CN110543897A - memory bank classifying method, system, terminal and storage medium - Google Patents
memory bank classifying method, system, terminal and storage medium Download PDFInfo
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- CN110543897A CN110543897A CN201910760277.7A CN201910760277A CN110543897A CN 110543897 A CN110543897 A CN 110543897A CN 201910760277 A CN201910760277 A CN 201910760277A CN 110543897 A CN110543897 A CN 110543897A
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F11/00—Error detection; Error correction; Monitoring
- G06F11/22—Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing
- G06F11/2205—Detection or location of defective computer hardware by testing during standby operation or during idle time, e.g. start-up testing using arrangements specific to the hardware being tested
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- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F18/00—Pattern recognition
- G06F18/20—Analysing
- G06F18/23—Clustering techniques
- G06F18/232—Non-hierarchical techniques
- G06F18/2321—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions
- G06F18/23213—Non-hierarchical techniques using statistics or function optimisation, e.g. modelling of probability density functions with fixed number of clusters, e.g. K-means clustering
Abstract
the invention provides a memory bank classifying method, a system, a terminal and a storage medium, wherein the memory bank classifying method comprises the following steps: setting basic parameter items and performance parameter items; carrying out basic parameter clustering according to basic parameter values of the memory banks by using a K-means clustering method, and carrying out performance parameter clustering according to performance parameter values of the memory banks; acquiring basic clustering distance and performance clustering distance between a target memory bank and other memory banks; and calculating the similar memory bank of the target memory bank according to the basic clustering distance and the performance clustering distance. The method and the device can quickly find the same type of memory bank of the memory bank needing to be replaced on the premise of not influencing the use of the client, save manpower and material resources and improve user experience.
Description
Technical Field
the invention belongs to the technical field of memory bank testing, and particularly relates to a memory bank classifying method, a memory bank classifying system, a memory bank classifying terminal and a memory medium.
background
The memory bank is one of core components of the server, and the performance and the stability of the server are strongly influenced. Memory banks can be classified more complicatedly in terms of performance, quality, manufacturer, capacity, process, yield, and the like. However, these classification bases are only classified according to the information of the memory manufacturer or supplier, but in practical applications, the classification is not necessarily suitable. Therefore, memory banks need to be classified and aggregated. Under the condition that the requirements on the performance and the stability of the server are more and more strict at present, the memory is one of important factors for restricting the performance and the stability of the server. The memory banks of the server are in the era of rapid development, and the memory banks of all manufacturers are not nearly the same. The server needs to perform a large number of tests before shipment, wherein the compatibility test of the memory bank and the server is very important, but the compatibility test or other tests of the memory bank do not classify the memory bank, and the classification basis is fuzzy or even unrelated. For example, there are currently 5 banks named as M1, M2, M3, M4, and M5 (not necessarily banks of the same brand), and 1 st server SV 1. When the product is delivered, SV1 needs to be matched with M1. There are times when an emergency situation arises (memory bank M1 is disabled or otherwise brought to a halt) where a quick and accurate replacement is required in order not to affect customer use. The memories M2, M3, M4 and M5 are completely tested on the server SV1, so that a large amount of manpower, material resources and financial resources are consumed, and most importantly, the memories cannot be replaced for clients in the first time.
Disclosure of Invention
in view of the above-mentioned deficiencies of the prior art, the present invention provides a method, a system, a terminal and a storage medium for classifying memory banks, which can quickly find out a substitute memory bank of a memory bank M1 by clustering memory banks M1, M2, M3, M4 and M5 through an artificial intelligence algorithm K-means and previous test data, so as to solve the above-mentioned technical problems.
in a first aspect, the present invention provides a memory bank classifying method, including:
Setting basic parameter items and performance parameter items;
Carrying out basic parameter clustering according to basic parameter values of the memory banks by using a K-means clustering method, and carrying out performance parameter clustering according to performance parameter values of the memory banks;
Acquiring basic clustering distance and performance clustering distance between a target memory bank and other memory banks;
And calculating the similar memory bank of the target memory bank according to the basic clustering distance and the performance clustering distance.
further, the setting of the basic parameter item and the performance parameter item includes:
Setting a memory bank manufacturer scale grade, a process grade, a production line yield and a manufacturer technical grade as basic parameter items;
And setting the performance, the power consumption, the RMT, the appearance test data result and the compatibility grade as performance parameter items.
Further, the performing basic parameter clustering according to the basic parameter values of the memory banks by using the K-means clustering method and performing performance parameter clustering according to the performance parameter values of the memory banks includes:
setting the number of clustering types;
clustering the memory banks into preset kinds and quantities by using a K-means clustering method according to the similarity of basic parameter values of the memory banks;
And clustering the memory banks into preset kinds and quantities by using a K-means clustering method according to the similarity of the performance parameter values of the memory banks.
Further, the calculating the similar memory bank of the target memory bank according to the basic clustering distance and the performance clustering distance includes:
setting a basic clustering distance weight and a performance clustering distance weight;
Calculating the clustering distance between the memory bank and the target memory bank according to the basic clustering distance, the basic clustering distance weight, the performance clustering distance and the performance clustering distance weight;
And selecting the memory bank with the minimum clustering distance with the target memory bank as a replacement memory bank of the target memory bank.
In a second aspect, the present invention provides a memory bank classifying system, including:
The project setting unit is used for configuring and setting a basic parameter item and a performance parameter item;
The clustering execution unit is configured for carrying out basic parameter clustering according to basic parameter values of the memory banks by using a K-means clustering method and carrying out performance parameter clustering according to performance parameter values of the memory banks;
The distance acquisition unit is configured to acquire the basic clustering distance and the performance clustering distance between the target memory bank and other memory banks;
and the same type obtaining unit is configured for calculating the same type memory bank of the target memory bank according to the basic clustering distance and the performance clustering distance.
further, the item setting unit includes:
The basic setting module is configured for setting the scale grade, the process grade, the production line yield and the manufacturer technical grade of the memory bank as basic parameter items;
and the performance setting module is configured to set the performance, the power consumption, the RMT, the appearance test data result and the compatibility grade as performance parameter items.
Further, the clustering execution unit includes:
the quantity setting module is configured for setting the quantity of the clustering types;
the basic clustering module is configured for clustering the memory banks into preset category quantity according to the similarity of basic parameter values of the memory banks by using a K-means clustering method;
And the performance clustering module is configured for clustering the memory banks into preset category quantity according to the similarity of the performance parameter values of the memory banks by using a K-means clustering method.
further, the homogeneous acquiring unit includes:
The weight setting module is configured for setting basic clustering distance weight and performance clustering distance weight;
the distance calculation module is configured for calculating the clustering distance between the memory bank and the target memory bank according to the basic clustering distance, the basic clustering distance weight, the performance clustering distance and the performance clustering distance weight;
and the distance screening module is configured to select the memory bank with the minimum clustering distance with the target memory bank as a replacement memory bank of the target memory bank.
In a third aspect, a terminal is provided, including:
A processor, a memory, wherein,
the memory is used for storing a computer program which,
The processor is used for calling and running the computer program from the memory so as to make the terminal execute the method of the terminal.
In a fourth aspect, a computer storage medium is provided having stored therein instructions that, when executed on a computer, cause the computer to perform the method of the above aspects.
The beneficial effect of the invention is that,
According to the memory bank classifying method, the system, the terminal and the storage medium, the memory banks are clustered according to basic parameters and performance parameters of the memory banks through an artificial intelligence algorithm K-means and previous test data, and therefore the substituted memory banks of the target memory bank can be found out quickly. The method and the device can quickly find the same type of memory bank of the memory bank needing to be replaced on the premise of not influencing the use of the client, save manpower and material resources and improve user experience.
In addition, the invention has reliable design principle, simple structure and very wide application prospect.
drawings
in order to more clearly illustrate the embodiments or technical solutions in the prior art of the present invention, the drawings used in the description of the embodiments or prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained based on these drawings without creative efforts.
FIG. 1 is a schematic flow diagram of a method of one embodiment of the invention.
FIG. 2 is a schematic block diagram of a system of one embodiment of the present invention.
fig. 3 is a schematic structural diagram of a terminal according to an embodiment of the present invention.
Detailed Description
In order to make those skilled in the art better understand the technical solution of the present invention, the technical solution in the embodiment of the present invention will be clearly and completely described below with reference to the drawings in the embodiment of the present invention, and it is obvious that the described embodiment is only a part of the embodiment of the present invention, and not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
the following explains key terms appearing in the present invention.
FIG. 1 is a schematic flow diagram of a method of one embodiment of the invention. The execution entity of fig. 1 may be a memory bank classifying system.
as shown in fig. 1, the method 100 includes:
step 110, setting basic parameter items and performance parameter items;
step 120, performing basic parameter clustering according to basic parameter values of the memory banks by using a K-means clustering method, and performing performance parameter clustering according to performance parameter values of the memory banks;
Step 130, acquiring basic clustering distances and performance clustering distances between the target memory bank and other memory banks;
and 140, calculating the similar memory bank of the target memory bank according to the basic clustering distance and the performance clustering distance.
optionally, as an embodiment of the present invention, the setting of the basic parameter item and the performance parameter item includes:
Setting a memory bank manufacturer scale grade, a process grade, a production line yield and a manufacturer technical grade as basic parameter items;
and setting the performance, the power consumption, the RMT, the appearance test data result and the compatibility grade as performance parameter items.
optionally, as an embodiment of the present invention, the performing basic parameter clustering according to basic parameter values of a memory bank and performing performance parameter clustering according to performance parameter values of the memory bank by using a K-means clustering method includes:
Setting the number of clustering types;
Clustering the memory banks into preset kinds and quantities by using a K-means clustering method according to the similarity of basic parameter values of the memory banks;
and clustering the memory banks into preset kinds and quantities by using a K-means clustering method according to the similarity of the performance parameter values of the memory banks.
optionally, as an embodiment of the present invention, the calculating a similar memory bank of a target memory bank according to a basic clustering distance and a performance clustering distance includes:
setting a basic clustering distance weight and a performance clustering distance weight;
Calculating the clustering distance between the memory bank and the target memory bank according to the basic clustering distance, the basic clustering distance weight, the performance clustering distance and the performance clustering distance weight;
and selecting the memory bank with the minimum clustering distance with the target memory bank as a replacement memory bank of the target memory bank.
In order to facilitate understanding of the present invention, the memory bank classifying method provided by the present invention is further described below by using the principle of the memory bank classifying method of the present invention and combining the process of classifying the memory bank in the embodiment.
specifically, the memory bank classifying method includes:
And S1, setting a basic parameter item and a performance parameter item.
the sub-attributes of each type of memory include two categories, one is a memory basic information category, and the other is an actual test data category. The basic information class of the memory comprises a manufacturer scale grade, a process grade, a production line yield and a manufacturer technical grade. The actual test class data includes performance, power consumption, RMT, appearance test data, and compatibility levels. In this embodiment, the manufacturer scale level is a, the process level is B, the production line yield is C, and the manufacturer technology level is D. Each memory has A, B, C and D parameters. The performance is set to E, the power consumption is set to F, the RMT is set to G, the appearance test data result is set to H, and the compatibility grade is set to I. Each memory has E, F, G, H and I parameters.
And S2, carrying out basic parameter clustering according to the basic parameter values of the memory banks by using a K-means clustering method, and carrying out performance parameter clustering according to the performance parameter values of the memory banks.
the K-means clustering algorithm is to randomly select K objects as initial clustering centers. The distance between each object and the respective seed cluster center is then calculated, and each object is assigned to the cluster center closest to it. The cluster centers and the objects assigned to them represent a cluster. The cluster center of a cluster is recalculated for each sample assigned based on the objects existing in the cluster. This process will be repeated until some termination condition is met. The termination condition may be that no (or minimum number) objects are reassigned to different clusters, no (or minimum number) cluster centers are changed again, and the sum of squared errors is locally minimal.
And (3) dividing all the memory banks into 5 classes which are named as K1 according to the basic memory information class parameter information (A, B, C and D parameters) by adopting a K-means algorithm. And (3) dividing all the memory banks into 5 classes which are named as K2 according to the basic memory information class parameter information (parameters such as E, F, G, H, I and the like) by adopting a K-means algorithm.
and S3, acquiring the basic clustering distance and the performance clustering distance between the target memory bank and other memory banks.
And collecting the clustering distance between other memory banks in the K1 cluster and the target memory bank, and marking the clustering distance as a basic clustering distance. And collecting the clustering distance between other memory banks in the K2 cluster and the target memory bank, and marking the clustering distance as a performance clustering distance.
And S4, calculating the similar memory bank of the target memory bank according to the basic clustering distance and the performance clustering distance.
And respectively selecting the basic similar memory bank with the minimum basic clustering distance to the target memory bank and the performance similar memory bank with the minimum performance clustering distance to the target memory bank. And if the basic similar memory bank and the performance similar memory bank are the same memory bank, outputting the memory bank as a replacement memory bank of the target memory bank. If the basic similar memory bank and the performance similar memory bank are not the same memory bank, acquiring a basic clustering distance and a performance clustering distance between the basic similar memory bank and a target memory bank, acquiring a basic clustering distance and a performance clustering distance between the performance similar memory bank and the target memory bank, and calculating a clustering distance between the performance similar memory bank and the target memory bank according to a preset basic clustering distance weight and a preset performance clustering distance weight and the following formula:
Clustering distance is basic clustering distance multiplied by basic clustering distance weight and performance clustering distance multiplied by performance clustering distance weight
And selecting the memory bank with the minimum clustering distance with the target memory bank as a replacement memory bank of the target memory bank.
For example, if the memory bank M1 cannot be used, it needs to be replaced by another memory bank.
if the K1 clustering mode is adopted, the memory bank M1 and the memory bank M2 are in the same class; in the K2 clustering manner, the memory bank M1 and the memory bank M2 are also in the same class. Such a case would replace bank M1 with bank M2.
if the K1 clustering mode is adopted, the memory bank M1 and the memory bank M2 are in the same class; in the clustering way of K2, the memory bank M1 and the memory bank M2 are not in the same class. In a K2 clustering mode, the memory bank M1 and the memory bank M3 are in the same class; in the clustering way of K1, the memory bank M1 and the memory bank M3 are not in the same class. Such a case would then require comparing the distance d131 of M3 to M1, the distance d121 of M2 to M1 in a classification of the form K1; comparing the distance d231 of M3 to M1, the distance d221 of M2 to M1 in the classification of form K12; a weight value is added during comparison, and if d131x 40% + d231x 60% > d121x 40% + d221x 60%, the memory bank M2 is selected to replace the memory bank M1; if d131x 40% + d231x 60% < d121x 40% + d221x 60%, the memory bank M1 is replaced by the memory bank M3.
In other embodiments of the present invention, the above formula may also be directly used to calculate the clustering distances between all memory banks and the target memory bank, and the memory bank with the smallest clustering distance is selected as the replacement memory bank of the target memory bank.
As shown in fig. 2, the system 200 includes:
a item setting unit 210 that configures and sets basic parameter items and performance parameter items;
A clustering execution unit 220 configured to perform basic parameter clustering according to the basic parameter values of the memory banks by using a K-means clustering method, and perform performance parameter clustering according to the performance parameter values of the memory banks;
A distance obtaining unit 230 configured to obtain a basic clustering distance and a performance clustering distance between the target memory bank and another memory bank;
and a homogeneous obtaining unit 240 configured to calculate homogeneous memory banks of the target memory bank according to the basic clustering distance and the performance clustering distance.
optionally, as an embodiment of the present invention, the item setting unit includes:
The basic setting module is configured for setting the scale grade, the process grade, the production line yield and the manufacturer technical grade of the memory bank as basic parameter items;
And the performance setting module is configured to set the performance, the power consumption, the RMT, the appearance test data result and the compatibility grade as performance parameter items.
Optionally, as an embodiment of the present invention, the cluster executing unit includes:
The quantity setting module is configured for setting the quantity of the clustering types;
the basic clustering module is configured for clustering the memory banks into preset category quantity according to the similarity of basic parameter values of the memory banks by using a K-means clustering method;
and the performance clustering module is configured for clustering the memory banks into preset category quantity according to the similarity of the performance parameter values of the memory banks by using a K-means clustering method.
Optionally, as an embodiment of the present invention, the homogeneous obtaining unit includes:
the weight setting module is configured for setting basic clustering distance weight and performance clustering distance weight;
the distance calculation module is configured for calculating the clustering distance between the memory bank and the target memory bank according to the basic clustering distance, the basic clustering distance weight, the performance clustering distance and the performance clustering distance weight;
and the distance screening module is configured to select the memory bank with the minimum clustering distance with the target memory bank as a replacement memory bank of the target memory bank.
Fig. 3 is a schematic structural diagram of a terminal system 300 according to an embodiment of the present invention, where the terminal system 300 may be used to execute the memory bank classifying method according to the embodiment of the present invention.
the terminal system 300 may include: a processor 310, a memory 320, and a communication unit 330. The components communicate via one or more buses, and those skilled in the art will appreciate that the architecture of the servers shown in the figures is not intended to be limiting, and may be a bus architecture, a star architecture, a combination of more or less components than those shown, or a different arrangement of components.
the memory 320 may be used for storing instructions executed by the processor 310, and the memory 320 may be implemented by any type of volatile or non-volatile storage terminal or combination thereof, such as Static Random Access Memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk. The executable instructions in memory 320, when executed by processor 310, enable terminal 300 to perform some or all of the steps in the method embodiments described below.
the processor 310 is a control center of the storage terminal, connects various parts of the entire electronic terminal using various interfaces and lines, and performs various functions of the electronic terminal and/or processes data by operating or executing software programs and/or modules stored in the memory 320 and calling data stored in the memory. The processor may be composed of an Integrated Circuit (IC), for example, a single packaged IC, or a plurality of packaged ICs connected with the same or different functions. For example, the processor 310 may include only a Central Processing Unit (CPU). In the embodiment of the present invention, the CPU may be a single operation core, or may include multiple operation cores.
a communication unit 330, configured to establish a communication channel so that the storage terminal can communicate with other terminals. And receiving user data sent by other terminals or sending the user data to other terminals.
The present invention also provides a computer storage medium, wherein the computer storage medium may store a program, and the program may include some or all of the steps in the embodiments provided by the present invention when executed. The storage medium may be a magnetic disk, an optical disk, a read-only memory (ROM) or a Random Access Memory (RAM).
Therefore, the invention clusters the memory bank according to the basic parameters and the performance parameters of the memory bank by the artificial intelligence algorithm K-means and the previous test data, and can quickly find out the substituted memory bank of the target memory bank. According to the invention, the similar memory banks of the memory banks needing to be replaced can be quickly found on the premise of not influencing the use of the client, so that the manpower and material resources are saved, and the user experience is improved.
Those skilled in the art will readily appreciate that the techniques of the embodiments of the present invention may be implemented as software plus a required general purpose hardware platform. Based on such understanding, the technical solutions in the embodiments of the present invention may be embodied in the form of a software product, where the computer software product is stored in a storage medium, such as a usb disk, a removable hard disk, a Read-Only Memory (ROM), a Random Access Memory (RAM), a magnetic disk or an optical disk, and the like, and the storage medium can store program codes, and includes instructions for enabling a computer terminal (which may be a personal computer, a server, or a second terminal, a network terminal, and the like) to perform all or part of the steps of the method in the embodiments of the present invention.
The same and similar parts in the various embodiments in this specification may be referred to each other. Especially, for the terminal embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant points can be referred to the description in the method embodiment.
in the embodiments provided by the present invention, it should be understood that the disclosed system, system and method can be implemented in other ways. For example, the above-described system embodiments are merely illustrative, and for example, the division of the units is only one logical functional division, and other divisions may be realized in practice, for example, a plurality of units or components may be combined or integrated into another system, or some features may be omitted, or not executed. In addition, the shown or discussed mutual coupling or direct coupling or communication connection may be an indirect coupling or communication connection through some interfaces, systems or units, and may be in an electrical, mechanical or other form.
The units described as separate parts may or may not be physically separate, and parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of the embodiment.
In addition, functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may exist alone physically, or two or more units are integrated into one unit.
Although the present invention has been described in detail by referring to the drawings in connection with the preferred embodiments, the present invention is not limited thereto. Various equivalent modifications or substitutions can be made on the embodiments of the present invention by those skilled in the art without departing from the spirit and scope of the present invention, and these modifications or substitutions are within the scope of the present invention/any person skilled in the art can easily conceive of the changes or substitutions within the technical scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (10)
1. a memory bank classifying method is characterized by comprising the following steps:
setting basic parameter items and performance parameter items;
carrying out basic parameter clustering according to basic parameter values of the memory banks by using a K-means clustering method, and carrying out performance parameter clustering according to performance parameter values of the memory banks;
Acquiring basic clustering distance and performance clustering distance between a target memory bank and other memory banks;
and calculating the similar memory bank of the target memory bank according to the basic clustering distance and the performance clustering distance.
2. The memory bank classifying method according to claim 1, wherein the setting of the basic parameter item and the performance parameter item includes:
Setting a memory bank manufacturer scale grade, a process grade, a production line yield and a manufacturer technical grade as basic parameter items;
And setting the performance, the power consumption, the RMT, the appearance test data result and the compatibility grade as performance parameter items.
3. the memory bank classifying method according to claim 1, wherein the performing basic parameter clustering according to the basic parameter values of the memory banks and performing performance parameter clustering according to the performance parameter values of the memory banks by using the K-means clustering method comprises:
Setting the number of clustering types;
clustering the memory banks into preset kinds and quantities by using a K-means clustering method according to the similarity of basic parameter values of the memory banks;
and clustering the memory banks into preset kinds and quantities by using a K-means clustering method according to the similarity of the performance parameter values of the memory banks.
4. the memory bank classifying method according to claim 1, wherein the calculating a homogeneous memory bank of a target memory bank according to a basic clustering distance and a performance clustering distance comprises:
setting a basic clustering distance weight and a performance clustering distance weight;
calculating the clustering distance between the memory bank and the target memory bank according to the basic clustering distance, the basic clustering distance weight, the performance clustering distance and the performance clustering distance weight;
And selecting the memory bank with the minimum clustering distance with the target memory bank as a replacement memory bank of the target memory bank.
5. A memory bank categorization system, comprising:
the project setting unit is used for configuring and setting a basic parameter item and a performance parameter item;
the clustering execution unit is configured for carrying out basic parameter clustering according to basic parameter values of the memory banks by using a K-means clustering method and carrying out performance parameter clustering according to performance parameter values of the memory banks;
the distance acquisition unit is configured to acquire the basic clustering distance and the performance clustering distance between the target memory bank and other memory banks;
and the same type obtaining unit is configured for calculating the same type memory bank of the target memory bank according to the basic clustering distance and the performance clustering distance.
6. the memory bank classifying system according to claim 5, wherein the item setting unit includes:
The basic setting module is configured for setting the scale grade, the process grade, the production line yield and the manufacturer technical grade of the memory bank as basic parameter items;
and the performance setting module is configured to set the performance, the power consumption, the RMT, the appearance test data result and the compatibility grade as performance parameter items.
7. the system of claim 5, wherein the clustering execution unit comprises:
the quantity setting module is configured for setting the quantity of the clustering types;
the basic clustering module is configured for clustering the memory banks into preset category quantity according to the similarity of basic parameter values of the memory banks by using a K-means clustering method;
and the performance clustering module is configured for clustering the memory banks into preset category quantity according to the similarity of the performance parameter values of the memory banks by using a K-means clustering method.
8. the bank classifying system according to claim 5, wherein the homogeneous obtaining unit comprises:
the weight setting module is configured for setting basic clustering distance weight and performance clustering distance weight;
The distance calculation module is configured for calculating the clustering distance between the memory bank and the target memory bank according to the basic clustering distance, the basic clustering distance weight, the performance clustering distance and the performance clustering distance weight;
and the distance screening module is configured to select the memory bank with the minimum clustering distance with the target memory bank as a replacement memory bank of the target memory bank.
9. a terminal, comprising:
A processor;
a memory for storing instructions for execution by the processor;
Wherein the processor is configured to perform the method of any one of claims 1-4.
10. a computer-readable storage medium, in which a computer program is stored which, when being executed by a processor, carries out the method according to any one of claims 1-4.
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