CN117234861A - Service monitoring method, device, storage medium and electronic equipment - Google Patents
Service monitoring method, device, storage medium and electronic equipment Download PDFInfo
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Abstract
The application discloses a service monitoring method, a device, a storage medium and electronic equipment, wherein the method comprises the following steps: determining a multi-dimensional three-dimensional index system based on a plurality of service items, at least one application module associated with the plurality of service items, at least one monitoring index associated with the plurality of application modules and fault problems associated with the plurality of monitoring indexes; determining an alarm standard of at least one service item based on the multidimensional three-dimensional index system; when a service system fails, obtaining a target alarm index related to a fault problem to which the fault belongs; based on at least one service item, the alarm standard comprises the service item of the target alarm index as an abnormal service item. The method utilizes a multidimensional three-dimensional index system and the alarm standard of the service item, can rapidly and effectively determine the abnormal service item, and effectively improves the operation and maintenance efficiency of service monitoring.
Description
Technical Field
The present application relates to the field of service monitoring, and in particular, to a service monitoring method, device, storage medium, and electronic apparatus.
Background
The monitoring of the service and the advance sensing of the service risk are important means for stopping the damage of the operation. Monitoring the service requires configuring monitoring indexes, identifying abnormality through various detection algorithms when the monitoring indexes change, and notifying operation and maintenance personnel in an alarm mode. However, with increasing traffic data volume, tight coupling between traffic data occurs, and problems occur in any application module on the entire traffic link, which may result in massive alarm generation.
Most of the existing service monitoring modes can only determine whether the application module is abnormal according to the monitoring index corresponding to the application module, but cannot determine which service item is abnormal, and further check and positioning are needed by operation and maintenance personnel.
Disclosure of Invention
The application provides a service monitoring method, a device, a storage medium and electronic equipment, and aims to quickly and effectively determine abnormal service items.
In order to achieve the above object, the present application provides the following technical solutions:
a method of traffic monitoring, comprising:
determining a multi-dimensional stereoscopic index system based on a plurality of service items, at least one application module associated with a plurality of service items, at least one monitoring index associated with a plurality of application modules and fault problems associated with a plurality of monitoring indexes;
determining an alarm standard of at least one service item based on the multi-dimensional three-dimensional index system; the alarm criteria include at least one alarm indicator; the alarm index is a monitoring index of which the index value does not accord with a threshold value interval;
when a service system fails, obtaining a target alarm index related to a fault problem to which the fault belongs;
based on the at least one service item, the alarm standard comprises the service item of the target alarm index as an abnormal service item.
Optionally, determining an alarm standard of at least one service item based on the multi-dimensional stereoscopic index system includes:
determining a data set based on the multi-dimensional stereo index system; the dataset includes a plurality of anomaly information; the anomaly information includes: in a specified time period when the business item is abnormal, an alarm index generated by an application module related to the business item;
training to obtain a classification model by utilizing the data set; the classification model is used for classifying at least one alarm index in the data set to obtain alarm standards of at least one service item.
Optionally, training to obtain a classification model by using the data set includes:
initializing a random forest model; the random forest model comprises a plurality of decision trees;
determining a plurality of training sets based on the data sets;
training the random forest model by utilizing the training sets;
based on the random forest model obtained after training, the model is used as a classification model.
Optionally, based on the service item in the at least one service item, the alarm standard includes a service item of the target alarm indicator, as an abnormal service item, including:
and taking the target alarm index as the input of the random forest model to obtain an abnormal service item output by the random forest model.
Optionally, the method further comprises:
if the number of the abnormal business items is a plurality of, determining at least one monitoring index associated with the abnormal business items based on the multi-dimensional three-dimensional index system;
determining a target monitoring index according to the occurrence time corresponding to at least one monitoring index; the occurrence time corresponding to the target monitoring index is earliest in at least one monitoring index;
determining a fault problem associated with the target monitoring index based on the multi-dimensional three-dimensional index system;
based on the fault problems associated with the target monitoring indicators, the fault problems are taken as root causes of the faults of the service system.
Optionally, the method further comprises:
obtaining a target graphic component corresponding to the target monitoring index from a designated graphic database;
and constructing a root cause positioning interface based on the target graphic assembly and the root cause, and displaying the root cause positioning interface.
Optionally, the method further comprises:
generating a first graph based on the alarm standard of the abnormal service item;
according to the target alarm index, the first graph is adjusted to obtain a second graph;
and visualizing the first graph and the second graph.
A traffic monitoring device, comprising:
the system determining unit is used for determining a multi-dimensional three-dimensional index system based on a plurality of service items, at least one application module associated with a plurality of service items, at least one monitoring index associated with a plurality of application modules and fault problems associated with a plurality of monitoring indexes;
the standard determining unit is used for determining an alarm standard of at least one service item based on the multi-dimensional three-dimensional index system; the alarm criteria include at least one alarm indicator; the alarm index is a monitoring index of which the index value does not accord with a threshold value interval;
the alarm determining unit is used for obtaining a target alarm index related to a fault problem to which the fault belongs when the service system fails;
and the abnormality determining unit is used for determining the abnormal service item based on the service item with the alarm standard containing the target alarm index in the at least one service item.
Optionally, the standard determining unit is specifically configured to:
determining a data set based on the multi-dimensional stereo index system; the dataset includes a plurality of anomaly information; the anomaly information includes: in a specified time period when the business item is abnormal, an alarm index generated by an application module related to the business item;
training to obtain a classification model by utilizing the data set; the classification model is used for classifying at least one alarm index in the data set to obtain alarm standards of at least one service item.
Optionally, the standard determining unit is specifically configured to:
initializing a random forest model; the random forest model comprises a plurality of decision trees;
determining a plurality of training sets based on the data sets;
training the random forest model by utilizing the training sets;
based on the random forest model obtained after training, the model is used as a classification model.
Optionally, the anomaly determination unit is specifically configured to:
and taking the target alarm index as the input of the random forest model to obtain an abnormal service item output by the random forest model.
Optionally, the root cause determining unit is further included for:
if the number of the abnormal business items is a plurality of, determining at least one monitoring index associated with the abnormal business items based on the multi-dimensional three-dimensional index system;
determining a target monitoring index according to the occurrence time corresponding to at least one monitoring index; the occurrence time corresponding to the target monitoring index is earliest in at least one monitoring index;
determining a fault problem associated with the target monitoring index based on the multi-dimensional three-dimensional index system;
based on the fault problems associated with the target monitoring indicators, the fault problems are taken as root causes of the faults of the service system.
Optionally, the root cause determining unit is further configured to:
obtaining a target graphic component corresponding to the target monitoring index from a designated graphic database;
and constructing a root cause positioning interface based on the target graphic assembly and the root cause, and displaying the root cause positioning interface.
Optionally, the system further comprises a visualization unit, wherein the visualization unit is used for:
generating a first graph based on the alarm standard of the abnormal service item;
according to the target alarm index, the first graph is adjusted to obtain a second graph;
and visualizing the first graph and the second graph.
A storage medium comprising a stored program, wherein the program when executed by a processor performs the traffic monitoring method.
An electronic device, comprising: a processor, a memory, and a bus; the processor is connected with the memory through the bus;
the memory is used for storing a program, and the processor is used for running the program, wherein the program is executed by the processor to execute the service monitoring method.
According to the technical scheme provided by the application, a multi-dimensional three-dimensional index system is determined based on a plurality of service items, at least one application module associated with the service items, at least one monitoring index associated with the application modules and a fault problem associated with the monitoring indexes. Based on the multidimensional stereoscopic index system, an alarm criterion of at least one service item is determined. When the service system fails, a target alarm index related to the failure problem to which the failure belongs is obtained. Based on at least one service item, the alarm standard comprises the service item of the target alarm index as an abnormal service item. The application utilizes the multidimensional three-dimensional index system and the alarm standard of the service item, can rapidly and effectively determine the abnormal service item, and effectively improves the operation and maintenance efficiency of service monitoring.
Drawings
In order to more clearly illustrate the embodiments of the application or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, it being obvious that the drawings in the following description are only some embodiments of the application, and that other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a schematic flow chart of a service monitoring method according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an association relationship according to an embodiment of the present application;
FIG. 3 is a schematic diagram of another association relationship provided in an embodiment of the present application;
FIG. 4 is a schematic diagram of a multi-dimensional three-dimensional index system according to an embodiment of the present application;
fig. 5 is a schematic flow chart of another service monitoring method according to an embodiment of the present application;
fig. 6 is a schematic diagram of a service system display page according to an embodiment of the present application;
fig. 7 is a flow chart of another service monitoring method according to an embodiment of the present application;
FIG. 8 is a schematic diagram of an embodiment of the present application;
FIG. 9 is a schematic diagram of another embodiment of the present application;
fig. 10 is a schematic structural diagram of a service monitoring device according to an embodiment of the present application.
Detailed Description
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
In the present disclosure, relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions, and the terms "comprise," "include," or any other variation thereof, are intended to cover a non-exclusive inclusion such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
As shown in fig. 1, a flow chart of a service monitoring method provided by an embodiment of the present application includes the following steps.
S101: and determining a multi-dimensional three-dimensional index system based on the plurality of service items, at least one application module associated with the plurality of service items, at least one monitoring index associated with the plurality of application modules and the fault problem associated with the plurality of monitoring indexes.
The association relationship among the plurality of service items, the plurality of application modules, the plurality of monitoring indexes and the plurality of fault problems can be obtained through combing the service flow.
In some examples, the processing of the business item involves at least one application module, each application module for processing a portion of the business data of the business item, each application module associated with at least one monitoring metric, each monitoring metric for monitoring a fault problem that may exist in the processing of the business data by the application module.
In one possible implementation, taking a payment device service as an example, as shown in fig. 2, the payment device service is connected to a service system through an external connection mechanism network and silver connection, and the payment device service relates to 12 application modules, namely an application component a, an application component B, an application component C, an application component D, an application component E, an application component F, an application component G, an application component H, an application component I, an application component J, an application component K and an application component L. As shown in fig. 3, the application component a associates a transaction code a, the application component B associates a transaction code B, the application component C associates a transaction code C, the application component D associates a transaction code D, the application component E associates a transaction code E, the application component F associates a transaction code F, the application component G associates a transaction code G, the application component H associates a transaction code H, the application component I associates a transaction code I, the application component J associates a transaction code J, and the application component K associates a transaction code K. Each transaction code shown in fig. 3 corresponds to a plurality of monitoring indexes, where the plurality of monitoring indexes at least includes transaction amount, service success rate, system success rate, average response time, average processing time, long transaction amount, and yield transaction rate.
In some examples, the business system involves at least one business item, each business item may involve the same or different application modules, the same application module may have different names for monitoring indexes in different business items, and for this reason, in the multi-dimensional three-dimensional index system determining process, the names of the monitoring indexes may be unified.
In some examples, the application modules are deployed on different operation platforms, and a multi-dimensional monitoring system in the whole field can be established by determining the multi-dimensional three-dimensional index system, so that the service monitoring is changed from single-dimensional index monitoring to multi-dimensional index monitoring, and the actual running condition of the service system can be reflected more truly.
In one possible implementation, the determined multidimensional stereoscopic index system is shown in fig. 4 based on a plurality of service items, at least one application module associated with the plurality of service items, at least one monitoring index associated with the plurality of application modules, and a fault problem associated with the plurality of monitoring indexes.
S102: based on the multidimensional stereoscopic index system, an alarm criterion of at least one service item is determined.
The alarm standard comprises at least one alarm index, and the alarm index is a monitoring index of which the index value does not accord with a threshold value interval.
In some examples, the machine learning model may be utilized to perform training learning on the multi-dimensional stereo index system to obtain the alarm standard of at least one service item, and generally speaking, the implementation process of determining the alarm standard of at least one service item may be simply understood as the attribution determining process of the alarm index, that is, determining which service items contain the alarm index.
Optionally, determining a specific implementation procedure of the alarm standard of at least one service item based on the multidimensional three-dimensional index system includes: determining a data set based on the multi-dimensional three-dimensional index system; the dataset includes a plurality of anomaly information; the anomaly information includes: in a specified time period when the business item is abnormal, an alarm index generated by an application module related to the business item; training to obtain a classification model by utilizing a data set; the classification model is used for classifying at least one alarm index in the dataset to obtain alarm criteria for at least one service item.
It can be understood that the operation of the service system is monitored by using the multidimensional three-dimensional index system so as to obtain the abnormal information of the service system in the past. Additionally, the classification model may employ a machine learning model.
In some examples, the classification model may employ a random forest model, and accordingly, a specific implementation process of training the classification model using the data set may be referred to as steps and an explanation of the steps shown in fig. 5.
S103: when the service system fails, a target alarm index related to the failure problem to which the failure belongs is obtained.
When the service system fails, a target alarm index related to a fault problem to which the fault belongs is determined based on a multi-dimensional three-dimensional index system, and the target alarm index may be associated with one or more application modules.
S104: based on at least one service item, the alarm standard comprises the service item of the target alarm index as an abnormal service item.
The abnormal service item is determined based on the comparison between the alarm standard of the service item and the target alarm index, so that abnormal check positioning can be effectively and accurately realized, check work by operation and maintenance personnel is avoided, and operation and maintenance efficiency of service monitoring is improved.
In some examples, the alert criteria for at least one business item are expressed based on a random forest model, with which a comparison between the alert criteria for a business item and a target alert indicator can be quickly achieved.
Optionally, the target alarm index is used as the input of the random forest model to obtain the abnormal business item output by the random forest model.
In some examples, traffic monitoring needs to determine the root cause of the failure of the traffic system, i.e., which failure problem is the primary cause of the failure of the traffic system, in addition to the need to troubleshoot the location anomaly traffic item.
Optionally, if the number of the abnormal service items is multiple, determining at least one monitoring index associated with the multiple abnormal service items based on a multidimensional three-dimensional index system; determining a target monitoring index according to the occurrence time corresponding to at least one monitoring index; the occurrence time corresponding to the target monitoring index is earliest in at least one monitoring index; determining a fault problem associated with the target monitoring index based on the multi-dimensional three-dimensional index system; based on the fault problems associated with the target monitoring indicators, as the root cause of the business system faults.
In some examples, to enable a user to intuitively learn the root cause, the root cause that causes the business system to fail may be presented through a graphical interface.
Optionally, a target graphic component corresponding to the target monitoring index can be obtained from the designated graphic database; and constructing a root cause positioning interface based on the target graphic component and the root cause, and displaying the root cause positioning interface.
In one possible implementation, a root cause location interface, as shown in FIG. 6, presents to a user a root cause that causes a business system to fail, including an application problem, a business problem, a database problem, a grid communication (Garbage Collection, gc) problem, a middleware problem, a storage problem, a network problem, and a host problem.
The process shown in S101-S104 can quickly and effectively determine abnormal service items by utilizing the multidimensional three-dimensional index system and the alarm standard of the service items, and effectively improve the operation and maintenance efficiency of service monitoring.
As shown in fig. 5, a flowchart of another service monitoring method according to an embodiment of the present application includes the following steps.
S501: initializing a random forest model.
Wherein the random forest model comprises a plurality of decision trees.
In some examples, parameters of the random forest model may be set by a technician according to actual conditions, and in general, features involved in initializing the random forest model may be determined based on alarm indicators in the dataset, and features may be obtained by performing feature engineering on the alarm indicators.
S502: based on the data sets, a plurality of training sets is determined.
The alarm indexes in the data set may have problems of missing values and abnormal values, and therefore, missing value processing and abnormal value processing are required to be performed on the data set so as to obtain an effective training set.
In one possible implementation, in addition to determining multiple training sets, a test set and a verification set are determined, and the data distribution among the training set, the test set and the verification set is ensured to be the same, so as to improve the training effect of the random forest model.
S503: training the random forest model by utilizing a plurality of training sets.
The random forest model can be trained by adopting a random search algorithm, the number of decision trees can be traversed from 100 to 500 in the training process of the model, the depth of the decision trees is traversed from 20 layers to 200 layers, each decision tree extends nodes until all leaves are pure, and the performance of the random forest model is evaluated by utilizing a confusion matrix.
S504: based on the random forest model obtained after training, the model is used as a classification model.
The random forest model obtained after training is used as a classification model, so that the operation and maintenance efficiency of service monitoring can be effectively improved, and operation and maintenance personnel can conveniently use the classification model to realize the investigation and positioning of abnormal service items.
The flow shown in S501 to S504 can construct a classification model using a random forest model.
Fig. 7 is a schematic flow chart of another service monitoring method according to an embodiment of the present application, including the following steps.
S701: and generating a first graph based on the alarm standard of the abnormal service item.
Wherein the alert criteria includes at least one alert indicator, and for this purpose, the first graph generated based on the alert criteria of the abnormal service item may be an irregular curved surface, as shown in fig. 8.
S702: and adjusting the first graph according to the target alarm index to obtain a second graph.
The first graph is adjusted according to the target alarm index, and the actual parameter of the first graph is adjusted to obtain the second graph, which may be an irregular curved surface in general.
S703: the first graphic and the second graphic are visualized.
In one possible embodiment, the visualization of the first graphic and the second graphic may be as shown in fig. 9.
The process shown in the above steps S701-S703 can realize the visualization of the abnormal business item, so that the operation and maintenance personnel can intuitively learn the abnormal business item.
Corresponding to the service monitoring method provided by the embodiment of the application, the embodiment of the application also provides a service monitoring device.
Fig. 10 is a schematic structural diagram of a service monitoring device according to an embodiment of the present application, which includes the following units.
The system determining unit 100 is configured to determine a multi-dimensional stereoscopic index system based on the plurality of service items, at least one application module associated with the plurality of service items, at least one monitoring index associated with the plurality of application modules, and a fault problem associated with the plurality of monitoring indexes.
A standard determining unit 200, configured to determine an alarm standard of at least one service item based on the multidimensional stereoscopic index system; the alarm criteria include at least one alarm indicator; the alarm index is a monitoring index of which the index value does not accord with the threshold value interval.
Alternatively, the standard determining unit 200 is specifically configured to: determining a data set based on the multi-dimensional three-dimensional index system; the dataset includes a plurality of anomaly information; the anomaly information includes: in a specified time period when the business item is abnormal, an alarm index generated by an application module related to the business item; training to obtain a classification model by utilizing a data set; the classification model is used for classifying at least one alarm index in the dataset to obtain alarm criteria for at least one service item.
Alternatively, the standard determining unit 200 is specifically configured to: initializing a random forest model; the random forest model comprises a plurality of decision trees; determining a plurality of training sets based on the data sets; training the random forest model by utilizing a plurality of training sets; based on the random forest model obtained after training, the model is used as a classification model.
And an alarm determining unit 300 for obtaining a target alarm index related to a fault problem to which the fault belongs when the service system fails.
The anomaly determination unit 400 is configured to, based on at least one service item, alert criteria including a service item of the target alert indicator as an anomaly service item.
Alternatively, the anomaly determination unit 400 specifically functions to: and taking the target alarm index as the input of the random forest model to obtain an abnormal service item output by the random forest model.
The root cause determination unit 500 is configured to: if the number of the abnormal business items is a plurality of, determining at least one monitoring index associated with the abnormal business items based on a multi-dimensional three-dimensional index system; determining a target monitoring index according to the occurrence time corresponding to at least one monitoring index; the occurrence time corresponding to the target monitoring index is earliest in at least one monitoring index; determining a fault problem associated with the target monitoring index based on the multi-dimensional three-dimensional index system; based on the fault problems associated with the target monitoring indicators, as the root cause of the business system faults.
Optionally, the root cause determining unit 500 is further configured to: obtaining a target graphic component corresponding to the target monitoring index from the designated graphic database; and constructing a root cause positioning interface based on the target graphic component and the root cause, and displaying the root cause positioning interface.
The visualization unit 600 is configured to: generating a first graph based on the alarm standard of the abnormal service item; adjusting the first graph according to the target alarm index to obtain a second graph; the first graphic and the second graphic are visualized.
The units can quickly and effectively determine abnormal service items by utilizing the multidimensional three-dimensional index system and the alarm standard of the service items, and effectively improve the operation and maintenance efficiency of service monitoring.
The application also provides a computer readable storage medium, which comprises a stored program, wherein the program executes the service monitoring method provided by the application.
The application also provides an electronic device, comprising: a processor, a memory, and a bus. The processor is connected with the memory through a bus, the memory is used for storing a program, and the processor is used for running the program, wherein the service monitoring method provided by the application is executed when the program runs.
Furthermore, the functions described above in embodiments of the application may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: a Field Programmable Gate Array (FPGA), an Application Specific Integrated Circuit (ASIC), an Application Specific Standard Product (ASSP), a system on a chip (SOC), a Complex Programmable Logic Device (CPLD), and the like.
Although the subject matter has been described in language specific to structural features and/or methodological acts, it is to be understood that the subject matter defined in the appended claims is not necessarily limited to the specific features or acts described above. Rather, the specific features and acts described above are example forms of implementing the claims.
While several specific implementation details are included in the above discussion, these should not be construed as limiting the scope of the application. Certain features that are described in the context of separate embodiments can also be implemented in combination in a single embodiment. Conversely, various features that are described in the context of a single embodiment can also be implemented in multiple embodiments separately or in any suitable subcombination.
The above description is only illustrative of the preferred embodiments of the present application and of the principles of the technology employed. It will be appreciated by persons skilled in the art that the scope of the disclosure referred to in the present application is not limited to the specific combinations of technical features described above, but also covers other technical features formed by any combination of the technical features described above or their equivalents without departing from the spirit of the disclosure. Such as the above-mentioned features and the technical features disclosed in the present application (but not limited to) having similar functions are replaced with each other.
Claims (10)
1. A method for monitoring traffic, comprising:
determining a multi-dimensional stereoscopic index system based on a plurality of service items, at least one application module associated with a plurality of service items, at least one monitoring index associated with a plurality of application modules and fault problems associated with a plurality of monitoring indexes;
determining an alarm standard of at least one service item based on the multi-dimensional three-dimensional index system; the alarm criteria include at least one alarm indicator; the alarm index is a monitoring index of which the index value does not accord with a threshold value interval;
when a service system fails, obtaining a target alarm index related to a fault problem to which the fault belongs;
based on the at least one service item, the alarm standard comprises the service item of the target alarm index as an abnormal service item.
2. The method of claim 1, wherein determining an alert criteria for at least one business item based on the multi-dimensional stereoscopic index system comprises:
determining a data set based on the multi-dimensional stereo index system; the dataset includes a plurality of anomaly information; the anomaly information includes: in a specified time period when the business item is abnormal, an alarm index generated by an application module related to the business item;
training to obtain a classification model by utilizing the data set; the classification model is used for classifying at least one alarm index in the data set to obtain alarm standards of at least one service item.
3. The method of claim 2, wherein training a classification model using the data set comprises:
initializing a random forest model; the random forest model comprises a plurality of decision trees;
determining a plurality of training sets based on the data sets;
training the random forest model by utilizing the training sets;
based on the random forest model obtained after training, the model is used as a classification model.
4. A method according to claim 3, wherein based on the at least one service item, the alert criteria comprises a service item of the target alert indicator as an abnormal service item, comprising:
and taking the target alarm index as the input of the random forest model to obtain an abnormal service item output by the random forest model.
5. The method as recited in claim 1, further comprising:
if the number of the abnormal business items is a plurality of, determining at least one monitoring index associated with the abnormal business items based on the multi-dimensional three-dimensional index system;
determining a target monitoring index according to the occurrence time corresponding to at least one monitoring index; the occurrence time corresponding to the target monitoring index is earliest in at least one monitoring index;
determining a fault problem associated with the target monitoring index based on the multi-dimensional three-dimensional index system;
based on the fault problems associated with the target monitoring indicators, the fault problems are taken as root causes of the faults of the service system.
6. The method as recited in claim 5, further comprising:
obtaining a target graphic component corresponding to the target monitoring index from a designated graphic database;
and constructing a root cause positioning interface based on the target graphic assembly and the root cause, and displaying the root cause positioning interface.
7. The method as recited in claim 1, further comprising:
generating a first graph based on the alarm standard of the abnormal service item;
according to the target alarm index, the first graph is adjusted to obtain a second graph;
and visualizing the first graph and the second graph.
8. A traffic monitoring device, comprising:
the system determining unit is used for determining a multi-dimensional three-dimensional index system based on a plurality of service items, at least one application module associated with a plurality of service items, at least one monitoring index associated with a plurality of application modules and fault problems associated with a plurality of monitoring indexes;
the standard determining unit is used for determining an alarm standard of at least one service item based on the multi-dimensional three-dimensional index system; the alarm criteria include at least one alarm indicator; the alarm index is a monitoring index of which the index value does not accord with a threshold value interval;
the alarm determining unit is used for obtaining a target alarm index related to a fault problem to which the fault belongs when the service system fails;
and the abnormality determining unit is used for determining the abnormal service item based on the service item with the alarm standard containing the target alarm index in the at least one service item.
9. A storage medium comprising a stored program, wherein the program when executed by a processor performs the traffic monitoring method of any of claims 1-7.
10. An electronic device, comprising: a processor, a memory, and a bus; the processor is connected with the memory through the bus;
the memory is configured to store a program, and the processor is configured to execute the program, wherein the program when executed by the processor performs the traffic monitoring method according to any one of claims 1 to 7.
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