WO2017084460A1 - 一种原因追溯方法 - Google Patents
一种原因追溯方法 Download PDFInfo
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- WO2017084460A1 WO2017084460A1 PCT/CN2016/102358 CN2016102358W WO2017084460A1 WO 2017084460 A1 WO2017084460 A1 WO 2017084460A1 CN 2016102358 W CN2016102358 W CN 2016102358W WO 2017084460 A1 WO2017084460 A1 WO 2017084460A1
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/25—Integrating or interfacing systems involving database management systems
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F16/00—Information retrieval; Database structures therefor; File system structures therefor
- G06F16/20—Information retrieval; Database structures therefor; File system structures therefor of structured data, e.g. relational data
- G06F16/21—Design, administration or maintenance of databases
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- the invention relates to a method for traceback of reasons and belongs to the technical field of computer artificial intelligence.
- each concrete entity object has the commonality of the class, and each cause causes a (group) effect (or phenomenon) to occur, and the effect caused by different causes ( Or phenomenon) will be different, this difference is the important characteristics and basis for distinguishing different causes.
- each internal node represents a property test.
- Each branch represents a test output.
- Each leaf node represents a category.
- the technical problem to be solved by the present invention is to provide a quick cause traceback method suitable for various accidents or errors in order to overcome the above problems.
- a method for traceability including the following steps:
- the causality knowledge base includes an abnormal phenomenon of a type of object and a cause of the abnormal phenomenon, and a causal relationship between the abnormal phenomenon and the cause thereof;
- S2 selecting an abnormal phenomenon of the current known state in the abnormal phenomenon list, forming a new causal relationship knowledge base according to the causal relationship in the causal knowledge base and recording the cause of the traceback;
- the step S2 is specifically: sequentially selecting an abnormal phenomenon that is currently known to occur, establishing a new cause group, recording the cause of the traceback, constructing a new phenomenon group, and establishing a new causal knowledge base;
- Step S3 Comparing the third reason group with the fourth reason group as a reference, and recording the reason that the fourth reason group and the third reason group are not duplicated as the cause of the traceback, and the fourth reason group Zhonghe
- the reason for the existence of the repetition in the third reason group establishes a new cause group.
- the "result information" further includes a causal relationship between the group of phenomena that have been confirmed to be in a state and the cause that is traced back.
- the third phenomenon group is used instead of the first phenomenon group, while the third phenomenon group is retained.
- an abnormal phenomenon can correspond to a plurality of types of objects
- the plurality of causal knowledge bases respectively established by the abnormal phenomena and the corresponding multi-class objects are simultaneously performed in steps S1-S3.
- each execution of the S2 step is referred to as a cause traceback
- the N-run S2 step is based on the new causal knowledge base for N+1 cause traceback.
- each step S1 to S3 is performed as a level 1 trace.
- N-level traceback N executions from step S1 to step S3 are referred to as N-level traceback, and the original causality knowledge base used by each level of traceability is different.
- the causality knowledge base is represented by a matrix: an anomaly phenomenon set P, comprising N anomalous elements, denoted as p i , that is, an i-th anomaly, i is from 1 to N; causing an abnormal phenomenon
- the set S contains M causes of the anomalous phenomenon, which is denoted as s j , that is, the jth cause of the abnormal phenomenon, j from 1 to M;
- the causal relationship set R containing N ⁇ M relationship elements, denoted as r , namely the relation between the i-th and j-th abnormal phenomenon causes abnormal phenomenon ij.
- the causality knowledge base is represented by a table structure: only a control relationship including an abnormal phenomenon and a cause of the abnormal phenomenon, and the record composed of the control relationship is unique in the table structure, and different control relationships are A collection of causal knowledge bases that make up an object class.
- the invention has the beneficial effects that the invention can trace the reason for different kinds of objects, and the invention can quickly find the cause of the abnormal phenomenon compared to the prior art adopting the traceback method of the tree structure, because the different kinds of objects can be The machine, computer program or various repeated specific events, so the invention can quickly find the cause for the abnormal situation of various industries, let the user solve the problem for the corresponding reason, save the user's own troubleshooting time, and make the production Or things can quickly return to normal, improve efficiency, and this method can also be used as the basic cognitive path of artificial intelligence robots. Intelligent robots can trace the abnormal phenomena identified by this method, find the results, and understand the occurrence of abnormal phenomena. The reason and the corresponding strategy to make it have the ability to solve the problem.
- Figure 1 is a flow chart of an embodiment of the present invention
- Figure 2 is a schematic view of an embodiment of the present invention.
- Figure 3 is a schematic view of another embodiment of the present invention.
- Figure 4 is a schematic view of another embodiment of the present invention.
- Figure 5 is a schematic view of another embodiment of the present invention.
- Figure 6 is a schematic view of another embodiment of the present invention.
- Figure 7 is a flow chart of the method of the present invention.
- Figure 8 is a diagram showing the traceback process of the watch structure in the present invention.
- Figure 9 is a diagram showing another traceback process of the watch structure of the present invention.
- a method for traceability of the reason as shown in FIG. 1 includes the following steps:
- the causal relationship knowledge base includes an abnormal phenomenon of a type of object and a cause of the abnormal phenomenon, and a causal relationship between the abnormal phenomenon and a cause causing the abnormality, wherein the type of the object is
- the causality knowledge base is a data set conforming to a specific structural rule in the artificial intelligence technology, and the present invention only provides each constituent element of the causal relationship knowledge base, the relationship between each element, and how to use the causal relationship knowledge base when performing traceability
- the information in the specific causal relationship knowledge base, the reasons and the relationship are supplemented by the technical personnel in the corresponding field, and the specific embodiment of the causal relationship knowledge base can be, but is not limited to, a matrix structure, such as a string table structure and other structures
- the anomalous phenomenon in the causality knowledge base is an abnormal or extreme situation occurring in an ordinary phenomenon, and is a phenomenon that is not easy to occur in different situations, and can be detected by using the prior art (for example, voltage and current). ), or common-sense knowledge (such as whether it is raining, whether the light is on), not experience and feelings (such as feeling a bit cold or feeling very stuffy);
- the invention can trace the cause of different kinds of objects and quickly find the cause of the abnormal phenomenon, because the different kinds of objects can be mechanical, computer programs or various repeated specific events of various industries, so the invention can be used in various industries.
- Abnormal conditions quickly find the cause, let the user solve the problem for the corresponding reason, save the user's own troubleshooting time, make the production or things quickly return to normal, improve the efficiency, and the method can also be used as the basic cognition of the artificial intelligence robot Pathway, intelligent robots can trace the abnormalities identified by this method, find the results, understand the causes of abnormal phenomena, and adopt corresponding strategies to make them have the ability to solve problems.
- the invention adopts the recording method of the same structure for the abnormal phenomena of different object classes and the cause of the abnormal phenomenon, and gives a specific embodiment for implementing the traceback method of the present method when adopting different record structures, and the advantages thereof are as follows:
- This method uses the same causal relationship knowledge base structure and the same kind of traceability method. After loading the causality knowledge base of different object classes, it can be applied to different fields for traceability.
- the step S2 is specifically: Select the abnormal phenomenon that is currently known to occur, establish a new cause group, record the reasons traced back, establish a new phenomenon group, and establish a new knowledge base of causality.
- the abnormal phenomenon of the current confirmation state is grouped: the first phenomenon group is to confirm the occurrence of the abnormal phenomenon, the second phenomenon group is to confirm the phenomenon that has not occurred, and the remaining phenomenon in which the phenomenon state is not confirmed is the third phenomenon group.
- the current first phenomenon group is missing, that is, when the confirmation existence phenomenon is not input, the current third phenomenon group is used instead, and the first phenomenon group is copied from the third phenomenon group. The three phenomenon groups are still retained.
- the reasons corresponding to the first phenomenon group, the second phenomenon group, and the third phenomenon group respectively form the first reason group, the second reason group, and the third reason group.
- the practical significance of these three cause groups is that the first cause group contains the cause of the abnormality, the second cause group contains the cause that must not have occurred, and the third cause group contains the reason for the need to confirm whether it has occurred.
- the reason for the repetition in the second cause group is removed in the first cause group to obtain the fourth cause group. Therefore, the fourth reason group only contains the possible reasons, which narrows the scope of the cause.
- the abnormal phenomenon that does not have a causal relationship with any of the new cause groups is deleted, that is, the abnormal phenomenon that is not caused by deleting any one of the new cause groups in the third phenomenon group, the remaining The part is a new phenomenon group.
- the "result information" further includes a causal relationship between the phenomenon group that has been confirmed and the cause that is traced back, but the result information is not
- the causal relationship knowledge base and the data generated in the traceback process may be output according to the needs of the user, and may be changed according to the actual use situation, and step S3 is responsible for outputting the cause traceback as the last step of the present invention. result.
- the reason that is traced back to is the output content necessary for step S3, and the output content and functions of S3 can be more abundant due to different usage requirements:
- some automation devices with redundant structures can initiate redundant system compensation capabilities after automatically detecting anomalies and trace back to the cause. Shorten the recovery time of the system and reduce the compensation cost as accurately as possible.
- each execution of the S2 step is called a cause traceback.
- the first run of S2 uses the original causal knowledge base, and each time a new causal knowledge base is used. Run N times S2 steps on a basic basis, so each level of traceback includes N+1 times of traceability.
- the scope of the cause of the anomaly, and the subsequent absence of each of the cause-returning anomalies will further narrow the cause of the anomaly, as shown in Figure 7, because of the new causal knowledge that is formed each time.
- the library is a narrowing of scope. Compared with the unprocessed original relational knowledge base, the number of anomalies listed is less, the scope of detection and identification needs to be more centralized, and the cost of inspection and testing is reduced to some extent. Increased efficiency.
- the method traces from initializing a raw relationship causal knowledge base in step S1 to the end of step S3, and ending the first-level traceback.
- the cause of the traceback is traced or verified to enable another object system.
- the original causal knowledge base is traced to the second level. This can be recursive to N-level traceability.
- each level of tracing is to narrow the cause to one subsystem of the system object of the system, and then to the subsystem based on the subsystem causality knowledge base. Traceability, further narrowing the scope of the cause, can be traced back to the root cause of the problem, in order to use economically effective methods to eliminate the cause of the anomaly from the root cause.
- the role of multi-level traceability is:
- the reason for the cause of the retrospective that is, the cause of the cause of the abnormality.
- the traceability of the cause of the cold is the secondary traceability.
- the causal relationship has a plurality of ways in which it can be expressed in the above-mentioned Embodiment 1, and in the present embodiment, the causality
- the relational knowledge base is preferably represented by a matrix, with the uppercase English letters representing the set, and the corresponding lowercase letters plus the subscripts as the elements of the set.
- the occurrence of a cause can cause
- the causal relationship between the cause s 1 and the anomalies p 1 and p 3 is 1 , that is, true, there is a true causal relationship between them, so when s 1 occurs, it will definitely cause an anomaly p
- the occurrence of 1 and p 3 the same reason, when s 2 occurs, it will definitely cause the occurrence of p 3 , so the matrix can be used to record the causes of anomalies and the anomalies that occur in a class of fully-perceived object systems, and The relationship between them.
- the original causality knowledge base adopts a matrix structure and records the relationship between the cause and the phenomenon with 0 and 1, the sub-steps in step S2 are performed in a manner suitable for the recording structure, and step S2 is specifically as follows.
- the original causality knowledge base is a set of 7 rows and 7 columns of S caused by 7 causes of anomalies, a set of 10 rows and 1 column of 10 anomalies, and 10 of 70 causal relationships. Row 7 columns set R, a large collection of together.
- a first phenomenon group set W for recording the existence phenomenon is formed. , that is, the elements corresponding to p 1 , p 2 are marked as true, denoted as 1, and the rest are all false, denoted as 0; form a second phenomenon group X for recording to confirm that the abnormal phenomenon has not occurred, that is, p 3 , p 4
- the corresponding element is marked as true, recorded as 1, and the rest are all false, recorded as 0; formed to record the unconfirmed third phenomenon group Y, wherein the unidentified state of the element is marked as true, recorded as 1, the rest are If it is false, it is recorded as 0;
- the scale of these three sets is the set of 1 column matrix with the same number of rows and the current causality knowledge base anomaly, which respectively correspond to the existence of p 1 , p 2 and the confirmation that
- the fifth cause group is a set E that has been initialized to be empty before step S2, which is a reason element for recording traceback, which is a preferred way of recording traceability reasons.
- the fifth cause group is a set E that has been initialized to be empty before step S2, which is a reason element for recording traceback, which is a preferred way of recording traceability reasons.
- five scales are the same as the current causality knowledge base, the number of elements of the same row of matrix sets A, B, C, D, F, wherein a reason group set A, a second reason group set B, and a third reason group set C are respectively derived from the first, second, and third phenomenon groups are calculated according to the relationship that can be caused by the abnormality recorded in the current causal knowledge base.
- Figure 5 shows that can be caused by the abnormality recorded in the current causal knowledge base.
- the first cause group and the second cause group after the inversion are performed 0 0 1 0
- the reason element value indicated in the set S in the current causality knowledge base is added to the fifth cause group.
- the elements in the fifth reason group have the following Boolean algebra, when When the value of the value is true, the reason j of the set S in the current causal knowledge base (where j is the serial number of the current causal knowledge base) is added to the fifth cause group.
- the reason j of the set S in the current causal knowledge base (where j is the serial number of the current causal knowledge base) is added to the fifth cause group.
- the step S2 of the level ends, and the reason traced back can be output; otherwise, the new reason group is And the third phenomenon group forms a new phenomenon group set Z, which does not contain anomalies unrelated to the new cause group.
- a new causal relationship knowledge base is established with the new cause group and the new phenomenon group in the current causality knowledge base as the reason and anomaly corresponding to the true value, and their causal relationship in the original causal knowledge base.
- the abnormal phenomena p 5 , p 7 , and p 9 are confirmed.
- p 8 does not exist, after the state of the abnormal phenomenon is input, based on the current causal knowledge base, In the fourth reason group, d 1 is true, and c 1 is false. Therefore, s 3 in the corresponding set S is added to the fifth cause group, that is, the reason for the traceback is recorded. Finally, the s 3 recorded in the fifth phenomenon group causes the abnormal phenomenon.
- FIG. 4 is the original causality knowledge base obtained by S1
- FIG. 5 is a process description for performing one traceability
- Figure 6 is a description of the process of the second traceback performed after the new cause group and the new phenomenon group according to the original causality knowledge base form a new causal knowledge base according to Figure 5, according to the third and the third
- the record of the four reasons group judges that the condition for the traceability of the level has not existed, and the traceability result can be output.
- the reason for the traceback is s 3 , which is recorded in the fifth cause group and output at step S3.
- the traceability process shown in this embodiment is one level and two traces. Through the state input of eight abnormal phenomena, the level is traced back.
- the causality knowledge base has a plurality of ways that can be expressed in the above-mentioned Embodiment 1, and in this embodiment,
- the causality knowledge base is preferably represented by a table structure. As shown in Table 2, the causes and anomalies of the causal phenomena recorded in the causality knowledge base represented by the matrix shown in Fig. 4 are described in the form of the cause of abnormal phenomena and the anomaly comparison table, and their Relationship between.
- the occurrence of s 1 causes an abnormal phenomenon p 1
- the occurrence of p 3 , s 2 causes the phenomenon of p 3 , p 4 , p 7 to occur.
- the initialization work step S1 of the original causality knowledge base is completed.
- the first, second, and third phenomenon groups are formed, according to Table 2
- the causal relationship of the record, the reason corresponding to the phenomenon recorded in the first phenomenon group is s 1 , s 3 , s 4 , s 7 , forming the first cause group, and correspondingly p 3 , p 4 in the second phenomenon group
- Removing the element s 1 repeated in the second cause group s 1 , s 2 , s 6 in the first cause group s 1 , s 3 , s 4 , s 7 forms a fourth cause group s 3 , s 4 , s 7 .
- the fifth reason group for initializing an empty is also used for recording the reason for the traceability, and the fourth reason group is used as a reference to compare with the third cause group to obtain:
- the remaining phenomenon elements after the phenomenon element p 6 unrelated to the three cause elements are formed in the third phenomenon group to form a new phenomenon group.
- the abnormal phenomena p 5 , p 7 , p 9 are confirmed in the list of abnormal phenomena in the new causal knowledge base formed after a traceback process, and p 8 does not exist, so there is the first and second as shown in FIG. 9 .
- the third phenomenon group and the corresponding first, second, and third cause groups perform a second traceback for the new causal knowledge base: the same cause element in the first cause group is removed from the first cause group After the remaining element s 3 , a fourth cause group is formed, of which there is only one cause element s 3 . Comparing the fourth reason group with the third reason group can be concluded:
- the reason for the abnormality that is traced back to is s 3 .
- the traceability of this level is traced by two traceback processes.
- the number of causes is 1.
- the causality knowledge base can implement the retrospective method. Therefore, the three main elements of the causality knowledge base in the present invention are fixed relationships among the three, which is specifically implemented in the use process. Setting as needed, as long as the causality knowledge base is established to use the traceability method of the present invention, no matter which data structure is used, it falls within the scope of the present invention.
- a system or apparatus employing the cause traceback method described in the above embodiments that is, any software system or hardware device in which the method can be used.
- embodiments of the present invention can be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment, or a combination of software and hardware. Moreover, the invention can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) including computer usable program code.
- computer-usable storage media including but not limited to disk storage, CD-ROM, optical storage, etc.
- the computer program instructions can also be stored in a computer readable memory that can direct a computer or other programmable data processing device to operate in a particular manner, such that the instructions stored in the computer readable memory produce an article of manufacture comprising the instruction device.
- the apparatus implements the functions specified in one or more blocks of a flow or a flow and/or block diagram of the flowchart.
- These computer program instructions can also be loaded onto a computer or other programmable data processing device such that a series of operational steps are performed on a computer or other programmable device to produce computer-implemented processing for execution on a computer or other programmable device.
- the instructions provide steps for implementing the functions specified in one or more of the flow or in a block or blocks of a flow diagram.
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Abstract
Description
第一原因组 | 1 | 0 | 1 | 0 |
第二原因组 | 1 | 0 | 0 | 1 |
取反后的第二原因组 | 0 | 1 | 1 | 0 |
第一原因组和取反后的第二原因组进行与运算 | 0 | 0 | 1 | 0 |
第四原因组 | 0 | 0 | 1 | 0 |
引起异常现象的原因 | 异常现象 |
s1 | p1 |
s1 | p3 |
s2 | p3 |
s2 | p4 |
s2 | p7 |
s3 | p2 |
s3 | p5 |
s3 | p7 |
s3 | p9 |
s4 | p1 |
s4 | p8 |
s4 | p10 |
s5 | p10 |
s6 | p4 |
s6 | p6 |
s6 | p7 |
s7 | p1 |
s7 | p8 |
引起异常现象的原因 | 异常现象 |
s3 | p5 |
s3 | p7 |
s3 | p9 |
s4 | p8 |
s4 | p10 |
s7 | p8 |
Claims (10)
- 一种原因追溯方法,其特征在于,包括以下步骤:S1:初始化因果关系知识库,所述因果关系知识库中包括一类对象的异常现象和引起该异常现象的原因,以及异常现象和其原因之间的因果关系;S2:在异常现象列表中选出当前已知状态的异常现象,根据因果关系知识库中的因果关系,形成新的因果关系知识库并记录追溯到的原因;S3:输出追溯到的原因作为结果信息。
- 如权利要求1所述的原因追溯方法,其特征在于,所述步骤S2具体为:依次选出当前已知是否发生的异常现象,建立新的原因组,记录追溯到的原因,建新的现象组,建立新的因果关系知识库;“建立所述新的原因组,记录追溯到的原因”具体如下:把所述异常现象拆分成确认已发生的第一现象组、确认未发生的第二现象组和未被确认状态的第三现象组;分别在所述因果关系知识库中找到第一现象组、第二现象组和第三现象组对应的原因组形成第一原因组、第二原因组和第三原因组;以第一原因组为基础,删除其中与第二原因组重复的原因后形成第四原因组;以第四原因组为基准在第三原因组中进行比较,将所述第四原因组中与所述第三原因组中不重复的原因记录为追溯到的原因,将所述第四原因组中与所述第三原因组中存在重复的原因建立新的原因组,当所述第四原因组中已经不存在与所述第三原因组有重复的原因时,则表示继续追溯的条件已经不存在,将执行步骤S3。建立新的现象组的过程如下:在所述第三现象组中删除与所述新的原因组中任何一个原因不存在因果关系的异常现象,剩余的异常现象为新的现象组;建立所述新的因果关系知识库过程如下:根据原始因果关系知识库中记录的因果关系,为新的原因组和新的现象组建立因果关系,形成新的因果关系知识库。
- 如权利要求1或2所述的原因追溯方法,其特征在于,所述“结果信息”还包括已经被确认过状态的现象组和被追溯到的原因之间对应的因果关系。
- 如权利要求1-3任一项所述的原因追溯方法,其特征在于,当所述第一现象组缺损时,即输入未包括确认存在现象时,则采用所述第三现象组替代所述第一现象组,同时保留第三现象组。
- 如权利要求1-4任一项所述的原因追溯方法,其特征在于,当一个异常现象可对应多类对象时,则以该异常现象分别与对应的多类对象建立的多个因果关系知识库,同时并行执行步骤S1-S3。
- 如权利要求1-5任一项所述的原因追溯方法,其特征在于,每执行一次S2步骤称为一次原因追溯,以新的因果关系知识库为基础运行N次运行S2步骤为N+1次原因追溯。
- 如权利要求1-6任一项所述的原因追溯方法,其特征在于,每执行完一次S1步骤至S3步骤称为1级追溯。
- 如权利要求1-7任一项所述的原因追溯方法,其特征在于,还包括“N级追溯”步骤:N次执行从S1步骤至S3步骤称为N级追溯,且每一级追溯所使用的原始因果关系知识库不同。
- 如权利要求1-8任一项所述的原因追溯方法,其特征在于,所述因果关系知识库通过矩阵来表示:异常现象集合P,包含N个异常现象元素,记做pi,即第i个异常现象,i从1到N;引起异常现象的原因的集合S,包含M个引起异常现象的原因元素,记做sj,即第j个引起异常现象的原因,j从1到M;因果关系集合R,包含N×M个关系元素,记做rij,即第i个异常现象和第j个引起异常现象的原因之间的关系。
- 如权利要求1-9任一项所述的原因追溯方法,其特征在于,所述因果关系知识库通过表结构来表示:仅包含异常现象和能引起该异常现象的原因的对照关系,由该对照关系组成的记录在表结构中具有唯一性,不同对照关系的集合组成某对象类的因果关系知识库。
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