Log information conversion method and device, electronic equipment and computer readable medium

Abstract

Embodiments of the present disclosure disclose log information conversion methods and apparatuses, electronic devices, and computer-readable media. A specific implementation of the method includes: obtaining a set of plug-in information groups from a preset plug-in information directory; monitoring the plug-in information groups in the preset plug-in information directory to obtain a monitoring result; in response to determining that the monitoring result indicates that there are new plug-in information groups in the preset plug-in information directory, determining each of the existing new plug-in information groups as a new plug-in information group set; updating the plug-in information groups in the set of plug-in information groups based on the new plug-in information group set to update the set of plug-in information groups; obtaining log information to be converted and conversion requirement information corresponding to the log information to be converted; and generating conversion information corresponding to the log information to be converted based on the conversion requirement information and the updated set of plug-in information groups. This implementation makes the generated conversion information more accurate, thereby reducing the number of log information conversion failures.

Description

Technical Field

[0001] The embodiments disclosed herein relate to the field of computer technology, and more specifically to log information conversion methods, apparatus, electronic devices, and computer-readable media. Background Technology

[0002] Log information transformation is a technology that transforms log information. Currently, the common approach to transforming log information is to directly match fixed parsing plugins from a static plugin manager based on the keywords required for the transformation, and then generate corresponding transformation information for the log information to be transformed based on the fixed parsing plugins and the log information to be transformed.

[0003] However, when using the above method to transform log information, the following technical problems often arise:

[0004] First, since the plugins in the static plugin manager are fixed, when there is no plugin in the plugin manager that meets the requirements for log information conversion, the obtained plugin information group will be incorrect, resulting in incorrect log information and increasing the number of log information conversion failures.

[0005] Secondly, directly matching fixed parsing plugins from a static plugin manager based on keywords in the log information transformation requirements can lead to low accuracy in matching plugins, as the same keyword may have different meanings in different contexts. When incorrect transformation information is obtained based on incorrect plugins (i.e., incorrect plugin information groups), re-matching and transformation are required, wasting computing resources.

[0006] The information disclosed in this background section is only intended to enhance the understanding of the background of the inventive concept, and therefore may contain information that does not constitute prior art known to those skilled in the art. Summary of the Invention

[0007] The summary portion of this disclosure is intended to provide a brief overview of the concepts, which will be described in detail in the detailed description portion. This summary portion is not intended to identify key or essential features of the claimed technical solutions, nor is it intended to limit the scope of the claimed technical solutions.

[0008] Some embodiments of this disclosure provide log information conversion methods, apparatuses, electronic devices, and computer-readable media to address one or more of the technical problems mentioned in the background section above.

[0009] In a first aspect, some embodiments of this disclosure provide a log information conversion method, which includes: obtaining a set of plugin information groups from a preset plugin information directory; monitoring the plugin information groups in the preset plugin information directory to obtain monitoring results; in response to determining that the monitoring results indicate the existence of newly added plugin information groups in the preset plugin information directory, determining each existing newly added plugin information group as a set of newly added plugin information groups; updating the plugin information groups in the set of newly added plugin information groups to update the set of plugin information groups; obtaining log information to be converted and conversion requirement information corresponding to the log information to be converted; determining the plugin information group corresponding to the conversion requirement information based on the conversion requirement information and the updated set of plugin information groups; and generating conversion information corresponding to the log information to be converted based on the generated plugin information group and the log information to be converted.

[0010] Secondly, some embodiments of this disclosure provide a log information conversion apparatus, comprising: a first acquisition unit configured to acquire a set of plugin information groups from a preset plugin information directory; a monitoring unit configured to monitor the plugin information groups in the preset plugin information directory and obtain a monitoring result; a first determination unit configured to, in response to determining that the monitoring result indicates the existence of new plugin information groups in the preset plugin information directory, determine each existing new plugin information group as a new plugin information group set; an update unit configured to update the plugin information groups in the plugin information group set based on the new plugin information group set, thereby updating the plugin information group set; a second acquisition unit configured to acquire log information to be converted and conversion requirement information corresponding to the log information to be converted; a second determination unit configured to determine the plugin information group corresponding to the conversion requirement information based on the conversion requirement information and the updated plugin information group set; and a generation unit configured to generate conversion information corresponding to the log information to be converted based on the generated plugin information group and the log information to be converted.

[0011] Thirdly, some embodiments of this disclosure provide an electronic device, including: one or more processors; and a storage device having one or more programs stored thereon, wherein when the one or more programs are executed by the one or more processors, the one or more processors implement the method described in any implementation of the first aspect above.

[0012] Fourthly, some embodiments of this disclosure provide a computer-readable medium having a computer program stored thereon, wherein the program, when executed by a processor, implements the method described in any of the implementations of the first aspect above.

[0013] The above embodiments of this disclosure have the following beneficial effects: the log information conversion method of some embodiments of this disclosure can continuously update the plugin information groups in the plugin information group set, reducing the number of log information conversion failures. Specifically, the reason for the error in the converted log information and the increase in the number of log information conversion failures is that, since the plugins in the static plugin manager are fixed, when there are no plugins in the plugin manager that meet the log information conversion requirements, the obtained plugins, i.e., plugin information groups, are incorrect, thus leading to incorrect converted log information and an increase in the number of log information conversion failures. Based on this, the log information conversion method of some embodiments of this disclosure first obtains a plugin information group set from a preset plugin information directory. Thus, an unupdated plugin information group set for determining the plugin information group corresponding to the conversion requirement information can be obtained. Second, the plugin information groups in the preset plugin information directory are monitored to obtain monitoring results. Thus, the plugin information groups in the preset plugin information directory can be monitored in real time to obtain monitoring results indicating whether there are new plugin information groups in the preset plugin information directory. Then, in response to determining that the monitoring results indicate that there are new plugin information groups in the preset plugin information directory, each existing new plugin information group is determined as a new plugin information group set. Therefore, the newly added plugin information group set used to update the plugin information group set can be determined. Next, based on the newly added plugin information group set, the plugin information groups in the aforementioned plugin information group set are updated to update the plugin information group set. This update results in a more complete plugin information group set. Then, the log information to be converted and the corresponding conversion requirement information are obtained. Based on the conversion requirement information and the updated plugin information group set, the plugin information group corresponding to the conversion requirement information is determined. This yields the plugin information group used to generate conversion information. Finally, based on the generated plugin information group and the log information to be converted, the conversion information corresponding to the log information to be converted is generated. This generates log conversion information corresponding to the conversion requirement. Because real-time monitoring of the plugin information groups in the preset plugin information directory is employed, when the monitoring results indicate the existence of a newly added plugin information group in the preset plugin information directory, the plugin information groups in the plugin information group set are updated in real-time to update the plugin information group set. Based on the conversion requirement information and the updated, more complete plugin information group set, a plugin information group that better meets the conversion requirement information is determined. This makes the generated transformation information more accurate, thereby reducing the number of log information transformation failures. Attached Figure Description

[0014] The above and other features, advantages, and aspects of the embodiments of this disclosure will become more apparent from the accompanying drawings and the following detailed description. Throughout the drawings, the same or similar reference numerals denote the same or similar elements. It should be understood that the drawings are schematic, and elements are not necessarily drawn to scale.

[0015] Figure 1 This is a flowchart of some embodiments of the log information conversion method according to this disclosure;

[0016] Figure 2 These are schematic diagrams illustrating the structure of some embodiments of the log information conversion apparatus according to this disclosure;

[0017] Figure 3 This is a schematic diagram of the structure of an electronic device suitable for implementing some embodiments of the present disclosure. Detailed Implementation

[0018] Embodiments of this disclosure will now be described in more detail with reference to the accompanying drawings. While some embodiments of this disclosure are shown in the drawings, it should be understood that this disclosure can be implemented in various forms and should not be construed as limited to the embodiments set forth herein. Rather, these embodiments are provided to provide a more thorough and complete understanding of this disclosure. It should be understood that the accompanying drawings and embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of protection of this disclosure.

[0019] It should also be noted that, for ease of description, only the parts relevant to the invention are shown in the accompanying drawings. Unless otherwise specified, the embodiments and features described in this disclosure can be combined with each other.

[0020] It should be noted that the concepts of "first" and "second" mentioned in this disclosure are used only to distinguish different devices, modules or units, and are not used to limit the order of functions performed by these devices, modules or units or their interdependencies.

[0021] It should be noted that the terms "a" and "a plurality of" used in this disclosure are illustrative rather than restrictive, and those skilled in the art should understand that, unless otherwise expressly indicated in the context, they should be understood as "one or more".

[0022] The names of messages or information exchanged between multiple devices in the embodiments of this disclosure are for illustrative purposes only and are not intended to limit the scope of such messages or information.

[0023] This disclosure will now be described in detail with reference to the accompanying drawings and embodiments.

[0024] Figure 1A flow 100 of some embodiments of the log information conversion method according to this disclosure is shown. The log information conversion method includes the following steps:

[0025] Step 101: Obtain the plugin information group set from the preset plugin information directory.

[0026] In some embodiments, the execution entity of the log information conversion method (e.g., a computing device) can obtain a set of plugin information groups from a preset plugin information directory via a wired or wireless connection. The plugin information in the plugin information group may include, but is not limited to, at least one of the following: plugin identifier, plugin description information, and plugin version. The preset plugin information directory can be a directory within an application that stores various plugin information groups. For example, the preset plugin information directory is a preset folder or a preset JAR file. The plugin identifier can be a unique identifier for the plugin. The plugin can be represented by a plugin information group. The plugin information in the plugin information group may include, but is not limited to, at least one of the following: the plugin function code, plugin identifier, plugin description information, and configuration information. The plugin description information can be a textual description of the plugin function. The plugin version can be a plugin number or label used in software or an application to identify the version of the plugin. For example, the plugin OK Lite's version could be OK9.0.

[0027] It should be noted that the aforementioned wireless connection methods may include, but are not limited to, 3G / 4G connection, WiFi connection, Bluetooth connection, WiMAX connection, Zigbee connection, UWB (ultra wideband) connection, and other currently known or future wireless connection methods.

[0028] Step 102: Monitor the plugin information group in the preset plugin information directory and obtain the monitoring results.

[0029] In some embodiments, the execution entity can monitor the plugin information groups in the preset plugin information directory and obtain monitoring results. These monitoring results can indicate whether there is information about newly added plugin information groups in the preset plugin information directory. The newly added plugin information groups can be plugin information groups used to update the plugin information group set.

[0030] In some optional implementations of certain embodiments, the aforementioned execution entity can monitor the plugin information group in the aforementioned preset plugin information directory through the following steps to obtain monitoring results:

[0031] The first step is to determine the time when the plugin information group set is first obtained from the preset plugin information directory as the preset initial time.

[0032] The second step is to set the start time of the periodic timer to the preset initial time mentioned above.

[0033] The third step involves performing the following processing steps based on the preset initial time:

[0034] Sub-step one: Obtain the system time as the current time.

[0035] Sub-step two: In response to determining that the current time is greater than or equal to the preset initial time, perform the following monitoring steps based on the preset initial time:

[0036] The first sub-step involves determining the directory information of the preset plugin information directory corresponding to the preset initial time as the first directory information. This directory information can be structured data used to organize and manage various plugin information groups and is continuously updated according to time. For example, the directory information may include, but is not limited to, at least one of the following: plugin identifier and plugin version. When the preset plugin information directory contains plugin information group A, plugin information group B, plugin information group C, and plugin information group D, the directory information of the preset plugin information directory can be (plugin A, plugin B, plugin C, plugin D). After a preset time period, if a new plugin information group is added to the preset plugin information directory, and the preset plugin information directory includes plugin information group A, plugin information group B, plugin information group C, plugin information group D, plugin information group E, and plugin information group F, then the directory information of the corresponding preset plugin information directory after the preset time period can be (plugin A, plugin B, plugin C, plugin D, plugin E, plugin F). The preset time period can be 5 minutes.

[0037] The second sub-step involves, in response to determining that the periodic timer meets the system time acquisition condition, acquiring the system time as the second directory information acquisition time. The aforementioned system time acquisition condition can be that, after a preset initial time, the time interval of the periodic timer reaches a preset time interval. The preset time interval can be 1 minute.

[0038] The third sub-step is to determine the directory information of the preset plugin information directory corresponding to the second directory information acquisition time as the second directory information.

[0039] The fourth sub-step, in response to determining that the first directory information and the second directory information are the same, identifies the information indicating that no new plugin information group exists in the preset plugin information directory as the monitoring result. For example, the first directory information could be (plugin A, plugin B, plugin C, plugin D). The second directory information could be (plugin A, plugin B, plugin C, plugin D). Since the first directory information and the second directory information are the same, the monitoring result indicates that no new plugin information group exists in the preset plugin information directory.

[0040] The fifth sub-step, in response to the determination that the monitoring result indicates that there is no new plugin information group in the preset plugin information directory, updates the preset initial time to the second directory information acquisition time, and executes the above monitoring steps again.

[0041] The sixth sub-step, in response to the determination that the first directory information and the second directory information are different, identifies the information indicating the existence of a newly added plugin information group in the preset plugin information directory as the monitoring result. For example, the first directory information could be (plugin A, plugin B, plugin C, plugin D). The second directory information could be (plugin A, plugin B, plugin C, plugin D, plugin E, plugin F). Since the first directory information and the second directory information are different, the monitoring result can indicate the existence of a newly added plugin information group in the preset plugin information directory. Here, the newly added plugin information group can be plugin information group E and plugin information group F.

[0042] Sub-step three: In response to determining that the current time is less than the preset initial time, the above processing steps are executed again after a second preset time interval.

[0043] Step 103: In response to determining that the monitoring results indicate the existence of new plugin information groups in the preset plugin information directory, the existing new plugin information groups are determined as a set of new plugin information groups.

[0044] In some embodiments, the execution entity may, in response to determining that the monitoring result indicates the existence of a new plugin information group in the preset plugin information directory, determine each existing new plugin information group as a new plugin information group set.

[0045] In some optional implementations of certain embodiments, the execution entity may, in response to determining that the monitoring results indicate the existence of newly added plugin information groups in the preset plugin information directory, determine each existing newly added plugin information group as a set of newly added plugin information groups:

[0046] The first step is to delete the first directory information from the second directory information. The first directory information can be a subset of the second directory information. For example, the first directory information can be (plugin A, plugin B, plugin C, plugin D). The second directory information can be (plugin A, plugin B, plugin C, plugin D, plugin E, plugin F). The first directory information (plugin A, plugin B, plugin C, plugin D) can be a subset of the second directory information (plugin A, plugin B, plugin C, plugin D, plugin E, plugin F).

[0047] The second step is to identify the second directory information after deleting the first directory information as the newly added directory information.

[0048] The third step is to determine the new plugin information group corresponding to the newly added directory information as the new plugin information group set.

[0049] Step 104: Based on the newly added plugin information group set, update the plugin information groups in the plugin information group set to update the plugin information group set.

[0050] In some embodiments, the execution entity may update the plugin information group in the plugin information group set based on the newly added plugin information group set, thereby updating the plugin information group set.

[0051] In some optional implementations of certain embodiments, the aforementioned execution entity may update the plugin information group set by updating the plugin information group set based on the newly added plugin information group set through the following steps:

[0052] The first step is to identify the plugin information groups corresponding to the newly added plugin information groups in the plugin information group set as the plugin information group set to be removed. In practice, firstly, the executing entity can identify the plugin identifiers of each plugin information group in the plugin information group set as the first plugin identifier set. Then, the executing entity can identify the plugin identifiers of each newly added plugin information group in the newly added plugin information group set as the second plugin identifier set. Next, the executing entity can use a traversal comparison algorithm to find each plugin identifier in the first plugin identifier set that is identical to the plugin identifier in the second plugin identifier set. Finally, the executing entity can identify each plugin information group in the plugin information group set corresponding to each found plugin identifier as the plugin information group set to be removed.

[0053] The second step is to delete the plugin information group set to be removed from the above plugin information group set in order to perform an initial update to the plugin information group set.

[0054] The third step is to determine the initial set of plugin information groups after the initial update as the initial set of plugin information groups to be updated.

[0055] The fourth step involves combining the initial set of plugin information groups to be updated with the newly added set of plugin information groups to obtain the combined set of plugin information groups, which serves as the updated set of plugin information groups. In practice, the executing entity can determine the combined set of plugin information groups as the sum of the initial set of plugin information groups to be updated and the newly added set of plugin information groups, which is then used as the updated set of plugin information groups.

[0056] Step 105: Obtain the log information to be converted and the corresponding conversion requirement information of the log information to be converted.

[0057] In some embodiments, the aforementioned executing entity can obtain log information to be converted and corresponding conversion requirement information. The log information to be converted can be unstructured text information automatically recorded by computer systems and applications, representing events, states, and behaviors, which is then converted into structured information. For example, the log information to be converted could be "User 123 successfully logged in at 14:32:14 on 2023-07-13, accessed page A at 14:35:21 on 2023-07-13, and clicked button B at 14:38:05 on 2023-07-13." The conversion requirement information is the requirement to convert the log information into a preset format. For example, the conversion requirement information could be to convert the log information from unstructured text information into a structured JSON format.

[0058] Step 106: Based on the conversion requirement information and the updated set of plugin information groups, determine the plugin information group corresponding to the conversion requirement information.

[0059] In some embodiments, the execution entity may determine the plug-in information group corresponding to the conversion requirement information based on the conversion requirement information and the updated plug-in information group set.

[0060] In some optional implementations of certain embodiments, the execution entity may determine the plugin information group corresponding to the aforementioned conversion requirement information based on the aforementioned conversion requirement information and the updated plugin information group set through the following steps:

[0061] The first step involves inputting the aforementioned conversion requirement information into a pre-trained related word set extraction model to obtain a related word set. This related word set extraction model can be a neural network model that takes the conversion requirement information as input and the related word set as output, converting the information into semantically labeled related words based on part-of-speech tags. For example, the neural network model can include, but is not limited to, at least one of the following: a Bag of Words model and an RNN model.

[0062] The second step is to perform the following similarity determination steps for each plugin information group in the updated plugin information group set:

[0063] The first sub-step involves determining the semantic similarity between each associated word in the associated word set and the aforementioned plugin information group as the first semantic similarity, thus obtaining a first semantic similarity set. In practice, firstly, for each piece of plugin information in the plugin information group, the executing entity can input the associated word and the plugin information into a word vector model to determine the semantic similarity between the associated word and the plugin information, thus obtaining a semantic similarity set. Then, the executing entity can determine the average of the semantic similarities in the semantic similarity set as the first semantic similarity.

[0064] The second sub-step involves determining the average value of each first semantic similarity in the aforementioned first semantic similarity set as the semantic similarity to be screened.

[0065] The third step is to define each of the identified semantic similarities to be screened as a set of semantic similarities to be screened.

[0066] The fourth step is to identify the semantic similarities in the set of semantic similarities that meet the preset filtering criteria as the target semantic similarities. The preset filtering criteria can be that the semantic similarity to be filtered is the one with the highest semantic similarity in the set of semantic similarities to be filtered.

[0067] The fifth step is to determine the plugin information group corresponding to the target semantic similarity as the plugin information group corresponding to the conversion requirement information.

[0068] Optionally, the above-mentioned related word set extraction model can be trained through the following steps:

[0069] The first step is to obtain a first sample set. This first sample set includes sample conversion requirement information, a set of actual part-of-speech tags corresponding to the sample conversion requirement information, and a set of target related words corresponding to the sample conversion requirement information. The part-of-speech tags in the set can be words with tagged parts of speech. For example, the part-of-speech tag could be "love" (verb). The related words in the target related word set are keywords obtained by removing pronouns and stop words from the set of tags. For example, the set of tags could be {I (pronoun), love (verb), playing (verb), football (noun)}, and the related word set could be {love (verb), playing (verb), football (noun)}.

[0070] The second step involves training the associated word set extraction model based on the first sample set:

[0071] Sub-step one involves inputting the sample transformation requirement information of at least one first sample in the first sample set into the input layer of the first initial neural network to obtain the initial word set corresponding to each of the at least one first sample. The first initial neural network includes the input layer, a first semantic processing layer, a second semantic processing layer, a part-of-speech tagging prediction layer, and an output layer. The input layer of the first initial neural network can take the sample transformation requirement information as input data and the initial word set as output data. The sample transformation requirement information can be text information. The initial words in the initial word set can be words after removing pronouns and stop words. For example, the sample transformation requirement information can be "I love playing football," and the initial word set can be {love, playing, football}.

[0072] Sub-step two involves inputting the initial word set corresponding to each of the at least one first sample into the first semantic processing layer to obtain the semantic vector set corresponding to each of the at least one first sample. The first semantic processing layer can be a word embedding layer.

[0073] Sub-step three involves inputting the semantic vector set corresponding to each of the at least one first sample into the second semantic processing layer to obtain the semantic encoding set corresponding to each of the at least one first sample. The second semantic processing layer can be an encoding layer.

[0074] Sub-step four involves inputting the semantic encoding set corresponding to each of the at least one first sample into the part-of-speech tagging prediction layer to obtain the part-of-speech tag probability information corresponding to each of the at least one first sample. The part-of-speech tagging prediction layer can be a fully connected layer. The part-of-speech tag probability information can be the probability distribution information of the part-of-speech tag for each word in the part-of-speech tagging task. For example, the part-of-speech tag probability information could be "love (verb) probability can be 0.7, love (noun) probability can be 0.3, playing (verb) probability can be 0.8, playing (noun) probability can be 0.2, and football (noun) probability can be 1".

[0075] Sub-step five involves inputting the part-of-speech tag probability information corresponding to each of the at least one first sample into the output layer to obtain the sample predicted part-of-speech tagging word set corresponding to each of the at least one first sample. The output layer can be an activation layer. The sample predicted part-of-speech tagging words in the sample predicted part-of-speech tagging word set are words with tagged parts of speech predicted by the model. For example, the sample predicted part-of-speech tagging word set could be {love (verb), playing (verb), football (noun)}.

[0076] Sub-step six involves comparing the predicted part-of-speech tagging set for each of the at least one first sample with the actual part-of-speech tagging set for the corresponding sample predicted part-of-speech tagging set to obtain a first comparison result. In practice, the execution entity can use the cross-entropy loss function to compare and determine the gap between the predicted part-of-speech tagging set and the corresponding actual part-of-speech tagging set.

[0077] Sub-step seven: For the sample conversion requirement information of each of the at least one first sample mentioned above, update the sample conversion requirement information with the sample prediction part-of-speech tagging word set corresponding to the sample conversion requirement information, so as to update the first sample.

[0078] Sub-step eight: Determine at least one updated first sample as the second sample set.

[0079] Sub-step nine involves inputting the part-of-speech tagging set of at least one second sample in the second sample set into the second initial neural network to obtain the sample extraction associated word set corresponding to each of the at least one second sample.

[0080] Sub-step ten involves comparing the extracted related word set corresponding to each of the at least one second sample with the corresponding target related word set to obtain a second comparison result. In practice, the execution entity can use the cross-entropy loss function to compare and determine the gap between the extracted related word set and the corresponding target related word set.

[0081] Sub-step eleven involves determining whether the first initial neural network and the second initial neural network have achieved a preset optimization objective based on the first comparison result, the second comparison result, and the preset weights. The optimization objective may be that the loss function value is less than or equal to a preset value.

[0082] Sub-step twelve: In response to determining that the first initial neural network and the second initial neural network have achieved the above optimization objective, the first initial neural network and the second initial neural network are used as the trained associated word set extraction model.

[0083] Sub-step thirteen involves adjusting the network parameters of the first and second initial neural networks in response to the determination that they have not achieved the aforementioned optimization objective. A first sample set is then formed using unused first samples. The adjusted first and second initial neural networks are then used to re-execute the aforementioned associated word set extraction model training steps. As an example, the back propagation algorithm (BP algorithm) can be used to adjust the network parameters of the first and second initial neural networks.

[0084] The above technical solution and its related content, as an inventive point of this disclosure, solve the second technical problem mentioned in the background: "Directly matching fixed parsing plugins from a static plugin manager based on keywords of log information conversion requirements can lead to low accuracy in matching plugins because the same keyword may have different meanings in different contexts. When incorrect conversion information is obtained based on incorrect plugins (i.e., incorrect plugin information groups), re-matching and conversion are required, wasting computing resources." Factors leading to wasted computing resources often include: directly matching fixed parsing plugins from a static plugin manager based on keywords of log information conversion requirements; the same keyword may have different meanings in different contexts, leading to low accuracy in matching plugins; and re-matching and conversion are required when incorrect conversion information is obtained based on incorrect plugins (i.e., incorrect plugin information groups), thus wasting computing resources. Solving these factors can reduce the waste of computing resources. To achieve this effect, this disclosure uses the following steps: First, obtaining a first sample set, wherein the first sample in the first sample set includes sample conversion requirement information, a set of real part-of-speech tags corresponding to the sample conversion requirement information, and a set of sample target related words corresponding to the sample conversion requirement information. The second step involves training the associated word set extraction model based on the first sample set: Sub-step one: Input the sample transformation requirement information of at least one first sample in the first sample set into the input layer of the first initial neural network to obtain the initial word set corresponding to each of the at least one first sample. The first initial neural network includes the input layer, the first semantic processing layer, the second semantic processing layer, the part-of-speech tagging prediction layer, and the output layer. This yields the initial word set after removing pronouns and stop words. Sub-step two: Input the initial word set corresponding to each of the at least one first sample into the first semantic processing layer to obtain the semantic vector set corresponding to each of the at least one first sample. This yields the semantic vector set representing the semantic relationship information of each initial word. Sub-step three: Input the semantic vector set corresponding to each of the at least one first sample into the second semantic processing layer to obtain the semantic encoding set corresponding to each of the at least one first sample. Sub-step four: Input the semantic encoding set corresponding to each of the at least one first sample into the part-of-speech tagging prediction layer to obtain the part-of-speech tag probability information corresponding to each of the at least one first sample. Therefore, the probability distribution information of the part-of-speech tags used to generate the predicted part-of-speech tagging set can be obtained. Sub-step five involves inputting the part-of-speech tag probability information corresponding to each of the at least one first sample into the output layer to obtain the sample predicted part-of-speech tagging set corresponding to each of the at least one first sample. Thus, the sample predicted part-of-speech tagging set used to generate the sample extraction associated word set can be obtained.Sub-step six: Compare the predicted part-of-speech tagging set for each of the at least one first samples with the actual part-of-speech tagging set for the corresponding predicted part-of-speech tagging set to obtain a first comparison result. This provides a first comparison result for determining whether the first initial neural network and the second initial neural network have achieved the preset optimization objective. Sub-step seven: For the sample transformation requirement information of each of the at least one first samples, update the sample transformation requirement information with the predicted part-of-speech tagging set for the corresponding sample transformation requirement information to update the first samples. Sub-step eight: Determine at least one updated first sample as the second sample set. This provides the training data, i.e., the second sample set, for training the second initial neural network. Sub-step nine: Input the predicted part-of-speech tagging set of at least one second sample in the second sample set into the second initial neural network to obtain the sample extraction associated word set corresponding to each of the at least one second sample. Sub-step ten: Compare the sample extraction associated word set corresponding to each of the at least one second sample with the corresponding sample target associated word set to obtain a second comparison result. Therefore, a second comparison result can be obtained to determine whether the first initial neural network and the second initial neural network have reached the preset optimization objective. Sub-step eleven: Based on the first comparison result, the second comparison result, and the preset weights, determine whether the first initial neural network and the second initial neural network have reached the preset optimization objective. Sub-step twelve: In response to determining that the first initial neural network and the second initial neural network have reached the above optimization objective, use the first initial neural network and the second initial neural network as the trained associated word set extraction model. Therefore, a successfully trained associated word set extraction model can be obtained. Sub-step thirteen: In response to determining that the first initial neural network and the second initial neural network have not reached the above optimization objective, adjust the network parameters of the first initial neural network and the second initial neural network, and use unused first samples to form a first sample set. Using the adjusted first initial neural network and the second initial neural network, execute the above associated word set extraction model training steps again. This is because the associated word set extraction model was generated by training the first initial neural network and the second initial neural network. By using a related word extraction model, the conversion requirement information is converted into part-of-speech tagged keywords based on the contextual semantics. When the same keyword has different meanings in different contexts, its unique part of speech can also be determined, thereby improving the accuracy of the matching plugin, reducing the number of rematching conversions, and thus reducing the waste of computing resources.

[0085] Step 107: Based on the generated plugin information group and the log information to be converted, generate the conversion information corresponding to the log information to be converted.

[0086] In some embodiments, the executing entity can generate conversion information corresponding to the log information to be converted based on the generated plugin information group and the log information to be converted. In practice, the executing entity can input the plugin information group and the log information to be converted into a parser to obtain the conversion information corresponding to the log information to be converted. The parser can be a log parser.

[0087] The above embodiments of this disclosure have the following beneficial effects: the log information conversion method of some embodiments of this disclosure can continuously update the plugin information groups in the plugin information group set, reducing the number of log information conversion failures. Specifically, the reason for the error in the converted log information and the increase in the number of log information conversion failures is that, since the plugins in the static plugin manager are fixed, when there are no plugins in the plugin manager that meet the log information conversion requirements, the obtained plugins, i.e., plugin information groups, are incorrect, thus leading to incorrect converted log information and an increase in the number of log information conversion failures. Based on this, the log information conversion method of some embodiments of this disclosure first obtains a plugin information group set from a preset plugin information directory. Thus, an unupdated plugin information group set for determining the plugin information group corresponding to the conversion requirement information can be obtained. Second, the plugin information groups in the preset plugin information directory are monitored to obtain monitoring results. Thus, the plugin information groups in the preset plugin information directory can be monitored in real time to obtain monitoring results indicating whether there are new plugin information groups in the preset plugin information directory. Then, in response to determining that the monitoring results indicate that there are new plugin information groups in the preset plugin information directory, each existing new plugin information group is determined as a new plugin information group set. Therefore, the newly added plugin information group set used to update the plugin information group set can be determined. Next, based on the newly added plugin information group set, the plugin information groups in the aforementioned plugin information group set are updated to update the plugin information group set. This update results in a more complete plugin information group set. Then, the log information to be converted and the corresponding conversion requirement information are obtained. Based on the conversion requirement information and the updated plugin information group set, the plugin information group corresponding to the conversion requirement information is determined. This yields the plugin information group used to generate conversion information. Finally, based on the generated plugin information group and the log information to be converted, the conversion information corresponding to the log information to be converted is generated. This generates log conversion information corresponding to the conversion requirement. Because real-time monitoring of the plugin information groups in the preset plugin information directory is employed, when the monitoring results indicate the existence of a newly added plugin information group in the preset plugin information directory, the plugin information groups in the plugin information group set are updated in real-time to update the plugin information group set. Based on the conversion requirement information and the updated, more complete plugin information group set, a plugin information group that better meets the conversion requirement information is determined. This makes the generated transformation information more accurate, thereby reducing the number of log information transformation failures.

[0088] Further reference Figure 2 As an implementation of the methods shown in the figures, this disclosure provides some embodiments of a log information conversion device, which are similar to... Figure 1Corresponding to the method embodiments shown, the device can be specifically applied to various electronic devices.

[0089] like Figure 2 As shown, the log information conversion device 200 in some embodiments includes: a first acquisition unit 201, a monitoring unit 202, a first determination unit 203, an update unit 204, a second acquisition unit 205, a second determination unit 206, and a generation unit 207. The system comprises the following components: a first acquisition unit 201 configured to acquire a set of plugin information groups from a preset plugin information directory; a monitoring unit 202 configured to monitor the plugin information groups in the preset plugin information directory and obtain monitoring results; a first determination unit 203 configured to determine each existing new plugin information group as a set of new plugin information groups in response to the determination that the monitoring results indicate the existence of new plugin information groups in the preset plugin information directory; an update unit 204 configured to update the plugin information groups in the set of new plugin information groups based on the set of new plugin information groups; a second acquisition unit 205 configured to acquire log information to be converted and corresponding conversion requirement information; a second determination unit 206 configured to determine the plugin information group corresponding to the conversion requirement information based on the conversion requirement information and the updated set of plugin information groups; and a generation unit 207 configured to generate conversion information corresponding to the log information to be converted based on the generated plugin information group and the log information to be converted.

[0090] It is understandable that the units described in the device 200 are related to the reference. Figure 1 The steps in the method described above correspond to each other. Therefore, the operations, features, and beneficial effects described above for the method also apply to the device 200 and the units contained therein, and will not be repeated here.

[0091] The following is for reference. Figure 3 It shows a schematic diagram of the structure of an electronic device 300 suitable for implementing some embodiments of the present disclosure. Figure 3 The electronic device shown is merely an example and should not be construed as limiting the functionality and scope of the embodiments of this disclosure.

[0092] like Figure 3As shown, the electronic device 300 may include a processing unit (e.g., a central processing unit, a graphics processing unit, etc.) 301, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 302 or a program loaded from a storage device 308 into a random access memory (RAM) 303. The RAM 303 also stores various programs and data required for the operation of the electronic device 300. The processing unit 301, ROM 302, and RAM 303 are interconnected via a bus 304. An input / output (I / O) interface 305 is also connected to the bus 304.

[0093] Typically, the following devices can be connected to I / O interface 305: input devices 306 including, for example, touchscreens, touchpads, keyboards, mice, cameras, microphones, accelerometers, gyroscopes, etc.; output devices 307 including, for example, liquid crystal displays (LCDs), speakers, vibrators, etc.; storage devices 308 including, for example, magnetic tapes, hard disks, etc.; and communication devices 309. Communication device 309 allows electronic device 300 to communicate wirelessly or wiredly with other devices to exchange data. Although Figure 3 An electronic device 300 with various devices is shown; however, it should be understood that it is not required to implement or possess all of the devices shown. More or fewer devices may be implemented or possessed alternatively. Figure 3 Each box shown can represent a device or multiple devices as needed.

[0094] In particular, according to some embodiments of this disclosure, the processes described above with reference to the flowcharts can be implemented as computer software programs. For example, some embodiments of this disclosure include a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for performing the methods shown in the flowcharts. In such embodiments, the computer program can be downloaded and installed from a network via communication device 309, or installed from storage device 308, or installed from ROM 302. When the computer program is executed by processing device 301, it performs the functions defined in the methods of some embodiments of this disclosure.

[0095] It should be noted that, in some embodiments of this disclosure, the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example,—but not limited to—an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of a computer-readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination thereof. In some embodiments of this disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In some embodiments of this disclosure, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. Such propagated data signals may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium can be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium can be transmitted using any suitable medium, including but not limited to: wires, optical fibers, RF (radio frequency), etc., or any suitable combination thereof.

[0096] In some implementations, clients and servers can communicate using any currently known or future-developed network protocol such as HTTP (Hypertext Transfer Protocol) and can interconnect with digital data communication (e.g., communication networks) of any form or medium. Examples of communication networks include local area networks (“LANs”), wide area networks (“WANs”), the Internet (e.g., the Internet of Things), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future-developed networks.

[0097] A computer-readable medium may be contained within an electronic device or may exist independently, not assembled into the electronic device. The computer-readable medium carries one or more programs that, when executed by the electronic device, cause the electronic device to: obtain a set of plugin information groups from a preset plugin information directory; monitor the plugin information groups in the preset plugin information directory and obtain monitoring results; in response to determining that the monitoring results indicate the existence of new plugin information groups in the preset plugin information directory, identify each existing new plugin information group as a set of new plugin information groups; update the plugin information groups in the set of new plugin information groups based on the set of new plugin information groups to update the plugin information group set; obtain log information to be converted and corresponding conversion requirement information for the log information to be converted; determine the plugin information group corresponding to the conversion requirement information based on the conversion requirement information and the updated set of plugin information groups; and generate conversion information corresponding to the log information to be converted based on the generated plugin information group and the log information to be converted.

[0098] Computer program code for performing operations of some embodiments of this disclosure can be written in one or more programming languages ​​or a combination thereof, including object-oriented programming languages ​​such as Java, Smalltalk, and C++, and conventional procedural programming languages ​​such as the "C" language or similar programming languages. The program code can be executed entirely on the user's computer, partially on the user's computer, as a standalone software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving remote computers, the remote computer can be connected to the user's computer via any type of network—including a local area network (LAN) or a wide area network (WAN)—or can be connected to an external computer (e.g., via the Internet using an Internet service provider).

[0099] The flowcharts and block diagrams in the accompanying drawings illustrate the architecture, functionality, and operation of possible implementations of systems, methods, and computer program products according to various embodiments of this disclosure. In this regard, each block in a flowchart or block diagram may represent a module, segment, or portion of code containing one or more executable instructions for implementing a specified logical function. It should also be noted that in some alternative implementations, the functions indicated in the blocks may occur in a different order than those indicated in the drawings. For example, two consecutively indicated blocks may actually be executed substantially in parallel, and they may sometimes be executed in reverse order, depending on the functions involved. It should also be noted that each block in the block diagrams and / or flowcharts, and combinations of blocks in the block diagrams and / or flowcharts, can be implemented using a dedicated hardware-based system that performs the specified function or operation, or using a combination of dedicated hardware and computer instructions.

[0100] The units described in some embodiments of this disclosure can be implemented in software or hardware. The described units can also be housed in a processor; for example, a processor can be described as including a first acquisition unit, a monitoring unit, a first determination unit, an update unit, a second acquisition unit, a second determination unit, and a generation unit. The names of these units do not necessarily limit the specific unit; for example, a monitoring unit can also be described as "a unit that monitors the plugin information group in the aforementioned preset plugin information directory and obtains monitoring results."

[0101] The functions described above in this document can be performed, at least in part, by one or more hardware logic components. For example, exemplary types of hardware logic components that can be used, without limitation, include: Field Programmable Gate Arrays (FPGAs), Application-Specific Integrated Circuits (ASICs), Application Standard Products (ASSPs), System-on-Chip (SoCs), Complex Programmable Logic Devices (CPLDs), and so on.

[0102] The above description is merely a selection of preferred embodiments of this disclosure and an explanation of the technical principles employed. Those skilled in the art should understand that the scope of the invention involved in the embodiments of this disclosure is not limited to technical solutions formed by specific combinations of the above-described technical features, but should also cover other technical solutions formed by arbitrary combinations of the above-described technical features or their equivalents without departing from the above-described inventive concept. For example, technical solutions formed by substituting the above-described features with (but not limited to) technical features with similar functions disclosed in the embodiments of this disclosure.

Claims

1. A log information conversion method, comprising: Retrieve the plugin information group set from the preset plugin information directory; Monitor the plugin information groups in the preset plugin information directory and obtain the monitoring results; In response to determining that the monitoring result indicates the existence of a new plugin information group in the preset plugin information directory, each existing new plugin information group is determined as a new plugin information group set; Based on the newly added plugin information group set, the plugin information groups in the plugin information group set are updated to update the plugin information group set. Obtain the log information to be converted and the corresponding conversion requirement information for the log information to be converted; Based on the conversion requirement information and the updated set of plugin information groups, determine the plugin information group corresponding to the conversion requirement information; Based on the generated plugin information group and the log information to be converted, conversion information corresponding to the log information to be converted is generated.

2. The method according to claim 1, wherein, The monitoring of the plugin information groups in the preset plugin information directory, and the resulting monitoring results, include: The time when the first set of plugin information groups is obtained from the preset plugin information directory is set as the preset initial time. The start time of the periodic timer is determined as the preset initial time; Based on the preset initial time, the following processing steps are performed: Get the system time as the current time; In response to determining that the current time is greater than or equal to a preset initial time, the following monitoring steps are performed based on the preset initial time: The directory information of the preset plugin information directory corresponding to the preset initial time is determined as the first directory information; In response to the determination period timer meeting the system time acquisition condition, the system time is acquired as the second directory information acquisition time; The directory information of the preset plugin information directory corresponding to the second directory information acquisition time is determined as the second directory information; In response to the determination that the information in the first directory is the same as the information in the second directory, the information indicating that there is no new plugin information group in the preset plugin information directory is determined as the monitoring result; In response to the determination that the monitoring results indicate that there is no new plugin information group in the preset plugin information directory, the preset initial time is updated to the second directory information acquisition time, and the monitoring steps are executed again. In response to the determination that the information in the first directory is different from the information in the second directory, the information indicating that there is a new plugin information group in the preset plugin information directory is determined as the monitoring result; In response to determining that the current time is less than the preset initial time, the processing steps are executed again after a second preset time interval.

3. The method according to claim 2, wherein, The second directory information includes the first directory information; and in response to determining that the monitoring result indicates the existence of a new plugin information group in the preset plugin information directory, the existing new plugin information groups are determined as a set of new plugin information groups, including: Delete the first directory information from the second directory information; The information in the second directory after deleting the information in the first directory will be identified as newly added directory information. Each newly added plugin information group corresponding to the newly added directory information is determined as a set of newly added plugin information groups.

4. The method according to claim 1, wherein, The step of updating the plugin information groups in the plugin information group set based on the newly added plugin information group set, to update the plugin information group set, includes: Each plugin information group corresponding to the newly added plugin information group in the plugin information group set is determined as the plugin information group set to be removed; Remove the plugin information group set to be removed from the plugin information group set to perform an initial update to the plugin information group set; The initially updated set of plugin information groups is determined as the initial set of plugin information groups to be updated. The initial set of plugin information groups to be updated is combined with the newly added set of plugin information groups to obtain the combined set of plugin information groups as the updated set of plugin information groups.

5. The method according to claim 1, wherein, The step of determining the plugin information group corresponding to the conversion requirement information based on the conversion requirement information and the updated plugin information group set includes: The conversion requirement information is input into a pre-trained related word set extraction model to obtain the related word set; For each plugin information group in the updated plugin information group set, perform the following similarity determination steps: Based on each associated word in the associated word set and the plugin information group, the semantic similarity between the associated word and the plugin information group is determined as the first semantic similarity, thus obtaining the first semantic similarity set; The average value of each first semantic similarity in the first semantic similarity set is determined as the semantic similarity to be screened; Each identified semantic similarity to be screened is defined as a set of semantic similarities to be screened. The semantic similarity that meets the preset filtering conditions in the set of semantic similarities to be filtered is determined as the target semantic similarity; The plugin information group corresponding to the target semantic similarity is determined as the plugin information group corresponding to the conversion requirement information.

6. A log information conversion device, comprising: The first acquisition unit is configured to acquire a set of plugin information groups from a preset plugin information directory; The monitoring unit is configured to monitor the plugin information group in the preset plugin information directory and obtain monitoring results; The first determining unit is configured to determine each existing new plugin information group as a set of new plugin information groups in response to determining that the monitoring result indicates that there is a new plugin information group in the preset plugin information directory. The update unit is configured to update the plugin information groups in the plugin information group set based on the newly added plugin information group set, so as to update the plugin information group set. The second acquisition unit is configured to acquire the log information to be converted and the corresponding conversion requirement information of the log information to be converted. The second determining unit is configured to determine the plug-in information group corresponding to the conversion requirement information based on the conversion requirement information and the updated plug-in information group set. The generation unit is configured to generate conversion information corresponding to the log information to be converted based on the generated plugin information group and the log information to be converted.

7. An electronic device, comprising: One or more processors; A storage device on which one or more programs are stored; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1 to 5.

8. A computer-readable medium having a computer program stored thereon, wherein, When the program is executed by the processor, it implements the method as described in any one of claims 1 to 5.

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