A monitoring system and method for abnormal information of multiple systems

Abstract

The present invention provides a monitoring system and method for multi-system abnormal information, including a data acquisition module, a data processing module and a data display module, wherein the data acquisition module is connected to the data processing module, and the data processing module is connected to the data display module; the present invention uses the data acquisition module to monitor data points to collect and monitor multi-dimensional data in real time, and then calculates the service health score according to the collected data through the data processing module, so that by comprehensively considering the monitoring data of different dimensions, the impact of the data on the business can be more accurately evaluated, thereby improving the monitoring efficiency and accuracy of abnormal data; by presetting the alarm strategy in the data processing module, the false alarm rate is reduced and the credibility of the alarm is improved. When the preset alarm conditions are met, the data is monitored and displayed on a large panel through the data display module, and an early warning is issued so as to promptly notify the user to respond.

Description

Technical Field

[0001] The invention relates to a monitoring system, in particular to a monitoring system for abnormal information of multiple systems, belonging to the technical field of abnormal information monitoring. Background Art

[0002] As the business of an enterprise continues to develop, the business process will become more and more complex, and the business data interaction between multiple systems of the enterprise will be frequent and complex.

[0003] Traditional monitoring systems mainly monitor from a technical perspective, such as server status, network status, database performance, etc. However, these technical monitoring systems cannot fully cover all aspects of business data, resulting in the inability to timely discover and handle changes in the business. The solution is usually to use manual inspections to regularly check the operating status and business data of each system to ensure the normal operation of the business. However, this method is not only inefficient, but also prone to errors, and cannot meet the company's requirements for business continuity and data accuracy. For this reason, a monitoring system and method for multi-system abnormal information is proposed. Summary of the invention

[0004] In view of this, the present invention provides a multi-system abnormal information monitoring system and method to solve or alleviate the technical problems existing in the prior art and at least provide a beneficial choice.

[0005] The technical solution of the embodiment of the present invention is implemented as follows: a monitoring system for multi-system abnormal information includes a data acquisition module, a data processing module and a data display module, the data acquisition module is connected to the data processing module, and the data processing module is connected to the data display module;

[0006] The data collection module is used to collect monitoring data in real time in multiple dimensions by using monitoring data embedding points;

[0007] The data processing module is used to calculate the service health score based on the collected data and generate a visual service dependency topology diagram;

[0008] Among them, the data display module is used to display the processed data to the user and issue an early warning.

[0009] Further preferably, the monitoring data embedding point is located at a key point in the business link;

[0010] The monitoring data includes the QPS of the interface, average RT, error data, service change data and cloud control change messages.

[0011] Further preferably, the computing service health score is calculated based on defined health indicators and weights corresponding to each indicator, and a preset alarm strategy is used to determine whether an alarm condition is met based on the calculation result;

[0012] Among them, the health score algorithm adopts a weighted average algorithm or a comprehensive scoring algorithm.

[0013] Further preferably, the data processing module is also used to process the collected data;

[0014] Among them, processing includes data cleaning, data unification and data aggregation.

[0015] Further preferably, the visual service dependency topology diagram is generated by a service dependency model, and the service dependency model is deployed in the data processing module.

[0016] Further preferably, the service dependency model is established according to the dependency matrix, and the service dependency model is converted into a visual graph by using a graph drawing tool;

[0017] Among them, the graph drawing tool adopts Graphview, Lucidchart or Gephi tool.

[0018] Further preferably, the data display module is displayed in a monitoring dashboard, and the service dependency topology diagram and health indicator data are integrated into the monitoring dashboard for display through the data display module.

[0019] A monitoring method for a monitoring system based on multi-system abnormal information comprises the following steps:

[0020] S1. Add monitoring data points at key points in the business chain to collect monitoring data;

[0021] S2. Process and analyze the collected monitoring data to obtain monitoring data of various dimensions;

[0022] S3. According to the preset alarm strategy, determine whether the service link meets the preset alarm condition. If it does, execute S4. If it does not, return to execute S1.

[0023] S4, sending alarm information to the terminal device;

[0024] S5. Synchronize the data to the visual monitoring dashboard for display.

[0025] Further preferably, in S1, monitoring data is collected by a data collection module;

[0026] In S2 and S3, the data is processed, analyzed and judged by the data processing module;

[0027] In S4 and S5, the alarm information is sent and the monitoring dashboard is displayed through the data display module.

[0028] Further preferably, in S1, a timed task trigger point is added in the service link to perform a timed test on the monitoring system.

[0029] The embodiment of the present invention has the following advantages due to the adoption of the above technical solution:

[0030] 1. The present invention utilizes the data acquisition module to monitor data points to collect and monitor multi-dimensional data in real time, and then calculates the service health score based on the collected data through the data processing module, so that by comprehensively considering the monitoring data of different dimensions, it can more accurately evaluate the impact of data on the business, thereby improving the monitoring efficiency and accuracy of abnormal data.

[0031] 2. The present invention presets an alarm strategy in the data processing module to reduce the false alarm rate and improve the credibility of the alarm. When the preset alarm conditions are met, the data display module monitors the data and issues an early warning so that the user can be notified in time to respond.

[0032] The above summary is for illustrative purposes only and is not intended to be limiting in any way. In addition to the illustrative aspects, embodiments and features described above, further aspects, embodiments and features of the present invention will be readily apparent by reference to the accompanying drawings and the following detailed description. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] In order to more clearly illustrate the embodiments of the present application 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 only some embodiments of the present application. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0034] Figure 1 It is a flow chart of the data acquisition module of the present invention;

[0035] Figure 2 It is a flow chart of the data processing module of the present invention;

[0036] Figure 3 It is a schematic diagram of the alarm flow of the data processing module and the data display module of the present invention. DETAILED DESCRIPTION

[0037] In the following, only some exemplary embodiments are briefly described. As those skilled in the art will appreciate, the described embodiments may be modified in various ways without departing from the spirit or scope of the present invention. Therefore, the drawings and descriptions are considered to be exemplary and non-restrictive in nature.

[0038] It should be noted that the terms "first", "second", "symmetrical", "array", etc. are only used to distinguish between description and position description purposes, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the definition of "first", "symmetrical", etc. can explicitly or implicitly include one or more of these features; similarly, when the number of certain features is not limited in the form of words such as "two" or "three", it should be noted that this feature also explicitly or implicitly includes one or more feature quantities.

[0039] The embodiments of the present invention are described in detail below with reference to the accompanying drawings.

[0040] Business chain: refers to a business process composed of multiple systems, which realize business functions through data interaction;

[0041] Monitoring data: refers to various types of data collected during operation, such as performance indicators, abnormal information, etc.;

[0042] Alarm information: refers to the alarm information sent to the terminal device when the monitoring data meets the preset conditions, including the alarm type, alarm time and alarm indicators.

[0043] Embodiment 1

[0044] like Figure 1-3 As shown, an embodiment of the present invention provides a monitoring system for abnormal information of multiple systems, including a data acquisition module, a data processing module and a data display module, wherein the data acquisition module is connected to the data processing module, and the data processing module is connected to the data display module;

[0045] Among them, the data collection module is used to collect monitoring data in real time in multiple dimensions by using monitoring data embedding points;

[0046] The data processing module is used to calculate the service health score based on the collected data and generate a visual service dependency topology diagram;

[0047] Among them, the data display module is used to display the processed data to the user and issue an early warning.

[0048] In one embodiment, Figure 1 As shown, the monitoring data embedding point is located at the key point in the business link;

[0049] Monitoring data includes interface QPS, average RT, error data, service change data, and cloud control change messages;

[0050] The monitoring data module uses monitoring data points to collect the QPS, average RT, error data, service change data, and cloud control change message data of the business link interface;

[0051] The QPS (query rate per second) data of an interface refers to the number of requests that an interface can process per second within a certain period of time. This indicator is an important parameter for measuring interface performance and can reflect the processing capacity of the interface and the throughput of the system;

[0052] Average RT data, or average response time, refers to the average time it takes for a system to respond to a request. This indicator is another important parameter for measuring system performance and can reflect the response efficiency of the system;

[0053] Error data refers to erroneous or abnormal data that occurs during data collection, processing, or analysis. These errors may be caused by problems with the data source, failure of the data collection equipment, interference during data transmission, defects in the data processing algorithm, or human input errors.

[0054] Service change data refers to the record of modifications and adjustments made to the service itself during the service operation process. These changes may be to fix bugs, improve performance, add new features, improve user experience, or adapt to changes in the external environment;

[0055] Cloud control change messages refer to the management and delivery of information about service changes in cloud computing environments through automated tools and process control systems. These messages contain relevant details of the change, such as the type, time, scope of impact, required resources, etc., and are usually intended to notify and coordinate related teams or systems so that they can respond or adjust accordingly.

[0056] In one embodiment, the service health score is calculated based on the defined health indicators and the weights corresponding to the indicators, and a preset alarm strategy is used to determine whether the alarm condition is met based on the calculation result;

[0057] Among them, the algorithm for health score adopts weighted average algorithm;

[0058] By presetting the alarm strategy in the data processing module, the false alarm rate can be reduced and the credibility of the alarm can be improved;

[0059] Among them, the alarm strategy is mainly to set thresholds for each health indicator and issue an alarm when the threshold is exceeded;

[0060] Weighted average algorithm: Each indicator is assigned a weight, which reflects the relative importance of the indicator in the evaluation. The total score is obtained by adding up all the weighted indicator values.

[0061] The weighted average algorithm is simple, intuitive and easy to understand, but its disadvantage is that the weight distribution has a great influence on the results and needs to be set accurately;

[0062] In one embodiment, the data processing module is further used to process the collected data;

[0063] Among them, processing includes data cleaning, data unification and data aggregation;

[0064] Data cleaning is the first step in data processing, identifying and correcting errors or inconsistencies in the data set, including handling missing values, correcting errors, removing duplicate records, etc.

[0065] Data unification refers to converting data from different sources into consistent formats and standards to ensure the consistency and comparability of data throughout the organization. Unified data can promote cross-departmental or cross-system data sharing and integration, and improve data readability and usability. It also helps eliminate decision-making biases caused by data inconsistency;

[0066] Data aggregation is the process of combining multiple data sources or data sets into a single, comprehensive data set. The aggregated data can provide more comprehensive information, facilitate macro analysis and decision-making. It can reduce the complexity of data and make it easier to manage and analyze.

[0067] In one embodiment, the visual service dependency topology map is generated by a service dependency model, and the service dependency model is deployed in the data processing module;

[0068] By deploying the service dependency model in the data processing module, after the data processing module completes data processing, the service dependency model is used to generate a visual service dependency topology diagram based on the processed data.

[0069] In one embodiment, the service dependency model is established based on the dependency matrix, and the service dependency model is converted into a visual graph by using a graph drawing tool;

[0070] The dependency relationship between services is represented by a matrix. The rows and columns of the matrix represent different services, and the values ​​represent the degree of dependency between two services. Then, a service dependency model is constructed based on the dependency relationship.

[0071] Among them, the graph drawing tool adopts Graphv iz tool;

[0072] Graphview is an open source graph visualization tool that uses the DOT language to describe the graph structure and then generates a visualization graph through its command line tool or graphical interface tool. Graphview is suitable for creating structured service dependency topology graphs.

[0073] In one embodiment, the display mode of the data display module is a monitoring dashboard, and the service dependency topology diagram and health indicator data are integrated into the monitoring dashboard for display through the data display module.

[0074] A monitoring method for a monitoring system based on multi-system abnormal information comprises the following steps:

[0075] S1. Add monitoring data points at key points in the business chain to collect monitoring data;

[0076] S2. Process and analyze the collected monitoring data to obtain monitoring data of various dimensions;

[0077] S3. According to the preset alarm strategy, determine whether the service link meets the preset alarm condition. If it does, execute S4. If it does not, return to execute S1.

[0078] S4, sending alarm information to the terminal device;

[0079] S5. Synchronize the data to the visual monitoring dashboard for display.

[0080] In one embodiment, in S1, monitoring data is collected by a data collection module;

[0081] In S2 and S3, the data is processed, analyzed and judged through the data processing module;

[0082] In S4 and S5, alarm information is sent and the monitoring dashboard is displayed through the data display module.

[0083] In one embodiment, Figure 3 As shown, in S1, a timed task trigger point is added in the service link to perform a timed test on the monitoring system;

[0084] By setting up scheduled task trigger points, the system's autonomous operation and maintenance capabilities can be greatly improved, labor costs can be reduced, and efficient, stable and safe operation of the system can be ensured.

[0085] Embodiment 2

[0086] like Figure 1-3 As shown, an embodiment of the present invention provides a monitoring system for abnormal information of multiple systems, including a data acquisition module, a data processing module and a data display module, wherein the data acquisition module is connected to the data processing module, and the data processing module is connected to the data display module;

[0087] Among them, the data collection module is used to collect monitoring data in real time in multiple dimensions by using monitoring data embedding points;

[0088] The data processing module is used to calculate the service health score based on the collected data and generate a visual service dependency topology diagram;

[0089] Among them, the data display module is used to display the processed data to the user and issue an early warning.

[0090] In one embodiment, Figure 1 As shown, the monitoring data embedding point is located at the key point in the business link;

[0091] Monitoring data includes interface QPS, average RT, error data, service change data, and cloud control change messages;

[0092] The monitoring data module uses monitoring data points to collect the QPS, average RT, error data, service change data, and cloud control change message data of the business link interface;

[0093] The QPS (query rate per second) data of an interface refers to the number of requests that an interface can process per second within a certain period of time. This indicator is an important parameter for measuring interface performance and can reflect the processing capacity of the interface and the throughput of the system;

[0094] Average RT data, or average response time, refers to the average time it takes for a system to respond to a request. This indicator is another important parameter for measuring system performance and can reflect the response efficiency of the system;

[0095] Error data refers to erroneous or abnormal data that occurs during data collection, processing, or analysis. These errors may be caused by problems with the data source, failure of the data collection equipment, interference during data transmission, defects in the data processing algorithm, or human input errors.

[0096] Service change data refers to the record of modifications and adjustments made to the service itself during the service operation process. These changes may be to fix bugs, improve performance, add new features, improve user experience, or adapt to changes in the external environment;

[0097] Cloud control change messages refer to the management and delivery of information about service changes in cloud computing environments through automated tools and process control systems. These messages contain relevant details of the change, such as the type, time, scope of impact, required resources, etc., and are usually intended to notify and coordinate related teams or systems so that they can respond or adjust accordingly.

[0098] In one embodiment, the service health score is calculated based on the defined health indicators and the weights corresponding to the indicators, and a preset alarm strategy is used to determine whether the alarm condition is met based on the calculation result;

[0099] Among them, the algorithm for health score adopts a comprehensive scoring algorithm;

[0100] By presetting the alarm strategy in the data processing module, the false alarm rate can be reduced and the credibility of the alarm can be improved;

[0101] Among them, the alarm strategy is mainly to set thresholds for each health indicator and issue an alarm when the threshold is exceeded;

[0102] Comprehensive scoring algorithm: Compared with the weighted average algorithm, it includes more processing steps, such as maximum and minimum standardization, principal component analysis, cluster analysis, etc., which aims to extract the main information of the indicators and reduce the multicollinearity problem between indicators. The comprehensive scoring algorithm can use factor analysis, linear or nonlinear models, etc. to integrate multiple indicators. These methods can handle the relationship between indicators more flexibly.

[0103] Scoring algorithms usually give a comprehensive score that can be used to rank or classify different services or objects.

[0104] The advantage of a comprehensive scoring algorithm is that it can better handle the relationships between indicators, but the calculations are usually more complex and require more data analysis experience and expertise to design and implement.

[0105] In one embodiment, the data processing module is further used to process the collected data;

[0106] Among them, processing includes data cleaning, data unification and data aggregation;

[0107] Data cleaning is the first step in data processing, identifying and correcting errors or inconsistencies in the data set, including handling missing values, correcting errors, removing duplicate records, etc.

[0108] Data unification refers to converting data from different sources into consistent formats and standards to ensure the consistency and comparability of data throughout the organization. Unified data can promote cross-departmental or cross-system data sharing and integration, and improve data readability and usability. It also helps eliminate decision-making biases caused by data inconsistency;

[0109] Data aggregation is the process of combining multiple data sources or data sets into a single, comprehensive data set. The aggregated data can provide more comprehensive information, facilitate macro analysis and decision-making. It can reduce the complexity of data and make it easier to manage and analyze.

[0110] In one embodiment, the visual service dependency topology map is generated by a service dependency model, and the service dependency model is deployed in the data processing module;

[0111] By deploying the service dependency model in the data processing module, after the data processing module completes data processing, the service dependency model is used to generate a visual service dependency topology diagram based on the processed data.

[0112] In one embodiment, the service dependency model is established based on the dependency matrix, and the service dependency model is converted into a visual graph by using a graph drawing tool;

[0113] The dependency relationship between services is represented by a matrix. The rows and columns of the matrix represent different services, and the values ​​represent the degree of dependency between two services. Then, a service dependency model is constructed based on the dependency relationship.

[0114] Among them, the graphics drawing tool adopts the Lucidchart tool;

[0115] Lucidchart is an online diagramming tool that provides automatic layout capabilities and can be used to create flowcharts, UML diagrams, etc. It supports team collaboration and can be exported to multiple formats, including PDF and SVG.

[0116] In one embodiment, the display mode of the data display module is a monitoring dashboard, and the service dependency topology diagram and health indicator data are integrated into the monitoring dashboard for display through the data display module.

[0117] A monitoring method for a monitoring system based on multi-system abnormal information comprises the following steps:

[0118] S1. Add monitoring data points at key points in the business chain to collect monitoring data;

[0119] S2. Process and analyze the collected monitoring data to obtain monitoring data of various dimensions;

[0120] S3. According to the preset alarm strategy, determine whether the service link meets the preset alarm condition. If it does, execute S4. If it does not, return to execute S1.

[0121] S4, sending alarm information to the terminal device;

[0122] S5. Synchronize the data to the visual monitoring dashboard for display.

[0123] In one embodiment, in S1, monitoring data is collected by a data collection module;

[0124] In S2 and S3, the data is processed, analyzed and judged through the data processing module;

[0125] In S4 and S5, alarm information is sent and the monitoring dashboard is displayed through the data display module.

[0126] In one embodiment, Figure 3 As shown, in S1, a timed task trigger point is added in the service link to perform a timed test on the monitoring system;

[0127] By setting up scheduled task trigger points, the system's autonomous operation and maintenance capabilities can be greatly improved, labor costs can be reduced, and efficient, stable and safe operation of the system can be ensured.

[0128] Embodiment 3

[0129] like Figure 1-3 As shown, an embodiment of the present invention provides a monitoring system for abnormal information of multiple systems, including a data acquisition module, a data processing module and a data display module, wherein the data acquisition module is connected to the data processing module, and the data processing module is connected to the data display module;

[0130] Among them, the data collection module is used to collect monitoring data in real time in multiple dimensions by using monitoring data embedding points;

[0131] The data processing module is used to calculate the service health score based on the collected data and generate a visual service dependency topology diagram;

[0132] Among them, the data display module is used to display the processed data to the user and issue an early warning.

[0133] In one embodiment, Figure 1 As shown, the monitoring data embedding point is located at the key point in the business link;

[0134] Monitoring data includes interface QPS, average RT, error data, service change data, and cloud control change messages;

[0135] The monitoring data module uses monitoring data points to collect the QPS, average RT, error data, service change data, and cloud control change message data of the business link interface;

[0136] The QPS (query rate per second) data of an interface refers to the number of requests that an interface can process per second within a certain period of time. This indicator is an important parameter for measuring interface performance and can reflect the processing capacity of the interface and the throughput of the system;

[0137] Average RT data, or average response time, refers to the average time it takes for a system to respond to a request. This indicator is another important parameter for measuring system performance and can reflect the response efficiency of the system;

[0138] Error data refers to erroneous or abnormal data that occurs during data collection, processing, or analysis. These errors may be caused by problems with the data source, failure of the data collection equipment, interference during data transmission, defects in the data processing algorithm, or human input errors.

[0139] Service change data refers to the record of modifications and adjustments made to the service itself during the service operation process. These changes may be to fix bugs, improve performance, add new features, improve user experience, or adapt to changes in the external environment;

[0140] Cloud control change messages refer to the management and delivery of information about service changes in cloud computing environments through automated tools and process control systems. These messages contain relevant details of the change, such as the type, time, scope of impact, required resources, etc., and are usually intended to notify and coordinate related teams or systems so that they can respond or adjust accordingly.

[0141] In one embodiment, the service health score is calculated based on the defined health indicators and the weights corresponding to the indicators, and a preset alarm strategy is used to determine whether the alarm condition is met based on the calculation result;

[0142] Among them, the algorithm for health score adopts a comprehensive scoring algorithm;

[0143] By presetting the alarm strategy in the data processing module, the false alarm rate can be reduced and the credibility of the alarm can be improved;

[0144] Among them, the alarm strategy is mainly to set thresholds for each health indicator and issue an alarm when the threshold is exceeded;

[0145] Comprehensive scoring algorithm: Compared with the weighted average algorithm, it includes more processing steps, such as maximum and minimum standardization, principal component analysis, cluster analysis, etc., which aims to extract the main information of the indicators and reduce the multicollinearity problem between indicators. The comprehensive scoring algorithm can use factor analysis, linear or nonlinear models, etc. to integrate multiple indicators. These methods can handle the relationship between indicators more flexibly.

[0146] Scoring algorithms usually give a comprehensive score that can be used to rank or classify different services or objects.

[0147] The advantage of a comprehensive scoring algorithm is that it can better handle the relationships between indicators, but the calculations are usually more complex and require more data analysis experience and expertise to design and implement.

[0148] In one embodiment, the data processing module is further used to process the collected data;

[0149] Among them, processing includes data cleaning, data unification and data aggregation;

[0150] Data cleaning is the first step in data processing, identifying and correcting errors or inconsistencies in the data set, including handling missing values, correcting errors, removing duplicate records, etc.

[0151] Data unification refers to converting data from different sources into consistent formats and standards to ensure the consistency and comparability of data throughout the organization. Unified data can promote cross-departmental or cross-system data sharing and integration, and improve data readability and usability. It also helps eliminate decision-making biases caused by data inconsistency;

[0152] Data aggregation is the process of combining multiple data sources or data sets into a single, comprehensive data set. The aggregated data can provide more comprehensive information, facilitate macro analysis and decision-making. It can reduce the complexity of data and make it easier to manage and analyze.

[0153] In one embodiment, the visual service dependency topology map is generated by a service dependency model, and the service dependency model is deployed in the data processing module;

[0154] By deploying the service dependency model in the data processing module, after the data processing module completes data processing, the service dependency model is used to generate a visual service dependency topology diagram based on the processed data.

[0155] In one embodiment, the service dependency model is established based on the dependency matrix, and the service dependency model is converted into a visual graph by using a graph drawing tool;

[0156] The dependency relationship between services is represented by a matrix. The rows and columns of the matrix represent different services, and the values ​​represent the degree of dependency between two services. Then, a service dependency model is constructed based on the dependency relationship.

[0157] Among them, the graphics drawing tool adopts Geph i tool;

[0158] Gephi is an open source network analysis tool that provides a rich set of layout algorithms and visualization options suitable for processing large and complex network data.

[0159] In one embodiment, the display mode of the data display module is a monitoring dashboard, and the service dependency topology diagram and health indicator data are integrated into the monitoring dashboard for display through the data display module.

[0160] A monitoring method for a monitoring system based on multi-system abnormal information comprises the following steps:

[0161] S1. Add monitoring data points at key points in the business chain to collect monitoring data;

[0162] S2. Process and analyze the collected monitoring data to obtain monitoring data of various dimensions;

[0163] S3. According to the preset alarm strategy, determine whether the service link meets the preset alarm condition. If it does, execute S4. If it does not, return to execute S1.

[0164] S4, sending alarm information to the terminal device;

[0165] S5. Synchronize the data to the visual monitoring dashboard for display.

[0166] In one embodiment, in S1, monitoring data is collected by a data collection module;

[0167] In S2 and S3, the data is processed, analyzed and judged through the data processing module;

[0168] In S4 and S5, alarm information is sent and the monitoring dashboard is displayed through the data display module.

[0169] In one embodiment, Figure 3 As shown, in S1, a timed task trigger point is added in the service link to perform a timed test on the monitoring system;

[0170] By setting up scheduled task trigger points, the system's autonomous operation and maintenance capabilities can be greatly improved, labor costs can be reduced, and efficient, stable and safe operation of the system can be ensured.

[0171] The above are only specific embodiments of the present invention, but the protection scope of the present invention is not limited thereto. Any person skilled in the art can easily think of various changes or substitutions within the technical scope disclosed by the present invention, which should be included in the protection scope of the present invention. Therefore, the protection scope of the present invention should be based on the protection scope of the claims.

Claims

1. A monitoring system for abnormal information of multiple systems, comprising a data acquisition module, a data processing module and a data display module, characterized in that: The data acquisition module is connected to the data processing module, and the data processing module is connected to the data display module; The data collection module is used to collect monitoring data in real time in multiple dimensions by using monitoring data embedding points; The data processing module is used to calculate the service health score based on the collected data and generate a visual service dependency topology diagram; Among them, the data display module is used to display the processed data to the user and issue an early warning.

2. The multi-system abnormal information monitoring system according to claim 1 is characterized in that: The monitoring data embedding point is located at a key point in the business link; The monitoring data includes the QPS of the interface, average RT, error data, service change data and cloud control change messages.

3. The multi-system abnormal information monitoring system according to claim 1 is characterized in that: The computing service health score is calculated based on the defined health indicators and the weights corresponding to each indicator, and the preset alarm strategy is used to determine whether the alarm condition is met based on the calculation result; Among them, the health score algorithm adopts a weighted average algorithm or a comprehensive scoring algorithm.

4. The multi-system abnormal information monitoring system according to claim 1 is characterized in that: The data processing module is also used to process the collected data; Among them, processing includes data cleaning, data unification and data aggregation.

5. The multi-system abnormal information monitoring system according to claim 1 is characterized in that: The visual service dependency topology diagram is generated through a service dependency model, and the service dependency model is deployed in a data processing module.

6. The multi-system abnormal information monitoring system according to claim 5 is characterized in that: The service dependency model is established according to the dependency matrix, and the service dependency model is converted into a visual graph by using a graph drawing tool; Among them, the graph drawing tool adopts Graphviz, Lucidchart or Gephi tool.

7. The multi-system abnormal information monitoring system according to claim 3 is characterized in that: The data display module is displayed in a monitoring dashboard, and the service dependency topology diagram and health indicator data are integrated into the monitoring dashboard for display through the data display module.

8. A monitoring method for a multi-system abnormal information monitoring system according to any one of claims 1 to 7, characterized in that: The following steps are involved: S1. Add monitoring data points at key points in the business chain to collect monitoring data; S2. Process and analyze the collected monitoring data to obtain monitoring data of various dimensions; S3. According to the preset alarm strategy, determine whether the service link meets the preset alarm condition. If it does, execute S4. If it does not, return to execute S1. S4, sending alarm information to the terminal device; S5. Synchronize the data to the visual monitoring dashboard for display.

9. The multi-system abnormal information monitoring system and method according to claim 8, characterized in that: In S1, monitoring data is collected by a data collection module; In S2 and S3, the data is processed, analyzed and judged by the data processing module; In S4 and S5, the alarm information is sent and the monitoring dashboard is displayed through the data display module.

10. The multi-system abnormal information monitoring system and method according to claim 8, characterized in that: In S1, a timing task trigger point is added in the service link to perform a timing test on the monitoring system.

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