Alarm identification method and device, electronic equipment and computer readable storage medium

Abstract

The invention relates to the technical field of alarm recognition and the field of smart medical treatment, and provides an alarm recognition method and device, electronic equipment and a computer readable storage medium, and the method comprises the steps: carrying out the matching from a database according to preliminary alarm type information, and obtaining corresponding solution information; under the condition that the credibility value is greater than a preset credibility threshold value, performing accuracy analysis processing on the preliminary alarm category information and the solution information to obtain alarm accuracy information and scheme accuracy information; and under the condition that at least one of the alarm accuracy information and the scheme accuracy information does not meet a preset accuracy requirement, performing updating processing on training parameters of the determiner. Through the technical scheme, the alarm information in the intelligent medical system can be accurately identified and processed, the alarm rule does not need to be manually configured due to the change of the alarm information, the workload of manual configuration is reduced, and the system maintenance is facilitated.

Description

Technical Field

[0001] The embodiments of the present application relate to, but are not limited to, the field of alarm recognition, and in particular, to an alarm recognition method, device, electronic device, and computer-readable storage medium. Background Art

[0002] Monitoring alarms are alarms generated during the operation of a big data platform. For example, in systems such as a smart medical system, a property insurance business system, and an elderly care service business system, a monitoring platform can be used to collect and process the alarms generated during the system operation. Subsequently, the monitoring platform is also required to identify and classify the collected alarms; since the monitoring data, compared with historical data, characteristics such as time period, fluctuation range, and data volume size will all affect the credibility of alarm recognition; in order to make the alarm recognition and classification more reasonable and accurate, it is necessary to manually and meticulously configure the alarm rules, which will result in the alarm rule configuration being too complex and not facilitating system maintenance. Summary of the Invention

[0003] The following is an overview of the subject matter described in detail in this document. This overview is not intended to limit the scope of protection of the claims.

[0004] To solve the problems mentioned in the above background art, the embodiments of the present application provide an alarm recognition method, device, electronic device, and computer-readable storage medium, which can convert complex monitoring rules into intelligent alarm determination, thereby reducing the manual configuration workload and facilitating system maintenance.

[0005] In a first aspect, the embodiments of the present application provide an alarm recognition method, including:

[0006] Reading system operation information from a preset business system;

[0007] Performing message category analysis processing on the system operation information to obtain potential alarm information;

[0008] Inputting the potential alarm information into a preset determiner for recognition and determination processing to obtain a preliminary recognition result, where the training parameters of the determiner are loaded through a preset database, and the preliminary recognition result includes preliminary alarm category information and a credibility value;

[0009] Matching corresponding solution information from the database according to the preliminary alarm category information;

[0010] When the confidence value is greater than a preset confidence threshold, the preliminary alarm category information and the solution information are fed back to the service system to perform accuracy analysis processing on the preliminary alarm category information and the solution information, so as to obtain alarm accuracy information corresponding to the preliminary alarm category information and solution accuracy information corresponding to the solution information;

[0011] When at least one of the alarm accuracy information and the solution accuracy information does not meet the preset accuracy requirement, update processing is performed on the training parameters of the discriminator.

[0012] In a second aspect, an embodiment of the present application further provides an alarm recognition device, including:

[0013] A reading unit, configured to read system operation information from a preset service system;

[0014] An analysis unit, configured to perform message category analysis processing on the system operation information to obtain potential alarm information;

[0015] An identification unit, configured to input the potential alarm information into a preset discriminator for identification and determination processing to obtain a preliminary identification result, where the training parameters of the discriminator are obtained by loading through a preset database, and the preliminary identification result includes preliminary alarm category information and a confidence value;

[0016] A matching unit, configured to match corresponding solution information from the database according to the preliminary alarm category information;

[0017] An execution unit, configured to, when the confidence value is greater than a preset confidence threshold, feed back the preliminary alarm category information and the solution information to the service system to perform accuracy analysis processing on the preliminary alarm category information and the solution information, so as to obtain alarm accuracy information corresponding to the preliminary alarm category information and solution accuracy information corresponding to the solution information;

[0018] An update unit, configured to perform update processing on the training parameters of the discriminator when at least one of the alarm accuracy information and the solution accuracy information does not meet the preset accuracy requirement.

[0019] In a third aspect, an embodiment of the present application further provides an electronic device, including: a memory, a processor, and a computer program stored on the memory and executable on the processor, where when the processor executes the computer program, the alarm recognition method described in the first aspect above is implemented.

[0020] Fourthly, an embodiment of the present application further provides a computer-readable storage medium storing computer-executable instructions for executing the alarm recognition method as described in the first aspect above.

[0021] According to the alarm recognition method provided by the embodiment of the present application, it has at least the following beneficial effects: in the process of alarm recognition, first read the system operation information from a preset business system; then perform message category analysis and processing on the system operation information to obtain potential alarm information; then input the potential alarm information into a preset discriminator for recognition and determination processing to obtain a preliminary recognition result, where the training parameters of the discriminator are loaded from a preset database, and the preliminary recognition result includes preliminary alarm category information and a confidence value; then match the corresponding solution information from the database according to the preliminary alarm category information; when the confidence value is greater than a preset confidence threshold, feedback the preliminary alarm category information and the solution information to the business system to perform accuracy analysis and processing on the preliminary alarm category information and the solution information to obtain alarm accuracy information corresponding to the preliminary alarm category information and solution accuracy information corresponding to the solution information; when at least one of the alarm accuracy information and the solution accuracy information does not meet the preset accuracy requirements, update the training parameters of the discriminator. Through the above technical solution, input the potential alarm information into the discriminator for recognition and determination processing to obtain a preliminary recognition result, and then match the corresponding solution information from the database according to the preliminary alarm category information in the preliminary recognition result; and when the confidence value is greater than the preset confidence threshold, feedback the preliminary alarm category information and the solution information to the business system to perform accuracy analysis and processing on the preliminary alarm category information and the solution information, which can transform complex monitoring rules into intelligent alarm determination, thereby reducing the manual configuration workload and facilitating system maintenance. BRIEF DESCRIPTION OF THE DRAWINGS

[0022] The drawings are used to provide a further understanding of the technical solutions of the present application and constitute a part of the specification. They are used together with the embodiments of the present application to explain the technical solutions of the present application and do not constitute a limitation to the technical solutions of the present application.

[0023] Figure 1 is a schematic flowchart of an alarm recognition method provided by an embodiment of the present application;

[0024] Figure 2 is Figure 1 a schematic flowchart of a specific implementation manner of step S200 in

[0025] Figure 3 is Figure 1 a schematic flowchart of a specific implementation manner of step S300 in

[0026] Figure 4 is Figure 1 A schematic flowchart of a specific implementation manner of step S400 in

[0027] Figure 5 is Figure 1 A schematic flowchart of a specific implementation manner of step S500 in

[0028] Figure 6 is Figure 1 A schematic flowchart of a specific implementation manner of step S600 in

[0029] Figure 7 is Figure 1 A schematic flowchart of a specific implementation manner after step S600 in

[0030] Figure 8 A schematic diagram of an alarm recognition device provided by an embodiment of the present application;

[0031] Figure 9 A schematic diagram of an electronic device provided by an embodiment of the present application. Specific implementation manner

[0032] In order to make the purpose, technical solutions and advantages of the present application clearer, the present application will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0033] It should be noted that although functional module division is performed in the device schematic diagram and the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in a different order from the module division in the device or the order in the flowchart. The terms "first", "second", etc. in the specification, claims and the above drawings are used to distinguish similar objects and do not necessarily need to describe a specific order or sequence.

[0034] It should be noted that unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the technical field to which the present application belongs. The terms used herein are only for the purpose of describing the embodiments of the present application and are not intended to limit the present application.

[0035] The embodiments of the present application can acquire and process relevant data based on artificial intelligence technology. Among them, artificial intelligence (AI) is a theory, method, technology and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend and expand human intelligence, perceive the environment, acquire knowledge and use knowledge to obtain the best results.

[0036] AI is a new technological science that studies and develops theories, methods, technologies, and application systems for simulating, extending, and expanding human intelligence; artificial intelligence is a branch of computer science. Artificial intelligence attempts to understand the essence of intelligence and produce a new intelligent machine that can respond in a way similar to human intelligence. The research in this field includes robots, speech recognition, image recognition, natural language processing, and expert systems, etc. Artificial intelligence can simulate the information process of human consciousness and thinking. Artificial intelligence also refers to the theory, method, technology, and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0037] The basic technologies of artificial intelligence generally include technologies such as sensors, dedicated artificial intelligence chips, cloud computing, distributed storage, big data processing technology, operation / interaction systems, and mechatronics, etc. The software technologies of artificial intelligence mainly include several major directions such as computer vision technology, robotics, biometric technology, speech processing technology, natural language processing technology, and machine learning / deep learning.

[0038] Artificial intelligence is AI. AI is the theory, method, technology, and application system that uses a digital computer or a machine controlled by a digital computer to simulate, extend, and expand human intelligence, perceive the environment, acquire knowledge, and use knowledge to obtain the best results.

[0039] The servers involved in artificial intelligence technology can be independent servers or cloud servers that provide basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, Content Delivery Network (CDN), as well as big data and artificial intelligence platforms.

[0040] The present application provides an alarm recognition method, apparatus, electronic device, and computer-readable storage medium. In the process of alarm recognition, first, system operation information is read from a preset business system; then, message category analysis and processing are performed on the system operation information to obtain potential alarm information; then, the potential alarm information is input into a preset discriminator for recognition and determination processing to obtain a preliminary recognition result. Among them, the training parameters of the discriminator are obtained by loading from a preset database. The preliminary recognition result includes preliminary alarm category information and a credibility value; then, corresponding solution information is matched from the database according to the preliminary alarm category information; when the credibility value is greater than a preset credibility threshold, the preliminary alarm category information and the solution information are fed back to the business system to perform accuracy analysis and processing on the preliminary alarm category information and the solution information, so as to obtain alarm accuracy information corresponding to the preliminary alarm category information and solution accuracy information corresponding to the solution information; when at least one of the alarm accuracy information and the solution accuracy information does not meet the preset accuracy requirement, the training parameters of the discriminator are updated. Through the above technical solution, the potential alarm information is input into the discriminator for recognition and determination processing to obtain a preliminary recognition result, and then corresponding solution information is matched from the database according to the preliminary alarm category information in the preliminary recognition result; and when the credibility value is greater than the preset credibility threshold, the preliminary alarm category information and the solution information are fed back to the business system to perform accuracy analysis and processing on the preliminary alarm category information and the solution information, which can transform complex monitoring rules into intelligent alarm determination, thereby reducing the manual configuration workload and facilitating system maintenance.

[0041] The alarm recognition method provided by the embodiments of the present application relates to the technical field of alarm recognition. The alarm recognition method provided by the embodiments of the present application can be applied to a terminal, can also be applied to a server side, or can be software running on a terminal or a server side. In some embodiments, the terminal can be a smart phone, a tablet computer, a notebook computer, a desktop computer, etc.; the server side can be configured as an independent physical server, can also be configured as a server cluster or a distributed system composed of multiple physical servers, or can be configured as a cloud server providing basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, and big data and artificial intelligence platforms.

[0042] This application can be used in numerous general-purpose or special-purpose computer system environments or configurations. For example: personal computers, server computers, handheld or portable devices, tablet devices, multiprocessor systems, microprocessor-based systems, set-top boxes, programmable consumer electronics, network PCs, minicomputers, mainframe computers, distributed computing environments including any of the above systems or devices, and so on. This application can be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. This application can also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules can be located in local and remote computer storage media including storage devices.

[0043] It should be noted that in each specific embodiment of this application, when it comes to relevant processing based on data related to the user's identity or characteristics, such as user information, user behavior data, user historical data, and user location information, the user's permission or consent will be obtained first. Moreover, the collection, use, and processing of these data will comply with relevant laws, regulations, and standards. In addition, when this application embodiment needs to obtain the user's sensitive personal information, the user's separate permission or separate consent will be obtained through methods such as pop-up windows or redirecting to a confirmation page. After clearly obtaining the user's separate permission or separate consent, the necessary user-related data for the normal operation of this application embodiment will be obtained.

[0044] The following further elaborates on the embodiments of this application in conjunction with the accompanying drawings.

[0045] As Figure 1 shown, Figure 1 is a flowchart of an alarm recognition method provided by an embodiment of this application. The alarm recognition method includes the following steps:

[0046] Step S100: Read the system operation information from a preset business system.

[0047] The alarm recognition method provided by the embodiments of the present application, in the process of alarm recognition, can first read and process relevant system operation information from a preset business system to prepare for subsequent alarm recognition. Among them, in some embodiments of the present application, the business system can be a property insurance business system, an old-age insurance business system, a smart medical and health system, etc.; the system operation information in the embodiments of the present application is the information generated by the relevant business system during operation. Exemplarily, in the property insurance business system, relevant system operation information will be generated when a user purchases insurance using the system. For another example, in the smart medical system, relevant system operation information will also be generated when a user uses the system to make an appointment for registration.

[0048] Step S200: Analyze and process the message categories of the system operation information to obtain potential alarm information.

[0049] The alarm recognition method provided by the embodiments of the present application, after reading the system operation information from a preset business system, can analyze and process the message categories of the system operation information to obtain potential alarm information; among them, the potential alarm information is the risk or abnormal information that may exist in the system. Analyzing and processing the message categories of the system operation information can obtain potential alarm information, which prepares for subsequent alarm recognition.

[0050] Exemplarily, in the vehicle insurance business system, the system operation information in the vehicle insurance business system can be analyzed and processed for message categories to obtain potential alarm information in the vehicle insurance business system. Subsequently, the potential alarm information can be further analyzed and processed to further identify the alarm information in the vehicle insurance business system. For another example, in the smart medical system, the system operation information in the smart medical system can also be analyzed and processed for message categories, and potential alarm information in the smart medical system can also be obtained, which can also prepare for subsequent alarm recognition in the smart medical system.

[0051] As Figure 2 shown, in step S200, analyzing and processing the message categories of the system operation information to obtain potential alarm information may include the following steps:

[0052] Step S210, perform information splitting processing on the system operation information to obtain multiple operation information blocks;

[0053] Step S220, perform information screening processing on the multiple operation information blocks according to preset alarm marking information to obtain potential alarm information.

[0054] For steps S210 to S220, in the process of performing message category analysis on the system operation information to obtain potential warning information, first, the system operation information can be split to obtain multiple operation information blocks; then, according to the preset warning marker information, the multiple operation information blocks can be screened to obtain the corresponding potential warning information. Through the above technical solution, potential warning information can be determined from the system operation information simply and quickly.

[0055] It should be noted that after obtaining the system operation information, the system operation information can be split to obtain multiple operation information blocks; then, according to the preset warning marker information, the obtained multiple operation information blocks can be screened to obtain the corresponding potential warning information. The warning marker information is the marker used to represent the warning information. If there is warning marker information in an operation information block, the information corresponding to the corresponding operation information block is used as potential warning information. Through the above setting, the determination of potential warning information can be made more accurate.

[0056] Exemplarily, in a smart healthcare system, after obtaining the system operation information from the smart healthcare system, the system operation information can be split to obtain multiple operation information blocks; then, according to the warning marker information, screening can be performed from the multiple operation information blocks, and thus potential warning information can be obtained to prepare for subsequent warning identification.

[0057] Step S300: Input the potential warning information into a preset discriminator for identification and determination processing to obtain a preliminary identification result. Among them, the training parameters of the discriminator are loaded through a preset database, and the preliminary identification result includes preliminary warning category information and a credibility value.

[0058] For the warning identification method provided in the embodiment of the present application, after performing message category analysis on the system operation information to obtain potential warning information, the potential warning information can be input into a preset discriminator for identification and determination processing to obtain a preliminary identification result; among them, the training parameters of the discriminator can be loaded through a preset database, and the preliminary identification result includes preliminary warning category information and a credibility value. Subsequently, warning identification processing can be performed according to the preliminary warning category information and the credibility value. Through the above technical solution, the discriminator can be used to perform identification and determination processing on the warning information, and then the complex monitoring rules can be transformed into intelligent warning determination, thereby reducing the manual configuration workload and facilitating system maintenance.

[0059] It should be noted that after obtaining the potential warning information, the potential warning information can be input into a discriminator for identification and determination processing to obtain a preliminary identification result. Among them, in the process of using the discriminator to process the potential warning information, the training parameters of the discriminator can be loaded from a preset database. The preliminary identification result includes preliminary warning category information and a credibility value. Subsequently, further judgment processing of the warning information can be carried out based on the preliminary warning category information and the credibility value, which can not only improve the accuracy of warning information identification but also speed up the efficiency of warning identification.

[0060] It should be noted that the discriminator includes a data input module, a determination module, and a result output module. In the process of identifying and determining the potential warning information, first, the data input module is used for preprocessing. Then, the determination module is used for feature extraction. Finally, the result output module is used for classification processing.

[0061] It should be noted that the preliminary warning category information is the warning type to which the potential warning information belongs, and the credibility value represents the credibility of warning identification and determination. The larger the credibility value, the more accurate the identification and determination of the potential warning information. The smaller the credibility value, the greater the chance of identification error in the identification and determination of the potential warning information.

[0062] It should be noted that the training parameters of the discriminator are stored in a preset database. When the discriminator needs to process the potential warning information, it will first read the latest training parameters from the preset database to process the potential warning information.

[0063] As Figure 3 shown, in step S300, the discriminator includes a data input module, a determination module, and a result output module. Inputting the potential warning information into a preset discriminator for identification and determination processing to obtain a preliminary identification result may include the following steps:

[0064] Step S310, preprocessing the potential warning information based on the data input module to obtain preprocessed warning information;

[0065] Step S320, extracting features from the preprocessed warning information based on the determination module to obtain warning feature information;

[0066] Step S330, classifying the warning feature information based on the result output module to obtain a preliminary identification result.

[0067] For steps S310 to S330, in the process of inputting potential warning information into a preset discriminator for identification and determination processing to obtain a preliminary identification result, first, the potential warning information is preprocessed by a data input module to obtain preprocessed warning information; then, warning feature information is extracted from the preprocessed warning information by a determination module; finally, the warning feature information is classified by a result output module to obtain a preliminary identification result. Through the above technical solution, the identification and determination of potential warning information can be more accurate and fast.

[0068] It should be noted that in the process of preprocessing potential warning information in the embodiment of the present application, it may include data cleaning, standardization, noise reduction, and preliminary screening processing of potential warning information. Among them, data cleaning can remove invalid or incorrect data, such as filtering potential warning information with incorrect formats; standardization can convert data into a unified format, such as unifying timestamp formats, field names, etc.; noise reduction is to remove duplicate potential warning information or significantly irrelevant noise data; preliminary screening is to screen out warnings containing important information according to preset rules. Extracting warning feature information from the preprocessed warning information by the determination module can extract information that effectively describes the data features, facilitating subsequent classification and decision-making processing by other modules.

[0069] Step S400: Match corresponding solution information from the database according to the preliminary warning category information.

[0070] After the warning recognition method provided by the embodiment of the present application inputs potential warning information into a preset discriminator for identification and determination processing to obtain a preliminary identification result, corresponding solution information can be matched from a preset database according to the preliminary warning category information in the preliminary identification result, providing a response method for the warning information and bringing a better user experience to users.

[0071] Exemplarily, in a smart healthcare system, when a user uses the smart healthcare system to handle a registration process, the smart healthcare system will generate corresponding system operation information; then the system operation information is read from the smart healthcare system; then, by performing message category analysis processing on the obtained system operation information, potential warning information generated during the user's registration process using the system can be obtained; then the potential warning information is input into a preset discriminator for identification and determination processing to obtain a preliminary identification result; subsequently, corresponding solution information can be matched from a preset database according to the preliminary warning category information in the preliminary identification result. For example, if the user's registration fails, the user can be advised to perform a reservation registration operation again.

[0072] Such as Figure 4As shown, in step S400, obtaining corresponding solution information by matching from the database according to the preliminary alarm category information may include the following steps:

[0073] Step S410, extracting an alarm category flag from the preliminary alarm category information;

[0074] Step S420, screening and obtaining solution information that matches the alarm category flag from the database according to the alarm category flag.

[0075] For steps S410 to S420, in the process of obtaining corresponding solution information by matching from the database according to the preliminary alarm category information, first extract the alarm category flag from the preliminary alarm category information; then, solution information that matches the alarm category flag can be screened and obtained from the database according to the alarm category flag. Through the above technical solution, solution information that matches the alarm category flag can be simply and quickly screened and obtained from the database, bringing a good experience to users.

[0076] Exemplarily, in the process of matching solution information, first extract the alarm category flag from the preliminary alarm category information. For example, the extracted alarm category flag is "02", and then solution information that matches the alarm category flag "02" can be screened and obtained from the database according to the alarm category flag "02". The whole matching process is simple, convenient and accurate.

[0077] Step S500: When the credibility value is greater than a preset credibility threshold, feedback the preliminary alarm category information and the solution information to the service system to perform accuracy analysis and processing on the preliminary alarm category information and the solution information, so as to obtain alarm accuracy information corresponding to the preliminary alarm category information and solution accuracy information corresponding to the solution information.

[0078] For the alarm recognition method provided in the embodiments of the present application, after using the discriminator to determine the preliminary recognition result, the preliminary alarm category information and the credibility value can be determined from the preliminary recognition result; then, corresponding solution information can be obtained by matching from the database according to the preliminary alarm category information; when the credibility value is greater than a preset credibility threshold, the preliminary alarm category information and the solution information will be fed back to the service system; subsequently, the service system can be used to perform accuracy analysis and processing on the preliminary alarm category information and the solution information, and finally, alarm accuracy information corresponding to the preliminary alarm category information and solution accuracy information corresponding to the solution information can be obtained. When at least one of the alarm accuracy information and the solution accuracy information does not meet the preset accuracy requirement, the training parameters of the discriminator can be updated to make subsequent alarm recognition more accurate.

[0079] It should be noted that the confidence threshold in the embodiments of the present application can be set according to actual needs. When the confidence value is greater than the pre-set confidence threshold, the preliminary alarm category information and solution information can be fed back to the business system, so as to prepare for the subsequent verification and processing of the accuracy of the preliminary alarm category information and solution information.

[0080] It should be noted that in the intelligent medical system, when the confidence value is greater than the pre-set confidence threshold, the preliminary alarm category information and solution information can be fed back to the intelligent medical system. Then, the intelligent medical system can be used to perform accuracy analysis and processing on the preliminary alarm category information and solution information. Finally, the alarm accuracy information corresponding to the preliminary alarm category information and the solution accuracy information corresponding to the solution information can be obtained. Through the above technical solution, the alarm recognition of the intelligent medical system can be made more accurate and reliable. Or, in the property insurance business system, when the confidence value is greater than the pre-set confidence threshold, the preliminary alarm category information and solution information can be fed back to the intelligent medical system. Then, the property insurance business system can be used to perform accuracy analysis and processing on the preliminary alarm category information and solution information. Finally, the alarm accuracy information corresponding to the preliminary alarm category information and the solution accuracy information corresponding to the solution information can be obtained. Through the above technical solution, the alarm recognition of the property insurance business system can also be made more accurate and reliable.

[0081] As Figure 5 shown, in step S500, performing accuracy analysis and processing on the preliminary alarm category information and solution information to obtain the alarm accuracy information corresponding to the preliminary alarm category information and the solution accuracy information corresponding to the solution information may include the following steps:

[0082] Step S510, predicting the accuracy rate of the preliminary alarm category information to obtain the alarm accuracy rate information; and predicting the recall rate of the preliminary alarm category information to obtain the alarm recall rate information;

[0083] Step S520, predicting the effectiveness of the solution information to obtain the solution effectiveness information; and predicting the precision of the solution information to obtain the solution precision information;

[0084] Step S530, determining the alarm accuracy information according to the alarm accuracy rate information and the alarm recall rate information; and determining the solution accuracy information according to the solution effectiveness information and the solution precision information.

[0085] For steps S510 to S530, in the process of performing accuracy analysis and processing on the preliminary alarm category information and the solution information to obtain the alarm accuracy information corresponding to the preliminary alarm category information and the solution accuracy information corresponding to the solution information, first, the accuracy rate of the preliminary alarm category information is predicted to obtain the alarm accuracy rate information; and the recall rate of the preliminary alarm category information is predicted to obtain the alarm recall rate information; then, the effectiveness of the solution information is predicted and processed to obtain the solution effectiveness information; and the precision of the solution information is predicted to obtain the solution precision information; finally, based on the alarm accuracy rate information and the alarm recall rate information, the alarm accuracy information can be determined; and based on the solution effectiveness information and the solution precision information, the solution accuracy information can be determined. Through the above technical solution, the alarm recognition can be made more accurate.

[0086] It should be noted that in the process of performing accuracy analysis and processing on the preliminary alarm category information, first, the accuracy rate of the preliminary alarm category information is predicted to obtain the alarm accuracy rate information; and in addition, the recall rate of the preliminary alarm category information needs to be predicted and processed to obtain the alarm recall rate information. In the process of performing accuracy analysis and processing on the solution information, first, the effectiveness of the solution information is predicted and processed to obtain the solution effectiveness information; and the precision of the solution information is predicted and processed to obtain the solution precision information. Finally, the alarm accuracy information of the preliminary alarm category information can be determined based on the alarm accuracy rate information and the alarm recall rate information; and the solution accuracy information of the solution information can be determined based on the solution effectiveness information and the solution precision information.

[0087] It should be noted that in the property insurance business system, when the preliminary alarm category information and the solution information are obtained, the accuracy rate of the preliminary alarm category information is predicted and processed to obtain the alarm accuracy rate information; the recall rate of the preliminary alarm category information can also be predicted and processed to obtain the alarm recall rate information; through the above alarm accuracy rate information and alarm recall rate information, more accurate identification and processing of the preliminary alarm category information in the property insurance business system can be performed. And the effectiveness of the solution information is predicted and processed to obtain the solution effectiveness information; the precision of the solution information can also be predicted and processed to obtain the solution precision information; through the above solution effectiveness information and solution precision information, more accurate identification and processing of the solution information in the property insurance business system can be performed.

[0088] Step S600: When at least one of the alarm accuracy information and the solution accuracy information does not meet the preset accuracy requirement, update the training parameters of the discriminator.

[0089] In the warning recognition method provided by the embodiment of the present application, when at least one of the warning accuracy information and the solution accuracy information does not meet the pre-set accuracy requirement, the training parameters of the discriminator are adjusted to make subsequent warning recognition more accurate, thereby improving the accuracy of warning recognition.

[0090] It should be noted that at least one of the warning accuracy information and the solution accuracy information not meeting the pre-set accuracy requirement means that either any one of the warning accuracy information and the solution accuracy information does not meet the pre-set accuracy requirement, or both the warning accuracy information and the solution accuracy information do not meet the pre-set accuracy requirement. Exemplarily, the warning accuracy information and the solution accuracy information can be presented in the form of percentages. For example, the percentage corresponding to the warning accuracy information is 60%, the percentage corresponding to the solution accuracy information is 70%, and the percentage corresponding to the accuracy requirement is 50%. Therefore, in such a case, both the warning accuracy information and the solution accuracy information meet the accuracy requirement.

[0091] As Figure 6 shown, when at least one of the warning accuracy information and the solution accuracy information does not meet the pre-set accuracy requirement, the update process of the training parameters of the discriminator may include the following steps:

[0092] Step S610, when the warning accuracy information does not meet the pre-set accuracy requirement, adjust the training parameters of the discriminator according to the warning accuracy information;

[0093] Step S620, when the solution accuracy information does not meet the pre-set accuracy requirement, adjust the training parameters of the discriminator according to the solution accuracy information;

[0094] Step S630, when both the warning accuracy information and the solution accuracy information do not meet the pre-set accuracy requirement, adjust the training parameters of the discriminator according to the warning accuracy information and the solution accuracy information.

[0095] For steps S610 to S630, in the process of updating the training parameters of the discriminator when at least one of the alarm accuracy information and the solution accuracy information does not meet the preset accuracy requirements, when the alarm accuracy information does not meet the preset accuracy requirements, the training parameters of the discriminator are updated and adjusted according to the alarm accuracy information; when the solution accuracy information does not meet the preset accuracy requirements, the training parameters of the discriminator are adjusted according to the solution accuracy information; when both the alarm accuracy information and the solution accuracy information do not meet the preset accuracy requirements, the training parameters of the discriminator are adjusted according to the alarm accuracy information and the solution accuracy information; through the above technical solution, the training parameters of the discriminator are adjusted, so that the subsequent identification of alarm information can be more accurate.

[0096] It should be noted that when both the alarm accuracy information and the solution accuracy information meet the preset accuracy requirements, there is no need to adjust the training parameters of the discriminator, and the original training parameters can be continuously loaded into the discriminator subsequently to implement the identification process of the alarm.

[0097] Exemplarily, in a smart healthcare system, after obtaining the alarm accuracy information and the solution accuracy information, when the alarm accuracy information does not meet the preset accuracy requirements, the training parameters of the discriminator can be adjusted according to the alarm accuracy information; when the solution accuracy information does not meet the preset accuracy requirements, the training parameters of the discriminator can be adjusted according to the solution accuracy information; when both the alarm accuracy information and the solution accuracy information do not meet the preset accuracy requirements, the training parameters of the discriminator can be adjusted according to the alarm accuracy information and the solution accuracy information. Through the above technical solution, the alarm identification of the smart healthcare system can be more accurate.

[0098] As Figure 7 shown, when at least one of the alarm accuracy information and the solution accuracy information does not meet the preset accuracy requirements, after updating the training parameters of the discriminator, the following steps may be included:

[0099] Step S710, input the potential alarm information into the updated discriminator for identification and determination processing to obtain the final identification result.

[0100] For step S710, when at least one of the alarm accuracy information and the solution accuracy information does not meet the preset accuracy requirement, the training parameters of the discriminator can be updated. Subsequently, the obtained potential alarm information can be continuously input into the updated discriminator for identification and determination processing, and finally the final identification result can be obtained. Based on the above technical solution, the potential alarm information is identified and determined again based on the updated discriminator to improve the accuracy of alarm identification.

[0101] In addition, as Figure 8 shown, an embodiment of the present application further provides an alarm identification device 10, including:

[0102] A reading unit 100, configured to read system operation information from a preset service system;

[0103] An analysis unit 200, configured to perform message category analysis processing on the system operation information to obtain potential alarm information;

[0104] An identification unit 300, configured to input the potential alarm information into a preset discriminator for identification and determination processing to obtain a preliminary identification result, where the training parameters of the discriminator are obtained by loading through a preset database, and the preliminary identification result includes preliminary alarm category information and a confidence value;

[0105] A matching unit 400, configured to match corresponding solution information from the database according to the preliminary alarm category information;

[0106] An execution unit 500, configured to, when the confidence is greater than a preset confidence threshold, feed back the preliminary alarm category information and the solution information to the service system to perform accuracy analysis processing on the preliminary alarm category information and the solution information, and obtain alarm accuracy information corresponding to the preliminary alarm category information and solution accuracy information corresponding to the solution information;

[0107] An updating unit 600, configured to update the training parameters of the discriminator when at least one of the alarm accuracy information and the solution accuracy information does not meet the preset accuracy requirement.

[0108] It should be noted that during the process of alarm recognition, first, system operation information is read from a preset business system; then, message category analysis and processing are performed on the system operation information to obtain potential alarm information; then, the potential alarm information is input into a preset discriminator for recognition and determination processing, and a preliminary recognition result can be obtained. Among them, the training parameters of the discriminator are loaded from a preset database. The preliminary recognition result includes preliminary alarm category information and a credibility value; then, corresponding solution information is matched from the database according to the preliminary alarm category information; when the credibility value is greater than a preset confidence threshold, the preliminary alarm category information and the solution information are fed back to the business system to perform accuracy analysis and processing on the preliminary alarm category information and the solution information, so as to obtain alarm accuracy information corresponding to the preliminary alarm category information and solution accuracy information corresponding to the solution information; when at least one of the alarm accuracy information and the solution accuracy information does not meet the preset accuracy requirement, the training parameters of the discriminator are updated. Through the above technical solution, the potential alarm information is input into the discriminator for recognition and determination processing to obtain a preliminary recognition result, and then corresponding solution information is matched from the database according to the preliminary alarm category information in the preliminary recognition result; and when the credibility value is greater than the preset confidence threshold, the preliminary alarm category information and the solution information are fed back to the business system to perform accuracy analysis and processing on the preliminary alarm category information and the solution information, which can transform complex monitoring rules into intelligent alarm determination, thereby reducing the manual configuration workload and facilitating system maintenance.

[0109] The specific implementation manner of the alarm recognition device 10 is basically the same as the specific embodiments of the above alarm recognition method, and will not be elaborated here.

[0110] In addition, as Figure 9 shown, an embodiment of the present application further provides an electronic device 700, which includes: a memory 720, a processor 710, and a computer program stored on the memory 720 and executable on the processor 710.

[0111] The processor 710 and the memory 720 can be connected through a bus or other means.

[0112] The non-transitory software program and instructions required to implement the alarm recognition method of the above embodiments are stored in the memory 720, and when executed by the processor 710, they execute the alarm recognition methods of the above embodiments.

[0113] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed to multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0114] In addition, an embodiment of the present application further provides a computer-readable storage medium. The computer-readable storage medium stores computer-executable instructions, which are executed by a processor 710 or a controller, for example, executed by a processor 710 in the above device embodiment, so that the processor 710 can execute the alarm recognition method in the above embodiment.

[0115] The above embodiments can be combined for use. The modules with the same name in different embodiments may be the same or different.

[0116] The specific embodiments of the present application are described above. Other embodiments are within the scope of the appended claims. In some cases, the actions or steps recited in the claims may be executed in a different order than in the embodiments and still achieve the desired result. Additionally, the processes depicted in the drawings do not necessarily have to be executed in the particular order or continuous order shown to achieve the desired result. In certain embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0117] Each embodiment in the present application is described in a progressive manner. The same or similar parts among the embodiments can be referred to each other, and the key point of each embodiment is to illustrate the differences from other embodiments. In particular, for the device, equipment, and computer-readable storage medium embodiments, since they are basically similar to the method embodiments, the description is relatively simple, and the relevant parts can refer to the partial description of the method embodiments.

[0118] The device, equipment, and computer-readable storage medium provided by the embodiments of the present application correspond to the method. Therefore, the device, equipment, and non-volatile computer storage medium also have beneficial technical effects similar to the corresponding method. Since the beneficial technical effects of the method have been described in detail above, the beneficial technical effects of the corresponding device, equipment, and computer storage medium will not be elaborated here.

[0119] In the 1990s, improvements to a technology could be clearly distinguished as either hardware improvements (e.g., improvements to circuit structures such as diodes, transistors, switches, etc.) or software improvements (improvements to method flows). However, with the development of technology, many method flow improvements today can be regarded as direct improvements to hardware circuit structures. Almost all designers obtain the corresponding hardware circuit structure by programming the improved method flow into the hardware circuit. Therefore, it cannot be said that an improvement to a method flow cannot be implemented using a hardware entity module. For example, a Programmable Logic Device (PLD) (such as a Field Programmable Gate Array (FPGA)) is an integrated circuit whose logical function is determined by the user programming the device. Designers can program themselves to "integrate" a digital system onto a single PLD, without having to ask a chip manufacturer to design and fabricate a dedicated integrated circuit chip. Moreover, nowadays, instead of manually fabricating integrated circuit chips, this programming is mostly implemented using "logic compiler" software, which is similar to the software compilers used in program development and writing. The original code before compilation also has to be written in a specific programming language, which is called a Hardware Description Language (HDL), and there is not just one kind of HDL, but many kinds, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc. Currently, the most commonly used ones are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also be aware that by simply performing a little logical programming on the method flow using the above-mentioned several hardware description languages and programming it into an integrated circuit, it is easy to obtain the hardware circuit that implements the logical method flow.

[0120] The controller can be implemented in any suitable manner. For example, the controller can take the form of, for example, a microprocessor or a processor and a computer-readable medium that stores computer-readable program code (such as software or firmware) executable by the (micro)processor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller, and an embedded microcontroller. Examples of the controller include, but are not limited to, the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20, and Silicone Labs C8051F320. The memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art also know that in addition to implementing the controller in the form of pure computer-readable program code, it is entirely possible to logically program the method steps to enable the controller to be implemented in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers, embedded microcontrollers, etc. to achieve the same function. Therefore, such a controller can be considered a hardware component, and the devices included therein for implementing various functions can also be regarded as the structures within the hardware component. Or even, the devices for implementing various functions can be regarded as either software modules for implementing the method or structures within the hardware component.

[0121] The systems, devices, modules, or units illustrated in the above embodiments can be specifically implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be, for example, a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or any combination of these devices.

[0122] For the convenience of description, the above devices are described by dividing them into various units according to their functions. Of course, when implementing the embodiments of the present application, the functions of the various units can be implemented in the same or multiple software and / or hardware.

[0123] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the embodiments of the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the embodiments of the present application can take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memories, CD-ROMs, optical memories, etc.) that contain computer-usable program code.

[0124] This specification is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present application. It should be understood that each flow and / or block in the flowchart and / or block diagram, and combinations of flows and / or blocks in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing device to produce a machine, such that the instructions executed by the processor of the computer or other programmable data processing device generate means for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0125] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to operate in a specific manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0126] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in one or more of the flows Figure 1 one or more of the flows and / or blocks Figure 1 or means for implementing the functions specified in one or more of the blocks.

[0127] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0128] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory such as read-only memory (ROM) or flash memory (Flash RAM). The memory is an example of computer-readable media.

[0129] A computer-readable medium includes both permanent and non-permanent, removable and non-removable media, and information storage can be implemented by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information accessible by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0130] It should also be noted that the term "comprises," "comprising," or any other variation thereof is intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but also other elements not expressly listed, or elements that are inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising a..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that comprises the element.

[0131] In the embodiments of the present application, "at least one" means one or more, and "a plurality" means two or more. "And / or" describes the association relationship of associated objects and indicates that three relationships can exist. For example, A and / or B can represent the cases of A existing alone, A and B existing simultaneously, and B existing alone. Here, A and B can be singular or plural. The character " / " generally indicates that the associated objects before and after are in an "or" relationship. "At least one of the following" and its similar expressions refer to any combination of these items, including any combination of single items or plural items. For example, at least one of a, b, and c can represent: a, b, c, a and b, a and c, b and c, or a and b and c, where a, b, and c can be single or multiple.

[0132] Embodiments of the present application may be described in the general context of computer-executable instructions executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc. that perform specific tasks or implement specific abstract data types. Embodiments of the present application may also be practiced in a distributed computing environment where tasks are performed by remote processing devices connected through a communication network. In a distributed computing environment, program modules may be located in local and remote computer storage media including storage devices.

[0133] The various embodiments in the present application are described in a progressive manner. For the same or similar parts among the various embodiments, reference can be made to each other. Each embodiment focuses on the differences from other embodiments. In particular, for system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and for the relevant parts, reference can be made to the description of the method embodiments.

[0134] The above description is only for the embodiments of the present application and is not intended to limit the present application. For those skilled in the art, various changes and modifications can be made to the present application. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present application shall be included within the scope of the claims of the present application.

Claims

1. An alarm recognition method, characterized in that, Including: Reading system operation information from a preset business system; Performing message category analysis and processing on the system operation information to obtain potential warning information; Inputting the potential warning information into a preset discriminator for identification and determination processing to obtain a preliminary identification result, wherein the training parameters of the discriminator are loaded through a preset database, and the preliminary identification result includes preliminary warning category information and a credibility value; Matching corresponding solution information from the database according to the preliminary warning category information; When the credibility value is greater than a preset confidence threshold, feeding back the preliminary warning category information and the solution information to the business system to perform accuracy analysis and processing on the preliminary warning category information and the solution information, so as to obtain warning accuracy information corresponding to the preliminary warning category information and solution accuracy information corresponding to the solution information; When at least one of the warning accuracy information and the solution accuracy information does not meet the preset accuracy requirement, updating the training parameters of the discriminator.

2. The alarm recognition method according to claim 1, wherein The performing message category analysis and processing on the system operation information to obtain potential warning information includes: Performing information splitting processing on the system operation information to obtain a plurality of operation information blocks; Performing information screening processing on the plurality of operation information blocks according to preset warning mark information to obtain the potential warning information.

3. The alarm recognition method according to claim 1, wherein The discriminator includes a data input module, a determination module, and a result output module. The inputting the potential warning information into a preset discriminator for identification and determination processing to obtain a preliminary identification result includes: Performing preprocessing on the potential warning information based on the data input module to obtain preprocessed warning information; Performing feature extraction on the preprocessed warning information based on the determination module to obtain warning feature information; Performing classification processing on the warning feature information based on the result output module to obtain the preliminary identification result.

4. The alarm recognition method according to claim 1, wherein The matching corresponding solution information from the database according to the preliminary warning category information includes: Extracting a warning category mark from the preliminary warning category information; Screening and obtaining the solution information matching the warning category mark from the database according to the warning category mark.

5. The alarm recognition method according to claim 1, wherein The performing accuracy analysis and processing on the preliminary warning category information and the solution information to obtain warning accuracy information corresponding to the preliminary warning category information and solution accuracy information corresponding to the solution information includes: Performing accuracy rate prediction on the preliminary warning category information to obtain warning accuracy rate information; and performing recall rate prediction on the preliminary warning category information to obtain warning recall rate information; Performing effectiveness prediction on the solution information to obtain solution effectiveness information; and performing precision prediction on the solution information to obtain solution precision information; Determine the alarm accuracy information based on the alarm accuracy rate information and the alarm recall rate information; and determine the solution accuracy information based on the solution effectiveness information and the solution precision information.

6. The alarm recognition method according to claim 1, wherein, When at least one of the alarm accuracy information and the solution accuracy information does not meet the preset accuracy requirement, the updating process of the training parameters of the discriminator includes: When the alarm accuracy information does not meet the preset accuracy requirement, adjust the training parameters of the discriminator according to the alarm accuracy information; When the solution accuracy information does not meet the preset accuracy requirement, adjust the training parameters of the discriminator according to the solution accuracy information; When both the alarm accuracy information and the solution accuracy information do not meet the preset accuracy requirement, adjust the training parameters of the discriminator according to the alarm accuracy information and the solution accuracy information.

7. The alarm recognition method according to claim 1, wherein After the training parameters of the discriminator are updated when at least one of the alarm accuracy information and the solution accuracy information does not meet the preset accuracy requirement, the method further includes: Input the potential alarm information into the updated discriminator for identification and determination processing to obtain a final identification result.

8. An alarm recognition device, characterized in that, including: A reading unit for reading system operation information from a preset business system; An analysis unit for performing message category analysis processing on the system operation information to obtain potential alarm information; An identification unit for inputting the potential alarm information into a preset discriminator for identification and determination processing to obtain a preliminary identification result, wherein the training parameters of the discriminator are loaded through a preset database, and the preliminary identification result includes preliminary alarm category information and a credibility value; A matching unit for matching corresponding solution information from the database according to the preliminary alarm category information; An execution unit for, when the credibility value is greater than a preset credibility threshold, feeding back the preliminary alarm category information and the solution information to the business system to perform accuracy analysis processing on the preliminary alarm category information and the solution information to obtain alarm accuracy information corresponding to the preliminary alarm category information and solution accuracy information corresponding to the solution information; An updating unit for updating the training parameters of the discriminator when at least one of the alarm accuracy information and the solution accuracy information does not meet the preset accuracy requirement.

9. An electronic device, comprising: A memory, a processor, and a computer program stored on the memory and executable on the processor, wherein the processor implements the alarm identification method according to any one of claims 1 to 7 when executing the computer program.

10. A computer-readable storage medium storing computer-executable instructions, characterized in that, The computer-executable instructions are used to execute the alarm identification method according to any one of claims 1 to 7.

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