Risk prediction analysis method and device based on full-link tracking

Abstract

The present invention provides a risk prediction and analysis method and device based on full-link tracking, the method comprising: obtaining all business process information of the current system in real time based on business logs and call chains in full-link monitoring; adding each functional unit, the dependency relationship and impact weight between each functional unit to the corresponding field of the database table according to the field information in a pre-established database table, obtaining a new database table and performing regression analysis; when any field in the new database table changes, updating the corresponding field in the new database table to obtain the latest version of the database table; when any functional unit has a problem, searching the latest version of the database table for the functional unit associated with the functional unit, predicting the subsequent affected functional units based on the dependency relationship and impact weight between the functional unit and the associated functional units, and determining the risk to the functional unit. The above method improves the accuracy and completeness of anomaly identification and analysis.

Description

Technical Field

[0001] The present invention relates to the field of full-link monitoring technology, and in particular to a risk prediction and analysis method and device based on full-link tracking. Background Art

[0002] The businesses carried by today's systems are becoming increasingly important. When a problem occurs in a functional unit of the system, even if timely emergency handling can be achieved, it will still have a great impact. Therefore, the ability to detect risks and anomalies in advance is becoming increasingly important, and the demand for problem analysis and prediction is becoming increasingly strong.

[0003] Existing technologies use full-link monitoring to monitor system performance and business operation status in real time. However, this approach cannot provide predictive analysis and early warning of system performance and business operation status. When a problem occurs in a process, it is impossible to immediately analyze and predict the subsequent problems that may arise in subsequent functional units due to the problem in that functional unit.

[0004] From this, we can see that the existing full-link monitoring method's abnormality identification and analysis is incomplete, has low accuracy, is prone to omissions, and takes a long time to analyze. Summary of the Invention

[0005] In view of this, an embodiment of the present invention provides a risk prediction analysis method and device based on full-link tracking to achieve the purpose of improving the accuracy and completeness of anomaly identification and analysis, avoiding omissions, and reducing analysis time.

[0006] To achieve the above objectives, the embodiments of the present invention provide the following technical solutions:

[0007] A first aspect of an embodiment of the present invention discloses a risk prediction and analysis method based on full-link tracing, the method comprising:

[0008] Based on the service log and the call chain in the full-link monitoring, all business process information of the current system is obtained in real time. The complete business process information includes the order in which each functional unit performs related operations, the dependencies and influence weights between each functional unit. The call chain is the service call relationship between the client and server services in the network communication through the call chain tracking number traceid, the node identification number id and the parent node identification number parentid;

[0009] According to the field information in the pre-established database table, the functional units, the dependencies between the functional units, and the influence weights are added to the corresponding fields in the database table to obtain a new database table;

[0010] Performing regression analysis on the new database table, and when any field in the new database table is changed, updating the corresponding field in the new database table to obtain the latest version of the database table;

[0011] When a problem occurs in any of the functional units, the functional units associated with the functional unit are searched in the latest version of the database table, and the subsequent affected functional units are predicted based on the dependency and impact weight between the functional unit and the associated functional units, and the risk posed to the functional unit is determined.

[0012] Optionally, the function units, the dependencies between the function units, and the influence weights are added to the corresponding fields of the database table according to the field information in the pre-established database table to obtain a new database table, including:

[0013] If the dependency relationship between the functional units is a direct dependency relationship, setting the influence weights between the functional units to a first value;

[0014] If the dependency relationship between the functional units is an indirect dependency relationship, setting the influence weight between the functional units to a second value;

[0015] If there is no dependency relationship between the functional units, setting the influence weights between the functional units to a third value;

[0016] According to the field information in the pre-established database table, the dependency relationships and corresponding influence weights between the functional units are added to the corresponding fields in the database table to obtain a new database table.

[0017] Optionally, performing regression analysis on the new database table and updating the corresponding field in the new database table when any field in the new database table changes to obtain the latest version of the database table includes:

[0018] Analyzing the dependencies between the functional units and the functional units obtained in real time based on the service log and the call chain in the full-link monitoring;

[0019] If there is a new dependency relationship between the functional units, setting the influence weights between the new functional units according to the new dependency relationship between the functional units;

[0020] The corresponding influence weight fields in the new database table are updated using the influence weights between the new functional units to obtain the latest version of the database table.

[0021] Optionally, performing regression analysis on the new database table and updating the corresponding field in the new database table when any field in the new database table changes to obtain the latest version of the database table includes:

[0022] Analyzing the dependencies between the functional units and the functional units obtained in real time based on the service log and the call chain in the full-link monitoring;

[0023] If a problem occurs in any of the functional units, determining a subsequent functional unit causing an error caused by the functional unit, and determining a dependency relationship between the functional unit and the subsequent functional unit causing the error;

[0024] Obtaining, according to a dependency relationship between the functional unit and the subsequent functional unit that fails, an influence weight between the functional unit in the new database table and the subsequent functional unit that fails;

[0025] adding one to the influence weight between the functional unit and the subsequent functional unit that fails, to obtain a new influence weight between the functional unit and the subsequent functional unit that fails;

[0026] The corresponding impact weight field in the new database table is updated using the new impact weight between the functional unit and the subsequent functional unit that fails, to obtain the latest version of the database table.

[0027] Optionally, when a problem occurs in any of the functional units, searching the latest version of the database table for functional units associated with the functional unit, and predicting subsequent functional units affected based on the dependency relationship and impact weight between the functional unit and the associated functional units, and determining the risk posed to the functional unit, including:

[0028] When a problem occurs in any of the functional units, searching for the functional units associated with the functional unit in the latest version of the database table based on the dependency relationships and impact weights between the functional units in the latest version of the database table, and determining the dependency relationships and impact weights between the functional unit and the associated functional units;

[0029] According to the dependency relationship and influence weight between the functional unit and the associated functional units, all functional units that will be subsequently affected are predicted, and the risks to which the functional units are subject are determined.

[0030] A second aspect of an embodiment of the present invention discloses a risk prediction and analysis device based on full-link tracing, the device comprising:

[0031] The acquisition module is used to obtain all business process information of the current system in real time based on the business log and the call chain in the full-link monitoring. The complete business process information includes the order in which each functional unit performs related operations, the dependencies and influence weights between each functional unit. The call chain is the service call relationship between the client and server services in network communication through the call chain tracking number traceid, the node identification number id and the parent node identification number parentid;

[0032] An adding module, configured to add the functional units, the dependencies between the functional units, and the influence weights to the corresponding fields of the database table according to the field information in the pre-established database table, to obtain a new database table;

[0033] An update module is used to perform regression analysis on the new database table, and when any field in the new database table is changed, update the corresponding field in the new database table to obtain the latest version of the database table;

[0034] The prediction module is used to search the functional units associated with the functional unit in the latest version of the database table when a problem occurs in any of the functional units, predict the functional units that will be affected subsequently based on the dependency and impact weight between the functional unit and the associated functional units, and determine the risks posed to the functional units.

[0035] Optionally, the adding module includes:

[0036] a first setting unit, configured to set the influence weights between the functional units to a first value if the dependency relationship between the functional units is a direct dependency relationship;

[0037] a second setting unit, configured to set the influence weights between the functional units to a second value if the dependency relationship between the functional units is an indirect dependency relationship;

[0038] a third setting unit, configured to set the influence weights between the functional units to a third value if there is no dependency relationship between the functional units;

[0039] The adding unit is used to add the dependency relationships and corresponding influence weights between the functional units to the corresponding fields of the database table according to the field information in the pre-established database table to obtain a new database table.

[0040] Optionally, the update module includes:

[0041] A first analysis unit is configured to analyze the dependency relationships between the functional units and the functional units obtained in real time based on the service log and the call chain in the full-link monitoring;

[0042] a setting unit, configured to set, if a new dependency relationship exists between the functional units, an influence weight between the new functional units according to the new dependency relationship between the functional units;

[0043] The first updating unit is configured to update the corresponding influence weight fields in the new database table by using the influence weights between the new functional units to obtain the latest version of the database table.

[0044] Optionally, the update module further includes:

[0045] A second analysis unit is configured to analyze the dependencies between the functional units and the functional units obtained in real time based on the service log and the call chain in the full-link monitoring;

[0046] a determining unit configured to, if a problem occurs in any of the functional units, determine a subsequent functional unit causing an error caused by the functional unit, and determine a dependency relationship between the functional unit and the subsequent functional unit causing the error;

[0047] an obtaining unit, configured to obtain, according to a dependency relationship between the functional unit and the subsequent functional unit that fails, an influence weight between the functional unit in the new database table and the subsequent functional unit that fails;

[0048] an adding unit, configured to add one to the influence weight between the functional unit and the subsequent functional unit that fails, to obtain a new influence weight between the functional unit and the subsequent functional unit that fails;

[0049] The second updating unit is configured to update the corresponding impact weight field in the new database table by using the new impact weight between the functional unit and the subsequent erroneous functional unit to obtain the latest version of the database table.

[0050] Optionally, the prediction module is specifically used to:

[0051] When a problem occurs in any of the functional units, based on the dependency relationships and impact weights between the various functional units in the latest version of the database table, the functional units associated with the functional unit are searched in the latest version of the database table, and the dependency relationships and impact weights between the functional unit and the associated functional units are determined; based on the dependency relationships and impact weights between the functional unit and the associated functional units, all subsequent functional units that will be affected are predicted, and the risks posed to the functional units are determined.

[0052] Based on the above-mentioned embodiment of the present invention, a risk prediction and analysis method and device based on full-link tracing are provided. The method includes: based on business logs and call chains in full-link monitoring, real-time acquisition of all business process information of the current system, the all business process information including the order in which each functional unit performs related operations, the dependencies and impact weights between each functional unit, and the call chain is a service call relationship between the client and server services in network communication through the call chain tracking number traceid, the current node identification number id, and the parent node identification number parentid. According to field information in a pre-established database table, the dependencies and impact weights between each functional unit are added to the corresponding fields of the database table to obtain a new database table; regression analysis is performed on the new database table, and when any field in the new database table changes, the corresponding field in the new database table is updated to obtain the latest version of the database table; when a problem occurs with any of the functional units, the functional unit associated with the functional unit is searched in the latest version of the database table, and based on the dependencies and impact weights between the functional unit and the associated functional units, the subsequently affected functional units are predicted, and the risk to the functional unit is determined. In this solution, the functional units, dependencies between functional units, and impact weights obtained through full-link monitoring are added to the corresponding fields in the database table. When a problem occurs in any functional unit, the dependencies and impact weights between functional units in the latest version of the database table are used to predict the subsequent affected functional units and determine the risks to the functional units, thereby improving the accuracy and completeness of anomaly identification and analysis, avoiding omissions, and reducing analysis time. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are merely embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on the provided drawings without paying any creative work.

[0054] Figure 1 A schematic diagram of a process for risk prediction and analysis based on full-link tracing provided by an embodiment of the present invention;

[0055] Figure 2 A schematic diagram of a process for adding various functional units, dependencies between various functional units, and influence weights to corresponding fields in a database table, provided by an embodiment of the present invention;

[0056] Figure 3A schematic diagram of a process for updating corresponding fields in a new database table provided by an embodiment of the present invention;

[0057] Figure 4 A schematic diagram of another process for updating corresponding fields in a new database table provided by an embodiment of the present invention;

[0058] Figure 5 A schematic diagram of a process for predicting subsequently affected functional units provided by an embodiment of the present invention;

[0059] Figure 6 A schematic structural diagram of a risk prediction and analysis device based on full-link tracking provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0060] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative efforts are within the scope of protection of the present invention.

[0061] In this application, the terms "comprises," "comprising," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus that includes a list of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not preclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0062] The terms "first," "second," "third," "fourth," and so on (if any) in the specification and claims of this application and the accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a particular order or sequence. It should be understood that the terms used in this manner are interchangeable where appropriate, so that the embodiments described herein can be implemented in an order other than that shown or described herein.

[0063] In order to facilitate understanding of the technical solution of the present invention, the technical terms appearing in the present invention are explained:

[0064] Full-link monitoring: Monitoring the entire process from the start of a request to the final response, which may pass through multiple services.

[0065] Functional unit: refers to a specific function when the system is running.

[0066] Impact weight: refers to the likelihood of impact on the function.

[0067] As can be seen from the background technology, the existing full-link monitoring method has incomplete anomaly identification and analysis, low accuracy, easy omissions, and long analysis time.

[0068] Therefore, an embodiment of the present invention provides a risk prediction and analysis method and device based on full-link tracking. In this solution, the various functional units, the dependencies between the functional units, and the impact weights obtained through full-link monitoring are added to the corresponding fields of the database table. When a problem occurs in any functional unit, the subsequent affected functional units are predicted based on the dependencies and impact weights between the functional units in the latest version of the database table, and the risks to the functional units are determined, thereby improving the accuracy and completeness of anomaly identification and analysis, avoiding omissions, and reducing analysis time.

[0069] like Figure 1 FIG. 1 is a flow chart of a risk prediction and analysis method based on full-link tracing provided by an embodiment of the present invention. The method mainly includes the following steps:

[0070] Step S101: Based on the business log and the call chain in the full-link monitoring, all business process information of the current system is obtained in real time.

[0071] In step S101 , all business process information includes the order in which the functional units perform related operations, the dependencies between the functional units, and the impact weights.

[0072] The call chain is a service call relationship between the client and server services in network communication through the call chain tracking number traceid, the node identification number id and the parent node identification number parentid.

[0073] In the process of implementing step S101, when performing full-link tracking or full-link monitoring, in order to facilitate subsequent risk prediction analysis, it is necessary to obtain relevant data information, that is: based on the business log of the current system and the call chain in the full-link monitoring, all business process information of the current system is obtained in real time, that is, the order in which each functional unit of the current system performs related operations, the dependencies between each functional unit and the impact weights are obtained in real time.

[0074] It is understandable that the call chain in the full-link monitoring is analyzed in combination with the business log of the current system to obtain all the business process information of the current system in real time.

[0075] Step S102: According to the field information in the pre-established database table, each functional unit, the dependency relationship between each functional unit and the influence weight are added to the corresponding fields of the database table to obtain a new database table.

[0076] In the process of implementing step S102, a database table is pre-established, and relevant field information is set in the database table. Then, according to the field information, each functional unit, the dependency relationship and influence weight between each functional unit are added to the corresponding field of the database table to obtain a new database table.

[0077] Step S103: performing regression analysis on the new database table.

[0078] In the specific implementation of step S103 , after the new database table is initially constructed, a regression update process will be entered, that is, regression analysis will be performed on the new database table.

[0079] Step S104: Determine whether any field in the new database table has been changed. If so, execute step S105; if not, return to execute step S103.

[0080] Step S105: Update the corresponding fields in the new database table to obtain the latest version of the database table.

[0081] In the specific implementation of step S105 , on the premise that any field in the new database table is determined to be changed, the changed field in the new database table is determined, and the changed field in the new database table is updated to obtain the latest version of the database table.

[0082] Step S106: Determine whether any functional unit has a problem. If so, execute step S107; if not, continue to execute step S106.

[0083] Step S107: Find the functional units associated with the functional units in the latest version of the database table, predict the functional units that will be affected subsequently based on the dependency and impact weight between the functional units and the associated functional units, and determine the risks to the functional units.

[0084] In the process of implementing step S107, if a problem occurs in any functional unit, the functional unit associated with the functional unit is searched in the latest version of the database table, the dependency relationship and impact weight between the functional unit and the associated functional units are determined, and based on the dependency relationship and impact weight between the functional unit and the associated functional units, the subsequent affected functional units are predicted and the risks to the functional units are determined.

[0085] It is understandable that when a problem occurs in a functional unit, by querying the latest version of the database table, all processes that may be affected and the degree of impact can be analyzed and predicted. That is, all functional units that may be affected and the degree of impact can be analyzed and predicted, and the analysis results can be promptly issued through early warnings, which can promptly remind operation and maintenance personnel to deal with it in advance and reduce error costs.

[0086] It should be noted that step S106 to step S107 is the process of implementing the alarm reminder function.

[0087] Taking steps S106 to S107 as an example, assuming that a problem occurs in functional unit A, first, the functional units associated with functional unit A are searched in the latest version of the database table, that is, the functional units associated with functional unit A are found to be functional unit B, functional unit C, and functional unit D.

[0088] Based on the dependency and impact weights between functional unit A and the associated functional units B, C, and D, predict the subsequently affected functional units E and F, and determine the risks to which functional units E and F are exposed.

[0089] A risk prediction and analysis method based on full-link tracing provided by an embodiment of the present invention obtains all business process information of the current system in real time based on the call chain in the business log and full-link monitoring. All business process information includes the order in which each functional unit performs related operations, the dependency relationship and impact weight between each functional unit. The call chain is a service call relationship in which the services of the client and server in the network communication are connected through the call chain tracking number traceid, the node identification number id and the parent node identification number parentid; according to the field information in the pre-established database table, each functional unit, the dependency relationship and impact weight between each functional unit are added to the corresponding field of the database table to obtain a new database table; regression analysis is performed on the new database table, and when any field in the new database table changes, the corresponding field in the new database table is updated to obtain the latest version of the database table; when a problem occurs with any functional unit, the functional unit associated with the functional unit is found in the latest version of the database table, and the subsequent affected functional units are predicted based on the dependency relationship and impact weight between the functional unit and the associated functional units, and the risk of the functional unit is determined. In this solution, the functional units, dependencies between functional units, and impact weights obtained through full-link monitoring are added to the corresponding fields in the database table. When a problem occurs in any functional unit, the dependencies and impact weights between functional units in the latest version of the database table are used to predict the subsequent affected functional units and determine the risks to the functional units, thereby improving the accuracy and completeness of anomaly identification and analysis, avoiding omissions, and reducing analysis time.

[0090] Based on the risk prediction and analysis method based on full-link tracing provided by the above embodiment of the present invention, step S102 is executed to add each functional unit, the dependency relationship between each functional unit and the influence weight to the corresponding field of the database table according to the field information in the pre-established database table to obtain a new database table, such as Figure 2 FIG. 1 is a flow chart of adding various functional units, dependencies between various functional units, and influence weights to corresponding fields in a database table, provided by an embodiment of the present invention, and mainly includes the following steps:

[0091] Step S201: Determine the dependency relationship between various functional units.

[0092] It should be noted that if the dependency relationship between the functional units is a direct dependency relationship, step S202 is executed; if the dependency relationship between the functional units is an indirect dependency relationship, step S203 is executed; if there is no dependency relationship between the functional units, step S204 is executed.

[0093] Step S202: setting the influence weights between the functional units to a first value.

[0094] Optionally, in a specific embodiment, the first value is 5.

[0095] In the specific implementation of step S202 , if it is determined that the dependency relationship between the functional units is a direct dependency relationship, the influence weights between the functional units are set to a first value.

[0096] Step S203: setting the influence weights between the functional units to a second value.

[0097] Optionally, in a specific embodiment, the second value is 1.

[0098] In the specific implementation of step S203 , if the dependency relationship between the functional units is an indirect dependency relationship, the influence weight between the functional units is set to the second value.

[0099] Step S204: setting the influence weights between the functional units to a third value.

[0100] Optionally, in a specific embodiment, the third value is 0.

[0101] In the specific implementation of step S204 , if it is determined that there is no dependency between the functional units, the influence weights between the functional units are set to a third value.

[0102] Step S205: According to the field information in the pre-established database table, the dependency relationships and corresponding influence weights between the functional units are added to the corresponding fields in the database table to obtain a new database table.

[0103] In the process of implementing step S205, a database table is pre-established, and relevant field information is set in the database table. Then, according to the field information, each functional unit, the dependency relationship between each functional unit and the corresponding influence weight are added to the corresponding field of the database table to obtain a new database table.

[0104] A risk prediction and analysis method based on full-link tracing provided in an embodiment of the present invention determines the dependency relationships between various functional units, and sets the influence weights between various functional units according to the determined dependency relationships between various functional units. Thus, according to the field information in a pre-established database table, the dependency relationships between various functional units and the corresponding influence weights are added to the corresponding fields in the database table, thereby improving the accuracy and completeness of anomaly identification and analysis, avoiding omissions, and reducing analysis time.

[0105] Based on the risk prediction and analysis method based on full-link tracing provided by the above embodiment of the present invention, step S103 is executed to perform regression analysis on the new database table, step S104 is executed to determine whether any field in the new database table has been changed, and step S105 is executed to update the corresponding field in the new database table to obtain the latest version of the database table. Figure 3 FIG. 1 is a flow chart of updating corresponding fields in a new database table provided by an embodiment of the present invention, which mainly includes the following steps:

[0106] Step S301: Based on the service log and the call chain in the full-link monitoring, the dependencies between the functional units and the functional units obtained in real time are analyzed.

[0107] In the specific implementation process of step S301, after the new database table is initially constructed, it will enter the regression update process, that is: based on the business log and the call chain in the full-link monitoring, the dependencies between each functional unit and each functional unit obtained in real time are analyzed.

[0108] It is understandable that the business logs and the full-link call chain are continuously combined to analyze the various functional units and the dependencies between the functional units obtained in real time.

[0109] Step S302: Determine whether there is a new dependency relationship between functional units. If so, execute step S303; if not, return to execute step S301.

[0110] Step S303: according to the new dependency relationship between the functional units, the influence weights between the new functional units are set.

[0111] In the specific implementation of step S303 , if it is determined that there is a new dependency relationship between the functional units, the influence weights between the new functional units are set according to the new dependency relationship between the functional units.

[0112] Step S304: using the new influence weights between the functional units, update the corresponding influence weight fields in the new database table to obtain the latest version of the database table.

[0113] During the specific implementation of step S304, if new dependencies between functional units are discovered, the impact weight values ​​in the new database need to be updated in a timely manner. That is, the impact weights between the new functional units are used to update the corresponding impact weight fields in the new database table to obtain the latest version of the database table.

[0114] A risk prediction and analysis method based on full-link tracing provided in an embodiment of the present invention determines that there are new dependencies between functional units, and uses the influence weights between the new functional units to update the corresponding influence weight fields in the new database table, thereby improving the accuracy and completeness of anomaly identification and analysis, avoiding omissions, and reducing analysis time.

[0115] Based on the risk prediction and analysis method based on full-link tracing provided by the above embodiment of the present invention, step S103 is executed to perform regression analysis on the new database table, step S104 is executed to determine whether any field in the new database table has been changed, and step S105 is executed to update the corresponding field in the new database table to obtain the latest version of the database table. Figure 4 FIG. 1 is a flow chart of another method for updating corresponding fields in a new database table provided by an embodiment of the present invention, which mainly includes the following steps:

[0116] Step S401: Based on the service log and the call chain in the full-link monitoring, the dependencies between the functional units and the functional units obtained in real time are analyzed.

[0117] In the specific implementation process of step S401, after the new database table is initially constructed, it will enter the regression update process, that is: based on the business log and the call chain in the full-link monitoring, the dependencies between each functional unit and each functional unit obtained in real time are analyzed.

[0118] It is understandable that the business logs and the full-link call chain are continuously combined to analyze the various functional units and the dependencies between the functional units obtained in real time.

[0119] Step S402: Determine whether any functional unit has a problem. If so, execute step S403; if not, return to execute step S401.

[0120] In the process of implementing step S402, it is determined whether any functional unit has a problem based on the actual operation and maintenance analysis. If so, it means that any functional unit has a problem, and step S403 is executed; if not, it means that any functional unit has no problem, and the process returns to step S401.

[0121] Step S403: determining a subsequent erroneous functional unit caused by the functional unit, and determining a dependency relationship between the functional unit and the subsequent erroneous functional unit.

[0122] In the specific implementation of step S403 , it is determined that a problem occurs in any functional unit, and then the functional units causing subsequent errors caused by the functional unit are determined, and the dependency relationship between the functional unit and the subsequent functional units causing errors is determined.

[0123] Step S404: Obtaining the influence weight between the functional unit in the new database table and the functional unit that subsequently fails according to the dependency relationship between the functional unit and the functional unit that subsequently fails.

[0124] In the process of specifically implementing step S404, based on the dependency relationship between the determined functional unit and the subsequent erroneous functional unit, the influence weight between the functional unit and the subsequent erroneous functional unit in the new database table is searched, and the influence weight between the functional unit and the subsequent erroneous functional unit is obtained.

[0125] Step S405: adding one to the influence weight between the functional unit and the subsequent erroneous functional unit to obtain a new influence weight between the functional unit and the subsequent erroneous functional unit.

[0126] In the specific implementation of step S405 , the obtained influence weight between the functional unit and the subsequent erroneous functional unit is added by one to obtain a new influence weight between the functional unit and the subsequent erroneous functional unit.

[0127] Step S406: using the new impact weight between the functional unit and the subsequent erroneous functional unit, update the corresponding impact weight field in the new database table to obtain the latest version of the database table.

[0128] In the specific implementation of step S406 , the corresponding impact weight field in the new database table is updated using the new impact weight between the functional unit and the subsequent erroneous functional unit to obtain the latest version of the database table.

[0129] Take an example to illustrate the above steps. Suppose there is a problem with functional unit A, and it is determined that the functional unit that causes the subsequent error caused by functional unit A is B. Then, the dependency relationship between functional unit A and the subsequent error functional unit B is determined to be a direct dependency relationship, and the influence weight between functional unit A and the subsequent error functional unit B in the new database table is obtained as 5. The influence weight between functional unit A and the subsequent error functional unit B is added by one, and the new influence weight between functional unit A and the subsequent error functional unit B is obtained as 6. The new influence weight of 6 between functional unit A and the subsequent error functional unit B is used to update the corresponding influence weight field in the new database table to obtain the latest version of the database table.

[0130] A risk prediction and analysis method based on full-link tracing provided by an embodiment of the present invention, when it is determined that a problem occurs in any functional unit, the impact weight between the functional unit and the subsequent erroneous functional units is increased by one according to the determined dependency relationship between the functional unit and the subsequent erroneous functional units, and the corresponding impact weight field in the new database table is updated using the new impact weight, thereby improving the accuracy and completeness of anomaly identification and analysis, avoiding omissions, and reducing analysis time.

[0131] Based on the risk prediction and analysis method based on full-link tracing provided by the above embodiment of the present invention, step S106 is executed to determine whether any functional unit has a problem, and step S107 is executed to find the functional unit associated with the functional unit in the latest version of the database table, and the subsequent affected functional units are predicted according to the dependency relationship and influence weight between the functional unit and the associated functional units, and the process of determining the risk of the functional unit is as follows: Figure 5 FIG. 1 is a flow chart of a method for predicting a functional unit that will be affected subsequently, provided by an embodiment of the present invention, and mainly includes the following steps:

[0132] Step S501: Determine whether any functional unit has a problem. If so, execute step S502; if not, continue to execute step S501.

[0133] Step S502: According to the dependency relationship and influence weight between each functional unit in the latest version database table, search for the functional unit associated with the functional unit in the latest version database table, and determine the dependency relationship and influence weight between the functional unit and the associated functional unit.

[0134] In the process of specifically implementing step S502, if it is determined that a problem occurs in any functional unit, the functional unit associated with the functional unit in the latest version of the database table is searched based on the dependency relationship and impact weight between the various functional units in the latest version of the database table, and the dependency relationship and impact weight between the functional unit and the associated functional units are determined.

[0135] Step S503: predict all functional units that will be subsequently affected based on the dependency relationship and impact weights between the functional unit and the associated functional units, and determine the risks posed to the functional units.

[0136] Based on the above content, it can be understood that by querying the latest version of the database table, all processes that may be affected and the degree of impact can be analyzed and predicted, that is, all functional units that may be affected and the degree of impact can be analyzed and predicted, and the analysis results can be promptly issued through early warnings, which can promptly remind operation and maintenance personnel to handle them in advance and reduce error costs.

[0137] It should be noted that the above content is the implementation process of the alarm reminder function.

[0138] A risk prediction and analysis method based on full-link tracing provided in an embodiment of the present invention, when it is determined that a problem occurs in any functional unit, the subsequent affected functional units are predicted based on the dependencies and impact weights between the functional units in the latest version of the database table, and the risks posed to the functional units are determined, thereby improving the accuracy and completeness of anomaly identification and analysis, avoiding omissions, and reducing analysis time.

[0139] The above-mentioned embodiment of the present invention Figure 1 Corresponding to the risk prediction and analysis method based on full-link tracing shown in FIG, the embodiment of the present invention also provides a risk prediction and analysis device based on full-link tracing, such as Figure 6 As shown, the risk prediction and analysis device based on full-link tracking includes: an acquisition module 61, an adding module 62, an updating module 63 and a prediction module 64.

[0140] The acquisition module 61 is used to obtain all business process information of the current system in real time based on the business log and the call chain in the full-link monitoring.

[0141] Among them, all business process information includes the order in which each functional unit performs related operations, the dependencies and influence weights between each functional unit, and the call chain is the service call relationship between the client and server services in network communication through the call chain tracking number traceid, the node identification number id and the parent node identification number parentid.

[0142] The adding module 62 is configured to add each functional unit, the dependency relationship between each functional unit and the influence weight to the corresponding fields of the database table according to the field information in the pre-established database table, so as to obtain a new database table.

[0143] The updating module 63 is used to perform regression analysis on the new database table. When any field in the new database table is changed, the corresponding field in the new database table is updated to obtain the latest version of the database table.

[0144] The prediction module 64 is used to find the functional units associated with the functional unit in the latest version of the database table when a problem occurs in any functional unit, predict the subsequent affected functional units based on the dependency and impact weight between the functional unit and the associated functional units, and determine the risks faced by the functional units.

[0145] It should be noted that the specific principles and execution processes of each module in the risk prediction and analysis device based on full-link tracking disclosed in the above-mentioned embodiment of the present invention are the same as the risk prediction and analysis method based on full-link tracking implemented in the above-mentioned present invention. Please refer to the corresponding parts of the risk prediction and analysis method based on full-link tracking disclosed in the above-mentioned embodiment of the present invention, and no further details will be given here.

[0146] A risk prediction and analysis device based on full-link tracing provided by an embodiment of the present invention obtains all business process information of the current system in real time based on the call chain in the business log and full-link monitoring. All business process information includes the order in which each functional unit performs related operations, the dependency relationship and impact weight between each functional unit. The call chain is a service call relationship in which the services of the client and server in the network communication are connected through the call chain tracking number traceid, the node identification number id and the parent node identification number parentid; according to the field information in the pre-established database table, each functional unit, the dependency relationship and impact weight between each functional unit are added to the corresponding field of the database table to obtain a new database table; regression analysis is performed on the new database table, and when any field in the new database table changes, the corresponding field in the new database table is updated to obtain the latest version of the database table; when a problem occurs with any functional unit, the functional unit associated with the functional unit is found in the latest version of the database table, and the subsequent affected functional units are predicted based on the dependency relationship and impact weight between the functional unit and the associated functional units, and the risk of the functional unit is determined. In this solution, the functional units, dependencies between functional units, and impact weights obtained through full-link monitoring are added to the corresponding fields in the database table. When a problem occurs in any functional unit, the dependencies and impact weights between functional units in the latest version of the database table are used to predict the subsequent affected functional units and determine the risks to the functional units, thereby improving the accuracy and completeness of anomaly identification and analysis, avoiding omissions, and reducing analysis time.

[0147] Optional, based on the above Figure 6 The risk prediction and analysis device based on full-link tracing shown in the figure, the adding module 62 includes:

[0148] The first setting unit is configured to set the influence weights between the functional units to a first value if the dependency relationship between the functional units is a direct dependency relationship.

[0149] The second setting unit is configured to set the influence weights between the functional units to a second value if the dependency relationship between the functional units is an indirect dependency relationship.

[0150] The third setting unit is configured to set the influence weights between the functional units to a third value if there is no dependency between the functional units.

[0151] The adding unit is used to add the dependency relationships and corresponding influence weights between various functional units to the corresponding fields of the database table according to the field information in the pre-established database table to obtain a new database table.

[0152] A risk prediction and analysis device based on full-link tracking provided by an embodiment of the present invention determines the dependency relationships between various functional units, and sets the influence weights between various functional units according to the determined dependency relationships between various functional units. Thus, according to the field information in a pre-established database table, the dependency relationships between various functional units and the corresponding influence weights are added to the corresponding fields in the database table, thereby improving the accuracy and completeness of anomaly identification and analysis, avoiding omissions, and reducing analysis time.

[0153] Optional, based on the above Figure 6 The risk prediction and analysis device based on full-link tracing shown, the updating module 63, includes:

[0154] The first analysis unit is used to analyze the dependencies between the functional units and the functional units obtained in real time based on the business log and the call chain in the full-link monitoring.

[0155] The setting unit is configured to set the influence weights between the new functional units according to the new dependency between the functional units if there is a new dependency between the functional units.

[0156] The first updating unit is used to update the corresponding influence weight fields in the new database table by using the influence weights between the new functional units to obtain the latest version of the database table.

[0157] A risk prediction and analysis device based on full-link tracking provided by an embodiment of the present invention determines that there are new dependencies between functional units, and uses the influence weights between the new functional units to update the corresponding influence weight fields in the new database table, thereby improving the accuracy and completeness of anomaly identification and analysis, avoiding omissions, and reducing analysis time.

[0158] Optional, based on the above Figure 6 The risk prediction and analysis device based on full-link tracing shown, the updating module 63, further includes:

[0159] The second analysis unit is used to analyze the dependencies between the functional units and the functional units obtained in real time based on the service log and the call chain in the full-link monitoring;

[0160] The determination unit is used to determine the subsequent erroneous functional units caused by the functional unit if a problem occurs in any functional unit, and to determine the dependency relationship between the functional unit and the subsequent erroneous functional units.

[0161] The obtaining unit is used to obtain the influence weight between the functional unit in the new database table and the subsequent functional unit that fails according to the dependency relationship between the functional unit and the subsequent functional unit that fails.

[0162] The adding unit is used to add one to the influence weight between the functional unit and the subsequent erroneous functional unit to obtain a new influence weight between the functional unit and the subsequent erroneous functional unit.

[0163] The second updating unit is used to update the corresponding impact weight field in the new database table by using the new impact weight between the functional unit and the subsequent erroneous functional unit to obtain the latest version of the database table.

[0164] A risk prediction and analysis device based on full-link tracing provided by an embodiment of the present invention, when it is determined that a problem occurs in any functional unit, the impact weight between the functional unit and the subsequent erroneous functional units is increased by one according to the determined dependency relationship between the functional unit and the subsequent erroneous functional units, and the corresponding impact weight field in the new database table is updated using the new impact weight, thereby improving the accuracy and completeness of anomaly identification and analysis, avoiding omissions, and reducing analysis time.

[0165] Optional, based on the above Figure 6 In the risk prediction and analysis device based on full-link tracing shown, the prediction module 64 is specifically configured to:

[0166] When a problem occurs in any functional unit, the functional units associated with the functional unit in the latest version of the database table are searched based on the dependency relationships and impact weights between the functional units in the latest version of the database table, and the dependency relationships and impact weights between the functional unit and the associated functional units are determined; based on the dependency relationships and impact weights between the functional unit and the associated functional units, all functional units that will be affected subsequently are predicted, and the risks posed to the functional units are determined.

[0167] A risk prediction and analysis device based on full-link tracing provided in an embodiment of the present invention predicts the subsequent affected functional units and determines the risks posed to the functional units based on the dependencies and impact weights between the functional units in the latest version of the database table when a problem is determined in any functional unit, thereby improving the accuracy and completeness of anomaly identification and analysis, avoiding omissions, and reducing analysis time.

[0168] Each embodiment in this specification is described in a progressive manner. The same or similar parts between the embodiments can be referred to each other. Each embodiment focuses on 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. For relevant parts, refer to the partial description of the method embodiment. The system and system embodiments described above are merely schematic, wherein the units described as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units, that is, they may be located in one place, or they may be distributed on multiple network units. Some or all of the modules can be selected according to actual needs to achieve the purpose of the solution of this embodiment. A person of ordinary skill in the art can understand and implement it without expending creative work.

[0169] Professionals may further appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, computer software, or a combination of the two. In order to clearly illustrate the interchangeability of hardware and software, the above description has generally described the components and steps of each example according to their functions. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians may use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0170] The above description of the disclosed embodiments is intended to enable one skilled in the art to implement or use the present invention. Various modifications to these embodiments will be readily apparent to one skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present invention. Therefore, the present invention is not limited to the embodiments shown herein but is intended to conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A risk prediction and analysis method based on full-link tracking, characterized in that: The method comprises: Based on the service log and the call chain in the full-link monitoring, all business process information of the current system is obtained in real time. The complete business process information includes the order in which each functional unit performs related operations, the dependencies and influence weights between each functional unit. The call chain is the service call relationship between the client and server services in the network communication through the call chain tracking number traceid, the node identification number id and the parent node identification number parentid; According to the field information in the pre-established database table, the functional units, the dependencies between the functional units, and the influence weights are added to the corresponding fields in the database table to obtain a new database table; Performing regression analysis on the new database table, and when any field in the new database table is changed, updating the corresponding field in the new database table to obtain the latest version of the database table; When a problem occurs in any of the functional units, searching the latest version of the database table for the functional units associated with the functional unit, predicting the subsequently affected functional units based on the dependency relationship and impact weight between the functional unit and the associated functional units, and determining the risk posed to the functional unit; According to the field information in the pre-established database table, the functional units, the dependencies between the functional units, and the influence weights are added to the corresponding fields of the database table to obtain a new database table, including: If the dependency relationship between the functional units is a direct dependency relationship, setting the influence weights between the functional units to a first value; If the dependency relationship between the functional units is an indirect dependency relationship, setting the influence weight between the functional units to a second value; If there is no dependency relationship between the functional units, setting the influence weights between the functional units to a third value; According to the field information in the pre-established database table, the dependency relationships and corresponding influence weights between the functional units are added to the corresponding fields in the database table to obtain a new database table.

2. The method according to claim 1, characterized in that The regression analysis is performed on the new database table, and when any field in the new database table is changed, the corresponding field in the new database table is updated to obtain the latest version of the database table, including: Analyzing the dependencies between the functional units and the functional units obtained in real time based on the service log and the call chain in the full-link monitoring; If there is a new dependency relationship between the functional units, setting the influence weights between the new functional units according to the new dependency relationship between the functional units; The corresponding influence weight fields in the new database table are updated using the influence weights between the new functional units to obtain the latest version of the database table.

3. The method according to claim 1, characterized in that The regression analysis is performed on the new database table, and when any field in the new database table is changed, the corresponding field in the new database table is updated to obtain the latest version of the database table, including: Analyzing the dependencies between the functional units and the functional units obtained in real time based on the service log and the call chain in the full-link monitoring; If a problem occurs in any of the functional units, determining a subsequent functional unit causing an error caused by the functional unit, and determining a dependency relationship between the functional unit and the subsequent functional unit causing the error; Obtaining, according to a dependency relationship between the functional unit and the subsequent functional unit that fails, an influence weight between the functional unit in the new database table and the subsequent functional unit that fails; adding one to the influence weight between the functional unit and the subsequent functional unit that fails, to obtain a new influence weight between the functional unit and the subsequent functional unit that fails; The corresponding impact weight field in the new database table is updated using the new impact weight between the functional unit and the subsequent functional unit that fails, to obtain the latest version of the database table.

4. The method according to claim 1, wherein When a problem occurs in any of the functional units, searching the latest version of the database table for the functional units associated with the functional unit, and predicting the subsequently affected functional units based on the dependency relationship and impact weight between the functional unit and the associated functional units, and determining the risk to the functional unit, including: When a problem occurs in any of the functional units, searching for the functional units associated with the functional unit in the latest version of the database table based on the dependency relationships and impact weights between the functional units in the latest version of the database table, and determining the dependency relationships and impact weights between the functional unit and the associated functional units; According to the dependency relationship and influence weight between the functional unit and the associated functional units, all functional units that will be subsequently affected are predicted, and the risks to which the functional units are subject are determined.

5. A risk prediction and analysis device based on full-link tracking, characterized in that: The device comprises: The acquisition module is used to obtain all business process information of the current system in real time based on the business log and the call chain in the full-link monitoring. The complete business process information includes the order in which each functional unit performs related operations, the dependencies and influence weights between each functional unit. The call chain is the service call relationship between the client and server services in network communication through the call chain tracking number traceid, the node identification number id and the parent node identification number parentid; An adding module, configured to add the functional units, the dependencies between the functional units, and the influence weights to the corresponding fields of the database table according to the field information in the pre-established database table, to obtain a new database table; An update module is used to perform regression analysis on the new database table, and when any field in the new database table is changed, update the corresponding field in the new database table to obtain the latest version of the database table; a prediction module configured to, when a problem occurs in any of the functional units, search the latest version of the database table for a functional unit associated with the functional unit, predict the subsequent functional units that will be affected based on the dependency and impact weight between the functional unit and the associated functional units, and determine the risk posed to the functional unit; Wherein, the adding module includes: a first setting unit, configured to set the influence weights between the functional units to a first value if the dependency relationship between the functional units is a direct dependency relationship; a second setting unit, configured to set the influence weights between the functional units to a second value if the dependency relationship between the functional units is an indirect dependency relationship; a third setting unit, configured to set the influence weights between the functional units to a third value if there is no dependency relationship between the functional units; The adding unit is used to add the dependency relationships and corresponding influence weights between the functional units to the corresponding fields of the database table according to the field information in the pre-established database table to obtain a new database table.

6. The device according to claim 5, characterized in that The update module includes: A first analysis unit is configured to analyze the dependency relationships between the functional units and the functional units obtained in real time based on the service log and the call chain in the full-link monitoring; a setting unit, configured to set, if a new dependency relationship exists between the functional units, an influence weight between the new functional units according to the new dependency relationship between the functional units; The first updating unit is configured to update the corresponding influence weight fields in the new database table by using the influence weights between the new functional units to obtain the latest version of the database table.

7. The device according to claim 5, characterized in that The update module further includes: A second analysis unit is configured to analyze the dependencies between the functional units and the functional units obtained in real time based on the service log and the call chain in the full-link monitoring; a determining unit configured to, if a problem occurs in any of the functional units, determine a subsequent functional unit causing an error caused by the functional unit, and determine a dependency relationship between the functional unit and the subsequent functional unit causing the error; an obtaining unit, configured to obtain, according to a dependency relationship between the functional unit and the subsequent functional unit that fails, an influence weight between the functional unit in the new database table and the subsequent functional unit that fails; an adding unit, configured to add one to the influence weight between the functional unit and the subsequent functional unit that fails, to obtain a new influence weight between the functional unit and the subsequent functional unit that fails; The second updating unit is configured to update the corresponding impact weight field in the new database table by using the new impact weight between the functional unit and the subsequent erroneous functional unit to obtain the latest version of the database table.

8. The device according to claim 5, characterized in that The prediction module is specifically used to: When a problem occurs in any of the functional units, based on the dependency relationships and impact weights between the various functional units in the latest version of the database table, the functional units associated with the functional unit are searched in the latest version of the database table, and the dependency relationships and impact weights between the functional unit and the associated functional units are determined; based on the dependency relationships and impact weights between the functional unit and the associated functional units, all subsequent functional units that will be affected are predicted, and the risks posed to the functional units are determined.

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