A multi-layer collaborative data aggregation method for multi-division heterogeneous concurrent services

Abstract

The application relates to the technical field of electric power, and particularly discloses a multi-layer cooperative data aggregation method for multi-substation heterogeneous concurrent services, which comprises the following steps: S1, constructing a master station aggregation attribute matrix of the multi-substation heterogeneous concurrent services; S2, constructing a bandwidth allocation constraint condition; S3, constructing a bandwidth resource optimization problem based on the bandwidth allocation constraint condition, with the maximum bandwidth allocation benefit in a substation as a target; S4, solving the bandwidth resource optimization problem for each substation, and determining an expected aggregation time delay of the services; S5, when the expected aggregation time delay meets the time delay requirements of the services, outputting the master station aggregation attribute matrix; S6, when the expected aggregation time delay does not meet the time delay requirements of the services, updating the master station aggregation attribute matrix, returning to execute S2 to S5, and outputting the master station aggregation attribute matrix as a multi-layer cooperative data aggregation scheme until the expected aggregation time delays of the residual services of the master station aggregation attribute matrix all meet the respective requirements.

Description

Technical Field

[0001] This invention relates to the field of power technology, and specifically to a multi-layer collaborative data aggregation method for heterogeneous concurrent services in multiple transformer areas. Background Technology

[0002] With the advancement of new power system construction, the types of services in distribution network areas are becoming increasingly diverse, exhibiting significant heterogeneity and concurrency characteristics. For example, concurrent services may include high-frequency electricity consumption information collection, low-latency distributed energy regulation, and high-bandwidth video surveillance and inspection. The data from these services is typically aggregated through substation nodes in the distribution area and then uploaded to the cloud master station for unified analysis and processing (i.e., master station aggregation).

[0003] However, existing multi-server data aggregation solutions have the following bottlenecks when dealing with heterogeneous concurrent services: Rigid bandwidth resource allocation: Traditional methods usually use static allocation or simple polling mechanisms, which fail to dynamically allocate the limited uplink bandwidth of substation nodes according to the latency sensitivity of services. This results in high-priority services timeout due to insufficient bandwidth, while low-priority services consume too many resources.

[0004] The multi-layered collaboration strategy is too simplistic: existing technologies often rely entirely on cloud aggregation (leading to high bandwidth pressure and uncontrollable latency) or entirely on sub-site processing (leading to a lack of global data view), lacking a mechanism to dynamically switch between "main site aggregation" and "sub-site aggregation" based on real-time network status and business needs.

[0005] Multi-zone concurrent interference: In multi-zone concurrent scenarios, there is a lack of unified consideration for the aggregation latency of cross-zone services. Only the transmission within a single zone is considered, ignoring the decisive impact of the slowest link on the overall aggregation latency of the main service station.

[0006] Therefore, there is an urgent need for a multi-layer collaborative data aggregation method for heterogeneous concurrent services in multiple stations, which can ensure the timeliness requirements of critical services under bandwidth-constrained conditions through latency-driven bandwidth allocation and dynamic switching of substation / master station aggregation modes. Summary of the Invention

[0007] To address the technical challenge of ensuring latency for multi-layer collaborative data aggregation under limited uplink bandwidth constraints in multi-server concurrent scenarios with heterogeneous services (such as high-frequency data acquisition, equipment control, and video surveillance), this invention aims to provide a multi-layer collaborative data aggregation method for heterogeneous concurrent services across multiple servers. By constructing a latency-driven bandwidth allocation optimization model and a dynamic degradation mechanism, adaptive scheduling of cloud-edge resources can be achieved.

[0008] This invention provides a multi-layer collaborative data aggregation method for heterogeneous concurrent services across multiple server areas, comprising: Step S1: Construct a master station aggregation attribute matrix for heterogeneous concurrent services across multiple server zones based on the cloud-edge-device collaborative architecture; the master station aggregation attribute matrix includes: cloud master station, multiple server zones corresponding to the cloud master station, multiple services corresponding to each server zone, latency requirements and data size of each service; Step S2: Construct bandwidth allocation constraints to ensure that the total allocatable bandwidth for services within the distribution area does not exceed the uplink available bandwidth of its respective substation node, and that the bandwidth allocated to each service is greater than 0. Step S3: Based on the bandwidth allocation constraints, construct a bandwidth resource optimization problem with the objective of maximizing bandwidth allocation revenue within the distribution area; Step S4: Solve the bandwidth resource optimization problem for each distribution area to determine the expected aggregation latency of the service; the expected aggregation latency is determined based on the maximum expected master-to-subsite uplink latency in the distribution areas involved in the service. Step S5: When the expected aggregation latency meets the latency requirements of each service, output the main station aggregation attribute matrix; Step S6: When the expected aggregation latency does not meet the latency requirements of each service, remove the service with the lowest latency requirement in the current main station aggregation attribute matrix and change the service to execute the sub-station aggregation scheme. Update the main station aggregation attribute matrix and return to execute steps S2 to S5 until the expected aggregation latency of the remaining services in the main station aggregation attribute matrix meets their respective requirements. Output the main station aggregation attribute matrix as a multi-layer collaborative data aggregation scheme.

[0009] In one possible implementation, step S1 includes: The main site's aggregated attribute matrix can be represented by the following formula: in, This represents the aggregated attribute matrix of the main station for heterogeneous concurrent services across multiple server areas. This represents the set of business IDs aggregated by the main site. Indicates the first The latency requirements of each service.

[0010] In one possible implementation, step S2 includes: The bandwidth allocation constraints are expressed by the following formula: in, This indicates the first constraint condition. This indicates the second constraint condition. Indicates the area Available uplink bandwidth, Indicates the first The bandwidth allocated to each service. This represents the set of business IDs that are aggregated on the main site.

[0011] In one possible implementation, step S3 includes: The bandwidth resource optimization problem can be expressed by the following formula: in, Assign a bandwidth vector. Indicates the first The bandwidth allocated to each service. This represents the set of business IDs aggregated by the main site. Indicates the area Business The bandwidth allocation revenue of the allocated bandwidth.

[0012] In one possible implementation, the bandwidth allocation vector is represented by the following formula. : in, Indicates the area Business The allocated bandwidth, This represents the set of business IDs that are aggregated on the main site.

[0013] In one possible implementation, the transformer area is represented by the following formula. Business The bandwidth allocation benefit of the allocated bandwidth: in, Indicates the area Business The allocated bandwidth, Indicates business In Taiwan The amount of data that needs to be aggregated Indicates the first The latency requirements of each service.

[0014] In one possible implementation, step S4 includes: Calculate the expected aggregation latency using the following formula: in, Indicates business The expected aggregation latency, Represents the set of all transformer area codes. Indicates the first The business in the first The optimal bandwidth allocated to each station area Indicates the first The business in the first The amount of data that needs to be aggregated for each station area.

[0015] In one possible implementation, the expected aggregate latency is determined based on the following formula to determine whether it meets the latency requirements of each service: in, This indicates that the expected aggregation latency of the main site's aggregation services all meet their respective requirements. This indicates that the condition is not met and the next step needs to be performed. This indicates the relationship between the expected aggregation latency of the business and the required quantities for each.

[0016] In one possible implementation, the expected aggregation latency of the business and the respective quantitative requirements are expressed by the following formula. : in, Indicates the first Latency requirements for each service This represents the set of business IDs aggregated by the main site. Indicates business The expected aggregation latency.

[0017] In one possible implementation, step S6 includes: Update the main site aggregation matrix according to the following formula. : in, Indicates the first Latency requirements for each service This represents the set of business IDs aggregated by the main site. Indicates business The expected aggregation latency, This represents the set of remaining main site aggregated service numbers after removing the services with the lowest latency requirements from the current main site aggregated attribute matrix.

[0018] The multi-layer collaborative data aggregation method for heterogeneous concurrent services in multiple stations provided by this invention achieves adaptive collaborative scheduling of master station-substation resources by constructing a latency-driven bandwidth allocation optimization model and a dynamic degradation mechanism. Attached Figure Description

[0019] Figure 1This is a flowchart illustrating a multi-layer collaborative data aggregation method for heterogeneous concurrent services across multiple server areas, provided as an embodiment of the present invention. Detailed Implementation

[0020] The embodiments of the present invention will be further described in detail below with reference to the accompanying drawings and examples. The following detailed description of the embodiments and the accompanying drawings are used to illustrate the principles of the present invention by way of example, but should not be used to limit the scope of the present invention. That is, the present invention is not limited to the described preferred embodiments, and the scope of the present invention is defined by the claims.

[0021] In the description of this invention, it should be noted that, unless otherwise stated, "a plurality of" means two or more; the terms "first," "second," etc., are used for descriptive purposes only and should not be construed as indicating or implying relative importance; those skilled in the art can understand the specific meaning of the above terms in this invention as appropriate.

[0022] Figure 1 A flowchart illustrating a multi-layer collaborative data aggregation method for heterogeneous concurrent services across multiple server areas, as provided in an embodiment of the present invention, is shown below. Figure 1 As shown, this invention provides a multi-layer collaborative data aggregation method for heterogeneous concurrent services across multiple server areas, comprising: Step S1: Construct a main station aggregation attribute matrix for heterogeneous concurrent services across multiple server zones based on the cloud-edge-device collaborative architecture; The main station's aggregated attribute matrix includes: the cloud main station, the multiple zones corresponding to the cloud main station, the multiple services corresponding to each zone, and the latency requirements and data size of each service.

[0023] In one possible implementation, the main site aggregation attribute matrix is ​​represented by the following formula: in, This represents the aggregated attribute matrix of the main station for heterogeneous concurrent services across multiple server areas. This represents the set of business IDs aggregated by the main site. Indicates the first The latency requirements of different services vary, hence the term heterogeneous services.

[0024] Step S2: Construct bandwidth allocation constraints to ensure that the total allocatable bandwidth for services within the distribution area does not exceed the uplink available bandwidth of its respective substation node, and that the bandwidth allocated to each service is greater than 0. After determining business requirements, the carrying capacity of the underlying physical links must be considered. This step aims to establish hard boundaries for bandwidth allocation to prevent resource overload. Since the uplink bandwidth of the substation gateway devices in each zone is limited, if the total bandwidth allocated to services exceeds this limit, data packets will be lost or severely queued, leading to uncontrollable latency. Furthermore, bandwidth allocation cannot be zero; otherwise, services will be unable to establish connections.

[0025] If the system allocates bandwidth without restriction, it will result in: 1) Network congestion: Data packets exceeding the physical bandwidth will be queued in the router buffer, introducing unpredictable queuing delays and disrupting the QoS of latency-sensitive services.

[0026] 2) Service interruption: In extreme cases, this can lead to packet loss, causing the service transmission to fail completely.

[0027] Therefore, the following dual constraint mechanism is constructed. The bandwidth allocation constraint is expressed by the following formula: in, This represents the first constraint, indicating the bandwidth allocated to a single service. The value must be greater than 0 to ensure the feasibility of main site aggregation; this is to ensure that each service participating in main site aggregation can obtain the bandwidth resources required for the minimum transmission unit, and avoid the business stagnation caused by "zero bandwidth". The second constraint states that the total allocatable bandwidth for aggregated services of heterogeneous master stations within a distribution area must not exceed the uplink available bandwidth of its subordinate substation nodes. This simulates the traffic shaping function of the network interface to ensure that the total bandwidth request allocated to all services in the distribution area does not exceed the carrying capacity of the physical link. Indicates the area Available uplink bandwidth, Indicates the first The bandwidth allocated to each service. This represents the set of business IDs that are aggregated on the main site.

[0028] In one example, assume that the available uplink bandwidth of area A is... Given a bandwidth of 100Mbps, there are two services: Service 1 requests 30Mbps and Service 2 requests 40Mbps. The total bandwidth is 70Mbps ≤ 100Mbps, satisfying the constraint. If Service 2 requests 80Mbps, the total bandwidth becomes 110Mbps > 100Mbps, violating the constraint. The system will reject this allocation scheme or adjust it in subsequent optimizations.

[0029] Step S3: Based on the bandwidth allocation constraints, construct a bandwidth resource optimization problem with the objective of maximizing bandwidth allocation revenue within the distribution area; To address the question of "how to allocate bandwidth under limited conditions," this step proposes a latency-driven benefit quantification method. Its core idea is to shift the optimization objective of bandwidth allocation from simple "fairness" or "maximizing throughput" to "minimizing the perceived latency of critical services."

[0030] This code defines a non-linear benefit function. This function exhibits diminishing marginal returns: when the bandwidth allocated to a certain service is extremely low, increasing the bandwidth even slightly can significantly reduce latency, resulting in a high benefit; however, when bandwidth is already abundant, further increases in bandwidth have a negligible improvement on latency, resulting in a low benefit. The system will automatically allocate bandwidth to services with "high marginal benefits" (i.e., more urgent needs).

[0031] In one possible implementation, the bandwidth resource optimization problem can be expressed by the following formula: in, Assign a bandwidth vector. express The bandwidth allocated to the first service. This represents the set of business IDs aggregated by the main site. Indicates the area Business The bandwidth allocation revenue of the allocated bandwidth.

[0032] In one possible implementation, the bandwidth allocation vector is represented by the following formula. : in, Indicates the area Business The allocated bandwidth, This represents the set of business IDs that are aggregated on the main site.

[0033] In one possible implementation, the transformer area is represented by the following formula. Business The bandwidth allocation benefit of the allocated bandwidth: in, Indicates the area Business The allocated bandwidth, Indicates business In Taiwan The amount of data that needs to be aggregated Indicates the first The latency requirements of each service.

[0034] Step S4: Solve the bandwidth resource optimization problem for each distribution area and determine the expected aggregation latency of the service; The expected aggregation latency is determined based on the maximum expected uplink latency between the main station and the substation in the area involved in the business.

[0035] The final completion time of a cross-regional global service does not depend on the region with the fastest data transmission, but on the slowest link. This is like filling a wooden bucket with water; the water level is determined by the shortest stave.

[0036] In one possible implementation, the expected aggregation latency is calculated according to the following formula: in, Indicates business The expected aggregation latency, Represents the set of all transformer area codes. Indicates the first The business in the first The optimal bandwidth allocated to each station area Indicates the first The business in the first The amount of data that needs to be aggregated for each station area.

[0037] In one possible implementation, the expected aggregate latency is determined based on the following formula to see if it meets the latency requirements of each service: in, This indicates that the expected aggregation latency of the main site's aggregation services all meet their respective requirements. This indicates that the condition is not met and the next step needs to be performed. This indicates the relationship between the expected aggregation latency of the business and the required quantities for each.

[0038] In one possible implementation, the expected aggregation latency of the business and the respective quantitative requirements are expressed by the following formula. : in, Indicates the first Latency requirements for each service This represents the set of business IDs aggregated by the main site. Indicates business The expected aggregation latency.

[0039] Step S5: When the expected aggregation latency meets the latency requirements of each service, output the main site aggregation attribute matrix; Step S6: When the expected aggregation latency does not meet the latency requirements of each service, remove the service with the lowest latency requirement in the current main station aggregation attribute matrix and change the service to execute the sub-station aggregation scheme. Update the main station aggregation attribute matrix and return to execute steps S2 to S5 until the expected aggregation latency of the remaining services in the main station aggregation attribute matrix meets their respective requirements. Output the main station aggregation attribute matrix as a multi-level collaborative data aggregation scheme.

[0040] In one possible implementation, step S6 includes: Update the main site aggregation matrix according to the following formula. : in, Indicates the first Latency requirements for each service This represents the set of business IDs aggregated by the main site. Indicates business The expected aggregation latency, This indicates the removal of the current main site's aggregated attribute matrix. The set of remaining main site aggregation service numbers after the service with the lowest latency requirement. The removed services are then reassigned to sub-site aggregation schemes. This process is repeated to update the main site aggregation matrix, iterating until the expected aggregation latency of the remaining main site aggregation services meets their respective requirements.

[0041] To better understand the practical effects of this invention, a specific digital scenario is provided: Set the uplink available bandwidth for transformer area A to 100Mbps. Concurrent services include: Service 1 (Relay Protection): latency requirement 20ms, data volume 50Mb. Service 2 (Environmental Video): latency requirement 200ms, data volume 80Mb.

[0042] The system constructed a matrix and discovered that both services required uploading to the main station. Attempting to allocate bandwidth according to the default ratio revealed that the bandwidth allocated to Service 1 was insufficient for transmission within 20ms, triggering a timeout alarm. The system detected that Service 2's latency requirement of 200ms was significantly higher than Service 1's 20ms. Therefore, it was decided to remove Service 2 and instead perform sub-site storage and analysis locally in Zone A, thus no longer consuming the valuable 100Mbps uplink bandwidth. After updating the matrix, all 100Mbps bandwidth in Zone A was allocated to Service 1. The recalculated expected aggregation latency for Service 1 was 0.05ms, far below the 20ms limit. The system output the final solution: Service 1 uses main station aggregation, and Service 2 uses sub-site aggregation.

[0043] Compared with existing technologies, the multi-layer collaborative data aggregation method for heterogeneous concurrent services across multiple server areas provided by this invention has the following significant advantages: 1) Latency-driven resource optimization: By introducing an exponential revenue function, bandwidth resources are tilted towards high-time-sensitive services, maximizing the overall transmission efficiency of the system.

[0044] 2) Adaptive degradation mechanism: The proposed iterative elimination strategy (step S6) can intelligently sacrifice non-critical services (those with the lowest latency requirements) when bandwidth is extremely tight, so as to ensure the success rate of master station aggregation for critical services (such as power grid control).

[0045] 3) Global perspective consideration: The maximum value is taken when calculating the expected aggregation latency, which effectively solves the business timeout problem caused by "long tail latency" in multi-server concurrent scenarios.

[0046] 4) High engineering practicality: The method has a clear logic and can be deployed on the existing power distribution IoT business master station system without large-scale hardware transformation.

[0047] The above are merely specific embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

[0048] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. The 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 present invention. It should 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, special-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 and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 The 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 functions specified in one or more boxes. Although preferred embodiments of the invention have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of the embodiments of the invention. Finally, it should be noted that in this document, relational terms such as "first" and "second" are used only to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "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.

[0049] The methods and apparatus provided by the present invention have been described in detail above. Specific examples have been used to illustrate the principles and implementation methods of the present invention. The descriptions of the above embodiments are only for the purpose of helping to understand the methods and core ideas of the present invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of the present invention. Therefore, the content of this specification should not be construed as a limitation of the present invention.

[0050] In the description of this specification, references to terms such as "an embodiment," "some embodiments," "example," "specific example," or "a specific embodiment" or "some examples," etc., indicate that a specific feature, structure, material, or characteristic described in connection with that embodiment or example is included in at least one embodiment or example of the invention. In this specification, illustrative expressions of terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples.

[0051] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of this application, and are not intended to limit them. Although this application has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of this application.

Claims

1. A multi-layer collaborative data aggregation method for heterogeneous concurrent services across multiple server areas, characterized in that, include: Step S1: Construct a main station aggregation attribute matrix for heterogeneous concurrent services across multiple server areas based on the cloud-edge-device collaborative architecture; the main station aggregation attribute matrix uses the expected aggregation latency of each service as its primary element. Step S2: Construct bandwidth allocation constraints to ensure that the total allocatable bandwidth for services within the distribution area does not exceed the uplink available bandwidth of its respective substation node, and that the bandwidth allocated to each service is greater than 0. Step S3: Based on the bandwidth allocation constraints, construct a bandwidth resource optimization problem with the objective of maximizing bandwidth allocation revenue within the distribution area; Step S4: Solve the bandwidth resource optimization problem for each distribution area to determine the expected aggregation latency of the service; the expected aggregation latency is determined based on the maximum expected master-to-subsite uplink latency in the distribution areas involved in the service. Step S5: When the expected aggregation latency meets the latency requirements of each service, output the main station aggregation attribute matrix; Step S6: When the expected aggregation latency does not meet the latency requirements of each service, remove the service with the lowest latency requirement in the current main station aggregation attribute matrix and change the service to execute the sub-station aggregation scheme. Update the main station aggregation attribute matrix and return to execute steps S2 to S5 until the expected aggregation latency of the remaining services in the main station aggregation attribute matrix meets their respective requirements. Output the main station aggregation attribute matrix as a multi-layer collaborative data aggregation scheme.

2. The multi-layer collaborative data aggregation method according to claim 1, characterized in that, Step S1 includes: The main site's aggregated attribute matrix can be represented by the following formula: in, This represents the aggregated attribute matrix of the main station for heterogeneous concurrent services across multiple server areas. This represents the set of business IDs aggregated by the main site. Indicates the first The latency requirements of each service.

3. The multi-layer collaborative data aggregation method according to claim 1, characterized in that, Step S2 includes: The bandwidth allocation constraints are expressed by the following formula: in, This indicates the first constraint condition. This indicates the second constraint condition. Indicates the area Available uplink bandwidth, Indicates the first The bandwidth allocated to each service This represents the set of business IDs that are aggregated on the main site.

4. The multi-layer collaborative data aggregation method according to claim 1, characterized in that, Step S3 includes: The bandwidth resource optimization problem can be expressed by the following formula: in, Assign a bandwidth vector. Indicates the first The bandwidth allocated to each service This represents the set of business IDs aggregated by the main site. Indicates the area Business The revenue from bandwidth allocation.

5. The multi-layer collaborative data aggregation method according to claim 4, characterized in that, Also includes: The bandwidth allocation vector is represented by the following formula. : in, Indicates the area Business The allocated bandwidth, This represents the set of business IDs that are aggregated on the main site.

6. The multi-layer collaborative data aggregation method according to claim 4, characterized in that, Also includes: The following formula represents the transformer area. Business The bandwidth allocation benefit of the allocated bandwidth: in, Indicates the area Business The allocated bandwidth, Indicates business In Taiwan The amount of data that needs to be aggregated Indicates the first The latency requirements of each service.

7. The multi-layer collaborative data aggregation method according to claim 1, characterized in that, Step S4 includes: Calculate the expected aggregation latency using the following formula: in, Indicates business The expected aggregation latency, Represents the set of all transformer area codes. Indicates the i-th business in the i-th order. The optimal bandwidth allocated to each station area Indicates the first The business in the first The amount of data that needs to be aggregated for each station area.

8. The multi-layer collaborative data aggregation method according to claim 1, characterized in that, Also includes: Determine whether the expected aggregate latency meets the latency requirements of each service using the following formula: in, This indicates that the expected aggregation latency of the main site's aggregation services all meet their respective requirements. This indicates that the condition is not met and the next step needs to be performed. This indicates the relationship between the expected aggregation latency of the business and the required quantities for each.

9. The multi-layer collaborative data aggregation method according to claim 8, characterized in that, Also includes: The following formula represents the relationship between the expected aggregation latency of the business and the respective required quantities. : in, Indicates the first Latency requirements for each service This represents the set of business IDs aggregated by the main site. Indicates business The expected aggregation latency.

10. The multi-layer collaborative data aggregation method according to claim 1, characterized in that, Step S6 includes: Update the main site aggregation matrix according to the following formula. : in, Indicates the first Latency requirements for each service This represents the set of business IDs aggregated by the main site. Indicates business The expected aggregation latency, This represents the set of remaining main site aggregated service numbers after removing the services with the lowest latency requirements from the current main site aggregated attribute matrix.

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