Supply chain data management method, system and device based on industrial Internet of Things and medium

Abstract

The invention discloses a supply chain data management method, system and device based on the industrial Internet of Things and a medium, and relates to the technical field of industrial Internet of Things data processing, and the method comprises the steps: obtaining to-be-managed data and an inevitably managed region, obtaining region nodes, and obtaining a real-time load and historical transmission information; planning a data transmission path, obtaining a bearing index, obtaining the data transmission path with the bearing index smaller than a preset threshold value, and taking the data transmission path as a to-be-analyzed path; obtaining a dynamic risk score of each area node in the to-be-analyzed path based on the evaluation model, the real-time load, the historical transmission information and the inevitable management area, and obtaining the transmission cost of the to-be-analyzed path based on the constraint model and the dynamic risk score of each area node in the to-be-analyzed path; and obtaining the to-be-analyzed path with the lowest transmission cost, taking the to-be-analyzed path as a target transmission path, and transmitting the to-be-managed data according to the target transmission path. The method has the advantages of being good in data transmission planning effect, high in data transmission planning efficiency, stable and reliable.

Description

Technical Field

[0001] The present invention relates to the technical field of industrial Internet of Things data processing, and particularly relates to a supply chain data management method, system, device and medium based on the industrial Internet of Things. Background Art

[0002] The application of industrial Internet of Things technology in supply chain management is facing increasingly complex data transmission challenges. Specifically, with the intelligent upgrade of the manufacturing industry, a large amount of heterogeneous data has been generated in each link of the supply chain (production, warehousing, transportation, distribution). These data need to be interacted in real time among multiple geographically dispersed nodes to support intelligent decision-making. However, there are significant defects in the existing data transmission solutions in this situation.

[0003] Firstly, the traditional method adopts a static node selection mechanism, without fully considering the dynamic load changes of nodes within each management area. When the computing resources in the production area suddenly drop due to a sudden equipment failure, or when the data transmission peak is triggered by an inventory count in the warehousing area, it is very easy to cause key nodes to be overloaded, resulting in packet loss or increased transmission delay.

[0004] Secondly, the existing risk assessment model only selects paths based on the current load level, ignoring the associated impact of the historical transmission error rate of nodes and the characteristics of the management areas to which they belong. For example, the stability of the mobile network in the transportation area is much lower than that of the fixed warehousing area, but the existing algorithms do not incorporate such area characteristics into the weight calculation, resulting in high-risk nodes being repeatedly selected.

[0005] Finally, the traditional calculation of carrying capacity uses a simple arithmetic average method. When there are a few overloaded nodes in a path, the average effect of a large number of low-load nodes will mask the existence of key bottleneck points. For example, when the load rate of a node in a distribution area reaches 120% of the theoretical maximum due to abnormal GPS positioning, while the load of other nodes in the same path is only 30%, the path carrying index calculated by the traditional method will be seriously distorted, resulting in system operation and maintenance personnel being unable to accurately predict the potential failure risks of key paths.

[0006] These problems have a chain reaction in a complex supply chain environment: the manufacturing execution system (MES) may incorrectly adjust the scheduling plan due to the delayed receipt of production data, the warehouse management system (WMS) may cause the picking path planning to fail due to the unsynchronized real-time inventory data, and the transportation management system (TMS) may cause the delivery route to deviate due to the loss of GPS positioning data. Summary of the Invention

[0007] Aiming at the defects in the prior art, the present invention provides a supply chain data management method, system, device and medium based on the industrial Internet of Things.

[0008] A supply chain data management method based on industrial Internet of Things, comprising: obtaining data to be managed and a plurality of mandatory management areas, obtaining a plurality of area nodes within the mandatory management areas, and obtaining the real-time load and historical transmission information of each area node; planning a data transmission path composed of any one area node within each mandatory management area, obtaining a bearing index of the data transmission path according to the real-time load of each area node within the data transmission path, obtaining a data transmission path with a bearing index less than a preset threshold as a path to be analyzed; obtaining a dynamic risk score of each area node in the path to be analyzed based on an evaluation model, the real-time load, historical transmission information and the mandatory management area where the area nodes in the path to be analyzed are located, and obtaining a transmission cost of the path to be analyzed based on a constraint model and the dynamic risk scores of each area node in the path to be analyzed; obtaining a path to be analyzed with the lowest transmission cost as a target transmission path, and transmitting the data to be managed according to the target transmission path.

[0009] Optionally, obtaining a bearing index of the data transmission path according to the real-time load of each area node within the data transmission path includes: obtaining the theoretical maximum load of each area node in the data transmission path; obtaining an average increment threshold of the data transmission path; obtaining a bearing index of the data transmission path according to the average increment threshold of the data transmission path, the real-time load of each area node therein and the theoretical maximum load.

[0010] Optionally, obtaining a bearing index of the data transmission path according to the average increment threshold of the data transmission path, the real-time load of each area node and the theoretical maximum load is expressed as: ; where is the bearing index of the kth data transmission path, is the number of area nodes of the kth data transmission path, is the average increment threshold (only when it exceeds is it included in the average value calculation, and at the same time, ensuring that the minimum value participating in the average value calculation is to avoid the situation that the contribution of a small number of overloaded area nodes is suppressed due to too many area nodes with low real-time load and too small average value), is the real-time load of the ith area node in the kth data transmission path, is the theoretical maximum load of the ith area node in the kth data transmission path.

[0011] ​Optionally, obtaining the dynamic risk score of each regional node in the path to be analyzed based on the evaluation model, the real-time load of the regional nodes in the path to be analyzed, the historical transmission information and the necessary management area of the regional nodes includes: obtaining a historical time window, and obtaining the number of historical errors of each regional node in the path to be analyzed within 1, 2, ..., h historical time windows from the current moment; obtaining the regional weight corresponding to the necessary management area, and obtaining the theoretical maximum load of each regional node in the path to be analyzed; obtaining the dynamic risk score carrying index of each regional node in the path to be analyzed based on the evaluation model, the real-time load, the theoretical maximum load, the historical number of errors and the regional weight of each regional node in the path to be analyzed.

[0012] Optionally, the evaluation model for obtaining a dynamic risk score for each regional node in the path to be analyzed based on the evaluation model, the real-time load, the theoretical maximum load, the number of historical errors, and the regional weight of each regional node in the path to be analyzed is expressed as follows: ;in, is the dynamic risk score of the i-th regional node in the m-th path to be analyzed, is the regional weight of the management area that the i-th regional node in the m-th path to be analyzed must pass through, is the number of historical errors between the i-th regional node in the m-th path to be analyzed and the current time within j historical time windows, is the upper limit of the number of historical time windows, is the real-time load of the i-th regional node in the m-th path to be analyzed, is the theoretical maximum load of the i-th regional node in the m-th path to be analyzed.

[0013] Optionally, the constraint model in obtaining the transmission cost of the path to be analyzed based on the constraint model and the dynamic risk score of each regional node in the path to be analyzed is expressed as: ;in, is the transmission cost of the mth path to be analyzed, is the dynamic risk score of the i-th regional node in the m-th path to be analyzed, is the number of regional nodes of the mth path to be analyzed.

[0014] There is also provided a supply chain data management system based on the industrial Internet of Things. The system includes a service platform, a management platform, and a sensing network platform that are communicatively connected in sequence. The management platform includes: a data acquisition module, configured to acquire data to be managed and a plurality of mandatory management areas, acquire a plurality of area nodes within the mandatory management areas, and acquire the real-time load and historical transmission information of each area node; a data path selection module, configured to plan a data transmission path composed of any one area node within each mandatory management area, and obtain a bearing index of the data transmission path according to the real-time load of each area node within the data transmission path, and obtain a data transmission path with a bearing index less than a preset threshold as a path to be analyzed; a data path evaluation module, configured to obtain a dynamic risk score of each area node in the path to be analyzed based on an evaluation model, the real-time load, historical transmission information, and the mandatory management area where the area node in the path to be analyzed is located, and obtain the transmission cost of the path to be analyzed based on a constraint model and the dynamic risk scores of each area node in the path to be analyzed; a data management module, configured to obtain the path to be analyzed with the lowest transmission cost as the target transmission path, and transmit the data to be managed according to the target transmission path.

[0015] Optionally, the data path selection module is further configured to: obtain the theoretical maximum load of each area node in the data transmission path; obtain the average increment threshold of the data transmission path; and obtain the bearing index of the data transmission path according to the average increment threshold of the data transmission path, the real-time load of each area node therein, and the theoretical maximum load.

[0016] There is also provided an electronic device, including: a memory having a computer program stored thereon; a processor, configured to execute the computer program in the memory to implement the above-mentioned supply chain data management method based on the industrial Internet of Things.

[0017] There is also provided a non-transitory computer-readable storage medium having a computer program stored thereon, and when the program is executed by a processor, it implements the above-mentioned supply chain data management method based on the industrial Internet of Things.

[0018] The beneficial effects of the present invention are embodied in: In the entire supply chain data management method based on the Industrial Internet of Things, a function division based on necessary management areas and a node real-time load monitoring mechanism break through the limitations of traditional static path planning. By dynamically collecting the hardware resource status and historical transmission characteristics of nodes in production, warehousing, transportation, distribution and other links, a dynamic resource portrait covering the entire supply chain is formed, enabling path planning to accurately identify sudden load fluctuations. Further, a dual-dimensional bearing index calculation model is proposed. On the basis of considering the theoretical maximum processing capacity of nodes, a dynamically adjusted average increment threshold is introduced, which not only avoids the dilution effect of low-load nodes on the evaluation results, but also ensures the authenticity and timeliness of path bearing capacity evaluation by focusing on monitoring abnormal nodes exceeding the threshold. Further, by superimposing regional characteristic weights, time decay mechanisms and risk multiplier effects, the cognitive bias of traditional linear superposition models is broken through, and hidden risks can be accurately quantified, making the calculation of transmission costs more in line with the risk conduction law in actual business. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] In order 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 denoted by similar reference numerals. In the drawings, the elements or parts are not necessarily drawn to scale.

[0020] Figure 1 It is a schematic diagram of the steps of the supply chain data management method based on the Industrial Internet of Things of the present invention; Figure 2 It is a partial schematic diagram of the steps of S2 in the supply chain data management method based on the Industrial Internet of Things of the present invention; Figure 3 It is a partial schematic diagram of the steps of S3 in the supply chain data management method based on the Industrial Internet of Things of the present invention; Figure 4 It is a schematic diagram of the composition of the supply chain data management system based on the Industrial Internet of Things of the present invention; Figure 5 It is a schematic diagram of the composition of the optimized Industrial Internet of Things related to the present invention; Figure 6 It is a block diagram of an electronic device shown in an embodiment of the present invention.

[0021] REFERENCE NUMERALS 700 - Electronic device, 701 - Processor, 702 - Memory, 703 - Multimedia component, 704 - I / O interface, 705 - Communication component. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] To make the objectives, 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 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. Components of the embodiments of the present invention described and illustrated herein generally may be arranged and designed in a variety of different configurations.

[0023] 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 invention, but merely represents 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.

[0024] It should be noted that: Similar reference numerals and letters denote similar items in the following drawings. Therefore, once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. In addition, the terms "first", "second", etc. are only used for descriptive distinction and cannot be construed as indicating or implying relative importance.

[0025] As Figure 1 shown, a supply chain data management method based on the industrial Internet of Things is provided, including: S1. Obtain the data to be managed and multiple mandatory management areas, obtain multiple area nodes within the mandatory management areas, and obtain the real-time load and historical transmission information of each area node; S2. Plan a data transmission path composed of any one area node within each mandatory management area, obtain the bearing index of the data transmission path according to the real-time load of each area node in the data transmission path, and obtain the data transmission path with the bearing index less than the preset threshold as the path to be analyzed; S3. Obtain the dynamic risk scores of each area node in the path to be analyzed based on the evaluation model, the real-time load, historical transmission information, and the mandatory management area where the area nodes in the path to be analyzed are located, and obtain the transmission cost of the path to be analyzed based on the constraint model and the dynamic risk scores of each area node in the path to be analyzed; S4. Obtain the path to be analyzed with the lowest transmission cost as the target transmission path, and transmit the data to be managed according to the target transmission path.

[0026] In this embodiment, it should be noted that in S1, a dynamic supply chain data management infrastructure is constructed. First, through the industrial Internet of Things platform, it docks with multi-source devices such as production line sensors, warehouse RFID tags, transportation vehicle GPS modules, and distribution terminals, and real-time aggregates heterogeneous data streams such as production progress, inventory status, and logistics coordinates. According to the business logic, the supply chain is divided into four necessary management areas: production, warehousing, transportation, and distribution. The node devices deployed in each area have the characteristic of single-function specialization: the nodes in the production area are specialized in parsing equipment operation parameters and process indicators, the warehousing nodes focus on the dynamic of goods in and out and temperature and humidity monitoring data, the transportation nodes are responsible for collecting vehicle positions and fuel consumption information, and the distribution nodes process terminal receipt and route optimization instructions. On the premise of maintaining the same type of data processing function, these area nodes synchronously upload their real-time load status (such as CPU utilization rate, memory occupancy rate) and historical transmission records (including data transmission error times data), forming a dynamic resource portrait covering all supply chain links.

[0027] Taking the automotive parts supply chain as an example, when performing supply chain data management, the nodes in the production area continuously receive the vibration frequency data of stamping machines, the warehousing nodes real-time track the bin coordinates of tire inventory, the transportation nodes monitor the real-time position coordinates of transport trucks, and the distribution nodes obtain the arrival time requirements of each 4S store. Although the node clusters in each management area have different geographical distributions, they all follow the principle of "same function in the same area". For example, all production area nodes only process equipment working condition data to ensure the verticalization of data processing logic; all warehousing area nodes uniformly dock with the warehousing management database to avoid cross-functional data mixing. Through the Internet of Things edge computing gateway, continuously collect the instantaneous load peaks (such as the length of the data processing queue) and historical transmission characteristics (such as the data transmission success rate in the past 24 hours) of each node. These multi-dimensional data establish a dynamically changing decision-making basis for subsequent path planning, enabling the subsequent steps to accurately identify the immediate service capabilities and potential risk characteristics of each node.

[0028] In S2, as a key stage of path planning and bearing assessment, its core lies in establishing a path screening mechanism that can dynamically identify transmission bottlenecks. First, traverse all combinations of nodes in the necessary management areas to generate a complete set of transmission paths covering all aspects of production, warehousing, transportation, and distribution. For each candidate path, by comprehensively analyzing the dynamic relationship between the real-time load and the theoretical processing capacity of each node, a multi-dimensional bearing capacity evaluation system is constructed: on the one hand, based on the node hardware configuration (such as the number of CPU cores and network interface bandwidth of the edge server), the theoretical maximum load is preset to reflect the upper limit of data processing of the node under ideal conditions; on the other hand, a dynamically adjusted average increment threshold is introduced to identify nodes with abnormal load fluctuations. For example, in the scenario of transporting auto parts, a certain path includes a stamping machine monitoring node (production area), a three-dimensional warehouse management node (warehousing area), an in-vehicle GPS relay node (transportation area), and a 4S store distribution terminal node (distribution area). By comparing the current data processing request volume of each node with the theoretical maximum processing capacity, potential resource bottlenecks can be captured - when the data queue length of the in-vehicle GPS node approaches its processing limit due to sudden network congestion, even if the loads of other nodes are at a low level, the overall bearing capacity of this path will still be accurately identified as a high-risk state. Optimize the path screening logic through two dimensions: first, calculate the load deviation degree of each node in the path, and focus on monitoring abnormal nodes that exceed the average increment threshold to avoid the dilution effect of low-load nodes on the overall evaluation result.

[0029] For example, when the temperature control warehousing node in the cold chain logistics path triggers high-frequency data transmission due to a sudden temperature alarm, its load rate may break through the normal level in a short time. At this time, the influence weight of this node on the overall path bearing index will be automatically amplified. At the same time, the maximum load node in the path is additionally extracted as the key bottleneck point. For example, if the node responsible for signing and receiving data processing in a certain distribution path experiences a sharp increase in resource occupancy due to the firmware upgrade of the terminal device, even if the loads of other nodes are balanced, this path will still be marked as a potentially unstable path. This evaluation mechanism can effectively identify local overload problems that are easily masked by traditional methods, ensuring that the set of paths for subsequent analysis truly reflects the dynamic characteristics of the actual transmission environment.

[0030] In S3, a multi-dimensional dynamic risk model is constructed to break through the limitations of traditional algorithms. First, the historical reliability of nodes is analyzed through a time decay mechanism: for each regional node, transmission error records within multiple historical time windows are traced, and higher weights are assigned to recent error events. For example, if the GPS relay node in a certain transportation area has consecutive data packet losses within the last 3 hours, its risk contribution will be significantly higher than similar events a week ago. At the same time, differential weight coefficients are set in combination with the inherent characteristics of different management areas. For example, transportation areas are given a higher basic risk coefficient due to the complex mobile network environment, while warehousing areas are set with a lower weight due to the strong stability of wired networks. This risk assessment mechanism in the space-time dimension can accurately capture the essential characteristics of node risks. For example, although the current load of the equipment monitoring node in a certain production area is normal, due to its high electromagnetic interference environment, the historical error rate is high. By superimposing the regional weight and the error records after time decay, such hidden risk points can be effectively identified. At the level of transmission cost calculation, a risk multiplier effect model is adopted to replace the traditional linear superposition method.

[0031] Taking the cross-border supply chain of electronic products as an example, when a certain path includes production nodes in Southeast Asian factories (high humidity environment risk), transportation nodes of sea containers (satellite signal instability), and distribution nodes of overseas warehouses (multilingual compatibility issues), even if the independent risk score of each node is only at a medium level, its transmission cost will show an exponential growth trend through the multiplication formula. This modeling method is more in line with the chain reaction characteristics of risk conduction in actual business scenarios. For example, when the historical temperature data transmission of the refrigerated truck transportation node in the cold chain logistics path fails frequently, even if its current load is within the safe range, due to the high weight attribute of the transportation area to which the node belongs and the recent error records, the transmission cost estimate of the entire path will be automatically increased, thus avoiding choosing a transmission route with seemingly balanced load but potential hazards.

[0032] In S4, by globally comparing the transmission costs of all paths to be analyzed, the path with the lowest transmission cost is selected as the final transmission channel. Taking cross-border pharmaceutical cold chain monitoring as an example, when the satellite communication of a certain sea container monitoring node in the target transmission path is delayed due to extreme weather, the path re-evaluation process is immediately triggered: first, the bearing index is recalculated based on the latest load status of each node currently, then the dynamic risk score is updated by combining the sudden increase in the historical error records of this node, and finally a new transmission cost ranking is generated and automatically switched to the sub-optimal path. This closed-loop optimization mechanism not only realizes the rapid elimination of faulty paths, but also dynamically adapts to the changes in the supply chain environment. For example, during the e-commerce promotion period, when the load of the distribution area node fluctuates due to a sharp increase in orders, the path selection strategy can be adjusted in real time, and node combinations with a low historical error rate and strong regional stability are preferentially selected, so as to achieve the best balance between data throughput and transmission reliability and achieve excellent supply chain data management effects.

[0033] In summary, in the entire supply chain data management method based on the industrial Internet of Things, first, based on the functional division of the necessary management areas and the real-time load monitoring mechanism of nodes, the limitations of traditional static path planning are broken through. By dynamically collecting the hardware resource status and historical transmission characteristics of nodes in links such as production, warehousing, transportation, and distribution, a dynamic resource portrait covering the entire supply chain is formed, enabling path planning to accurately identify sudden load fluctuations. Further, a dual-dimensional bearing index calculation model is proposed. On the basis of considering the theoretical maximum processing capacity of nodes, a dynamically adjusted average increment threshold is introduced, which not only avoids the dilution effect of low-load nodes on the evaluation results (such as the overloading phenomenon caused by abnormal GPS of distribution nodes), but also ensures the authenticity and timeliness of path bearing capacity evaluation by focusing on monitoring abnormal nodes exceeding the threshold (such as sudden high-frequency data transmission of temperature-controlled warehousing nodes in cold chain logistics). Further, by superimposing regional characteristic weights, a time decay mechanism (the error record in the past 3 hours has multiple times higher weight than the record 1 day ago), and a risk multiplier effect (the node risk multiplication formula), the cognitive bias of traditional linear superposition models is broken through, and hidden risks can be accurately quantified (such as production nodes with a high historical error rate in a high electromagnetic interference environment), making the calculation of transmission costs more in line with the risk conduction law in actual business (such as the collaborative risk between seaport container nodes and overseas warehouse nodes).

[0034] As Figure 2 shown, in one embodiment, obtaining the bearing index of the data transmission path according to the real-time load of each regional node in the data transmission path in S2 includes: S21. Obtain the theoretical maximum load of each regional node in the data transmission path; S22. Obtain the average increment threshold of the data transmission path; S23. Obtain the bearing index of the data transmission path according to the average increment threshold of the data transmission path, the real-time load and the theoretical maximum load of each regional node therein.

[0035] In this embodiment, it should be noted that in S21, the theoretical maximum load is determined based on the comprehensive analysis of node hardware configuration and historical operation data. For example, the edge server in the production area sets the maximum number of data packets that can be stably processed per unit time according to its CPU core count, memory capacity, and historical peak data processing volume; the mobile gateway in the transportation area determines its maximum concurrent connection count in combination with parameters such as 4G / 5G module bandwidth and satellite communication stability. Taking intelligent warehousing as an example, the RFID reader of a certain warehousing node calculates the maximum number of goods scans that can be processed simultaneously based on the number of antenna arrays, label recognition speed, and peak data processing performance during past Double Eleven periods, providing a standardized benchmark for subsequent load rate calculation.

[0036] In S22, the average increment threshold is dynamically adjusted according to the business characteristics of the management area to which the node belongs. For example, in the transportation area, due to the complex mobile network environment, the threshold is set to 50% of the theoretical maximum load, while in the warehousing area, due to the stable wired network, it is set to 70%.

[0037] In S23, the path stability is evaluated through a dual dimension: on the one hand, first identify the abnormal nodes that exceed the average increment threshold, and then replace the other abnormal nodes that do not exceed the average increment threshold with the average increment threshold to participate in the mean calculation, which not only avoids the dilution effect of low-load nodes but also highlights the impact of abnormal nodes; on the other hand, extract the node with the maximum load in the path as the key bottleneck. For example, in cross-border logistics, the load rate of the sea container tracking node reaches the theoretical limit due to satellite signal interruption. Even if the loads of other nodes are normal, this path is still determined to be a high-risk path due to the existence of the node with the maximum load.

[0038] In one implementation, the bearing index of the data transmission path obtained in S23 based on the average increment threshold of the data transmission path, the real-time load of each regional node therein, and the theoretical maximum load is expressed as: ; where is the bearing index of the k-th data transmission path, is the number of regional nodes of the k-th data transmission path, is the average increment threshold, is the real-time load of the i-th regional node in the k-th data transmission path, is the theoretical maximum load of the i-th regional node in the k-th data transmission path.

[0039] In this implementation, it should be noted that in, the average increment threshold is set as the admission line for load participation in the calculation. Only when the real-time load of the node exceeds , it is calculated according to the actual value; otherwise, is used as the reference value. For example, in the logistics distribution scenario, it is set that = 50%: the load rate of a distribution node is 120% (exceeding ), and 120% is directly used for calculation. The loads of the other three nodes are 5%, 10%, and 35% respectively (all lower than ), and they are uniformly calculated according to = 50%. This improvement completely eliminates the downward effect of low-load nodes on the average value. In the traditional algorithm, the average load rate of the four nodes is (30 + 40 + 45 + 120) / 4 = 58.75%, while after correction by this method, it is (50 + 50 + 50 + 120) / 4 = 67.5%, which more truly reflects the actual impact of overloaded nodes.

[0040] Further, in , the node data with the highest load rate in the path is separately extracted and directly superimposed on the evaluation index. In the previous example, the maximum load rate of 120% is added as an independent item. The final index is 67.5% + 120% = 187.5%. Compared with the evaluation result of 58.75% of the traditional method, the index value of this method is amplified by 3.2 times, strongly highlighting the existence of the key bottleneck node.

[0041] For example, assume a transmission path containing 4 nodes: Production node: load rate 15% (theoretical maximum 100%); Warehouse node: load rate 30% (theoretical 100%); Shipping node: load rate 80% (theoretical 120% due to satellite module expansion); Overseas warehouse node: load rate 80% (theoretical 100%). Parameter setting: = 60%. Threshold correction item calculation: Production node: 15% is less than 60%, take 60 / 100 = 0.6; Warehouse node: 30% < 60%, take 60 / 100 = 0.6; Shipping node: take 80 / 120 ≈ 0.67; Overseas warehouse node: take 80 / 100 = 0.8. Then (0.6 + 0.6 + 0.67 + 0.8) / 4 ≈ 0.67, 0.8. The final bearing index is 1.47.

[0042] As Figure 3 shown, in one embodiment, in S3, obtaining the dynamic risk scores of each regional node in the path to be analyzed based on the evaluation model, the real-time load of the regional nodes in the path to be analyzed, the historical transmission information, and the necessary management area where they are located includes: S31. Obtain the historical time window, and respectively obtain the historical error times of each regional node in the path to be analyzed within 1, 2,..., h historical time windows from the current moment; S32. Obtain the regional weight corresponding to the necessary management area, and obtain the theoretical maximum load of each regional node in the path to be analyzed; S33. Obtain the dynamic risk score bearing index of each regional node in the path to be analyzed based on the evaluation model, the real-time load, the theoretical maximum load, the historical error times, and the regional weight of each regional node in the path to be analyzed.

[0043] In this embodiment, it should be noted that in S31, a time series analysis model for node reliability is constructed. Through the sliding time window mechanism, with time periods such as hours or days as the unit of the historical time window, the transmission error times within 1, 2,..., h historical time windows are statistically analyzed.

[0044] In S32, according to the inherent risk characteristics of different supply chain links, regional weight coefficients are preset in advance: the transportation area is given a weight of 0.9 times due to the volatility of the mobile network, and the warehousing area is set to 0.6 times due to environmental stability. At the same time, combined with the theoretical maximum load calculated in S21, a standardized benchmark for node bearing capacity is established.

[0045] In S33, the evaluation model performs a coupled calculation of the real-time load rate, the historical error time decay value, and the regional weight. In the scenario of automotive parts supply, on-vehicle diagnostic nodes frequently experience historical errors due to electromagnetic interference. Even if the current load is normal, the risk value after superimposing the regional weight will still trigger an early warning, avoiding the selection of data transmission paths containing such nodes, thereby preventing assembly errors caused by data delays on the production line.

[0046] In one implementation, the evaluation model in S33 for obtaining the dynamic risk scores of each regional node in the path to be analyzed based on the evaluation model, the real-time load of each regional node in the path to be analyzed, the theoretical maximum load, the historical error count, and the regional weight is expressed as: ; where is the dynamic risk score of the i-th regional node in the m-th path to be analyzed, is the regional weight of the mandatory management area where the i-th regional node in the m-th path to be analyzed is located, is the historical error count of the i-th regional node in the m-th path to be analyzed within j historical time windows from the current moment, is the upper limit of the number of historical time windows, is the real-time load of the i-th regional node in the m-th path to be analyzed, is the theoretical maximum load of the i-th regional node in the m-th path to be analyzed.

[0047] In this implementation, it should be noted that in it means that the historical error count decays exponentially according to the number of time windows. For example, at 1 time window from the current moment it is at 2 time windows from the current moment it is at j time windows from the current moment it is . For example, the error counts of a cross-border sea freight node in the past 1 hour (j = 1), 2 hours (j = 2), and 3 hours (j = 3) are 5, 8, and 9 times respectively; compared with the traditional method where the average error count per hour in the past 3 hours is 3 times, after calculation by this evaluation model, 8, increasing the impact degree of recent errors by 2.7 times (3 vs 8).

[0048] Furthermore, by setting the regional weight Complete regional characteristic correction. For example, the transportation area is given a weight of 0.9 times, and the warehousing area is set to 0.6 times. Under the same number of error occurrences, the calculated value when the mandatory management area is the transportation area is 1.5 times that when the mandatory management area is the warehousing area.

[0049] Furthermore, is the real-time load rate, which is used to complete dynamic correction. If the load rate of a certain production node reaches 90% of the theoretical maximum load, this item contributes 0.9.

[0050] In one implementation, the constraint model in obtaining the transmission cost of the path to be analyzed based on the constraint model and the dynamic risk scores of each regional node in the path to be analyzed in S3 is expressed as: ; where is the transmission cost of the m-th path to be analyzed, is the dynamic risk score of the i-th regional node in the m-th path to be analyzed, is the number of regional nodes in the m-th path to be analyzed.

[0051] In this implementation, it should be noted that the product is used instead of linear superposition to reflect the synergistic amplification effect of multi-node risk events; when the risk of any node in the path significantly increases, the total cost increases exponentially; the traditional linear superposition cannot reflect the chain reaction of node risks. For example, for two nodes with a risk value of 1, the total risk calculated by the traditional method is 2, while the calculation of this model is (1 + 1) × (1 + 1) = 4, which is more in line with the superposition effect of multi-node failures in actual business. At the same time, each node term is to ensure that when the node risk = 0 (ideal risk-free), this item has no amplification effect on the total cost (multiplied by 1); when is large (high risk), this item makes the total cost increase exponentially, and its engineering significance is to avoid the interference of zero-risk nodes on the evaluation and at the same time amplify the influence of key bottleneck nodes.

[0052] For example, assume that the path contains 3 nodes with risk values of 0.5, 1.0, and 0.2 respectively, = 1.5 × 2.0 × 1.2 = 3.6, the result of the traditional linear model is 0.5 + 1.0 + 0.2 = 1.7, and the result of this model is magnified by 2.12 times; if a node with a risk value of 0.5 is added to the path: = 3.6 × (1 + 0.5) = 5.4, and the cost increase rate reaches 50%, which is much higher than the 25% of the linear model (1.7 + 0.5 = 2.2), solving the problem that the traditional method cannot effectively reflect the marginal impact of newly added nodes on system stability.

[0053] Such as Figure 4As shown, a supply chain data management 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 communicatively connected in sequence. The management platform includes: A data acquisition module, configured to acquire data to be managed, multiple mandatory management areas, multiple area nodes within the mandatory management areas, and the real-time load and historical transmission information of each area node; A data path selection module, configured to plan a data transmission path composed of any one area node within each mandatory management area, obtain a bearing index of the data transmission path according to the real-time load of each area node within the data transmission path, and obtain a data transmission path with a bearing index less than a preset threshold as a path to be analyzed; A data path evaluation module, configured to obtain a dynamic risk score of each area node in the path to be analyzed based on an evaluation model, the real-time load, historical transmission information, and the mandatory management area where the area node in the path to be analyzed is located, and obtain the transmission cost of the path to be analyzed based on a constraint model and the dynamic risk scores of each area node in the path to be analyzed; A data management module, configured to obtain the path to be analyzed with the lowest transmission cost as a target transmission path, and transmit the data to be managed according to the target transmission path.

[0054] In one embodiment, the data path selection module is further configured to: obtain the theoretical maximum load of each area node in the data transmission path; obtain the average increment threshold of the data transmission path; and obtain the bearing index of the data transmission path according to the average increment threshold of the data transmission path, the real-time load of each area node therein, and the theoretical maximum load.

[0055] In one embodiment, the data path evaluation module is further configured to: obtain a historical time window, and respectively obtain the historical error times of each area node in the path to be analyzed within 1, 2,..., h historical time windows from the current moment; obtain the area weight corresponding to the mandatory management area, and obtain the theoretical maximum load of each area node in the path to be analyzed; and obtain the dynamic risk score bearing index of each area node in the path to be analyzed based on the evaluation model, the real-time load, theoretical maximum load, historical error times, and area weight of each area node in the path to be analyzed.

[0056] In this embodiment, it should be noted that regarding the above supply chain data management system based on the industrial Internet of Things, the specific manner of performing operations has been described in detail in the embodiments of the supply chain data management method based on the industrial Internet of Things, and will not be elaborated here.

[0057] It should also be noted that the entire supply chain data management system based on the industrial Internet of Things can be applied to the optimized industrial Internet of Things, such asFigure 5 As 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 corresponding service demand wishes of users.

[0058] The physical entities of the user platform include various user terminals, such as mobile phones, computers, dedicated terminals, etc., and realize the services at the user end through the combination with the user information system software.

[0059] The service platform is configured as an API server or other servers used to establish communications between the management platform and the user platform to achieve corresponding functions; the physical entities of the service platform include various servers.

[0060] The management platform is configured to perform at least one of device operation status monitoring management, data monitoring management, device parameter management, and life cycle management; the management platform is the operation and coordination platform of the Internet of Things and 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.

[0061] The sensing 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 sensing 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.

[0062] The object platform is configured to perform specific production control, detection, measurement, and other production work; the physical entities of the production object include various production devices, sensors, etc.

[0063] Figure 6 It is a block diagram of an electronic device for a supply chain data management method based on an industrial Internet of Things shown according to an exemplary embodiment. As Figure 6 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.

[0064] 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 supply chain 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. 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, sent and received 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, magnetic disk or optical disk. 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 here. Therefore, the corresponding communication component 705 may include: a Wi-Fi module, a Bluetooth module, an NFC module, and so on.

[0065] In an exemplary embodiment, the electronic device 700 may 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 supply chain data management method based on the industrial Internet of Things.

[0066] 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 supply chain data management method based on the industrial Internet of Things are implemented. For example, the computer-readable storage medium may be the above-mentioned memory 702 including program instructions, and the above program instructions may be executed by the processor 701 of the electronic device 700 to complete the above-mentioned supply chain data management method based on the industrial Internet of Things.

[0067] 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 portion for executing the above-mentioned supply chain data management method based on the industrial Internet of Things when executed by the programmable device.

[0068] 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.

[0069] In addition, it should be noted that, in the above specific embodiments, the various specific technical features described can be combined in any appropriate manner without conflict. To avoid unnecessary repetition, the present disclosure does not separately describe various possible combination manners.

[0070] Furthermore, 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.

[0071] 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 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 various 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. A supply chain data management method based on the industrial Internet of Things, characterized in that, Including: Obtain the data to be managed and multiple mandatory management areas, obtain multiple area nodes within the mandatory management areas, and obtain the real-time load and historical transmission information of each area node; Plan a data transmission path composed of any one area node within each mandatory management area, obtain the bearing index of the data transmission path according to the real-time load of each area node within the data transmission path, and obtain the data transmission path with the bearing index less than the preset threshold as the path to be analyzed; Based on the evaluation model, the real-time load, historical transmission information, and the mandatory management area where the area nodes in the path to be analyzed are located, obtain the dynamic risk scores of each area node in the path to be analyzed, and obtain the transmission cost of the path to be analyzed based on the constraint model and the dynamic risk scores of each area node in the path to be analyzed; Obtain the path to be analyzed with the lowest transmission cost as the target transmission path, and transmit the data to be managed according to the target transmission path.

2. The supply chain data management method based on industrial Internet of Things according to claim 1, characterized in that, The obtaining the bearing index of the data transmission path according to the real-time load of each area node within the data transmission path includes: Obtain the theoretical maximum load of each area node in the data transmission path; Obtain the average increment threshold of the data transmission path; Obtain the bearing index of the data transmission path according to the average increment threshold of the data transmission path, the real-time load of each area node within it, and the theoretical maximum load.

3. The supply chain data management method based on the industrial Internet of Things according to claim 2, characterized in that, The obtaining the bearing index of the data transmission path according to the average increment threshold of the data transmission path, the real-time load of each area node within it, and the theoretical maximum load is expressed as: ; wherein, is the bearer metric of the k-th data transmission path, is the number of regional nodes of the k-th data transmission path, is the average increment threshold (only when it exceeds will it be included in the average value calculation, and at the same time ensure that the minimum value participating in the average value calculation is to avoid the situation where too many regional nodes in the low real-time load area lead to too small an average value and suppress the contribution of a small number of overloaded regional nodes), is the real-time load of the i-th regional node in the k-th data transmission path, is the theoretical maximum load of the i-th regional node in the k-th data transmission path. ​ 4. The supply chain data management method based on industrial Internet of Things according to claim 1, characterized in that, The obtaining the dynamic risk scores of each area node in the path to be analyzed based on the evaluation model, the real-time load, historical transmission information, and the mandatory management area where the area nodes in the path to be analyzed are located includes: Obtain the historical time window, and respectively obtain the historical error counts of each area node in the path to be analyzed within 1, 2,..., h historical time windows from the current moment; Obtain the area weight corresponding to the mandatory management area, and obtain the theoretical maximum load of each area node in the path to be analyzed; Based on the evaluation model, the real-time load, theoretical maximum load, historical error counts, and area weight of each area node in the path to be analyzed, obtain the dynamic risk score bearing index of each area node in the path to be analyzed.

5. The supply chain data management method based on industrial Internet of Things according to claim 4, characterized in that The evaluation model in the obtaining the dynamic risk scores of each area node in the path to be analyzed based on the evaluation model, the real-time load, theoretical maximum load, historical error counts, and area weight of each area node in the path to be analyzed is expressed as: ; wherein, is the dynamic risk score of the i-th regional node in the m-th path to be analyzed, is the regional weight of the mandatory management region where the i-th regional node in the m-th path to be analyzed is located, is the number of historical errors within j historical time windows from the current moment for the i-th regional node in the m-th path to be analyzed, is the upper limit of the number of historical time windows, is the real-time load of the i-th regional node in the m-th path to be analyzed, is the theoretical maximum load capacity of the i-th regional node in the m-th path to be analyzed.

6. The supply chain data management method based on industrial Internet of Things according to claim 1, wherein The constraint model in the obtaining the transmission cost of the path to be analyzed based on the constraint model and the dynamic risk scores of each area node in the path to be analyzed is expressed as: ; wherein, is the transmission cost of the m-th path to be analyzed, is the dynamic risk score of the i-th regional node in the m-th path to be analyzed, is the number of regional nodes of the m-th path to be analyzed.

7. A supply chain data management system based on the industrial Internet of Things, characterized in that, The system includes a management platform, a sensor network platform, and an object platform that are communicatively connected in sequence. The management platform includes: A data acquisition module, which is used to obtain the data to be managed and multiple mandatory management areas, obtain multiple area nodes within the mandatory management areas, and obtain the real-time load and historical transmission information of each area node; A data path selection module, configured to plan a data transmission path composed of any one of the area nodes in each necessary management area, obtain a bearing index of the data transmission path according to the real-time loads of the area nodes in the data transmission path, and obtain a data transmission path with a bearing index less than a preset threshold as a path to be analyzed; A data path evaluation module, configured to obtain dynamic risk scores of each area node in the path to be analyzed based on an evaluation model, the real-time loads of the area nodes in the path to be analyzed, historical transmission information, and the necessary management area where they are located, and obtain the transmission cost of the path to be analyzed based on a constraint model and the dynamic risk scores of each area node in the path to be analyzed; A data management module, configured to obtain the path to be analyzed with the lowest transmission cost as the target transmission path, and transmit the data to be managed according to the target transmission path.

8. The supply chain data management system based on the industrial Internet of Things according to claim 7, characterized in that The data path selection module is further configured to: Obtain the theoretical maximum load of each area node in the data transmission path; Obtain the average increment threshold of the data transmission path; Obtain the bearing index of the data transmission path according to the average increment threshold of the data transmission path, the real-time loads of each area node therein, and the theoretical maximum load.

9. An electronic device, characterized in that, Including: A memory, on which a computer program is stored; A processor, configured to execute the computer program in the memory to implement the supply chain data management method based on industrial Internet of Things according to 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 the processor, it implements the supply chain data management method based on industrial Internet of Things according to any one of claims 1 to 6.

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