Data backup method, device and equipment of intelligent manufacturing management system and storage medium

Abstract

The invention discloses a data backup method, device and equipment of an intelligent manufacturing management system and a storage medium, and relates to the technical field of data backup, the data backup method of the intelligent manufacturing management system comprises the following steps: calculating parameter data according to a target data type set corresponding to a to-be-processed data set to obtain a reference data set; performing clustering processing on the to-be-processed data set according to the plurality of data clustering centers corresponding to the reference data set and a preset similarity threshold to obtain a plurality of sub to-be-processed data sets; determining backup state information of each sub-to-be-processed data set according to the database backup information to obtain a backup state information set; and performing data backup on each sub to-be-processed data set according to the backup state information set, a preset backup time threshold and a target data type set corresponding to the to-be-processed data set to obtain a target data backup result. And the utilization rate of system resources is improved, so that the backup efficiency of the to-be-processed data is improved.

Description

Technical Field

[0001] The present application relates to the technical field of data backup, and particularly to a data backup method, device, equipment and storage medium for an intelligent manufacturing management system. Background Art

[0002] When the existing intelligent manufacturing management system performs data backup, when the data collected by a certain data collector has not changed, the existing solutions all overwrite the original data in the database with the data currently collected by the data collector, resulting in low efficiency during data backup; and when performing backup, the data collected by the data collector is backed up according to the pre-set data backup priority, resulting in low utilization rate of system resources, thus resulting in low efficiency during data backup. Summary of the Invention

[0003] The main purpose of the present application is to provide a data backup method, device, equipment and storage medium for an intelligent manufacturing management system, aiming to solve the technical problem of low efficiency of the data backup method of the existing intelligent manufacturing management system.

[0004] To achieve the above object, the present application proposes a data backup method for an intelligent manufacturing management system, and the data backup method for the intelligent manufacturing management system includes: Calculating parameter data according to the target data type set corresponding to the to-be-processed data set to obtain a reference data set; Performing clustering processing on the to-be-processed data set according to multiple data clustering centers corresponding to the reference data set and a preset similarity threshold to obtain multiple sub-to-be-processed data sets; Determining the backup status information of each sub-to-be-processed data set according to the database backup information to obtain a backup status information set; Performing data backup on each sub-to-be-processed data set according to the backup status information set, a preset backup time threshold, and the target data type set corresponding to the to-be-processed data set to obtain a target data backup result.

[0005] In an embodiment, the step of performing clustering processing on the to-be-processed data set according to multiple data clustering centers corresponding to the reference data set and a preset similarity threshold to obtain multiple sub-to-be-processed data sets includes: Calculating the data similarity corresponding to each to-be-processed data according to the first feature vector set corresponding to the to-be-processed data set and the second feature vector set corresponding to multiple data clustering centers corresponding to the reference data set; Comparing the data similarity with the preset similarity threshold to obtain a similarity comparison result; Complete the clustering process for each piece of data to be processed according to the similarity comparison result, and obtain multiple subsets of data to be processed.

[0006] In one embodiment, the step of performing data backup on each subset of data to be processed according to the backup status information set, the preset backup time threshold, and the set of target data types corresponding to the data set to be processed, to obtain a target data backup result, includes: Perform data partitioning on each subset of data to be processed corresponding to the backup status information of the to-be-backup status in the backup status information set, to obtain a first target data set to be processed and a second target data set to be processed; Calculate weights for the first target data set to be processed and the second target data set to be processed respectively according to the target backup time and the data processing priority, to obtain a first weight information set corresponding to the first target data set to be processed and a second weight information set corresponding to the second target data set to be processed; Perform classification processing on the first target data set to be processed and the second target data set to be processed respectively according to the set of target data types corresponding to the data set to be processed, to obtain multiple third target data sets to be processed corresponding to the first target data set to be processed and multiple fourth target data sets to be processed corresponding to the second target data set to be processed; Perform data backup on each subset of data to be processed according to the multiple third target data sets to be processed corresponding to the first target data set to be processed and the multiple fourth target data sets to be processed corresponding to the second target data set to be processed, to obtain a target data backup result.

[0007] In one embodiment, the step of calculating weights for the first target data set to be processed and the second target data set to be processed respectively according to the target backup time and the data processing priority, to obtain a first weight information set corresponding to the first target data set to be processed and a second weight information set corresponding to the second target data set to be processed, includes: Obtain a weight gain coefficient and a system sensitivity coefficient; Perform mean calculation according to the data processing priority, to obtain an average processing priority; Calculate weights for the first target data set to be processed and the second target data set to be processed respectively according to the target backup time, the weight gain coefficient, the system sensitivity coefficient, the data processing priority, and the average processing priority, to obtain a first weight information set corresponding to the first target data set to be processed and a second weight information set corresponding to the second target data set to be processed.

[0008] In one embodiment, the step of performing data backup on each sub-data set to be processed according to a plurality of third target data sets to be processed corresponding to the first target data set to be processed and a plurality of fourth target data sets to be processed corresponding to the second target data set to be processed to obtain a target data backup result includes: Performing weight calculation according to a first weight information set, a second weight information set, a first target data set to be processed, and a second target data set to be processed to obtain a third weight information set and a fourth weight information set; Performing priority calculation according to the third weight information set, the fourth weight information set, a plurality of third target data sets to be processed, and a plurality of fourth target data sets to be processed to obtain a first processing priority and a second processing priority; Performing data backup on each sub-data set to be processed according to a preset backup time, the first processing priority, and the second processing priority to obtain a target data backup result.

[0009] In one embodiment, the step of performing data backup on each sub-data set to be processed according to a preset backup time, the first processing priority, and the second processing priority to obtain a target data backup result includes: Determining a first preset backup time and a second preset backup time according to the preset backup time; Performing data backup on the third target data to be processed corresponding to each sub-data set to be processed according to the first preset backup time and the first processing priority to obtain a first data backup result; Performing data backup on the fourth target data to be processed corresponding to each sub-data set to be processed according to the second preset backup time and the second processing priority to obtain a second data backup result; Obtaining a target data backup result according to the first data backup result and the second data backup result.

[0010] In one embodiment, the step of performing data division on each sub-data set to be processed corresponding to the backup status information in the backup status information set with the backup status being the to-be-backup status to obtain a first target data set to be processed and a second target data set to be processed includes: Inputting each sub-data set to be processed corresponding to the backup status information in the backup status information set with the backup status being the to-be-backup status into a backup time prediction model to obtain a target backup time; When the target backup time is greater than a backup time threshold, determining the sub-data set to be processed corresponding to the target backup time as the first target data set to be processed; When the target backup time is not greater than the backup time threshold, determining the sub-data set to be processed corresponding to the target backup time as the second target data set to be processed.

[0011] In addition, to achieve the above object, the present application also provides a data backup device for an intelligent manufacturing management system, where the data backup device for the intelligent manufacturing management system includes: A calculation module, configured to calculate parameter data according to a target data type set corresponding to a to-be-processed data set, and obtain a reference data set; A clustering module, configured to perform clustering processing on the to-be-processed data set according to a plurality of data clustering centers corresponding to the reference data set and a preset similarity threshold, and obtain a plurality of sub-to-be-processed data sets; A processing module, configured to determine backup status information of each sub-to-be-processed data set according to the database backup information, and obtain a backup status information set; A backup module, configured to perform data backup on each sub-to-be-processed data set according to the backup status information set, a preset backup time threshold, and a target data type set corresponding to the to-be-processed data set, and obtain a target data backup result.

[0012] In addition, to achieve the above object, the present application also provides a data backup device for an intelligent manufacturing management system, where the device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, and the computer program is configured to implement the steps of the data backup method for the intelligent manufacturing management system as described above.

[0013] In addition, to achieve the above object, the present application also provides a storage medium, where the storage medium is a computer-readable storage medium, and a computer program is stored on the storage medium, and when the computer program is executed by a processor, the steps of the data backup method for the intelligent manufacturing management system as described above are implemented.

[0014] In addition, to achieve the above object, the present application also provides a computer program product, where the computer program product includes a computer program, and when the computer program is executed by a processor, the steps of the data backup method for the intelligent manufacturing management system as described above are implemented.

[0015] In this application, reference data sets are obtained by calculating parameter data according to the target data type sets corresponding to the data sets to be processed; the data sets to be processed are clustered according to multiple data clustering centers corresponding to the reference data sets and a preset similarity threshold to obtain multiple sub-data sets to be processed; backup status information for each sub-data set to be processed is determined according to database backup information to obtain a backup status information set; data backup is performed on each sub-data set to be processed according to the backup status information set, a preset backup time threshold, and the target data type sets corresponding to the data sets to be processed to obtain a target data backup result. This improves the utilization rate of system resources and thus improves the efficiency of data backup for the data sets to be processed. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] The drawings herein are incorporated into and constitute a part of this specification, showing embodiments consistent with this application and, together with the specification, used to explain the principles of this application.

[0017] To more clearly illustrate the technical solutions in the embodiments of this application or in the prior art, the following briefly introduces the drawings required for use in the description of the embodiments or the prior art. Obviously, for those of ordinary skill in the art, other drawings can also be obtained based on these drawings without creative efforts.

[0018] Figure 1 It is a schematic flowchart provided for Embodiment 1 of the data backup method of the intelligent manufacturing management system of this application; Figure 2 It is a schematic flowchart provided for Embodiment 2 of the data backup method of the intelligent manufacturing management system of this application; Figure 3 It is a schematic module structure diagram of the data backup device of the intelligent manufacturing management system in the embodiments of this application; Figure 4 It is a schematic device structure diagram of the hardware operating environment involved in the data backup method of the intelligent manufacturing management system in the embodiments of this application.

[0019] The implementation, functional features, and advantages of the objectives of this application will be further described with reference to the embodiments and the drawings. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0020] It should be understood that the specific embodiments described herein are only used to explain the technical solutions of this application and are not used to limit this application.

[0021] To better understand the technical solutions of this application, the following will be described in detail in combination with the drawings in the specification and the specific embodiments.

[0022] The main solution of the embodiment of the present application is: calculate parameter data according to the set of target data types corresponding to the set of data to be processed to obtain a reference data set; perform clustering processing on the set of data to be processed according to multiple data clustering centers corresponding to the reference data set and a preset similarity threshold to obtain multiple sub-sets of data to be processed; determine the backup status information of each sub-set of data to be processed according to the database backup information to obtain a backup status information set; perform data backup on each sub-set of data to be processed according to the backup status information set, a preset backup time threshold, and the set of target data types corresponding to the set of data to be processed to obtain a target data backup result.

[0023] When the existing intelligent manufacturing management system performs data backup, when the data collected by a certain data collector has not changed, the existing solution is to overwrite the original data in the database with the data currently collected by the data collector, resulting in low efficiency during data backup; and when performing backup, the data collected by the data collector is backed up according to the pre-set data backup priority, resulting in low utilization rate of system resources, thus resulting in low efficiency during data backup.

[0024] In the present application, parameter data is calculated according to the set of target data types corresponding to the set of data to be processed to obtain a reference data set; clustering processing is performed on the set of data to be processed according to multiple data clustering centers corresponding to the reference data set and a preset similarity threshold to obtain multiple sub-sets of data to be processed; the backup status information of each sub-set of data to be processed is determined according to the database backup information to obtain a backup status information set; data backup is performed on each sub-set of data to be processed according to the backup status information set, a preset backup time threshold, and the set of target data types corresponding to the set of data to be processed to obtain a target data backup result. This improves the utilization rate of system resources, thereby improving the efficiency of backing up the data to be processed.

[0025] It should be noted that the execution subject of this embodiment can be a computing service device with data processing, network communication, and program running functions, such as a tablet computer, a personal computer, a mobile phone, etc., or a data backup device of an intelligent manufacturing management system capable of implementing the above functions. Hereinafter, taking the data backup device of the intelligent manufacturing management system as the execution subject as an example, this embodiment and the following embodiments will be described.

[0026] Based on this, the embodiment of the present application provides a data backup method for an intelligent manufacturing management system, referring to Figure 1 , Figure 1 is a schematic flowchart of the first embodiment of the data backup method for the intelligent manufacturing management system of the present application.

[0027] In this embodiment, the data backup method of the intelligent manufacturing management system includes steps S10 to S40: Step S10, calculate parameter data according to the target data type set corresponding to the data set to be processed, and obtain a reference data set; It should be noted that in this embodiment, by using the clustering method, the type corresponding to each data to be processed in the data set to be processed is accurately determined, and K sub-data sets to be processed are obtained. By determining the processing priorities corresponding to the K third target data sets and the K fourth target data sets in the K sub-data sets to be processed according to the weight information corresponding to each sub-data set to be processed, the system can back up different types of data to be processed in the data set to be processed according to their corresponding importance at different time periods. Among them, K represents the number of target data types in the target data type set. By the above method, the utilization rate of system resources is improved, and thus the efficiency of backing up the data to be processed in the data set to be processed is improved.

[0028] It can be understood that the data set to be processed refers to the data transmitted through the data transmission bus carried by the data set collector on the intelligent manufacturing production line, and the reference data set refers to a set composed of K reference data selected from the data set to be processed.

[0029] In specific implementation, K reference data in the data set to be processed are calculated according to the target data type set through an iterative method to obtain a reference data set; the reference data in the reference data set are determined as clustering centers to obtain K clustering centers. Specifically, the method of calculating K reference data in the data set to be processed according to the target data type set through an iterative method can be as follows:

[0030] In the formula, represents the jth clustering center among the K clustering centers, The initial value of can be K data to be processed randomly selected from the data set to be processed. K represents the number of target data types in the target data type set, and j = 1, 2, 3... K; C j represents the cluster with as the clustering center; |C j | represents the number of data to be processed in the cluster with as the clustering center; x j represents the jth data to be processed in the data set to be processed.

[0031] It should be noted that in this embodiment, the method for obtaining the data set to be processed may be to obtain the data to be processed by receiving the data transmitted through the data transmission bus carried by the data collector on the intelligent manufacturing production line. Among them, the data to be processed in the data set to be processed also includes the time stamp corresponding to the data to be processed, and the time stamp represents the time information when the data to be processed is collected.

[0032] Step S20: Perform clustering processing on the data set to be processed according to the multiple data clustering centers corresponding to the reference data set and a preset similarity threshold to obtain multiple sub-data sets to be processed; It can be understood that the data clustering centers are determined by the reference data in the reference data set. The preset similarity threshold refers to the similarity critical value preset between the data to be processed and each of the K clustering centers. The sub-data set to be processed refers to the subset after the clustering processing of the data set to be processed.

[0033] In a specific implementation, the data to be processed in the data set to be processed is vectorized by using a general vectorization processing method to obtain a set of vectors to be processed; feature extraction is performed on each vector to be processed in the set of vectors to be processed to obtain M sets of first feature vectors; feature extraction is performed on the K clustering centers to obtain K sets of second feature vectors; the similarity between the data to be processed in the data set to be processed and each of the K clustering centers is calculated according to the M sets of first feature vectors and the K sets of second feature vectors, and it is determined whether the similarity between the data to be processed and each of the K clustering centers is less than or equal to the preset similarity threshold. If the similarity between a certain data to be processed in the data set to be processed and a certain clustering center among the K clustering centers is greater than the preset similarity threshold, then the data to be processed is determined to be an element in the corresponding cluster of the clustering, and the clustering of the data to be processed in the data set to be processed is completed according to the above judgment rule to obtain K sub-data sets to be processed. Among them, M represents the number of data to be processed corresponding to the data set to be processed.

[0034] It should be noted that for an intelligent manufacturing production line, different production tasks require different processes, and the quantity and type of data generated during the production process are also different. Therefore, before processing the data to be processed in the data set to be processed, it is necessary to obtain the corresponding data processing rule in the database according to the production task on the intelligent manufacturing production line to obtain the target data processing rule. Among them, the data processing rule includes the processing priority corresponding to different types of data and the reference data corresponding to different types of data, which can be determined by historical data or the work experience of relevant practitioners.

[0035] In a feasible implementation manner, step S20 may include steps A11 to A13: Step A11, calculate the similarity degree of each piece of data to be processed according to the first feature vector set corresponding to the data set to be processed and the second feature vector set corresponding to multiple data clustering centers of the reference data set; It should be noted that the first feature vector set is obtained by performing feature extraction on each to-be-processed vector in the to-be-processed vector set, the second feature vector set refers to the feature vector set obtained by performing feature extraction on K clustering centers, and the data similarity degree refers to the similarity degree between the to-be-processed data and each of the K clustering centers.

[0036] In a specific implementation, the to-be-processed data in the to-be-processed data set is vectorized by using a general vectorization processing method to obtain a to-be-processed vector set; feature extraction is performed on each to-be-processed vector in the to-be-processed vector set to obtain M first feature vector sets; feature extraction is performed on K clustering centers to obtain K second feature vector sets; and the similarity degree between the to-be-processed data in the to-be-processed data set and each of the K clustering centers is calculated according to the M first feature vector sets and the K second feature vector sets.

[0037] It should be noted that the similarity degree between the to-be-processed data in the to-be-processed data set and each of the K clustering centers is calculated according to the following formula based on the M first feature vector sets and the K second feature vector sets:

[0038] In the formula, represents the similarity degree between the to-be-processed data in the to-be-processed data set and each of the K clustering centers; represents a similarity factor, which can be determined by user input or system default; M represents the number of the first feature vector sets; K represents the number of clustering centers; || represents absolute value calculation; a ij represents the j-th second feature vector in the second feature vector set corresponding to the i-th clustering center among the K clustering centers; b ij represents the j-th first feature vector in the first feature vector set corresponding to the i-th to-be-processed data among the M to-be-processed data; B i represents the i-th to-be-processed vector in the to-be-processed vector set; represents the average modulus length of the to-be-processed vectors in the to-be-processed vector set; A i represents the vector representation of the reference data corresponding to the i-th clustering center among the K clustering centers; |A i| represents the norm length after vectorizing the reference data corresponding to the $i$-th clustering center among the $K$ clustering centers.

[0039] Step A12: Compare the data similarity with a preset similarity threshold to obtain a similarity comparison result. It can be understood that the similarity comparison result refers to the comparison result between the data to be processed and each clustering center among the $K$ clustering centers and the preset similarity threshold.

[0040] In a specific implementation, by comparing the similarity between the data to be processed and each clustering center among the $K$ clustering centers with the preset similarity critical value between the data to be processed and each clustering center among the $K$ clustering centers, the comparison result between the similarity between the data to be processed and each clustering center among the $K$ clustering centers and the preset similarity critical value between the data to be processed and each clustering center among the $K$ clustering centers is obtained.

[0041] Step A13: Complete the clustering process of each data to be processed according to the similarity comparison result to obtain multiple sub-data sets to be processed.

[0042] In a specific implementation, determine whether the similarity between the data to be processed and each clustering center among the $K$ clustering centers is less than or equal to the preset similarity threshold. If the similarity between a certain data to be processed in the data set to be processed and a certain clustering center among the $K$ clustering centers is greater than the preset similarity threshold, then determine this data to be processed as an element in the corresponding cluster of this clustering center, and complete the clustering of the data to be processed in the data set to be processed according to the above judgment rule to obtain $K$ sub-data sets to be processed.

[0043] Step S30: Determine the backup status information of each sub-data set to be processed according to the database backup information to obtain a backup status information set. It can be understood that the database backup information refers to the backup log information stored in the database, and the backup status information includes the backed-up status and the non-backed-up status.

[0044] In a specific implementation, time information regarding the backup of each sub-data to be processed is obtained from the backup log of the database; current time information is obtained to obtain reference time information; it is determined whether the time information of the backup of the sub-data to be processed is the same as the reference time information, and backup status information corresponding to each sub-data to be processed is generated according to the determination result to obtain a set of backup status information. Among them, the determination rule for determining whether the time information of the backup of the sub-data to be processed is the same as the reference time information may specifically be: if the time information of the backup of the sub-data to be processed is the same as the reference time, the backup status information corresponding to the sub-data to be processed is set to "backed up"; if the time information of the backup of the sub-data to be processed is different from the reference time, the backup status information corresponding to the sub-data to be processed is set to "not backed up".

[0045] Step S40, perform data backup on each sub-data set to be processed according to the set of backup status information, the preset backup time threshold, and the set of target data types corresponding to the data set to be processed, to obtain a target data backup result.

[0046] It can be understood that the preset backup time threshold refers to the critical value of the backup time of the data to be processed set in advance. The set of target data types includes the first set of target data to be processed and the second set of target data to be processed. The target data backup result refers to the backup result of the data to be processed.

[0047] In a specific implementation, different backup schemes are adopted for the data to be processed in each sub-data set to be processed among the K sub-data sets to be processed according to the backup status information in the set of backup status information, so as to complete the backup processing of the sub-data to be processed in the K sub-data sets to be processed.

[0048] In this embodiment, reference data sets are obtained by calculating parameter data according to the set of target data types corresponding to the data set to be processed; the data set to be processed is clustered according to multiple data clustering centers corresponding to the reference data sets and a preset similarity threshold to obtain multiple sub-data sets to be processed; the backup status information of each sub-data set to be processed is determined according to the database backup information to obtain a set of backup status information; data backup is performed on each sub-data set to be processed according to the set of backup status information, the preset backup time threshold, and the set of target data types corresponding to the data set to be processed, to obtain a target data backup result. The utilization rate of system resources is improved, thereby improving the efficiency when backing up the data to be processed.

[0049] Based on the first embodiment of the present application, in the second embodiment of the present application, for the same or similar content as in the above-mentioned first embodiment, reference may be made to the above introduction and will not be elaborated hereinafter. On this basis, please refer to Figure 2In the data backup method of the intelligent manufacturing management system, step S40 further includes steps S41 to S44: Step S41, perform data partitioning on each sub-pending data corresponding to the pending backup status in the backup status information set to obtain a first target pending data set and a second target pending data set; It can be understood that the first target pending data set refers to the data set composed of pending data whose backup time is greater than the preset backup time threshold, and the second target pending data set refers to the data set composed of pending data whose backup time is not greater than the preset backup time threshold.

[0050] In a specific implementation, if the backup information corresponding to the sub-pending data is "not backed up", then the sub-pending data is determined as the target pending data; the target pending data is input into a pre-trained backup time prediction model to predict the backup time of the target pending data, and the obtained target backup time is obtained; it is judged whether the target backup time is greater than the preset backup time threshold. If the target backup time is greater than the preset backup time threshold, the target pending data is determined as the first target pending data; if the target backup time is less than or equal to the preset backup time threshold, the target pending data is determined as the second target pending data. Classify the sub-pending data in the K sub-pending data sets according to the above judgment rules to obtain a first target pending data set and a second target pending data set.

[0051] It should be noted that if the backup information corresponding to the sub-pending data is "already backed up", then obtain the time stamp corresponding to the sub-pending data; judge whether the time stamp corresponding to the sub-pending data is consistent with the time stamp corresponding to the data in the database. If they are consistent, control the data set collector on the intelligent manufacturing production line to re-collect the data; if they are inconsistent, update the time stamp corresponding to the data in the database corresponding to the sub-pending data to the current time stamp.

[0052] In a feasible implementation manner, step S41 may include steps B11 to B13: Step B11, input each sub-pending data corresponding to the pending backup status in the backup status information set into the backup time prediction model to obtain the target backup time; It can be understood that the backup time prediction model refers to a pre-trained backup time prediction model, and the target backup time refers to the predicted backup time of the sub-pending data.

[0053] In a specific implementation, if the backup information corresponding to the sub-data to be processed is "not backed up", then the sub-data to be processed is determined as the target data to be processed; the target data to be processed is input into a pre-trained backup time prediction model to predict the backup time of the target data to be processed, and the obtained target backup time is obtained.

[0054] Step B12, when the target backup time is greater than the backup time threshold, determine the sub-data to be processed corresponding to the target backup time as the first target set of data to be processed; It can be understood that the predicted backup time of the sub-data to be processed is compared with the preset critical value of the backup time of the data to be processed. When the predicted backup time of the sub-data to be processed is greater than the preset critical value of the backup time of the data to be processed, the corresponding data to be processed is classified as the first target data to be processed, and then the first target set of data to be processed is obtained.

[0055] Step B13, when the target backup time is not greater than the backup time threshold, determine the sub-data to be processed corresponding to the target backup time as the second target set of data to be processed.

[0056] It can be understood that when the predicted backup time of the sub-data to be processed is not greater than the preset critical value of the backup time of the data to be processed, the corresponding data to be processed is classified as the second target data to be processed, and then the second target set of data to be processed is obtained.

[0057] Step S42, calculate the weights of the first target set of data to be processed and the second target set of data to be processed respectively according to the target backup time and the data processing priority, and obtain the first weight information set corresponding to the first target set of data to be processed and the second weight information set corresponding to the second target set of data to be processed; It can be understood that the data processing priority refers to the processing priority of the data to be processed. The first weight information set refers to the set of weight information of each data in the first target set of data to be processed when backing up, and the second weight information set refers to the set of weight information of each data in the second target set of data to be processed when backing up.

[0058] In a specific implementation, calculate the weight information of the first target data to be processed when backing up according to the target backup time and the processing priority of the first target data to be processed in the first target set of data to be processed, and obtain the first weight information set; calculate the weight information of the second target data to be processed when backing up according to the target backup time and the processing priority of the second target data to be processed in the second target set of data to be processed, and obtain the second weight information set.

[0059] In a feasible implementation manner, step S42 may include steps C11 to C13: Step C11, obtaining a weight benefit coefficient and a system sensitivity coefficient; It can be understood that the weight benefit coefficient refers to a parameter for measuring the contribution degree of the processing weight coefficient of the data to be processed to the total benefit, and the system sensitivity coefficient refers to the sensitivity coefficient of the intelligent manufacturing management system.

[0060] In a specific implementation, the weight benefit coefficient can be determined by user input or system default, and the sensitivity coefficient of the system can be obtained by system default, which is used to represent the sensitivity of the system when obtaining the first weight information corresponding to the first target data to be processed in the first target data set to be processed.

[0061] Step C12, performing a mean calculation according to the data processing priority to obtain an average processing priority; It can be understood that the average processing priority refers to the average value corresponding to the processing priorities of each target data to be processed in the target data set to be processed. By performing a mean calculation on the processing priorities of each target data to be processed in the first target data set to be processed and the second target data set to be processed respectively, the average processing priority is obtained.

[0062] Step C13, performing a weight calculation on the first target data set to be processed and the second target data set to be processed respectively according to the target backup time, the weight benefit coefficient, the system sensitivity coefficient, the data processing priority, and the average processing priority, to obtain a first weight information set corresponding to the first target data set to be processed and a second weight information set corresponding to the second target data set to be processed.

[0063] It can be understood that in this embodiment, the weight information of the first target data to be processed during backup is calculated according to the target backup time and the processing priority corresponding to the first target data to be processed in the first target data set to be processed by the method shown in the following formula, to obtain the first weight information set (similarly, the second weight information set can be calculated):

[0064] M in the formula i represents the mutual influence degree between any two first target data to be processed in the first target data set to be processed; represents the second extreme minimum difference between any two first target data to be processed in the first target data set to be processed; x a represents the a-th first target data to be processed in the first target data set to be processed; x brepresents the b-th first data set to be processed in the first target data set to be processed; p represents the identification coefficient, where p is greater than 0 and less than 1. The larger p is, the higher the recognition rate of the first target data to be processed in the first target data set to be processed. Conversely, the lower the recognition rate of the first target data to be processed in the first target data set to be processed. It can be determined by user input or system default; Q p represents the first weight information corresponding to the first target data to be processed in the first target data set to be processed; N i represents the weight gain coefficient, which can be determined by user input or system default; c1 and c2 represent the sensitivity coefficients of the system, which can be determined by system default. They are used to represent the sensitivity of the system when obtaining the first weight information corresponding to the first target data to be processed in the first target data set to be processed. The smaller the values of c1 and c2, the higher the sensitivity of the system, the higher the accuracy of the obtained first weight, and the greater the corresponding volatility; conversely, the lower the sensitivity of the server, the lower the accuracy of the obtained first weight, and the smaller the corresponding volatility; Y represents the target backup time corresponding to the first target data to be processed, and Y is less than 1 and greater than 0; Q c represents the processing priority corresponding to the first target data to be processed in the first target data set to be processed; Q M represents the average value corresponding to the processing priority of the first target data to be processed in the first target data set to be processed.

[0065] Step S43: Classify the first target data set to be processed and the second target data set to be processed according to the corresponding target data type sets of the data sets to be processed, to obtain multiple third target data sets to be processed corresponding to the first target data set to be processed and multiple fourth target data sets to be processed corresponding to the second target data set to be processed; It can be understood that the target data type set refers to the set of data types generated during the production process of the intelligent manufacturing production line. The multiple third target data sets to be processed refer to the classified first target data set to be processed, and the multiple fourth target data sets to be processed refer to the classified second target data set to be processed.

[0066] In a specific implementation, the first target data to be processed in the first target data to be processed set is classified according to the target data types in the target data type set, and K third target data to be processed sets are obtained; among them, the third target data to be processed set in the K third target data to be processed sets is a subset of the first target data to be processed set; the second target data to be processed in the second target data to be processed set is classified according to the target data types in the target data type set, and K fourth target data to be processed sets are obtained; among them, the fourth target data to be processed set in the K fourth target data to be processed sets is a subset of the second target data to be processed set.

[0067] Step S44: Perform data backup on each sub-data set to be processed according to the multiple third target data sets to be processed corresponding to the first target data set to be processed and the multiple fourth target data sets to be processed corresponding to the second target data set to be processed, and obtain a target data backup result.

[0068] It can be understood that data backup is performed on each sub-data set to be processed based on the multiple third target data sets to be processed and the multiple fourth target data sets to be processed obtained by dividing according to data types, and finally a backup result of the data to be processed is obtained.

[0069] In a feasible implementation manner, step S44 may include steps D11 to D13: Step D11: Calculate weights according to the first weight information set, the second weight information set, the first target data set to be processed, and the second target data set to be processed, and obtain a third weight information set and a fourth weight information set; It can be understood that the third weight information set refers to the weight information set corresponding to the first target data to be processed in each sub-data set to be processed among the K sub-data sets to be processed, and the fourth weight information set refers to the weight information set corresponding to the second target data to be processed in each sub-data set to be processed among the K sub-data sets to be processed.

[0070] In a specific implementation, the method for obtaining the third weight information in the third weight information set may be to calculate the weighted average of the weight information corresponding to the first target data to be processed in each of the K subsets of data to be processed, so as to obtain the third weight information set; the method for calculating the fourth weight information in the fourth weight information set is the same as the method for obtaining the third weight information set, that is, according to the first weight information in the first weight information set, calculate the weight information corresponding to the first target data to be processed in each of the K subsets of data to be processed, so as to obtain the third weight information set; according to the second weight information in the second weight information set, calculate the weight information corresponding to the second target data to be processed in each of the K subsets of data to be processed, so as to obtain the fourth weight information set.

[0071] Step D12, perform priority calculation according to the third weight information set, the fourth weight information set, the multiple third target data sets to be processed, and the multiple fourth target data sets to be processed, so as to obtain a first processing priority and a second processing priority; It can be understood that the first processing priority refers to the set of processing priorities corresponding to each of the K third target data sets to be processed, and the second processing priority refers to the set of processing priorities corresponding to each of the K fourth target data sets to be processed.

[0072] In a specific implementation, determine the processing priority corresponding to each of the K third target data sets to be processed according to the third weight information set, so as to obtain the first processing priority; determine the processing priority corresponding to each of the K fourth target data sets to be processed according to the fourth weight information set, so as to obtain the second processing priority.

[0073] Step D13, perform data backup on each subset of data to be processed according to the preset backup time, the first processing priority, and the second processing priority, so as to obtain a target data backup result.

[0074] It can be understood that the preset backup time includes a first preset backup time and a second preset backup time.

[0075] In a specific implementation, the third target data to be processed in the K third target data sets to be processed is backed up according to the first priority based on the first preset backup time; the fourth target data to be processed in the K fourth target data sets to be processed is backed up according to the second priority based on the second preset backup time. Among them, the first preset backup time and the second preset backup time can be determined by user input or system default. The second preset backup time can be any time, and the first preset backup time can be when the system resources are abundant.

[0076] In a feasible implementation manner, step D13 may include steps E11 to E14: Step E11, determine the first preset backup time and the second preset backup time according to the preset backup time; It can be understood that the first preset backup time refers to the preset time for backing up the K third target data sets to be processed, and the second preset backup time refers to the preset time for backing up the K fourth target data sets to be processed.

[0077] Step E12, perform data backup on the third target data to be processed corresponding to each sub-data set to be processed according to the first preset backup time and the first processing priority, and obtain a first data backup result; It can be understood that the first data backup result refers to the result of backing up the third target data to be processed.

[0078] In a specific implementation, the third target data to be processed in the K third target data sets to be processed is backed up according to the first priority based on the first preset backup time, that is, the third target data to be processed in the K third target data sets to be processed is backed up according to the first priority at the preset time for backing up the K third target data sets to be processed, and finally a first data backup result is obtained.

[0079] Step E13, perform data backup on the fourth target data to be processed corresponding to each sub-data set to be processed according to the second preset backup time and the second processing priority, and obtain a second data backup result; It can be understood that the second data backup result refers to the result of backing up the fourth target data to be processed.

[0080] In a specific implementation, the fourth target data to be processed in the K fourth target data sets to be processed is backed up according to the second priority based on the second preset backup time, that is, the fourth target data to be processed in the K fourth target data sets to be processed is backed up according to the second priority at the preset time for backing up the K fourth target data sets to be processed, and finally a second data backup result is obtained.

[0081] Step E14: Obtain a target data backup result based on the first data backup result and the second data backup result.

[0082] In a specific implementation, the results of backing up the third target data to be processed and the results of backing up the fourth target data to be processed are aggregated to obtain a target data backup result.

[0083] In this embodiment, the sub-data to be processed corresponding to the backup status information of the to-be-backed-up status in the backup status information set are partitioned to obtain a first target data set to be processed and a second target data set to be processed; weight calculations are respectively performed on the first target data set to be processed and the second target data set to be processed according to the target backup time and the data processing priority to obtain a first weight information set corresponding to the first target data set to be processed and a second weight information set corresponding to the second target data set to be processed; classification processing is respectively performed on the first target data set to be processed and the second target data set to be processed according to the target data type set corresponding to the data set to be processed to obtain a plurality of third target data sets to be processed corresponding to the first target data set to be processed and a plurality of fourth target data sets to be processed corresponding to the second target data set to be processed; data backup is performed on each sub-data set to be processed according to the plurality of third target data sets to be processed corresponding to the first target data set to be processed and the plurality of fourth target data sets to be processed corresponding to the second target data set to be processed to obtain a target data backup result. Through the above method, the efficiency of backing up the data to be processed in the data set to be processed is improved.

[0084] It should be noted that the above examples are only for understanding the present application and do not constitute a limitation on the data backup method of the intelligent manufacturing management system of the present application. Based on this technical concept, more forms of simple transformations are within the protection scope of the present application.

[0085] The present application also provides a data backup device for an intelligent manufacturing management system. Please refer to Figure 3 , the data backup device of the intelligent manufacturing management system includes: Calculation module 10, configured to calculate parameter data according to the target data type set corresponding to the data set to be processed to obtain a reference data set; Clustering module 20, configured to perform clustering processing on the data set to be processed according to a plurality of data clustering centers corresponding to the reference data set and a preset similarity threshold to obtain a plurality of sub-data sets to be processed; Processing module 30, configured to determine the backup status information of each sub-data set to be processed according to the database backup information to obtain a backup status information set; A backup module 40 for performing data backup on each sub-set of data to be processed according to the backup status information set, a pre-set backup time threshold, and a set of target data types corresponding to the set of data to be processed, to obtain a target data backup result.

[0086] Optionally, the clustering module 20 is further configured to: Calculate a data similarity corresponding to each data to be processed according to a first set of feature vectors corresponding to the set of data to be processed and a second set of feature vectors corresponding to a plurality of data clustering centers corresponding to the set of reference data; Compare the data similarity with a pre-set similarity threshold to obtain a similarity comparison result; Complete the clustering process of each data to be processed according to the similarity comparison result to obtain a plurality of sub-sets of data to be processed.

[0087] Optionally, the backup module 40 is further configured to: Perform data partitioning on each sub-set of data to be processed in the backup status information set where the backup status information is in the to-be-backup status, to obtain a first set of target data to be processed and a second set of target data to be processed; Calculate weight information for the first set of target data to be processed and the second set of target data to be processed respectively according to a target backup time and a data processing priority, to obtain a first set of weight information corresponding to the first set of target data to be processed and a second set of weight information corresponding to the second set of target data to be processed; Perform classification processing on the first set of target data to be processed and the second set of target data to be processed respectively according to the set of target data types corresponding to the set of data to be processed, to obtain a plurality of third sets of target data to be processed corresponding to the first set of target data to be processed and a plurality of fourth sets of target data to be processed corresponding to the second set of target data to be processed; Perform data backup on each sub-set of data to be processed according to the plurality of third sets of target data to be processed corresponding to the first set of target data to be processed and the plurality of fourth sets of target data to be processed corresponding to the second set of target data to be processed, to obtain a target data backup result.

[0088] Optionally, the backup module 40 is further configured to: Obtain a weight gain coefficient and a system sensitivity coefficient; Perform a mean calculation according to a data processing priority to obtain an average processing priority; Calculate the weights of the first target data sets to be processed and the second target data sets to be processed respectively according to the target backup time, the weight profit coefficient, the system sensitivity coefficient, the data processing priority, and the average processing priority, to obtain the first weight information set corresponding to the first target data sets to be processed and the second weight information set corresponding to the second target data sets to be processed.

[0089] Optionally, the backup module 40 is further configured to: Calculate the weights of the first weight information set, the second weight information set, the first target data sets to be processed, and the second target data sets to be processed to obtain a third weight information set and a fourth weight information set; Calculate the priorities of the third weight information set, the fourth weight information set, multiple third target data sets to be processed, and multiple fourth target data sets to be processed to obtain a first processing priority and a second processing priority; Perform data backup on each sub-data set to be processed according to the preset backup time, the first processing priority, and the second processing priority to obtain a target data backup result.

[0090] Optionally, the backup module 40 is further configured to: Determine a first preset backup time and a second preset backup time according to the preset backup time; Perform data backup on the third target data to be processed corresponding to each sub-data set to be processed according to the first preset backup time and the first processing priority to obtain a first data backup result; Perform data backup on the fourth target data to be processed corresponding to each sub-data set to be processed according to the second preset backup time and the second processing priority to obtain a second data backup result; Obtain a target data backup result according to the first data backup result and the second data backup result.

[0091] Optionally, the backup module 40 is further configured to: Input each sub-data to be processed corresponding to the backup status information in the backup status information set with the backup status of to-be-backup into the backup time prediction model to obtain a target backup time; When the target backup time is greater than the backup time threshold, determine the sub-data to be processed corresponding to the target backup time as the first target data sets to be processed; When the target backup time is not greater than the backup time threshold, determine the sub-data to be processed corresponding to the target backup time as the second target data sets to be processed.

[0092] The data backup device of the intelligent manufacturing management system provided by this application adopts the data backup method of the intelligent manufacturing management system in the above-mentioned embodiment, and can solve the technical problem of low efficiency of the existing data backup method for intelligent manufacturing management systems. Compared with the prior art, the beneficial effects of the data backup device of the intelligent manufacturing management system provided by this application are the same as those of the data backup method of the intelligent manufacturing management system provided by the above-mentioned embodiment, and other technical features in the data backup device of the intelligent manufacturing management system are the same as the features disclosed in the above-mentioned embodiment method, and will not be elaborated here.

[0093] This application provides a data backup device for an intelligent manufacturing management system. The data backup device for an intelligent manufacturing management system includes: at least one processor; and a memory communicatively connected to the at least one processor; wherein, the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor so that the at least one processor can execute the data backup method of the intelligent manufacturing management system in the first embodiment above.

[0094] Refer to the following Figure 4 , which shows a schematic structural diagram of a data backup device for an intelligent manufacturing management system suitable for implementing the embodiments of this application. The data backup device for an intelligent manufacturing management system in the embodiments of this application may include, but is not limited to, mobile terminals such as mobile phones, laptop computers, digital broadcast receivers, PDAs (Personal Digital Assistants), PADs (Portable Application Descriptions), PMPs (Portable Media Players), in-vehicle terminals (such as in-vehicle navigation terminals), etc., and fixed terminals such as digital TVs, desktop computers, etc. Figure 4 The data backup device for the intelligent manufacturing management system shown is only an example, and should not impose any limitations on the functions and usage scope of the embodiments of this application.

[0095] As Figure 4As shown, the data backup device of the intelligent manufacturing management system may include a processing device 1001 (such as a central processing unit, a graphics processing unit, etc.), which can perform various appropriate actions and processes according to the program stored in the read-only memory (ROM: Read Only Memory) 1002 or the program loaded from the storage device 1003 into the random access memory (RAM: Random Access Memory) 1004. In the RAM 1004, various programs and data required for the operation of the data backup device of the intelligent manufacturing management system are also stored. The processing device 1001, the ROM 1002, and the RAM 1004 are connected to each other through a bus 1005. The input / output (I / O) interface 1006 is also connected to the bus. Generally, the following systems may be connected to the I / O interface 1006: an input device 1007 including, for example, a touch screen, a touchpad, a keyboard, a mouse, an image sensor, a microphone, an accelerometer, a gyroscope, etc.; an output device 1008 including, for example, a liquid crystal display (LCD: Liquid Crystal Display), a speaker, a vibrator, etc.; a storage device 1003 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 can allow the data backup device of the intelligent manufacturing management system to communicate with other devices wirelessly or wiredly to exchange data. Although the figure shows the data backup device of the intelligent manufacturing management system with various systems, it should be understood that it is not required to implement or have all the shown systems. More or fewer systems may be implemented or had alternatively.

[0096] In particular, according to the embodiments disclosed in the present application, the process described above with reference to the flowchart can be implemented as a computer software program. For example, the embodiments disclosed in the present application include a computer program product, which includes a computer program carried on a computer-readable medium, and the computer program contains program codes for executing the method shown in the flowchart. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device, or installed from the storage device 1003, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above functions defined in the method of the embodiments disclosed in the present application are executed.

[0097] The data backup device of the intelligent manufacturing management system provided by the present application adopts the data backup method of the intelligent manufacturing management system in the above embodiment, and can solve the technical problem of low efficiency of the existing data backup method of the intelligent manufacturing management system. Compared with the prior art, the beneficial effects of the data backup device of the intelligent manufacturing management system provided by the present application are the same as those of the data backup method of the intelligent manufacturing management system provided by the above embodiment, and other technical features in the data backup device of the intelligent manufacturing management system are the same as the features disclosed in the method of the previous embodiment, and will not be elaborated here.

[0098] It should be understood that each part disclosed in this application can be implemented by hardware, software, firmware or a combination thereof. In the description of the above embodiments, specific features, structures, materials or characteristics can be combined in a suitable manner in any one or more embodiments or examples.

[0099] As mentioned above, it is only the specific implementation manner of this application, but the protection scope of this application is not limited thereto. Any person skilled in the art can easily think of changes or substitutions within the technical scope disclosed in this application, and all of them should be covered by the protection scope of this application. Therefore, the protection scope of this application should be subject to the protection scope of the claims.

[0100] This application provides a computer-readable storage medium having computer-readable program instructions (i.e., computer programs) stored thereon, and the computer-readable program instructions are used to execute the data backup method of the intelligent manufacturing management system in the above embodiments.

[0101] The computer-readable storage medium provided by this application can be, for example, a USB flash drive, but is not limited to electrical, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or components, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM) or flash memory, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above. In this embodiment, the computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or combined with an instruction execution system, device or component. The program code contained on the computer-readable storage medium can be transmitted by any appropriate medium, including but not limited to: wires, optical cables, RF (Radio Frequency), etc., or any suitable combination of the above.

[0102] The above computer-readable storage medium can be included in the data backup device of the intelligent manufacturing management system; it can also exist separately and not be assembled into the data backup device of the intelligent manufacturing management system.

[0103] The above computer-readable storage medium carries one or more programs, which, when executed by the data backup device of the intelligent manufacturing management system, cause the data backup device of the intelligent manufacturing management system to: calculate parameter data according to the target data type set corresponding to the to-be-processed data set to obtain a reference data set; perform clustering processing on the to-be-processed data set according to multiple data clustering centers corresponding to the reference data set and a preset similarity threshold to obtain multiple sub-to-be-processed data sets; determine the backup status information of each sub-to-be-processed data set according to the database backup information to obtain a backup status information set; perform data backup on each sub-to-be-processed data set according to the backup status information set, a preset backup time threshold, and the target data type set corresponding to the to-be-processed data set to obtain a target data backup result.

[0104] Computer program code for performing the operations of this application may be written in one or more programming languages or combinations thereof. The programming languages include object-oriented programming languages such as Java, Smalltalk, C++, and also include conventional procedural programming languages such as the "C" language or similar programming languages. The program code may execute entirely on the user's computer, partially on the user's computer, execute as a stand-alone software package, execute partially on the user's computer and partially on a remote computer, or execute entirely on the remote computer or server. In the case of a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (for example, by using an Internet service provider to connect through the Internet).

[0105] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of this application. In this regard, each block in the flowchart or block diagram may represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks may occur in a different order than marked in the accompanying drawings. For example, two consecutive blocks shown may actually be executed substantially in parallel, and they may sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram and / or flowchart, and the combination of blocks in the block diagram and / or flowchart, may be implemented by a dedicated hardware-based system for performing the specified functions or operations, or may be implemented by a combination of dedicated hardware and computer instructions.

[0106] The modules involved in the embodiments of the present application can be implemented in software or in hardware. Among them, the name of the module does not constitute a limitation to the unit itself in some cases.

[0107] The readable storage medium provided by the present application is a computer-readable storage medium. The computer-readable storage medium stores computer-readable program instructions (i.e., computer programs) for executing the data backup method of the above-mentioned intelligent manufacturing management system, and can solve the technical problem of low efficiency of the data backup method of the existing intelligent manufacturing management system. Compared with the prior art, the beneficial effects of the computer-readable storage medium provided by the present application are the same as those of the data backup method of the intelligent manufacturing management system provided by the above embodiments, and will not be elaborated here.

[0108] The present application also provides a computer program product, including a computer program, and the steps of the data backup method of the intelligent manufacturing management system as described above are implemented when the computer program is executed by a processor.

[0109] The computer program product provided by the present application can solve the technical problem of low efficiency of the data backup method of the existing intelligent manufacturing management system. Compared with the prior art, the beneficial effects of the computer program product provided by the present application are the same as those of the data backup method of the intelligent manufacturing management system provided by the above embodiments, and will not be elaborated here.

[0110] The above are only some embodiments of the present application, and do not limit the patent scope of the present application. All equivalent structural transformations made under the technical concept of the present application by using the content of the specification and drawings of the present application, or directly / indirectly applied to other related technical fields are included in the patent protection scope of the present application.

Claims

1. A data backup method for an intelligent manufacturing management system, characterized in that, The data backup method of the intelligent manufacturing management system includes: Calculating parameter data according to the target data type set corresponding to the data set to be processed to obtain a reference data set; Performing clustering processing on the data set to be processed according to multiple data clustering centers corresponding to the reference data set and a preset similarity threshold to obtain multiple sub-data sets to be processed; Determining the backup status information of each sub-data set to be processed according to the database backup information to obtain a backup status information set; Performing data backup on each sub-data set to be processed according to the backup status information set, a preset backup time threshold, and the target data type set corresponding to the data set to be processed to obtain a target data backup result.

2. The method according to claim 1, characterized in that, The step of performing clustering processing on the data set to be processed according to multiple data clustering centers corresponding to the reference data set and a preset similarity threshold to obtain multiple sub-data sets to be processed includes: Calculating the similarity of each data to be processed according to the first feature vector set corresponding to the data set to be processed and the second feature vector set corresponding to multiple data clustering centers corresponding to the reference data set to obtain the data similarity of each data to be processed; Comparing the data similarity with the preset similarity threshold to obtain a similarity comparison result; Completing the clustering processing of each data to be processed according to the similarity comparison result to obtain multiple sub-data sets to be processed.

3. The method according to claim 1, characterized in that, The step of performing data backup on each sub-data set to be processed according to the backup status information set, a preset backup time threshold, and the target data type set corresponding to the data set to be processed to obtain a target data backup result includes: Dividing the data of each sub-data set to be processed with the backup status information of the to-be-backed-up status in the backup status information set to obtain a first target data set to be processed and a second target data set to be processed; Calculating weights for the first target data set to be processed and the second target data set to be processed respectively according to the target backup time and data processing priority to obtain a first weight information set corresponding to the first target data set to be processed and a second weight information set corresponding to the second target data set to be processed; Classifying the first target data set to be processed and the second target data set to be processed respectively according to the target data type set corresponding to the data set to be processed to obtain multiple third target data sets to be processed corresponding to the first target data set to be processed and multiple fourth target data sets to be processed corresponding to the second target data set to be processed; Performing data backup on each sub-data set to be processed according to the multiple third target data sets to be processed corresponding to the first target data set to be processed and the multiple fourth target data sets to be processed corresponding to the second target data set to be processed to obtain a target data backup result.

4. The method according to claim 3, wherein The step of calculating weights for the first target data set to be processed and the second target data set to be processed respectively according to the target backup time and the data processing priority to obtain the first weight information set corresponding to the first target data set to be processed and the second weight information set corresponding to the second target data set to be processed includes: Obtain a weight benefit coefficient and a system sensitivity coefficient; Perform a mean calculation according to the data processing priority to obtain an average processing priority; Calculate weights for the first target data set to be processed and the second target data set to be processed respectively according to the target backup time, the weight benefit coefficient, the system sensitivity coefficient, the data processing priority, and the average processing priority to obtain the first weight information set corresponding to the first target data set to be processed and the second weight information set corresponding to the second target data set to be processed.

5. The method according to claim 3, characterized in that, The step of performing data backup on each sub-data set to be processed according to the multiple third target data sets to be processed corresponding to the first target data set to be processed and the multiple fourth target data sets to be processed corresponding to the second target data set to be processed to obtain a target data backup result includes: Calculate weights according to the first weight information set, the second weight information set, the first target data set to be processed, and the second target data set to be processed to obtain a third weight information set and a fourth weight information set; Calculate priorities according to the third weight information set, the fourth weight information set, the multiple third target data sets to be processed, and the multiple fourth target data sets to be processed to obtain a first processing priority and a second processing priority; Perform data backup on each sub-data set to be processed according to the pre-set backup time, the first processing priority, and the second processing priority to obtain a target data backup result.

6. The method according to claim 5, wherein The step of performing data backup on each sub-data set to be processed according to the pre-set backup time, the first processing priority, and the second processing priority to obtain a target data backup result includes: Determine a first pre-set backup time and a second pre-set backup time according to the pre-set backup time; Perform data backup on the third target data to be processed corresponding to each sub-data set to be processed according to the first pre-set backup time and the first processing priority to obtain a first data backup result; Perform data backup on the fourth target data to be processed corresponding to each sub-data set to be processed according to the second pre-set backup time and the second processing priority to obtain a second data backup result; Obtain a target data backup result according to the first data backup result and the second data backup result.

7. The method according to claim 3, characterized in that, The step of partitioning each sub-data to be processed corresponding to the backup status information in the backup status information set with the backup status being the to-be-backed-up status to obtain a first target data set to be processed and a second target data set to be processed includes: Input each sub-data to be processed corresponding to the backup status information in the backup status information set with the backup status being the to-be-backed-up status into a backup time prediction model to obtain a target backup time; When the target backup time is greater than the backup time threshold, determining the sub-pending data corresponding to the target backup time as the first target set of pending data; When the target backup time is not greater than the backup time threshold, determining the sub-pending data corresponding to the target backup time as the second target set of pending data.

8. A data backup device for an intelligent manufacturing management system, characterized in that, The device includes: A calculation module, configured to calculate parameter data according to the set of target data types corresponding to the set of pending data, to obtain a set of reference data; A clustering module, configured to perform clustering processing on the set of pending data according to a plurality of data clustering centers corresponding to the set of reference data and a preset similarity threshold, to obtain a plurality of sub-sets of pending data; A processing module, configured to determine the backup status information of each sub-set of pending data according to the database backup information, to obtain a set of backup status information; A backup module, configured to perform data backup on each sub-set of pending data according to the set of backup status information, a preset backup time threshold, and the set of target data types corresponding to the set of pending data, to obtain a target data backup result.

9. A data backup device for an intelligent manufacturing management system, characterized in that, The device includes: a memory, a processor, and a computer program stored on the memory and executable on the processor, the computer program being configured to implement the steps of the data backup method of the intelligent manufacturing management system according to any one of claims 1 to 7.

10. A storage medium, characterized in that, The storage medium is a computer-readable storage medium, on which a computer program is stored, and the computer program, when executed by a processor, implements the steps of the data backup method of the intelligent manufacturing management system according to any one of claims 1 to 7.

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