CN113176975B

CN113176975B - Method and device for processing monitoring data, storage medium and electronic equipment

Info

Publication number: CN113176975B
Application number: CN202110341003.1A
Authority: CN
Inventors: 王占; 邹康; 闻英友; 吕昕东; 葛东; 窦丽莉
Original assignee: Neusoft Cloud Technology Co ltd; Neusoft Corp
Current assignee: Neusoft Cloud Technology Co ltd; Neusoft Corp
Priority date: 2021-03-30
Filing date: 2021-03-30
Publication date: 2023-10-31
Anticipated expiration: 2041-03-30
Also published as: CN113176975A

Abstract

The disclosure relates to a method, a device, a storage medium and an electronic device for processing monitoring data, and relates to the technical field of electronic information processing, comprising the following steps: the method comprises the steps of obtaining an initial monitoring graph corresponding to a target task, wherein the target task comprises a first number of processing flows, and the initial monitoring graph comprises a first number of nodes and a second number of directed edges. And generating an intermediate monitoring graph according to the initial monitoring graph, wherein the intermediate monitoring graph is a directed acyclic graph, determining the level of each node according to the intermediate monitoring graph, no directed edge exists between nodes with the same level in the intermediate monitoring graph, and the level of a source node of each directed edge is larger than that of a final node of the directed edge. According to the level of each node, generating a monitoring tree corresponding to the intermediate monitoring graph, arranging each node in the monitoring tree according to the level sequence of the node, displaying a first number of nodes according to the structure indicated by the monitoring tree, and displaying each directed edge in a second number of directed edges between the nodes at two ends of the directed edge.

Description

Method and device for processing monitoring data, storage medium and electronic equipment

Technical Field

The present disclosure relates to the field of electronic information processing technologies, and in particular, to a method and apparatus for processing monitoring data, a storage medium, and an electronic device.

Background

With the continuous development of electronic information technology, cloud computing, big data and other technologies, various industries begin to adopt a distributed cluster deployment structure to realize various services. The business may include a plurality of processing flows, each processing flow may be implemented by a different application module, and accordingly, the application modules may use different development platforms, different programming languages, and may be distributed on a large number of servers. To learn about the execution process or execution state of a service, each process flow needs to be monitored, so that each process flow in the current task can be monitored by means of a distributed tracking tool (e.g., dapper). In general, the distributed tracking tool displays each process flow in a form of a table or a form of a graph, however, the form of the table cannot intuitively display the relationship between the process flows, the process flows displayed in the form of the graph are randomly arranged, the execution sequence between the process flows cannot be clearly displayed, and it is difficult for a user to obtain effective information therefrom.

Disclosure of Invention

In order to solve the problems in the prior art, the present disclosure provides a method, an apparatus, a storage medium, and an electronic device for processing monitoring data.

According to a first aspect of an embodiment of the present disclosure, there is provided a method for processing monitoring data, the method including:

acquiring an initial monitoring graph corresponding to a target task, wherein the target task comprises a first number of processing flows, the initial monitoring graph comprises a first number of nodes and a second number of directed edges, each node corresponds to one processing flow, and each directed edge is used for indicating the data flow direction between the nodes at two ends of the directed edge;

generating an intermediate monitoring graph according to the initial monitoring graph, wherein the intermediate monitoring graph comprises a first number of nodes and a third number of directed edges, is a directed acyclic graph, and is smaller than or equal to the second number;

determining the hierarchy of each node according to the intermediate monitoring graph, wherein the directed edges do not exist between the nodes with the same hierarchy in the intermediate monitoring graph, and the hierarchy of the source node of each directed edge in the intermediate monitoring graph is larger than the hierarchy of the end node of the directed edge;

Generating a monitoring tree corresponding to the intermediate monitoring graph according to the hierarchy of each node, wherein the monitoring tree comprises a first number of nodes, a root node is the node with zero degree in the initial monitoring graph, leaf nodes are the nodes with zero degree in the initial monitoring graph, and each node is arranged in the monitoring tree according to the size sequence of the hierarchy of the node;

and displaying the first number of the nodes according to the structure indicated by the monitoring tree, and displaying each directed edge of the second number of the directed edges between the nodes at two ends of the directed edge.

Optionally, before the generating an intermediate monitoring graph according to the initial monitoring graph, the method further includes:

performing topological sorting on the initial monitoring graph to determine whether a loop exists in the initial monitoring graph according to a topological sequence output by the topological sorting; or,

performing depth-first search on the initial monitoring graph to determine whether a loop exists in the initial monitoring graph according to a search result output by the depth-first search;

the generating an intermediate monitoring graph according to the initial monitoring graph comprises the following steps:

And if a loop exists in the initial monitoring graph, deleting the designated edge from the initial monitoring graph to obtain the intermediate monitoring graph.

Optionally, the topologically ordering the initial monitoring graph includes:

deleting the node with zero degree in the initial monitoring graph from the initial monitoring graph, and putting the node into a first sequence; deleting the node with zero degree in the initial monitoring graph from the initial monitoring graph, and putting the node into a second sequence;

determining a first target node with the largest difference between the degree and the incidence degree in the initial monitoring graph, deleting the first target node from the initial monitoring graph, and putting the first target node into the first sequence;

repeating the steps of determining the first target node with the largest difference between the degree and the degree of incidence in the initial monitoring graph, deleting the first target node from the initial monitoring graph, and putting the first target node into the first sequence until the degree of incidence of all the nodes in the initial monitoring graph is zero or the degree of emergence of all the nodes in the initial monitoring graph is zero;

placing the node with zero degree in the initial monitoring graph into the first sequence, and placing the node with zero degree in the initial monitoring graph into the second sequence;

And splicing the first sequence and the second sequence to obtain the topological sequence.

Optionally, the determining a hierarchy of each node according to the intermediate monitoring graph includes:

setting the level of each second target node in the intermediate monitoring graph as a first level, wherein the second target nodes are nodes with zero degree in the intermediate monitoring graph;

for each second target node, determining the hierarchy of the third target node corresponding to the second target node according to the number of directed edges between the third target node and the second target node, wherein the third target node is any node which is not the second target node in the intermediate monitoring graph;

and determining the hierarchy of the third target node according to the hierarchy of each second target node corresponding to the third target node, wherein the hierarchy of the third target node is larger than the first hierarchy.

Optionally, the generating a monitor tree corresponding to the intermediate monitor graph according to the hierarchy of each node includes:

generating an initial monitoring tree according to the level of each node, wherein the nodes with the same level in the initial monitoring tree are positioned in the same layer;

And according to the level of each node and the level of the corresponding adjacent node, adjusting the number of levels separated between the node and the corresponding adjacent node to obtain the monitoring tree, wherein the sum of the number of levels separated between each node and the corresponding adjacent node in the monitoring tree is minimum, and the adjacent node is the node with the directed edge between the adjacent node and the node.

Optionally, the adjusting the number of levels separated between each node and the corresponding adjacent node according to the level of the node and the level of the corresponding adjacent node includes:

for each node, if the number of levels separated from the node and the corresponding adjacent node is greater than 1, reducing the number of levels separated from the node and the corresponding adjacent node to obtain an intermediate monitoring tree;

and determining the compactness of each directed edge in the intermediate monitoring tree, and adjusting the directed edge with negative compactness to obtain the monitoring tree, wherein the compactness is determined according to two connected areas obtained by deleting the directed edge from the intermediate monitoring tree.

Optionally, before the reducing the number of levels of separation between the node and the corresponding neighboring node, adjusting the number of levels of separation between the node and the corresponding neighboring node according to the level of each of the nodes and the level of the corresponding neighboring node, further includes:

Determining a tightening tree according to the initial monitoring tree, wherein the tightening tree belongs to the initial monitoring tree, and the number of levels separated from each node included in the tightening tree and a corresponding adjacent node is equal to 1;

the reducing the number of levels of separation between the node and the corresponding adjacent node comprises:

if the node points to the corresponding adjacent node and belongs to the compact tree, increasing the hierarchy of the adjacent node corresponding to the node;

if the node points to the corresponding adjacent node and the node does not belong to the compact tree, the hierarchy of the node is reduced.

Optionally, the determining the compactness of each directed edge in the intermediate monitoring tree and adjusting the directed edge with negative compactness includes:

deleting each directed edge and each target directed edge in the middle monitoring tree to obtain two communication areas, wherein the number of levels of the intervals between the nodes at two ends of each target directed edge is larger than 1;

determining the compactness of the directed edges according to the quantity and the direction of the directed edges existing between the two communication areas in the middle monitoring tree;

If the compactness of the directed edge is negative, increasing the hierarchy of the source node of the directed edge according to the compactness of the directed edge.

deleting the root node directed edge and the target directed edge from the intermediate monitoring tree aiming at the root node directed edge of the intermediate monitoring tree to obtain two communication areas, wherein the number of levels separated between the nodes at two ends of the target directed edge is more than 1, and the root node directed edge is the directed edge of any source node in the intermediate monitoring tree as the root node; determining the compactness of the directed edges of the root node according to the quantity and the direction of the directed edges existing between the two connected areas in the intermediate monitoring tree;

determining the compactness of each directed edge in the intermediate monitoring tree according to the sum of the compactness of other directed edges corresponding to the directed edge and the outgoing degree and the incoming degree of the source node of the directed edge, wherein any one end of the other directed edges is the source node of the directed edge;

According to a second aspect of embodiments of the present disclosure, there is provided a processing apparatus for monitoring data, the apparatus comprising:

the system comprises an acquisition module, a processing module and a processing module, wherein the acquisition module is used for acquiring an initial monitoring graph corresponding to a target task, the target task comprises a first number of processing flows, the initial monitoring graph comprises a first number of nodes and a second number of directed edges, each node corresponds to one processing flow, and each directed edge is used for indicating the data flow direction between the nodes at the two ends of the directed edge;

the processing module is used for generating an intermediate monitoring graph according to the initial monitoring graph, wherein the intermediate monitoring graph comprises a first number of nodes and a third number of directed edges, is a directed acyclic graph, and is smaller than or equal to the second number;

the determining module is used for determining the hierarchy of each node according to the intermediate monitoring graph, the directed edges do not exist between the nodes with the same hierarchy in the intermediate monitoring graph, and the hierarchy of the source node of each directed edge in the intermediate monitoring graph is larger than the hierarchy of the end node of the directed edge;

the generation module is used for generating a monitoring tree corresponding to the intermediate monitoring graph according to the hierarchy of each node, wherein the monitoring tree comprises a first number of nodes, a root node is the node with zero degree in the initial monitoring graph, leaf nodes are the nodes with zero degree in the initial monitoring graph, and each node is arranged in the monitoring tree according to the size sequence of the hierarchy of the node;

And the display module is used for displaying the first number of the nodes according to the structure indicated by the monitoring tree, and displaying each directed edge of the second number of the directed edges between the nodes at the two ends of the directed edge.

Optionally, the apparatus further comprises:

the judging module is used for carrying out topological sorting on the initial monitoring graph before the intermediate monitoring graph is generated according to the initial monitoring graph so as to determine whether a loop exists in the initial monitoring graph according to a topological sequence output by the topological sorting; or, performing depth-first search on the initial monitoring graph to determine whether a loop exists in the initial monitoring graph according to a search result output by the depth-first search;

the processing module is used for: and if a loop exists in the initial monitoring graph, deleting the designated edge from the initial monitoring graph to obtain the intermediate monitoring graph.

Optionally, the judging module is configured to:

Optionally, the determining module includes:

the initialization sub-module is used for setting the level of each second target node in the intermediate monitoring graph as a first level, wherein the second target nodes are nodes with zero degree in the intermediate monitoring graph;

the first determining submodule is used for determining the hierarchy of a third target node corresponding to the second target node according to the number of directed edges between the third target node and the second target node, wherein the third target node is any node which is not the second target node in the intermediate monitoring graph;

And the second determining submodule is used for determining the hierarchy of the third target node according to the hierarchy of each second target node corresponding to the third target node, and the hierarchy of the third target node is larger than the first hierarchy.

Optionally, the generating module includes:

the first generation sub-module is used for generating an initial monitoring tree according to the level of each node, and the nodes with the same level in the initial monitoring tree are positioned in the same layer;

and the second generation submodule is used for adjusting the number of levels separated from the corresponding adjacent node according to the level of each node and the level of the corresponding adjacent node so as to obtain the monitoring tree, wherein the sum of the number of levels separated from each node and the corresponding adjacent node in the monitoring tree is minimum, and the adjacent node is the node with the directed edge.

Optionally, the second generating submodule is configured to:

Optionally, the second generating sub-module is further configured to:

before the number of levels of separation between the node and the corresponding adjacent node is reduced, determining a tightening tree according to the initial monitoring tree, wherein the tightening tree belongs to the initial monitoring tree, and the number of levels of separation between each node included in the tightening tree and the corresponding adjacent node is equal to 1;

the second generating submodule is used for:

Optionally, the second generating sub-module is further configured to:

According to a third aspect of the disclosed embodiments, there is provided a computer readable storage medium having stored thereon a computer program which when executed by a processor implements the steps of the method of the first aspect of the disclosed embodiments.

According to a fourth aspect of embodiments of the present disclosure, there is provided an electronic device, comprising:

a memory having a computer program stored thereon;

a processor for executing the computer program in the memory to implement the steps of the method of the first aspect of the embodiments of the present disclosure.

By the technical proposal, the method firstly obtains the initial monitoring graph comprising a first number of nodes and a second number of directed edges, each node corresponds to a processing flow of the target task, and each directed edge is used for indicating a data flow direction between the nodes at two ends of the directed edge. And then generating an intermediate monitoring graph without loops according to the initial monitoring graph, and determining the level of each node according to the intermediate monitoring graph, wherein directed edges do not exist among nodes with the same level in the intermediate monitoring graph, and the level of a source node of each directed edge is larger than that of a final node of the directed edge. And generating a monitoring tree corresponding to the intermediate monitoring graph according to the node level, wherein the monitoring tree comprises a first number of nodes, and each node is arranged in the monitoring tree according to the level sequence of the node. And finally, displaying the first number of nodes according to the structure indicated by the monitoring tree, and displaying the directed edges between the nodes at two ends of each directed edge aiming at each directed edge. According to the method and the device, each node in the initial monitoring graph corresponding to the target task is layered, and the monitoring tree is generated according to the level of each node, so that the directed edges between the nodes are displayed according to the monitoring tree, the data flow direction between each processing flow in the target task can be clearly reflected, the execution sequence between each processing flow can be displayed in a layered mode, and the display efficiency and the display definition are improved.

Additional features and advantages of the present disclosure will be set forth in the detailed description which follows.

Drawings

The accompanying drawings are included to provide a further understanding of the disclosure, and are incorporated in and constitute a part of this specification, illustrate the disclosure and together with the description serve to explain, but do not limit the disclosure. In the drawings:

FIG. 1 is a flow chart illustrating a method of processing monitoring data according to an exemplary embodiment;

FIG. 2 is a schematic diagram of an initial monitoring graph and an intermediate monitoring graph, shown in accordance with an exemplary embodiment;

FIG. 3 is a schematic diagram of a monitoring tree, shown according to an exemplary embodiment;

FIG. 4 is a flowchart illustrating another method of processing monitoring data according to an exemplary embodiment;

FIG. 5 is a schematic diagram illustrating a topology sequence according to an exemplary embodiment;

FIG. 6 is a flowchart illustrating another method of processing monitoring data according to an exemplary embodiment;

FIG. 7 is a schematic diagram of another topological sequence shown in accordance with an exemplary embodiment;

FIG. 8 is a flowchart illustrating another method of processing monitoring data according to an exemplary embodiment;

FIG. 9 is a schematic diagram of a spanning monitoring tree, shown in accordance with an exemplary embodiment;

FIG. 10 is a schematic diagram of another spanning monitoring tree, shown in accordance with an exemplary embodiment;

FIG. 11 is a block diagram illustrating a processing device for monitoring data according to an exemplary embodiment;

FIG. 12 is a block diagram of another monitoring data processing apparatus according to an exemplary embodiment;

FIG. 13 is a block diagram of another monitoring data processing apparatus according to an exemplary embodiment;

FIG. 14 is a block diagram of another monitoring data processing apparatus according to an exemplary embodiment;

fig. 15 is a block diagram of an electronic device, according to an example embodiment.

Detailed Description

Reference will now be made in detail to exemplary embodiments, examples of which are illustrated in the accompanying drawings. When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present disclosure. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present disclosure as detailed in the accompanying claims.

Fig. 1 is a flowchart illustrating a method of processing monitoring data according to an exemplary embodiment, as shown in fig. 1, the method may include the steps of:

Step 101, obtaining an initial monitoring graph corresponding to a target task, wherein the target task comprises a first number of processing flows, the initial monitoring graph comprises a first number of nodes and a second number of directed edges, each node corresponds to one processing flow, and each directed edge is used for indicating the data flow direction between the nodes at the two ends of the directed edge.

For example, the target task may be monitored by a distributed tracking tool to obtain an initial monitoring graph corresponding to the target task. The target task may be understood as a task to be monitored, which is implemented by adopting a distributed cluster deployment structure, and includes a first number of processing flows, where each processing flow may be implemented by a different application module, and each application module may depend on each other or may be independent of each other, which is not specifically limited in this disclosure. The initial monitoring Graph may be understood as a directed Graph (english) output by the distributed tracking tool for exposing the target task, where the directed Graph includes a first number of nodes and a second number of directed edges. Each node corresponds to a process flow, and it is understood that in the initial monitoring graph, the node can represent a corresponding process flow, and further, the node can also represent attribute information (such as an execution state, application module information, an IP address, etc.) of the corresponding process flow. Each directed edge corresponds to one data flow in the target task, that is, the directed edge is expressed in the target task, the processing flow corresponding to the source node of the directed edge will send data to the processing flow corresponding to the destination node of the directed edge, or the processing flow corresponding to the source node of the directed edge will be accessed, and the processing flow corresponding to the destination node of the directed edge will be accessed. In the initial monitoring graph output by the distributed tracking tool, each node is often arranged randomly, as shown in (a) in fig. 2, the nodes are disordered, a user cannot comprehensively know a target task by looking at the initial monitoring graph, and effective information is difficult to obtain.

Step 102, generating an intermediate monitoring graph according to the initial monitoring graph, wherein the intermediate monitoring graph comprises a first number of nodes and a third number of directed edges, the intermediate monitoring graph is a directed acyclic graph, and the third number is smaller than or equal to the second number.

For example, since loops add complexity to the topology and also affect the layering of each node, after the initial monitoring graph is obtained, an intermediate monitoring graph that does not include loops may be generated from the initial monitoring graph. First, whether a loop exists in the initial monitoring graph can be judged, if the loop exists in the initial monitoring graph, a designated edge can be deleted from the initial monitoring graph to delete the loop in the initial monitoring graph, so that a directed acyclic graph (English: directed Acyclic Graph, abbreviated: DAG) is obtained, namely, an intermediate monitoring graph. If no loop exists in the initial monitoring graph, the initial monitoring graph can be directly used as an intermediate monitoring graph. That is, the intermediate monitor graph is a directed acyclic graph and includes all of the first number of nodes described above, and a third number of directed edges, the third number being less than the second number (i.e., the designated edges are deleted) in the case where loops are present in the initial monitor graph, and the third number being equal to the second number in the case where no loops are present in the initial monitor graph.

Specifically, in the case that a loop exists in the initial monitoring graph, the designated edge can be determined according to the topological sequence (English: topological Order) corresponding to the initial monitoring graph. For example, the first number of nodes may be arranged in a preset direction (e.g., left to right or right to left) so that as many directed edges as possible are the same as the preset direction, then the directed edges opposite to the preset direction may be determined as the directed edges forming the loop, and these edges may be deleted as designated edges, thereby obtaining the intermediate monitoring graph. The initial monitoring graph may also be searched by a depth first search (english: depth First Search, abbreviated: DFS), if a loop is detected to be present, any directed edge in the loop may be deleted as the designated edge to destroy the loop, and when the entire DFS process is completed, an intermediate monitoring graph may be obtained. Taking the initial monitoring diagram shown in (b) of fig. 2 as an example, six nodes including node 1, node 2, node 3, node 4, node 5 and node 6, and directed edges between the respective nodes are included. The directed edge of node 4 to node 3 may be deleted as the designated edge to obtain an intermediate monitoring graph, as shown in fig. 2 (c).

Step 103, determining the level of each node according to the intermediate monitoring graph, wherein no directed edges exist between nodes with the same level in the intermediate monitoring graph, and the level of the source node of each directed edge in the intermediate monitoring graph is larger than the level of the end node of the directed edge.

By way of example, since there is no loop in the intermediate monitoring graph, a first number of nodes may be layered according to the intermediate monitoring graph to determine a hierarchy (which may be denoted as rank) for each node. In the intermediate monitoring graph, no directed edges exist between nodes with the same hierarchy, and the hierarchy of the source node of each directed edge in the intermediate monitoring graph is larger than the hierarchy of the end node of the directed edge, that is, the hierarchy of each node is determined according to the pointing relationship between each node in the intermediate monitoring graph. For example, the level of a node with zero degree in the intermediate monitoring graph may be determined to be 0, then the level of a node with a directed edge between the node with zero degree may be determined to be 1, and so on until the level of each node is obtained. Taking the intermediate monitoring graph shown in (c) of fig. 2 as an example, the node with the degree of egress being zero includes: node 6 and node 4. First, the level of the node 4 and the node 6 is determined to be 0, a directed edge exists between the node 5 and the nodes 6 and 4, then the level of the node 5 is determined to be 1, a directed edge exists between the node 3 and the node 5, then the level of the node 3 is determined to be 2, a directed edge exists between the node 1 and the node 3, and then the level of the node 1 is determined to be 3. Meanwhile, there is a directed edge between the node 2 and the node 4, then the level of the node 2 is determined to be 1. Thus, the levels corresponding to the node 1, the node 2, the node 3, the node 4, the node 5 and the node 6 are respectively: 3. 1, 2, 0, 1, 0.

And 104, generating a monitoring tree corresponding to the intermediate monitoring graph according to the hierarchy of each node, wherein the monitoring tree comprises a first number of nodes, the root node is a node with zero degree in the initial monitoring graph, the leaf nodes are nodes with zero degree in the initial monitoring graph, and each node is arranged in the monitoring tree according to the sequence of the hierarchy of the node.

Step 105, displaying the first number of nodes according to the structure indicated by the monitoring tree, and displaying each directed edge of the second number of directed edges between the nodes at two ends of the directed edge.

For example, after determining the level of each node, a monitoring tree corresponding to the intermediate monitoring graph may be generated according to the order of the level of each node, where the monitoring tree includes all the first number of nodes, the root node is a node with zero degree in the initial monitoring graph, and the leaf node is a node with zero degree in the initial monitoring graph. It will be appreciated that the first number of nodes is divided into a fourth number of levels, and then the monitor tree is divided into a fourth number of levels, and each node is then populated into the monitor tree in accordance with the corresponding level. Taking the intermediate monitoring graph shown in fig. 2 (c) as an example, six (i.e., the first number) nodes are divided into four (i.e., the fourth number) levels, then the monitoring tree is also divided into four levels, node 1 is filled into level 3 in the monitoring tree, node 2 is filled into level 1 in the monitoring tree, and so on, and the resulting monitoring tree is shown in fig. 3 (a). And then, the first number of nodes can be displayed according to the structure indicated by the monitoring tree, namely, the first number of nodes are arranged in layers for display, so that the execution sequence among each processing flow can be displayed in layers. And then, displaying each directed edge in the second number of directed edges between the nodes at two ends of the directed edge, so that all the directed edges included in the initial monitoring graph (including the designated edges deleted under the condition that the loop exists in the initial monitoring graph) can be displayed, and the data flow direction between each processing flow is accurately reflected. The user can directly check the relation and the execution sequence among the processing flows, so that the state of the target task is comprehensively known, and the display efficiency and the definition are effectively improved. Taking (a) in fig. 2 as an example of an initial monitoring diagram, the nodes and the directed edges displayed through the steps can be shown in (b) in fig. 3, so that the relation and the execution sequence between the nodes are clear, and a user can quickly acquire effective information.

Further, after displaying the first number of nodes according to the structure indicated by the monitoring tree, the method may further include: for each node, attribute information of a processing flow corresponding to the node is displayed on the node, so that the dimension of information display is increased, and a user can acquire more information about a target task. Wherein the attribute information may include: the execution state of the processing flow, the application module information corresponding to the processing flow, the flow size of the processing flow, the flow ID of the processing flow, the IP address of the server corresponding to the processing flow, and the like. For example, the attribute information includes the execution state of the processing flow, the execution state can be represented by the color of the node, red represents a serious alarm, yellow represents a normal alarm, and blue represents a normal, so that a user can quickly and intuitively determine the abnormal processing flow, and quickly and accurately locate the problem according to the execution sequence of the abnormal processing flow in the target task.

In summary, the present disclosure first obtains an initial monitoring graph including a first number of nodes and a second number of directed edges, where each node corresponds to a process flow of a target task, and each directed edge is used to indicate a data flow direction between nodes at two ends of the directed edge. And then generating an intermediate monitoring graph without loops according to the initial monitoring graph, and determining the level of each node according to the intermediate monitoring graph, wherein directed edges do not exist among nodes with the same level in the intermediate monitoring graph, and the level of a source node of each directed edge is larger than that of a final node of the directed edge. And generating a monitoring tree corresponding to the intermediate monitoring graph according to the node level, wherein the monitoring tree comprises a first number of nodes, and each node is arranged in the monitoring tree according to the level sequence of the node. And finally, displaying the first number of nodes according to the structure indicated by the monitoring tree, and displaying the directed edges between the nodes at two ends of each directed edge aiming at each directed edge. According to the method and the device, each node in the initial monitoring graph corresponding to the target task is layered, and the monitoring tree is generated according to the level of each node, so that the directed edges between the nodes are displayed according to the monitoring tree, the data flow direction between each processing flow in the target task can be clearly reflected, the execution sequence between each processing flow can be displayed in a layered mode, and the display efficiency and the display definition are improved.

FIG. 4 is a flowchart illustrating another method of processing monitoring data, as shown in FIG. 4, according to an exemplary embodiment, the method may further include, prior to step 102:

and 106a, performing topological sorting on the initial monitoring graph to determine whether a loop exists in the initial monitoring graph according to the topological sequence output by the topological sorting. Or,

and 106b, performing depth-first search on the initial monitoring graph to determine whether a loop exists in the initial monitoring graph according to the search result output by the depth-first search.

Accordingly, the implementation manner of step 102 may be: if a loop exists in the initial monitoring graph, deleting the designated edge from the initial monitoring graph to obtain an intermediate monitoring graph.

For example, before determining the intermediate monitoring graph, it is necessary to determine whether a loop exists in the initial monitoring graph. The initial monitoring graph may be processed by a topology ordering or depth-first search algorithm to determine whether loops exist in the initial monitoring graph.

In one implementation, the initial monitoring graph may be topologically ordered to determine whether loops exist in the initial monitoring graph based on a topological sequence of topologically ordered outputs:

step 1) deleting the node with zero degree in the initial monitoring graph from the initial monitoring graph, and putting the node into the first sequence. And deleting the node with zero degree in the initial monitoring graph from the initial monitoring graph, and putting the node into the second sequence.

And 2) determining a first target node with the largest difference between the degree and the incidence degree in the initial monitoring graph, deleting the first target node from the initial monitoring graph, and putting the first target node into a first sequence.

Step 3) repeating the step 2) until the ingress degree or egress degree of all nodes in the initial monitoring graph is zero.

And 4) placing the node with zero degree in the initial monitoring graph into the first sequence, and placing the node with zero degree in the initial monitoring graph into the second sequence.

Step 5) splicing the first sequence and the second sequence to obtain a topological sequence.

Taking the initial monitoring diagram shown in (b) of fig. 2 as an example, the node 1 with the degree of entry being zero is deleted from the initial monitoring diagram, and put into the first sequence, where the first sequence is [ node 1], and at the same time, the node 6 with the degree of exit being zero is deleted from the initial monitoring diagram, and put into the second sequence, where the second sequence is [ node 6]. Then, differences between the outbound degrees and inbound degrees of the node 2, the node 3, the node 4 and the node 5 are respectively determined, wherein the difference between the outbound degree (namely 2) and the inbound degree (namely 1) of the node 3 is the largest, then the node 3 is determined as a first target node, the node 3 is deleted from the initial monitoring graph, and a first sequence is put in, and the first sequence is [ node 1, node 3]. And determining the difference between the outbound degree and the inbound degree of the node 2, the node 4 and the node 5. At this time, the ingress degree of the node 2 and the node 5 is zero, and the egress degree of the node 4 is zero, then the node 2 and the node 5 may be put into a first sequence, where the first sequence is [ node 1, node 3, node 2, node 5], and the node 4 is put into a second sequence, where the second sequence is [ node 4, node 6]. Finally, splicing the first sequence and the second sequence to obtain a topological sequence: [ node 1, node 3, node 2, node 5, node 4, node 6]. The topology sequence shown in fig. 5 can be obtained by arranging six nodes from left to right according to the topology sequence. Wherein the directed edge from right to left (i.e., the directed edge pointing from node 4 to node 3, indicated by the dotted line in fig. 5) is the designated edge, and deleting the designated edge, the intermediate monitoring graph shown in fig. 2 (c) is obtained.

In another implementation, a depth-first search may also be performed on the initial monitor map to determine whether a loop exists in the initial monitor map based on a search result output from the depth-first search.

For example, DFS may be performed on the initial monitor graph, and in the process of performing DFS on each node that has not been accessed, if a node that has been accessed before is accessed again, it is determined that a loop exists in the initial monitor graph. If a loop is detected, any directed edge in the loop can be deleted as a designated edge to destroy the loop, and when the whole DFS process is completed, an intermediate monitoring graph can be obtained.

FIG. 6 is a flowchart illustrating another method of processing monitoring data, as shown in FIG. 6, according to an exemplary embodiment, step 103 may include:

step 1031, setting the level of each second target node in the intermediate monitoring graph as the first level, where the second target node is a node with zero degree in the intermediate monitoring graph.

Step 1032, for each second target node, determining a level of the third target node corresponding to the second target node according to the number of directed edges between the third target node and the second target node, where the third target node is any node in the intermediate monitoring graph that is not the second target node.

Step 1033, determining a hierarchy of the third target node according to the hierarchy of the third target node corresponding to each second target node, wherein the hierarchy of the third target node is larger than the first hierarchy.

For example, in a manner of determining a hierarchy of each node in the intermediate monitoring graph, a second target node with a degree of zero in the intermediate monitoring graph may be determined first, and the second target node may be one or more. Thereafter, the level of each second target node is set to a first level, where the first level is the lowest level (i.e., the lowest level), e.g., may be set to 0 (or 1). Then, for a third target node that is not a second target node, a hierarchy of the third target node corresponding to the second target node may be determined based on a number of directed edges between the third target node and the second target node. For example, if there are 3 directed edges between the third target node and a certain second target node, then the level of the third target node corresponding to the second target node may be determined as 0+3=3. The third target node may correspond to a different hierarchy for a different second target node, and the hierarchy of the third target node may be determined as the highest hierarchy in the corresponding second target node. For example, the third target node has a hierarchy of 2 and 3 corresponding to two second target nodes, respectively, and then the hierarchy of the third target node may be determined to be 3.

The initial monitoring graph comprises 7 nodes including node A, node B, node C, node D, node E, node F and node G, and the corresponding topological sequence is shown in FIG. 7. The second target node is determined to be the node D, the node F and the node G, and the corresponding hierarchy is 0, 0 and 0. For the node D, a directed edge exists between the node B and the node D, the level of the node B corresponding to the node D is 1, two directed edges exist between the node A and the node D, and the level of the node A corresponding to the node D is 2. For node F, node E corresponds to node F at a level of 1, node B corresponds to node F at a level of 2, and node a corresponds to node F at a level of 3. For node G, node C corresponds to node G at a level of 1 and node a corresponds to node G at a level of 2. Then, the level of the node a corresponding to the node D is 2, the level of the corresponding node F is 3, the level of the corresponding node G is 2, the level of the node a may be the maximum value thereof, i.e. 3, and so on, the levels of the node a, the node B, the node C, the node D, the node E, the node F, and the node G may be respectively: 3. 2, 0, 1, 0.

FIG. 8 is a flowchart illustrating another method of processing monitoring data, as shown in FIG. 8, according to an exemplary embodiment, step 104 may include:

In step 1041, an initial monitoring tree is generated according to the level of each node, where the nodes with the same level in the initial monitoring tree are located in the same layer.

Step 1042, adjusting the number of levels separated between each node and the corresponding adjacent node according to the level of each node and the level of the corresponding adjacent node to obtain a monitoring tree, wherein the sum of the number of levels separated between each node and the corresponding adjacent node in the monitoring tree is minimum, and the adjacent node is a node with a directed edge between the adjacent node and the node.

For example, after determining the hierarchy of each node, an initial monitoring tree may be generated. It will be understood that if the first number of nodes is divided into the fourth number of levels, then the initial monitoring tree is also divided into the fourth number of levels, and then each node is filled into the initial monitoring tree according to the corresponding level, so that the nodes with the same level in the initial monitoring tree are located in the same level. Taking the topological sequence shown in fig. 7 as an example, the levels of node a, node B, node C, node D, node E, node F and node G are respectively: 3. 2, 0, 1, 0. The initial monitoring tree generated from the hierarchy of 7 nodes is shown in fig. 9 (a).

There may be instances in the initial monitoring tree where one or more directed edges span multiple levels (also understood as the length of the directed edges is too long), resulting in an insufficiently compact initial monitoring tree. As shown in fig. 9 (a), where node B points to the directed edge of node D, spans three levels of 2, 1, and 0, and is too long. Thus, the number of levels separated between each node and the corresponding adjacent node may be adjusted according to the level of each node and the level of the corresponding adjacent node to minimize the number of levels separated between each node and the corresponding adjacent node (i.e., minimize the length of each directed edge), thereby obtaining a monitoring tree. The sum of the number of levels of each node in the monitoring tree and the number of levels of the corresponding adjacent nodes is the smallest, which is understood as that the monitoring tree is the tightest, that is, the number of levels of each node and the corresponding adjacent node in the first number of nodes is calculated respectively, and the obtained number of levels is summed to obtain the smallest result.

An implementation of how the number of layers separating each node from the corresponding neighboring node is adjusted is described below:

in an application scenario, the implementation of step 1042 may include:

step 6) for each node, if the number of levels separated from the node and the corresponding adjacent node is greater than 1, reducing the number of levels separated from the node and the corresponding adjacent node to obtain an intermediate monitoring tree.

For example, for each node, a number of layers separating the node from the corresponding neighboring node may be determined. If the number of levels of separation between the node and the corresponding neighboring node is greater than 1, which indicates that the length of the directed edge between the node and the corresponding neighboring node is too long, the number of levels of separation between the node and the corresponding neighboring node can be reduced (i.e., the length of the directed edge between the node and the corresponding neighboring node is shortened), so as to obtain an intermediate monitoring tree.

Specifically, before step 6), step 1042 may further include:

step 8) determining a tightening tree according to the initial monitoring tree, wherein the tightening tree belongs to the initial monitoring tree, and the number of levels of separation between each node included in the tightening tree and the corresponding adjacent node is equal to 1. That is, the compact tree is derived from the initial monitoring tree, and includes a number of levels spaced between nodes at each end of the directed edge equal to 1.

Accordingly, the implementation manner of the step 6) may be:

if the node points to the corresponding adjacent node and the node belongs to the compact tree, the hierarchy of the adjacent node corresponding to the node is increased. If the node points to a corresponding adjacent node and the node does not belong to a compact tree, the hierarchy of the node is reduced.

Taking the initial monitoring tree shown in fig. 9 (a) as an example, a compact tree is generated, as shown in fig. 9 (B), which includes node a, node B, node C, node E, and node F. The number of levels between the node B and the adjacent node D is 2-0=2, and is greater than 1, the node B points to the node D, and the node B belongs to the compact tree, so that the node D can be moved up one level (i.e., the level of the node D is modified to 1), and similarly, the node G can be moved up one level (i.e., the level of the node G is modified to 1), and the intermediate monitoring tree shown in (c) of fig. 9 can be obtained.

Step 7) determining the compactness of each directed edge in the intermediate monitoring tree, and adjusting the directed edge with negative compactness to obtain the monitoring tree, wherein the compactness is determined according to two connected areas obtained by deleting the directed edge from the intermediate monitoring tree.

For example, to further avoid the situation that the length of the directed edge is still too long, the compactness of each directed edge in the intermediate monitoring tree may be determined separately, and if there is a directed edge with negative compactness, the directed edge may be adjusted to obtain the monitoring tree with the smallest sum of the number of layers separated between each node and the corresponding adjacent node. Wherein the compactness is determined from two connected regions obtained by deleting the directed edge from the intermediate monitoring tree.

The implementation of step 7) may include the following two:

mode one:

first, for each directed edge in the intermediate monitoring tree, deleting the directed edge and the target directed edge from the intermediate monitoring tree to obtain two connected areas, wherein the number of levels of separation between nodes at two ends of the target directed edge is greater than 1.

And then determining the compactness of the directed edges according to the number and the direction of the directed edges existing between the two connected areas in the middle monitoring tree.

Finally, if the compactness of the directed edge is negative, increasing the hierarchy of the source node of the directed edge according to the compactness of the directed edge.

For example, the target directed edge in the intermediate monitoring tree may be determined first, and then for each directed edge in the intermediate monitoring tree except for the target directed edge, the directed edge and the target directed edge may be deleted from the intermediate monitoring tree first, at which time two connected regions are obtained. And then determining the number and the direction of the directed edges existing between the two communication areas in the middle monitoring tree, thereby obtaining the compactness of the directed edges. Specifically, a connected region including the source node of the directed edge may be taken as a source region, and a connected region including the destination node of the directed edge may be taken as a target region, where the compactness of the directed edge=the number of directed edges directed from the source region to the target region—the number of directed edges directed from the target region to the source region. Finally, if the compactness of the directed edge is negative, the hierarchy of the source node of the directed edge may be increased according to the compactness of the directed edge.

Taking the intermediate monitoring tree shown in (a) of fig. 10 as an example, the intermediate monitoring tree includes a node a, a node b, a node c, a node d, a node e, a node f, a node g, and a node h. Firstly, determining target directed edges with the number of levels larger than 1 between nodes at two ends: edge ae (indicating that node a points to node e), and edge ad (indicating that node a points to node d). Taking the compactness of the edge fh (indicating that the node f points to the node h) as an example, the edge ae, the edge ad and the edge fh can be deleted from the intermediate monitoring tree to obtain two connected regions, wherein the source region includes: node d, node e, node f, the target area includes: in the intermediate monitoring tree, the number of directed edges from the source area to the target area is 1 (namely, edges fh), the number of directed edges from the target area to the source area is 2 (namely, edges ae and ad), and the compactness of the edges fh is 1-2= -1. The compactness of edge fh is negative, then the level of the source node of edge fh (i.e., node f) may be increased by the absolute value of the compactness of the directed edge, i.e., by 1. For example, by taking the compactness of the edge ab (indicating that the node a points to the node b) as an example, the edge ae, the edge ad and the edge ab can be deleted from the intermediate monitoring tree, so as to obtain two connected areas, wherein the source area includes: node a, target area includes: node b, node c, node d, node e, node f, node g, node h. The number of directed edges pointing from the source region to the target region is 3 (i.e., edges ab, ae, ad), the number of directed edges pointing from the target region to the source region is 0, and the compactness of edge ab is 3-0=3. The compactness of the edge ab is positive, and no adjustment of the edge ab is required. The above steps are performed for each directed edge, and a monitor tree can be obtained as shown in (b) of fig. 10.

Mode two:

firstly, aiming at the directed edges of the root nodes of the intermediate monitoring tree, deleting the directed edges of the root nodes and the directed edges of the target from the intermediate monitoring tree to obtain two communication areas, wherein the number of levels of separation between the nodes at two ends of the directed edges of the target is larger than 1, and the directed edges of the root nodes are the directed edges of any source node in the intermediate monitoring tree as the root nodes. And determining the compactness of the directed edges of the root node according to the number and the direction of the directed edges existing between the two connected areas in the intermediate monitoring tree.

And then, aiming at each directed edge in the intermediate monitoring tree, determining the compactness of the directed edge according to the sum of the compactness of other directed edges corresponding to the directed edge and the outgoing degree and the incoming degree of the source node of the directed edge, wherein any one end of the other directed edge is the source node of the directed edge.

By way of example, the tightness of each directed edge may also be determined in another way. The target directed edge in the intermediate monitoring tree may be determined first, and then for each root node directed edge in the intermediate monitoring tree, the root node directed edge and the target directed edge may be deleted from the intermediate monitoring tree first, at which time two connected regions may be obtained. The root node directed edge is the directed edge of which any source node in the intermediate monitoring tree is the root node, and the root node directed edge is not the target directed edge. The manner of determining the compactness of the directed edge of the root node is the same as that in the first mode, and is not described herein.

Then, for each directed edge which is not a root node directed edge and is not a target directed edge in the intermediate monitoring tree, the compactness of the directed edge can be determined according to the sum of the compactibility of other directed edges corresponding to the directed edge and the outgoing degree and the incoming degree of the source node of the directed edge, wherein any end of the other directed edge is the source node of the directed edge, and the other directed edge is not the directed edge. Specifically, the compactness of the directed edge=the sum of the compactibility of the other directed edges+the outgoing degree of the source node of the directed edge-the incoming degree of the source node of the directed edge. Finally, if the compactness of the directed edge is negative, the hierarchy of the source node of the directed edge may be increased according to the compactness of the directed edge.

Also taking the intermediate monitoring tree shown in fig. 10 (a) as an example. Firstly, determining a target directed edge: edge ae and edge ad. The edge ab is a root node directed edge, and the corresponding compactness is 3. Taking the edge bc (indicating that the node b points to the node c) as an example, the other directed edges corresponding to the edge bc are the edge ab, the outgoing degree of the source node (i.e. the node b) of the edge bc is 1, and the incoming degree of the node b is 1, then the compactness of the edge bc=3+1-1=3.

Fig. 11 is a block diagram of an apparatus for processing monitoring data according to an exemplary embodiment, and as shown in fig. 11, the apparatus 200 may include the following modules:

the obtaining module 201 is configured to obtain an initial monitoring graph corresponding to a target task, where the target task includes a first number of processing flows, and the initial monitoring graph includes a first number of nodes and a second number of directed edges, each node corresponds to a processing flow, and each directed edge is configured to indicate a data flow direction between nodes at two ends of the directed edge.

The processing module 202 is configured to generate an intermediate monitoring graph according to the initial monitoring graph, where the intermediate monitoring graph includes a first number of nodes and a third number of directed edges, and is a directed acyclic graph, and the third number is less than or equal to the second number.

The determining module 203 is configured to determine a hierarchy of each node according to an intermediate monitoring graph, where no directed edge exists between nodes with the same hierarchy in the intermediate monitoring graph, and a hierarchy of source nodes of each directed edge in the intermediate monitoring graph is greater than a hierarchy of end nodes of the directed edge.

The generating module 204 is configured to generate, according to the hierarchy of each node, a monitoring tree corresponding to the intermediate monitoring graph, where the monitoring tree includes a first number of nodes, a root node is a node with zero degree of entry in the initial monitoring graph, a leaf node is a node with zero degree of exit in the initial monitoring graph, and each node is arranged in the monitoring tree according to the size order of the hierarchy of the node.

The display module 205 is configured to display the first number of nodes according to the structure indicated by the monitor tree, and display each of the second number of directed edges between the nodes at both ends of the directed edge.

Fig. 12 is a block diagram of another apparatus for processing monitoring data according to an exemplary embodiment, and as shown in fig. 12, the apparatus 200 may further include:

the judging module 206 is configured to topologically sort the initial monitoring graph before generating the intermediate monitoring graph according to the initial monitoring graph, so as to determine whether a loop exists in the initial monitoring graph according to the topological sequence output by the topological sorting. Or, performing depth-first search on the initial monitoring graph to determine whether a loop exists in the initial monitoring graph according to the search result output by the depth-first search.

Accordingly, the processing module 202 is configured to delete the designated edge from the initial monitoring chart to obtain the intermediate monitoring chart if a loop exists in the initial monitoring chart.

In one application scenario, the judging module 206 may be configured to perform the following steps:

Fig. 13 is a block diagram of another processing apparatus for monitoring data according to an exemplary embodiment, and as shown in fig. 13, the determining module 203 may include:

an initialization submodule 2031 is configured to set a level of each second target node in the intermediate monitoring graph to be a first level, where the second target node is a node with a degree of zero in the intermediate monitoring graph.

A first determining submodule 2032, configured to determine, for each second target node, a hierarchy of a third target node corresponding to the second target node according to the number of directed edges between the third target node and the second target node, where the third target node is any node in the intermediate monitoring graph that is not the second target node.

A second determining submodule 2033, configured to determine a level of the third target node according to the level of each second target node corresponding to the third target node, where the level of the third target node is greater than the first level.

Fig. 14 is a block diagram of another monitoring data processing apparatus, according to an exemplary embodiment, and as shown in fig. 14, the generating module 204 may include:

the first generation submodule 2041 is configured to generate an initial monitoring tree according to the level of each node, where the nodes with the same level in the initial monitoring tree are located in the same layer.

And a second generating submodule 2042, configured to adjust the number of levels separated from the corresponding adjacent node according to the level of each node and the level of the corresponding adjacent node, so as to obtain a monitoring tree, where the sum of the number of levels separated from each node and the corresponding adjacent node in the monitoring tree is the smallest, and the adjacent node is a node with a directed edge.

In an application scenario, the second generating sub-module 2042 may be used to perform the following steps:

In another application scenario, the second generating submodule 2042 is further configured to perform:

step 8) before reducing the number of levels of separation between the node and the corresponding adjacent node, determining a tightening tree according to the initial monitoring tree, wherein the tightening tree belongs to the initial monitoring tree, and the number of levels of separation between each node included in the tightening tree and the corresponding adjacent node is equal to 1.

Accordingly, the implementation manner of the step 6) may be:

if the node points to the corresponding adjacent node and the node belongs to the compact tree, the hierarchy of the adjacent node corresponding to the node is increased.

If the node points to a corresponding adjacent node and the node does not belong to a compact tree, the hierarchy of the node is reduced.

In yet another application scenario, the implementation manner of step 7) may be:

The specific manner in which the various modules perform the operations in the apparatus of the above embodiments have been described in detail in connection with the embodiments of the method, and will not be described in detail herein.

Fig. 15 is a block diagram of an electronic device 300, according to an example embodiment. As shown in fig. 15, the electronic device 300 may include: a processor 301, a memory 302. The electronic device 300 may also include one or more of a multimedia component 303, an input/output (I/O) interface 304, and a communication component 305.

The processor 301 is configured to control the overall operation of the electronic device 300 to perform all or part of the steps in the above-mentioned method for processing the monitoring data. The memory 302 is used to store various types of data to support operation at the electronic device 300, which may include, for example, instructions for any application or method operating on the electronic device 300, as well as application-related data, such as contact data, transceived messages, pictures, audio, video, and the like. The Memory 302 may be implemented by any type or combination of volatile or non-volatile Memory devices, such as static random access Memory (Static Random Access Memory, SRAM for short), electrically erasable programmable Read-Only Memory (Electrically Erasable Programmable Read-Only Memory, EEPROM for short), erasable programmable Read-Only Memory (Erasable Programmable Read-Only Memory, EPROM for short), programmable Read-Only Memory (Programmable Read-Only Memory, PROM for short), read-Only Memory (ROM for short), magnetic Memory, flash Memory, magnetic disk, or optical disk. The multimedia component 303 may include a screen and an audio component. Wherein the screen may be, for example, a touch screen, the audio component being for outputting and/or inputting audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signals may be further stored in the memory 302 or transmitted through the communication component 305. The audio assembly further comprises at least one speaker for outputting audio signals. The I/O interface 304 provides an interface between the processor 301 and other interface modules, which may be a keyboard, mouse, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 305 is used for wired or wireless communication between the electronic device 300 and other devices. Wireless communication, such as Wi-Fi, bluetooth, near field communication (Near Field Communication, NFC for short), 2G, 3G, 4G, NB-IOT, eMTC, or other 5G, etc., or one or a combination of more of them, is not limited herein. The corresponding communication component 305 may thus comprise: wi-Fi module, bluetooth module, NFC module, etc.

In an exemplary embodiment, the electronic device 300 may be implemented by one or more application specific integrated circuits (Application Specific Integrated Circuit, abbreviated as ASIC), digital signal processor (Digital Signal Processor, abbreviated as DSP), digital signal processing device (Digital Signal Processing Device, abbreviated as DSPD), programmable logic device (Programmable Logic Device, abbreviated as PLD), field programmable gate array (Field Programmable Gate Array, abbreviated as FPGA), controller, microcontroller, microprocessor, or other electronic component for performing the above-described method of processing monitoring data.

In another exemplary embodiment, a computer readable storage medium is also provided, comprising program instructions which, when executed by a processor, implement the steps of the method of processing monitoring data described above. For example, the computer readable storage medium may be the memory 302 including the program instructions described above, which are executable by the processor 301 of the electronic device 300 to perform the method of processing the monitoring data described above.

In another exemplary embodiment, a computer program product is also provided, which comprises a computer program executable by a programmable apparatus, the computer program having code portions for performing the above-mentioned method of processing monitoring data when being executed by the programmable apparatus.

The preferred embodiments of the present disclosure have been described in detail above with reference to the accompanying drawings, but the present disclosure is not limited to the specific details of the above embodiments, and various simple modifications may be made to the technical solutions of the present disclosure within the scope of the technical concept of the present disclosure, and all the simple modifications belong to the protection scope of the present disclosure.

In addition, the specific features described in the foregoing embodiments may be combined in any suitable manner, and in order to avoid unnecessary repetition, the present disclosure does not further describe various possible combinations.

Moreover, any combination between the various embodiments of the present disclosure is possible as long as it does not depart from the spirit of the present disclosure, which should also be construed as the disclosure of the present disclosure.

Claims

1. A method of processing monitoring data, the method comprising:

2. The method of claim 1, wherein prior to said generating an intermediate monitoring graph from said initial monitoring graph, said method further comprises:

3. The method of claim 2, wherein the topologically ordering the initial monitoring graph comprises:

4. The method of claim 1, wherein said determining a hierarchy of each of said nodes from said intermediate monitoring graph comprises:

5. The method of claim 4, wherein the generating the monitor tree corresponding to the intermediate monitor graph according to the hierarchy of each node comprises:

6. The method of claim 5, wherein adjusting the number of levels of separation between each of the nodes and the corresponding neighboring node based on the level of the node and the level of the corresponding neighboring node comprises:

7. The method of claim 6, wherein said adjusting the number of levels of separation between the node and the corresponding neighboring node based on the level of each of the nodes and the level of the corresponding neighboring node, before said reducing the number of levels of separation between the node and the corresponding neighboring node, further comprises:

8. A device for processing monitored data, said device comprising:

9. A computer readable storage medium, on which a computer program is stored, characterized in that the program, when being executed by a processor, implements the steps of the method according to any one of claims 1-7.

10. An electronic device, comprising:

a memory having a computer program stored thereon;

a processor for executing the computer program in the memory to implement the steps of the method of any one of claims 1-7.