Production scheduling method based on NB-iot acquisition technology

Abstract

The invention discloses a production scheduling method based on an NB-IoT (Narrow Band Internet of Things) acquisition technology. The method comprises the following steps: carrying out legality verification on accessed equipment, dynamically distributing network resources to equipment in a queue to be processed, locally processing data by utilizing an edge computing technology, dynamically adjusting the bandwidth of the equipment according to real-time requirements, and sending the network resources to the equipment in the queue to be processed. A machine learning algorithm is used to predict production abnormity and adjust a production plan in advance, and a data caching function is set at an equipment end. According to the invention, the demand of manual intervention is reduced through automatic scheduling, and finally, the production efficiency is greatly improved and the operation cost is effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of Internet of Things, and in particular to a production scheduling method based on NB-iot acquisition technology. Background Art

[0002] In the context of Industry 4.0, NB-IoT (Narrowband Internet of Things) technology has been widely used in production data collection and transmission due to its low power consumption and wide coverage. However, the existing production scheduling methods have some obvious defects when facing large-scale equipment networking and complex production processes, such as large data transmission delay, insufficient scheduling flexibility, unreasonable resource allocation, and slow response to equipment abnormalities. In particular, with the sharp increase in data volume, traditional data collection and processing methods are difficult to meet the real-time requirements, resulting in serious impact on the accuracy and timeliness of production scheduling decisions. In addition, the lack of effective redundant design and intelligent prediction means also makes the system less reliable when facing network failures or equipment abnormalities, thereby increasing downtime and maintenance costs. These problems limit the improvement of production efficiency and the competitiveness of enterprises. Therefore, there is an urgent need for a new production scheduling method that can overcome the above-mentioned defects to achieve intelligent and automated management of the production process. Summary of the invention

[0003] 1. Technical Problems Solved

[0004] The technical problems to be solved by the present invention are the various problems mentioned in the above background technology, and a production scheduling method based on NB-iot acquisition technology is provided.

[0005] 2. Technical Solution

[0006] In order to solve the above technical problems, the technical solution provided by the present invention is: a production scheduling method based on NB-IoT acquisition technology, the method comprising the following steps:

[0007] S1. Verify the legitimacy of connected devices, register legitimate devices and add them to the processing queue, and classify devices into different priorities according to their importance and type;

[0008] S2. Dynamically allocate network resources to devices in the processing queue, collect and process the data of the devices, summarize the processing results to the result management module, apply network slicing technology to optimize network resource allocation, and set high priority transmission for data related to production scheduling;

[0009] S3. Use edge computing technology to process data locally, reduce data transmission requirements, and reduce data transmission volume through data compression and aggregation technology;

[0010] S4, dynamically adjust device bandwidth according to real-time demand, combine other low-latency wireless communication technologies with NB-IoT to build a hybrid network, and design redundant communication paths to ensure data transmission;

[0011] S5. Use machine learning algorithms to predict production anomalies and adjust production plans in advance;

[0012] S6. Set up data caching function on the device side to prepare for network delays or interruptions, use lightweight communication protocols and compact data formats, regularly monitor network performance and update device firmware and software.

[0013] As an improvement, the legitimacy verification step includes verifying the identity information of the device, disconnecting the illegal device and sending a prompt message to the user.

[0014] As an improvement, the step of dynamically allocating network resources includes adjusting the bandwidth of the device according to the current task requirements of the device, and automatically increasing the bandwidth allocation of key devices in emergency situations.

[0015] As an improvement, the data compression and aggregation step includes using a compression algorithm to reduce the size of the data packet and aggregating the data of multiple sampling points.

[0016] As an improvement, the step of applying a machine learning algorithm includes analyzing historical production data to predict future production anomalies and adjusting the production scheduling strategy based on the prediction results.

[0017] As an improvement, it also includes regular updates of the firmware of NB-IoT devices and the software of the management platform to improve system performance and stability.

[0018] As an improvement, the application of the edge computing technology includes performing preliminary data processing and analysis on a local server or gateway, making production scheduling decisions in real time and immediately executing adjustment commands.

[0019] As an improvement, the construction of the hybrid network architecture also includes combining other wireless communication technologies such as LoRa and 5G with NB-IoT to jointly build a network, using high-transmission rate communication methods to handle tasks that require high real-time performance.

[0020] 3. Beneficial Effects

[0021] The advantages of the present invention compared with the prior art are:

[0022] The present invention proposes a production scheduling method based on NB-IoT acquisition technology, which verifies the legitimacy of access devices and manages them by priority, dynamically allocates network resources, optimizes network slicing technology to ensure high-priority transmission of critical data, uses edge computing to reduce data transmission requirements and improves response speed, uses data compression and aggregation technology to reduce data volume, dynamically adjusts bandwidth according to real-time needs, builds a hybrid network architecture to ensure the reliability and real-time performance of data transmission, uses machine learning to predict production anomalies and adjust production plans in advance, sets data caching functions to cope with network delays or interruptions, uses lightweight protocols and compact data formats, and regularly updates device firmware and software to improve system performance and stability, thereby significantly improving the real-time, accuracy and reliability of production scheduling, reducing delays and downtime in the production process, optimizing resource allocation and energy management, enhancing system reliability and security, and reducing the need for manual intervention through automated scheduling, ultimately achieving a significant increase in production efficiency and an effective reduction in operating costs. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] Figure 1 This is a step diagram of a production scheduling method based on NB-iot acquisition technology of the present invention.

[0024] Figure 2 It is an operational implementation process of a production scheduling method based on NB-iot acquisition technology in the present invention. DETAILED DESCRIPTION

[0025] 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 only part of the embodiments of the present invention, rather than all the embodiments; based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention.

[0026] like Figure 1 — Figure 2 As shown, a production scheduling method based on NB-IoT acquisition technology includes the following steps:

[0027] S1. Verify the legitimacy of connected devices, register legitimate devices and add them to the processing queue, and classify devices into different priorities according to their importance and type;

[0028] S2. Dynamically allocate network resources to devices in the processing queue, collect and process the data of the devices, summarize the processing results to the result management module, apply network slicing technology to optimize network resource allocation, and set high priority transmission for data related to production scheduling;

[0029] S3. Use edge computing technology to process data locally, reduce data transmission requirements, and reduce data transmission volume through data compression and aggregation technology;

[0030] S4, dynamically adjust device bandwidth according to real-time demand, combine other low-latency wireless communication technologies with NB-IoT to build a hybrid network, and design redundant communication paths to ensure data transmission;

[0031] S5. Use machine learning algorithms to predict production anomalies and adjust production plans in advance;

[0032] S6. Set up data caching function on the device side to prepare for network delays or interruptions, use lightweight communication protocols and compact data formats, regularly monitor network performance and update device firmware and software.

[0033] Specifically, the legitimacy verification step includes verifying the identity information of the device, disconnecting the illegal device and sending a prompt message to the user.

[0034] Specifically, the step of dynamically allocating network resources includes adjusting the bandwidth of the equipment according to the current task requirements of the equipment, and automatically increasing the bandwidth allocation of key equipment in an emergency. The step of data compression and aggregation includes using a compression algorithm to reduce the size of the data packet and aggregating the data from multiple sampling points. The step of applying a machine learning algorithm includes analyzing historical production data to predict future production anomalies and adjusting the production scheduling strategy based on the prediction results.

[0035] Specifically, it also includes regularly updating the firmware of NB-IoT devices and the software of the management platform to improve system performance and stability. The application of the edge computing technology includes performing preliminary data processing and analysis on the local server or gateway, making production scheduling decisions in real time and immediately executing adjustment commands. The construction of the hybrid network architecture also includes combining other wireless communication technologies such as LoRa and 5G with NB-IoT to jointly build a network, and using high-transmission rate communication methods to process tasks that require high real-time performance.

[0036] The working principle of the present invention is as follows: first, the legitimacy of the devices connected to the network is verified, and after confirming that the device identity information is correct, it is registered and added to the queue to be processed, and the devices are divided into different priorities according to their importance and type; second, network resources are dynamically allocated to the devices in the queue to be processed, the data of the devices are centrally processed and the processing results are summarized in the result management module, and network slicing technology is applied to optimize network resource allocation, and high priority transmission is set for data related to production scheduling; then, edge computing technology is used to perform preliminary data processing and analysis on the local server or gateway to reduce data transmission requirements, and data compression and aggregation technology are used to reduce the amount of data transmission; then, according to real-time Dynamically adjust device bandwidth according to demand, automatically increase bandwidth allocation for key devices when necessary, combine other low-latency wireless communication technologies (such as LoRa, 5G, etc.) with NB-IoT to build a hybrid network, and design redundant communication paths to ensure the reliability of data transmission; in addition, use machine learning algorithms to analyze historical production data to predict future production anomalies, and adjust production plans in advance based on the prediction results; finally, set up data caching function on the device side to cope with network delays or interruptions, use lightweight communication protocols and compact data formats, regularly monitor network performance, and promptly update the firmware of NB-IoT devices and the software of the management platform to improve system performance and stability.

[0037] Through the above steps, the method of the present invention achieves high efficiency, accuracy and reliability in the production scheduling process, which helps to improve overall production efficiency, reduce costs, and enhance the competitiveness of enterprises.

[0038] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device.

[0039] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

[0040] The present invention and its embodiments are described above, and such description is not restrictive. The drawings show only one embodiment of the present invention, and the actual structure is not limited thereto. In short, if ordinary technicians in the field are inspired by it, without departing from the purpose of the invention, they can design a structure and embodiment similar to the technical solution without creativity, which should belong to the protection scope of the present invention.

Claims

1. A production scheduling method based on NB-IoT acquisition technology, characterized in that: The method comprises the following steps: S1. Verify the legitimacy of connected devices, register legitimate devices and add them to the processing queue, and classify devices into different priorities according to their importance and type; S2. Dynamically allocate network resources to devices in the processing queue, collect and process the data of the devices, summarize the processing results to the result management module, apply network slicing technology to optimize network resource allocation, and set high priority transmission for data related to production scheduling; S3. Use edge computing technology to process data locally, reduce data transmission requirements, and reduce data transmission volume through data compression and aggregation technology; S4, dynamically adjust device bandwidth according to real-time demand, combine other low-latency wireless communication technologies with NB-IoT to build a hybrid network, and design redundant communication paths to ensure data transmission; S5. Use machine learning algorithms to predict production anomalies and adjust production plans in advance; S6. Set up data caching function on the device side to prepare for network delays or interruptions, use lightweight communication protocols and compact data formats, regularly monitor network performance and update device firmware and software.

2. A production scheduling method based on NB-IoT acquisition technology as claimed in claim 1, characterized in that: The legitimacy verification step includes verifying the identity information of the device, disconnecting the illegal device and sending a prompt message to the user.

3. A production scheduling method based on NB-IoT acquisition technology as claimed in claim 1, characterized in that: The step of dynamically allocating network resources includes adjusting the bandwidth of the device according to the current task requirements of the device and automatically increasing the bandwidth allocation of key devices in emergency situations.

4. A production scheduling method based on NB-IoT acquisition technology as claimed in claim 1, characterized in that: The data compression and aggregation steps include using a compression algorithm to reduce the size of the data packet and aggregating the data of multiple sampling points.

5. The production scheduling method based on NB-IoT acquisition technology according to claim 1, characterized in that: The step of applying the machine learning algorithm includes analyzing historical production data to predict future production anomalies and adjusting the production scheduling strategy based on the prediction results.

6. A production scheduling method based on NB-IoT acquisition technology as claimed in claim 1, characterized in that: It also includes regular updates of the firmware of NB-IoT devices and the software of the management platform to improve system performance and stability.

7. The production scheduling method based on NB-IoT acquisition technology according to claim 1, characterized in that: The application of edge computing technology includes preliminary data processing and analysis on local servers or gateways, making production scheduling decisions in real time and immediately executing adjustment commands.

8. The production scheduling method based on NB-IoT acquisition technology according to claim 1, characterized in that: The construction of the hybrid network architecture also includes combining other wireless communication technologies such as LoRa and 5G with NB-IoT to jointly build a network, using high-transmission rate communication methods to handle tasks that require high real-time performance.

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