Production data management method and cloud platform for edge computing

Abstract

The production data management method and cloud platform for edge computing provided by the embodiment of the present application, by responding to being in the state of automated production line monitoring, if an abnormal production line monitoring project intended to express abnormal production line monitoring is identified, a set of automated production line data sets is determined, and when it is determined that no real-time automated production line monitoring project intended to express real-time automated production line monitoring is identified in the monitoring process of the automated production line data set, the current data set is used as an abnormal data set based on the abnormal production line monitoring project, which can not only realize the adaptive collection of data sets in the monitoring process, but also accurately and reliably determine the abnormal data sets, thereby improving the quality of production data monitoring and generated data management. In this way. After determining the abnormal data set, the debugging and optimization processing of the relevant automated production line can be carried out, thereby ensuring the normal and stable operation of the automated production line.

Description

Technical Field

[0001] The present application relates to the field of edge computing and data management technology, and in particular to a production data management method and cloud platform applied to edge computing. Background Art

[0002] With the rapid development of science and technology, the manufacturing industry has also undergone great changes. For example, for production data monitoring and data generation management, the traditional method is based on manual on-site management. This method cannot effectively and quickly monitor abnormal situations in the production process, and cannot ensure production quality in the production process. Therefore, there is a need to provide an automated production line monitoring technology. Summary of the invention

[0003] In order to improve the technical problems existing in the related technologies, the present application provides a production data management method and a cloud platform applied to edge computing.

[0004] In the first aspect, the embodiment of the present application provides a production data management method applied to edge computing, which is applied to a production data management cloud platform, and the method at least includes: in response to being in an automated production line monitoring state, when an abnormal production line monitoring project intended to express abnormal production line monitoring is identified, a group of automated production line data sets are determined, and the abnormal production line monitoring project is related to the production task or monitoring node; in response to determining that the monitoring process of the automated production line data set does not identify a real-time automated production line monitoring project intended to express real-time automated production line monitoring, the automated production line data set is used as an abnormal data set based on the abnormal production line monitoring project. In this way, the embodiment of the present application is applied, by responding to being in an automated production line monitoring state, if an abnormal production line monitoring project intended to express abnormal production line monitoring is identified, a group of automated production line data sets are determined, and when it is determined that the monitoring process of the automated production line data set does not identify a real-time automated production line monitoring project intended to express real-time automated production line monitoring, the current data set is used as an abnormal data set based on the abnormal production line monitoring project, which can not only realize the adaptive collection of data sets in the monitoring process, but also accurately and reliably determine the abnormal data set, thereby improving the quality of production data monitoring and generated data management. In this way. After the abnormal data set is determined, the relevant automated production line can be debugged and optimized to ensure the normal and stable operation of the automated production line.

[0005] Optionally, the identification of abnormal production line monitoring items intended to express abnormal production line monitoring includes: determining whether the current automated production line information has continued for a first target cumulative period, the current automated production line information being the information in the currently determined automated production line log; when it is identified that the current automated production line information has continued for the first target cumulative period, determining to identify abnormal production line monitoring items intended to express abnormal production line monitoring.

[0006] Optionally, the identification of abnormal production line monitoring items intended to express abnormal production line monitoring includes: determining whether the production line monitoring edge terminal has been at the current node for a second target accumulation period, the current node being the task node when the production line monitoring edge terminal currently obtains the automated production line log; when it is identified that the production line monitoring edge terminal has been at the current node for a second target accumulation period, it is determined that the abnormal production line monitoring items intended to express abnormal production line monitoring are identified.

[0007] Optionally, the identifying of abnormal production line monitoring items intended to express abnormal production line monitoring includes: determining whether the production line monitoring edge terminal has been at the current node for a second target accumulation period, the current node being the task node when the production line monitoring edge terminal currently obtains the automated production line log; upon identifying that the production line monitoring edge terminal has been at the current node for a second target accumulation period, determining whether the current automated production line information has been at the first target accumulation period, the current automated production line information being the information in the currently determined automated production line log;

[0008] When it is identified that the current automated production line information has continued for the first target accumulation period, it is determined to identify an abnormal production line monitoring item intended to express abnormal production line monitoring.

[0009] Optionally, determining whether the current automated production line information has continued for a first target accumulation period includes: determining whether the current automated production line information has continued for a third target accumulation period, the third target accumulation period being less than the first target accumulation period;

[0010] When it is identified that the current automated production line information has continued for the third target accumulation period, analyze whether the first real-time automated production line monitoring item has been retained in the monitoring process of the current automated production line data set, and the first real-time automated production line monitoring item refers to the difference between the information in two consecutive automated production line logs that have been obtained; if so, determine a set of automated production line data sets; if not, continue to determine whether the current automated production line information has continued for the first target accumulation period.

[0011] Optionally, determining whether the production line monitoring edge terminal has been at the current node for a second target accumulation period includes:

[0012] Determine whether the production line monitoring edge terminal has been at the current node for a fourth target accumulation period, and the fourth target accumulation period is less than the second target accumulation period;

[0013] When it is identified that the production line monitoring edge terminal has been in the current node for the fourth target accumulation period, it is analyzed whether the second real-time automated production line monitoring item has been retained in the monitoring process of the current automated production line data set, and the second real-time automated production line monitoring item refers to the difference in tasks when obtaining two consecutive automated production line logs; if so, a set of automated production line data sets is determined; if not, it is continued to determine whether the production line monitoring edge terminal has been in the current node for the second target accumulation period.

[0014] Optionally, the method further comprises at least one of the following:

[0015] If a first real-time automated production line monitoring item is identified in the monitoring process of the automated production line data set, a first significant keyword is added, wherein the first significant keyword is intended to express that the first real-time automated production line monitoring item has been retained in the monitoring process of the automated production line data set, and the first real-time automated production line monitoring item refers to a difference in information between two consecutive automated production line logs that have been obtained;

[0016] If a second real-time automated production line monitoring item is identified in the monitoring process of the automated production line data set, a second significant keyword is added. The second significant keyword is intended to express that the second real-time automated production line monitoring item has been retained in the monitoring process of the automated production line data set. The second real-time automated production line monitoring item refers to the difference in tasks when obtaining two consecutive automated production line logs.

[0017] Optionally, the determining that no real-time automated production line monitoring item intended to express real-time automated production line monitoring is identified in the monitoring process of the automated production line data set comprises:

[0018] If it is determined that the first significant keyword or the second significant keyword is not added in the monitoring process of the automated production line data set, it is determined that no real-time automated production line monitoring item intended to express real-time automated production line monitoring is identified in the monitoring process of the automated production line data set.

[0019] Optionally, when an abnormal production line monitoring item intended to express abnormal production line monitoring is identified, the method further comprises: if it is determined that the first significant keyword or the second significant keyword is not added in the monitoring process of the automated production line data set, adding a third significant keyword, wherein the third significant keyword is intended to express the abnormal production line monitoring item;

[0020] The method of using the automated production line data set as an abnormal data set based on the abnormal production line monitoring item includes: based on the reserved third significant keyword, using the automated production line data set as an abnormal data set

[0021] The method further includes: in response to being in an automated production line monitoring state, if the abnormal production line monitoring item is not identified, then when it is identified that the monitoring accumulation period of the current automated production line data set reaches the target accumulation period, a set of automated production line data sets is determined.

[0022] In a second aspect, the present application also provides a production data management cloud platform, including a processor and a memory; the processor and the memory are communicatively connected, and the processor is used to read a computer program from the memory and execute it to implement the method described above. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The accompanying drawings herein are incorporated in and constitute a part of the specification, illustrate embodiments consistent with the present application, and together with the description, are used to explain the principles of the present application.

[0024] Figure 1 It is a hardware structure diagram of a production data management cloud platform provided in an embodiment of the present application.

[0025] Figure 2 It is a flow chart of a production data management method applied to edge computing provided in an embodiment of the present application. DETAILED DESCRIPTION

[0026] Exemplary embodiments will be described in detail herein, examples of which are shown in the accompanying drawings. When the following description refers to the drawings, the same numbers in different drawings represent the same or similar elements unless otherwise indicated. The implementations described in the following exemplary embodiments do not represent all implementations consistent with the present application. Instead, they are merely examples of devices and methods consistent with some aspects of the present application as detailed in the appended claims.

[0027] It should be noted that the terms "first", "second", etc. in the specification and claims of this application and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence.

[0028] The method embodiments provided in the embodiments of the present application can be executed in a production data management cloud platform, a computer device or a similar computing device. Taking running on a production data management cloud platform as an example, Figure 1 1 is a hardware structure diagram of a production data management cloud platform for implementing a production data management method for edge computing in an embodiment of the present application. Figure 1As shown, the production data management cloud platform 10 may include one or more ( Figure 1 Only one is shown in the figure) a processor 102 (the processor 102 may include but is not limited to a processing device such as a microprocessor MCU or a programmable logic device FPGA) and a memory 104 for storing data. Optionally, the above-mentioned production data management cloud platform may also include a transmission device 106 for communication functions. It can be understood by those skilled in the art that Figure 1 The structure shown is for illustration only and does not limit the structure of the above-mentioned production data management cloud platform. Figure 1 More or fewer components as shown, or with Figure 1 Different configurations are shown.

[0029] The memory 104 can be used to store computer programs, for example, software programs and modules of application software, such as a computer program corresponding to a production data management method applied to edge computing in an embodiment of the present application. The processor 102 executes various functional applications and data processing by running the computer program stored in the memory 104, that is, implementing the above method. The memory 104 may include a high-speed random access memory, and may also include a non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memory. In some instances, the memory 104 may further include a memory remotely arranged relative to the processor 102, and these remote memories may be connected to the production data management cloud platform 10 via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0030] The transmission device 106 is used to receive or send data via a network. The specific example of the above network may include a wireless network provided by a communication provider of the production data management cloud platform 10. In one example, the transmission device 106 includes a network adapter (Network Interface Controller, referred to as NIC), which can be connected to other network devices through a base station so as to communicate with the Internet. In one example, the transmission device 106 can be a radio frequency (Radio Frequency, referred to as RF) module, which is used to communicate with the Internet wirelessly.

[0031] Based on this, see Figure 2 , Figure 2 It is a flow chart of a production data management method applied to edge computing provided by an embodiment of the present invention. The method is applied to a production data management cloud platform and may further include the technical solutions described below.

[0032] Step 101, in response to being in an automated production line monitoring state, when an abnormal production line monitoring item intended to express abnormal production line monitoring is identified, a set of automated production line data sets is determined, wherein the abnormal production line monitoring item is related to a production task or a monitoring node.

[0033] In an illustrative embodiment, the identification of abnormal production line monitoring items intended to express abnormal production line monitoring as described in step 101 may specifically include the following three implementation methods.

[0034] The first implementation method is to determine whether the current automated production line information has continued for a first target accumulation period, wherein the current automated production line information is the information in the currently determined automated production line log; when it is identified that the current automated production line information has continued for the first target accumulation period, an abnormal production line monitoring item intended to express abnormal production line monitoring is determined and identified.

[0035] The second implementation method determines whether the production line monitoring edge terminal has been in the current node for a second target accumulation period, and the current node is the task node when the production line monitoring edge terminal currently obtains the automated production line log; when it is identified that the production line monitoring edge terminal has been in the current node for a second target accumulation period, it is determined to identify abnormal production line monitoring items intended to express abnormal production line monitoring.

[0036] A third implementation method is to determine whether the production line monitoring edge terminal has been in the current node for a second target accumulation period, where the current node is the task node when the production line monitoring edge terminal currently obtains the automated production line log; upon identifying that the production line monitoring edge terminal has been in the current node for a second target accumulation period, determine whether the current automated production line information has been in the first target accumulation period, where the current automated production line information is the information in the currently determined automated production line log; upon identifying that the current automated production line information has been in the first target accumulation period, determine to identify an abnormal production line monitoring item intended to express abnormal production line monitoring.

[0037] When the above implementation is implemented, abnormal production line monitoring items can be fully identified.

[0038] In an illustrative embodiment, the third implementation method described in determining whether the current automated production line information has continued for the first target accumulation period may specifically include: determining whether the current automated production line information has continued for the third target accumulation period, the third target accumulation period being less than the first target accumulation period; upon identifying that the current automated production line information has continued for the third target accumulation period, analyzing whether the first real-time automated production line monitoring item has been retained in the monitoring process of the current automated production line data set, the first real-time automated production line monitoring item referring to differences in information between two consecutive automated production line logs that have been obtained; if so, determining a set of automated production line data sets; if not, continuing to determine whether the current automated production line information has continued for the first target accumulation period.

[0039] In an illustrative embodiment, the third implementation described in determining whether the production line monitoring edge terminal has been at the current node for a second target accumulation period may specifically include: determining whether the production line monitoring edge terminal has been at the current node for a fourth target accumulation period, the fourth target accumulation period being less than the second target accumulation period; upon identifying that the production line monitoring edge terminal has been at the current node for a fourth target accumulation period, analyzing whether a second real-time automated production line monitoring item has been retained in the monitoring process of the current automated production line data set, the second real-time automated production line monitoring item referring to differences in tasks when obtaining two consecutive automated production line logs; if so, determining a set of automated production line data sets; if not, continuing to determine whether the production line monitoring edge terminal has been at the current node for a second target accumulation period.

[0040] Step 102: In response to determining that no real-time automated production line monitoring items intended to express real-time automated production line monitoring are identified in the monitoring process of the automated production line data set, the automated production line data set is treated as an abnormal data set based on the abnormal production line monitoring items.

[0041] On the basis of the above steps 101 and 102, the method further includes at least one of the following: if a first real-time automated production line monitoring item is identified in the monitoring process of the automated production line data set, a first significant keyword is added, the first significant keyword is intended to express that the first real-time automated production line monitoring item has been retained in the monitoring process of the automated production line data set, and the first real-time automated production line monitoring item refers to the difference in information between two consecutive automated production line logs that have been obtained; if a second real-time automated production line monitoring item is identified in the monitoring process of the automated production line data set, a second significant keyword is added, the second significant keyword is intended to express that the second real-time automated production line monitoring item has been retained in the monitoring process of the automated production line data set, and the second real-time automated production line monitoring item refers to the difference in tasks when obtaining two consecutive automated production line logs. This can improve the efficiency of the difference in tasks between two consecutive automated production line logs.

[0042] It can be understood that the above-described determination that no real-time automated production line monitoring item intended to express real-time automated production line monitoring is identified in the monitoring process of the automated production line data set may specifically include: if it is determined that the first significant keyword or the second significant keyword is not added in the monitoring process of the automated production line data set, then it is determined that no real-time automated production line monitoring item intended to express real-time automated production line monitoring is identified in the monitoring process of the automated production line data set. In this way, the monitoring process of the automated production line data set can be identified in a targeted manner.

[0043] In an illustrative embodiment, when the abnormal production line monitoring item intended to express abnormal production line monitoring is identified as recorded in step 101, the method may further specifically include: if it is determined that the first significant keyword or the second significant keyword has not been added in the monitoring process of the automated production line data set, then adding a third significant keyword, wherein the third significant keyword is intended to express the abnormal production line monitoring item; the automated production line data set is treated as an abnormal data set based on the abnormal production line monitoring item, including: based on the retained third significant keyword, treating the automated production line data set as an abnormal data set.

[0044] In an illustrative embodiment, the method may further include: in response to being in an automated production line monitoring state, if the abnormal production line monitoring item is not identified, then when it is identified that the monitoring accumulation period of the current automated production line data set reaches the target accumulation period, determining a set of automated production line data sets. This can ensure the integrity of the automated production line data set.

[0045] In summary, by responding to being in the state of automated production line monitoring, if an abnormal production line monitoring item intended to express abnormal production line monitoring is identified, a set of automated production line data sets is determined, and when it is determined that no real-time automated production line monitoring item intended to express real-time automated production line monitoring is identified in the monitoring process of the automated production line data set, the current data set is used as an abnormal data set based on the abnormal production line monitoring item, which can not only realize the adaptive collection of data sets in the monitoring process, but also accurately and reliably determine the abnormal data set, thereby improving the quality of production data monitoring and generated data management. In this way. After determining the abnormal data set, the relevant automated production line can be debugged and optimized to ensure the normal and stable operation of the automated production line.

[0046] Furthermore, a readable storage medium is provided, on which a program is stored, and the program implements the above method when executed by a processor.

[0047] In several embodiments provided in the embodiments of the present application, it should be understood that the disclosed device and method can also be implemented in other ways. The device and method embodiments described above are merely schematic, for example, the flowcharts and block diagrams in the accompanying drawings show the possible architecture, functions and operations of the device, method and computer program product according to multiple embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, a program segment or a part of a code, and the module, a program segment or a part of a code contains one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order from the order marked in the accompanying drawings. For example, two consecutive boxes can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of boxes in the block diagram and / or flowchart can be implemented with a dedicated hardware-based system that performs a specified function or action, or can be implemented with a combination of dedicated hardware and computer instructions.

[0048] In addition, the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist separately, or two or more modules may be integrated to form an independent part.

[0049] If the function is implemented in the form of a software function module and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application is essentially or the part that contributes to the prior art or the part of the technical solution can be embodied in the form of a software product, which is stored in a storage medium and includes several instructions to enable a computer device (which can be a personal computer, a media service server 10, 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: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk and other media that can store program code. It should be noted that in this article, the terms "include", "comprise" or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also includes other elements that are not explicitly listed, or also includes elements inherent to such a process, method, article or device. Without more constraints, an element defined by the phrase "comprising a..." does not exclude the existence of other identical elements in the process, method, article or apparatus comprising the element.

[0050] The above description is only the preferred embodiment of the present application and is not intended to limit the present application. For those skilled in the art, the present application may have various modifications and variations. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included in the protection scope of the present application.

Claims

1. A production data management method applied to edge computing, characterized in that: Applied to a production data management cloud platform, the method at least includes: In response to being in an automated production line monitoring state, when an abnormal production line monitoring item intended to express abnormal production line monitoring is identified, a set of automated production line data sets is determined, wherein the abnormal production line monitoring item is associated with a production task or a monitoring node; In response to determining that no real-time automated production line monitoring items intended to express real-time automated production line monitoring are identified in the monitoring process of the automated production line data set, treating the automated production line data set as an abnormal data set based on the abnormal production line monitoring items; The method further comprises at least one of the following: If a first real-time automated production line monitoring item is identified in the monitoring process of the automated production line data set, a first significant keyword is added, wherein the first significant keyword is intended to express that the first real-time automated production line monitoring item has been retained in the monitoring process of the automated production line data set, and the first real-time automated production line monitoring item refers to a difference in information between two consecutive automated production line logs that have been obtained; If a second real-time automated production line monitoring item is identified in the monitoring process of the automated production line data set, a second significant keyword is added, wherein the second significant keyword is intended to express that the second real-time automated production line monitoring item has been retained in the monitoring process of the automated production line data set, and the second real-time automated production line monitoring item refers to a difference in tasks when obtaining two consecutive automated production line logs; Among them, the determining that no real-time automated production line monitoring item intended to express real-time automated production line monitoring is identified in the monitoring process of the automated production line data set includes: if it is determined that the first significant keyword or the second significant keyword is not added in the monitoring process of the automated production line data set, then determining that no real-time automated production line monitoring item intended to express real-time automated production line monitoring is identified in the monitoring process of the automated production line data set.

2. The method according to claim 1, characterized in that The identification of abnormal production line monitoring items intended to express abnormal production line monitoring includes: Determine whether the current automated production line information has lasted for the first target accumulation period, wherein the current automated production line information is information in the currently determined automated production line log; When it is identified that the current automated production line information has continued for the first target accumulation period, it is determined to identify an abnormal production line monitoring item intended to express abnormal production line monitoring.

3. The method according to claim 1, characterized in that The identification of abnormal production line monitoring items intended to express abnormal production line monitoring includes: Determine whether the production line monitoring edge terminal has been in the current node for a second target accumulation period, where the current node is the task node when the production line monitoring edge terminal currently obtains the automated production line log; When it is identified that the production line monitoring edge terminal has been at the current node for a second target accumulation period, it is determined that an abnormal production line monitoring item intended to express abnormal production line monitoring is identified.

4. The method according to claim 1, characterized in that The identification of abnormal production line monitoring items intended to express abnormal production line monitoring includes: Determine whether the production line monitoring edge terminal has been in the current node for a second target accumulation period, where the current node is the task node when the production line monitoring edge terminal currently obtains the automated production line log; When it is identified that the production line monitoring edge terminal has been in the current node for a second target accumulation period, it is determined whether the current automated production line information has been in the first target accumulation period, wherein the current automated production line information is information in the currently determined automated production line log; When it is identified that the current automated production line information has continued for the first target accumulation period, it is determined to identify an abnormal production line monitoring item intended to express abnormal production line monitoring.

5. The method according to claim 2 or 4, characterized in that Determine whether the current automated production line information has continued for the first target accumulation period, including: Determine whether the current automated production line information has lasted for a third target accumulation period, and the third target accumulation period is less than the first target accumulation period; When it is identified that the current automated production line information has continued for the third target accumulation period, analyze whether the first real-time automated production line monitoring item has been retained in the monitoring process of the current automated production line data set, and the first real-time automated production line monitoring item refers to the difference between the information in two consecutive automated production line logs that have been obtained; if so, determine a set of automated production line data sets; if not, continue to determine whether the current automated production line information has continued for the first target accumulation period.

6. The method according to claim 3 or 4, characterized in that Determining whether the production line monitoring edge terminal has been at the current node for a second target accumulation period includes: Determine whether the production line monitoring edge terminal has been at the current node for a fourth target accumulation period, and the fourth target accumulation period is less than the second target accumulation period; When it is identified that the production line monitoring edge terminal has been in the current node for the fourth target accumulation period, it is analyzed whether the second real-time automated production line monitoring item has been retained in the monitoring process of the current automated production line data set, and the second real-time automated production line monitoring item refers to the difference in tasks when obtaining two consecutive automated production line logs; if so, a set of automated production line data sets is determined; if not, it is continued to determine whether the production line monitoring edge terminal has been in the current node for the second target accumulation period.

7. The method according to claim 1, characterized in that When an abnormal production line monitoring item intended to express abnormal production line monitoring is identified, the method further includes: if it is determined that the first significant keyword or the second significant keyword is not added in the monitoring process of the automated production line data set, adding a third significant keyword, the third significant keyword intended to express the abnormal production line monitoring item; The taking the automated production line data set as an abnormal data set based on the abnormal production line monitoring item includes: taking the automated production line data set as an abnormal data set based on the reserved third significant keyword; The method further includes: in response to being in an automated production line monitoring state, if the abnormal production line monitoring item is not identified, then when it is identified that the monitoring accumulation period of the current automated production line data set reaches the target accumulation period, a set of automated production line data sets is determined.

8. A production data management cloud platform, characterized in that: It comprises a processor and a memory; the processor and the memory are communicatively connected, and the processor is used to read a computer program from the memory and execute it to implement the method described in any one of claims 1 to 7.

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