A data computing method and system for coordinating air aggregation and edge offloading

Abstract

The application discloses a data computing method and system for cooperation of air aggregation and edge unloading, and belongs to the technical field of communication signal processing. The method comprises the following steps: constructing a UAV-assisted computing integration scene, the ground node has edge computing and AirComp heterogeneous requirements, the UAV has edge server and AirComp aggregation center functions; establishing an air-ground communication link model and system constraints; constructing a joint optimization problem with the minimum system total energy consumption as the target; adopting energy-aware cooperative scheduling and BCD iterative solution until the optimal strategy is obtained through energy consumption convergence. The system comprises a ground node, a UAV, communication modeling, constraint construction, optimization construction, cooperative scheduling, iterative solution, and convergence judgment modules. The application realizes global cooperation of computing unloading and air aggregation, reduces system energy consumption under the premise of ensuring computing accuracy, improves the energy efficiency and coverage capability of the UAV-assisted computing system, and is suitable for 6G space-air-ground integrated network, wide-area perception, emergency communication and other scenes.

Description

Technical Field

[0001] This invention belongs to the field of signal processing technology, specifically relating to a data calculation method and system for collaborative air aggregation and edge offloading. Background Technology

[0002] With the continuous evolution of 6G communication technology, future wireless communication networks are rapidly developing towards the convergence of ubiquitous connectivity, integrated communication and computing, and integrated air-space-ground systems. Compared to 5G's focus on high speed and massive connectivity, 6G networks not only need to provide traditional high-speed communication capabilities, but also need to achieve breakthroughs in multiple dimensions such as ultra-low latency, high reliability, and high energy efficiency to support complex business scenarios such as intelligent manufacturing, environmental monitoring, wide-area sensing, vehicle-to-everything (V2X) communication, and smart cities. Against this backdrop, communication-computing convergence, which tightly integrates communication and computing resources, is gradually becoming an important direction for the 6G system, and communication-computing convergence is becoming one of the core capabilities of future networks.

[0003] With the deep integration of communication and computing, Mobile Edge Computing (MEC) has emerged. By deploying servers at the network edge, MEC can significantly reduce the latency and bandwidth pressure caused by data transmission, enabling resource-constrained terminals to complete complex computing tasks within limited resources. However, fixed edge servers are limited by deployment location, infrastructure conditions, and coverage, resulting in significant limitations in applications in remote areas, post-disaster scenarios, or complex terrains. Meanwhile, with the rise of distributed computing and large-scale sensing tasks, systems are placing higher demands on energy-efficient data aggregation and function computation mechanisms. To further improve the efficiency of distributed computing and reduce system-level communication overhead, Over-the-Air Computation (AirComp) technology has received widespread attention in recent years. AirComp utilizes the superposition characteristics of wireless channels to directly achieve multi-node data aggregation and computation during transmission, making it particularly suitable for scenarios such as distributed learning, statistical calculation, and sensor data fusion. Compared to traditional point-to-point communication methods, AirComp can significantly reduce communication latency and energy consumption.

[0004] However, as these network architectures continue to evolve, energy issues have gradually become one of the core bottlenecks restricting the sustainable operation of the system. On the one hand, key nodes in wireless networks, such as UAVs, high-altitude platforms, and satellites, often rely on onboard power supplies. Their available energy is strictly limited by factors such as battery capacity, refueling cycles, and flight missions, thus facing strict energy constraints. Especially for UAVs, their flight energy consumption, communication energy consumption, and computing energy consumption are intertwined. Without a reasonable joint design, mission duration will be significantly shortened and system service capabilities reduced. On the other hand, wireless networks often serve remote users beyond cellular coverage. These terminals are mostly self-powered and energy-sensitive devices, with limited communication and computing capabilities, making them particularly sensitive to energy consumption. Simultaneously, to support diverse emerging applications, the communication and computing demands in wireless networks are often energy-intensive, and the complexity of the network architecture and the large number of nodes make energy optimization increasingly challenging. Summary of the Invention

[0005] The technical problem to be solved by this invention is to address the shortcomings of the prior art by providing a data computing method and system for collaborative air aggregation and edge offloading. This method organically combines the mobility advantages of UAVs with their dual functions in edge computing and AirComp. Through dynamic planning of UAV flight paths and joint design of communication and computing resources, it achieves unified scheduling and joint optimization of task offloading, data fusion, and computing aggregation. This addresses the technical problems in existing UAV-assisted networks where communication and computing resources are fragmented, unable to simultaneously and efficiently support heterogeneous computing tasks, resulting in excessive overall system energy consumption, low spectrum utilization, and task processing latency that cannot meet the stringent requirements of IoT applications.

[0006] The present invention adopts the following technical solution: A data computation method for collaborative air aggregation and edge offloading includes the following steps: The scenario involves constructing an integrated communication and computing system assisted by unmanned aerial vehicles (UAVs), where ground nodes have heterogeneous computing requirements, including edge computing requirements based on task offloading and function aggregation requirements based on AirComp in the air; the UAVs also serve as edge computing servers and AirComp aggregation centers. Model the air-to-ground wireless communication link between the ground node and the UAV, establish the channel gain relationship, and obtain the mathematical expressions for the UAV's received signals and computational task processing. A unified system model is constructed by introducing constraints on edge computing task processing capabilities, AirComp function aggregation accuracy, and air-to-ground communication resources. With minimizing the overall system energy consumption as the optimization objective, and with computation offloading strategy, AirComp aggregation parameters, and communication resource allocation as optimization variables, a collaborative optimization problem of joint computation offloading and air-to-air aggregation is established. An energy-aware collaborative design approach is adopted to jointly schedule the computation offloading decision and the AirComp aggregation process, thereby characterizing the coupling relationship between communication resources and computing resources. Under the premise of satisfying all constraints, the joint optimization problem is solved iteratively, and the computation offloading strategy, AirComp aggregation parameters and communication resource allocation scheme are dynamically updated. Repeat the joint scheduling and iterative solution steps described above until the overall system energy consumption converges, and obtain the optimal combination of cooperative strategies for computational offloading and in-flight aggregation.

[0007] Preferably, the ground node includes A collection of terminal devices with AirComp computing requirements. This indicates a collection of terminal devices with edge computing needs. It means, and The drone is configured to simultaneously receive and process data from a set. AirComp aggregated signals from mid-terminal devices and signals from collections Edge computing task data of terminal devices.

[0008] Preferably, the air-to-ground wireless communication link modeling adopts a time-slot scheduling-based model, dividing the flight mission cycle into... There are three time slots; within each time slot, AirComp nodes upload data simultaneously, and edge computing nodes are scheduled in a round-robin fashion, allowing only one edge computing node at a time. Unload the task.

[0009] Preferably, the air-to-ground wireless communication channel is modeled as a Ricean channel model, specifically:

[0010] in, This represents the path loss at the reference distance. Indicates time slot With ground nodes Distance to UAV Indicates in time slot node Channel between UAV and UAV factor, and They represent time slots respectively ,node The line-of-sight and non-line-of-sight components of the channel between the UAV and the UAV.

[0011] Preferably, the air-to-ground communication resource constraints include: transmit power of edge offloading nodes satisfy ;in, Indicates edge unloading node Maximum transmission power; AirComp node Transmission power Limited to: ;in, Indicates the AirComp node Maximum transmission power; The aggregation accuracy constraint of the AirComp function is that the mean square error (MSE) of each time slot is less than or equal to a preset threshold. The mean square error (MSE) is calculated based on the difference between the UAV recovered signal and the expected received signal.

[0012] Preferably, the edge computing task processing capability constraints include: The total amount of unloaded data in all time slots meets the total amount of data that the ground nodes need to unload. The amount of data that a drone can process in each time slot is less than or equal to the upper limit of processing capacity determined by the drone's maximum computing frequency and the overhead per bit of data processing. The amount of data that an edge computing node can upload in each time slot is less than or equal to the data transmission limit determined by the achievable transmission rate of that time slot.

[0013] Preferably, the total system energy consumption includes: the uplink transmission energy consumption of terminal devices with AirComp requirements on the ground node side throughout the entire mission cycle, the uplink transmission energy consumption of terminal devices with edge computing requirements during the flight mission cycle, and the computing energy consumption of the UAV when handling the unloading task. The optimization variables include: the transmission power of the AirComp node, the transmission power of the edge computing node, the receiver scaling factor of the drone, the amount of task offloading data of the edge computing node, and the scheduling binary variable of the edge computing node.

[0014] Preferably, an energy-aware collaborative design method is adopted to jointly schedule the computational offloading decision and the AirComp aggregation process, specifically as follows: The joint optimization problem is decomposed into scaling factor optimization subproblem, power allocation optimization subproblem and scheduling optimization subproblem using the block coordinate descent (BCD) framework. To address the non-convex constraints in the power allocation optimization subproblem, auxiliary variables are introduced for relaxation, transforming it into a convex problem. For the integer programming variables in the scheduling optimization subproblem, relax them into continuous variables and transform them into a convex problem form.

[0015] Preferably, the condition for determining the convergence of the overall system energy consumption is: the decrease in the total system energy consumption obtained in the current iteration compared to the total system energy consumption obtained in the previous iteration is less than a set threshold; when the convergence condition is met, the current computation offloading strategy, AirComp aggregation parameters, and communication resource allocation scheme are output as the optimal collaborative strategy combination.

[0016] Secondly, embodiments of the present invention provide a data computing system for collaborative over-the-air aggregation and edge offloading, comprising: The ground node module is used to generate heterogeneous computing requirements, which include edge computing tasks and AirComp function aggregation tasks. The drone module includes an edge computing server and an AirComp aggregation center, which are used to simultaneously receive and process edge computing tasks and AirComp aggregation signals from the ground node module; The communication link modeling module, connected to the ground node module and the UAV module, is used to establish an air-to-ground wireless communication channel model between the ground node and the UAV, and to obtain mathematical expressions for the UAV's received signals and computational task processing. The constraint construction module is used to introduce constraints on edge computing task processing capabilities, AirComp aggregation accuracy, and air-to-ground communication resources to build a unified system model. An optimization problem construction module, connected to the constraint construction module, is used to establish a collaborative optimization problem of joint computation offloading and over-the-air aggregation with the goal of minimizing the overall energy consumption of the system. The collaborative scheduling module is used to jointly schedule the computation offloading decision and the AirComp aggregation process using an energy-aware collaborative design method, and to characterize the coupling relationship between communication resources and computing resources. The iterative solution module, connected to the collaborative scheduling module, is used to iteratively solve the joint optimization problem under the premise of satisfying various constraints, and dynamically update the computation unloading strategy, AirComp aggregation parameters and communication resource allocation scheme. The convergence judgment module, connected to the iterative solution module, is used to determine whether the overall energy consumption of the system has converged, and outputs the optimal combination of cooperative strategies when convergence occurs.

[0017] Thirdly, a computer device includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the aforementioned collaborative over-the-air aggregation and edge offloading data calculation method.

[0018] Fourthly, embodiments of the present invention provide a computer-readable storage medium including a computer program, which, when executed by a processor, implements the steps of the above-described collaborative over-the-air aggregation and edge offloading data calculation method.

[0019] Fifthly, a chip includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the steps of the aforementioned collaborative over-the-air aggregation and edge offloading data calculation method.

[0020] In a sixth aspect, embodiments of the present invention provide an electronic device, including a computer program, which, when executed by the electronic device, implements the steps of the above-described collaborative over-the-air aggregation and edge offloading data calculation method.

[0021] Compared with the prior art, the present invention has at least the following beneficial effects: A collaborative data computation method for aerial aggregation and edge offloading is proposed, which for the first time uses a drone simultaneously as an edge computing server and an AirComp aggregation center, solving the problem of parallel processing of heterogeneous computing needs. With minimizing total system energy consumption as the unified goal, it achieves global coordination between computation offloading and aerial aggregation, avoiding local optima. Through joint optimization of computation offloading strategies, AirComp parameters, and communication resources, it achieves deep coupling and scheduling of communication and computing resources. It employs iterative optimization and convergence judgment to ensure the stability of the solution, balancing computational accuracy and energy efficiency, adapting to the requirements of 6G integrated air-space-ground networks, and overcoming the bottlenecks of limited coverage of fixed edge servers and low energy efficiency of single computing modes. It provides a standardized and implementable core process for drone-assisted general computing systems, applicable to complex scenarios such as wide-area perception, post-disaster emergency response, and remote area coverage, possessing strong versatility and high practicality.

[0022] Furthermore, by classifying and partitioning requirements, physical isolation and logical decoupling of the two types of computing services are achieved, avoiding task interference. Optimal computing modes are matched for different task characteristics, with AirComp adapting to rapid data aggregation and edge offloading adapting to high-complexity tasks, improving the system's processing targeting. Clear division of labor among terminal devices simplifies scheduling logic and reduces the complexity of system modeling and optimization. The non-overlapping set design ensures that signal transmission and computing processing do not conflict, improving system stability and reliability. At the same time, it maximizes the advantages of AirComp's low latency and high-performance edge computing, achieving a dual improvement in system efficiency and flexibility, and adapting to large-scale heterogeneous terminal access scenarios.

[0023] Furthermore, time-slotted scheduling achieves time-domain isolation between the two types of services, avoiding co-channel interference and ensuring transmission reliability; AirComp synchronous uploading fully utilizes the superposition characteristics of wireless channels to improve aggregation efficiency; edge node polling offloading avoids channel contention, reduces the probability of transmission conflicts, and improves data transmission success rate; the unified time-slot framework simplifies resource allocation logic and facilitates collaborative management of communication and computing resources; time-domain parallel processing improves system throughput, adapts to multi-terminal, high-traffic service scenarios, while reducing the complexity of scheduling algorithms, ensuring real-time performance, and meeting the requirements of ultra-low latency services.

[0024] Furthermore, the Ricean channel accurately characterizes the transmission characteristics of UAVs at high altitudes, primarily based on line-of-sight while also considering small-scale fading, closely reflecting the actual air-to-ground communication environment. The model includes path loss, Ricean factor, and line-of-sight / non-line-of-sight components, exhibiting high modeling accuracy and providing a reliable basis for subsequent power allocation and scaling factor optimization. It realistically reflects the impact of ground obstacles and UAV attitude changes on the channel, improving the accuracy of the system model. The optimization scheme based on the accurate channel model is more in line with actual deployment, reducing the gap between theory and practice, improving the feasibility of the scheme, and ensuring the accuracy of AirComp aggregation and the stability of edge offloading transmission.

[0025] Furthermore, power constraints are aligned with the limitations of terminal hardware capabilities, preventing damage to equipment due to over-power transmission; ensuring terminal energy consumption remains within a safe range, extending the battery life of self-powered terminals; controlling uplink transmission power reduces inter-node interference and improves channel utilization; unified power constraint rules simplify resource allocation logic and facilitate global optimization; and a balance is struck between hardware feasibility and system energy efficiency, preventing individual nodes from consuming excessive resources with high power, ensuring fairness among multiple nodes, and improving the overall stability and battery life of the system.

[0026] Furthermore, MSE precision constraints ensure the reliability of over-the-air aggregation computation and meet business precision requirements; quantified precision indicators provide clear constraints for algorithm optimization, balancing precision and energy consumption; preset thresholds can be flexibly adjusted according to business scenarios to adapt to applications with different precision requirements; precision constraints avoid error accumulation and ensure the availability of aggregation results; and energy consumption optimization is achieved while ensuring precision, solving the problem of balancing AirComp precision and energy efficiency and improving system practicality.

[0027] Furthermore, comprehensive energy consumption modeling covers all core energy consumption aspects of the system without omissions or redundancies, ensuring the comprehensiveness of optimization objectives; it coordinates the energy consumption of terminals and drones at the system level to avoid localized energy saving leading to an increase in overall energy consumption; the precise energy consumption model provides a reliable objective function for iterative optimization, improving optimization results; it comprehensively considers energy consumption components, closely matches the actual energy consumption distribution of the system operation, ensuring that energy-saving solutions are real and effective, maximizing system energy efficiency, and extending the working time of drones and ground terminals.

[0028] Furthermore, BCD decomposition breaks down non-convex complex optimization problems into simpler subproblems, reducing the difficulty of solving them; modular optimization facilitates parallel processing and improves solution efficiency; subproblems are independent yet collaborative, ensuring global optimality; scaling factor, power, and scheduling are optimized separately to address optimization challenges at each stage; it adapts to the real-time requirements of UAV systems, quickly obtaining the optimal strategy, balancing solution accuracy and speed, and improving the algorithm's practicality and applicability.

[0029] Furthermore, quantified convergence criteria ensure stable algorithm termination and avoid infinite iteration; the energy consumption reduction assessment aligns with the optimization objective, ensuring sufficiently good optimization results; thresholds can be flexibly adjusted to balance the number of iterations and optimization effects; explicit convergence rules enhance algorithm controllability; rapid convergence ensures system real-time performance, adapts to dynamically changing air-to-ground communication environments, ensures timely updates to optimization schemes, and improves system response speed.

[0030] It is understood that the beneficial effects of the second to sixth aspects mentioned above can be found in the relevant descriptions in the first aspect mentioned above, and will not be repeated here.

[0031] In summary, this invention enables the synergistic use of UAV edge computing and AirComp, and through time-slot scheduling, precise modeling, global energy consumption optimization, and BCD decomposition, it takes into account the needs of heterogeneous computing, accuracy and energy efficiency, and solves problems such as limited coverage of fixed edge servers, low energy efficiency of single computing mode, and fragmentation of communication and computing resources. It improves system stability, real-time performance and deployability, and is compatible with 6G air-space-ground integrated networks.

[0032] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Attached Figure Description

[0033] Figure 1 A model diagram of the wireless transmission system constructed for this invention; Figure 2 This is a flowchart of the present invention; Figure 3 This is a schematic diagram illustrating the change in system energy consumption with the number of iterations in this invention; Figure 4 This is a schematic diagram illustrating the change in system energy consumption as a function of the MSE threshold. Figure 5 A schematic diagram of a computer device provided in an embodiment of the present invention; Figure 6 This is a block diagram of a chip provided according to an embodiment of the present invention.

[0034] Among them, 60. Computer equipment; 61. Processor; 62. Memory; 63. Computer program; 600. Electronic device; 610. Processing unit; 620. Storage unit; 6201. Random access memory unit; 6202. Cache memory unit; 6203. Read-only memory unit; 6204. Program / utility; 6205. Program module; 630. Bus; 640. Display unit; 650. Input / output interface; 660. Network adapter; 700. External device. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. 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.

[0036] In the description of this invention, it should be understood that the terms "comprising" and "including" indicate the presence of the described features, integrals, steps, operations, elements and / or components, but do not exclude the presence or addition of one or more other features, integrals, steps, operations, elements, components and / or collections thereof.

[0037] It should also be understood that the terminology used in this specification is for the purpose of describing particular embodiments only and is not intended to limit the invention. As used in this specification and the appended claims, the singular forms “a,” “an,” and “the” are intended to include the plural forms unless the context clearly indicates otherwise.

[0038] It should also be further understood that the term "and / or" as used in this specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes such combinations. For example, A and / or B can represent three cases: A alone, A and B simultaneously, and B alone. Additionally, the character " / " in this invention generally indicates that the preceding and following objects have an "or" relationship.

[0039] It should be understood that although terms such as first, second, third, etc., may be used in the embodiments of the present invention to describe the preset range, these preset ranges should not be limited to these terms. These terms are only used to distinguish the preset ranges from one another. For example, without departing from the scope of the embodiments of the present invention, the first preset range may also be referred to as the second preset range, and similarly, the second preset range may also be referred to as the first preset range.

[0040] Depending on the context, the word "if" as used here can be interpreted as "when," "when," "in response to determination," or "in response to detection." Similarly, depending on the context, the phrase "if determination" or "if detection (of the stated condition or event)" can be interpreted as "when determination," "in response to determination," "when detection (of the stated condition or event)," or "in response to detection (of the stated condition or event)."

[0041] The accompanying drawings illustrate various structural schematic diagrams according to embodiments disclosed in this invention. These drawings are not to scale, and some details have been enlarged for clarity, and some details may have been omitted. The shapes of the various regions and layers shown in the drawings, as well as their relative sizes and positional relationships, are merely exemplary and may deviate from reality due to manufacturing tolerances or technical limitations. Furthermore, those skilled in the art can design regions / layers with different shapes, sizes, and relative positions as needed.

[0042] This invention provides a data computation method for coordinated air aggregation and edge offloading, taking into account the coexistence of heterogeneous computing needs in UAV-assisted communication computing systems and the difficulty of balancing energy efficiency and performance with a single computing mode, and studies the coordinated mechanism of energy-sensing computation offloading and air aggregation. First, a UAV-assisted integrated communication and computing system scenario is constructed. In this scenario, ground nodes simultaneously have heterogeneous computing needs, including edge computing needs based on task offloading and function aggregation needs based on airborne computing (AirComp). The problem of energy efficiency and performance being difficult to balance in a single computing mode is analyzed, and a unified communication and computing system model is established accordingly. Then, in the constructed system model, the UAV is simultaneously set as an edge computing server and an AirComp aggregation center. The air-to-ground communication relationship between ground nodes and UAVs is characterized, and offloading and processing models for edge computing tasks and AirComp function aggregation models are established respectively. Constraints on edge computing task processing capacity, AirComp aggregation accuracy, and air-to-ground communication resources are introduced. Next, with the optimization objective of minimizing the overall system energy consumption, the optimization problem model of joint computing offloading and airborne aggregation is constructed by comprehensively considering the data transmission energy consumption on the ground node side and the computing energy consumption on the UAV side, revealing the coupling relationship between communication resources and computing resources at the system level. Finally, by collaboratively designing and jointly optimizing the optimization problem, unified scheduling and energy-aware management of heterogeneous computing needs are achieved. While ensuring the quality of edge computing services and the aggregation accuracy of AirComp, the overall energy consumption of the system is effectively reduced, thereby improving the energy efficiency of the UAV-assisted communication computing system.

[0043] Please see Figure 2 The present invention provides a data calculation method for collaborative air aggregation and edge offloading, comprising the following steps: S1. Construct a UAV-assisted communication and computing integrated system scenario. In the scenario, there are several ground nodes and one UAV. The ground nodes have heterogeneous computing needs, including computing tasks that need to be processed by the edge server carried by the UAV through task offloading, and computing tasks that rely on AirComp to complete function aggregation. The UAV simultaneously undertakes the dual functions of edge computing server and AirComp aggregation center. Please see Figure 1 There are a total of A set of terminal devices, represented as ... Position modeling is performed in the Cartesian coordinate system, where the first... The location coordinates of each device can be represented as follows: Due to the different task characteristics of various applications, some terminals are more suitable for directly performing function aggregation operations through AirComp. Terminal devices with AirComp computing requirements use a set of... This indicates that another group of terminals has tasks with a larger computational load or higher requirements for computational accuracy, making them more suitable for edge offloading. These terminal devices use a collection... This indicates that the resulting hybrid architecture can simultaneously support rapid data aggregation and high-complexity task processing, improving the overall computational efficiency and flexibility of the system. These two sets are mutually exclusive, i.e. Their union represents the set of all terminal devices in the system, denoted as . .Regulation and These represent the number of ground devices performing AirComp and edge computing, respectively.

[0044] S2. Based on the system scenario constructed in step S1, model the air-to-ground wireless communication link between the ground node and the UAV, respectively characterize the data transmission model of the computation offloading task and the reception model of the AirComp aggregated signal, establish the channel gain relationship between the ground node and the UAV, and obtain the mathematical expressions of the UAV side received signal and the computation task processing process. To better coordinate the coexistence and scheduling of the two types of computing services during communication, a time-slot-based scheduling model is adopted. This model covers the entire flight mission cycle. Divided into average Each time slot is represented as The set of flight time points can be represented as And the time interval is Considering that UAVs typically operate at relatively high deployment altitudes in the airspace, most transmission links between them and ground nodes can maintain line-of-sight conditions. However, due to the inevitable influence of buildings, trees, vehicles, and the UAV's own attitude changes during signal propagation, a non-negligible small-scale fading is introduced. Therefore, to realistically characterize the wireless propagation characteristics between ground nodes and UAVs, the ground node... The channel between the UAV and the UAV is modeled as a Ricean channel model, as shown below:

[0045] in, This represents the path loss at the reference distance. Indicates time slot With ground nodes Distance to UAV Indicates in time slot node Channel between UAV and UAV factor, and They represent time slots respectively ,node The line-of-sight and non-line-of-sight components of the channel between the UAV and the UAV.

[0046] In the time slot Inside, all that belong to the set AirComp will simultaneously upload the data it senses or processes locally. This is used for real-time function aggregation. Meanwhile, for collections... Nodes with edge offloading requirements are scheduled in a round-robin fashion, in each time slot. Only one edge computing node is allowed inside. Perform task unloading and instruct it to unload data packets to the UAV within the current time slot. For edge-side computational processing, the UAV simultaneously receives superimposed signals from AirComp users and offloaded data from scheduled edge users in the same time slot, thus achieving parallel processing of two types of services. For ease of modeling, the signals transmitted by the ground terminal equipment are assumed to be independent, with a normalized mean of zero and a variance of one. Within each time slot, binary variables are used. Indicates which device is being scheduled:

[0047] Among them, in time slots terminal equipment When selected, ,otherwise, The aggregated signal received by the UAV consists of two parts: a preprocessed signal sent by a device with AirComp service and a signal sent by a device with edge computing tasks, which can be represented as:

[0048] S3. Based on step S2, introduce task processing capability constraints for edge computing offloading tasks, introduce function aggregation accuracy constraints for AirComp computing, and combine air-to-ground communication resource constraints to construct a unified system model that includes communication and computing constraints. To ensure energy constraints and hardware feasibility of the terminal equipment, the transmit power of the edge offloading node must meet the following constraints:

[0049] in, Indicates edge unloading node Maximum transmission power.

[0050] AirComp node The transmission power is limited by:

[0051] in, Indicates the AirComp node Maximum transmission power.

[0052] In the AirComp framework, UAVs need to perform function-level aggregation of uplink signals from multiple ground-based terminal devices. Based on this, in time slots... The target aggregation function that the UAV wants to compute is expressed as:

[0053] To obtain the theoretically desired aggregation result, the UAV needs to recover the objective function value from the received superimposed signals. Therefore, in the time slot... A receiver scaling factor (Rx-scaling factor) is introduced on the UAV side. This is used to adjust the amplitude of the original received signal, resulting in the recovered signal:

[0054] In each time slot The receiving side, based on the received signal The obtained recovery signal With the expected received signal The precision error (MSE) between the two is expressed as:

[0055] To ensure the reliability of AirComp aggregation results under mixed transmission, the system design requires that the MSE of each time slot be less than a preset threshold. In this invention, it is assumed that the MSE threshold is the same in each time slot, that is:

[0056] In the time slot UAV and nodes performing edge computing The data transfer rate between them is:

[0057] To ensure that the total amount of data to be unloaded by the device can be completed within the task cycle, the total unloading data in all time slots must meet the following conditions:

[0058] Let the processing cost per bit of data be... CPU cycles, and the maximum computing frequency of the UAV is Then in the time slot The amount of data that a UAV can process must meet the following requirements:

[0059] set up For edge computing nodes In the time slot If the achievable transmission rate is met, then the data that can be uploaded must satisfy the following:

[0060] S4. Based on the system model constructed in step S3, the overall system energy consumption is minimized as the optimization objective function. The overall system energy consumption includes the data transmission energy consumption on the ground node side and the computing energy consumption on the UAV side. The computing offloading strategy, AirComp aggregation parameters and communication resource allocation are used as optimization variables to establish a joint optimization problem of computing offloading and air aggregation. The total uplink power consumption of a terminal device with AirComp requirements throughout the entire task cycle is expressed as follows:

[0061] For a node performing an edge computing task, its uplink transmission energy consumption during the flight mission cycle is:

[0062] The computational energy consumption of a UAV when handling unloading tasks can be expressed as:

[0063] In summary, the total energy consumption of the system during the task cycle can be written as:

[0064] An energy minimization optimization problem was constructed to achieve coordinated optimization between AirComp aggregation, edge offloading, and UAV computation. This optimization problem is formally represented as:

[0065] S5. For the joint optimization problem obtained in step S4, an energy-aware collaborative design method is adopted to jointly schedule the computation offloading decision and the AirComp aggregation process, characterize the coupling relationship between communication resources and computing resources at the system level, and achieve unified optimization of heterogeneous computing requirements. The problem formed in step S4 is a non-convex problem, which is difficult to solve efficiently. Therefore, a solution algorithm based on the BCD framework is proposed as follows. The problem formed in step S4 is decomposed into three sub-problems:

[0066] The issue of power allocation between edge computing offload node devices and AirComp nodes:

[0067] After obtaining the optimization results for all the aforementioned variables, the next step is to determine which edge computing offloading node should be scheduled to perform task offloading in each time slot for the UAV. Therefore, the scheduling optimization problem can be formulated as:

[0068] S6. Under the premise of satisfying the constraints of edge computing task processing capacity, AirComp function aggregation accuracy and air-to-ground communication resources, the joint optimization problem is solved iteratively, and the computing offloading strategy, AirComp aggregation parameters and communication resource allocation scheme are dynamically updated. The joint optimization subproblem of determining the scaling factor of the UAV and the amount of task unloading data of each edge computing unloading node in each time slot is a joint convex problem that can be solved directly.

[0069] For solving the power allocation subproblem between edge computing offloading node devices and AirComp nodes, the constraint regarding the amount of offloaded data is as follows:

[0070] Regarding the variable to be optimized and It is a non-convex constraint, and an auxiliary variable is introduced. Let's relax the non-convex terms:

[0071] At this point, we can obtain the relaxed inequality:

[0072] Based on the above analysis, the subproblem has been transformed into a convex problem.

[0073] For solving the scheduling optimization subproblem, since integer programming typically has high computational complexity, these binary variables are relaxed to continuous variables, i.e.:

[0074] After relaxation, the resulting problem is a convex optimization problem because the objective function is convex and all constraints are affine with respect to the variables. Based on the above analysis, this subproblem has been transformed into a convex problem form.

[0075] S7. Repeat steps S5 to S6 until the overall system energy consumption converges, and obtain the optimal collaborative strategy combination of computation offloading and over-the-air aggregation. While ensuring the service quality of heterogeneous computing tasks, the overall energy consumption of the UAV-assisted communication computing system is effectively reduced.

[0076] In another embodiment of the present invention, a data computing system for collaborative air aggregation and edge offloading is provided. This system can be used to implement the above-mentioned data computing method for collaborative air aggregation and edge offloading. Specifically, the data computing system for collaborative air aggregation and edge offloading includes a ground node module, a UAV module, a communication link modeling module, a constraint construction module, an optimization problem construction module, a collaborative scheduling module, an iterative solution module, and a convergence judgment module.

[0077] The ground node module is used to generate heterogeneous computing requirements, which include edge computing tasks and AirComp function aggregation tasks. The drone module includes an edge computing server and an AirComp aggregation center, which are used to simultaneously receive and process edge computing tasks and AirComp aggregation signals from the ground node module; The communication link modeling module, connected to the ground node module and the UAV module, is used to establish an air-to-ground wireless communication channel model between the ground node and the UAV, and to obtain mathematical expressions for the UAV's received signals and computational task processing. The constraint construction module is used to introduce constraints on edge computing task processing capabilities, AirComp aggregation accuracy, and air-to-ground communication resources to build a unified system model. An optimization problem construction module, connected to the constraint construction module, is used to establish a collaborative optimization problem of joint computation offloading and over-the-air aggregation with the goal of minimizing the overall energy consumption of the system. The collaborative scheduling module is used to jointly schedule the computation offloading decision and the AirComp aggregation process using an energy-aware collaborative design method, and to characterize the coupling relationship between communication resources and computing resources. The iterative solution module, connected to the collaborative scheduling module, is used to iteratively solve the joint optimization problem under the premise of satisfying various constraints, and dynamically update the computation unloading strategy, AirComp aggregation parameters and communication resource allocation scheme. The convergence judgment module, connected to the iterative solution module, is used to determine whether the overall energy consumption of the system has converged, and outputs the optimal combination of cooperative strategies when convergence occurs.

[0078] This invention provides a terminal device comprising a processor and a memory. The memory stores a computer program, which includes program instructions. The processor executes the program instructions stored in the computer storage medium. The processor may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. It is the computing and control core of the terminal, suitable for implementing one or more instructions, specifically suitable for loading and executing one or more instructions to achieve corresponding method flows or corresponding functions. The processor described in this embodiment can be used for the operation of a data computing method that coordinates over-the-air aggregation and edge offloading, including: This paper constructs a UAV-assisted integrated communication and computing system scenario, in which ground nodes have heterogeneous computing needs, including edge computing needs based on task offloading and function aggregation needs based on AirComp. The UAV simultaneously serves as an edge computing server and an AirComp aggregation center. The air-to-ground wireless communication link between the ground nodes and the UAV is modeled, establishing channel gain relationships and obtaining mathematical expressions for the UAV's received signals and computational task processing. Constraints on edge computing task processing capabilities, AirComp function aggregation accuracy, and air-to-ground communication resources are introduced to construct a unified system model. With minimizing overall system energy consumption as the optimization objective and computation offloading strategy, AirComp aggregation parameters, and communication resource allocation as optimization variables, a joint optimization problem of computation offloading and air aggregation is established. An energy-aware collaborative design method is used to jointly schedule the computation offloading decision and the AirComp aggregation process, characterizing the coupling relationship between communication and computing resources. Under the premise of satisfying all constraints, the joint optimization problem is iteratively solved, dynamically updating the computation offloading strategy, AirComp aggregation parameters, and communication resource allocation scheme. The above joint scheduling and iterative solution steps are repeated until the overall system energy consumption converges, obtaining the optimal combination of collaborative strategies for computation offloading and air aggregation.

[0079] Please see Figure 5 The terminal device is a computer device. In this embodiment, the computer device 60 includes a processor 61, a memory 62, and a computer program 63 stored in the memory 62 and executable on the processor 61. When executed by the processor 61, the computer program 63 implements the data calculation method for collaborative over-the-air aggregation and edge offloading in this embodiment. To avoid repetition, these details are not elaborated here. Alternatively, when executed by the processor 61, the computer program 63 implements the functions of each model / unit in the data calculation system for collaborative over-the-air aggregation and edge offloading in this embodiment. To avoid repetition, these details are not elaborated here.

[0080] Computer device 60 can be a desktop computer, laptop, handheld computer, cloud server, or other computing device. Computer device 60 may include, but is not limited to, a processor 61 and a memory 62. Those skilled in the art will understand that... Figure 5 This is merely an example of computer device 60 and does not constitute a limitation on computer device 60. It may include more or fewer components than shown, or combine certain components, or different components. For example, computer device may also include input / output devices, network access devices, buses, etc.

[0081] The processor 61 may be a Central Processing Unit (CPU), or other general-purpose processors, graphics processing units (GPUs), tensor processing units (TPUs), digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor.

[0082] The memory 62 can be an internal storage unit of the computer device 60, such as a hard disk or memory of the computer device 60. The memory 62 can also be an external storage device of the computer device 60, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc. equipped on the computer device 60.

[0083] Furthermore, the memory 62 may include both internal storage units of the computer device 60 and external storage devices. The memory 62 is used to store computer programs and other programs and data required by the computer device. The memory 62 can also be used to temporarily store data that has been output or will be output.

[0084] Please see Figure 6 The terminal device is an electronic device 600, which is manifested in the form of a general-purpose computing device. The components of the electronic device may include, but are not limited to: at least one processing unit 610, at least one storage unit 620, a bus 630 connecting different platform components (including storage unit 620 and processing unit 610), a display unit 640, etc.

[0085] The storage unit stores program code, which can be executed by the processing unit 610 to perform the steps described in the method section of this specification according to various exemplary embodiments of the present invention. For example, the processing unit 610 can perform actions such as... Figure 2 The steps are shown in the figure.

[0086] Storage unit 620 may include a readable medium in the form of a volatile storage unit, such as random access memory (RAM) 6201 and / or cache memory 6202, and may further include a read-only memory (ROM) 6203.

[0087] Storage unit 620 may also include a program / utility 6204 having a set (at least one) program module 6205, such program module 6205 including but not limited to: operating system, one or more application programs, other program modules and program data, each or some combination of these examples may include an implementation of a network environment.

[0088] Bus 630 can represent one or more of several types of bus structures, including a memory cell bus or memory cell controller, a peripheral bus, a graphics acceleration port, a processing unit, or a local bus using any of the multiple bus structures.

[0089] Electronic device 600 can also communicate with one or more external devices 700 (e.g., keyboard, pointing device, Bluetooth device, etc.), and with one or more devices that enable a user to interact with electronic device 600, and / or with any device that enables electronic device 600 to communicate with one or more other computing devices (e.g., router, modem). This communication can be performed via input / output interface 650. Furthermore, electronic device 600 can also communicate with one or more networks (e.g., local area network, wide area network, and / or public network, such as the Internet) via network adapter 660. Network adapter 660 can communicate with other modules of electronic device 600 via bus 630. It should be understood that, although not shown in the figures, other hardware and / or software modules can be used in conjunction with electronic device 600, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage platforms.

[0090] Example 4 This invention also provides a storage medium, specifically a computer-readable storage medium, which is a memory device in a terminal device for storing programs and data. It is understood that the computer-readable storage medium here can include both built-in storage media in the terminal device and extended storage media supported by the terminal device; it can be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. The computer-readable storage medium provides storage space that stores the terminal's operating system. Furthermore, the storage space also stores one or more instructions suitable for loading and execution by a processor, which can be one or more computer programs (including program code). More specific examples of the computer-readable storage medium include: an electrical connection with one or more wires, a portable disk, a hard disk, random access memory, read-only memory, erasable programmable read-only memory, optical fiber, portable compact disk read-only memory, optical storage device, magnetic storage device, or any suitable combination thereof.

[0091] Computer-readable storage media also include data signals propagated in baseband or as part of a carrier wave, carrying readable program code. Such propagated data signals can take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination thereof. A readable storage medium can also be any readable medium other than a readable storage medium that can send, propagate, or transmit a program for use by or in connection with an instruction execution system, apparatus, or device. The program code contained on the readable storage medium can be transmitted using any suitable medium, including but not limited to wireless, wired, optical fiber, radio frequency, etc., or any suitable combination thereof.

[0092] Program code for performing the operations of this invention can be written in any combination of one or more programming languages, including object-oriented programming languages ​​such as Java and C++, and conventional procedural programming languages ​​such as C or similar languages. The program code can execute entirely on the user's computing device, partially on the user's computing device, as a standalone software package, partially on the user's computing device and partially on a remote computing device, or entirely on a remote computing device or server. In cases involving remote computing devices, the remote computing device can be connected to the user's computing device via any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., via the Internet using an Internet service provider).

[0093] One or more instructions stored in a computer-readable storage medium can be loaded and executed by a processor to implement the corresponding steps of the data calculation method related to cooperative over-the-air aggregation and edge offloading in the above embodiments; one or more instructions in the computer-readable storage medium are loaded and executed by the processor to perform the following steps: This paper constructs a UAV-assisted integrated communication and computing system scenario, in which ground nodes have heterogeneous computing needs, including edge computing needs based on task offloading and function aggregation needs based on AirComp. The UAV simultaneously serves as an edge computing server and an AirComp aggregation center. The air-to-ground wireless communication link between the ground nodes and the UAV is modeled, establishing channel gain relationships and obtaining mathematical expressions for the UAV's received signals and computational task processing. Constraints on edge computing task processing capabilities, AirComp function aggregation accuracy, and air-to-ground communication resources are introduced to construct a unified system model. With minimizing overall system energy consumption as the optimization objective and computation offloading strategy, AirComp aggregation parameters, and communication resource allocation as optimization variables, a joint optimization problem of computation offloading and air aggregation is established. An energy-aware collaborative design method is used to jointly schedule the computation offloading decision and the AirComp aggregation process, characterizing the coupling relationship between communication and computing resources. Under the premise of satisfying all constraints, the joint optimization problem is iteratively solved, dynamically updating the computation offloading strategy, AirComp aggregation parameters, and communication resource allocation scheme. The above joint scheduling and iterative solution steps are repeated until the overall system energy consumption converges, obtaining the optimal combination of collaborative strategies for computation offloading and air aggregation.

[0094] The databases involved in the embodiments provided in this application may include at least one type of relational database and non-relational database. Non-relational databases may include, but are not limited to, blockchain-based distributed databases. The processors involved in the embodiments provided in this application may be general-purpose processors, central processing units, graphics processing units, digital signal processors, programmable logic devices, quantum computing-based data processing logic devices, etc., and are not limited to these.

[0095] To make the objectives, technical solutions, and advantages of the embodiments of the present invention clearer, the technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. The components of the embodiments of the present invention described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present invention provided in the accompanying drawings is not intended to limit the scope of the claimed invention, but merely to illustrate selected embodiments of the invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort are within the scope of protection of the present invention.

[0096] Simulation environment settings To verify the effectiveness of the collaborative optimization algorithm proposed in this invention, we conducted Monte Carlo simulations in a typical urban IoT scenario. The number of ground nodes was set... =20, with AirComp nodes and edge computing nodes each accounting for half. The drone's flight altitude is fixed at 100 meters, and the channel model adopts Ricean fading (…). =10dB). The comparison algorithms include: (1) Orthogonal Resource Allocation (OMA): Time-frequency resources are orthogonally divided into two types of tasks and optimized independently. (2) Fixed scheduling method: randomly select edge computing nodes and do not perform joint optimization; (3) The method of the present invention.

[0097] Please see Figure 3 The changes in the total energy consumption of the system in each iteration are presented. Figure 3 As can be seen, the system energy consumption shows a continuous downward trend as the number of iterations increases, indicating that the proposed algorithm can stably reduce the objective function value and achieve convergence within a reasonable number of iterations.

[0098] Please see Figure 4 The impact of AirComp aggregation accuracy on system energy consumption was evaluated. Simulation results show that as the MSE threshold gradually increases, the total system energy consumption decreases significantly. This is because a larger MSE threshold indicates that the system can tolerate greater computational errors during aggregation, meaning it has a higher tolerance for signal superposition errors. In this way, AirComp nodes can reduce the stringent requirements for signal amplitude alignment and interference suppression while maintaining computational accuracy.

[0099] In this scenario, AirComp nodes can transmit data with lower power, significantly reducing uplink power consumption. Simultaneously, due to the relaxed overall computational error constraints, the receiver does not need to maintain a high signal amplification gain, resulting in an optimal receiver scaling factor. This also reduces the energy consumption at the receiving end. Overall, a higher MSE threshold provides the system with greater flexibility in balancing computational accuracy and energy consumption, enabling the system to perform hybrid computing tasks in a more energy-efficient manner, thereby effectively improving overall energy efficiency.

[0100] Under the same mean square error (MSE) accuracy constraint ( Under the premise of 0.01, the method of this invention can achieve the goal at a lower power cost. This shows that the invention does not simply sacrifice computational accuracy for low power consumption, but rather maximizes energy efficiency while ensuring the accuracy of data fusion through precise beamforming (scaling factor optimization) and power control.

[0101] In summary, this invention provides a data computation method and system for coordinated airborne aggregation and edge offloading. By simultaneously using a UAV as an edge computing server and an airborne computing (AirComp) aggregation center, it achieves integrated collaborative processing of computation offloading and airborne aggregation, effectively solving the problems of limited coverage of fixed edge servers and the inability of a single computing mode to balance energy efficiency and performance. This invention constructs a unified system model for air-to-ground communication and computing, introducing power constraints, computing power constraints, and aggregation accuracy constraints. It performs joint optimization with the goal of minimizing total system energy consumption, employing time-slot scheduling and block coordinate descent (BCD) algorithms for efficient solution. This significantly reduces the energy consumption of ground terminal transmission and UAV computing while ensuring the reliability of AirComp aggregation and the quality of edge computing services. Compared with traditional solutions, this invention enables global collaborative allocation of communication and computing resources, improving system energy efficiency, convergence speed, and scenario adaptability. It is suitable for 6G air-to-ground integrated network scenarios such as wide-area sensing, emergency communication, and smart cities, possessing strong practicality and engineering application value.

[0102] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the above-described division of functional units and modules is merely an example. In practical applications, the above functions can be assigned to different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiments can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit. Furthermore, the specific names of the functional units and modules are only for easy differentiation and are not intended to limit the scope of protection of this application. The specific working process of the units and modules in the above system can be referred to the corresponding process in the foregoing method embodiments, and will not be repeated here.

[0103] In the above embodiments, the descriptions of each embodiment have different focuses. For parts that are not described in detail or recorded in a certain embodiment, please refer to the relevant descriptions of other embodiments.

[0104] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed in this invention can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0105] In the embodiments provided by this invention, it should be understood that the disclosed devices / terminals and methods can be implemented in other ways. For example, the device / terminal embodiments described above are merely illustrative. For instance, the division of modules or units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0106] The units described as separate components may or may not be physically separate. The components shown as units may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the units can be selected to achieve the purpose of this embodiment according to actual needs.

[0107] Furthermore, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0108] If the integrated module / unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, all or part of the processes in the methods of the above embodiments can also be implemented by a computer program instructing related hardware. The computer program can be stored in a computer-readable storage medium, and when executed by a processor, it can implement the steps of the various method embodiments described above. The computer program includes computer program code, which can be in the form of source code, object code, executable files, or certain intermediate forms. The computer-readable medium can include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, a read-only memory (ROM), a random-access memory (RAM), an electrical carrier signal, a telecommunication signal, and a software distribution medium, etc.

[0109] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus, and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0110] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0111] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0112] The above content is only for illustrating the technical concept of the present invention and should not be construed as limiting the scope of protection of the present invention. Any modifications made to the technical solution based on the technical concept proposed in this invention shall fall within the scope of protection of the claims of this invention.

Claims

1. A data calculation method for collaborative aerial aggregation and edge unloading, characterized in that, Includes the following steps: The scenario involves constructing an integrated communication and computing system assisted by unmanned aerial vehicles (UAVs), where ground nodes have heterogeneous computing requirements, including edge computing requirements based on task offloading and function aggregation requirements based on airborne computing (AirComp). The drone serves as both an edge computing server and an AirComp aggregation center. Model the air-to-ground wireless communication link between the ground node and the UAV, establish the channel gain relationship, and obtain the mathematical expressions for the UAV's received signals and computational task processing. A unified system model is constructed by introducing constraints on edge computing task processing capabilities, AirComp function aggregation accuracy, and air-to-ground communication resources. With minimizing the overall system energy consumption as the optimization objective, and with computation offloading strategy, AirComp aggregation parameters, and communication resource allocation as optimization variables, a collaborative optimization problem of joint computation offloading and air-to-air aggregation is established. An energy-aware collaborative design approach is adopted to jointly schedule the computation offloading decision and the AirComp aggregation process, thereby characterizing the coupling relationship between communication resources and computing resources. Under the premise of satisfying all constraints, the joint optimization problem is solved iteratively, and the computation offloading strategy, AirComp aggregation parameters and communication resource allocation scheme are dynamically updated. Repeat the joint scheduling and iterative solution steps described above until the overall system energy consumption converges, and obtain the optimal combination of cooperative strategies for computational offloading and in-flight aggregation.

2. The data calculation method for collaborative aerial aggregation and edge unloading according to claim 1, characterized in that, The ground node includes A collection of terminal devices with AirComp computing requirements. This indicates a collection of terminal devices with edge computing needs. It means, and The drone is configured to simultaneously receive and process data from a set. AirComp aggregated signals from mid-terminal devices and signals from collections Edge computing task data of terminal devices.

3. The data calculation method for collaborative aerial aggregation and edge unloading according to claim 1, characterized in that, The air-to-ground wireless communication link model adopts a time-slot scheduling-based model, dividing the flight mission cycle into... There are three time slots; within each time slot, AirComp nodes upload data simultaneously, and edge computing nodes are scheduled in a round-robin fashion, allowing only one edge computing node at a time. Unload the task.

4. The data calculation method for collaborative aerial aggregation and edge unloading according to claim 3, characterized in that, The air-to-ground wireless communication channel is modeled as a Ricean channel model, specifically: in, This represents the path loss at the reference distance. Indicates time slot With ground nodes Distance to UAV Indicates in time slot node Channel between UAV and UAV factor, and They represent time slots respectively ,node The line-of-sight and non-line-of-sight components of the channel between the UAV and the UAV.

5. The data calculation method for collaborative aerial aggregation and edge unloading according to claim 1, characterized in that, The air-to-ground communication resource constraints include: transmit power of edge offloading nodes satisfy ;in, Indicates edge unloading node Maximum transmission power; AirComp node Transmission power Limited to: ;in, Indicates the AirComp node Maximum transmission power; The aggregation accuracy constraint of the AirComp function is that the mean square error (MSE) of each time slot is less than or equal to a preset threshold. The mean square error (MSE) is calculated based on the difference between the UAV recovered signal and the expected received signal.

6. The data calculation method for collaborative aerial aggregation and edge unloading according to claim 1, characterized in that, The edge computing task processing capability constraints include: The total amount of unloaded data in all time slots meets the total amount of data that the ground nodes need to unload. The amount of data that a drone can process in each time slot is less than or equal to the upper limit of processing capacity determined by the drone's maximum computing frequency and the overhead per bit of data processing. The amount of data that an edge computing node can upload in each time slot is less than or equal to the data transmission limit determined by the achievable transmission rate of that time slot.

7. The data calculation method for collaborative aerial aggregation and edge unloading according to claim 1, characterized in that, The overall system energy consumption includes: the uplink transmission energy consumption of terminal devices with AirComp requirements on the ground node side throughout the entire mission cycle, the uplink transmission energy consumption of terminal devices with edge computing requirements during the flight mission cycle, and the computing energy consumption of the UAV when handling the unloading task. The optimization variables include: the transmission power of the AirComp node, the transmission power of the edge computing node, the receiver scaling factor of the drone, the amount of task offloading data of the edge computing node, and the scheduling binary variable of the edge computing node.

8. The data calculation method for collaborative aerial aggregation and edge unloading according to claim 1, characterized in that, An energy-aware collaborative design approach is adopted to jointly schedule the computation offloading decision and the AirComp aggregation process, specifically as follows: The joint optimization problem is decomposed into scaling factor optimization subproblem, power allocation optimization subproblem and scheduling optimization subproblem using the block coordinate descent (BCD) framework. To address the non-convex constraints in the power allocation optimization subproblem, auxiliary variables are introduced for relaxation, transforming it into a convex problem. For the integer programming variables in the scheduling optimization subproblem, relax them into continuous variables and transform them into a convex problem form.

9. The data calculation method for collaborative aerial aggregation and edge unloading according to claim 1, characterized in that, The condition for determining the convergence of the overall system energy consumption is: the decrease in the total system energy consumption obtained in the current iteration compared to the total system energy consumption obtained in the previous iteration is less than a set threshold; when the convergence condition is met, the current computation offloading strategy, AirComp aggregation parameters, and communication resource allocation scheme are output as the optimal combination of collaborative strategies.

10. A data computing system for collaborative aerial aggregation and edge offloading, characterized in that, include: The ground node module is used to generate heterogeneous computing requirements, which include edge computing tasks and AirComp function aggregation tasks. The drone module includes an edge computing server and an AirComp aggregation center, which are used to simultaneously receive and process edge computing tasks and AirComp aggregation signals from the ground node module; The communication link modeling module, connected to the ground node module and the UAV module, is used to establish an air-to-ground wireless communication channel model between the ground node and the UAV, and to obtain mathematical expressions for the UAV's received signals and computational task processing. The constraint construction module is used to introduce constraints on edge computing task processing capabilities, AirComp aggregation accuracy, and air-to-ground communication resources to build a unified system model. An optimization problem construction module, connected to the constraint construction module, is used to establish a collaborative optimization problem of joint computation offloading and over-the-air aggregation with the goal of minimizing the overall energy consumption of the system. The collaborative scheduling module is used to jointly schedule the computation offloading decision and the AirComp aggregation process using an energy-aware collaborative design method, and to characterize the coupling relationship between communication resources and computing resources. The iterative solution module, connected to the collaborative scheduling module, is used to iteratively solve the joint optimization problem under the premise of satisfying various constraints, and dynamically update the computation unloading strategy, AirComp aggregation parameters and communication resource allocation scheme. The convergence judgment module, connected to the iterative solution module, is used to determine whether the overall energy consumption of the system has converged, and outputs the optimal combination of cooperative strategies when convergence occurs.

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