Mobile network resource scheduling method based on cloud computing

Abstract

The invention discloses a mobile network resource scheduling method based on cloud computing, which relates to the technical field of information operation, and comprises the following steps: dividing service allocation areas of mobile phone operation service providers, and collecting network demand resources; constructing a service area plane graph, and carrying out resource use positioning on the user to obtain pairing behavior data; identifying the pairing behavior data, obtaining node resource type consumption, constructing a node consumption change graph according to the node resource type consumption, and performing consumption division to obtain graded consumption sections; carrying out region grading division in the service region planar graph, obtaining a feature aggregation region, constructing a service twin space, constructing a dynamic twin channel for the service region planar graph, carrying out region dynamic regulation and control on the service region planar graph according to the dynamic twin channel based on the service twin space, and obtaining a dynamic scheduling scheme; therefore, the use condition of network resources can be better known, more accurate resource allocation and dynamic regulation and control can be realized, the network performance can be improved, and the operation cost can be reduced.

Description

Technical Field

[0001] This invention relates to the field of information operation technology, specifically a mobile network resource scheduling method based on cloud computing. Background Technology

[0002] With the rapid development of the mobile internet, mobile service providers face enormous challenges in the field of information operation. To meet users' demands for high-speed, stable network services, mobile network resource scheduling has become one of the key technologies for mobile service providers. However, traditional resource allocation methods are often based on static rules and cannot be dynamically adjusted according to real-time network demand and resource conditions. This leads to uneven resource allocation, with some areas experiencing overload while others suffer from resource waste. Changes in user access volume and service types cause significant fluctuations in network load, which traditional scheduling methods struggle to handle, easily resulting in network congestion and decreased service quality.

[0003] Therefore, this invention aims to propose a cloud computing-based mobile network resource scheduling method. By monitoring network resources and user needs in real time, and dynamically allocating and scheduling resources according to scheduling strategies and algorithms, it achieves load balancing and distribution of services, meets the growing needs of users, and provides mobile phone operators with an efficient information operation solution. Summary of the Invention

[0004] The objective of this invention can be achieved through the following technical solutions: A cloud computing-based mobile network resource scheduling method includes the following steps: Step S1: Divide the service allocation areas of mobile operators and collect the network demand resources of the service allocation areas; Step S2: Construct a service area map based on the service allocation area, locate users' resource usage based on the service area map, and obtain matching behavior data; Step S3: Identify the type of the pairing behavior data, obtain the consumption of node resource types, construct a node consumption change map, set threshold boundaries for the node consumption change map to divide the consumption, and obtain the hierarchical consumption segments. Step S4: Divide the service area into hierarchical regions in the service area plan map to obtain feature clustering regions, construct a service twin space, build a dynamic twin channel for the service area plan map, and perform dynamic regional regulation of the service area plan map based on the service twin space and the dynamic twin channel to obtain a dynamic scheduling scheme.

[0005] Preferably, the process of collecting and allocating network demand resources in the service area includes: By restricting the geographical scope of mobile service providers, they can obtain service allocation areas. Set up the data collection terminal for the obtained service allocation area to obtain the raw material collection terminal; Resources are collected through the raw material collection terminal to obtain the network's required resources.

[0006] Preferably, the process of constructing a service area map based on the service allocation area includes: A service area plan is generated based on the service allocation area. The location of the raw material collection terminal on the service area plan is marked as a raw material collection node, and a transmission link is constructed between the raw material collection terminal and the raw material collection node. Obtain the network demand resources from the raw material collection end, and upload the network demand resources to the raw material collection node through the transmission link.

[0007] Preferably, the process of obtaining pairing behavior data includes: Filter network resources by address to obtain the addresses used by users; Mark the obtained user addresses on the service area map to obtain the user nodes; Based on the usage of network resources by user nodes, the system matches the usage of network resources to obtain matching behavior data and uploads the matched matching behavior data to the user nodes.

[0008] Preferably, the process of constructing a node consumption change graph includes: Based on network resource demand, the consumption of pairing behavior data is identified to obtain the resource consumption of the behavior. Perform data volume statistics on the resources consumed by the obtained behaviors to obtain the resource consumption of each node type; Construct a two-dimensional Cartesian coordinate system about the nodes used by the user based on the obtained node resource type consumption; The obtained node resource type consumption is uploaded to a two-dimensional rectangular coordinate system. A node consumption curve is generated based on the obtained node resource type consumption, and the two-dimensional rectangular coordinate system containing the node consumption curve is recorded as a node consumption change graph.

[0009] Preferably, the process of obtaining the graded consumption range includes: Set threshold boundaries based on the node consumption change graph, upload the obtained threshold boundaries to the node consumption change graph, perform extreme value statistics on the obtained node consumption change graph, and obtain the extreme values ​​of consumption. Set the gliding scale based on the extreme value of consumption, and translate the threshold boundary line according to the obtained gliding scale based on the node consumption change map. Mark the threshold boundary line after translation and positioning in the node consumption change map. The consumption change graph of nodes is classified according to the threshold boundary line to obtain the consumption range of each level.

[0010] Preferably, the process of obtaining the feature clustering region includes: Based on the service area map, the consumption segments of each level are characterized to obtain the user nodes with level identification. Based on the obtained hierarchical identifiers, user nodes are displayed as features on the service area map. Based on the feature display results, regional boundaries are defined to obtain feature clustering areas, and these feature clustering areas are marked on the service area map.

[0011] Preferably, the process of constructing a service twin space includes: A service twin space is constructed based on the service area plan map. The obtained service area plan map is uploaded to the service twin space, and the service twin space is used to perform spatial virtualization on the service area plan map to obtain the region twin model. A dynamic twin channel is constructed based on the service twin space as the feature aggregation region, and the obtained dynamic twin channel is statically configured to obtain a static transmission port. The obtained static transmission port is marked at the starting point of the dynamic twin channel.

[0012] Preferably, the process of obtaining a dynamic scheduling scheme includes: By using the service twin space to identify the usage of feature clusters, the resource consumption results can be obtained. Determine the flow direction of resources based on resource consumption results to obtain the direction of resource flow. Based on the obtained resource flow direction, a pre-transmission command is issued to the regional twin model, and based on the received pre-transmission command, a channel opening command is issued to the static transmission port. The static transmission port transmits and displays the dynamic twin channel according to the received channel opening command, and dynamically transmits the feature aggregation area through the service twin space according to the displayed dynamic twin channel, and generates an initial scheduling scheme based on the dynamic transmission process. The initial scheduling scheme is supplemented and transmitted to obtain a dynamic scheduling scheme.

[0013] Compared with the prior art, the beneficial effects of the present invention are: 1. Dividing service areas for mobile operators and constructing service area maps allows for real-time monitoring of network resource usage and prediction of future resource demands; collecting user behavior data and mobile network resource information within the service area, transforming user behavior data into the service area map, and monitoring resource consumption based on user behavior data facilitates resource optimization; classifying consumption levels based on resource consumption to obtain tiered consumption segments; and enabling dynamic adjustment of service areas based on real-time data for more flexible resource scheduling.

[0014] 2. Based on the tiered consumption segments, feature markers are applied to the service area map. Clustered areas of similar feature markers are statistically analyzed to obtain feature clustered areas. This allows operators to more accurately identify resource consumption hotspots, thereby achieving more precise resource allocation. A service twin space is constructed, and dynamic twin channels are built for feature clustered areas for dynamic resource transfer, enabling intelligent resource transfer, improving resource transfer speed, reducing transmission loss, and generating dynamic scheduling schemes based on the intelligent resource transfer process, making resource scheduling more flexible and efficient. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0016] Figure 1 This is a schematic diagram of the present invention. Detailed Implementation

[0017] The technical solution of the present invention will be clearly and completely described below with reference to the embodiments. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0018] like Figure 1 As shown, a cloud computing-based mobile network resource scheduling method includes the following steps: Step S1: Divide the service allocation areas of mobile operators and collect the network demand resources of the service allocation areas; Step S2: Construct a service area map based on the service allocation area, locate users' resource usage based on the service area map, and obtain matching behavior data; Step S3: Identify the type of the pairing behavior data, obtain the consumption of node resource types, construct a node consumption change map, set threshold boundaries for the node consumption change map to divide the consumption, and obtain the hierarchical consumption segments. Step S4: Divide the service area into hierarchical regions in the service area plan map to obtain feature clustering regions, construct a service twin space, build a dynamic twin channel for the service area plan map, and perform dynamic regional regulation of the service area plan map based on the service twin space and the dynamic twin channel to obtain a dynamic scheduling scheme.

[0019] It needs further explanation that, in the specific implementation process, mobile network resource utilization by mobile service providers faces some limitations, making it difficult to meet dynamically changing network demands and the needs of large-scale user access. By introducing cloud computing technology, mobile network resources can be dynamically allocated, network load fluctuations can be balanced, and diverse user needs can be met. By understanding user behavior, operators can optimize network performance, improve user experience, and ensure the effective utilization of network resources. Firstly, it is necessary to collect mobile network resource information for mobile service providers. The specific process includes: By restricting the geographical scope of mobile service providers, they can obtain service allocation areas. The term "regional limitation" refers to the geographical area where mobile network resources can be dispatched, defined according to the operational service scope of the mobile service provider. This area is the service allocation area, representing the geographical area that the mobile service provider's network services can cover.

[0020] The data collection terminal is set in the obtained service allocation area to obtain the raw material collection terminal. The data collection terminal setting means selecting a suitable information collection location in the service allocation area and setting the data collection terminal, which is the raw material collection terminal, and can collect all data information in the area covered by the raw material collection terminal.

[0021] Resources are collected through raw material collection terminals to obtain network demand resources, and the obtained network demand resources are associated with the corresponding raw material collection terminals. That is, each network demand resource can be obtained from a specific raw material collection terminal. The resource collection refers to the mobile network operator collecting relevant information about mobile network resources in the service allocation area, denoted as network demand resources. The network demand resources include user demand resources and resource type data. User demand resources represent various activities of users when using mobile network services, such as different types of services used by users, network usage time, geographical location, and user behavior patterns. Different types of services used by users may include voice calls, SMS, social media, video streaming, and online games. User behavior patterns include, but are not limited to, users' internet browsing habits, application usage frequency, and data transmission patterns. Resource type data represents the resource types of mobile network resources, including base station resources, spectrum resources, wireless transmission resources, network equipment resources, computing resources, storage resources, and energy resources.

[0022] A service area map is constructed based on the service allocation area. Based on this service area map, user resource usage is located, and matching behavior data is obtained. The specific process includes: A service area plan is generated based on the obtained service allocation area. The service area plan is a map generated based on the structure of the service allocation area, including but not limited to buildings, roads and green facilities within the service allocation area, and has the same function and structure as the actual service allocation area.

[0023] Mark the location of the raw material collection terminal on the service area map as the raw material collection node, and construct the transmission link between the raw material collection terminal and the raw material collection node; The transmission link is used for bidirectional information transmission, transmitting information between the raw material collection end and the raw material collection node.

[0024] The network demand resources of the raw material collection end are obtained, and the obtained network demand resources are uploaded to the raw material collection node through the transmission link. The network demand resources are displayed at the corresponding raw material collection node on the service area map. That is, the network demand resources of the corresponding collection location can be obtained through the raw material collection node on the service area map.

[0025] Based on the obtained network demand resources, the addresses are filtered to obtain the user's usage address; The geological screening refers to filtering out the geographical location corresponding to the network usage of mobile phone users based on network demand resources, which is the user's usage address.

[0026] The obtained user addresses are marked on the service area map to obtain user nodes, which represent the corresponding positions of mobile phone users' user addresses on the service area map.

[0027] Based on the usage of network resources by user nodes, matching behavior data is obtained and the matched behavior data is uploaded to user nodes, so that user nodes can directly obtain information about the resources used by users. Furthermore, the usage matching refers to the statistical analysis of the amount of resource data used by each mobile phone user at the user's usage node to obtain the resource information used at the user's usage node, which is the matching behavior data. The matching behavior data includes, but is not limited to, the network service type used by the user, the time of network use, the geographical location of use, and the user behavior pattern, which are included in the user's resource needs. Specifically, the matched pairing behavior data represents the network behavior information of mobile phone users at the user's access node, which is used to determine the amount of network resources required at the user's access node, so as to facilitate intelligent dynamic scheduling of network resources for users to meet the user's network surfing needs during peak periods.

[0028] The pairing behavior data is type-identified to obtain the node resource type consumption. A node consumption change graph is constructed, and threshold boundaries are set on the node consumption change graph to divide the consumption into hierarchical consumption segments. The specific process includes: Based on the obtained network resource demand, the consumption of the obtained pairing behavior data is identified to obtain the resource consumption of the behavior; The consumption identification refers to separating the paired behavior data of each user node to obtain the resources consumed for each paired behavior data, which is the behavior consumption resource. For example, if a user plays games on their mobile phone at the user node between 8 pm and 9 pm, the corresponding consumed resources include bandwidth resources, wireless channel resources, spectrum resources, computing resources, and server resources. Therefore, bandwidth resources, wireless channel resources, spectrum resources, computing resources, and server resources are the behavior consumption resources of the user at the user node.

[0029] Perform data volume statistics on the resources consumed by the obtained behaviors to obtain the resource consumption of each node type; Furthermore, the data volume statistics refer to the statistics of the data volume of each type of resource consumed for each user action. Based on the resource types of mobile network resources included in the network demand resources, the data volume consumed by each resource type at the user's node can be matched; that is, the resource data volume consumed by the same resource type is counted, which is the node resource type consumption, representing the consumption of each resource type at the user's node. For example, playing games consumes bandwidth resources, wireless channel resources, spectrum resources, computing resources, and server resources. The amount of data consumed by each type of resource is recorded as the consumption of a node resource type. For example, the amount of resources consumed by the wireless channel is recorded as the consumption of a node resource type, and computing resources are recorded as the consumption of a node resource type.

[0030] Based on the obtained node resource type consumption, a two-dimensional Cartesian coordinate system is constructed regarding the user's used nodes. The horizontal axis of the two-dimensional Cartesian coordinate system represents the user's used nodes. In the service area plan, the user's used nodes are marked sequentially on the horizontal axis of the two-dimensional Cartesian coordinate system according to the order. The vertical axis of the two-dimensional Cartesian coordinate system represents the node resource type consumption. Depending on the type of consumption, the vertical axis changes with the type of consumption. For example, when analyzing the resource consumption of the wireless channel, the vertical axis of the corresponding two-dimensional Cartesian coordinate system represents the node resource type consumption of the wireless channel, while the horizontal axis remains unchanged. That is, each type of resource consumption corresponds to a two-dimensional Cartesian coordinate system.

[0031] The obtained node resource type consumption is uploaded to a two-dimensional rectangular coordinate system. A node consumption curve is generated based on the obtained node resource type consumption, and the two-dimensional rectangular coordinate system containing the node consumption curve is recorded as a node consumption change graph. It should be further explained that, in the specific implementation process, the generated node consumption curve is determined by the type of node resource consumption. For example, there are spectrum resource node consumption curves and computing resource node consumption curves. Then there are corresponding spectrum resource node consumption change graphs and computing resource node consumption change graphs. This means that there is a corresponding node consumption change graph for each resource type node consumption curve. In this case, generating the node consumption curve means connecting the node resource type consumption in the order of nodes to obtain the node consumption curve.

[0032] A threshold boundary line is set for the obtained node consumption change graph. The threshold boundary line is a number of straight lines that are parallel to the horizontal axis and can be moved up and down. The distance between two adjacent threshold boundary lines is set according to the consumption of node resource type.

[0033] The obtained threshold boundary line is uploaded to the node consumption change map. Extreme value statistics are performed on the obtained node consumption change map to obtain the extreme values ​​of consumption. The extreme values ​​of consumption include the maximum value of consumption and the minimum value of consumption. The extreme value statistics represent the identification of the largest node resource type consumption in the node consumption change graph, denoted as the maximum consumption value, and the identification of the smallest node resource type consumption, denoted as the minimum consumption value.

[0034] Set the glide scale based on the obtained extreme value of consumption, and translate the threshold boundary line according to the obtained glide scale based on the node consumption change map. Mark the threshold boundary line after translation and positioning in the node consumption change map. The consumption change map of the nodes is classified according to the obtained threshold boundary line to obtain the consumption range of each level. It should be further explained that, in the specific implementation process, the gliding scale is determined based on the maximum and minimum consumption values ​​among the extreme consumption values. The difference between the maximum and minimum consumption values ​​is obtained and denoted as the consumption difference. The number of intervals is selected according to the magnitude of the consumption difference. For example, if the consumption difference is 'a' and the number of intervals is 5, then the spacing between each interval is... And round up to the nearest integer, i.e., the gliding scale is ,like =4.1, then the sliding scale is 5, which means that the distance between two adjacent threshold boundaries is 5, and both are located above the horizontal axis. The distance between the first threshold boundary and the horizontal axis is 5, and the last threshold boundary is located above or exactly contains the maximum consumption value. That is, the number of threshold boundaries is determined by the extreme consumption value. The translational positioning means, in the node consumption change graph, sequentially translating the threshold boundary line according to the distance of the sliding scale to reach the corresponding position; The consumption grading is represented in the node consumption change graph. Based on the position of the threshold boundary line, the node consumption curve can be divided into curve segments of different intervals. The curve segment falling within each pair of adjacent threshold boundary lines is recorded as a graded consumption segment. For example, the part of the node consumption curve falling between the horizontal axis and the first threshold boundary line is recorded as a graded consumption segment, and the part of the node consumption curve falling between the first threshold boundary line and the second threshold boundary line is recorded as a graded consumption segment. In particular, the lower part includes the node consumption curves on the threshold boundary line. For example, if a node in the node consumption curve falls on the eighth threshold boundary line, then this node is divided into a graded consumption segment between the eighth threshold boundary line and the ninth threshold boundary line. Specifically, based on the node consumption change graph corresponding to different types of consumed resources, the consumption of each type of resource used by a user on a node can be divided according to the corresponding node consumption change graph to obtain the corresponding tiered consumption range.

[0035] The service area plan is hierarchically divided into regions to obtain feature clustering areas, and a service twin space is constructed. A dynamic twin channel is then built for the service area plan. Based on the service twin space and the dynamic twin channel, the service area plan is dynamically adjusted to obtain a dynamic scheduling scheme. The specific process includes: Based on the service area map, the obtained tiered consumption segments are characterized to obtain tiered user nodes. The feature identifier represents the assignment of corresponding feature markers to different tiered consumption segments in the service area plan. In this embodiment, different colors are used to distinguish different levels of tiered consumption segments. For example, in the spectrum resource node consumption change diagram, after consumption tiering, there are 6 tiered consumption segments, which are marked in order from near to far from the horizontal axis as blue, green, yellow, orange, red, and purple. Each user node in each tiered consumption segment corresponds to a color, and this applies to the consumption of spectrum resources. For other types of resource consumption by user nodes, different colors can be used to identify them, thus obtaining the corresponding tiered user node. The obtained tiered user node is a node with a feature identifier that can distinguish different resource consumption levels.

[0036] Based on the obtained hierarchical identifier user nodes, feature display is performed on the service area plan map. Based on the feature display results, area boundaries are performed to obtain feature clustering areas. The obtained feature clustering areas are marked on the service area plan map. The marking indicates that each feature clustering area is marked on the service area plan map, that is, the service area plan map includes all feature clustering areas. It should be further explained that, in the specific implementation process, the feature display means that the user nodes are displayed in the service area plan according to the consumption level of the corresponding node resource type. That is, the user nodes in the original location are replaced with user nodes with hierarchical labels. In other words, the user nodes after feature display have color feature labels, which can distinguish the consumption level of the corresponding resource consumption type according to different colors, and quickly distinguish the resource consumption. By simply observing the distribution of different colors in the service area plan, the amount of resources consumed by the user can be directly obtained. By using the service area plan with different resource consumption type features, the impact of different types of resources on the analysis is reduced, and targeted consumption analysis is facilitated. The region decomposition refers to dividing the regions with the same color into a single region, denoted as a feature cluster region, based on the feature colors corresponding to the hierarchical user nodes displayed in the feature display results. This indicates that within this region, the feature identifiers of the hierarchical user nodes are the same, i.e., they are identified by the same color. Specifically, for regions divided into the same area, as long as the proportion of the same color within the region reaches p%, then this region can be denoted as a feature cluster region. For example, if the proportion of yellow in the region reaches 90%, and 90% is the p% corresponding to the proportion requirement, then this region can be divided into a feature cluster region. This means that within this region, the resource consumption type corresponding to the consumption hierarchical classification of the region can be obtained. Based on the consumption type, the resource consumption level of the users in this region can be determined as yellow. Based on the hierarchical consumption range matched by yellow, the resource consumption status of the users in this region can be judged, which facilitates resource scheduling for this region. Specifically, all divisible areas in the service area plan are divided into regions to obtain corresponding feature clustering regions. The area of ​​each feature clustering region is greater than S, where S represents the minimum division area. As long as a region with an area greater than S can be divided and the color ratio reaches p%, it can be recorded as a feature clustering region.

[0037] A service twin space is constructed based on the service area plan map. The obtained service area plan map is uploaded to the service twin space, and the service twin space is used to perform spatial virtualization on the service area plan map to obtain the region twin model. It should be further explained that, in the specific implementation process, the service twin space is a virtual twin space generated based on the spatial range of the service area plan. The twin space has the same structure and function as the space where the real service area plan is located, and is used to provide resource transfer in a virtual environment for the service area plan, thereby reducing the impact on the actual network. The spatial virtualization refers to the transformation of the service area plan map into a three-dimensional plan map model through the service twin space, which is the area twin model. The structure and function of the obtained area twin model are exactly the same as the service area plan map, except that it is a three-dimensional form transformation, while the service area plan map is a two-dimensional plan map form. In particular, the corresponding feature clustering region is no longer a two-dimensional planar region in the region twin model, but a three-dimensional feature clustering region volume model. However, the region range does not change and remains the same as the radiation range of the feature clustering region.

[0038] Based on the service twin space, a dynamic twin channel is constructed for the obtained feature aggregation region, and the obtained dynamic twin channel is statically configured to obtain a static transmission port. The obtained static transmission port is marked at the starting point of the dynamic twin channel, where the starting point represents the connection point between the dynamic twin channel and the feature aggregation region. It should be further explained that, in the specific implementation process, the constructed dynamic twin channel is a virtual channel in the service twin space, which is a virtual channel twinned from the real information transmission channel for transmitting mobile network resources. Moreover, the thickness of the dynamic twin channel can be dynamically changed according to the amount of mobile network resource data to be transmitted. That is, the larger the amount of resource data to be transmitted, the thicker the dynamic twin channel, and the more resource data can be transmitted. In this way, the most resource data can be transmitted in the shortest time, ensuring the network resources required by the transmission area. The static setting indicates that the dynamic twin channel is generally not displayed. It will only be displayed when there is a need to transmit resource data. Therefore, a switch button for displaying the channel is required. The static transmission port set at this time is the button used to control the switch of the dynamic twin channel. When the static transmission port is turned on, the dynamic twin channel is displayed and normal resource data transmission can be carried out. When the static transmission port is turned off, the dynamic twin channel is hidden and resource data transmission cannot be carried out. The static transmission port is located at the connection point between the feature aggregation area and the dynamic twin channel, that is, the contact point between the feature aggregation area and the dynamic twin channel is the starting point.

[0039] By identifying the usage of feature clustering regions through the service twin space, resource consumption results are obtained, including regions with low and high resource consumption. Furthermore, the usage identification means that in the service twin space, based on the color of the feature identifier corresponding to each feature cluster area and the feature color of the hierarchical user node, the feature cluster area corresponding to the first hierarchical user node is recorded as the low-consumption area, that is, the resource consumption level closest to the horizontal axis of the node consumption change graph, indicating the least resource consumption. The feature cluster area corresponding to the last hierarchical user node is recorded as the high-consumption area, that is, the resource consumption level farthest from the horizontal axis of the node consumption change graph, indicating the area with the most resource consumption.

[0040] The obtained resource consumption results are used to determine the flow direction of resources. The flow direction determination means setting the flow direction of resource transfer based on the low-consumption area and the high-consumption area included in the resource consumption result. The resource flow direction indicates that the resource is transferred from the low-consumption area to the high-consumption area.

[0041] Based on the obtained resource flow direction, a pre-transmission command is issued to the regional twin model, and based on the received pre-transmission command, a channel opening command is issued to the static transmission port. It should be further explained that, in the specific implementation process, the pre-transmission instruction means that, according to the resource flow direction, an instruction is issued to the low-consumption area to prepare for the transmission of resource data. That is, the resources of the low-consumption area are integrated and prepared for transmission to the high-consumption area. After receiving the pre-transmission instruction, the static transmission port of the low-consumption area issues a channel opening instruction to the static transmission port, controlling the static transmission port to open the dynamic twin channel and prepare for information transmission.

[0042] The static transmission port transmits and displays the dynamic twin channel according to the received channel opening command, and dynamically transmits the feature aggregation area through the service twin space according to the displayed dynamic twin channel, and generates an initial scheduling scheme based on the dynamic transmission process. Furthermore, the transmission display indicates that the static transmission port opens the dynamic twin channel after receiving the channel opening instruction, that is, displays the hidden dynamic twin channel. In the service twin space, half of the maximum value of the node resource type consumption in the feature aggregation area to be transmitted is obtained and recorded as the prepared call resource, indicating that the resource amount of the prepared call resource is half of the maximum value of the node resource type consumption. The resources to be invoked are sent to the static transmission port. The width of the dynamic twin channel is adjusted according to the amount of data of the resources to be invoked, so that the resources to be invoked can reach the other end of the dynamic twin channel in the shortest time, that is, the high-volume consumption area closest to the resources to be invoked. This not only quickly transmits excess network resource information, but also achieves transmission to the nearest location, reducing channel loss. The network resources transmitted are the resources to be invoked for the corresponding resource type. Different types of network resource data can be invoked simultaneously. If the origin and destination are the same, the width of the dynamic twin channel needs to be widened. If the destinations are different, a channel link between the origin and the high-volume consumption area needs to be established to facilitate the rapid invocation of network resources and alleviate resource usage pressure.

[0043] The initial scheduling scheme is supplemented and transmitted to obtain a dynamic scheduling scheme; It should be further explained that, in the specific implementation process, the supplementary transmission means that after internal redundant resource calls in the service area plan, there are still areas with unsatisfactory resource consumption. In this case, external resource data needs to be introduced to supplement resources to meet the high-demand areas. The introduced resource call process is supplemented and recorded to obtain a new resource call scheme, which is recorded as a dynamic scheduling scheme. For example, for area D, after internal resource calls in the service area plan, the spectrum resources are still in a high-consumption area. Therefore, the number of base stations in area D can be increased to meet the network needs of users in area D.

[0044] The preferred embodiments of the present invention disclosed above are merely illustrative of the invention. These preferred embodiments do not exhaustively describe all details, nor do they limit the invention to any specific implementation. Clearly, many modifications and variations can be made based on the content of this specification. This specification selects and specifically describes these embodiments to better explain the principles and practical applications of the invention, thereby enabling those skilled in the art to better understand and utilize the invention. The invention is limited only by the claims and their full scope and equivalents.

Claims

1. A mobile network resource scheduling method based on cloud computing, characterized in that, Includes the following steps: Step S1: Divide the service allocation areas of mobile operators and collect the network demand resources of the service allocation areas; Step S2: Construct a service area map based on the service allocation area, locate users' resource usage based on the service area map, and obtain matching behavior data; Step S3: Identify the type of the pairing behavior data, obtain the consumption of node resource types, construct a node consumption change map, set threshold boundaries for the node consumption change map to divide the consumption, and obtain the hierarchical consumption segments. Step S4: Divide the service area into hierarchical regions in the service area plan map to obtain feature clustering regions, construct a service twin space, build a dynamic twin channel for the service area plan map, and perform dynamic regional regulation of the service area plan map based on the service twin space and the dynamic twin channel to obtain a dynamic scheduling scheme.

2. The mobile network resource scheduling method based on cloud computing according to claim 1, characterized in that, The process of collecting and allocating network resources in a service area includes: By restricting the geographical scope of mobile service providers, they can obtain service allocation areas. Set up the data collection terminal for the obtained service allocation area to obtain the raw material collection terminal; Resources are collected through the raw material collection terminal to obtain the network's required resources.

3. The mobile network resource scheduling method based on cloud computing according to claim 2, characterized in that, The process of constructing a service area map based on the service allocation area includes: A service area plan is generated based on the service allocation area. The location of the raw material collection terminal on the service area plan is marked as a raw material collection node, and a transmission link is constructed between the raw material collection terminal and the raw material collection node. Obtain the network demand resources from the raw material collection end, and upload the network demand resources to the raw material collection node through the transmission link.

4. The mobile network resource scheduling method based on cloud computing according to claim 1, characterized in that, The process of obtaining pairing behavior data includes: Filter network resources by address to obtain the addresses used by users; Mark the obtained user addresses on the service area map to obtain the user nodes; Based on the usage of network resources by user nodes, the system matches the usage of network resources to obtain matching behavior data and uploads the matched matching behavior data to the user nodes.

5. The mobile network resource scheduling method based on cloud computing according to claim 4, characterized in that, The process of constructing a node consumption change graph includes: Based on network resource demand, the consumption of pairing behavior data is identified to obtain the resource consumption of the behavior. Perform data volume statistics on the resources consumed by the obtained behaviors to obtain the resource consumption of each node type; Construct a two-dimensional Cartesian coordinate system about the nodes used by the user based on the obtained node resource type consumption; The obtained node resource type consumption is uploaded to a two-dimensional rectangular coordinate system. A node consumption curve is generated based on the obtained node resource type consumption, and the two-dimensional rectangular coordinate system containing the node consumption curve is recorded as a node consumption change graph.

6. The mobile network resource scheduling method based on cloud computing according to claim 1, characterized in that, The process of obtaining the tiered consumption range includes: Set threshold boundaries based on the node consumption change graph, upload the obtained threshold boundaries to the node consumption change graph, perform extreme value statistics on the obtained node consumption change graph, and obtain the extreme values ​​of consumption. Set the gliding scale based on the extreme value of consumption, and translate the threshold boundary line according to the obtained gliding scale based on the node consumption change map. Mark the threshold boundary line after translation and positioning in the node consumption change map. The consumption change graph of nodes is classified according to the threshold boundary line to obtain the consumption range of each level.

7. The mobile network resource scheduling method based on cloud computing according to claim 1, characterized in that, The process of obtaining feature clustering regions includes: Based on the service area map, the consumption segments of each level are characterized to obtain the user nodes with level identification. Based on the obtained hierarchical identifiers, user nodes are displayed as features on the service area map. Based on the feature display results, regional boundaries are defined to obtain feature clustering areas, and these feature clustering areas are marked on the service area map.

8. The mobile network resource scheduling method based on cloud computing according to claim 1, characterized in that, The process of building a service twin space includes: A service twin space is constructed based on the service area plan map. The obtained service area plan map is uploaded to the service twin space, and the service twin space is used to perform spatial virtualization on the service area plan map to obtain the region twin model. A dynamic twin channel is constructed based on the service twin space as the feature aggregation region, and the obtained dynamic twin channel is statically configured to obtain a static transmission port. The obtained static transmission port is marked at the starting point of the dynamic twin channel.

9. A mobile network resource scheduling method based on cloud computing according to claim 8, characterized in that, The process of obtaining a dynamic scheduling scheme includes: By using the service twin space to identify the usage of feature clusters, the resource consumption results can be obtained. Determine the flow direction of resources based on resource consumption results to obtain the direction of resource flow. Based on the obtained resource flow direction, a pre-transmission command is issued to the regional twin model, and based on the received pre-transmission command, a channel opening command is issued to the static transmission port. The static transmission port transmits and displays the dynamic twin channel according to the received channel opening command, and dynamically transmits the feature aggregation area through the service twin space according to the displayed dynamic twin channel, and generates an initial scheduling scheme based on the dynamic transmission process. The initial scheduling scheme is supplemented and transmitted to obtain a dynamic scheduling scheme.

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