Dynamic resource allocation method and system for space-air-ground integrated network

Abstract

The invention discloses a dynamic resource allocation method and system for an air-space-ground integrated network. The method comprises the following steps: deploying a plurality of task processing models with different processing precisions on all nodes of the air-space-ground integrated network; collecting a task generated by the user equipment; the nodes of the space-air-ground integrated network adaptively select task processing models according to the characteristics and data types of the tasks; according to the precision of the task processing model, task allocation is carried out by taking the total cost as a target function; during task allocation, tasks are allocated to a ground-based network and a space-based network firstly, and when the number of the tasks exceeds a task number threshold value of the ground-based network and the space-based network, the tasks exceeding the task number threshold value are allocated to the space-based network for processing. Through the innovative calculation unloading method and the self-adaptive distributed calculation model, the task processing capability of the space-air-ground integrated network and the service quality of the network are improved.

Description

Technical Field

[0001] The present invention relates to the field of wireless communications, and in particular to a method and system for dynamically allocating resources for an air-ground-space integrated network. Background Art

[0002] With the development of wireless communication technology and telecommunication technology, 5G networks have made significant progress in bandwidth, capacity, and latency. The heterogeneous data generated by large-scale intelligent devices has promoted the rapid development of the telecommunications industry, but the growing demand for data computing and communication between devices has put higher demands on ground communication and computing networks. However, ground communication and computing networks rely on data base stations. Due to factors such as economic development level and geographical environment, many regions lack communication infrastructure, and lack communication-related functions such as data storage and computing, power supply, and system maintenance, making it difficult to meet the needs of various services in the digital society.

[0003] A single ground communication computing network is far from meeting current communication needs, and it is even more unable to support the strategic needs of the digital society to move seamlessly from 5G to the 6G era. As one of the core technologies of the 6G era, the integrated air-ground-air network is based on the ground-based network, supplemented and extended by the space-based network and the air-based network, providing intelligent and efficient information services for various network applications in a wide area, and plays a key role in constructing the 6G network architecture. Unlike a single ground communication network, the integrated air-ground-air network can get rid of the limitations of the geographical environment. It can not only be used in scenarios such as smart cities, but also has an irreplaceable role in smart agriculture, natural environment monitoring, disaster management and prevention, marine exploration and other fields.

[0004] Satellites, as an important component of the integrated space-ground network, are crucial to the overall performance of the network. At present, most satellites can only realize data transmission and communication functions. A small number of computing satellites are limited by power consumption, heat dissipation, computing hardware, physical size, etc., and their computing power is relatively limited. Therefore, for the integrated space-ground network, it is crucial to dynamically allocate heterogeneous resources according to the mission requirements of the satellite's corresponding area and the availability of the satellite itself. However, most resource allocation methods for the integrated space-ground network only consider the satellite's orbit, while ignoring the satellite's own computing power and the service quality requirements of the area where the satellite is located. Summary of the invention

[0005] The present invention proposes a method and system for dynamic resource allocation for an air-space-ground integrated network, which solves the problem that the existing resource allocation method for an air-space-ground integrated network ignores the service quality requirements and the computing power of the satellite itself.

[0006] In order to solve the above technical problems, the present invention provides a method for dynamic resource allocation for an air-ground integrated network, comprising the following steps:

[0007] Deploy multiple task processing models with different processing precisions on all nodes of an integrated air-space-ground-integrated network; collect tasks generated by user devices; the nodes of the integrated air-space-ground-integrated network adaptively select task processing models according to the characteristics and data types of the tasks; perform task allocation based on the precision of the task processing model and taking the total cost as the objective function; when allocating tasks, first allocate them to ground-based networks and space-based networks, and when the number of tasks exceeds the task number threshold of the ground-based networks and the space-based networks, allocate the tasks exceeding the task number threshold to the air-based network for processing.

[0008] Preferably, the characteristics of the task include accuracy priority, efficiency priority, minimum energy consumption, accuracy and efficiency balance, accuracy and energy consumption balance, efficiency and energy consumption balance, accuracy, efficiency and energy consumption balance; the data types of the task include text tasks, image tasks, voice tasks, text and image tasks, text, image and voice tasks.

[0009] Preferably, the total cost objective function includes communication delay, computing time cost and global task processing accuracy during node aggregation, and when tasks are assigned to air-based network processing, the total cost objective function also includes the time cost of node synchronization between satellites.

[0010] Preferably, the expression of the global task processing accuracy is:

[0011]

[0012] In the formula, A max is the global task processing accuracy; T i P is the task quantity threshold of node i; T i G is the number of tasks assigned to node i; M is the number of task processing models; Indicates whether model m is selected by node i. If selected, otherwise A m is the accuracy of the task processing model m; α is the weight coefficient; N is the number of nodes; E i is the energy consumption of node i; is the maximum energy that node i can use for task processing; represents the total energy of node i at time T; represents the total energy of node i at time Tt; It represents the minimum energy consumption required for node i to maintain its normal operation.

[0013] Preferably, the expression of the communication delay when the nodes are aggregated is:

[0014] T comm =δ1*(T SG +T GA +T AS +N SS *T SS );

[0015]

[0016] In the above formula, T comm is the communication delay when nodes are aggregated; δ1 and δ2 are weight coefficients; T SG Represents the transmission delay between the satellite and the ground equipment; T GA represents the transmission delay between the ground equipment and the aerial drone; T AS represents the transmission delay between the drone and the satellite; T SS Represents the transmission delay between satellites; N SS Indicates the number of retransmissions between satellites; represents the transmission delay between nodes; M model Represents the decay ratio of the task processing model; BW AB represents the bandwidth between node A and node B; p AB represents the transmission power between node A and node B; h AB Indicates that the channel fading between node A and node B is sparse; σ is the noise function; is the propagation delay between node A and node B; L AB is the length of the physical link between node A and node B; V AB is the propagation speed in the wireless medium between node A and node B.

[0017] Preferably, the expression for calculating the time cost is:

[0018]

[0019] Where, T comp is the calculation time cost; δ1 and δ2 are weight coefficients; T train represents the computation time cost of nodes in the ground-based network; and represents the aggregate time cost of aerial drone nodes and satellite nodes respectively; FLOPS model Indicates the floating point operations of the task processing model; Indicates the amount of data for the task; N ep Indicates the number of calculations; FLOPS indicates the floating point operations per second of the node; Indicates the number of task processing models selected by the node.

[0020] Preferably, the expression of the time cost of inter-satellite node synchronization is:

[0021]

[0022] Where N S is the number of satellites; and Respectively represent the transmission time cost and propagation delay between satellites; FLOPS S Indicates the floating point operations per second of the satellite node; M model Represents the decay ratio of the task processing model.

[0023] Preferably, the expression of the total cost objective function is:

[0024]

[0025] T SAG =T comm +T comp +T sync ;

[0026] Where A represents the number of node coordination strategies; λ1 and λ2 are adjustable weight coefficients; T comm T is the communication delay when nodes are aggregated; comp is the time cost of calculation; T sync A is the synchronization time cost of the satellite node; max Processing precision for global tasks.

[0027] A resource dynamic allocation system for an air-space-ground integrated network, applicable to the above-mentioned resource dynamic allocation method for an air-space-ground integrated network, comprising: a data acquisition module, a judgment module, an adaptive collaboration module and a result aggregation module;

[0028] The data acquisition module is used to collect and update parameter information of all nodes in the air-ground integrated network;

[0029] The judgment module is used to poll the ground-based network in the integrated space-air-ground network and the space-based network covering the ground-based network area to see whether there is a need to switch the task processing model. When there is a switching demand, the judgment module judges the relationship between the number of tasks generated by the user equipment and the task thresholds of the ground-based network and the space-based network, and sends the judgment result to the adaptive collaboration module;

[0030] The adaptive collaboration module is used to adaptively select a collaborative network and a task processing model for each node to participate in task processing;

[0031] The result aggregation module is used to receive the task processing results of the adaptive collaboration module, notify the judgment module and feed back the corresponding task processing results to the terminal device.

[0032] Preferably, the parameter information includes wireless communication bandwidth, data size of the task, accuracy of the task processing model, energy consumption of the task processing model, energy consumption of the node, and maximum energy that the node can use for task processing.

[0033] The benefits of the present invention include at least:

[0034] 1. The calculation offloading method adopted by the present invention has less calculation amount during the execution process, and the calculation complexity is also reduced, which not only reduces the burden of a single computing node, but also improves the operation efficiency of the entire network;

[0035] 2. Taking into full consideration the task requirements of users, an adaptive distributed computing model is designed, which can dynamically adjust the allocation and scheduling of computing resources according to task requirements to ensure that each task can be processed on the most appropriate computing node;

[0036] In general, the present invention not only improves the task processing capability of the integrated air-space-ground network, but also significantly improves the service quality of the network through an innovative computing offloading method and an adaptive distributed computing model. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 is a flow chart of a method according to an embodiment of the present invention;

[0038] Figure 2 A structural diagram of an adaptive distributed computing system according to an embodiment of the present invention;

[0039] Figure 3 A comparison chart of average accuracy between the method of the embodiment of the present invention and the existing method;

[0040] Figure 4 A comparison chart of average energy consumption between the method of the embodiment of the present invention and the existing method;

[0041] Figure 5 This is a comparison chart of average processing time between the method of the embodiment of the present invention and the existing method. DETAILED DESCRIPTION

[0042] The following is a clear and complete description of the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work belong to the protection scope of the present invention.

[0043] When providing various IoT information services, the integrated space-ground network needs to meet different service quality requirements in terms of data transmission delay and reliability, data calculation accuracy and efficiency. However, optimizing all indicators from the three perspectives of network design, data communication, and task processing will cause a large amount of energy, storage, communication, and computing resource overhead, which is not suitable for resource-constrained nodes in the integrated space-ground network. Therefore, in the context of space-ground integration, how to combine information service quality and design model-adaptive distributed computing methods to reduce space-ground integration computing delays while meeting information service needs is of great significance for achieving optimal resource allocation between satellites and improving the task processing capabilities and service quality of the integrated space-ground network.

[0044] Embodiment 1

[0045] like Figure 1 As shown, an embodiment of the present invention provides a method for dynamic resource allocation for an air-ground integrated network, comprising the following steps:

[0046] Step S1: Set up multiple task processing models according to the task type and service characteristics required by the user, and deploy the perception model on each node of the air-ground integrated network The perception model includes various task processing models. The perception model collects and updates the parameter information of all nodes, including: wireless communication bandwidth BW, task data size D, task processing model accuracy A m , by model m i Energy consumption for processing information E m , node energy consumption E i , the maximum energy that a node can use for information processing

[0047] Specifically, the air-ground integrated network set by the present invention includes three parts: ground-based network, space-based network and air-based network. The ground-based network and the space-based network pre-deploy complete task processing models. Due to its own resource limitations, the air-based network only deploys the segmented task processing model, and multiple task processing models with different parameters are deployed in each node at the same time. The nodes involved in the present invention are no longer limited to the limitations of traditional computer architectures. All devices with wireless transmission and computing capabilities can be considered as nodes, such as mobile phones, smart cars, drones flying in the sky, satellites of various orbits in space, etc.

[0048] The selection of the task processing model in the present invention is performed with reference to the settings in Table 1 below, where the first row indicates the set data type, the first column indicates the set task characteristics, and the rest is the set task processing model.

[0049] Table 1 Task processing model selection comparison table

[0050]

[0051] Step S2: Collect the tasks generated by the user equipment, assign the tasks to the nodes closest to the user equipment in the air-ground-integrated network, and randomly select the task processing model. When the network environment changes, poll the ground edge nodes that make up the ground-based network in the air-ground-integrated network and the UAV nodes covering the ground-based network area to see if the selection of the task processing model changes. When the task type or data characteristics processed by the node change, the task is reallocated.

[0052] Specifically, network environment changes include factors that may cause network environment fluctuations, such as task type conversion, node sleep and wake-up, and changes in user needs. The criteria for judging the above network environment changes are as follows:

[0053] Task type conversion: The judgment criterion is whether the data type processed by the task processing model changes. If it changes, the task type changes; otherwise, it does not change.

[0054] Node sleep and wake-up: The judgment standard is whether the working state of the node changes. If the node changes from a non-working state to a working state or is always in a working state within a specified continuous time, the node is in an awakened state; if the node changes from a working state to a non-working state or is always in a non-working state within a specified continuous time, the node is in a sleep state.

[0055] Changes in user needs: The judgment standard is whether the task characteristics have changed, such as whether accuracy priority, efficiency priority, energy saving priority, etc. have changed. If so, the user needs have changed, otherwise, they will not change.

[0056] Step S3: Determine the number of tasks T assigned to each node in the ground-based network and the space-based network i G and the number of tasks T that are processed on node i within the specified time i P The relationship between the ground-based network and the space-based network is adaptively selected to participate in the task processing. When the number of tasks processed by the nodes in the ground-based network and the space-based network exceeds the set task processing threshold, step S4 is executed, otherwise step S5 is executed. The adaptive selection of the collaborative network participating in information processing should meet the following conditions:

[0057]

[0058] Step S4: Collect the data type and characteristics of the new task, and adaptively extract the data from the perception model according to the data type and characteristics of the new task. Select the corresponding task processing model in the ground-based network and the space-based network according to the number of tasks T assigned to each node. iG and the number of tasks T that are processed on node i within the specified time i P Calculate the overall task processing accuracy A of the ground-based network and the space-based network sum , time transmission cost T ga , and the total system mission cost C of the ground-based network and the space-based network is obtained AG .

[0059] Node i can adaptively select the optimal task processing model, which not only meets the processing accuracy A required by the task, but also reduces the system energy consumption. The overall task processing accuracy A of the ground-based network and the space-based network sum In addition to being related to the task processing accuracy of each node i, it is also related to the number of tasks T assigned to node i i G and the number of tasks that can be processed on node i within the specified control time T i P Related, that is, satisfying the following relationship:

[0060]

[0061] Where M is the number of task processing models; Indicates whether model m is selected by node i. If selected, otherwise A m is the accuracy of the task processing model m; α is the weight coefficient; N is the number of nodes; E i is the energy consumption of node i; is the maximum energy that node i can use for task processing; represents the total energy of node i at time T; represents the total energy of node i at time Tt; It represents the minimum energy consumption required for node i to maintain its normal operation.

[0062] The time transmission cost T of all nodes in the ground-based network and the space-based network ga The calculation method is:

[0063]

[0064] In the formula, Indicates whether the communication path established by device q between nodes i and j is used. If so, otherwise represents the task that device q forwards from node i to node j for processing; D represents the data size of the task; h ij represents the number of hops between node i and node j; L represents the time cost of a single hop.

[0065] Generated in node i and in the node set Cost of tasks completed The calculation method is:

[0066]

[0067] Among them, v ij represents the cost of the task forwarded from node i to node j, which is calculated as:

[0068]

[0069] The total system mission cost C of the ground-based network and the space-based network AG The calculation method is:

[0070]

[0071] Where A represents the number of node coordination strategies, where the node coordination strategies are automatically generated by the system; β, ε, and η are adjustable weight coefficients.

[0072] After the system automatically generates multiple node system strategies, according to the total cost of the system task C AG The node coordination strategy with the smallest total task cost is selected as the node coordination strategy within the specified time of this task.

[0073] Step S5: Assign the tasks that exceed the task threshold to the air-based network for processing, and let the ground-based network and the space-based network process the remaining tasks, collect the data types and characteristics of the new tasks, and adaptively extract the data from the perception model according to the data types and characteristics of the new tasks. Select the corresponding task processing model in and calculate the communication delay T when the task processing model of each node in the air-based network is aggregated comm , calculate the time cost T comp , the time cost of synchronizing the task processing model between satellites T sync , the global task processing accuracy of the air-space-ground integrated network A max , we get the total system mission cost C of the space-ground integrated network SAG .

[0074] Specifically, the global task processing accuracy A of the space-ground integrated network max The calculation method of is the same as the overall task processing accuracy A of the ground-based network and the space-based network in step S4 sum The calculation method is the same as that of , and the node range involved is all nodes in the integrated air-space-ground network, while step S4 only involves nodes in the ground-based network and the space-based network.

[0075] Communication delay T when task processing model of each node in the space-ground integrated network is aggregated comm It is composed of the communication delay between devices at different levels in the network and is calculated as follows:

[0076] T comm =δ1*(T SG +T GA +T AS +N SS *T SS );

[0077] Among them, δ1 is the weight coefficient; T SG Represents the transmission delay between the satellite and the ground equipment; T GA represents the transmission delay between the ground equipment and the aerial drone; T AS represents the transmission delay between the drone and the satellite; T SS Represents the transmission delay between satellites; N SS Indicates the number of retransmissions between satellites.

[0078] T SG , T GA , T AS and T SS The calculation method is:

[0079]

[0080] Among them, AB∈{SG,GA,AS,SS}, G represents ground nodes, A represents aerial drone nodes, and S represents satellite nodes; represents the transmission delay between nodes; M model Represents the decay ratio of the task processing model; BW AB Indicates the bandwidth between device A and device B; p AB Indicates the transmission power between device A and device B; h AB Indicates that the channel fading between device A and device B is sparse; σ is the noise function; is the propagation delay, which can be expressed by the length L of the physical link between device A and device B. AB and the propagation speed V in the wireless medium AB Calculated; T AB Represents the end-to-end delay between nodes.

[0081] Computational time cost of the space-ground integrated network T comp It consists of the computational time cost and aggregation time cost of the task processing model, and is calculated as follows:

[0082]

[0083] Among them, δ2 is the weight coefficient; T train represents the computation time cost of nodes in the ground-based network; and represents the aggregate time cost of aerial drone nodes and satellite nodes respectively; FLOPS model Indicates the floating point operations of the task processing model; Indicates the amount of data for the task; N ep Indicates the number of calculations; FLOPS indicates the floating point operations per second of ground equipment; Indicates the number of models selected by the node.

[0084] Synchronization time cost T of task processing model between satellites in space-based network sync The calculation method is:

[0085]

[0086] Among them, N S is the number of satellites; and Respectively represent the transmission time cost and propagation delay between satellites; FLOPS S Indicates the number of floating point operations per second used for calculations on the satellite.

[0087] The total time cost T of task processing in the space-ground integrated network SAG The calculation method is:

[0088] T SAG =T comm +T comp +T sync .

[0089] The total system mission cost C of the integrated air-space-ground network SAG The calculation method is:

[0090]

[0091] Among them, λ1 and λ2 are adjustable weight coefficients.

[0092] According to the total cost of system tasks C SAG The node coordination strategy with the minimum cost is selected as the node coordination strategy within the specified time of this task.

[0093] Step S6: According to the strategy selected in step S4 or step S5, the task is completed within the specified time and the task processing result is fed back to the user terminal. When the specified time is over, return to step S1.

[0094] Embodiment 2

[0095] This embodiment provides a resource dynamic allocation system for an air-ground integrated network. Figure 2 As shown, the system includes:

[0096] The data acquisition module 201 is used to collect and update the parameter information of all computing nodes before the specified time of task execution, including: wireless communication bandwidth, data size of the task, accuracy of the task model, energy consumption of processing tasks according to the model, node energy consumption, and maximum energy that the node can use for task processing.

[0097] The judgment module 202 is used to poll the ground edge nodes that constitute the ground-based network in the integrated air-ground-space network and the drone nodes covering the ground-based network area to see whether there is a need to switch the task model when the network environment changes. When there is a switching demand, it further judges the relationship between the number of information generated by the node and the number of tasks completed within the specified time, and sends the judgment result to the adaptive collaboration module.

[0098] The adaptive collaboration module 203 is used to adaptively select a collaborative network to participate in task processing. The adaptive collaboration module determines the results of the module transmission, selects a collaborative network consisting of ground-based and space-based nodes or a collaborative network consisting of ground-based, space-based and air-based nodes, and calculates the total cost of the system task under various schemes, selects the scheme with the minimum task cost as the node collaboration strategy, executes the task and sends the result to the result aggregation module.

[0099] The result aggregation module 204 is used to receive the task processing results of the adaptive collaboration module, notify the judgment module 202 and feed back the corresponding task processing results to the terminal device.

[0100] The present invention provides a method and system for dynamic resource allocation for an integrated air-space-ground-in-space network. The computational offloading method adopted has a small amount of computation, low computational complexity, and is easy to implement and has strong practicality. Different from other methods of processing data using specific types of artificial intelligence models, the present invention fully considers the characteristics of the nodes such as energy consumption and computing resource consumption, and service requirements such as data processing accuracy, and proposes a matching task processing model adaptive decision-making method based on the node's own characteristics, thereby improving the task processing capability of the integrated air-space-ground-in-space network.

[0101] In general, the present invention effectively reduces the energy consumption of the integrated air-ground network and reduces the processing waiting delay of user equipment while meeting service requirements. This has very important practical significance and application value for information service processing in areas where ground communication and computing networks are not perfect and underdeveloped.

[0102] Figure 3 , Figure 4 , Figure 5The average task accuracy, average task energy consumption and average algorithm processing time of the Enhanced method of the present invention are compared with those of the existing self-processing algorithm Self-Processing, Greedy algorithm Greedy, shortest path algorithm SP and proportional distribution algorithm PD. It can be seen that the method of the present invention reduces the average energy consumption and average processing time of processing tasks while improving the task accuracy.

[0103] The technical features of the above embodiments can be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. Only the preferred embodiments of the present invention are expressed. The description is more specific and detailed, but it cannot be understood as limiting the scope of the patent of the present invention. As long as there is no contradiction in the combination of these technical features, they should be considered as the scope recorded in this specification.

[0104] It should be noted that, for those skilled in the art, several modifications and improvements can be made without departing from the concept of the present invention, and these modifications and improvements all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for dynamic resource allocation for an air-ground integrated network, characterized in that: The following steps are involved: Deploy multiple task processing models with different processing precisions on all nodes of the integrated space-ground network; collect tasks generated by user devices; The nodes of the integrated air-space-ground network adaptively select the task processing model according to the characteristics and data type of the task; according to the accuracy of the task processing model, the tasks are allocated with the total cost as the objective function; when allocating tasks, they are first allocated to the ground-based network and the space-based network. When the number of tasks exceeds the task number threshold of the ground-based network and the space-based network, the tasks exceeding the task number threshold are allocated to the air-based network for processing.

2. The method for dynamic resource allocation for an air-ground integrated network according to claim 1, characterized in that: The characteristics of the task include accuracy priority, efficiency priority, minimum energy consumption, accuracy and efficiency balance, accuracy and energy consumption balance, efficiency and energy consumption balance, accuracy, efficiency and energy consumption balance; the data types of the task include text tasks, image tasks, speech tasks, text and image tasks, text, image and speech tasks.

3. The method for dynamic resource allocation for an air-ground integrated network according to claim 1, characterized in that: The total cost objective function includes communication delay, computing time cost and global task processing accuracy when nodes are aggregated. When tasks are assigned to air-based network processing, the total cost objective function also includes the time cost of node synchronization between satellites.

4. The method for dynamic resource allocation for an air-ground integrated network according to claim 3, characterized in that: The expression of the global task processing accuracy is: In the formula, A max Processing accuracy for global tasks; T i P is the task quantity threshold of node i; T i G is the number of tasks assigned to node i; M is the number of task processing models; Indicates whether model m is selected by node i. If selected, otherwise A m is the accuracy of the task processing model m; α is the weight coefficient; N is the number of nodes; E i is the energy consumption of node i; is the maximum energy that node i can use for task processing; represents the total energy of node i at time T; represents the total energy of node i at time Tt; It represents the minimum energy consumption required for node i to maintain its normal operation.

5. The method for dynamic resource allocation for an air-ground integrated network according to claim 3, characterized in that: The expression of the communication delay when the nodes are aggregated is: T comm =δ1*(T SG +T GA +T AS +N SS *T SS ); In the above formula, T comm is the communication delay when nodes are aggregated; δ1 and δ2 are weight coefficients; T SG Represents the transmission delay between the satellite and the ground equipment; T GA represents the transmission delay between the ground equipment and the aerial drone; T AS represents the transmission delay between the drone and the satellite; T SS Represents the transmission delay between satellites; N SS Indicates the number of retransmissions between satellites; represents the transmission delay between nodes; M model Represents the decay ratio of the task processing model; BW AB represents the bandwidth between node A and node B; p AB represents the transmission power between node A and node B; h AB Indicates that the channel fading between node A and node B is sparse; σ is the noise function; is the propagation delay between node A and node B; L AB is the length of the physical link between node A and node B; V AB is the propagation speed in the wireless medium between node A and node B.

6. The method for dynamic resource allocation for an air-ground integrated network according to claim 3, characterized in that: The expression of the computation time cost is: Where, T comp is the calculation time cost; δ1 and δ2 are weight coefficients; T train represents the computation time cost of nodes in the ground-based network; and represents the aggregate time cost of aerial drone nodes and satellite nodes respectively; FLOPS model Indicates the floating point operations of the task processing model; Indicates the amount of data for the task; N ep Indicates the number of calculations; FLOPS indicates the floating point operations per second of the node; Indicates the number of task processing models selected by the node.

7. The method for dynamic resource allocation for an air-ground integrated network according to claim 3, characterized in that: The expression of the time cost of node synchronization between satellites is: Where N S is the number of satellites; and Respectively represent the transmission time cost and propagation delay between satellites; FLOPS S Indicates the floating point operations per second of the satellite node; M model Represents the decay ratio of the task processing model.

8. The method for dynamic resource allocation for an air-ground integrated network according to claim 3, characterized in that: The expression of the total cost objective function is: T SAG =T comm +T comp +T sync ; Where A represents the number of node coordination strategies; λ1 and λ2 are adjustable weight coefficients; T comm T is the communication delay when nodes are aggregated; comp is the time cost of calculation; T sync A is the synchronization time cost of the satellite node; max Processing precision for global tasks.

9. A resource dynamic allocation system for an air-space-ground integrated network, applicable to a resource dynamic allocation method for an air-space-ground integrated network as claimed in any one of claims 1 to 8, characterized in that: It includes data collection module, judgment module, adaptive collaboration module and result aggregation module; The data acquisition module is used to collect and update parameter information of all nodes in the air-ground integrated network; The judgment module is used to poll the ground-based network in the integrated space-air-ground network and the space-based network covering the ground-based network area to see whether there is a need to switch the task processing model. When there is a switching demand, the judgment module judges the relationship between the number of tasks generated by the user equipment and the task thresholds of the ground-based network and the space-based network, and sends the judgment result to the adaptive collaboration module; The adaptive collaboration module is used to adaptively select a collaborative network and a task processing model for each node to participate in task processing; The result aggregation module is used to receive the task processing results of the adaptive collaboration module, notify the judgment module and feed back the corresponding task processing results to the terminal device.

10. The resource dynamic allocation system for the air-ground integrated network according to claim 9, characterized in that: The parameter information includes wireless communication bandwidth, data size of the task, accuracy of the task processing model, energy consumption of the task processing model, energy consumption of the node, and maximum energy that the node can use for task processing.

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