CN116795602A - Method and device for constructing biological sample information database - Google Patents
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Abstract
The application discloses a construction method and a construction device of a biological sample information database, which particularly relate to the field of databases, and are characterized in that a data recovery effect coefficient is obtained by collecting fault parameters and recovery parameters of the database, judging is carried out by combining a data recovery effect threshold, the data recovery effect is evaluated, an operable coefficient is obtained by collecting resource parameters and performance parameters of the database under the condition of judging to generate a difficult signal, the performance of the database is evaluated by combining the operable threshold, the current state of the database is judged, after judging to generate a good signal, the data backup interval time of the database is obtained, deviation correction is carried out on the data backup interval time by the data recovery effect coefficient and the data recovery effect threshold, and the data recovery effect is compensated by modifying the size of the data backup interval time, so that the risk of losing biological sample data is reduced.
Description
Technical Field
The application relates to the field of databases, in particular to a method and a device for constructing a biological sample information database.
Background
To facilitate the censoring of biological sample data, traditional approaches typically enter the biological sample data into a database for storage. However, during database operation, various problems may be encountered, resulting in database operation failure. When a database fails, a built-in automatic failure recovery mechanism automatically attempts to recover the affected data and services, but due to different failure degrees, the data recovery often has a certain loss, so that the integrity of the data recovery is damaged.
To cope with these risks, conventional methods often regularly perform data backup as a protective measure. Data backup may make snapshot copies of the database at regular intervals and store the backup data in a reliable storage medium. In this way, even if the database fails, the state of the database can be restored by restoring the backup data, thereby reducing the risk of data loss.
However, there are some limitations to the conventional periodic backup method. Firstly, the backup frequency is fixed, and flexible adjustment cannot be performed according to the reliability of current data recovery and the performance condition of a database. If the backup frequency is too low, more data may be lost; and the backup frequency is too high, the consumption of system resources may be increased. Second, the security of data storage cannot be flexibly ensured by only relying on regular backup. If a failure occurs during the backup period, the data may not be backed up in time, thereby increasing the risk of data loss.
In order to solve the above problems, a technical solution is now provided.
Disclosure of Invention
In order to overcome the above-mentioned drawbacks of the prior art, embodiments of the present application provide a method for obtaining a usage performance state of a database by evaluating a data recovery effect of the database, in case that the data recovery effect is bad, and in case that the performance of the database satisfies flexible adjustment of a backup interval time, flexibly adjusting the data backup interval time in combination with the data recovery effect, thereby reducing loss by adjusting the data backup interval time in case that the data recovery effect is bad, so as to solve the problems set forth in the above-mentioned background art.
In order to achieve the above purpose, the present application provides the following technical solutions:
step S100: collecting fault parameters and recovery parameters of a database;
step S200: normalizing the fault parameters and the recovery parameters to obtain data recovery effect coefficients, comparing the data recovery effect coefficients with a data recovery effect threshold, and generating a feasible signal if the data recovery effect coefficients are smaller than the data recovery effect threshold; if the data recovery effect coefficient is greater than or equal to the data recovery effect threshold, generating an intractable signal;
step S300: after the difficult signal is obtained, collecting the resource parameter and the performance parameter of the database, and normalizing the resource parameter and the performance parameter to obtain an operable coefficient;
step S400: comparing the operable coefficient with the operable threshold, and generating a good signal if the operable coefficient is greater than or equal to the operable threshold; if the operational coefficient is smaller than the operational threshold, generating an intervention signal;
step S500: and after the excellent signal is acquired, acquiring data backup interval time, and synthesizing the data recovery effect coefficient, the data recovery effect threshold value and the database backup interval to obtain compensation backup interval time, wherein the compensation backup interval time is used for replacing the data backup interval time.
In a preferred embodiment, step S100 comprises the steps of:
the fault parameters comprise a fault fatal index, the recovery parameters comprise a data recovery missing dynamic attitude, and the performance parameters comprise a database performance index;
the logic for obtaining the failure deadly index is as follows:
step S101: recording the total running time of the database, namely the accumulated running time of the database from the starting to the current;
step S102: dividing the total database operation time by the occurrence times of faults to obtain the average time interval of all faults;
step S103: dividing the total database operation time by the occurrence times of the fatal faults to obtain the average occurrence time interval of the fatal faults; the fatal fault refers to a serious fault which causes the database to trigger data recovery;
step S104: the average time interval for the occurrence of a fatal fault is divided by the average time interval for the occurrence of all faults to obtain a fault fatal index.
In a preferred embodiment, the data recovery missing dynamic state acquisition logic is:
step S111: for each data recovery event, recording the corresponding data recovery missing proportion, namely the missing proportion between the original data and the recovered data, wherein the calculation formula is as follows: miss ratio = number of missing data points/number of original total data points;
step S112: recording the data recovery missing proportion of different data recovery events occurring in the time range t, and calibrating the data recovery missing proportion of the different data recovery events asI=1, 2, 3, 4, … …, n is a positive integer, byIs to acquire the standard deviation of the data recovery missing proportion fluctuation rate, will +.>The standard deviation of (2) is calibrated as X, and the calculation formula is as follows:wherein->And X is the standard deviation of the data recovery missing proportion of different data recovery events, and the data recovery missing dynamic attitude is obtained through the standard deviation of the data recovery missing proportion of different data recovery events.
In a preferred embodiment, step S300 comprises the steps of:
after the difficult signal is obtained, collecting the resource parameters and the performance parameters of the database, wherein the resource parameters comprise load empty rate, and the performance parameters comprise reading stability indexes;
the low load balancing index acquisition logic is as follows:
step S311: dividing the entire operating cycle into a plurality of time periods;
step S312: for each time period, calculating a low load time in the time period, wherein the low load time refers to a time when the CPU utilization is lower than a utilization threshold;
step S313: the proportion of the low load time in each time period is calculated, and the calculation formula is as follows: low load time ratio = low load time/total time of time period;
step S314: marking low load time ratios for different time periods asJ=1, 2, 3, 4, … …, m is a positive integer, by +.>Is marked as S, and the calculation formula is: />Wherein->And S is the standard deviation of the low load time proportion of different time periods, and the low load balance index is obtained through the standard deviation of the low load time proportion of different time periods.
In a preferred embodiment, the acquisition logic for reading the stability index is:
step S321: dividing an operation period of the database into a plurality of time periods, wherein each time period has the same time length;
step S322: recording the average of the read delays over each time period;
step S323: for each time period, judging whether the average value of the reading delay in the time period is lower than a reading delay threshold value, and counting the number of the time periods lower than the reading delay threshold value;
step S324: calculating the proportion of the time period below the reading delay threshold value to the whole operation period to obtain a reading stability index, wherein the calculation formula is as follows: read stability index = number of time periods below expected/total number of time periods.
In a preferred embodiment, step S500 specifically includes the steps of:
after obtaining the good signal, obtaining data backup interval time, and synthesizing the data recovery effect coefficient, the data recovery effect threshold value and the database backup interval time to obtain compensation backup interval time, wherein the calculation formula is as follows:wherein CC is a compensation interval for replacing data backup interval,/or->And RER and H1 are respectively a data recovery effect coefficient and a data recovery effect threshold for the data backup interval time.
The device for constructing the biological sample information database comprises a first acquisition unit, a first analysis unit, a second judgment unit and a comprehensive adjustment unit;
the first acquisition unit is used for acquiring fault parameters and recovery parameters of the database, generating fault parameter signals and recovery parameter signals and sending the fault parameter signals and the recovery parameter signals to the first analysis unit;
the first analysis unit is used for normalizing the fault parameters and the recovery parameters to obtain data recovery effect coefficients, comparing the data recovery effect coefficients with a data recovery effect threshold value, and generating a feasible signal if the data recovery effect coefficients are smaller than the data recovery effect threshold value; if the data recovery effect coefficient is larger than or equal to the data recovery effect threshold, generating a difficult signal, and transmitting the generated difficult signal to a second analysis unit;
the second analysis unit is used for acquiring the resource parameters and the performance parameters of the database after the difficult signal is obtained, normalizing the resource parameters and the performance parameters to obtain the operable coefficients, generating the operable coefficients and sending the operable coefficients to the second judgment unit;
the second judging unit is used for comparing the operable coefficient with the operable threshold, and generating a good signal if the operable coefficient is greater than or equal to the operable threshold; if the operational coefficient is smaller than the operational threshold, generating an intervention signal, generating a good signal and sending the good signal to the comprehensive regulation unit;
the comprehensive adjustment unit is used for obtaining data backup interval time after obtaining the excellent signal, and carrying out comprehensive on the data recovery effect coefficient, the data recovery effect threshold value and the database backup time interval to obtain compensation backup interval time, wherein the compensation backup interval time is used for replacing the data backup interval time.
The method and the device for constructing the biological sample information database have the technical effects and advantages that:
1. and acquiring the failure deadly index and the data recovery missing dynamic attitude of the database, and calculating to obtain a data recovery effect coefficient so as to evaluate the effect of the database in data recovery. The safety of biological sample data in the database can be analyzed, and deployment and safety measures can be timely warned. And comparing the data recovery effect coefficient with a data recovery effect threshold, and generating a feasible signal when the data recovery effect coefficient is greater than or equal to the data recovery effect threshold, wherein the feasible signal indicates that the data recovery effect of the database is good. And otherwise, when the data recovery effect coefficient is smaller than the data recovery effect threshold, generating an intractable signal which indicates that the data recovery effect of the database is poor. Through the clear judgment result and the warning signal, related personnel can be reminded of the state of data recovery, warning is carried out, corresponding measures are taken to solve the problem, and the safety and the integrity of biological sample data in a database are ensured;
2. after the difficult signal is obtained, the application acquires the low load balance index and the read stability index of the database to obtain the operational coefficient for evaluating the service performance condition of the database. The operational coefficients are compared to an operational threshold. If the operational coefficient is greater than or equal to the operational threshold, the database performance is poor, the database cannot be qualified for frequent data backup tasks, and an intervention signal is generated. If the operational coefficient is smaller than the operational threshold, the database performance is good, and reasonable data backup interval time adjustment operation can be dealt with, so that a good signal is generated; and (3) for the situation of generating the excellent signal, correcting the error by acquiring the original data backup interval time and combining the data recovery effect coefficient and the data recovery effect threshold value. Therefore, the size of the database backup time interval can be actively regulated under the condition that the database performance can be qualified for frequent backup data, so that the condition of poor data recovery effect is compensated, and the storage safety of biological sample data is ensured.
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FIG. 1 is a schematic flow chart of a method for constructing a biological sample information database according to the present application;
fig. 2 is a schematic structural diagram of a device for constructing a biological sample information database according to the present application.
The following description of the embodiments of the present application will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present application, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the application without making any inventive effort, are intended to be within the scope of the application.
DETAILED DESCRIPTION OF EMBODIMENT (S) OF INVENTION
FIG. 1 shows a method for constructing a biological sample information database according to the present application, which comprises the steps of:
step S100: collecting fault parameters and recovery parameters of a database;
step S200: normalizing the fault parameters and the recovery parameters to obtain data recovery effect coefficients, comparing the data recovery effect coefficients with a data recovery effect threshold, and generating a feasible signal if the data recovery effect coefficients are smaller than the data recovery effect threshold; if the data recovery effect coefficient is greater than or equal to the data recovery effect threshold, generating an intractable signal;
step S300: after the difficult signal is obtained, collecting the resource parameter and the performance parameter of the database, and normalizing the resource parameter and the performance parameter to obtain an operable coefficient;
step S400: comparing the operable coefficient with the operable threshold, and generating a good signal if the operable coefficient is greater than or equal to the operable threshold; if the operational coefficient is smaller than the operational threshold, generating an intervention signal;
step S500: and after the excellent signal is acquired, acquiring data backup interval time, and synthesizing the data recovery effect coefficient, the data recovery effect threshold value and the database backup interval to obtain compensation backup interval time, wherein the compensation backup interval time is used for replacing the data backup interval time.
In constructing a database, it is necessary to understand the data recovery capability of the database after encountering a failure, and evaluating the database recovery capability can help to better manage risks. Evaluating database data recovery capabilities may ensure that the integrity and accuracy of the data is maintained during the data recovery process. By identifying and evaluating weak links and potential risks of database data recovery, corresponding measures are facilitated to be taken to reduce risks of data loss and service interruption. By periodically evaluating the data recovery capability, problems can be discovered in advance and precautions taken to minimize potential data loss.
Step S100 includes the steps of:
the fault parameters comprise a fault fatal index, the recovery parameters comprise a data recovery missing dynamic attitude, and the performance parameters comprise a database performance index.
The logic for obtaining the failure deadly index is as follows:
step S101: recording the total running time of the database, namely the accumulated running time of the database from the starting to the current;
step S102: dividing the total database operation time by the occurrence times of faults to obtain the average time interval of all faults;
step S103: dividing the total database operation time by the occurrence times of the fatal faults to obtain the average occurrence time interval of the fatal faults; the fatal fault refers to a serious fault which causes the database to trigger data recovery;
step S104: the average time interval for the occurrence of a fatal fault is divided by the average time interval for the occurrence of all faults to obtain a fault fatal index.
The failure deadly index is used for measuring the relative frequency between the triggering data recovery failures and all the failures, and a larger failure deadly index indicates that the deadly failures in all the failures occupy higher proportion and occur more frequently, so that the data recovery task is aggravated, and the data loss risk is increased; on the contrary, the smaller failure deadly index indicates that the deadly failures occupy a lower proportion in all failures, the occurrence frequency is relatively rare, the database is relatively stable, and serious deadly failures are not easy to suffer, so that the safety and reliability of data are improved.
The acquisition logic of the data recovery missing dynamic attitude is as follows:
step S111: for each data recovery event, recording the corresponding data recovery missing proportion, namely the missing proportion between the original data and the recovered data, wherein the calculation formula is as follows: miss ratio = number of missing data points/number of original total data points;
missing data points refer to the number of data points where there is a missing in the original data and the recovered data, the original total number of data points refers to the original total number of data points;
step S112: recording the data recovery missing proportion of different data recovery events occurring in the time range t, and calibrating the data recovery missing proportion of the different data recovery events asI=1, 2, 3, 4, … …, n is a positive integer, byIs to acquire the standard deviation of the data recovery missing proportion fluctuation rate, will +.>The standard deviation of (2) is calibrated as X, and the calculation formula is as follows:wherein->For the average value of the data recovery missing proportion of different data recovery events, X is the standard deviation of the data recovery missing proportion of different data recovery events, and the data recovery missing proportion of different data recovery events is usedAnd (3) obtaining the data recovery missing dynamic attitude.
The missing dynamic attitude of data recovery reflects the change and fluctuation of the missing proportion in the data recovery process. A larger data recovery missing dynamic attitude indicates that the fluctuation of the data recovery missing proportion is larger in different data recovery events, i.e. there is a larger change in the missing proportion. This means that there is instability in the data recovery process, and the integrity of the data cannot be effectively guaranteed. Conversely, a smaller data recovery missing dynamic attitude indicates that the fluctuation of the data recovery missing proportion is smaller in different data recovery events, i.e. the missing proportion change is more stable. This means that the data recovery process is stable and the integrity of the data can be effectively ensured.
Step S200 includes the steps of:
the failure deadline index and the data recovery missing dynamic attitude are normalized to obtain a data recovery effect coefficient, for example, the data recovery effect coefficient can be calculated by the following formula:wherein RER is a data recovery effect coefficient, -/-, a>The failure deadline index and the data recovery lack dynamic attitude are respectively +.>Preset proportional coefficients of failure deadly index and data recovery missing dynamic attitude respectively, and +.>Are all greater than 0;
the data recovery effect coefficient is used for measuring the comprehensive performance of the database in terms of data recovery. Comprehensively considering indexes of failure deadly indexes and data recovery missing dynamic attitude, and obtaining a comprehensive evaluation value through normalization processing; the smaller data recovery effect coefficient indicates that the data recovery capability of the database is stronger and the data recovery effect is better. This means that when the database fails, the recovery process can effectively protect and recover the data, reducing the risk of data loss; conversely, a larger data recovery effect coefficient indicates that the database has weaker data recovery capability and poorer data recovery effect. This means that there is a greater risk of data loss during recovery when the database fails, resulting in a loss of data integrity.
Comparing the data recovery effect index with the data recovery effect threshold, if the data recovery effect index is greater than or equal to the data recovery effect threshold, indicating that the data recovery effect is not ideal, meaning that the database has the condition of data loss, inaccuracy or incompleteness in the data recovery process, the fluctuation of the loss proportion is larger, the fatality of the fault in the recovery process is higher, generating a difficult signal, and sending out an alarm signal; otherwise, if the data recovery effect index is smaller than the data recovery effect threshold, the data recovery effect in the database is good, which means that the database can keep the integrity and accuracy of the data in the data recovery process, the fluctuation of the missing proportion is smaller, the failure deadly in the recovery process is lower, which means that the data recovery work of the database is effectively executed and managed, and a feasible signal is generated.
According to the application, the data recovery effect coefficient is obtained through calculation by collecting the failure deadly index and the data recovery missing dynamic attitude of the database, so as to evaluate the effect of the database in data recovery. The safety of biological sample data in the database can be analyzed, and deployment and safety measures can be timely warned. And comparing the data recovery effect coefficient with a data recovery effect threshold, and generating a feasible signal when the data recovery effect coefficient is greater than or equal to the data recovery effect threshold, wherein the feasible signal indicates that the data recovery effect of the database is good. And otherwise, when the data recovery effect coefficient is smaller than the data recovery effect threshold, generating an intractable signal which indicates that the data recovery effect of the database is poor. Through the clear judgment result and the warning signal, related personnel can be reminded of the state of data recovery, warning is carried out, corresponding measures are taken to solve the problem, and the safety and the integrity of biological sample data in the database are ensured.
Data backup is one of the important means of protecting database data security, and can provide the ability to recover data in the event of a database failure or loss of data. When data backup is performed, certain requirements are required on the performance of the database to ensure the efficiency and reliability of the backup process, so that when the data backup strategy of the database is adjusted, the performance of the database needs to be firstly known and evaluated.
Step S300 includes the steps of:
after the difficult signal is obtained, the resource parameters and the performance parameters of the database are collected, wherein the resource parameters comprise load empty rate, and the performance parameters comprise reading stability indexes.
The low load balancing index acquisition logic is as follows:
step S311: dividing the entire operating cycle into a plurality of time periods;
step S312: for each time period, calculating a low load time in the time period, wherein the low load time refers to a time when the CPU utilization is lower than a utilization threshold;
step S313: the proportion of the low load time in each time period is calculated, and the calculation formula is as follows: low load time ratio = low load time/total time of time period;
step S314: marking low load time ratios for different time periods asJ=1, 2, 3, 4, … …, m is a positive integer, by +.>Is marked as S, and the calculation formula is: />Wherein->And S is the standard deviation of the low load time proportion of different time periods, and the low load balance index is obtained through the standard deviation of the low load time proportion of different time periods.
The low load balance index is used for reflecting the low load time dispersibility of the database operation, and the smaller low load balance index indicates that the proportion of the low load time in different time periods is close, and the distribution is balanced. This means that the low load time of the database is relatively evenly distributed over the entire operating period over the various time periods without significant time period preference or concentration. Under the condition, the load of the database is balanced, and the resource utilization is reasonable; the larger low load balance index indicates that the proportion of the low load time in different time periods is larger, and the distribution is unbalanced. This means that throughout the operating cycle there is a significantly higher proportion of the low load time for some periods than for other periods. In this case, the load of the database is unbalanced, and the situation of excessive or insufficient resources may exist in part of the time period, which is not beneficial to flexibly backing up at any time.
The acquisition logic for reading the stability index is:
step S321: dividing an operation period of the database into a plurality of time periods, wherein each time period has the same time length;
step S322: recording the average of the read delays over each time period;
step S323: for each time period, judging whether the average value of the reading delay in the time period is lower than a reading delay threshold value, and counting the number of the time periods lower than the reading delay threshold value;
step S324: calculating the proportion of the time period below the reading delay threshold value to the whole operation period to obtain a reading stability index, wherein the calculation formula is as follows: read stability index = number of time periods below expected/total number of time periods.
The reading stability index is used for measuring the reading stability degree of the database, and a larger reading stability index indicates that the database has a reading delay lower than expected in a larger proportion of time period, and reflects the instability of the reading performance of the database. A smaller read stability index indicates that the database has a read delay below expected for a smaller proportion of the time period, reflecting the relative stability of the database read performance.
Normalizing the low load balance index and the read stability index to obtain an operable coefficient;
for example, the operational coefficient can be obtained by the following formula:wherein OC is an operational coefficient, < ->Low load balancing index, read stability index, +.>Preset scaling factors of low load balancing index, read stability index, respectively, and +.>Are all greater than 0;
the operational coefficient is used for reflecting the operability and manageability of the database, the smaller operational coefficient indicates that the database has good performance in terms of low load balancing and reading stability, can be effectively managed and operated, has higher performance and stability, and means that the database can balance resource utilization in terms of load balancing, keeps stable delay level in the reading process and is convenient for backup operation at any time and any place; conversely, a larger operational coefficient indicates that the database has problems in terms of low load balancing and reading stability, and the operability is lower, which means that the database has unbalanced resource utilization in terms of load balancing or delay abnormality often occurs in the reading process, which is unfavorable for data backup.
The step S400 specifically includes the following steps:
comparing the operational coefficient with an operational threshold, if the operational coefficient is smaller than the operational threshold, the load balance and the reading stability of the database reach the expected level, the database has good performance, can be operated and managed normally, is convenient for backup operation at any time and any place, and generates good signals; if the operational coefficient is greater than or equal to the operational threshold, the load balancing or the reading stability of the database is indicated to have a problem, the performance of the database is poor, further optimization or adjustment is needed, an intervention signal is generated, and the fact that measures are needed to improve the performance and the stability of the database is indicated to be taken, so that the data backup is not facilitated.
The step S500 specifically includes the following steps:
after obtaining the good signal, obtaining data backup interval time, and synthesizing the data recovery effect coefficient, the data recovery effect threshold value and the database backup interval time to obtain compensation backup interval time, wherein the calculation formula is as follows:wherein CC is a compensation interval for replacing data backup interval,/or->And RER and H1 are respectively a data recovery effect coefficient and a data recovery effect threshold for the data backup interval time.
According to the application, after the difficult signal is obtained, the low load balance index and the read stability index of the database are collected to obtain the operable coefficient, so that the method is used for evaluating the service performance condition of the database. The operational coefficients are compared to an operational threshold. If the operational coefficient is greater than or equal to the operational threshold, the database performance is poor, the database cannot be qualified for frequent data backup tasks, and an intervention signal is generated. If the operational coefficient is smaller than the operational threshold, the database performance is good, and reasonable data backup interval time adjustment operation can be dealt with, so that a good signal is generated; and (3) for the situation of generating the excellent signal, correcting the error by acquiring the original data backup interval time and combining the data recovery effect coefficient and the data recovery effect threshold value. Therefore, the size of the database backup time interval can be actively regulated under the condition that the database performance can be qualified for frequent backup data, so that the condition of poor data recovery effect is compensated, and the storage safety of biological sample data is ensured.
Examples
FIG. 2 shows a device for constructing a biological sample information database, which comprises a first acquisition unit, a first analysis unit, a second judgment unit and a comprehensive adjustment unit;
the first acquisition unit is used for acquiring fault parameters and recovery parameters of the database, generating fault parameter signals and recovery parameter signals and sending the fault parameter signals and the recovery parameter signals to the first analysis unit;
the first analysis unit is used for normalizing the fault parameters and the recovery parameters to obtain data recovery effect coefficients, comparing the data recovery effect coefficients with a data recovery effect threshold value, and generating a feasible signal if the data recovery effect coefficients are smaller than the data recovery effect threshold value; if the data recovery effect coefficient is larger than or equal to the data recovery effect threshold, generating a difficult signal, and transmitting the generated difficult signal to a second analysis unit;
the second analysis unit is used for acquiring the resource parameters and the performance parameters of the database after the difficult signal is obtained, normalizing the resource parameters and the performance parameters to obtain the operable coefficients, generating the operable coefficients and sending the operable coefficients to the second judgment unit;
the second judging unit is used for comparing the operable coefficient with the operable threshold, and generating a good signal if the operable coefficient is greater than or equal to the operable threshold; if the operational coefficient is smaller than the operational threshold, generating an intervention signal, generating a good signal and sending the good signal to the comprehensive regulation unit;
the comprehensive adjustment unit is used for obtaining data backup interval time after obtaining the excellent signal, and carrying out comprehensive on the data recovery effect coefficient, the data recovery effect threshold value and the database backup time interval to obtain compensation backup interval time, wherein the compensation backup interval time is used for replacing the data backup interval time.
The above formulas are all formulas with dimensionality removed and numerical calculation, the formulas are formulas with the latest real situation obtained by software simulation through collecting a large amount of data, and preset parameters and threshold selection in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of a computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with embodiments of the present application are produced in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, specific working procedures of the above-described systems, apparatuses and units may refer to corresponding procedures in the foregoing method embodiments, and are not repeated herein.
In the several embodiments provided in the present application, it should be understood that the disclosed system, apparatus and method may be implemented in other manners. For example, the apparatus embodiments described above are merely illustrative, e.g., the division of the units is merely a logical function division, and there may be additional divisions when actually implemented, e.g., multiple units or components may be combined or integrated into another system, or some features may be omitted or not performed. Alternatively, the coupling or direct coupling or communication connection shown or discussed with each other may be an indirect coupling or communication connection via some interfaces, devices or units, which may be in electrical, mechanical or other forms.
The units described as separate units may or may not be physically separate, and units shown as units may or may not be physical units, may be located in one place, or may be distributed over a plurality of network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.
In addition, each functional unit in the embodiments of the present application may be integrated in one processing unit, or each unit may exist alone physically, or two or more units may be integrated in one unit.
The functions, if implemented in the form of software functional units and sold or used as a stand-alone product, may be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of a software product stored in a storage medium, comprising several instructions for causing a computer device (which may be a personal computer, a server, a network device, etc.) to perform all or part of the steps of the method according to the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely illustrative of the present application, and the present application is not limited thereto, and any person skilled in the art will readily recognize that variations or substitutions are within the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Finally: the foregoing description of the preferred embodiments of the application is not intended to limit the application to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and principles of the application are intended to be included within the scope of the application.
Claims (7)
1. The method for constructing the biological sample information database is characterized by comprising the following steps of:
step S100: collecting fault parameters and recovery parameters of a database;
step S200: normalizing the fault parameters and the recovery parameters to obtain data recovery effect coefficients, comparing the data recovery effect coefficients with a data recovery effect threshold, and generating a feasible signal if the data recovery effect coefficients are smaller than the data recovery effect threshold; if the data recovery effect coefficient is greater than or equal to the data recovery effect threshold, generating an intractable signal;
step S300: after the difficult signal is obtained, collecting the resource parameter and the performance parameter of the database, and normalizing the resource parameter and the performance parameter to obtain an operable coefficient;
step S400: comparing the operable coefficient with the operable threshold, and generating a good signal if the operable coefficient is greater than or equal to the operable threshold; if the operational coefficient is smaller than the operational threshold, generating an intervention signal;
step S500: and after the excellent signal is acquired, acquiring data backup interval time, and comprehensively calculating a data recovery effect coefficient, a data recovery effect threshold value and a database backup time interval to obtain compensation backup interval time which is used for replacing the data backup interval time.
2. The method for constructing a biological sample information database according to claim 1, wherein: step S100 includes the steps of:
the fault parameters comprise a fault fatal index, the recovery parameters comprise a data recovery missing dynamic attitude, and the performance parameters comprise a database performance index;
the logic for obtaining the failure deadly index is as follows:
step S101: recording the total running time of the database, namely the accumulated running time of the database from the starting to the current;
step S102: dividing the total database operation time by the occurrence times of faults to obtain the average time interval of all faults;
step S103: dividing the total database operation time by the occurrence times of the fatal faults to obtain the average occurrence time interval of the fatal faults; the fatal fault refers to a serious fault which causes the database to trigger data recovery;
step S104: the average time interval for the occurrence of a fatal fault is divided by the average time interval for the occurrence of all faults to obtain a fault fatal index.
3. The method for constructing a biological sample information database according to claim 2, wherein: the acquisition logic of the data recovery missing dynamic attitude is as follows:
step S111: for each data recovery event, recording the corresponding data recovery missing proportion, namely the missing proportion between the original data and the recovered data, wherein the calculation formula is as follows: miss ratio = number of missing data points/number of original total data points;
step S112: recording the data recovery missing proportion of different data recovery events occurring in the time range t, and calibrating the data recovery missing proportion of the different data recovery events asI=1, 2, 3, 4, … …, n is a positive integer, by +.>Is to acquire the standard deviation of the data recovery missing proportion fluctuation rate, will +.>The standard deviation of (2) is calibrated as X, and the calculation formula is as follows:wherein->And X is the standard deviation of the data recovery missing proportion of different data recovery events, and the data recovery missing dynamic attitude is obtained through the standard deviation of the data recovery missing proportion of different data recovery events.
4. A method of constructing a database of biological sample information according to claim 3, wherein: step S300 includes the steps of:
after the difficult signal is obtained, collecting the resource parameters and the performance parameters of the database, wherein the resource parameters comprise load empty rate, and the performance parameters comprise reading stability indexes;
the low load balancing index acquisition logic is as follows:
step S311: dividing the entire operating cycle into a plurality of time periods;
step S312: for each time period, calculating a low load time in the time period, wherein the low load time refers to a time when the CPU utilization is lower than a utilization threshold;
step S313: the proportion of the low load time in each time period is calculated, and the calculation formula is as follows: low load time ratio = low load time/total time of time period;
step S314: marking low load time ratios for different time periods asJ=1, 2, 3, 4, … …, m is a positive integer, by +.>Is marked as S, and the calculation formula is: />Wherein->And S is the standard deviation of the low load time proportion of different time periods, and the low load balance index is obtained through the standard deviation of the low load time proportion of different time periods.
5. The method for constructing a biological sample information database according to claim 4, wherein: the acquisition logic for reading the stability index is:
step S321: dividing an operation period of the database into a plurality of time periods, wherein each time period has the same time length;
step S322: recording the average of the read delays over each time period;
step S323: for each time period, judging whether the average value of the reading delay in the time period is lower than a reading delay threshold value, and counting the number of the time periods lower than the reading delay threshold value;
step S324: calculating the proportion of the time period below the reading delay threshold value to the whole operation period to obtain a reading stability index, wherein the calculation formula is as follows: read stability index = number of time periods below expected/total number of time periods.
6. The method for constructing a biological sample information database according to claim 5, wherein: the step S500 specifically includes the following steps:
after obtaining the good signal, obtaining data backup interval time, and synthesizing the data recovery effect coefficient, the data recovery effect threshold value and the database backup interval time to obtain compensation backup interval time, wherein the calculation formula is as follows:wherein CC is a compensation interval time used for replacing the data backup interval time,/>and RER and H1 are respectively a data recovery effect coefficient and a data recovery effect threshold for the data backup interval time.
7. A device for constructing a biological sample information database, which is used for realizing the construction method of any one of claims 1-6, and comprises a first acquisition unit, a first analysis unit, a second judgment unit and a comprehensive adjustment unit;
the first acquisition unit is used for acquiring fault parameters and recovery parameters of the database, generating fault parameter signals and recovery parameter signals and sending the fault parameter signals and the recovery parameter signals to the first analysis unit;
the first analysis unit is used for normalizing the fault parameters and the recovery parameters to obtain data recovery effect coefficients, comparing the data recovery effect coefficients with a data recovery effect threshold value, and generating a feasible signal if the data recovery effect coefficients are smaller than the data recovery effect threshold value; if the data recovery effect coefficient is larger than or equal to the data recovery effect threshold, generating a difficult signal, and transmitting the generated difficult signal to a second analysis unit;
the second analysis unit is used for acquiring the resource parameters and the performance parameters of the database after the difficult signal is obtained, normalizing the resource parameters and the performance parameters to obtain the operable coefficients, generating the operable coefficients and sending the operable coefficients to the second judgment unit;
the second judging unit is used for comparing the operable coefficient with the operable threshold, and generating a good signal if the operable coefficient is greater than or equal to the operable threshold; if the operational coefficient is smaller than the operational threshold, generating an intervention signal, generating a good signal and sending the good signal to the comprehensive regulation unit;
the comprehensive adjustment unit is used for obtaining data backup interval time after obtaining the excellent signal, and carrying out comprehensive on the data recovery effect coefficient, the data recovery effect threshold value and the database backup time interval to obtain compensation backup interval time, wherein the compensation backup interval time is used for replacing the data backup interval time.
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