Cloud platform operation and maintenance recommendation method, device, equipment and medium based on knowledge base

Abstract

The present invention relates to the technical field of operation and maintenance recommendation based on an operation and maintenance knowledge base, and in particular to a cloud platform operation and maintenance recommendation method, device, equipment and medium based on a knowledge base. The method of the present invention first obtains an abnormal description sentence; then performs word segmentation on the abnormal description sentence to obtain multiple entity word segments; then, according to the frequency of occurrence of the multiple entity word segments in the operation and maintenance knowledge text and the reverse file frequency, multiple candidate operation and maintenance knowledge texts are determined from the operation and maintenance knowledge base; finally, according to the probability of multiple abnormal causes, the probability of occurrence of entity word segments and the probability of occurrence of entity word segments in the operation and maintenance knowledge text under the condition of abnormal causes, the multiple cause reliability probabilities are determined, and according to the multiple cause reliability probabilities, a target operation and maintenance knowledge text is determined from the multiple candidate operation and maintenance knowledge texts. The method of the present invention improves the speed and accuracy of operation and maintenance knowledge base query, provides help for operation and maintenance work, and improves the efficiency of operation and maintenance.

Description

Technical Field

[0001] The present invention relates to the technical field of operation and maintenance recommendation based on an operation and maintenance knowledge base, and in particular to a cloud platform operation and maintenance recommendation method, device, equipment and medium based on a knowledge base. Background Art

[0002] Equipment operation and maintenance is a job that tests equipment maintenance experience, especially the operation and maintenance of meteorological big data cloud platform. Meteorological big data cloud platform is a platform that integrates computing and storage and is built according to the needs of big data computing. Meteorological big data cloud platform has high reliability requirements. For example, when an abnormality occurs in the cloud platform, it must be able to respond quickly and solve the problem in a timely manner. However, the special characteristics of meteorological big data cloud platform make its operation and maintenance different from that of general cloud platform to a certain extent. The operation and maintenance experience of general cloud platform cannot often be directly applied to meteorological big data cloud platform. Establishing and improving the operation and maintenance knowledge base of meteorological big data cloud platform can realize the accumulation and sharing of operation and maintenance experience, and help speed up the response speed to abnormalities of meteorological big data cloud platform.

[0003] Usually, the knowledge base includes relevant operation and maintenance knowledge items, such as solutions to common faults, platform upgrade processes, etc., which can facilitate operation and maintenance personnel to quickly query and solve problems and improve the efficiency of operation and maintenance. After the introduction of knowledge graph technology, the experience and knowledge in the operation and maintenance process are sorted and summarized to form a knowledge base, which can facilitate subsequent automated operation and maintenance work.

[0004] At present, the main problem in the use of operation and maintenance knowledge base is that it is inconvenient to find the operation and maintenance knowledge that best suits the current equipment abnormality from the knowledge base. Based on this, it is necessary to develop and design a cloud platform operation and maintenance recommendation method based on the knowledge base. Summary of the invention

[0005] The embodiments of the present invention provide a knowledge base-based cloud platform operation and maintenance recommendation method, device, equipment and medium, which are used to solve the problem in the prior art that it is not convenient to find the operation and maintenance knowledge that best matches the current device abnormality from the knowledge base.

[0006] In a first aspect, an embodiment of the present invention provides a cloud platform operation and maintenance recommendation method based on a knowledge base, comprising:

[0007] Get the exception description statement;

[0008] Segmenting the abnormal description sentence to obtain multiple entity segmentations;

[0009] Determine a plurality of candidate operation and maintenance knowledge texts from the operation and maintenance knowledge base according to the frequencies of occurrence of the plurality of entity segmentations in the operation and maintenance knowledge texts and the reverse file frequencies, wherein the reverse file frequencies represent the ratio of the operation and maintenance knowledge texts containing the entity segmentations to the total operation and maintenance knowledge texts;

[0010] The multiple cause reliability probabilities are determined based on the probabilities of multiple abnormal causes, the probability of the occurrence of entity segmentations, and the probability of the occurrence of entity segmentations in the operation and maintenance knowledge text under the abnormal cause conditions, and the target operation and maintenance knowledge text is determined from the multiple alternative operation and maintenance knowledge texts based on the multiple cause reliability probabilities, wherein each alternative operation and maintenance knowledge text corresponds to a cause reliability probability and an abnormal cause.

[0011] In a possible implementation, the segmentation of the abnormal description sentence to obtain multiple entity segmentations includes:

[0012] Extracting a first number of multiple characters from a predetermined position of the abnormal description statement;

[0013] If the multiple characters are not the target part of speech, entity matching is performed in the knowledge graph according to the multiple characters, wherein the knowledge graph includes multiple entities, and the entities of the knowledge graph are connected by relationships;

[0014] If there is no match, the value of the first number is increased, and the process jumps to the step of extracting a first number of multiple characters from a predetermined position of the exception description statement;

[0015] Otherwise, the matched entity and entities having multiple identical relationships with the matched entity are added to multiple entity segmentations;

[0016] The predetermined position is offset according to the predetermined position and the first number, and the process jumps to the step of extracting a first number of multiple characters from the predetermined position of the exception description statement.

[0017] In a possible implementation, determining a plurality of candidate operation and maintenance knowledge texts from an operation and maintenance knowledge base according to the frequencies of occurrence of the plurality of entity segmentations in the operation and maintenance knowledge text and the reverse file frequencies includes:

[0018] Determine multiple segmentation frequency queues according to the multiple entity segmentations, wherein each segmentation frequency queue corresponds to an operation and maintenance knowledge text, the segmentation frequency queue includes multiple segmentation frequency values, each segmentation frequency value corresponds to an entity segmentation, and the segmentation frequency value represents the probability of the entity segmentation appearing in the operation and maintenance knowledge text;

[0019] Constructing a reverse file frequency queue according to the frequencies of occurrence of the multiple entity participles in the operation and maintenance knowledge base, wherein the reverse file frequency queue includes multiple reverse file frequency values, each reverse file frequency value corresponding to an entity participle;

[0020] Determine a plurality of association values ​​according to the plurality of word segmentation frequency queues and the reverse file frequency queue, wherein each association value corresponds to an operation and maintenance knowledge text, and the association value represents the degree of association between the operation and maintenance knowledge text and the abnormal description statement;

[0021] According to the multiple association values, multiple candidate operation and maintenance knowledge texts are determined from the operation and maintenance knowledge base.

[0022] In a possible implementation, determining multiple word segmentation frequency queues according to the multiple entity word segmentations includes:

[0023] For each operation and maintenance knowledge text, perform the following steps respectively:

[0024] Taking out entity participles from the multiple entity participles in sequence as entity participles to be processed;

[0025] The word segmentation frequency value is calculated according to the first formula, wherein the first formula is:

[0026]

[0027] In the formula, is the word segmentation frequency value of the entity to be processed, is the number of times the entity segmentation to be processed appears in the operation and maintenance knowledge text, is the number of segmented words in the operation and maintenance knowledge text;

[0028] Add the word segmentation frequency value to the word segmentation frequency queue;

[0029] If the traversal of the multiple entity participles is not completed, jump to the step of sequentially taking out entity participles from the multiple entity participles as entity participles to be processed.

[0030] In a possible implementation, the step of constructing a reverse file frequency queue according to the frequencies of occurrence of the multiple entity segmentations in the operation and maintenance knowledge base includes:

[0031] Taking out entity participles from the multiple entity participles in sequence as entity participles to be processed;

[0032] The reverse document frequency value of the entity segmentation to be processed is calculated according to the second formula, wherein the second formula is:

[0033]

[0034] In the formula, is the reverse document frequency value of the entity segmentation to be processed, is the number of operation and maintenance knowledge texts with entity segmentation to be processed, is the number of operation and maintenance knowledge texts in the operation and maintenance knowledge base, is the first constant;

[0035] Adding the reverse file frequency value to a reverse file frequency queue;

[0036] If the traversal of the multiple entity participles is not completed, jump to the step of sequentially taking out entity participles from the multiple entity participles as entity participles to be processed.

[0037] In a possible implementation, the determining of multiple association values ​​according to the multiple word segmentation frequency queues and the reverse file frequency queues includes:

[0038] For each operation and maintenance knowledge text, the associated value is determined according to the third formula, wherein the third formula is:

[0039]

[0040] In the formula, is the associated value of the operation and maintenance knowledge text, is the first word frequency values, The reverse file frequency queue Inverse file frequency values, The total number of entity segmentations.

[0041] In a possible implementation, the determining of the multiple cause reliability probabilities according to the probabilities of the multiple abnormal causes, the probability of the occurrence of entity segmentation, and the probability of the occurrence of entity segmentation in the operation and maintenance knowledge text under the abnormal cause condition includes:

[0042] The cause reliability probability of each abnormal cause is determined according to the fourth formula, wherein the fourth formula is:

[0043]

[0044] In the formula, is the cause reliability probability, The first The probability of entity segmentation, is the safety risk probability, For the The probability of an entity segmentation word appearing in the operation and maintenance knowledge text.

[0045] In a second aspect, an embodiment of the present invention provides a cloud platform operation and maintenance recommendation device based on a knowledge base, which is used to implement the cloud platform operation and maintenance recommendation method based on a knowledge base as described in the first aspect or any possible implementation of the first aspect, and the cloud platform operation and maintenance recommendation device based on the knowledge base includes:

[0046] Description acquisition module, used to obtain the exception description statement;

[0047] A word segmentation module, used to segment the abnormal description sentence to obtain multiple entity segmentations;

[0048] A preliminary selection module, used to determine a plurality of candidate operation and maintenance knowledge texts from the operation and maintenance knowledge base according to the frequencies of occurrence of the plurality of entity segmentations in the operation and maintenance knowledge texts and the reverse file frequencies, wherein the reverse file frequencies represent the ratio of the operation and maintenance knowledge texts containing the entity segmentations to the total operation and maintenance knowledge texts;

[0049] as well as,

[0050] The operation and maintenance recommendation module is used to determine the multiple cause reliability probabilities according to the probabilities of multiple abnormal causes, the probability of the occurrence of entity segmentations, and the probability of the occurrence of entity segmentations in the operation and maintenance knowledge text under the abnormal cause conditions, and determine the target operation and maintenance knowledge text from the multiple alternative operation and maintenance knowledge texts according to the multiple cause reliability probabilities, wherein each alternative operation and maintenance knowledge text corresponds to a cause reliability probability and an abnormal cause.

[0051] In a third aspect, an embodiment of the present invention provides an electronic device, comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, and when the processor executes the computer program, it implements the steps of the method described in the first aspect or any possible implementation method of the first aspect.

[0052] In a fourth aspect, an embodiment of the present invention provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps of the method described in the first aspect or any possible implementation of the first aspect are implemented.

[0053] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0054] The embodiment of the present invention discloses a cloud platform operation and maintenance recommendation method based on a knowledge base, which first obtains an abnormal description sentence; then performs word segmentation on the abnormal description sentence to obtain multiple entity word segmentations; then determines multiple candidate operation and maintenance knowledge texts from the operation and maintenance knowledge base according to the frequency of occurrence of the multiple entity word segmentations in the operation and maintenance knowledge text and the reverse file frequency, wherein the reverse file frequency represents the ratio of the operation and maintenance knowledge text containing the entity word segmentations to the whole operation and maintenance knowledge text; finally, determines the multiple cause reliability probabilities according to the probabilities of multiple abnormal causes, the probability of occurrence of the entity word segmentations and the probability of occurrence of the entity word segmentations in the operation and maintenance knowledge text under the condition of the abnormal cause, and determines the target operation and maintenance knowledge text from the multiple candidate operation and maintenance knowledge texts according to the multiple cause reliability probabilities, wherein each candidate operation and maintenance knowledge text corresponds to a cause reliability probability and an abnormal cause. The implementation mode of the present invention determines alternative operation and maintenance knowledge texts by analyzing the frequency of occurrence of the word segmentation of the abnormal description sentence in the operation and maintenance knowledge text and the proportion of the operation and maintenance knowledge text containing the entity segmentation, and finally selects the operation and maintenance knowledge text most relevant to the abnormal description sentence from the alternative operation and maintenance knowledge texts through statistics and probability theory for push. The method of the present invention improves the speed and accuracy of the operation and maintenance knowledge base query, provides assistance to the operation and maintenance work, and improves the efficiency of the operation and maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0056] Figure 1 is a flow chart of a cloud platform operation and maintenance recommendation method based on a knowledge base provided in an embodiment of the present invention;

[0057] Figure 2 It is a schematic diagram of a method for determining alternative operation and maintenance knowledge texts provided by an embodiment of the present invention;

[0058] Figure 3 It is a functional block diagram of a cloud platform operation and maintenance recommendation device based on a knowledge base provided in an embodiment of the present invention;

[0059] Figure 4 It is a functional block diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0060] In the following description, specific details such as specific system structures and technologies are provided for the purpose of illustration rather than limitation so as to provide a thorough understanding of the embodiments of the present invention. However, it should be clear to those skilled in the art that the present invention may be implemented in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, and methods are omitted so as not to obscure the description of the present invention with unnecessary details.

[0061] In order to make the purpose, technical solutions and advantages of the present invention more clear, a specific implementation method will be described below in conjunction with the accompanying drawings.

[0062] The following is a detailed description of an embodiment of the present invention. This example is implemented based on the technical solution of the present invention, and provides a detailed implementation method and a specific operation process, but the protection scope of the present invention is not limited to the following embodiments.

[0063] Figure 1 A flowchart of a knowledge base-based cloud platform operation and maintenance recommendation method provided in an embodiment of the present invention.

[0064] like Figure 1 As shown, it shows a flow chart of the implementation of the cloud platform operation and maintenance recommendation method based on the knowledge base provided by the embodiment of the present invention, which is described in detail as follows:

[0065] In step 101, an exception description statement is obtained.

[0066] In step 102, the abnormal description sentence is segmented to obtain a plurality of entity segmentations.

[0067] In some implementations, step 102 includes:

[0068] Extracting a first number of multiple characters from a predetermined position of the abnormal description statement;

[0069] If the multiple characters are not the target part of speech, entity matching is performed in the knowledge graph according to the multiple characters, wherein the knowledge graph includes multiple entities, and the entities of the knowledge graph are connected by relationships;

[0070] If there is no match, the value of the first number is increased, and the process jumps to the step of extracting a first number of multiple characters from a predetermined position of the exception description statement;

[0071] Otherwise, the matched entity and entities having multiple identical relationships with the matched entity are added to multiple entity segmentations;

[0072] The predetermined position is offset according to the predetermined position and the first number, and the process jumps to the step of extracting a first number of multiple characters from the predetermined position of the exception description statement.

[0073] Exemplarily, the embodiments of the present invention accept operation and maintenance query statements, which are generally description statements of abnormal phenomena. Due to different expressions, when the operation and maintenance knowledge base is searched based on the operation and maintenance query statements alone, some operation and maintenance knowledge texts (operation and maintenance knowledge items) may be ignored. The processing of description statements in the embodiments of the present invention is to segment the abnormal description statements, usually by extracting one or two words from the statements, and the extracted words are matched with entities in the knowledge graph. It should be noted that the knowledge graph includes multiple entities, and each entity is connected to other entities through relationships to form a network. After the extracted words are matched in the entity, the entities with the same relationship with the entity are added to the multiple entity segmentations, otherwise, the number of words is adjusted. In this way, the segmentation of the abnormal description statement is completed.

[0074] The multiple word segments obtained through the above steps can be retrieved to a large extent through words with similar meanings, which expands the candidate targets and improves the search accuracy.

[0075] In step 103, multiple candidate operation and maintenance knowledge texts are determined from the operation and maintenance knowledge base based on the frequencies of occurrence of the multiple entity participles in the operation and maintenance knowledge text and the reverse file frequency, wherein the reverse file frequency represents the ratio of the operation and maintenance knowledge text containing the entity participles to the total operation and maintenance knowledge text.

[0076] In some implementations, step 103 includes:

[0077] Determine multiple segmentation frequency queues according to the multiple entity segmentations, wherein each segmentation frequency queue corresponds to an operation and maintenance knowledge text, the segmentation frequency queue includes multiple segmentation frequency values, each segmentation frequency value corresponds to an entity segmentation, and the segmentation frequency value represents the probability of the entity segmentation appearing in the operation and maintenance knowledge text;

[0078] Constructing a reverse file frequency queue according to the frequencies of occurrence of the multiple entity participles in the operation and maintenance knowledge base, wherein the reverse file frequency queue includes multiple reverse file frequency values, each reverse file frequency value corresponding to an entity participle;

[0079] Determine a plurality of association values ​​according to the plurality of word segmentation frequency queues and the reverse file frequency queue, wherein each association value corresponds to an operation and maintenance knowledge text, and the association value represents the degree of association between the operation and maintenance knowledge text and the abnormal description statement;

[0080] According to the multiple association values, multiple candidate operation and maintenance knowledge texts are determined from the operation and maintenance knowledge base.

[0081] In some implementations, determining a plurality of word segmentation frequency queues according to the plurality of entity word segmentations includes:

[0082] For each operation and maintenance knowledge text, perform the following steps respectively:

[0083] Taking out entity participles from the multiple entity participles in sequence as entity participles to be processed;

[0084] The word segmentation frequency value is calculated according to the first formula, wherein the first formula is:

[0085]

[0086] In the formula, is the word segmentation frequency value of the entity to be processed, is the number of times the entity segmentation to be processed appears in the operation and maintenance knowledge text, is the number of segmented words in the operation and maintenance knowledge text;

[0087] Add the word segmentation frequency value to the word segmentation frequency queue;

[0088] If the traversal of the multiple entity participles is not completed, jump to the step of sequentially taking out entity participles from the multiple entity participles as entity participles to be processed.

[0089] In some implementations, the step of constructing a reverse file frequency queue according to the frequencies of occurrence of the plurality of entity segmentations in the operation and maintenance knowledge base includes:

[0090] Taking out entity participles from the multiple entity participles in sequence as entity participles to be processed;

[0091] The reverse document frequency value of the entity segmentation to be processed is calculated according to the second formula, wherein the second formula is:

[0092]

[0093] In the formula, is the reverse document frequency value of the entity segmentation to be processed, is the number of operation and maintenance knowledge texts with entity segmentation to be processed, is the number of operation and maintenance knowledge texts in the operation and maintenance knowledge base, is the first constant;

[0094] Adding the reverse file frequency value to a reverse file frequency queue;

[0095] If the traversal of the multiple entity participles is not completed, jump to the step of sequentially taking out entity participles from the multiple entity participles as entity participles to be processed.

[0096] In some implementations, determining a plurality of association values ​​according to the plurality of word segmentation frequency queues and the reverse file frequency queue includes:

[0097] For each operation and maintenance knowledge text, the associated value is determined according to the third formula, wherein the third formula is:

[0098]

[0099] In the formula, is the associated value of the operation and maintenance knowledge text, is the first word frequency values, The reverse file frequency queue Inverse file frequency values, The total number of entity segmentations.

[0100] For example, Figure 2 As shown, the embodiment of the present invention calculates the frequency of each entity segmentation in the multiple entity segmentations 201 for each operation and maintenance knowledge text in the operation and maintenance knowledge base 202, and constructs the frequencies of these entity segmentations into a segmentation frequency queue 203. In this way, each operation and maintenance knowledge text corresponds to a segmentation frequency queue 203. At the same time, for each entity segmentation in the multiple entity segmentations 201, the frequency of its occurrence in the operation and maintenance knowledge text in the operation and maintenance knowledge base 202 (reverse file frequency value) is counted, and these frequency values ​​are constructed into a reverse file frequency queue 204. Through the segmentation frequency queue 203 and the reverse file frequency queue 204, we can analyze the correlation between each operation and maintenance knowledge text and the abnormal description statement.

[0101] In terms of determining the word segmentation frequency value, the embodiment of the present invention applies the first formula:

[0102]

[0103] In the formula, is the word segmentation frequency value of the entity to be processed, is the number of times the entity segmentation to be processed appears in the operation and maintenance knowledge text, is the number of segmented words in the operation and maintenance knowledge text.

[0104] In determining the reverse file frequency value, the embodiment of the present invention applies the second formula:

[0105]

[0106] In the formula, is the reverse document frequency value of the entity segmentation to be processed, is the number of operation and maintenance knowledge texts with entity segmentation to be processed, is the number of operation and maintenance knowledge texts in the operation and maintenance knowledge base, is the first constant.

[0107] Finally, the present invention calculates the correlation between each operation and maintenance knowledge text and the abnormal description statement through the third formula, and the third formula is:

[0108]

[0109] In the formula, is the associated value of the operation and maintenance knowledge text, is the first word frequency values, The reverse file frequency queue Inverse file frequency values, The total number of entity segmentations.

[0110] In step 104, the multiple cause reliability probabilities are determined based on the probabilities of multiple abnormal causes, the probability of the occurrence of entity segmentations, and the probability of the occurrence of entity segmentations in the operation and maintenance knowledge text under the abnormal cause conditions, and the target operation and maintenance knowledge text is determined from the multiple alternative operation and maintenance knowledge texts based on the multiple cause reliability probabilities, wherein each alternative operation and maintenance knowledge text corresponds to a cause reliability probability and an abnormal cause.

[0111] In some implementations, determining the multiple cause reliability probabilities according to the probabilities of multiple abnormal causes, the probability of entity segmentation appearing, and the probability of entity segmentation appearing in the operation and maintenance knowledge text under the abnormal cause condition includes:

[0112] The cause reliability probability of each abnormal cause is determined according to the fourth formula, wherein the fourth formula is:

[0113]

[0114] In the formula, is the cause reliability probability, The first The probability of entity segmentation, is the safety risk probability, For the The probability of an entity segmentation word appearing in the operation and maintenance knowledge text.

[0115] Exemplarily, in fact, multiple alternative operation and maintenance knowledge texts can be selected through the above steps. These operation and maintenance knowledge texts usually correspond to multiple abnormal causes. If the abnormal cause corresponding to the abnormal description statement is found from these abnormal causes, then the matching of the operation and maintenance knowledge text can be completed.

[0116] Based on the above concept, the implementation mode of the present invention calculates the probability of various abnormal causes when multiple entity segmentations appear based on statistical and probability principles. Obviously, the larger the probability value, the closer the abnormal cause is to the cause corresponding to the abnormal description sentence.

[0117] For example, if the exception description statement includes descriptions of insufficient space, too long task execution time, and new tasks, we can roughly infer that there is a problem in the task operation, resulting in a large amount of data occupying the data space.

[0118] The embodiment of the present invention determines the probability of the abnormal cause when the entity segmentation description appears based on the probability of the entity segmentation appearing when the abnormal cause occurs, the probability of the abnormal cause occurring, and the probability of the entity segmentation. Specifically, the fourth formula is used:

[0119]

[0120] In the formula, is the cause reliability probability, The first The probability of entity segmentation, is the safety risk probability, For the The probability of an entity segmentation word appearing in the operation and maintenance knowledge text.

[0121] The abnormal risks are ranked by the fourth formula, and the candidate operation and maintenance knowledge texts corresponding to the largest abnormal risks are selected as the target operation and maintenance knowledge texts for push. The cloud platform is maintained with these target operation and maintenance knowledge texts as references.

[0122] The cloud platform operation and maintenance recommendation method implementation method based on the knowledge base of the present invention first obtains an abnormal description sentence; then performs word segmentation on the abnormal description sentence to obtain multiple entity word segmentations; then determines multiple alternative operation and maintenance knowledge texts from the operation and maintenance knowledge base according to the frequency of occurrence of the multiple entity word segmentations in the operation and maintenance knowledge text and the reverse file frequency, wherein the reverse file frequency represents the ratio of the operation and maintenance knowledge text containing the entity word segmentations to the whole operation and maintenance knowledge text; finally, determines the multiple cause reliability probabilities according to the probabilities of multiple abnormal causes, the probability of occurrence of the entity word segmentations and the probability of occurrence of the entity word segmentations in the operation and maintenance knowledge text under the abnormal cause conditions, and determines the target operation and maintenance knowledge text from the multiple alternative operation and maintenance knowledge texts according to the multiple cause reliability probabilities, wherein each alternative operation and maintenance knowledge text corresponds to a cause reliability probability and an abnormal cause. The implementation mode of the present invention determines alternative operation and maintenance knowledge texts by analyzing the frequency of occurrence of the word segmentation of the abnormal description sentence in the operation and maintenance knowledge text and the proportion of the operation and maintenance knowledge text containing the entity segmentation, and finally selects the operation and maintenance knowledge text most relevant to the abnormal description sentence from the alternative operation and maintenance knowledge texts through statistics and probability theory for push. The method of the present invention improves the speed and accuracy of the operation and maintenance knowledge base query, provides assistance to the operation and maintenance work, and improves the efficiency of the operation and maintenance.

[0123] It should be understood that the size of the serial numbers of the steps in the above implementation does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the implementation method of the present invention.

[0124] The following is an implementation of the device of the present invention. For details not described in detail, reference may be made to the corresponding method implementation described above.

[0125] Figure 3 is a functional block diagram of a cloud platform operation and maintenance recommendation device based on a knowledge base provided in an embodiment of the present invention, referring to Figure 3 The cloud platform operation and maintenance recommendation device based on the knowledge base includes: a description acquisition module 301, a word segmentation module 302, a preliminary selection module 303 and an operation and maintenance recommendation module 304, wherein:

[0126] Description acquisition module 301, used to acquire an exception description statement;

[0127] A word segmentation module 302, used to segment the abnormal description sentence to obtain multiple entity segmentations;

[0128] A preliminary selection module 303 is used to determine a plurality of candidate operation and maintenance knowledge texts from the operation and maintenance knowledge base according to the frequencies of occurrence of the plurality of entity segmentations in the operation and maintenance knowledge texts and the reverse document frequencies, wherein the reverse document frequencies represent the ratio of the operation and maintenance knowledge texts containing the entity segmentations to the total operation and maintenance knowledge texts;

[0129] The operation and maintenance recommendation module 304 is used to determine the multiple cause reliability probabilities based on the probabilities of multiple abnormal causes, the probability of the occurrence of entity segmentations, and the probability of the occurrence of entity segmentations in the operation and maintenance knowledge text under the abnormal cause conditions, and determine the target operation and maintenance knowledge text from the multiple alternative operation and maintenance knowledge texts based on the multiple cause reliability probabilities, wherein each alternative operation and maintenance knowledge text corresponds to a cause reliability probability and an abnormal cause.

[0130] Figure 4 is a functional block diagram of an electronic device provided by an embodiment of the present invention. Figure 4 As shown, the electronic device 4 of this embodiment includes: a processor 400 and a memory 401, wherein the memory 401 stores a computer program 402 that can be run on the processor 400. When the processor 400 executes the computer program 402, the steps in the above-mentioned cloud platform operation and maintenance recommendation method based on the knowledge base and the implementation method are implemented, for example Figure 1 Steps 101 to 104 are shown.

[0131] Exemplarily, the computer program 402 may be divided into one or more modules / units, and the one or more modules / units are stored in the memory 401 and executed by the processor 400 to implement the present invention.

[0132] The electronic device 4 may be a computing device such as a desktop computer, a notebook, a PDA, or a cloud server. The electronic device 4 may include, but is not limited to, a processor 400 and a memory 401. Those skilled in the art will appreciate that Figure 4 It is only an example of the electronic device 4 and does not constitute a limitation of the electronic device 4. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the electronic device 4 may also include input and output devices, network access devices, buses, etc.

[0133] The processor 400 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0134] The memory 401 may be an internal storage unit of the electronic device 4, such as a hard disk or memory of the electronic device 4. The memory 401 may also be an external storage device of the electronic device 4, such as a plug-in hard disk, a smart media card (SMC), a secure digital (SD) card, a flash card, etc. equipped on the electronic device 4. Further, the memory 401 may also include both an internal storage unit and an external storage device of the electronic device 4. The memory 401 is used to store the computer program 402 and other programs and data required by the electronic device 4. The memory 401 may also be used to temporarily store data that has been output or is to be output.

[0135] Those skilled in the art can clearly understand that for the convenience and simplicity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned function allocation can be completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the implementation method can be integrated into a processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of this application. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method implementation method, which will not be repeated here.

[0136] In the above-mentioned embodiments, the description of each embodiment has its own emphasis. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0137] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of the present invention.

[0138] In the embodiments provided by the present invention, it should be understood that the disclosed devices / electronic devices and methods can be implemented in other ways. For example, the device / electronic device embodiments described above are only schematic. For example, the division of the modules or units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0139] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0140] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0141] If the integrated module / unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention implements all or part of the processes in the above-mentioned implementation method, and can also be completed by instructing the relevant hardware through a computer program. The computer program can be stored in a computer-readable storage medium. When the computer program is executed by the processor, the steps of the above-mentioned various methods and device implementation methods can be implemented. Among them, the computer program includes computer program code, and the computer program code can be in source code form, object code form, executable file or some intermediate form. The computer-readable medium may include: any entity or device that can carry the computer program code, recording medium, U disk, mobile hard disk, disk, optical disk, computer memory, read-only memory (ROM), random access memory (RAM), electrical carrier signal, telecommunication signal and software distribution medium.

[0142] The above-described embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, a person skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features may be replaced by equivalents. Such modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the protection scope of the present invention.

Claims

1. A cloud platform operation and maintenance recommendation method based on a knowledge base, characterized in that: include: Get the exception description statement; Segmenting the abnormal description sentence to obtain multiple entity segmentations; Determine a plurality of candidate operation and maintenance knowledge texts from the operation and maintenance knowledge base according to the frequencies of occurrence of the plurality of entity segmentations in the operation and maintenance knowledge texts and the reverse file frequencies, wherein the reverse file frequencies represent the ratio of the operation and maintenance knowledge texts containing the entity segmentations to the total operation and maintenance knowledge texts; Determine the multiple cause reliability probabilities according to the probabilities of multiple abnormal causes, the probability of entity segmentation appearing in the operation and maintenance knowledge text, and the probability of entity segmentation appearing in the operation and maintenance knowledge text under the abnormal cause condition, and determine the target operation and maintenance knowledge text from the multiple candidate operation and maintenance knowledge texts according to the multiple cause reliability probabilities, wherein each candidate operation and maintenance knowledge text corresponds to a cause reliability probability and an abnormal cause; in, The probability of multiple abnormal causes, Operation and maintenance knowledge text The probability of entity segmentation appearing and the probability of entity segmentation appearing in the operation and maintenance knowledge text under abnormal cause conditions determine the reliability probabilities of the multiple causes, including: The cause reliability probability of each abnormal cause is determined according to a first formula, wherein the first formula is: In the formula, is the cause reliability probability, The first The probability of entity segmentation, The probability of being the abnormal cause, For the The probability of entity segmentation appearing in the operation and maintenance knowledge text, The total number of entity segmentations.

2. The cloud platform operation and maintenance recommendation method based on the knowledge base according to claim 1, characterized in that: The multiple entity segmentations obtained by segmenting the abnormal description sentence include: Extracting a first number of multiple characters from a predetermined position of the abnormal description statement; If the multiple characters are not the target part of speech, entity matching is performed in the knowledge graph according to the multiple characters, wherein the knowledge graph includes multiple entities, and the entities of the knowledge graph are connected by relationships; If there is no match, the value of the first number is increased, and the process jumps to the step of extracting a first number of multiple characters from a predetermined position of the exception description statement; Otherwise, the matched entity and entities having multiple identical relationships with the matched entity are added to multiple entity segmentations; The predetermined position is offset according to the predetermined position and the first number, and the process jumps to the step of extracting a first number of multiple characters from the predetermined position of the exception description statement.

3. The cloud platform operation and maintenance recommendation method based on the knowledge base according to claim 1 is characterized in that: The determining of multiple candidate operation and maintenance knowledge texts from the operation and maintenance knowledge base according to the frequencies of occurrence of the multiple entity segmentations in the operation and maintenance knowledge text and the reverse file frequencies includes: Determine multiple segmentation frequency queues according to the multiple entity segmentations, wherein each segmentation frequency queue corresponds to an operation and maintenance knowledge text, the segmentation frequency queue includes multiple segmentation frequency values, each segmentation frequency value corresponds to an entity segmentation, and the segmentation frequency value represents the probability of the entity segmentation appearing in the operation and maintenance knowledge text; Constructing a reverse file frequency queue according to the frequencies of occurrence of the multiple entity participles in the operation and maintenance knowledge base, wherein the reverse file frequency queue includes multiple reverse file frequency values, each reverse file frequency value corresponding to an entity participle; Determine a plurality of association values ​​according to the plurality of word segmentation frequency queues and the reverse file frequency queue, wherein each association value corresponds to an operation and maintenance knowledge text, and the association value represents the degree of association between the operation and maintenance knowledge text and the abnormal description statement; According to the multiple association values, multiple candidate operation and maintenance knowledge texts are determined from the operation and maintenance knowledge base.

4. The cloud platform operation and maintenance recommendation method based on the knowledge base according to claim 3 is characterized in that: The step of determining a plurality of word segmentation frequency queues according to the plurality of entity word segmentations includes: For each operation and maintenance knowledge text, perform the following steps respectively: Taking out entity participles from the multiple entity participles in sequence as entity participles to be processed; The word segmentation frequency value is calculated according to the second formula, wherein the second formula is: In the formula, is the word segmentation frequency value of the entity to be processed, is the number of times the entity segmentation to be processed appears in the operation and maintenance knowledge text, is the number of segmented words in the operation and maintenance knowledge text; Add the word segmentation frequency value to the word segmentation frequency queue; If the traversal of the multiple entity participles is not completed, jump to the step of sequentially taking out entity participles from the multiple entity participles as entity participles to be processed.

5. The cloud platform operation and maintenance recommendation method based on the knowledge base according to claim 3 is characterized in that: The step of constructing a reverse file frequency queue according to the frequencies of occurrence of the multiple entity segmentations in the operation and maintenance knowledge base includes: Taking out entity participles from the multiple entity participles in sequence as entity participles to be processed; The reverse document frequency value of the entity segmentation to be processed is calculated according to the third formula, wherein the third formula is: In the formula, is the reverse document frequency value of the entity segmentation to be processed, is the number of operation and maintenance knowledge texts with entity segmentation to be processed, is the number of operation and maintenance knowledge texts in the operation and maintenance knowledge base, is the first constant; Adding the reverse file frequency value to a reverse file frequency queue; If the traversal of the multiple entity participles is not completed, jump to the step of sequentially taking out entity participles from the multiple entity participles as entity participles to be processed.

6. The cloud platform operation and maintenance recommendation method based on the knowledge base according to claim 3 is characterized in that: The determining of a plurality of associated values ​​according to the plurality of word segmentation frequency queues and the reverse file frequency queue comprises: For each operation and maintenance knowledge text, the associated value is determined according to the fourth formula, wherein the fourth formula is: In the formula, is the associated value of the operation and maintenance knowledge text, is the first word frequency values, The reverse file frequency queue Inverse file frequency values, The total number of entity segmentations.

7. A cloud platform operation and maintenance recommendation device based on a knowledge base, characterized in that: Used to implement the cloud platform operation and maintenance recommendation method based on a knowledge base as described in any one of claims 1 to 6, the cloud platform operation and maintenance recommendation device based on a knowledge base comprises: Description acquisition module, used to obtain the exception description statement; A word segmentation module, used to segment the abnormal description sentence to obtain multiple entity segmentations; A preliminary selection module, used to determine a plurality of candidate operation and maintenance knowledge texts from the operation and maintenance knowledge base according to the frequencies of occurrence of the plurality of entity segmentations in the operation and maintenance knowledge texts and the reverse file frequencies, wherein the reverse file frequencies represent the ratio of the operation and maintenance knowledge texts containing the entity segmentations to the total operation and maintenance knowledge texts; as well as, The operation and maintenance recommendation module is used to determine the multiple cause reliability probabilities according to the probabilities of multiple abnormal causes, the probability of the occurrence of entity segmentations, and the probability of the occurrence of entity segmentations in the operation and maintenance knowledge text under the abnormal cause conditions, and determine the target operation and maintenance knowledge text from the multiple alternative operation and maintenance knowledge texts according to the multiple cause reliability probabilities, wherein each alternative operation and maintenance knowledge text corresponds to a cause reliability probability and an abnormal cause.

8. An electronic device comprising a memory and a processor, wherein the memory stores a computer program that can be run on the processor, characterized in that: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

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