CN114676975A - Production data template production method and device - Google Patents
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Abstract
According to the production data template production method and device, the target production state multidimensional description bound to the automatic production task training with difference is described, and production data template mining is carried out on the determined automatic production line production data by means of the intelligent template production thread obtained through prior training, so that the dynamic production data template with the target production state multidimensional description can be timely and accurately obtained, the problem that the related technology is difficult to deal with the defects of different data dimensions and the production data template is incomplete is solved, and the completeness, the accuracy and the reliability of the production data template can be guaranteed as much as possible through the embodiment of the disclosure.
Description
Technical Field
The application relates to the technical field of production data processing, in particular to a production data template production method and device.
Background
The continuous development of science and technology promotes the continuous upgrading and optimization of automatic production, gradually improves the production efficiency, releases the productivity and makes a contribution to the development of the economic society. At present, various fields related to automatic production are wide, production data templates are usually introduced for assisting in effective production management, the production data templates can record all matters in the automatic production process, and guidance bases can be provided for subsequent production monitoring management, troubleshooting and equipment optimization. However, in practical applications, the inventors have found that it is difficult to ensure the integrity of the production data template during the creation of the production data template.
Disclosure of Invention
In view of this, the present application provides a method and an apparatus for producing a production data template.
In a first aspect, a production data template production method is provided, which is applied to a production data processing server, and the method at least includes:
determining the production data of the automatic production line to be analyzed, which is recorded by an intelligent production data collecting terminal, wherein the production data of the automatic production line to be analyzed comprises the production data of the automatic production line of a key production task; determining a target production state multi-dimensional description bound with the key production task through a mapping list between an automatic production task and the production state multi-dimensional description which is established in advance;
and on the premise of determining that the target production state multi-dimensional description comprises not less than one production state multi-dimensional description, determining that an intelligent template production thread with the performance of producing a production data template for the target production state multi-dimensional description exists, and mining the production data template of the automatic production line production data to be analyzed by means of the intelligent template production thread to obtain a dynamic production data template of the target production state multi-dimensional description of the key production task, wherein the intelligent template production thread is obtained by training a basic template production thread by means of the automatic production line production data of the dynamic production data template annotated with the target production state multi-dimensional description.
In an independently implemented embodiment, the determining the production data of the to-be-analyzed automation line recorded by the intelligent production data collecting terminal includes: determining the production data of the automatic production line to be analyzed of the production request to be accessed to the production line flow, which is recorded by the intelligent production data collecting terminal outside the positioning production line flow, wherein the key production task covers the production request;
after obtaining a dynamic production data template for the multi-dimensional description of the target production state for the key production task, the method further comprises: and activating a request pairing mechanism for matching the production line flow by a user on the premise of determining that the dynamic production data template aims to express that the production request meets the set requirement.
In an independently implemented embodiment, before determining the target production state multidimensional description bound to the key production task through a mapping list between the previously established automated production tasks and the production state multidimensional description, the method further comprises:
creating the mapping list between the automated production task and the multidimensional production state description, wherein the mapping list covers one or more of the following items:
On the premise that the automatic production task is determined to be a first automatic production task, generating a production state multi-dimensional description bound with the first automatic production task as a first production state multi-dimensional description, wherein the first production state multi-dimensional description does not include the production state multi-dimensional description;
and on the premise of determining that the automatic production task is a remaining automatic production task except the first automatic production task, generating a production state multi-dimensional description bound with the remaining automatic production task as a second production state multi-dimensional description, wherein the second production state multi-dimensional description covers the production state multi-dimensional description bound with a keyword label of the automatic production task.
In an independently implemented embodiment, before performing production data template mining on the to-be-analyzed automation line production data by means of the intelligent template production thread to obtain a dynamic production data template of the multi-dimensional description of the target production state of the key production task, the method further includes:
determining a training example, wherein the training example covers the production data of the automatic production line of the dynamic production data template annotated with the multidimensional description of the target production state;
And training the basic template production thread by means of the automatic production line production data of the dynamic production data template annotated with the multidimensional description of the target production state, so as to obtain the intelligent template production thread.
In a separately implemented embodiment, after obtaining the dynamic production data template for the multi-dimensional description of the goal production state for the key production task, the method further comprises: and outputting prompt information on the premise that the key production task is determined not to meet the set requirement through the dynamic production data template.
In a separately implemented embodiment, the goal production state multi-dimensional description comprises one or more of: describing the heating of production equipment; describing abnormal sound of production equipment; describing the environmental cleanliness of a production line; describing the environmental humidity of the production line; describing the network stability of the production line;
on the premise that the dynamic production data template determines that the key production task does not meet the set requirement, outputting prompt information comprises one or more of the following items:
outputting the prompt information on the premise that the target production state multi-dimensional description covers the production equipment heating description and the key production task does not reach an equipment heating safety index or is not matched with the equipment heating safety index based on a first adjustment strategy through the dynamic production data template;
Outputting the prompt information on the premise that the target production state multi-dimensional description covers the abnormal sound description of the production equipment and the key production task does not reach the equipment abnormal sound safety index or the equipment abnormal sound safety index is not matched based on a second adjustment strategy through the dynamic production data template;
outputting the prompt information on the premise that the multi-dimensional description of the target production state covers the description of the environmental cleanliness of the production line and the key production tasks are determined to have the environmental pollution of the production line through the dynamic production data template, and the quantized environmental pollution coefficient is larger than a first determination value;
outputting the prompt information on the premise that the multi-dimensional description of the target production state covers the description of the environmental humidity of the production line and the condition that the key production task is matched with a production line humidifier and the state coefficient of the production line humidifier is larger than a second judgment value is determined through the dynamic production data template;
and outputting the prompt information on the premise that the multi-dimensional description of the target production state covers the production line network stability description and the fact that the key production task is matched with a production line network analysis thread and the production line network stability evaluation is larger than a third judgment value is determined through the dynamic production data template.
In an independently implemented embodiment, after obtaining the dynamic production data template for the multi-dimensional description of the target production state for the key production task, the method further comprises: and marking the production data of the automatic production line to be analyzed on the premise of determining that the key production task does not meet the set requirement through the dynamic production data template.
In a second aspect, there is provided a production data template production apparatus comprising:
the description determining module is used for determining the production data of the automatic production line to be analyzed, which is recorded by the intelligent production data collecting terminal, wherein the production data of the automatic production line to be analyzed comprises the production data of the automatic production line of a key production task; determining a target production state multi-dimensional description bound with the key production task through a mapping list between an automatic production task and the production state multi-dimensional description which is established in advance;
and the data training module is used for determining that an intelligent template production thread for performing production data template production on the target production state multidimensional description exists on the premise of determining that the target production state multidimensional description comprises not less than one production state multidimensional description, and performing production data template mining on the automatic production line production data to be analyzed by means of the intelligent template production thread to obtain a dynamic production data template of the target production state multidimensional description of the key production task, wherein the intelligent template production thread is obtained by training a basic template production thread by means of automatic production line production data of the dynamic production data template annotated with the target production state multidimensional description.
According to the production data template production method and device provided by the embodiment of the application, the target production state multidimensional description bound to the automatic production task training with difference is subjected to production data template mining on the determined automatic production line production data by means of the intelligent template production thread obtained by the prior training, so that the dynamic production data template with the target production state multidimensional description can be timely and accurately obtained, the problem that the production data template is incomplete due to the fact that the related technology is difficult to deal with the defects of different data dimensions is solved, and the completeness, the accuracy and the reliability of the production data template can be guaranteed as much as possible through the embodiment of the application.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are required to be used in the embodiments will be briefly described below, it should be understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered as limiting the scope, and for those skilled in the art, other related drawings can be obtained from the drawings without inventive effort.
Fig. 1 is a flowchart of a method for producing a production data template according to an embodiment of the present disclosure.
Fig. 2 is a block diagram of a production data template production apparatus according to an embodiment of the present application.
Fig. 3 is an architecture diagram of a production data template production system according to an embodiment of the present application.
Detailed Description
In order to better understand the technical solutions, the technical solutions of the present application are described in detail below with reference to the drawings and specific embodiments, and it should be understood that the specific features in the embodiments and examples of the present application are detailed descriptions of the technical solutions of the present application, and are not limitations of the technical solutions of the present application, and the technical features in the embodiments and examples of the present application may be combined with each other without conflict.
Referring to fig. 1, a method for producing a production data template is shown, which may include the following steps 201-203.
Step 201, determining the production data of the automatic production line to be analyzed recorded by the intelligent production data collecting terminal, wherein the production data of the automatic production line to be analyzed comprises the production data of the automatic production line of the key production task.
Step 202, determining the target production state multidimensional description bound with the key production task through a mapping list between the automatic production task and the production state multidimensional description which is established in advance.
Step 203, on the premise that it is determined that the target production state multidimensional description includes at least one production state multidimensional description, determining that there is an intelligent template production thread for performing production data template production on the target production state multidimensional description, and performing production data template mining on the automatic production line production data to be analyzed by means of the intelligent template production thread to obtain a dynamic production data template of the target production state multidimensional description of the key production task, wherein the intelligent template production thread is obtained by training a basic template production thread by means of the automatic production line production data of the dynamic production data template annotated with the target production state multidimensional description.
In the embodiment of the disclosure, the target production state multidimensional description bound to the automated production task training with difference is subjected to production data template mining on the determined automated production line production data by means of the intelligent template production thread obtained by prior training, so that the dynamic production data template with the target production state multidimensional description can be timely and accurately obtained, the problem that the related technology is difficult to deal with the defects of different data dimensions and the production data template is incomplete is solved, and the integrity, the accuracy and the reliability of the production data template can be guaranteed as much as possible by the embodiment of the disclosure.
In the embodiment of the disclosure, the paired production data processing server determines the production data of the to-be-analyzed automatic production line recorded by the intelligent production data collecting terminal, wherein the production data of the to-be-analyzed automatic production line comprises the production data of the automatic production line of the key production task; determining a target production state multi-dimensional description bound with the key production task through a mapping list between an automatic production task and the production state multi-dimensional description which is established in advance; that is, for the target production state multidimensional description bound to the automated production task training with differences, for example, the target production state multidimensional description trained for the first production request is M production state multidimensional descriptions (the number of M is not less than one) included in the global production state multidimensional description, and the target production state multidimensional description trained for the second production request is N production state multidimensional descriptions (the number of N is not less than one) included in the global production state multidimensional description, it can be understood that the target production state multidimensional description trained for a certain production request may also be 0 in the global production state multidimensional description; on the premise that the multidimensional description of the target production state comprises not less than one multidimensional description of the production state, firstly, an intelligent template production thread bound to the multidimensional description of the target production state is determined, wherein the intelligent template production thread is obtained by training a basic template production thread by means of automatic production line production data of a dynamic production data template annotated with the multidimensional description of the target production state, then, production data template mining is carried out on automatic production line production data to be analyzed by means of the intelligent template production thread to obtain a dynamic production data template of the multidimensional description of the target production state of a key production task, for example, for a first production request, firstly, M intelligent template production threads bound to the first production request and having multidimensional description of the production state are determined, and then, the intelligent template production thread is used for advancing the automatic production line production data to be analyzed, which are recorded by the intelligent production data collection terminal, into a computer system And mining the production data template, analyzing whether the production data of the automatic production line to be analyzed is bound with the production thread of the intelligent template, thereby determining the dynamic production data template of the multidimensional description of the target production state (namely M multidimensional descriptions of the production state bound by the first production request), and determining the dynamic production data template of the multidimensional description of the production state bound by the key production task with different types from the production data of the automatic production line to be analyzed.
In an alternative embodiment, determining the production data of the automation line to be analyzed recorded by the intelligent production data collecting terminal may include the following: determining production data of an automatic production line to be analyzed of a production request to be accessed into a production line flow, which is recorded by an intelligent production data collecting terminal outside the production line flow, wherein a key production task covers the production request; after obtaining the dynamic production data template for the multi-dimensional description of the target production state of the key production task, the method further comprises the following steps: and activating a request pairing mechanism of a user matching production line flow on the premise of determining that the dynamic production data template aims at expressing that the production request meets the set requirement. Furthermore, the production data of the automatic production line to be analyzed of the production request (such as production line flow workers, management production request or other production requests) to be accessed to the production line flow can be recorded according to the intelligent production data collection terminal outside the positioning production line flow;
in an alternative embodiment, before determining the target production state multidimensional description bound to the key production task through a mapping list between the automatic production tasks and the production state multidimensional description established in advance, the method further comprises the following steps: creating a mapping list between the automated production task and the multidimensional description of the production state, wherein the mapping list covers one or more of the following items: on the premise that the automatic production task is determined to be a first automatic production task, generating a production state multi-dimensional description bound with the first automatic production task as a first production state multi-dimensional description, wherein the first production state multi-dimensional description does not include the production state multi-dimensional description; and on the premise of determining that the automatic production task is a residual automatic production task except the first automatic production task, generating a production state multi-dimensional description bound with the residual automatic production task as a second production state multi-dimensional description, wherein the second production state multi-dimensional description covers the production state multi-dimensional description bound with the keyword label of the automatic production task. In an embodiment of the present disclosure, before determining the target production state multidimensional description bound to the key production task, a mapping list between the automated production task and the production state multidimensional description is created, wherein the mapping list covers one or more of: on the premise that the automatic production task is determined to be a first automatic production task, generating a production state multi-dimensional description bound with the first automatic production task as a first production state multi-dimensional description, wherein the first production state multi-dimensional description does not include the production state multi-dimensional description; and on the premise of determining that the automatic production task is a residual automatic production task except the first automatic production task, generating a production state multi-dimensional description bound with the residual automatic production task as a second production state multi-dimensional description, wherein the second production state multi-dimensional description covers the production state multi-dimensional description bound with the keyword label of the automatic production task.
In an alternative embodiment, before performing production data template mining on the to-be-analyzed automation line production data by means of an intelligent template production thread to obtain a dynamic production data template for multidimensional description of a target production state of a key production task, the method further comprises the following steps: determining a training example, wherein the training example covers the automatic production line production data of the dynamic production data template annotated with the multidimensional description of the target production state; the basic template production thread is trained with the aid of the automation line production data of the dynamic production data template included in the training paradigm, which is annotated with a multidimensional description of the target production state, to obtain an intelligent template production thread. In the embodiment of the disclosure, before mining the production data template of the automatic production line production data to be analyzed by means of the intelligent template production thread to obtain the dynamic production data template of the multidimensional description of the target production state of the key production task, a training example is determined first, data is annotated and stored by means of related annotation equipment, and the training example covers the automatic production line production data of the dynamic production data template of the multidimensional description of the annotated target production state; and training the basic template production thread by means of the automatic production line production data of the dynamic production data template annotated with the multidimensional description of the target production state, so as to obtain an intelligent template production thread.
In an alternative embodiment, after obtaining the dynamic production data template for the multi-dimensional description of the goal production state of the critical production task, the method further comprises the following: and on the premise of determining that the key production task does not meet the set requirement through the dynamic production data template, outputting prompt information. In the embodiment of the disclosure, after the dynamic production data template for multidimensional description of the target production state of the key production task is obtained, if it is determined through the dynamic production data template that the key production task does not meet the set requirement, the prompt information may be output.
In an alternative embodiment, the goal production state multi-dimensional description includes one or more of: describing the heating of production equipment; abnormal sound description of production equipment; describing the environmental cleanliness of a production line; describing the environmental humidity of the production line; describing the network stability of the production line; on the premise that the key production task is determined not to meet the set requirement through the dynamic production data template, outputting prompt information comprises one or more of the following items: outputting prompt information on the premise that the target production state multi-dimensional description covers the production equipment heating description and the key production task does not reach the equipment heating safety index or the equipment heating safety index is not matched based on the first adjustment strategy through the dynamic production data template; outputting prompt information on the premise that the target production state multi-dimensional description covers the abnormal sound description of the production equipment and the key production task does not reach the abnormal sound safety index of the equipment or the abnormal sound safety index of the equipment is not matched based on a second adjustment strategy through the dynamic production data template; outputting prompt information on the premise that the target production state multi-dimensional description covers the production line environment cleanliness description and the key production tasks are determined to have production line environment pollution through the dynamic production data template, and the quantized environment pollution coefficient is larger than a first judgment value; outputting prompt information on the premise that the multi-dimensional description of the target production state covers the environmental humidity description of the production line and the condition that the key production task is matched with the production line humidifier and the state coefficient of the production line humidifier is larger than a second judgment value is determined through the dynamic production data template; and outputting prompt information on the premise that the target production state multi-dimensional description covers the production line network stability description and the key production tasks are determined to be matched with the production line network analysis thread through the dynamic production data template, and the production line network stability evaluation is greater than a third judgment value. In an embodiment of the disclosure, the goal production state multi-dimensional description includes one or more of: describing the heating of production equipment; abnormal sound description of production equipment; describing the environmental cleanliness of a production line; describing the environmental humidity of the production line; describing the network stability of the production line; and outputting prompt information on the premise that the key production task is determined not to meet the set requirement through the dynamic production data template.
(a) If the multi-dimensional description of the target production state bound by the first automatic production task comprises the heating description of the production equipment, outputting prompt information on the premise that the dynamic production data template determines that the first automatic production task does not reach the equipment heating safety index or the equipment heating safety index is not matched based on a first adjustment strategy;
(b) if the multidimensional description of the target production state bound by the second automatic production task comprises the abnormal sound description of the production equipment, outputting prompt information on the premise that the dynamic production data template determines that the second automatic production task does not reach the abnormal sound safety index of the equipment or does not match the abnormal sound safety index of the equipment based on a second adjustment strategy;
(c) if the multidimensional description of the target production state bound by the third automatic production task comprises a production line environment cleanliness description, outputting prompt information on the premise that the dynamic production data template determines that the third automatic production task has production line environment pollution and the quantized environment pollution coefficient is greater than a first judgment value;
(d) if the multidimensional description of the target production state bound by the fourth automated production task comprises the production line environment humidity description, outputting prompt information on the premise that the dynamic production data template determines that the fourth automated production task is matched with a production line humidifier and the state coefficient of the production line humidifier is greater than a second judgment value;
(e) If the multidimensional description of the target production state bound by the fifth automatic production task comprises a production line network stability description, when the dynamic production data template determines that the fifth automatic production task is matched with a production line network analysis thread and the evaluation of the production line network stability is greater than a third judgment value (on the premise, outputting prompt information;
in an alternative embodiment, after obtaining the dynamic production data template for the multi-dimensional description of the goal production state of the key production task, the method further comprises: and marking the production data of the automatic production line to be analyzed on the premise of determining that the key production task does not meet the set requirement through the dynamic production data template. In the embodiment of the disclosure, after the dynamic production data template of the multidimensional description of the target production state of the key production task is obtained, and on the premise that the key production task is determined not to meet the set requirement, the production data of the automatic production line to be analyzed is marked, and when the template is applied to an actual scene, the production data of the automatic production line to be analyzed which does not meet the set requirement can be marked.
On the basis of the above, please refer to fig. 2 in combination, there is provided a production data template production apparatus 200, applied to a production data template production system, the apparatus including:
The description determining module 210 is configured to determine the production data of the automatic production line to be analyzed, which is recorded by the intelligent production data collecting terminal, where the production data of the automatic production line to be analyzed includes the production data of an automatic production line of a key production task; determining a target production state multi-dimensional description bound with the key production task through a mapping list between an automatic production task and the production state multi-dimensional description which is established in advance;
the data training module 220 is configured to, on the premise that it is determined that the target production state multidimensional description includes at least one production state multidimensional description, determine that an intelligent template production thread exists for performing production data template production on the target production state multidimensional description, and perform production data template mining on the automatic production line production data to be analyzed by using the intelligent template production thread to obtain a dynamic production data template of the target production state multidimensional description of the key production task, where the intelligent template production thread is obtained by training a basic template production thread by using the automatic production line production data of the dynamic production data template annotated with the target production state multidimensional description.
On the basis of the above, please refer to fig. 3, which shows a production data template production system 300, which includes a processor 310 and a memory 320 communicating with each other, wherein the processor 310 is configured to read a computer program from the memory 320 and execute the computer program to implement the above method.
On the basis of the above, a computer-readable storage medium is also provided, on which a computer program stored is executed to implement the above-described method.
In summary, based on the above scheme, the target production state multidimensional description bound to the automated production task training with differences is subjected to production data template mining on the determined automated production line production data by means of the intelligent template production thread obtained by prior training, so that the dynamic production data template with the target production state multidimensional description can be timely and accurately obtained, the problem that the related technology is difficult to deal with the defects of different data dimensions and the production data template is incomplete is solved, and the integrity, the accuracy and the reliability of the production data template can be guaranteed as much as possible through the embodiment of the disclosure.
It should be appreciated that the system and its modules shown above may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules of the present application may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).
It is to be noted that different embodiments may produce different advantages, and in different embodiments, the advantages that may be produced may be any one or combination of the above, or any other advantages that may be obtained.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing detailed disclosure is to be considered as illustrative only and not limiting of the application. Various modifications, improvements and adaptations to the present application may occur to those skilled in the art, though not expressly described herein. Such alterations, modifications, and improvements are intended to be suggested herein and are intended to be within the spirit and scope of the exemplary embodiments of this application.
Also, this application uses specific language to describe embodiments of the application. Reference throughout this specification to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the present application is included in at least one embodiment of the present application. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the present application may be combined as appropriate.
Moreover, those skilled in the art will appreciate that aspects of the present application may be illustrated and described in terms of several patentable species or situations, including any new and useful combination of processes, machines, manufacture, or materials, or any new and useful improvement thereof. Accordingly, various aspects of the present application may be embodied entirely in hardware, entirely in software (including firmware, resident software, micro-code, etc.) or in a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present application may be represented as a computer product, including computer readable program code, in one or more computer readable media.
The computer storage medium may comprise a propagated data signal with the computer program code embodied therewith, for example, on baseband or as part of a carrier wave. The propagated signal may take any of a variety of forms, including electromagnetic, optical, etc., or any suitable combination. A computer storage medium may be any computer-readable medium that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code located on a computer storage medium may be propagated over any suitable medium, including radio, cable, fiber optic cable, RF, or the like, or any combination of the preceding.
Computer program code required for operation of various portions of the present application may be written in any one or more programming languages, including an object oriented programming language such as Java, Scala, Smalltalk, Eiffel, JADE, Emerald, C + +, C #, VB.NET, Python, and the like, a conventional programming language such as C, Visual Basic, Fortran 2003, Perl, COBOL 2002, PHP, ABAP, a dynamic programming language such as Python, Ruby, and Groovy, or other programming languages, and the like. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the latter scenario, the remote computer may be connected to the user's computer through any network format, such as a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet), or in a cloud computing environment, or as a service, such as a software as a service (SaaS).
Additionally, the order in which elements and sequences of the processes described herein are processed, the use of alphanumeric characters, or the use of other designations, is not intended to limit the order of the processes and methods described herein, unless explicitly claimed. While various presently contemplated embodiments of the invention have been discussed in the foregoing disclosure by way of example, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the foregoing description of embodiments of the application, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features are required than are expressly recited in the claims. Indeed, the embodiments may be characterized as having less than all of the features of a single disclosed embodiment.
Numerals describing the number of components, attributes, etc. are used in some embodiments, it being understood that such numerals used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially". Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the numbers allow for adaptive variation. Accordingly, in some embodiments, the numerical parameters used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, the numerical parameter should take into account the specified significant digits and employ a general digit preserving approach. Notwithstanding that the numerical ranges and parameters setting forth the broad scope of the range are approximations, in the specific examples, such numerical values are set forth as precisely as possible within the scope of the application.
Each patent, patent application publication, and other material, such as articles, books, specifications, publications, documents, and the like, cited in this application is hereby incorporated by reference in its entirety. Except where the application history document is inconsistent or conflicting with the present application as to the extent of the present claims, which are now or later appended to this application. It is to be understood that the descriptions, definitions and/or uses of terms in the attached materials of this application shall control if they are inconsistent or inconsistent with the statements and/or uses of this application.
Finally, it should be understood that the embodiments described herein are merely illustrative of the principles of embodiments of the present application. Other variations are also possible within the scope of the present application. Thus, by way of example, and not limitation, alternative configurations of the embodiments of the present application can be viewed as being consistent with the teachings of the present application. Accordingly, the embodiments of the present application are not limited to only those embodiments explicitly described and depicted herein.
The above are merely examples of the present application and are not intended to limit the present application. Various modifications and changes may occur to those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application should be included in the scope of the claims of the present application.
Claims (8)
1. A production data template production method, applied to a production data processing server, the method comprising at least:
determining production data of an automatic production line to be analyzed, which is recorded by an intelligent production data collecting terminal, wherein the production data of the automatic production line to be analyzed comprises production data of an automatic production line of a key production task; determining a target production state multi-dimensional description bound with the key production task through a mapping list between an automatic production task and the production state multi-dimensional description which is established in advance;
and on the premise that the target production state multi-dimensional description comprises not less than one production state multi-dimensional description, determining that an intelligent template production thread with the performance of producing a data template for the target production state multi-dimensional description exists, and mining the production data template of the automatic production line production data to be analyzed by means of the intelligent template production thread to obtain a dynamic production data template of the target production state multi-dimensional description of the key production task, wherein the intelligent template production thread is obtained by training a basic template production thread by means of automatic production line production data of the dynamic production data template annotated with the target production state multi-dimensional description.
2. The method of claim 1, wherein determining the automation line production data to be analyzed that is recorded by the intelligent production data collection terminal comprises: determining the production data of the automatic production line to be analyzed of the production request to be accessed to the production line flow, which is recorded by the intelligent production data collecting terminal outside the positioning production line flow, wherein the key production task covers the production request;
after obtaining a dynamic production data template for the multi-dimensional description of the target production state for the key production task, the method further comprises: and activating a request pairing mechanism for matching the production line flow by a user on the premise of determining that the dynamic production data template aims to express that the production request meets the set requirement.
3. The method of claim 1, wherein prior to determining the target production state multidimensional description bound to the key production task from a list of mappings between automation production tasks and production state multidimensional descriptions established in advance, the method further comprises:
creating the mapping list between the automated production task and the multidimensional production state description, wherein the mapping list covers one or more of the following items:
On the premise that the automatic production task is determined to be a first automatic production task, generating a production state multi-dimensional description bound with the first automatic production task as a first production state multi-dimensional description, wherein the first production state multi-dimensional description does not include the production state multi-dimensional description;
and on the premise of determining that the automatic production task is a remaining automatic production task except the first automatic production task, generating a production state multi-dimensional description bound with the remaining automatic production task as a second production state multi-dimensional description, wherein the second production state multi-dimensional description covers the production state multi-dimensional description bound with a keyword label of the automatic production task.
4. The method of claim 1, wherein prior to performing production data template mining on the to-be-analyzed automation line production data by means of the intelligent template production thread to obtain a dynamic production data template for the multi-dimensional description of the target production state of the key production task, the method further comprises:
determining a training example, wherein the training example covers the production data of the automatic production line of the dynamic production data template annotated with the multidimensional description of the target production state;
And training the basic template production thread by means of the automatic production line production data of the dynamic production data template annotated with the multidimensional description of the target production state, so as to obtain the intelligent template production thread.
5. The method of claim 1, wherein after obtaining the dynamic production data template for the multi-dimensional description of the target production state for the key production task, the method further comprises: and outputting prompt information on the premise that the key production task is determined not to meet the set requirement through the dynamic production data template.
6. The method of claim 5, wherein the goal production state multi-dimensional description comprises one or more of: describing the heating of production equipment; abnormal sound description of production equipment; describing the environmental cleanliness of a production line; describing the environmental humidity of the production line; describing the network stability of the production line;
on the premise that the dynamic production data template determines that the key production task does not meet the set requirement, outputting prompt information comprises one or more of the following items:
outputting the prompt information on the premise that the target production state multi-dimensional description covers the production equipment heating description and the key production task does not reach an equipment heating safety index or is not matched with the equipment heating safety index based on a first adjustment strategy through the dynamic production data template;
Outputting the prompt information on the premise that the target production state multi-dimensional description covers the abnormal sound description of the production equipment and the key production task does not reach the equipment abnormal sound safety index or the equipment abnormal sound safety index is not matched based on a second adjustment strategy through the dynamic production data template;
outputting the prompt information on the premise that the multi-dimensional description of the target production state covers the description of the environmental cleanliness of the production line and the key production tasks are determined to have the environmental pollution of the production line through the dynamic production data template, and the quantized environmental pollution coefficient is larger than a first determination value;
outputting the prompt information on the premise that the multi-dimensional description of the target production state covers the description of the environmental humidity of the production line and the condition that the key production task is matched with a production line humidifier and the state coefficient of the production line humidifier is larger than a second judgment value is determined through the dynamic production data template;
and outputting the prompt information on the premise that the target production state multi-dimensional description covers the production line network stability description and the key production task is determined to be matched with a production line network analysis thread through the dynamic production data template, and the production line network stability evaluation is greater than a third judgment value.
7. The method of claim 1, wherein after obtaining the dynamic production data template for the multi-dimensional description of the target production state for the key production task, the method further comprises: and marking the production data of the automatic production line to be analyzed on the premise that the key production task is determined not to meet the set requirement through the dynamic production data template.
8. A production data template production apparatus, comprising:
the description determining module is used for determining the production data of the automatic production line to be analyzed, which is recorded by the intelligent production data collecting terminal, wherein the production data of the automatic production line to be analyzed comprises the production data of the automatic production line of a key production task; determining a target production state multi-dimensional description bound with the key production task through a mapping list between an automatic production task and the production state multi-dimensional description which is established in advance;
and the data training module is used for determining that an intelligent template production thread for performing production data template production on the target production state multidimensional description exists on the premise of determining that the target production state multidimensional description comprises not less than one production state multidimensional description, and performing production data template mining on the automatic production line production data to be analyzed by means of the intelligent template production thread to obtain a dynamic production data template of the target production state multidimensional description of the key production task, wherein the intelligent template production thread is obtained by training a basic template production thread by means of the automatic production line production data of the dynamic production data template annotated with the target production state multidimensional description.
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