Early warning method and device for process machine, electronic equipment and medium

Abstract

The invention provides an early warning method and device for a process machine, electronic equipment and a medium, and is applied to a statistical process control (SPC) system.The method comprises the steps that a piece of production data is received in real time from a manufacturing execution system (MES), and the production data comprises equipment identification and product parameters; calling an out-of-control monitoring strategy bound with a target processing machine indicated by the equipment identifier; performing anomaly detection on the product parameters by using an out-of-control monitoring strategy; if it is identified that the product parameters are abnormal, forming an abnormal record, and storing the abnormal record in a folder corresponding to the target processing machine; identifying whether the abnormal record stored in the folder meets an out-of-control state early warning condition or not; and if yes, outputting an early warning signal for indicating that the target processing machine is in the out-of-control state. According to the invention, the product parameters of the target processing machine are monitored and the early warning signal is output, so that the production process of the target processing machine can be monitored in time.

Description

Technical Field

[0001] The present application relates to the technical field of production process control, and in particular to an early warning method, device, electronic equipment and medium for a process machine. Background Art

[0002] For experienced quality control experts, the nine judgment rules of the traditional SPC (Statistical Process Control) system can no longer meet their stringent quality management needs, especially when faced with the same process machine receiving values, when the abnormal situation of exceeding the specification limit occurs three times in a row under the same specifications. The probability of this abnormality is much lower than the range captured by the nine conventional rules. In response to this demand, the traditional SPC system is difficult to accurately identify. Summary of the invention

[0003] In view of this, the purpose of the present application is to provide an early warning method, device, electronic equipment and medium for a process machine, aiming to overcome at least one of the above-mentioned defects.

[0004] In a first aspect, the present application provides an early warning method for a process machine, which is applied to a statistical process control (SPC) system, and the method includes: receiving a piece of production data from a manufacturing execution system (MES) in real time, the production data including an equipment identification and product parameters; calling an out-of-control monitoring strategy bound to a target process machine indicated by the equipment identification; using the out-of-control monitoring strategy to perform abnormality detection on the product parameters; if an abnormality is identified in the product parameters, an abnormality record is formed and stored in a folder corresponding to the target process machine, the abnormality record includes an equipment identification, a parameter identification, an abnormality identification, a specification identification and a timestamp, the parameter identification is used to indicate an abnormal product parameter, the abnormality identification is used to indicate a product abnormality in the process machine, the specification identification is used to indicate the product specification corresponding to the abnormal product parameter, and the timestamp is used to indicate the time point when the abnormality occurs; identifying whether the abnormality record stored in the folder meets the out-of-control state early warning condition; if so, outputting an early warning signal indicating that the target process machine is in an out-of-control state.

[0005] In one possible implementation, MES obtains the production data in the following manner: receiving product parameters on multiple process machines collected in real time from an equipment automation EAP system; integrating each product parameter and the equipment identification of the process machine corresponding to the product parameter into one piece of production data, and sending it to the SPC system.

[0006] In one possible implementation, the out-of-control monitoring strategy includes a detection range that matches a product specification corresponding to a product parameter, wherein the product parameter is detected for abnormality in the following manner: determining whether the product parameter falls within the matched detection range; if the product parameter does not fall within the matched detection range, determining that the product parameter has an abnormality; if the product parameter falls within the matched detection range, determining that the product parameter does not have an abnormality.

[0007] In a possible implementation, the step of identifying whether the abnormal records stored in the folder meet the out-of-control state warning condition includes: sorting the abnormal records stored in the folder according to their corresponding timestamps; for each two adjacent abnormal records, calculating the time difference between the first timestamp of the first abnormal record and the second timestamp of the second abnormal record, and judging whether the time difference is consistent with the sampling interval, where the sampling interval is the time interval for collecting product parameters; if the time difference corresponding to the two most recent adjacent abnormal records is inconsistent with the sampling interval, it is determined that the out-of-control state warning condition is not met; if the time difference corresponding to each two adjacent abnormal records is consistent with the sampling interval, the number of abnormal records stored in the folder is counted; if the counted number is less than the first predetermined number, it is determined that the out-of-control state warning condition is not met; if the counted number is not less than the first predetermined number, it is determined that the out-of-control state warning condition is met.

[0008] In a possible implementation, the step of identifying whether the abnormal records stored in the folder meet the out-of-control state warning condition includes: classifying the abnormal records in the folder according to the specification identification to form multiple abnormal processing groups, wherein each abnormal processing group includes all abnormal records under a corresponding specification identification, and each abnormal processing group is cleared when a preset condition is met, and the preset condition includes after the first abnormal record is received in the abnormal processing group and a preset time has passed; judging whether the number of abnormal records in each abnormal processing group reaches a second predetermined number; if there are abnormal processing groups that reach the second predetermined number, determining that the out-of-control state warning condition is met, so that the specification identification used to characterize the abnormal processing group is carried in the output warning signal; if there are no abnormal processing groups that reach the second predetermined number, determining that the out-of-control state warning condition is not met.

[0009] In a possible implementation, it also includes: a plurality of abnormal intervals and a warning signal corresponding to each abnormal interval are pre-configured for the out-of-control state warning condition, wherein the step of outputting a warning signal for indicating that the target process machine is in an out-of-control state includes: extracting a preset abnormal record in an abnormal queue, wherein the abnormal queue is a plurality of abnormal records that meet the out-of-control state warning condition; identifying the abnormal interval into which the product parameters in the preset abnormal record fall, so as to output a warning signal corresponding to the abnormal interval.

[0010] In a possible implementation, the step of extracting a preset exception record in the exception queue includes: identifying the timestamp corresponding to each exception record in the exception queue to determine the timestamp closest to the time point among the timestamps, and determining the exception record corresponding to the timestamp as the preset exception record.

[0011] In a second aspect, the present application provides an early warning device for a process machine, the device comprising: a receiving module, for receiving a piece of production data from a manufacturing execution system MES in real time, the production data comprising an equipment identifier and a product parameter; a calling module, for calling an out-of-control monitoring strategy bound to a target process machine indicated by the equipment identifier; a detection module, for performing abnormality detection on the product parameters using the out-of-control monitoring strategy; a storage module, for forming an abnormality record if an abnormality is identified in the product parameter, and storing it in a folder corresponding to the target process machine, the abnormality record comprising an equipment identifier, a parameter identifier, an abnormality identifier, a specification identifier and a timestamp, the parameter identifier being used to indicate an abnormal product parameter, the abnormality identifier being used to indicate a product abnormality in the process machine, the specification identifier being used to indicate a product specification corresponding to the abnormal product parameter, and the timestamp being used to indicate a time point when the abnormality occurs; an identification module, for identifying whether the abnormality record stored in the folder meets an out-of-control state early warning condition; an output module, for outputting an early warning signal indicating that the target process machine is in an out-of-control state if the condition is met.

[0012] In a third aspect, the present application also provides an electronic device, comprising: a processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate through the bus, and when the machine-readable instructions are executed by the processor, the steps of the above method are performed.

[0013] In a fourth aspect, the present application further provides a computer-readable storage medium having a computer program stored thereon, and the computer program executes the steps of the above method when executed by a processor.

[0014] The present application provides an early warning method, device, electronic device and medium for a process machine, which is applied to a statistical process control SPC system, wherein the method includes: receiving a piece of production data from a manufacturing execution system MES in real time, the production data including equipment identification and product parameters; calling the out-of-control monitoring strategy bound to the target process machine indicated by the equipment identification; using the out-of-control monitoring strategy to perform abnormal detection on the product parameters; if an abnormality is identified in the product parameters, an abnormal record is formed and stored in a folder corresponding to the target process machine; identifying whether the abnormal record stored in the folder meets the out-of-control state early warning condition; if it does, outputting an early warning signal to indicate that the target process machine is in an out-of-control state. The present application realizes timely monitoring of the production process of the target process machine by monitoring the product parameters of the target process machine and outputting an early warning signal.

[0015] In order to make the above-mentioned objects, features and advantages of the present application more obvious and easy to understand, preferred embodiments are specifically cited below and described in detail with reference to the attached drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0017] Figure 1 A flow chart of an early warning method for a process tool provided in an embodiment of the present application;

[0018] Figure 2 A flowchart of the MES for obtaining production data provided in the embodiment of the present application;

[0019] Figure 3 A flowchart of abnormal detection of product parameters provided in an embodiment of the present application;

[0020] Figure 4 A flowchart for identifying abnormal records that meet out-of-control state warning conditions provided in an embodiment of the present application;

[0021] Figure 5 Another flowchart for identifying abnormal records that meet out-of-control state warning conditions provided in an embodiment of the present application;

[0022] Figure 6 A flowchart of outputting a warning signal provided in an embodiment of the present application;

[0023] Figure 7 A schematic diagram of the structure of an early warning device for a process machine provided in an embodiment of the present application;

[0024] Figure 8 A schematic diagram of the structure of an electronic device provided in an embodiment of the present application. DETAILED DESCRIPTION

[0025] To make the purpose, technical scheme and advantages of the embodiments of the present application clearer, the technical scheme in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application usually described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, each other embodiment obtained by those skilled in the art without making creative work belongs to the scope of protection of the present application.

[0026] First, the application scenarios to which the present application is applicable are introduced. The present application can be applied to an SPC system.

[0027] Research has found that for senior quality control experts, the nine judgment rules of the traditional SPC system can no longer meet their stringent quality management needs, especially when facing the same process machine collection values, that is, the actual process parameters or product characteristic values ​​automatically collected and recorded by process equipment such as injection molding machines, machining centers, and testing machines in the manufacturing process. When the abnormal situation exceeds the specification limit three times in a row under the same specification, the probability of this abnormality occurring is much lower than the range captured by the nine general rules. In response to this demand, the traditional SPC system is difficult to accurately identify.

[0028] Based on this, the embodiments of the present application provide an early warning method, device, electronic device and medium for a process machine to achieve real-time monitoring and efficient response measures in the production flow of the target process machine.

[0029] See also Figure 1 , Figure 1 This is a flow chart of an early warning method for a process tool provided in an embodiment of the present application. Figure 1 The early warning method for a process tool shown is implemented in an SPC system. Figure 1 As shown in , the early warning method for a process machine provided by the embodiment of the present application includes:

[0030] S101. Receive a piece of production data from a manufacturing execution system MES in real time.

[0031] Here, the SPC system interacts directly with the MES (Manufacturing Execution System) to capture and record data about the process machine in the production process in real time. As the core part of the informatization of the manufacturing industry, MES can monitor and guide the execution of the production process in real time, including production scheduling, inventory management, quality control, etc.

[0032] Below through Figure 2 This paper introduces the specific steps of MES to obtain production data.

[0033] Figure 2 A flowchart of the MES obtaining production data provided in an embodiment of the present application.

[0034] S201, receiving product parameters on multiple process machines collected in real time from an equipment automation EAP system.

[0035] Here, MES needs to first receive data from the EAP (Equipment Automation Program) system, which is responsible for monitoring and collecting the operating status and product parameters of each process machine in the factory, such as temperature, pressure, speed, etc. These parameters are crucial for evaluating product quality and production efficiency. The EAP system can obtain these parameters from multiple process machines in real time to ensure the comprehensiveness and real-time nature of the data.

[0036] S202, integrating each product parameter and the equipment identification of the process machine corresponding to the product parameter into a piece of production data, and sending the result to the SPC system.

[0037] Here, after receiving the product parameters from the EAP system, MES needs to associate these parameters with the equipment identification of their corresponding process machines to ensure that each piece of production data can be accurately traced back to the specific production equipment and production batch. After the association is completed, the system will send the integrated production data to the SPC system.

[0038] Specifically, the equipment identification is a unique identifier used to distinguish and identify different process machines in the factory. The equipment identification can be the serial number, model, location code, etc. of the equipment. Product parameters can be parameters for only one product specification, or they can include multiple product specifications and product parameters corresponding to each product specification, which can be selected according to customer needs.

[0039] In a possible embodiment, the target product may be a semiconductor. In a semiconductor production environment, EAP is responsible for controlling production equipment and collecting data from process machines in the production process in real time, such as product parameters. These data are essential for monitoring the stability of the production process and product quality. As the core management system of the production line, MES is responsible for real-time monitoring of the entire production line and recording the status changes of various elements related to production. When the EAP system collects product parameters, these data will be reported to the MES system in real time. The MES system will further process these product parameters for production process control, product quality monitoring, and production management decision support. At the same time, the MES system will also provide these product parameters to the SPC system for more in-depth analysis and monitoring.

[0040] return Figure 1 , S102, calling the out-of-control monitoring strategy bound to the target process machine indicated by the equipment identification.

[0041] Here, the out-of-control monitoring strategy includes a detection range that matches the product specifications corresponding to the product parameters.

[0042] S103. Utilize the out-of-control monitoring strategy to perform abnormal detection on product parameters.

[0043] In a preferred example of the present application, the out-of-control monitoring strategy includes a detection range that matches the product specifications corresponding to the product parameters.

[0044] Here, the out-of-control monitoring strategy is a set of preset rules or algorithms used to monitor product parameters during the production process and determine whether these parameters exceed the preset normal range. The out-of-control monitoring strategy is bound to a specific target process machine and includes a detection range that matches the product specifications corresponding to the product parameters. This means that for each target process machine, the system has a specific strategy to monitor the product parameters it produces and determine whether these product parameters are abnormal.

[0045] Below through Figure 3 This section describes the specific steps for detecting anomalies in product parameters.

[0046] Figure 3 A flowchart for abnormal detection of product parameters provided in an embodiment of the present application.

[0047] S301: Determine whether the product parameters fall within the matched detection range.

[0048] If the product parameter does not fall within the matched detection range, step S302 is executed to determine whether the product parameter is abnormal.

[0049] If the product parameter falls within the matched detection range, step S303 is executed to determine whether there is any abnormality in the product parameter.

[0050] return Figure 1 , S104, if it is identified that the product parameters are abnormal, an abnormal record is generated and stored in the folder corresponding to the target process machine.

[0051] In an embodiment of the present application, the exception record includes a device identifier, a parameter identifier, an exception identifier, a specification identifier and a timestamp. The parameter identifier is used to indicate that an abnormal product parameter exists. The exception identifier is used to indicate that a product abnormality occurs in a process machine. The specification identifier is used to indicate the product specification corresponding to the abnormal product parameter. The timestamp is used to indicate the time point when the abnormality occurs.

[0052] Here, an exception record is a document or data entry used to record abnormal situations found during the production process. The exception record is formed when an abnormality is identified in a product parameter. It contains multiple key information fields, such as equipment identification, parameter identification, exception identification, specification identification, and timestamp. Together, these information constitute a comprehensive description of the abnormal event. The equipment identification is used to indicate the target process machine where the abnormality occurred, which is convenient for tracking and locating the source of the problem. In the exception record, the parameter identification is used to indicate the specific product parameters with abnormalities, such as temperature, pressure, etc. The exception identification is used to clearly indicate that a product abnormality has occurred in the process machine. The timestamp is used to indicate the exact time point when the abnormality occurred, which helps relevant personnel understand the sequence and duration of the abnormality.

[0053] S105: Identify whether the abnormal records stored in the folder meet the out-of-control state warning condition.

[0054] Here, the present application provides two methods for determining whether the out-of-control state warning conditions are met.

[0055] Below through Figure 4 and Figure 5 Two specific steps are introduced to identify abnormal records that meet the out-of-control state warning conditions.

[0056] Figure 4 A flowchart for identifying abnormal records that meet out-of-control state warning conditions provided in an embodiment of the present application.

[0057] S401. Sort the exception records stored in the folder according to their corresponding timestamps.

[0058] S402: For each two adjacent abnormal records, calculate the time difference between the first timestamp of the first abnormal record and the second timestamp of the second abnormal record, and determine whether the time difference is consistent with the sampling interval.

[0059] Here, the sampling interval is the time interval at which the SPC system collects product parameters from the MES.

[0060] If the time difference corresponding to the two most recent adjacent abnormal records is inconsistent with the sampling interval, step S403 is executed to determine that the out-of-control state warning condition is not met.

[0061] If the time difference between each two adjacent abnormal records is consistent with the sampling interval, step S404 is executed to count the number of abnormal records stored in the folder.

[0062] If the counted number is less than the first predetermined number, the process returns to step S403 to determine that the out-of-control state warning condition is not met.

[0063] Here, the first predetermined number is a preset threshold value used to determine whether the number of abnormal records is sufficient to trigger an out-of-control state warning. For example, if the system is set to trigger an alarm only when the number of abnormal records reaches 3 or more, the first predetermined number is 3. In the embodiment of the present application, the first predetermined number is set to three, and these three consecutive out-of-control state warnings are recorded as R13 rules in the SPC system.

[0064] If the counted number is not less than the first predetermined number, step S405 is executed to determine whether the out-of-control state warning condition is met.

[0065] Figure 5 Another flowchart for identifying abnormal records that meet out-of-control state warning conditions provided in an embodiment of the present application.

[0066] S501. Classify the exception records in the folder according to the specification identifier to form a plurality of exception processing groups.

[0067] Among them, each exception processing group includes all exception records under a corresponding specification identification, and each exception processing group is cleared when the preset conditions are met. The preset conditions include that after the first exception record is received in the exception processing group, a preset time has passed. The preset condition means that after the first exception record is received, a preset time has passed, and the preset time can be four days.

[0068] S502: Determine whether the number of exception records in each exception processing group reaches a second predetermined number.

[0069] Here, the second predetermined number is also a preset threshold value, which is used to determine whether the number of abnormal records is sufficient to trigger the out-of-control state warning. For example, if the system is set to trigger the warning only when the number of abnormal records reaches 3 or more, the second predetermined number is 3, which is the same as the first predetermined number. If the counted number is not less than the second predetermined number, step S405 is executed to determine whether the out-of-control state warning condition is met.

[0070] In the embodiment of the present application, the second predetermined number is set to three, and these three consecutive out-of-control state warnings are recorded as R13 rule in the SPC system.

[0071] If there are exception processing groups reaching the second predetermined number, step S503 is executed to determine whether the out-of-control state warning condition is met, so that a specification identifier for characterizing the exception processing group is carried in the output warning signal.

[0072] Here, the specification identifier used to characterize the exception handling group can be output on the SPC system to remind the staff which specific product specification on the target process machine has an error, so that specific investigation can be carried out.

[0073] If there are no exception processing groups that reach the second predetermined number, step S504 is executed to determine whether the out-of-control state warning condition is not met.

[0074] return Figure 1 If the out-of-control state warning condition is met, step S106 is executed to output a warning signal indicating that the target process tool is in an out-of-control state.

[0075] In a preferred example of the present application, a plurality of abnormal intervals and warning signals corresponding to the abnormal intervals are pre-configured for the out-of-control state warning condition.

[0076] Below through Figure 6 This section introduces the specific process of outputting early warning signals.

[0077] Figure 6 A flowchart of outputting a warning signal provided in an embodiment of the present application.

[0078] S601: extract a preset exception record in the exception queue.

[0079] Here, the abnormal queue is a plurality of abnormal records that meet the out-of-control state warning condition, and the preset abnormal record refers to the abnormal record with the latest time point in the abnormal queue.

[0080] In a preferred example of the present application, the step of extracting a preset exception record in the exception queue includes: identifying the timestamp corresponding to each exception record in the exception queue to determine the timestamp closest to the time point in the corresponding timestamps, and determining the exception record corresponding to the timestamp as the preset exception record.

[0081] S602: Identify the abnormal interval into which the parameter identifier in the preset abnormal record falls, and output a warning signal corresponding to the abnormal interval.

[0082] In a preferred example of the present application, multiple abnormal intervals and warning signals corresponding to each abnormal interval are pre-configured for the out-of-control state warning condition, the multiple abnormal intervals include a first abnormal interval, the first abnormal interval corresponds to a first warning signal, the multiple abnormal intervals also include a second abnormal interval, the second abnormal interval corresponds to a second warning signal, the multiple abnormal intervals also include a third abnormal interval, the third abnormal interval corresponds to a third warning signal.

[0083] Here, the abnormal interval is set according to the out-of-control state warning condition, and is used to distinguish the numerical range of abnormal situations of different degrees. There are multiple abnormal intervals, including the first abnormal interval, the second abnormal interval and the third abnormal interval. Each interval corresponds to a different degree of abnormality and a warning signal. The warning signal refers to the response measures that the system or staff should take when the production parameters fall into a certain abnormal interval.

[0084] When it is identified that the product parameter in the preset abnormal record falls into the first abnormal interval, a first early warning signal is triggered to display a warning notification.

[0085] Here, when the value of the product parameter in the preset abnormal record falls into the first abnormal interval, it indicates that the production parameter has a slight abnormality, but it may not have a serious impact on the product quality or the production process. The system will trigger the first warning signal, recorded as Alarm, which usually displays a warning notification. This notification will remind the staff to pay attention to the abnormal situation of the production parameter and require them to conduct further observation and monitoring. The warning notification can be sent to relevant personnel through screen display, sound prompt or text message.

[0086] If the product parameters in the preset abnormal record fall into the second abnormal range, a second warning signal is triggered to control the stop of obtaining the product parameters of the target process machine.

[0087] Here, when the value of the production parameter further deviates from the normal range and falls into the second abnormal interval, it indicates that the abnormal situation in the production process has become more obvious and may have a certain impact on product quality or production efficiency. At this time, the system will trigger the second warning signal, recorded as HOLD, which usually controls the stop of obtaining the product parameters of the target process machine, which means that the system will suspend the monitoring and recording of the product parameters of the target process machine to avoid generating more data that may be affected by the abnormality. This measure helps staff focus on dealing with current problems and prevents the problem from getting worse.

[0088] If the product parameters in the preset abnormal record fall into the third abnormal range, a third warning signal is triggered to control the sending of a shutdown signal to the target process machine and notify the staff to inspect the target process machine.

[0089] Here, when the value of the production parameter deviates seriously from the normal range and falls into the third abnormal interval, it indicates that the abnormal situation in the production process has become very serious and may pose a serious threat to product quality or production safety. In this case, the system will trigger the third early warning signal, recorded as OCAP (Out-of-Control Action Plan), that is, sending a shutdown signal to the target process machine and notifying the staff to inspect the target process machine. The shutdown signal is an emergency control measure aimed at immediately stopping production activities that may cause quality problems. At the same time, notifying the staff to conduct an inspection is to find out the root cause of the problem and take necessary repair measures to ensure that production can return to normal.

[0090] Based on the same inventive concept, an early warning device for a process machine corresponding to the early warning method for a process machine is also provided in the embodiment of the present application. Since the principle of solving the problem by the device in the embodiment of the present application is similar to the above-mentioned early warning method for a process machine in the embodiment of the present application, the implementation of the device can refer to the implementation of the method, and the repeated parts will not be repeated.

[0091] See also Figure 7 , Figure 7 The schematic diagram of the structure of the early warning device for a process machine provided in the embodiment of the present application is as follows: Figure 7 As shown in FIG. , the early warning device 700 for a process tool includes:

[0092] The receiving module 701 is used to receive a piece of production data from the manufacturing execution system MES in real time. The production data includes equipment identification and product parameters.

[0093] The calling module 702 is used to call the out-of-control monitoring strategy bound to the target process tool indicated by the equipment identification.

[0094] The detection module 703 is used to perform abnormal detection on product parameters using the out-of-control monitoring strategy.

[0095] Storage module 704 is used to form an exception record and store it in a folder corresponding to the target process machine if an abnormality is identified in the product parameters. The exception record includes a device identifier, a parameter identifier, an exception identifier, a specification identifier and a timestamp. The parameter identifier is used to indicate that an abnormal product parameter exists. The exception identifier is used to indicate that a product abnormality occurs in the process machine. The specification identifier is used to indicate the product specification corresponding to the abnormal product parameter. The timestamp is used to indicate the time point when the abnormality occurs.

[0096] The identification module 705 is used to identify whether the abnormal records stored in the folder meet the out-of-control state warning condition.

[0097] The output module 706 is used to output a warning signal indicating that the target process tool is in an out-of-control state if the conditions are met.

[0098] See also Figure 8 , Figure 8 This is a schematic diagram of the structure of an electronic device provided in an embodiment of the present application. Figure 8 As shown in , the electronic device 800 includes a processor 810 , a memory 820 and a bus 830 .

[0099] The memory 820 stores machine-readable instructions executable by the processor 810. When the electronic device 800 is running, the processor 810 communicates with the memory 820 via the bus 830. When the machine-readable instructions are executed by the processor 810, the above-mentioned Figure 1 With ~ Figure 6 The specific implementation of the steps of the early warning method for a process tool in the method embodiment shown can be found in the method embodiment, which will not be described in detail here.

[0100] The present application also provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the computer program can execute the above-mentioned Figure 1 to Figure 6 The specific implementation of the steps of the early warning method for a process tool in the method embodiment shown can be found in the method embodiment, which will not be described in detail here.

[0101] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0102] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. The device embodiments described above are merely schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some communication interfaces, and the indirect coupling or communication connection of devices or units can be electrical, mechanical or other forms.

[0103] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0104] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0105] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a non-volatile computer-readable storage medium that is executable by a processor. Based on this understanding, the technical solution of the present application can essentially be embodied in the form of a software product, or in other words, the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present application. The aforementioned storage medium includes: various media that can store program codes, such as a USB flash drive, a mobile hard disk, a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk.

[0106] Finally, it should be noted that the above-described embodiments are only specific implementation methods of the present application, which are used to illustrate the technical solutions of the present application, rather than to limit them. The protection scope of the present application is not limited thereto. Although the present application is described in detail with reference to the above-mentioned embodiments, ordinary technicians in the field should understand that any technician familiar with the technical field can still modify the technical solutions recorded in the above-mentioned embodiments within the technical scope disclosed in the present application, or can easily think of changes, or make equivalent replacements for some of the technical features therein; and these modifications, changes or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present application, and should be included in the protection scope of the present application. Therefore, the protection scope of the present application shall be based on the protection scope of the claims.

Claims

1. An early warning method for a process machine, characterized in that: Applied to a statistical process control (SPC) system, the method comprises: receiving a piece of production data from a manufacturing execution system MES in real time, the production data including equipment identification and product parameters; Invoking the out-of-control monitoring strategy bound to the target process machine indicated by the equipment identification; Using the out-of-control monitoring strategy, abnormality detection is performed on the product parameters; If it is identified that the product parameter is abnormal, an abnormal record is formed and stored in the folder corresponding to the target process machine, the abnormal record includes a device identifier, a parameter identifier, an abnormal identifier, a specification identifier and a timestamp, the parameter identifier is used to indicate the product parameter with abnormality, the abnormal identifier is used to indicate that the process machine has a product abnormality, the specification identifier is used to indicate the product specification corresponding to the product parameter with abnormality, and the timestamp is used to indicate the time point when the abnormality occurs; Identify whether the abnormal records stored in the folder meet the out-of-control state warning condition; If the conditions are met, a warning signal is outputted to indicate that the target process tool is in an out-of-control state.

2. The method according to claim 1, characterized in that: MES obtains the production data in the following ways: Receive product parameters from multiple process machines collected in real time from the equipment automation EAP system; Each product parameter and the equipment identification of the process machine corresponding to the product parameter are integrated into a piece of production data and sent to the SPC system.

3. The method according to claim 1, characterized in that The out-of-control monitoring strategy includes a detection range that matches the product specifications corresponding to the product parameters, The product parameters are detected to be abnormal in the following ways: Determine whether the product parameters fall within the matched detection range; If the product parameter does not fall within the matched detection range, it is determined that the product parameter is abnormal; If the product parameter falls within the matched detection range, it is determined that there is no abnormality in the product parameter.

4. The method according to claim 1, characterized in that: The step of identifying whether the abnormal records stored in the folder meet the out-of-control state warning condition includes: Sorting the abnormal records stored in the folder according to their corresponding timestamps; For each two adjacent abnormal records, calculate the time difference between the first timestamp of the first abnormal record and the second timestamp of the second abnormal record, and determine whether the time difference is consistent with a sampling interval, where the sampling interval is a time interval for collecting product parameters; If the time difference corresponding to the two most recent adjacent abnormal records is inconsistent with the sampling interval, it is determined that the out-of-control state warning condition is not met; If the time difference between each two adjacent abnormal records is consistent with the sampling interval, the number of abnormal records stored in the folder is counted; If the counted number is less than the first predetermined number, it is determined that the out-of-control state warning condition is not met; If the counted number is not less than the first predetermined number, it is determined that the out-of-control state warning condition is met.

5. The method according to claim 1, characterized in that The step of identifying whether the abnormal records stored in the folder meet the out-of-control state warning condition includes: According to the specification identifier, the exception records under the folder are classified to form a plurality of exception processing groups, wherein each exception processing group includes all exception records under a corresponding specification identifier, and each exception processing group is cleared when a preset condition is met, wherein the preset condition includes that after the first exception record is received in the exception processing group, a preset time has passed; Determining whether the number of exception records in each exception processing group reaches a second predetermined number; If there are abnormal processing groups reaching the second predetermined number, it is determined that the out-of-control state warning condition is met, so that a specification identifier used to characterize the abnormal processing group is carried in the output warning signal; If there are no abnormality processing groups reaching the second predetermined number, it is determined that the out-of-control state warning condition is not satisfied.

6. The method according to claim 1, characterized in that Also includes: A plurality of abnormal intervals and warning signals corresponding to each abnormal interval are pre-configured for the out-of-control state warning condition. The step of outputting a warning signal indicating that the target process machine is in an out-of-control state includes: Extracting a preset abnormal record in an abnormal queue, wherein the abnormal queue is a plurality of abnormal records that meet the out-of-control state warning condition; The abnormal interval into which the product parameter in the preset abnormal record falls is identified to output a warning signal corresponding to the abnormal interval.

7. The method according to claim 6, characterized in that The step of extracting a preset exception record in the exception queue includes: The timestamp corresponding to each abnormal record in the abnormal queue is identified to determine a timestamp with the closest time point among the timestamps, and the abnormal record corresponding to the timestamp is determined as a preset abnormal record.

8. An early warning device for a process machine, characterized in that: The device comprises: A receiving module, used for receiving a piece of production data from a manufacturing execution system MES in real time, wherein the production data includes equipment identification and product parameters; A calling module, used for calling the out-of-control monitoring strategy bound to the target process machine indicated by the equipment identification; A detection module, used to perform abnormal detection on the product parameters using the out-of-control monitoring strategy; A storage module, for forming an abnormality record if an abnormality is identified in the product parameter, and storing it in a folder corresponding to the target process machine, wherein the abnormality record includes a device identifier, a parameter identifier, an abnormality identifier, a specification identifier, and a timestamp, wherein the parameter identifier is used to indicate the abnormal product parameter, the abnormality identifier is used to indicate that a product abnormality occurs in the process machine, the specification identifier is used to indicate the product specification corresponding to the abnormal product parameter, and the timestamp is used to indicate the time point when the abnormality occurs; An identification module, used to identify whether the abnormal records stored in the folder meet the out-of-control state warning condition; The output module is used to output a warning signal indicating that the target process tool is in an out-of-control state if the conditions are met.

9. An electronic device, characterized in that: include: A processor, a memory and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor and the memory communicate via the bus, and the processor executes the machine-readable instructions to perform the steps of any method as claimed in claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 7 are executed.

Images