time data acquisition and monitoring of an integrated digital twin platform, self-adaptive generation of control programs of all stations and programmed issuing of production instructions are carried out; when the production disturbance is sensed, dynamic optimization of a task sequence and real-time self-adaptive adjustment of processing parameters are executed; feedforward control and active compensation are realized in combination with a quality prediction model; constructing a digital thread to support tracing and analysis of the whole manufacturing process; and dynamic program verification of security interlocking and resource conflicts is cooperatively executed on multiple machines. Intelligentized, high-efficiency and high-quality integrated optimization control over the multi-station production process of the suspension spring is achieved.

The invention discloses a BIM-driven tunnel construction full life cycle management method and system, and relates to the technical field of industrial data processing and analysis, and the method comprises the steps: building a BIM model covering the full life cycle of a tunnel and a data mapping linkage relation; collecting monitoring data, accessing the monitoring data to the BIM model, and performing dynamic linkage evaluation to obtain a construction state result; and constructing a safety management mechanism according to an evaluation result and feeding back field guidance. The technical problem that in tunnel construction full-life-cycle management, a traditional industrial data processing mode cannot achieve full-life-cycle integration and cross-stage linkage of multi-source industrial data in the stages of design, construction and operation and maintenance is solved. The technical effects of full-life-cycle integration and cross-stage linkage processing of the industrial data in tunnel design, construction and operation and maintenance stages, improvement of the comprehensiveness, collaboration and timeliness of industrial data processing, and accurate acquisition of the construction state to optimize on-site safety management are achieved.

The invention relates to the technical field of automatic production control and industrial data processing, in particular to a PLC-based gypsum-based flame-retardant plate production line control system and method, and the system comprises a data collection module which collects sensor data in real time; the ideal state construction module is used for constructing a pure ideal state vector; the disturbance simulation module is used for generating a theoretical damaged state vector corresponding to a specific fault mode; the difference calculation module is used for generating real and theoretical deviation vectors; the coupling verification control module is used for calculating a similarity value between the actual deviation vector and the theoretical deviation vector; if the similarity value is larger than a preset judgment threshold value, it is judged that a real physical fault exists at present, and a PLC precise compensation instruction is generated and sent to a production line execution mechanism; otherwise, judging that non-physical noise exists at present, keeping the control parameters of the production line unchanged, and starting a dynamic filtering program; according to the method, the problem that physical faults and sensor clutters are difficult to distinguish in a high-noise environment is effectively solved, and missing report is avoided.

The invention discloses a food package fault monitoring method and system, and particularly relates to the field of food package fault monitoring, and the method comprises the steps: extracting a structure response variable of a food package object in an operation environment, constructing a disturbance expression sequence, and taking the disturbance expression sequence as a basis for path evolution recognition and industrial data processing analysis in the food package fault monitoring method; the method comprises the following steps: constructing a structure coupling mapping graph by analyzing a physical dependency relationship between food packaging structure arrangement and adjacent units; the change trend of a path disturbance parameter sequence formed by disturbance parameters corresponding to all paths in a structural path set is analyzed. By constructing a multi-level identification mechanism based on a structure disturbance expression sequence and a path propagation chain, industrial data capture and closed-loop traceability of a hidden defect propagation trend in a structure interdependence relationship between food packaging units are realized, so that the problem that the overall structure instability of a cluster type package cannot be identified in time is solved.

The invention provides a report loading processing method, system and device in an industrial scene and a medium, and belongs to the technical field of report loading. Data source information associated with a service entity identifier is obtained; establishing a dynamic binding relationship between business entity attributes and data source fields, and generating an index metadata template; acquiring data source information associated with the service entity identifier through a data registration component; establishing a calculation mapping relation between the index field and the data source field; generating a standardized configuration file; persisting the generated standardized configuration file to a database; a three-party incidence relation index among an index instance identifier, a service entity identifier and a data source identifier is established, in actual industrial data processing, even if network faults and other problems occur, it can be guaranteed that configuration files are accurately updated, when service requirements change, calculation rules of corresponding levels can be rapidly positioned and adjusted, and the service requirements are met. And the flexibility and accuracy of data processing are improved.

The invention relates to the technical field of industrial data processing, in particular to a model-driven FlinkSQL (Structured Query Language) dynamic optimization method, which comprises the following steps of: generating candidate SQL (Structured Query Language); carrying out parallel acquisition and noise reduction processing; constructing a causal dependency graph and embedding the graph; constructing a spatio-temporal feature vector and performing steady-state judgment; performing disturbance sandbox verification and stability judgment; a fault-tolerant strategy is injected, and DML is compiled and produced; and deploying tasks and monitoring and updating in real time. Physical signals of industrial equipment are stripped through a wavelet noise reduction technology, then operation indexes of the Flink platform are subjected to structured expression in combination with causal dependency graph embedding, quantitative classification is performed on current operation and ideal steady state, robustness is verified through multiple cycles, adaptive parallelism and a fusing strategy are injected into SQL, and the method has the advantages of being high in robustness and high in reliability. And the reference base point is automatically updated according to the monitoring result, so that the problems of low SQL execution efficiency, insufficient stability and difficulty in adapting to complex industrial scenes caused by neglecting environment change and system load fluctuation during operation are effectively solved.

The invention belongs to the technical field of industrial data processing, and particularly relates to a production enterprise energy consumption data prediction method based on machine learning. The method comprises the following steps: deploying an Internet of Things sensor to collect power and equipment operation data, and after three-stage preprocessing of anomaly detection, deletion repair and normalization, constructing a layered feature extraction network which comprises a time sequence feature layer, an equipment association feature layer and a modal fusion layer; the two features are respectively used for capturing power consumption features and equipment collaborative consumption features and fusing the features to generate depth feature vectors; and constructing a dynamic integrated prediction model, training three types of base models including a support vector machine and the like, screening a model entering an integrated pool by using JS divergence, and optimizing the weight through a genetic algorithm to obtain a final prediction result. The method can accurately capture the characteristics of energy consumption data, effectively improves the prediction precision, assists an enterprise in reasonably planning energy use, and reduces the cost.

The invention relates to the technical field of rare earth waste recovery, in particular to a rare earth waste recovery process monitoring system and method based on industrial data processing, and the method comprises the steps: importing component composition data and magnetic characteristic data of rare earth waste in a rare earth waste magnetic separation process into a rare earth waste magnetic separation state analysis model; analyzing the magnetic separation state of the rare earth waste in the magnetic separation process of the rare earth waste; importing the equipment state data of the recovery equipment into the recovery equipment processing state analysis model, and analyzing the equipment processing state of the recovery equipment in the rare earth waste magnetic separation process; according to the rare earth waste magnetic separation state analysis result and the equipment treatment state analysis result of the recovery equipment in the rare earth waste magnetic separation process, the magnetic separation treatment benefit of the rare earth waste is evaluated; and according to the magnetic separation treatment benefit evaluation result of the rare earth waste, optimization early warning is conducted on the subsequent recovery process of the rare earth waste. And by analyzing the equipment state and sorting parameters, the rare earth treatment benefit is improved, and the equipment energy consumption cost is reduced.

The invention relates to the technical field of industrial data processing, in particular to an industrial quality monitoring and analyzing system based on industrial Internet of Things, which comprises a data acquisition module, a drift detection module, a countermeasure compensation module, a dynamic optimization module and a quality monitoring module. According to the method, multi-dimensional parameters are collected through the industrial Internet of Things nodes and the sensors, real-time consistency and comprehensiveness of parameter data are ensured in combination with timestamp alignment and data verification, and fine recognition of parameter fluctuation in a dynamic environment is realized based on distribution deviation quantization and fluctuation value evaluation. By combining an optimization strategy of adjusting compensation factor weights in real time, adaptive adjustment of process parameters is more flexible and efficient, a dynamic analysis and matching strategy is utilized, a product quality fluctuation rate and a key parameter deviation value are evaluated in real time, machining quality is accurately controlled, product consistency and production efficiency are improved, and resource loss and adjustment delay caused by abnormities are reduced.

The invention relates to the technical field of optical cable fault detection, and discloses an optical cable fault detection method based on industrial data processing, which comprises the following steps: acquiring optical fiber splicing point data of an optical fiber in an optical cable, judging whether parameters of the optical fiber splicing point are normal, and verifying the accuracy of the parameters of the optical fiber in the optical cable; according to the optical cable fault detection method based on industrial data processing, whether the parameters of the optical fiber connection point are abnormal or not is judged, a simulation model is generated according to different interference conditions and different optical fiber conditions, and the optical cable fault detection accuracy is improved. The method comprises the following steps: acquiring parameters of an optical fiber connection point, judging whether the acquired parameters of the optical fiber connection point are accurate, automatically recovering to real data when the acquired parameters have interference, and automatically adjusting data needing to be transmitted when the parameters of the optical fiber connection point are abnormal, thereby reducing errors in data transmission, avoiding loss of a user, improving the detection accuracy and improving the detection efficiency. And the detection efficiency is improved.

The invention relates to the technical field of industrial data processing, and discloses a copper foil production monitoring method and system based on data storage optimization, and the method comprises the following steps: carrying out the periodic aggregation of high-frequency data when a production process is in a steady state, and carrying out the calculation of a newly recognized production process event when the production process is monitored to deviate from the steady state. Whether causal association exists or not is judged by calculating the correlation between the new event and the current activity event in time and physical dimensions, and if the association exists, a chained event ID inheriting a precursor event identifier is generated for the new event; if the event is an independent event, a brand-new root event ID is generated, context data before the event occurs is extracted in a backtracking mode, and the generated event ID is marked on a data set in a unified mode in combination with real-time data during the event occurrence period for permanent storage. According to the method, the storage configuration is obviously optimized, and the efficiency of fault causal chain tracing is improved through the structured chain ID.

The invention discloses an urban rainstorm disaster dynamic risk assessment method and system based on multi-source data fusion, and relates to the technical field of industrial data processing and big data, and the method comprises the steps: deploying an edge data network at a distributed source end, and determining urban dynamic data; constructing a simplified feature space, adding a time axial direction, and updating the simplified feature space; a multi-dimensional index system is introduced, a dynamic evaluation module is constructed, rainstorm disaster risk evaluation is executed, and a risk evaluation result is determined; executing interface display of the dynamic risk; the multi-dimensional index system comprises disaster-inducing factor danger, disaster-pregnancy environment exposure, disaster-bearing body vulnerability and disaster prevention and reduction capability. The technical problems that traditional urban rainstorm disaster risk assessment mainly depends on a single data source or a simple assessment model, multi-aspect factors are difficult to comprehensively consider and a multi-source data fusion effective mechanism is lacked are solved, accurate and comprehensive assessment of urban rainstorm disaster risks is achieved, and the urban rainstorm disaster risk assessment efficiency is improved. And scientific decision support is provided for urban emergency management.

The invention discloses a new energy electricity price accurate prediction method and system based on virtual power plant aggregation regulation and control, and relates to the technical field of industrial data processing. The method comprises the steps: S1, carrying out the preprocessing of meteorological environment data, output equipment data and battery power grid operation data; s2, constructing a variation dynamics analysis and mutation depth discrimination mechanism based on the meteorological environment data, and entering an output prediction and optimization process and a scheduling load response process in a layered manner; s3, executing energy storage collaborative optimization scheduling and parameter correction according to a meteorological output regression prediction analysis result; and S4, performing electricity price time sequence multi-source driving analysis, and reconstructing an electricity price fluctuation response strategy. The problems that new energy output violently fluctuates within a minute level due to sudden weather change, and electricity price prediction lags due to the fact that an existing prediction model depends on too low weather data updating frequency and cannot capture the rapid change in time are solved.

The invention belongs to the technical field of industrial data processing, and provides an intelligent material matching method and system fusing multi-modal features and fuzzy matching, and the technical scheme is that data in obtained electronic component list data is analyzed, key characters in character strings are identified, and a constructed mapping table is called to carry out mapping replacement on the key characters, so that the matching accuracy of the electronic component list data is improved. Obtaining each field parameter of the mapped electronic component; performing matching based on the constructed manufacturer alias knowledge graph and the mapped manufacturer field parameters to obtain a manufacturer matching result; screening the mapped material number parameters to obtain a material number candidate set, performing semantic similarity calculation based on the material number candidate set and a constructed special word vector model in the field of electronic components, when the similarity is greater than a set threshold, performing accurate matching, otherwise, triggering fuzzy matching, calculating a service score according to a matching result, and obtaining a service result; and performing multi-objective optimization based on a service score result to obtain an optimal matching scheme. And the matching accuracy and the purchasing decision-making efficiency are obviously improved.

The invention relates to the technical field of industrial data processing, and provides an industrial internet production scheduling method and system based on multiple modes. Industrial data in the production process are obtained through an intelligent sensor in the industrial internet, modal parameters corresponding to all modals are calculated, and space-time alignment is carried out to generate an industrial tensor; decomposing and combining the industrial tensor through a preset first decomposition mode and a preset second decomposition mode to obtain a data tensor; generating a scheduling strategy based on to-be-optimized parameters screened from a parameter space of production scheduling and a preset Lie algebra base, and constructing a strategy function based on the scheduling strategy and the data tensor; and adjusting a modal weight factor in the strategy function to optimize a scheduling strategy in the strategy function to generate a target strategy, and performing distributed control on each industrial device through the industrial control terminal. The digital twin base constructed through the intelligent sensor ensures the real-time performance of data, the scheduling and production efficiency is improved, and the idle rate of equipment is reduced.

The invention discloses an industrial software platform system based on a large model, and aims to improve the industrial data processing efficiency and the intelligent level by integrating a large model technology, domain knowledge and an information system interface. The system comprises a vertical large model module, an agent module and a universal API interface module. The vertical large model module adapts a general large model to a specific vertical field by using a GraphRAG technology and a LoRA fine tuning technology. The agent module comprises a memory module, a tool calling module and a planning module, and the autonomous decision-making ability of the large model is improved. The universal API interface module realizes seamless connection between a large model and an industrial information system, and opens up a gap between different information systems. According to the system, efficient data processing and intelligent decision making are achieved through knowledge graph construction, retrieval enhancement generation, model fine adjustment and a universal API interface, and the system adapts to market requirements and industrial environment changes.

The invention relates to the technical field of industrial data processing and intelligent control, and discloses an iron ore concentrate grinding medium intelligent regulation and control method and system based on real-time data processing, and the system comprises a sensing layer, an edge calculation layer, a cloud intelligent layer and an execution layer; according to the method, multi-dimensional data are obtained in real time and processed through a professional data preprocessing process, complex process characteristics are condensed into three core indexes including a particle size distribution index, a chemical component index and an energy consumption process index, and fuzzy worker experience is converted into an accurate numerical index; a reinforcement learning algorithm is introduced to enable the system to autonomously learn an optimal control strategy, so that the system has advanced intelligence of prediction, learning and global optimization; millisecond-level real-time response of index grading and medium regulation and control is achieved through the edge computing unit, timeliness of control is ensured, depth computing and model training are carried out based on cloud capacity, and automation and closed loop of the whole process from data collection to instruction execution are ensured.

An electrolytic cell data processing method for large model training relates to the technical field of industrial data processing in the electrolytic aluminum industry, and comprises the following steps in sequence: step S01, collecting static data and dynamic production data of an electrolytic cell; s02, setting a data sampling interval; s03, constructing a data set according to the time dimension; step S04, checking data missing; s05, data missing processing is carried out; step S06, performing continuous data curve analysis on each column of data, and detecting abnormal values existing in the data; and S07, data outlier analysis, data outlier processing, and storage of the processed data as a data set. By collecting, processing and optimizing static and dynamic data in the production process of the electrolytic cell, the quality and reliability of the data are effectively improved, and a high-quality data basis is provided for training and application of a large model.

The invention relates to the technical field of industrial data processing, in particular to an industrial Internet of Things data classification method and device and a storage medium, and the method comprises the steps: collecting sensor data in real time, and carrying out the filtering processing; performing time-frequency domain feature extraction on the sensor data to obtain data window features; analyzing a window comprehensive fluctuation index according to the sensor data, and performing type marking on the sensor data according to the window comprehensive fluctuation index; constructing a window feature vector based on the data window feature, and analyzing the type similarity based on the window feature vector and the type tag of the sensor data; constructing a data classification model based on the data window features and the type tag of the sensor data to obtain a data classification tag; and triggering a data classification model updating rule according to the type similarity and the type mark of the sensor data. According to the invention, accurate classification analysis of the Internet of Things sensor data is realized.

The invention relates to the technical field of data processing, in particular to an industrial data batch processing method and system, and the method comprises the steps: determining a command type value in a command type field in a unique protocol data unit according to a command type adopted by batch access; the command type value is used for indicating each target slave station to determine a read-write data type of batch access and providing a calculation mode of the target slave station; determining a first value and a second value respectively corresponding to a first address field and a second address field in the protocol data unit according to the target slave station segment to be accessed in batches and the command type value; the first value and the second value are used for each slave station to confirm whether the slave station is a target slave station according to the own address after determining the calculation mode; based on the unique protocol data unit, the batch processing messages are generated and sent to the target slave stations, and the problem that when an existing Ethercat protocol is applied to an industrial scene, the data processing efficiency is low can be solved.

The invention relates to the field of industrial data processing and analysis, in particular to an intelligent production system for polyester-viscose color spinning antistatic fabric and a medium. The method comprises the following specific implementation steps: collecting acoustic data streams generated by breakage of conductive fibers, synchronously collecting image data streams, carrying out refined classification and quantification on yarn hairiness and broken ends, fusing multi-source data by adopting Kalman filtering, and inputting the fused multi-source data into a gated recurrent neural network to predict future quality deviation. An optimal control action sequence is generated based on a model prediction control algorithm, and multiple technological parameters are actively adjusted to avoid production defects. According to the method, the fundamental transformation from passive defect detection to active defect avoidance is realized, the defect prediction precision and the control optimality are remarkably improved, and the diagnosis and insight ability of the production process is enhanced through refined defect classification.

The invention relates to the technical field of industrial data processing, in particular to an intelligent safety accident early warning method and system based on risk assessment, and the method comprises the steps: obtaining a mining operation event, extracting response situations before and after the operation event, checking the response situations with a reference response template, and judging a disturbance response according to the deviation degree; a ventilation network model is constructed, and when it is detected that a monitoring point is abnormal, management traceability is carried out along a traversal upstream path in combination with an air volume distribution proportion; predicting the propagation trajectory of the abnormal gas along the wind flow path and the time of reaching a downstream node based on the ventilation network model, and obtaining spatial correlation analysis; analyzing the disturbance response and the spatial correlation, calculating a coupling risk increment, and outputting a comprehensive risk value; judging the critical degree grade of the comprehensive risk value time series data; and determining a scheduling level according to the combination of the comprehensive risk value level and the critical degree level, when the critical index indicates to evolve to a dangerous state, improving the scheduling level in advance, and generating a differentiated resource scheduling suggestion in combination with the propagation trajectory.

The invention discloses a complex equipment state monitoring data missing value complementing method based on an adaptive time sequence diagram convolutional network, and relates to the technical field of industrial data processing, and the method comprises the steps: obtaining incomplete state monitoring data, carrying out the preprocessing of the incomplete state monitoring data, and generating a mask matrix according to the incomplete state monitoring data, then inputting the two features into a trained mask auto-encoder time sequence interpolation model, extracting spatial-temporal features of monitoring data in an incomplete state through an adaptive graph convolutional network layer, extracting time sequence features of a mask matrix through a second bidirectional gating cycle unit, and fusing the two features to obtain a mask auto-encoder time sequence interpolation model; and decoding and outputting through a decoder to obtain complemented state monitoring data. According to the scheme, the adaptive graph convolutional network is used for operation along the time dimension, the spatio-temporal characteristics of the data can be fully mined, and the bidirectional gating cycle unit is used for mining the time sequence characteristics from the mask data so as to reconstruct the missing data and complete the missing value.

The invention discloses an indoor environment monitoring method based on thermal comfort analysis, and particularly relates to the field of indoor environment industrial data processing and monitoring, which comprises the following steps: deploying a temperature and humidity sensor and a carbon dioxide sensor based on gridding, dynamically clustering a personnel dense area and migrating nodes, the Manhattan distance and random sampling consistency technology is combined to execute fault self-inspection and calibration; infrared and millimeter wave radars and a color depth camera are integrated to acquire radiation temperature and physiological data. The sensor layout is optimized through the dynamic clustering algorithm, and the fault self-inspection technology is integrated, so that the monitoring reliability of the dense area is improved; multi-modal data are aligned by using space-time diagram convolution, and privacy security is ensured in combination with a dual-encryption mechanism; the multi-region air distribution is coordinated based on the block chain to avoid conflicts; adopting reinforcement learning to construct a heat transfer model fused with physiological parameters to enhance simulation adaptability; and on the basis of regulation and control of federal learning linkage equipment, global optimization of industrial data processing of personalized thermal comfort and energy consumption balance is realized.

The invention relates to the technical field of plate processing production management and industrial data processing, in particular to a plate production order management system based on edge sealing drilling, which comprises an order data acquisition step: constructing a holographic data input layer, and acquiring a process data set and real-time performance process data; a performance benchmark construction step: establishing a dual reference system, generating an ideal performance time benchmark, and generating theoretical performance delay evaluation data in combination with a process complexity parameter; a performance difference analysis step: separating the noise and the signal, and respectively calculating an actual performance deviation and a theoretical performance deviation only containing a process complexity factor; an order state judgment step: analyzing the distribution mode similarity of the deviation, if the distribution mode similarity is higher than a threshold value, judging that compliance process delay exists and the delivery time is adjusted, and if the distribution mode similarity is lower than the threshold value, judging that abnormal performance risk exists and generating a management instruction; according to the method, confusion of processing endogenous complexity and exogenous abnormity is successfully stripped, and fine attribution oriented management is realized.

The invention discloses a streaming data compression and time sequence prediction integrated method for a geothermal monitoring platform, and relates to the technical field of industrial data processing. The method comprises the following steps of: 1, acquiring various original heterogeneous sensing protocol data from a plurality of wellheads and a plurality of layers of sensor arrays in real time on a wellhead gateway side of a geothermal monitoring platform; 2, calling a well layer wavelet domain self-adaptive compression encoder through a well site edge end processor, and performing real-time compression on the structured geothermal flow data; and 3, in a well site edge end processor, performing wavelet dynamic system coding on the multi-scale wavelet coefficient of the well layer wavelet domain compressed code stream, and outputting a temperature prediction sequence, a pressure prediction sequence and a flow velocity prediction sequence of multiple time steps in the future.

The invention belongs to the technical field of industrial data processing, and particularly relates to a production enterprise energy consumption data prediction method based on machine learning. The method comprises the following steps: deploying an Internet of Things sensor to collect power and equipment operation data, and after three-stage preprocessing of anomaly detection, deletion repair and normalization, constructing a layered feature extraction network which comprises a time sequence feature layer, an equipment association feature layer and a modal fusion layer; the two features are respectively used for capturing power consumption features and equipment collaborative consumption features and fusing the features to generate depth feature vectors; and constructing a dynamic integrated prediction model, training three types of base models including support vector machine regression and the like, screening the model entering an integrated pool by using JS divergence, and optimizing the weight through a genetic algorithm to obtain a final prediction result. The method can accurately capture the characteristics of energy consumption data, effectively improves the prediction precision, assists an enterprise in reasonably planning energy use, and reduces the cost.

The invention relates to the technical field of industrial data processing, in particular to a PDF drawing vector review method and system based on the creative technology, and the method comprises the steps: receiving a PDF drawing data stream through a creative security communication channel, and converting the PDF drawing data stream into a standardized graphic primitive containing a two-dimensional coordinate and engineering metadata through a sandbox security parser; and on the basis, a spatial semantic data structure integrating quadtree spatial indexes and semantic grouping information is constructed. According to the method, a dynamic performance benchmark test data model is also constructed; and during rendering, predicting and selecting an optimal rendering scheme capable of meeting preset target performance based on the model, efficiently generating an interactive graphic review interface by utilizing the data structure, and finally receiving and storing a user review instruction associated with the graphic object. By constructing the adaptive rendering model and fusing a data structure of space and semantic information, high-performance and safe vectorization interactive review of the PDF drawing in a credential environment is realized.

The invention relates to the technical field of industrial data processing, in particular to a capacitive touch screen test data management method based on an MES (Manufacturing Execution System), which comprises the following steps: acquiring a capacitive touch screen to be detected and performing a first test mode, wherein the first test mode tests the touch screen according to the first test temperature, the second test temperature, the test duration, the test period and the specified operation set; obtaining first test mode data after all the touch screens complete the first test mode; analyzing the data to obtain first abnormal data of the touch screen, wherein the first abnormal data comprises abnormal temperature and abnormal test operation of the touch screen; performing test optimization on the touch screen according to the abnormal temperature and the abnormal test operation, and performing a second test mode; obtaining second test mode data after all the touch screens complete the second test mode; analyzing the second test mode data to obtain second abnormal data; and according to the first abnormal data and the second abnormal data, judging whether the to-be-detected capacitive touch screen has a hardware fault or not.