Model and method applied to backup sampling for pre-judging production data loss

Abstract

The invention discloses a model and a method applied to backup sampling for pre-judging production data loss, and belongs to the technical field of data backup. The method comprises the following steps: pre-dividing a backup time slice to obtain a data loss ratio of each piece of production equipment in the backup time slice; depicting a backup behavior simulation rule curve of the production equipment based on the data loss ratio, and converting the backup behavior simulation rule curve into a digital rule straight line; one piece of production equipment is selected as a pre-judgment reference object, the other piece of production equipment is selected as a pre-judgment correlation object for correlation sampling, and the data loss behavior relation between the production equipment and the pre-judgment reference object is analyzed; and based on a result of the correlation sampling, evaluating a pre-judgment probability between the pre-judgment reference object and the pre-judgment correlation object, generating a pre-judgment list, and outputting the pre-judgment list to an operation and maintenance port. The reliability and safety of production data backup can be effectively improved, the risk of data loss is reduced, and operation and maintenance personnel can identify the production equipment possibly having the risk of data loss in advance, so that the distribution of operation and maintenance resources is optimized.

Description

Technical Field

[0001] The present invention relates to the technical field of data backup, and in particular to a model and method for predicting production data loss applied to backup sampling. Background Art

[0002] In modern industrial production, data backup is an important means to ensure production data security and business continuity; however, with the continuous expansion of production scale and the sharp increase in the amount of production data, the problem of data loss in the data backup process has become increasingly prominent, posing a considerable challenge to the production and operation of enterprises.

[0003] Traditional data backup methods often rely on fixed backup strategies and cycles. Due to performance differences between production equipment, there is a lack of dynamic adjustment capabilities based on the actual operating status of production equipment. This static backup strategy is difficult to adapt to various abnormal situations that may occur during the production process of production equipment, such as equipment failure, network interruption, etc., which may lead to incomplete or lost backup data. In addition, due to differences in the amount of data generated by different production equipment during the production process, data change rate, and data importance, the use of a unified backup strategy is often difficult to meet the personalized needs of different equipment, exacerbating the risk of data loss.

[0004] In order to address the above problems, the prior art generally attempts to establish a correlation model between data backup and data loss by collecting and analyzing various data generated by production equipment during operation, thereby achieving prediction and early warning of data loss risks; however, in actual applications, only the backup behavior of a single production device is considered, ignoring the possible mutual influence and correlation between production equipment, which is not conducive to achieving effective prediction or early warning of data loss risks of production equipment. Summary of the invention

[0005] The purpose of the present invention is to provide a model and method for predicting production data loss for backup sampling, so as to solve the problems raised in the above-mentioned background technology.

[0006] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0007] A model for predicting production data loss applied to backup sampling, the model includes a backup data acquisition module, a data loss behavior characterization module, a correlation sampling processing module and an operation and maintenance center module connected in sequence;

[0008] The backup data acquisition module is used to pre-divide the backup time segments and obtain the data loss ratio generated when each production device backs up production data within the backup time segment;

[0009] The data loss behavior characterization module describes the backup behavior simulation regularity curve of the production equipment based on the backup time segment and the data loss ratio, and converts the backup behavior simulation regularity curve into a backup behavior digital regularity straight line;

[0010] The associated sampling processing module is used to arbitrarily select a production device as a prejudgment reference object, arbitrarily select a production device other than the prejudgment reference object as a prejudgment associated object, and perform associated sampling of data loss behavior between the prejudgment reference object and the prejudgment associated object;

[0011] The operation and maintenance center module evaluates the prediction probability between the prediction reference object and the prediction associated object based on the result of the associated sampling to generate a prediction list and output it to the operation and maintenance port.

[0012] Further, the backup data acquisition module includes a backup encoding unit and a data acquisition unit connected in sequence;

[0013] The backup coding unit is used to evenly divide the time in a day into backup time segments and uniformly code each backup time segment; it is also used to uniformly code production equipment;

[0014] The data acquisition unit is used to record the backup time node of the production equipment to back up the production data, and identify the backup time segment to which the backup time node belongs. The data loss ratio is the ratio of the data packet capacity value lost when backing up the production data to the total data packet capacity value.

[0015] Further, the data loss behavior characterization module includes a punctuation unit and a feature conversion unit connected in sequence;

[0016] The punctuation unit is used to establish a backup behavior two-dimensional coordinate system, take the backup time segment as the abscissa independent variable of the backup behavior two-dimensional coordinate system, take the data loss ratio as the ordinate dependent variable of the backup behavior two-dimensional coordinate system, and then perform punctuation on the backup behavior two-dimensional coordinate system;

[0017] The feature conversion unit is used to smoothly connect the various punctuation points formed by the production equipment during backup in sequence to obtain the backup behavior simulation regular curve of the production equipment; it is also used to capture the punctuation points of each peak and trough in the backup behavior simulation regular curve, and convert the backup behavior simulation regular curve into a backup behavior digital regular straight line.

[0018] Further, the associated sampling processing module includes an object selection unit and an associated sampling analysis unit which are sequentially connected;

[0019] The object selection unit is used to select a production device as a prejudgment reference object, select any production device other than the prejudgment reference object as a prejudgment associated object, and associate the backup behavior digital regular straight line generated corresponding to the prejudgment associated object; and is also used to uniformly number each horizontal straight line in the backup behavior digital regular straight line, wherein the horizontal straight line includes a high horizontal straight line or a low horizontal straight line;

[0020] The associated sampling analysis unit is used to set a logical judgment function. If the horizontal line is a high horizontal line, the logical judgment function is set to 1; if the horizontal line is a low horizontal line, the logical judgment function is set to 0. Based on the logical judgment function, the backup time segment interval used for associated sampling is analyzed.

[0021] Further, the operation and maintenance center module includes an associated sampling statistics unit and a prediction list generation unit which are sequentially connected;

[0022] The associated sampling statistical unit is used to count all horizontal coordinate intervals obtained by performing associated sampling analysis between the predicted reference object and the predicted associated object, and generate an associated sampling set;

[0023] The prediction list generating unit is used to evaluate the prediction probability between the prediction reference object and the prediction associated object, and generate a prediction list, and send it to the operation and maintenance port.

[0024] A method for predicting production data loss applied to backup sampling, the method comprising the following steps:

[0025] Step S1: pre-dividing the backup time segment, and obtaining the data loss ratio generated when each production device backs up production data within the backup time segment;

[0026] Step S2: based on the backup time segment and the data loss ratio, characterize the backup behavior simulation regularity curve of the production equipment, and convert the backup behavior simulation regularity curve into a backup behavior digital regularity straight line;

[0027] Step S3: randomly selecting a production device as a prediction reference object, randomly selecting a production device other than the prediction reference object as a prediction associated object, and performing association sampling of data loss behaviors between the prediction reference object and the prediction associated object;

[0028] Step S4: Based on the result of the associated sampling, the prediction probability between the prediction reference object and the prediction associated object is evaluated to generate a prediction list, and output it to the operation and maintenance port.

[0029] Furthermore, the specific implementation process of step S1 includes:

[0030] The time in a day is evenly divided into backup time segments, and each backup time segment is uniformly coded. The i-th backup time segment is recorded as t i ; Unify the production equipment and record e production equipment as P e ;

[0031] Record production equipment P e Back up the backup time node of the production data and identify the backup time segment t to which the backup time node belongs i , the production equipment P e In the backup time segment t i The data loss ratio generated when backing up production data is recorded as CV (P e , t i ), the data loss ratio is the ratio of the data packet capacity value lost when backing up production data to the total data packet capacity value.

[0032] Furthermore, the specific implementation process of step S2 includes:

[0033] A two-dimensional coordinate system of backup behavior is established, and the backup time segment is used as the abscissa independent variable of the two-dimensional coordinate system of backup behavior, and the data loss ratio is used as the ordinate dependent variable of the two-dimensional coordinate system of backup behavior. Then, points are placed on the two-dimensional coordinate system of backup behavior, and the data loss ratio CV(P e , t i ) The corresponding punctuation is denoted as [t i , CV(P e , t i )];

[0034] Smoothly connect the production equipment P in sequence e The various punctuations formed during the backup process obtain the production equipment P e The backup behavior simulation regularity curve is denoted as AL(P e );

[0035] Capture the backup behavior simulation regularity curve AL(P e ) and the backup behavior simulation regular curve AL(P e ) is transformed into the backup behavior digital law straight line DL(P e ), the conversion process is as follows:

[0036] If the next punctuation point of the peak punctuation point is a trough punctuation point, the backup behavior simulation regular curve segment between the peak punctuation point and the trough punctuation point is fitted into a low horizontal straight line, the ordinate value of the low horizontal straight line is the ordinate value corresponding to the peak punctuation point, the starting point of the abscissa interval of the low horizontal straight line is the abscissa value corresponding to the peak punctuation point, and the end point of the abscissa interval of the low horizontal straight line is the abscissa value corresponding to the trough punctuation point;

[0037] If the next punctuation point of the trough punctuation point is a peak punctuation point, the backup behavior simulation regular curve segment between the trough punctuation point and the peak punctuation point is fitted into a high horizontal straight line, the ordinate value of the high horizontal straight line is the ordinate value corresponding to the trough punctuation point, the starting point of the abscissa interval of the high horizontal straight line is the abscissa value corresponding to the trough punctuation point, and the end point of the abscissa interval of the high horizontal straight line is the abscissa value corresponding to the peak punctuation point;

[0038] In the backup behavior simulation law curve AL(P e ) in the alternating order of the peak punctuation points and the trough punctuation points, and the backup behavior digital regularity straight line DL (P e );

[0039] In the above method, different from the design principle of analog-to-digital conversion, the low-level straight line of the present invention represents the growth law of the degree of data loss behavior, while the high-level straight line represents the reduction law of the degree of data loss behavior.

[0040] Furthermore, the specific implementation process of step S3 includes:

[0041] Production equipment P e In order to predict the reference object, in addition to the production equipment P e Randomly select the rth production equipment P r As the predicted associated object, the associated production equipment P r The corresponding backup behavior digital regularity straight line DL (P r );

[0042] The digital law straight line DL(P e ) are uniformly numbered, and the horizontal straight lines include high horizontal straight lines or low horizontal straight lines, and any x-th horizontal straight line is denoted as dl x (P e );

[0043] Snap horizontal line dl x (P e ) Backup behavior digital regularity straight line DL(P r ) and generate the associated sample set, denoted as S[dl x (P e )]={dl y (P r )|y∈[1,Y]}, where dl y (P r ) represents the digital law straight line DL(P r ) is the y-th horizontal line in the image, where Y represents the digital regularity line of backup behavior DL(Pr ) The total number of horizontal lines in

[0044] Set a logic judgment function f(α), where α represents a horizontal straight line. If the horizontal straight line α is a high-level straight line, f(α)=1; if the horizontal straight line α is a low-level straight line, f(α)=0;

[0045] Perform correlation sampling of data loss behavior:

[0046] If f[dl x (P e )]×f[dl y (P r )]=1, then extract the horizontal line dl x (P e ), denoted as T x =[t a , t b ], where t a represents the ath backup time segment, t b Indicates the bth backup time segment;

[0047] If f[dl x (P e )]×f[dl y (P r )]=0, then the horizontal line dl is not extracted x (P e )’s horizontal axis interval;

[0048] In the above method, a logical judgment function is set to quickly capture backup time segments with the same data loss behavior pattern, that is, backup time segments with high-level data loss behavior patterns.

[0049] Furthermore, the specific implementation process of step S4 includes:

[0050] Statistical backup behavior digital law straight line DL (P e ) and the backup behavior digital law straight line DL(P r ) to obtain all the horizontal axis intervals obtained by performing associated sampling analysis, and generate an associated sampling set, recorded as (P e , P r )={T x |x∈[1,β]}, β represents the digital law straight line DL(P e ) The total number of horizontal lines in

[0051] Production equipment P e To predict the reference object, evaluate the production equipment P e Related production equipment r The predicted probability Among them, NUM[as(P e , P r )] represents the associated sampling set as(P e , P r ), Represents the set obtained by performing a union operation between associated sampling sets The total number of horizontal axis intervals included in , R represents the total number of production equipment;

[0052] The production equipment as prediction related objects are sorted in descending order of prediction probability, and the production equipment P is generated. e The prediction list is recorded as list(P e ), when production equipment P e When data loss occurs, send a prediction list (P e ) to the operation and maintenance port.

[0053] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: the present invention provides a model and method for predicting production data loss in backup sampling, which can accurately capture the backup behavior characteristics of production equipment by pre-dividing the backup time segments and obtaining the data loss ratio of each production equipment in the backup time segments, and convert the backup behavior simulation regularity curve into a digital regularity straight line, so that the representation of data loss behavior is more intuitive and quantitative, so as to improve the accuracy of data loss prediction;

[0054] The data loss behavior between the predicted reference object and the predicted associated object is sampled, and the prediction probability is evaluated based on the results of the associated sampling, which helps operation and maintenance personnel to identify production equipment that may have data loss risks in advance, thereby optimizing the allocation of operation and maintenance resources, concentrating limited operation and maintenance resources on high-risk equipment, and improving operation and maintenance efficiency;

[0055] When data loss occurs in production equipment, operation and maintenance personnel can quickly refer to the prediction list and take corresponding preventive or remedial measures, thereby effectively avoiding or reducing the impact of data loss on the production system and enhancing system reliability;

[0056] The method and model of the present invention are not only suitable for predicting production data loss, but also can provide a scientific basis for the formulation of data backup strategies; through in-depth analysis of the backup behavior of production equipment, operation and maintenance personnel can formulate data backup plans more scientifically, reasonably arrange backup time and frequency, and ensure data security and integrity. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] The accompanying drawings are used to provide further understanding of the present invention and constitute a part of the specification. They are used to explain the present invention together with the embodiments of the present invention and do not constitute a limitation of the present invention.

[0058] Figure 1 It is a schematic diagram of the steps of a method for backup sampling and predicting production data loss of the present invention. DETAILED DESCRIPTION

[0059] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0060] In the first embodiment: a model for predicting production data loss applied to backup sampling is provided, the model comprising a backup data acquisition module, a data loss behavior characterization module, a correlation sampling processing module and an operation and maintenance center module connected in sequence;

[0061] The backup data acquisition module is used to pre-divide the backup time segments and obtain the data loss ratio generated when each production device backs up production data within the backup time segment;

[0062] Wherein, the backup data acquisition module comprises a backup encoding unit and a data acquisition unit connected in sequence;

[0063] The backup coding unit is used to evenly divide the time in a day into backup time segments and uniformly code each backup time segment; it is also used to uniformly code production equipment;

[0064] The data acquisition unit is used to record the backup time node of the production equipment to back up the production data, and identify the backup time segment to which the backup time node belongs, and the data loss ratio is the ratio of the data packet capacity value lost when backing up the production data to the total data packet capacity value;

[0065] The data loss behavior characterization module describes the backup behavior simulation regularity curve of the production equipment based on the backup time segment and the data loss ratio, and converts the backup behavior simulation regularity curve into a backup behavior digital regularity straight line;

[0066] Wherein, the data loss behavior characterization module includes a punctuation unit and a feature conversion unit connected in sequence;

[0067] The punctuation unit is used to establish a backup behavior two-dimensional coordinate system, take the backup time segment as the abscissa independent variable of the backup behavior two-dimensional coordinate system, take the data loss ratio as the ordinate dependent variable of the backup behavior two-dimensional coordinate system, and then perform punctuation on the backup behavior two-dimensional coordinate system;

[0068] The feature conversion unit is used to smoothly connect the various punctuation points formed by the production equipment during backup in sequence to obtain a backup behavior simulation regular curve of the production equipment; it is also used to capture the punctuation points of each peak and trough in the backup behavior simulation regular curve, and convert the backup behavior simulation regular curve into a backup behavior digital regular straight line;

[0069] The associated sampling processing module is used to arbitrarily select a production device as a prejudgment reference object, arbitrarily select a production device other than the prejudgment reference object as a prejudgment associated object, and perform associated sampling of data loss behavior between the prejudgment reference object and the prejudgment associated object;

[0070] Wherein, the associated sampling processing module includes an object selection unit and an associated sampling analysis unit which are sequentially connected;

[0071] The object selection unit is used to select a production device as a prejudgment reference object, select any production device other than the prejudgment reference object as a prejudgment associated object, and associate the backup behavior digital regular straight line generated corresponding to the prejudgment associated object; and is also used to uniformly number each horizontal straight line in the backup behavior digital regular straight line, wherein the horizontal straight line includes a high horizontal straight line or a low horizontal straight line;

[0072] The associated sampling analysis unit is used to set a logic judgment function. If the horizontal line is a high-level line, the logic judgment function is set to 1; if the horizontal line is a low-level line, the logic judgment function is set to 0. Based on the logic judgment function, the backup time segment interval for associated sampling is analyzed;

[0073] The operation and maintenance center module evaluates the prediction probability between the prediction reference object and the prediction associated object based on the result of the association sampling to generate a prediction list and output it to the operation and maintenance port;

[0074] Wherein, the operation and maintenance center module includes an associated sampling statistics unit and a prediction list generation unit connected in sequence;

[0075] The associated sampling statistical unit is used to count all horizontal coordinate intervals obtained by performing associated sampling analysis between the predicted reference object and the predicted associated object, and generate an associated sampling set;

[0076] The prediction list generating unit is used to evaluate the prediction probability between the prediction reference object and the prediction associated object, and generate a prediction list, and send it to the operation and maintenance port.

[0077] See also Figure 1 In the second embodiment, a method for predicting production data loss for backup sampling is provided, the method comprising the following steps:

[0078] Step S1: pre-dividing the backup time segment, and obtaining the data loss ratio generated when each production device backs up production data within the backup time segment;

[0079] For example, the time in a day is evenly divided into backup time segments, and each backup time segment is uniformly coded. The i-th backup time segment is recorded as t i ; Unify the production equipment and record e production equipment as P e ;

[0080] Record production equipment P e Back up the backup time node of the production data and identify the backup time segment t to which the backup time node belongs i , the production equipment P e In the backup time segment t i The data loss ratio generated when backing up production data is recorded as CV (P e , t i ), the data loss ratio is the ratio of the data packet capacity value lost when backing up production data to the total data packet capacity value;

[0081] Step S2: based on the backup time segment and the data loss ratio, characterize the backup behavior simulation regularity curve of the production equipment, and convert the backup behavior simulation regularity curve into a backup behavior digital regularity straight line;

[0082] Exemplarily, a two-dimensional coordinate system of backup behavior is established, the backup time segment is used as the abscissa independent variable of the two-dimensional coordinate system of backup behavior, the data loss ratio is used as the ordinate dependent variable of the two-dimensional coordinate system of backup behavior, and then points are placed on the two-dimensional coordinate system of backup behavior, and the data loss ratio CV(P e , t i ) The corresponding punctuation is denoted as [t i , CV(P e , t i )];

[0083] Smoothly connect the production equipment P in sequence e The various punctuations formed during the backup process obtain the production equipment P e The backup behavior simulation regularity curve is denoted as AL(P e );

[0084] Capture the backup behavior simulation regularity curve AL(P e ) and the backup behavior simulation regular curve AL(P e ) is transformed into the backup behavior digital law straight line DL(P e ), the conversion process is as follows:

[0085] If the next punctuation point of the peak punctuation point is a trough punctuation point, the backup behavior simulation regular curve segment between the peak punctuation point and the trough punctuation point is fitted into a low horizontal straight line, the ordinate value of the low horizontal straight line is the ordinate value corresponding to the peak punctuation point, the starting point of the abscissa interval of the low horizontal straight line is the abscissa value corresponding to the peak punctuation point, and the end point of the abscissa interval of the low horizontal straight line is the abscissa value corresponding to the trough punctuation point;

[0086] If the next punctuation point of the trough punctuation point is a peak punctuation point, the backup behavior simulation regular curve segment between the trough punctuation point and the peak punctuation point is fitted into a high horizontal straight line, the ordinate value of the high horizontal straight line is the ordinate value corresponding to the trough punctuation point, the starting point of the abscissa interval of the high horizontal straight line is the abscissa value corresponding to the trough punctuation point, and the end point of the abscissa interval of the high horizontal straight line is the abscissa value corresponding to the peak punctuation point;

[0087] In the backup behavior simulation law curve AL(P e ) in the alternating order of the peak punctuation points and the trough punctuation points, and the backup behavior digital regularity straight line DL (P e );

[0088] Step S3: randomly selecting a production device as a prediction reference object, randomly selecting a production device other than the prediction reference object as a prediction associated object, and performing association sampling of data loss behaviors between the prediction reference object and the prediction associated object;

[0089] For example, the production equipment P e In order to predict the reference object, in addition to the production equipment P e Randomly select the rth production equipment P r As the predicted associated object, the associated production equipment P r The corresponding backup behavior digital regularity straight line DL (P r );

[0090] The digital law straight line DL(P e ) are uniformly numbered, and the horizontal straight lines include high horizontal straight lines or low horizontal straight lines, and any x-th horizontal straight line is denoted as dl x (P e );

[0091] Snap horizontal line dl x (P e ) Backup behavior digital regularity straight line DL(P r ) and generate the associated sample set, denoted as S[dl x (P e )]={dl y (P r)|y∈[1,Y]}, where dl y (P r ) represents the digital law straight line DL(P r ) is the y-th horizontal line in the image, where Y represents the digital regularity line of backup behavior DL(P r ) The total number of horizontal lines in

[0092] Set a logic judgment function f(α), where α represents a horizontal straight line. If the horizontal straight line α is a high-level straight line, f(α)=1; if the horizontal straight line α is a low-level straight line, f(α)=0;

[0093] Perform correlation sampling of data loss behavior:

[0094] If f[dl x (P e )]×f[dl y (P r )]=1, then extract the horizontal line dl x (P e ), denoted as T x =[t a , t b ], where t a represents the ath backup time segment, t b Indicates the bth backup time segment;

[0095] If f[dl x (P e )]×f[dl y (P r )]=0, then the horizontal line dl is not extracted x (P e )’s horizontal axis interval;

[0096] Step S4: Based on the result of the association sampling, the prediction probability between the prediction reference object and the prediction associated object is evaluated to generate a prediction list, and output it to the operation and maintenance port;

[0097] For example, the statistical backup behavior digital regularity straight line DL (P e ) and the backup behavior digital law straight line DL(P r ) to obtain all the horizontal axis intervals obtained by performing associated sampling analysis, and generate an associated sampling set, recorded as (P e , P r )={T x |x∈[1,β]}, β represents the digital law straight line DL(P e ) The total number of horizontal lines in

[0098] Production equipment P eTo predict the reference object, evaluate the production equipment P e Related production equipment r The predicted probability Among them, NUM[as(P e , P r )] represents the associated sampling set as(P e , P r ), Represents the set obtained by performing a union operation between associated sampling sets The total number of horizontal axis intervals included in , R represents the total number of production equipment;

[0099] The production equipment as prediction related objects are sorted in descending order of prediction probability, and the production equipment P is generated. e The prediction list is recorded as list(P e ), when production equipment P e When data loss occurs, send a prediction list (P e ) to the operation and maintenance port.

[0100] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0101] Finally, it should be noted that the above is only a preferred embodiment of the present invention and is not intended to limit the present invention. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art can still modify the technical solutions described in the aforementioned embodiments or replace some of the technical features therein by equivalents. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included in the protection scope of the present invention.

Claims

1. A method for predicting production data loss applied to backup sampling, characterized in that: The method comprises the following steps: Step S1: pre-dividing the backup time segment, and obtaining the data loss ratio generated when each production device backs up production data within the backup time segment; Step S2: based on the backup time segment and the data loss ratio, characterize the backup behavior simulation regularity curve of the production equipment, and convert the backup behavior simulation regularity curve into a backup behavior digital regularity straight line; Step S3: randomly selecting a production device as a prediction reference object, randomly selecting a production device other than the prediction reference object as a prediction associated object, and performing association sampling of data loss behaviors between the prediction reference object and the prediction associated object; Step S4: Based on the result of the associated sampling, the prediction probability between the prediction reference object and the prediction associated object is evaluated to generate a prediction list, and output it to the operation and maintenance port.

2. According to claim 1, a method for predicting production data loss applied to backup sampling is characterized in that: The specific implementation process of step S1 includes: The time in a day is evenly divided into backup time segments, and each backup time segment is uniformly coded. The i-th backup time segment is recorded as t i ; Unify the production equipment and record e production equipment as P e ; Record production equipment P e Back up the backup time node of the production data and identify the backup time segment t to which the backup time node belongs i , the production equipment P e In the backup time segment t i The data loss ratio generated when backing up production data is recorded as CV (P e , t i ), the data loss ratio is the ratio of the data packet capacity value lost when backing up production data to the total data packet capacity value.

3. The method for predicting production data loss by backup sampling according to claim 2 is characterized in that: The specific implementation process of step S2 includes: A two-dimensional coordinate system of backup behavior is established, and the backup time segment is used as the abscissa independent variable of the two-dimensional coordinate system of backup behavior, and the data loss ratio is used as the ordinate dependent variable of the two-dimensional coordinate system of backup behavior. Then, points are placed on the two-dimensional coordinate system of backup behavior, and the data loss ratio CV(P e , t i ) The corresponding punctuation is denoted as [t i , CV(P e , t i )]; Smoothly connect the production equipment P in sequence e The various punctuations formed during the backup process obtain the production equipment P e The backup behavior simulation regularity curve is denoted as AL(P e ); Capture the backup behavior simulation regularity curve AL(P e ) and the backup behavior simulation regular curve AL(P e ) is transformed into the backup behavior digital law straight line DL(P e ), the conversion process is as follows: If the next punctuation point of the peak punctuation point is a trough punctuation point, the backup behavior simulation regular curve segment between the peak punctuation point and the trough punctuation point is fitted into a low horizontal straight line, the ordinate value of the low horizontal straight line is the ordinate value corresponding to the peak punctuation point, the starting point of the abscissa interval of the low horizontal straight line is the abscissa value corresponding to the peak punctuation point, and the end point of the abscissa interval of the low horizontal straight line is the abscissa value corresponding to the trough punctuation point; If the next punctuation point of the trough punctuation point is a peak punctuation point, the backup behavior simulation regular curve segment between the trough punctuation point and the peak punctuation point is fitted into a high horizontal straight line, the ordinate value of the high horizontal straight line is the ordinate value corresponding to the trough punctuation point, the starting point of the abscissa interval of the high horizontal straight line is the abscissa value corresponding to the trough punctuation point, and the end point of the abscissa interval of the high horizontal straight line is the abscissa value corresponding to the peak punctuation point; In the backup behavior simulation law curve AL(P e ) in the alternating order of the peak punctuation points and the trough punctuation points, and the backup behavior digital regularity straight line DL (P e ).

4. The method for predicting production data loss by backup sampling according to claim 3 is characterized in that: The specific implementation process of step S3 includes: Production equipment P e In order to predict the reference object, in addition to the production equipment P e Randomly select the rth production equipment P r As the predicted associated object, the associated production equipment P r The corresponding backup behavior digital regularity straight line DL (P r ); The digital law straight line DL(P e ) are uniformly numbered, and the horizontal straight lines include high horizontal straight lines or low horizontal straight lines, and any x-th horizontal straight line is denoted as dl x (P e ); Snap horizontal line dl x (P e ) Backup behavior digital regularity straight line DL(P r ) and generate the associated sample set, denoted as S[dl x (P e )]={dl y (P r )|y∈[1,Y]}, where dl y (P r ) represents the digital law straight line DL(P r ) is the y-th horizontal line in the image, where Y represents the digital regularity line of backup behavior DL(P r ) The total number of horizontal lines in Set a logic judgment function f(α), where α represents a horizontal straight line. If the horizontal straight line α is a high-level straight line, f(α)=1; if the horizontal straight line α is a low-level straight line, f(α)=0; Perform correlation sampling of data loss behavior: If f[dl x (P e )]×f[dl y (P r )]=1, then extract the horizontal line dl x (P e ), denoted as T x =[t a , t b ], where t a represents the ath backup time segment, t b Indicates the bth backup time segment; If f[dl x (P e )]×f[dl y (P r )]=0, then the horizontal line dl is not extracted x (P e )’s horizontal axis interval.

5. The method for predicting production data loss by backup sampling according to claim 4 is characterized in that: The specific implementation process of step S4 includes: Statistical backup behavior digital law straight line DL (P e ) and the backup behavior digital law straight line DL(P r ) to obtain all the horizontal axis intervals obtained by performing associated sampling analysis, and generate an associated sampling set, recorded as (P e , P r )={T x |x∈[1,β]}, β represents the digital law straight line DL(P e ) The total number of horizontal lines in Production equipment P e To predict the reference object, evaluate the production equipment P e Related production equipment r The predicted probability Among them, NUM[as(P e , P r )] represents the associated sampling set as(P e , P r ), Represents the set obtained by performing a union operation between associated sampling sets The total number of horizontal axis intervals included in , R represents the total number of production equipment; The production equipment as prediction related objects are sorted in descending order of prediction probability, and the production equipment P is generated. e The prediction list is recorded as list(P e ), when production equipment P e When data loss occurs, send a prediction list (P e ) to the operation and maintenance port.

6. A model for predicting production data loss applied to backup sampling, executing a method for predicting production data loss applied to backup sampling as claimed in any one of claims 1 to 5, characterized in that: The model includes a backup data acquisition module, a data loss behavior characterization module, a correlation sampling processing module and an operation and maintenance center module which are sequentially connected; The backup data acquisition module is used to pre-divide the backup time segments and obtain the data loss ratio generated when each production device backs up production data within the backup time segment; The data loss behavior characterization module describes the backup behavior simulation regularity curve of the production equipment based on the backup time segment and the data loss ratio, and converts the backup behavior simulation regularity curve into a backup behavior digital regularity straight line; The associated sampling processing module is used to arbitrarily select a production device as a prejudgment reference object, arbitrarily select a production device other than the prejudgment reference object as a prejudgment associated object, and perform associated sampling of data loss behavior between the prejudgment reference object and the prejudgment associated object; The operation and maintenance center module evaluates the prediction probability between the prediction reference object and the prediction associated object based on the result of the associated sampling to generate a prediction list and output it to the operation and maintenance port.

7. The model for predicting production data loss for backup sampling according to claim 6 is characterized in that: The backup data acquisition module includes a backup encoding unit and a data acquisition unit connected in sequence; The backup coding unit is used to evenly divide the time in a day into backup time segments and uniformly code each backup time segment; it is also used to uniformly code production equipment; The data acquisition unit is used to record the backup time node of the production equipment to back up the production data, and identify the backup time segment to which the backup time node belongs. The data loss ratio is the ratio of the data packet capacity value lost when backing up the production data to the total data packet capacity value.

8. The model for predicting production data loss for backup sampling according to claim 7 is characterized in that: The data loss behavior characterization module includes a punctuation unit and a feature conversion unit connected in sequence; The punctuation unit is used to establish a backup behavior two-dimensional coordinate system, take the backup time segment as the abscissa independent variable of the backup behavior two-dimensional coordinate system, take the data loss ratio as the ordinate dependent variable of the backup behavior two-dimensional coordinate system, and then perform punctuation on the backup behavior two-dimensional coordinate system; The feature conversion unit is used to smoothly connect the various punctuation points formed by the production equipment during backup in sequence to obtain the backup behavior simulation regular curve of the production equipment; it is also used to capture the punctuation points of each peak and trough in the backup behavior simulation regular curve, and convert the backup behavior simulation regular curve into a backup behavior digital regular straight line.

9. The model for predicting production data loss for backup sampling according to claim 8 is characterized in that: The associated sampling processing module includes an object selection unit and an associated sampling analysis unit connected in sequence; The object selection unit is used to select a production device as a prejudgment reference object, select any production device other than the prejudgment reference object as a prejudgment associated object, and associate the backup behavior digital regular straight line generated corresponding to the prejudgment associated object; and is also used to uniformly number each horizontal straight line in the backup behavior digital regular straight line, wherein the horizontal straight line includes a high horizontal straight line or a low horizontal straight line; The associated sampling analysis unit is used to set a logical judgment function. If the horizontal line is a high horizontal line, the logical judgment function is set to 1; if the horizontal line is a low horizontal line, the logical judgment function is set to 0. Based on the logical judgment function, the backup time segment interval used for associated sampling is analyzed.

10. The model for predicting production data loss for backup sampling according to claim 9 is characterized in that: The operation and maintenance center module includes an associated sampling statistics unit and a prediction list generation unit connected in sequence; The associated sampling statistical unit is used to count all horizontal coordinate intervals obtained by performing associated sampling analysis between the predicted reference object and the predicted associated object, and generate an associated sampling set; The prediction list generating unit is used to evaluate the prediction probability between the prediction reference object and the prediction associated object, and generate a prediction list, and send it to the operation and maintenance port.

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