Cloud edge collaborative data distribution method and system

Abstract

The invention provides a cloud edge collaborative data distribution method and system. Relates to a digital information transmission method and system. The method comprises the following steps: acquiring a service priority of to-be-distributed data; when the service priority is a high priority, routing the data to be distributed to a DDS (Direct Digital Synthesizer) transmission channel; adjusting the QoS strategy of the DDS transmission channel based on the current network state of the edge node; and distributing the data to be distributed through a DDS transmission channel according to the adjusted QoS strategy. When the service priority is a low priority, routing the data to be distributed to an MQTT transmission channel; and transmitting the to-be-distributed data to a cloud end in a zero analysis mode.

Description

Technical Field

[0001] This invention relates to a method and system for transmitting digital information, and more particularly to a cloud-edge collaborative data distribution method and system. Background Technology

[0002] Data Distribution Service (DDS), as a communication middleware for real-time systems, supports data transmission between devices in a cloud-edge collaborative architecture through a publish / subscribe model and static QoS (Quality of Service) policies (such as fixed priority and reliability levels). It is widely used in fields such as smart manufacturing and connected vehicles, and its core value lies in providing low-latency communication capabilities.

[0003] Existing technologies rely on cloud-edge data synchronization schemes based on predefined QoS levels, depend on edge caching mechanisms to cope with network fluctuations, and use protocol conversion gateways to enable heterogeneous devices (such as OPC UA devices (Open Platform Communications Unified Architecture)) to access the DDS network.

[0004] However, existing technology has three significant drawbacks:

[0005] 1. DDS's QoS policies are typically configured statically during system deployment, making them unsuitable for adaptive adjustments based on runtime network conditions (such as packet loss rate and bandwidth fluctuations). Although the DDS standard allows for some QoS policy modifications at runtime, the lack of effective network awareness and automatic adjustment mechanisms makes it difficult to guarantee the reliability of critical data transmission in dynamic network environments. This results in a significant increase in transmission delays for high-priority commands (such as industrial emergency stop signals) during sudden network congestion.

[0006] 2. Heterogeneous devices need to pass through a protocol conversion gateway, resulting in additional data parsing and reconstruction overhead, which reduces system transmission efficiency;

[0007] 3. Real-time commands and non-real-time data (such as device logs) share a single transmission channel, leading to bandwidth contention and increasing the risk of packet loss for critical commands. The root cause of these shortcomings lies in the lack of network status awareness and a service-tiered transmission mechanism. Summary of the Invention

[0008] To address the technical problems existing in the prior art, the present invention aims to provide a cloud-edge collaborative data distribution method and system that can overcome the rigid limitations of static QoS policies and resolve resource conflicts between high and low priority service flows.

[0009] To achieve the above-mentioned objectives, this invention provides a cloud-edge collaborative data distribution method, comprising the following steps:

[0010] Prioritize the acquisition of data to be distributed;

[0011] When the service priority is high, the data to be distributed is routed to the DDS transmission channel;

[0012] Adjust the QoS policy of the DDS transmission channel based on the current network status of the edge nodes;

[0013] The data to be distributed is distributed through the DDS transmission channel in accordance with the adjusted QoS policy.

[0014] According to one technical solution of the present invention, it further includes:

[0015] When the service priority is low, the data to be distributed is routed to the MQTT transmission channel;

[0016] The data to be distributed is transmitted to the cloud in a zero-parse manner.

[0017] According to one technical solution of the present invention, adjusting the QoS policy of the DDS transmission channel includes the following process:

[0018] Obtain at least one network state parameter of the current network state;

[0019] Based on the preset mapping relationship between network status parameters and QoS adjustment commands, the corresponding QoS adjustment commands are obtained;

[0020] Based on the QoS adjustment command, update the QoS policy of the DDS transmission channel.

[0021] In this embodiment,

[0022] According to a technical solution of the present invention, the process of obtaining the business priority of data to be distributed includes:

[0023] Obtain the business tags from the data to be distributed;

[0024] Based on the preset mapping relationship between business tags and business priorities, the corresponding business priority is determined based on the value of the business tag.

[0025] According to one technical solution of the present invention, the network state parameters include at least one of packet loss rate and bandwidth fluctuation rate.

[0026] According to one technical solution of the present invention, the QoS strategy includes at least one of reliability level, deadline, and transmission priority.

[0027] The present invention also provides a cloud-edge collaborative data distribution system, including edge nodes;

[0028] The edge nodes include:

[0029] A network probe is used to obtain at least one network status parameter of the current network status.

[0030] Business tiering system, used for:

[0031] Prioritize the acquisition of data to be distributed;

[0032] The data to be distributed with the highest priority is routed to the DDS transmission channel;

[0033] The data to be distributed with low priority is routed to the MQTT transmission channel;

[0034] Dynamic QoS engine, used for:

[0035] Based on the preset mapping relationship between network status parameters and QoS adjustment commands, the corresponding QoS adjustment commands are obtained;

[0036] Based on the QoS adjustment command, update the QoS policy of the DDS transmission channel.

[0037] According to one technical solution of the present invention, the edge node further includes:

[0038] The edge-cloud collaborative agent module is used to transmit data to be distributed via the MQTT transmission channel to the cloud in a zero-parse manner.

[0039] The present invention provides a cloud-edge collaborative data distribution method and system, which has the following beneficial effects:

[0040] 1. By introducing a closed-loop control circuit consisting of network probes, fuzzy controllers, and QoS actuators, a dynamic mapping between network state parameters and QoS policies is established through a dynamic QoS control mechanism, breaking through the rigid limitations of static policies.

[0041] 2. By adopting dual-channel service isolation and a hierarchical routing strategy based on semantic tags, resource conflicts between high and low priority service flows are resolved. Attached Figure Description

[0042] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the embodiments will be briefly described below. Obviously, the drawings described below are merely some embodiments of the present invention, and those skilled in the art can obtain other drawings based on these drawings without creative effort.

[0043] Figure 1 A flowchart illustrating a cloud-edge collaborative data distribution method according to an embodiment of the present invention;

[0044] Figure 2 The diagram illustrates the structure of a cloud-edge collaborative data distribution system according to one embodiment of the present invention. Detailed Implementation

[0045] The description of the embodiments in this specification should be taken in conjunction with the accompanying drawings, which should form part of the complete specification. In the drawings, the shape or thickness of the embodiments may be exaggerated and may be indicated in a simplified or convenient manner. Furthermore, parts of the various structures in the drawings will be described separately; it is worth noting that elements not shown in the figures or not described in words are in a form known to those skilled in the art.

[0046] The descriptions of the embodiments herein, including any references to directions and orientations, are for ease of description only and should not be construed as limiting the scope of the invention. The following description of preferred embodiments involves combinations of features, which may exist independently or in combination; the invention is not particularly limited to the preferred embodiments. The scope of the invention is defined by the claims. Figures 1-2 As shown; Specific Implementation Method 1

[0048] This embodiment of a cloud-edge collaborative data distribution method includes the following steps:

[0049] Prioritize the acquisition of data to be distributed;

[0050] When the service priority is high, the data to be distributed will be routed to the DDS transmission channel;

[0051] Adjust the QoS policy of the DDS transmission channel based on the current network status of the edge nodes;

[0052] Based on the adjusted QoS policy, the data to be distributed is distributed through the DDS transmission channel.

[0053] In this embodiment, the purpose is to address the problem of insufficient adaptability of static QoS policies in dynamic network environments and to achieve adaptive QoS control based on real-time network conditions.

[0054] A dynamic QoS policy engine is employed, which collects bandwidth fluctuation and packet loss rate parameters in real time through network probes deployed at edge nodes. Combined with the rule base built into the fuzzy controller (e.g., automatically increasing command priority when the packet loss rate exceeds a threshold), the QoS executor is driven to dynamically adjust DDS transmission parameters (including reliability level, deadline, and priority). This mechanism significantly reduces the transmission latency of high-priority commands during network congestion.

[0055] The aforementioned dynamic QoS policy engine adaptively adjusts the DDS transmission guarantee strength based on real-time network conditions: when network link quality is good (e.g., low packet loss rate, stable bandwidth), it lowers the reliability level, relaxes the deadline, and reduces transmission priority to free up system resources and bandwidth overhead; when network degradation occurs (e.g., increased packet loss rate, aggravated latency jitter), it automatically increases the reliability level of critical data, shortens the deadline, and raises the transmission priority to enhance the determinism and robustness of critical data transmission. This achieves a dynamic balance between service quality and resource consumption. Specific Implementation Method Two

[0057] This embodiment is a further explanation of embodiment one. The cloud-edge collaborative data distribution method in this embodiment also includes:

[0058] When the business priority is low, the data to be distributed will be routed to the MQTT transmission channel;

[0059] Data to be distributed is passed through to the cloud in a zero-resolution manner.

[0060] In this embodiment, the purpose is to eliminate the protocol conversion overhead when heterogeneous devices are connected; and to avoid resource contention and improve the reliability of critical data transmission through a service hierarchical isolation mechanism.

[0061] The high-priority channel uses the native DDS protocol to transmit real-time control commands, ensuring microsecond-level response; the low-priority channel achieves zero-parsing forwarding through the edge-cloud collaborative agent module, that is, non-real-time data (low-priority data to be distributed) is directly encapsulated as MQTT payload and passed to the cloud, avoiding protocol parsing overhead. Specific Implementation Method 3

[0063] This embodiment is a further explanation of embodiment one or two. In this embodiment, the process includes:

[0064] Obtain at least one network state parameter of the current network state;

[0065] Based on the preset mapping relationship between network status parameters and QoS adjustment commands, the corresponding QoS adjustment commands are obtained;

[0066] Update the QoS policy of the DDS transmission channel based on the QoS adjustment command.

[0067] In this embodiment, the network probe collects the network link status between the edge node and the target at fixed intervals (50ms~100ms), including:

[0068] Packet loss rate: The proportion of data packets lost per unit of time;

[0069] Bandwidth volatility: The standard deviation of currently available bandwidth relative to a baseline bandwidth;

[0070] The above parameters are processed (e.g., normalized) and then input into the fuzzy controller.

[0071] The fuzzy controller can employ a fuzzy inference system (such as the Mamdani type). An example of its built-in fuzzy rule base (the mapping relationship between preset network state parameters and QoS adjustment instructions) is as follows:

[0072] Table 1 Fuzzy Rules

[0073] Among them, high, medium, and low variables are defined by membership functions (such as triangular or trapezoidal membership functions), for example:

[0074] High packet loss rate: Packet loss rate ≥ 5%;

[0075] High bandwidth volatility: BFR (Bandwidth Fluctuation Range) ≥ 20%;

[0076] Normal latency: E2ED (End-to-End Delay) ≤ 10ms.

[0077] After defuzzification (using methods such as the centroid method), the output of the fuzzy controller generates specific DDS QoS parameter adjustment instructions, which are then applied by the QoS executor to the corresponding DDS DataWriter (publishing end) object. The specific adjustments include:

[0078] Reliability rating:

[0079] Switch between BEST_EFFORT_RELIABILITY and RELIABLE_RELIABILITY;

[0080] Deadline:

[0081] Dynamically modify the deadline.period field, for example, shorten it from the default 100ms to 70ms;

[0082] Transport Priority:

[0083] Adjust transport_priority.value, with a range of 0 to 255. Higher values ​​indicate higher priority. Specific Implementation Method Four

[0085] This embodiment is a further explanation of embodiment three. In this embodiment, the process of obtaining the business priority of the data to be distributed includes:

[0086] Retrieve business tags from the data to be distributed;

[0087] Based on the preset mapping relationship between business tags and business priorities, the corresponding business priority is determined based on the value of the business tag.

[0088] In this implementation, data to be distributed is automatically routed to the corresponding channel based on data tags by a service classifier, achieving service flow isolation. This solution significantly improves the access efficiency of heterogeneous devices while ensuring the reliability of critical instructions.

[0089] For example:

[0090] If the label contains emergency / control / critical, then the route is routed to the DDS high-priority channel.

[0091] If the tag is log / history / telemetry, it will be routed to the MQTT low priority channel (transmitted through the edge-cloud collaborative agent module), will not participate in DDS communication, and will not be affected by the QoS control mechanism. Detailed Implementation Method Five

[0093] This embodiment is a further explanation of embodiment three. In this embodiment, the network status parameters include at least one of packet loss rate and bandwidth fluctuation rate. Specific Implementation Method Six

[0095] This embodiment is a further explanation of embodiment five. In this embodiment, the QoS policy includes at least one of reliability level, deadline, and transmission priority. Detailed Implementation Method Seven

[0097] This embodiment of a cloud-edge collaborative data distribution system includes edge nodes;

[0098] Edge nodes include:

[0099] A network probe is used to obtain at least one network status parameter of the current network status.

[0100] Business tiering system, used for:

[0101] Prioritize the acquisition of data to be distributed;

[0102] Data with high priority is routed to the DDS transmission channel;

[0103] Data to be distributed with low priority is routed to the MQTT transmission channel;

[0104] Dynamic QoS engine, used for:

[0105] Based on the preset mapping relationship between network status parameters and QoS adjustment commands, the corresponding QoS adjustment commands are obtained;

[0106] Update the QoS policy of the DDS transmission channel based on the QoS adjustment command. Detailed Implementation Method Eight

[0108] This embodiment is a further explanation of embodiment seven. In this embodiment, the edge node also includes:

[0109] The edge-cloud collaborative agent module is used to transmit data to be distributed via the MQTT transmission channel to the cloud in a zero-parse manner.

[0110] In this embodiment, the cloud-edge collaborative data distribution system consists of a device layer, an edge node layer, and a cloud layer. The edge node integrates four core modules:

[0111] Network probe: Periodically samples network bandwidth and packet loss rate;

[0112] Service classifier: Parses data tags and routes them (e.g., if the tag contains "emergency", it directs the data to the DDS channel);

[0113] Dynamic QoS Engine: Outputs QoS adjustment instructions based on a fuzzy rule base;

[0114] Edge-cloud collaborative agent module: Encapsulates low-priority data into MQTT messages and transmits them to the cloud.

[0115] The aforementioned edge-cloud collaborative agent module is a functional unit deployed on the edge node. It receives low-priority data identified and routed by the business classifier, but does not perform protocol parsing or format reconstruction on the low-priority data. Instead, it directly encapsulates the original payload of the low-priority data into a message payload of the MQTT protocol. It then connects to the cloud MQTT message broker server through an MQTT client and transmits the encapsulated message to the cloud.

[0116] The overall workflow is as follows: Data sent from the device layer first enters the service classifier, where channels are allocated according to preset label rules. High-priority data enters the dynamic QoS engine, which adjusts the transmission strategy based on the real-time network status parameters of the network probe; low-priority data is encapsulated by the edge-cloud collaborative proxy module and then directly transmitted to the cloud.

[0117] Taking an emergency stop scenario in a smart factory as an example: when the PLC sends a command tagged " / press / emergency_stop", the service classifier routes it to the DDS channel; if the network probe detects that the bandwidth fluctuation rate exceeds the standard at this time, the fuzzy controller immediately shortens the deadline parameter of the command to ensure a rapid response.

[0118] The cloud-edge collaborative data distribution method and system of the present invention comprises the following steps: obtaining the service priority of the data to be distributed; when the service priority is high, routing the data to be distributed to the DDS transmission channel; adjusting the QoS policy of the DDS transmission channel based on the current network status of the edge node; distributing the data to be distributed through the DDS transmission channel according to the adjusted QoS policy. When the service priority is low, routing the data to be distributed to the MQTT transmission channel; and transmitting the data to be distributed to the cloud in a zero-resolution manner.

[0119] Furthermore, it should be noted that the present invention can be provided as a method, apparatus, or computer program product. Therefore, embodiments of the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Moreover, embodiments of the present invention can take the form of a computer program product implemented on one or more computer-usable storage media containing computer-usable program code.

[0120] Embodiments of the present invention are described with reference to flowchart illustrations and / or block diagrams of methods, terminal devices (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, embedded processor, or other programmable data processing terminal device to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing terminal device, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0121] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing terminal device to operate in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1The functions specified in one or more boxes. These computer program instructions may also be loaded onto a computer or other programmable data processing terminal equipment to cause a series of operational steps to be performed on the computer or other programmable terminal equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable terminal equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0122] It should also be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or terminal device that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or terminal device. Without further limitations, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or terminal device that includes said element.

[0123] Finally, it should be noted that the above description represents a preferred embodiment of the present invention. It should be pointed out that although preferred embodiments have been described, those skilled in the art, once they understand the basic inventive concept of the present invention, can make various improvements and modifications without departing from the principles described herein. These improvements and modifications should also be considered within the scope of protection of the present invention. Therefore, the appended claims are intended to be interpreted as including both the preferred embodiments and all changes and modifications falling within the scope of the embodiments of the present invention.

Claims

1. A cloud-edge collaborative data distribution method, characterized in that, The steps are as follows: Prioritize the acquisition of data to be distributed; When the service priority is high, the data to be distributed is routed to the DDS transmission channel; Adjust the QoS policy of the DDS transmission channel based on the current network status of the edge nodes; The data to be distributed is distributed through the DDS transmission channel in accordance with the adjusted QoS policy.

2. The cloud-edge collaborative data distribution method according to claim 1, characterized in that, Also includes: When the service priority is low, the data to be distributed is routed to the MQTT transmission channel; The data to be distributed is transmitted to the cloud in a zero-parse manner.

3. The cloud-edge collaborative data distribution method according to claim 1 or 2, characterized in that, Adjusting the QoS policy of the DDS transmission channel includes the following process: Obtain at least one network state parameter of the current network state; Based on the preset mapping relationship between network status parameters and QoS adjustment commands, the corresponding QoS adjustment commands are obtained; Based on the QoS adjustment command, update the QoS policy of the DDS transmission channel.

4. The cloud-edge collaborative data distribution method according to claim 3, characterized in that, The process of prioritizing data to be distributed includes: Obtain the business tags from the data to be distributed; Based on the preset mapping relationship between business tags and business priorities, the corresponding business priority is determined based on the value of the business tag.

5. The cloud-edge collaborative data distribution method according to claim 3, characterized in that, The network state parameters include at least one of packet loss rate and bandwidth fluctuation rate.

6. The cloud-edge collaborative data distribution method according to claim 5, characterized in that, QoS policies include at least one of reliability level, deadline, and transmission priority.

7. A cloud-edge collaborative data distribution system, characterized in that, Including edge nodes; The edge nodes include: A network probe is used to obtain at least one network status parameter of the current network status. Business tiering system, used for: Prioritize the acquisition of data to be distributed; The data to be distributed with the highest priority is routed to the DDS transmission channel; The data to be distributed with low priority is routed to the MQTT transmission channel; Dynamic QoS engine, used for: Based on the preset mapping relationship between network status parameters and QoS adjustment commands, the corresponding QoS adjustment commands are obtained; Based on the QoS adjustment command, update the QoS policy of the DDS transmission channel.

8. The cloud-edge collaborative data distribution system according to claim 7, characterized in that, Edge nodes also include: The edge-cloud collaborative agent module is used to transmit data to be distributed via the MQTT transmission channel to the cloud in a zero-parse manner.

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