Industrial data coding and transmission optimization method, system and equipment based on industrial internet of things, and medium

Abstract

The invention discloses an industrial data coding and transmission optimization method, system and device based on the industrial Internet of Things and a medium, and relates to the technical field of data transmission processing, and the method comprises the steps: obtaining to-be-processed industrial data, information source features and information source length; when the data volume exceeds the information source length, forming a to-be-sent data packet, and obtaining a first sequential code; planning a basic transmission path, and obtaining a second sequence code; associating the first sequence code and the second sequence code with the to-be-sent data packet to form a to-be-transmitted data set, and transmitting the to-be-transmitted data set according to a basic transmission path corresponding to the second sequence code; and receiving a to-be-transmitted data set, obtaining a transmission path optimization strategy according to the first sequence code of the received to-be-transmitted data set, and carrying out data transmission of a subsequent to-be-sent data packet according to the transmission path optimization strategy. The invention also provides an industrial data coding and transmission optimization system based on the industrial Internet of Things. The device has the advantages of being good in transmission effect, high in transmission efficiency, stable and reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of data transmission processing, and particularly to an industrial data encoding and transmission optimization method, system, device and medium based on the industrial Internet of Things. Background Art

[0002] With the rapid development of the Industrial Internet of Things (IIoT for short), the acquisition, encoding and efficient transmission of industrial data have become the basis for realizing key applications such as intelligent manufacturing, remote monitoring, and predictive maintenance. In the industrial environment, various sensors, devices and control systems continuously generate a large amount of data, which contains the real-time state of the production process, equipment health status, energy consumption information, etc., and is of great significance for improving the operation efficiency of enterprises, optimizing the production process, and reducing the downtime due to faults. However, the particularity and complexity of industrial data have brought huge challenges to data encoding and transmission.

[0003] Existing data encoding and transmission methods often adopt a fixed data packet size and a single transmission path, ignoring the dynamic characteristics of different industrial data sources and the complexity of the network environment; for example, some data may require more frequent sampling and transmission due to the rapid changes in the production process, while some data may be relatively stable and do not require such high-frequency updates; in this way, adopting a unified data packet length and transmission strategy will not only lead to low data transmission efficiency, but also may increase unnecessary network burden due to encoding redundancy, and even cause data loss or transmission delay in some cases, affecting subsequent data analysis and decision-making. In addition, there are numerous transmission nodes in the industrial Internet of Things, and the network environment is complex and changeable. A fixed transmission path is difficult to adapt to the dynamic changes of the network conditions, such as node failures, network congestion, etc., which further exacerbates the instability and unreliability of data transmission. Summary of the Invention

[0004] Aiming at the defects in the prior art, the present invention provides an industrial data encoding and transmission optimization method, system, device and medium based on the industrial Internet of Things.

[0005] An industrial data encoding and transmission optimization method based on the industrial Internet of Things, comprising: obtaining industrial data to be processed based on the industrial Internet of Things, obtaining the source characteristics corresponding to the industrial data to be processed, and obtaining the source length corresponding to the industrial data to be processed according to the source characteristics corresponding to the industrial data to be processed; when the data volume of the industrial data to be processed exceeds the source length, packing the industrial data to be processed to form a data packet to be sent, and obtaining a continuously increasing first sequential code corresponding to the current data packet to be sent based on the first coding model; planning a plurality of basic transmission paths including different transmission nodes, and obtaining a second sequential code corresponding to each basic transmission path based on the position data of the plurality of transmission nodes in each basic transmission path; associating the first sequential code and the second sequential code with the data packet to be sent to form a data set to be transmitted, and transmitting the data set to be transmitted according to the basic transmission path corresponding to the second sequential code; receiving the data set to be transmitted, obtaining a transmission path optimization strategy according to the first sequential code of the received data set to be transmitted, and carrying out data transmission of subsequent data packets to be sent according to the transmission path optimization strategy.

[0006] Optionally, obtaining a transmission path optimization strategy according to the first sequential code of the received data set to be transmitted includes: if the first sequential code of the currently received data set to be transmitted and the first sequential code of the previously received data set to be transmitted are not continuous, and the first sequential code of the currently received data set to be transmitted is greater than the first sequential code of the previously received data set to be transmitted, then obtaining the interval difference between the first sequential code of the currently received data set to be transmitted and the first sequential code of the previously received data set to be transmitted; obtaining the maximum interval difference among a plurality of interval differences within a preset time period, and obtaining the second sequential code of the currently received data set to be transmitted corresponding to the maximum interval difference, and using the basic transmission path corresponding to the second sequential code as the optimized transmission path.

[0007] Optionally, carrying out data transmission of subsequent data packets to be sent according to the transmission path optimization strategy includes: associating the first sequential code with the subsequent data packets to be sent to form a new data set to be transmitted, and transmitting the new data set to be transmitted according to the optimized transmission path.

[0008] Optionally, obtaining a continuously increasing first sequential code corresponding to the current data packet to be sent based on the first coding model includes: setting a continuous increment value and an initial coding value corresponding to the first formed data packet to be sent; obtaining the number of times of forming data packets to be sent between the first formed data packet to be sent and the current data packet to be sent; obtaining a continuously increasing first sequential code corresponding to the current data packet to be sent based on the first coding model, the number of times of forming data packets to be sent, the initial coding value, and the continuous increment value.

[0009] Optionally, the first coding model in the first sequential coding that continuously increases and corresponds to the current data packet to be sent is obtained based on the first coding model, the number of times the data packet to be sent is formed, the initial coding value, and the continuous increment value, which is expressed as: ; where is the i-th first sequential coding, is the number of times the data packet to be sent is formed, is the continuous increment value, is the initial coding value.

[0010] Optionally, obtaining the second sequential coding corresponding to each basic transmission path based on the location data of multiple transmission nodes in each basic transmission path includes: obtaining all the transmission nodes in the j-th basic transmission path, and obtaining the corresponding unique identifier according to the location data of the obtained transmission nodes; obtaining the second sequential coding corresponding to the j-th basic transmission path based on the order of the transmission nodes in the j-th basic transmission path and the unique identifier corresponding to each transmission node.

[0011] Optionally, the system includes a management platform, a sensing network platform, and an object platform that are communicatively connected in sequence. The management platform includes: a comprehensive acquisition module, configured to acquire the industrial data to be processed based on the industrial Internet of Things, acquire the source characteristics corresponding to the industrial data to be processed, and acquire the source length corresponding to the industrial data to be processed according to the source characteristics corresponding to the industrial data to be processed; a judgment and sequential coding module, configured to, when the data volume of the industrial data to be processed exceeds the source length, pack the industrial data to be processed to form a data packet to be sent, and obtain the first sequential coding that continuously increases and corresponds to the current data packet to be sent based on the first coding model; a location coding module, configured to plan multiple basic transmission paths including different transmission nodes, and obtain the second sequential coding corresponding to each basic transmission path based on the location data of multiple transmission nodes in each basic transmission path; a transmission module, configured to associate the first sequential coding and the second sequential coding with the data packet to be sent to form a data set to be transmitted, and transmit the data set to be transmitted according to the basic transmission path corresponding to the second sequential coding; an optimization module, configured to receive the data set to be transmitted, obtain a transmission path optimization strategy according to the first sequential coding of the received data set to be transmitted, and perform subsequent data transmission of the data packet to be sent according to the transmission path optimization strategy.

[0012] Optionally, the optimization module is further configured to: if the first sequence code of the currently received data set to be transmitted is not continuous with the first sequence code of the previously received data set to be transmitted, and the first sequence code of the currently received data set to be transmitted is greater than the first sequence code of the previously received data set to be transmitted, then obtain the interval difference between the first sequence code of the currently received data set to be transmitted and the first sequence code of the previously received data set to be transmitted; obtain the maximum interval difference among multiple interval differences within a preset time period, and obtain the second sequence code of the currently received data set to be transmitted corresponding to the maximum interval difference, and use the basic transmission path corresponding to the second sequence code as the optimized transmission path; associate the first sequence code with subsequent data packets to be sent to form a new data set to be transmitted, and transmit the new data set to be transmitted according to the optimized transmission path.

[0013] An electronic device is further provided, including: a memory on which a computer program is stored; a processor configured to execute the computer program in the memory to implement the above-mentioned industrial data encoding and transmission optimization method based on the industrial Internet of Things.

[0014] A non-transitory computer-readable storage medium is further provided, on which a computer program is stored, and when the program is executed by a processor, the above-mentioned industrial data encoding and transmission optimization method based on the industrial Internet of Things is implemented.

[0015] The beneficial effects of the present invention are as follows: In the entire industrial data encoding and transmission optimization method based on the industrial Internet of Things, firstly, by deeply analyzing the source characteristics of different industrial data sources, the dynamic adjustment of the data packet size is realized, significantly improving the data transmission efficiency and avoiding coding redundancy and increased network burden caused by a fixed data packet size; at the same time, the first sequence code is introduced to ensure the uniqueness, orderliness and traceability of data packets during the transmission process, providing strong support for data recombination, error detection and recovery; in addition, by planning multiple basic transmission paths and assigning a unique second sequence code to each path, not only the data shunting and load balancing are realized, but also the system is given a high degree of flexibility and adaptability, enabling it to dynamically adjust the data transmission path according to the real-time changes of the network environment and data characteristics, effectively coping with uncertain factors such as node failures and network congestion, and ensuring the stability, reliability and timeliness of data transmission; therefore, this optimization method that comprehensively considers data characteristics, network environment and transmission node uncertainty greatly improves the efficiency and quality of data transmission in the industrial Internet of Things, laying a solid foundation for subsequent data analysis, decision-making and production optimization. Description of the Drawings

[0016] To more clearly illustrate the specific embodiments of the present invention or the technical solutions in the prior art, the following will briefly introduce the drawings required for the description of the specific embodiments or 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 to scale.

[0017] Figure 1 Schematic diagram of the steps of the industrial data encoding and transmission optimization method based on the industrial Internet of Things of the present invention; Figure 2 Partial schematic diagram of the steps of S5 in the industrial data encoding and transmission optimization method based on the industrial Internet of Things of the present invention; Figure 3 Partial schematic diagram of the steps of S2 in the industrial data encoding and transmission optimization method based on the industrial Internet of Things of the present invention; Figure 4 Partial schematic diagram of the steps of S3 in the industrial data encoding and transmission optimization method based on the industrial Internet of Things of the present invention; Figure 5 Schematic diagram of the composition of the industrial data encoding and transmission optimization system based on the industrial Internet of Things of the present invention; Figure 6 Schematic diagram of the composition of the optimized industrial Internet of Things related to the present invention; Figure 7 Block diagram of an electronic device shown in an embodiment of the present invention.

[0018] Reference numerals: 700 - Electronic device, 701 - Processor, 702 - Memory, 703 - Multimedia component, 704 - I / O interface, 705 - Communication component. Specific embodiments

[0019] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are some, but not all, of the embodiments of the present invention. Usually, the components of the embodiments of the present invention described and shown in the accompanying drawings here can be arranged and designed in various different configurations.

[0020] Therefore, the following detailed description of the embodiments of the present invention provided in the drawings is not intended to limit the scope of the claimed present invention, but merely represents the selected embodiments of the present invention. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts fall within the scope of protection of the present invention.

[0021] It should be noted that similar reference numerals and letters indicate similar items in the following figures. Therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. In addition, terms such as "first", "second", etc. are only used for distinguishing descriptions and cannot be understood as indicating or implying relative importance.

[0022] As Figure 1 shown, an industrial data encoding and transmission optimization method based on the industrial Internet of Things is provided, including: S1. Obtain the industrial data to be processed based on the industrial Internet of Things, obtain the source characteristics corresponding to the industrial data to be processed, and obtain the source length corresponding to the industrial data to be processed according to the source characteristics corresponding to the industrial data to be processed; S2. When the data volume of the industrial data to be processed exceeds the source length, pack the industrial data to be processed to form a data packet to be sent, and obtain a first sequential code that continuously increases and corresponds to the current data packet to be sent based on the first encoding model; S3. Plan multiple basic transmission paths including different transmission nodes, and obtain a second sequential code corresponding to each basic transmission path based on the position data of multiple transmission nodes in each basic transmission path; S4. Associate the first sequential code and the second sequential code with the data packet to be sent to form a data set to be transmitted, and transmit the data set to be transmitted according to the basic transmission path corresponding to the second sequential code; S5. Receive the data set to be transmitted, obtain a transmission path optimization strategy according to the first sequential code of the received data set to be transmitted, and carry out the data transmission of subsequent data packets to be sent according to the transmission path optimization strategy.

[0023] In this embodiment, it should be noted that in S1, first, the industrial data to be processed is obtained through various sensors or data acquisition devices of the industrial Internet of Things. These data may include, but are not limited to, various types of physical quantities or status information such as temperature, pressure, humidity, vibration, etc. While obtaining these data, in-depth analysis will be carried out on each data type to extract its unique source characteristics. The source characteristics may cover multiple dimensions such as the source of the data, size, fluctuation range, change frequency, importance level, and correlation with other data. For example, for temperature data, it may be found that it changes frequently and is crucial for production safety, so it is given a higher priority and a shorter data update cycle; while for humidity data, if its change is relatively stable and has little impact on production, a longer sampling interval and a lower data priority may be set. Based on these source characteristics, the source length corresponding to each data type, that is, the size of the data packet, will be further determined; in this way, the source length can also be dynamic and will be adjusted according to the real-time characteristics and requirements of the data to ensure the efficient transmission and processing of the data.

[0024] Taking an intelligent factory as an example, various sensors are deployed in this factory to monitor all aspects of the production line. Among them, the sensor used to monitor the temperature inside the reactor finds that the temperature data fluctuates greatly within a short period of time, and this fluctuation is directly related to the product quality and production safety. Therefore, in step S1, the source characteristics of this temperature data will be analyzed, its high-frequency variation and key characteristics will be identified, and a relatively short source length will be set accordingly, such as 10k. In contrast, the sensor data for monitoring the humidity in the warehouse is relatively stable with little change, and a relatively long source length may be set, such as 200k. In this way, the size of the source length can be flexibly adjusted according to the characteristics and requirements of different data, laying a solid foundation for subsequent coding and transmission optimization.

[0025] In S2, the formation of data packets and coding is further refined. When the amount of industrial data to be processed under the same source characteristics accumulates to exceed the source length determined in step S1, the data sending mechanism will be triggered. First, it will be packed to form a data packet to be sent. At the same time, according to the characteristics and importance of the data, as well as the preset first coding model, a unique and continuously increasing first sequential coding will be assigned to the data packet to be sent. This coding is the unique identifier of the data packet identity. It can not only ensure the traceability of the data packet during the transmission process, but also provide an important basis for subsequent data recombination, error detection and recovery. The construction of the first coding model takes into account the timing of the data and the possible concurrent transmission requirements to ensure that each data packet can be accurately identified and located in the complex and changeable industrial Internet of Things environment.

[0026] Taking the temperature control of the intelligent factory as an example, assume that in S1, 10k has been determined as the source length according to the source characteristics of the temperature data, that is, whenever the amount of temperature data accumulates to 10k, a data packet to be sent will be formed. In step S2, when the temperature sensor continuously monitors and accumulates 10k of temperature data, the data packet sending mechanism will be triggered. At this time, according to the first coding model and the timing of the current data packet to be sent, a unique first sequential coding will be generated for this data packet, such as "1442", indicating that this is the 1442nd data packet to be sent. Subsequently, when the temperature data accumulates to 10k again, the next consecutive first sequential coding will be generated, such as "1443". In this way, each data packet to be sent is given a unique and ordered identifier. Even if data packets are lost or out of order during the transmission process, the receiving end can quickly locate, report errors and request retransmission of the lost data packets according to the first sequential coding, thus ensuring the integrity and continuity of the data.

[0027] In S3, it involves the planning and encoding of the transmission path. In this step, first, according to the actual situation of the industrial Internet of Things, multiple basic transmission paths containing different transmission nodes are planned. The selection of these paths takes into account multiple factors such as the geographical location of the nodes, network bandwidth, transmission delay, node stability, and potential failure risks, and is generally obtained based on existing historical information. Subsequently, the position data of the transmission nodes in each basic transmission path is collected and analyzed, which includes the physical location of the nodes or their logical location in the network. Based on this information, a unique second-order code is generated for each basic transmission path. This code is not only an identifier of the path identity but also an important basis for subsequent transmission path optimization decisions.

[0028] Continuing with the example of temperature control in a smart factory, in S3, three basic transmission paths may be planned: Path 1 goes from the data aggregation point through Data Relay Point 1, Data Relay Point 3, and Data Relay Point 6 and is sent to the central control; Path 2 goes from the data aggregation point through Data Relay Point 2, Data Relay Point 3, and Data Relay Point 4 and is sent to the central control; Path 3 goes from the data aggregation point through Data Relay Point 2 and Data Relay Point 5 and is sent to the central control. Next, a unique second-order code is generated for each path. For example, Path 1 is encoded as "T136", Path 2 as "T234", and Path 3 as "T25". These codes can not only clearly identify and manage different transmission paths. More importantly, during the data transmission process, if it is detected that a certain path (such as Path 2) has a decrease in transmission efficiency due to network congestion or node failure, it can quickly locate the problem path according to the second-order code "T234" and, based on the transmission path optimization strategy, dynamically adjust the data transmission path. For example, the subsequent temperature data can be transmitted through Path 1 or Path 3 to ensure that the temperature data can reach the central control in a timely and accurate manner, providing strong support for the temperature control of the factory.

[0029] In S4, it is necessary to associate the previously generated first-order encoding and second-order encoding with the data packet to be sent to form a complete data set to be transmitted. This association process ensures that each data packet has a unique identity identifier and transmission path identifier, and also provides an important basis for subsequent data transmission, reception, and possible error detection and recovery. Specifically, according to the basic transmission path corresponding to the second-order encoding, the data set to be transmitted will be sent to the specified transmission path. By allocating different data packets to the corresponding transmission paths according to the second-order encoding generated in S3, data shunting and load balancing are achieved. In addition, the continuity of the first-order encoding also ensures the orderliness of the data packets during transmission. Even if data packets are lost or out of order, the receiving end can quickly locate and request retransmission according to the first-order encoding, thus ensuring the integrity and accuracy of the data. This association and transmission mechanism fully considers the dynamic characteristics of data, the complexity of the network environment, and the uncertainty of transmission nodes in the industrial Internet of Things, providing a strong guarantee for the efficient and stable transmission of data.

[0030] In S5, not only the reception of data needs to be processed, but also the transmission strategy needs to be dynamically adjusted according to the received data situation. In S5, first, the data set to be transmitted generated according to S4 is received. This data set contains the first-order encoding and second-order encoding of the data packets. During the reception process, the first-order encoding of each data packet will be recorded in detail as the basis for the orderly reception and subsequent processing of the data packets. At the same time, the received data packets will be deeply analyzed, especially focusing on the continuity of the first-order encoding, to detect whether there are data packet losses or transmission delays. If discontinuity or delay is found, the error handling mechanism will be immediately started, including but not limited to requesting retransmission of the lost data packets, adjusting the data transmission rate, or re-planning the transmission path. This deep analysis and dynamic adjustment mechanism requires a high degree of intelligence and adaptability to be able to respond in real time to changes in the network environment and data characteristics, ensuring the integrity and accuracy of data transmission. In addition, step S5 also implies the continuous optimization of the transmission path. According to historical transmission data and the current network status, the optimal transmission path will be dynamically selected to further improve the efficiency and stability of data transmission.

[0031] For example, if it is detected that the transmission delay on path 2 ("T234") increases, it may automatically switch the subsequent temperature data to path 1 ("T136") or path 3 ("T25") for transmission to ensure the timely arrival of the data. Thus, it has comprehensive network monitoring capabilities, fast data analysis capabilities, and flexible transmission path adjustment capabilities, ensuring that data can also be efficiently and stably transmitted to the destination in the complex and changeable industrial Internet of Things environment.

[0032] In summary, in the entire industrial data encoding and transmission optimization method based on the industrial Internet of Things, by deeply analyzing the source characteristics of different industrial data sources, the dynamic adjustment of the data packet size is realized, significantly improving the data transmission efficiency and avoiding the coding redundancy and increased network burden caused by a fixed data packet size. At the same time, the first-order coding is introduced to ensure the uniqueness, orderliness, and traceability of data packets during transmission, providing strong support for data recombination, error detection, and recovery. In addition, by planning multiple basic transmission paths and assigning a unique second-order coding to each path, not only the data shunting and load balancing are achieved, but also the system is endowed with high flexibility and adaptability, enabling it to dynamically adjust the data transmission path according to the real-time changes in the network environment and data characteristics, effectively coping with uncertain factors such as node failures and network congestion, and ensuring the stability, reliability, and timeliness of data transmission. Therefore, this optimization method that comprehensively considers data characteristics, network environment, and transmission node uncertainty greatly improves the efficiency and quality of data transmission in the industrial Internet of Things, laying a solid foundation for subsequent data analysis, decision-making, and production optimization.

[0033] Figure 2 It is a partial step schematic diagram of S5 in the industrial data encoding and transmission optimization method based on the industrial Internet of Things of the present invention. In one embodiment, the obtaining of the transmission path optimization strategy according to the first-order coding of the received data set to be transmitted in S5 includes: S51. If the first-order coding of the currently received data set to be transmitted is not continuous with the first-order coding of the previously received data set to be transmitted, and the first-order coding of the currently received data set to be transmitted is greater than the first-order coding of the previously received data set to be transmitted, then obtain the interval difference between the first-order coding of the currently received data set to be transmitted and the first-order coding of the previously received data set to be transmitted; S52. Obtain the maximum interval difference among multiple interval differences within a preset time period, and obtain the second-order coding of the currently received data set to be transmitted corresponding to the maximum interval difference, and use the basic transmission path corresponding to the second-order coding as the optimized transmission path.

[0034] In this embodiment, it should be noted that in S51, discontinuity and delay problems in the data transmission process are detected. In this step, a detailed comparative analysis is performed on the first-order encoding of each received data set to be transmitted. If it is found that the first-order encoding of the currently received data set to be transmitted is discontinuous with the first-order encoding of the previously received data set to be transmitted, and the current encoding value is greater than the previous one, this means that there are packet losses, transmission delays, and early arrival in the data transmission process. At this time, the interval difference between these two encodings will be immediately calculated, and this difference directly reflects the severity of data loss and delay or the degree of early arrival of transmission, that is, the interval difference reflects the time advantage obtained when the currently received data set to be transmitted is transmitted through the basic transmission path. In this way, step S51 can not only detect abnormalities in data transmission in a timely manner, but also provide accurate data support for subsequent transmission path optimization, ensuring the integrity and timeliness of data transmission.

[0035] In S52, first, a preset time period is set, and it only needs to ensure that there are multiple interval differences within the preset time period. Then, from these calculated multiple time interval differences, the largest one is determined. This largest interval difference is not only a quantitative indicator, but more intuitively shows the maximum amount of time that can be saved when the currently received data set to be transmitted is transmitted through the basic transmission path, reflecting the significant time advantage of this basic transmission path; Therefore, based on this consideration, the basic transmission path that shows the greatest potential for time savings is selected as the optimized transmission path for the subsequent transmission process.

[0036] In one embodiment, the data transmission of the subsequent data packets to be sent in S5 according to the transmission path optimization strategy includes: S53. Associate the first-order encoding with the subsequent data packets to be sent to form a new data set to be transmitted, and transmit the new data set to be transmitted according to the optimized transmission path.

[0037] In this embodiment, it should be noted that in S53, it involves precisely associating the first-order encoding generated in the previous steps with the subsequent industrial data packets to be sent to form a new dataset to be transmitted, and performing the actual data transmission according to the transmission path (i.e., the optimized transmission path) selected through in-depth analysis and optimization before. In this process, the system needs to ensure that each data packet to be sent is assigned a unique and continuous first-order encoding, which not only carries the identity information of the data packet but also ensures the orderliness and traceability of the data packet during transmission and reception. Subsequently, the system will identify the basic transmission path with the greatest time advantage as the current optimized transmission path according to the second-order encoding corresponding to the maximum interval difference determined in step S52. Immediately afterwards, the system will perform efficient data transmission on the newly generated dataset to be transmitted according to the network configuration and transmission protocol of this optimized path. In this way, it can process the encoding, path selection, data transmission, and possible error detection and recovery of data packets in real time, ensuring that data can be transmitted to the destination in the fastest and most stable manner in the complex and changeable industrial Internet of Things environment, providing solid data support for subsequent data analysis, decision-making, and production optimization.

[0038] It should also be noted that after transmitting the new dataset to be transmitted according to the optimized transmission path for a certain period of time, it can return to the steps of S3, re-plan multiple basic transmission paths, and then cycle through the subsequent steps to select a new optimized transmission path, so as to achieve dynamic optimization; by continuously cycling through this optimization process, the system can continuously adapt to the changes in the network environment and data characteristics, and achieve continuous improvement in data transmission efficiency and quality.

[0039] Figure 3 This is a schematic diagram of some steps of S2 in the industrial data encoding and transmission optimization method based on the industrial Internet of Things of the present invention. In one embodiment, the steps of obtaining the continuously increasing first-order encoding corresponding to the current data packet to be sent based on the first encoding model in S2 include: S21. Set a continuous increment value and an initial encoding value corresponding to the first formation of the data packet to be sent; S22. Obtain the number of times the data packet to be sent is formed between the first formation of the data packet to be sent and the current data packet to be sent; S23. Based on the first encoding model, the number of times the data packet to be sent is formed, the initial encoding value, and the continuous increment value, obtain the continuously increasing first-order encoding corresponding to the current data packet to be sent.

[0040] In this embodiment, it should be noted that in S21, two key parameters need to be set first: the continuous increment value and the initial coding value. The continuous increment value is a fixed value that determines the increase in the coding of each subsequent data packet relative to the previous one. It ensures the continuity and uniqueness of the coding. Even in high-concurrency scenarios of data transmission, it can accurately distinguish each data packet. The selection of this increment value needs to comprehensively consider the generation frequency of data packets, the bandwidth of the transmission network, and the processing capacity of the receiving end to ensure that the generation of coding does not become a bottleneck in data transmission. The initial coding value is the coding assigned to the first data packet to be sent. It serves as the starting point of the entire coding sequence. Although its selection is relatively flexible, it is usually set to a value that is easy to identify and process, such as "1" or "1000", etc., for subsequent coding calculations and data management. The setting of these two parameters lays a solid foundation for the first coding model, enabling each data packet to be assigned a unique and continuously increasing coding, thus meeting various requirements in the data transmission process.

[0041] In S22, it is necessary to track the data acquisition situation in real time. When the accumulated data reaches the preset source length, the formation of data packets is triggered. By recording the time when each data packet to be sent is formed during this process, the system can accurately calculate the number of times the data packet to be sent is formed. This number not only reflects the data generation speed but also provides a necessary basis for subsequent coding generation.

[0042] In S23, based on the previously set continuous increment value, initial coding value, and the calculated number of times the data packet to be sent is formed, the first sequential coding corresponding to the current data packet to be sent can be generated according to the first coding model. That is, add the initial coding value to (the number of times the data packet to be sent is formed - 1) multiplied by the continuous increment value. This coding not only ensures that each data packet has a unique identity but also, through its continuity, provides strong support for error detection, data packet recombination, and transmission path optimization during data transmission. More importantly, the generation of this coding is completely automated and can be dynamically adjusted according to the real-time generation situation of data, thus meeting the requirements of high efficiency and flexibility in data transmission in the industrial Internet of Things.

[0043] In one embodiment, the first coding model in obtaining the continuously increasing first sequential coding corresponding to the current data packet to be sent based on the first coding model, the number of times the data packet to be sent is formed, the initial coding value, and the continuous increment value in S23 is expressed as: ; where is the i-th first sequential coding, is the number of times the data packet to be sent is formed, is the continuous increment value, is the initial coding value.

[0044] In this embodiment, it should be noted that through this item, it is ensured that each first-order coding increases continuously based on the sending order of the data packets to be sent. This helps the receiving party to correctly reorganize and sort the received data packets according to the coding order. As an initial offset, it can ensure that the coding sequence starts from a specific value instead of always starting from 0 or 1, which helps to meet specific coding requirements or avoid coding conflicts in specific application scenarios. and The values of can be flexibly set as needed. For example, if a larger interval between codings is desired, the value of can be increased ; if the coding is desired to start from a specific numerical value, the corresponding value can be set. This setting method enables the coding scheme to easily adapt to different data packet sending scenarios and requirements. At the same time, the calculation speed of the entire linear function is fast and will not bring additional computational burden to the data packet coding process.

[0045] Figure 4 is a schematic diagram of partial steps of S3 in the industrial data coding and transmission optimization method based on industrial Internet of Things of the present invention. In one embodiment, obtaining the second-order coding corresponding to each basic transmission path based on the position data of multiple transmission nodes in each basic transmission path in S3 includes: S31. Obtain all the transmission nodes in the jth basic transmission path, and obtain the corresponding unique identifier according to the position data of the obtained transmission nodes; S32. Obtain the second-order coding corresponding to the jth basic transmission path based on the order of the transmission nodes in the jth basic transmission path and the unique identifier corresponding to each transmission node.

[0046] In this embodiment, it should be noted that in S31, first, for the jth basic transmission path, obtain the position data of all the transmission nodes on this path. These position data can be the actual coordinates of the nodes in the physical space (such as longitude and latitude, altitude, etc.), or can cover the logical position information of the nodes in the network architecture (such as IP address, network level, etc.). Subsequently, assign a unique and stable identifier to each transmission node. This identifier is the unique representative of the node identity and can help quickly locate and identify the nodes in the network. In this way, step S31 ensures that each transmission node is accurately incorporated into the coding system of the basic transmission path, laying a solid foundation for the subsequent generation of the second-order coding.

[0047] In S32, an orderly encoding process is performed on the transmission nodes on the j-th basic transmission path. This process first requires strict sorting of the transmission nodes according to their actual order in the path. The sorting basis may include multiple factors such as the physical location, logical location, transmission priority, and network latency of the nodes to ensure that the encoding can truly reflect the relative positions and transmission characteristics of the nodes in the path. Subsequently, a unique and highly recognizable second-order encoding is generated for the j-th basic transmission path by using the unique identifier assigned to each node in step S31. This encoding is not only an accurate identification of the path identity but also an important basis for subsequent data transmission, path optimization, and fault handling.

[0048] Specifically, first, clarify the set of unique identifiers to be encoded. The set of unique identifiers is composed of the unique identifiers corresponding to all transmission nodes in the j-th basic transmission path. Then, sequentially concatenate multiple unique identifiers in the set of unique identifiers according to the transmission order of the j-th basic transmission path. A specific delimiter (such as an underscore) can be used to distinguish different identifiers. Finally, the concatenated string is used as the second-order encoding. This encoding contains the information of all unique identifiers and is organized according to the order of the transmission path.

[0049] An industrial data encoding and transmission optimization system based on the industrial Internet of Things is also provided. The system includes a management platform, a sensing network platform, and an object platform that are sequentially communicatively connected. The management platform includes: An integrated acquisition module for acquiring industrial data to be processed based on the industrial Internet of Things, acquiring the source characteristics corresponding to the industrial data to be processed, and obtaining the source length corresponding to the industrial data to be processed according to the source characteristics corresponding to the industrial data to be processed; A judgment and sequential encoding module for packing the industrial data to be processed and forming a data packet to be sent when the data volume of the industrial data to be processed exceeds the source length, and obtaining a continuously increasing first-order encoding corresponding to the current data packet to be sent based on the first encoding model; A position encoding module for planning multiple basic transmission paths containing different transmission nodes and obtaining the second-order encoding corresponding to each basic transmission path based on the position data of multiple transmission nodes in each basic transmission path and the second encoding model; A transmission module for associating the first-order encoding and the second-order encoding with the data packet to be sent and forming a data set to be transmitted, and transmitting the data set to be transmitted according to the basic transmission path corresponding to the second-order encoding; An optimization module for receiving the data set to be transmitted, obtaining a transmission path optimization strategy according to the first-order encoding of the received data set to be transmitted, and carrying out data transmission of subsequent data packets to be sent according to the transmission path optimization strategy.

[0050] In one embodiment, the optimization module is further configured to: if the first sequence code of the currently received data set to be transmitted is not continuous with the first sequence code of the previously received data set to be transmitted, and the first sequence code of the currently received data set to be transmitted is greater than the first sequence code of the previously received data set to be transmitted, obtain the interval difference between the first sequence code of the currently received data set to be transmitted and the first sequence code of the previously received data set to be transmitted; obtain the maximum interval difference among multiple interval differences within a preset time period, and obtain the second sequence code of the currently received data set to be transmitted corresponding to the maximum interval difference, and use the basic transmission path corresponding to the second sequence code as the optimized transmission path; associate the first sequence code with subsequent data packets to be sent to form a new data set to be transmitted, and transmit the new data set to be transmitted along the optimized transmission path.

[0051] In one embodiment, the position coding module is further configured to: obtain all transmission nodes in the j-th basic transmission path, and obtain the corresponding unique identifier according to the position data of the obtained transmission nodes; obtain the second sequence code corresponding to the j-th basic transmission path based on the second coding model, the sequence of transmission nodes in the j-th basic transmission path, and the unique identifier corresponding to each transmission node.

[0052] In this embodiment, it should be noted that for the above industrial data coding and transmission optimization system based on the industrial Internet of Things, the specific manner of performing operations has been described in detail in the embodiments of the industrial data coding and transmission optimization method based on the industrial Internet of Things, and will not be elaborated here.

[0053] It should also be noted that the entire industrial data coding and transmission optimization system based on the industrial Internet of Things can be applied to the optimized industrial Internet of Things. Figure 5 This is a schematic diagram of the composition of the industrial data coding and transmission optimization system based on the present invention. Figure 6 This is a schematic diagram of the composition of the optimized industrial Internet of Things related to the present invention. As Figure 5 and Figure 6 shown, the optimized industrial Internet of Things includes a user platform, a service platform, a management platform, a sensing network platform, and an object platform that establish communications in sequence; The user platform is configured to provide the function of front-end services to users; users obtain the required perception service information through the user platform, process the perception service information, and convert it into user perception information; users analyze the user perception information and make corresponding decisions based on their own wishes, and convert the user perception information into user control information through the corresponding information system and send it to the service platform, thereby showing the user's corresponding service demand intention.

[0054] The physical entities of the user platform include various user terminals, such as mobile phones, computers, dedicated terminals, etc. By combining with the user information system software, the services at the user side are realized.

[0055] The service platform is configured as an API server or other servers for establishing communication between the management platform and the user platform to implement corresponding functions; the physical entities of the service platform include various servers.

[0056] The management platform is configured to perform at least one of device operation status monitoring and management, data monitoring and management, device parameter management, and life cycle management; the management platform is the operation and coordination platform of the Internet of Things, which may include various management sub-platforms, and different management sub-platforms execute different management services; the physical entities of the management platform include various servers.

[0057] The sensor network platform is configured to perform at least one of network management, instruction management, device status management, data protocol management, data parsing, data classification, data transmission monitoring, and data transmission security management. The sensor network platform provides functions such as communication transmission, parsing, identification, and classification of data, avoiding the direct aggregation of data from various object platforms in the management platform, resulting in redundant data in the management platform and low data processing efficiency; the physical entities of the object platform include various gateways, edge computing devices, etc.

[0058] The object platform is configured to perform specific production control, detection, metering and other production work; the physical entities of the production object include various production devices, sensors, etc.

[0059] Figure 7 It is a block diagram of an electronic device for an industrial data encoding and transmission optimization method based on an industrial Internet of Things shown according to an exemplary embodiment. As Figure 7 shown, the electronic device 700 may include: a processor 701, a memory 702. The electronic device 700 may further include one or more of a multimedia component 703, an I / O interface 704 (input / output interface), and a communication component 705.

[0060] Among them, 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 industrial data encoding and transmission optimization 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. These data may include, for example, instructions for any application or method operating on the electronic device 700, as well as application-related data, such as contact data, received and sent messages, pictures, audio, video, and so on. 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 (SRAM), Electrically Erasable Programmable Read-Only Memory (EEPROM), Erasable Programmable Read-Only Memory (EPROM), Programmable Read-Only Memory (PROM), Read-Only Memory (ROM), magnetic memory, flash memory, a magnetic disk, or an optical disc. The multimedia component 703 may include a screen and an audio component. Among them, the screen may be 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, and the microphone is used to receive external audio signals. The received audio signals 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 above-mentioned other interface modules may be a keyboard, a mouse, 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 herein. Therefore, the corresponding communication component 705 may include: a Wi-Fi module, a Bluetooth module, an NFC module, and so on.

[0061] In an exemplary embodiment, the electronic device 700 can be implemented by one or more application specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components, and is used to execute the above-mentioned industrial data encoding and transmission optimization method based on the industrial Internet of Things.

[0062] In another exemplary embodiment, a computer-readable storage medium including program instructions is further provided. When the program instructions are executed by a processor, the steps of the above-mentioned industrial data encoding and transmission optimization 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 industrial data encoding and transmission optimization method based on the industrial Internet of Things.

[0063] In another exemplary embodiment, a computer program product is further provided. The computer program product includes a computer program that can be executed by a programmable device, and the computer program has a code part for executing the above-mentioned industrial data encoding and transmission optimization method based on the industrial Internet of Things when executed by the programmable device.

[0064] The preferred embodiments of the present disclosure have been 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 scope of the technical concept of the present disclosure, various simple modifications can be made to the technical solutions of the present disclosure, and these simple modifications all fall within the protection scope of the present disclosure.

[0065] In addition, it should be noted that, in the above specific embodiments, the various specific technical features described can be combined in any suitable manner without conflict. To avoid unnecessary repetition, the present disclosure does not separately describe various possible combination methods.

[0066] In addition, any combination can be made between various different embodiments of the present disclosure as long as it does not violate the idea of the present disclosure, and it should also be regarded as the content disclosed by the present disclosure.

[0067] 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 them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that they can still modify the technical solutions described in the foregoing embodiments, or perform equivalent replacements on some or all of the technical features; and these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and the description of the present invention.

Claims

1. An industrial data encoding and transmission optimization method based on industrial Internet of Things, characterized in that: include: Based on the industrial Internet of Things, the industrial data to be processed is obtained, and the source characteristics corresponding to the industrial data to be processed are obtained, and the source length corresponding to the industrial data to be processed is obtained according to the source characteristics corresponding to the industrial data to be processed; When the amount of the to-be-processed industrial data exceeds the source length, the to-be-processed industrial data is packaged to form a data packet to be sent, and a first sequential code that increases continuously and corresponds to the current data packet to be sent is obtained based on the first coding model; Planning a plurality of basic transmission paths including different transmission nodes, and acquiring a second sequence code corresponding to each basic transmission path based on position data of a plurality of transmission nodes in each basic transmission path; Associating the first sequence code and the second sequence code with the data packet to be sent to form a data set to be transmitted, and transmitting the data set to be transmitted according to the basic transmission path corresponding to the second sequence code; A data set to be transmitted is received, and a transmission path optimization strategy is obtained according to a first sequence code of the received data set to be transmitted, and data transmission of subsequent data packets to be sent is carried out according to the transmission path optimization strategy.

2. The industrial data encoding and transmission optimization method based on the industrial Internet of Things according to claim 1 is characterized in that: The step of obtaining a transmission path optimization strategy according to the first sequence encoding of the received data set to be transmitted comprises: If the first sequence code of the currently received data set to be transmitted is not continuous with the first sequence code of the previously received data set to be transmitted, and the first sequence code of the currently received data set to be transmitted is greater than the first sequence code of the previously received data set to be transmitted, then obtaining an interval difference between the first sequence code of the currently received data set to be transmitted and the first sequence code of the previously received data set to be transmitted; The maximum interval difference of multiple interval differences within a preset time period is obtained, and the second sequence code of the currently received data set to be transmitted corresponding to the maximum interval difference is obtained, and the basic transmission path corresponding to the second sequence code is used as the optimized transmission path.

3. The industrial data encoding and transmission optimization method based on industrial Internet of Things according to claim 2 is characterized in that: The subsequent data transmission of the data packet to be sent according to the transmission path optimization strategy includes: The first sequence code is associated with subsequent data packets to be sent to form a new data set to be transmitted, and the new data set to be transmitted is transmitted along the optimized transmission path.

4. The industrial data encoding and transmission optimization method based on industrial Internet of Things according to claim 1 is characterized in that: The step of obtaining the first sequential codes that are continuously increased and correspond to the current data packet to be sent based on the first coding model comprises: Setting the continuous increment value and the initial encoding value corresponding to the first formation of the data packet to be sent; Obtain the number of times a to-be-sent data packet is formed between the first formation of a to-be-sent data packet and the current to-be-sent data packet; A first sequential code that increases continuously and corresponds to a current data packet to be sent is obtained based on the first coding model, the number of times the data packet to be sent is formed, the initial coding value and the continuous increment value.

5. The industrial data encoding and transmission optimization method based on the industrial Internet of Things according to claim 4 is characterized in that: The first coding model in the first sequential coding that is obtained based on the first coding model, the number of times the data packet to be sent is formed, the initial coding value and the continuous increment value and corresponds to the current data packet to be sent is expressed as: ;in, is the i-th first order code, is the number of packets to be sent. is a continuous increment value, is the initial encoding value.

6. The industrial data encoding and transmission optimization method based on industrial Internet of Things according to claim 1 is characterized in that: The step of acquiring the second sequence code corresponding to each basic transmission path based on the position data of multiple transmission nodes in each basic transmission path includes: Acquire all transmission nodes in the j-th basic transmission path, and acquire corresponding unique identifiers according to the acquired position data of the transmission nodes; A second sequence code corresponding to the jth basic transmission path is obtained based on the sequence of transmission nodes in the jth basic transmission path and unique identifiers corresponding to each transmission node.

7. An industrial data encoding and transmission optimization system based on industrial Internet of Things, characterized in that: The system includes a management platform, a sensor network platform and an object platform which are sequentially connected in communication, and the management platform includes: A comprehensive acquisition module, used to acquire the industrial data to be processed based on the industrial Internet of Things, and to acquire the source characteristics corresponding to the industrial data to be processed, and to acquire the source length corresponding to the industrial data to be processed according to the source characteristics corresponding to the industrial data to be processed; A judgment and sequence encoding module, used for packaging the to-be-processed industrial data to form a data packet to be sent when the data volume of the to-be-processed industrial data exceeds the source length, and obtaining a first sequence code that increases continuously and corresponds to the current data packet to be sent based on the first encoding model; A position coding module, used for planning a plurality of basic transmission paths including different transmission nodes, and obtaining a second sequence code corresponding to each basic transmission path based on the position data of a plurality of transmission nodes in each basic transmission path; A transmission module, used for associating the first sequence code and the second sequence code with the data packet to be sent to form a data set to be transmitted, and transmitting the data set to be transmitted according to the basic transmission path corresponding to the second sequence code; The optimization module is used to receive the data set to be transmitted, obtain the transmission path optimization strategy according to the first sequence encoding of the received data set to be transmitted, and carry out the data transmission of the subsequent data packets to be sent according to the transmission path optimization strategy.

8. The industrial data encoding and transmission optimization system based on industrial Internet of Things according to claim 7 is characterized in that: The optimization module is also used to: If the first sequence code of the currently received data set to be transmitted is not continuous with the first sequence code of the previously received data set to be transmitted, and the first sequence code of the currently received data set to be transmitted is greater than the first sequence code of the previously received data set to be transmitted, then obtaining an interval difference between the first sequence code of the currently received data set to be transmitted and the first sequence code of the previously received data set to be transmitted; Obtaining a maximum interval difference of a plurality of interval differences within a preset time period, and obtaining a second sequence code of a currently received data set to be transmitted corresponding to the maximum interval difference, and using a basic transmission path corresponding to the second sequence code as an optimized transmission path; The first sequence code is associated with subsequent data packets to be sent to form a new data set to be transmitted, and the new data set to be transmitted is transmitted along the optimized transmission path.

9. 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 industrial data encoding and transmission optimization method based on the industrial Internet of Things as described in any one of claims 1 to 6.

10. 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 industrial data encoding and transmission optimization method based on the industrial Internet of Things as described in any one of claims 1 to 6 is implemented.

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