A method and apparatus for determining the tolerance of an alarm baseband.

Abstract

This invention provides a method and apparatus for determining the tolerance of an alarm baseband. The method involves: parsing a monitoring data file to extract the monitored object and its corresponding original alarm baseband; obtaining historical indicator data of the monitored object; and determining the tolerance for adjusting the original alarm baseband based on the historical indicator data. In this solution, the monitored object and its corresponding original alarm baseband are extracted from the monitoring data file, and historical indicator data of the monitored object is obtained. The tolerance for adjusting the original alarm baseband is determined through the historical indicator data, eliminating the need for users to rely on personal experience to set the tolerance, thereby reducing the risk of false alarms and missed alarms.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and specifically to a method and apparatus for determining the tolerance of an alarm baseband. Background Technology

[0002] When monitoring business systems, business metrics (such as transaction volume and average processing response time) and corresponding basebands are typically used to monitor the system's operational status. To control the sensitivity of alarms, the baseband width needs to be controlled, and the baseband tolerance is a parameter that controls the baseband width.

[0003] Currently, users still set the baseband tolerance to adjust the baseband width. However, users mainly rely on personal experience to set the tolerance, which can lead to a significant deviation between the adjusted baseband and the actual value, resulting in a higher risk of false alarms and missed alarms. Summary of the Invention

[0004] In view of this, embodiments of the present invention provide a method and apparatus for determining the tolerance of an alarm baseband, in order to solve the risks of high false alarm risk and missed alarm risk caused by the method of having the user set the tolerance of the baseband.

[0005] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0006] The first aspect of this invention discloses a method for determining the tolerance of an alarm baseband, the method comprising:

[0007] Parse the monitoring data file to extract the monitored object and the original alarm baseband corresponding to the monitored object;

[0008] Obtain historical indicator data of the monitored object;

[0009] Based on the historical indicator data, the tolerance level used to adjust the original alarm baseband is determined.

[0010] Preferably, determining the tolerance for adjusting the original alarm baseband based on the historical indicator data includes:

[0011] Based on the historical indicator data and the original alarm baseband corresponding to the monitored object, and combined with the preset ratio coefficient, the target curve corresponding to the monitored object is calculated.

[0012] For each monitored object, calculate the distance between the target curve and the historical indicator data corresponding to the monitored object;

[0013] The target curve corresponding to the largest distance is determined as the total target curve;

[0014] Calculate a first transformation coefficient between the overall target curve and the target alarm baseband to obtain the tolerance for adjusting the original alarm baseband, wherein the target alarm baseband is the original alarm baseband corresponding to the monitored object corresponding to the overall target curve.

[0015] Preferably, calculating a first transformation coefficient between the total target curve and the target alarm baseband to obtain a tolerance for adjusting the original alarm baseband includes:

[0016] Divide the time window;

[0017] Based on the values ​​of the total target curve and the target alarm baseband at each moment, the first transformation coefficient between the total target curve and the target alarm baseband within the time window is calculated using the least squares method to obtain the tolerance for adjusting the original alarm baseband.

[0018] Preferably, after determining the tolerance for adjusting the original alarm baseband based on the historical indicator data, the method further includes:

[0019] Obtain the baseline corresponding to the original alarm baseband;

[0020] The adjusted original alarm baseband is calculated based on the tolerance and the baseline.

[0021] Preferably, the method further includes:

[0022] Obtain real-time indicator data of the monitored object, and calculate the baseband deviation between the real-time indicator data and the alarm baseband of the monitored object currently being used. The alarm baseband of the current application is: the original alarm baseband before adjustment or the original alarm baseband after adjustment corresponding to the monitored object.

[0023] If the rate of decrease of the baseband deviation exceeds a threshold within a preset time period, a service relocation prompt message will be output.

[0024] When a confirmation instruction for the business migration prompt is detected, the migration start object and migration end object are determined;

[0025] Using the data form and water level of the starting object before and after the relocation, calculate the second transformation coefficient; and using the data form and water level of the ending object before and after the relocation, calculate the third transformation coefficient.

[0026] The alarm baseband currently applied to the monitored object in the migration initiation object is updated according to the second transformation coefficient, and the alarm baseband currently applied to the monitored object in the migration endpoint object is updated according to the third transformation coefficient.

[0027] A second aspect of this invention discloses a device for determining the tolerance of an alarm baseband, the device comprising:

[0028] The parsing unit is used to parse the monitoring data file to extract the monitoring object and the original alarm baseband corresponding to the monitoring object;

[0029] The first acquisition unit is used to acquire historical indicator data of the monitored object;

[0030] The first determining unit is used to determine the tolerance level for adjusting the original alarm baseband based on the historical indicator data.

[0031] Preferably, the first determining unit includes:

[0032] The first calculation module is used to calculate the target curve corresponding to the monitored object based on the historical indicator data and the original alarm baseband corresponding to the monitored object, combined with a preset ratio coefficient.

[0033] The second calculation module is used to calculate the distance between the target curve and the historical indicator data corresponding to each monitored object.

[0034] The determination module is used to determine the target curve corresponding to the largest distance as the total target curve;

[0035] The third calculation module is used to calculate the first transformation coefficient between the total target curve and the target alarm baseband to obtain the tolerance for adjusting the original alarm baseband, wherein the target alarm baseband is the original alarm baseband corresponding to the monitored object corresponding to the total target curve.

[0036] Preferably, the third calculation module is specifically used for: dividing a time window; and calculating a first transformation coefficient between the total target curve and the target alarm baseband within the time window based on the values ​​at each moment of the total target curve and the target alarm baseband using the least squares method, so as to obtain the tolerance for adjusting the original alarm baseband.

[0037] Preferably, the device further includes:

[0038] The second acquisition unit is used to acquire the baseline corresponding to the original alarm baseband;

[0039] An adjustment unit is used to calculate the adjusted original alarm baseband based on the tolerance and the baseline.

[0040] Preferably, the device further includes:

[0041] The processing unit is used to acquire real-time indicator data of the monitored object and calculate the baseband deviation between the real-time indicator data and the alarm baseband currently applied to the monitored object. The alarm baseband currently applied is either the original alarm baseband before adjustment or the original alarm baseband after adjustment corresponding to the monitored object.

[0042] The output unit is used to output a service relocation prompt message if the rate of decrease of the baseband deviation within a preset time period is greater than a threshold.

[0043] The second determining unit is used to determine the migration start object and the migration end object when a confirmation instruction for the business migration prompt information is detected.

[0044] The calculation unit is used to calculate the second transformation coefficient using the data form and water level of the starting object before and after the relocation, and to calculate the third transformation coefficient using the data form and water level of the ending object before and after the relocation.

[0045] The update unit is used to update the alarm baseband currently applied to the monitored object in the migration start object according to the second transformation coefficient, and to update the alarm baseband currently applied to the monitored object in the migration end object according to the third transformation coefficient.

[0046] The present invention provides a method and apparatus for determining the tolerance of an alarm baseband, based on the above embodiments. The method involves: parsing a monitoring data file to extract the monitored object and its corresponding original alarm baseband; obtaining historical indicator data of the monitored object; and determining the tolerance for adjusting the original alarm baseband based on the historical indicator data. In this solution, the monitored object and its corresponding original alarm baseband are extracted from the monitoring data file, and historical indicator data of the monitored object is obtained. The tolerance for adjusting the original alarm baseband is determined using the historical indicator data, eliminating the need for users to rely on personal experience to set the tolerance, thereby reducing the risk of false alarms and missed alarms. Attached Figure Description

[0047] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0048] Figure 1 A flowchart illustrating a method for determining the tolerance of an alarm baseband according to an embodiment of the present invention;

[0049] Figure 2This is a schematic diagram of module interaction for a method to determine the tolerance of an alarm baseband, provided in an embodiment of the present invention.

[0050] Figure 3 A flowchart of the algorithm for determining tolerance provided in an embodiment of the present invention;

[0051] Figure 4 A schematic diagram of baseband deviation provided in an embodiment of the present invention;

[0052] Figure 5 Another schematic diagram of baseband deviation provided in an embodiment of the present invention;

[0053] Figure 6 This is a structural block diagram of a device for determining the tolerance of an alarm baseband, provided in an embodiment of the present invention. Detailed Implementation

[0054] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0055] In this application, the terms "comprising," "including," or any other variations thereof are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0056] As the background technology indicates, currently, users still set the baseband tolerance to adjust its width. However, users primarily rely on personal experience when setting the tolerance, which can lead to a significant deviation between the adjusted baseband and the actual value, resulting in a higher risk of false alarms and missed alarms.

[0057] Therefore, embodiments of the present invention provide a method and apparatus for determining the tolerance of an alarm baseband. This involves extracting the monitored object and its corresponding original alarm baseband from a monitoring data file, and obtaining historical indicator data of the monitored object. The tolerance for adjusting the original alarm baseband is determined using this historical indicator data, eliminating the need for users to rely on personal experience to set the tolerance, thereby reducing the risk of false alarms and missed alarms.

[0058] It should be noted that when using business metrics to monitor business systems, the alarm baseband is the final alarm threshold. The alarm baseband is calculated from the baseline (also known as the alarm baseline) and the tolerance; specifically, alarm baseband = baseline * (1 + tolerance). The tolerance value ranges from -100% to +100%, and the positive or negative value of the tolerance depends on whether the baseline is the upper or lower baseline.

[0059] It should be further noted that when using business metrics and corresponding alarm basebands for monitoring, different types of business metrics have different alarm methods. The alarm method can be used to determine whether the baseline of the business metric is the upper baseline or the lower baseline. If an alarm is triggered when the business metric is greater than the corresponding alarm baseband, then the baseline of the business metric is the upper baseline. If an alarm is triggered when the business metric is less than the corresponding alarm baseband, then the baseline of the business metric is the lower baseline.

[0060] For example: For business metric A, if an alarm is triggered when business metric A is greater than the alarm baseband corresponding to business metric A, then the baseline constituting the alarm baseband corresponding to business metric A is the upper baseline. For business metric B, if an alarm is triggered when business metric B is less than the alarm baseband corresponding to business metric B, then the baseline constituting the alarm baseband corresponding to business metric B is the lower baseline.

[0061] See Figure 1 The flowchart illustrates a method for determining the tolerance of an alarm baseband according to an embodiment of the present invention. The method includes:

[0062] Step S101: Parse the monitoring data file to extract the monitored object and the corresponding original alarm baseband.

[0063] In the specific implementation step S101, the monitoring data file transmitted by the application monitoring system is received. The content of the monitoring data file is then structured and parsed according to the rules governing its content, thereby extracting data such as the monitored object, the corresponding original alarm baseband, and time. The monitored objects include, but are not limited to, deployment units, application servers, and transaction codes.

[0064] Specifically, a calculation task can be generated, and its configuration parameters (such as time period, alarm metrics, and task number) can be configured. This task is then triggered to parse the monitored objects and their corresponding raw alarm basebands from the monitoring data file. Specifically, the monitored objects and their corresponding raw alarm basebands for the application monitoring system's overview view, deployment unit view, AP view, and transaction code view are parsed from the monitoring data file.

[0065] Step S102: Obtain historical indicator data of the monitored object.

[0066] In the specific implementation step S102, after determining the monitoring object, the data indicator name corresponding to the monitoring object is obtained, as well as the specified time range is obtained.

[0067] Retrieve historical indicator data within the specified time range according to the data indicator name. The retrieved historical indicator data is the historical indicator data of the monitored object. The specified time range is a period of time prior to the current time (the length of the time range is set according to actual needs).

[0068] Step S103: Determine the tolerance for adjusting the original alarm baseband based on historical indicator data.

[0069] It should be noted that the values ​​in the historical indicator data are equivalent to the actual values. When calculating the tolerance, the original alarm baseband should be as close as possible to the actual value without any additional alarms. In the specific implementation step S103, based on the historical indicator data and the original alarm baseband corresponding to the monitored object, combined with the preset ratio coefficient, the target curve corresponding to the monitored object is calculated. A corresponding target curve is calculated for each monitored object.

[0070] Specifically, for a given monitored object, a segmentation curve can be obtained based on the historical metric data (i.e., the actual value) and the original alarm baseband value of the monitored object, combined with a preset scaling factor 'a'. This segmentation curve is the target curve for the monitored object. The preset scaling factor 'a' is an adjustable parameter, and its default value is 0.5.

[0071] For each monitored object, calculate the distance between the target curve and historical indicator data corresponding to that object. The target curve corresponding to the largest distance is determined as the overall target curve. The overall target curve is the target curve furthest from the true value.

[0072] In some embodiments, the overall target curve can be determined as follows: After calculating the target curve corresponding to the first monitored object, the target curve corresponding to the first monitored object is used as the overall target curve; the target curve corresponding to the next monitored object is then calculated, and the target curve corresponding to the next monitored object is compared with the overall target curve (comparing the distance to the true value). If the distance between the target curve of the next monitored object and the true value is greater than that of the overall target curve, the overall target curve is updated to the target curve of the next monitored object; if the distance between the target curve of the next monitored object and the true value is not greater than that of the overall target curve, the overall target curve remains unchanged; the target curve corresponding to the next monitored object is then calculated, and the aforementioned method is used to determine whether to update the overall target curve, and so on, until the final overall target curve is determined. In other words, for each target curve corresponding to a monitored object, the target curve is compared with the overall target curve, and so on, until the final overall target curve is determined.

[0073] A first transformation coefficient is calculated between the overall target curve and the target alarm baseband to obtain the tolerance for adjusting the original alarm baseband. This target alarm baseband is the original alarm baseband corresponding to the monitored object corresponding to the overall target curve. Specifically, time windows are divided, for example, dividing a 24-hour day into 12 time windows. Based on the values ​​of the overall target curve and the target alarm baseband at each moment, the first transformation coefficient between the overall target curve and the target alarm baseband within the time window is calculated using the least squares method to obtain the tolerance for adjusting the original alarm baseband. The first transformation coefficient within the time window is the tolerance for adjusting the original alarm baseband.

[0074] In some embodiments, the specific method for calculating the first transformation coefficient may be: scanning the curve values ​​at all times, calculating the required transformation coefficient using the target curve value and the corresponding original alarm baseband value; since the baseline corresponding to the original alarm baseband may be an upper baseline or a lower baseline, a set of values ​​with high transformation coefficients is retained for the upper baseline, and a set of values ​​with low transformation coefficients is retained for the lower baseline; after scanning the target curves of all monitored objects, the total target curve and the first transformation coefficient can be determined. The aforementioned operations such as "retaining a set of values ​​with high transformation coefficients" and "retaining a set of values ​​with low transformation coefficients" can be used to find the first transformation coefficient.

[0075] Preferably, since all monitored objects under a single view of a system use the same set of tolerances, after calculating the tolerances, the original alarm baseband of each monitored object is adjusted using the calculated tolerances.

[0076] Specifically, the baseline corresponding to the original alarm baseband of the monitored object is obtained; based on the tolerance and the baseline, combined with the formula "alarm baseband = baseline * (1 + tolerance)", the adjusted original alarm baseband of the monitored object is calculated.

[0077] In some embodiments, after the tolerance is calculated and the original alarm baseband is adjusted, the alarm effect of the calculated tolerance can be evaluated by formula (1) using the mean absolute percentage error.

[0078]

[0079] In formula (1), n ​​is the number of time points. Let y be the adjusted original alarm baseband value of the monitored object at time i. i This represents the actual value of the monitored object at time i.

[0080] It should be noted that since the probability distribution of the monitored object's business metrics generally follows a Gaussian distribution, the K-Sigma method can be used to calculate the dynamic alarm baseband. The alarm baseband can be calculated using "mean ± 3 * std", where mean is the average of the historical metric data over a period before and after the same point in time, and std is the variance of the historical metric data over the same period before and after the same point in time. Since business systems typically operate on weekdays and non-weekdays, the dynamic alarm baseband also needs to differentiate between weekdays and non-weekdays to ensure greater accuracy.

[0081] In the process of determining the tolerance mentioned in step S103, positive and negative samples can be marked according to the normal and outlier values ​​in the historical indicator data, and then the tolerance can be solved by linear transformation and least squares method, so that the alarm baseband calculated by the tolerance is more reasonable.

[0082] Let the target curve be f(t), where f(t) is a sequence of length L; l(t) represents the lower limit, h(t) represents the upper limit, and a is a parameter between 0 and 1. For details of f(t), please refer to formula (2).

[0083] f(t)=l(t)+a×h(t)(2)

[0084] When a is 0, the target curve equals the lower limit; when a is 0, the target curve equals the lower limit. a When the value is 1, the target curve is equal to the upper limit. Find an optimization method to make the original alarm baseband as close as possible to the target curve. The optimization method is expressed as the linear transformation shown in formula (3). The specific implementation method is to solve the optimization problem shown in formula (4) (that is, the least squares method).

[0085] c(t)=n×b(t)+m(3)

[0086]

[0087] Formulas (3) and (4) are general formulas for optimization using the least squares method, which find parameters n and m to make the original alarm baseband as close as possible to the target curve.

[0088] In this embodiment of the invention, the monitored object and its corresponding original alarm baseband are extracted from the monitoring data file, and historical indicator data of the monitored object are obtained. The tolerance level for adjusting the original alarm baseband is determined using the historical indicator data, eliminating the need for users to rely on personal experience to set the tolerance level, thereby reducing the risk of false alarms and missed alarms.

[0089] It is understandable that the contents of steps S101 to S103 above can be executed through multiple modules, such as... Figure 2 A schematic diagram illustrating the module interaction for determining the tolerance of the alarm baseband. Figure 2 It includes the MLOps-Task module, the Kafka module, the Algorithm Service - Backend module, the Algorithm Service - Database module, and the Algorithm Service - Algorithm module; Figure 2 In this context, APM stands for Application Monitoring System, and the MLOps-Task module is also known as the tolerance calculation algorithm task scheduling module; combined with Figure 2 The following sections describe the functions of each module.

[0090] The MLOps-Task module acts as the task initiator (the initiator of computational tasks), periodically receiving monitoring data files from the application monitoring system from a specified directory via file transfer. It parses the monitored objects from these files, including the application monitoring system's overview view, deployment unit view, AP view, and transaction code view, and generates configuration parameters for the computational tasks. These configuration parameters, along with the data parsed from the monitoring data files, are then sent to the algorithm service backend module via the Kafka module.

[0091] Algorithm Service - Backend Module: Consumes data from the Kafka module and performs the following steps: Obtains the specified data metric name and machine / deployment unit name (objectInstance); Obtains the specified time range; After obtaining the data metric name and machine / deployment unit name, retrieves historical metric data and raw alarm baseband from the Algorithm Service - Database Module (e.g., InfluxDB); Writes the historical metric data and raw alarm baseband to the Kafka module according to the specified structure, and sends the historical metric data and raw alarm baseband to the Algorithm Service - Algorithm Module for calculation through the Kafka module; After receiving the tolerance feedback from the Algorithm Service - Algorithm Module through the Kafka module, writes the tolerance to the application monitoring system.

[0092] Algorithm Service - Algorithm Module: Consumes historical metric data and raw alarm baseband pushed by the Algorithm Service - Backend Module through the Kafka module; executes the tolerance algorithm to calculate the tolerance; writes the tolerance to the Kafka module so that the tolerance can be fed back to the Algorithm Service - Backend Module through the Kafka module.

[0093] Regarding the aforementioned "tolerance algorithm," through... Figure 3 The flowchart of the algorithm for determining tolerance is shown as an example. Figure 3 Includes the following:

[0094] Load the configuration file and listen for Kafka messages, and receive data for a single object (i.e., historical metric data and raw alarm baseband for a single monitored object).

[0095] Instantiate the target generator and bind the data loader; the data loader is used to load historical data (i.e., historical indicator data), load baseline data (i.e., original alarm baseband), and perform data interpolation and completion; the target generator (or target curve generator) generates the target curve corresponding to the monitored object based on the historical indicator data and the original alarm baseband, and a corresponding target curve is calculated for each monitored object. During the execution of the above, the tolerance algorithm needs to be initialized, configuration parameters checked, and configuration parameters initialized.

[0096] For each monitored object, a target curve is calculated and compared with the overall target curve until the final overall target curve is determined. A time window is defined, and a first transformation coefficient (i.e., tolerance) is calculated between the overall target curve and the target alarm baseband within that time window. Based on this tolerance, the adjusted original alarm baseband of the monitored object is calculated.

[0097] The above is an explanation of how to calculate tolerance.

[0098] It is worth noting that during the operation of a business system, there may be instances of business component migration (migrating the transaction portion of a business object to another business object), where business traffic is switched from a high-load machine to a low-load machine to improve the stability of the business system. When such a switch occurs, the alarm baseband of the currently applied application of the business object involved in the switch also needs to be adjusted accordingly; otherwise, false alarms and missed alarms may occur.

[0099] After the business unit is migrated, the alarm baseband of the current application will be significantly close to the business metrics after the migration, which will generate a large number of alarms. Therefore, it is necessary to identify the business unit migration scenario and adjust the alarm baseband of the current application accordingly.

[0100] In some embodiments, real-time metric data of the monitored object is acquired, and the baseband deviation between the real-time metric data and the alarm baseband currently used by the monitored object is calculated. The alarm baseband currently used is either the original alarm baseband before adjustment or the original alarm baseband after adjustment corresponding to the monitored object. Alternatively, the alarm baseband currently used is the alarm baseband currently used by the monitored object to monitor whether an alarm is triggered.

[0101] It should be noted that the baseband deviation is calculated based on the true value and the baseline corresponding to the alarm baseband. Since there is an upper baseline and a lower baseline, it is necessary to determine whether the baseline is the upper baseline or the lower baseline before calculating the baseband deviation.

[0102] Specifically, if the baseline corresponding to the alarm baseband is the lower baseline, when calculating the baseband deviation, the baseband deviation corresponding to the lower baseline is calculated as (actual value - baseline value) / actual value.

[0103] If the baseline corresponding to the alarm baseband is the upper baseline, when calculating the baseband deviation, the baseband deviation corresponding to the upper baseline is calculated as (baseline value - actual value) / actual value.

[0104] Understandably, if the baseband deviation of a monitored object suddenly decreases (or even becomes less than 0) and persists for a period of time, it indicates that the alarm baseband of the current application is very close to the true value. This could be a fault or a business migration.

[0105] like Figure 4 As shown in the provided diagram of baseband deviation, the baseline of the transaction volume indicator is the lower baseline. The baseband deviation is calculated by comparing the actual value with the lower baseline. If the baseband deviation suddenly decreases and persists for a period of time, it may be due to a fault or a business relocation.

[0106] For example Figure 5 Another diagram of the provided baseband deviation shows that the baseline of the average response time indicator is the upper baseline. The baseband deviation is calculated by comparing the actual value with the upper baseline. If the baseband deviation drops sharply and continues for a period of time within a certain period of time, it may be due to a fault or a business migration.

[0107] After calculating the baseband deviation, it is determined whether the rate of decrease of the baseband deviation within a preset time period exceeds a threshold. If the rate of decrease of the baseband deviation within the preset time period exceeds the threshold (i.e., the baseband deviation decreases sharply and persists for a certain period of time), a service migration prompt message is output. This service migration prompt message is displayed to the user (e.g., the administrator) so that the user can determine whether service migration has occurred based on the prompt message.

[0108] When a confirmation command for a business migration notification is detected, the migration start and end objects are determined. Specifically, after the user confirms the business migration, a confirmation command for the business migration notification is detected. At this time, the migration start and end objects (i.e., the migration scope) entered by the user are obtained, and the migration time entered by the user is determined.

[0109] The second transformation coefficient is calculated using the data form and water level of the starting object before and after the relocation. Specifically, the data form and water level of the starting object before and after the relocation are linearly transformed to obtain the second transformation coefficient.

[0110] The third transformation coefficient is calculated by using the data form and water level of the relocation endpoint object before and after the relocation. Specifically, the data form and water level of the relocation endpoint object before and after the relocation are linearly transformed to obtain the third transformation coefficient.

[0111] The alarm baseband currently used by the monitored objects in the migration initiation object is updated according to the second transformation coefficient, and the alarm baseband currently used by the monitored objects in the migration endpoint object is updated according to the third transformation coefficient. This adapts to the alarm baseband requirements applied within 24 hours after the migration.

[0112] Understandably, after updating the alarm baseband of the migration start and end objects, to ensure the accuracy of the alarm baseband, it is necessary to recalculate the alarm baseband of the migration start and end objects daily before collecting complete cycle data. It is worth noting that the metric data used in recalculating the alarm baseband of the migration start and end objects is the metric data collected after the migration is completed.

[0113] Corresponding to the method for determining the tolerance of an alarm baseband provided in the above embodiments of the present invention, see also... Figure 6 The present invention also provides a structural block diagram of a device for determining the tolerance of an alarm baseband, the device comprising: a parsing unit 601, a first acquisition unit 602 and a first determination unit 603;

[0114] The parsing unit 601 is used to parse the monitoring data file to extract the monitored object and the corresponding original alarm baseband.

[0115] The first acquisition unit 602 is used to acquire historical indicator data of the monitored object.

[0116] The first determining unit 603 is used to determine the tolerance for adjusting the original alarm baseband based on historical indicator data.

[0117] In this embodiment of the invention, the monitored object and its corresponding original alarm baseband are extracted from the monitoring data file, and historical indicator data of the monitored object are obtained. The tolerance level for adjusting the original alarm baseband is determined using the historical indicator data, eliminating the need for users to rely on personal experience to set the tolerance level, thereby reducing the risk of false alarms and missed alarms.

[0118] Preferred, combined Figure 6 The first determining unit 603, as shown, includes a first calculation module, a second calculation module, a determining module, and a third calculation module; the execution principle of each module is as follows:

[0119] The first calculation module is used to calculate the target curve corresponding to the monitored object based on the historical indicator data and original alarm baseband of the monitored object, combined with a preset ratio coefficient.

[0120] The second calculation module is used to calculate the distance between the target curve and historical indicator data for each monitored object.

[0121] The determination module is used to determine the target curve corresponding to the maximum distance as the total target curve.

[0122] The third calculation module is used to calculate the first transformation coefficient between the total target curve and the target alarm baseband to obtain the tolerance for adjusting the original alarm baseband. The target alarm baseband is the original alarm baseband corresponding to the monitored object corresponding to the total target curve.

[0123] In the specific implementation, the third calculation module is used to: divide the time window; based on the values ​​of the total target curve and the target alarm baseband at each moment, and combined with the least squares method to calculate the first transformation coefficient between the total target curve and the target alarm baseband within the time window, so as to obtain the tolerance for adjusting the original alarm baseband.

[0124] Preferred, combined Figure 6 The determining device, as shown, also includes:

[0125] The second acquisition unit is used to acquire the baseline corresponding to the original alarm baseband.

[0126] The adjustment unit is used to calculate the adjusted original alarm baseband based on the tolerance and baseline.

[0127] Preferred, combined Figure 6 The determining device, as shown, also includes:

[0128] The processing unit is used to acquire real-time indicator data of the monitored object and calculate the baseband deviation between the real-time indicator data and the alarm baseband currently applied to the monitored object. The alarm baseband currently applied is either the original alarm baseband before adjustment or the original alarm baseband after adjustment corresponding to the monitored object.

[0129] The output unit is used to output a service relocation prompt message if the rate of decrease of baseband deviation within a preset time period is greater than a threshold.

[0130] The second determining unit is used to determine the migration start object and migration end object when a confirmation instruction for the business migration prompt information is detected.

[0131] The calculation unit is used to calculate the second transformation coefficient using the data form and water level of the starting object before and after the relocation, and to calculate the third transformation coefficient using the data form and water level of the ending object before and after the relocation.

[0132] The update unit is used to update the alarm baseband of the monitored object currently applied in the migration start object according to the second transformation coefficient, and to update the alarm baseband of the monitored object currently applied in the migration end object according to the third transformation coefficient.

[0133] In summary, this invention provides a method and apparatus for determining the tolerance of an alarm baseband. It extracts the monitored object and its corresponding original alarm baseband from a monitoring data file and obtains historical indicator data of the monitored object. The tolerance for adjusting the original alarm baseband is determined using this historical indicator data, eliminating the need for users to rely on personal experience to set the tolerance, thereby reducing the risk of false alarms and missed alarms.

[0134] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to mutually. Each embodiment focuses on describing the differences from other embodiments. In particular, for system or system embodiments, since they are basically similar to method embodiments, the description is relatively simple, and relevant parts can be referred to the descriptions in the method embodiments. The systems and system embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs. Those skilled in the art can understand and implement this without creative effort.

[0135] Those skilled in the art will further recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of both. To clearly illustrate the interchangeability of hardware and software, the components and steps of the various examples have been generally described in terms of functionality in the foregoing description. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0136] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for determining the tolerance of an alarm baseband, characterized in that, The method includes: Parse the monitoring data file to extract the monitored object and the original alarm baseband corresponding to the monitored object; Obtain historical indicator data of the monitored object; Based on the historical index data, the tolerance level used to adjust the original alarm baseband is determined; Based on the historical index data, the tolerance for adjusting the original alarm baseband is determined, including: Based on the historical indicator data and the original alarm baseband corresponding to the monitored object, and combined with a preset ratio coefficient, the target curve corresponding to the monitored object is calculated, and the target curve is a segmented curve. For each monitored object, calculate the distance between the target curve and the historical indicator data corresponding to the monitored object; The target curve corresponding to the largest distance is determined as the total target curve; Calculate a first transformation coefficient between the overall target curve and the target alarm baseband to obtain the tolerance for adjusting the original alarm baseband, wherein the target alarm baseband is the original alarm baseband corresponding to the monitored object corresponding to the overall target curve.

2. The method according to claim 1, characterized in that, Calculating a first transformation coefficient between the total target curve and the target alarm baseband to obtain the tolerance for adjusting the original alarm baseband includes: Divide the time window; Based on the values ​​of the total target curve and the target alarm baseband at each moment, the first transformation coefficient between the total target curve and the target alarm baseband within the time window is calculated using the least squares method to obtain the tolerance for adjusting the original alarm baseband.

3. The method according to any one of claims 1-2, characterized in that, After determining the tolerance level for adjusting the original alarm baseband based on the historical indicator data, the method further includes: Obtain the baseline corresponding to the original alarm baseband; The adjusted original alarm baseband is calculated based on the tolerance and the baseline.

4. The method according to any one of claims 1-2, characterized in that, The method further includes: Obtain real-time indicator data of the monitored object, and calculate the baseband deviation between the real-time indicator data and the alarm baseband of the monitored object currently being used. The alarm baseband of the current application is: the original alarm baseband before adjustment or the original alarm baseband after adjustment corresponding to the monitored object. If the rate of decrease of the baseband deviation exceeds a threshold within a preset time period, a service relocation prompt message will be output. When a confirmation instruction for the business migration prompt is detected, the migration start object and migration end object are determined; Using the data form and water level of the starting object before and after the relocation, calculate the second transformation coefficient; and using the data form and water level of the ending object before and after the relocation, calculate the third transformation coefficient. The alarm baseband currently applied to the monitored object in the migration initiation object is updated according to the second transformation coefficient, and the alarm baseband currently applied to the monitored object in the migration endpoint object is updated according to the third transformation coefficient.

5. A device for determining the tolerance of an alarm baseband, characterized in that, The device includes: The parsing unit is used to parse the monitoring data file to extract the monitoring object and the original alarm baseband corresponding to the monitoring object; The first acquisition unit is used to acquire historical indicator data of the monitored object; The first determining unit is used to determine the tolerance level for adjusting the original alarm baseband based on the historical indicator data. The first determining unit includes: The first calculation module is used to calculate the target curve corresponding to the monitored object based on the historical indicator data and the original alarm baseband of the monitored object, combined with a preset ratio coefficient. The target curve is a segmented curve. The second calculation module is used to calculate the distance between the target curve and the historical indicator data corresponding to each monitored object. The determination module is used to determine the target curve corresponding to the largest distance as the total target curve; The third calculation module is used to calculate the first transformation coefficient between the total target curve and the target alarm baseband to obtain the tolerance for adjusting the original alarm baseband, wherein the target alarm baseband is the original alarm baseband corresponding to the monitored object corresponding to the total target curve.

6. The apparatus according to claim 5, characterized in that, The third calculation module is specifically used for: dividing the time window; based on the values ​​of the total target curve and the target alarm baseband at each moment, and using the least squares method to calculate the first transformation coefficient between the total target curve and the target alarm baseband within the time window, so as to obtain the tolerance for adjusting the original alarm baseband.

7. The apparatus according to any one of claims 5-6, characterized in that, The device further includes: The second acquisition unit is used to acquire the baseline corresponding to the original alarm baseband; An adjustment unit is used to calculate the adjusted original alarm baseband based on the tolerance and the baseline.

8. The apparatus according to any one of claims 5-6, characterized in that, The device further includes: The processing unit is used to acquire real-time indicator data of the monitored object and calculate the baseband deviation between the real-time indicator data and the alarm baseband currently applied to the monitored object. The alarm baseband currently applied is either the original alarm baseband before adjustment or the original alarm baseband after adjustment corresponding to the monitored object. The output unit is used to output a service relocation prompt message if the rate of decrease of the baseband deviation within a preset time period is greater than a threshold. The second determining unit is used to determine the migration start object and the migration end object when a confirmation instruction for the business migration prompt information is detected. The calculation unit is used to calculate the second transformation coefficient using the data form and water level of the starting object before and after the relocation, and to calculate the third transformation coefficient using the data form and water level of the ending object before and after the relocation. The update unit is used to update the alarm baseband currently applied to the monitored object in the migration start object according to the second transformation coefficient, and to update the alarm baseband currently applied to the monitored object in the migration end object according to the third transformation coefficient.

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