Operation and maintenance method, system, device, equipment, medium and program product

Abstract

This disclosure provides an operation and maintenance method applied to servers, applicable to the fields of financial technology and artificial intelligence. The method includes: acquiring alarm information from a monitoring system; matching the alarm information with preset operation information in an operation terminal database to obtain target preset operation information; generating an alarm voice based on the target preset operation information and sending the alarm voice to the target operation terminal corresponding to the target preset operation information; inputting the alarm information into a prediction model to obtain predicted handling information; sending the predicted handling information and the alarm voice to the operation and maintenance terminal corresponding to the alarm information; the operation and maintenance terminal, in response to receiving the predicted handling information and alarm voice sent by the server, collecting the voice signal emitted by the operation and maintenance object to obtain dialogue voice; and, in response to receiving the dialogue voice from the operation and maintenance terminal, retrieving the target operation and maintenance operation from the operation and maintenance knowledge base based on the dialogue voice, and sending the target operation and maintenance operation to the operation and maintenance terminal.

Description

Technical Field

[0001] This disclosure relates to the fields of financial technology and artificial intelligence, and more specifically to an operation and maintenance method, system, device, equipment, medium and program product. Background Technology

[0002] With the rapid development of financial technology, the number of devices such as application terminals and servers is growing rapidly, which places higher demands on equipment operation and maintenance. Typically, the operation of application terminals and servers is monitored, and when a monitored device generates an alarm, the alarm information is sent to maintenance personnel via SMS or email, and the terminal device is then maintained.

[0003] In realizing the concept disclosed herein, the inventors discovered at least the following problems in the related technology: if maintenance personnel cannot arrive at the site in a timely manner to handle the situation, the equipment will continue to operate, and the risk will continue to increase, resulting in low efficiency of maintenance. Summary of the Invention

[0004] In view of the above problems, this disclosure provides an operation and maintenance method, system, device, equipment, medium and program product.

[0005] According to a first aspect of this disclosure, an operation and maintenance method is provided, applied to a server, comprising: acquiring alarm information from a monitoring system, the monitoring system being used to monitor the operating parameters of at least one operating terminal, and generating alarm information in response to the operating parameters meeting preset conditions; matching the alarm information with preset operating information in an operating terminal database to obtain target preset operating information matching the alarm information, the preset operating information being used to instruct the operating terminal to execute an operation command; generating an alarm voice based on the target preset operating information, and sending the alarm voice to the target operating terminal corresponding to the target preset operating information; inputting the alarm information into a prediction model to obtain predicted handling information, the predicted handling information representing a handling strategy matching the alarm information; sending the predicted handling information and the alarm voice to the operation and maintenance terminal corresponding to the alarm information, the operation and maintenance terminal being used to, in response to receiving the predicted handling information and the alarm voice sent by the server, collect the voice signal emitted by the operation and maintenance object to obtain dialogue voice; in response to receiving the dialogue voice from the operation and maintenance terminal, searching in an operation and maintenance knowledge base based on the dialogue voice to obtain a target operation and maintenance operation, and sending the target operation and maintenance operation to the operation and maintenance terminal, the target operation and maintenance operation representing an operation and maintenance answer matching the dialogue voice.

[0006] According to embodiments of this disclosure, matching alarm information with preset operation information in the operation terminal database to obtain target preset operation information that matches the alarm information includes: matching alarm information with at least one alarm tag in the operation terminal database to obtain a target alarm tag that matches the alarm information; and determining the target preset operation information that matches the alarm information from the operation terminal database based on the target alarm tag.

[0007] According to embodiments of this disclosure, generating an alarm voice based on target preset operation information includes: performing entity recognition on the target preset operation information according to preset field rules to obtain target operation instructions; generating voice alarm information based on the alarm level and target operation instructions in the alarm information; and generating alarm voice based on the voice features of the voice alarm information.

[0008] According to embodiments of this disclosure, generating alarm voice based on the voice features of voice alarm information includes: decomposing the voice alarm information into text to obtain at least one field, the field including at least one character; performing language modeling on the at least one field to obtain semantic information of the at least one field; performing prosodic analysis on the at least one field to obtain pronunciation information of the characters in the at least one field; and performing speech synthesis on the voice alarm information based on the semantic information and pronunciation information of the at least one field to obtain alarm voice.

[0009] According to embodiments of this disclosure, inputting alarm information into a prediction model to obtain predicted handling information includes: inputting alarm information into the convolutional layer of the prediction model to obtain operation and maintenance features; determining target historical alarm information with alarm levels from a historical alarm information set based on the alarm levels in the alarm information, and determining target historical operation and maintenance features corresponding to the target historical alarm information; calculating the similarity between the target historical operation and maintenance features and the operation and maintenance features to obtain a similarity result; and determining predicted handling information from a historical alarm handling information set based on the similarity result.

[0010] According to embodiments of this disclosure, in response to receiving a dialogue voice from an operations and maintenance terminal, retrieving the target operations and maintenance operation from an operations and maintenance knowledge base based on the dialogue voice includes: in response to receiving the dialogue voice from the operations and maintenance terminal, extracting acoustic features from the dialogue voice to obtain an acoustic feature vector, wherein the dialogue voice is obtained by the operations and maintenance terminal collecting the voice signal emitted by the operations and maintenance object; inputting the acoustic feature vector into an acoustic model to obtain a feature score of the acoustic feature vector, wherein the acoustic model is trained using a labeled voice dataset; inputting the dialogue voice into a language model to obtain a predicted text sequence corresponding to the dialogue voice; and decoding the dialogue voice based on the feature score of the acoustic feature vector and the predicted text sequence to obtain the dialogue text.

[0011] A second aspect of this disclosure provides an operation and maintenance method applied to a target operating terminal, comprising: responding to receiving an alarm voice sent by a server, executing an operation command corresponding to the alarm voice, wherein the alarm voice is obtained by applying the method to the server according to any of the above operation and maintenance methods.

[0012] A third aspect of this disclosure provides an operation and maintenance method applied to an operation and maintenance terminal, comprising: in response to receiving predictive handling information sent by a server, collecting voice signals emitted by the operation and maintenance object to obtain dialogue voice, wherein the predictive handling information is obtained by applying the above-mentioned operation and maintenance method to the server.

[0013] A fourth aspect of this disclosure provides an operation and maintenance system, comprising: a monitoring system for monitoring the operating parameters of at least one operating terminal, and generating alarm information in response to the operating parameters meeting preset conditions; a server for obtaining alarm information from the monitoring system; matching the alarm information with preset operating information in an operating terminal database to obtain target preset operating information matching the alarm information, the preset operating information being used to instruct the operating system to execute operating instructions; generating an alarm voice based on the target preset operating information, and sending the alarm voice to a target operating terminal corresponding to the target preset operating information; and a target operating terminal for executing an alarm upon receiving the alarm voice sent by the server. The server provides operation instructions corresponding to alarm voice messages; it also inputs alarm information into a prediction model to obtain predicted handling information, which represents a handling strategy matching the alarm information; it sends the predicted handling information and alarm voice messages to the maintenance terminal corresponding to the alarm information; the maintenance terminal, in response to receiving the predicted handling information from the server, collects the voice signal emitted by the maintenance object to obtain dialogue voice; the server also responds to receiving the dialogue voice from the maintenance terminal, searches the maintenance knowledge base based on the dialogue voice to obtain the target maintenance operation, and sends the target maintenance operation to the maintenance terminal, which represents the maintenance answer matching the dialogue voice.

[0014] The fifth aspect of this disclosure provides an operation and maintenance device applied to a server, comprising: an acquisition module for acquiring alarm information from a monitoring system, the monitoring system for monitoring the operating parameters of at least one operating terminal and generating alarm information in response to the operating parameters meeting preset conditions; a matching module for matching the alarm information with preset operating information in an operating terminal database to obtain target preset operating information matching the alarm information, the preset operating information being used to instruct the operating terminal to execute an operation command; and a generation module for generating an alarm voice based on the target preset operating information and sending the alarm voice to the target operating terminal corresponding to the target preset operating information. The first input module is used to input alarm information into the prediction model to obtain predicted handling information, which represents the handling strategy matching the alarm information. The sending module is used to send the predicted handling information and alarm voice to the operation and maintenance terminal corresponding to the alarm information. The operation and maintenance terminal is used to collect the voice signal emitted by the operation and maintenance object in response to receiving the predicted handling information and alarm voice sent by the server to obtain the dialogue voice. In response to receiving the dialogue voice from the operation and maintenance terminal, it searches in the operation and maintenance knowledge base according to the dialogue voice to obtain the target operation and maintenance operation, and sends the target operation and maintenance operation to the operation and maintenance terminal. The target operation and maintenance operation represents the operation and maintenance answer matching the dialogue voice.

[0015] The sixth aspect of this disclosure provides an operation and maintenance device applied to a target operating terminal, comprising: an execution module, configured to execute an operation command corresponding to the alarm voice in response to receiving an alarm voice sent by a server, wherein the alarm voice is obtained according to any of the above-described operation and maintenance methods applied to the server.

[0016] The seventh aspect of this disclosure provides an operation and maintenance device applied to an operation and maintenance terminal, comprising: a data acquisition module, configured to, in response to receiving predictive handling information sent by a server, acquire voice signals emitted by the operation and maintenance object to obtain dialogue voice, wherein the predictive handling information is obtained according to any of the above-described operation and maintenance methods applied to the server.

[0017] An eighth aspect of this disclosure provides an electronic device comprising: one or more processors; and a memory for storing one or more computer programs, wherein the one or more processors execute the one or more computer programs to implement the steps of the method described above.

[0018] The ninth aspect of this disclosure also provides a computer-readable storage medium having a computer program or instructions stored thereon, which, when executed by a processor, implement the steps of the above-described method.

[0019] The tenth aspect of this disclosure also provides a computer program product, including a computer program or instructions that, when executed by a processor, implement the steps of the above-described method.

[0020] According to embodiments of this disclosure, alarm information is obtained from the monitoring system, and the alarm information is matched with preset operation information in the operation terminal database to obtain target preset operation information that matches the alarm information. Then, an alarm voice is generated based on the target preset operation information and sent to the target operation terminal corresponding to the target preset operation information. Even if the maintenance object cannot handle the situation on-site in a timely manner, the risk of the target operation terminal continuing to operate incorrectly can be reduced.

[0021] The alarm information is input into the prediction model to obtain the predicted handling information; the predicted handling information is sent to the operation and maintenance terminal corresponding to the alarm information so that the operation and maintenance terminal can quickly and accurately perform operation and maintenance operations on the target operation terminal based on the accurate predicted handling information.

[0022] When maintenance users have questions about predicted handling information, the system searches the maintenance knowledge base based on the user's voice messages to obtain the target maintenance operation. This can quickly answer the user's questions, making it easier for the user to select the appropriate maintenance operation and greatly improving the efficiency of maintenance. Attached Figure Description

[0023] The foregoing contents, as well as other objects, features, and advantages of this disclosure, will become clearer from the following description of embodiments with reference to the accompanying drawings, in which:

[0024] Figure 1 This diagram illustrates an application scenario of the operation and maintenance method according to an embodiment of the present disclosure.

[0025] Figure 2 A flowchart illustrating an operation and maintenance method applied to a server according to an embodiment of the present disclosure is shown schematically.

[0026] Figure 3 A flowchart illustrating an operation and maintenance method applied to a target operating terminal according to an embodiment of the present disclosure is shown schematically.

[0027] Figure 4 A flowchart illustrating an operation and maintenance method applied to an operation and maintenance terminal according to an embodiment of the present disclosure is shown schematically.

[0028] Figure 5 A schematic diagram of an operation and maintenance system according to an embodiment of the present disclosure is shown.

[0029] Figure 6A A schematic diagram of an operation and maintenance system according to another embodiment of the present disclosure is shown;

[0030] Figure 6B The diagram illustrates the interaction of an operation and maintenance method according to an embodiment of the present disclosure.

[0031] Figure 7This schematically illustrates a structural block diagram of an operation and maintenance apparatus for a server according to an embodiment of the present disclosure;

[0032] Figure 8 This schematically illustrates a structural block diagram of an operation and maintenance device applied to a target operating terminal according to an embodiment of the present disclosure;

[0033] Figure 9 This schematically illustrates a structural block diagram of an operation and maintenance device applied to an operation and maintenance terminal according to an embodiment of the present disclosure; and

[0034] Figure 10 A block diagram schematically illustrates an electronic device suitable for implementing an operation and maintenance method according to an embodiment of the present disclosure. Detailed Implementation

[0035] The embodiments of the present disclosure will now be described with reference to the accompanying drawings. However, it should be understood that these descriptions are exemplary only and are not intended to limit the scope of the disclosure. In the following detailed description, numerous specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure for ease of explanation. However, it will be apparent that one or more embodiments may be practiced without these specific details. Furthermore, descriptions of well-known structures and techniques are omitted in the following description to avoid unnecessarily obscuring the concepts of the present disclosure.

[0036] The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit this disclosure. The terms “comprising,” “including,” etc., as used herein indicate the presence of the stated features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0037] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein are to be interpreted in a manner consistent with the context of this specification, and not in an idealized or overly rigid way.

[0038] When using expressions such as "at least one of A, B and C", they should generally be interpreted in accordance with the meaning that is commonly understood by those skilled in the art (e.g., "a system having at least one of A, B and C" should include, but is not limited to, a system having A alone, a system having B alone, a system having C alone, a system having A and B, a system having A and C, a system having B and C, and / or a system having A, B and C, etc.).

[0039] It should be noted that in the embodiments of this application, certain software, components, models and other existing solutions in the industry may be mentioned. These should be regarded as exemplary and are only intended to illustrate the feasibility of implementing the technical solution of this application. However, it does not mean that the applicant has used or necessarily used the solution.

[0040] In the technical solution disclosed herein, the user information (including but not limited to user personal information, user image information, user device information, such as location information) and data (including but not limited to data used for analysis, stored data, and displayed data) involved are all information and data authorized by the user or fully authorized by all parties. Furthermore, the collection, storage, use, processing, transmission, provision, disclosure, and application of related data all comply with relevant laws, regulations, and standards, necessary confidentiality measures have been taken, and they do not violate public order and good morals. Corresponding operation entry points are provided for users to choose to authorize or refuse.

[0041] In scenarios involving automated decision-making using personal information, the methods, devices, and systems provided in this disclosure all offer users corresponding entry points for choosing to agree to or reject the automated decision-making results. If the user chooses to reject, the process proceeds to the expert decision-making stage. Here, "automated decision-making" refers to the activity of automatically analyzing and evaluating an individual's behavioral habits, interests, or economic, health, and credit status through computer programs, and then making a decision. Here, "expert decision-making" refers to the activity of making decisions by personnel who specialize in a particular field, possess specialized experience, knowledge, and skills, and have reached a certain level of professional expertise.

[0042] The system monitors the operation of devices such as application terminals and servers. When a monitored device generates an alarm, the alarm information is sent to maintenance personnel via SMS or email. If maintenance personnel cannot respond to the situation on-site in a timely manner, the equipment continues to operate, and the risk will continue to escalate, resulting in low efficiency in alarm handling.

[0043] In view of this, embodiments of the present disclosure provide an operation and maintenance method applied to a server, comprising: obtaining alarm information from a monitoring system, the monitoring system being used to monitor the operating parameters of at least one operating terminal, and generating alarm information in response to the operating parameters meeting preset conditions; matching the alarm information with preset operating information in an operating terminal database to obtain target preset operating information matching the alarm information, the preset operating information being used to instruct the operating terminal to execute an operation command; generating an alarm voice based on the target preset operating information, and sending the alarm voice to the target operating terminal corresponding to the target preset operating information; inputting the alarm information into a prediction model to obtain predicted handling information, the prediction model being trained using a historical alarm information set and a historical alarm handling information set; and sending the predicted handling information to the operation and maintenance terminal corresponding to the alarm information.

[0044] Figure 1 The diagram illustrates an application scenario of the operation and maintenance method according to an embodiment of the present disclosure.

[0045] like Figure 1 As shown, application scenario 100 according to this embodiment may include a monitoring system 101, a target operation terminal 102, an operation and maintenance terminal 103, a network 104, and a server 105. The network 104 serves as a medium for providing communication links between the monitoring system 101, the target operation terminal 102, the operation and maintenance terminal 103, and the server 105. The network 104 may include various connection types, such as wired or wireless communication links, or fiber optic cables, etc.

[0046] Users can interact with the monitoring system 101, target operation terminal 102, maintenance terminal 103, and network 104 to receive or send messages, etc. Various communication client applications can be installed on the monitoring system 101, target operation terminal 102, maintenance terminal 103, etc., such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social media platform software, etc. (for example only).

[0047] The monitoring system 101, the target operation terminal 102, the maintenance terminal 103, and various electronic devices with displays and web browsing capabilities, including but not limited to smartphones, tablets, laptops, and desktop computers, etc.

[0048] Server 105 can be a server that provides various services, such as a backend management server that supports users using monitoring system 101, target operation terminal 102, maintenance terminal 103, and the website they browse (for example only). The backend management server can analyze and process data such as received user requests, and feed back the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0049] It should be noted that the operation and maintenance methods provided in this disclosure embodiment can generally be executed by server 105. Correspondingly, the operation and maintenance devices provided in this disclosure embodiment can generally be installed in server 105. The operation and maintenance methods provided in this disclosure embodiment can also be executed by a server or server cluster that is different from server 105 and capable of communicating with monitoring system 101, target operation terminal 102, operation and maintenance terminal 103, and / or server 105. Correspondingly, the operation and maintenance devices provided in this disclosure embodiment can also be installed in a server or server cluster that is different from server 105 and capable of communicating with monitoring system 101, target operation terminal 102, operation and maintenance terminal 103, and / or server 105.

[0050] It should be understood that Figure 1The number of terminal devices, networks, and servers shown is merely illustrative. Depending on implementation needs, any number of terminal devices, networks, and servers can be included.

[0051] Figure 2 A flowchart illustrating an operation and maintenance method applied to a server according to an embodiment of the present disclosure is shown schematically.

[0052] like Figure 2 As shown, the operation and maintenance method applied to the server in this embodiment includes operations S210 to S260.

[0053] In operation S210, alarm information is obtained from the monitoring system. The monitoring system is used to monitor the operating parameters of at least one operating terminal and generates alarm information in response to the operating parameters meeting preset conditions.

[0054] According to embodiments of this disclosure, the monitoring system can be a basic monitoring system, a log monitoring system, an application monitoring system, a batch monitoring system, etc. For example, a basic monitoring system can be used to monitor the operational status of the operating terminal's hardware and software, cloud platform, database, middleware, and applications. A log monitoring system can be used to monitor database, middleware, and application logs generated by the operating terminal. An application monitoring system can be used to monitor the transaction status of the operating terminal, such as transaction volume, business success rate, system success rate, response time, processing time, and long transactions. A batch monitoring system can be used to monitor batch tasks on the operating terminal.

[0055] According to embodiments of this disclosure, operating parameters may include the operating status of components of the operating terminal, processed transaction data, processing volume of batch tasks, log data, etc.

[0056] According to embodiments of this disclosure, the preset condition may be exceeding a monitoring indicator. For example, the operating parameter may be the transaction volume of the operating terminal, and the monitoring indicator may be 1000 transactions. In response to the transaction volume of the operating terminal exceeding 1000 transactions, an alarm message is generated. For example, the monitoring indicator may be configured with multiple alarm threshold levels, and the alarm level is determined based on the alarm threshold level exceeded by the operating parameter.

[0057] According to embodiments of this disclosure, alarm information may include information about whether operating parameters meet preset conditions. For example, alarm information may include the operating terminal that issued the alarm, monitoring indicators, alarm level, alarm status (e.g., pending status, processed status), alarm generation time, host name, IP (Internet Protocol) address, alarm event, and other information.

[0058] In operation S220, the alarm information is matched with the preset operation information in the operation terminal database to obtain the target preset operation information that matches the alarm information.

[0059] According to embodiments of this disclosure, preset operation information is used to instruct the operation terminal to execute operation commands. For example, the preset operation information may be emergency handling steps set by the maintenance terminal. Based on the operation commands in the preset operation information, the operation terminal can be suspended according to the emergency handling steps to reduce further erroneous operations and mitigate risks.

[0060] According to embodiments of this disclosure, the operating terminal database can store preset operating information.

[0061] For example, preset operation information can have a unique identifier, which can be used to identify the preset operation information corresponding to the alarm information. For example, preset operation information can have a mapping relationship with alarm information, and the mapping relationship can determine the preset operation information corresponding to the alarm information.

[0062] For example, the alarm information is compared with the preset operation information in the operation terminal database to calculate the similarity, and the preset operation information corresponding to the alarm information is determined based on the result of the similarity calculation.

[0063] According to embodiments of this disclosure, target preset operation information can characterize the emergency handling steps of the target operating terminal that issued the alarm information.

[0064] During operation S230, an alarm voice is generated based on the target preset operation information, and the alarm voice is sent to the target operation terminal corresponding to the target preset operation information.

[0065] According to embodiments of this disclosure, since alarm information is extensive, to facilitate timely acquisition of key alarm information by the maintenance terminal, the alarm information can first be matched with preset operation information in the operation terminal database to obtain target preset operation information that matches the alarm information. Then, based on the target preset operation information, a concise and refined alarm voice is generated.

[0066] For example, the alarm voice may indicate the following: Level 1 alarm, please suspend business operations.

[0067] When operating S240, the alarm information is input into the prediction model to obtain the predicted handling information.

[0068] According to embodiments of this disclosure, the prediction model is trained using a set of historical alarm information and a set of historical alarm handling information.

[0069] According to embodiments of this disclosure, predictive handling information represents a handling strategy that matches alarm information. The predictive handling information can be at least one alarm handling strategy recommended to the operation and maintenance terminal.

[0070] According to embodiments of this disclosure, the prediction model can be a machine learning model, such as a support vector machine model, a random forest model, a deep learning model, etc.

[0071] According to embodiments of this disclosure, the historical alarm information set can be composed of alarm information collected by the monitoring system. The historical alarm handling information set can be composed of alarm handling information recorded by the operating terminal. It should be noted that there can be a mapping relationship between the alarm handling information in the historical alarm handling information set and the alarm information in the historical alarm information set, so as to realize the association query between the alarm handling information in the historical alarm information set and the alarm information in the historical alarm information set.

[0072] When operating the S250, predictive handling information and alarm voice are sent to the maintenance terminal corresponding to the alarm information.

[0073] According to embodiments of this disclosure, the maintenance terminal may be an electronic device that is easy for the maintenance object to carry or an maintenance platform controlled by the maintenance object.

[0074] According to embodiments of this disclosure, the operation and maintenance terminal is used to collect the voice signal emitted by the operation and maintenance object in response to receiving the predictive handling information and alarm voice sent by the server, and obtain the dialogue voice.

[0075] According to embodiments of this disclosure, if the maintenance object has questions about the predicted handling information, it can send a voice signal to the maintenance terminal. The maintenance terminal can then interact with the server to provide maintenance answers corresponding to the voice dialogue.

[0076] According to embodiments of this disclosure, the dialogue voice represents the operation and maintenance object's operation and maintenance related issues regarding predictive handling information.

[0077] In operation S260, in response to receiving a voice dialogue from the operation and maintenance terminal, the system searches the operation and maintenance knowledge base based on the voice dialogue to obtain the target operation and maintenance operation, and then sends the target operation and maintenance operation to the operation and maintenance terminal.

[0078] According to embodiments of this disclosure, the target maintenance operation representation is matched with the maintenance response in the dialogue voice.

[0079] According to embodiments of this disclosure, a similarity analysis is performed between the spoken dialogue and historical spoken dialogue to determine if similar voices exist. If similar voices exist, historical maintenance operations corresponding to the similar voices can be retrieved from the maintenance knowledge base. If no similar voices exist, the spoken dialogue is converted into text, and the text is used to search the maintenance knowledge base to obtain the target maintenance operation.

[0080] According to embodiments of this disclosure, the operations and maintenance knowledge base can store massive amounts of relevant operations and maintenance information. For example, historical operations and maintenance records, operations and maintenance book information, operations and maintenance webpage information, etc.

[0081] According to embodiments of this disclosure, the target maintenance operation may be specific operational information for one step in the predictive handling information.

[0082] According to embodiments of this disclosure, alarm information is obtained from the monitoring system, and the alarm information is matched with preset operation information in the operation terminal database to obtain target preset operation information that matches the alarm information. Then, an alarm voice is generated based on the target preset operation information and sent to the target operation terminal corresponding to the target preset operation information. Even if the maintenance object cannot handle the situation on-site in a timely manner, the risk of the target operation terminal continuing to operate incorrectly can be reduced.

[0083] The alarm information is input into the prediction model to obtain the predicted handling information; the predicted handling information is sent to the operation and maintenance terminal corresponding to the alarm information so that the operation and maintenance terminal can quickly and accurately perform operation and maintenance operations on the target operation terminal based on the accurate predicted handling information.

[0084] When maintenance users have questions about predicted handling information, the system searches the maintenance knowledge base based on the user's voice messages to obtain the target maintenance operation. This can quickly answer the user's questions, making it easier for the user to select the appropriate maintenance operation and greatly improving the efficiency of maintenance.

[0085] According to embodiments of this disclosure, matching alarm information with preset operation information in the operation terminal database to obtain target preset operation information that matches the alarm information includes: matching alarm information with at least one alarm tag in the operation terminal database to obtain a target alarm tag that matches the alarm information; and determining the target preset operation information that matches the alarm information from the operation terminal database based on the target alarm tag.

[0086] According to embodiments of this disclosure, an alarm tag can be a unique identifier representing the handling method of an alarm event. For example, an alarm tag of "1.03" indicates that "1" represents the first alarm event in the operating terminal database, and "03" represents the third handling method in the corresponding operating terminal database for that alarm event.

[0087] The alarm information includes information such as the operating terminal that issued the alarm, monitoring indicators, alarm level, alarm status (e.g., pending status, processed status), alarm generation time, host name, IP (Internet Protocol) address, and alarm event.

[0088] Based on the storage method of alarm events and preset operation information in the operator terminal database, the alarm events in the alarm information are encoded. The encoded information of the alarm events in the alarm information is then matched with at least one alarm tag in the operator terminal database to obtain the target alarm tag that matches the alarm information.

[0089] According to embodiments of this disclosure, by matching alarm information with at least one alarm tag in the operating terminal database, a target alarm tag matching the alarm information is obtained; based on the target alarm tag, target preset operation information matching the alarm information is determined from the operating terminal database, thereby achieving rapid acquisition of target preset operation information and improving alarm efficiency.

[0090] According to embodiments of this disclosure, generating an alarm voice based on target preset operation information includes: performing entity recognition on the target preset operation information according to preset field rules to obtain target operation instructions; generating voice alarm information based on the alarm level and target operation instructions in the alarm information; and generating alarm voice based on the voice features of the voice alarm information.

[0091] According to embodiments of this disclosure, entity recognition is an important task in Natural Language Processing (NLP). Entities with specific meanings are identified from preset operational information of the target; for example, an entity may be an operational instruction, time, etc.

[0092] According to embodiments of this disclosure, the preset field rules can be text structure information of preset operation information. For example, the first 5 characters in the preset operation information are operation instructions.

[0093] According to embodiments of this disclosure, the server can directly send target operation instructions to the target operation terminal. The target operation terminal is used to execute the target operation instructions upon receiving them from the server. Simultaneously, alarm voice prompts facilitate quick access to the alarm level and target operation instructions for the maintenance personnel.

[0094] For example, voice alarm messages can be generated based on the operating system, alarm level, and target operation command in the alarm information.

[0095] According to embodiments of this disclosure, voice features can characterize the acoustic features of voice alarm information.

[0096] According to embodiments of this disclosure, entity recognition of target preset operation information is performed using preset field rules. Compared with recognition using a pre-trained machine learning model, key information (target operation instructions) can be obtained simply and quickly.

[0097] According to embodiments of this disclosure, generating alarm voice based on the voice features of voice alarm information includes: decomposing the voice alarm information into text to obtain at least one field, the field including at least one character; performing language modeling on the at least one field to obtain semantic information of the at least one field; performing prosodic analysis on the at least one field to obtain pronunciation information of the characters in the at least one field; and performing speech synthesis on the voice alarm information based on the semantic information and pronunciation information of the at least one field to obtain alarm voice.

[0098] According to embodiments of this disclosure, voice alarm information is decomposed into words or phrases to obtain at least one field. The at least one field is then identified to ensure correct lexical boundaries and grammatical understanding.

[0099] According to embodiments of this disclosure, the correct pronunciation, stress, and semantics of at least one field are reconstructed based on contextual understanding and language rules of at least one field to complete the language modeling of voice alarm information.

[0100] According to embodiments of this disclosure, prosodic analysis is performed on at least one field to determine the pronunciation characteristics of each character, including syllable division, stress position, pauses, etc., in order to simulate natural language.

[0101] According to embodiments of this disclosure, the speech synthesizer can be based on a pre-trained speech synthesis model. The speech synthesizer is used to synthesize speech from voice alarm information based on semantic and pronunciation information from at least one field, to obtain alarm speech.

[0102] According to embodiments of this disclosure, a continuous sound wave signal, i.e., an audible audio stream, is generated based on the speech parameters of the alarm voice. The synthesized speech needs to undergo certain optimization processing to improve sound quality, eliminate mechanical sounds, and make it sound more natural.

[0103] According to embodiments of this disclosure, voice alarm information is decomposed into text to obtain at least one field; language modeling is performed on the at least one field to obtain semantic information of the at least one field, thereby realizing semantic information extraction of the field. Prosodic analysis is performed on the at least one field to obtain pronunciation information of the text in the at least one field, thereby realizing speech information extraction of the field. Based on the semantic and pronunciation information of the at least one field, speech synthesis is performed on the voice alarm information to obtain alarm speech, enabling maintenance objects to quickly identify key information in the alarm speech and perform maintenance operations in a timely manner.

[0104] According to embodiments of this disclosure, inputting alarm information into a prediction model to obtain predicted handling information includes: inputting alarm information into the convolutional layer of the prediction model to obtain operation and maintenance features; determining target historical alarm information with alarm levels from a historical alarm information set based on the alarm levels in the alarm information, and determining target historical operation and maintenance features corresponding to the target historical alarm information; calculating the similarity between the target historical operation and maintenance features and the operation and maintenance features to obtain a similarity result; and determining predicted handling information from a historical alarm handling information set based on the similarity result.

[0105] Since the alarm voice is only intended to enable the maintenance object to obtain critical alarm information in a timely manner and prevent the risk from being further amplified by misoperation, the target preset operation information given does not have a detailed solution and is difficult to fundamentally solve the alarm event problem. Therefore, a more precise processing solution is needed.

[0106] According to embodiments of this disclosure, the prediction model can be a neural network model, and the convolutional layer of the neural network model can extract features from the alarm information to obtain operation and maintenance features.

[0107] According to embodiments of this disclosure, historical alarm information in the historical alarm information set has alarm level labels. The alarm level in the alarm information can be matched with the alarm level labels of the historical alarm information in the historical alarm information set to obtain the target historical alarm information. The target historical alarm information represents the same alarm level as the alarm information.

[0108] According to embodiments of this disclosure, the target historical operation and maintenance characteristics can be obtained by inputting the target historical alarm information into the convolutional layer of the prediction model.

[0109] According to embodiments of this disclosure, by calculating the similarity between target historical operation and maintenance characteristics and operation and maintenance characteristics, the historical alarm information most similar to the alarm information is determined from the target historical alarm information with the same alarm level as the alarm information.

[0110] Since there can be a mapping relationship between the alarm handling information in the historical alarm handling information set and the alarm information in the historical alarm information set, it is possible to realize the association query between the alarm handling information in the historical alarm information set and the alarm information in the historical alarm information set.

[0111] Therefore, based on the historical alarm information most similar to the alarm information, the historical alarm handling information most related to the alarm information can be queried, and the historical alarm handling information most related to the alarm information can be identified as the predictive handling information.

[0112] According to embodiments of this disclosure, by determining target historical alarm information with alarm levels from a historical alarm information set based on the alarm levels in the alarm information, and determining the target historical operation and maintenance characteristics corresponding to the target historical alarm information, accurate alarm level positioning can be achieved. The similarity between the target historical operation and maintenance characteristics and the operation and maintenance characteristics is calculated to obtain a similarity result; based on the similarity result, predictive handling information is determined from the historical alarm handling information set, enabling the operation and maintenance object to perform operation and maintenance on the target operating terminal using fast and accurate predictive handling information.

[0113] According to embodiments of this disclosure, in response to receiving a dialogue voice from an operations and maintenance terminal, retrieving the target operations and maintenance operation from an operations and maintenance knowledge base based on the dialogue voice includes: in response to receiving the dialogue voice from the operations and maintenance terminal, extracting acoustic features from the dialogue voice to obtain an acoustic feature vector, wherein the dialogue voice is obtained by the operations and maintenance terminal collecting the voice signal emitted by the operations and maintenance object; inputting the acoustic feature vector into an acoustic model to obtain a feature score of the acoustic feature vector, wherein the acoustic model is trained using a labeled voice dataset; inputting the dialogue voice into a language model to obtain a predicted text sequence corresponding to the dialogue voice; and decoding the dialogue voice based on the feature score of the acoustic feature vector and the predicted text sequence to obtain the dialogue text.

[0114] According to embodiments of this disclosure, the voice signal emitted by the maintenance object is preprocessed by filtering, framing, etc., to obtain the dialogue voice.

[0115] According to embodiments of this disclosure, the spoken dialogue is converted from the time domain to the frequency domain to obtain acoustic feature vectors. The time domain, also known as the time-domain, has time as the independent variable; that is, the horizontal axis represents time, and the vertical axis represents the signal variation. Its dynamic signal is a function describing the value of the signal at different times. The frequency domain, also known as the frequency range domain, has frequency as the independent variable; that is, the horizontal axis represents frequency, and the vertical axis represents the amplitude of the signal at that frequency.

[0116] According to embodiments of this disclosure, the acoustic model can be obtained through supervised learning training using a labeled speech dataset.

[0117] According to embodiments of this disclosure, the language model can be constructed based on linguistic theories, and the language model can calculate the probability of a word sequence corresponding to a sound signal.

[0118] According to embodiments of this disclosure, acoustic feature vectors can be converted into text to be decoded using an existing dictionary. Then, based on the feature scores of the acoustic feature vectors and the predicted text sequence, the text to be decoded is decoded to obtain the dialogue text.

[0119] The system can be accessed by the operations and maintenance personnel through voice dialogue with the operating system based on the recommended predictive action suggestions, in order to obtain relevant content (knowledge search) from the operations and maintenance knowledge database.

[0120] According to embodiments of this disclosure, by responding to received voice dialogue from the maintenance terminal, the voice dialogue is converted into voice dialogue text, which facilitates subsequent retrieval and enables the maintenance object to obtain relevant content related to the maintenance knowledge database and predictive handling opinions, thereby improving maintenance efficiency.

[0121] Figure 3 A flowchart illustrating an operation and maintenance method applied to a target operating terminal according to an embodiment of the present disclosure is shown schematically.

[0122] like Figure 3 As shown, the operation and maintenance method applied to the target operating terminal in this embodiment includes operation S310.

[0123] When operating S310, in response to receiving an alarm voice from the server, it executes the operation command corresponding to the alarm voice, which is obtained based on the operation and maintenance methods applied to the server.

[0124] Figure 4 A flowchart illustrating an operation and maintenance method applied to an operation and maintenance terminal according to an embodiment of the present disclosure is shown schematically.

[0125] like Figure 4 As shown, the operation and maintenance method applied to the operation and maintenance terminal in this embodiment includes operation S410.

[0126] When operating S410, in response to receiving predictive handling information sent by the server, the voice signal emitted by the maintenance object is collected to obtain the dialogue voice. The predictive handling information is obtained based on the maintenance method applied to the server.

[0127] According to embodiments of this disclosure, after the maintenance object further understands the content of the predictive action information, it can issue a voice signal to the maintenance terminal. For example, if the predictive action information includes reducing the storage capacity of the operating terminal, the maintenance terminal can issue a voice signal with the following content: how to reduce the storage capacity of the operating terminal.

[0128] For example, in response to a received voice dialogue from an operations and maintenance (O&M) terminal, the server extracts acoustic features from the dialogue to obtain an acoustic feature vector. The dialogue dialogue is obtained by the O&M terminal collecting the voice signal emitted by the O&M object. The acoustic feature vector is then input into an acoustic model to obtain its feature score. This acoustic model is trained using a labeled speech dataset. The dialogue dialogue is then input into a language model to obtain a predicted text sequence. Based on the feature score of the acoustic feature vector and the predicted text sequence, the dialogue dialogue is decoded to obtain the dialogue text. The dialogue text is then retrieved from an O&M knowledge database, which can be deployed on the server. The retrieved information is then sent to the O&M terminal.

[0129] Figure 5 A schematic diagram of an operation and maintenance system according to an embodiment of the present disclosure is shown.

[0130] like Figure 5 As shown, the operation and maintenance system 500 in this embodiment includes a monitoring system 510, a server 520, a target operation terminal 530, and an operation and maintenance terminal 540.

[0131] The monitoring system 510 is used to monitor the operating parameters of at least one operating terminal and generate alarm information in response to the operating parameters meeting preset conditions.

[0132] Server 520 is used to obtain alarm information from the monitoring system; match the alarm information with preset operation information in the operation terminal database to obtain target preset operation information that matches the alarm information. The preset operation information is used to instruct the operating system to execute operation commands; generate alarm voice according to the target preset operation information, and send the alarm voice to the target operation terminal corresponding to the target preset operation information.

[0133] The target operation terminal 530 is used to respond to the alarm voice sent by the server and execute the operation command corresponding to the alarm voice.

[0134] Server 520 is also used to input alarm information into the prediction model to obtain prediction and handling information, which represents the handling strategy that matches the alarm information; and to send prediction and handling information and alarm voice to the operation and maintenance terminal corresponding to the alarm information.

[0135] The maintenance terminal 540 is used to respond to the predicted handling information and alarm voice sent by the server, collect the voice signal issued by the maintenance object, and obtain the dialogue voice.

[0136] Server 520 is also used to respond to the received dialogue voice from the operation and maintenance terminal, search the operation and maintenance knowledge base according to the dialogue voice, obtain the target operation and maintenance operation, and send the target operation and maintenance operation to the operation and maintenance terminal. The target operation and maintenance operation represents the operation and maintenance answer that matches the dialogue voice.

[0137] Figure 6A A schematic diagram of an operation and maintenance system according to another embodiment of the present disclosure is shown.

[0138] like Figure 6A As shown, the server in this embodiment has a voice recognition module, an alarm recognition module, and a user management module.

[0139] When the monitoring system generates an alarm, it accurately locates the root cause of the alarm based on the characteristics of the monitoring indicators and transmits the alarm information to the server via an interface in a preset format. The alarm information includes the source of the alarm's operating terminal, alarm level, alarm status, alarm generation time, hostname, IP address, and alarm content.

[0140] The server's alarm identification module is used to respond to alarm information received from the monitoring system and classify the alarm information by alarm level, alarm event, and operating terminal. For example, operating terminal classification can be based on IP address, alarm event classification can be based on alarm content, and alarm level classification can be determined based on alarm threshold levels.

[0141] The user management module is used to classify maintenance objects and bind them to corresponding operation terminals. When an alarm is generated by the corresponding operation terminal, the corresponding maintenance object is notified to handle it.

[0142] For example, alarm levels are classified (levels 1-5), alarm events are classified (operating system maintenance group, database maintenance group, middleware maintenance group, network maintenance group, storage maintenance group, application maintenance group, etc.), and operation terminals are classified (payment settlement operation terminal, product operation operation terminal, etc.). The maintenance objects and operation terminals in the user management module are bound one by one through configuration.

[0143] Different operational and maintenance strategies can be implemented based on different alarm levels.

[0144] For example, for alarms at levels 1-5, the server can be used to send alarm voice messages to the operating terminal to notify on-duty personnel. The server's alarm recognition module is used to determine the alarm level of the alarm information. When all alarm information meets the requirements of levels 1-5, the alarm information in the preset format is processed into natural speech, and alarm voice is obtained through analysis, processing, and speech synthesis technology.

[0145] For example, for alarms at levels 1-3, the server can send an SMS message to the maintenance terminal to notify the maintenance recipient. The server's alarm identification module is used to determine the alarm level of the alarm information. When all alarm information meets the requirements of levels 1-3, the alarm information in the preset format is simply converted and then sent via SMS through the server's notification center.

[0146] For example, for Level 1 alarms, the server can send a call request to the operations and maintenance (O&M) terminal to notify the corresponding O&M object by phone. Calls can be made sequentially according to the priority of the O&M object, for example, by calling O&M personnel A, B, and then the supervisor. The server's alarm recognition module is used to determine the alarm level of the alarm information. When all alarm information meets the Level 1 criteria, the alarm information in a preset format undergoes natural language processing, and the alarm voice is obtained through analysis, processing, and speech synthesis technology. The call is then initiated by calling the notification center and the dialogue engine to broadcast the alarm voice to the O&M terminal.

[0147] Voice recognition can automatically trigger corresponding operation and maintenance operations. It can invoke the notification center to make phone calls; it can recognize alarm information and broadcast the alarm messages; and the dialogue engine is used for dialogue and interaction between the operation and maintenance terminal and the server.

[0148] For example, for high-level alarms, the maintenance user interacts with the server based on the actual alarm situation and provides handling suggestions. The server responds to the received voice communication from the maintenance user terminal, extracts acoustic features from the voice signal emitted by the maintenance user, and obtains an acoustic feature vector. The voice communication is obtained by the maintenance user terminal collecting the voice signal emitted by the maintenance user. The acoustic feature vector is input into an acoustic model to obtain its feature score. The acoustic model is trained using a labeled voice dataset. The voice communication is then input into a language model to obtain a predicted text sequence corresponding to the voice communication. Based on the feature score of the acoustic feature vector and the predicted text sequence, the voice communication is decoded to obtain the dialogue text.

[0149] The server is also used to search the operations and maintenance knowledge database based on the dialogue text provided by the operations and maintenance user, obtaining at least one piece of information. This information is then converted into spoken language and fed back to the operations and maintenance user. The user can then select one or more pieces of information for confirmation and processing.

[0150] For high-level alarms, the maintenance personnel can communicate with the server and obtain auxiliary handling information based on the alarm details. For example, automatic alarm recovery can be achieved by automatically creating event tickets through the work order terminal, enabling timely handling of high-risk alarms.

[0151] For example, the server can also be used to call backend operation terminals, which can be automated operation terminals or work order operation terminals. Automated operation terminals and work order operation terminals can be used to perform relevant operation and maintenance operations.

[0152] Figure 6B The diagram illustrates the interaction of the operation and maintenance method according to an embodiment of the present disclosure.

[0153] like Figure 6B As shown, the operation and maintenance method of this embodiment includes S601~S614.

[0154] In operation S601, the alarm information is matched with the preset operation information in the operation terminal database to obtain the target preset operation information that matches the alarm information.

[0155] When operating S602, an alarm voice is generated based on the target preset operation information.

[0156] In operation S603, an alarm voice is sent to the target operation terminal corresponding to the target preset operation information.

[0157] When operating S604, in response to receiving an alarm voice from the server, the operation command corresponding to the alarm voice is executed.

[0158] When operating S605, alarm information is input into the prediction model to obtain prediction and handling information.

[0159] When operating S606, predictive handling information and alarm voice are sent to the maintenance terminal corresponding to the alarm information.

[0160] When operating S607, in response to receiving predictive handling information and alarm voice messages from the server, the system collects the voice signals emitted by the maintenance object to obtain the dialogue voice.

[0161] When operating S608, send voice messages to the server.

[0162] In operation S609, acoustic features are extracted from the dialogue speech to obtain acoustic feature vectors.

[0163] In operation S610, the acoustic feature vector is input into the acoustic model to obtain the feature score of the acoustic feature vector.

[0164] When operating S611, the dialogue speech is input into the language model to obtain the predicted text sequence corresponding to the dialogue speech.

[0165] In operation S612, the dialogue speech is decoded based on the feature scores of the acoustic feature vector and the predicted text sequence to obtain the dialogue text.

[0166] When operating S613, the target operation is obtained by retrieving the operation and maintenance knowledge database based on the dialogue text.

[0167] When operating S614, the target maintenance operation is sent to the maintenance terminal.

[0168] Figure 7 A schematic block diagram of an operation and maintenance device applied to a server according to an embodiment of the present disclosure is shown.

[0169] like Figure 7 As shown, the server maintenance device 700 of this embodiment includes an acquisition module 710, a matching module 720, a generation module 730, a first input module 740, a sending module 750, and a retrieval module 760.

[0170] The acquisition module 710 is used to acquire alarm information from the monitoring system. The monitoring system monitors the operating parameters of at least one operating terminal and generates alarm information in response to the operating parameters meeting preset conditions. In one embodiment, the acquisition module 710 can be used to perform the operation S210 described above, which will not be repeated here.

[0171] The matching module 720 is used to match alarm information with preset operation information in the operation terminal database to obtain target preset operation information that matches the alarm information. The preset operation information is used to instruct the operation terminal to execute operation commands. In one embodiment, the matching module 720 can be used to execute the operation S220 described above, which will not be repeated here.

[0172] The generation module 730 is used to generate an alarm voice based on the target preset operation information and send the alarm voice to the target operation terminal corresponding to the target preset operation information. In one embodiment, the generation module 730 can be used to perform the operation S230 described above, which will not be repeated here.

[0173] The first input module 740 is used to input alarm information into the prediction model to obtain prediction and handling information. The prediction model is trained using a set of historical alarm information and a set of historical alarm handling information. In one embodiment, the first input module 740 can be used to perform the operation S240 described above, which will not be repeated here.

[0174] The sending module 750 is used to send predictive handling information and alarm voice to the operation and maintenance terminal corresponding to the alarm information. The operation and maintenance terminal, in response to receiving the predictive handling information and alarm voice from the server, collects the voice signal emitted by the object being maintained to obtain the dialogue voice. In one embodiment, the sending module 750 can be used to perform the operation S250 described above, which will not be repeated here.

[0175] The retrieval module 760 is configured to, in response to receiving a voice dialogue from the maintenance terminal, search the maintenance knowledge base based on the voice dialogue to obtain the target maintenance operation, and send the target maintenance operation to the maintenance terminal. The target maintenance operation represents the maintenance answer that matches the voice dialogue. In one embodiment, the retrieval module 760 may be used to perform the operation S260 described above, which will not be repeated here.

[0176] According to embodiments of this disclosure, the matching module 720 includes a matching submodule and a determining submodule. The matching submodule is used to match alarm information with at least one alarm tag in the operation terminal database to obtain a target alarm tag that matches the alarm information. The determining submodule is used to determine target preset operation information that matches the alarm information from the operation terminal database based on the target alarm tag.

[0177] According to embodiments of this disclosure, the generation module 730 includes an entity recognition submodule, a first generation submodule, and a second generation submodule. The entity recognition submodule is used to perform entity recognition on the target preset operation information according to preset field rules to obtain the target operation command. The first generation submodule is used to generate voice alarm information based on the alarm level and the target operation command in the alarm information. The second generation submodule is used to generate alarm voice based on the voice features of the voice alarm information.

[0178] According to embodiments of this disclosure, the second generation submodule includes a text decomposition unit, a language modeling unit, a prosodic analysis unit, and a speech synthesis unit. The text decomposition unit decomposes the voice alarm information into text to obtain at least one field, where each field includes at least one character. The language modeling unit performs language modeling on the at least one field to obtain semantic information for that field. The prosodic analysis unit performs prosodic analysis on the at least one field to obtain pronunciation information for the characters in that field. The speech synthesis unit synthesizes the voice alarm information into speech based on the semantic and pronunciation information of the at least one field to obtain alarm speech.

[0179] According to embodiments of this disclosure, the first input module 740 includes a first input unit, a first determination unit, a calculation unit, and a second determination unit. The first input unit is used to input alarm information into the convolutional layer of the prediction model to obtain operational features. The first determination unit is used to determine target historical alarm information with alarm levels from a historical alarm information set based on the alarm levels in the alarm information, and to determine the target historical operational features corresponding to the target historical alarm information. The calculation unit is used to calculate the similarity between the target historical operational features and operational features to obtain a similarity result. The second determination unit is used to determine predicted handling information from a historical alarm handling information set based on the similarity result.

[0180] According to embodiments of this disclosure, the retrieval module 760 includes an acoustic feature extraction submodule, a first input submodule, a second input submodule, and a decoding submodule. The acoustic feature extraction submodule is used to extract acoustic features from the received dialogue voice from the maintenance terminal to obtain an acoustic feature vector. The dialogue voice is obtained by the maintenance terminal collecting the voice signal emitted by the maintenance object. The first input submodule is used to input the acoustic feature vector into an acoustic model to obtain a feature score of the acoustic feature vector. The acoustic model is trained using a labeled speech dataset. The second input submodule is used to input the dialogue voice into a language model to obtain a predicted text sequence corresponding to the dialogue voice. The decoding submodule is used to decode the dialogue voice based on the feature score of the acoustic feature vector and the predicted text sequence to obtain the dialogue text.

[0181] According to embodiments of this disclosure, any multiple modules among the acquisition module 710, matching module 720, generation module 730, first input module 740, sending module 750, and retrieval module 760 can be combined into one module, or any one of these modules can be split into multiple modules. Alternatively, at least some of the functions of one or more of these modules can be combined with at least some of the functions of other modules and implemented in one module. According to embodiments of this disclosure, at least one of the acquisition module 710, matching module 720, generation module 730, first input module 740, sending module 750, and retrieval module 760 can be at least partially implemented as hardware circuitry, such as a field-programmable gate array (FPGA), a programmable logic array (PLA), a system-on-a-chip, a system-on-a-substrate, a system-on-package, an application-specific integrated circuit (ASIC), or implemented in hardware or firmware by any other reasonable means of integrating or packaging the circuitry, or implemented in software, hardware, or firmware, or in any suitable combination of any of these three implementation methods. Alternatively, at least one of the acquisition module 710, matching module 720, generation module 730, first input module 740, sending module 750 and retrieval module 760 may be implemented at least partially as a computer program module, which can perform corresponding functions when the computer program module is run.

[0182] Figure 8 The diagram illustrates a structural block diagram of an operation and maintenance device applied to a target operating terminal according to an embodiment of the present disclosure.

[0183] like Figure 8 As shown, the maintenance device 800 applied to the target operating terminal in this embodiment includes an execution module 810.

[0184] The execution module 810 is used to respond to the alarm voice sent by the server and execute the operation command corresponding to the alarm voice. The alarm voice is obtained by applying any of the above-mentioned operation and maintenance methods to the server.

[0185] Figure 9 The diagram illustrates a structural block diagram of an operation and maintenance device applied to an operation and maintenance terminal according to an embodiment of the present disclosure.

[0186] like Figure 9 As shown, the maintenance device 900 applied to the maintenance terminal in this embodiment includes a data acquisition module 910.

[0187] The acquisition module 910 is used to acquire the voice signal emitted by the maintenance object in response to receiving the predictive handling information sent by the server, and obtain the dialogue voice. The predictive handling information is obtained by applying any of the above maintenance methods to the server.

[0188] Figure 10 A block diagram schematically illustrates an electronic device suitable for implementing an operation and maintenance method according to an embodiment of the present disclosure.

[0189] like Figure 10 As shown, an electronic device 1000 according to an embodiment of the present disclosure includes a processor 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage portion 1008 into a random access memory (RAM) 1003. The processor 1001 may include, for example, a general-purpose microprocessor (e.g., a CPU), an instruction set processor and / or an associated chipset and / or a special-purpose microprocessor (e.g., an application-specific integrated circuit (ASIC)), etc. The processor 1001 may also include onboard memory for caching purposes. The processor 1001 may include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0190] RAM 1003 stores various programs and data required for the operation of electronic device 1000. Processor 1001, ROM 1002, and RAM 1003 are interconnected via bus 1004. Processor 1001 performs various operations of the method flow according to embodiments of the present disclosure by executing programs in ROM 1002 and / or RAM 1003. It should be noted that the programs may also be stored in one or more memories other than ROM 1002 and RAM 1003. Processor 1001 may also perform various operations of the method flow according to embodiments of the present disclosure by executing programs stored in said one or more memories.

[0191] According to embodiments of this disclosure, the electronic device 1000 may further include an input / output (I / O) interface 1005, which is also connected to a bus 1004. The electronic device 1000 may also include one or more of the following components connected to the input / output (I / O) interface 1005: an input section 1006 including a keyboard, mouse, etc.; an output section 1007 including a cathode ray tube (CRT), liquid crystal display (LCD), etc., and a speaker, etc.; a storage section 1008 including a hard disk, etc.; and a communication section 1009 including a network interface card such as a LAN card, modem, etc. The communication section 1009 performs communication processing via a network such as the Internet. A drive 1010 is also connected to the input / output (I / O) interface 1005 as needed. A removable medium 1011, such as a disk, optical disk, magneto-optical disk, semiconductor memory, etc., is installed on the drive 1010 as needed so that computer programs read from it can be installed into the storage section 1008 as needed.

[0192] This disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or it may exist independently and not assembled into the device / apparatus / system. The computer-readable storage medium carries one or more programs that, when executed, implement the method according to the embodiments of this disclosure.

[0193] According to embodiments of this disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, such as including, but not limited to: portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof. In this disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. For example, according to embodiments of this disclosure, the computer-readable storage medium may include ROM 1002 and / or RAM 1003 and / or one or more memories other than ROM 1002 and RAM 1003 described above.

[0194] Embodiments of this disclosure also include a computer program product comprising a computer program containing program code for performing the methods shown in the flowchart. When the computer program product is run on a computer system, the program code enables the computer system to implement the operation and maintenance methods provided in the embodiments of this disclosure.

[0195] When the computer program is executed by the processor 1001, it performs the functions defined in the system / apparatus of this disclosure embodiments. According to embodiments of this disclosure, the systems, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0196] In one embodiment, the computer program may rely on a tangible storage medium such as an optical storage device or a magnetic storage device. In another embodiment, the computer program may also be transmitted and distributed in the form of signals over a network medium, and may be downloaded and installed via the communication section 1009, and / or installed from a removable medium 1011. The program code contained in the computer program can be transmitted using any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination thereof.

[0197] In such an embodiment, the computer program can be downloaded and installed from a network via communication section 1009, and / or installed from removable medium 1011. When the computer program is executed by processor 1001, it performs the functions defined in the system of this disclosure embodiment. According to embodiments of this disclosure, the systems, devices, apparatuses, modules, units, etc., described above can be implemented by computer program modules.

[0198] According to embodiments of this disclosure, program code for executing the computer programs provided in embodiments of this disclosure can be written in any combination of one or more programming languages. Specifically, these computational programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages ​​include, but are not limited to, languages ​​such as Java, C++, Python, "C", or similar programming languages. The program code can execute entirely on a user's computing device, partially on a user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0199] 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 a block diagram or flowchart, and combinations of blocks in a block diagram or flowchart, may 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.

[0200] Those skilled in the art will understand that the features described in the various embodiments of this disclosure can be combined and / or combined in various ways, even if such combinations or combinations are not explicitly described in this disclosure. In particular, the features described in the various embodiments of this disclosure can be combined and / or combined in various ways without departing from the spirit and teachings of this disclosure. All such combinations and / or combinations fall within the scope of this disclosure.

[0201] The embodiments of this disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of this disclosure. Although various embodiments have been described above, this does not mean that the measures in the various embodiments cannot be used advantageously in combination. Various substitutions and modifications can be made by those skilled in the art without departing from the scope of this disclosure, and all such substitutions and modifications should fall within the scope of this disclosure.

Claims

1. An operation and maintenance method applied to a server, characterized in that, The method includes: The alarm information is obtained from the monitoring system, which is used to monitor the operating parameters of at least one operating terminal and generate the alarm information in response to the operating parameters meeting preset conditions. The alarm information is matched with preset operation information in the operation terminal database to obtain target preset operation information that matches the alarm information. The preset operation information is used to instruct the operation terminal to execute operation instructions. Based on the target preset operation information, an alarm voice is generated and sent to the target operation terminal corresponding to the target preset operation information; The alarm information is input into the prediction model to obtain the predicted handling information, which represents the handling strategy that matches the alarm information. The predictive handling information and the alarm voice are sent to the operation and maintenance terminal corresponding to the alarm information. The operation and maintenance terminal is used to collect the voice signal emitted by the operation and maintenance object in response to receiving the predictive handling information and the alarm voice sent by the server, and obtain the dialogue voice. In response to receiving a voice dialogue from the operation and maintenance terminal, the system searches the operation and maintenance knowledge base based on the voice dialogue to obtain a target operation and maintenance operation, and sends the target operation and maintenance operation to the operation and maintenance terminal. The target operation and maintenance operation represents an operation and maintenance answer that matches the voice dialogue.

2. The method according to claim 1, characterized in that, The step of matching the alarm information with preset operation information in the operation terminal database to obtain target preset operation information that matches the alarm information includes: The alarm information is matched with at least one alarm tag in the operating terminal database to obtain a target alarm tag that matches the alarm information; Based on the target alarm tag, target preset operation information matching the alarm information is determined from the operation terminal database.

3. The method according to claim 1 or 2, characterized in that, The step of generating an alarm voice based on the target preset operation information includes: Entity recognition is performed on the target preset operation information according to preset field rules to obtain the target operation instruction; Based on the alarm level and the target operation command in the alarm information, generate voice alarm information; The alarm voice is generated based on the voice characteristics of the voice alarm information.

4. The method according to claim 3, characterized in that, Generating the alarm voice based on the voice features of the voice alarm information includes: The voice alarm information is decomposed into text to obtain at least one field, and the field includes at least one character. Language modeling is performed on the at least one field to obtain the semantic information of the at least one field; Perform prosodic analysis on the at least one field to obtain the pronunciation information of the text in the at least one field; Based on the semantic information and pronunciation information of the at least one field, the voice alarm information is synthesized to obtain the alarm voice.

5. The method according to claim 3, characterized in that, The step of inputting the alarm information into the prediction model to obtain the prediction and handling information includes: The alarm information is input into the convolutional layer of the prediction model to obtain the operation and maintenance features; Based on the alarm level in the alarm information, determine the target historical alarm information with the alarm level from the historical alarm information set, and determine the target historical operation and maintenance characteristics corresponding to the target historical alarm information; Calculate the similarity between the target historical operation and maintenance characteristics and the operation and maintenance characteristics to obtain the similarity result; Based on the similarity results, predictive handling information is determined from the historical alarm handling information set.

6. The method according to claim 1, characterized in that, The step of responding to the received dialogue voice from the operation and maintenance terminal and retrieving the target operation and maintenance operation from the operation and maintenance knowledge base based on the dialogue voice includes: In response to receiving the dialogue voice from the operation and maintenance terminal, acoustic features are extracted from the dialogue voice to obtain an acoustic feature vector. The dialogue voice is obtained by the operation and maintenance terminal collecting the voice signal emitted by the operation and maintenance object. The acoustic feature vector is input into the acoustic model to obtain the feature score of the acoustic feature vector. The acoustic model is trained using a labeled speech dataset. The dialogue speech is input into a language model to obtain a predicted text sequence corresponding to the dialogue speech; The dialogue speech is decoded based on the feature scores of the acoustic feature vector and the predicted text sequence to obtain the dialogue text; The target operation is obtained by searching the operation and maintenance knowledge base based on the dialogue text.

7. An alarm notification method, applied to a target operating terminal, characterized in that, The method includes: In response to receiving an alarm voice message from the server, an operation instruction corresponding to the alarm voice message is executed, wherein the alarm voice message is obtained by the method according to any one of claims 1 to 6.

8. An alarm notification method, applied to an operation and maintenance terminal, characterized in that, The method includes: In response to receiving the predictive handling information sent by the server, the voice signal emitted by the operation and maintenance object is collected to obtain the dialogue voice, wherein the predictive handling information is obtained by the method according to any one of claims 1 to 6.

9. An alarm notification system, characterized in that, The system includes: A monitoring system is used to monitor the operating parameters of at least one operating terminal and generate alarm information in response to the operating parameters meeting preset conditions. The server is configured to obtain the alarm information from the monitoring system; match the alarm information with preset operation information in the operation terminal database to obtain target preset operation information that matches the alarm information, the preset operation information being used to instruct the operating system to execute operation commands; generate an alarm voice based on the target preset operation information, and send the alarm voice to the target operation terminal corresponding to the target preset operation information; The target operating terminal is used to execute an operation command corresponding to the alarm voice in response to receiving an alarm voice sent by the server. The server is also used to input the alarm information into the prediction model to obtain prediction and handling information; and to send the prediction and handling information and the alarm voice to the operation and maintenance terminal corresponding to the alarm information. The maintenance terminal is used to collect the voice signal emitted by the maintenance object in response to receiving the predictive handling information and the alarm voice sent by the server, and obtain the dialogue voice. The server is further configured to, in response to receiving a dialogue voice from the operation and maintenance terminal, search the operation and maintenance knowledge base according to the dialogue voice to obtain a target operation and maintenance operation, and send the target operation and maintenance operation to the operation and maintenance terminal, wherein the target operation and maintenance operation represents an operation and maintenance answer that matches the dialogue voice.

10. An operation and maintenance device applied to a server, characterized in that, The device includes: The acquisition module is used to acquire alarm information from the monitoring system, which is used to monitor the operating parameters of at least one operating terminal and generate the alarm information in response to the operating parameters meeting preset conditions. The matching module is used to match the alarm information with preset operation information in the operation terminal database to obtain target preset operation information that matches the alarm information. The preset operation information is used to instruct the operation terminal to execute operation instructions. The generation module is used to generate an alarm voice based on the target preset operation information and send the alarm voice to the target operation terminal corresponding to the target preset operation information. The first input module is used to input the alarm information into the prediction model to obtain predicted handling information, wherein the predicted handling information represents the handling strategy that matches the alarm information. The sending module is used to send the predicted handling information and the alarm voice to the operation and maintenance terminal corresponding to the alarm information. The operation and maintenance terminal is used to collect the voice signal emitted by the operation and maintenance object in response to receiving the predicted handling information and the alarm voice sent by the server, and obtain the dialogue voice. The retrieval module is used to respond to the received dialogue voice from the operation and maintenance terminal, search the operation and maintenance knowledge base according to the dialogue voice, obtain the target operation and maintenance operation, and send the target operation and maintenance operation to the operation and maintenance terminal. The target operation and maintenance operation represents the operation and maintenance answer that matches the dialogue voice.

11. An operation and maintenance device, applied to a target operating terminal, characterized in that, The device includes: An execution module is configured to, in response to receiving an alarm voice sent by a server, execute an operation instruction corresponding to the alarm voice, wherein the alarm voice is obtained by the method according to any one of claims 1 to 6.

12. An operation and maintenance device, applied to an operation and maintenance terminal, characterized in that, The device includes: The acquisition module is used to acquire the voice signal emitted by the operation and maintenance object in response to receiving the predictive handling information sent by the server, and to obtain the dialogue voice. The predictive handling information is obtained by the method according to any one of claims 1 to 6.

13. An electronic device, comprising: One or more processors; Memory, used to store one or more computer programs. The characteristic feature is that the one or more processors execute the one or more computer programs to implement the steps of the method according to any one of claims 1 to 8.

14. A computer-readable storage medium having a computer program or instructions stored thereon, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 8.

15. A computer program product, comprising a computer program or instructions, characterized in that, When the computer program or instructions are executed by a processor, they implement the steps of the method according to any one of claims 1 to 8.

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