Manufacturing industry equipment data acquisition and processing system

Abstract

The invention discloses a manufacturing industry equipment data acquisition and processing system, which relates to the technical field of data processing, and comprises the following steps: calculating an initial backup coefficient of each piece of sub-data, if a cloud EMS system has no backup data of the collector, marking the collector as a new backup collector, and sequentially uploading each piece of sub-data according to the sequence of the initial backup coefficients; if the cloud EMS system has the backup information of the collector, the predicted backup completion time is calculated, if the predicted backup completion time is less than the preset backup time, the backup adopts a real-time backup strategy, and the difference data of each piece of sub-data is ordered and uploaded in sequence; if it is predicted that the backup completion time is larger than the preset backup time, a non-real-time backup strategy is adopted for the backup, the data are sequentially uploaded to the cloud EMS according to the non-real-time updating coefficient sequence, and the backup data of the collector are updated. And which data needs to be backed up preferentially is determined more intelligently, so that the overall backup efficiency is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of data security protection, and in particular to a manufacturing equipment data collection and processing system. Background Art

[0002] With the advent of the industrial age, smart factories have become an important trend in the development of the manufacturing industry. In smart factories, various sensors, equipment and systems continuously generate a large amount of data, which is essential for optimizing production processes, improving production efficiency and ensuring product quality. However, data collection and backup, as a key link in smart factory data management, cannot be ignored. Frequent security incidents such as data leakage, illegal access, and malicious tampering seriously threaten the interests of enterprises and collectors. At present, there are many data security backup technologies on the market, such as snapshots, data mirroring, RAID, and off-site backup. However, these technologies still have many limitations when dealing with big data backup needs: low backup efficiency. In the face of massive data, traditional backup methods often take too long and are difficult to meet the needs of rapid recovery; large resource consumption. The backup process requires a large amount of storage space and computing resources, which increases the operating costs of enterprises; insufficient flexibility, unable to perform intelligent backup according to the real-time changes and importance of data, resulting in redundant backup data or omission of key data; security needs to be improved, and backup data still has the risk of being stolen or tampered with during storage and transmission.

[0003] In the Chinese invention application with application publication number CN118277164A, a data backup and recovery method and system based on cloud computing is disclosed, including step S1: obtaining allocation parameters of files to be backed up; dividing files to be backed up into write hot data, read hot data and cold data; step S2: obtaining local parameters; backing up write hot data or read hot data locally according to the file size of the files to be backed up; step S3: configuring a non-local server computing platform according to local parameters to back up cold data; creating a snapshot for the files to be backed up; judging whether the files to be backed up are complete; if not, restoring the files to be backed up according to the snapshot to obtain a comparison file; if the data of the files to be backed up are complete, no processing is performed; step S4: summarizing the comparison files and feeding them back to the collector; In the above invention application, data is divided into important data (read hot data and write hot data) and cold data according to the frequency of use of data by the collector. The important data (read hot data and write hot data) is backed up to the local server, and the cold data is backed up to the non-local server. According to the data processing rate of the server, the data sending rate of the non-local server is adjusted to improve the user experience of the collector. However, in the actual backup process, when the amount of data to be changed is large and the network speed is slow, some write hot data may undergo multiple changes before being uploaded to the server. At this time, if such data is uploaded first, it will lead to frequent data uploads, causing the data to occupy too many resources during peak hours, while the remaining data has been waiting to be uploaded to the server for backup, resulting in low overall backup efficiency. The occupation of system resources (such as CPU, memory, disk I / O, etc.) will also increase, resulting in a decline in the overall performance of the system and affecting the normal operation of other businesses.

[0004] To this end, the present invention provides a manufacturing equipment data collection and processing system. Summary of the invention

[0005] 1. Technical issues to be resolved In view of the shortcomings of the prior art, the present invention provides a manufacturing equipment data collection and processing system. The present invention analyzes the real-time backup status of the collector, marks it as new backup, real-time backup and non-real-time backup, and adopts different backup strategies. It is dynamically adjusted according to the changes in the collector data to adapt to different backup needs. The storage and bandwidth resources of the cloud EMS system can be more effectively utilized, which enables the system to more intelligently decide which data needs to be backed up first and which data can be processed later, thereby improving the overall efficiency of the backup and avoiding occupying too many resources during peak hours, thereby solving the technical problems recorded in the background technology.

[0006] (II) Technical solution To achieve the above objectives, the present invention is implemented through the following technical solutions: A manufacturing equipment data collection and processing system, comprising: The data collection module divides the data of the collector into sub-data and obtains the data volume of each sub-data , Historical access time and historical change time , calculate the initial backup coefficient of each sub-data If the cloud EMS system does not have backup data for the collector, the collector is marked as a new backup collector and each sub-data is backed up according to the initial backup coefficient. Upload the EMS system backups in the cloud in descending order; Data analysis module: if the cloud EMS system already has the backup information of the collector, compare the cloud EMS system backup data with the collector data to obtain the difference data volume of each sub-data , calculate the predicted backup completion time Tt .

[0007] First update module, if the backup completion time is predicted Tt If the backup time is less than the preset backup time, the backup will adopt the real-time backup strategy, based on the difference in data volume of each sub-data and the initial backup factor Calculate the real-time update coefficient for each sub-data , update the difference data of each sub-data according to the real-time update coefficient Upload the data to the cloud EMS system in order from largest to smallest, and update the backup data of the collector; The second update module predicts the backup completion time Tt If the backup time is greater than the preset backup time, the backup will adopt a non-real-time backup strategy based on the real-time change time of each sub-data. and real-time change difference data volume , calculate the real-time change coefficient of each sub-data , and calculate the non-real-time update coefficient of each sub-data , based on the non-real-time update coefficient Upload the data in order from largest to smallest to the cloud EMS system to update the collector backup data.

[0008] Furthermore, the collector data is obtained and divided into sub-data according to the data storage directory. The data volume of each sub-data is obtained through database management tools (such as MySQL Workbench, Navicat, etc.) , and use system monitoring tools (such as Nagios, Zabbix, Prometheus, etc.) to obtain the historical access time of each sub-data and historical change time .

[0009] Database management tools (such as MySQL Workbench, Navicat, etc.) are important tools for database administrators and developers when managing and maintaining databases. These tools usually provide a graphical interface to facilitate the collector to create, modify, delete, backup, restore, optimize and monitor databases.

[0010] System monitoring tools (such as Nagios, Zabbix, Prometheus, etc.) System monitoring tools are an indispensable and important part of IT operation and maintenance. They can monitor the performance, health and security of the system in real time, and provide system administrators with necessary data support to ensure the stable operation of the system.

[0011] Further, get the data volume of each sub-data , Historical access time and historical change time , after dimensionless processing, calculate the initial backup coefficient of each sub-data :

[0012] in, i Indicates the number of each sub-data, i= 1, 2, ..., k , a Indicates the time sequence number of each access to the same sub-data. a= 1, 2, ..., m , b Indicates the time sequence number of each historical change of the same sub-data. b= 1, 2, ..., n .

[0013] Furthermore, the cloud EMS system checks whether there is a device backup list for the collector. If the cloud EMS system does not have backup data for the collector, the collector is marked as a new backup collector, and each sub-data is backed up according to the initial backup coefficient. Upload the cloud EMS system backup in order from largest to smallest.

[0014] Furthermore, if the cloud EMS system already has the backup information of the collector, use database comparison tools (such as DBeaver, Toad Data Point, etc.) or data comparison software (such as Beyond Compare, WinMerge, etc.) to compare the cloud EMS system backup data with the collector data to obtain the difference data volume of each sub-data. . And use network performance testing tools (such as iperf, PsPing, etc.) to detect the maximum network communication volume of the collector Tx .

[0015] Network performance testing tools (such as iperf, PsPing, etc.) are important tools for evaluating and optimizing network performance. They can help collectors understand key indicators such as network transmission speed, latency, packet loss rate, etc., so as to discover potential network problems and optimize them.

[0016] Furthermore, the difference data volume of each sub-data is obtained and maximum network traffic Tx , calculate the predicted backup completion time Tt : The corresponding predicted backup completion time Tt The calculation formula is as above.

[0017] Furthermore, if the backup completion time is predicted Tt If the backup time is less than the preset backup time, the backup will adopt the real-time backup strategy to obtain the difference data volume of each sub-data and the initial backup factor , after linear normalization, calculate the real-time update coefficient of each sub-data : The preset backup time is preset by the collector or uses the default backup time.

[0018] Furthermore, when adopting the real-time backup strategy, the difference data of each sub-data is updated according to the real-time update coefficient Upload the data in order from largest to smallest to the cloud EMS system to update the collector backup data.

[0019] Furthermore, if the backup completion time is predicted Tt If it is greater than the preset backup time, the non-real-time backup strategy is adopted for this backup. The system monitoring tool is used to obtain the real-time change time of each sub-data after the collector starts the backup. and real-time change difference data volume , calculate the real-time change coefficient of each sub-data :

[0020] in, c Indicates the time sequence number of each real-time change of the same sub-data. c= 1, 2, ..., w .

[0021] Furthermore, obtain the real-time change coefficient of each sub-data and the initial backup factor , after linear normalization, calculate the non-real-time update coefficient of each sub-data : When adopting a non-real-time backup strategy, the difference data of each sub-data is updated according to the non-real-time update coefficient Upload the data in order from largest to smallest to the cloud EMS system to update the collector backup data.

[0022] Furthermore, the data uploaded to the cloud EMS system includes general network data and low-power network data; and the cloud EMS system and ERP perform two-way data transmission and communication.

[0023] General network data includes WiFi, 4G, 5G and WAN data. The bus signal is input through the general communication data collector. The general counting collector inputs pulses and level signals and outputs andon and buzzer signals. The general network data input bus signals include CAN, RS232, RS485, ETHERNET, and USB. The general network data input pulses and level signals include the total number of production and manufacturing count pulses, the number of qualified production and manufacturing count pulses, the equipment power supply signal, and the abnormal trigger signal. The low-power network data input pulses and level signals are input through the low-power counting collector. The low-power network data input pulses and level signals include the total production quantity counting pulses, the qualified production quantity counting pulses, the equipment power supply signal, and the abnormal trigger signal. The bus signal is input through the low-power communication data collector. The low-power network data input bus signals include CAN, RS232, and RS485.

[0024] (III) Beneficial effects The present invention provides a manufacturing equipment data collection and processing system, which has the following beneficial effects: 1. Divide the collector data into sub-data and obtain the data volume of each sub-data , Historical access time and historical change time , calculate the initial backup coefficient of each sub-data If the cloud EMS system does not have backup data for the collector, the collector is marked as a new backup collector and each sub-data is backed up according to the initial backup coefficient. Uploading cloud EMS system backups in descending order can identify which data is more critical or active for the collector. This helps prioritize the backup of important or frequently changing data, ensuring the timeliness and integrity of the data. Even if the initial data backup is interrupted, the collector's key data can be safely protected.

[0025] 2. If the cloud EMS system already has the backup information of the collector, compare the cloud EMS system backup data with the collector data to obtain the difference in data volume for each sub-data , calculate the predicted backup completion time Tt , if the backup completion time is predicted Tt If the backup time is less than the preset backup time, the backup will adopt the real-time backup strategy, based on the difference in data volume of each sub-data and the initial backup factor Calculate the real-time update coefficient for each sub-data , update the difference data of each sub-data according to the real-time update coefficient Uploading the data to the cloud EMS system in order from large to small and updating the collector backup data can make more effective use of the storage and bandwidth resources of the cloud EMS system. Important data will be uploaded first, while less important data can be backed up when resources are more abundant to avoid occupying too many resources during peak hours. Dynamic adjustments can be made based on changes in collector data to meet different backup needs.

[0026] 3. Predict backup completion time Tt If the backup time is greater than the preset backup time, the backup will adopt a non-real-time backup strategy based on the real-time change time of each sub-data. and real-time change difference data volume , calculate the real-time change coefficient of each sub-data , and calculate the non-real-time update coefficient of each sub-data , based on the non-real-time update coefficient The data is uploaded to the cloud EMS system in order from large to small, and the collector backup data is updated. This enables the system to more intelligently decide which data needs to be backed up first and which data can be processed later, thereby improving the overall efficiency of the backup. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 The present invention is a structural schematic diagram of a manufacturing equipment data acquisition and processing system. DETAILED DESCRIPTION

[0028] 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.

[0029] See also Figure 1 The present invention provides a manufacturing equipment data collection and processing system, comprising: The data collection module divides the data of the collector into sub-data and obtains the data volume of each sub-data , Historical access time and historical change time , calculate the initial backup coefficient of each sub-data If the cloud EMS system does not have backup data for the collector, the collector is marked as a new backup collector and each sub-data is backed up according to the initial backup coefficient. Upload the EMS system backups in the cloud in descending order; Obtain the collector data and divide it into sub-data according to the data storage directory. Obtain the data volume of each sub-data through database management tools (such as MySQL Workbench, Navicat, etc.) , and use system monitoring tools (such as Nagios, Zabbix, Prometheus, etc.) to obtain the historical access time of each sub-data and historical change time .

[0030] Database management tools (such as MySQL Workbench, Navicat, etc.) are important tools for database administrators and developers when managing and maintaining databases. These tools usually provide a graphical interface to facilitate the collector to create, modify, delete, backup, restore, optimize and monitor databases.

[0031] System monitoring tools (such as Nagios, Zabbix, Prometheus, etc.) System monitoring tools are an indispensable and important part of IT operation and maintenance. They can monitor the performance, health and security of the system in real time, and provide system administrators with necessary data support to ensure the stable operation of the system.

[0032] Get the amount of data for each sub-data , Historical access time and historical change time , after dimensionless processing, calculate the initial backup coefficient of each sub-data :

[0033] in, i Indicates the number of each sub-data, i= 1, 2, ..., k , a Indicates the time sequence number of each access to the same sub-data. a= 1, 2, ..., m , b Indicates the time sequence number of each historical change of the same sub-data. b= 1, 2, ..., n .

[0034] Check whether there is a device backup list for the collector in the cloud EMS system. If there is no backup data for the collector in the cloud EMS system, mark the collector as a new backup collector and set each sub-data according to the initial backup coefficient. Upload the cloud EMS system backup in order from largest to smallest.

[0035] Obtain the data from the collector and divide it into sub-data, and obtain the data volume of each sub-data , Historical access time and historical change time , calculate the initial backup coefficient of each sub-data If the cloud EMS system does not have backup data for the collector, the collector is marked as a new backup collector and each sub-data is backed up according to the initial backup coefficient. Uploading cloud EMS system backups in descending order can identify which data is more critical or active for the collector. This helps prioritize the backup of important or frequently changing data, ensuring the timeliness and integrity of the data. Even if the initial data backup is interrupted, the collector's key data can be safely protected.

[0036] Data analysis module: if the cloud EMS system already has the backup information of the collector, compare the cloud EMS system backup data with the collector data to obtain the difference data volume of each sub-data , calculate the predicted backup completion time Tt .

[0037] If the cloud EMS system already has the backup information of the collector, use database comparison tools (such as DBeaver, ToadData Point, etc.) or data comparison software (such as Beyond Compare, WinMerge, etc.) to compare the cloud EMS system backup data with the collector data to obtain the difference data volume of each sub-data. . And use network performance testing tools (such as iperf, PsPing, etc.) to detect the maximum network communication volume of the collector Tx .

[0038] Network performance testing tools (such as iperf, PsPing, etc.) are important tools for evaluating and optimizing network performance. They can help collectors understand key indicators such as network transmission speed, latency, packet loss rate, etc., so as to discover potential network problems and optimize them.

[0039] Get the difference data volume of each sub-data and maximum network traffic Tx , calculate the predicted backup completion time Tt : The corresponding predicted backup completion time Tt The calculation formula is as above.

[0040] First update module, if the backup completion time is predicted TtIf the backup time is less than the preset backup time, the backup will adopt the real-time backup strategy, based on the difference in data volume of each sub-data and the initial backup factor Calculate the real-time update coefficient for each sub-data , update the difference data of each sub-data according to the real-time update coefficient Upload the data to the cloud EMS system in order from largest to smallest, and update the backup data of the collector; If you want to predict the backup completion time Tt If the backup time is less than the preset backup time, the backup will adopt the real-time backup strategy to obtain the difference data volume of each sub-data and the initial backup factor , after linear normalization, calculate the real-time update coefficient of each sub-data : The preset backup time is preset by the collector or uses the default backup time.

[0041] When adopting the real-time backup strategy, the difference data of each sub-data is updated according to the real-time update coefficient Upload the data in order from largest to smallest to the cloud EMS system to update the collector backup data.

[0042] Enable monitoring and logging in the backup task to record the difference in data volume during the backup process. and the actual amount of backup data , calculate the proportion of redundant backup data of the collector Because : Among them, the amount of data difference Refers to the difference between the data stored in the cloud EMS system before the backup starts and the data stored in the cloud EMS system after the backup is completed. The actual backup data volume It refers to the total amount of data transferred from the collector to the cloud EMS system during the backup process.

[0043] If the collector has excess backup data Because More than 1, and the backup completion time is predicted Tt If it is greater than 0.8 preset backup time, adjust the preset backup time so that the current predicted backup completion time Tt If the time is greater than the preset backup time and the non-real-time backup policy conditions are met, the non-real-time backup policy is enabled.

[0044] If the cloud EMS system already has the backup information of the collector, compare the cloud EMS system backup data with the collector data to obtain the difference data volume of each sub-data , calculate the predicted backup completion time Tt , if the backup completion time is predicted Tt If the backup time is less than the preset backup time, the backup will adopt the real-time backup strategy, based on the difference in data volume of each sub-data and the initial backup factor Calculate the real-time update coefficient for each sub-data , update the difference data of each sub-data according to the real-time update coefficient Uploading the data to the cloud EMS system in order from large to small and updating the collector backup data can make more effective use of the storage and bandwidth resources of the cloud EMS system. Important data will be uploaded first, while less important data can be backed up when resources are more abundant to avoid occupying too many resources during peak hours. Dynamic adjustments can be made based on changes in collector data to meet different backup needs.

[0045] The second update module predicts the backup completion time Tt If the backup time is greater than the preset backup time, the backup will adopt a non-real-time backup strategy based on the real-time change time of each sub-data. and real-time change difference data volume , calculate the real-time change coefficient of each sub-data , and calculate the non-real-time update coefficient of each sub-data , based on the non-real-time update coefficient Upload the data in order from largest to smallest to the cloud EMS system to update the collector backup data.

[0046] If you want to predict the backup completion time Tt If it is greater than the preset backup time, the non-real-time backup strategy is adopted for this backup. The system monitoring tool is used to obtain the real-time change time of each sub-data after the collector starts the backup. and real-time change difference data volume , calculate the real-time change coefficient of each sub-data :

[0047] in, c Indicates the time sequence number of each real-time change of the same sub-data. c= 1, 2, ..., w .

[0048] Get the real-time change coefficient of each sub-data and the initial backup factor , after linear normalization, calculate the non-real-time update coefficient of each sub-data : When adopting a non-real-time backup strategy, the difference data of each sub-data is updated according to the non-real-time update coefficient Upload the data in order from largest to smallest to the cloud EMS system to update the collector backup data.

[0049] Predict backup completion time Tt If the backup time is greater than the preset backup time, the backup will adopt a non-real-time backup strategy based on the real-time change time of each sub-data. and real-time change difference data volume , calculate the real-time change coefficient of each sub-data , and calculate the non-real-time update coefficient of each sub-data , based on the non-real-time update coefficient The data is uploaded to the cloud EMS system in order from large to small, and the collector backup data is updated. This enables the system to more intelligently decide which data needs to be backed up first and which data can be processed later, thereby improving the overall efficiency of the backup.

[0050] The data uploaded to the cloud EMS system includes general network data and low-power network data; and the cloud EMS system and ERP conduct two-way data transmission and communication.

[0051] General network data includes WiFi, 4G, 5G and WAN data. The bus signal is input through the general communication data collector. The general counting collector inputs pulses and level signals and outputs andon and buzzer signals. The general network data input bus signals include CAN, RS232, RS485, ETHERNET, and USB. The general network data input pulses and level signals include the total number of production and manufacturing count pulses, the number of qualified production and manufacturing count pulses, the equipment power supply signal, and the abnormal trigger signal. The low-power network data input pulses and level signals are input through the low-power counting collector. The low-power network data input pulses and level signals include the total production quantity counting pulses, the qualified production quantity counting pulses, the equipment power supply signal, and the abnormal trigger signal. The bus signal is input through the low-power communication data collector. The low-power network data input bus signals include CAN, RS232, and RS485.

[0052] The above embodiments may be implemented in whole or in part by software, hardware, firmware or any other combination thereof. When implemented using software, the above embodiments may be implemented in whole or in part in the form of a computer program product. A person of ordinary skill in the art may appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein may be implemented in electronic hardware, or in a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution.

[0053] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0054] The above description is only a specific implementation manner of the present application, but the protection scope of the present application is not limited thereto. Any technician familiar with the technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application.

Claims

1. In a manufacturing equipment data collection and processing system, the characteristics are: include: The data collection module divides the data of the collector into sub-data and obtains the data volume of each sub-data , Historical access time and historical change time , calculate the initial backup coefficient of each sub-data If the cloud EMS system does not have backup data for the collector, the collector is marked as a new backup collector and each sub-data is backed up according to the initial backup coefficient. Upload the EMS system backups in the cloud in descending order; Data analysis module: if the cloud EMS system already has the backup information of the collector, compare the cloud EMS system backup data with the collector data to obtain the difference data volume of each sub-data , calculate the predicted backup completion time Tt; First update module, if the backup completion time is predicted Tt If the backup time is less than the preset backup time, the backup will adopt the real-time backup strategy, based on the difference in data volume of each sub-data and the initial backup factor Calculate the real-time update coefficient for each sub-data , update the difference data of each sub-data according to the real-time update coefficient Upload the data to the cloud EMS system in order from largest to smallest, and update the backup data of the collector; The second update module predicts the backup completion time Tt If the backup time is greater than the preset backup time, the backup will adopt a non-real-time backup strategy based on the real-time change time of each sub-data. and real-time change difference data volume , calculate the real-time change coefficient of each sub-data , and calculate the non-real-time update coefficient of each sub-data , based on the non-real-time update coefficient Upload the data in order from largest to smallest to the cloud EMS system to update the collector backup data.

2. A manufacturing equipment data collection and processing system according to claim 1, characterized in that: Obtain the collector data, and divide the collector data into sub-data according to the data storage directory, and obtain the data volume of each sub-data through the database management tool , and use system monitoring tools to obtain the historical access time of each sub-data and historical change time .

3. A manufacturing equipment data collection and processing system according to claim 2, characterized in that: Get the amount of data for each sub-data , Historical access time and historical change time , after dimensionless processing, calculate the initial backup coefficient of each sub-data : ； in, i Indicates the number of each sub-data, i= 1, 2, ..., k , a Indicates the time sequence number of each access to the same sub-data. a= 1, 2, ..., m , b Indicates the time sequence number of each historical change of the same sub-data. b= 1, 2, ..., n .

4. A manufacturing equipment data collection and processing system according to claim 3, characterized in that: Check whether there is a device backup list for the collector in the cloud EMS system. If the cloud EMS system does not have backup data for the collector, the collector is marked as a new backup collector and each sub-data is backed up according to the initial backup coefficient. Upload the cloud EMS system backup in order from largest to smallest.

5. A manufacturing equipment data collection and processing system according to claim 4, characterized in that: If the cloud EMS system already has the backup information of the collector, use the database comparison tool to compare the cloud EMS system backup data with the collector data to obtain the difference data volume of each sub-data and use the network performance test tool to detect the maximum network communication volume of the collector Tx .

6. A manufacturing equipment data collection and processing system according to claim 5, characterized in that: Get the difference data volume of each sub-data and maximum network traffic Tx , calculate the predicted backup completion time Tt : ； If you want to predict the backup completion time Tt If the backup time is less than the preset backup time, the backup will adopt the real-time backup strategy to obtain the difference data volume of each sub-data and the initial backup factor , after linear normalization, calculate the real-time update coefficient of each sub-data : ； When adopting the real-time backup strategy, the difference data of each sub-data is updated according to the real-time update coefficient Upload the data in order from largest to smallest to the cloud EMS system to update the collector backup data.

7. A manufacturing equipment data collection and processing system according to claim 6, characterized in that: Enable monitoring and logging in the backup task to record the difference in data volume during the backup process. and the actual amount of backup data , calculate the proportion of redundant backup data of the collector Pg : ； Among them, the amount of data difference Refers to the difference between the data stored in the cloud EMS system before the backup starts and the data stored in the cloud EMS system after the backup is completed. The actual backup data volume It refers to the total amount of data transferred from the collector to the cloud EMS system during the backup process; If the collector has excess backup data Pg More than 1, and the backup completion time is predicted Tt If it is greater than 0.8 preset backup time, adjust the preset backup time so that the current predicted backup completion time Tt If the time is greater than the preset backup time and the non-real-time backup policy conditions are met, the non-real-time backup policy is enabled.

8. A manufacturing equipment data collection and processing system according to claim 7, characterized in that: If you want to predict the backup completion time Tt If it is greater than the preset backup time, the non-real-time backup strategy is adopted for this backup. The system monitoring tool is used to obtain the real-time change time of each sub-data after the collector starts the backup. and real-time change difference data volume , calculate the real-time change coefficient of each sub-data : ； in, c Indicates the time sequence number of each real-time change of the same sub-data. c= 1, 2, ..., w .

9. A manufacturing equipment data collection and processing system according to claim 8, characterized in that: Get the real-time change coefficient of each sub-data and the initial backup factor , after linear normalization, calculate the non-real-time update coefficient of each sub-data : ； When adopting a non-real-time backup strategy, the difference data of each sub-data is updated according to the non-real-time update coefficient Upload the data in order from largest to smallest to the cloud EMS system to update the collector backup data.

10. A manufacturing equipment data collection and processing system according to claim 9, characterized in that: The data uploaded to the cloud EMS system includes general network data and low-power network data, and the cloud EMS system and ERP conduct two-way data transmission and communication; General network data includes WiFi, 4G, 5G and WAN data. The bus signal is input through the general communication data collector. The general counting collector inputs pulses and level signals and outputs andon and buzzer signals. The general network data input bus signals include CAN, RS232, RS485, ETHERNET, and USB. The general network data input pulses and level signals include the total number of production and manufacturing count pulses, the number of qualified production and manufacturing count pulses, the equipment power supply signal, and the abnormal trigger signal. The low-power network data input pulses and level signals are input through the low-power counting collector. The low-power network data input pulses and level signals include the total production quantity counting pulses, the qualified production quantity counting pulses, the equipment power supply signal, and the abnormal trigger signal. The bus signal is input through the low-power communication data collector. The low-power network data input bus signals include CAN, RS232, and RS485.

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