CN118260294A - Manufacturing pain signal summarizing method, system, medium and equipment based on AI - Google Patents
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Abstract
The application discloses a method, a system, a medium and equipment for summarizing pain signals in manufacturing industry based on AI, wherein the method comprises the following steps: acquiring and standardizing the source data of each accessed manufacturing system to be monitored to obtain standardized data of each manufacturing system to be monitored; carrying out real-time processing on the standardized data of each manufacturing system to be monitored to generate real-time pain signals of each manufacturing system to be monitored; carrying out off-line processing on the standardized data of each manufacturing system to be monitored to generate off-line pain signals of each manufacturing system to be monitored; the real-time pain signals and off-line pain signals of each manufacturing system to be monitored are aggregated and stored. Therefore, by adopting the embodiment of the application, secondary development of each manufacturing system to be monitored can be avoided, and unified summarization processing of the source data and signals of each manufacturing system to be monitored is realized, so that the pain signal collection efficiency of each manufacturing system to be monitored is improved.
Description
Technical Field
The application relates to the technical field of computers, in particular to a method, a system, a medium and equipment for summarizing pain signals in manufacturing industry based on AI.
Background
With popularization of information technology, a plurality of online running systems exist in each internet company, each system is respectively responsible for different services, and when an abnormality or problem occurs in a single system, the service of the system is interrupted or the running of other systems is influenced. Therefore, in order to ensure safe operation of each system, the data of each system needs to be analyzed in real time to obtain pain signals for timely processing.
In the related technology, when analyzing the data of each system, secondary development is needed for each system, the secondary development period of the system is long, and the normal operation of the service is influenced, so that the development and maintenance are difficult; meanwhile, the data of each system are analyzed independently, unified summarizing processing of data sources and signals of each system cannot be achieved, and the problems of high cost and data island in data processing are caused, so that pain signal collection efficiency of each manufacturing industry system to be monitored is reduced.
Disclosure of Invention
The embodiment of the application provides a method, a system, a medium and electronic equipment for summarizing pain signals in manufacturing industry based on AI. The following presents a simplified summary in order to provide a basic understanding of some aspects of the disclosed embodiments. This summary is not an extensive overview and is intended to neither identify key/critical elements nor delineate the scope of such embodiments. Its sole purpose is to present some concepts in a simplified form as a prelude to the more detailed description that is presented later.
In a first aspect, an embodiment of the present application provides a method for summarizing pain signals in an AI-based manufacturing industry, where the method includes:
acquiring and standardizing the source data of each accessed manufacturing system to be monitored to obtain standardized data of each manufacturing system to be monitored;
generating real-time pain signals and off-line pain signals of all manufacturing systems to be monitored according to standardized data of all manufacturing systems to be monitored; the real-time pain signals are obtained by carrying out flow processing on the standardized data of each manufacturing system to be monitored through artificial intelligence, and the offline pain signals are obtained by carrying out batch processing on the standardized data of each manufacturing system to be monitored; the real-time pain signal is used for signal feedback of the real-time service function of the target system, and the offline pain signal is used for signal feedback of the offline service function of the target system;
The real-time pain signals and off-line pain signals of each manufacturing system to be monitored are summarized and stored in a database of the target system.
Optionally, generating the real-time pain signal and the off-line pain signal of each manufacturing system to be monitored according to the standardized data of each manufacturing system to be monitored includes:
carrying out stream processing on the standardized data of each manufacturing system to be monitored to generate real-time pain signals of each manufacturing system to be monitored;
carrying out batch processing on the standardized data of each manufacturing system to be monitored to generate an off-line pain signal of each manufacturing system to be monitored;
The method for generating the off-line pain signal of each manufacturing system to be monitored comprises the following steps of:
Carrying out batch division on the standardized data of each manufacturing system to be monitored to obtain batch processing data of each manufacturing system to be monitored;
storing batch data of each manufacturing system to be monitored to an offline database;
Traversing and extracting abnormal information existing in batch processing data of each manufacturing system to be monitored stored in an offline database;
And converting the abnormal information existing in the batch data of each manufacturing system to be monitored into an off-line pain signal of each manufacturing system to be monitored.
Optionally, the streaming processing is performed on the standardized data of each manufacturing system to be monitored, to generate a real-time pain signal of each manufacturing system to be monitored, including:
Capturing and extracting change data existing in standardized data of each manufacturing system to be monitored in real time to obtain change data of each manufacturing system to be monitored;
Carrying out streaming processing on the changed data of each manufacturing system to be monitored to obtain streaming data of each manufacturing system to be monitored; streaming includes filtering, converting, and aggregating data;
Determining a real-time data change characteristic sequence corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period based on the streaming data of each manufacturing system to be monitored;
And generating real-time pain signals of the manufacturing systems to be monitored according to the real-time data change characteristic sequences corresponding to each preset monitoring field of the manufacturing systems to be monitored in the preset period.
Optionally, the streaming data of each manufacturing system to be monitored includes Kafka data, file system data and database log information;
Based on the streaming data of each manufacturing system to be monitored, determining real-time data change feature sequences corresponding to a plurality of preset monitoring fields of each manufacturing system to be monitored in a preset period comprises the following steps:
Loading a data model for monitoring the flow data of each manufacturing system to be monitored, wherein the data model comprises a plurality of preset monitoring fields;
performing data mapping on Kafka data, file system data and database log information included in the stream data of each manufacturing system to be monitored so as to convert the Kafka data, the file system data and the database log information into format data of each preset monitoring field in a data model;
Window division is carried out on the format data of each preset monitoring field according to the preset period, so that the format data of each preset monitoring field in each time window is obtained;
and calculating the characteristic value of the format data of each preset monitoring field in each time window to obtain a real-time data change characteristic sequence of each preset monitoring field of each manufacturing system to be monitored.
Optionally, the real-time data change feature sequence includes a maximum value, a minimum value, an average value, and a standard deviation;
Generating real-time pain signals of each manufacturing system to be monitored according to the real-time data change characteristic sequence corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period, wherein the real-time pain signals comprise:
Inputting the maximum value, the minimum value, the average value and the standard deviation corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period into a pre-trained system anomaly monitoring model, and outputting service function operation information corresponding to each preset monitoring field of each manufacturing system to be monitored in the preset period;
Judging whether the operation of each manufacturing system to be monitored is abnormal in a preset period according to the operation information of the service function corresponding to each preset monitoring field of each manufacturing system to be monitored in the preset period and a preset operation abnormality knowledge base;
A real-time pain signal of an abnormal operation is generated for a manufacturing system to be monitored having the abnormal operation, and a real-time pain signal of a normal operation is generated for a manufacturing system to be monitored having no abnormal operation.
Optionally, a mapping relation between the monitoring field and the abnormal information corresponding to the monitoring field is configured in a pre-established operation abnormal knowledge base;
According to the business function operation information corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period and a preset operation abnormality knowledge base, judging whether the manufacturing system to be monitored has operation abnormality in the preset period or not, comprising the following steps:
Acquiring abnormal information corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period from the mapping relation;
Judging whether the service function operation information corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period is consistent with the abnormal information corresponding to each preset monitoring field of each manufacturing system to be monitored in the preset period;
if yes, determining that the manufacturing system to be monitored has abnormal operation in a preset period;
if not, determining that the manufacturing system to be monitored does not have abnormal operation in a preset period;
wherein, the method comprises the following steps of generating a pre-established abnormal operation knowledge base, comprising:
receiving a plurality of monitoring fields from a client input;
Collecting abnormal logs of each manufacturing system to be monitored;
Taking each monitoring field as an excavation object, excavating and aggregating abnormal key description from the abnormal logs of each manufacturing system to be monitored to obtain the abnormal information of each monitoring field;
And storing the mapping relation between each monitoring field and the abnormal information of each monitoring field to obtain a pre-established operation abnormal knowledge base.
Optionally, generating the pre-trained system anomaly monitoring model includes:
establishing a system anomaly monitoring model by adopting a neural network based on artificial intelligence;
Acquiring historical change data of each manufacturing system to be monitored;
Generating a historical data change characteristic sequence corresponding to each preset monitoring field corresponding to each manufacturing system to be monitored according to the historical change data of each manufacturing system to be monitored;
Marking service function operation information corresponding to each preset monitoring field according to the historical data change characteristic sequence corresponding to each preset monitoring field, and generating a model training sample;
inputting a model training sample into a system anomaly monitoring model, and outputting a loss value;
and when the loss value reaches the minimum, generating a pre-trained system anomaly monitoring model.
In a second aspect, embodiments of the present application provide an AI-based manufacturing pain signal summary system, the system comprising:
the source data standardization module is used for acquiring and standardizing the source data of each accessed manufacturing system to be monitored to obtain standardized data of each manufacturing system to be monitored;
The pain signal generating module is used for generating real-time pain signals and off-line pain signals of all manufacturing systems to be monitored according to the standardized data of all the manufacturing systems to be monitored; the real-time pain signals are obtained by carrying out flow processing on the standardized data of each manufacturing system to be monitored through artificial intelligence, and the offline pain signals are obtained by carrying out batch processing on the standardized data of each manufacturing system to be monitored; the real-time pain signal is used for signal feedback of the real-time service function of the target system, and the offline pain signal is used for signal feedback of the offline service function of the target system;
And the signal summarizing and storing module is used for summarizing and storing the real-time pain signals and the off-line pain signals of each manufacturing system to be monitored into a database of the target system.
In a third aspect, embodiments of the present application provide a computer storage medium having stored thereon a plurality of instructions adapted to be loaded by a processor and to perform the above-described method steps.
In a fourth aspect, an embodiment of the present application provides an electronic device, which may include: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method steps described above.
The technical scheme provided by the embodiment of the application can have the following beneficial effects:
In the embodiment of the application, on one hand, the AI-based manufacturing pain signal summarizing method does not affect each connected manufacturing system to be monitored, so that secondary development of each manufacturing system to be monitored is avoided, meanwhile, source data of each manufacturing system to be monitored is uniformly connected to be subjected to batch processing and stream processing respectively to obtain pain signals, and the unified summarizing of the source data and signals of each manufacturing system to be monitored can be realized, thereby improving the pain signal collecting efficiency of each manufacturing system to be monitored; on the other hand, in the stream processing process, according to the real-time data change characteristic sequence corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period, the real-time pain sense signals of each manufacturing system to be monitored are generated, the data change characteristic sequence can reflect the state of real-time change of the system, and the pain sense signal identification accuracy of each manufacturing system to be monitored can be improved by combining a pre-trained system abnormity monitoring model and a pre-established operation abnormity knowledge base, so that the safety of the manufacturing system to be monitored is improved.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the application as claimed.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the application and together with the description, serve to explain the principles of the application.
FIG. 1 is a schematic flow chart of an AI-based pain signal summarizing method for manufacturing according to an embodiment of the application;
FIG. 2 is a schematic diagram of a user interface for pain signals provided by the present application;
FIG. 3 is a schematic diagram of a user interface for configuring preset monitor fields according to the present application;
FIG. 4 is a logic diagram of a feature sequence key code within each time window provided by the present application;
FIG. 5 is a schematic diagram of an AI-based manufacturing pain signal summary system according to one embodiment of the application;
fig. 6 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
The following description and the drawings sufficiently illustrate specific embodiments of the application to enable those skilled in the art to practice them.
It should be understood that the described embodiments are merely some, but not all, embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples do not represent all implementations consistent with the application. Rather, they are merely examples of systems and methods that are consistent with aspects of the application as detailed in the accompanying claims.
In the description of the present application, it should be understood that the terms "first," "second," and the like are used for descriptive purposes only and are not to be construed as indicating or implying relative importance. The specific meaning of the above terms in the present application will be understood in specific cases by those of ordinary skill in the art. Furthermore, in the description of the present application, unless otherwise indicated, "a plurality" means two or more. "and/or", describes an association relationship of an association object, and indicates that there may be three relationships, for example, a and/or B, and may indicate: a exists alone, A and B exist together, and B exists alone. The character "/" generally indicates that the context-dependent object is an "or" relationship.
The application provides an AI-based manufacturing pain signal summarizing method, an AI-based manufacturing pain signal summarizing system, an AI-based manufacturing pain signal summarizing medium and an AI-based manufacturing pain signal summarizing electronic device, so as to solve the problems in the related technical problems. In the embodiment of the application, on one hand, the AI-based manufacturing pain signal summarizing method does not affect each connected manufacturing system to be monitored, so that secondary development of each manufacturing system to be monitored is avoided, meanwhile, source data of each manufacturing system to be monitored is uniformly connected to be subjected to batch processing and stream processing respectively to obtain pain signals, and the unified summarizing of the source data and signals of each manufacturing system to be monitored can be realized, thereby improving the pain signal collecting efficiency of each manufacturing system to be monitored; on the other hand, in the stream processing process, according to the real-time data change characteristic sequence corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period, the real-time pain sense signal of each manufacturing system to be monitored is generated, the data change characteristic sequence can reflect the state of real-time change of the system, and the pain sense signal identification accuracy of each manufacturing system to be monitored can be improved by combining a pre-trained system anomaly monitoring model and a pre-established operation anomaly knowledge base, so that the safety of the manufacturing system to be monitored is improved, and the method is described in detail by adopting an exemplary embodiment.
The method for summarizing pain signals in AI-based manufacturing according to the embodiments of the present application will be described in detail with reference to fig. 1 to 4. The method may be implemented in dependence on a computer program and may be run on an AI-based manufacturing pain signal summarization system based on von neumann systems. The computer program may be integrated in the application or may run as a stand-alone tool class application.
Referring to fig. 1, a flow chart of an AI-based manufacturing pain signal summarizing method is provided in an embodiment of the application. As shown in fig. 1, the method according to the embodiment of the present application may include the following steps:
S101, acquiring and standardizing the source data of each accessed manufacturing system to be monitored to obtain standardized data of each manufacturing system to be monitored;
The AI is artificial intelligence, and each manufacturing system to be monitored refers to an online system in charge of different services in an Internet company, such as a system for managing customer relationship and sales flow, an enterprise resource planning system and other systems with different sizes; or may be a third party company's system. The standardization is to clean and convert the data of each manufacturing system to be monitored according to a preset unified format so as to ensure the consistency of the data format and the structure. The source data is business data in a system database of each manufacturing system to be monitored, such as data generated by user interaction with the system, such as click streams, page accesses, user sessions, etc., or transaction records of the system, such as order information, payment records, user behavior, etc.
In some embodiments, in the process of obtaining and standardizing the source data of each accessed manufacturing system to be monitored to obtain the standardized data of each manufacturing system to be monitored, firstly obtaining the source data of each accessed manufacturing system to be monitored, then performing operations such as field renaming, data type conversion, code conversion and the like on the source data of each manufacturing system to be monitored, and finally obtaining the standardized data of each manufacturing system to be monitored.
S102, generating real-time pain signals and off-line pain signals of all manufacturing systems to be monitored according to standardized data of all manufacturing systems to be monitored;
The pain sense is the sense of business risk by enterprises, and the pain sense signal can provide accurate, real-time and intelligent business risk sensing and management capability for the enterprises; the real-time pain signals are obtained by carrying out flow processing on the standardized data of each manufacturing system to be monitored through artificial intelligence, and the offline pain signals are obtained by carrying out batch processing on the standardized data of each manufacturing system to be monitored; the real-time pain signal is used for signal feedback of the real-time business function of the target system, and the off-line pain signal is used for signal feedback of the off-line business function of the target system.
The target system is a platform system for generating and collecting pain sense signals from a plurality of manufacturing systems to be monitored and sending the pain sense signals to a platform for unified monitoring and management. The signal feedback of the real-time service function of the target system can quickly respond to the system state change, usually in a period of seconds to minutes, such as server load, transaction processing time, user activity and the like in the performance of the real-time monitoring system, for example, supporting automatic resource expansion in the instant service decision, triggering alarm, real-time recommendation and the like.
The signal feedback processing period of the offline service function of the target system may vary from several minutes to several hours, for example, analysis of user behavior trends related to historical data, sales data analysis, etc., such as a scenario that does not require immediate response, for example, generation of a day report, a week report, month statistics, etc.
In an embodiment of the present application, in a process of generating a real-time pain signal and an off-line pain signal of each manufacturing system to be monitored according to standardized data of each manufacturing system to be monitored, the method includes: carrying out stream processing on the standardized data of each manufacturing system to be monitored to generate real-time pain signals of each manufacturing system to be monitored; and carrying out batch processing on the standardized data of each manufacturing system to be monitored to generate an off-line pain signal of each manufacturing system to be monitored. The user operation interface of the pain signal is shown in fig. 2, for example.
The batch processing process has low real-time requirement and does not need a complex processing process. Thus, in some embodiments, the process of batch processing standardized data for each manufacturing system to be monitored to generate an off-line pain signal for each manufacturing system to be monitored includes: carrying out batch division on the standardized data of each manufacturing system to be monitored to obtain batch processing data of each manufacturing system to be monitored; storing batch data of each manufacturing system to be monitored to an offline database; traversing and extracting abnormal information existing in batch processing data of each manufacturing system to be monitored stored in an offline database; and converting the abnormal information existing in the batch data of each manufacturing system to be monitored into an off-line pain signal of each manufacturing system to be monitored.
The application provides a stream processing technical means to improve the pain signal identification accuracy of each manufacturing system to be monitored.
In some embodiments, the process of streaming the standardized data of each manufacturing system to be monitored to generate the real-time pain signal of each manufacturing system to be monitored includes: capturing and extracting change data existing in standardized data of each manufacturing system to be monitored in real time to obtain change data of each manufacturing system to be monitored; carrying out streaming processing on the changed data of each manufacturing system to be monitored to obtain streaming data of each manufacturing system to be monitored; streaming includes filtering, converting, and aggregating data; determining a real-time data change characteristic sequence corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period based on the streaming data of each manufacturing system to be monitored; and generating real-time pain signals of the manufacturing systems to be monitored according to the real-time data change characteristic sequences corresponding to each preset monitoring field of the manufacturing systems to be monitored in the preset period. The user interface for configuring the preset monitoring field is shown in fig. 3, for example.
The streaming data of each manufacturing system to be monitored comprises Kafka data, file system data and database log information.
In some embodiments, in determining a real-time data change feature sequence corresponding to a plurality of preset monitoring fields of each manufacturing system to be monitored in a preset period based on streaming data of each manufacturing system to be monitored, the method includes: loading a data model for monitoring the flow data of each manufacturing system to be monitored, wherein the data model comprises a plurality of preset monitoring fields; performing data mapping on Kafka data, file system data and database log information included in the stream data of each manufacturing system to be monitored so as to convert the Kafka data, the file system data and the database log information into format data of each preset monitoring field in a data model; window division is carried out on the format data of each preset monitoring field according to the preset period, so that the format data of each preset monitoring field in each time window is obtained; and calculating the characteristic value of the format data of each preset monitoring field in each time window to obtain a real-time data change characteristic sequence of each preset monitoring field of each manufacturing system to be monitored. A logic diagram of the characteristic sequence key code within each time window is shown, for example, in fig. 4.
The real-time data change characteristic sequence comprises a maximum value, a minimum value, an average value and a standard deviation.
In some embodiments, the process of generating the real-time pain signal of each manufacturing system to be monitored according to the real-time data change feature sequence corresponding to each preset monitoring field of each manufacturing system to be monitored in the preset period includes: inputting the maximum value, the minimum value, the average value and the standard deviation corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period into a pre-trained system anomaly monitoring model, and outputting service function operation information corresponding to each preset monitoring field of each manufacturing system to be monitored in the preset period; judging whether the operation of each manufacturing system to be monitored is abnormal in a preset period according to the operation information of the service function corresponding to each preset monitoring field of each manufacturing system to be monitored in the preset period and a preset operation abnormality knowledge base; a real-time pain signal of an abnormal operation is generated for a manufacturing system to be monitored having the abnormal operation, and a real-time pain signal of a normal operation is generated for a manufacturing system to be monitored having no abnormal operation.
The mapping relation between the monitoring field and the abnormal information corresponding to the monitoring field is configured in the pre-established operation abnormal knowledge base.
In some embodiments, determining whether an operation abnormality exists in each manufacturing system to be monitored in a preset period according to service function operation information corresponding to each preset monitoring field of each manufacturing system to be monitored in the preset period and a preset operation abnormality knowledge base includes: acquiring abnormal information corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period from the mapping relation; judging whether the service function operation information corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period is consistent with the abnormal information corresponding to each preset monitoring field of each manufacturing system to be monitored in the preset period; if yes, determining that the manufacturing system to be monitored has abnormal operation in a preset period; if not, determining that the manufacturing system to be monitored does not have abnormal operation in the preset period.
Specifically, the pre-established operational anomaly knowledge base may be generated according to the following steps, including: receiving a plurality of monitoring fields from a client input; collecting abnormal logs of each manufacturing system to be monitored; taking each monitoring field as an excavation object, excavating and aggregating abnormal key description from the abnormal logs of each manufacturing system to be monitored to obtain the abnormal information of each monitoring field; and storing the mapping relation between each monitoring field and the abnormal information of each monitoring field to obtain a pre-established operation abnormal knowledge base.
Specifically, a pre-trained system anomaly monitoring model may be generated according to the following steps, including: establishing a system anomaly monitoring model by adopting a neural network based on artificial intelligence; acquiring historical change data of each manufacturing system to be monitored; generating a historical data change characteristic sequence corresponding to each preset monitoring field corresponding to each manufacturing system to be monitored according to the historical change data of each manufacturing system to be monitored; marking service function operation information corresponding to each preset monitoring field according to the historical data change characteristic sequence corresponding to each preset monitoring field, and generating a model training sample; inputting a model training sample into a system anomaly monitoring model, and outputting a loss value; and when the loss value reaches the minimum, generating a pre-trained system anomaly monitoring model.
S103, summarizing and storing real-time pain signals and off-line pain signals of each manufacturing system to be monitored into a database of a target system.
The target system is a system for realizing specific service scenes according to the real-time pain signals and the off-line pain signals of the manufacturing system to be monitored, and the specific service scenes are used for carrying out service early warning based on the real-time pain signals and the off-line pain signals.
In some embodiments, after obtaining the real-time pain signal and the off-line pain signal of each manufacturing system to be monitored, the database of the target system may be connected, and the real-time pain signal and the off-line pain signal of each manufacturing system to be monitored may be summarized according to different service scenarios, and the summarized real-time pain signal and off-line pain signal may be stored recently.
In the embodiment of the application, on one hand, the AI-based manufacturing pain signal summarizing method does not affect each connected manufacturing system to be monitored, so that secondary development of each manufacturing system to be monitored is avoided, meanwhile, source data of each manufacturing system to be monitored is uniformly connected to be subjected to batch processing and stream processing respectively to obtain pain signals, and the unified summarizing of the source data and signals of each manufacturing system to be monitored can be realized, thereby improving the pain signal collecting efficiency of each manufacturing system to be monitored; on the other hand, in the stream processing process, according to the real-time data change characteristic sequence corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period, the real-time pain sense signals of each manufacturing system to be monitored are generated, the data change characteristic sequence can reflect the state of real-time change of the system, and the pain sense signal identification accuracy of each manufacturing system to be monitored can be improved by combining a pre-trained system abnormity monitoring model and a pre-established operation abnormity knowledge base, so that the safety of the manufacturing system to be monitored is improved.
The following are system embodiments of the present application that may be used to perform method embodiments of the present application. For details not disclosed in the system embodiments of the present application, please refer to the method embodiments of the present application.
Referring to fig. 5, a schematic diagram of an AI-based manufacturing pain signal summary system according to an exemplary embodiment of the application is shown. The AI-based manufacturing pain signal summary system may be implemented as all or a portion of an electronic device via software, hardware, or a combination of both. The system 1 includes a source data normalization module 10, a pain signal generation module 20, and a signal summary storage module 30.
The source data standardization module 10 is configured to acquire and standardize source data of each accessed manufacturing system to be monitored, and obtain standardized data of each manufacturing system to be monitored;
A pain signal generating module 20 for generating real-time pain signals and off-line pain signals of each manufacturing system to be monitored according to standardized data of each manufacturing system to be monitored; the real-time pain signals are obtained by carrying out flow processing on the standardized data of each manufacturing system to be monitored through artificial intelligence, and the offline pain signals are obtained by carrying out batch processing on the standardized data of each manufacturing system to be monitored; the real-time pain signal is used for signal feedback of the real-time service function of the target system, and the offline pain signal is used for signal feedback of the offline service function of the target system;
The signal summary storage module 30 is used for summarizing and storing the real-time pain signals and the off-line pain signals of each manufacturing system to be monitored to the database of the target system.
It should be noted that, when the AI-based manufacturing pain signal aggregation system provided in the above embodiment performs the AI-based manufacturing pain signal aggregation method, only the division of the above functional modules is used for illustration, and in practical application, the above functional allocation may be performed by different functional modules according to needs, that is, the internal structure of the device is divided into different functional modules to perform all or part of the functions described above. In addition, the AI-based manufacturing pain signal summary system provided in the above embodiment and the AI-based manufacturing pain signal summary method embodiment belong to the same concept, and detailed implementation processes of the method embodiments are shown in the embodiment, and are not repeated here.
The foregoing embodiment numbers of the present application are merely for the purpose of description, and do not represent the advantages or disadvantages of the embodiments.
In the embodiment of the application, on one hand, the AI-based manufacturing pain signal summarizing method does not affect each connected manufacturing system to be monitored, so that secondary development of each manufacturing system to be monitored is avoided, meanwhile, source data of each manufacturing system to be monitored is uniformly connected to be subjected to batch processing and stream processing respectively to obtain pain signals, and the unified summarizing of the source data and signals of each manufacturing system to be monitored can be realized, thereby improving the pain signal collecting efficiency of each manufacturing system to be monitored; on the other hand, in the stream processing process, according to the real-time data change characteristic sequence corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period, the real-time pain sense signals of each manufacturing system to be monitored are generated, the data change characteristic sequence can reflect the state of real-time change of the system, and the pain sense signal identification accuracy of each manufacturing system to be monitored can be improved by combining a pre-trained system abnormity monitoring model and a pre-established operation abnormity knowledge base, so that the safety of the manufacturing system to be monitored is improved.
The present application also provides a computer readable medium having stored thereon program instructions which, when executed by a processor, implement the AI-based manufacturing pain signal summary method provided by the various method embodiments described above.
The present application also provides a computer program product containing instructions that, when run on a computer, cause the computer to perform the AI-based manufacturing pain signal summary method of the various method embodiments described above.
Referring to fig. 6, a schematic structural diagram of an electronic device is provided in an embodiment of the present application. As shown in fig. 6, the electronic device 1000 may include: at least one processor 1001, at least one network interface 1004, a user interface 1003, a memory 1005, at least one communication bus 1002.
Wherein the communication bus 1002 is used to enable connected communication between these components.
The user interface 1003 may include a Display screen (Display) and a Camera (Camera), and the optional user interface 1003 may further include a standard wired interface and a wireless interface.
The network interface 1004 may optionally include a standard wired interface, a wireless interface (e.g., WI-FI interface), among others.
Wherein the processor 1001 may include one or more processing cores. The processor 1001 connects various parts within the overall electronic device 1000 using various interfaces and lines, performs various functions of the electronic device 1000 and processes data by executing or executing instructions, programs, code sets, or instruction sets stored in the memory 1005, and invoking data stored in the memory 1005. Alternatively, the processor 1001 may be implemented in at least one hardware form of digital signal Processing (DIGITAL SIGNAL Processing, DSP), field-Programmable gate array (Field-Programmable GATE ARRAY, FPGA), programmable logic array (Programmable Logic Array, PLA). The processor 1001 may integrate one or a combination of several of a central processing unit (Central Processing Unit, CPU), an image processor (Graphics Processing Unit, GPU), and a modem, etc. The CPU mainly processes an operating system, a user interface, an application program and the like; the GPU is used for rendering and drawing the content required to be displayed by the display screen; the modem is used to handle wireless communications. It will be appreciated that the modem may not be integrated into the processor 1001 and may be implemented by a single chip.
The Memory 1005 may include a random access Memory (Random Access Memory, RAM) or a Read-Only Memory (Read-Only Memory). Optionally, the memory 1005 includes a non-transitory computer readable medium (non-transitory computer-readable storage medium). The memory 1005 may be used to store instructions, programs, code, sets of codes, or sets of instructions. The memory 1005 may include a stored program area and a stored data area, wherein the stored program area may store instructions for implementing an operating system, instructions for at least one function (such as a touch function, a sound playing function, an image playing function, etc.), instructions for implementing the above-described respective method embodiments, etc.; the storage data area may store data or the like referred to in the above respective method embodiments. The memory 1005 may also optionally be at least one storage system located remotely from the processor 1001. As shown in fig. 6, an operating system, a network communication module, a user interface module, and an AI-based manufacturing pain signal summary application may be included in a memory 1005, which is a computer storage medium.
In the electronic device 1000 shown in fig. 6, the user interface 1003 is mainly used for providing an input interface for a user, and acquiring data input by the user; and the processor 1001 may be configured to invoke the AI-based manufacturing pain signal summary application stored in the memory 1005 and specifically:
acquiring and standardizing the source data of each accessed manufacturing system to be monitored to obtain standardized data of each manufacturing system to be monitored;
generating real-time pain signals and off-line pain signals of all manufacturing systems to be monitored according to standardized data of all manufacturing systems to be monitored; the real-time pain signals are obtained by carrying out flow processing on the standardized data of each manufacturing system to be monitored through artificial intelligence, and the offline pain signals are obtained by carrying out batch processing on the standardized data of each manufacturing system to be monitored; the real-time pain signal is used for signal feedback of the real-time service function of the target system, and the offline pain signal is used for signal feedback of the offline service function of the target system;
The real-time pain signals and off-line pain signals of each manufacturing system to be monitored are summarized and stored in a database of the target system.
In one embodiment, the processor 1001, when executing the generation of the real-time pain signal and the off-line pain signal for each manufacturing system to be monitored based on the standardized data for each manufacturing system to be monitored, specifically performs the following operations:
carrying out stream processing on the standardized data of each manufacturing system to be monitored to generate real-time pain signals of each manufacturing system to be monitored;
carrying out batch processing on the standardized data of each manufacturing system to be monitored to generate an off-line pain signal of each manufacturing system to be monitored;
The method for generating the off-line pain signal of each manufacturing system to be monitored comprises the following steps of:
Carrying out batch division on the standardized data of each manufacturing system to be monitored to obtain batch processing data of each manufacturing system to be monitored;
storing batch data of each manufacturing system to be monitored to an offline database;
Traversing and extracting abnormal information existing in batch processing data of each manufacturing system to be monitored stored in an offline database;
And converting the abnormal information existing in the batch data of each manufacturing system to be monitored into an off-line pain signal of each manufacturing system to be monitored.
In one embodiment, the processor 1001 performs the following operations in performing the streaming of the standardized data of each manufacturing system to be monitored to generate the real-time pain signal of each manufacturing system to be monitored:
Capturing and extracting change data existing in standardized data of each manufacturing system to be monitored in real time to obtain change data of each manufacturing system to be monitored;
Carrying out streaming processing on the changed data of each manufacturing system to be monitored to obtain streaming data of each manufacturing system to be monitored; streaming includes filtering, converting, and aggregating data;
Determining a real-time data change characteristic sequence corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period based on the streaming data of each manufacturing system to be monitored;
And generating real-time pain signals of the manufacturing systems to be monitored according to the real-time data change characteristic sequences corresponding to each preset monitoring field of the manufacturing systems to be monitored in the preset period.
In one embodiment, the processor 1001, when executing the determining, based on the streaming data of each manufacturing system to be monitored, a real-time data change feature sequence corresponding to a plurality of preset monitoring fields of each manufacturing system to be monitored in a preset period, specifically performs the following operations:
Loading a data model for monitoring the flow data of each manufacturing system to be monitored, wherein the data model comprises a plurality of preset monitoring fields;
performing data mapping on Kafka data, file system data and database log information included in the stream data of each manufacturing system to be monitored so as to convert the Kafka data, the file system data and the database log information into format data of each preset monitoring field in a data model;
Window division is carried out on the format data of each preset monitoring field according to the preset period, so that the format data of each preset monitoring field in each time window is obtained;
and calculating the characteristic value of the format data of each preset monitoring field in each time window to obtain a real-time data change characteristic sequence of each preset monitoring field of each manufacturing system to be monitored.
In one embodiment, the processor 1001, when executing the real-time data change feature sequence corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period, generates the real-time pain signal of each manufacturing system to be monitored, specifically performs the following operations:
Inputting the maximum value, the minimum value, the average value and the standard deviation corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period into a pre-trained system anomaly monitoring model, and outputting service function operation information corresponding to each preset monitoring field of each manufacturing system to be monitored in the preset period;
Judging whether the operation of each manufacturing system to be monitored is abnormal in a preset period according to the operation information of the service function corresponding to each preset monitoring field of each manufacturing system to be monitored in the preset period and a preset operation abnormality knowledge base;
A real-time pain signal of an abnormal operation is generated for a manufacturing system to be monitored having the abnormal operation, and a real-time pain signal of a normal operation is generated for a manufacturing system to be monitored having no abnormal operation.
In one embodiment, the processor 1001 determines, when executing the service function operation information corresponding to each preset monitoring field of each manufacturing system to be monitored in the preset period and the preset operation abnormality knowledge base, whether there is an operation abnormality in each manufacturing system to be monitored in the preset period, specifically performs the following operations:
Acquiring abnormal information corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period from the mapping relation;
Judging whether the service function operation information corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period is consistent with the abnormal information corresponding to each preset monitoring field of each manufacturing system to be monitored in the preset period;
if yes, determining that the manufacturing system to be monitored has abnormal operation in a preset period;
If not, determining that the manufacturing system to be monitored does not have abnormal operation in the preset period.
In one embodiment, the processor 1001, when executing the generation of the pre-established operational anomaly repository, specifically performs the following:
receiving a plurality of monitoring fields from a client input;
Collecting abnormal logs of each manufacturing system to be monitored;
Taking each monitoring field as an excavation object, excavating and aggregating abnormal key description from the abnormal logs of each manufacturing system to be monitored to obtain the abnormal information of each monitoring field;
And storing the mapping relation between each monitoring field and the abnormal information of each monitoring field to obtain a pre-established operation abnormal knowledge base.
In one embodiment, the processor 1001, when executing the generation of the pre-trained system anomaly monitoring model, specifically performs the following:
establishing a system anomaly monitoring model by adopting a neural network based on artificial intelligence;
Acquiring historical change data of each manufacturing system to be monitored;
Generating a historical data change characteristic sequence corresponding to each preset monitoring field corresponding to each manufacturing system to be monitored according to the historical change data of each manufacturing system to be monitored;
Marking service function operation information corresponding to each preset monitoring field according to the historical data change characteristic sequence corresponding to each preset monitoring field, and generating a model training sample;
inputting a model training sample into a system anomaly monitoring model, and outputting a loss value;
and when the loss value reaches the minimum, generating a pre-trained system anomaly monitoring model.
In the embodiment of the application, on one hand, the AI-based manufacturing pain signal summarizing method does not affect each connected manufacturing system to be monitored, so that secondary development of each manufacturing system to be monitored is avoided, meanwhile, source data of each manufacturing system to be monitored is uniformly connected to be subjected to batch processing and stream processing respectively to obtain pain signals, and the unified summarizing of the source data and signals of each manufacturing system to be monitored can be realized, thereby improving the pain signal collecting efficiency of each manufacturing system to be monitored; on the other hand, in the stream processing process, according to the real-time data change characteristic sequence corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period, the real-time pain sense signals of each manufacturing system to be monitored are generated, the data change characteristic sequence can reflect the state of real-time change of the system, and the pain sense signal identification accuracy of each manufacturing system to be monitored can be improved by combining a pre-trained system abnormity monitoring model and a pre-established operation abnormity knowledge base, so that the safety of the manufacturing system to be monitored is improved.
Those skilled in the art will appreciate that implementing all or part of the above-described methods in accordance with the embodiments may be accomplished by a computer program that instructs associated hardware, and that the AI-based manufacturing pain signal summary program may be stored on a computer-readable storage medium, which program, when executed, may include the steps of the embodiments of the methods described above. The storage medium of the program based on the collection of the pain signals in the manufacturing industry of AI can be a magnetic disk, an optical disk, a read-only memory, a random access memory, etc.
The foregoing disclosure is illustrative of the present application and is not to be construed as limiting the scope of the application, which is defined by the appended claims.
Claims (10)
1. A method for summarizing pain signals in AI-based manufacturing industries, the method comprising:
acquiring and standardizing the source data of each accessed manufacturing system to be monitored to obtain standardized data of each manufacturing system to be monitored;
Generating real-time pain signals and off-line pain signals of all manufacturing systems to be monitored according to the standardized data of all manufacturing systems to be monitored; the real-time pain signals are obtained by carrying out flow processing on the standardized data of each manufacturing system to be monitored through artificial intelligence, and the offline pain signals are obtained by carrying out batch processing on the standardized data of each manufacturing system to be monitored; the real-time pain signal is used for signal feedback of the real-time service function of the target system, and the off-line pain signal is used for signal feedback of the off-line service function of the target system;
and summarizing and storing the real-time pain signals and the off-line pain signals of the manufacturing systems to be monitored into a database of the target system.
2. The method of claim 1, wherein generating the real-time pain signal and the off-line pain signal for each manufacturing system to be monitored based on the standardized data for each manufacturing system to be monitored comprises:
carrying out stream processing on the standardized data of each manufacturing system to be monitored to generate real-time pain signals of each manufacturing system to be monitored;
Carrying out batch processing on the standardized data of each manufacturing system to be monitored to generate an off-line pain signal of each manufacturing system to be monitored;
the method for generating the offline pain signal of each manufacturing system to be monitored comprises the following steps of:
dividing the standardized data of each manufacturing system to be monitored into batches to obtain batch processing data of each manufacturing system to be monitored;
storing batch processing data of each manufacturing system to be monitored to an offline database;
Traversing and extracting abnormal information existing in batch processing data of each manufacturing system to be monitored, which are stored in the offline database;
and converting the abnormal information existing in the batch data of each manufacturing system to be monitored into an offline pain signal of each manufacturing system to be monitored.
3. The method of claim 2, wherein streaming the standardized data for each of the manufacturing systems to be monitored to generate the real-time pain signal for each of the manufacturing systems to be monitored comprises:
Capturing and extracting change data existing in the standardized data of each manufacturing system to be monitored in real time to obtain the change data of each manufacturing system to be monitored;
Performing streaming processing on the changed data of each manufacturing system to be monitored to obtain streaming data of each manufacturing system to be monitored; the streaming process includes filtering, converting, and aggregating data;
Determining a real-time data change characteristic sequence corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period based on the streaming data of each manufacturing system to be monitored;
and generating real-time pain signals of the manufacturing systems to be monitored according to the real-time data change characteristic sequences corresponding to each preset monitoring field of the manufacturing systems to be monitored in the preset period.
4. A method according to claim 3, wherein the streaming data of each manufacturing system to be monitored comprises Kafka data, file system data and database log information;
the determining, based on the streaming data of each manufacturing system to be monitored, a real-time data change feature sequence corresponding to a plurality of preset monitoring fields of each manufacturing system to be monitored in a preset period includes:
loading a data model for monitoring flow data of each manufacturing system to be monitored, wherein the data model comprises a plurality of preset monitoring fields;
Performing data mapping on Kafka data, file system data and database log information included in the stream data of each manufacturing system to be monitored so as to convert the Kafka data, the file system data and the database log information into format data of each preset monitoring field in the data model;
window division is carried out on the format data of each preset monitoring field according to a preset period to obtain the format data of each preset monitoring field in each time window;
and calculating the characteristic value of the format data of each preset monitoring field in each time window to obtain a real-time data change characteristic sequence of each preset monitoring field of each manufacturing system to be monitored.
5. A method according to claim 3, wherein the real-time data change signature sequence comprises a maximum value, a minimum value, an average value, and a standard deviation;
The generating a real-time pain signal of each manufacturing system to be monitored according to the real-time data change feature sequence corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period comprises:
Inputting the maximum value, the minimum value, the average value and the standard deviation corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period into a pre-trained system anomaly monitoring model, and outputting service function operation information corresponding to each preset monitoring field of each manufacturing system to be monitored in the preset period;
Judging whether the manufacturing system to be monitored has abnormal operation in a preset period according to the business function operation information corresponding to each preset monitoring field of the manufacturing system to be monitored in the preset period and a preset operation abnormality knowledge base;
A real-time pain signal of an abnormal operation is generated for a manufacturing system to be monitored having the abnormal operation, and a real-time pain signal of a normal operation is generated for a manufacturing system to be monitored having no abnormal operation.
6. The method according to claim 5, wherein a mapping relationship between a monitoring field and anomaly information corresponding to the monitoring field is configured in the pre-established operation anomaly knowledge base;
The determining whether the operation abnormality exists in the manufacturing system to be monitored in the preset period according to the operation information of the service function corresponding to each preset monitoring field of the manufacturing system to be monitored in the preset period and the pre-established operation abnormality knowledge base comprises the following steps:
Acquiring abnormal information corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period from the mapping relation;
Judging whether service function operation information corresponding to each preset monitoring field of each manufacturing system to be monitored in a preset period is consistent with abnormal information corresponding to each preset monitoring field of each manufacturing system to be monitored in the preset period;
if yes, determining that the manufacturing system to be monitored has abnormal operation in a preset period;
if not, determining that the manufacturing system to be monitored does not have abnormal operation in a preset period;
wherein, the method comprises the following steps of generating a pre-established abnormal operation knowledge base, comprising:
receiving a plurality of monitoring fields from a client input;
collecting abnormal logs of each manufacturing system to be monitored;
taking each monitoring field as an excavation object, excavating and aggregating abnormal key description from the abnormal logs of each manufacturing system to be monitored to obtain abnormal information of each monitoring field;
and storing the mapping relation between each monitoring field and the abnormal information of each monitoring field to obtain a pre-established operation abnormal knowledge base.
7. The method of claim 5, wherein generating the pre-trained system anomaly monitoring model comprises:
establishing a system anomaly monitoring model by adopting a neural network based on artificial intelligence;
acquiring historical change data of each manufacturing system to be monitored;
Generating a historical data change characteristic sequence corresponding to each preset monitoring field corresponding to each manufacturing system to be monitored according to the historical change data of each manufacturing system to be monitored;
Marking service function operation information corresponding to each preset monitoring field according to the historical data change characteristic sequence corresponding to each preset monitoring field, and generating a model training sample;
inputting the model training sample into the system anomaly monitoring model, and outputting a loss value;
and when the loss value reaches the minimum, generating a pre-trained system abnormity monitoring model.
8. An AI-based manufacturing pain signal summarizing device, the device comprising:
the source data standardization module is used for acquiring and standardizing the source data of each accessed manufacturing system to be monitored to obtain standardized data of each manufacturing system to be monitored;
The pain signal generation module is used for generating real-time pain signals and off-line pain signals of the manufacturing systems to be monitored according to the standardized data of the manufacturing systems to be monitored; the real-time pain signals are obtained by carrying out flow processing on the standardized data of each manufacturing system to be monitored through artificial intelligence, and the offline pain signals are obtained by carrying out batch processing on the standardized data of each manufacturing system to be monitored; the real-time pain signal is used for signal feedback of the real-time service function of the target system, and the off-line pain signal is used for signal feedback of the off-line service function of the target system;
and the signal summarizing and storing module is used for summarizing and storing the real-time pain signals and the off-line pain signals of the manufacturing system to be monitored into the database of the target system.
9. A computer storage medium storing a plurality of instructions adapted to be loaded by a processor and to perform the method of any of claims 1-7.
10. An apparatus, comprising: a processor and a memory; wherein the memory stores a computer program adapted to be loaded by the processor and to perform the method according to any of claims 1-7.
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