MEC edge cloud service-oriented modeling method

Abstract

The invention relates to the field of edge computing, and discloses an MEC-oriented edge cloud service modeling method, which comprises the following steps of: predicting possible service fields in a target area; deducing a service demand dimension and a predicted development life cycle of the service field to the MEC edge cloud; obtaining a demand matrix of the business field to the MEC edge cloud in combination with each business demand dimension and the predicted development life cycle thereof; corresponding importance weights are given to the business fields according to the demand matrixes of the business fields, and corresponding demand weights are given to the business demand dimensions; calculating a business demand popularity value of the MEC edge cloud in the region; constructing an MEC-oriented edge cloud service demand popularity distribution map; and carrying out MEC edge cloud node layout according to the business demand popularity distribution diagram. According to the method, the requirement of the MEC edge cloud is driven by the business, the scientificity and rationality of MEC edge cloud node planning and construction are greatly improved, the construction of an efficient and green edge computing power network is facilitated, and the method has very high practical value and generalizability.

Description

Technical Field

[0001] The present invention relates to the field of edge computing, and in particular to a method for modeling MEC edge cloud services. Background Art

[0002] This section merely provides background information related to the present disclosure and is not necessarily prior art.

[0003] With the national digital transformation and new infrastructure initiatives, new infrastructure such as 5G networks, Internet Data Centers (IDCs), Mobile Edge Computing (MEC), artificial intelligence, and the Industrial Internet have become key national strategic priorities. Leveraging data centers as a foundation, accelerating the development of a multi-faceted computing system integrating "edge computing, intelligent computing, and supercomputing," and accelerating the construction of new infrastructure for green data centers to support the development of the digital economy have become crucial tools for local governments at all levels to implement national strategies. Driven by new applications, technologies, and policies, the development of new infrastructure such as cloud computing, edge computing, and 5G is poised for significant growth.

[0004] Numerous technologies and methods exist for planning MEC edge cloud nodes, with varying effectiveness. For example, major operators primarily leverage existing fixed-line resources to achieve fixed-mobile edge convergence, fully utilizing existing content delivery network (CDN) nodes. Through centralized data center transformation, they introduce edge computing into operator gateways and devices. Their edge computing platforms can flexibly distribute traffic to different networks based on service type or demand, enabling intelligent content distribution. They leverage existing public, private, and hybrid clouds, CDNs, and core aggregation data centers for unified planning and construction. Initially, they adopt an incremental approach. As business expands, they implement unified planning for new and expanded resource pools and gradually integrate existing resource pools into unified management. With the continued development of edge computing, some operators and enterprises are planning and constructing MEC for fixed-mobile convergence. Leveraging their vast cloud and network resources and cloud-edge collaboration capabilities, they are planning and deploying MEC edge nodes based on a multi-tiered architecture encompassing provinces, cities, and counties, with the MEC platform at the core. These methods are effective in the early stages of MEC development when it is not sensitive to business needs. They can save planning and construction time, reduce construction investment, and quickly meet business needs. However, with the continuous development and changes in business needs, especially for high-demand scenarios such as high-density computing, high-traffic access, ultra-low latency, high security, and cloud-edge integration, the layout and construction of MEC nodes can no longer meet the needs, limiting the availability and performance of related applications for MEC edge clouds, and restricting the large-scale deployment and development of MEC edge clouds. Summary of the Invention

[0005] Purpose of the invention: In view of the shortcomings of the existing technology, the present invention provides a method for modeling MEC edge cloud services.

[0006] In order to solve the above technical problems, the present invention discloses a business modeling method for MEC edge cloud, which is applied to the business-driven MEC edge cloud planning, layout and construction. The business modeling method for MEC edge cloud includes the following steps:

[0007] Step 1: predict possible business areas within a target area, which may be a province, city, county, administrative district, or other area.

[0008] Step 2: Based on the business domain obtained in step 1, derive the business demand dimensions of the business domain for the MEC edge cloud and its expected development life cycle, assign corresponding demand weights to each business demand dimension, and assign corresponding business expected development life cycle weights to the business expected development life cycle.

[0009] Step 3: Combine the business demand dimensions obtained in step 2 and their expected development lifecycles to obtain the demand matrix of the business field for MEC edge cloud.

[0010] Step 4: Assign corresponding importance weights to the business areas based on the business area demand matrix obtained in step 3.

[0011] Step 5: Based on the importance weight obtained in step 4, the business demand heat value for MEC edge cloud in the region is calculated through a weighted algorithm.

[0012] Step 6: Repeat steps 2 to 5 based on the predicted new business areas.

[0013] Step 7: Construct a MEC edge cloud business demand heat distribution map based on the business demand heat values within the region obtained in step 6.

[0014] Step 8: Layout MEC edge cloud nodes based on the regional business demand heat distribution map obtained in step 7.

[0015] Furthermore, the possible business areas within the area described in step 1 refer to business areas that have business needs for the MEC edge cloud, including business areas 1 to N, where N is the number of business areas.

[0016] Furthermore, the business demand dimensions of the possible business areas for the MEC edge cloud described in step 2 include business demand dimension 1 to business demand dimension N; the business expected development life cycle includes business expected development life cycle 1 to business expected development life cycle N, and one business area corresponds to one demand dimension and one expected development life cycle.

[0017] The business requirement dimensions include low latency, large edge computing power, large bandwidth, high security, storage capacity, cloud-edge collaboration, and edge-edge collaboration. Based on the importance of different business domains within a region to MEC edge cloud requirements, corresponding business requirement dimension weights are assigned, and business domains are assigned corresponding business requirement dimension weights. The business requirement dimension weights range from business requirement dimension weight 1 to business requirement dimension weight N, with a value range of [0, 1]. A value of 1 indicates that the business requirement dimension is the most important, and a value of 0 indicates that the business requirement dimension is the least important.

[0018] The business requirement dimension weight ranges from 1 to N, and the default initial value is 1.

[0019] The expected business lifecycle refers to the year in which the MEC edge cloud business in the business field is expected to be launched. The expected MEC edge cloud business lifecycle weight of the corresponding business field is assigned according to the difference between the year in which the MEC edge cloud business is expected to be launched and the year in which the MEC edge cloud business is modeled; the expected business lifecycle weight includes expected business lifecycle weight 1 to expected business lifecycle weight N, and the value range is [0, 1].

[0020] The greater the difference between the year in which the MEC edge cloud service is expected to be launched and the year in which the MEC edge cloud service is modeled, the smaller the weight value of the expected service life cycle.

[0021] The expected business lifecycle weight ranges from 1 to N, with the default initial value being 1.

[0022] Furthermore, the MEC edge cloud demand matrix described in step 3 includes the delay sensitivity dimension and the large bandwidth and edge large computing power sensitivity dimension.

[0023] The delay sensitivity dimension is determined based on the weight of the low-latency business demand dimension in the business demand dimension, including delay sensitivity dimension 1 to delay sensitivity dimension N; the large bandwidth and edge large computing power sensitivity dimension is determined based on the weight of the large bandwidth and edge large computing power demand dimensions in the business demand dimension, including large bandwidth and edge large computing power sensitivity dimension 1 to large bandwidth and edge large computing power sensitivity dimension N.

[0024] The higher the weight of the low-latency business demand dimension in the business demand dimension, the more sensitive the business field is to latency, and the larger the latency sensitivity dimension value is;

[0025] The higher the product of the weights of the large bandwidth and edge large computing power demand dimensions in the business demand dimension, the more sensitive the business field is to large bandwidth and edge large computing power, and the larger the sensitivity dimension value of large bandwidth and edge large computing power.

[0026] Furthermore, step 4 includes:

[0027] Step 4-1: Calculate the importance weight of the business area based on the demand matrix. The importance weight of the business area is the product of the delay sensitivity dimension and the large bandwidth and edge large computing power sensitivity dimension; the delay sensitivity dimension of the business is equal to the delay demand dimension weight of the business, and the large bandwidth and edge large computing power sensitivity dimension of the business is equal to the product of the large bandwidth and edge large computing power demand dimension weights of the business.

[0028] Step 4-2: Repeat step 4-1 to obtain the importance weights of business areas 1 to N in the region.

[0029] In step 4-3, the importance weights of business areas 1 to N obtained in step 4-2 are normalized to obtain normalized importance weights of business areas 1 to N.

[0030] Furthermore, the normalization of the importance weights of the business areas in step 4-3 includes:

[0031] Step 4-3-1: sum up the importance weights of business areas 1 to N in the region to obtain the total importance weight value in the region.

[0032] In step 4-3-2, the importance weights of business areas 1 to N are divided by the total importance weight value to calculate the corresponding ratios.

[0033] Step 4-3-3: reassign the calculated corresponding ratios as the importance weights of business areas 1 to N.

[0034] Furthermore, the business demand heat value for MEC edge cloud in the region calculated by the weighted algorithm in step 5 includes:

[0035] Step 5-1: Predict the number of potential users in business areas 1 to N within the region and the expected life cycle of the business.

[0036] Step 5-2: assign corresponding business expected development life cycle weights from 1 to N based on the business expected development life cycle.

[0037] Step 5-3: Calculate the service demand heat value for MEC edge cloud in the region based on the data obtained in steps 5-1 and 5-2.

[0038] Furthermore, the calculation of the service demand heat value for MEC edge cloud in the region in step 5-4 includes:

[0039] Step 5-4-1, calculate the business demand heat value of a single business field in the region. The business demand heat value of a single business field is equal to the product of the importance weight of the business field, the expected life cycle weight of the corresponding business, and the number of potential users of the corresponding business field.

[0040] Step 5-4-2: Calculate the total business demand heat value in the region, and add up the business demand heat values from business area 1 to business area N in the region.

[0041] Step 5-4-3: Modify the coefficient of business demand heat value based on regional importance.

[0042] In step 6, the map of all regions involved in the MEC edge cloud business planning is used as the base. According to the business demand heat value of each region for the MEC edge cloud calculated in the map base, different heat value colors are assigned to each region to construct the heat distribution map of the MEC edge cloud business demand for all regions in the MEC edge cloud business planning.

[0043] Beneficial Effects: The MEC edge cloud business modeling method of the present invention can significantly improve the scientificity and rationality of MEC edge cloud node planning and layout, improve the satisfaction rate of business needs, and build an efficient and green edge computing network. This method has the advantages of being simple to implement, easy to implement, highly efficient, having excellent overall planning and layout effects, and achieving business-driven MEC on-demand deployment and cloud-edge collaborative balance. It effectively promotes the planning and implementation of distributed MEC edge computing networks and solves the problem that MEC edge node planning and deployment cannot meet business needs.

[0044] Compared with the existing technology, it has the following advantages and effects:

[0045] The MEC edge cloud business modeling method of the present invention adopts a weighted algorithm based on the importance weight of the business field and the weight of the business demand dimension to construct a business demand heat distribution map, which is used to guide the planning, layout and deployment of MEC edge nodes, and has good effect.

[0046] This invention adopts a business modeling method to ensure the scientific and reasonable planning and deployment of MEC edge nodes, while greatly improving the business demand satisfaction rate, which is conducive to building an efficient and green distributed edge computing network.

[0047] The present invention makes full use of existing IP metropolitan area networks and bearer network node computer rooms, data center computer rooms, metropolitan area networks and other resources, facilitating the rapid and low-cost construction of cloud-network integration, cloud-edge collaboration, and edge multi-point collaboration systems, revitalizing existing computer rooms, networks, ports and other resources, reducing costs and increasing efficiency.

[0048] The present invention can carry out the overall planning and layout of the MEC edge computing network based on the demand heat distribution map driven by business. According to the business development life cycle, a deployment implementation roadmap of MEC edge cloud nodes can be formulated. Combined with the construction investment plan, the construction and implementation of MEC edge cloud nodes can be carried out in accordance with the principle of unified planning and step-by-step implementation, bringing better economic, social and ecological benefits. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments, and the above and / or other advantages of the present invention will become more apparent.

[0050] Figure 1 A schematic diagram of a process for MEC edge cloud business modeling method provided in an embodiment of the present application Figure 1 .

[0051] Figure 2 A schematic diagram of a process for MEC edge cloud business modeling method provided in an embodiment of the present application Figure 2 .

[0052] Figure 3 This is a distribution map of MEC service demand heat values within the city provided in the embodiment of this application. DETAILED DESCRIPTION

[0053] The embodiments of the present invention will be described below with reference to the accompanying drawings.

[0054] The embodiment of the present application discloses a method for modeling MEC edge cloud business, which is applied to the business-driven MEC edge cloud planning, layout and construction.

[0055] like Figure 1 As shown, the business modeling method for MEC edge cloud includes the following steps:

[0056] Step 1: Predict possible business areas within the target area.

[0057] Step 2: Based on the business domain obtained in step 1, derive the business demand dimensions of the business domain for the MEC edge cloud and its expected development life cycle, assign corresponding demand weights to each business demand dimension, and assign corresponding business expected development life cycle weights to the business expected development life cycle.

[0058] Step 3: Combine the business demand dimensions obtained in step 2 and their expected development lifecycles to obtain the demand matrix of the business field for MEC edge cloud.

[0059] Step 4: Assign corresponding importance weights to the business areas based on the business area demand matrix obtained in step 3.

[0060] Step 5: Based on the importance weight obtained in step 4, the business demand heat value for MEC edge cloud in the region is calculated through a weighted algorithm.

[0061] Step 6: Repeat steps 2 to 5 based on the predicted new business areas.

[0062] Step 7: Construct a MEC edge cloud business demand heat distribution map based on the business demand heat values within the region obtained in step 6.

[0063] Step 8: Layout MEC edge cloud nodes based on the regional business demand heat distribution map obtained in step 7.

[0064] Specifically, such as Figure 2 As shown, the possible business areas within the area described in step 1 include business areas 1 to N. In this embodiment, typical business areas include government agencies, tertiary hospitals, key enterprises, colleges and universities, large venues, scenic spots, and streets.

[0065] The business demand dimensions of the possible business areas for the MEC edge cloud described in step 2 include business demand dimension 1 to business demand dimension N; the business expected development life cycle includes business expected development life cycle 1 to business expected development life cycle N. In this embodiment, typical business expected development life cycles range from one year, two years, three years, five years, and ten years.

[0066] The business demand dimensions mainly refer to low latency, large edge computing power, large bandwidth, high security, storage capacity, cloud-edge collaboration, and edge-edge collaboration demand dimensions, and corresponding business demand dimension weights are assigned according to different business demand dimensions.

[0067] The expected business life cycle mainly refers to the year in which the MEC edge cloud business in the business field is expected to be launched. The weight of the MEC edge cloud business expected to be launched in the corresponding business field is assigned according to the difference between the year in which the MEC edge cloud business is expected to be launched and the year in which the MEC edge cloud business is modeled; the weight of the business expected life cycle includes a weight of 1 to a weight of N. In this embodiment, the typical business expected life cycle is within five years, and its weight is assigned to 1, and other life cycles are assigned linearly.

[0068] Furthermore, the MEC edge cloud demand matrix described in step 3 includes a delay sensitivity dimension and a large bandwidth and edge large computing power sensitivity dimension; the delay sensitivity dimension is mainly determined based on the weight of the service delay demand dimension, including delay sensitivity dimension 1 to delay sensitivity dimension N; the large bandwidth and edge large computing power sensitivity dimension is mainly determined based on the weight of the service large bandwidth and edge large computing power demand dimension, including large bandwidth and edge large computing power sensitivity dimension 1 to large bandwidth and edge large computing power sensitivity dimension N; in this embodiment, the MEC edge cloud service demand matrix is constructed with the delay sensitivity dimension as the horizontal axis and the large bandwidth and edge large computing power sensitivity dimension as the vertical axis, and the focus of the horizontal axis and the vertical axis constitutes the importance node of the business field.

[0069] Furthermore, step 4 includes:

[0070] Step 4-1: Calculate the importance weight of the business area based on the demand matrix. The importance weight of the business area is the product of the delay sensitivity dimension and the large bandwidth and edge large computing power sensitivity dimension; the delay sensitivity dimension of the business is equal to the delay demand dimension weight of the business, and the large bandwidth and edge large computing power sensitivity dimension of the business is equal to the product of the large bandwidth and edge large computing power demand dimension weights of the business.

[0071] Step 4-2: Repeat step 4-1 to obtain the importance weights of business areas 1 to N in the region.

[0072] In step 4-3, the importance weights of business areas 1 to N obtained in step 4-2 are normalized to obtain normalized importance weights of business areas 1 to N.

[0073] Furthermore, the business demand heat value for MEC edge cloud in the region calculated by the weighted algorithm in step 5 includes:

[0074] Step 5-1: predict the number of potential users in business areas 1 to N within the region.

[0075] Step 5-2: predict the expected life cycle of the businesses in business areas 1 to N within the region.

[0076] Step 5-3: assign corresponding business expected development life cycle weights from 1 to N based on the business expected development life cycle.

[0077] Step 5-4: Calculate the business demand heat value for MEC edge cloud in the region.

[0078] Furthermore, the calculation of the service demand heat value for MEC edge cloud in the region in step 5-4 includes:

[0079] Step 5-4-1, calculate the business demand heat value of a single business field in the region. The business demand heat value of a single business field is equal to the product of the importance weight of the business field, the expected life cycle weight of the corresponding business, and the number of potential users of the corresponding business field.

[0080] Step 5-4-2: Calculate the total business demand heat value in the region, and add up the business demand heat values from business area 1 to business area N in the region.

[0081] Step 5-4-3: Modify the business demand heat value by coefficient according to the regional importance. In this embodiment, the typical coefficient modification index is the regional GDP index.

[0082] In this embodiment, the business areas within the region are subdivided into government agencies, tertiary hospitals, key enterprises, colleges and universities, large venues, scenic spots, streets, etc. The business demand matrix and business demand heat value model of the MEC edge cloud for the business areas are constructed step by step as shown in Table 1 below.

[0083]

[0084] Demand heat value RV of XX business field = ∑RVn

[0085] =∑Mbn*number of business locations +∑Bwn*number of business locations +∑Dtn*number of business locations +∑San*number of business locations +∑Tin*number of business locations +…

[0086] The total business demand heat value in the region = ∑XX business area demand heat value RV.

[0087] In step 6, a city map is used as a base, and different heat value colors are assigned to each area according to the business demand heat value of each administrative area for MEC edge cloud calculated in the city.

[0088] Step 7: Based on step 6, construct a heat distribution map of the city's MEC edge cloud business demand according to the regional business demand heat value and the assigned color. Draw the distribution map of the MEC business demand heat value in the city (the values in the figure are the heat values of each administrative district in the region) as follows Figure 3 shown.

[0089] Step 8: Layout MEC edge cloud nodes based on the city-wide business demand heat distribution map obtained in step 7.

[0090] The present invention provides a method for modeling services for MEC edge cloud services. There are many methods and approaches for implementing this technical solution. The above is only a specific embodiment of the present invention. It should be noted that those skilled in the art can make several improvements and modifications without departing from the principles of the present invention. These improvements and modifications should also be considered within the scope of protection of the present invention. Any components not specified in this embodiment can be implemented using existing technologies.

Claims

1. A method for modeling MEC edge cloud services, characterized in that: The following steps are involved: Step 1: predict possible business areas within the target area; Step 2: Based on the business domain obtained in step 1, derive the business demand dimensions and expected development lifecycle of the business domain for the MEC edge cloud, assign corresponding demand weights to each business demand dimension, and assign corresponding expected development lifecycle weights to the business development lifecycle; Step 3: Combine the business demand dimensions obtained in step 2 and their expected development lifecycles to obtain the demand matrix of the business field for MEC edge cloud; Step 4: assign corresponding importance weights to the business areas according to the business area demand matrix obtained in step 3; Step 5: Based on the importance weights obtained in step 4, the service demand heat value for the MEC edge cloud in the region is calculated through a weighted algorithm; Step 6: Repeat steps 2 to 5 based on the predicted new business areas. Step 7: Construct a MEC edge cloud service demand heat distribution map based on the service demand heat values within the region obtained in step 6. Step 8: Layout MEC edge cloud nodes based on the regional business demand heat distribution map obtained in step 7.

2. The MEC edge cloud service modeling method according to claim 1, characterized in that: The possible business areas within the area refer to business areas that have business needs for MEC edge cloud, including business areas 1 to N, where N is the number of business areas.

3. The MEC edge cloud service modeling method according to claim 2, characterized in that: The business demand dimensions mentioned in step 2 include business demand dimensions 1 to business demand dimensions N; the business expected development lifecycle includes business expected development lifecycle 1 to business expected development lifecycle N. Each business area corresponds to one demand dimension and one expected development lifecycle; The business demand dimensions include low latency, large edge computing power, large bandwidth, high security, storage capacity, cloud-edge collaboration, and edge-edge collaboration demand dimensions. According to different business areas in the region, the importance of their business demand dimensions to the MEC edge cloud needs varies, and corresponding business demand dimension weights are assigned to their business demand dimensions; the business demand dimension weights include business demand dimension weight 1 to business demand dimension weight N, with a value range of [0, 1]; a value of 1 indicates that the importance of the business demand dimension is the most important, and a value of 0 indicates that the importance of the business demand dimension is the least important; Initialize the business requirement dimension weight from 1 to the business requirement dimension weight N. The default initial value is 1; The expected service lifecycle refers to the year in which the MEC edge cloud service in the business field is expected to be launched. The expected service lifecycle weight of the MEC edge cloud service in the corresponding business field is assigned based on the difference between the expected service lifecycle and the year in which the MEC edge cloud service is modeled. The expected service lifecycle weight ranges from expected service lifecycle weight 1 to expected service lifecycle weight N, with a value range of [0, 1]. The greater the difference between the year in which the MEC edge cloud service is expected to be launched and the year in which the MEC edge cloud service is modeled, the smaller the weight value of the expected service life cycle; The expected business lifecycle weight ranges from 1 to N, with the default initial value being 1.

4. The MEC edge cloud service modeling method according to claim 3 is characterized in that: The MEC edge cloud demand matrix described in step 3 includes the delay sensitivity dimension and the large bandwidth and edge large computing power sensitivity dimension; The delay sensitivity dimension is determined based on the weight of the low-latency business demand dimension in the business demand dimension, including delay sensitivity dimension 1 to delay sensitivity dimension N; the large bandwidth and edge large computing power sensitivity dimension is determined based on the weight of the large bandwidth and edge large computing power demand dimensions in the business demand dimension, including large bandwidth and edge large computing power sensitivity dimension 1 to large bandwidth and edge large computing power sensitivity dimension N; The higher the weight of the low-latency business demand dimension in the business demand dimension, the more sensitive the business field is to latency, and the larger the latency sensitivity dimension value is; The higher the product of the weights of the large bandwidth and edge large computing power demand dimensions in the business demand dimension, the more sensitive the business field is to large bandwidth and edge large computing power, and the larger the sensitivity dimension value of large bandwidth and edge large computing power.

5. The MEC edge cloud service modeling method according to claim 4, characterized in that: Step 4 includes: Step 4-1: Calculate the importance weight of the service domain based on the demand matrix. The importance weight of the service domain is the product of the latency sensitivity dimension and the high bandwidth and edge computing power sensitivity dimensions. The latency sensitivity dimension of the service is equal to the latency demand dimension weight of the service. The high bandwidth and edge computing power sensitivity dimension of the service is equal to the product of the high bandwidth and edge computing power demand dimension weights of the service. Step 4-2: Repeat step 4-1 to obtain the importance weights of business areas 1 to N in the region; In step 4-3, the importance weights of business areas 1 to N obtained in step 4-2 are normalized to obtain normalized importance weights of business areas 1 to N.

6. A method for modeling MEC edge cloud services according to claim 5, characterized in that: The normalization of the importance weights of business areas in step 4-3 includes: Step 4-3-1: sum up the importance weights of business areas 1 to N in the region to obtain the total importance weight value of the region; Step 4-3-2: Divide the importance weights of business areas 1 to N by the total importance weight value to calculate the corresponding ratios; Step 4-3-3: reassign the calculated corresponding ratios as the importance weights of business areas 1 to N.

7. The MEC edge cloud service modeling method according to claim 6, characterized in that: The business demand heat value for MEC edge cloud in the region is calculated by weighted algorithm in step 5, including: Step 5-1: Forecast the number of potential users in business areas 1 to N within the region and the expected life cycle of the business; Step 5-2: assign a corresponding business expected development life cycle weight of 1 to business expected development life cycle weight N based on the business expected development life cycle; Step 5-3: Calculate the service demand heat value for MEC edge cloud in the region based on the data obtained in steps 5-1 and 5-2.

8. A method for modeling MEC edge cloud services according to claim 7, characterized in that: The service demand heat value for MEC edge cloud in the calculation area in step 5-3 includes: Step 5-4-1: Calculate the business demand heat value of a single business area in the region. The business demand heat value of a single business area is the product of the importance weight of the business area, the expected life cycle weight of the corresponding business, and the number of potential users of the corresponding business area. Step 5-4-2: Calculate the total business demand heat value in the region by accumulating the business demand heat values from business area 1 to business area N in the region; Step 5-4-3: Modify the coefficient of business demand heat value based on regional importance.

9. A method for modeling MEC edge cloud services according to claim 8, characterized in that: In step 6, the map of all regions involved in the MEC edge cloud business planning is used as the base. According to the business demand heat value of each region for the MEC edge cloud calculated in the map base, different heat value colors are assigned to each region to construct the MEC edge cloud business demand heat distribution map of all regions in the MEC edge cloud business planning.

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