Fault alarm method and system, storage medium and electronic equipment

Abstract

The invention belongs to the technical field of passenger service, and provides a fault alarm method and system, a storage medium and electronic equipment, and the method comprises the steps: obtaining front-end processing information, and generating front-end behavior data according to the front-end processing information; dividing the front-end behavior data into a plurality of business data segments, and determining business process information corresponding to the plurality of business data segments; performing similarity matching on the business process information and a preset standard process, and determining the business process information which does not accord with a preset matching condition as abnormal business information; determining an influence type and a corresponding influence event according to the abnormal service information and the occurrence frequency of the abnormal service information in unit time; and generating a fault alarm notification based on the influence type and the corresponding influence event. The fault solving efficiency and accuracy are improved; the specific reason of the fault can be accurately identified, and powerful support is provided for subsequent repair; existing security vulnerabilities or risk points can be found, and the security of the departure system is integrally improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of passenger service, and in particular, relates to a fault alarm method, system, storage medium and electronic equipment. Background Art

[0002] The departure system is a key system for airlines and airports to implement functions such as passenger check-in, flight data control, and load balancing. Its stability and reliability are crucial to ensuring the normal operation of flights and the safety of passenger travel.

[0003] With the increasing complexity of the departure system and the continuous growth in the number of passengers, the risk of failure faced by the departure system is gradually increasing. The use of manual diagnosis methods in traditional technologies has huge differences in the real-time and accuracy of fault location due to personal experience and ability. In addition, when a failure occurs, the central operation and maintenance team cannot grasp the situation of the on-site user business processing being blocked. Summary of the invention

[0004] To solve the above problems, the present disclosure provides a fault alarm method, system, storage medium and electronic device, which collect front-end processing information and determine multiple business process information, and determine abnormal business information through business process information and preset standard processes; determine whether a fault occurs according to the error type and frequency of occurrence of the abnormal business information, and when a fault is confirmed, determine the impact type and corresponding impact event according to the corresponding abnormal business data, calculate the fault score, determine the fault level, and generate and feedback a fault report notification.

[0005] The present invention is achieved through the following technical solutions:

[0006] In a first aspect, an embodiment of the present disclosure provides a fault alarm method, the method comprising:

[0007] Acquire front-end processing information, and generate front-end behavior data according to the front-end processing information;

[0008] Dividing the front-end behavior data into a plurality of business data segments, and determining business process information corresponding to the plurality of business data segments;

[0009] The business process information is matched with a preset standard process for similarity, and the business process information that does not meet the preset matching condition is determined as abnormal business information;

[0010] Determine the impact type and the corresponding impact event according to the abnormal business information and the number of occurrences of the abnormal business information per unit time;

[0011] Based on the impact type and the corresponding impact event, a fault alarm notification is generated.

[0012] Furthermore,

[0013] The front-end processing information is collected in real time, the front-end processing information is converted into a data format, and the front-end behavior data is generated.

[0014] Furthermore,

[0015] Removing duplicate data, erroneous data and irrelevant data from the front-end behavior data;

[0016] Supplement the missing values ​​in the front-end behavior data and fill the missing values ​​in the same format;

[0017] Generate a plurality of business data segments from the front-end behavior data according to the time sequence of the business;

[0018] The data formats and measurement units of all the business data segments are unified, and the business process information corresponding to each business data segment is determined.

[0019] Furthermore,

[0020] Determine the preset standard process corresponding to the business process information, and perform similarity matching between the business process information and the corresponding preset standard process;

[0021] A reference percentage is set as the preset matching condition, and the business process information whose similarity matching result is less than the reference percentage is determined as the abnormal business information; wherein the abnormal business information includes the error type of this business.

[0022] Furthermore,

[0023] According to the error reporting type of the abnormal business information, count the number of occurrences of the abnormal business information within a unit time;

[0024] If the number of occurrences of the abnormal business information of the same error type in a unit time exceeds a preset threshold, the impact type and the corresponding impact event are determined based on all the abnormal business information corresponding to this error type.

[0025] Furthermore,

[0026] Based on the error type of the abnormal service information, the preset threshold of the number of occurrences of the abnormal service information of the same error type within a unit time is set.

[0027] Furthermore,

[0028] A fault weight is set according to the impact type, a fault score is set according to the impact event, a fault score is determined through weighted calculation, and a fault level is determined according to the fault score.

[0029] In a second aspect, based on the same inventive concept, the embodiment of the present disclosure further provides a fault alarm system, the system comprising: a data acquisition module, a data processing module, a behavior analysis module, a fault diagnosis module and an alarm notification module;

[0030] The data acquisition module is used to obtain front-end processing information and generate front-end behavior data according to the front-end processing information;

[0031] The data processing module is used to divide the front-end behavior data into multiple business data segments and determine the business process information corresponding to the multiple business data segments;

[0032] The behavior analysis module is used to perform similarity matching between the business process information and a preset standard process, and to determine the business process information that does not meet the preset matching conditions as abnormal business information;

[0033] The fault diagnosis module is used to determine the impact type and the corresponding impact event according to the abnormal business information and the number of occurrences of the abnormal business information per unit time;

[0034] The alarm notification module is used to generate a fault alarm notification based on the impact type and the corresponding impact event.

[0035] On the third aspect, based on the same inventive concept, the embodiment of the present disclosure also provides a computer-readable storage medium, storing one or more programs, which, when executed, can implement the aforementioned fault alarm method.

[0036] In a fourth aspect, based on the same inventive concept, an embodiment of the present disclosure further provides an electronic device, including a processor, a communication interface, the aforementioned computer-readable storage medium, and a communication bus. The processor, the communication interface, and the computer-readable storage medium communicate with each other via the communication bus; the processor is used to execute the program stored in the aforementioned computer-readable storage medium.

[0037] Compared with the prior art, the present invention has the following advantages:

[0038] 1. Collect front-end processing information, determine user (passenger) behavior based on the front-end processing information, match the business process information corresponding to the user behavior with the preset standard process, quickly locate the links where problems may occur, reduce the blindness and randomness in traditional troubleshooting, and improve the efficiency and accuracy of troubleshooting.

[0039] 2. Determining whether a fault occurs through business process information corresponding to user behavior helps to accurately identify the specific cause of the fault and provide strong support for subsequent repairs.

[0040] 3. By matching the business process information corresponding to the user behavior with the standard process information, it is possible to determine whether there is a fault, discover the existing security loopholes or risk points, and take corresponding security measures in time, thereby improving the overall security of the departure system and ensuring the safety of flights and passengers.

[0041] Other features and advantages of the present disclosure will be described in the following description, and partly become apparent from the description, or be understood by implementing the present disclosure. The purpose and other advantages of the present disclosure can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] In order to more clearly illustrate the embodiments of the present disclosure or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present disclosure. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0043] Figure 1 A flow chart of a fault alarm method provided by an embodiment of the present disclosure;

[0044] Figure 2 A block diagram of a fault alarm system provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0045] In order to make the purpose, technical solution and advantages of the embodiments of the present disclosure clearer, the technical solution in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the drawings in the embodiments of the present disclosure. Obviously, the described embodiments are part of the embodiments of the present disclosure, not all of the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present disclosure.

[0046] First, Figure 1 A flow chart of a fault alarm method provided by an embodiment of the present disclosure is as follows: Figure 1 As shown, the embodiment of the present disclosure provides a fault alarm method, including:

[0047] S1: Obtain front-end processing information, and generate front-end behavior data based on the front-end processing information.

[0048] S2: Divide the front-end behavior data into multiple business data segments, and determine the business process information corresponding to the multiple business data segments.

[0049] S3: Perform similarity matching between the business process information and the preset standard process, and determine the business process information that does not meet the preset matching conditions as abnormal business information.

[0050] S4: Determine the impact type and the corresponding impact event according to the abnormal business information and the number of occurrences of the abnormal business information per unit time.

[0051] S5: Generate and feedback a fault alarm notification based on the impact type and the corresponding impact event.

[0052] In the disclosed embodiment, front-end processing information is collected and multiple business process information is determined. Abnormal business information is determined through the business process information and preset standard processes. Whether a fault has occurred is determined based on the error type and frequency of occurrence of the abnormal business information. When a fault is confirmed, the impact type and corresponding impact event are determined based on the corresponding abnormal business data, and a fault report notification is generated and fed back.

[0053] In some examples, obtaining the front-end processing information and generating the front-end behavior data according to the front-end processing information include:

[0054] S11: Collect front-end processing information in real time, convert the front-end processing information into a data format, and generate front-end behavior data.

[0055] Specifically, the front-end processing information is obtained in real time through the departure front-end system, which ensures the real-time nature of the information and facilitates the discovery of potential faults; the obtained front-end processing information is converted into a data format to generate front-end behavior data. Front-end behavior data includes user behavior data and error pop-up box data. User behavior data includes business scenarios and operation information. Business scenarios may include: login, query, check-in, boarding, change, cancellation, etc.; operation information includes user ID, operation time, operation business ID, content, method, result, etc. Error pop-up box data includes: error code, error description, error time, transaction number information, etc., which are used to determine the error type later.

[0056] In some examples, the front-end behavior data is divided into a plurality of business data segments, and the business process information of the plurality of business data segments is determined, including:

[0057] S21: Remove duplicate data, erroneous data and irrelevant data from front-end behavior data.

[0058] S22: Supplement the missing values ​​in the front-end behavior data and fill the missing values ​​in the same format.

[0059] S23: Generate multiple business data segments according to the time sequence of the business according to the front-end behavior data; each business data segment corresponds to one business.

[0060] Specifically, the front-end behavior data may include duplicate data, erroneous data, irrelevant data, and missing values, so the front-end behavior data needs to be processed. Clean the front-end behavior data to remove duplicate data, erroneous data, and irrelevant data in the front-end behavior data to improve data quality. Supplement missing values ​​for the front-end behavior data and fill the missing values ​​in the same format. Sort the front-end behavior data and generate multiple business data segments in chronological order of the business. Each business data segment corresponds to a business, for example: a business data segment corresponds to a business operation of a user in the departure system. When the business corresponding to this business data segment is abnormal and an error pop-up box occurs, the error pop-up box data will also be recorded in this business data segment.

[0061] S24: Unify the data formats and measurement units of all business data segments, and determine the business process information corresponding to each business data segment.

[0062] Specifically, all business data segments are standardized, the data format and measurement unit of each behavior data segment are unified, the business process information is determined based on the business data segments, and each business data segment corresponds to a business process information; wherein, a business process information includes the business scenario and process information of this business determined by the business data segment. If an error pop-up box is generated, the business process information also includes the error pop-up box information of this business.

[0063] In some examples, the business process information is matched with a preset standard process for similarity, and the business process information that does not meet the preset matching conditions is determined as abnormal business information, including:

[0064] S31: Determine the preset standard process corresponding to the business process information, and perform similarity matching between the business process information and the corresponding preset standard process.

[0065] Specifically, the standard process of each business is preset, the business scenario corresponding to the business process information is determined, and the business process information is matched with the preset standard process of the corresponding business scenario for similarity. The preset matching condition is that the similarity between the business process information and the preset standard process is greater than or equal to 80%.

[0066] S32: Setting a reference percentage as a preset matching condition, and determining the business process information whose similarity matching result is less than the reference percentage as abnormal business information; wherein the abnormal business information includes the error type of this business.

[0067] Specifically, the preset matching condition is that the similarity between the business process information and the preset standard process is greater than or equal to 80%, that is, the reference percentage is 80%. When the similarity between the business process information and the preset standard process is greater than or equal to 80%, it meets the preset matching condition and the business process information is determined to be normal; when the similarity between the business process information and the preset standard process is less than 80%, it does not meet the preset matching condition. If the similarity matching result is less than 80%, it is determined that there is an abnormality in this business process information and it is determined to be abnormal business information, wherein the abnormal business information includes the error pop-up box information of this business, and the error pop-up box information includes error code, error description, error time, transaction number and other information, and the error type can be determined by the error code, error description, error time, transaction number and other information.

[0068] In some examples, before determining the impact type and the corresponding impact event according to the abnormal business information and the number of occurrences of the abnormal business information per unit time, the process includes:

[0069] S41': Based on the error type of the abnormal business information, a preset threshold for the number of occurrences of abnormal business information of the same error type within a unit time is set.

[0070] Specifically, all abnormal business information is analyzed, and based on the error type of the abnormal business information, a preset threshold for the number of occurrences of abnormal business information of the same error type within a unit time is set.

[0071] In some examples, the impact type and the corresponding impact event are determined based on the abnormal business information and the number of occurrences of the abnormal business information per unit time, including:

[0072] S41: According to the error type of the abnormal business information, count the number of occurrences of the abnormal business information within a unit time.

[0073] S42: If the number of occurrences of abnormal business information of the same error type in a unit time exceeds a preset threshold, the impact type and the corresponding impact event are determined according to all abnormal business information corresponding to the error type.

[0074] In some examples, the fault alarm method further includes:

[0075] The fault weight is set according to the impact type, the fault score is set according to the impact event, the fault score is determined through weighted calculation, and the fault level is determined according to the fault score.

[0076] Specifically, after determining the impact type and the corresponding impact event, the fault weight is set according to the impact type, the fault score is set according to the impact event, weighted calculation is performed to determine the fault score, and the fault level is determined according to the fault score. When a fault alarm notification is generated based on the impact type and the corresponding impact event, the fault score and the fault level corresponding to the fault score are added to the fault alarm notification.

[0077] Embodiment 1:

[0078] Through the departure front-end system, front-end processing information is collected in real time to generate front-end behavior data; among them, the departure front-end system is used for the departure system (DGU I system) to interact with passengers, including the front-end interface, through which users perform check-in and other operations. At the same time, when abnormal conditions occur, the front-end interface will also provide abnormal reminders to users.

[0079] Front-end processing information includes all paths taken by users during operations, and records each step of the interaction process. At the same time, when an abnormal situation occurs, it will also be recorded. Convert the front-end processing information into a data format to generate front-end behavior data, which includes user behavior data and error pop-up box data. User behavior data includes business scenarios and operation information. Business scenarios may include: login, query, check-in, boarding, ticket change, cancellation, etc.; operation information includes user ID, operation time, operation business ID, content, method, result, etc. Error pop-up box data includes: error code, error description, error time, transaction number information, etc., which are used to determine the error type later.

[0080] Front-end behavior data may include duplicate data, erroneous data, irrelevant data, and missing values, so it is necessary to process the front-end behavior data. Clean the front-end behavior data to remove duplicate data, erroneous data, and irrelevant data in the front-end behavior data to improve data quality. Supplement missing values ​​for the front-end behavior data and fill the missing values ​​in the same format. Sort the front-end behavior data and generate multiple business data segments in the chronological order of the business. Each business data segment corresponds to a business, for example: a business data segment corresponds to a business operation of a user in the departure system. When the business corresponding to this business data segment is abnormal and an error pop-up box occurs, the error pop-up box data will also be recorded in this business data segment.

[0081] Furthermore, all business data segments are standardized, the data format and measurement unit of each behavior data segment are unified, the business process information is determined based on the business data segments, and each business data segment corresponds to a business process information; wherein, a business process information includes the business scenario and process information of this business determined by the business data segment. If an error pop-up box is generated, the business process information also includes the error pop-up box information of this business.

[0082] Preset the standard process for each business, determine the business scenario corresponding to the business process information, and match the business process information with the preset standard process of the corresponding business scenario. The preset matching condition is that the similarity between the business process information and the preset standard process is greater than or equal to 80%. When the similarity between the business process information and the preset standard process is greater than or equal to 80%, it meets the preset matching condition and the business process information is determined to be normal; when the similarity between the business process information and the preset standard process is less than 80%, it does not meet the preset matching condition. If the similarity matching result is less than 80%, it is determined that there is an abnormality in this business process information and it is determined to be abnormal business information. Among them, the abnormal business information includes the error pop-up box information of this business. The error pop-up box information includes error code, error description, error time, transaction number and other information. The error type can be determined by the error code, error description, error time, transaction number and other information.

[0083] Taking passenger check-in as an example, passenger check-in usually includes 5 steps, passenger search ORDERNO01, passenger check-in ORDERNO02, ORDERNO03, ORDERNO04, closing the page and returning to the home page ORDERNO05; the complete execution of ORDERNO01 to ORDERNO05 is a preset standard process. The preset matching condition is that the similarity between the business process information and the preset standard process is greater than or equal to 80%. The business process information corresponding to each business data segment is used to perform similarity matching with the preset standard process. For example, the business process information only includes ORDERNO01, ORDERNO02 and ORDERNO05. This business process information is matched with the preset standard process, and the matching result is less than 80%. Therefore, it does not meet the preset matching condition, and this business process information is determined as abnormal business information.

[0084] Use the existing and real-time generated business process information to match the preset business process to determine multiple abnormal business information. Understandably, in actual situations, there is not only business process information corresponding to the passenger check-in business, but also business process information corresponding to various other businesses, different airlines, and different airports; the generated abnormal business information also corresponds to various businesses, different airlines, and different airports.

[0085] All abnormal business information is analyzed, and based on the error type of the abnormal business information, a preset threshold for the number of occurrences of abnormal business information of the same error type per unit time is set, which can be expressed in the form of preset fault rules.

[0086] Table 1 is a table of preset fault rules in the embodiment of the present disclosure. The preset fault rules include rule name, preset threshold, unit time, error type and whether to pop up a box. The error type of Rule 1 is system error. Since the impact of system errors is relatively large, 10 occurrences within 5 minutes are set as the preset threshold; abnormal business information with the error type of system error, if the number of occurrences within 5 minutes exceeds 10 times, is determined to be in compliance with the preset fault rules, and the impact type and corresponding impact event are determined based on the abnormal business information corresponding to Rule 1. The error type of Rule 2 is business error, and 20 occurrences within 10 minutes are set as the preset threshold; abnormal business information with the error type of business error, if the number of occurrences within 10 minutes exceeds 20 times, is determined to be in compliance with the preset fault rules, and the impact type and corresponding impact event are determined based on the abnormal business information corresponding to Rule 2. The error type of rule 3 is no error but is identified as abnormal business information through matching. It is abnormal business information that does not meet the preset matching conditions in other situations. The preset threshold is 60 times within 10 minutes. If it occurs 60 times within 10 minutes, it is necessary to further analyze the abnormal business information to determine the fault event. For example: determine more specific error content through other information such as operation information and business scenarios in the abnormal business information, and determine the impact type and corresponding impact event.

[0087] In the disclosed embodiment, according to the error type of abnormal business information, the number of occurrences of abnormal business information in a unit time is counted. If the number of occurrences of abnormal business information of the same error type in a unit time exceeds a preset threshold, it is determined that the business or system corresponding to this error type has indeed failed, and the impact type and the corresponding impact event are determined based on all abnormal business information corresponding to this error type. The abnormal business information includes the error pop-up box information and the business scenario and process information of this business. The specific business or content corresponding to the abnormal business information is determined through the error pop-up box information, business scenario and process information, and the impact type and the corresponding impact event are further determined. According to the impact type and the corresponding impact event, the fault score is calculated to determine the fault level.

[0088] It should be noted that Table 1 provided in the embodiment of the present disclosure is only an example. In actual situations, the preset fault rules may also include specific information such as affected business functions and affected airline / airport customers. The number of occurrences of abnormal business information per unit time may be counted according to the preset fault rules, and the influencing events may be determined.

[0089] Rule Name Preset Threshold Unit time Error type Whether to pop up a box Rule 1 10 times Within 5 minutes System error yes Rule 2 20 times Within 10 minutes Business error yes Rule 3 60 times Within 10 minutes No error -

[0090] Table 1

[0091] The fault weight is set according to the impact type, the fault score is set according to the impact event, the fault score is determined by weighted calculation, and the fault level is determined according to the fault score. As shown in Table 2, each impact type has a corresponding set of impact events, score ranges and fault weights. Different impact events correspond to different fault scores; impact types include: affected business functions, affected airline / airport customers, affected passenger business volume, impact duration, and abnormal behavior volume. The impact events corresponding to the affected business functions are different businesses, and different scores are assigned according to the importance of the affected business functions, with a score range of 0-10 points. The fault weight of the affected business functions when calculating the fault score is 30%; the impact events corresponding to the affected airlines / airport customers are different airports, and different scores are assigned according to the customer attributes such as the scale and cooperative relationship of the affected airlines / airports, with a score range of 0-10 points. The fault weight of the affected airlines / airport customers when calculating the fault score is 20%; the impact event corresponding to the passenger business volume is the number of passengers scheduled to take off in the next 3 hours. The more passengers are expected, the greater the score assigned, with a score range of 0-10 points. The fault weight of the passenger business volume when calculating the fault score is 20%; the impact event corresponding to the impact duration is, based on the receipt of the corresponding first The calculation starts from the first abnormal business information. If the corresponding abnormal business information is continuously received in the future, 1 point will be added for every 10 minutes, and the score range is 0-10 points. The fault weight of the impact duration when calculating the fault score is 20%; the impact event corresponding to the abnormal behavior magnitude is the ratio of the maximum actual number of abnormal business information within the unit time range to the preset threshold value within the unit time. For example, taking Rule 2 in Table 1 as an example, the unit time is 10 minutes, and the actual impact duration is 12 minutes. Then, the number of abnormal business information corresponding to the [0-10]th minute, the [1-11]th minute and the [2-12]th minute in these 12 minutes are counted respectively. From the three statistical numbers, select a maximum value and calculate it with the preset threshold value within the unit time; the score range corresponding to the abnormal behavior magnitude is 0-10 points, and the fault weight when calculating the fault score is 10%.

[0092] In the embodiment of the present disclosure, at the counter of Airport A, service B has 40 business abnormality information within 10 minutes, and the impact duration is determined to be 30 minutes through the first abnormal business information, and the passenger business volume affected is determined to be 1,100 people based on the flight situation in the next 3 hours and the passengers that may be affected in the future. Understandably, since it is a business error, using Rule 2, it appears 40 times within 10 minutes, which exceeds the preset threshold, so it is determined to be a fault.

[0093] Continuing to refer to Table 2, the fault score is calculated according to the impact event, impact type and corresponding fault score and fault weight; the fault score of business B is 8 points, and the corresponding fault weight is 30%; the fault score of airport A is 10 points, and the corresponding fault weight is 20%; the passenger business volume affected is 1,100 people, and the corresponding fault score of the passenger business volume affected is 7 points, and the corresponding fault weight is 20%; the impact duration is 30 minutes, because the time that the rules are continuously met is accumulated, 1 point is added for every 10 minutes, the fault score corresponding to the impact duration is 3 points, and the fault weight is 20%; the fault score calculation method of the abnormal behavior magnitude is that the maximum actual number of abnormal business information within the unit time range is divided by the preset threshold value within the unit time, that is, 40 divided by 20, the fault score is 2 points, and the fault weight is 10%; the final fault score is determined to be: fault score = 8*0.3+10*0.2+7*0.2+3*0.2+2*0.1=6.6.

[0094]

[0095] Table 2

[0096] In the embodiment of the present disclosure, as shown in Table 3, the fault classification includes four levels: A, B, C, and D. Level A is a major fault, and the corresponding fault score is 9-10 points. Level A faults usually indicate that the system is completely interrupted or the core business functions cannot be used, which has a major impact on the business; Level B is a serious fault, and the corresponding fault score is 6-8 points. Level B faults usually indicate that some key functions of the system are interrupted or the performance is seriously degraded, which has a great impact on the business; Level C is a general fault, and the corresponding fault score is 2-5 points. Level C faults usually indicate that non-critical functions of the system are affected or the performance is degraded, but the overall impact on the business is small; Level D is a minor fault, and the corresponding fault score is 1 point. Level D faults are usually not regarded as faults, but are only recorded as general problems. Therefore, in the embodiment of the present disclosure, the calculated fault score is 6.6, and the fault classification is Level B, which is identified as a serious fault.

[0097]

[0098] Table 3

[0099] In the disclosed embodiment, based on the impact type and the corresponding impact event, as well as the fault score and the fault level corresponding to the fault score, a fault alarm notification is generated and fed back. After the impact type and impact event corresponding to the fault are determined and the fault level is determined, the departure system (DGUI system) automatically generates a fault alarm notification and returns the on-site log and interface error screenshot for further investigation and location; at the same time, the generated fault alarm notification is notified to the system maintenance personnel through various methods such as email, SMS, system interface, etc., so as to evaluate and initiate relevant emergency plans.

[0100] Second, Figure 2 A block diagram of a fault alarm system provided by an embodiment of the present disclosure, such as Figure 2 As shown, based on the same inventive concept, the embodiment of the present disclosure also provides a fault alarm system, and the system includes: a data acquisition module, a data processing module, a behavior analysis module, a fault diagnosis module and an alarm notification module. The data acquisition module is used to obtain front-end processing information, and generate front-end behavior data according to the front-end processing information. The data processing module is used to divide the front-end behavior data into multiple business data segments, and determine the business process information corresponding to the multiple business data segments; the behavior analysis module is used to match the business process information with the preset standard process for similarity, and the business process information that does not meet the preset matching conditions is determined as abnormal business information; the fault diagnosis module is used to determine the impact type and the corresponding impact event according to the abnormal business information and the number of occurrences of the abnormal business information per unit time. The alarm notification module is used to generate a fault alarm notification based on the impact type and the corresponding impact event.

[0101] On the third aspect, based on the same inventive concept, the embodiment of the present disclosure also provides a computer-readable storage medium, storing one or more programs, which, when executed, can implement the aforementioned fault alarm method.

[0102] In a fourth aspect, based on the same inventive concept, an embodiment of the present disclosure further provides an electronic device, including a processor, a communication interface, the aforementioned computer-readable storage medium, and a communication bus. The processor, the communication interface, and the computer-readable storage medium communicate with each other via the communication bus. The processor is used to execute the program stored in the aforementioned computer-readable storage medium.

[0103] It should be noted that the electrical connection between the above-mentioned units does not necessarily mean the connection between lines. An indirect connection method can be applied to the embodiments of the present disclosure as long as the purpose of the present disclosure is achieved.

[0104] Although the present disclosure has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present disclosure.

Claims

1. A fault alarm method, characterized in that: The method comprises: Acquire front-end processing information, and generate front-end behavior data according to the front-end processing information; Dividing the front-end behavior data into a plurality of business data segments, and determining business process information corresponding to the plurality of business data segments; The business process information is matched with a preset standard process for similarity, and the business process information that does not meet the preset matching condition is determined as abnormal business information; Determine the impact type and the corresponding impact event according to the abnormal business information and the number of occurrences of the abnormal business information per unit time; Based on the impact type and the corresponding impact event, a fault alarm notification is generated.

2. The method according to claim 1, characterized in that The obtaining of the front-end processing information and generating the front-end behavior data according to the front-end processing information includes: The front-end processing information is collected in real time, the front-end processing information is converted into a data format, and the front-end behavior data is generated.

3. The method according to claim 1, characterized in that The dividing the front-end behavior data into a plurality of business data segments and determining the business process information corresponding to the plurality of business data segments includes: Removing duplicate data, erroneous data and irrelevant data from the front-end behavior data; Supplement the missing values ​​in the front-end behavior data and fill the missing values ​​in the same format; Generate a plurality of business data segments from the front-end behavior data according to the time sequence of the business; The data formats and measurement units of all the business data segments are unified, and the business process information corresponding to each business data segment is determined.

4. The method according to claim 1, characterized in that The business process information is matched with the preset standard process for similarity, and the business process information that does not meet the preset matching conditions is determined as abnormal business information, including: Determine the preset standard process corresponding to the business process information, and perform similarity matching between the business process information and the corresponding preset standard process; A reference percentage is set as the preset matching condition, and the business process information whose similarity matching result is less than the reference percentage is determined as the abnormal business information; wherein the abnormal business information includes the error type of this business.

5. The method according to claim 1, characterized in that Determining the impact type and the corresponding impact event according to the abnormal business information and the number of occurrences of the abnormal business information per unit time includes: According to the error reporting type of the abnormal business information, count the number of occurrences of the abnormal business information within a unit time; If the number of occurrences of the abnormal business information of the same error type in a unit time exceeds a preset threshold, the impact type and the corresponding impact event are determined based on all the abnormal business information corresponding to this error type.

6. The method according to any one of claim 5, characterized in that: Before determining the impact type and the corresponding impact event according to the abnormal business information and the number of occurrences of the abnormal business information per unit time, the method includes: Based on the error type of the abnormal service information, the preset threshold of the number of occurrences of the abnormal service information of the same error type within a unit time is set.

7. The method according to claim 1, characterized in that The fault alarm method further includes: A fault weight is set according to the impact type, a fault score is set according to the impact event, a fault score is determined through weighted calculation, and a fault level is determined according to the fault score.

8. A fault warning system, characterized in that: The system includes: a data acquisition module, a data processing module, a behavior analysis module, a fault diagnosis module and an alarm notification module; The data acquisition module is used to obtain front-end processing information and generate front-end behavior data according to the front-end processing information; The data processing module is used to divide the front-end behavior data into multiple business data segments and determine the business process information corresponding to the multiple business data segments; The behavior analysis module is used to perform similarity matching between the business process information and a preset standard process, and to determine the business process information that does not meet the preset matching conditions as abnormal business information; The fault diagnosis module is used to determine the impact type and the corresponding impact event according to the abnormal business information and the number of occurrences of the abnormal business information per unit time; The alarm notification module is used to generate a fault alarm notification based on the impact type and the corresponding impact event.

9. A computer-readable storage medium storing one or more programs, characterized in that: When the one or more programs are executed, the fault alarm method described in any one of claims 1 to 7 can be implemented.

10. An electronic device comprising a processor, a communication interface, the computer-readable storage medium of claim 9, and a communication bus; wherein: The processor, the communication interface, and the computer-readable storage medium communicate with each other via a communication bus; It is characterized in that The processor is configured to execute a program stored in a computer-readable storage medium.

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