Industrial equipment maintenance system for industrial internet data acquisition

Abstract

The invention relates to the technical field of industrial equipment maintenance, in particular to an industrial equipment maintenance system for industrial internet data acquisition. The system comprises a fragmented storage database and a maintenance associated data calling module. According to the method, the associated industrial equipment is matched according to the real-time associated data through the fragmented storage database, the corresponding real-time associated data is divided, meanwhile, the influence degree of each associated data is calculated, the storage data and the storage period are planned according to the influence degrees, real-time data cleaning is performed, and normal data and abnormal data are divided to be stored in order. Normal storage of conventional data is guaranteed, real-time calling is facilitated, abnormal data can be stored in advance, when monitoring data calling needs to be carried out in the maintenance work process, the abnormal data can be preferentially supplied according to the storage priority, the abnormal reason analysis efficiency is improved, and the maintenance efficiency is improved. Meanwhile, the storage period of each piece of monitoring data and the storage of the database are reasonably planned according to the storage priority, and the storage state is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial equipment maintenance, and more specifically, to an industrial equipment maintenance system for industrial Internet data acquisition. Background Art

[0002] Industrial equipment maintenance is a core link to ensure production safety, extend equipment life, and maintain production efficiency.

[0003] Traditional industrial equipment maintenance mainly includes the following work processes:

[0004] First step, through monitoring devices deployed at different positions in the industrial area, real-time monitoring information is obtained and stored.

[0005] Second step, when an industrial equipment malfunctions and needs to be repaired, historical monitoring information needs to be combined for analysis to obtain the cause of the anomaly.

[0006] Third step, based on the analysis results of the monitoring information, the ultimate cause of the anomaly is determined.

[0007] Although the above method can help maintenance staff handle abnormal industrial equipment in a timely manner and provide data reference, the following problems still exist:

[0008] First, the number of devices operating in the industrial area is huge, so the number of deployed monitoring devices and the amount of monitored data will increase accordingly, resulting in a large amount of final monitoring data. In most cases, the monitoring data is routine, leading to the continuous accumulation of irrelevant data in the database, which will not only cause a storage burden on the database, but also the redundant data will interfere with later data matching.

[0009] Second, when it is necessary to compare historical data to obtain the cause of abnormal equipment, due to the large amount and disorder of the data, a large amount of time will be consumed in the matching process, resulting in a low efficiency of analyzing the cause of the anomaly.

[0010] To address the above problems, there is an urgent need for an industrial equipment maintenance system that can orderly update the collected industrial Internet data. Summary of the Invention

[0011] The purpose of the present invention is to provide an industrial equipment maintenance system for industrial Internet data acquisition to solve the problems raised in the above background art.

[0012] To achieve the above object, an industrial equipment maintenance system for industrial Internet data acquisition is provided, including a data acquisition module. The data acquisition module monitors the real-time associated data of industrial equipment through monitoring devices deployed in the industrial area and uploads the real-time associated data to the control center through the Internet. It also includes a sharded storage database and a maintenance associated data retrieval module.

[0013] Among them, the sharded storage database matches and associates industrial devices according to real-time associated data, divides the corresponding real-time associated data, calculates the influence degree of each associated data at the same time, plans the storage data and storage period according to the influence degree, and performs real-time data cleaning;

[0014] The maintenance associated data retrieval module is used to respond to the maintenance information of abnormal industrial devices, identify the current abnormal industrial devices, match and obtain the associated data stored in the sharded storage database, and retrieve them in the order of the influence degree of the associated data.

[0015] As a further improvement of this technical solution, the data acquisition module includes a layout monitoring device acquisition unit and a data correlation degree calculation unit;

[0016] Among them, the layout monitoring device acquisition unit is used to obtain the layout positions of each monitoring device, mark each monitoring device, obtain the monitoring data of the corresponding position in real time, and combine the historical monitoring information to obtain the standard range of each monitoring data, and mark the monitoring data exceeding the standard range as abnormal data;

[0017] The data correlation degree calculation unit combines the historical monitoring information to calculate the influence effect of each monitoring data matched by the industrial device on it, and marks it as the correlation degree.

[0018] As a further improvement of this technical solution, the method for calculating the influence effect of each monitoring data matched by the industrial device in the data correlation degree calculation unit includes the following steps:

[0019] S1201. Set the unit statistical time , and collect the abnormal monitoring data items corresponding to the same abnormal industrial device within the unit statistical time ;

[0020] S1202. Obtain the occurrence probability of each abnormal monitoring data item ;

[0021] S1203. Define the initial weight of each abnormal monitoring data item according to the occurrence probability ; ;

[0022] S1204. Reflect the correlation degree through the size of the initial weight ;

[0023] As a further improvement of this technical solution, the method for the sharded storage database to divide the corresponding real-time associated data includes the following steps:

[0024] S201. Partition the database, mark the area numbers, and store the monitoring data associated with different industrial devices in different areas;

[0025] S202. Divide the storage order of each item of monitoring data corresponding to each area according to the degree of association of the monitoring data.

[0026] As a further improvement of this technical solution, the method for calculating the influence degree of each associated data in the sharded storage database includes the following steps:

[0027] S203. Combine the normal range of the monitoring data and the initial weight , define the unit overflow value of each abnormal monitoring data , and each unit overflow value corresponds to the same unit weight ;

[0028] S204. Calculate the sorting weight of the monitoring data stored in the same batch of industrial equipment in each storage area , where is the excess value of the current abnormal monitoring data exceeding the normal range;

[0029] S205. Update the order of the monitoring data stored in the same batch according to the sorting weight .

[0030] As a further improvement of this technical solution, the method for defining the unit overflow value of each abnormal monitoring data in S203 includes the following steps:

[0031] S2031. Combine the industry rules for industrial equipment maintenance to obtain the abnormal levels of various abnormalities of industrial equipment;

[0032] S2032. Obtain the values of each item of monitoring data under different abnormal levels and calculate the final average value of the levels;

[0033] S2033. Calculate the difference between the average values of adjacent levels for the same item of monitoring data, mark it as the adjacent average difference, and take the average value of each adjacent average difference, and mark it as the unit overflow value .

[0034] As a further improvement of this technical solution, the method for planning the stored data and storage period according to the influence degree in the sharded storage database includes the following steps:

[0035] S206. According to the sorting weight of each item of monitoring data , obtain its corresponding sorting in the partition;

[0036] S207. Divide the concentrated areas of each partition according to the sorting, and define the risk coefficient R of each concentrated area;

[0037] S208. Combine the database storage capacity and the actual required storage volume to define the reference period of each partition , and calculate the storage period of each concentrated area.

[0038] As a further improvement of this technical solution, the exponential decay algorithm is used to calculate the storage period of each concentrated area in S208, and its algorithm formula is as follows:

[0039] ;

[0040] Where is the storage period of different concentrated areas, is the natural constant, is the risk slope coefficient, and the value is 0.2, is the risk coefficient, and its range is , taking positive integers.

[0041] Compared with the prior art, the beneficial effects of the present invention:

[0042] In the industrial equipment maintenance system for industrial Internet data collection, the sharded storage database matches and associates industrial equipment according to real-time associated data, divides the corresponding real-time associated data, calculates the influence degree of each associated data at the same time, plans the storage data and storage period according to the influence degree, conducts real-time data cleaning, divides the storage order of regular data and abnormal data, not only ensures the normal storage of regular data and is convenient for real-time calling, but also can advance the storage order of abnormal data. When monitoring data needs to be called during the maintenance process, abnormal data can be preferentially supplied according to the storage priority, improving the efficiency of abnormal cause analysis. At the same time, the storage period of each monitoring data is reasonably planned according to the storage priority, and the storage of the database is improved. Description of the Drawings

[0043] Figure 1 is the overall system structure block diagram of the present invention;

[0044] Figure 2 is the database partition schematic diagram of the present invention;

[0045] Figure 3 is the method step diagram for calculating the influence effect of each monitoring data matched by industrial equipment on the present invention;

[0046] Figure 4 is the method step diagram for dividing the corresponding real-time associated data of the present invention;

[0047] Figure 5 This is a flowchart of the method for calculating the influence degree of each associated data in the present invention;

[0048] Figure 6 This is a flowchart of the method for defining the unit overflow value of each abnormal monitoring data in the present invention;

[0049] Figure 7 This is a flowchart of the method for planning the storage data and storage period according to the influence degree in the present invention.

[0050] The meanings of each label in the figure are as follows:

[0051] 10. Data acquisition module; 110. Acquisition unit of the monitoring device for layout control; 120. Data correlation degree calculation unit;

[0052] 20. Sharded storage database;

[0053] 30. Maintenance associated data retrieval module. Specific implementation mode

[0054] Next, the technical solutions in the embodiments of the present invention will be clearly and completely described in conjunction with the accompanying drawings in the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments in the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

[0055] Please refer to Figure 1 As shown, an industrial equipment maintenance system for industrial Internet data acquisition is provided, including a data acquisition module 10. The data acquisition module 10 monitors the real-time associated data of industrial equipment through monitoring devices deployed in industrial areas, and uploads the real-time associated data to the control center through the Internet. It also includes a sharded storage database 20 and a maintenance associated data retrieval module 30;

[0056] Among them, the sharded storage database 20 matches and associates industrial equipment according to the real-time associated data, divides the corresponding real-time associated data, calculates the influence degree of each associated data at the same time, plans the storage data and storage period according to the influence degree, and performs real-time data cleaning;

[0057] The maintenance associated data retrieval module 30 is used to respond to the information to be repaired of abnormal industrial equipment, identify the current abnormal industrial equipment, match and obtain the associated data stored in the sharded storage database 20, and retrieve it in the order of the influence degree of the associated data.

[0058] In specific use, this solution provides an industrial equipment maintenance system capable of updating the collected industrial Internet data in an orderly manner. The specific solution is as follows:

[0059] First, it is necessary to collect monitoring data in advance. The data collection module 10 obtains real-time associated data of industrial equipment through monitoring devices deployed in industrial areas, such as temperature monitoring and power consumption monitoring of electrical equipment, and uploads the real-time associated data to the control center through the Internet;

[0060] Since most of the equipment inside the factory is in a stable operation state in most cases, the collected monitoring data is within the normal range. And these normal data also need to be stored. Similarly, when abnormal data appears, that is, when the current monitoring data exceeds the normal range, these abnormal data need to be collected and stored even more. And different monitoring data reflect the operation states of the corresponding industrial equipment. Therefore, if the corresponding relationships of each monitoring data can be reasonably divided, the efficiency of analyzing the causes of abnormalities in the later stage can be improved. The present invention matches and associates industrial equipment according to real-time associated data through the sharded storage database 20, divides the corresponding real-time associated data, calculates the influence degree of each associated data at the same time, plans the stored data and the storage period according to the influence degree, and performs real-time data cleaning;

[0061] Finally, the maintenance associated data retrieval module 30 responds to the information to be repaired of the abnormal industrial equipment, identifies the current abnormal industrial equipment, matches and obtains the associated data stored in the sharded storage database 20, and retrieves it in the order of the influence degree of the associated data.

[0062] The present invention matches and associates industrial equipment according to real-time associated data through the sharded storage database 20, divides the corresponding real-time associated data, calculates the influence degree of each associated data at the same time, plans the stored data and the storage period according to the influence degree, performs real-time data cleaning, divides the storage order of normal data and abnormal data, not only ensures the normal storage of normal data and is convenient for real-time retrieval, but also can advance the storage order of abnormal data. When the monitoring data needs to be retrieved during the maintenance process, the abnormal data can be preferentially supplied according to the storage priority, improving the efficiency of analyzing the causes of abnormalities, reducing the comparison volume of irrelevant data, and reasonably planning the storage period of each monitoring data according to the storage priority, improving the storage status of the database.

[0063] In addition, the data collection module 10 includes a deployed monitoring device collection unit 110 and a data correlation degree calculation unit 120;

[0064] Among them, the deployed monitoring device collection unit 110 is used to obtain the deployment positions of each monitoring device, mark each monitoring device, obtain the monitoring data of the corresponding position in real time, and combine the historical monitoring information to obtain the standard range of each monitoring data, and mark the monitoring data that exceeds the standard range as abnormal data;

[0065] The data correlation calculation unit 120 calculates the impact of each piece of monitoring data matched to the industrial equipment in combination with historical monitoring information, and marks it as the correlation degree.

[0066] Further, the method for calculating the impact of each piece of monitoring data matched to the industrial equipment in the data correlation calculation unit 120 includes the following steps:

[0067] S1201. Define the unit statistical time , and collect the abnormal monitoring data items corresponding to the same abnormal industrial equipment within the unit statistical time ;

[0068] S1202. Obtain the occurrence probability of each abnormal monitoring data item ;

[0069] S1203. Define the initial weight of each abnormal monitoring data item according to the occurrence probability ; ;

[0070] S1204. Reflect the correlation degree through the magnitude of the initial weight ;

[0071] In specific use, when an industrial equipment has an abnormality, the corresponding monitoring data will have a single abnormality or multiple abnormalities, and the abnormal values will also vary. That is to say, the correlation degrees of different monitoring data with the industrial equipment are different. Therefore, during the monitoring data collection process, the monitoring device layout acquisition unit 110 acquires the layout positions of each monitoring device, marks each monitoring device, obtains the monitoring data at the corresponding positions in real time, and combines historical monitoring information to obtain the standard range of each piece of monitoring data, and marks the monitoring data exceeding the standard range as abnormal data;

[0072] Since multiple pieces of monitoring data are in a real-time acquisition state and the upload frequencies are the same, in order to ensure the orderliness of the uploaded data and avoid upload disorders, the data correlation calculation unit 120 calculates the impact of each piece of monitoring data matched to the industrial equipment in combination with historical monitoring information. First, by defining the unit statistical time , collect the abnormal monitoring data items corresponding to the same abnormal industrial equipment within the unit statistical time . For example, when an industrial equipment has an operation failure, during historical monitoring, the monitoring data provided by multiple monitoring devices associated with this industrial equipment shows abnormalities, including temperature monitoring data, pressure monitoring data, and current monitoring data, but they do not all occur simultaneously. Therefore, the correlation degrees of each piece of monitoring data with its corresponding industrial equipment are different, and it is necessary to obtain the occurrence probability of each abnormal monitoring data item , and according to the occurrence probability Define the initial weights of each abnormal monitoring data item , the initial weight The higher the initial weight, the higher the corresponding correlation degree. Plan the upload order of the monitoring data to be uploaded according to the correlation degree, and ensure the orderliness of the monitoring data upload.

[0073] Furthermore, the method for the segmented storage database 20 to divide the corresponding real-time correlation data includes the following steps:

[0074] S201. Perform partition processing on the database and mark the area numbers. Different areas store the monitoring data associated with different industrial devices;

[0075] S202. Divide the storage order of the corresponding monitoring data in each area according to the correlation degree of the monitoring data;

[0076] The method for calculating the influence degree of each correlation data in the segmented storage database 20 includes the following steps:

[0077] S203. Combine the normal range of the monitoring data and the initial weight , and define the unit overflow value of each abnormal monitoring data , and each unit overflow value corresponding unit weight is the same;

[0078] S204. Calculate the sorting weight of the monitoring data stored in the same batch of industrial devices in each storage area , where is the excess value of the current abnormal monitoring data exceeding the normal range;

[0079] S205. Update the order of the monitoring data stored in the same batch according to the sorting weight ;

[0080] The method for defining the unit overflow value of each abnormal monitoring data in S203 includes the following steps:

[0081] S2031. Combine the industrial equipment maintenance industry rules to obtain the abnormal levels of various abnormalities of the industrial equipment;

[0082] S2032. Obtain the values of the monitoring data under different abnormal levels and calculate the final average level value;

[0083] S2033. Calculate the difference between the average level values of adjacent levels for the same monitoring data item, mark it as the adjacent average difference, and take the average of each adjacent average difference, and mark it as the unit overflow value .

[0084] During specific use, since the data stored in the database is used for later comparison and analysis of fault causes, orderly partitioning of the monitoring data corresponding to each industrial device can improve the efficiency of later data comparison. At this time, the database is partitioned and area numbers are marked, and different areas store the monitoring data associated with different industrial devices;

[0085] When there is no abnormal monitoring data, according to the degree of association of the monitoring data, the storage order of the corresponding monitoring data in each area is partitioned, as Figure 2 shown, where and are respectively the monitoring data associated with the same industrial device, and the corresponding values do not exceed the normal range and are in the same storage area. n is the total amount of associated monitoring data of the current industrial device, and the corresponding order is sorted according to the degree of association of the monitoring data. The monitoring data with a higher order is in a high order, such as orders Ⅰ, Ⅱ, and Ⅲ in the figure are all high orders, the ones in the middle are in a medium order, such as orders Ⅳ and Ⅴ in the figure, and the monitoring data with a lower order is in the order to be deleted, such as order M in the figure;

[0086] When abnormal monitoring data appears, in order to be able to respond to abnormal industrial devices in a timely manner and give priority to the abnormal monitoring data, order update is required. At this time, combined with the normal range of the monitoring data and the initial weight , the unit overflow value of each abnormal monitoring data is defined , and each unit overflow value corresponds to the same unit weight For example, for the monitoring data types and , the corresponding data ranges are different. Since the unit weights updated for each monitoring data are different, the unit overflow values corresponding to each monitoring data will be different. The definition method of the unit overflow value is as follows:

[0087] First, in combination with the industrial equipment maintenance industry rules, obtain the anomaly levels of various anomalies of industrial equipment. For example, there are three levels of anomalies in the mechanical equipment on the assembly line. The first level is the appearance of defective products, the second level is that the quantity of defective products exceeds the normal value, and the third level is the stop of operation. Also, obtain the values of various monitoring data under different anomaly levels, and calculate the final average level value. There will be a certain error in the values of the same monitoring data in the same anomaly level under different circumstances. Therefore, it is necessary to calculate the average level value as the ideal value of the monitoring data in the current anomaly level to improve the accuracy. Finally, calculate the difference between the average level values of adjacent levels for the same monitoring data, mark it as the adjacent average difference, and take the average of each adjacent average difference, mark it as the unit overflow value ;

[0088] Complete the definition of the unit overflow value After that, calculate the sorting weight of the monitoring data stored in the same batch of industrial equipment in each storage area , where is the excess value of the current abnormal monitoring data exceeding the normal range. For example, the monitoring data types and are both abnormal monitoring data. According to the above calculation formula, the final sorting weight is obtained, and > > . At this time, the corresponding order will be updated to in the first order, in the second order, in the third order

[0089] Specifically, the method for planning the stored data and storage period according to the influence degree in the sharded storage database 20 includes the following steps:

[0090] S206. According to the sorting weight of each monitoring data, obtain its corresponding partition sorting;

[0091] S207. Divide the order concentration areas of each partition according to the sorting, and define the risk coefficient R of each concentration area;

[0092] S208. Combine the database storage capacity and the actual required storage capacity to define the benchmark period of each partition, and calculate the storage period of each concentration area;

[0093] In S208, the exponential decay algorithm is used to calculate the storage period of each concentration area, and its algorithm formula is as follows:

[0094] ;

[0095] Among them is the storage period of different concentrated areas is the natural constant is the risk slope coefficient, and the value is 0.2 is the risk coefficient, and its range is , taking positive integers

[0096] In specific use, during the process of planning the storage period for monitoring data of different orders, since abnormal monitoring data needs to be used as a reference for later industrial equipment maintenance, the corresponding storage period needs to be extended. For regular data, its data reference effect is poor and it is easy to occupy the storage space of the database. Therefore, it is necessary to reasonably plan the storage period for different monitoring data. First, according to the sorting weight , obtain its corresponding partition sorting, and divide the concentrated areas of each partition according to the sorting. As Figure 2 shown, among them, the high-order, low-order, and to-be-deleted orders are all concentrated areas of the order, and the risk coefficient R of each concentrated area is defined. Finally, combined with the database storage capacity and the actual required storage volume, the reference period of each partition is defined. The reference period of each partition is determined by the administrator according to the actual needs. For the case of a large database memory, its reference period is relatively long. Calculate the storage period of each concentrated area. This scheme uses an exponential decay algorithm for calculation, and its algorithm formula is as follows:

[0097] ;

[0098] Among them is the storage period of different concentrated areas is the risk slope coefficient, and the value is 0.2 is the risk coefficient, and its range is , that is, the risk coefficient of the high order is , the risk coefficient of the low order is 2, and the risk coefficient of the to-be-deleted order is , for example, when the reference period is 50 days, the storage period of its high order = = = 91 days, that is, the storage period of this high order is 91 days

[0099] The basic principles, main features and advantages of the present invention have been shown and described above. Those skilled in the art should understand that the present invention is not limited by the above embodiments. The above embodiments and the descriptions in the specification are only preferred examples of the present invention and are not used to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention will have various changes and improvements, and these changes and improvements all fall within the scope of the present invention claimed. The scope of the present invention claimed is defined by the appended claims and their equivalents.

Claims

1. An industrial equipment maintenance system for industrial Internet data acquisition, comprising a data acquisition module (10). The data acquisition module (10) monitors real-time associated data of industrial equipment through monitoring devices deployed in industrial areas and uploads the real-time associated data to a control center via the Internet. It is characterized in that: It further includes a sharded storage database (20) and a maintenance associated data retrieval module (30); Among them, the sharded storage database (20) matches associated industrial equipment according to real-time associated data, divides the corresponding real-time associated data, calculates the influence degree of each associated data, plans the stored data and storage period according to the influence degree, and performs real-time data cleaning; The maintenance associated data retrieval module (30) is used to respond to the information to be repaired of abnormal industrial equipment, identify the current abnormal industrial equipment, match and obtain the associated data stored in the sharded storage database (20), and retrieve it in the order of the influence degree of the associated data.

2. The industrial equipment maintenance system for industrial Internet data collection according to claim 1, wherein: The data acquisition module (10) includes a layout monitoring device acquisition unit (110) and a data correlation degree calculation unit (120); Among them, the layout monitoring device acquisition unit (110) is used to obtain the layout positions of each monitoring device, mark each monitoring device, obtain the monitoring data of the corresponding position in real time, and combine historical monitoring information to obtain the standard range of each monitoring data, and mark the monitoring data exceeding the standard range as abnormal data; The data correlation degree calculation unit (120) combines historical monitoring information to calculate the influence effect of each monitoring data matched by the industrial equipment on it, and marks it as the correlation degree.

3. The industrial equipment maintenance system for industrial Internet data acquisition according to claim 2, characterized in that: The method for calculating the influence effect of each monitoring data matched by the industrial equipment in the data correlation degree calculation unit (120) includes the following steps: S1201. Determine the statistical time of the unit , and collect the abnormal monitoring data items corresponding to the same abnormal industrial equipment within the statistical time of the unit ; S1202. Obtain the occurrence probability of each abnormal monitoring data item ; S1203. Define the initial weights of each abnormal monitoring data item according to the occurrence probability ;​ S1204. Reflect the degree of association through the initial weight The size reflects the degree of association.

4. The industrial equipment maintenance system for industrial Internet data acquisition according to claim 1, wherein: The method for dividing the corresponding real-time associated data by the sharded storage database (20) includes the following steps: S201. Perform partition processing on the database and mark the area numbers, and store the monitoring data associated with different industrial equipment in different areas; S202. Divide the storage order of each item of monitoring data corresponding to each area according to the magnitude of the correlation degree of the monitoring data.

5. The industrial equipment maintenance system for industrial Internet data collection according to claim 4, characterized in that: The method for calculating the influence degree of each associated data in the sharded storage database (20) includes the following steps: S203. Combine the normal range of the monitoring data and the initial weights , define the unit overflow values of the abnormal monitoring data items , and for each unit overflow value , the corresponding unit weight is the same; S204. Calculate the sorting weight of the monitoring data stored in the same batch of industrial equipment in each storage area , where is the overvalue of the current abnormal monitoring data whose value exceeds the normal range S205. Update the ranking of the monitoring data stored in the same batch according to the sorting weight. Update the ranking of the monitoring data stored in the same batch.

6. The industrial equipment maintenance system for industrial Internet data acquisition according to claim 5, characterized in that: Define the unit overflow value of each abnormal monitoring data in S203 The method includes the following steps: S2031. Combine the industrial equipment maintenance industry rules to obtain the abnormal levels of various abnormalities of the industrial equipment; S2032. Obtain the numerical values of each monitoring data under different abnormal levels, and calculate the final average level value; S2033. Calculate the difference between the average values of adjacent levels for the same monitoring data, mark it as the adjacent average difference, and take the average of each adjacent average difference, mark it as the unit overflow value .

7. The industrial equipment maintenance system for industrial Internet data collection according to claim 4, characterized in that: The method for planning the stored data and storage period according to the influence degree in the sharded storage database (20) includes the following steps: S206. Obtain the sorting of its corresponding partition according to the sorting weights of the monitoring data items , and obtain the sorting of its corresponding partition S207. Divide the concentrated areas of each partition according to the sorting, and define the risk coefficient R of each concentrated area; S208. Define the reference period of each partition in combination with the database storage capacity and the actual required storage volume. Calculate the storage periods of each centralized area.

8. The industrial equipment maintenance system for industrial Internet data collection according to claim 7, characterized in that: The exponential decay algorithm is used to calculate the storage period of each concentrated area in S208, and its algorithm formula is as follows: ； Among them is the storage period of different concentrated areas is the natural constant is the risk slope coefficient, and the value is 0.2 is the risk coefficient, and its range is , taking positive integers

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