CN112906891A - Expert system knowledge base construction method and device based on machine learning - Google Patents
Expert system knowledge base construction method and device based on machine learning Download PDFInfo
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Abstract
The invention discloses a method and a device for constructing an expert system knowledge base based on machine learning, wherein the method comprises the following steps: selecting a plurality of fault phenomena which are mutually associated to complete fault description, and generating corresponding knowledge items in an expert system knowledge base or directly acquiring corresponding maintenance records according to the fault description; acquiring a maintenance record generated by examining knowledge items by experts in the industry; and inputting the maintenance record into a machine learning model, and updating the fault phenomenon frequency, the fault reason frequency and the incidence frequency of the correlation of the fault phenomenon frequency and the fault reason frequency in the expert system knowledge base. The embodiment of the invention provides a report entry of knowledge items and maintenance records for a user, continuously perfects a knowledge base and optimizes a diagnosis model by means of an expert auditing process, greatly expands a knowledge acquisition way, and effectively solves the bottleneck problem about knowledge acquisition commonly existing in a fault diagnosis expert system.
Description
Technical Field
The invention relates to the technical field of artificial intelligence, in particular to a method and a device for constructing an expert system knowledge base based on machine learning.
Background
The expert system is based on expert knowledge in the field of fault diagnosis, so that a computer can simulate the thinking mode of human experts to form a system with expert level and capability of solving complex problems in the field. The amount and quality of the acquired knowledge will directly affect the diagnostic efficiency and accuracy of the expert system. Knowledge acquisition is a complex problem that is closely related to domain experts, expert system builders, and the expert system itself, and is recognized as a "bottleneck" in the construction of expert systems. There are two main approaches to knowledge acquisition: namely, manual migration and machine learning.
Manual migration requires repeated communication between a knowledge engineer and a domain expert, acquiring knowledge from the domain expert, and then inputting the knowledge into a knowledge base by the knowledge engineer. In many cases, domain experts are difficult to clearly teach their own empirical knowledge, especially the problem of intuitive solution, the effectiveness, the priori and the pertinence of the acquired knowledge, depend on human factors in the acquisition process to a certain extent, and have perceptual and blind characteristics. This method is therefore inefficient and the knowledge obtained is not accurate.
Machine learning requires the assistance of artificial intelligence and knowledge engineering techniques to acquire knowledge and update the knowledge base. The system can directly talk with the expert, learn the knowledge required by the expert system from the original information provided by the expert, and can also summarize new knowledge from a large number of learning samples, find possible errors in the knowledge, continuously complete the knowledge by self, and establish a knowledge base with excellent performance and perfect knowledge. There are many methods of machine learning, which can be classified into: mechanical learning, analogy learning, inductive learning, observational learning, interpretation-based learning, case-based learning, neural network-based learning, genetic algorithm-based learning, rough set theory-based learning, and the like, which have been developed in recent years. But machine learning requires a large amount of training sample data as a support.
The fault diagnosis expert system established by the expert system at the present stage can only provide basic fault diagnosis reasoning logic, but can not solve the problem of diagnosis logic outside the knowledge base.
Disclosure of Invention
The invention aims to provide a method and a device for constructing an expert system knowledge base based on machine learning, and aims to solve the problem that the fault diagnosis expert system at the present stage usually depends on an initialized knowledge base established by cooperation of a knowledge engineer and experts in the fault diagnosis field, the completeness and the effectiveness of the knowledge base initialized in the mode are difficult to ensure due to the influence of human factors, and meanwhile, the completeness and the optimization of the knowledge base become the bottleneck problem of the expert diagnosis system due to the lack of a large amount of sample data required by machine learning.
The invention provides a method for constructing an expert system knowledge base based on machine learning, which comprises the following steps:
s1, selecting a plurality of correlated fault phenomena to complete fault description, and generating corresponding knowledge items in an expert system knowledge base or directly acquiring corresponding maintenance records according to the fault description;
s2, obtaining a maintenance record generated by examining and verifying knowledge items by an expert in the industry;
and S3, inputting the maintenance record into the machine learning model, and updating the fault phenomenon frequency, the fault reason frequency and the incidence frequency of the correlation of the fault phenomenon frequency and the fault reason frequency in the expert system knowledge base.
The invention provides an expert system knowledge base construction device based on machine learning, which comprises:
a knowledge acquisition module: selecting a plurality of fault phenomena which are mutually associated to complete fault description, and generating corresponding knowledge items in an expert system knowledge base or directly acquiring corresponding maintenance records according to the fault description;
a knowledge analysis module: acquiring a maintenance record generated by examining knowledge items by experts in the industry;
updating the knowledge base module: and inputting the maintenance record into a machine learning model, and updating the fault phenomenon frequency, the fault reason frequency and the incidence frequency of the correlation of the fault phenomenon frequency and the fault reason frequency in the expert system knowledge base.
The embodiment of the invention also provides an expert system knowledge base construction device based on machine learning, which comprises the following steps: the computer program is executed by the processor to realize the steps of the expert system knowledge base construction method based on machine learning.
The embodiment of the invention also provides a computer readable storage medium, wherein an implementation program for information transmission is stored on the computer readable storage medium, and the program is executed by a processor to realize the steps of the expert system knowledge base construction method based on machine learning.
The embodiment of the invention provides a report entry of knowledge items and maintenance records for a user, continuously perfects a knowledge base and optimizes a diagnosis model by means of an expert auditing process, greatly expands a knowledge acquisition way, and effectively solves the bottleneck problem about knowledge acquisition commonly existing in a fault diagnosis expert system.
The foregoing description is only an overview of the technical solutions of the present invention, and the embodiments of the present invention are described below in order to make the technical means of the present invention more clearly understood and to make the above and other objects, features, and advantages of the present invention more clearly understandable.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings can be obtained by those skilled in the art without creative efforts.
FIG. 1 is a flow chart of a method of constructing a knowledge base of an expert system based on machine learning according to an embodiment of the method of the present invention;
FIG. 2 is a schematic diagram of a knowledge parsing warehousing process of an embodiment of the method of the present invention;
FIG. 3 is a schematic diagram of an expert system knowledge base construction device based on machine learning according to a first embodiment of the present invention;
fig. 4 is a schematic diagram of an expert system knowledge base construction device based on machine learning according to a second embodiment of the present invention.
Detailed Description
The technical solutions of the present invention will be described clearly and completely with reference to the following embodiments, and it should be understood that the described embodiments are some, but not all, embodiments of the present invention. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
In the description of the present invention, it is to be understood that the terms "center", "longitudinal", "lateral", "length", "width", "thickness", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer", "clockwise", "counterclockwise", and the like, indicate orientations and positional relationships based on those shown in the drawings, and are used only for convenience of description and simplicity of description, and do not indicate or imply that the device or element being referred to must have a particular orientation, be constructed and operated in a particular orientation, and thus, should not be considered as limiting the present invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, features defined as "first", "second", may explicitly or implicitly include one or more of the described features. In the description of the present invention, "a plurality" means two or more unless specifically defined otherwise. Furthermore, the terms "mounted," "connected," and "connected" are to be construed broadly and may, for example, be fixedly connected, detachably connected, or integrally connected; can be mechanically or electrically connected; they may be connected directly or indirectly through intervening media, or they may be interconnected between two elements. The specific meanings of the above terms in the present invention can be understood in specific cases to those skilled in the art.
Method embodiment
According to an embodiment of the present invention, a method for constructing an expert system knowledge base based on machine learning is provided, fig. 1 is a flowchart of the method for constructing the expert system knowledge base based on machine learning according to the embodiment of the present invention, and as shown in fig. 1, the method for constructing the expert system knowledge base based on machine learning according to the embodiment of the present invention specifically includes:
s101, selecting a plurality of correlated fault phenomena to complete fault description, and generating corresponding knowledge items or directly acquiring corresponding maintenance records in an expert system knowledge base according to the fault description;
specifically, the maintenance record comprises a logical relation and a relation weight of a fault phenomenon and a fault reason acquired by an expert in the industry;
more specifically, the fault description is retrieved through an expert system knowledge base, if a fault diagnosis result is obtained, the generated maintenance record is directly obtained, and if the fault diagnosis result is not obtained, the fault reason after the self-maintenance of the user is obtained and a knowledge item is generated; or, the past experience information of the user is obtained to directly report the fault reason, the fault phenomenon and the association thereof to generate a knowledge item;
in this embodiment, the knowledge reporting stage: when a user reports, the user firstly needs to select the equipment model and the part name, clearly fills in the fault phenomenon and the fault reason, selects and fills in the maintenance guarantee requirement and the maintenance process steps, and submits the real name to report after confirming that no error exists, so that the system can conveniently evaluate and count the knowledge contribution. Fig. 2 is a schematic diagram of a knowledge parsing warehousing process according to an embodiment of the method of the present invention, as shown in fig. 2.
Specifically, there are three cases when reporting:
(1) normal conditions are as follows: in the fault description stage, a user completes fault description by selecting fault phenomena and associated fault phenomena, then an intelligent fault diagnosis model enumerates fault diagnosis results through knowledge reasoning, and finally, after the user confirms fault reasons and completes actual fault removal, a system automatically generates maintenance records.
(2) Abnormal conditions are as follows: when a user cannot retrieve a fault phenomenon to be reported by means of the fault diagnosis terminal or a fault diagnosis model cannot provide a proper fault solution, if the user can effectively eliminate a fault according to own maintenance experience, the experience can be used as a new knowledge item to report knowledge.
(3) Other cases are as follows: the user can also arrange the previous maintenance experience and independently report the knowledge by means of the experience module in the maintenance terminal. S102, obtaining a maintenance record generated by examining knowledge items by an expert in the industry;
specifically, the reasonability and operability of the knowledge items are judged, and a decision of sorting and entering or direct deletion is made;
in the example of the invention, the knowledge auditing stage: knowledge auditing is handled by a knowledge expert role that has deep research on the construction principles and maintenance knowledge of the selected equipment. Firstly, the reasonability and operability of knowledge are required to be clearly reported so as to make an audit opinion which is adopted or ignored; secondly, reporting items of the adopted knowledge, disassembling fault phenomena and fault reasons, and carrying out standardized description processing; and finally, establishing a diagnosis logic relation between the fault phenomenon and the fault reason, and determining a weight coefficient related to the cause and effect.
And S103, inputting the maintenance record into the machine learning model, and updating the fault phenomenon frequency, the fault reason frequency and the incidence frequency of the correlation of the fault phenomenon frequency and the fault reason frequency in the expert system knowledge base.
In the present example, the knowledge-binning stage: after the knowledge examination and the knowledge disassembly pretreatment are completed, the existing knowledge items are retrieved by means of a knowledge base management module, if necessary, newly-added item warehousing operation can be performed, meanwhile, a diagnosis inference logic relation is established in a correlation selection mode, the occurrence frequency of correlation can be edited, and further, the cause-and-effect weight configuration of the fault phenomenon and the fault reason is realized.
The embodiment of the invention provides a report entry of knowledge items and maintenance records for a user, continuously perfects a knowledge base and optimizes a diagnosis model by means of an expert auditing process, greatly expands a knowledge acquisition way, and effectively solves the bottleneck problem about knowledge acquisition commonly existing in a fault diagnosis expert system.
Apparatus embodiment one
According to an embodiment of the present invention, there is provided an expert system knowledge base construction apparatus based on machine learning, fig. 3 is a schematic diagram of an expert system knowledge base construction apparatus based on machine learning according to an embodiment of the apparatus of the present invention, and as shown in fig. 3, an expert system knowledge base construction apparatus based on machine learning according to an embodiment of the present invention specifically includes:
the knowledge acquisition module 301: selecting a plurality of fault phenomena which are mutually associated to complete fault description, and generating corresponding knowledge items in an expert system knowledge base or directly acquiring corresponding maintenance records according to the fault description;
in this embodiment, the maintenance record includes the logical relationship and the relationship weight of the failure phenomenon and the failure cause obtained by the expert in the industry;
specifically, the fault description is retrieved through an expert system knowledge base, if a fault diagnosis result is obtained, the generated maintenance record is directly obtained, and if the fault diagnosis result is not obtained, the fault reason after the self-maintenance of the user is obtained and a knowledge item is generated; or, the past experience information of the user is obtained to directly report the fault reason, the fault phenomenon and the association thereof to generate a knowledge item;
knowledge analysis module 302: acquiring a maintenance record generated by examining knowledge items by experts in the industry;
in the embodiment, the reasonability and operability of the knowledge items are judged, and a decision of sorting and entering or direct deletion is made;
update knowledge base module 303: and inputting the maintenance record into a machine learning model, and updating the fault phenomenon frequency, the fault reason frequency and the incidence frequency of the correlation of the fault phenomenon frequency and the fault reason frequency in the expert system knowledge base.
The embodiment of the present invention is a system embodiment corresponding to the above method embodiment, and specific operations of each module may be understood with reference to the description of the method embodiment, which is not described herein again.
Device embodiment II
The embodiment of the present invention provides an expert system knowledge base construction device based on machine learning, and fig. 4 is a schematic diagram of an expert system knowledge base construction device based on machine learning according to a second embodiment of the present invention, as shown in fig. 4, including: a memory 401, a processor 402 and a computer program stored on the memory 401 and executable on the processor 402, which computer program when executed by the processor 303 performs the method steps of:
s101, selecting a plurality of correlated fault phenomena to complete fault description, and generating corresponding knowledge items or directly acquiring corresponding maintenance records in an expert system knowledge base according to the fault description;
in this embodiment, the maintenance record includes the logical relationship and the relationship weight of the failure phenomenon and the failure cause obtained by the expert in the industry;
specifically, the fault description is retrieved through an expert system knowledge base, if a fault diagnosis result is obtained, the generated maintenance record is directly obtained, and if the fault diagnosis result is not obtained, the fault reason after the self-maintenance of the user is obtained and a knowledge item is generated; or, the past experience information of the user is obtained to directly report the fault reason, the fault phenomenon and the association thereof to generate a knowledge item;
s102, obtaining a maintenance record generated by examining knowledge items by an expert in the industry;
in the embodiment, the reasonability and operability of the knowledge items are judged, and a decision of sorting and entering or direct deletion is made;
and S103, inputting the maintenance record into the machine learning model, and updating the fault phenomenon frequency, the fault reason frequency and the incidence frequency of the correlation of the fault phenomenon frequency and the fault reason frequency in the expert system knowledge base.
Device embodiment III
The embodiment of the invention provides a computer readable storage medium, wherein an implementation program for information transmission is stored on the computer readable storage medium, and when being executed by a processor 402, the implementation program realizes the following method steps:
s101, selecting a plurality of correlated fault phenomena to complete fault description, and generating corresponding knowledge items or directly acquiring corresponding maintenance records in an expert system knowledge base according to the fault description;
in this embodiment, the maintenance record includes the logical relationship and the relationship weight of the failure phenomenon and the failure cause obtained by the expert in the industry;
specifically, the fault description is retrieved through an expert system knowledge base, if a fault diagnosis result is obtained, the generated maintenance record is directly obtained, and if the fault diagnosis result is not obtained, the fault reason after the self-maintenance of the user is obtained and a knowledge item is generated; or, the past experience information of the user is obtained to directly report the fault reason, the fault phenomenon and the association thereof to generate a knowledge item;
s102, obtaining a maintenance record generated by examining knowledge items by an expert in the industry;
in the embodiment, the reasonability and operability of the knowledge items are judged, and a decision of sorting and entering or direct deletion is made;
and S103, inputting the maintenance record into the machine learning model, and updating the fault phenomenon frequency, the fault reason frequency and the incidence frequency of the correlation of the fault phenomenon frequency and the fault reason frequency in the expert system knowledge base.
The computer-readable storage medium of the embodiment includes, but is not limited to: ROM, RAM, magnetic or optical disks, and the like.
It will be apparent to those skilled in the art that the modules or steps of the present invention described above may be implemented by a general purpose computing device, they may be centralized on a single computing device or distributed across a network of multiple computing devices, and alternatively, they may be implemented by program code executable by a computing device, such that they may be stored in a storage device and executed by a computing device, and in some cases, the steps shown or described may be performed in an order different than that described herein, or they may be separately fabricated into individual integrated circuit modules, or multiple ones of them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
Finally, it should be noted that: the above embodiments are only used to illustrate the technical solution of the present invention, and not to limit the same; while the invention has been described in detail and with reference to the foregoing embodiments, it will be understood by those skilled in the art that: the technical solutions described in the foregoing embodiments may still be modified, or some or all of the technical features may be equivalently replaced; and the modifications or the substitutions do not make the essence of the corresponding technical solutions depart from the scope of the technical solutions of the embodiments of the present invention.
Claims (10)
1. A method for constructing an expert system knowledge base based on machine learning is characterized by comprising the following steps:
s1, selecting a plurality of correlated fault phenomena to complete fault description, and generating corresponding knowledge items in an expert system knowledge base or directly acquiring corresponding maintenance records according to the fault description;
s2, obtaining a maintenance record generated by examining the knowledge items by an expert in the industry;
and S3, inputting the maintenance record into a machine learning model, and updating the fault phenomenon frequency, the fault reason frequency and the incidence frequency of the correlation of the fault phenomenon frequency and the fault reason frequency in the expert system knowledge base.
2. The method according to claim 1, wherein the step S2 further comprises:
judging the reasonability and operability of the knowledge items, and making a decision of sorting and entering or directly deleting.
3. The method of claim 1, wherein the service record comprises logical relationships and relational weights of the fault phenomena and fault causes obtained by the industry experts.
4. The method according to claim 1, wherein the step S1 specifically includes:
searching the fault description through an expert system knowledge base, directly acquiring a generated maintenance record if a fault diagnosis result is obtained, and acquiring a fault reason after the self maintenance of a user and generating a knowledge item if the fault diagnosis result is not obtained; or, the past experience information of the user is acquired to directly report the fault reason, the fault phenomenon and the association thereof to generate the knowledge item.
5. An expert system knowledge base construction device based on machine learning is characterized by comprising the following steps:
a knowledge acquisition module: selecting a plurality of correlated fault phenomena to complete fault description, and generating corresponding knowledge items in the expert system knowledge base or directly acquiring corresponding maintenance records according to the fault description;
a knowledge analysis module: acquiring a maintenance record generated by examining the knowledge items by an expert in the industry;
updating the knowledge base module: and inputting the maintenance record into a machine learning model, and updating the fault phenomenon frequency, the fault reason frequency and the incidence frequency of the correlation of the fault phenomenon frequency and the fault reason frequency in the expert system knowledge base.
6. The apparatus of claim 5, wherein the knowledge analysis module further comprises: judging the reasonability and operability of the knowledge items, and making a decision of sorting and entering or directly deleting.
7. The apparatus of claim 5, wherein the service record comprises logical relationships and relational weights of the fault phenomena and fault causes obtained by the industry experts.
8. The apparatus of claim 5, wherein the knowledge acquisition module further comprises: searching the fault description through an expert system knowledge base, directly acquiring a generated maintenance record if a fault diagnosis result is obtained, and acquiring a fault reason after the self maintenance of a user and generating a knowledge item if the fault diagnosis result is not obtained; or, the past experience information of the user is acquired to directly report the fault reason, the fault phenomenon and the association thereof to generate the knowledge item.
9. An expert system knowledge base construction device based on machine learning is characterized by comprising the following steps: memory, a processor and a computer program stored on the memory and executable on the processor, the computer program, when executed by the processor, implementing the steps of the machine learning based expert system knowledge base construction method of any one of claims 1 to 4.
10. A computer-readable storage medium, on which an information transfer implementation program is stored, which when executed by a processor implements the steps of the machine learning-based expert system knowledge base construction method according to any one of claims 1 to 4.
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