Source network load storage data aggregation processing method, system, equipment and medium

Abstract

The invention discloses a source network load storage data aggregation processing method, system and device and a medium, mainly relates to the technical field of aggregation processing, and is used for solving the problems of data transmission conflict and lack of a dynamic adjustment mechanism for bandwidth allocation caused by bandwidth sharing in the existing scheme. Comprising the following steps: adjusting a real-time bandwidth according to the priority of a dedicated channel and the hop count of an edge computing node corresponding to the dedicated channel in a data aggregation layer; the communication layer transmits the equipment data to the data aggregation layer through an internet communication protocol; the data aggregation layer pushes the device data to a real-time edge computing node in a streaming manner according to a preset processing logic, stores the device data to an offline analysis platform, and uploads a processing result returned by the edge computing node to the application layer; and the application layer issues the operation instruction to the data aggregation layer, the data aggregation layer stores the operation instruction and issues the operation instruction to the communication layer, and the dedicated channel bandwidth is adjusted again according to the operation instruction pre-stored in the communication layer and a dedicated channel bandwidth allocation strategy.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of polymerization processing, in particular to a source network load storage data aggregation processing method, system, device and medium. BACKGROUND

[0002] The current data processing of the source network load storage system adopts a traditional network architecture. Generally, the data generated by physical layer devices (such as distributed power sources, energy storage devices, load terminals, etc.) is transmitted to the communication layer through shared network bandwidth, and then uniformly forwarded to the data aggregation layer by the communication layer. The data aggregation layer generally adopts a centralized processing mode, first stores all device data to a central server, then performs batch processing according to a preset order, and uploads the results to the application layer after processing is completed. The application layer generates operation instructions according to the results and feeds back to the physical layer devices for execution through the original route. In this process, the network bandwidth is not differentiated, and various device data (such as real-time monitoring data and historical statistical data) share the same transmission channel, and the data processing relies on the central node computing power, and the role of the edge computing node is limited to simple data collection and forwarding. The existing technology has the following defects: first, shared bandwidth causes data transmission conflict, and real-time device data (such as load regulation instruction feedback data) is easily disturbed by non-critical data, causing transmission delay; second, the bandwidth allocation lacks a dynamic adjustment mechanism, and cannot be flexibly optimized according to the actual working conditions such as device data priority and edge computing node hop count, resulting in low network resource utilization and affecting the operation stability and efficiency of the entire system. SUMMARY

[0003] The present application provides a source network load storage data aggregation processing method, system, device and medium to solve the problems of data transmission conflict caused by shared bandwidth and lack of dynamic adjustment mechanism in the existing bandwidth allocation.

[0004] In a first aspect, the present application provides a source network load storage data aggregation processing method, which comprises: dividing the network bandwidth of the physical layer and the communication layer into a plurality of dedicated channels; configuring the type and priority of each dedicated channel; determining the dedicated channel corresponding to the physical device based on the type of the physical device in the physical layer; The physical device transmits device data to the communication layer through the corresponding dedicated channel and the device communication protocol; in the transmission process, the real-time bandwidth of the dedicated channel is dynamically adjusted according to the priority of the dedicated channel and the hop count of the edge computing node corresponding to the dedicated channel in the data aggregation layer; The communication layer transmits the device data to the data aggregation layer through an Internet communication protocol; the data aggregation layer pushes the device data to the real-time edge computing node in a streaming manner and stores the device data to an offline analysis platform according to a preset processing logic, and uploads the processing result returned by the edge computing node to the application layer; The application layer issues an operation instruction to the data aggregation layer, the data aggregation layer stores the operation instruction, and issues the operation instruction to the communication layer, and adjusts the bandwidth of the special channel again according to the pre-stored operation instruction and the special channel bandwidth allocation strategy of the communication layer; and issues the operation instruction to the physical layer; The physical layer executes the operation instruction.

[0005] In an implementation manner of the present application, the network bandwidth of the physical layer and the communication layer is divided into a plurality of special channels, specifically including: The network bandwidth of the physical layer and the communication layer is divided into a plurality of special channels by using an IP small particle hard slicing technology.

[0006] In an implementation manner of the present application, in the transmission process, the real-time bandwidth of the special channel is dynamically adjusted according to the priority of the special channel and the hop number of the corresponding edge computing node in the data aggregation layer, specifically including: The real-time bandwidth of the i-th special channel is calculated by the formula: Wherein, Total bandwidth, The priority weight corresponding to the priority of the i-th special channel; The attenuation coefficient, The hop number of the corresponding edge computing node in the data aggregation layer, N represents the total number of special channels, The priority weight corresponding to the priority of the j-th special channel.

[0007] In an implementation manner of the present application, the device data includes device ID code, collection time, and collection data. Before the data aggregation layer pushes the device data to the real-time edge computing node in a streaming manner and stores the device data to an offline analysis platform according to a preset processing logic, the method further includes: The data aggregation layer aligns the device data from a plurality of physical devices by using a sliding time window interpolation algorithm according to the collection time of the device data.

[0008] In an implementation manner of the present application, the communication layer transmits the device data to the data aggregation layer through an Internet communication protocol, specifically including: ​​The IP small-particle hard slicing technology is adopted to divide the network bandwidth of the communication layer and the data aggregation layer into a plurality of dedicated channels, and the dedicated channels between the communication layer and the data aggregation layer and the dedicated channels between the physical layer and the communication layer are configured with one-to-one correspondence.

[0009] In a second aspect, the application provides a source-network-load data aggregation processing system, which comprises: A channel configuration module is configured to divide the network bandwidth of the physical layer and the communication layer into a plurality of dedicated channels, configure the type and priority of each dedicated channel, and determine the dedicated channel corresponding to the physical device based on the type of the physical device in the physical layer. The physical layer is configured to transmit the device data of the physical device to the communication layer through the corresponding dedicated channel and the device communication protocol, and dynamically adjust the real-time bandwidth of the dedicated channel according to the priority of the dedicated channel and the hop number of the corresponding edge computing node in the data aggregation layer during the transmission process. The communication layer is configured to transmit the device data to the data aggregation layer through the Internet communication protocol. The data aggregation layer is configured to push the device data to the real-time edge computing node in a streaming manner and store it to the offline analysis platform according to the preset processing logic, and upload the processing result returned by the edge computing node to the application layer. The application layer is configured to issue operation instructions to the data aggregation layer, and the data aggregation layer stores the operation instructions and issues the operation instructions to the communication layer. The communication layer is further configured to adjust the bandwidth of the dedicated channel again according to the pre-stored operation instructions and the dedicated channel bandwidth allocation strategy, and issue the operation instructions to the physical layer; each physical device of the physical layer executes the operation instructions.

[0010] In an implementation manner of the application, the channel configuration module comprises a channel configuration unit, which is configured to divide the network bandwidth of the physical layer and the communication layer into a plurality of dedicated channels by using the IP small-particle hard slicing technology.

[0011] In an implementation manner of the application, the physical layer comprises an adjustment unit, which is configured to calculate the real-time bandwidth of the ith dedicated channel by using the formula: wherein, B represents the total bandwidth, wi represents the priority weight corresponding to the priority of the ith dedicated channel; α represents the attenuation coefficient, N represents the hop number of the corresponding edge computing node in the data aggregation layer, and N represents the total number of dedicated channels, wj represents the priority weight corresponding to the priority of the jth dedicated channel.​​

[0012] In a third aspect, the present application provides a source-network-load-storage data aggregation processing device, the device comprising: a processor; and a memory having stored thereon executable code that, when executed, causes the processor to perform any of the source-network-load-storage data aggregation processing methods described above.

[0013] In a fourth aspect, the present application provides a non-volatile computer storage medium having stored thereon computer instructions that, when executed, implement any of the source-network-load-storage data aggregation processing methods described above.

[0014] From the above technical solutions, the present application has the following advantages: By dividing the network bandwidth into several dedicated channels and configuring independent types and priorities for each channel, physical isolation transmission of critical device data is achieved. Real-time data such as load regulation instructions can exclusively occupy high-priority channels, eliminating transmission conflicts with non-critical data and ensuring low-latency characteristics of instruction feedback data from a physical layer. At the same time, the fixed allocation mechanism of dedicated channels avoids resource contention under traditional shared bandwidth, enabling physical devices to transmit data stably through pre-matched channels and improving the determinism and reliability of data transmission.

[0015] The dynamic bandwidth adjustment mechanism based on dedicated channel priority and edge computing node hop count enables real-time optimization of network resources according to actual working conditions. When high-priority data volume surges or edge node hop count increases, the system automatically increases the bandwidth proportion of the corresponding channel to ensure the transmission efficiency of critical data. The linkage between operation instruction issuance and bandwidth strategy forms a closed-loop control, ensuring that bandwidth allocation always matches device operating conditions. This dynamic adaptation capability not only improves bandwidth utilization but also enhances the system's adaptability to complex working conditions through coordinated response of the communication layer-data aggregation layer-application layer, thereby improving the overall operational stability of the source-network-load-storage system. BRIEF DESCRIPTION OF DRAWINGS

[0016] In order to more clearly illustrate the technical solutions of the present application, the following will briefly introduce the drawings needed to be used in the description. Obviously, the drawings in the following description are only some embodiments of the present application, and other drawings can be obtained by those skilled in the art without creative labor.

[0017] Figure 1 is a source-network-load-storage data aggregation processing method flowchart provided by an embodiment of the present application.

[0018] Figure 2is a schematic diagram of an internal structure of a source network load data aggregation processing system provided by an embodiment of the present application.

[0019] Figure 3 is a schematic diagram of an internal structure of a source network load data aggregation processing device provided by an embodiment of the present application. DETAILED DESCRIPTION

[0020] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative efforts fall within the protection scope of the present application.

[0021] Those skilled in the art should understand that the embodiments described below are only preferred embodiments of the present disclosure, and do not represent the only way to implement the present disclosure. The preferred embodiments are only used to explain the technical principles of the present disclosure, and are not used to limit the protection scope of the present disclosure. Based on the preferred embodiments provided in the present disclosure, all other embodiments obtained by those skilled in the art without creative efforts still fall within the protection scope of the present disclosure.

[0022] It should also be noted that the terms "comprising", "including", or any other variant thereof are intended to cover non-exclusive inclusion, so that a process, method, article, or apparatus that includes a list of elements not only includes those elements, but also includes other elements not explicitly listed, or other elements inherent in such a process, method, article, or apparatus. Without more limitations, the element defined by the statement "comprising a" does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes the element.

[0023] The technical solutions provided by the embodiments of the present application will be described in detail below with reference to the drawings.

[0024] The embodiments provide a source network load data aggregation processing method, as shown in Figure 1 The method provided by the embodiments of the present application mainly includes the following steps: In step 110, the network bandwidth of the physical layer and the communication layer is divided into a plurality of dedicated channels; the type and priority of each dedicated channel are configured; and the dedicated channel corresponding to the physical device is determined based on the type of the physical device in the physical layer.

[0025] In some embodiments, the network bandwidth of the physical layer and the communication layer is divided into a plurality of dedicated channels, specifically including: The network bandwidth of the physical layer and the communication layer is divided into several dedicated channels by using the IP small-particle hard slice technology.

[0026] Based on the above description, the configuration of the dedicated channel involved in this step can clearly distinguish different types and priorities of network traffic, ensuring that high-priority services (such as real-time communication or critical data transmission) obtain stable bandwidth resources, avoiding delay or packet loss problems caused by network congestion. Secondly, the type of physical layer device is directly bound to the dedicated channel, simplifying the network management process and reducing the configuration complexity caused by dynamic allocation of resources, especially suitable for multi-device heterogeneous network environment. In addition, the hard slice technology reduces the interference risk between different service streams through physical isolation channels, improving the reliability and predictability of the overall network. This division method does not introduce additional performance loss, but optimizes based on existing hardware resources, so there is no need to rely on specific device upgrades in actual deployment, and the compatibility is strong.

[0027] Step 120, the physical device transmits device data to the communication layer through the corresponding dedicated channel and device communication protocol; during transmission, the real-time bandwidth of the dedicated channel is dynamically adjusted according to the priority of the dedicated channel and the number of hops of the corresponding edge computing node of the dedicated channel in the data aggregation layer.

[0028] During transmission, the real-time bandwidth of the dedicated channel is dynamically adjusted according to the priority of the dedicated channel and the number of hops of the corresponding edge computing node of the dedicated channel in the data aggregation layer, specifically including: The real-time bandwidth of the i-th dedicated channel is calculated by the formula: Wherein, Total bandwidth, Priority weight corresponding to the priority of the i-th dedicated channel; Decay coefficient, Number of hops of the corresponding edge computing node in the data aggregation layer, N represents the total number of dedicated channels, Priority weight corresponding to the priority of the j-th dedicated channel.

[0029] ​​Based on the above description, the bandwidth dynamic allocation mechanism based on priority and hop count ensures that high-priority services (such as real-time control or critical monitoring data) can obtain more sufficient bandwidth resources according to the weight, thereby reducing transmission delay and improving the timeliness of critical data. Secondly, by introducing the attenuation coefficient and hop count calculation, the system can automatically adapt to network topology changes, such as hierarchical expansion of edge computing nodes or link state fluctuations, avoiding resource waste or congestion caused by fixed bandwidth allocation. In addition, the formulaic bandwidth adjustment method provides a quantifiable configuration basis for network management, simplifying the need for manual intervention, especially suitable for complex scenarios with multiple edge node collaboration. This dynamic adjustment mechanism does not rely on the addition of new hardware or protocol modification, but optimizes the existing bandwidth allocation logic to improve performance, thus having strong compatibility with existing network architecture and not introducing additional communication overhead.

[0030] Step 130, the communication layer transmits device data to the data aggregation layer through an Internet communication protocol; the data aggregation layer pushes device data to the real-time edge computing node in a streaming manner and stores it to the offline analysis platform according to a preset processing logic, and uploads the processing result returned by the edge computing node to the application layer.

[0031] Among them, the communication layer transmits device data to the data aggregation layer through an Internet communication protocol, specifically including: An IP small particle hard slicing technology is adopted to divide the network bandwidth of the communication layer and the data aggregation layer into a plurality of dedicated channels, and the dedicated channels between the communication layer and the data aggregation layer and the dedicated channels between the physical layer and the communication layer are configured with one-to-one correspondence.

[0032] It should be noted that the device data includes device ID code, collection time, and collection data. Before the data aggregation layer pushes device data to the real-time edge computing node in a streaming manner and stores it to the offline analysis platform according to a preset processing logic, the method further includes: The data aggregation layer aligns device data from a plurality of physical devices according to the collection time of the device data through a sliding time window interpolation algorithm.

[0033] Based on the above description, the correspondence of the dedicated channel in this step ensures that the transmission path of the physical layer device data is completely consistent with the path from the communication layer to the data aggregation layer, avoiding the re-routing or protocol conversion of the data stream during cross-layer transmission, thereby reducing the transmission delay and packet loss risk. Secondly, the processing mode of streaming to the real-time edge computing node enables device data to be quickly processed and fed back, especially suitable for application scenarios with high real-time requirements (such as industrial control or intelligent monitoring), while the offline storage mechanism provides complete historical records for subsequent data analysis. In addition, the hard isolation feature of the dedicated channel avoids bandwidth contention between different service streams, ensuring the transmission stability of critical data, and the preset of layered processing logic (real-time calculation and offline storage) further simplifies the system architecture, reducing the complexity brought by dynamic decision-making. This design does not introduce new communication protocols or hardware modifications, but optimizes existing bandwidth resources and data processing procedures to improve overall efficiency, so it has strong compatibility with existing networks and does not introduce additional management overhead.

[0034] Step 140, the application layer issues operation instructions to the data aggregation layer, the data aggregation layer stores the operation instructions, and issues the operation instructions to the communication layer, according to the pre-stored operation instructions and dedicated channel bandwidth allocation strategy of the communication layer, adjusts the dedicated channel bandwidth again; at the same time, the operation instructions are issued to the physical layer; the physical layer executes the operation instructions.

[0035] Based on the above description, the step-by-step storage and forwarding of operation instructions in the application layer, data aggregation layer and communication layer ensures the integrity and traceability of instruction transmission, and the linkage of pre-stored instructions and dedicated channel bandwidth allocation strategy enables network resources to be adjusted again according to actual needs, such as automatically increasing the bandwidth priority of the instruction transmission channel during device-intensive operation periods. Secondly, the physical layer devices directly execute the operation instructions from the data aggregation layer, avoiding the delay or protocol conversion overhead that may be introduced by traditional multi-layer forwarding, especially suitable for scenarios that require fast response (such as remote control or parameter adjustment). In addition, the re-adjustment mechanism of the dedicated channel bandwidth cooperates with the physical device operation, for example, when a critical device executes a high-priority instruction, the system can automatically allocate more bandwidth resources to it, thereby reducing the risk of instruction transmission interruption or timeout. This design does not rely on complex algorithms or add new hardware, but optimizes the correlation between existing instruction flow and bandwidth management to improve system flexibility, so it has less impact on existing architecture and does not introduce additional communication load.

[0036] Based on the foregoing description, the embodiment realizes physical isolation transmission of key device data by dividing the network bandwidth into several dedicated channels and configuring independent types and priorities for each channel. The data with high real-time requirements such as load regulation instructions can exclusively occupy high-priority channels, eliminating transmission conflicts with non-key data and ensuring low-delay characteristics of instruction feedback data from the physical layer. At the same time, the fixed allocation mechanism of the dedicated channel avoids resource contention under the traditional shared bandwidth, enabling physical devices to stably transmit data through pre-matched channels and improving the determinacy and reliability of data transmission.

[0037] The dynamic bandwidth adjustment mechanism based on the priority of the dedicated channel and the hop count of the edge computing node enables the network resources to be optimized in real time according to the actual working conditions. When the amount of high-priority data increases or the hop count of the edge node increases, the system automatically increases the bandwidth proportion of the corresponding channel to ensure the transmission efficiency of key data. The linkage between the issuance of operation instructions and the bandwidth strategy forms a closed-loop control, ensuring that the bandwidth allocation always matches the device running state. This dynamic adaptation capability not only improves the bandwidth utilization rate, but also enhances the system's adaptability to complex working conditions through the coordinated response of the communication layer-data aggregation layer-application layer, thereby improving the overall operation stability of the source network load storage system.

[0038] In addition, the application Figure 2 A source network load storage data aggregation processing system is provided in the embodiment of the application. As shown in Figure 2 The system provided by the embodiment of the application mainly comprises: The channel configuration module 210 is configured to divide the network bandwidth of the physical layer 220 and the communication layer 230 into several dedicated channels, configure the types and priorities of the dedicated channels, and determine the dedicated channel corresponding to the physical device based on the type of the physical device in the physical layer 220.

[0039] The channel configuration module 210 comprises a channel configuration unit, configured to divide the network bandwidth of the physical layer 220 and the communication layer 230 into several dedicated channels by using the IP small particle hard slicing technology.

[0040] The physical layer 220 is configured to transmit the device data of the physical device to the communication layer 230 through the corresponding dedicated channel and the device communication protocol, and dynamically adjust the real-time bandwidth of the dedicated channel according to the priority of the dedicated channel and the hop count of the edge computing node corresponding to the dedicated channel in the data aggregation layer 240 during the transmission process.

[0041] The physical layer 220 comprises an adjustment unit, configured to calculate the real-time bandwidth of the ith dedicated channel by using the formula: wherein,​​ represents the total bandwidth, represents the priority weight corresponding to the priority of the i-th dedicated channel; represents the attenuation coefficient, represents the hop number of the corresponding edge computing node in the data aggregation layer 240, and N represents the total number of dedicated channels, represents the priority weight corresponding to the priority of the j-th dedicated channel.

[0042] The communication layer 230 is configured to transmit the device data to the data aggregation layer 240 through an Internet communication protocol.

[0043] The data aggregation layer 240 is configured to push the device data to the real-time edge computing node in a streaming manner and store the device data to the offline analysis platform according to a preset processing logic, and upload the processing result returned by the edge computing node to the application layer 250.

[0044] The application layer 250 is configured to issue an operation instruction to the data aggregation layer 240, the data aggregation layer 240 stores the operation instruction, and issues the operation instruction to the communication layer 230.

[0045] The communication layer 230 is further configured to pre-store the operation instruction and a dedicated channel bandwidth allocation strategy, and adjust the dedicated channel bandwidth again; meanwhile, the operation instruction is issued to the physical layer 220; and the physical layer 220 executes the operation instruction.

[0046] The above is the method embodiment in the present application. Based on the same inventive concept, the present application embodiment also provides a source network and cargo storage data aggregation processing device. As shown in the figure, Figure 3 The device includes a processor and a memory having executable code stored thereon, wherein when the executable code is executed, the processor executes a source network and cargo storage data aggregation processing method as described in one of the above embodiments.

[0047] Specifically, the server end divides the network bandwidth of the physical layer and the communication layer into several dedicated channels; configures the type and priority of each dedicated channel; determines the dedicated channel corresponding to the physical device based on the type of the physical device in the physical layer; the physical device transmits device data to the communication layer through the corresponding dedicated channel and the device communication protocol; in the transmission process, the real-time bandwidth of the dedicated channel is dynamically adjusted according to the priority of the dedicated channel and the hop number of the edge computing node corresponding to the dedicated channel in the data aggregation layer; the communication layer transmits the device data to the data aggregation layer through the Internet communication protocol; the data aggregation layer pushes the device data to the real-time edge computing node in a streaming manner and stores it to the offline analysis platform according to the preset processing logic, and uploads the processing result returned by the edge computing node to the application layer; the application layer issues an operation instruction to the data aggregation layer, the data aggregation layer stores the operation instruction, and issues the operation instruction to the communication layer, and adjusts the bandwidth of the dedicated channel again according to the pre-stored operation instruction and dedicated channel bandwidth allocation strategy of the communication layer; at the same time, the operation instruction is issued to the physical layer; the physical devices of the physical layer execute the operation instruction.

[0048] In addition, the embodiment of the present application further provides a non-volatile computer storage medium, which stores executable instructions, and when the executable instructions are executed, a source network load data aggregation processing method as described above is realized.

[0049] The above description of disclosed embodiments enables one of ordinary skill in the art to make or use the application. Various modifications to these embodiments will be readily apparent to those of ordinary skill in the art, and the generic principles defined herein can be applied to other embodiments without departing from the spirit or scope of the application. Therefore, the present application is not to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A source network load storage data aggregation processing method, characterized in that, The method comprises: The network bandwidth of the physical layer and the communication layer is divided into a plurality of dedicated channels; the type and priority of each dedicated channel are configured; based on the type of the physical device in the physical layer, the dedicated channel corresponding to the physical device is determined; The physical device transmits device data to the communication layer through the corresponding dedicated channel and the device communication protocol; during the transmission process, the real-time bandwidth of the dedicated channel is dynamically adjusted according to the priority of the dedicated channel and the hop number of the edge computing node corresponding to the dedicated channel in the data aggregation layer; The communication layer transmits the device data to the data aggregation layer through the Internet communication protocol; the data aggregation layer pushes the device data to the real-time edge computing node in a streaming manner and stores the device data to the offline analysis platform according to the preset processing logic; and the processing result returned by the edge computing node is uploaded to the application layer; The application layer issues an operation instruction to the data aggregation layer, the data aggregation layer stores the operation instruction, and issues the operation instruction to the communication layer; according to the pre-stored operation instruction and dedicated channel bandwidth allocation strategy of the communication layer, the dedicated channel bandwidth is adjusted again; at the same time, the operation instruction is issued to the physical layer; the physical devices of the physical layer execute the operation instruction.

2. The source-network-shoal data aggregation processing method of claim 1, wherein, The network bandwidth of the physical layer and the communication layer is divided into a plurality of dedicated channels, specifically comprising: The network bandwidth of the physical layer and the communication layer is divided into a plurality of dedicated channels by using the IP small particle hard slicing technology.

3. The source-network-bearer-data aggregation processing method of claim 1, wherein, During the transmission process, the real-time bandwidth of the dedicated channel is dynamically adjusted according to the priority of the dedicated channel and the hop number of the edge computing node corresponding to the dedicated channel in the data aggregation layer, specifically comprising: Through the formula: , calculating the real-time bandwidth of the ith dedicated channel ; wherein, denotes the total bandwidth, denotes the priority weight corresponding to the priority of the ith dedicated channel; denotes the attenuation coefficient, denotes the hop number of the corresponding edge computing node in the data aggregation layer, and N denotes the total number of dedicated channels, denotes the priority weight corresponding to the priority of the jth dedicated channel.

4. The source-network-shoal data aggregation processing method of claim 1, wherein, The device data includes device ID code, collection time and collection data; Before the device data is pushed to the real-time edge computing node in a streaming manner and stored to the offline analysis platform according to the preset processing logic in the data aggregation layer, the method further comprises: The data aggregation layer aligns the device data from a plurality of physical devices by using the sliding time window interpolation algorithm according to the collection time of the device data.

5. The source-network-bearer-data aggregation processing method of claim 1, wherein, The communication layer transmits the device data to the data aggregation layer through the Internet communication protocol, specifically comprising: The network bandwidth of the communication layer and the data aggregation layer is divided into a plurality of dedicated channels by using the IP small particle hard slicing technology, and the dedicated channels between the communication layer and the data aggregation layer and the dedicated channels between the physical layer and the communication layer are configured with one-to-one correspondence.

6. A source network repository data aggregation processing system, comprising: The system comprises: A channel configuration module is configured to divide the network bandwidth of the physical layer and the communication layer into a plurality of dedicated channels; configure the type and priority of each dedicated channel; and determine the dedicated channel corresponding to the physical device based on the type of the physical device in the physical layer; The physical layer is configured to transmit device data of the physical device to the communication layer through the corresponding dedicated channel and the device communication protocol; during the transmission process, the real-time bandwidth of the dedicated channel is dynamically adjusted according to the priority of the dedicated channel and the hop number of the edge computing node corresponding to the dedicated channel in the data aggregation layer; The communication layer is configured to transmit the device data to the data aggregation layer through the Internet communication protocol; The data aggregation layer is configured to push the device data to the real-time edge computing node and store the device data to the offline analysis platform according to preset processing logic, and upload the processing result returned by the edge computing node to the application layer. The application layer is configured to send an operation instruction to the data aggregation layer, and the data aggregation layer stores the operation instruction and sends the operation instruction to the communication layer. The communication layer is further configured to adjust the bandwidth of the special channel according to the pre-stored operation instruction and the special channel bandwidth allocation strategy, and send the operation instruction to the physical layer.

7. The source network repository function data aggregation handling system of claim 6, wherein, The channel configuration module includes a channel configuration unit, The physical layer and the communication layer are divided into a plurality of special channels by using an IP small granule hard slice technology.

8. The source network repository function data aggregation handling system of claim 6, wherein, The physical layer includes an adjustment unit, The device includes: , calculating the real-time bandwidth of the ith dedicated channel ; wherein, denotes the total bandwidth, denotes the priority weight corresponding to the priority of the ith dedicated channel; denotes the attenuation coefficient, denotes the hop number of the corresponding edge computing node in the data aggregation layer, and N denotes the total number of dedicated channels, denotes the priority weight corresponding to the priority of the jth dedicated channel.

9. A source network repository function data aggregation processing device, comprising: a processor; and a memory having executable code stored thereon, which, when executed, causes the processor to perform the source network load data aggregation processing method of any one of claims 1-5. having computer instructions stored thereon, which, when executed, implement the source network load data aggregation processing method of any one of claims 1-5.

10. A non-transitory computer storage medium, comprising, ​

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