Intelligent fault early warning method and device for civil aviation system

Abstract

The invention discloses an intelligent fault early warning method and device for a civil aviation system, and belongs to the technical field of civil aviation information. Obtaining original log data and analyzing the original log data to obtain error information data; using a preset machine learning algorithm to analyze the data in the related database to obtain calculation time configuration and fault possibility configuration; analyzing the error information data according to calculation time configuration and fault possibility configuration to obtain fault information data; and according to a preset alarm rule, sending the fault information data to carry out fault alarm. According to the method, the system log is analyzed in real time, and the error information is analyzed by adopting a statistics and comparison algorithm, so that the fault in the business problem can be quickly and accurately identified, the complexity and low efficiency of traditional manual analysis and troubleshooting are avoided, and the fault identification speed is remarkably improved.

Description

Technical Field

[0001] The present invention relates to the field of civil aviation information technology, and in particular to an intelligent fault early warning method and device for a civil aviation system. Background Art

[0002] In the daily operation and maintenance of civil aviation systems, fault warning and rapid response are crucial to ensure the stable operation of the system. Traditional fault warning methods mainly rely on the experience and manual analysis of operation and maintenance personnel, which is inadequate when faced with complex and ever-changing business problems. In particular, for non-hardware faults such as business logic errors and service access anomalies, due to their diverse manifestations and difficulty in pre-definition, they often require analysis and judgment through the integration of multiple log information, business indicators and system status.

[0003] However, with the continuous expansion of the scale of the civil aviation system and the increase in business complexity, the amount of log data generated by the system has expanded dramatically, and traditional manual analysis methods can no longer meet the needs of efficient and accurate fault warning. In addition, the error information of business problems is often uncertain, and the same error code may represent different degrees of failure in different scenarios, which makes the warning method based on fixed rules difficult to apply. Therefore, it is particularly important to develop an intelligent fault warning method and device that can automatically and intelligently analyze system logs, quickly and accurately identify faults in business problems, and provide detailed fault information and response suggestions. Summary of the invention

[0004] To solve the above problems, the present invention provides a method and device for intelligent fault warning of civil aviation system. By real-time analysis of system logs and analysis of error information using statistical and comparative algorithms, faults in business problems can be quickly and accurately identified, thus avoiding the tediousness and inefficiency of traditional manual analysis and troubleshooting, and significantly improving the speed of fault identification.

[0005] The above objectives can be achieved through the following solutions:

[0006] An intelligent fault warning method for a civil aviation system comprises: establishing a database related to fault information; acquiring and parsing original log data to obtain error information data; using a preset machine learning algorithm to analyze data in a related database to obtain a calculation time configuration and a fault possibility configuration; analyzing the error information data according to the calculation time configuration and the fault possibility configuration to obtain fault information data; and sending the fault information data for fault alarm according to a preset alarm rule.

[0007] Optionally, obtaining the original log data and parsing it to obtain error information data includes: reading the original log data one by one; determining whether the original log data contains an error code or an exception; if so, extracting the error information data in the original log data, performing standardization processing and storing it in a preset error information database; if not, performing standardization processing on the original log data and storing it in a preset standard information database.

[0008] Optionally, the use of a preset machine learning algorithm to analyze the data in the relevant database to obtain the computing time configuration and the fault possibility configuration includes: obtaining data from the error information database and the standard information database to obtain first sample data; using a preset machine learning algorithm to analyze the impact of different computing time configurations in different time periods on the error rate of the first sample data, and calculating the optimal computing time configuration to obtain the preset time; obtaining data from a preset fault information database and a preset error rate database to obtain second sample data; using a preset machine learning algorithm to perform statistical analysis on the second sample data based on the growth of error information data and the status of fault information data of different services, calculate the optimal fault possibility configuration, and obtain a first threshold, a second threshold, and a third threshold.

[0009] Optionally, the error information data is analyzed according to the calculation time configuration and the fault possibility configuration to obtain the fault information data, including: extracting newly generated and unprocessed error information data from the error information database; judging, according to the error information database, whether the error code of the error information data is generated for the first time; if so, obtaining the standard information data of the service corresponding to the error information data from the standard information database; judging, according to the standard information database, the number of visits to the service corresponding to the error information data on the previous day, and analyzing the judgment result to obtain the fault information data; and storing the fault information data in the fault information database.

[0010] Optionally, judging the number of visits to the service corresponding to the error information data on the previous day according to the standard information database, and analyzing the judgment result to obtain fault information data includes: judging whether the service corresponding to the error information data had visits on the previous day according to the standard information database; if not, the service corresponding to the error information data is a newly commissioned service, and the service is judged to be normal; if yes, the service corresponding to the error information data is not a newly commissioned service, and the service is judged to be faulty, and the fault information data is obtained.

[0011] Optionally, judging whether the error code of the error information data is generated for the first time based on the error information database includes: if the error code of the error information data is not generated for the first time, extracting the total number of errors of the service corresponding to the error information data within the preset time from the error information database; obtaining the total number of visits to the service corresponding to the error information data within the preset time from the standard information database; obtaining the error information data of the service corresponding to the error information data based on the total number of errors and the total number of visits, and calculating the error rate ER in the error information data of the service SY. SY ,have

[0012]

[0013] Where, SE SY is the total number of errors reported by service SY within the preset time, TV SY The total number of visits to the service SY within a preset time; storing the error information data in the error rate database; and analyzing and obtaining fault information data according to the error rate in the error information data.

[0014] Optionally, analyzing the fault information data based on the error rate in the error rate information data includes: obtaining the maximum error rate of the service corresponding to the error information data from the error rate database; calculating a fault proportion value using the maximum error rate and the error rate in the error information data; judging the size of the fault proportion value based on the optimal fault possibility configuration of the service corresponding to the error information data, and obtaining the fault information data based on the judgment result.

[0015] Optionally, the optimal fault probability configuration of the service corresponding to the error information data is used to determine the size of the fault proportion value, and the fault information data is obtained according to the judgment result, including: determining whether the fault proportion value is greater than the first threshold value; if not, determining that the service is normal; if so, determining whether the fault proportion value is greater than the second threshold value; if not, determining that the fault probability is low, and obtaining first fault data; if so, determining whether the fault proportion value is greater than the third threshold value; if not, determining that the fault probability is medium, and obtaining second fault data; if so, determining that the fault probability is high, and obtaining third fault data; storing the first fault data, the second fault data, and the third fault data in the fault information database.

[0016] Optionally, sending the fault information data for fault alarm according to preset alarm rules includes: obtaining alarm rules from a preset alarm rule database; obtaining current fault information data from the fault information database; generating alarm information and performing a fault alarm according to the alarm rules and the current fault information data; judging whether a fault exists according to the alarm information; if so, updating the corresponding fault information data status in the fault information database to confirmed, and updating the corresponding error information data status in the error rate database to deleted; if not, updating the corresponding fault information data status in the fault information database to false alarm.

[0017] Based on the same inventive concept, the present invention also provides an intelligent fault warning device for a civil aviation system, the device comprising: a database storage module, used to establish a database related to fault information; a log parsing module, used to obtain and parse original log data, obtain error information data and store it in a related database; a fault rule management module, used to use a preset machine learning algorithm to analyze the data in the related database to obtain a calculation time configuration and a fault possibility configuration; a fault analysis module, used to analyze the error information data according to the calculation time configuration and the fault possibility configuration to obtain fault information data; a fault alarm module, used to send the fault information data for fault alarm according to a preset alarm rule.

[0018] Compared with the prior art, the present invention has the following advantages:

[0019] 1. The present invention analyzes the error information by real-time analysis of system logs and statistical and comparative algorithms, which can quickly and accurately identify faults in business problems, avoiding the tediousness and inefficiency of traditional manual analysis and troubleshooting, and significantly improving the speed of fault identification. At the same time, the fault probability is judged by the error rate per unit time rather than the error quantity alone, and the threshold is dynamically adjusted in combination with historical data, which improves the accuracy of fault judgment and reduces the false alarm rate.

[0020] 2. The present invention adopts an automatic threshold algorithm based on error rate, which can dynamically adjust the threshold according to historical data, making the early warning system more adaptable to different service scenarios and time periods, and improving the adaptability and flexibility of the system;

[0021] 3. The present invention not only provides basic fault information during early warning, but also further clarifies the possibility of fault by calculating the multiple relationship between the current error rate and the historical maximum error rate, and divides it into low, medium and high levels, providing more detailed fault information and response basis for operation and maintenance personnel, so that operation and maintenance personnel can make decisions quickly;

[0022] 4. The present invention uses statistical machine learning and algorithm analysis to dynamically adjust the optimal computing time configuration and fault possibility configuration according to the impact of different computing time configurations on the error rate in different time periods, as well as the growth of error information data and fault information data status of different services, thereby further optimizing the performance of the system.

[0023] Other features and advantages of the present invention will be described in the following description, and partly become apparent from the description, or understood by practicing the present invention. The purpose and other advantages of the present invention can be realized and obtained by the structures pointed out in the description, claims and drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the embodiments of the present invention 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 invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0025] Figure 1 It is a flow chart of an intelligent fault warning method for a civil aviation system according to an embodiment of the present invention.

[0026] Figure 2 Schematic diagram of the workflow of the log parsing module of the embodiment of the present invention.

[0027] Figure 3 4 is a schematic diagram of the working process of the fault analysis module according to an embodiment of the present invention.

[0028] Figure 4 A schematic diagram of a structure of an intelligent fault warning device for a civil aviation system according to an embodiment of the present invention DETAILED DESCRIPTION

[0029] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0030] Reference Figure 1 An embodiment of the present invention proposes an intelligent fault warning method for a civil aviation system. By real-time parsing of system logs and using statistical and comparative algorithms to analyze error information, it can quickly and accurately identify faults in business problems, avoiding the tediousness and inefficiency of traditional manual analysis and troubleshooting, and significantly improving the speed of fault identification.

[0031] The method of this embodiment specifically includes:

[0032] Establish a database related to fault information;

[0033] Specifically, the relevant databases include an error information database, a standard information database, a fault information database, an error rate database, a fault rule database and an alarm rule database.

[0034] Get the original log data and parse it to get the error information data;

[0035] Specifically, the recorded original logs need to be recorded according to preset rules and specifications to facilitate log analysis, and can be stored in disk files or databases, etc.; the original logs are parsed to analyze the error codes appearing in the logs, and the error information is stored in the error information database; the original log data is standardized to remove useless information, and the standardized log data is stored in standard data storage; the information that needs to be recorded in the original log data may include: service access time, service name, access result, error code or exception information, other scenario information, and other access details, some of which, such as abnormal information such as long processing time, requires the business system to make a judgment and actively record the abnormal information.

[0036] Analyze the data in the relevant database using the preset machine learning algorithm to obtain the calculation time configuration and fault possibility configuration;

[0037] Analyzing the error information data according to the calculation time configuration and the fault possibility configuration to obtain fault information data;

[0038] Specifically, the error information that needs to be analyzed is obtained from the error information database; the fault rules are obtained from the fault rule database, and the optimal computing configuration and the optimal fault possibility configuration corresponding to the service are obtained according to the fault rules; the error information is analyzed according to the fault rules; the data required for the analysis process mainly depends on the data in the standard information database, the data in the error information database itself, and the data in the fault information database; if it is determined to be a fault after analysis, the fault information will be stored in the fault information database.

[0039] According to the preset alarm rules, the fault information data is sent to perform a fault alarm.

[0040] Specifically, the analyzed fault information is obtained from the fault information database and notified to the system operation and maintenance personnel through SMS, email, WeChat notification, etc. After the operation and maintenance personnel receive the alarm information, if they manually confirm that a fault does exist, they will update the fault status to confirmed; otherwise, if no fault exists, the fault status will be updated to false alarm, thereby further providing information for judgment and improving the accuracy of analysis.

[0041] Specifically, during implementation, it is necessary to first set the fault rules and alarm rules for each service. If the initial settings are inappropriate, they can be adjusted according to historical data and the results after implementation. By real-time analysis of raw log data, the use of statistical and comparative algorithms combined with machine learning technology has achieved automation and intelligence in intelligent fault warning, significantly improved the speed and accuracy of fault identification, reduced the false alarm rate, and provided operation and maintenance personnel with detailed fault information and response basis, which helps to improve operation and maintenance efficiency, reduce operation and maintenance costs, and enhance the stability and reliability of the civil aviation system.

[0042] Optionally, the obtaining of original log data and parsing to obtain error information data includes:

[0043] Read raw log data by entry;

[0044] Determine whether the original log data has an error code or anomaly;

[0045] If yes, extracting the error information data in the original log data, performing standardization processing and storing the data in a preset error information database;

[0046] Specifically, the error information data in the error information database may include: the service name where the error occurs, the error code or exception information, the error time, and the server where the error occurs.

[0047] If not, the original log data is standardized and stored in a preset standard information database.

[0048] Specifically, the standard information data in the standard information database may include: service name, log time, and the server where the log is located.

[0049] For example, Figure 2 As shown, the system log is parsed line by line in real time; the system log is read line by line, and it is determined whether there is error information through error codes or exceptions. If there is error information, the error information is standardized and recorded in the error information database. The main recorded information may be the service name that generated the error, the error code or exception information, the error time, and the server where the error occurred; if there is no error information, the log is standardized and parsed, and the standardized data is recorded in the standard information database. The main recorded information may be the service name, log time, and the server where the log is located.

[0050] Optionally, the using of a preset machine learning algorithm to analyze data in a relevant database to obtain a computing time configuration and a fault probability configuration includes:

[0051] Acquire data from the error information database and the standard information database to obtain first sample data;

[0052] Using a preset machine learning algorithm, analyzing the impact of different computing time configurations of the first sample data in different time periods on the error rate, calculating the optimal computing time configuration, and obtaining a preset time;

[0053] Acquire data from a preset fault information database and a preset error rate database to obtain second sample data;

[0054] Using a preset machine learning algorithm, according to the growth of error information data and fault information data status of different services, the second sample data is statistically analyzed to calculate the optimal fault possibility configuration to obtain the first threshold, the second threshold and the third threshold.

[0055] Specifically, the fault rules in the fault rule database may include: service name, rule name (such as: calculation period configuration when an error occurs, fault possibility), rule value (such as: 6:00-9:00, XX minutes; 9:00-20:00, SS minutes; 20:00-6:00, KK minutes; low fault possibility = 2≥HEMR>1; medium fault possibility = 3≥HEMR>2; high fault possibility = HEMR>3).

[0056] Exemplarily, using data from the error information database and the standard information database, a statistical machine learning algorithm is used to analyze the impact of different computing time configuration error rates in different time periods, and the optimal value of the preset time within the data sample is selected and updated to the fault rule database; using data from the fault information database and the error rate database, a statistical analysis is performed on the fault information data and the error information data, and based on the growth trend of the error rate in the error information data and the actual situation of the fault information data, the optimal fault possibility configuration, i.e., the optimal first threshold, the optimal second threshold and the optimal third threshold, is selected and updated to the fault rule database.

[0057] Optionally, the analyzing the error information data according to the calculation time configuration and the fault possibility configuration to obtain the fault information data includes:

[0058] extracting newly generated and unprocessed error information data from the error information database;

[0059] According to the error information database, determining whether the error code of the error information data is generated for the first time;

[0060] If yes, obtaining standard information data of the service corresponding to the error information data from the standard information database;

[0061] According to the standard information database, determine the number of visits to the service corresponding to the error information data on the previous day, and analyze the determination result to obtain the fault information data;

[0062] The fault information data is stored in the fault information database.

[0063] Specifically, the fault information data in the fault information database may include: service name, error code or exception information, error rate, historical maximum error rate, ratio value to historical maximum error rate, fault possibility, and whether the error occurs for the first time.

[0064] For example, Figure 3 As shown, by analyzing the newly generated error information data in the error information database, calculating the error rate, determining whether to alarm, and determining the possibility of a fault; obtaining the newly generated unprocessed error information data from the error information database, the error information data may be: service SY, error code 1001, the server reporting the error is 10.201.xx.xx, and the time when the error was generated is 2024.6.20 06:15:21; determining according to the error information database, obtaining from the error information database whether the error code 1001 of the service SY is generated for the first time; if it is generated for the first time, determining the previous access volume of the service SY, thereby obtaining the fault information data and storing it in the fault information database, the fault information data may include: service name, error code or abnormal information, fault possibility (low, medium, high), and whether the error occurs for the first time.

[0065] Optionally, judging the number of visits to the service corresponding to the error information data on the previous day according to the standard information database, and analyzing the judgment result to obtain the fault information data includes:

[0066] According to the standard information database, determining whether the service corresponding to the error information data had visits in the previous day;

[0067] If not, the service corresponding to the error information data is a newly commissioned service, and the service is determined to be normal;

[0068] If so, the service corresponding to the error information data is not a newly launched service, and a service failure is determined to obtain fault information data.

[0069] Exemplarily, if the error code 1001 of the service SY obtained from the error information database is generated for the first time, it is checked from the standard data storage whether the service SY had any visits on the previous day. If there were no visits on the previous day, the service SY is a newly commissioned service, otherwise it is not a newly commissioned service; if it is not a newly commissioned service, it is determined to be a fault, and the fault information data is stored in the fault information database; if it is a newly commissioned service, it is determined to be normal, and the analysis ends.

[0070] Optionally, judging, according to the error information database, whether the error code of the error information data is generated for the first time comprises:

[0071] If the error code of the error information data is not generated for the first time, extracting the total number of errors reported by the service corresponding to the error information data within the preset time from the error information database;

[0072] Acquire the total number of visits to the service corresponding to the error information data within the preset time from the standard information database;

[0073] According to the total number of errors and the total number of visits, the error information data of the service corresponding to the error information data is obtained, and the error rate ER in the error information data of the service SY is SY ,have

[0074]

[0075] Where, SE SY is the total number of errors reported by service SY within the preset time, TV SY The total number of visits to the service SY within the preset time;

[0076] Specifically, the error information data in the error rate database includes service name, error code or exception information, time, error rate, and status (pending, normal, deleted).

[0077] Storing the error information data in a preset error rate database;

[0078] According to the error rate in the error information data, the fault information data is analyzed and obtained.

[0079] Exemplarily, the total number of errors SE for the service, such as SY, the error code, such as 1001, within a preset time (the optimal computing time configuration is determined according to the machine learning result) is obtained and calculated from the error information database. SU , obtain and calculate the total number of visits to the service such as SY in the same period of time from the standard information database SY , then according to Get the error rate ER of this service SY, thereby obtaining error information data, and storing the error information data in the error rate database. The error information data may include: service name, error code or exception information, time, error rate, and status (pending at this time).

[0080] Optionally, the analyzing and obtaining the fault information data according to the error rate in the error rate information data includes:

[0081] Acquire the maximum value of the error rate of the service corresponding to the error information data from the error rate database;

[0082] Calculate the fault ratio value by using the maximum value of the error rate and the error rate in the error information data;

[0083] According to the optimal fault possibility configuration of the service corresponding to the error information data, the size of the fault proportion value is judged, and the fault information data is obtained according to the judgment result.

[0084] Exemplarily, the maximum historical error rate of the error of the service is obtained from the error rate database, such as the maximum error rate P-ER(SY-1001) of the error code 1001 of the service SY; the error rate ER of the error code 1001 of the current service SY is calculated. SU The ratio of the historical maximum error rate P-ER(SY-1001) to HMER(SY-1001) = ER SY / P-ER (SY-1001), obtain the fault rule about the ratio value from the fault rule database. If the fault rule is set to HMER<1, the error is considered normal and the data status of the error rate database is updated to normal; if HMER>1, it is considered that there may be a fault.

[0085] Optionally, judging the magnitude of the fault proportion value according to the optimal fault possibility configuration of the service corresponding to the error information data, and obtaining the fault information data according to the judgment result includes:

[0086] Determine whether the fault ratio value is greater than the first threshold; if not, determine that the service is normal;

[0087] If yes, then determine whether the fault ratio value is greater than the second threshold; if no, then determine that the fault possibility is low, and obtain first fault data;

[0088] If yes, then determine whether the fault ratio value is greater than the third threshold; if no, then determine that the fault possibility is medium, and obtain second fault data;

[0089] If yes, the possibility of the fault is judged to be high, and the third fault data is obtained;

[0090] The first fault data, the second fault data and the third fault data are stored in the fault information database.

[0091] Exemplarily, a fault rule is obtained from a fault rule database, and the fault probability (low / medium / high) is determined according to the fault ratio value. When the fault ratio value is less than a first threshold value, the state of the error information data in the error rate database is updated to normal service. For example, the first threshold value is set to 1, the second threshold value is set to 2, and the third threshold value is set to 3. The fault rule is: if 2≥HMER>1, the fault probability is low; if 3≥HMER>2, the fault probability is medium; if HMER>3, the fault probability is high. Then, the fault information data, i.e., the first fault data, the second fault data, and the third fault data are stored in the fault information database. The fault information data may include: service name (SY), error code or exception information (1001), error rate ER SY , historical maximum error rate P-ER (SY-1001), proportional relationship with the historical maximum error rate HMER (SY-1001), fault probability (low / medium / high), whether the error occurs for the first time; the first threshold can be set to 1, the second threshold to 2, and the third threshold to 3 as the initial setting, and then configured according to the actual alarm and fault conditions and the statistical machine learning results. For example, when the fault ratio value HMER (SY-1001) of the error code 1001 of the service SY has a multiple of 2, it is determined that there has been a fault, and the configuration ratio here is adjusted accordingly. When HMER (SY-1001) is set ≥ 2, the fault probability is high, and the low and medium configuration values ​​are adjusted in the same way. It can be adjusted proportionally, for example, it can be adjusted downward according to the ratio of 2 / 3.

[0092] Optionally, sending the fault information data to perform a fault alarm according to a preset alarm rule includes:

[0093] Obtain alarm rules from a preset alarm rule database;

[0094] Specifically, the alarm rules in the alarm rule database may include: failure probability, alarm mode, and alarm sender.

[0095] Acquire current fault information data from the fault information database;

[0096] Generate alarm information and perform fault alarm according to the alarm rules and current fault information data;

[0097] Exemplarily, the fault information data is obtained from the fault information database, and according to the alarm rules in the alarm rule database, the system operation and maintenance personnel are informed through SMS, email, WeChat notification, etc.

[0098] According to the alarm information, determine whether a fault exists;

[0099] If yes, then the corresponding fault information data status in the fault information database is updated to confirmed, and the corresponding error information data status in the error rate database is updated to deleted;

[0100] If not, the corresponding fault information data status in the fault information database is updated to false alarm.

[0101] For example, after receiving the alarm information, the system operation and maintenance personnel confirm whether a fault exists; if a fault exists, the status of the fault information data in the fault information database is updated to confirmed, and the relevant information in the error rate database is updated to deleted (to avoid subsequent fault judgment based on this standard); if the fault does not exist, the status of the fault information data in the fault information database is updated to false alarm, and the error rate database is updated according to the actual situation. If the error rate deviates too much from the error rate under normal circumstances, the relevant information in the error rate database can be updated to deleted (not used as a basis for subsequent analysis), otherwise it is updated to normal.

[0102] Based on the same inventive concept, Figure 4 As shown, the present invention also provides an intelligent fault warning device for a civil aviation system, the device comprising:

[0103] A database storage module is used to establish a database related to fault information;

[0104] The log parsing module is used to obtain and parse the original log data, obtain the error information data and store it in the relevant database;

[0105] A fault rule management module is used to analyze the data in the relevant database using a preset machine learning algorithm to obtain a calculation time configuration and a fault possibility configuration;

[0106] A fault analysis module, used to analyze the error information data according to the calculation time configuration and the fault possibility configuration to obtain fault information data;

[0107] The fault alarm module is used to send the fault information data to perform a fault alarm according to a preset alarm rule.

[0108] It should be noted that the electrical connection between the above-mentioned units does not necessarily mean the connection between the lines. The indirect connection method can be applied to the embodiments of the present invention as long as the purpose of the present invention is achieved. The above is only an exemplary embodiment of the present invention and cannot be used to limit the scope of the present invention.

[0109] That is, any equivalent changes and modifications made according to the teachings of the present invention are still within the scope of the present invention. After considering the disclosure of the specification and the truth of practice, those skilled in the art will easily think of other embodiments of the present invention. This application is intended to cover any variation, use or adaptive change of the present invention, which follows the general principles of the present invention and includes common knowledge or customary technical means in the art that are not described in the present invention.

Claims

1. A civil aviation system intelligent fault early warning method, characterized in that: The method comprises: Establish a database related to fault information; Get the original log data and parse it to get the error information data; Analyze the data in the relevant database using the preset machine learning algorithm to obtain the calculation time configuration and fault possibility configuration; Analyzing the error information data according to the calculation time configuration and the fault possibility configuration to obtain fault information data; According to the preset alarm rules, the fault information data is sent to perform a fault alarm.

2. The intelligent fault warning method for civil aviation system according to claim 1, characterized in that: The original log data is obtained and parsed to obtain error information data including: Read raw log data by entry; Determine whether the original log data has an error code or anomaly; If yes, extracting the error information data in the original log data, performing standardization processing and storing the data in a preset error information database; If not, the original log data is standardized and stored in a preset standard information database.

3. The intelligent fault warning method for civil aviation system according to claim 2, characterized in that: The method of using a preset machine learning algorithm to analyze the data in the relevant database to obtain the calculation time configuration and the fault possibility configuration includes: Acquire data from the error information database and the standard information database to obtain first sample data; Using a preset machine learning algorithm, analyze the impact of different computing time configurations of the first sample data in different time periods on the error rate, calculate the optimal computing time configuration, and obtain the preset time; Acquire data from a preset fault information database and a preset error rate database to obtain second sample data; Using a preset machine learning algorithm, according to the growth of error information data and fault information data status of different services, a statistical analysis is performed on the second sample data, and the optimal fault possibility configuration is calculated to obtain the first threshold, the second threshold and the third threshold.

4. The intelligent fault early warning method for civil aviation system according to claim 3, characterized in that: The analyzing the error information data according to the calculation time configuration and the fault possibility configuration to obtain the fault information data includes: extracting newly generated and unprocessed error information data from the error information database; According to the error information database, determining whether the error code of the error information data is generated for the first time; If yes, obtaining standard information data of the service corresponding to the error information data from the standard information database; According to the standard information database, determine the number of visits to the service corresponding to the error information data on the previous day, and analyze the determination result to obtain the fault information data; The fault information data is stored in the fault information database.

5. The intelligent fault early warning method for civil aviation system according to claim 4, characterized in that: The method of determining the number of visits to the service corresponding to the error information data on the previous day according to the standard information database, and analyzing the determination result to obtain the fault information data includes: According to the standard information database, determining whether the service corresponding to the error information data had visits in the previous day; If not, the service corresponding to the error information data is a newly commissioned service, and the service is determined to be normal; If so, the service corresponding to the error information data is not a newly launched service, and a service failure is determined to obtain fault information data.

6. The intelligent fault early warning method for civil aviation system according to claim 4, characterized in that: The determining, based on the error information database, whether the error code of the error information data is generated for the first time comprises: If the error code of the error information data is not generated for the first time, extracting the total number of errors reported by the service corresponding to the error information data within the preset time from the error information database; Acquire the total number of visits to the service corresponding to the error information data within the preset time from the standard information database; According to the total number of errors and the total number of visits, the error information data of the service corresponding to the error information data is obtained, and the error rate ER in the error information data of the service SY is SY ,have In the formula, SE SY is the total number of errors reported by service SY within the preset time, TV SY The total number of visits to the service SY within the preset time; storing the error information data in the error rate database; According to the error rate in the error information data, the fault information data is analyzed and obtained.

7. The intelligent fault early warning method for civil aviation system according to claim 6, characterized in that: The analyzing and obtaining the fault information data according to the error rate in the error rate information data includes: Acquire the maximum value of the error rate of the service corresponding to the error information data from the error rate database; Calculate the fault ratio value by using the maximum value of the error rate and the error rate in the error information data; According to the optimal fault possibility configuration of the service corresponding to the error information data, the size of the fault proportion value is judged, and the fault information data is obtained according to the judgment result.

8. The intelligent fault early warning method for civil aviation system according to claim 7, characterized in that: The determining of the magnitude of the fault ratio value according to the optimal fault possibility configuration of the service corresponding to the error information data, and obtaining the fault information data according to the determination result comprises: Determine whether the fault ratio value is greater than the first threshold; if not, determine that the service is normal; If yes, then determine whether the fault ratio value is greater than the second threshold; if no, then determine that the fault possibility is low, and obtain first fault data; If yes, then determine whether the fault ratio value is greater than the third threshold; if no, then determine that the fault possibility is medium, and obtain second fault data; If yes, the possibility of the fault is judged to be high, and the third fault data is obtained; The first fault data, the second fault data and the third fault data are stored in the fault information database.

9. The intelligent fault early warning method for civil aviation system according to claim 6, characterized in that: The sending of the fault information data for fault alarm according to the preset alarm rule includes: Obtain alarm rules from a preset alarm rule database; Acquire current fault information data from the fault information database; Generate alarm information and perform fault alarm according to the alarm rules and current fault information data; According to the alarm information, determine whether a fault exists; If yes, then the corresponding fault information data status in the fault information database is updated to confirmed, and the corresponding error information data status in the error rate database is updated to deleted; If not, the corresponding fault information data status in the fault information database is updated to false alarm.

10. An intelligent fault warning device for a civil aviation system, characterized in that: The device comprises: A database storage module is used to establish a database related to fault information; The log parsing module is used to obtain and parse the original log data, obtain the error information data and store it in the relevant database; A fault rule management module is used to analyze the data in the relevant database using a preset machine learning algorithm to obtain a calculation time configuration and a fault possibility configuration; A fault analysis module, used to analyze the error information data according to the calculation time configuration and the fault possibility configuration to obtain fault information data; The fault alarm module is used to send the fault information data to perform a fault alarm according to a preset alarm rule.

Images