Alarm information processing method, device, computer equipment and computer storage medium

Abstract

The present application discloses an alarm information processing method, device, computer equipment and computer storage medium, including: receiving various types of alarm information generated by a monitoring system or other alarm sources in real time through a preset API interface or a monitoring message queue; performing structured extraction and semantic understanding on the received alarm information to obtain the alarm content of the alarm information; extracting key information from the alarm content, and determining the classification and urgency of the alarm information based on the key information; adjusting voice parameters according to the urgency, and generating an alarm audio file with corresponding language parameters based on the classification and the alarm content through a pre-built multilingual language synthesis system; obtaining the recipient and contact information of the alarm information, calling the recipient according to the contact information, and pushing the audio file to the recipient. Optimize alarm classification and urgency assessment, improve alarm response efficiency, and enhance the accessibility of alarm notifications.

Description

Technical Field

[0001] The present application relates to the field of alarm processing, and in particular to an alarm information processing method, device, computer equipment and computer storage medium. Background Art

[0002] The existing technology has problems such as low efficiency, high professional skills requirements, and high audit risks in alarm information processing. In the existing technology, most alarms in the alarm information are false alarms, which need to be checked one by one, wasting a lot of operation and maintenance manpower. The operation and maintenance personnel need to quickly judge each alarm scenario and quickly deal with it according to various disposal methods. If the disposal is not timely, it will lead to a large amount of alarm information hoarding, which is easy to miss and will also bring huge risks. The data center has strict monitoring management requirements, and the relevant personnel are not notified in time and the problem of untimely handling is difficult for the existing methods to adapt to the real-time and automation requirements, resulting in inaccurate alarm classification and urgency assessment, and low alarm response efficiency and accessibility. Summary of the invention

[0003] The purpose of the present application is to provide an alarm information processing method, apparatus, computer equipment and computer storage medium to at least solve the current problems of inaccurate alarm classification and urgency assessment, and low alarm response efficiency and accessibility.

[0004] In order to solve the above technical problems, the present application provides a method for processing alarm information, comprising:

[0005] Receive various alarm information generated by the monitoring system or other alarm sources in real time through the preset API interface or monitoring message queue;

[0006] Performing structured extraction and semantic understanding on the received alarm information to obtain the alarm content of the alarm information;

[0007] Extracting key information from the alarm content, and determining the classification and urgency of the alarm information based on the key information;

[0008] Adjusting the voice parameters according to the urgency, and generating an alarm audio file with corresponding language parameters based on the classification and the alarm content through a pre-built multilingual speech synthesis system;

[0009] The recipient and contact information of the alarm information are obtained, the recipient is called according to the contact information, and the audio file is pushed to the recipient.

[0010] Optionally, after receiving various alarm information generated by the monitoring system or other alarm sources, the step of:

[0011] Deduplication processing is performed on the received alarm information. After duplication of the alarm information is eliminated, subsequent steps are performed, wherein deduplication processing on the received alarm information includes:

[0012] Calculating the characteristic value of the alarm information based on a preconfigured hash algorithm to generate a unique hash value for the alarm information;

[0013] The hash value of the alarm information is compared with the hash value list of processed alarm information. If there is a duplicate, the alarm information is ignored; if there is no duplicate, the hash value of the alarm information is added to the hash value list of processed alarm information.

[0014] Optionally, the structured extraction of the received alarm information includes:

[0015] Cleaning the received warning information to remove irrelevant characters, spaces and format errors in the warning information;

[0016] Identifying and separating metadata in the cleaned alarm information;

[0017] The metadata is verified by a preset verification rule. If the metadata does not conform to the verification rule, the alarm information is marked as an invalid alarm and corresponding error processing is performed.

[0018] Optionally, after the pre-built multilingual language synthesis system generates an alarm audio file corresponding to the language parameters based on the classification and the alarm content, the method further includes:

[0019] Provide a user interface for receiving a specific prefix or suffix text input by a user;

[0020] Recognize the prefix or suffix text by the multilingual speech synthesis system, and embed it into the beginning or end of the generated alarm audio file;

[0021] Extracting key content of the alarm information, and generating an alarm summary according to the key content;

[0022] The alarm summary is configured as summary information of the alarm audio file.

[0023] Optionally, the generating of an alarm audio file corresponding to the language parameters based on the classification and the alarm content by a pre-built multilingual language synthesis system includes:

[0024] Use regular expressions to match sensitive information in the alarm content;

[0025] Performing a preconfigured replacement on the sensitive information to obtain desensitized alarm content;

[0026] The multilingual speech synthesis system generates an alarm audio file with corresponding language parameters based on the classification and the desensitized alarm content.

[0027] Optionally, after determining the classification and urgency of the alarm information based on the key information, the method further includes:

[0028] Based on the machine learning model, the characteristics of the alarm information and the historical alarm processing data are analyzed, and processing suggestions for different alarm information are automatically generated.

[0029] Optionally, after pushing the audio file to the receiving object, the method further includes:

[0030] Record the playback record of the audio file, the time and result of the alarm processing;

[0031] The alarm information processing result is generated according to the play record, the time and result of the alarm processing, and fed back to the statistical object of the alarm information.

[0032] Optionally, extracting key information from the alarm content and determining the classification and urgency of the alarm information based on the key information includes:

[0033] Extracting a service topology diagram of a service node corresponding to the alarm content according to the alarm content;

[0034] Pulling public opinion information associated with the key information based on the key information;

[0035] Based on a preset public opinion root cause analysis model and the public opinion information, generate a root cause topology diagram representing the cause of the public opinion information;

[0036] A logical match is performed based on the business topology map and the root cause topology map. When the logical match value between the business topology map and the root cause topology map is greater than a preset matching threshold, the classification and urgency of the alarm information are determined based on the public sentiment information and the heat value of the public sentiment information.

[0037] In order to solve the above technical problems, the present application also provides an alarm information processing device, including:

[0038] The alarm data module is used to receive various alarm information generated by the monitoring system or other alarm sources in real time through the preset API interface or monitoring message queue;

[0039] A content recognition module is used to perform structured extraction and semantic understanding on the received alarm information to obtain the alarm content of the alarm information;

[0040] An alarm classification module is used to extract key information from the alarm content and determine the classification and urgency of the alarm information based on the key information;

[0041] A speech synthesis module, used for adjusting speech parameters according to the urgency, and generating an alarm audio file of corresponding language parameters based on the classification and the alarm content through a pre-built multilingual speech synthesis system;

[0042] The alarm push module is used to obtain the recipient and contact information of the alarm information, call the recipient according to the contact information, and push the audio file to the recipient.

[0043] In order to solve the above technical problems, the present application also provides a computer device, including a memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor executes the steps of the above-mentioned alarm information processing method.

[0044] In order to solve the above technical problem, the present application also provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the above-mentioned alarm information processing method.

[0045] The beneficial effects of the embodiments created by the present application are as follows: various types of alarm information generated by a monitoring system or other alarm sources are received in real time through a preset API interface or a monitoring message queue; structured extraction and semantic understanding of the received alarm information are performed to obtain the alarm content of the alarm information; key information in the alarm content is extracted, and the classification and urgency of the alarm information are determined based on the key information; voice parameters are adjusted according to the urgency, and an alarm audio file with corresponding language parameters is generated based on the classification and the alarm content through a pre-built multilingual language synthesis system; the recipient and contact information of the alarm information are obtained, and a call is made according to the contact information. The method further comprises: calling the receiving object and pushing the audio file to the receiving object; by receiving and processing the alarm information in real time, the method can quickly respond to various alarms from the monitoring system or other alarm sources, reduce the response time, and improve the overall alarm processing efficiency; by extracting key information from the alarm content and determining the classification and urgency of the alarm, the method can more accurately classify the alarm and evaluate its urgency, thereby giving priority to alarms with high urgency; at the same time, by obtaining the contact information of the receiving object and directly playing the alarm audio file to it, the method ensures that the alarm information can be directly and effectively conveyed to the relevant personnel, thereby improving the accessibility and reception rate of the alarm notification. BRIEF DESCRIPTION OF THE DRAWINGS

[0046] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the following description of the embodiments in conjunction with the accompanying drawings, in which:

[0047] Figure 1 A basic flow chart of a method for processing alarm information according to a specific embodiment of the present application;

[0048] Figure 2 A schematic diagram of the basic structure of an alarm information processing device according to a specific embodiment of the present application;

[0049] Figure 3 This is a basic structural block diagram of a computer device according to a specific embodiment of the present application. DETAILED DESCRIPTION

[0050] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be interpreted as limiting the present application.

[0051] Those skilled in the art will appreciate that, unless otherwise stated, the singular forms "a", "an", "said" and "the" used herein may also include plural forms. It should be further understood that the term "comprising" used in the specification of the present application refers to the presence of the features, integers, steps, operations, elements and / or components, but does not exclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0052] It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as generally understood by those skilled in the art to which this application belongs. It should also be understood that terms such as those defined in common dictionaries should be understood to have meanings consistent with the meanings in the context of the prior art, and will not be interpreted with idealized or overly formal meanings unless specifically defined as here.

[0053] It will be understood by those skilled in the art that the term "terminal" as used herein includes both devices with wireless signal receivers, which are devices with only wireless signal receivers without transmission capability, and devices with receiving and transmitting hardware, which are devices with receiving and transmitting hardware capable of performing two-way communication on a two-way communication link. Such devices may include: cellular or other communication devices, which have a single-line display or a multi-line display or a cellular or other communication device without a multi-line display; PCS (Personal Communications Service), which may combine voice, data processing, fax and / or data communication capabilities; PDA (Personal Digital Assistant), which may include a radio frequency receiver, a pager, Internet / intranet access, a web browser, a notepad, a calendar and / or a GPS (Global Positioning System) receiver; conventional laptop and / or palmtop computers or other devices, which have and / or include a conventional laptop and / or palmtop computer or other device with a radio frequency receiver. The "terminal" used here can be portable, transportable, installed in a vehicle (air, sea and / or land), or suitable and / or configured to operate locally, and / or in a distributed form, at any other location on the earth and / or in space. The "terminal" used here can also be a communication terminal, an Internet terminal, a music / video playback terminal, such as a PDA, a MID (Mobile Internet Device) and / or a mobile phone with music / video playback function, or a smart TV, a set-top box and other devices.

[0054] The hardware referred to by the names such as "server", "client", and "service node" in this application is essentially an electronic device with capabilities equivalent to those of a personal computer. It is a hardware device that has the necessary components revealed by the von Neumann principle, such as a central processing unit (including an arithmetic unit and a controller), a memory, an input device, and an output device. The computer program is stored in its memory, and the central processing unit calls the program stored in the external memory into the internal memory for execution, executes the instructions in the program, and interacts with the input and output devices to complete specific functions.

[0055] It should be pointed out that the concept of "server" referred to in this application can also be extended to the case of server clusters. According to the network deployment principle understood by those skilled in the art, the servers should be logically divided. In physical space, these servers can be independent of each other but can be called through interfaces, or integrated into a physical computer or a set of computer clusters. Those skilled in the art should understand this flexibility, and should not use it to restrict the implementation of the network deployment method of this application.

[0056] Unless expressly specified, one or more technical features of the present application can be deployed on a server for implementation and accessed by a client through a remote call to obtain an online service interface provided by the server, or can be directly deployed and run on a client for access.

[0057] The AI ​​models and large language models cited or may be cited in this application, unless explicitly specified, can be deployed on a remote server and remotely called on the client, or can be deployed and directly called by a client with sufficient device capabilities. In some embodiments, when it runs on the client, its corresponding intelligence can be obtained through transfer learning so as to reduce the requirements for the client hardware operating resources and avoid excessive occupation of the client hardware operating resources.

[0058] Unless explicitly specified, the various data involved in this application can be stored remotely on a server or on a local terminal device, as long as it is suitable for being called by the technical solution of this application.

[0059] Those skilled in the art should be aware that, although the various methods of the present application are described based on the same concept and thus present commonality with each other, unless otherwise specified, these methods can be independently executed. Similarly, for each embodiment disclosed in the present application, they are all proposed based on the same inventive concept, therefore, concepts with the same expression, and concepts that are appropriately changed for convenience despite different expressions, should be understood as equivalent.

[0060] Unless the mutually exclusive relationship between the embodiments to be disclosed in this application is explicitly stated, the relevant technical features involved in each embodiment can be cross-combined to flexibly construct a new embodiment, as long as such combination does not deviate from the creative spirit of this application and can meet the needs of the prior art or solve certain deficiencies in the prior art. Those skilled in the art should be aware of this flexibility.

[0061] See also Figure 1 , Figure 1 The figure is a basic flow chart of the alarm information processing method of this embodiment.

[0062] like Figure 1 As shown, including:

[0063] S1100, receiving various alarm information generated by the monitoring system or other alarm sources in real time through a preset API interface or a monitoring message queue;

[0064] This embodiment is applied to the alarm information push scenarios in multiple different fields. This embodiment can access different systems to warn the alarm information generated by different systems. Specifically, this embodiment can be completed by one system. The system receives various types of alarm information generated by the monitoring system or other alarm sources in real time through a preset API interface or a monitoring message queue. The API interface supports the standard protocols of multiple alarm sources to achieve cross-platform alarm information integration. The alarm sources include but are not limited to network monitoring systems, security event management systems, environmental monitoring systems, etc., and real-time data access is achieved through standardized API interfaces or message queues.

[0065] It should be noted that this embodiment also configures a monitoring function for processing alarm information from the message queue, and ensures that the alarm information can be correctly parsed and received by configuring the preset network protocol and interface parameters. This embodiment also configures an exception handling mechanism to ensure that retries can be performed when there is a network delay or data format error, and to maintain continuous processing of alarm information from the message queue, such as configuring a heartbeat detection function to monitor the availability of the API interface and the message queue to ensure the stability and reliability of the system.

[0066] It should be noted that this embodiment is also configured to authenticate the alarm source when receiving alarm information in real time to ensure the legitimacy of the information source. The specific implementation method is to implement an authentication mechanism, requiring the alarm source to pass identity authentication before sending alarm information, including through digital certificates, API keys or OAuth and other security protocols. After receiving the alarm information, the system will check whether each alarm information contains valid authentication credentials, and only the information that passes the verification will be further processed.

[0067] S1200, performing structured extraction and semantic understanding on the received alarm information to obtain alarm content of the alarm information;

[0068] After receiving various types of alarm information generated by the monitoring system or other alarm sources in real time through a preset API interface or a monitoring message queue, the received alarm information is subjected to structured extraction and semantic understanding, the alarm content of the alarm information is obtained, and the alarm information is subjected to text parsing, so that key data in the alarm information, such as timestamp, alarm level, impact range, etc., can be identified and extracted. When performing structured extraction on the received alarm information, natural language processing technology is used to improve the extraction efficiency. The specific implementation method is to apply natural language processing (NLP) technology, such as named entity recognition (NER) and dependency syntax analysis, to automatically identify and extract key data in the alarm information, so as to quickly identify entities, events and relationships in the alarm information, thereby improving the speed and accuracy of information extraction; in the process of semantic understanding, machine learning algorithms are used to improve the accuracy of key information extraction. The specific implementation method is to use pre-trained natural language processing models such as BERT or LSTM to perform in-depth analysis on the alarm information to identify complex patterns and associations, thereby improving the ability to understand the semantics of the alarm information, identify and extract key information, and adjust the information extraction rules according to the model output to improve the accuracy and efficiency of extraction.

[0069] It should be noted that this embodiment can identify and extract structured data in alarm information, and can also use regular expressions and natural language processing technology, such as TF-IDF algorithm, to identify key words and phrases, and convert unstructured text into structured data, and analyze the semantic content of the alarm information, extract key information such as alarm type, time, severity level, etc., and store it in a structured format, such as JSON or XML, for subsequent processing. In addition, the above model can continuously learn and optimize based on historical alarm data to improve the accuracy of alarm information extraction.

[0070] It should be noted that this embodiment also uses semantic role annotation based on deep learning to identify key semantic roles such as the subject of action and the object of action in the alarm information. At the same time, a context-aware parsing engine is deployed, which can dynamically adjust the parsing strategy according to the historical context and current status of the alarm information to improve the accuracy of information extraction. In addition, users are allowed to correct the parsing results, and the system will learn these corrections to optimize future parsing results.

[0071] S1300, extracting key information from the alarm content, and determining the classification and urgency of the alarm information based on the key information;

[0072] After performing structured extraction and semantic understanding on the received alarm information and obtaining the alarm content of the alarm information, extract the key information in the alarm content, and determine the classification and urgency of the alarm information based on the key information. Specifically, based on the preset rules and machine learning models, the structured alarm data extracted in step S1200 is analyzed. The system will automatically classify each alarm information and evaluate its urgency based on the alarm type, scope of impact, historical data and other relevant factors. The classification may include categories such as errors, warnings, and information, while the urgency may be high, medium, or low. In addition, the system will implement a dynamic adjustment mechanism to automatically adjust the classification rules and urgency assessment criteria based on real-time feedback and historical data analysis results to improve the adaptability and accuracy of the system.

[0073] It should be noted that, in this embodiment, when determining the classification and urgency of the alarm information based on the key information, the system can continuously update itself according to the real-time data stream and historical alarm cases to adapt to changes in the alarm mode. The evaluation of the urgency is integrated into a multi-factor decision analysis framework, which considers multiple dimensions such as the reliability of the alarm source, historical repetition rate, and potential impact to achieve a more refined urgency evaluation.

[0074] S1400, adjusting the voice parameters according to the urgency, and generating an alarm audio file with corresponding language parameters based on the classification and the alarm content through a pre-built multilingual speech synthesis system;

[0075] After extracting the key information in the alarm content and determining the classification and urgency of the alarm information based on the key information, the system will receive the structured alarm information and convert it into text in a specific language by adjusting the voice parameters such as speech speed, pitch, volume, etc. according to the classification and urgency of the alarm. Then, using speech synthesis technology such as text-to-speech (TTS) engine, an alarm audio file is generated. Emergency alarms will have a more urgent tone and faster speech speed, while non-emergency alarms use calmer voice parameters.

[0076] It should be pointed out that this embodiment optimizes the classification algorithm based on historical alarm data when determining the classification and urgency of alarm information. The specific implementation method is to analyze the historical alarm data stored in the database, extract the frequency, time, location and other features of the alarm, and use these features to train the classification algorithm model to improve the accuracy of alarm classification.

[0077] It should be pointed out that this embodiment selects different voice intensities and speech speeds according to the urgency of the alarm information when adjusting the voice parameters. The specific implementation method is to set a mapping table between the urgency and the voice parameters. For example, when the urgency is high, the voice intensity increases and the speech speed increases. When the urgency is low, the voice intensity decreases and the speech speed slows down. This logic is implemented through programming to automatically adjust the parameters of the speech synthesis system.

[0078] It should be pointed out that when adjusting the voice parameters according to the degree of urgency, this embodiment selects different speech synthesis engines according to the alarm type. The specific implementation method is to integrate multiple speech synthesis engines, and each engine is optimized for different types of alarm information. For example, emergency alarms use clear and high-pitched voice engines, while general alarms use more peaceful voice engines. The system automatically selects the most suitable speech synthesis engine according to the alarm type.

[0079] It should be pointed out that this embodiment builds a unified multilingual speech synthesis system to achieve cross-language voice cloning to meet the needs of users of different languages, enhance the emotional expressiveness of synthesized speech, enable it to express rich emotions and speaking styles, so as to improve the communication effect of alarm information, and through few-sample / zero-sample speech synthesis technology, it is possible to clone the voice of a new speaker with a small amount or even zero sample data to provide personalized alarm voice notifications.

[0080] S1500: Obtain a recipient and contact information of the alarm information, call the recipient according to the contact information, and push the audio file to the recipient.

[0081] After adjusting the voice parameters according to the urgency and generating an alarm audio file with corresponding language parameters based on the classification and the alarm content through a pre-built multilingual speech synthesis system, the contact information of the recipient, including telephone number, email address, etc., is retrieved from the database. Then, the system will send a notification to the recipient using an automatic dialer or email service. For telephone notifications, the system will automatically dial the phone and play the alarm audio file generated in step 4 after the call is connected. For email notifications, the system will send an email containing an audio file attachment or link to ensure that the recipient can access the alarm information. The system will record the sending status of all notifications for easy tracking and auditing. In addition, the system will implement a feedback mechanism that allows the recipient to provide feedback on the alarm reception status through phone buttons, SMS replies, or emails. The system will optimize future alarm notification processes based on these feedbacks.

[0082] It should be noted that the implementation of this step will be linked to an intelligent notification scheduling system that can intelligently select the best contact method and time based on the recipient's availability and preferences. For example, the system will analyze the recipient's calendar information and communication history to determine the best time to contact. In addition, a multi-channel notification mechanism will be implemented that can automatically try email, SMS or other instant messaging tools when the phone cannot be connected. The system will also integrate an intelligent retry strategy to dynamically adjust the retry frequency and time based on historical connection rates and predictive models.

[0083] In the above implementation manner, various types of alarm information generated by the monitoring system or other alarm sources are received in real time through a preset API interface or a monitoring message queue; the received alarm information is subjected to structured extraction and semantic understanding to obtain the alarm content of the alarm information; key information in the alarm content is extracted, and the classification and urgency of the alarm information are determined based on the key information; voice parameters are adjusted according to the urgency, and an alarm audio file with corresponding language parameters is generated based on the classification and the alarm content through a pre-built multilingual language synthesis system; the recipient and contact information of the alarm information are obtained, and the recipient is called according to the contact information. The method can quickly respond to various alarms from the monitoring system or other alarm sources by receiving and processing alarm information in real time, reduce response time, and improve the overall alarm processing efficiency. By extracting key information from the alarm content and determining the classification and urgency of the alarm, the method can more accurately classify the alarm and evaluate its urgency, thereby giving priority to alarms with high urgency. At the same time, by obtaining the contact information of the receiving object and directly playing the alarm audio file to it, the method ensures that the alarm information can be directly and effectively conveyed to relevant personnel, thereby improving the accessibility and reception rate of the alarm notification.

[0084] In some implementations, after S1100 receives various alarm information generated by a monitoring system or other alarm sources, it further includes the following steps:

[0085] S1110, performing deduplication processing on the received alarm information, and executing subsequent steps only after the alarm information is no longer duplicated; wherein the deduplication processing on the received alarm information includes:

[0086] S1111 calculates the characteristic value of the alarm information based on a preconfigured hash algorithm to generate a unique hash value for the alarm information;

[0087] S1112 compares the hash value of the alarm information with the hash value list of processed alarm information. If there is a duplicate, ignore the alarm information; if there is no duplicate, add the hash value of the alarm information to the hash value list of processed alarm information.

[0088] After receiving various types of alarm information generated by the monitoring system or other alarm sources, this embodiment performs deduplication processing on the received alarm information. Only after the alarm information is excluded from duplication, the subsequent steps are executed. Specifically, the system will configure an efficient hash algorithm, such as SHA-256, to generate a unique hash value for each alarm information. The characteristic values ​​of the alarm information include, but are not limited to, key fields such as the unique identifier of the alarm, timestamp, alarm level, and alarm content summary. These fields are combined and input into the hash function to generate a fixed-length hash value, which serves as the unique fingerprint of the alarm information.

[0089] The system will maintain a dynamically updated hash value database or memory data structure for storing and retrieving the hash values ​​of processed alarm information. When a new alarm message arrives, the system will calculate its hash value and compare it with the hash value in the database in real time. If the hash value is found to already exist, it indicates that the alarm message is a duplicate and the system will automatically ignore this alarm to avoid unnecessary processing. If the hash value is not found in the database, the system will accept the alarm message and add its hash value to the database for quick comparison in the future.

[0090] It should be noted that the system also configures a regular cleanup mechanism to manage the size of the hash value database to prevent memory overflow or performance degradation. This mechanism can be based on the life cycle of the alarm information, for example, only retaining the alarm hash values ​​within the last 24 hours, or dynamically adjusting the retention policy based on the urgency and importance of the alarm information. In this way, the system can effectively reduce the processing of duplicate alarms and improve the efficiency and response speed of the overall alarm processing process.

[0091] This implementation method effectively avoids the interference of duplicate alarms by implementing deduplication processing of alarm information, and improves the efficiency and accuracy of alarm processing. This measure reduces multiple responses to the same alarm event, saves system resources, reduces the false alarm rate, and improves the reliability of the alarm system. At the same time, the deduplication mechanism ensures the cleanliness of the alarm database, providing a more accurate data basis for subsequent alarm analysis and response, thereby optimizing the entire alarm management process.

[0092] In some implementations, the step S1200 extracts structured information from the received alarm information, including:

[0093] S1211, cleaning the received warning information to remove irrelevant characters, spaces and format errors in the warning information;

[0094] When this embodiment performs structured extraction on the received alarm information, the received alarm information is first cleaned to remove irrelevant characters, spaces and format errors in the alarm information. Specifically, unnecessary elements in the alarm information, such as irrelevant characters, redundant spaces and format errors, are removed. This process involves the use of regular expressions to identify and remove unnecessary symbols and spaces, and to correct or standardize the format of the alarm information through predefined format templates. For example, the date and time stamp may be standardized to ISO format, while numbers and letters may be formatted to comply with specific encoding rules. The cleaned alarm information will be more standardized, facilitating subsequent structured extraction and semantic understanding.

[0095] It should be noted that the alarm information cleaning module will use a text processing algorithm to automatically identify and remove irrelevant content such as non-printing characters and control characters in the alarm information. The module will also identify and delete redundant blank characters, including continuous spaces and invisible Unicode blank characters. In addition, the module will automatically correct format errors according to predefined alarm information format rules, such as XML, JSON or application-specific formats, such as fixing label closure problems, adjusting field order, etc. During the cleaning process, the system will record information before and after cleaning for auditing and backtracking when necessary. In some embodiments, in order to identify unrecognizable blank characters in the alarm information, after extracting the content of the alarm information, the font type, font size and paragraph format of the alarm information are identified, and according to the above parameters obtained by identification, the alarm information is restored to generate restoration information, and then the restored information and the paragraph length and length of the alarm information are compared, and invisible blank characters and other information can be identified.

[0096] S1212, identifying and separating metadata in the cleaned alarm information;

[0097] This embodiment cleans the received alarm information, removes irrelevant characters, spaces and format errors in the alarm information, and then identifies and separates the metadata in the cleaned alarm information. The system will use natural language processing technology, especially named entity recognition (NER) technology, to identify metadata in the alarm information, such as timestamps, device IDs, location information, etc. These metadata usually contain key operational information, which is crucial for the classification and response of alarms. The system will be configured with a metadata pattern matching engine that can identify metadata according to preset patterns and separate it from the main content of the alarm information. The separated metadata will be stored in a structured format, such as a JSON object, for subsequent processing and analysis.

[0098] It should be noted that in the cleaned alarm information, the system will use predefined patterns and regular expressions to identify metadata. For example, the system will configure patterns to identify dates, times, serial numbers, and device identifiers in specific formats. Once the metadata is identified, the system will use a parser to separate it from the main text of the alarm information and store the metadata in a structured data structure, such as a key-value pair or a database field. In addition, the system will perform a preliminary quality check on the separated metadata to ensure that they conform to the expected data type and format, providing accurate context information for subsequent alarm processing.

[0099] S1213: Verify the metadata using a preset verification rule. If the metadata does not conform to the verification rule, mark the alarm information as an invalid alarm and perform corresponding error processing.

[0100] After identifying and separating the cleaned metadata in the alarm information, the metadata is verified by preset verification rules. If the metadata does not meet the verification rules, the alarm information is marked as an invalid alarm and corresponding error handling is performed. This step involves a verification engine that verifies the metadata according to a preset set of rules. For example, whether the timestamp is within a reasonable range, whether the device ID meets specific encoding rules, whether the location information is within the service area, etc. If the metadata does not meet these verification rules, the system will mark the alarm information as invalid and trigger the error handling process. Error handling may include recording error logs, notifying operation and maintenance personnel, or automatically trying to re-acquire alarm information. This verification mechanism ensures that only accurate and complete alarm information will be further processed, thereby improving the reliability and efficiency of alarm processing.

[0101] It should be noted that in one embodiment, the system configures a metadata verification framework, which contains a series of predefined verification rules for checking the legitimacy and consistency of metadata. For example, the system will verify whether the timestamp is within a reasonable time range, whether the device ID is in a known device list, and whether the serial number conforms to a specific format. If the metadata fails to pass the verification, the system will automatically mark the alarm information as invalid and record detailed error information. Invalid alarms will be isolated in a separate storage area and trigger a notification process to notify the operation and maintenance team for manual review. At the same time, the system will generate an error report detailing the reasons for the verification failure to facilitate quick location of the problem and take corrective measures.

[0102] This implementation significantly improves the quality and reliability of alarm data by adding structured extraction and metadata verification steps to the alarm information processing process. This improvement ensures that only accurate, complete and properly formatted alarm information is further processed and responded to, thereby reducing false alarms and duplicate alarms and improving the efficiency of the alarm system. At the same time, by cleaning and verifying metadata, the system can identify and locate problems more quickly, speed up fault response time, reduce operation and maintenance costs, and enhance the stability of the alarm system and user trust.

[0103] In some implementations, after the S1400 generates an alarm audio file corresponding to the language parameters based on the classification and the alarm content through a pre-built multilingual language synthesis system, the step further includes:

[0104] S1411: providing a user interface for receiving a specific prefix or suffix text input by a user;

[0105] In this embodiment, after the alarm audio file corresponding to the language parameters is generated, a user interface is also provided to allow the user to enter the custom prefix or suffix text they wish to add to the beginning or end of the alarm audio file. This UI can be a web form, a mobile application interface, or a desktop application window, which will provide a text box for the user to enter text, and there will be clear instructions that the content entered by the user will be added to the alarm audio. In order to improve the user experience, the UI will include an input validation function to ensure that the text entered by the user meets the predetermined format requirements, such as length restrictions, special character filtering, etc. In addition, the UI will provide a preview function so that the user can hear the speech synthesis effect of the prefix or suffix text before final confirmation.

[0106] S1412: recognizing the prefix or suffix text through the multilingual speech synthesis system, and embedding it into the beginning or end of the generated alarm audio file;

[0107] Furthermore, after the user enters the prefix or suffix text through the UI, the system passes these texts to the multilingual speech synthesis system (TTS). The TTS system will recognize these texts and convert them into speech. The system will configure the TTS engine to support multiple languages ​​and dialects to meet the needs of different users. The converted speech will be embedded in the specified position of the alarm audio file, that is, the user can choose to add the prefix speech to the beginning of the audio file, or add the suffix speech to the end of the audio file. This process will be automated to ensure that each generated alarm audio file meets the user's personalized needs while maintaining the professionalism and consistency of the alarm information.

[0108] S1413: extracting key content of the alarm information, and generating an alarm summary according to the key content;

[0109] Furthermore, the system will be equipped with an alarm summary generation module, which is responsible for extracting key content from the complete alarm information. This will involve natural language processing techniques such as keyword extraction, sentence summarization, or machine learning models to identify the most relevant parts of the alarm information. The extracted key content will be compiled into a concise summary that can quickly convey the core information of the alarm. The summary generation module will be designed to be highly configurable, allowing users to customize the length and detail of the summary to suit different alarm scenarios and user preferences.

[0110] S1414: Configuring the alarm summary as summary information of the alarm audio file.

[0111] Further, once the alarm summary is generated, the system will configure this summary as metadata or summary information of the alarm audio file. This can be achieved by embedding the summary text in the ID3 tag (for MP3 files) or other appropriate metadata fields of the audio file. In this way, when the audio file is played, the player can read and display this summary, providing the user with a quick preview of the information. In addition, this summary information can also be used for search and indexing purposes, making the alarm audio files easier to retrieve and manage. The system will ensure the accuracy and update of the summary information to reflect the latest status of the alarm information.

[0112] This implementation enhances the personalization and pertinence of alarm information by providing a user interface to receive custom prefix or suffix text and embed it into the alarm audio file. This improvement makes the alarm notification more in line with the needs of specific users or scenarios, improves the relevance of information and the user's attention. At the same time, an alarm summary is generated and used as the summary information of the audio file, making the alarm content more concise and clear, and easy to quickly understand and respond to. These measures jointly improve the user experience and operational efficiency of the alarm system, and ensure the effective communication and timely processing of alarm information.

[0113] In some implementations, S1400 generates an alarm audio file corresponding to the language parameters based on the classification and the alarm content through a pre-built multi-language language synthesis system, including:

[0114] S1421. Use a regular expression to match sensitive information in the alarm content;

[0115] In this embodiment, in the process of generating an alarm audio file with corresponding language parameters based on the classification and the alarm content by a pre-built multilingual language synthesis system, the system will integrate a regular expression engine to identify and match sensitive information in the alarm content. Sensitive information may include personal identity information, financial data, internal system passwords, etc. The system will pre-define a set of regular expression rules that are specifically designed to identify the above types of sensitive data. Whenever the alarm content is received, the regular expression engine will automatically scan the text to find any fragments that match the predefined rules. This process ensures the rapid identification of sensitive information and lays the foundation for subsequent desensitization processing. In order to improve the accuracy and coverage of the match, the system will allow security experts to regularly update and optimize regular expression rules to adapt to new data protection requirements and potential sensitive information patterns.

[0116] S1422. Perform a preconfigured replacement on the sensitive information to obtain desensitized alarm content;

[0117] After using regular expressions to match sensitive information in the alert content, once sensitive information is identified, the system will automatically apply pre-configured replacement strategies to desensitize the alert content. This may involve replacing sensitive data with asterisks (*), desensitizing suffixes, or other non-sensitive placeholders. For example, the numbers in a personal phone number may be replaced with "XXX-XXX-XXXX", while a password may be simply replaced with "[hidden]". The replacement operation will be performed by a desensitization module that understands different types of sensitive information and applies corresponding replacement rules. The desensitization module will also ensure that the replacement operation does not destroy the structure and semantics of the alert content to avoid affecting the quality and comprehensibility of the alert audio file. In addition, the system will record logs of the desensitization operation for audit and compliance checks.

[0118] S1423. Generate an alarm audio file of corresponding language parameters based on the classification and the desensitized alarm content through the multilingual speech synthesis system.

[0119] After the sensitive information is pre-configured to replace the desensitized alarm content, the system will use a multilingual speech synthesis system (TTS) to convert the desensitized alarm content into an audio file after the alarm content is desensitized. The TTS system will select appropriate language and voice parameters such as speech speed, pitch, volume, etc. based on the classification of the alarm and the language preferences of the target audience. The system will configure the TTS engine to support multiple languages ​​and dialects to ensure that the alarm information can be understood by recipients from different language backgrounds. When generating the audio file, the system will consider the urgency and nature of the alarm and adjust the parameters of the speech synthesis to convey the appropriate sense of urgency or importance. The generated audio file will contain the desensitized alarm content while maintaining the original structure and intent, ensuring that the information is conveyed both safely and effectively.

[0120] This implementation effectively protects the security of personal and corporate sensitive data by adding sensitive information identification and desensitization steps in the alarm information processing. This measure prevents the leakage of sensitive information during the alarm notification process, reduces the risk of data leakage, and ensures the compliance of the alarm information. When the desensitized alarm content is converted into an audio file, it not only protects privacy but also retains the core meaning of the alarm information, so that the alarm notification can quickly convey key information while meeting security requirements, thereby improving the security and reliability of the alarm system.

[0121] In some implementations, after S1300 determines the classification and urgency of the alarm information based on the key information, the method further includes:

[0122] S1311. Based on the machine learning model, analyze the characteristics of the alarm information and the historical alarm processing data, and automatically generate processing suggestions for different alarm information.

[0123] In this embodiment, after determining the classification and urgency of the alarm information based on the key information, the characteristics of the alarm information and the historical alarm processing data are analyzed based on the machine learning model, and processing suggestions for different alarm information are automatically generated. Specifically, first, the system collects the characteristics of the alarm information, such as alarm frequency, duration, scope of impact, and historical solutions. Then, the model uses these characteristics and historical alarm data to train a classifier that can identify alarm patterns and predict the best processing strategy. For example, the model may identify that certain types of alarms are often associated with specific hardware failures and recommend an on-site inspection. In addition, the system will implement a feedback mechanism that allows operation and maintenance personnel to evaluate and adjust the suggestions generated by the model. These feedbacks will be used to further train and optimize the model to improve the accuracy and practicality of the suggestions. In this way, the system can automatically generate specific processing suggestions for different types of alarm information, thereby improving the efficiency and effectiveness of alarm response.

[0124] Furthermore, the system accesses the characteristic data of alarm information and historical alarm processing records. Using this data, the machine learning model will identify alarm patterns and effective processing strategies through supervised learning algorithms such as random forests or gradient boosting trees. During the model training process, cross-validation will be used to evaluate its performance and select the best model for deployment. In addition, the system will implement an online learning mechanism that enables the model to self-update and optimize based on the latest alarm data and user feedback. This continuous learning process ensures that the processing suggestions can adapt to new alarm patterns and changing environmental conditions, improving the adaptability and effectiveness of alarm responses.

[0125] This implementation method integrates machine learning models to analyze alarm information and automatically generates processing suggestions, which significantly improves the intelligence level of alarm response. This automated suggestion generation mechanism not only reduces the burden of manual analysis, but also speeds up response and improves processing efficiency. The model can learn from historical data and continuously optimize the accuracy of suggestions, thereby reducing misjudgments and missed judgments and enhancing the accuracy of alarm processing. In addition, this method also improves the adaptive ability of the alarm system, enabling it to cope with complex and changing alarm scenarios and provide users with more intelligent and efficient alarm management services.

[0126] In some implementations, after the step S1500 pushes the audio file to the receiving object, the step further includes:

[0127] S1511, recording the playback record of the audio file, the time and result of the alarm processing;

[0128] After pushing the audio file to the receiving object, this embodiment records the playback record of the audio file, the time and result of the alarm processing. In this step, the system will implement a recording function to track the playback status of the audio file and the relevant data of the alarm processing. Specifically, every time the audio file is played, the system will automatically record the start time, end time and playback status (for example, whether it is played completely or interrupted). At the same time, the system will record the start time and end time of the alarm processing, as well as the processing results, such as whether the alarm is resolved, the measures taken and the feedback of the processing personnel. These data will be stored in the database and associated with the corresponding alarm event for subsequent query and analysis.

[0129] S1512: Generate the alarm information processing result according to the playback record, the time and result of the alarm processing, and feed it back to the statistical object of the alarm information.

[0130] Furthermore, after recording the playback record of the audio file, the time and result of the alarm processing, the alarm information processing result is generated according to the playback record, the time and result of the alarm processing, and fed back to the statistical object of the alarm information, which includes calculating the average time of alarm response and processing, alarm resolution rate, repeated alarm frequency and other key performance indicators. The system will use data analysis tools such as SQL queries, data mining algorithms or machine learning models to identify trends and patterns in alarm processing. The generated results will be presented in the form of a report and fed back to the statistical object of the alarm information, that is, the administrator or decision maker of the alarm management system, through email, web interface or mobile application. This feedback information will help them evaluate the efficiency of the alarm processing process, identify improvement points, and make more data-driven decisions.

[0131] In this step, the system will develop an automated report generation module that can extract key data points from the alarm processing database and use this data to generate detailed alarm processing reports. The report will include statistical charts and trend analysis of key indicators such as alarm response time, processing efficiency, and user feedback, providing an intuitive view for the statistical object. In addition, the system will implement an intelligent notification mechanism that automatically sends alerts to the statistical object when abnormal patterns are detected or key performance indicators exceed preset thresholds, prompting timely management and intervention. This enhanced feedback mechanism helps to quickly identify and resolve potential alarm processing problems, further improving the effectiveness and responsiveness of the alarm system.

[0132] This implementation achieves transparency and traceability of the alarm processing process by recording the playback of audio files and the details of alarm processing. This detailed recording and analysis helps monitor the efficiency of alarm response, optimize the alarm processing process, reduce processing time, and improve problem solving rate. In addition, the generated processing result report provides valuable data support for management, making decisions more accurate and helping to improve the emergency response capabilities and service quality of the entire organization. The automated intelligent notification mechanism further ensures that quick action can be taken at critical moments, enhancing the system's proactive early warning function.

[0133] In some implementations, S1300 includes:

[0134] S1311. Extracting a service topology map of a service node corresponding to the alarm content according to the alarm content;

[0135] In this implementation, the alarm information is ultimately used to feedback client problems caused by service node errors.

[0136] The alarm information includes: the number and location of the service node where the error occurred. The service node where the error occurred can be accurately located based on the number and location. Then, based on the specific function of the service node in the entire service chain, a topology map of the service node is generated. The topology map records the upstream and downstream service structures of all functional services of the service node.

[0137] S1312, pulling public opinion information associated with the key information based on the key information;

[0138] When an error occurs in a backend business node, the first to be impacted is not the operating logic within the business system, but the client user who is performing the business. For example, when a business error occurs on a platform, the first to discover it may not be the team managing the business system, but the user who is performing the business. Therefore, after extracting key information, it is necessary to retrieve the public opinion information associated with the key information based on the key information.

[0139] In some implementations, in order to enhance the accuracy of public opinion information collection, user portraits related to the business content are retrieved based on the business content contained in the key information, and the commonly used applications of such users are analyzed based on the user portraits. When performing public opinion retrieval, the retrieval scope is determined to be in commonly used applications.

[0140] S1313, based on a preset public opinion root cause analysis model and the public opinion information, generating a root cause topology diagram representing the cause of the public opinion information;

[0141] The public opinion root cause analysis model is a neural network model that has been trained to a convergent state. It infers the root causes of the problems that lead to public opinion information based on the content input into the model by the user. After the collected public opinion information is input into the public opinion root cause analysis model, the public opinion root cause analysis model will extract the characteristic parameters in the public opinion information, match the corresponding core root cause elements based on the characteristic parameters, and infer the upstream and downstream root cause elements of the core root cause elements based on the core root cause elements, thereby generating a root cause topology map.

[0142] S1314. Perform logical matching based on the business topology map and the root cause topology map. When the logical matching value of the business topology map and the root cause topology map is greater than a preset matching threshold, determine the classification and urgency of the alarm information based on the public sentiment information and the heat value of the public sentiment information.

[0143] Perform image matching on the business topology map and the root cause topology map. Since the business content undertaken by the business node is often relatively simple, the business topology map is also relatively simple. However, there are many reasons for the appearance of a certain public opinion information, so the root cause topology map is relatively complex. Perform image matching on the business topology map and the root cause topology map. When the logical matching value of the business topology map and the root cause topology map is greater than the preset matching threshold, it means that the business topology map has found the same topological posture in the root cause topology map, and further indicates that the error of the business node is a factor that causes the outbreak of public opinion information. At this time, the classification of the alarm information is determined according to the content of the public opinion information. The degree of urgency is determined according to the heat value of the public opinion information. Generally, the higher the heat value, the higher the corresponding degree of urgency.

[0144] Determine the business topology of the business node based on the alarm content of the business node, obtain the corresponding public opinion information based on the key information, and deduce the root cause topology of the public opinion information based on the public opinion information. After comparing the two images, determine the correlation between the business node error and the public opinion information based on the matching results. Finally, determine the classification and urgency of the alarm information based on the public opinion information and the heat value of the public opinion information. This solution breaks through the matching and combination of business node errors and public opinion information, so that the management system can quickly determine the alarm classification and urgency based on the public opinion information. It truly combines public opinion feedback and internal system error warnings, allowing managers to intuitively understand the impact of business errors based on public opinion results, and can also make the classification and level division results more real, direct and accurate.

[0145] Please refer to Figure 2 , Figure 2 It is a schematic diagram of the basic structure of the alarm information processing device of this embodiment.

[0146] like Figure 2 As shown, an alarm information processing device includes: an alarm data module 1100, which is used to receive various types of alarm information generated by a monitoring system or other alarm sources in real time through a preset API interface or a monitoring message queue; a content recognition module 1200, which is used to perform structured extraction and semantic understanding on the received alarm information to obtain the alarm content of the alarm information; an alarm classification module 1300, which is used to extract key information in the alarm content, and determine the classification and urgency of the alarm information based on the key information; a speech synthesis module 1400, which is used to adjust the speech parameters according to the urgency, and generate an alarm audio file with corresponding language parameters based on the classification and the alarm content through a pre-built multilingual language synthesis system; an alarm push module 1500, which is used to obtain the recipient and contact information of the alarm information, call the recipient according to the contact information, and push the audio file to the recipient.

[0147] The above-mentioned alarm information processing device is used to obtain the recipient and contact information of the alarm information, call the recipient according to the contact information, and push the audio file to the recipient.

[0148] In order to solve the above technical problems, the present application also provides an alarm information processing device, including:

[0149] The alarm data module is used to receive various alarm information generated by the monitoring system or other alarm sources in real time through the preset API interface or monitoring message queue;

[0150] A content recognition module is used to perform structured extraction and semantic understanding on the received alarm information to obtain the alarm content of the alarm information;

[0151] An alarm classification module is used to extract key information from the alarm content and determine the classification and urgency of the alarm information based on the key information;

[0152] A speech synthesis module, used for adjusting speech parameters according to the urgency, and generating an alarm audio file of corresponding language parameters based on the classification and the alarm content through a pre-built multilingual speech synthesis system;

[0153] The alarm push module is used to obtain the recipient and contact information of the alarm information, call the recipient according to the contact information, and push the audio file to the recipient.

[0154] In order to solve the above technical problems, the present application also provides a computer device, including a memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor executes the steps of the above-mentioned alarm information processing method.

[0155] In order to solve the above technical problem, the present application also provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the above-mentioned alarm information processing method.

[0156] The beneficial effects of the embodiments created by the present application are as follows: various types of alarm information generated by a monitoring system or other alarm sources are received in real time through a preset API interface or a monitoring message queue; structured extraction and semantic understanding of the received alarm information are performed to obtain the alarm content of the alarm information; key information in the alarm content is extracted, and the classification and urgency of the alarm information are determined based on the key information; voice parameters are adjusted according to the urgency, and an alarm audio file with corresponding language parameters is generated based on the classification and the alarm content through a pre-built multilingual language synthesis system; the recipient and contact information of the alarm information are obtained, and a call is made according to the contact information. The method further comprises: calling the receiving object and pushing the audio file to the receiving object; by receiving and processing the alarm information in real time, the method can quickly respond to various alarms from the monitoring system or other alarm sources, reduce the response time, and improve the overall alarm processing efficiency; by extracting key information from the alarm content and determining the classification and urgency of the alarm, the method can more accurately classify the alarm and evaluate its urgency, thereby giving priority to alarms with high urgency; at the same time, by obtaining the contact information of the receiving object and directly playing the alarm audio file to it, the method ensures that the alarm information can be directly and effectively conveyed to the relevant personnel, thereby improving the accessibility and reception rate of the alarm notification.

[0157] To solve the above technical problems, the present application also provides a computer device. Figure 3 , Figure 3 This is a basic structural block diagram of the computer device in this embodiment.

[0158] like Figure 3 As shown, a schematic diagram of the internal structure of a computer device. The computer device includes a processor, a non-volatile storage medium, a memory, and a network interface connected via a system bus. Among them, the non-volatile storage medium of the computer device stores an operating system, a database, and computer-readable instructions, and the database may store a control information sequence, and the computer-readable instructions are executed by the processor. The processor of the computer device is used to provide computing and control capabilities to support the operation of the entire computer device. The memory of the computer device may store computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor can execute an alarm information processing method. The network interface of the computer device is used to connect and communicate with a terminal. Those skilled in the art can understand that Figure 3 The structure shown in the figure is only a block diagram of a part of the structure related to the solution of the present application, and does not constitute a limitation on the computer device to which the solution of the present application is applied. The specific computer device may include more or fewer components than those shown in the figure, or combine certain components, or have a different arrangement of components.

[0159] In this embodiment, the processor is used to execute Figure 2 The specific functions of the alarm data module 1100, the content identification module 1200, the alarm classification module 1300, the speech synthesis module 1400 and the alarm push module 1500 are stored in the memory. The program code and various data required to execute the above modules are stored in the memory. The network interface is used to transmit data between the user terminal or the server. The memory in this embodiment stores the program code and data required to execute all sub-modules in the alarm information processing device, and the server can call the program code and data of the server to execute the functions of all sub-modules.

[0160] The above-mentioned computer device receives various types of alarm information generated by the monitoring system or other alarm sources in real time through a preset API interface or a monitoring message queue; performs structured extraction and semantic understanding on the received alarm information to obtain the alarm content of the alarm information; extracts key information from the alarm content, and determines the classification and urgency of the alarm information based on the key information; adjusts the voice parameters according to the urgency, and generates an alarm audio file with corresponding language parameters based on the classification and the alarm content through a pre-built multilingual language synthesis system; obtains the recipient and contact information of the alarm information, and calls the recipient according to the contact information. The method can quickly respond to various alarms from the monitoring system or other alarm sources by receiving and processing alarm information in real time, reduce response time, and improve the overall alarm processing efficiency. By extracting key information from the alarm content and determining the classification and urgency of the alarm, the method can more accurately classify the alarm and evaluate its urgency, thereby giving priority to alarms with high urgency. At the same time, by obtaining the contact information of the receiving object and directly playing the alarm audio file to it, the method ensures that the alarm information can be directly and effectively conveyed to the relevant personnel, thereby improving the accessibility and reception rate of the alarm notification.

[0161] The present application also provides a storage medium storing computer-readable instructions. When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the alarm information processing method described in any of the above embodiments.

[0162] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiments can be implemented by instructing the relevant hardware through a computer program, and the computer program can be stored in a computer-readable storage medium. When the program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, the aforementioned storage medium can be a non-volatile storage medium such as a disk, an optical disk, a read-only memory (ROM), or a random access memory (RAM).

[0163] Those skilled in the art will appreciate that the various operations, methods, steps, measures, and schemes in the processes discussed in this application may be alternated, altered, combined, or deleted. Further, other steps, measures, and schemes in the various operations, methods, and processes discussed in this application may also be alternated, altered, rearranged, decomposed, combined, or deleted. Further, the steps, measures, and schemes in the prior art that are similar to those disclosed in this application may also be alternated, altered, rearranged, decomposed, combined, or deleted.

[0164] The above description is only a partial implementation method of the present application. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present application. These improvements and modifications should also be regarded as the scope of protection of the present application.

Claims

1. A method for processing alarm information, characterized in that: The following steps are involved: Receive various alarm information generated by the monitoring system or other alarm sources in real time through the preset API interface or monitoring message queue; Performing structured extraction and semantic understanding on the received alarm information to obtain the alarm content of the alarm information; Extracting key information from the alarm content, and determining the classification and urgency of the alarm information based on the key information; Adjusting the voice parameters according to the urgency, and generating an alarm audio file with corresponding language parameters based on the classification and the alarm content through a pre-built multilingual speech synthesis system; Acquire a recipient and contact information of the alarm information, call the recipient according to the contact information, and push the audio file to the recipient; The extracting key information from the alarm content and determining the classification and urgency of the alarm information based on the key information includes: Extracting a service topology diagram of a service node corresponding to the alarm content according to the alarm content; Pulling public opinion information associated with the key information based on the key information; Based on a preset public opinion root cause analysis model and the public opinion information, generate a root cause topology diagram representing the cause of the public opinion information; A logical match is performed based on the business topology map and the root cause topology map. When the logical match value between the business topology map and the root cause topology map is greater than a preset matching threshold, the classification and urgency of the alarm information are determined based on the public sentiment information and the heat value of the public sentiment information.

2. The alarm information processing method according to claim 1, characterized in that: After receiving various alarm information generated by the monitoring system or other alarm sources, the method further includes the following steps: Deduplication processing is performed on the received alarm information. After duplication of the alarm information is eliminated, subsequent steps are performed, wherein deduplication processing on the received alarm information includes: Calculating the characteristic value of the alarm information based on a preconfigured hash algorithm to generate a unique hash value for the alarm information; The hash value of the alarm information is compared with the hash value list of processed alarm information. If there is a duplicate, the alarm information is ignored; if there is no duplicate, the hash value of the alarm information is added to the hash value list of processed alarm information.

3. The alarm information processing method according to claim 1, characterized in that: The structured extraction of the received alarm information includes: Cleaning the received warning information to remove irrelevant characters, spaces and format errors in the warning information; Identifying and separating metadata in the cleaned alarm information; The metadata is verified by a preset verification rule. If the metadata does not conform to the verification rule, the alarm information is marked as an invalid alarm and corresponding error processing is performed.

4. The alarm information processing method according to claim 1, characterized in that: After the pre-built multilingual language synthesis system generates an alarm audio file corresponding to the language parameters based on the classification and the alarm content, the method further includes: Provide a user interface for receiving a specific prefix or suffix text input by a user; Recognize the prefix or suffix text by the multilingual speech synthesis system, and embed it into the beginning or end of the generated alarm audio file; Extracting key content of the alarm information, and generating an alarm summary according to the key content; The alarm summary is configured as summary information of the alarm audio file.

5. The alarm information processing method according to claim 1, characterized in that: The generating of an alarm audio file corresponding to the language parameters based on the classification and the alarm content by a pre-built multilingual language synthesis system includes: Use regular expressions to match sensitive information in the alarm content; Performing a preconfigured replacement on the sensitive information to obtain desensitized alarm content; The multilingual speech synthesis system generates an alarm audio file with corresponding language parameters based on the classification and the desensitized alarm content.

6. The method for processing alarm information according to claim 1, characterized in that: After pushing the audio file to the receiving object, the method further includes: Record the playback record of the audio file, the time and result of the alarm processing; The alarm information processing result is generated according to the play record, the time and result of the alarm processing, and fed back to the statistical object of the alarm information.

7. An alarm information processing device, characterized in that: include: The alarm data module is used to receive various alarm information generated by the monitoring system or other alarm sources in real time through the preset API interface or monitoring message queue; A content recognition module is used to perform structured extraction and semantic understanding on the received alarm information to obtain the alarm content of the alarm information; An alarm classification module is used to extract key information from the alarm content and determine the classification and urgency of the alarm information based on the key information; A speech synthesis module, used for adjusting speech parameters according to the urgency, and generating an alarm audio file of corresponding language parameters based on the classification and the alarm content through a pre-built multilingual speech synthesis system; An alarm push module, used to obtain a recipient and contact information of the alarm information, call the recipient according to the contact information, and push the audio file to the recipient; The alarm push module is also used for: Extracting a service topology diagram of a service node corresponding to the alarm content according to the alarm content; Pulling public opinion information associated with the key information based on the key information; Based on a preset public opinion root cause analysis model and the public opinion information, generate a root cause topology diagram representing the cause of the public opinion information; A logical match is performed based on the business topology map and the root cause topology map. When the logical match value between the business topology map and the root cause topology map is greater than a preset matching threshold, the classification and urgency of the alarm information are determined based on the public sentiment information and the heat value of the public sentiment information.

8. A computer device, characterized in that: The method comprises a memory and a processor, wherein the memory stores computer-readable instructions, and when the computer-readable instructions are executed by the processor, the processor executes the steps of the alarm information processing method according to any one of claims 1 to 6.

9. A storage medium storing computer-readable instructions, characterized in that: When the computer-readable instructions are executed by one or more processors, the one or more processors execute the steps of the alarm information processing method according to any one of claims 1 to 6.

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