CN112463564B - Method and device for determining associated index influencing host state - Google Patents
Method and device for determining associated index influencing host state Download PDFInfo
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/34—Recording or statistical evaluation of computer activity, e.g. of down time, of input/output operation ; Recording or statistical evaluation of user activity, e.g. usability assessment
- G06F11/3452—Performance evaluation by statistical analysis
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- G—PHYSICS
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/30—Monitoring
- G06F11/3055—Monitoring arrangements for monitoring the status of the computing system or of the computing system component, e.g. monitoring if the computing system is on, off, available, not available
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- G06F18/00—Pattern recognition
- G06F18/20—Analysing
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- G06F18/241—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches
- G06F18/2411—Classification techniques relating to the classification model, e.g. parametric or non-parametric approaches based on the proximity to a decision surface, e.g. support vector machines
Abstract
The invention discloses a method and a device for determining an associated index affecting the state of a host, which can be used in the financial field or other technical fields, and the method comprises the following steps: acquiring a time sequence serialization vector of a host state and a time sequence serialization vector of an index state of a target index, wherein the time sequence serialization vector of the host state and the time sequence serialization vector of the index state of the target index comprise a first numerical value and a second numerical value, the first numerical value is used for representing an abnormal state, and the second numerical value is used for representing a normal state; determining a Euclidean distance between the time sequence serialization vector of the host state and the time sequence serialization vector of the index state of the target index; and if the Euclidean distance is smaller than a preset threshold value, determining the target index as an associated index affecting the state of the host. The invention realizes more accurate and rapid confirmation of whether one host index is the associated index of the host state, and is beneficial to improving the effect of host abnormality attribution.
Description
Technical Field
The invention relates to the technical field of host exception analysis, in particular to a method and a device for determining an associated index affecting a host state.
Background
The large-scale host abnormality investigation is an important work of system operation and maintenance personnel, and how to quickly and accurately locate the source of the abnormality is a key for effectively solving the problem. The host has various host indexes in operation, and determining which host indexes have larger influence on the operation state of the host can effectively improve the host abnormity attribution efficiency. At present, how to confirm whether a host index has a great influence on the running state of the host depends on manual experience, and the accuracy and the reliability are not high.
Disclosure of Invention
The invention provides a method and a device for determining an associated index affecting a host state in order to solve the technical problems in the background art.
To achieve the above object, according to one aspect of the present invention, there is provided a method of determining an association index affecting a state of a host, the method comprising:
acquiring a time sequence serialization vector of a host state and a time sequence serialization vector of an index state of a target index, wherein the time sequence serialization vector of the host state and the time sequence serialization vector of the index state of the target index comprise a first numerical value and a second numerical value, the first numerical value is used for representing an abnormal state, and the second numerical value is used for representing a normal state;
determining a Euclidean distance between the time sequence serialization vector of the host state and the time sequence serialization vector of the index state of the target index;
and if the Euclidean distance is smaller than a preset threshold value, determining the target index as an associated index affecting the state of the host.
Optionally, before determining the euclidean distance between the time-series serialized vector of the host state and the time-series serialized vector of the target state of the target indicator, the method further includes:
and adding a preset offset to all second values in the time sequence serialization vector of the index state of the target index.
Optionally, the determining the euclidean distance between the time-series serialization vector of the host state and the time-series serialization vector of the target indicator state includes:
performing dimension reduction processing on the time sequence serialization vector of the host state and the time sequence serialization vector of the index state of the target index respectively;
and calculating the Euclidean distance between the time sequence serialization vector of the host state after the dimension reduction processing and the time sequence serialization vector of the index state of the target index after the dimension reduction processing.
Optionally, the method for determining the association index affecting the state of the host further includes:
carrying out serialization processing on the host state data in a preset time period to obtain a time sequence serialization vector of the host state;
and carrying out serialization processing on the index state data of the target index in the preset time period to obtain a time sequence serialization vector of the index state of the target index.
To achieve the above object, according to another aspect of the present invention, there is provided an apparatus for determining an association index affecting a state of a host, the apparatus comprising:
a time sequence serialization vector obtaining unit, configured to obtain a time sequence serialization vector of a host state and a time sequence serialization vector of an index state of a target index, where the time sequence serialization vector of the host state and the time sequence serialization vector of the index state of the target index include a first value and a second value, where the first value is used to represent an abnormal state, and the second value is used to represent a normal state;
a euclidean distance calculating unit configured to determine a euclidean distance between the time-series serialization vector of the host state and the time-series serialization vector of the target index state;
and the determining unit is used for determining that the target index is an associated index influencing the state of the host computer when the Euclidean distance is smaller than a preset threshold value.
Optionally, the apparatus for determining the association index affecting the state of the host further includes:
and the offset processing unit is used for adding a preset offset to all second values in the time sequence serialization vectors of the index states of the target indexes.
Optionally, the euclidean distance calculating unit includes:
the dimension reduction processing module is used for respectively carrying out dimension reduction processing on the time sequence serialization vector of the host state and the time sequence serialization vector of the index state of the target index;
the calculation module is used for calculating the Euclidean distance of the time sequence serialization vector of the host state after the dimension reduction processing and the time sequence serialization vector of the index state of the target index after the dimension reduction processing.
Optionally, the apparatus for determining the association index affecting the state of the host further includes:
the first time sequence serialization vector generation unit is used for carrying out serialization processing on the host state data in a preset time period to obtain a time sequence serialization vector of the host state;
and the second time sequence serialization vector generation unit is used for carrying out serialization processing on the index state data of the target index in the preset time period to obtain the time sequence serialization vector of the index state of the target index.
To achieve the above object, according to another aspect of the present invention, there is also provided a computer device including a memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing the steps in the above method of determining an associated index affecting a host state when executing the computer program.
To achieve the above object, according to another aspect of the present invention, there is also provided a computer-readable storage medium storing a computer program which, when executed in a computer processor, implements the steps of the above-described method of determining an association indicator affecting a host state.
The beneficial effects of the invention are as follows: according to the embodiment of the invention, the Euclidean distance between the time sequence serialization vector of the host state and the time sequence serialization vector of the index state of the target index is calculated, so that whether the target index is the associated index influencing the host state is determined, whether one host index has a larger influence on the host running state or not is accurately and rapidly confirmed, and the effect of host abnormity attribution is improved.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required in the embodiments or the description of the prior art will be briefly described, and it is obvious that the drawings in the following description are some embodiments of the present invention, and other drawings may be obtained according to these drawings without inventive effort for a person skilled in the art. In the drawings:
FIG. 1 is a first flowchart of a method of determining an association indicator that affects host status in accordance with an embodiment of the present invention;
FIG. 2 is a flow chart of determining Euclidean distance according to an embodiment of the present invention;
FIG. 3 is a second flowchart of a method of determining an association indicator that affects host status in accordance with an embodiment of the present invention;
FIG. 4 is a block diagram of an apparatus for determining an association indicator that affects host status in accordance with an embodiment of the present invention;
FIG. 5 is a schematic diagram of a computer device according to an embodiment of the invention.
Detailed Description
In order that those skilled in the art will better understand the present invention, a technical solution in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in which it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present invention without making any inventive effort, shall fall within the scope of the present invention.
It will be appreciated by those skilled in the art that embodiments of the present invention may be provided as a method, system, or computer program product. Accordingly, the present invention may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and the like) having computer-usable program code embodied therein.
It is noted that the terms "comprises" and "comprising," and any variations thereof, in the description and claims of the present invention and in the foregoing figures, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed or inherent to such process, method, article, or apparatus.
It should be noted that, without conflict, the embodiments of the present invention and features of the embodiments may be combined with each other. The invention will be described in detail below with reference to the drawings in connection with embodiments.
It should be noted that the method and apparatus for determining the associated index for determining the host state of the present invention may be used in the financial field, or other technical fields.
Fig. 1 is a first flowchart of a method for determining an associated indicator affecting a host state according to an embodiment of the present invention, as shown in fig. 1, the method for determining an associated indicator affecting a host state according to the present invention includes steps S101 to S103.
Step S101, a time-series serialization vector of a host state and a time-series serialization vector of an index state of a target index are obtained, wherein the time-series serialization vector of the host state and the time-series serialization vector of the index state of the target index comprise a first value and a second value, the first value is used for representing an abnormal state, and the second value is used for representing a normal state.
In the embodiment of the present invention, the time-sequence serialization vector of the host state is obtained by serializing the host state data in a preset time period. The host state includes an abnormal state and a normal state. In the embodiment of the invention, the serialization processing specifically includes that the abnormal state is represented by a first numerical value, and the normal state is represented by a second numerical value. In an alternative embodiment of the present invention, the first value may be 1 and the second value may be 0. For example, when the host state S is defined as 1 at the time T and is defined as 0 at the time T, the host state S is at T 1 ～T N The time interval can be serialized into a vector { S ] consisting of 0 and 1 1 ，S 2 ，......,S N }。
In the embodiment of the present invention, similar to the time-series serialization vector of the host state, the time-series serialization vector of the target index state is obtained by serializing the index state data of the target index in the preset time period. The index state also includes an abnormal state and a normal state. For example, collecting database state indexes, wherein the rule is that the starting is normal, the serialization is 0, the downing is abnormal, and the serialization is 1; and (3) collecting replication delay indexes, wherein the rule is that the delay is not more than 1 hour, the delay is normal, the serialization is 0, and the delay is abnormal, and the serialization is 1. The database index and the replication delay index K are at T 1 ～T N The time intervals can be serialized into a vector { K ] consisting of 0 and 1 1 ，K 2 ，......,K N }。
In alternative embodiments of the present invention, the target index may be a variety of indices of host operation, such as database index, replication delay index, etc.
Step S102, determining a euclidean distance between the time-series serialization vector of the host state and the time-series serialization vector of the target indicator state.
Step S103, if the Euclidean distance is smaller than a preset threshold, determining the target index as an associated index affecting the state of the host.
In the embodiment of the present invention, if the euclidean distance is greater than or equal to a preset threshold, it is indicated that the target indicator is not related to the host state.
Therefore, the invention determines whether the target index is the associated index influencing the host state by calculating the Euclidean distance between the time sequence serialization vector of the host state and the time sequence serialization vector of the index state of the target index. Compared with the existing manual experience judgment, whether one host index has a large influence on the running state of the host or not can be accurately and rapidly confirmed, and the effect of host abnormity attribution is improved.
In one embodiment of the present invention, before determining the euclidean distance between the time-series serialized vector of the host state and the time-series serialized vector of the target state of the target pointer in step S102, the method of the present invention further includes:
and adding a preset offset to all second values in the time sequence serialization vector of the index state of the target index.
The invention defines the correlation between the host state and the index, and the specific table 1 is shown below. The abnormal host state of the index is strong correlation, the abnormal host state of the index is normal, the abnormal host state of the index is irrelevant, the normal index is normal, and the abnormal/normal host state of the index is weak correlation.
TABLE 1
The influence indexes of the host state in the real scene can be multiple, namely, under the condition that a single index is normal, the association state of the host state can be normal or abnormal, namely, a 0- >1\0- >0 weak correlation scene. In order to distinguish the difference between the strong correlation 1- >1 and the weak correlation 0- >0, the uncorrelated 1- >0 and the weak correlation 0- >1, the invention also corrects the index normal scene, namely the offset of the index value increased by delta, wherein delta is epsilon (0, 1), as shown in the following table 2. Specifically, the present invention increases all second values in the time-series serialization vector of the target index state by a preset offset δ.
| Index (I) | 1 | 1 | 0+δ | 0+δ |
| Host state | 1 | 0 | 1 | 0 |
| Correlation of | Strong correlation | Uncorrelated with | Weak correlation | Weak correlation |
TABLE 2
Fig. 2 is a flowchart of determining a euclidean distance according to an embodiment of the present invention, and as shown in fig. 2, the determining the euclidean distance between the time-series serialization vector of the host state and the time-series serialization vector of the target indicator in step S102 specifically includes step S201 and step S202.
Step S201, performing a dimension reduction process on the time-series serialization vector of the host state and the time-series serialization vector of the target index state.
Host state S i At T 1 ～T N Serialization toWherein->Index K i At T 1 ～T N Serialization of->Wherein->N tends to take a larger value, so the host state S i Sum index K i Is a high-dimensional vector. The present invention also requires projecting high-dimensional vectors into low-dimensional vectors in order to improve the computational efficiency and accuracy of the computation results.
In the embodiment of the invention, the step can adopt a principal component analysis method (Principal components analysis) to perform dimension reduction processing on the time sequence serialization vector of the host state to obtain the feature vector of the host state, and adopts a principal component analysis method (Principal components analysis) to perform dimension reduction processing on the time sequence serialization vector of the index state of the target index to obtain the feature vector of the target index. And obtaining the characteristic vector of the host state and the characteristic vector of the target index as low-dimensional vectors.
Step S202, calculating Euclidean distance of the time sequence serialization vector of the host state after the dimension reduction processing and the time sequence serialization vector of the index state of the target index after the dimension reduction processing.
In the embodiment of the invention, the time sequence serialization vector of the host state after the dimension reduction processing is the characteristic vector of the host state, and the index state of the target index after the dimension reduction processing is the characteristic vector of the target index. The Euclidean distance between the characteristic vector of the host state and the characteristic vector of the target index is calculated, and the result is the Euclidean distance between the time sequence serialization vector of the host state and the time sequence serialization vector of the target index state
FIG. 3 is a second flowchart of a method for determining an associated indicator affecting a host status according to an embodiment of the present invention, as shown in FIG. 3, in which the method for determining an associated indicator affecting a host status according to an embodiment of the present invention further includes step S301 and step S302.
Step S301, performing serialization processing on the host state data in a preset time period to obtain a time-sequence serialization vector of the host state.
Step S302, performing serialization processing on the index state data of the target index in the preset time period to obtain a time-sequence serialization vector of the index state of the target index.
As can be seen from the above embodiments, the present invention proposes a method of determining an association indicator affecting a host state having at least the following advantages:
1. the invention realizes the association analysis of the abnormal state of the host by using a machine learning method, and has better expansibility compared with the traditional mode which relies on manual experience.
2. According to the invention, the indexes are subjected to binary serialization according to the normal and abnormal rules, so that key factors affecting abnormal states are quantized and key information is ensured.
3. According to the method, after index serialization, the normal index quantized value is corrected based on the multi-factor influence hypothesis, and a real application scene is fitted.
It should be noted that the steps illustrated in the flowcharts of the figures may be performed in a computer system such as a set of computer executable instructions, and that although a logical order is illustrated in the flowcharts, in some cases the steps illustrated or described may be performed in an order other than that illustrated herein.
Based on the same inventive concept, the embodiment of the present invention further provides a device for determining an association indicator affecting a host state, which can be used to implement the method for determining an association indicator affecting a host state described in the above embodiment, as described in the following embodiments. Since the principle of solving the problem by the device for determining the association indicator affecting the host state is similar to that of the method for determining the association indicator affecting the host state, embodiments of the device for determining the association indicator affecting the host state can refer to embodiments of the method for determining the association indicator affecting the host state, and the repetition is omitted. As used below, the term "unit" or "module" may be a combination of software and/or hardware that implements the intended function. While the means described in the following embodiments are preferably implemented in software, implementation in hardware, or a combination of software and hardware, is also possible and contemplated.
FIG. 4 is a block diagram of an apparatus for determining an association indicator affecting a host status according to an embodiment of the present invention, as shown in FIG. 4, where the apparatus for determining an association indicator affecting a host status according to an embodiment of the present invention includes: a time-series serialization vector acquisition unit 1, a euclidean distance calculation unit 2, and a determination unit 3.
A time-series serialization vector obtaining unit 1, configured to obtain a time-series serialization vector of a host state and a time-series serialization vector of an index state of a target index, where the time-series serialization vector of the host state and the time-series serialization vector of the index state of the target index include a first value and a second value, where the first value is used to represent an abnormal state, and the second value is used to represent a normal state.
And a euclidean distance calculating unit 2 configured to determine a euclidean distance between the time-series serialized vector of the host state and the time-series serialized vector of the target index state.
And the determining unit 3 is used for determining the target index as an associated index affecting the state of the host when the Euclidean distance is smaller than a preset threshold value.
In one embodiment of the present invention, the apparatus for determining an associated indicator of a host state according to the present invention further includes:
and the offset processing unit is used for adding a preset offset to all second values in the time sequence serialization vectors of the index states of the target indexes.
In one embodiment of the present invention, the euclidean distance calculating unit includes:
the dimension reduction processing module is used for respectively carrying out dimension reduction processing on the time sequence serialization vector of the host state and the time sequence serialization vector of the index state of the target index;
the calculation module is used for calculating the Euclidean distance of the time sequence serialization vector of the host state after the dimension reduction processing and the time sequence serialization vector of the index state of the target index after the dimension reduction processing.
In one embodiment of the present invention, the apparatus for determining an associated indicator of a host state according to the present invention further includes:
the first time sequence serialization vector generation unit is used for carrying out serialization processing on the host state data in a preset time period to obtain a time sequence serialization vector of the host state;
and the second time sequence serialization vector generation unit is used for carrying out serialization processing on the index state data of the target index in the preset time period to obtain the time sequence serialization vector of the index state of the target index.
To achieve the above object, according to another aspect of the present application, there is also provided a computer apparatus. As shown in fig. 5, the computer device includes a memory, a processor, a communication interface, and a communication bus, where a computer program executable on the processor is stored on the memory, and when the processor executes the computer program, the steps in the method of the above embodiment are implemented.
The processor may be a central processing unit (Central Processing Unit, CPU). The processor may also be any other general purpose processor, digital signal processor (Digital Signal Processor, DSP), application specific integrated circuit (Application Specific Integrated Circuit, ASIC), field programmable gate array (Field-Programmable Gate Array, FPGA) or other programmable logic device, discrete gate or transistor logic, discrete hardware components, or any combination thereof.
The memory is used as a non-transitory computer readable storage medium for storing non-transitory software programs, non-transitory computer executable programs, and units, such as corresponding program units in the above-described method embodiments of the invention. The processor executes the various functional applications of the processor and the processing of the composition data by running non-transitory software programs, instructions and modules stored in the memory, i.e., implementing the methods of the method embodiments described above.
The memory may include a memory program area and a memory data area, wherein the memory program area may store an operating system, at least one application program required for a function; the storage data area may store data created by the processor, etc. In addition, the memory may include high-speed random access memory, and may also include non-transitory memory, such as at least one magnetic disk storage device, flash memory device, or other non-transitory solid state storage device. In some embodiments, the memory may optionally include memory located remotely from the processor, the remote memory being connectable to the processor through a network. Examples of such networks include, but are not limited to, the internet, intranets, local area networks, mobile communication networks, and combinations thereof.
The one or more units are stored in the memory, which when executed by the processor, performs the method in the above embodiments.
The details of the computer device may be correspondingly understood by referring to the corresponding relevant descriptions and effects in the above embodiments, and will not be repeated here.
To achieve the above object, according to another aspect of the present application, there is also provided a computer readable storage medium storing a computer program which, when executed in a computer processor, implements the steps of the above-described method of determining an associated indicator affecting a host state. It will be appreciated by those skilled in the art that implementing all or part of the above-described embodiment method may be implemented by a computer program to instruct related hardware, where the program may be stored in a computer readable storage medium, and the program may include the above-described embodiment method when executed. Wherein the storage medium may be a magnetic Disk, an optical Disk, a Read-Only Memory (ROM), a random access Memory (RandomAccessMemory, RAM), a Flash Memory (Flash Memory), a Hard Disk (HDD), a Solid State Drive (SSD), or the like; the storage medium may also comprise a combination of memories of the kind described above.
It will be apparent to those skilled in the art that the modules or steps of the invention described above may be implemented in a general purpose computing device, they may be concentrated on a single computing device, or distributed across a network of computing devices, or they may alternatively be implemented in program code executable by computing devices, such that they may be stored in a memory device for execution by the computing devices, or they may be separately fabricated into individual integrated circuit modules, or multiple modules or steps within them may be fabricated into a single integrated circuit module. Thus, the present invention is not limited to any specific combination of hardware and software.
The above description is only of the preferred embodiments of the present invention and is not intended to limit the present invention, but various modifications and variations can be made to the present invention by those skilled in the art. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention should be included in the protection scope of the present invention.
Claims (8)
1. A method of determining an association indicator that affects a state of a host, comprising:
acquiring a time sequence serialization vector of a host state and a time sequence serialization vector of an index state of a target index, wherein the time sequence serialization vector of the host state and the time sequence serialization vector of the index state of the target index comprise a first numerical value and a second numerical value, the first numerical value is used for representing an abnormal state, and the second numerical value is used for representing a normal state; defining the correlation between the host state and the index, wherein the correlation is defined to be strong when the host state and the index state are abnormal states, the host state and the index state are both the first numerical value, the correlation is defined to be weak when the host state and the index state are both normal states, the correlation is defined to be weak when the host state is the second numerical value, the index state is the normal state, the correlation is defined to be weak when the host state is the first numerical value, the index state is the second numerical value, and the correlation is defined to be irrelevant when the host state is the normal state, the index state is the abnormal state, and the host state is the second numerical value;
adding a preset offset to all second values in the time sequence serialization vector of the index state of the target index to distinguish between a strong correlation of the first value for both the host state and the index state and a weak correlation of the second value for both the host state and the index state, and between an uncorrelation of the first value for the second value index state and a weak correlation of the second value for the first value index state for the host state;
determining a Euclidean distance between the time sequence serialization vector of the host state and the time sequence serialization vector of the index state of the target index;
and if the Euclidean distance is smaller than a preset threshold value, determining the target index as an associated index affecting the state of the host.
2. The method of determining an associated indicator of a host state of claim 1, wherein determining a euclidean distance between a time-sequential serialization vector of the host state and a time-sequential serialization vector of an indicator state of the target indicator comprises:
performing dimension reduction processing on the time sequence serialization vector of the host state and the time sequence serialization vector of the index state of the target index respectively;
and calculating the Euclidean distance between the time sequence serialization vector of the host state after the dimension reduction processing and the time sequence serialization vector of the index state of the target index after the dimension reduction processing.
3. The method of determining an associated indicator of a host state of claim 1, further comprising:
carrying out serialization processing on the host state data in a preset time period to obtain a time sequence serialization vector of the host state;
and carrying out serialization processing on the index state data of the target index in the preset time period to obtain a time sequence serialization vector of the index state of the target index.
4. An apparatus for determining an association indicator that affects a state of a host, comprising:
a time sequence serialization vector obtaining unit, configured to obtain a time sequence serialization vector of a host state and a time sequence serialization vector of an index state of a target index, where the time sequence serialization vector of the host state and the time sequence serialization vector of the index state of the target index include a first value and a second value, where the first value is used to represent an abnormal state, and the second value is used to represent a normal state; defining the correlation between the host state and the index, wherein the correlation is defined to be strong when the host state and the index state are abnormal states, the host state and the index state are both the first numerical value, the correlation is defined to be weak when the host state and the index state are both normal states, the correlation is defined to be weak when the host state is the second numerical value, the index state is the normal state, the correlation is defined to be weak when the host state is the first numerical value, the index state is the second numerical value, and the correlation is defined to be irrelevant when the host state is the normal state, the index state is the abnormal state, and the host state is the second numerical value;
an offset processing unit, configured to add a preset offset to all second values in the time-series serialization vector of the index states of the target index, so as to distinguish a difference between a strong correlation of the first value for both the host state and the index state and a weak correlation of the second value for both the host state and the index state, and distinguish a difference between an uncorrelation of the first value for the index state of the second value and a weak correlation of the second value for the index state of the first value for the host state of the second value;
a euclidean distance calculating unit configured to determine a euclidean distance between the time-series serialization vector of the host state and the time-series serialization vector of the target index state;
and the determining unit is used for determining that the target index is an associated index influencing the state of the host computer when the Euclidean distance is smaller than a preset threshold value.
5. The apparatus for determining an associated index for a host state according to claim 4, wherein the euclidean distance calculating unit comprises:
the dimension reduction processing module is used for respectively carrying out dimension reduction processing on the time sequence serialization vector of the host state and the time sequence serialization vector of the index state of the target index;
the calculation module is used for calculating the Euclidean distance of the time sequence serialization vector of the host state after the dimension reduction processing and the time sequence serialization vector of the index state of the target index after the dimension reduction processing.
6. The apparatus for determining an associated indicator of a host state of claim 4, further comprising:
the first time sequence serialization vector generation unit is used for carrying out serialization processing on the host state data in a preset time period to obtain a time sequence serialization vector of the host state;
and the second time sequence serialization vector generation unit is used for carrying out serialization processing on the index state data of the target index in the preset time period to obtain the time sequence serialization vector of the index state of the target index.
7. A computer device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, characterized in that the processor implements the method of any of claims 1 to 3 when executing the computer program.
8. A computer readable storage medium storing a computer program, characterized in that the computer program when executed in a computer processor implements the method of any one of claims 1 to 3.
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