44 results about "Statistical process control" patented technology

The invention provides a production control method and system based on SPC alarm linkage process locking, and relates to the technical field of quality management and control, and the method comprises the steps: deploying an SPC sensing node used for collecting technological parameters at a cutting edge position where a machining tool of production equipment is in contact with a workpiece, or at a contact point where a probe of online measurement equipment is in contact with the surface of the workpiece; the method comprises the following steps: acquiring process parameter data of a key production process in real time through an SPC sensing node deployed at a cutting edge position or a contact point; inputting the process parameter data as real-time data or small sample expansion data into an SPC judgment engine; and performing statistical process control analysis on the real-time data or the small sample expansion data through an SPC judgment engine so as to model data clusters which are densely distributed and have stable parameters in the data into a virtual reference curved surface representing a normal state domain. According to the invention, the accuracy of abnormity determination can be improved, and timely management and control of quality abnormity and minimization of production loss can be realized.

The invention discloses a welding wire production control system and method based on visual feedback, and belongs to the technical field of precision machining. The system comprises a multi-mode intelligent sensing subsystem, a self-adaptive model prediction controller and a safety and exception handling subsystem, and a two-stage control architecture of a quality closed loop and an execution closed loop is constructed. According to the method, the microcosmic roughness, macroscopic geometry, defects and other multi-dimensional quality characteristics of the surface of the welding wire are obtained in real time through multi-modal sensing and fused into a comprehensive quality index; carrying out rolling optimization by adopting a model prediction control algorithm for updating parameters on line, and generating a control instruction; meanwhile, trend early warning based on statistical process control and sudden fault diagnosis based on model residual analysis are operated in parallel, and active safety protection is achieved. According to the method, the problems of unstable quality, control lag and passive safety caused by supplied material fluctuation and tool time variation in the welding wire flaking machining process are solved, and self-adaptive accurate control and intelligent safety protection in the machining process are achieved.

The invention provides a quality control system based on a multivariate exponential weighted moving average control chart, and belongs to the technical field of quality management and statistical process control. The system comprises a quality data acquisition module, a data preprocessing module, a statistical process control module, an anomaly judgment module and a quality evaluation and improvement module, and is used for monitoring and controlling the production process of a product with a plurality of quality characteristics in real time. The method comprises the following steps of: acquiring quality data of a plurality of quality characteristics in a production process by a system, preprocessing the quality data, and constructing a multivariate quality characteristic data set; estimating a mean vector and a covariance matrix in a controlled state based on the historical quality data, performing weighted updating on the multivariate quality data by adopting a multivariate exponential weighted moving average method, calculating a corresponding MEWMA statistic and generating an MEWMA control chart; and comparing the MEWMA statistical magnitude with a preset control limit to judge whether the production process is in an out-of-control state or not, and outputting early warning information and a corresponding quality evaluation result and improvement suggestion when abnormality is detected. The method can comprehensively analyze related information among multivariate quality characteristics, improves the sensitivity and accuracy of anomaly detection in the production process, and effectively improves the quality control level of the production process in the manufacturing industry.

The invention discloses an automatic fault detection and diagnosis system and method based on an artificial intelligence optimization operation system and a function library, and belongs to the technical field of intelligent building and industrial equipment operation and maintenance. The system comprises a data acquisition and preprocessing layer, a fault detection layer, a fault diagnosis layer, an intelligent recommendation layer and a visual display layer. The method comprises the following steps: collecting equipment data in real time and preprocessing; performing anomaly detection and performance trend analysis in combination with statistical process control and a machine learning algorithm; performing root cause diagnosis by using a Bayesian reasoning model; classifying faults according to an energy performance type, a controllability type and a thermal comfort correlation type, and evaluating priorities through a multi-dimensional weighted scoring model; and intelligently generating a solution and updating the knowledge base. According to the method, whole-process automation from early warning, diagnosis, classification to recommendation is achieved, the equipment reliability, the energy efficiency and the operation and maintenance intelligent level are improved, and the method is suitable for application scenes such as artificial intelligence middleware and computer visual and auditory software.

The invention provides a non-uniform ion implantation process monitoring method based on template matching image recognition, and the method comprises the steps: 1, carrying out the to-be-monitored non-uniform ion implantation of a wafer, carrying out the modeling according to the measurement data of the wafer, and obtaining a template used for monitoring the dose / resistance distribution of the non-uniform ion implantation; step 2, carrying out resistance measurement on the batch of wafers after the non-uniform ion implantation, and drawing a resistance distribution diagram according to the coordinates and the resistance values of the measurement point positions; and 3, carrying out image identification comparison operation on the template obtained in the step 1 and the resistance distribution diagram obtained in the step 2 to obtain a similarity score, and carrying out statistical process control and control on the similarity score. Abnormalities such as deviation and rotation of dose distribution patterns of the non-uniform ion implantation process are effectively captured, and monitoring of the non-uniform ion implantation process is achieved.

The invention discloses an intelligent wearable watch case precision detection method based on visual inspection, and relates to the technical field of machine vision, and the method comprises the steps: collecting a watch surface point cloud and a watch case image, carrying out the point cloud denoising and image distortion correction, and obtaining registration multi-modal data; performing multi-layer pyramid convolution on the registered multi-modal data, and outputting geometric-texture features of the watchcase; curvature gradient calculation and spatial relation modeling are carried out on the geometric-texture features of the watchcase, and curvature distribution parameters and a spatial relation adjacency matrix are obtained; the curvature distribution parameters are used as nodes of the topological structure, the spatial relation adjacent matrix is used as edges of the topological structure, and a case geometrical relation graph is constructed. According to the method, the watch surface point cloud and the watchcase image are fused and subjected to multi-layer pyramid convolution, the comprehensiveness and positioning precision of watchcase defect recognition are improved, and meanwhile, the engineering practicability of the detection process is effectively improved by utilizing a graph attention defect prediction model and an SPC statistical process control algorithm.

The invention provides an alumina carrier with a honeycomb-like structure and an additive manufacturing forming method thereof, and belongs to the field of advanced ceramic and structured reaction engineering. The alumina carrier is prepared by adopting a photoetching additive manufacturing technology, and high-precision consistency of intra-batch or inter-batch size and quality is realized by establishing a geometric manufacturable threshold system of a honeycomb-like structure, combining anisotropic shrinkage pre-compensation, local thickening correction and photoetching process in-situ metering closed-loop parameter adjustment and matching with a statistical process control acceptance release system. The unit characteristic scale L, the inner wall normal vector minimum thickness t and the fillet radius r of the honeycomb-like alumina carrier meet a specific constraint relationship, the total porosity is greater than 30%, the internal porosity is 20-30%, the dimensional deviation delta L / L is less than or equal to 10%, the mass deviation delta m / m is less than or equal to 10%, the process capability index Cpk is greater than or equal to 1.33, and the honeycomb-like alumina carrier is suitable for scenes such as medicine synthesis and fine chemical engineering which have strict requirements on carrier consistency.

The invention discloses a robust failure judgment method, system and equipment for a low-cost indoor environment sensor group and a medium, and the core of the method is to construct a serial five-step processing assembly line, utilize redundant information of the sensor group and combine robust statistics and statistical process control theories to judge the robustness failure of the indoor environment sensor group. According to the method, accurate decoupling of environmental events and sensor faults is achieved, and the problems that an existing method cannot distinguish environmental fluctuation and sensor degradation, and judgment lacks robustness and quantitative basis are solved. According to the method, the instantaneous deviation is judged, and misinformation caused by short-term fluctuation is avoided by setting the cumulant and the minimum duration time. The sensitivity to progressive degradation is improved.

The invention discloses a real-time assembly error recognition and early warning method based on multi-dimensional dynamic threshold adjustment. The method comprises the following steps: extracting a multi-modal assembly state descriptor and a field assembly information sequence; field assembly information fragments are intercepted for similarity calculation; constructing a time sequence prediction module, and taking the field assembly information sequence as input to predict the assembly state of the next frame; constructing a weighted Euclidean distance matrix of the field fragment and the template fragment, and calculating a minimum cumulative distance to quantify the difference between the two sequences; a dynamic threshold model is established based on statistical process control, and whether errors exist in the assembly process or not is judged according to the model; based on an incremental dynamic time planning algorithm and a real-time data acquisition module, a real-time identification system is constructed to realize early warning and standard step pushing, and a real-time adaptive quality control closed loop is formed. According to the method, a real-time and self-adaptive quality control closed-loop system is formed through integration with multi-mode sensing and AR guide technologies, and an effective solution is provided for a complex industrial assembly scene.

Computer Device for Statistical Process Control Visualization

The invention discloses a blowing and sucking integrated fan and a control system. The blowing and sucking integrated fan comprises a shell. The guide mechanism composed of the arc pieces and the fixing pipes is adopted, airflow is guided to form the spiral air duct, the air speed is remarkably increased, and the problem of insufficient wind power caused by unreasonable design of an existing fan air duct is solved; according to the invention, by arranging the efficiency analysis module, accurate modeling and dynamic management are carried out on the charging efficiency of the battery in a mode of charging while operating, and the optimal charging trigger threshold value can be comprehensively calculated based on multiple parameters such as real-time electric quantity, temperature and aging degree; the problem of low efficiency possibly caused by a traditional fixed low-electric-quantity charging strategy is effectively avoided, and the charging opportunity is intelligently judged on the premise that the charging efficiency is guaranteed, so that the single-time operation time of the battery is prolonged, and invalid charging circulation is reduced; the fault early warning module is arranged, and a deviation coefficient method and a time sequence trend analysis method based on statistical process control are adopted, so that dual health monitoring is carried out on core operation parts of the fan.

The application discloses a slab continuous casting machine crystallizer liquid level control method and an automatic casting method, and belongs to the technical field of metallurgy. In the liquid level control stage one, the crystallizer liquid level is raised from the initial liquid level value to the first liquid level set value; in the liquid level control stage two, the crystallizer liquid level is raised from the first liquid level set value to the second liquid level set value, and the second liquid level set value is the liquid level when the casting blank starts to be pulled out. The control method determines whether the parameters are abnormal through parameter collection and parameter analysis, adjusts the stopper opening degree for the crystallizer liquid level control according to the determination result, carries out single variable statistical process control and multivariable statistical process control through single variable analysis and multivariable analysis, and then adjusts the control stopper opening degree value, so that the crystallizer liquid level control in the automatic casting operation is realized, unnecessary shutdown and adjustment time are avoided, the efficiency of the continuous casting production line is maximally improved, the production capacity of the production equipment is fully utilized, and the utilization rate of the production line is greatly improved.

The present application relates to the technical field of image detection, and discloses a card surface topography defect detection method and system based on a relief effect, which comprises the following steps: double-frequency structured light adaptive projection of a laminated card, relief three-dimensional height map reconstruction based on phase deflection, automatic segmentation of a relief area based on a variational level set, analysis of relief profile curvature continuity and defect detection, and relief depth statistical process control. The present application eliminates mirror reflection interference of a laminated layer through a double-frequency phase shift algorithm, and the measurement accuracy of the relief depth reaches ±5 microns, and the detection accuracy reaches 99.5%.

The invention provides a statistical process control method and system based on a large language model. The system comprises the large language model and a statistical process control model. Decoupling the statistical process control model and the large language model on the architecture; the large language model is used for receiving an analysis request input by a user; reasoning the analysis request, and determining an analysis intention and an input parameter of the user; according to the analysis intention, a predefined Schema parameter mode of a target analysis tool and an input parameter, generating a tool calling instruction for the target analysis tool in the statistical process control model; sending the tool calling instruction to a statistical process control model, and receiving returned analysis data determined based on a statistical algorithm library; and analyzing the analysis data, generating an analysis result in a natural language form, and feeding back the analysis result to the user. In this way, the problems that in the prior art, a statistical process control analysis tool is complex in operation process, high in requirement for professional knowledge of a user, low in use efficiency and unreliable in calculation result of a universal AI model can be solved.

The invention relates to the field of water treatment automation, and discloses an intelligent disinfection method, system, medium and equipment combining feedforward optimization and feedback correction, and the method comprises the following steps: in a feedforward stage, based on measurable parameters such as dosage, water inflow, water temperature, turbidity, pH and the like, predicting factory residual chlorine by utilizing a machine learning model, and optimizing and calculating basic dosage by combining a genetic algorithm; in the feedback stage, a short-time-delay chlorine consumption prediction model is established, and the deviation between the actual chlorine consumption and the theoretical chlorine consumption predicted after dosing is calculated; and monitoring the deviation in real time by using a statistical process control method, judging that unmonitored water quality disturbance exists when the deviation exceeds a statistical control limit, and calculating the compensation dosage according to the magnitude of the deviation. And superposing the optimal basic dosing amount and the compensation dosing amount to obtain a final execution dosing amount, and controlling a dosing pump to execute dosing.

This application discloses a coating system and a coating method, wherein the system includes: a statistical process control system, a manufacturing execution system, and a controller; the controller is configured to generate a roll change signal when it detects that the winding device of the coating machine performs a tape breakage operation and connects the tape to the target film roll; the statistical process control system is configured to, in response to the roll change signal, send a processing request for the film roll number to the manufacturing execution system, obtain a first film roll number of the target film roll sent by the manufacturing execution system based on the processing request, and create a first film roll order based on the first film roll number; wherein the first film roll order is used to record the production process information of the target film roll during the coating process; the manufacturing execution system is configured to, in response to the processing request, determine the first film roll number of the target film roll and send the first film roll number to the statistical process control system.

The invention discloses an SLCC converter valve state diagnosis method based on a multi-parameter correlation residual error. The method comprises the following steps: constructing a multi-parameter health correlation model HCM; collecting multi-parameter operation data in real time; calculating an expected healthy temperature under the current working condition; calculating a real-time residual error; and performing diagnostic decision-making based on residual error statistical process control. According to the method, early warning is realized, + 3 DEG C tiny temperature drift caused by aging of heat dissipation silicone grease can be stripped from strong working condition interference by detecting whether the real-time residual error breaks through the statistical threshold value or not, the sub-health hidden danger is found in advance, and the problem of diagnosis lag is solved. According to the method, the normal influence of the working condition is automatically stripped, the diagnosis basis is a'non-existing 'residual value, and false alarm and missing alarm under heavy load or light load of a traditional method are avoided.

The invention relates to a coking coal ash composition control method based on partial least squares, which comprises the following steps: collecting n coal ash samples, detecting the mass contents of SiO2, Al2O3, Fe2O3, CaO, MgO, Na2O, K2O, TiO2, P2O5 and MnO2 in the components of each coal ash sample, generating an n * 10 independent variable matrix X, detecting the CSR value of each coal ash sample, and generating an n * 1 dependent variable matrix Y; standardizing the independent variable matrix X and the dependent variable matrix Y, and marking the standardized matrixes as Xs and Ys; constructing a latent variable regression model; training the regression model, and evaluating the performance of the trained regression model; screening the first four important components according to the VIP values; defining a sample set of target range CSR values; and calculating the total amount of coal ash components with the top four VIPs of each target range CSR sample. The control range of the coal ash components is determined through VIP analysis and statistical process control.

The invention discloses a silk hydrophilic non-woven fabric production process online optimization method, and relates to the field of textile intelligent manufacturing, and the method comprises the steps: obtaining synchronization process data of a preset process node of a production line, the synchronization process data comprising a cloth cover image subjected to time-space alignment and a corresponding upstream process parameter; calculating and generating a microscopic uniformity characteristic time sequence based on the cloth cover image; performing correlation analysis on the characteristic time sequence and the corresponding process parameter time sequence, and identifying a process disturbance source; performing real-time monitoring on the microscopic uniformity characteristic time sequence by adopting a statistical process control method, and performing abnormal early warning according to trend prediction to generate an early warning signal; and according to the early warning signal and the process disturbance source, querying a preset compensation rule to generate a process parameter compensation instruction of the downstream process and executing the process parameter compensation instruction. The quantitative characteristics of the microscopic uniformity of the cloth cover are extracted in real time, a process disturbance source is traced, and prospective statistical early warning is combined, so that closed-loop optimization of the production process and improvement of the product quality are realized.

The present application relates to the technical field of enterprise operation, in particular to an enterprise operation early warning monitoring method and system based on data analysis, the method comprising: acquiring index data of multiple dimensions of enterprise operation; performing time series decomposition on the index of each dimension, constructing a dynamic baseline that dynamically changes with the business cycle, and calculating the deviation intensity of the actual value of each index based on the dynamic baseline; inputting the deviation intensity into a statistical process control model and an anomaly detection model respectively to obtain first and second outputs; nonlinearly fusing the first and second outputs, and calculating a fusion risk score and a corresponding confidence level according to the divergence between the first and second outputs; determining an early warning level according to the fusion risk score and the confidence level, and generating a structured disposal scheme; collecting feedback data on the disposal scheme, and dynamically updating the threshold coefficient of the dynamic baseline, the anomaly detection model, and the fusion weight of the nonlinear fusion according to the feedback data.

The invention discloses a city elevator control access control system fault predictive maintenance method based on AI big data, and relates to the technical field of elevator control access control systems. The method comprises the steps of executing preliminary detection; calculating an operation risk coefficient in combination with a statistical process control theory, and calculating an environmental influence risk coefficient according to an environmental stress accumulation theory integral; mechanical scratches are recognized through three-dimensional Hough transformation, a mechanical wear value is calculated in combination with a material mechanics weight model, textural features of the electronic component are extracted through a three-dimensional gray-level co-occurrence matrix, and an aging value is calculated based on the statistical deviation degree; a first fault probability is calculated by fusing multi-dimensional risk coefficient weighting, and first-round maintenance judgment is made according to a threshold value; evaluating structural deformation by means of a current harmonic distortion rate, measuring circuit looseness by means of contact resistance change, and complementally calculating a second fault probability; and executing graded maintenance or conventional maintenance according to the second fault probability threshold. According to the method, through multi-dimensional risk fusion and non-disassembly detection, the fault prediction accuracy is improved, the operation and maintenance cost is reduced, and the service life of equipment is prolonged.

The invention provides an anomaly detection system and an anomaly detection method, and relates to the technical field of industrial process monitoring, and the detection system comprises a data collection module which is used for obtaining original data and carrying out the preprocessing of the original data, and obtaining temporary data; the calculation and analysis module is used for analyzing and processing the temporary data based on a preset statistical process control method to obtain analysis data; the preset statistical process control method comprises an MSPC method and an SPC method; the fusion analysis module is used for obtaining a comprehensive index according to the analysis data based on a preset fusion algorithm; and the abnormity diagnosis module is used for determining abnormity information according to the comprehensive index and the analysis data. According to the method, full-process optimization and collaboration from data preprocessing to anomaly judgment are realized, and through organic cooperation of multiple modules and multiple methods, various potential anomalies can be captured more sensitively and comprehensively, and the probability of false alarm and missing alarm can be effectively reduced.

The invention provides a lithium ion storage battery attenuation level and consistency rapid evaluation method, which is characterized by comprising the following steps: S1, determining a battery performance baseline and an initial state; s2, establishing a battery consistency evaluation model and a battery consistency evaluation system, wherein the consistency evaluation model is compiled according to the requirements in Q / RJ / Z232-2021 Statistical Process Control Application Guide for Space Products; s3, the batteries in batches are selected for an accelerated life test, and according to the initial state T1 and the accelerated aging state T2, the capacity retention rate eta 1, the energy retention rate eta 2 and the internal resistance expansion rate eta 3 are calculated; and S4, evaluating the battery consistency level by using the consistency evaluation model, wherein whether the production batch is abnormal or not is determined according to the point distribution on the control chart. According to the method, the defects of the to-be-characterized battery are subjected to consistency characterization after being exposed in a life test; the problems of long time consumption and low efficiency of the traditional life test are solved; and the battery production consistency is comprehensively and accurately evaluated.

The invention provides a power system redundancy alarm identification method and device, and the method comprises the steps: obtaining an alarm number sequence according to an original alarm record data set, and obtaining an alarm number time sequence according to the alarm number sequence; continuously updating the posterior probability of the alarm number time sequence to obtain the posterior probability of the change point moment; comparing the posterior probability with a preset threshold value to obtain a change point set; filtering the alarm paragraphs in the change point set by using a statistical process control chart method to obtain statistical invalid fluctuation segments and effective abnormal segments; and performing multi-dimensional classification and redundancy processing on the alarm information in the statistical invalid fluctuation section to obtain an alarm section compression report. The embodiment of the invention has real-time performance, interpretability and easy deployment, can be widely applied to various electric power automation scenes such as a transformer substation, a local master station and a cloud centralized platform, and has relatively high engineering feasibility and popularization value.

The invention discloses a method and a device for preprocessing industrial generated data containing high-dimensional noise, and relates to the field of data preprocessing, and the method comprises the following steps: outputting a sampling time point set and a multi-dimensional observation data set by collecting multi-dimensional function type observation data in a set time interval; and representing the real function of each dimension observation value as a primary function linear combination, extracting effective features, and outputting a primary function set and an expansion coefficient. And constructing a design matrix, and establishing association between the multi-dimensional observation data and the effective features. A combined objective function is constructed by combining multivariable synchronous processing requirements and signal importance differences, and a penalty matrix is constructed through an inner product of a second derivative of a primary function. And selecting a smoothing parameter by minimizing a generalized cross validation criterion, solving a target function to obtain an estimation expansion coefficient, and constructing and outputting a smoothing function as input data of a statistical process control or fault diagnosis model. The method solves the problems that in the prior art, effective features are prone to being lost, multi-dimensional collaboration is not considered, and smooth parameter selection lacks self-adaption.

This invention provides a deep learning model training optimization method based on multi-dimensional intelligent analysis. It achieves accurate quantitative judgment of training status based on statistical process control methods, realizes early warning of overfitting through multi-dimensional analysis, realizes deep understanding of learning dynamics by applying causal analysis and machine learning methods, establishes a multi-model integration framework to realize accurate prediction of training results, constructs a multi-dimensional risk assessment system to realize proactive management of training risks, and ensures the practical usability of optimization effect through training-inference correlation analysis.

The present application belongs to the technical field of precision strip steel production quality control, and particularly relates to a precision strip steel statistical process control method for small batch and multi-variety, and the specific steps are as follows: step S1: establishing a precision strip steel variety feature database; step S2: grouping historical varieties based on a K-means clustering algorithm; step S3: determining the cluster to which the current variety belongs; step S4: extracting historical quality data of the same cluster variety and removing outliers by using a 3σ criterion; step S5: data fusion based on similarity weighting; step S6: adaptive control limit calculation; step S7: multi-quality characteristic comprehensive control; step S8: abnormal mode identification; step S9: process capability evaluation; and step S10: control chart dynamic updating. The method effectively solves the problems of insufficient data, low historical data utilization, single-variable control limitations, fixed control limits and weak abnormal diagnosis capability by using K-means clustering analysis and data fusion technology and adaptive control limit calculation.

The invention belongs to the technical field of data analysis and statistical process control, and discloses an abnormal value screening and control limit determining method for finite single variable reliability data based on a normal quantile, which specifically comprises the following steps: acquiring a finite single variable reliability data set to be analyzed; sorting the finite single variable reliability data sets, and calculating an empirical cumulative distribution function value of each data point based on the sorted finite single variable reliability data sets; calculating a standard normal distribution quantile corresponding to each empirical cumulative distribution function value; and establishing a function relation model between a standard normal distribution quantile and an observed value of the sorted finite univariate reliability data set, substituting a target cumulative distribution function value into the function relation model, and carrying out extrapolation calculation to obtain an extreme quantile, so that normal, non-normal and even multi-modal distribution data can be effectively processed.

The application provides a self-adaptive smoke fire detection method suitable for ancient building environment, and relates to the technical field of fire detection, and comprises the following steps: collecting smoke concentration data of a smoke detector in an ancient building environment, removing invalid data by using an SPC statistical process control method to obtain an optimal data set; based on the optimal data set, calculating the correlation support degree between each smoke detector, and constructing a support matrix; the application effectively removes invalid data by using an SPC control chart, objectively evaluates the reliability of the detector based on the correlation support degree, dynamically integrates multi-sensor information by using a self-adaptive weighted fusion algorithm, and designs a trend threshold algorithm and a field model analysis algorithm for single detector and multi-detector scenes respectively, so that the fire determination strategy and the alarm threshold can be adaptively adjusted according to real-time environmental parameters and interference characteristics, the high sensitivity to real fire is retained, and frequent false alarms caused by traditional use of fire, ventilation airflow and humidity change are effectively inhibited.

The invention relates to the technical field of industrial furnace temperature control, and discloses an industrial furnace intelligent temperature control system based on data acquisition, which comprises a data acquisition module, a temperature analysis module, an intelligent control module, an early warning feedback module, a user interaction module and a remote maintenance module, according to the method, trend analysis is carried out in combination with historical temperature data, and the short-term temperature trend is predicted, so that the system can pre-judge the future change trend of the temperature, a control instruction is generated in advance, the problems of large inertia and large delay in the thermal process in the furnace are solved, temperature overshoot and adjustment oscillation are restrained under the complex and changeable working conditions, and the control efficiency is improved. The technical requirements of high-precision constant-temperature control are met, the temperature data are monitored in real time, a statistical process control method is used for anomaly detection, the system can conduct intelligent diagnosis, anomaly identification and root cause analysis triggering at the initial stage of abnormal temperature fluctuation, and therefore the product quality stability is guaranteed, and equipment safety faults are prevented.