CN112631817B - Problem diagnosis method and system and electronic equipment - Google Patents
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Abstract
The embodiment of the invention provides a problem diagnosis method, a problem diagnosis system and electronic equipment. Wherein the method comprises the following steps: acquiring a target problem to be analyzed; determining a first target agenda corresponding to the target problem; selecting a first analysis subunit according to a rule corresponding to a first target agenda, taking a target problem as an entry parameter of the first analysis subunit, and executing the first analysis subunit; determining an operation to be executed according to the analysis result and the rule of the first analysis subunit; when the operation to be executed is to select the second analysis subunit, executing the second analysis subunit and determining the operation to be executed; when the operation to be executed is to select a second target agenda, taking the analysis result of the first analysis subunit as a new problem, and determining a second target agenda corresponding to the new problem; when the operation to be performed is to output a diagnosis result, the analysis result of the first analysis subunit is determined as a diagnosis result of the target problem. The efficiency of problem diagnosis can be improved.
Description
Technical Field
The present invention relates to the field of operation and maintenance technologies, and in particular, to a problem diagnosis method, system and electronic device.
Background
During the operation and maintenance process, the staff may encounter various problems, for example, the device may not be able to establish a connection with the remote server, the program may not be started normally, the system crashes, and the like. In order for the worker to be able to accurately solve these problems, it is necessary to determine the cause of the occurrence of the problems.
However, for the same problem, there may be a plurality of different reasons that can cause the problem to occur, so in the related art, a worker with certain experience needs to manually analyze the problem to determine the reason causing the problem.
This mode is comparatively loaded down with trivial details and has certain requirement to the staff, consequently is unfavorable for the implementation, efficiency is lower.
Disclosure of Invention
The embodiment of the invention aims to provide a problem diagnosis method, a problem diagnosis system and electronic equipment, so as to improve the problem diagnosis efficiency. The specific technical scheme is as follows:
in a first aspect of embodiments of the present invention, there is provided a problem diagnosis method, including:
acquiring a target problem to be analyzed;
determining a first target agenda corresponding to the target problem according to a preset corresponding relation between a problem and an agenda, wherein the first target agenda comprises at least one analysis subunit and a rule for indicating a scheduling path among the analysis subunits;
selecting a first analysis subunit according to a rule corresponding to the first target agenda, taking the target problem as an entry parameter of the first analysis subunit, and executing the first analysis subunit;
determining an operation to be executed according to the analysis result of the first analysis subunit and the rule;
when the operation to be executed is to select a second analysis subunit, taking the analysis result of the first analysis subunit as the input parameter of the second analysis subunit, executing the second analysis subunit, and determining the operation to be executed according to the analysis result of the second analysis subunit and the rule;
when the operation to be executed is to select a second target agenda, taking an analysis result of the first analysis subunit as a new problem, and determining a second target agenda corresponding to the new problem according to the preset corresponding relationship between the problem and the agenda;
when the operation to be executed is outputting a diagnosis result, determining the analysis result of the first analysis subunit as the diagnosis result of the target problem.
In a possible embodiment, the performing the analysis process of the first analysis subunit or the second analysis subunit comprises:
determining a target experiment tool according to the input parameters of the analysis subunit and the mapping relation between a preset experiment tool and a condition to be analyzed, wherein the condition to be analyzed is a hypothesis condition for reasoning the cause of the problem in the analysis subunit; calling the target experiment tool to perform an experiment to obtain an experiment result; determining an analysis result of the analysis subunit according to the experiment result;
or,
determining input information of an experimental tool associated with the analysis subunit according to the input parameters of the analysis subunit, and calling the experimental tool associated with the analysis subunit according to the input information to obtain an experimental result; and determining the analysis result of the analysis subunit according to the experimental result.
In a possible embodiment, when the number of the target experiment tools is greater than 1 or the number of the experiment tools associated with the analysis subunit is greater than 1, calling each experiment tool in a preset order;
determining an analysis result of the analysis subunit according to the experiment result, including:
and determining the analysis result of the analysis subunit according to the experiment result of each experiment tool.
In a possible embodiment, the determining, according to the analysis result of the first analysis subunit and the rule, an operation to be performed includes:
judging whether a next analysis subunit meeting the analysis result exists on a scheduling path corresponding to the first analysis subunit according to the analysis result of the first analysis subunit and the rule;
if so, determining that the operation to be executed is to select a second analysis subunit;
if not, judging whether the analysis result of the first analysis subunit comprises a problem source or not;
when the analysis result of the first analysis subunit comprises a problem source, determining that the operation to be executed is to select a second target agenda;
when the analysis result of the first analysis subunit is determined not to include the problem source, determining that the operation to be performed is to output a diagnosis result.
In a second aspect of embodiments of the present invention, there is provided a problem diagnosis system including:
a knowledge graph unit;
the knowledge graph unit comprises a rule engine subunit and a data factory subunit;
the rules engine subunit comprises a plurality of agendas each comprising at least one parsing subunit and rules for indicating scheduling paths between the respective parsing subunits;
the data factory subunit is used for storing a preset corresponding relation between the agenda and the problem;
the rule engine subunit is used for determining a first target agenda corresponding to the target problem to be analyzed according to a preset corresponding relation between the problem and the agenda;
the first target agenda is used for selecting a first analysis subunit according to a rule corresponding to the first target agenda, taking the target problem as an entry parameter of the first analysis subunit, and executing the first analysis subunit; determining an operation to be executed according to the analysis result of the first analysis subunit and the rule;
the first target agenda is further used for taking the analysis result of the first analysis subunit as the input parameter of the second analysis subunit, executing the second analysis subunit and determining the operation to be executed according to the analysis result of the second analysis subunit and the rule when the operation to be executed is to select the second analysis subunit;
the rule engine subunit is configured to, when the operation to be executed is to select a second target agenda, use an analysis result of the first analysis subunit as a new problem, and determine a second target agenda corresponding to the new problem according to the preset correspondence between the problem and the agenda;
the rule engine subunit is further configured to determine, when the operation to be performed is an output diagnosis result, an analysis result of the first analysis subunit as a diagnosis result of the target problem.
In one possible embodiment, the system further comprises an experiment toolbox unit comprising a plurality of experiment tools;
the first target agenda executing the first analysis subunit or executing an analysis process of the second analysis subunit, including:
determining a target experiment tool according to the parameters of the analysis subunit and the mapping relation between a preset experiment tool and a condition to be analyzed, wherein the condition to be analyzed is a hypothesis condition for reasoning the cause of the problem in the analysis subunit; calling the target experiment tool to perform an experiment to obtain an experiment result;
or,
determining input information of an experimental tool associated with the analysis subunit according to the input parameters of the analysis subunit, and calling the experimental tool associated with the analysis subunit according to the input information to obtain an experimental result;
and determining the analysis result of the analysis subunit according to the experimental result.
In one possible embodiment, the problem diagnosis system further comprises an agenda development interface for receiving an input agenda as a new agenda for the rules engine subunit.
In one possible embodiment, the problem diagnosis system further comprises an experiment tool development interface for receiving an input experiment tool as a new experiment tool of the experiment tool box unit.
In a third aspect of embodiments of the present invention, there is provided an electronic device, including:
a memory for storing a computer program;
a processor adapted to perform the method steps of any of the above first aspects when executing a program stored in the memory.
In a fourth aspect of the embodiments of the present invention, a computer-readable storage medium is provided, in which a computer program is stored, and the computer program, when executed by a processor, implements the method steps of any one of the above first aspects.
The embodiment of the invention has the following beneficial effects:
the problem diagnosis method, the problem diagnosis system and the electronic equipment provided by the embodiment of the invention can start from the target problem, convert the diagnosis of the target problem into the diagnosis of a new problem obtained by iteration through continuous iteration of the target problem, so that the reason leading to the target problem is gradually approached, and finally, a diagnosis result which can be used for expressing the reason leading to the target problem is obtained. The process can be automatically completed by a machine, and an experienced worker is not required to perform manual analysis, so that the efficiency of problem diagnosis can be effectively improved, and the target problem can be timely solved by the worker.
Of course, it is not necessary for any product or method to achieve all of the above-described advantages at the same time for practicing the invention.
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 only some embodiments of the present invention, and it is obvious for those skilled in the art that other embodiments can be obtained by using the drawings without creative efforts.
FIG. 1 is a schematic flow chart of a problem diagnosis method according to an embodiment of the present invention;
fig. 2 is a schematic flowchart of a method for determining an operation to be performed according to an embodiment of the present invention;
FIG. 3a is a schematic diagram of a problem diagnosis system according to an embodiment of the present invention;
FIG. 3b is a schematic diagram of another structure of the problem diagnosis system according to the embodiment of the present invention;
fig. 4 is a schematic structural diagram of an agenda provided in an embodiment of the present invention;
FIG. 5 is a schematic diagram of an experimental tool according to an embodiment of the present invention;
fig. 6 is a schematic flowchart of a process for performing offline fault diagnosis on a device by using the problem diagnosis method according to the embodiment of the present invention;
fig. 7 is a schematic structural diagram of an electronic device according to an embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. 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 order to more clearly explain the problem diagnosis method provided by the embodiment of the present invention, some terms are explained below:
generating a formula rule: the term Production was first proposed by american mathematician boster (e.post). Boster proposed a computational model called a boster machine according to alternative rules, each rule in the model then being called a generator. In the following, the term is used in many fields with modifications and extensions. For example, a grammar rule in a formal language is called a production. Production is also referred to as production rules, or rules for short.
Reverse reasoning: reverse inference (backward inference) is one of the heuristics in problem solving strategies. The problem solving strategy is that starting from the target state of the problem, the problem is recurred to the initial state step by step according to the logic sequence formed by the sub targets.
Machine learning: one simple way to explain machine learning is to imagine a child pushing down on a table with glass. Without this having been encountered before, the child cannot predict the result. But as he grows and learns, he understands what happens even though he does not fully understand why. This is machine learning at work.
Machine reasoning: imagine now that a child who had pushed the glass off a table now knows the physics of motion and gravity. Even if this has not been encountered before, he can guess something that inevitably happens. The child can apply the same logic to another object on the table, adjust the knowledge and apply it to the tv remote control on the same table, as he knows why this happens. This is machine reasoning. Machine inference is more human-like in the AI spectrum, closely related to big data surveys, and therefore it is more flexible than machine learning. However, machine reasoning requires heuristics and strategies, which are usually done by knowledgeable domain experts. Machine reasoning is best suited for deterministic scenarios. That is, it is determined whether an event is real or whether it will occur. Knowing this, it is possible to work well with machine learning and machine reasoning.
A rule engine: the rule engine is developed by an inference engine, is a component embedded in an application program, and realizes the separation of business decisions from application program codes and the writing of the business decisions by using a predefined semantic module. And receiving data input, interpreting business rules, and making business decisions according to the business rules.
An expert system: the expert system is the most important and active application field in artificial intelligence, and realizes the major breakthrough of artificial intelligence from theoretical research to practical application and from general reasoning strategy discussion to application of special knowledge. The expert system is an important branch of early artificial intelligence, and can be regarded as a computer intelligent program system with special knowledge and experience, and generally adopts knowledge representation and knowledge inference technology in artificial intelligence to simulate complex problems which can be solved by field experts.
Performing deduction: namely, the hypothesis deduction, after questions are presented on the basis of observation and analysis, a hypothesis for explaining the questions is presented through inference and imagination, deductive reasoning is performed according to the hypothesis, and the conclusion of the deductive reasoning is verified through experiments. If the experimental results are in accordance with the expected conclusions, the hypothesis is proved to be correct, otherwise, the hypothesis is indicated to be wrong.
Iteration: an iteration is the activity of a repetitive feedback process, usually with the aim of approximating a desired goal or result. Iterations differ from recursion in that: recursion is extended by itself and iteration is getting new results and replacing itself.
Referring to fig. 1, fig. 1 is a schematic flow chart of a problem diagnosis method provided by an embodiment of the present invention, where the method may be applied to any electronic device with problem diagnosis capability, or may also be applied to any software with problem diagnosis function, and the method may include, without limitation:
s101, acquiring a target problem to be analyzed.
S102, determining a first target agenda corresponding to the target problem according to a preset corresponding relation between the problem and the agenda, wherein the first target agenda comprises at least one analysis subunit and a rule for indicating a scheduling path between the analysis subunits.
S103, according to the rule corresponding to the first target agenda, selecting a first analysis subunit, taking the target problem as the entry parameter of the first analysis subunit, and executing the first analysis subunit.
And S104, determining to-be-executed operation according to the analysis result and the rule of the first analysis subunit.
And S105, when the operation to be executed is used for selecting the second analysis subunit, taking the analysis result of the first analysis subunit as the input parameter of the second analysis subunit, executing the second analysis subunit, and determining the operation to be executed according to the analysis result and the rule of the second analysis subunit.
And S106, when the operation to be executed is used for selecting a second target agenda, taking the analysis result of the first analysis subunit as a new problem, and determining the second target agenda corresponding to the new problem according to the preset corresponding relation between the problem and the agenda.
S107, when the operation is to be performed as the output diagnosis result, the analysis result of the first analysis subunit is determined as the diagnosis result of the target problem.
By adopting the embodiment, starting from the target problem, the diagnosis of the target problem is converted into the diagnosis of a new problem obtained by iteration through continuous iteration of the target problem, so that the reason causing the target problem is gradually approached, and a diagnosis result capable of representing the reason causing the target problem is finally obtained. The process can be automatically completed by a machine, and an experienced worker is not required to perform manual analysis, so that the efficiency of problem diagnosis can be effectively improved, and the target problem can be timely solved by the worker.
In S101, the target problem may be different according to different application scenarios, and for convenience of description, the target problem is taken as an example for explaining the device offline, and for scenarios in which the target problem is another problem, the principle is the same, and therefore, details are not described here again.
In S102, the preset mapping relationship between the questions and the agenda may be set according to actual requirements, for example, agenda a is designed to perform inference on question 1 during development, then question 1 may be used as the question corresponding to agenda a in the mapping relationship, each agenda may correspond to one or more questions, and the questions corresponding to different agendas may be the same or different.
When there is a corresponding relationship between the target problem and multiple agendas, one agenda may be randomly selected from the multiple agendas having a corresponding relationship with the target problem as a first target agenda, an agenda having the highest priority may be selected from the multiple agendas as the first target agenda according to a preset priority, or a first target agenda may be selected from the multiple agendas according to other selection methods, which is not limited in this embodiment.
The rules for indicating the scheduling paths among the analysis subunits are determined according to a reverse reasoning, that is, in the scheduling paths indicated by the rules, the problem for analysis of any analysis subunit should be a sub-problem of the problem for analysis of the last analysis subunit adjacent to the any analysis subunit. Illustratively, assuming that analysis subunit 1 is designed for analyzing problem 1, analysis subunit 2 is designed for analyzing problem 2, analysis subunit 3 is designed for analyzing problem 3, and problem 2 is a sub-problem of problem 1 and problem 3 is a sub-problem of problem 2, the scheduling effort indicated by the rule may be: analysis subunit 1 → analysis subunit 2 → analysis subunit 3.
The rules may be expressed in different ways, for example, in one possible embodiment the rules may be expressed in the form of files written in a rule programming language (e.g., CLPS), and in other possible embodiments the rules may be expressed in the form of files written in a programming language (e.g., JAVA, PYTHON, JS, C + +, etc.).
In S103, the rule corresponding to the first target agenda is a rule included in the first target agenda and used for indicating a scheduling path between each analysis subunit in the first target agenda, and the first analysis subunit may be the first analysis subunit located in the scheduling path indicated by the rule.
The analysis subunit can be regarded as a function, the entry of which is a question and the output of which is an analysis result. Executing the first analysis subunit can be regarded as calling the function to map the problem, and obtaining a corresponding analysis result.
In S104, three operations to be executed may be determined, which are: selecting a second analysis subunit, selecting a second target agenda, and outputting a diagnosis result. For these three different operations to be executed, detailed descriptions will be given below, and are not described here.
In S105, the second analysis subunit is an analysis subunit in the first target agenda and is a different analysis subunit than the first analysis subunit. The second analysis subunit may be determined according to the analysis result of the first analysis subunit and the rule, in the scheduling path indicated by the rule, if there is only one analysis subunit next to the first analysis subunit, the analysis subunit next to the first analysis subunit may be determined as the second analysis subunit, and if there are a plurality of analysis subunits next to the first analysis subunit, one second analysis subunit may be determined from the plurality of analysis subunits next to the first analysis subunit according to the analysis result of the first analysis subunit.
The execution of the second analysis subunit is similar to the execution of the first analysis subunit described above, except that the execution logic implemented by the first and second analysis subunits is different. The analysis process of the first and second analytical subunits will therefore be explained in a unified manner below.
In a possible embodiment, the target experiment tool may be determined according to the entry of the analysis subunit and the mapping relationship between the preset experiment tool and the condition to be analyzed. And calling a target experiment tool to perform an experiment to obtain an experiment result. And determining the analysis result of the analysis subunit according to the experimental result.
Wherein, the condition to be analyzed is a hypothesis condition used for reasoning the cause of the problem in the analysis subunit. In the mapping relationship between the experimental tool and the condition to be analyzed, the experimental tool should have the capability of performing an experiment for verifying whether the corresponding condition to be analyzed is satisfied. For example, assuming that the cause of the target problem to be analyzed is login failure, the experimental tool corresponding to the condition to be analyzed in the mapping relationship should have the capability of performing an experiment for verifying whether the cause of the target problem is login failure, for example, the experimental tool may be an experimental tool having a ping (a computer network diagnosis function) function. Calling a target experiment tool to perform an experiment means calling the target experiment tool to perform an experiment for verifying whether a condition to be analyzed is established, and an obtained experiment result can be used for indicating whether the condition to be analyzed is established.
In another possible embodiment, the input information of the experiment tool associated with the analysis subunit may also be determined according to the input parameters of the analysis subunit, and the experiment tool associated with the analysis subunit may be called according to the input information to obtain the experiment result. And determining the analysis result of the analysis subunit according to the experimental result.
The input information of each experimental tool may be input information input by a developer when developing the experimental tool, or may be input information input by a relevant person according to a function of each experimental tool. The content included in the input information differs according to the application scenario. At least information describing the capabilities of the experimental tool should be included in the input information.
The experimental tool associated with the analysis subunit may be an experimental tool associated with the analysis subunit by searching the input information of each experimental tool for a keyword of the analysis subunit, and if the keyword can be matched with the input information of the experimental tool, the experimental tool may be regarded as the experimental tool associated with the analysis subunit.
The experimental tool associated with the target experimental tool or analysis subunit may be one experimental tool or a plurality of experimental tools. In the case that the experiment tool associated with the target experiment tool or the analysis subunit is one experiment tool, the result obtained by invoking the one experiment tool may be obtained. In the case that the experiment tools associated with the target experiment tool or the analysis subunit include a plurality of experiment tools, that is, when the number of the target experiment tools is greater than 1 or the number of the experiment tools associated with the analysis subunit is greater than 1, the experiment tools may be called in a preset order to perform an experiment.
The preset sequence can be set by related personnel according to actual needs or experience. Or may be set according to a preset rule, which is not limited in this embodiment. In the embodiment, a plurality of different experimental tools can be called to be combined together to carry out experiments according to actual requirements, so that the experimental result is more accurate.
In S106, in the preset correspondence between the questions and the agenda, one agenda should have the capability of performing question diagnosis on the corresponding question, and the determined second target agenda corresponding to the new question should have the capability of performing question diagnosis on the new question.
It is understood that as the problem diagnosis proceeds, the target problem may change, i.e., a new problem may be generated. The first target agenda may not have the capability to perform problem diagnosis on the new problem, in which case a second target agenda having the capability to perform problem diagnosis on the new problem needs to be selected to continue the problem diagnosis on the new problem.
The process of performing problem diagnosis on the new problem by the second target agenda is the same as the process of performing problem diagnosis on the target problem by the first target agenda, so reference may be made to the foregoing descriptions in S103-S105 and the following S107, and details are not repeated here.
If the new problem is gradually changed into another new problem in the subsequent process of performing problem diagnosis on the new problem by using the second target agenda, and the second target agenda does not have the capability of performing problem diagnosis on the another new problem, a third target agenda corresponding to the another new problem can be determined again according to the preset corresponding relationship between the problem and the agenda, and so on.
In S107, if the cause of the target problem has been uniquely determined from the analysis result and the rule of the first analysis subunit, it may be determined that the operation to be performed is to output a diagnosis result at this time. It is understood that as the problem diagnosis is performed, part of the reasons causing the problem are eliminated, for example, taking the target problem as the device offline, six reasons causing the device offline may be possible, and five of the reasons are gradually eliminated as the problem diagnosis is performed, and the remaining one is the real reason causing the device offline.
To more clearly describe the problem diagnosis method provided by the embodiment of the present invention, a determination manner of an operation to be executed is described below, and referring to fig. 2, fig. 2 is a schematic flow chart of the determination method of the operation to be executed provided by the embodiment of the present invention, and the method may include:
s201, judging whether a next analysis subunit meeting the analysis result exists on the dispatching path corresponding to the first analysis subunit according to the analysis result of the first analysis subunit and the rule. If there is a next analysis subunit on the dispatch path that matches the analysis result, S202 is performed, and if there is no next analysis subunit on the dispatch path that matches the analysis result, S203 is performed.
The next analysis subunit corresponding to the analysis result may refer to a problem that the next analysis subunit is designed for analysis in the state represented by the analysis result. For example, assuming that the next analysis subunit of the analysis subunit 1 in the scheduling path is the analysis subunit 2, and the problem that the analysis subunit 2 is designed to analyze exists only when the device is online, if the analysis result output by the analysis subunit 1 indicates that the device is offline, the analysis subunit 2 does not conform to the analysis result output by the analysis subunit 1, and if the analysis result output by the analysis subunit 1 indicates that the device is online, the analysis subunit 2 conforms to the analysis result output by the analysis subunit 1.
S202, determining that the operation to be executed is to select a second analysis subunit.
And the selected second analysis subunit is the next analysis subunit. It can be understood that if there is a next analysis subunit on the scheduling path that matches the analysis result, the next analysis subunit that matches the analysis result should be called to continue the problem diagnosis.
S203, determining whether the analysis result of the first analysis subunit includes a problem source, if the analysis result of the first analysis subunit includes a problem source, performing S204, and if the analysis result of the first analysis subunit does not include a problem source, performing S205.
If there is no next analysis subunit on the scheduling path that meets the analysis result, it may be considered that the first target agenda has been executed completely at this time, and the new problem obtained by the first analysis subunit is beyond the capability range of the first target agenda, and at this time, if the diagnosis result has not been obtained, the new target agenda should be selected to continue the problem diagnosis. Therefore, if the analysis result of the first analysis subunit does not include the problem source, it can be considered that the cause of the target problem has been obtained, and at this time it can be determined that the operation to be performed is the output diagnosis result. If the analysis result of the first analysis subunit includes a source of the problem, it may be determined that the operation to be performed is to select a second target agenda.
And S204, determining that the operation to be executed is to select a second target agenda.
When the second target agenda is selected, the agenda corresponding to the analysis result of the first analysis subunit may be determined as the second target agenda according to the preset corresponding relationship between the question and the agenda.
And S205, determining the operation to be executed as an output diagnosis result.
In order to more clearly describe the problem diagnosis manner provided by the embodiment of the present invention, a problem diagnosis system provided by the embodiment of the present invention will be described below. Referring to fig. 3a, fig. 3a is a schematic diagram of a framework of a problem diagnosis system according to an embodiment of the present invention, which may include:
a knowledge map (KonwledgGraph) unit 100.
The knowledge graph unit 100 includes a Rule Engine (Rule Engine) subunit 110 and a data factory (DataFactory) subunit 120. The rules engine subunit 110 includes a plurality of agenda (agenda) 111. Each agenda 111 includes at least one parsing subunit and rules for indicating scheduling paths between the respective parsing subunits.
The data factory subunit 120 is configured to store a preset correspondence between an agenda and a question.
The rule engine subunit 110 is configured to determine a first target agenda corresponding to the target question to be analyzed according to a preset correspondence between the question and the agenda.
The first target agenda is used for selecting a first analysis subunit according to a rule corresponding to the first target agenda, taking a target problem as an entry parameter of the first analysis subunit, executing the first analysis subunit, and determining an operation to be executed according to an analysis result and the rule of the first analysis subunit;
the first target agenda is also used for taking the analysis result of the first analysis subunit as the input parameter of the second analysis subunit, executing the second analysis subunit and determining the operation to be executed according to the analysis result and the rule of the second analysis subunit when the operation to be executed is used for selecting the second analysis subunit;
the rule engine subunit 110 is configured to, when the operation to be performed is to select a second target agenda, use an analysis result of the first analysis subunit as a new problem, and determine, according to a correspondence between the preset problem and an agenda, a second target agenda corresponding to the new problem;
the rule engine subunit 110 is further configured to determine, when the operation to be performed is an output diagnosis result, an analysis result of the first analysis subunit as a diagnosis result of the target problem.
The rule engine sub-unit 110 may include a rule engine, and the rule engine is configured to schedule the agenda 111 in the rule engine sub-unit 110.
In one possible implementation, the problem diagnosis system may also include, as shown in fig. 3b, an experiment kit unit 200 including a plurality of experiment tools 210;
the first target agenda executing the first analysis subunit or executing an analysis process of the second analysis subunit, including:
determining a target experiment tool according to the parameters of the analysis subunit and the mapping relation between a preset experiment tool and a condition to be analyzed, wherein the condition to be analyzed is a hypothesis condition for reasoning the cause of the problem in the analysis subunit; calling the target experiment tool to perform an experiment to obtain an experiment result;
or,
determining input information of an experimental tool associated with the analysis subunit according to the input parameters of the analysis subunit, and calling the experimental tool associated with the analysis subunit according to the input information to obtain an experimental result;
and determining the analysis result of the analysis subunit according to the experiment result.
The experiment tool box unit 200 may include an experiment tool management subunit, and the experiment tool management subunit is configured to manage and schedule the experiment tools 210 in the experiment tool box unit 200.
In one possible embodiment, the problem diagnosis system may further comprise an agenda development interface for receiving an input agenda as a new agenda for the rules engine subunit. The input agenda may be input by the user through any means, and the input agenda may be developed by the user based on expert experience accumulated during the practice. It is understood that the problem that each agenda can analyze is limited, and the agenda included in the problem diagnosis system is also limited, and in a practical application scenario, a user may encounter various problems that need to be diagnosed, so that it may be difficult to effectively and accurately diagnose the problems by the problem diagnosis system. By adopting the embodiment, the user can update the agenda in the problem diagnosis system according to the expert experience accumulated by the user and develop a new agenda according to the accumulated experience and the actual demand, so that the problem diagnosis system can diagnose more problems, and the applicability and the accuracy of the problem diagnosis system are effectively improved.
In another possible embodiment, the problem diagnosis system further includes a laboratory tool development interface for receiving an input laboratory tool as a new laboratory tool of the laboratory kit unit. The input experimental tool may be input by a user through any device, and the input experimental tool may be developed by the user according to expert experience accumulated in the practical process. It is understood that the experiment that each experimental tool can perform is limited, and the experimental tools included in the problem diagnosis system are also limited, and in a practical application scenario, a user may encounter various problems that need to perform problem diagnosis, and thus may need to perform experiments on various hypothesis conditions. However, due to the limited experimental tools in the problem diagnosis system, the problem diagnosis system may have difficulty in effectively and accurately performing experiments on these hypothesis conditions, so that the problem diagnosis system may not be able to accurately diagnose some problems.
By adopting the embodiment, the user can update the experimental tool in the problem diagnosis system according to the self-accumulated expert experience and develop a new experimental tool according to the accumulated experience and the actual demand, so that the problem diagnosis system can verify more hypothesis conditions, and the applicability and the accuracy of the problem diagnosis system are effectively improved.
The agenda 111 is shown in fig. 4 and consists of an agenda encapsulation and analysis subunit. The different agendas 111 are isolated from each other, and the rules engine subunit 110 assigns questions to each agenda 111. The input to the agenda 111 is Facts (Facts) including the problem that occurred and the current actual conditions, for example, in the case of offline fault diagnosis of a device, the Facts may include the target problem, i.e., that the device has failed offline, and may also include current status parameters of the device. The output of the agenda 111 is the conclusion.
In one possible embodiment, step messages generated by the agenda 111 during operation may support subscriptions to enable the relevant personnel to learn the detailed information and status of the current reasoning, and may include step message identifications, step message contents, and question identifications, where the question identifications are used to uniquely identify the target questions that the agenda 111 infers, i.e., the same question has the same question identification, and different questions have different question identifications. The relevant person can use the problem identification to screen the reasoning process of one or more concerned problems. After being subscribed, the step message may be delivered to a preset message middleware, such as ActiveMQ, kafka, etc., by the agenda 111, or may provide a query of the step message to the relevant person in a manner of directly opening an interface by the agenda 111.
The agenda package refers to a model described in a language with a certain structuring. The encapsulation of the agenda may be described in a conventional formatting language such as XML or JSON, or may be described in a computer language other than XML or JSON. The agenda package may be used to express the questions of the reasoning supported by the agenda to which it pertains, and may include an agenda identification, an agenda version, a set of questions supported, a set of rules, an interpreter, an executor, a set of experimental tools relied upon to validate hypothetical conditions, and the like.
Wherein a rule set is a set of rules. The rules may be abstracted from expert experience of a problem, and the rules may be used to indicate scheduling paths between analysis subunits, and inference of a problem requires a series of rules to support.
The interpreter is used for interpreting the rule set to form a machine understandable language. The executor is used for executing the machine understandable language based on the rule set, and comprises an inference process, a verification process and the like.
The data factory subunit 120 can be a relational database, such as a graph database, or other types of databases, such as a non-relational database Nosql. The data factory subunit 120 exists as a knowledge base. The data factory sub-unit 120 can support local deployment, distributed deployment, or provide services through cloud computing. The way in which the data factory sub-unit 120 stores the correspondence between the problem and the agenda may be different according to different application scenarios, for example, the correspondence may be stored in a dictionary, and for example, the following may be shown:
Q1--->agenda index001
Q2--->agenda index002
Q3--->agenda index003
the dictionary above indicates that agenda index001 corresponds to question Q1, agenda index002 corresponds to question Q2, and agenda index003 corresponds to question Q3.
The experiment tool may be as shown in fig. 5, including experiment tool packaging and processes. Different experimental tools are isolated from each other, that is, different experimental tools may not have an association relationship. The experiment tool package is used for describing the experiment tool, and the content of the experiment tool package can include an experiment tool identifier, an experiment tool version, a capability set supported by the experiment tool, a capability identifier, a capability version, a configuration associated with the capability, and the like. It is understood that fig. 5 is only a schematic diagram of a possible structure of an experimental tool, and in other possible embodiments, a plurality of processes may be included in an experimental tool, which is not limited in this embodiment.
The analysis subunit in the agenda may invoke the experiment tool, and the experiment tool on which the analysis subunit in the agenda depends may be described in the agenda package, and the agenda will invoke the experiment tool according to the experiment tool on which the agenda depends described in the agenda package. When the experiment tool is called, the analysis subunit in the agenda can determine the experiment tool identifier and the experiment tool version, and usually takes the experiment tool to be called, the capability identifier supported by the experiment tool, the input item of the experiment tool capability, the configuration set associated with the experiment tool capability, and the like as input parameters, and the calling result returns the capability set supported by the experiment tool, the capability identifier, the output item of the calling capability, and the like.
Illustratively, the inputs may be as shown in the following table:
the output can be shown in the following table:
the experimental tool may be configured according to actual requirements, and for example, if one experimental tool is used to detect whether a TCP connection is normal, retry times, connection timeout time, and the like may be configured according to actual requirements. And whether to restart the experimental tool may also be included in the configuration item.
Referring to fig. 6, fig. 6 is a schematic flow chart illustrating a process of performing offline fault diagnosis on a device by applying the problem diagnosis method provided by the embodiment of the present invention, where the process may include:
s601, selecting the problem to be processed.
S602, discovering that the device is off-line on the application system page and fault reasoning needs to be executed.
S603, please input the index of the device to be diagnosed.
Prompt information for prompting the user to input the device index to be diagnosed may be displayed on a preset display device, so that the user inputs the device index to be diagnosed.
After the problem diagnosis system obtains the problem to be analyzed, the steps S602 and S603 may not be executed, and the rule engine subunit may directly execute step S604, and determine the agenda corresponding to the problem to be analyzed according to the correspondence between the problem and the agenda stored in the data plant subunit.
S604, the rule engine retrieves the rule domain mapped by the question from the data factory subunit.
And S605, calling a corresponding agenda according to the rule domain, and inputting the questions as Facts.
S606, the agenda is combined into the parameter.
Such as a business and index combination.
And executing each analysis subunit according to the rule which is corresponding to the agenda and is used for indicating the scheduling path among each analysis subunit.
And S607, calling the full-text search according to the combined keyword for searching.
And S608, outputting a plurality of log records at different time points.
And S609, extracting and summarizing the log data.
S610, selecting the data with the nearest time point, and analyzing the semi-structured data replay.
And determining the operation to be executed according to the analysis result of the analysis subunit and the rule.
S611, judging whether the reason is login failure, if so, executing S612, and if not, executing S623.
And S612, calling the webon _ hiksdk to log in the equipment.
Wherein, the webson _ hiksdk is an experimental tool for detecting whether the equipment is on-line or not.
S613, detecting whether the result is on-line, if so, executing S614, and if not, executing S618
And S614, calling the webson _ logsearch retrieval log to report the result.
The webson _ logsearch is an experimental tool for detecting whether the device is online in the log reporting result.
And S615, judging whether the searched reported result is on-line or not, if so, executing S616, and if not, executing S617.
S616, obtaining a diagnosis result 1: upper application system problem.
And after the diagnosis result is output, the problem diagnosis process is finished.
S617, obtaining a diagnosis result 2: the present system addresses the problem.
And after the diagnosis result is output, the problem diagnosis process is finished.
And S618, invoking the webon _ ping detection device.
The method comprises the steps that the device is connected with the equipment, wherein the device is connected with the equipment through the communication interface, and the device is connected with the equipment through the communication interface.
S619, detecting whether the device is on line, if so, executing S614, and if not, executing S620 if the ping function is disabled.
S620, calling the webson _ tcpcon detection device.
The device comprises a detection module, a detection module and a display module, wherein the detection module is used for detecting whether the device is on-line, and the detection module is used for detecting whether the device is on-line.
And S621, detecting whether the equipment is on line or not, if so, executing S614, and if not, executing S622.
S622, obtaining diagnostic result 3: there is no problem.
And after the diagnosis result is output, the problem diagnosis process is finished.
S623, calling the webson _ logsearch to retrieve the latest 1 state change log.
S624, whether the device on-line notification exists, if so, S612 is executed, and if not, S625 is executed.
S625, whether the offline change notification exists or not is judged, if yes, S627 is executed, and if not, S626 is executed.
S626, obtaining a diagnosis result 4: more expert experience is required.
And after the diagnosis result is output, the problem diagnosis process is finished.
And S627, calling the webon _ hiksdk to log in the equipment.
S628, checking whether the login is successful, if yes, executing S629, and if not, executing S630.
S629, obtaining a diagnosis result 5: vendor SDK problem.
And after the diagnosis result is output, the problem diagnosis process is finished.
S630, calling the webon _ ping detection device.
S631, detecting whether the equipment is on line, if so, executing S629, and if not, executing S632.
S632, calling the webson _ tcpcon detection device.
S633, whether the equipment is on line or not is detected, if yes, S629 is executed, and if not, S634 is executed.
S634, obtaining diagnosis result 6: the current equipment is actually off-line due to the change of the state, and no problem exists.
And after the diagnosis result is output, the problem diagnosis process is finished.
The problem diagnosis method provided by the embodiment of the invention can start from the target problem, and convert the diagnosis of the target problem into the diagnosis of a new (sub) problem obtained by iteration through continuous iteration of the target problem, so that the reason leading to the target problem is gradually approached, and the diagnosis result capable of being used for expressing the reason leading to the target problem is finally obtained. The process can be automatically completed by a machine, and an experienced worker is not required to perform manual analysis, so that the efficiency of problem diagnosis can be effectively improved, and the target problem can be timely solved by the worker.
An embodiment of the present invention further provides an electronic device, as shown in fig. 7, including:
a memory 701 for storing a computer program;
the processor 702 is configured to implement the following steps when executing the program stored in the memory 701:
acquiring a target problem to be analyzed;
determining a first target agenda corresponding to the target problem according to a preset corresponding relation between a problem and an agenda, wherein the first target agenda comprises at least one analysis subunit and a rule for indicating a scheduling path among the analysis subunits;
selecting a first analysis subunit according to a rule corresponding to the first target agenda, taking the target problem as an entry parameter of the first analysis subunit, and executing the first analysis subunit;
determining an operation to be executed according to the analysis result of the first analysis subunit and the rule;
when the operation to be executed is to select a second analysis subunit, taking the analysis result of the first analysis subunit as the input parameter of the second analysis subunit, executing the second analysis subunit, and determining the operation to be executed according to the analysis result of the second analysis subunit and the rule;
when the operation to be executed is to select a second target agenda, taking an analysis result of the first analysis subunit as a new problem, and determining a second target agenda corresponding to the new problem according to the preset corresponding relationship between the problem and the agenda;
when the operation to be performed is an output diagnosis result, determining the analysis result of the first analysis subunit as the diagnosis result of the target problem.
In a possible embodiment, the performing the analysis process of the first analysis subunit or the second analysis subunit comprises:
determining a target experiment tool according to the input parameters of the analysis subunit and the mapping relation between a preset experiment tool and a condition to be analyzed, wherein the condition to be analyzed is a hypothesis condition for reasoning the cause of the problem in the analysis subunit; calling the target experiment tool to carry out an experiment to obtain an experiment result; determining an analysis result of the analysis subunit according to the experiment result;
or,
determining input information of the experiment tools related to the analysis subunit according to the input parameters of the analysis subunit, and calling the experiment tools related to the analysis subunit according to the input information to obtain an experiment result; and determining the analysis result of the analysis subunit according to the experiment result.
In a possible embodiment, when the number of the target experiment tools is greater than 1 or the number of the experiment tools associated with the analysis subunit is greater than 1, calling the experiment tools in a preset order;
determining an analysis result of the analysis subunit according to the experiment result, including:
and determining the analysis result of the analysis subunit according to the experiment result of each experiment tool.
In a possible embodiment, the determining, according to the analysis result of the first analysis subunit and the rule, an operation to be performed includes:
judging whether a next analysis subunit meeting the analysis result exists on a scheduling path corresponding to the first analysis subunit according to the analysis result of the first analysis subunit and the rule;
if so, determining that the operation to be executed is to select a second analysis subunit;
if not, judging whether the analysis result of the first analysis subunit comprises a problem source or not;
when the analysis result of the first analysis subunit is determined to comprise a problem source, determining that the operation to be executed is to select a second target agenda;
and when the analysis result of the first analysis subunit does not comprise a problem source, determining the operation to be executed as an output diagnosis result.
The aforementioned electronic device may refer to the Memory, which may include a Random Access Memory (RAM) or a Non-Volatile Memory (NVM), such as at least one disk Memory. Alternatively, the memory may be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, including a Central Processing Unit (CPU), a Network Processor (NP), and the like; but also Digital Signal Processors (DSPs), application Specific Integrated Circuits (ASICs), field Programmable Gate Arrays (FPGAs) or other Programmable logic devices, discrete Gate or transistor logic devices, discrete hardware components.
In yet another embodiment provided by the present invention, a computer-readable storage medium is further provided, in which a computer program is stored, and the computer program, when executed by a processor, implements the steps of any of the above problem diagnosis methods.
In yet another embodiment provided by the present invention, there is also provided a computer program product containing instructions which, when run on a computer, cause the computer to perform any of the problem diagnosis methods of the above embodiments.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, it may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the invention to occur, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored in a computer readable storage medium or transmitted from one computer readable storage medium to another computer readable storage medium, for example, the computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)) or wireless (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy Disk, hard Disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), among others.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrases "comprising a," "8230," "8230," or "comprising" does not exclude the presence of additional like elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, embodiments of the system, the electronic device, the computer-readable storage medium, and the computer program product are substantially similar to the method embodiments, so that the description is simple, and reference may be made to some descriptions of the method embodiments for relevant points.
The above description is only for the preferred embodiment of the present invention, and is not intended to limit the scope of the present invention. Any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention shall fall within the protection scope of the present invention.
Claims (10)
1. A method of problem diagnosis, the method comprising:
acquiring a target problem to be analyzed;
determining a first target agenda corresponding to the target problem according to a preset corresponding relation between the problem and an agenda, wherein the first target agenda comprises at least one analysis subunit and a rule for indicating a scheduling path among the analysis subunits, and in the scheduling path indicated by the rule, the problem used for analysis by any analysis subunit is a subproblem of the problem used for analysis by the last analysis subunit adjacent to the any analysis subunit;
selecting a first analysis subunit according to a rule corresponding to the first target agenda, taking the target problem as an entry parameter of the first analysis subunit, and executing the first analysis subunit;
determining an operation to be executed according to the analysis result of the first analysis subunit and the rule;
when the operation to be executed is to select a second analysis subunit, taking an analysis result of the first analysis subunit as an input parameter of the second analysis subunit, executing the second analysis subunit, and determining the operation to be executed according to the analysis result of the second analysis subunit and the rule, wherein the second analysis subunit is an analysis subunit different from the first analysis subunit in the first target session;
when the operation to be executed is to select a second target agenda, taking an analysis result of the first analysis subunit as a new problem, and determining a second target agenda corresponding to the new problem according to the preset corresponding relationship between the problem and the agenda;
when the operation to be performed is an output diagnosis result, determining the analysis result of the first analysis subunit as the diagnosis result of the target problem.
2. The method of claim 1, wherein the performing the analysis process of the first analysis subunit or the second analysis subunit comprises:
determining a target experiment tool according to the input parameters of the analysis subunit and the mapping relation between a preset experiment tool and a condition to be analyzed, wherein the condition to be analyzed is a hypothesis condition for reasoning the cause of the problem in the analysis subunit; calling the target experiment tool to carry out an experiment to obtain an experiment result; determining an analysis result of the analysis subunit according to the experiment result;
or,
determining input information of the experiment tools related to the analysis subunit according to the input parameters of the analysis subunit, and calling the experiment tools related to the analysis subunit according to the input information to obtain an experiment result; and determining the analysis result of the analysis subunit according to the experimental result.
3. The method according to claim 2, wherein when the number of target experiment tools is greater than 1 or the number of experiment tools associated with the analysis subunit is greater than 1, the respective experiment tools are invoked in a preset order;
determining an analysis result of the analysis subunit according to the experiment result, including:
and determining the analysis result of the analysis subunit according to the experiment result of each experiment tool.
4. The method according to claim 1, wherein the determining, according to the analysis result of the first analysis subunit and the rule, an operation to be performed comprises:
judging whether a next analysis subunit meeting the analysis result exists on a scheduling path corresponding to the first analysis subunit according to the analysis result of the first analysis subunit and the rule;
if so, determining that the operation to be executed is to select a second analysis subunit;
if not, judging whether the analysis result of the first analysis subunit comprises a problem source;
when the analysis result of the first analysis subunit is determined to comprise a problem source, determining that the operation to be executed is to select a second target agenda;
and when the analysis result of the first analysis subunit does not comprise a problem source, determining the operation to be executed as an output diagnosis result.
5. An issue diagnostic system, comprising:
a knowledge graph unit;
the knowledge graph unit comprises a rule engine subunit and a data factory subunit;
the rule engine subunit comprises a plurality of agendas, each agenda comprises at least one analysis subunit and a rule for indicating a scheduling path among the analysis subunits, and in the scheduling path indicated by the rule, a problem which is used by any analysis subunit to analyze is a sub-problem of a problem which is used by a previous analysis subunit adjacent to the analysis subunit;
the data factory subunit is used for storing a preset corresponding relation between an agenda and a problem;
the rule engine subunit is used for determining a first target agenda corresponding to the target problem to be analyzed according to a preset corresponding relation between the problem and the agenda;
the first target agenda is used for selecting a first analysis subunit according to a rule corresponding to the first target agenda, taking the target problem as an entry parameter of the first analysis subunit, and executing the first analysis subunit; determining an operation to be executed according to the analysis result of the first analysis subunit and the rule;
the first target agenda is further used for taking the analysis result of the first analysis subunit as the input parameter of the second analysis subunit, executing the second analysis subunit and determining the operation to be executed according to the analysis result of the second analysis subunit and the rule when the operation to be executed is to select the second analysis subunit, wherein the second analysis subunit is the analysis subunit different from the first analysis subunit in the first target agenda;
the rule engine subunit is configured to, when the operation to be executed is to select a second target agenda, use an analysis result of the first analysis subunit as a new problem, and determine a second target agenda corresponding to the new problem according to the preset correspondence between the problem and the agenda;
the rule engine subunit is further configured to determine, when the operation to be performed is an output diagnosis result, an analysis result of the first analysis subunit as a diagnosis result of the target problem.
6. The system of claim 5, further comprising an experiment toolbox unit comprising a plurality of experiment tools;
the first target agenda executing the first analysis subunit or executing an analysis process of the second analysis subunit, including:
determining a target experiment tool according to the parameters of the analysis subunit and the mapping relation between a preset experiment tool and a condition to be analyzed, wherein the condition to be analyzed is a hypothesis condition for reasoning the cause of the problem in the analysis subunit; calling the target experiment tool to carry out an experiment to obtain an experiment result;
or,
determining input information of an experimental tool associated with the analysis subunit according to the input parameters of the analysis subunit, and calling the experimental tool associated with the analysis subunit according to the input information to obtain an experimental result;
and determining the analysis result of the analysis subunit according to the experimental result.
7. The system of claim 5, wherein the problem diagnosis system further comprises an agenda development interface for receiving an input agenda as a new agenda for the rules engine subunit.
8. The system of claim 5, wherein the problem diagnosis system further comprises a laboratory tool development interface for receiving an input laboratory tool as a new laboratory tool for the laboratory toolbox unit.
9. An electronic device, comprising:
a memory for storing a computer program;
a processor for implementing the method steps of any of claims 1 to 4 when executing a program stored in the memory.
10. A computer-readable storage medium, characterized in that a computer program is stored in the computer-readable storage medium, which computer program, when being executed by a processor, carries out the method steps of any one of claims 1 to 4.
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