A multi-scenario process monitoring and early warning method

Abstract

The present invention discloses a process monitoring and early warning method for multiple scenarios, including real-time operation monitoring, process trend monitoring and indicator statistical monitoring; real-time operation monitoring includes: collecting real-time operation data in the production process and performing online analysis, and giving an alarm when an abnormality is found; process trend monitoring includes: collecting process trend data in the production process and performing analysis to obtain the process execution status on site, and performing corresponding process improvements based on the analysis results; indicator statistical monitoring includes: collecting indicator statistical data in the production process and performing analysis to obtain key indicator information, and displaying it in an intuitive manner to provide a basis for decision makers; wherein, real-time operation monitoring, process trend monitoring and indicator statistical monitoring configure monitoring rules according to needs to meet the monitoring needs of different scenarios and meet the differentiated needs of different users.

Description

Technical Field

[0001] The present invention relates to the technical field of manufacturing industry, and in particular to a multi-scenario oriented process monitoring and early warning method. Background Art

[0002] With the deep integration of industrial Internet technology and industrial production, industrial production data has exploded, and massive data has been widely collected and archived. These data contain information on the status of materials, equipment, and production. Due to the characteristics of high continuity, numerous equipment, severe coupling between variables, and easy changes in the production environment, the safe operation of the production process and the stability of product quality have always been the focus of attention in the industrial field. By monitoring and warning the key process parameters and various indicators of the production process, providing different monitoring scenarios for different personnel roles, ensuring the safe production of the industrial process and improving product quality has become a popular research topic for scholars.

[0003] At present, there are some documents that study process monitoring and early warning. For example, the patent "Steel Production Process Monitoring Alarm System and Method" (201910012859.7) detects process parameters in different processes in the production of steel materials, and alarms when process parameters exceed the limit, and classifies the alarm signals. The patent "A Hot Rolling Process Monitoring Based on Digital Twin" (202110173217.2) first establishes a three-dimensional entity model and a process model on the virtual end, collects production process data, and then corresponds the three-dimensional model with the actual data. The data is judged according to the calculated value of the process model and an early warning is given. This patent mainly introduces three-dimensional modeling and less about process monitoring. The patent "A Production Process Monitoring System" (202110277756.0) includes a data acquisition module, an abnormal state recognition module, and a production process level module. It can collect production data, identify abnormal parameters of machinery, personnel and environmental parameters in the production workshop, give corresponding alarms, and finally automatically register and generate a QR code. The data collected by this patent is non-continuous, and the monitoring method is relatively simple. Although the above-mentioned existing technologies can monitor and alarm production data, they cannot meet the differentiated needs of multi-scenario monitoring. Summary of the invention

[0004] The purpose of the present invention is to provide a process monitoring and early warning method for multiple scenarios, which can meet the differentiated needs of monitoring scenarios, design different monitoring contents and rules for different personnel roles, and collect data of different granularities according to the data items required for the monitoring content, so as to realize the application of the monitoring and early warning method in production and management.

[0005] To solve the above technical problems, the embodiments of the present invention provide the following solutions:

[0006] A multi-scenario process monitoring and early warning method, including real-time operation monitoring, process trend monitoring, and indicator statistical monitoring;

[0007] The real-time operation monitoring includes: collecting real-time operation data during the production process, analyzing the real-time operation data online, and issuing an alarm when an abnormality is found;

[0008] The process trend monitoring includes: collecting process trend data in the production process, analyzing the process trend data to obtain the process execution status on site, and making corresponding process improvements based on the analysis results;

[0009] The indicator statistical monitoring includes: collecting indicator statistical data in the production process, analyzing the indicator statistical data, obtaining key indicator information, and displaying it in an intuitive way to provide a basis for decision makers;

[0010] Among them, the real-time operation monitoring, the process trend monitoring and the indicator statistical monitoring are configured with monitoring rules according to requirements to meet the monitoring requirements of different scenarios.

[0011] Preferably, for real-time operation monitoring, the timeliness of data is required to be high. After the real-time operation data collection is completed, abnormal status is captured in real time, and displayed and alarmed in the most intuitive way;

[0012] For process trend monitoring, the timeliness of data is less demanding. Process trend statistical analysis is performed according to different time periods, or according to different production processes and different units to obtain the on-site process execution status under different analysis conditions, as well as the changing trend of process execution status.

[0013] For indicator statistical monitoring, the timeliness requirements of data are relatively low. The changes of different indicators in different units, different products, and different time axes are displayed from top to bottom and from global to local.

[0014] Preferably, for a hot rolling mill, the real-time operation data include: rough rolling dephosphorization pressure, finishing rolling dephosphorization pressure, dephosphorization header inlet DS height, dephosphorization header inlet OS height, dephosphorization header outlet DS height, dephosphorization header outlet OS height, anti-stripping water start signal up, anti-stripping water start signal down, upper roll gap lubrication water flow up, upper roll gap lubrication water flow down, working roll cooling water temperature deviation, inter-stand cooling water temperature deviation;

[0015] The process trend data include: rolling force of the roughing mill stand, rolling force of the finishing mill stand, measured value of roll gap deviation on both sides of the stand, stand stiffness OS, stand stiffness DS, looper angle, looper front tension, actual tension of the coiler, guide plate operating side pressure, roughing outlet temperature, finishing outlet temperature, coiling temperature;

[0016] The statistical data of the indicators include: total raw material inventory, raw material surplus inventory, total finished product inventory, finished product surplus inventory, total daily output, monthly total output, number of rolled blocks on the day, output trend in the past 7 days, steel unplanned rate, comprehensive unplanned rate, one-time hit rate, appearance quality unqualified rate, performance quality unqualified rate.

[0017] Preferably, the monitoring rules are from upper and lower tolerance rules or configurable monitoring rules issued from a manufacturing execution system. The configurable monitoring rules can be selected and modified according to a template, including formula editing, rule copying, and rule adding.

[0018] Preferably, the monitoring rules can be displayed with different alarm levels, where orange is the lowest alarm level and red is the highest alarm level, and each monitoring item can be configured with up to 3 alarm levels.

[0019] Preferably, monitoring and alarming according to the upper and lower tolerance rules issued by the manufacturing execution system as monitoring rules includes:

[0020] For the rolling force RF, it is calculated as follows:

[0021]

[0022] Among them, RF i is the rolling force data of any sampling point i, RF Lower_Tol ,RF Upper_Tol is the upper and lower tolerance of the rolling force RF, Monitor_Result is the rolling force monitoring result, ok means no alarm, no means an alarm occurs.

[0023] Preferably, monitoring alarms according to configurable monitoring rules include:

[0024] Monitoring rules are configured for discrete data through expressions, or rules are configured after any characteristic value calculation is performed on the process curve; the characteristic value calculation includes the calculation of maximum value, minimum value, range value, and standard deviation;

[0025] For the rolling force RF curve, the characteristic value calculation formula is as follows:

[0026] RF max =max(RF0, RF1, RF2...RF n-2 ,RF n-1 ,RF n );

[0027] RF min =min(RF0, RF1, RF2...RF n-2 ,RF n-1 ,RFn );

[0028] RF extrem =RF max -RF min ;

[0029]

[0030]

[0031] Among them, RF max ,RF min ,RF extrem ,RF avg ,RF std The maximum value, minimum value, extreme value, mean value, and standard deviation of the rolling force RF curve collected, RF0, RF1, RF2, RF n-2 ,RF n-1 ,RF n is the data point collected on the rolling force RF curve;

[0032] After the characteristic value calculation of the rolling force RF curve is completed, the monitoring rules are configured according to the monitoring requirements to form a monitoring rule base, and the monitoring rule base is converted into machine language. The monitoring rules are used to perform real-time abnormality judgment on various parameters of the production process, and the judgment results are displayed on the real-time monitoring operation screen to realize automatic alarm for abnormal events;

[0033]

[0034]

[0035]

[0036]

[0037] Among them, RF extrem_max ,RF std_max It indicates the upper limit of the extreme value and the upper limit of the standard deviation allowed by the rolling force RF curve. Monitor_min_Result, Monitor_max_Result, Monitor_extrem-Result and Monitor_std_Result are the monitoring results of the minimum, maximum, extreme value and standard deviation of the rolling force. ok means no alarm and no means an alarm occurs.

[0038] Preferably, the configurable monitoring rules are single rules or combined rules, or form customized monitoring scenarios to meet different needs.

[0039] The beneficial effects brought about by the technical solution provided by the embodiment of the present invention include at least:

[0040] The process monitoring and early warning method for multiple scenarios provided by the present invention includes real-time operation monitoring, process trend monitoring, and indicator statistical monitoring. According to the manufacturing production process specifications and indicator ranges, the entire manufacturing process and various intermediate process parameters, indicator parameters, etc. are monitored to provide complete monitoring data items for monitoring applications, and different monitoring contents and monitoring rules are designed for different application scenarios to achieve multi-directional monitoring and meet the monitoring needs of different scenarios. The present invention avoids relying on manpower for continuous and uninterrupted monitoring, reduces on-site production accidents, and transforms post-analysis of quality into in-process control; statistical analysis is performed on the changing patterns and abnormal conditions of product quality, process parameters, etc. to dig out potential problems; and for managers, monitoring information of key indicators is provided to assist decision-making, fully meeting the differentiated needs of different users. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the description of the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0042] Figure 1 It is a general schematic diagram of a multi-scenario process monitoring and early warning method provided by an embodiment of the present invention;

[0043] Figure 2 It is a schematic diagram of the monitoring rule configuration process provided by an embodiment of the present invention.

[0044] As shown in the figure, in order to clearly implement the structure of the embodiment of the present invention, specific structures and devices are marked in the figure, but this is only for illustrative purposes and is not intended to limit the present invention to the specific structure, device and environment. According to specific needs, ordinary technicians in this field can adjust or modify these devices and environments, and the adjustments or modifications made are still included in the scope of protection of the present invention. DETAILED DESCRIPTION

[0045] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0046] The embodiment of the present invention provides a multi-scenario process monitoring and early warning method, such as Figure 1As shown, the method includes real-time operation monitoring, process trend monitoring and indicator statistical monitoring;

[0047] The real-time operation monitoring includes: collecting real-time operation data during the production process, analyzing the real-time operation data online, and issuing an alarm when an abnormality is found;

[0048] The process trend monitoring includes: collecting process trend data in the production process, analyzing the process trend data to obtain the process execution status on site, and making corresponding process improvements based on the analysis results;

[0049] The indicator statistical monitoring includes: collecting indicator statistical data in the production process, analyzing the indicator statistical data, obtaining key indicator information, and displaying it in an intuitive way to provide a basis for decision makers;

[0050] Among them, the real-time operation monitoring, the process trend monitoring and the indicator statistical monitoring are configured with monitoring rules according to requirements to meet the monitoring requirements of different scenarios.

[0051] Furthermore, for real-time operation monitoring, the timeliness of data is required to be high, and the data collection granularity is also required to be high. After completing the real-time operation data collection, abnormal status is captured in real time, and displayed and alarmed in the most intuitive and eye-catching way, so as to facilitate operators to discover faults in time and deal with abnormal or dangerous production conditions as soon as possible, avoid the expansion and aggravation of hazards, and ensure safe and stable production and stable product quality.

[0052] For process trend monitoring, the timeliness of data is less demanding. Process trend statistical analysis is performed according to different time periods, or according to different production processes and different units, to obtain the on-site process execution status under different analysis conditions, as well as the changing trend of process execution status, so as to optimize and improve the process. Process trend monitoring focuses on the law of data changes in monitoring content, emphasizes the effectiveness of monitoring methods, and can dig out potential problems through data.

[0053] For indicator statistical monitoring, the corresponding users are management personnel, and the timeliness requirements of data are relatively low. The changes of different indicators in different units, different products, and different time axes are displayed in a top-down and from global to local manner to provide managers with a basis for decision-making.

[0054] Taking the hot rolling mill as an example, corresponding monitoring data is obtained from the data platform for different application scenarios.

[0055] Among them, the real-time operation data include: rough rolling dephosphorization pressure, finishing rolling dephosphorization pressure, dephosphorization manifold inlet DS height, dephosphorization manifold inlet OS height, dephosphorization manifold outlet DS height, dephosphorization manifold outlet OS height, anti-stripping water start signal up, anti-stripping water start signal down, upper roll gap lubrication water flow up, upper roll gap lubrication water flow down, working roll cooling water temperature deviation, inter-frame cooling water temperature deviation, etc.

[0056] The process trend data include: rolling force of the roughing mill stand, rolling force of the finishing mill stand, measured value of roll gap deviation on both sides of the stand, stand stiffness OS, stand stiffness DS, looper angle, looper front tension, actual tension of the coiler, guide plate operating side pressure, roughing outlet temperature, finishing outlet temperature, coiling temperature, etc.

[0057] The statistical data of the indicators include: total raw material inventory, raw material surplus inventory, total finished product inventory, finished product surplus inventory, total daily output, monthly total output, number of rolled blocks on the day, output trend over the past 7 days, steel unplanned rate, comprehensive unplanned rate, one-time hit rate, appearance quality unqualified rate, performance quality unqualified rate, etc.

[0058] After the monitoring content is formulated, the monitoring rules come from the upper and lower tolerance rules issued from the manufacturing execution system or configurable monitoring rules. The configurable monitoring rules can be selected and modified according to the template, including formula editing, rule copying, rule adding, etc. The monitoring rules can add different alarm levels for display, among which the orange alarm level is the lowest and the red alarm level is the highest. Each monitoring item can be configured with up to 3 alarm levels.

[0059] Specifically, the monitoring alarm is performed based on the upper and lower tolerance rules issued by the manufacturing execution system as monitoring rules, including:

[0060] Taking hot rolling force RF as an example, the calculation method is as follows:

[0061]

[0062] Among them, RF i is the rolling force data of any sampling point i, RF Lower_Tol ,RF Upper_Tol is the upper and lower tolerance of the rolling force RF, Monitor_Result is the rolling force monitoring result, ok means no alarm, no means an alarm occurs.

[0063] Furthermore, monitoring alarms based on configurable monitoring rules include:

[0064] Configure monitoring rules for discrete data through expressions, or calculate any characteristic values ​​of the process curve and then configure rules, such as Figure 2The characteristic value calculation includes the calculation of maximum value, minimum value, range value, standard deviation, etc.

[0065] For the rolling force RF curve, the characteristic value calculation formula is as follows:

[0066] RF max =max(RF0, RF1, RF2...RF n-2 ,RF n-1 ,RF n );

[0067] RF min =min(RF0, RF1, RF2...RF n-2 ,RF n-1 ,RF n );

[0068] RF extrem =RF max -RF min ;

[0069]

[0070]

[0071] Among them, RF max ,RF min ,RF extrem ,RF avg ,RF std The maximum value, minimum value, extreme value, mean value, and standard deviation of the rolling force RF curve collected, RF0, RF1, RF2, RF n-2 ,RF n-1 ,RF n are the data points collected on the rolling force RF curve.

[0072] After the characteristic value calculation of the rolling force RF curve is completed, the monitoring rules are configured according to the monitoring requirements to form a monitoring rule library, and the monitoring rule library is converted into machine language. The monitoring rules are used to perform real-time abnormality judgment on various parameters of the production process, and the judgment results are displayed on the real-time monitoring operation screen to realize automatic alarm for abnormal events.

[0073]

[0074]

[0075]

[0076]

[0077] Among them, RF extrem_max,RF std_max It indicates the upper limit of the extreme value and the upper limit of the standard deviation allowed by the rolling force RF curve. Monitor_min_Result, Monitor_max_Result, Monitor_extrem_Result and Monitor_std_Result are the monitoring results of the minimum, maximum, extreme value and standard deviation of the rolling force. "ok" means no alarm and "no" means an alarm occurs.

[0078] Among them, the configurable monitoring rules are single rules or combined rules, or form customized monitoring scenarios to meet different needs.

[0079] The process monitoring and early warning method for multiple scenarios provided by the present invention monitors the entire manufacturing process and various intermediate process parameters, indicator parameters, etc. according to the manufacturing production process specifications and indicator ranges, provides complete monitoring data items for monitoring applications, and designs different monitoring contents and monitoring rules for different application scenarios to achieve multi-directional monitoring and meet the monitoring needs of different scenarios. The present invention avoids relying on manpower for continuous and uninterrupted monitoring, reduces on-site production accidents, and transforms post-analysis of quality into in-process control; statistical analysis is performed on the changing rules and abnormal conditions of product quality, process parameters, etc. to dig out potential problems; and for managers, it provides monitoring information of key indicators to assist decision-making, fully meeting the differentiated needs of different users.

[0080] It should be noted that the references to "one embodiment", "embodiment", "exemplary embodiment", "some embodiments" and the like in the specification indicate that the embodiments described may include specific features, structures or characteristics, but not every embodiment may include the specific features, structures or characteristics. In addition, when a specific feature, structure or characteristic is described in conjunction with an embodiment, it should be within the knowledge of a person skilled in the art to implement such feature, structure or characteristic in conjunction with other embodiments (whether or not explicitly described).

[0081] In general, a term can be understood, at least in part, from its use in context. For example, depending, at least in part, on the context, the term "one or more" as used herein can be used to describe any feature, structure, or characteristic in the singular sense, or can be used to describe a combination of features, structures, or characteristics in the plural sense. Additionally, the term "based on" can be understood as not necessarily intended to convey an exclusive set of factors, but can instead, depending, at least in part, on the context, allow for the presence of other factors that are not necessarily explicitly described.

[0082] As used herein, the term "nominal / nominal" refers to an expected or target value for a characteristic or parameter of a component or process operation set during the design phase of a production or manufacturing process, as well as a range of values ​​above and / or below the expected value. The range of values ​​may be due to slight variations in the manufacturing process or tolerances. As used herein, the term "approximately" indicates a value of a given quantity that may vary based on a particular technology node associated with the subject semiconductor device. Based on a particular technology node, the term "approximately" may indicate a value of a given quantity that varies, for example, within 5%-15% of the value (e.g., ±5%, ±10%, or ±15% of the value).

[0083] It will be understood that the meaning of “on,” “over,” and “above” in this disclosure should be interpreted in the broadest manner, so that “on” means not only “directly on” something, but also includes the meaning of being “on” something with intervening features or layers therebetween, and “on” or “over” means not only “on” or “above” something, but also includes the meaning of being “on” or “above” something with no intervening features or layers therebetween.

[0084] Additionally, spatially relative terms such as "under," "beneath," "lower," "above," "upper," and the like may be used herein for descriptive convenience to describe the relationship of one element or feature to another element or features, as shown in the accompanying drawings. Spatially relative terms are intended to encompass different orientations of the device in use or operation in addition to the orientation depicted in the accompanying drawings. The device may be oriented in other ways, and the spatially relative descriptors used herein may be similarly interpreted accordingly.

[0085] The present invention covers any substitution, modification, equivalent method and scheme made on the essence and scope of the present invention. In order to make the public have a thorough understanding of the present invention, specific details are described in detail in the following preferred embodiments of the present invention, but those skilled in the art can fully understand the present invention without the description of these details. In addition, in order to avoid unnecessary confusion about the essence of the present invention, well-known methods, processes, procedures, components and circuits are not described in detail.

[0086] A person skilled in the art will appreciate that all or part of the steps in the above-mentioned embodiment method can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, such as ROM / RAM, a disk, an optical disk, etc.

[0087] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.

Claims

1. A multi-scenario process monitoring and early warning method, characterized in that: Including real-time operation monitoring, process trend monitoring and indicator statistical monitoring; The real-time operation monitoring includes: collecting real-time operation data during the production process, analyzing the real-time operation data online, and issuing an alarm when an abnormality is found; The process trend monitoring includes: collecting process trend data in the production process, analyzing the process trend data to obtain the process execution status on site, and making corresponding process improvements based on the analysis results; The indicator statistical monitoring includes: collecting indicator statistical data in the production process, analyzing the indicator statistical data, obtaining key indicator information, and displaying it in an intuitive way to provide a basis for decision makers; Among them, the real-time operation monitoring, the process trend monitoring and the indicator statistical monitoring are configured with monitoring rules according to the needs to meet the monitoring needs of different scenarios; The monitoring rules are derived from upper and lower tolerance rules or configurable monitoring rules issued from the manufacturing execution system. The configurable monitoring rules can be selected and modified according to the template, including formula editing, rule copying, and rule adding. The monitoring alarms are performed based on the upper and lower tolerance rules issued by the manufacturing execution system as monitoring rules, including: For the rolling force RF, it is calculated as follows: Among them, RF i is the rolling force data of any sampling point i, RF Lower_Tol ,RF Upper_Tol is the upper and lower tolerances of the rolling force RF, Monitor_Result is the rolling force monitoring result, ok means no alarm, no means an alarm occurs; Monitoring alarms based on configurable monitoring rules include: Monitoring rules are configured for discrete data through expressions, or rules are configured after any characteristic value calculation is performed on the process curve; the characteristic value calculation includes the calculation of maximum value, minimum value, range value, and standard deviation; For the rolling force RF curve, the characteristic value calculation formula is as follows: RF max =max(RF0、RF1、RF2……RF n-2 、RF n-1 、RF n ); RF min =min(RF0、RF1、RF2……RF n-2 、RF n-1 、RF n ); RF extrem =RF max -RF min ; Among them, RF max ,RF min ,RF extrem ,RF avg ,RF std The maximum, minimum, extreme value, mean, and standard deviation of the rolling force RF curve collected, RF0, RF1, RF2, RF n-2 ,RF n-1 ,RF n is the data point collected on the rolling force RF curve; After the characteristic value calculation of the rolling force RF curve is completed, the monitoring rules are configured according to the monitoring requirements to form a monitoring rule base, and the monitoring rule base is converted into machine language. The monitoring rules are used to perform real-time abnormality judgment on various parameters of the production process, and the judgment results are displayed on the real-time monitoring operation screen to realize automatic alarm for abnormal events; Among them, RF extrem_max ,RF std_max It indicates the upper limit of the extreme value and the upper limit of the standard deviation allowed by the rolling force RF curve. Monitor_min_Result, Monitor_max_Result, Monitor_extrem_Result and Monitor_std_Result are the monitoring results of the minimum, maximum, extreme value and standard deviation of the rolling force. "ok" means no alarm and "no" means an alarm occurs.

2. The multi-scenario process monitoring and early warning method according to claim 1 is characterized in that: For real-time operation monitoring, the timeliness of data is highly required. After completing the real-time operation data collection, abnormal status is captured in real time, and displayed and alarmed in the most intuitive way; For process trend monitoring, the timeliness of data is less demanding. Process trend statistical analysis is performed according to different time periods, or according to different production processes and different units to obtain the on-site process execution status under different analysis conditions, as well as the changing trend of process execution status. For indicator statistical monitoring, the timeliness requirements of data are relatively low. The changes of different indicators in different units, different products, and different time axes are displayed from top to bottom and from global to local.

3. The multi-scenario process monitoring and early warning method according to claim 1 is characterized in that: For the hot rolling mill, the real-time operation data include: rough rolling dephosphorization pressure, finishing rolling dephosphorization pressure, dephosphorization header inlet DS height, dephosphorization header inlet OS height, dephosphorization header outlet DS height, dephosphorization header outlet OS height, anti-stripping water opening signal up, anti-stripping water opening signal down, upper roll gap lubrication water flow up, upper roll gap lubrication water flow down, working roll cooling water temperature deviation, inter-stand cooling water temperature deviation; The process trend data include: rolling force of the roughing mill stand, rolling force of the finishing mill stand, measured value of roll gap deviation on both sides of the stand, stand stiffness OS, stand stiffness DS, looper angle, looper front tension, actual tension of the coiler, guide plate operating side pressure, roughing outlet temperature, finishing outlet temperature, coiling temperature; The statistical data of the indicators include: total raw material inventory, raw material surplus inventory, total finished product inventory, finished product surplus inventory, total daily output, monthly total output, number of rolled blocks on the day, output trend in the past 7 days, steel unplanned rate, comprehensive unplanned rate, one-time hit rate, appearance quality unqualified rate, performance quality unqualified rate.

4. The multi-scenario process monitoring and early warning method according to claim 1 is characterized in that: The monitoring rules can be displayed with different alarm levels, where orange is the lowest alarm level and red is the highest. Each monitoring item can be configured with up to 3 alarm levels.

5. The multi-scenario process monitoring and early warning method according to claim 1 is characterized in that: The configurable monitoring rules can be single rules or combined rules, or form customized monitoring scenarios to meet different needs.

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