Production data management method, system, equipment and medium based on industrial Internet of Things

Abstract

The present invention discloses a production data management method, system, equipment and medium based on industrial Internet of Things, which relates to the technical field of industrial Internet of Things data processing, including obtaining data acquisition nodes, and obtaining the initial importance of data acquisition nodes and the total amount of historical production data; obtaining multiple periodic production data, the total amount of periodic production data and the completeness of data acquisition; obtaining the data volume change rate, and obtaining the redundancy of each data acquisition node, and obtaining the first correction index of the data acquisition node; obtaining the physical distance and network topology distance between data acquisition nodes, and obtaining the second correction index of the data acquisition node; obtaining the real-time importance according to the first correction index, the second correction index and the initial importance of the data acquisition node; and processing multiple periodic production data in sequence according to the order of real-time importance. The present invention has the advantages of realizing dynamic adjustment, comprehensive and reliable correction, and timely and accurate correction.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial Internet of Things data processing, and in particular to a production data management method, system, equipment and medium based on the industrial Internet of Things. Background Art

[0002] With the rapid development of Industrial Internet of Things (IIoT), the collection, processing and analysis of production data have become the key links to improve enterprise production efficiency, optimize resource allocation and realize intelligent manufacturing. IIoT builds a highly interconnected production ecosystem by deploying a large number of sensors, actuators and other intelligent devices, which can collect various data in the production process in real time and accurately, providing strong data support for enterprise decision support, production scheduling and quality control.

[0003] However, with the surge in the number of data collection nodes, how to efficiently and orderly manage these massive and complex production data has become an urgent problem to be solved.

[0004] Traditional data management methods often focus on the simple collection and storage of data, while ignoring the correlation and redundancy between data and the actual operating status of the collection nodes, resulting in inefficient data processing, important data being submerged in a large amount of useless or duplicate information, and it is difficult to effectively tap the potential value of the data; especially in a continuous production cycle, the dynamic changes in data volume, the integrity of data collection, and the physical and network relationships between nodes are all important factors affecting data quality and processing efficiency. For example, some data collection nodes may have incomplete data collection due to remote locations or poor network conditions, while some nodes may cause data redundancy due to repeated collection of similar data, increasing the burden of data processing; in addition, the importance of different nodes in the production process is not static and needs to be dynamically adjusted according to real-time production conditions. Summary of the invention

[0005] In view of the defects in the prior art, the present invention provides a production data management method, system, equipment and medium based on industrial Internet of Things. The method can dynamically evaluate and adjust the real-time importance of each data acquisition node, so as to give priority to key data, improve data processing efficiency, and ensure the accuracy and timeliness of production data.

[0006] A production data management method based on the industrial Internet of Things comprises: obtaining multiple data collection nodes in the industrial Internet of Things in the nth production cycle, and obtaining the initial importance of the data collection nodes and the total amount of historical production data of the n-1th production cycle; obtaining multiple periodic production data collected by multiple data collection nodes in the nth production cycle, and obtaining the total amount of periodic production data and data collection completeness of each data collection node according to the periodic production data; obtaining the data volume change rate according to the total amount of historical production data and the total amount of periodic production data, and obtaining the redundancy of each data collection node according to the data volume change rate, the data collection completeness and the data type of the periodic production data, and obtaining a first correction index of the data collection node according to the redundancy; obtaining the physical distance and network topological distance between each data collection node, and calculating the spatial correlation of the data collection node according to the physical distance and the network topological distance, and obtaining a second correction index of the data collection node according to the spatial correlation; obtaining the real-time importance according to the first correction index, the second correction index and the initial importance of the data collection node; and processing the periodic production data of the multiple data collection nodes in sequence according to the order of the real-time importance of the multiple data collection nodes.

[0007] Preferably, obtaining the redundancy of each data acquisition node based on the data volume change rate, data collection completeness and data type of periodic production data includes: obtaining other data acquisition nodes with the same data type as the ith data acquisition node and using them as similar data acquisition points, obtaining the difference in node data volume change rate and the difference in node data collection completeness between the ith data acquisition node and the similar data acquisition nodes; obtaining a first number of differences in node data volume change rates that are less than a first preset threshold and a second number of differences in node data collection completeness that are less than a second preset threshold, and obtaining the redundancy of the ith data acquisition node based on the first number and the second number.

[0008] Preferably, the redundancy of the i-th data acquisition node is obtained according to the first quantity and the second quantity and is expressed as: ;in, is the redundancy of the ith data collection node, is the first quantity corresponding to the i-th data collection node, is the second number corresponding to the i-th data collection node, is the number of similar data collection points corresponding to the i-th data collection node.

[0009] Preferably, the first correction index of the data acquisition node obtained according to the redundancy is expressed as: ;in, is the first correction index of the i-th data collection node, is the first initial correction value, is the adjustment coefficient.

[0010] Preferably, the spatial correlation of the data collection nodes is calculated based on the physical distance and the network topological distance and is expressed as: , ;in, is the spatial correlation of the ith data collection node, is the number of data collection nodes, is the physical distance between the i-th data collection node and the j-th data collection node, is the network topological distance between the i-th data collection node and the j-th data collection node, and is the weight coefficient.

[0011] Preferably, the second correction index of the data acquisition node obtained according to the spatial correlation is expressed as: ;in, is the second correction index of the i-th data collection node, is the second initial correction value.

[0012] Preferably, the real-time importance is obtained according to the first correction index, the second correction index and the initial importance of the data collection node as follows: ;in, is the real-time importance of the i-th data collection node, is the initial importance of the i-th data collection node.

[0013] A production data management system based on the industrial Internet of Things is also provided. The system includes a management platform, a sensor network platform and an object platform that are sequentially connected in communication. The sensor network platform includes: a node confirmation module, which is used to obtain multiple data collection nodes in the industrial Internet of Things in the nth production cycle, and obtain the initial importance of the data collection nodes and the total amount of historical production data of the n-1th production cycle; a production data collection module, which is used to obtain multiple periodic production data collected by multiple data collection nodes in the nth production cycle, and obtain the total amount of periodic production data and data collection completeness based on the periodic production data; a first calculation module, which is used to obtain the data volume change rate based on the total amount of historical production data and the total amount of periodic production data, and obtain the data volume change rate based on the total amount of historical production data and the total amount of periodic production data. The redundancy of each data acquisition node is obtained according to the data volume change rate, data acquisition completeness and data type of periodic production data, and the first correction index of the data acquisition node is obtained according to the redundancy; the second calculation module is used to obtain the physical distance and network topological distance between each data acquisition node, and calculate the spatial correlation of the data acquisition node according to the physical distance and the network topological distance, and obtain the second correction index of the data acquisition node according to the spatial correlation; the third calculation module is used to obtain the real-time importance according to the first correction index, the second correction index and the initial importance of the data acquisition node; the decision module is used to process the periodic production data of multiple data acquisition nodes in sequence according to the order of the real-time importance of multiple data acquisition nodes.

[0014] An electronic device is also provided, comprising: a memory on which a computer program is stored; and a processor for executing the computer program in the memory to implement the above-mentioned production data management method based on industrial Internet of Things.

[0015] A non-temporary computer-readable storage medium is also provided, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned production data management method based on the industrial Internet of Things is implemented.

[0016] The beneficial effects of the present invention are embodied in:

[0017] In the entire production data management method based on industrial Internet of Things, by comprehensively counting data collection nodes and introducing the concepts of initial importance and total amount of historical data, a scientific basis is provided for the subsequent setting of data processing priorities; further, by collecting periodic production data in real time or at regular intervals, and calculating the total amount of data and the completeness of collection, enterprises can use storage and computing resources more effectively, and timely discover and solve potential data collection problems; further, by calculating the rate of change of data volume, evaluating redundancy and assigning the first correction index, the system can dynamically adjust the data processing priority of each node, improve data processing efficiency and ensure that key data is analyzed in time; further, considering the physical and network relationships between nodes, calculating spatial correlation and assigning the second correction index, it is helpful to optimize the production process and reduce resource waste; further, the real-time importance is calculated by combining multiple indicators, and the data is processed in order, ensuring that the most influential production data is analyzed and used in time and accurately; in summary, it not only improves the efficiency and pertinence of data processing, but also provides strong data support for decision support, production scheduling and quality control, which helps to realize intelligent manufacturing and optimal allocation of resources. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] In order to more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for the specific embodiments or the description of the prior art. In all the drawings, similar elements or parts are generally identified by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn according to the actual scale.

[0019] Figure 1 A schematic diagram of the steps of the production data management method based on the industrial Internet of Things of the present invention;

[0020] Figure 2 It is a schematic diagram of step S3 in the production data management method based on industrial Internet of Things of the present invention;

[0021] Figure 3 It is a schematic diagram of the composition of the production data management system based on the industrial Internet of Things of the present invention;

[0022] Figure 4 It is a schematic diagram of the composition of the optimized industrial Internet of Things involved in the present invention;

[0023] Figure 5 The present invention is a block diagram of an electronic device according to an embodiment of the present invention.

[0024] Reference numerals:

[0025] 700 - electronic device, 701 - processor, 702 - memory, 703 - multimedia component, 704 - input / output (I / O) interface, 705 - communication component. DETAILED DESCRIPTION

[0026] In order to make the purpose, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Generally, the components of the embodiments of the present invention described and shown in the drawings here can be arranged and designed in various different configurations.

[0027] Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the invention claimed for protection, but merely represents selected embodiments of the present invention. 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.

[0028] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in the subsequent drawings. In addition, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0029] like Figure 1 As shown, a production data management method based on industrial Internet of Things is provided, including:

[0030] S1. In the nth production cycle, multiple data collection nodes in the industrial Internet of Things are obtained, and the initial importance of the data collection nodes and the total amount of historical production data in the n-1th production cycle are obtained;

[0031] S2. Acquire multiple periodic production data collected by multiple data collection nodes in the nth production period, and acquire the total amount of periodic production data and data collection completeness of each data collection node according to the periodic production data;

[0032] S3. Obtain the data volume change rate according to the total amount of historical production data and the total amount of periodic production data, obtain the redundancy of each data collection node according to the data volume change rate, data collection completeness and the data type of periodic production data, and obtain the first correction index of the data collection node according to the redundancy;

[0033] S4, obtaining the physical distance and network topological distance between each data collection node, and calculating the spatial correlation of the data collection node according to the physical distance and the network topological distance, and obtaining the second correction index of the data collection node according to the spatial correlation;

[0034] S5, obtaining the real-time importance according to the first correction index, the second correction index and the initial importance of the data collection node;

[0035] S6. Processing the periodic production data of the multiple data collection nodes in sequence according to the order of their real-time importance.

[0036] In this embodiment, it should be noted that in S1, at the beginning of the nth production cycle, all nodes involved in data collection are identified and obtained from the industrial Internet of Things system. This step ensures the comprehensiveness and accuracy of subsequent analysis, because each node is a key component in the production data network. At the same time, the system will record the initial importance of each data collection node. If n=1, then this initial importance is usually based on the node device type, the location of the node in the production process, the type of production data collected, and other factors. If n≥2, it can be based on the historical situation of the n-1th production cycle or the real-time importance of the n-1th production cycle. In addition, S1 will also obtain the total amount of historical production data of the n-1th production cycle as a basis for evaluating the change in the amount of data in the current cycle.

[0037] For example, in the welding process of an automobile manufacturer, multiple sensors may be deployed to monitor welding quality, temperature, speed and other parameters. In the S1 stage, the system will identify and record all these sensors (i.e., data acquisition nodes), and assign an initial importance to each sensor based on their location on the production line, the importance of the monitoring parameters, and past data performance. At the same time, the system will review the total data volume of the previous production cycle (such as last week) so that at the beginning of a new cycle, it can accurately determine the growth or decrease trend of the data volume, thereby providing a basis for subsequent adjustments to the data processing strategy.

[0038] In summary, S1 ensures the integrity and consistency of data management by comprehensively counting data collection nodes, avoiding data loss or analysis bias caused by missing nodes; further, S1 provides a scientific basis for subsequent data processing priority setting by introducing the concepts of initial importance and total amount of historical data, so that resources can be more reasonably allocated to those nodes and data types that have a significant impact on production.

[0039] In S2, all cycle production data collected by multiple data collection nodes in the nth production cycle are obtained. By collecting data from each node in real time or periodically, the system can build a data set that fully reflects the current production status. Subsequently, the system calculates the total amount of cycle production data of each data collection node based on these cycle production data. This indicator reflects the scale of data generated by the node in the cycle and helps to evaluate the data contribution of the node. At the same time, the system also analyzes the completeness of data collection, that is, the ratio of the actual data collection amount of the data collection node to the maximum amount that can be collected, which is crucial for identifying missing or abnormal data.

[0040] In summary, the statistics of the total amount of periodic production data and the completeness of data collection provide a basis for node contribution evaluation, resource allocation and optimization, enabling enterprises to use storage and computing resources more effectively for production data; at the same time, the evaluation of data collection completeness helps to timely discover and solve potential data collection problems, such as sensor failures, network interruptions, etc., thereby ensuring the continuity and reliability of production data.

[0041] In S3, the data volume change rate is first calculated by comparing the total amount of cycle production data in the nth production cycle with the total amount of historical production data in the n-1th production cycle. This indicator reveals the growth or decrease trend of the data volume, which is of great significance for adjusting the data storage strategy and optimizing the data processing process. Subsequently, the system comprehensively considers the data volume change rate, data collection completeness, and the data type of the cycle production data to evaluate the redundancy of each data collection node. Nodes with high redundancy may mean that the data they collect is highly duplicated with other nodes or has low value for current production analysis. Finally, based on the redundancy, the system assigns a first correction indicator to each data collection node, which is used to adjust the initial importance of the node to reflect its actual value in the current production cycle.

[0042] Taking the quality inspection link in a smart factory as an example, in the S3 stage, the system may find that a camera node used to detect surface defects of products has a significant increase in the amount of data collected in several consecutive production cycles, but the data type is highly similar and there is a high redundancy with the data of other nodes. By calculating the redundancy and applying the first correction index, the system can reduce the importance of the node in the current cycle, thereby reducing the frequent processing of its data and allocating more resources to nodes that collect novel and critical data, such as sensors that detect the internal structure or performance parameters of products. Such adjustments not only improve the efficiency of data processing, but also ensure that key data can be analyzed in a timely and accurate manner, thereby supporting the optimization of production decisions.

[0043] In S4, the spatial relationship between data collection nodes is analyzed, which includes two aspects: physical distance and network topological distance. Physical distance refers to the location spacing of nodes in the actual production environment, while network topological distance reflects the degree of connection between nodes in the network structure. By combining these two factors, the system can calculate the spatial correlation of each data collection node, which measures the spatial interdependence and influence of nodes on other nodes. Subsequently, based on the spatial correlation, the system assigns a second correction indicator to each node to further adjust its importance. Nodes with high spatial correlation may mean that they are in a critical position in the production process, or that their data has an important impact on the data analysis of other nodes.

[0044] Taking the intelligent logistics system as an example, in the S4 stage, for a sensor node located at the entrance of the warehouse, although the amount of data is not large, it is at the starting point of the logistics process and has close physical and network connections with multiple other nodes (such as access control systems, weighing equipment, etc.), so it has a high spatial correlation. By calculating the spatial correlation and applying the second correction index, the system can increase the importance of the node and ensure that its data is processed in a timely manner, thereby providing accurate information support for subsequent logistics scheduling and inventory management. Such adjustments help optimize the logistics process, reduce waiting time and resource waste, and improve overall operational efficiency.

[0045] In S5, the first correction index and the second correction index calculated previously are combined with the initial importance of the data collection node to comprehensively evaluate the real-time importance of each node in the current production cycle. The real-time importance directly determines the priority of subsequent data processing and analysis. By comprehensively considering the change in the amount of data of the node, the integrity of data collection, the data type, the spatial correlation and the initial value of the node, the system can derive a comprehensive and dynamic node importance evaluation system. The calculation of real-time importance usually involves a certain weight distribution and algorithm model to ensure the accuracy and fairness of the evaluation results.

[0046] Taking the fault monitoring system in the smart grid as an example, in the S5 stage, the system may comprehensively calculate the real-time importance of each node based on the first correction index (reflecting data volume changes and redundancy), the second correction index (reflecting spatial correlation) and the initial importance (based on factors such as equipment type and historical information) of each monitoring node (such as transformers, lines, switches, etc.). In this process, the system may find that a node located on a critical transmission line has a high real-time importance due to its important location and close connection with multiple other nodes, although its data volume is not large. Based on such evaluation results, the system can give priority to processing the data of the node, discover potential fault risks in a timely manner, and take corresponding preventive measures to ensure the stable operation of the power grid.

[0047] In S6, the data collection nodes are sorted according to their real-time importance calculated previously, and the periodic production data of each node is processed in this order. When processing the periodic production data of each node, a variety of methods can be adopted, including but not limited to storage, calculation, cleaning, conversion, analysis, backup and visualization, etc., and the appropriate processing method can be selected according to specific needs and scenarios. By giving priority to the data of nodes with high importance, enterprises can ensure that the most influential information for production decisions, quality control, efficiency improvement, etc. is analyzed and used in a timely and accurate manner. This processing method not only improves the efficiency of data processing, but also ensures the pertinence and effectiveness of data processing.

[0048] To sum up, in the entire production data management method based on the Industrial Internet of Things, through a series of refined steps, efficient and orderly management of massive and complex production data in the Industrial Internet of Things is achieved. Its beneficial effects are mainly reflected in the following aspects: First, by comprehensively counting the data collection nodes and introducing the concepts of initial importance and total amount of historical data, a scientific basis is provided for the subsequent setting of data processing priorities; second, by collecting periodic production data in real time or at regular intervals and calculating the total amount of data and the completeness of collection, enterprises can use storage and computing resources more effectively, and at the same time promptly discover and solve potential data collection problems; then, by calculating the rate of change of data volume, evaluating redundancy and assigning the first correction index, the system can dynamically adjust the data processing priority of each node, improve data processing efficiency and ensure that key data is analyzed in a timely manner; in addition, considering the physical and network relationships between nodes, calculating spatial correlation and assigning the second correction index, it is helpful to optimize the production process and reduce resource waste; finally, the real-time importance is calculated by combining multiple indicators, and the data is processed in order, ensuring that the most influential production data is analyzed and utilized in a timely and accurate manner; in summary, it not only improves the efficiency and pertinence of data processing, but also provides strong data support for decision support, production scheduling and quality control, which helps to realize intelligent manufacturing and optimal allocation of resources.

[0049] like Figure 2 As shown, in one embodiment, obtaining the redundancy of each data collection node according to the data volume change rate, data collection completeness and data type of periodic production data in S3 includes:

[0050] S31, obtaining other data collection nodes with the same data type as the i-th data collection node and using them as similar data collection points, and obtaining the difference in data volume change rate and data collection completeness between the i-th data collection node and the similar data collection node;

[0051] S32, obtaining a first number of differences in data volume change rates that are less than a first preset threshold and a second number of differences in data collection completeness that are less than a second preset threshold, and obtaining the redundancy of the i-th data collection node according to the first number and the second number;

[0052] S33, repeat S31 and S32 to obtain the redundancy of multiple data collection nodes.

[0053] In this embodiment, it should be noted that in S31, a preliminary evaluation of redundancy is performed for each data acquisition node (taking the i-th node as an example). First, the system will identify all other data acquisition nodes with the same data type as the i-th data acquisition node, and mark these nodes as "similar data acquisition points". The purpose of this step is to find out those nodes that may collect similar or duplicate data as the i-th node, so as to further analyze the redundancy between them. Next, the "difference in node data volume change rate" and "difference in node data collection integrity" between the i-th data acquisition node and each similar data acquisition node will be calculated. The difference in node data volume change rate reflects the difference in the data volume growth or decrease trend between the two nodes, while the difference in node data collection integrity reveals their difference in data collection integrity. There is a certain connection between the difference in the rate of change of node data volume and the difference in the completeness of node data collection. When the difference in data collection completeness between two data collection nodes is small, the amount of data they collect is relatively close, which means that the difference in their rate of change of data volume is more likely to be reliable, because when the collection capabilities of the two nodes are similar, their collection results for the same data source will be more consistent; conversely, if the difference in data collection completeness between the two nodes is large, then the difference in their rate of change of data volume may be affected by the difference in collection capabilities, resulting in unreliable results.

[0054] Assume that in a smart factory, there are two temperature sensors (node ​​A and node B) deployed on adjacent production lines to monitor the temperature of the production environment. In the S31 stage, the system first identifies node A and node B as similar data collection points because their data types are both temperature. Then, the system calculates the difference in the node data volume change rate and the difference in node data collection completeness between node A and node B. If both differences are small, it means that the data collected by node A and node B are very similar, and there may be a high degree of redundancy.

[0055] In S32, the redundancy of the i-th data acquisition node is quantified. This step is based on the results of the difference in the node data volume change rate and the difference in the node data collection integrity calculated in S31. Specifically, the system will first set two preset thresholds: the first preset threshold is used to determine the acceptable range of the difference in the node data volume change rate, and the second preset threshold is used to determine the acceptable range of the difference in the node data collection integrity; these two thresholds are set according to the needs of the actual application scenario and the characteristics of the data, aiming to distinguish the level of data redundancy. Then, the first number of node data volume change rate differences less than the first preset threshold and the second number of node data collection integrity differences less than the second preset threshold are calculated; these two numbers respectively reflect the number of nodes similar to the i-th data acquisition node in terms of data volume change rate and data collection integrity, and the differences between these nodes and the i-th node are within an acceptable range. Finally, the system will comprehensively evaluate the redundancy of the i-th data acquisition node based on the first quantity and the second quantity; this can be achieved in a variety of ways, for example, adding the two quantities to obtain a total score, or assigning different weights according to their relative importance and then performing a weighted summation; no matter which method is used, the purpose is to obtain a quantitative indicator that can reflect the redundancy of the i-th data acquisition node.

[0056] Taking the temperature sensor in the smart factory as an example, it is assumed that the system has set a first preset threshold (such as 5%) and a second preset threshold (such as 2%) to determine the acceptable range of the difference in the node data volume change rate and the difference in the node data collection integrity. In S31, the system calculates that the difference in the node data volume change rate between node A (the i-th data collection node) and node B is 3%, and the difference in the node data collection integrity is 1%, both of which are less than their respective preset thresholds. In this case, the first number and the second number are both 1. Based on these numbers, the system can evaluate that the redundancy of node A is high because there is at least one node with the same data type and the data difference is within an acceptable range.

[0057] In S33, for each data acquisition node (not just the i-th node), the system will execute steps S31 and S32. Finally, the system will traverse all data acquisition nodes and repeat the above process to obtain the redundancy of all data acquisition nodes in the entire system.

[0058] In one embodiment, the step of obtaining the redundancy of the i-th data acquisition node according to the first quantity and the second quantity in S32 is expressed as:

[0059] ;in, is the redundancy of the ith data collection node, is the first quantity corresponding to the i-th data collection node, is the second number corresponding to the i-th data collection node, is the number of similar data collection points corresponding to the i-th data collection node.

[0060] In this embodiment, it should be noted that: represents the redundancy of the i-th data collection node. This redundancy is calculated by first and the second quantity The average value of the data is divided by the total number of similar data collection points. Come get it. It is the number of differences in the node data volume change rates that are less than the first preset threshold, which reflects the number of nodes that are similar to the i-th data collection node in terms of data volume change rate. is the number of differences in node data collection completeness that are less than the second preset threshold, which reflects the number of nodes that are similar to the i-th data collection node in terms of data collection completeness. is the total number of other data collection nodes (i.e., similar data collection points) with the same data type as the i-th data collection node. The purpose of the formula is to combine the two aspects of similarity (data volume change rate and data collection completeness) to obtain a comprehensive redundancy indicator, which can help us understand to what extent the i-th data collection node overlaps or is redundant with other nodes.

[0061] Assume that there are 5 data collection nodes (node ​​A, node B, node C, node D, node E; A, B, C, D, or E belongs to i), all of which are used to monitor temperature. In this example, we focus on the redundancy of node A (let i=A). Identify similar data collection points: B, C, D, E (they are all temperature monitoring nodes on the same production line). Calculate the difference in the rate of change of the data volume and determine the number of nodes that are less than the first preset threshold. Assumptions =2, which means that there are two nodes (such as B and C) that are very similar to node A in terms of data volume change rate. Calculate the difference in data collection completeness and determine the number of nodes that are less than the second preset threshold. Assumptions =3, which means that there are three nodes (such as B, C, and D) that are very similar to node A in terms of data collection completeness. Calculate the total number of similar data collection points In this example, =4. Apply the formula to calculate redundancy: . This is an extremely high level of redundancy.

[0062] In one embodiment, the first correction index of the data acquisition node obtained according to the redundancy is expressed as:

[0063] ;in, is the first correction index of the i-th data collection node, is the first initial correction value, is the adjustment coefficient.

[0064] In this embodiment, it should be noted that It is the first correction index of the i-th data collection node. This index is used for subsequent system optimization and adjustment, such as resource allocation and data collection frequency adjustment. is the first initial correction value, which is a basic value, usually determined by the overall system requirements or design, and can be fixed or dynamically adjusted. ,So ,therefore Also decided The maximum value of . is the adjustment factor, used to adjust the redundancy The degree of influence on the correction index, its value can be adjusted according to the actual situation to achieve the best system performance. is the redundancy of the ith data collection node calculated in the above implementation method. is obtained by changing the initial correction value Minus Multiply by the redundancy This fully reflects that the higher the redundancy of the node, the better the correction index The lower the real-time importance, the lower the resource allocation of the data collection node or the lower the data collection frequency. Based on the above implementation, let i=A, ,set up , ,but .

[0065] In one embodiment, the spatial correlation of the data collection nodes is calculated based on the physical distance and the network topological distance and is expressed as:

[0066] , ;in, is the spatial correlation of the ith data collection node, is the number of data collection nodes, is the physical distance between the i-th data collection node and the j-th data collection node, is the network topological distance between the i-th data collection node and the j-th data collection node, and is the weight coefficient.

[0067] In this embodiment, it should be noted that and As weight coefficients, they represent the importance of physical distance and network topological distance in calculating spatial association. and The relative weight between the two can be flexibly controlled to adapt to different application scenarios and requirements; at the same time, by setting , which can ensure that when calculating spatial association, the contributions of physical distance and network topological distance can be compared and weighted on a unified scale, thereby improving the accuracy and flexibility of the calculation. The sum of the inverse of the physical distance between the i-th node and all other nodes is calculated and multiplied by the weight , which fully reflects that the closer the physical distance is, the larger the reciprocal is. Similarly, The sum of the inverse of the network topological distance between the i-th node and all other nodes is calculated and multiplied by the weight , which fully reflects that the shorter the network topology distance (i.e. the better the connectivity), the larger the reciprocal value is. The greater the contribution.

[0068] Assume that there is a system with 4 data collection nodes. The physical distance and network topology distance between the nodes are shown in the following table:

[0069]

[0070] Assume θ=0.6, μ=0.4, and calculate the spatial correlation of node 1 :

[0071] .

[0072] In one embodiment, the second correction index of the data acquisition node is obtained according to the spatial correlation as follows:

[0073] ;in,

[0074] is the second correction index of the i-th data collection node, is the second initial correction value.

[0075] In this embodiment, it should be noted that It is a constant, representing the basic correction amount when the spatial correlation is not considered. It can be adjusted according to the needs of the actual application scenario to adapt to different data correction requirements. along with The decrease is gradually decreasing with the increase of Gradually increases, this characteristic makes the correction index exist When changing, it can increase more and more smoothly, avoiding The correction index changes dramatically due to mutations, that is, the exponential function can The linear change mapping is This nonlinear mapping relationship can better adapt to the complex association relationship in practical applications, making the correction more accurate; at the same time, since the range of the exponential function is (0, 1], The range of is [0, 1), which means that no matter What value to take, correct the indicator will be limited to a reasonable range to avoid The correction index is distorted due to being too large or too small.

[0076] Assume that there are three data collection nodes (i=1, 2, 3), and the spatial correlations are , , Now we substitute k=2 into the formula to calculate: For node 1, ; For node 2: ; For node 3: .

[0077] In one embodiment, the real-time importance is obtained according to the first correction index, the second correction index and the initial importance of the data collection node as follows:

[0078] ;in,

[0079] is the real-time importance of the i-th data collection node, is the initial importance of the i-th data collection node.

[0080] In this embodiment, it should be noted that Combined with the first correction indicator , Second Correction Index and initial importance ; Among them, multiplication It can ensure that when calculating the real-time importance, the initial importance is corrected. The initial importance of the node is used as a benchmark consideration. Different data collection nodes may have different initial importances, allowing personalized adjustments based on the specific initial state of each node, thereby obtaining a more accurate real-time importance. In the formula, dividing by 2 is actually an averaging of the two correction indicators. If the two correction indicators are directly added without averaging, then when the value of one indicator is much larger than the other, the real-time importance may be greatly affected, causing the result to be biased towards extreme values. Dividing by 2 can avoid this situation and ensure that the two correction indicators have a balanced impact on the real-time importance. Furthermore, since the first correction indicator and the second correction indicator may change over time, the real-time importance can also be adjusted dynamically to reflect the actual importance of the node at the current moment.

[0081] Based on the above implementation, there are three data collection nodes, set , calculated =0.7875, =0.3626; for node 2: =1.1, =0.7870; for node 3: =0.6, =1.2642. Then the final calculated , , .

[0082] The order of processing the periodic production data collected by the data collection nodes is node 2, node 3 to node 1.

[0083] like Figure 3 As shown, a production data management system based on the industrial Internet of Things is also provided, the system includes a service platform, a management platform and a sensor network platform which are sequentially connected in communication, and the management platform includes:

[0084] The node confirmation module is used to obtain multiple data collection nodes in the industrial Internet of Things in the nth production cycle, and obtain the initial importance of the data collection nodes and the total amount of historical production data in the n-1th production cycle;

[0085] The production data collection module is used to obtain multiple periodic production data collected by multiple data collection nodes in the nth production cycle, and obtain the total amount of periodic production data and data collection completeness according to the periodic production data;

[0086] A first calculation module is used to obtain a data volume change rate according to the total amount of historical production data and the total amount of periodic production data, and to obtain the redundancy of each data collection node according to the data volume change rate, the data collection completeness and the data type of the periodic production data, and to obtain a first correction index of the data collection node according to the redundancy;

[0087] A second calculation module is used to obtain the physical distance and network topological distance between each data collection node, calculate the spatial correlation of the data collection node according to the physical distance and the network topological distance, and obtain a second correction index of the data collection node according to the spatial correlation;

[0088] A third calculation module is used to obtain the real-time importance according to the first correction index, the second correction index and the initial importance of the data collection node;

[0089] The decision module is used to process the periodic production data of multiple data collection nodes in sequence according to the order of the real-time importance of the multiple data collection nodes.

[0090] In this embodiment, it should be noted that, regarding the above-mentioned production data management system based on industrial Internet of Things, the specific method of performing operations has been described in detail in the implementation of the production data management method based on industrial Internet of Things, and will not be elaborated here.

[0091] It should also be noted that the entire industrial Internet of Things-based production data management system can be applied to the optimized industrial Internet of Things, such as Figure 4 As shown, the optimized industrial Internet of Things includes a user platform, a service platform, a management platform, a sensor network platform and an object platform that establish communication in sequence;

[0092] The user platform is configured to provide front-end service functions to users; users obtain the required perceived service information through the user platform, process the perceived service information, and convert it into user perceived information; users analyze the user perceived information and make corresponding decisions based on their own wishes, and convert the user perceived information into user control information through the corresponding information system and send it to the service platform, thereby expressing the user's corresponding service demand intention.

[0093] The physical entities of the user platform include various user terminals, such as mobile phones, computers, special terminals, etc., which realize user-side services through integration with user information system software.

[0094] The service platform is configured as an API server or other server used to establish communication between the management platform and the user platform to implement corresponding functions; the physical entity of the service platform includes various servers.

[0095] The management platform is configured to perform at least one of equipment operation status monitoring management, data monitoring management, equipment parameter management, and life cycle management; the management platform is an operational coordination platform for the Internet of Things, which may include various management sub-platforms, and different management sub-platforms perform different management services; the physical entities of the management platform include various servers.

[0096] The sensor network platform is configured to perform at least one of network management, command management, device status management, data protocol management, data analysis, data classification, data transmission monitoring, and data transmission security management. The sensor network platform provides data communication, analysis, identification, classification, and other functions, avoiding the direct aggregation of data from various object platforms on the management platform, resulting in redundant data on the management platform and low data processing efficiency; the physical entities of the object platform include various gateways, edge computing devices, etc.

[0097] The object platform is configured to carry out specific production work such as production control, detection, and measurement; the physical entities of the production objects include various production equipment, sensors, etc.

[0098] Figure 5 is a block diagram of an electronic device showing a production data management method based on the industrial Internet of Things according to an exemplary embodiment. Figure 5 As shown, the electronic device 700 may include: a processor 701 , a memory 702 . The electronic device 700 may also include one or more of a multimedia component 703 , an input / output (I / O) interface 704 , and a communication component 705 .

[0099] The processor 701 is used to control the overall operation of the electronic device 700 to complete all or part of the steps in the above-mentioned production data management method based on the industrial Internet of Things. The memory 702 is used to store various types of data to support the operation of the electronic device 700. For example, these data may include instructions for any application or method used to operate on the electronic device 700, and application-related data, such as contact data, messages sent and received, pictures, audio, video, etc. The memory 702 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (Static Random Access Memory, referred to as SRAM), electrically erasable programmable read-only memory (Electrically Erasable Programmable Read-Only Memory, referred to as EEPROM), erasable programmable read-only memory (Erasable Programmable Read-Only Memory, referred to as EPROM), programmable read-only memory (Programmable Read-Only Memory, referred to as PROM), read-only memory (Read-Only Memory, referred to as ROM), magnetic memory, flash memory, magnetic disk or optical disk. The multimedia component 703 may include a screen and an audio component. The screen may be, for example, a touch screen, and the audio component is used to output and / or input audio signals. For example, the audio component may include a microphone for receiving external audio signals. The received audio signal may be further stored in the memory 702 or sent through the communication component 705. The audio component also includes at least one speaker for outputting audio signals. The I / O interface 704 provides an interface between the processor 701 and other interface modules, and the other interface modules may be keyboards, mice, buttons, etc. These buttons may be virtual buttons or physical buttons. The communication component 705 is used for wired or wireless communication between the electronic device 700 and other devices. Wireless communication, such as Wi-Fi, Bluetooth, Near Field Communication (NFC), 2G, 3G, 4G, NB-IOT, eMTC or other 5G, etc., or a combination of one or more of them, is not limited here. Therefore, the corresponding communication component 705 may include: Wi-Fi module, Bluetooth module, NFC module, etc.

[0100] In an exemplary embodiment, the electronic device 700 can be implemented by one or more application specific integrated circuits (ASIC), digital signal processors (DSP), digital signal processing devices (DSPD), programmable logic devices (PLD), field programmable gate arrays (FPGA), controllers, microcontrollers, microprocessors or other electronic components to execute the above-mentioned production data management method based on industrial Internet of Things.

[0101] In another exemplary embodiment, a computer-readable storage medium including program instructions is also provided, and when the program instructions are executed by a processor, the steps of the above-mentioned production data management method based on the industrial Internet of Things are implemented. For example, the computer-readable storage medium can be the above-mentioned memory 702 including program instructions, and the above-mentioned program instructions can be executed by the processor 701 of the electronic device 700 to complete the above-mentioned production data management method based on the industrial Internet of Things.

[0102] In another exemplary embodiment, a computer program product is also provided, which includes a computer program that can be executed by a programmable device, and the computer program has a code portion for executing the above-mentioned production data management method based on the industrial Internet of Things when executed by the programmable device.

[0103] The preferred embodiments of the present disclosure are described in detail above in conjunction with the accompanying drawings; however, the present disclosure is not limited to the specific details in the above embodiments. Within the technical concept of the present disclosure, a variety of simple modifications can be made to the technical solution of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0104] It should also be noted that the various specific technical features described in the above specific embodiments can be combined in any suitable manner without contradiction. In order to avoid unnecessary repetition, the present disclosure will not further describe various possible combinations.

[0105] In addition, various embodiments of the present disclosure may be arbitrarily combined, and as long as they do not violate the concept of the present disclosure, they should also be regarded as the contents disclosed by the present disclosure.

[0106] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or replace some or all of the technical features therein with equivalents. These modifications or replacements do not make the essence of the corresponding technical solutions deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be included in the scope of the claims and specification of the present invention.

Claims

1. A production data management method based on industrial Internet of Things, characterized in that: The method comprises: In the nth production cycle, multiple data collection nodes in the industrial Internet of Things are obtained, and the initial importance of the data collection nodes and the total amount of historical production data in the n-1th production cycle are obtained; Acquire multiple periodic production data collected by multiple data collection nodes in the nth production period, and acquire the total amount of periodic production data and data collection completeness of each data collection node according to the periodic production data; Obtain the data volume change rate according to the total amount of historical production data and the total amount of periodic production data, obtain the redundancy of each data collection node according to the data volume change rate, data collection completeness and the data type of periodic production data, and obtain the first correction index of the data collection node according to the redundancy; Wherein, the method of obtaining the redundancy of each data collection node according to the data volume change rate, data collection integrity and data type of periodic production data includes: obtaining other data collection nodes of the same data type as the i-th data collection node and using them as similar data collection points, obtaining the difference in node data volume change rate and the difference in node data collection integrity between the i-th data collection node and the similar data collection node; obtaining a first number of differences in node data volume change rate less than a first preset threshold and a second number of differences in node data collection integrity less than a second preset threshold, and obtaining the redundancy of the i-th data collection node according to the first number and the second number; The redundancy of the i-th data acquisition node obtained according to the first quantity and the second quantity is expressed as: ;in, is the redundancy of the ith data collection node, is the first quantity corresponding to the i-th data collection node, is the second number corresponding to the i-th data collection node, is the number of similar data collection points corresponding to the i-th data collection node; Obtaining the physical distance and network topological distance between each data collection node, and calculating the spatial correlation of the data collection node according to the physical distance and the network topological distance, and obtaining the second correction index of the data collection node according to the spatial correlation; Obtaining real-time importance according to the first correction index, the second correction index and the initial importance of the data collection node; The periodic production data of the multiple data collection nodes are processed in sequence according to the order of the real-time importance of the multiple data collection nodes.

2. The production data management method based on industrial Internet of Things according to claim 1 is characterized in that: The first correction index of the data acquisition node obtained according to the redundancy is expressed as: ;in, is the first correction index of the i-th data collection node, is the first initial correction value, is the adjustment coefficient.

3. The production data management method based on industrial Internet of Things according to claim 2 is characterized in that: The spatial correlation degree of the data acquisition nodes calculated based on the physical distance and the network topology distance is expressed as: , ;in, is the spatial correlation of the ith data collection node, is the number of data collection nodes, is the physical distance between the i-th data collection node and the j-th data collection node, is the network topological distance between the i-th data collection node and the j-th data collection node, and is the weight coefficient.

4. The production data management method based on industrial Internet of Things according to claim 3 is characterized in that: The second correction index of obtaining the data acquisition node according to the spatial correlation is expressed as: ;in, is the second correction index of the i-th data collection node, is the second initial correction value.

5. The production data management method based on industrial Internet of Things according to claim 4 is characterized in that: The real-time importance is obtained according to the first correction index, the second correction index and the initial importance of the data collection node as follows: ;in, is the real-time importance of the i-th data collection node, is the initial importance of the i-th data collection node.

6. A production data management system based on industrial Internet of Things, characterized in that: The system includes a service platform, a management platform and a sensor network platform which are connected in sequence, and the management platform includes: The node confirmation module is used to obtain multiple data collection nodes in the industrial Internet of Things in the nth production cycle, and obtain the initial importance of the data collection nodes and the total amount of historical production data in the n-1th production cycle; The production data collection module is used to obtain multiple periodic production data collected by multiple data collection nodes in the nth production cycle, and obtain the total amount of periodic production data and data collection completeness according to the periodic production data; The first calculation module is used to obtain the data volume change rate according to the total amount of historical production data and the total amount of periodic production data, and obtain the redundancy of each data collection node according to the data volume change rate, the data collection integrity and the data type of the periodic production data, and obtain the first correction index of the data collection node according to the redundancy; wherein, the redundancy of each data collection node according to the data volume change rate, the data collection integrity and the data type of the periodic production data includes: obtaining other data collection nodes with the same data type as the i-th data collection node and using them as similar data collection points, obtaining the difference in the node data volume change rate and the difference in the node data collection integrity between the i-th data collection node and the similar data collection node; obtaining a first number of differences in node data volume change rates less than a first preset threshold and a second number of differences in node data collection integrity less than a second preset threshold, and obtaining the redundancy of the i-th data collection node according to the first number and the second number; wherein, the redundancy of the i-th data collection node obtained according to the first number and the second number is expressed as: ;in, is the redundancy of the ith data collection node, is the first quantity corresponding to the i-th data collection node, is the second number corresponding to the i-th data collection node, is the number of similar data collection points corresponding to the i-th data collection node; A second calculation module is used to obtain the physical distance and network topological distance between each data collection node, calculate the spatial correlation of the data collection node according to the physical distance and the network topological distance, and obtain a second correction index of the data collection node according to the spatial correlation; A third calculation module is used to obtain the real-time importance according to the first correction index, the second correction index and the initial importance of the data collection node; The decision module is used to process the periodic production data of multiple data collection nodes in sequence according to the order of the real-time importance of the multiple data collection nodes.

7. An electronic device, characterized in that: include: a memory having a computer program stored thereon; A processor, configured to execute the computer program in the memory to implement the production data management method based on the industrial Internet of Things as described in any one of claims 1 to 5.

8. A non-transitory computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, the production data management method based on the industrial Internet of Things described in any one of claims 1 to 5 is implemented.

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