CN115599650A - Health degree evaluation method and device of IT system, electronic equipment and storage medium - Google Patents
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Abstract
The embodiment of the application provides a health degree evaluation method and device of an Internet technology IT system, electronic equipment and a storage medium, and relates to the technical field of computers. The method comprises the following steps: the method comprises the steps of obtaining a tree structure unit model of an IT system to be evaluated, wherein the tree structure unit model comprises at least two system levels from a low level to a high level; sequentially calculating the real-time health degree score of each node in each system level from a lower layer to a higher layer until the real-time health degree score of each node in the highest system level is obtained; and acquiring a weighted value of each node in the highest system level, and acquiring a real-time health score of the IT system to be evaluated based on the real-time health score and the weighted value of each node in the highest system level. This scheme can carry out the layering to any IT system through adopting tree structure unit model, has good expansibility, can real time monitoring simultaneously and obtain the health degree score that has high sensitivity, has improved the quantization accuracy.
Description
Technical Field
The present application relates to the field of computer technologies, and in particular, to a method and an apparatus for health assessment of an IT system, an electronic device, and a storage medium.
Background
Under the trend of global enterprise digital transformation, more and more company businesses appear in the internet, and meanwhile, an IT system is required to be used for managing the company businesses, so that a lot of convenience is brought to users.
At present, the existing IT system health degree evaluation methods mainly include an Analytic Hierarchy Process (AHP), a threshold-alarm based quantitative evaluation method, a strong association rule mining method, and the like. The analytic hierarchy process is mainly based on subjectively and qualitatively providing importance degree rating to calculate the weight division among all factors (indexes). The quantitative evaluation method based on threshold value-alarm distributes weight according to expert experience, calculates index threshold value, divides index performance value into 6 health degree grades according to the threshold value, and qualitatively judges the health degree grades according to the comparison between the performance value and the threshold value. The strong association rule mining method adopts a bit-dividing statistical method to divide fluctuation degree intervals to construct an index feature variable set, mines the strong association relation of feature variables based on support degree and confidence threshold values, and judges whether a system is healthy or not by using statistical frequency difference among strong association features.
In the prior art, the method has the problem of insufficient universality, such as a threshold-alarm-based quantitative evaluation method and a strong association rule mining method, when the method is applied to health degree scores of different systems, the same model is difficult to multiplex on other similar systems. Meanwhile, the problem of insufficient quantization precision exists.
Disclosure of Invention
The purpose of this application is to solve at least one of the above technical defects, and the technical solution provided by this application embodiment is as follows:
in a first aspect, an embodiment of the present application provides a method for assessing health of an internet technology IT system, including:
acquiring a tree structure unit model of an IT system to be evaluated, wherein the tree structure unit model comprises at least two system levels from a lower level to a higher level;
sequentially calculating the real-time health degree score of each node in each system level from the lower layer to the upper layer until the real-time health degree score of each node in the highest system level is obtained; the method comprises the steps that for each node of the lowest system level, real-time health degree scores of the nodes are obtained based on real-time data corresponding to the nodes, for each node of other system levels except the lowest system level, the weight value of each sub-node corresponding to the node in the system level lower than the lowest system level is obtained, and the real-time health degree scores of the nodes are obtained based on the weight value and the real-time health degree scores of each sub-node corresponding to the node;
and acquiring a weighted value of each node in the highest system level, and acquiring a real-time health score of the IT system to be evaluated based on the real-time health score and the weighted value of each node in the highest system level.
In an optional embodiment of the present application, the obtaining a real-time health score of a node based on real-time data corresponding to the node specifically includes:
acquiring a preset amount of historical data corresponding to the nodes;
carrying out Box-cox transformation on each historical data to obtain corresponding first normalized data, and carrying out Box-cox transformation on the real-time data of the nodes to obtain corresponding second normalized data; wherein transformation parameters of the Box-cox transformation are determined based on a preset amount of historical data;
and acquiring a real-time health degree score of the node by utilizing a three-sigma 3-sigma criterion based on each first normalized data and the second normalized data.
In an optional embodiment of the present application, the obtaining a real-time health score of the node by using a 3-sigma criterion based on each of the first normalization data and the second normalization data includes:
based on each first normalized data, acquiring a corresponding mean value and a standard deviation by using a 3-sigma criterion;
and acquiring the real-time health degree score of the node based on the second normalized data, the mean value and the standard deviation.
In an optional embodiment of the present application, the obtaining a weight value of each child node corresponding to a node in a system level lower by one level specifically includes:
acquiring historical health degree scores of sub-nodes corresponding to the nodes in a system level one level lower than the system level;
acquiring an entropy value of each child node based on the historical health degree score of each child node;
and acquiring the weight value of each child node based on the entropy value of each child node.
In an optional embodiment of the present application, the obtaining an entropy value of each child node based on the historical health score of each child node specifically includes:
acquiring the health degree score ratio of each sub node in the node based on the historical health degree score of each sub node;
and acquiring the entropy value of each child node based on the health degree score ratio.
In an optional embodiment of the present application, the obtaining of the real-time health degree score of the node based on the weighted value and the real-time health degree score of each sub-node corresponding to the node specifically includes:
and weighting and summing the real-time health degree scores corresponding to the sub-nodes based on the weighted values of the sub-nodes corresponding to the nodes to obtain the real-time health degree scores of the nodes.
In an optional embodiment of the present application, the obtaining a real-time health score of an IT system to be evaluated based on a real-time health score and a weight value of each node in a highest system level includes:
and based on the weight value of each node in the highest system level, carrying out weighted summation on the real-time health degree score of each node to obtain the real-time health degree score of the IT system to be evaluated.
In a second aspect, an embodiment of the present application provides an apparatus for assessing health of an internet technology IT system, including:
the system comprises a tree structure unit obtaining module, a tree structure unit evaluating module and a tree structure unit evaluating module, wherein the tree structure unit obtaining module is used for obtaining a tree structure unit model of the IT system to be evaluated, and the tree structure unit model comprises at least two system levels from a lower layer to a higher layer;
the node health degree score acquisition module is used for sequentially calculating the real-time health degree score of each node in each system level from a low layer to a high layer until the real-time health degree score of each node in the highest system level is obtained; for each node of the lowest system level, acquiring a real-time health degree score of the node based on real-time data corresponding to the node, for each node of other system levels except the lowest system level, acquiring a weight value of each sub-node corresponding to the node in the system level lower than the lowest system level, and acquiring the real-time health degree score of the node based on the weight value and the real-time health degree score of each sub-node corresponding to the node;
and the system health degree score obtaining module is used for obtaining the weighted value of each node in the highest system level and obtaining the real-time health degree score of the IT system to be evaluated based on the real-time health degree score and the weighted value of each node in the highest system level.
In an optional embodiment of the present application, the node health score obtaining module is specifically configured to:
acquiring a preset amount of historical data corresponding to the nodes;
carrying out Box-cox transformation on each historical data to obtain corresponding first normalized data, and carrying out Box-cox transformation on the real-time data of the nodes to obtain corresponding second normalized data; wherein transformation parameters of the Box-cox transformation are determined based on a preset amount of historical data;
and acquiring a real-time health degree score of the node by using a three-sigma 3-sigma criterion based on each first normalization data and the second normalization data.
In an optional embodiment of the present application, the node health score obtaining module is further configured to:
based on each first normalized data, acquiring a corresponding mean value and standard deviation by using a 3-sigma criterion;
and acquiring the real-time health degree score of the node based on the second normalized data, the mean value and the standard deviation.
In an optional embodiment of the present application, the node health score obtaining module is further configured to:
acquiring historical health degree scores of sub-nodes corresponding to the nodes in a system level one level lower than the system level;
acquiring an entropy value of each child node based on the historical health degree score of each child node;
and acquiring the weight value of each child node based on the entropy value of each child node.
In an optional embodiment of the present application, the node health score obtaining module is further configured to:
acquiring the health degree score ratio of each sub node in the node based on the historical health degree score of each sub node;
and acquiring the entropy value of each child node based on the health degree score ratio.
In an optional embodiment of the present application, the node health score obtaining module is further configured to:
and weighting and summing the real-time health degree scores corresponding to the sub-nodes based on the weighted values of the sub-nodes corresponding to the nodes to obtain the real-time health degree scores of the nodes.
In an optional embodiment of the present application, the system health score obtaining module is specifically configured to:
and based on the weight value of each node in the highest system level, carrying out weighted summation on the real-time health degree score of each node to obtain the real-time health degree score of the IT system to be evaluated.
In a third aspect, an embodiment of the present application provides an electronic device, including a memory, a processor, and a computer program stored on the memory;
a processor executes the computer program to implement the method as provided in an embodiment of the first aspect or any alternative embodiment of the first aspect.
In a fourth aspect, this application provides a computer-readable storage medium, on which a computer program is stored, where the computer program, when executed by a processor, implements the method provided in the embodiments of the first aspect or any optional embodiment of the first aspect.
The technical scheme provided by the embodiment of the application has the following beneficial effects:
by acquiring the tree structure unit model of the IT system to be evaluated, various IT systems can be divided into at least two system levels from a low level to a high level, and the universality is better; meanwhile, the health degree score and the weight value of each node are obtained from the lowest level based on the real-time data and the historical data of each node, and the specific value of the final health degree score of the system is obtained based on the health degree score and the weight value of each node, so that the method has high quantization precision.
Drawings
In order to more clearly illustrate the technical solutions in the embodiments of the present application, the drawings used in the description of the embodiments of the present application will be briefly described below.
Fig. 1 is a schematic flowchart of a health assessment method of an internet technology IT system according to an embodiment of the present disclosure;
FIG. 2 is a tree-structured unit model of a host dimension in an example of an embodiment of the present application;
FIG. 3 is a tree-structured unit model of a database dimension in one example of an embodiment of the present application;
FIG. 4 is a tree-structured unit model of a business dimension in an example of an embodiment of the present application;
FIG. 5 is a schematic diagram illustrating the steps of a method for health assessment of an IT system in an example of an embodiment of the present application;
FIG. 6 is a diagram illustrating a health scoring process of a tree unit structure model multiplexing calculation step according to an embodiment of the present application;
FIG. 7 is a flowchart illustrating a process of calculating a health score of each node according to an embodiment of the present application;
FIG. 8 is a flowchart illustrating a process of calculating weight values of nodes according to an example of the application;
FIG. 9 is a flowchart illustrating a training process for calculating a system health score according to an embodiment of the present application;
FIG. 10 is a flowchart illustrating an example system health score calculation inference process according to an embodiment of the present application;
fig. 11 is a block diagram illustrating a health assessment apparatus of an internet technology IT system according to an embodiment of the present disclosure;
fig. 12 is a schematic structural diagram of an electronic device according to an embodiment of the present application.
Detailed Description
Embodiments of the present application are described below in conjunction with the drawings in the present application. It should be understood that the embodiments set forth below in connection with the drawings are exemplary descriptions for explaining technical solutions of the embodiments of the present application, and do not limit the technical solutions of the embodiments of the present application.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, information, data, steps, operations, elements, and/or components, but do not preclude the presence or addition of other features, information, data, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or wirelessly coupled. The term "and/or" as used herein indicates at least one of the items defined by the term, e.g., "a and/or B" may be implemented as "a", or as "B", or as "a and B".
To make the objects, technical solutions and advantages of the present application more clear, embodiments of the present application will be described in further detail below with reference to the accompanying drawings.
The technical solutions of the embodiments of the present application and the technical effects produced by the technical solutions of the present application will be described below through descriptions of several exemplary embodiments. It should be noted that the following embodiments may be referred to, referred to or combined with each other, and the description of the same terms, similar features, similar implementation steps and the like in different embodiments is not repeated.
Fig. 1 is a schematic flowchart of a method for assessing health of an internet technology IT system according to an embodiment of the present disclosure, where an execution subject of the method may be a terminal (e.g., a computer, a mobile phone, etc.), and as shown in fig. 1, the method may include:
step S101, a tree structure unit model of an IT system to be evaluated is obtained, wherein the tree structure unit model comprises at least two system levels from a low level to a high level.
Wherein, the tree structure unit model is a topological structure model. In the present embodiment, the system hierarchy (hereinafter, referred to as hierarchy) may be "dimension", "grouping", "instance", "index". Each hierarchy includes one or more nodes, and if a node in a higher hierarchy has a connection relationship with one or more nodes in a lower hierarchy, the nodes in the lower hierarchy may be referred to as child nodes of the node in the higher hierarchy, and the nodes in the higher hierarchy may be referred to as parent nodes of the nodes in the lower hierarchy.
It should be noted that, the number and the specific meaning of each "index" child node corresponding to each different "instance" child node under the same "grouping" parent node are the same.
Wherein, the dimension can be host, middleware, database, service, application, network, etc. In the tree-structured unit model, the "grouping" hierarchy may be present or absent (when the number of nodes of the "grouping" hierarchy is one, it can be regarded as no "grouping" hierarchy) according to the difference of the "dimension" hierarchy, for example, in the "database" dimension, the "grouping" may be the type of the database such as "oracle" or the like, and in the "host" dimension, there may be no "grouping" hierarchy. The nodes in the "instance" hierarchy may be "memory instances", "CPU (central processing unit) instances", etc. The nodes in the "index" hierarchy may be "CPU usage", "memory usage", and the like.
According to the structure information of the IT system to be evaluated, the corresponding tree structure unit model can comprise one or more first unit models corresponding to different dimensions, for example, the first unit model corresponding to the dimension of a 'host' and the first unit model corresponding to the dimension of a 'database' can be included. Further, each of the first unit models may include one or more second unit models that are sequentially stacked.
Specifically, each second unit model includes the hierarchy of "dimension", "grouping" (optional), "instance", "index" in order. If a first unit model includes a plurality of sequentially stacked second unit models, then for two adjacent second unit models, the lowest level ("index" level) of the second unit model at a high level is used as the highest level ("dimension" level) of the second unit model at a low level, i.e. the "index" level of the second unit model at a higher level is used as the "dimension" level of the second unit model at a lower level in the two second unit models, and the model is passed through hierarchically.
It should be noted that, each "dimension" level is from high to low to its first "index" level, all of which constitute a second unit model, and each of the highest "dimension" level to the lowest "index" level constitutes a first unit model. Finally, the plurality of first unit models form a tree structure unit model of the IT system to be evaluated.
For example, a tree-structured unit model of an IT system to be evaluated is composed of a first unit model of three dimensions, including a first unit model of "host" dimension as shown in fig. 2, a first unit model of "database" dimension as shown in fig. 3, and a first unit model of "business" dimension as shown in fig. 4.
As shown in fig. 2, the first unit model includes five levels, which are divided into two second unit models. The second unit model of the high level comprises a dimension level, an instance level and an index level from high to low. The highest level of the second unit models of the lower and higher levels is the lowest level in the second unit models of the higher levels, i.e., model transfer is performed between the two second unit models. Then, the second unit models of the low and high levels include a "dimension" level, an "instance" level, and an "index" level in order from high to low.
In the second unit model of the high hierarchy, the "dimension" hierarchy includes a "host" node, the "instance" hierarchy includes nodes such as "host instance 1" to "host instance n", and the "index" hierarchy includes nodes such as "CPU", "memory", and "disk", wherein the "host" node in the "dimension" hierarchy has a connection relationship with the "host instance 1" to "host instance n" node in the "instance" hierarchy, so the "host" node can be regarded as a parent node of the "host instance 1" to "host instance n" node, and the "host instance 1" to "host instance n" node can be regarded as a child node of the "host" node. Each node in the "example" level and the "index" level may have a corresponding parent-child node relationship according to the connection relationship in the graph or according to the above-mentioned description, which is not repeated herein. In the second unit model unit of the low hierarchy, "instance" hierarchy includes nodes such as "CPU instance 1", "memory instance 1", "disk instance 1" to "disk instance n", and "index" hierarchy includes nodes such as "CPU utilization", "memory utilization", "disk utilization", and the like. Wherein each node in the new "dimension" level and each node in the new "instance" level and each node in the new "indicator" level may also have a corresponding parent-child relationship according to the foregoing statements. The highest "dimension" level to the lowest "index" level, i.e., two second unit models, may constitute one first unit model.
As shown in fig. 3, the first unit model includes six levels, which are divided into two second unit models. The second unit model of the high level comprises a dimension level, a grouping level, an instance level and an index level from high to low. The highest level of the second unit models of the lower and higher levels is the lowest level of the second unit models of the higher levels, i.e., model transfer is performed between the two second unit models. Then, the second unit models of the low and high levels include a "dimension" level, an "instance" level, and an "index" level in order from high to low.
In the second unit model of the high hierarchy, the "dimension" hierarchy includes "database" nodes, the "grouping" hierarchy includes "Oracle", "Redis" (two database types) and other nodes, the "instance" hierarchy includes "Oracle instance 1" to "Oracle instance n", "Redis instance 1" to "Redis instance n" and other nodes, and the "index" hierarchy includes "tablespace", "database lock", "database session", "CPU usage", "memory usage", "Key (Key) hit" and other nodes. In the index hierarchy, the node tablespace, the database lock and the database session can be used as nodes in a dimension hierarchy of a new second unit model, that is, the node tablespace, the database lock and the database session can be used as a new dimension hierarchy of a low-level second model unit, in the low-level second unit model unit, an instance hierarchy comprises nodes such as tablespace instances 1 to tablespace instances n, database lock instances and database session instances, and an index hierarchy comprises nodes such as a table space utilization rate, a database lock number and a database session number. The nodes in each level may have corresponding parent-child node relationships according to the connection relationships in the graph, and in the manner described in fig. 2. The highest "dimension" level to the lowest "index" level, i.e., two second unit models, may constitute one first unit model.
As shown in fig. 4, the first unit model includes four levels, and is formed by a second unit model alone. The second unit model comprises a dimension level, a grouping level, an instance level and an index level from high to low.
In the second unit model, the "dimension" level includes "service" nodes, the "grouping" level includes "mobile (C)", "family (H)", "government enterprise (B)", "emerging (H)", and other nodes, the "instance" level includes "service instance 1" to "service instance n" and other nodes, and the "index" level includes "call volume", "average duration", "success rate", and other nodes. The nodes in each level may have corresponding parent-child node relationships according to the connection relationships in the graph, and in the manner described in fig. 2. The highest "dimension" level to the lowest "index" level, i.e., a second unit model, may constitute a first unit model.
Step S102, sequentially calculating the real-time health degree score of each node in each system level from a low level to a high level until the real-time health degree score of each node in the highest system level is obtained; the method comprises the steps of obtaining real-time health degree scores of nodes on the basis of real-time data corresponding to the nodes for each node of the lowest system level, obtaining weight values of sub-nodes corresponding to the nodes in a system level lower than the lowest system level for each node in other system levels except the lowest system level, and obtaining the real-time health degree scores of the nodes on the basis of the weight values and the real-time health degree scores of the sub-nodes corresponding to the nodes.
As can be seen from the foregoing description, each node in different levels of the tree structure unit model has different meanings. For example, in the "index" hierarchy, each node may represent specific values such as "CPU usage", "memory usage", and the like, and in the "instance" hierarchy, each node may represent specific examples such as "CPU instance", "memory instance", and the like.
Wherein the health score is a specific percentage number that may carry a plurality of decimal points, such as "80%", "70%", "80.01%", "80.013%" and the like.
The real-time data is a specific value of each node at the current time corresponding to the lowest level (i.e., "index" level) acquired by the terminal, for example, a node in the "index" level of a certain tree structure unit model represents a CPU utilization rate, and the real-time utilization rate of the CPU instance in the IT system to be evaluated is 20%, and then the terminal acquires data "20%" and takes the data as the real-time data of the node.
Specifically, for the lowest system level (i.e. the lowest "index" level) in the tree-structured unit model, the real-time health degree score of each node in the level is obtained through related mathematical transformation according to the obtained real-time data of each node of the lowest "index" level. Meanwhile, historical data of each node of the lowest index level is required to be acquired, and the weight value of each node of the level (namely the weight value of each node in the corresponding father node) is acquired according to the acquired historical data. The historical data is a specific value before the current time of each node of the lowest hierarchy.
For any node of other levels except the lowest index level in the tree structure unit model, the real-time health degree score and the weight value of one or more sub-nodes corresponding to the node are determined firstly, and then the real-time health degree score corresponding to the node can be obtained based on the real-time health degree score and the weight value of each sub-node of the node. Meanwhile, the weighted values of the node in all child nodes corresponding to the parent node of the node are required to be obtained, so that the real-time health degree score of the parent node of the node is obtained conveniently.
According to the process, the real-time health degree score of the node in the highest system level in the tree structure unit model can be obtained.
It should be noted that, in the calculation process, the terminal needs to obtain the real-time data and the historical data of each node in the lowest "index" level, and the real-time health degree score and the weight value are obtained through mathematical transformation calculation. The real-time health degree score and the weight value of each node in other levels except the lowest level are calculated through mathematical transformation. That is to say, in the scheme, the terminal can complete the health degree score calculation of all other nodes only by acquiring the real-time data and the historical data of each node in the lowest index level.
Step S103, obtaining the weighted value of each node in the highest system level, and obtaining the real-time health degree score of the IT system to be evaluated based on the real-time health degree score and the weighted value of each node in the highest system level.
The tree structure unit model may include a plurality of first unit models, a highest "dimension" level in the plurality of first unit models may be regarded as a same level, that is, a highest system level, and nodes in each "dimension" level may be regarded as child nodes of the entire IT system to be evaluated.
Specifically, if the real-time health degree score of the highest dimension level in the plurality of first unit models is obtained through the step S102, the weight value of each dimension node is calculated according to the historical health degree scores of all the dimension nodes of the first unit models, and finally the real-time health degree score of the IT system to be evaluated is obtained according to the real-time health degree score and the weight value of each dimension node.
According to the scheme provided by the application, various IT systems can be divided into at least two system levels from a low level to a high level by acquiring the tree structure unit model of the IT system to be evaluated, and the universality is better; meanwhile, the health degree score and the weight value of each node are obtained from the lowest level based on the real-time data and the historical data of each node, and the specific value of the final health degree score of the IT system to be evaluated is obtained finally based on the health degree score and the weight value of each node, so that the method has high quantization precision.
Specifically, as shown in fig. 5, when the IT system to be evaluated is obtained, historical data of each node of the lowest "index" level is obtained, box-cox transformation is performed based on the historical data, a historical health score is calculated by using a 3sigma criterion, and related parameters are obtained. And then carrying out entropy value weighting based on the historical health degree score and the related parameters to obtain the weight value of each node. And acquiring real-time data of each node of the lowest index level, and calculating the real-time data based on related parameters and weighted values obtained by historical data transformation to obtain a real-time health score of the IT system to be evaluated.
Further, as shown in fig. 6, after the real-time data of each node in the lowest "index" level is obtained, real-time health degree scoring is performed on each node in the lowest "index" level and entropy value weighting is completed, then real-time health degree scoring of each node in the "instance" level above the node is obtained based on the real-time health degree scoring and the weight value of each node in the lowest "index" level, and entropy value weighting is completed on each node in the "instance" level based on the real-time health degree scoring of each node in the "instance" level to obtain the weight value of each node in the "instance" level. And then obtaining the real-time health degree score of the grouping node at the upper layer based on the real-time health degree score and the weight value of each node in the instance hierarchy, and completing entropy value weighting on each node in the grouping hierarchy based on the real-time health degree score of each node in the grouping hierarchy to obtain the weight value of each node in the grouping hierarchy (if no grouping hierarchy exists, only one virtual grouping exists and the weight is 100%). At this time, it is determined whether the "dimension" level is the other "index" level and is obtained as a new "dimension" level. If so, repeating the previous operation until the obtained dimension level is not obtained as a new dimension level; if not, calculating the real-time health degree score of the highest dimension level, obtaining the real-time health degree score of each node of the highest dimension level based on the real-time health degree score and the weight value of each node in the lower grouping level or the example level, and completing entropy value weighting on each node of the highest dimension level based on the real-time health degree score of each node of the highest dimension level to obtain the weight value of each node of the highest dimension level. And finally, calculating the real-time health degree score of the IT system to be evaluated based on the real-time health degree score and the weight value of each highest dimension level node.
In an optional embodiment of the present application, the obtaining a real-time health score of a node based on real-time data corresponding to the node specifically includes:
acquiring a preset amount of historical data corresponding to the nodes;
performing Box-cox transformation on each historical data to obtain corresponding first normalized data, and performing Box-cox transformation on real-time data of nodes to obtain corresponding second normalized data; wherein transformation parameters of the Box-cox transformation are determined based on a preset amount of historical data;
and acquiring a real-time health degree score of the node by using a three-sigma 3-sigma criterion based on each first normalization data and the second normalization data.
Wherein a predetermined amount of historical data is stored inThirty fewer. The first normalized data and the second normalized data are Box-cox transformation formula
And obtaining the transformation parameter of box-cox transformation as lambda in a formula, wherein c is a constant and is used for ensuring that the yi + c is greater than 0 during logarithmic calculation, and the lambda is obtained from the obtained historical data according to a maximum likelihood rule. And respectively calculating to obtain first normalized data and second normalized data according to the obtained lambda. And finally, calculating to obtain the real-time health degree score of each node by using a 3-sigma criterion according to the obtained first normalized data and the second normalized data.
Further, when y i And when the acquired historical data is acquired, the calculated y is the first normalized data. When y is i And when the acquired real-time data is acquired, the calculated y is second normalized data.
In an optional embodiment of the present application, the obtaining a real-time health score of the node by using a 3-sigma criterion based on each of the first normalization data and the second normalization data includes:
based on each first normalized data, acquiring a corresponding mean value and standard deviation by using a 3-sigma criterion;
and acquiring the real-time health degree score of the node based on the second normalized data, the mean value and the standard deviation.
Specifically, as shown in fig. 7, after the terminal acquires a plurality of historical data, a transformation parameter λ of Box-cox transformation is obtained according to a maximum likelihood rule, first normalized data of each historical data is obtained according to λ, and normal distribution of all historical data is obtained. And obtaining the mean value and the standard deviation of the normal distribution based on a 3-sigma criterion according to the obtained normal distribution. The second normalized data of the real-time data is based on the mean value and the standard deviation of the normal distribution and utilizes the 3-sigma rule
And obtaining the real-time health degree score of the node. Where x is the second normalized data, μ is the mean, and σ is the standard deviation.
It should be noted that both the mean value and the standard deviation in the 3-sigma criterion are obtained from the first normalized data corresponding to the historical data, the second normalized data corresponding to the real-time data are directly transformed according to the obtained mean value and standard deviation by using the 3-sigma criterion to obtain a deviation value of the health degree, that is, the degree of deviation of the node from the health state, and then the deviation value is subtracted by 100% to obtain the real-time health degree score of the node.
Wherein the deviation values of the health degree are evenly divided over the intervals [0, ± 3 σ ], abnormal data larger than 3 σ and smaller than-3 σ will be reduced to 3 σ and enlarged to-3 σ.
In an optional embodiment of the present application, the obtaining a weight value of each child node corresponding to a node in a system level lower by one level specifically includes:
acquiring historical health degree scores of sub-nodes corresponding to the nodes in a system level one level lower than the system level;
acquiring an entropy value of each child node based on the historical health degree score of each child node;
and acquiring the weight value of each child node based on the entropy value of each child node.
Specifically, as shown in fig. 8, the historical health degree score of each node of the lowest "index" level is obtained by using a 3-sigma rule according to the first normalized data, the mean value and the standard deviation of the normal distribution corresponding to each node of the lowest "index" level. Then obtaining the entropy e of each node of the lowest index level according to the historical health degree score of each node of the lowest index level j Then according to the "entropy e j + coefficient of difference d j Calculating a difference coefficient d according to the principle of =1 ″ j Finally according to the formula
Calculating to obtain the weight value w of each node of the lowest index level j . Where m represents the number of child nodes in a parent node, and j represents the jth index.
For nodes outside the lowest index level and the highest dimension level, the historical health degree score of each node can be calculated according to the historical health degree score and the weight value of each sub-node, and then the weight value of each node is calculated according to the historical health degree score of each node in the same mode as the lowest level node.
As can be seen from the foregoing description, the node of the highest "dimension" level can be regarded as a child node of the IT system to be evaluated, and therefore, the weight value of the node of the highest "dimension" level, i.e., the weight value of the node of the highest "dimension" level below the parent node of the IT system to be evaluated. Specifically, after obtaining the calculated historical health degree score of each highest dimension level node, the entropy value of each node is obtained according to the above description scheme, and finally the weight value corresponding to each node is obtained according to the entropy value of each node.
In an optional embodiment of the present application, the obtaining an entropy value of each child node based on the historical health score of each child node specifically includes:
acquiring the health degree score ratio of each sub node in the node based on the historical health degree score of each sub node;
and acquiring the entropy value of each child node based on the health degree score ratio.
In particular, according to the formula
And calculating to obtain the health degree score ratio of each child node. Wherein x is ij Represents the historical health score, p, of the ith sub-node under the jth node of the same level ij And the total historical health degree score is the sum of the historical health degree scores of the ith sub-node and the n th sub-node under the jth node of the same level. n represents the total number of child nodes under the node, and m represents the total number of nodes in the hierarchy. Obtaining the health degree score ratio of each sub-node according to the formula, and obtaining the health degree score ratio of each sub-node according to the formula
Calculating to obtain entropy e of each sub-node j . Wherein,
as shown in fig. 9, the process of obtaining Box-cox transformation, 3-sigma criterion transformation related transformation parameters, and obtaining weight values of nodes through entropy weighting may be regarded as a training process. Training is carried out based on historical data, a tree structure unit model of the IT system to be evaluated is established, the historical data is imported as index performance data, and whether the historical data meets normal distribution or not is checked at the moment: if not, carrying out Box-cox transformation to enable the Box-cox transformation to present normal distribution to obtain a related transformation parameter (lambda mentioned in the embodiment above), and then carrying out single index 3sigma scoring (3-sigma criterion used in the embodiment above); if yes, directly obtaining transformation parameters and carrying out single index 3sigma scoring. And obtaining single index scoring parameters (mean value and standard deviation) through 3sigma scoring. And then, performing entropy value weighting (by using the weight value calculation formula mentioned in the above embodiment) to obtain a weight value of each node, and finally performing weighted summation calculation on each node to obtain a system real-time health score.
In an optional embodiment of the present application, the obtaining of the real-time health degree score of the node based on the weighted value and the real-time health degree score of each sub-node corresponding to the node specifically includes:
and weighting and summing the real-time health degree scores corresponding to the sub-nodes based on the weighted values of the sub-nodes corresponding to the nodes to obtain the real-time health degree scores of the nodes.
Specifically, for nodes outside the lowest index level, multiplying the weight value corresponding to each child node by the real-time health degree score of each child node, and finally adding the multiplication results of each child node to obtain the real-time health degree score of the node.
It should be noted that, when the difference between the scoring result and the real-time health of the system is large, the weight can be set through manual intervention for optimization, such as reducing the weight of the node with the most deductions and increasing the weight of the node with the least deductions. Meanwhile, with data accumulation, real-time data can be used as historical data for iterative training to be optimized, and partial node weight can be specified according to prior knowledge such as expert experience and system characteristics. Wherein the training phase only generates unspecified partial node weights.
In an optional embodiment of the present application, the obtaining a real-time health score of an IT system to be evaluated based on a real-time health score and a weight value of each node in a highest system level includes:
and based on the weight value of each node in the highest system level, carrying out weighted summation on the real-time health degree score of each node to obtain the real-time health degree score of the IT system to be evaluated.
Specifically, the weighted values corresponding to the dimension nodes in the highest system level are multiplied by the real-time health degree scores corresponding to the dimension nodes, and finally the multiplication results of the dimension nodes in the highest system level are added to obtain the real-time health degree score of the IT system to be evaluated.
As shown in fig. 10, the process of calculating the real-time wellness score may be considered an inference process. And importing the real-time data of each node of the lowest index level into a system tree structure unit model as index performance data, and sequentially carrying out Box-cox transformation and single index 3sigma scoring on the real-time data to obtain the real-time health score of each node. And finally, calculating to obtain the real-time health degree score of the whole system according to the real-time health degree score and the weighted value of each node from low to high.
Fig. 11 is a block diagram illustrating a health evaluation apparatus of an internet technology IT system according to an embodiment of the present invention, and as shown in fig. 11, the health evaluation apparatus 1100 of the internet technology IT system may include: a tree structure unit obtaining module 1101, a node health degree score obtaining module 1102, and a system health degree score obtaining module 1103, wherein,
the tree structure unit obtaining module 1101 is configured to obtain a tree structure unit model of the IT system to be evaluated, where the tree structure unit model includes at least two system levels from a lower level to a higher level;
the node health degree score obtaining module 1102 is used for sequentially calculating the real-time health degree score of each node in each system level from a low level to a high level until the real-time health degree score of each node in the highest system level is obtained; the method comprises the steps that for each node of the lowest system level, real-time health degree scores of the nodes are obtained based on real-time data corresponding to the nodes, for each node of other system levels except the lowest system level, the weight value of each sub-node corresponding to the node in the system level lower than the lowest system level is obtained, and the real-time health degree scores of the nodes are obtained based on the weight value and the real-time health degree scores of each sub-node corresponding to the node;
the system health score obtaining module 1103 is configured to obtain a weighted value of each node in the highest system level, and obtain a real-time health score of the IT system to be evaluated based on the real-time health score and the weighted value of each node in the highest system level.
According to the scheme provided by the application, various IT systems can be divided into at least two system levels from a low level to a high level by acquiring the tree structure unit model of the IT system to be evaluated, and the universality is better; meanwhile, the health degree score and the weight value of each node are obtained from the lowest level based on the real-time data and the historical data of each node, and the specific numerical value of the final health degree score of the system is obtained based on the health degree score and the weight value of each node, so that the method has high quantization precision.
In an optional embodiment of the present application, the node health score obtaining module is specifically configured to:
acquiring a preset amount of historical data corresponding to the nodes;
carrying out Box-cox transformation on each historical data to obtain corresponding first normalized data, and carrying out Box-cox transformation on the real-time data of the nodes to obtain corresponding second normalized data; wherein transformation parameters of the Box-cox transformation are determined based on a preset amount of historical data;
and acquiring a real-time health score of the node by using a three-sigma 3-sigma three-sigma criterion based on each first normalization data and the second normalization data.
In an optional embodiment of the present application, the node health score obtaining module is further configured to:
based on each first normalized data, acquiring a corresponding mean value and a standard deviation by using a 3-sigma criterion;
and acquiring the real-time health degree score of the node based on the second normalized data, the mean value and the standard deviation.
In an optional embodiment of the present application, the node health score obtaining module is further configured to:
acquiring historical health degree scores of sub-nodes corresponding to the nodes in a system level one level lower than the system level;
acquiring an entropy value of each child node based on the historical health degree score of each child node;
and acquiring the weight value of each child node based on the entropy value of each child node.
In an optional embodiment of the present application, the node health score obtaining module is further configured to:
acquiring the health degree score ratio of each sub node in the node based on the historical health degree score of each sub node;
and acquiring entropy values of the child nodes based on the health degree score ratio.
In an optional embodiment of the present application, the node health score obtaining module is further configured to:
and weighting and summing the real-time health degree scores corresponding to the sub-nodes based on the weighted values of the sub-nodes corresponding to the nodes to obtain the real-time health degree scores of the nodes.
The system health degree score obtaining module is specifically used for:
and weighting and summing the real-time health degree scores of the nodes based on the weighted values of the nodes in the highest system level to obtain the real-time health degree score of the IT system to be evaluated.
Referring now to fig. 12, shown is a schematic diagram of an electronic device (e.g., a terminal device or a server that performs the method shown in fig. 1) 1200 suitable for implementing embodiments of the present application. The electronic device in the embodiments of the present application may include, but is not limited to, a mobile terminal such as a mobile phone, a notebook computer, a digital broadcast receiver, a PDA (personal digital assistant), a PAD (tablet), a PMP (portable multimedia player), a vehicle terminal (e.g., a car navigation terminal), a wearable device, and the like, and a stationary terminal such as a digital TV, a desktop computer, and the like. The electronic device shown in fig. 12 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present application.
The electronic device includes: a memory for storing a program for executing the method of the above-mentioned method embodiments and a processor; the processor is configured to execute programs stored in the memory. The processor herein may be referred to as the processing device 1201 described below, and the memory may include at least one of a Read Only Memory (ROM) 1202, a Random Access Memory (RAM) 1203, and a storage device 1208, which are described below:
as shown in fig. 12, the electronic device 1200 may include a processing means (e.g., a central processing unit, a graphics processor, etc.) 1201 that may perform various appropriate actions and processes in accordance with a program stored in a Read Only Memory (ROM) 1202 or a program loaded from a storage device 1208 into a Random Access Memory (RAM) 1203. In the RAM1203, various programs and data necessary for the operation of the electronic apparatus 1200 are also stored. The processing apparatus 1201, the ROM 1202, and the RAM1203 are connected to each other by a bus 1204. An input/output (I/O) interface 1205 is also connected to bus 1204.
Generally, the following devices may be connected to the I/O interface 1205: input devices 1206 including, for example, a touch screen, touch pad, keyboard, mouse, camera, microphone, accelerometer, gyroscope, or the like; output devices 1207 including, for example, a Liquid Crystal Display (LCD), speakers, vibrators, or the like; storage devices 1208 including, for example, magnetic tape, hard disk, etc.; and a communication device 1209. The communication device 1209 may allow the electronic apparatus 1200 to communicate wirelessly or by wire with other apparatuses to exchange data. While fig. 12 illustrates an electronic device having various means, it is to be understood that not all illustrated means are required to be implemented or provided. More or fewer devices may alternatively be implemented or provided.
In particular, according to embodiments of the application, the processes described above with reference to the flow diagrams may be implemented as computer software programs. For example, embodiments of the present application include a computer program product comprising a computer program carried on a non-transitory computer readable medium, the computer program containing program code for performing the method illustrated by the flow chart. In such an embodiment, the computer program may be downloaded and installed from a network through the communication device 1209, or installed from the storage device 1208, or installed from the ROM 1202. The computer program, when executed by the processing apparatus 1201, performs the above-described functions defined in the methods of embodiments of the present application.
It should be noted that the computer readable storage medium mentioned in the present application can be a computer readable signal medium or a computer readable storage medium or any combination of the two. A computer readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples of the computer readable storage medium may include, but are not limited to: an electrical connection having one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing. In the present application, a computer readable storage medium may be any tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. In this application, however, a computer readable signal medium may include a propagated data signal with computer readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A computer readable signal medium may be any computer readable medium that is not a computer readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a computer readable medium may be transmitted using any appropriate medium, including but not limited to: electrical wires, optical cables, RF (radio frequency), etc., or any suitable combination of the foregoing.
In some embodiments, the clients, servers may communicate using any currently known or future developed network Protocol, such as HTTP (HyperText Transfer Protocol), and may be interconnected with any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include a local area network ("LAN"), a wide area network ("WAN"), the Internet (e.g., the Internet), and peer-to-peer networks (e.g., ad hoc peer-to-peer networks), as well as any currently known or future developed network.
The computer readable medium may be embodied in the electronic device; or may exist separately without being assembled into the electronic device.
The computer readable medium carries one or more programs which, when executed by the electronic device, cause the electronic device to:
acquiring a tree structure unit model of an IT system to be evaluated, wherein the tree structure unit model comprises at least two system levels from a lower level to a higher level; sequentially calculating the real-time health degree score of each node in each system level from a lower layer to a higher layer until the real-time health degree score of each node in the highest system level is obtained; the method comprises the steps that for each node of the lowest system level, real-time health degree scores of the nodes are obtained based on real-time data corresponding to the nodes, for each node of other system levels except the lowest system level, the weight value of each sub-node corresponding to the node in the system level lower than the lowest system level is obtained, and the real-time health degree scores of the nodes are obtained based on the weight value and the real-time health degree scores of each sub-node corresponding to the node; and acquiring a weighted value of each node in the highest system level, and acquiring a real-time health score of the IT system to be evaluated based on the real-time health score and the weighted value of each node in the highest system level.
Computer program code for carrying out operations for aspects of the present application may be written in any combination of one or more programming languages, including but not limited to an object oriented programming language such as Java, smalltalk, C + +, and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computer, partly on the user's computer, as a stand-alone software package, partly on the user's computer and partly on a remote computer or entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or the connection may be made to an external computer (for example, through the Internet using an Internet service provider).
The flowchart and block diagrams in the figures illustrate the architecture, functionality, and operation of possible implementations of systems, methods and computer program products according to various embodiments of the present application. In this regard, each block in the flowchart or block diagrams may represent a module, segment, or portion of code, which comprises one or more executable instructions for implementing the specified logical function(s). It should also be noted that, in some alternative implementations, the functions noted in the block may occur out of the order noted in the figures. For example, two blocks shown in succession may, in fact, be executed substantially concurrently, or the blocks may sometimes be executed in the reverse order, depending upon the functionality involved. It will also be noted that each block of the block diagrams and/or flowchart illustration, and combinations of blocks in the block diagrams and/or flowchart illustration, can be implemented by special purpose hardware-based systems which perform the specified functions or acts, or combinations of special purpose hardware and computer instructions.
The modules or units described in the embodiments of the present application may be implemented by software or hardware. Where the name of a module or unit does not in some cases constitute a limitation of the unit itself, for example, the first constraint obtaining module may also be described as a "module that obtains first constraints".
The functions described herein above may be performed, at least in part, by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field Programmable Gate Arrays (FPGAs), application Specific Integrated Circuits (ASICs), application Specific Standard Products (ASSPs), systems on a chip (SOCs), complex Programmable Logic Devices (CPLDs), and the like.
In the context of this application, a machine-readable medium may be a tangible medium that can contain, or store a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of a machine-readable storage medium would include an electrical connection based on one or more wires, a portable computer diskette, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
It should be understood that, although the steps in the flowcharts of the figures are shown in order as indicated by the arrows, the steps are not necessarily performed in order as indicated by the arrows. The steps are not performed in the exact order shown and may be performed in other orders unless explicitly stated herein. Moreover, at least a portion of the steps in the flow chart of the figure may include multiple sub-steps or multiple stages, which are not necessarily performed at the same time, but may be performed at different times, which are not necessarily performed in sequence, but may be performed alternately or alternately with other steps or at least a portion of the sub-steps or stages of other steps.
The foregoing is only a partial embodiment of the present invention, and it should be noted that, for those skilled in the art, various modifications and decorations can be made without departing from the principle of the present invention, and these modifications and decorations should also be regarded as the protection scope of the present invention.
Claims (10)
1. A health degree assessment method of an Internet technology IT system is characterized by comprising the following steps:
the method comprises the steps of obtaining a tree structure unit model of an IT system to be evaluated, wherein the tree structure unit model comprises at least two system levels from a lower level to a higher level;
sequentially calculating the real-time health degree score of each node in each system level from a lower layer to a higher layer until the real-time health degree score of each node in the highest system level is obtained; for each node of the lowest system level, acquiring a real-time health degree score of the node based on real-time data corresponding to the node, for each node of other system levels except the lowest system level, acquiring a weight value of each sub-node corresponding to the node in the system level of the lower level, and acquiring the real-time health degree score of the node based on the weight value of each sub-node corresponding to the node and the real-time health degree score;
and acquiring a weighted value of each node in the highest system level, and acquiring a real-time health score of the IT system to be evaluated based on the real-time health score and the weighted value of each node in the highest system level.
2. The method according to claim 1, wherein the obtaining the real-time health score of the node based on the real-time data corresponding to the node specifically comprises:
acquiring a preset amount of historical data corresponding to the nodes;
performing Box-cox transformation on each historical data to obtain corresponding first normalized data, and performing Box-cox transformation on the real-time data of the nodes to obtain corresponding second normalized data; wherein transformation parameters of the Box-cox transformation are determined based on the preset amount of historical data;
and acquiring a real-time health score of the node by using a three-sigma 3-sigma criterion based on each first normalization data and the second normalization data.
3. The method of claim 2, wherein obtaining the real-time health score for the node using 3-sigma criteria based on the respective first and second normalized data comprises:
based on each first normalized data, acquiring a corresponding mean value and a standard deviation by using a 3-sigma criterion;
and acquiring the real-time health degree score of the node based on the second normalized data, the mean value and the standard deviation.
4. The method according to claim 1 or 2, wherein obtaining the weight value of each sub-node corresponding to the node in a system level one level lower includes:
acquiring historical health degree scores of sub-nodes corresponding to the nodes in a system level one level lower than the node;
acquiring an entropy value of each child node based on the historical health degree score of each child node;
and acquiring the weight value of each child node based on the entropy value of each child node.
5. The method according to claim 4, wherein the obtaining an entropy value of each child node based on the historical health score of each child node specifically includes:
acquiring the health degree score ratio of each child node in the node based on the historical health degree score of each child node;
and acquiring the entropy value of each child node based on the health degree score ratio.
6. The method according to claim 1, wherein the obtaining of the real-time health degree score of the node based on the weight value and the real-time health degree score of each sub-node corresponding to the node specifically includes:
and weighting and summing the real-time health degree scores corresponding to the sub-nodes based on the weight values of the sub-nodes corresponding to the nodes to obtain the real-time health degree scores of the nodes.
7. The method of claim 1, wherein obtaining the real-time health score of the IT system under evaluation based on the real-time health score and the weight value of each node in the highest system level comprises:
and weighting and summing the real-time health degree scores of the nodes based on the weight values of the nodes in the highest system level to obtain the real-time health degree score of the IT system to be evaluated.
8. An internet technology IT system health assessment apparatus, comprising:
the system comprises a tree structure unit obtaining module, a tree structure unit obtaining module and a tree structure unit evaluating module, wherein the tree structure unit obtaining module is used for obtaining a tree structure unit model of an IT system to be evaluated, and the tree structure unit model comprises at least two system levels from a lower level to a higher level;
the node health degree score acquisition module is used for sequentially calculating the real-time health degree score of each node in each system level from a low layer to a high layer until the real-time health degree score of each node in the highest system level is obtained; for each node of the lowest system level, acquiring a real-time health degree score of the node based on real-time data corresponding to the node, for each node of other system levels except the lowest system level, acquiring a weight value of each sub-node corresponding to the node in the system level of the lower level, and acquiring the real-time health degree score of the node based on the weight value of each sub-node corresponding to the node and the real-time health degree score;
and the system health degree score obtaining module is used for obtaining the weighted value of each node in the highest system level and obtaining the real-time health degree score of the IT system to be evaluated based on the real-time health degree score and the weighted value of each node in the highest system level.
9. An electronic device comprising a memory, a processor and a computer program stored on the memory, characterized in that the processor executes the computer program to implement the steps of the method of any of claims 1-7.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the steps of the method according to any one of claims 1 to 7.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11612295B2 (en) | 2021-01-04 | 2023-03-28 | Beijing Roborock Technology Co., Ltd. | Autonomous cleaning device |
| CN116521517A (en) * | 2023-02-09 | 2023-08-01 | 海看网络科技(山东)股份有限公司 | IPTV system health degree assessment method based on service topology multi-model fusion |
-
2022
- 2022-10-26 CN CN202211321470.9A patent/CN115599650A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US11612295B2 (en) | 2021-01-04 | 2023-03-28 | Beijing Roborock Technology Co., Ltd. | Autonomous cleaning device |
| CN116521517A (en) * | 2023-02-09 | 2023-08-01 | 海看网络科技(山东)股份有限公司 | IPTV system health degree assessment method based on service topology multi-model fusion |
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