Two-stage joint optimization and collaborative transmission method for space-based Internet of Things

Abstract

This invention relates to a two-stage joint optimization and collaborative transmission method for space-based Internet of Things (IoT), belonging to the field of satellite network transmission technology. It addresses the shortcomings of existing single-stage rate allocation strategies, which struggle to coordinate resource allocation and task scheduling, failing to balance resource consumption and service quality, leading to transmission bottlenecks or resource waste. The method employs a two-stage collaborative mechanism to achieve optimized collaborative transmission. This mechanism establishes a two-stage collaborative framework between the first and second stages, adding inter-stage collaborative constraints in the second stage's resource optimization. Specifically: In the first stage, utility-driven collaborative sensing aims to maximize network utility and path efficiency by constructing a standardized objective function; in the second stage, resource optimization maintains synergy by using the optimal service order obtained in the first stage as a fixed parameter to optimize the transmission rate allocation strategy, constructing an objective function to minimize system resource consumption. This invention is applicable to data collaborative transmission in the agricultural field and can be applied to farmland monitoring.

Description

Technical Field

[0001] This invention relates to the field of satellite network transmission technology, and more specifically to a space-based Internet of Things (IoT) communication technology field. Background Technology

[0002] The application of space-based Internet of Things (IoT) networks in agriculture primarily involves the collaboration of space-based facilities such as satellite remote sensing and BeiDou navigation with ground-based sensors, drones, and intelligent agricultural machinery to construct an integrated "air-space-ground" monitoring system. This system enables real-time dynamic perception and data fusion analysis of key elements such as farmland environment, crop growth, soil moisture, and pests and diseases, providing scientific decision support for precision irrigation, fertilization, pesticide application, and disaster early warning. Compared to traditional agriculture's reliance on manual experience and scattered equipment, its advantages lie in overcoming spatial and temporal limitations, achieving large-scale, high-precision, and full-cycle monitoring, significantly improving resource utilization efficiency and operational standardization. Simultaneously, intelligent decision-making models drive the transformation of agricultural production from "human intervention" to "autonomous execution." However, due to the significant spatiotemporal correlation of task requirements, the transmission of large amounts of data within a limited spatial and temporal range can easily lead to uneven network traffic, causing network congestion and affecting transmission efficiency.

[0003] Chinese patent application CN 118138108 A, entitled "Remote Sensing Data Aggregation and Forwarding Method Based on Spatiotemporal Correlation," proposes a method for remote sensing data aggregation and forwarding based on spatiotemporal correlation. This method designs a demand aggregation mechanism based on spatiotemporal correlation, comprehensively considering user location and data spatiotemporal attributes. It uniformly allocates transmission paths for demands with strong spatiotemporal correlation to improve data forwarding decision efficiency. Based on a network utility maximization framework, it comprehensively considers link status and user needs for the forwarding of aggregated demands, maximizing the demand forwarding rate while balancing network load. However, in complex dynamic network environments, due to limited satellite link bandwidth and equipment power constraints, it is difficult to meet the high-frequency, low-latency data requests from multiple users. The lack of a coordination mechanism between resource allocation and task scheduling, relying solely on single-stage rate allocation to maximize network utility, makes it difficult to balance resource consumption and service quality, easily leading to transmission bottlenecks or resource waste.

[0004] Therefore, the core technical problem currently facing the application of space-based Internet of Things in agriculture is that, due to the significant spatiotemporal correlation of farmland monitoring tasks, the concentrated transmission of a large amount of data within a limited spatiotemporal range can easily lead to uneven network traffic, causing congestion and reducing transmission efficiency. At the same time, in the complex dynamic network environment where satellite link bandwidth is limited and equipment power is limited, the existing single-stage rate allocation strategy is difficult to coordinate resource allocation and task scheduling, and cannot balance resource consumption and service quality, resulting in transmission bottlenecks or resource waste. Summary of the Invention

[0005] This invention addresses the shortcomings of existing single-stage rate allocation strategies, which struggle to coordinate resource allocation and task scheduling, fail to balance resource consumption and service quality, and result in transmission bottlenecks or resource waste.

[0006] A two-stage joint optimization and collaborative transmission method for space-based Internet of Things (IoT) is proposed. This method is implemented based on a three-layer "space-ground-air" network framework. In this method:

[0007] User needs Data is segmented and transmitted via multiple paths based on spatiotemporal attributes for data aggregation and forwarding; a spatiotemporal correlation factor is introduced. The spatiotemporal correlation factor Including user space association factors and data spatiotemporal correlation factors User space correlation factor Indicates user location characteristics, data spatiotemporal correlation factor To represent the spatiotemporal characteristics of data, GeoSOT encoding is introduced, combined with a scheduling strategy based on Euclidean distance, to further refine user spatial association and data spatiotemporal association, and an overlap factor is introduced. Based on overlap factor Identify overlapping data areas, which are transmitted only once during transmission; construct a UAV-assisted transmission model, a communication equipment model, and a link weight model; establish a mapping relationship between demand and user services; and establish a correspondence between aggregated demand and UAV service order through a service mapping function.

[0008] The two-stage joint optimization and collaborative transmission method adopts a two-stage collaborative mechanism, which establishes a two-stage collaborative mechanism between the first stage and the second stage. In the resource optimization of the second stage, inter-stage collaborative constraints are added to ensure that a certain level of utility is maintained while optimizing resource consumption.

[0009] Further optimization of the scheme: In the first stage, utility-driven collaborative perception is implemented. With the goal of maximizing network utility and path efficiency, a standardized objective function is constructed, and the following constraints are set: spatiotemporal correlation constraints, path selection constraints, link selection constraints, device power constraints, UAV power constraints, service order and distance constraints. The optimization problem in the first stage is divided into a transmission rate optimization sub-problem P1 and a service order optimization sub-problem P2. The transmission rate optimization sub-problem P1 is used to solve for the optimal transmission rate based on a fixed service order; the service order optimization sub-problem P2 is used to solve for the optimal service order with a fixed transmission rate.

[0010] Further optimization of the scheme: In the second stage, resource optimization effectiveness is maintained synergistically: Under the premise of ensuring service quality, with the goal of minimizing system resource consumption, the network resource utilization efficiency is optimized. The optimal service order obtained in the first stage is used as a fixed parameter to optimize the transmission rate allocation strategy, and the objective function is constructed with the goal of minimizing system resource consumption.

[0011] Further optimization of the solution, the user requirements For: regional scope +Time range +At the user level, in the Named Data Network (NDN), the user's location information and user needs are transmitted through a naming mechanism. The spatiotemporal attribute information is encapsulated, transmitted, segmented, and transmitted back via multiple paths.

[0012] Further optimization of the solution: the aggregated requirements are as follows: for any two user requirements, if the following conditions are met: Then, by aggregating any two user requests, the aggregated request is obtained as follows:

[0013] .

[0014] A further preferred embodiment is the service mapping function M:

[0015] ,

[0016] The demand after aggregation is Map it to the corresponding service order .

[0017] Further optimization involves standardizing the objective function described in the first stage, transforming the maximization problem into a minimization framework to unify the optimization direction. The maximization problem is as follows:

[0018] ,

[0019] The minimum framework is:

[0020] ,

[0021] in, For a standardized network utility function, For the standardized path efficiency function, For standardized relaxation penalty terms, For path efficiency weights, To relax the penalty weights, S represents the order in which drones provide data forwarding services to ground-based IoT devices. For user needs in the first phase An optimal set of transmission rates is allocated.

[0022] In a further preferred embodiment, the method for solving the optimal transmission rate subproblem P1 based on a fixed service order is as follows: the optimal transmission rate is solved based on the Lagrange multiplier method and the KKT optimality condition; the method for solving the optimal service order subproblem P2 based on a fixed transmission rate is as follows: the optimal service order is solved through dynamic programming or chaotic evolution optimization algorithms.

[0023] Further optimization of the solution, the objective function in the second stage:

[0024] ,

[0025] in For power consumption standard items, For energy consumption standard items, For load balancing standard items , , These are the weighting coefficients. This represents the transmission rate that has been further optimized in the second phase.

[0026] In a further optimized approach, the inter-stage collaborative constraint added in the second stage of resource optimization is: introducing a resource-aware collaborative function. And tolerance mapping function Furthermore, the following collaborative constraints are added to the second-stage resource optimization model:

[0027] ,

[0028] in It is the dynamic adjustment of utility that reduces tolerance.

[0029] The beneficial effects of this invention are as follows:

[0030] This invention introduces a two-stage aggregation-forwarding strategy and a UAV-assisted transmission mechanism to construct a systematic solution based on spatial aggregation and centered on utility-resource collaborative optimization. The method first achieves task-level aggregation based on user spatial location and GeoSOT encoding, reducing redundant data transmission and significantly lowering bandwidth consumption. Then, through a two-stage joint optimization mechanism, it maximizes network utility while collaboratively optimizing transmission paths, service sequences, and resource allocation, effectively balancing link load and device energy consumption. In agricultural IoT applications, this method not only expands network coverage in remote areas and improves the real-time transmission guarantee level of critical data such as disaster early warning, but also enhances the overall system stability through energy consumption control and load balancing, thereby achieving efficient, energy-saving, and reliable space-based IoT data transmission in complex environments.

[0031] The two-stage joint optimization and collaborative transmission method for space-based Internet of Things described in this invention is applicable to the realization of collaborative data transmission in the agricultural field, for example, it can be applied to the field of farmland monitoring. Attached Figure Description

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0033] Figure 1 This is a schematic diagram of the "sky-ground-space" three-layer network framework proposed in this invention;

[0034] Figure 2 This is a schematic diagram of the two-stage joint optimization and collaborative transmission method for space-based Internet of Things proposed in this invention.

[0035] Figure 3 This is a diagram of the two-stage collaborative optimization framework proposed in this invention. Detailed Implementation

[0036] The technical solutions in the embodiments of the present invention will be clearly and completely described below. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. 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.

[0037] Implementation Method 1

[0038] This embodiment provides a two-stage remote sensing data aggregation and forwarding optimization method based on a utility maximization framework. The method is implemented based on a three-layer "space-ground-air" network framework. In this method:

[0039] User location information and user needs The spatiotemporal attribute information is encapsulated, transmitted, segmented, and multi-pathed back for data aggregation and forwarding; a spatiotemporal correlation factor is introduced. The spatiotemporal correlation factor Including user space association factors and data spatiotemporal correlation factors User space correlation factor Indicates user location characteristics, data spatiotemporal correlation factor To represent the spatiotemporal characteristics of data, GeoSOT encoding is introduced, combined with a scheduling strategy based on Euclidean distance, to further refine user spatial association and data spatiotemporal association, and an overlap factor is introduced. Based on overlap factor Identify overlapping data regions; these regions are transmitted only once during the transmission process.

[0040] Construct a drone-assisted transmission model, a communication equipment model, and a link weight model; establish a mapping relationship between demand and user services; and establish a correspondence between aggregated demand and drone service sequence through a service mapping function.

[0041] The two-stage joint optimization and collaborative transmission method adopts a two-stage collaborative mechanism, which establishes a two-stage collaborative mechanism between the first stage and the second stage. In the resource optimization of the second stage, inter-stage collaborative constraints are added to ensure that a certain level of utility is maintained while optimizing resource consumption.

[0042] Implementation Method 2

[0043] This implementation method further defines implementation method one, and provides an example to illustrate the "sky-ground-space" three-layer network framework, such as... Figure 1 As shown.

[0044] The "space-ground-air" three-layer network framework includes the LEO data source layer, the UAV layer, and the terrestrial IoT layer. The LEO data source layer is a satellite network composed of LEO remote sensing satellites, used for data acquisition and relay transmission. The UAV layer acts as a relay node, used to link the terrestrial IoT layer and the satellite layer for data forwarding. The terrestrial IoT layer is composed of various IoT devices and is the source and destination of data.

[0045] A network can be represented as a multidimensional extended graph. ,in:

[0046] Node set ,

[0047] Represents a set of satellite nodes.

[0048] Represents a set of drone nodes.

[0049] Represents the set of ground equipment nodes;

[0050] Link set ,

[0051] Indicates inter-satellite links.

[0052] This indicates the link between the satellite and the drone.

[0053] This indicates the link between the drone and ground equipment.

[0054] This indicates the link between ground equipment.

[0055] This indicates the effective connection time of the link, for each link. Its effective connection time is defined as: ,in Indicates the first The duration of each time slot.

[0056] Implementation Method 3

[0057] This embodiment further defines embodiment one, and provides examples to illustrate the data aggregation and forwarding, such as... Figure 2 As shown.

[0058] The user requirements Defined as: regional scope +Time range +User level.

[0059] In NDN, a naming mechanism is used to encapsulate user location information and its spatiotemporal attributes in interest packets:

[0060] ,

[0061] in refer to The specific content—including type, plot attributes, time attributes, and corresponding levels—is used for intra-satellite data retrieval; to achieve aggregation of needs and unified allocation of paths, the following is added to the header of the interest package: Fields representing users Spatial positioning.

[0062] After receiving an interest packet, a node in NDN parses its type and time-space attributes, searches for relevant entries in CS, PIT, and FIB, and segments the data that meets the user's requirements to obtain the data packet requested by the user. Remote sensing image data of the region, and using Indicate its size, use multipath for backhaul, and the naming format in the data packet is as follows:

[0063] ,

[0064] To achieve demand aggregation and unified path allocation, add the following to the packet header: fields, where This indicates the information of the path used when sending back data during the routing process (i.e., the path interface information in the node, where...). It is the local node identifier. (Corresponding next-hop node identifier), this identifier is used during automatic resolution based on the link's validity period. This allows local nodes to be informed of the next-hop node information in advance, ensuring the reliability of the backhaul link. These are the user location identifier and the LEO identifier where the request is located, respectively, used to assist in request aggregation.

[0065] Implementation Method 4

[0066] This embodiment further defines embodiment one, and provides examples to illustrate the user space association and data spatiotemporal association, such as... Figure 2 As shown.

[0067] I. Spatiotemporal Correlation Measurement Based on GeoSOT Encoding

[0068] To quantify the spatiotemporal correlation among remote sensing data, a spatiotemporal correlation factor is introduced. The spatiotemporal correlation factor Including user space association factors and data spatiotemporal correlation factors :

[0069]

[0070] in It is a triple representing the user's location and the spatiotemporal characteristics of the data.

[0071] Further refined into user spatial distance and data spatiotemporal distance:

[0072] User space association (using GeoSOT encoding):

[0073]

[0074] Data time correlation:

[0075] ,

[0076] Data spatial association (using GeoSOT encoding):

[0077] ,

[0078] Spatiotemporal correlation of data:

[0079] ,

[0080] in This indicates that two calculations are performed. The length of the common prefix of the encoding, and These are the prefix thresholds for user space association and data space association, respectively.

[0081] II. Aggregation of Related Requirements

[0082] For any two user needs, if the following conditions are met: Then, any two user requests will be aggregated into a set of related requests:

[0083] ,

[0084] in For set identifier, The required quantity in the set.

[0085] For overlapping data regions, they are transmitted only once during transmission, and an overlap factor is introduced. Determine overlapping region data:

[0086] ,

[0087] use Encoding can estimate the degree of spatial overlap by the proportion of common prefix lengths:

[0088] Spatial overlap

[0089] ,

[0090] It is the degree of time overlap

[0091] ,

[0092] in Spatial weight parameters , This is the maximum time difference considered.

[0093] The formula for calculating task size remains unchanged:

[0094] ,

[0095] in It is the main requirement identifier. It is a demand The original size.

[0096] The size of the set after association is:

[0097] ,

[0098] in It is the basic unit of data blocks. It is a demand The corresponding number of data blocks.

[0099] Implementation Method 5

[0100] This embodiment further defines embodiment one, providing examples to illustrate the UAV-assisted transmission model, communication equipment model, link weight model, and the mapping relationship between demand and user services. Figure 2 As shown.

[0101] I. Unmanned Aerial Vehicle (UAV) Assisted Transmission Model

[0102] The drone's flight distance is defined based on the distance between IoT devices:

[0103] ,

[0104] in Indicates time slot The order of services within, Indicates time slot The Middle One device being serviced, For equipment and Spacing.

[0105] Drone battery level updates:

[0106] ,

[0107] Among them Communication energy consumption coefficient For flight energy consumption coefficient, This represents the time slot length.

[0108] II. Communication Power Model

[0109] Relationship between device power and transmission rate:

[0110] ,

[0111] The device power is limited by the maximum power limit:

[0112] ,

[0113] .

[0114] III. Link Weight Model

[0115] Link weight is defined as a function of effective connection time, bandwidth, and round-trip time:

[0116] ,

[0117] in It is the link state factor, reflecting the dynamic characteristics of the link, and is defined as:

[0118] ,

[0119] here It is the standardized queue length, with a value in the range [0,1]. It is a standardized energy consumption level, with a value range of [0,1]. and This is a weighting coefficient, with a value range of (0,1), used to control the degree to which queue length and energy consumption affect link weight. This allows link weight to reflect the degree of network congestion and energy consumption.

[0120] To ensure that the link weight remains within a reasonable range under extreme conditions, when or When the value is close to 1, the following adjustment strategy is adopted:

[0121] ,

[0122] in It is a preset minimum link state factor value (e.g., 0.1) to ensure that the link can still be selected even under congestion or high energy consumption conditions.

[0123] The probability of link selection is directly proportional to the weight:

[0124] .

[0125] IV. Demand and User Service Mapping

[0126] Establish a mapping relationship between demand aggregation and drone service sequence, and define the service mapping function:

[0127] ,

[0128] For each aggregated demand set Map it to the corresponding service order :

[0129] ,

[0130] in It is a demand Service hours window.

[0131] Implementation Method Six

[0132] This embodiment further defines embodiment one and provides an example to illustrate the optimization of the first stage, such as... Figure 2 As shown.

[0133] I. Standardized Objective Function in the First Stage

[0134] The standardized objective function described in the first stage transforms the maximization problem into a minimization framework to unify the optimization direction. The maximization problem is as follows:

[0135] ,

[0136] The minimization framework is as follows:

[0137] ,

[0138] The standardized components are defined as follows:

[0139] A standardized network utility function represents a comprehensive indicator of the transmission efficiency of all tasks in the system.

[0140] ,

[0141] A standardized path efficiency function represents the degree of optimization of the drone service path.

[0142] ,

[0143] The standardized slack penalty term represents the overall degree of violation of various constraints.

[0144] ,

[0145] parameter It is a small positive number used to ensure the legality of the domain of the logarithmic function; , and These are standardized reference values; It is the path efficiency weight; It is a relaxation of penalty weights.

[0146] The expanded objective function is:

[0147] .

[0148] II. Constraints

[0149] Spatiotemporal correlation constraints: ,

[0150] Path selection constraints: ,

[0151] Link capacity constraints: Ensure that the selected link has sufficient bandwidth to transmit aggregated data.

[0152] ,

[0153] Link rate constraints: Ensure link speed The total transmission rate does not exceed its capacity.

[0154] ,

[0155] Device power constraints: Limit the power consumption of each device, where It is the penalty coefficient. These are slack variables that limit the total power consumption of the system. It is the penalty coefficient. It is a slack variable.

[0156] ,

[0157] ,

[0158] .

[0159] Drone power constraint: Ensure that the drone's battery level does not fall below a minimum threshold at any time, where It is the penalty coefficient. It is a slack variable.

[0160] ,

[0161] .

[0162] Service order and distance constraints:

[0163] ,

[0164] ,

[0165] in, The relaxation penalty coefficient is updated as follows: ,in It is a growth factor. This is an upper limit, ensuring that the slack variable gradually approaches zero during iteration. The specific initial value of the slack coefficient is set as follows: (Equipment power constraints) (Total power constraint) (Power constraints). These different initial values ​​reflect the priority of the constraints, with power constraints, as hard constraints, having the highest priority. These constraints ensure the rationality of resource allocation, while the introduction of slack variables makes the model more flexible and capable of handling complex situations in real-world scenarios.

[0166] III. Solving the transmission rate optimization subproblem P1

[0167] Fixed service order Solve for the optimal transmission rate.

[0168] ,

[0169] Fixed service order , As a constant term, the actual optimization objective of P1 simplifies to:

[0170] ,

[0171] The standardized network utility function is defined as follows:

[0172] ,

[0173] The standardized relaxation penalty term is defined as follows:

[0174] ,

[0175] in, It is a task Priority weights, It is a task Size, It is a task transmission rate It is a small positive number, ensuring that the domain of the logarithmic function is valid. It is a standardized reference value. It is to relax the penalty weight. It is a penalty for violating constraints related to transmission rate.

[0176]

[0177] ,

[0178] ,

[0179] ,

[0180] ,

[0181] ,

[0182] ,

[0183] ,

[0184] .

[0185] The objective function contains logarithmic terms. The objective function is strictly concave, guaranteeing the uniqueness of the global optimum. In the service ordering subproblem P1 of transmission rate optimization... and drone flight distance Treating it as a fixed parameter, not as an optimization variable, the Lagrangian function is constructed as follows:

[0186]

[0187]

[0188]

[0189] ,

[0190] in These are Lagrange multipliers, all of which are non-negative. It is a Lagrange multiplier constrained by link rate. It is a Lagrange multiplier constrained by the power consumption of the drone. It is the Lagrange multiplier of the equipment power constraint. Lagrange multipliers corresponding to the total power constraint of the equipment. The Lagrange multipliers corresponding to the link capacity constraint. and These are all known constants, not optimization variables.

[0191] According to the KKT optimality condition, taking the partial derivative of the Lagrange function and setting it to zero yields the following about... The optimality condition.

[0192] For transmission rate Find the partial derivatives:

[0193] ,

[0194] in Task Use links and Task In the time slot transmission .

[0195] Solving for the optimal transmission rate:

[0196] .

[0197] IV. Solving the service order optimization subproblem P2

[0198] (1) Solving by dynamic programming algorithm

[0199]

[0200]

[0201] ,

[0202] ,

[0203] ,

[0204] ,

[0205] ,

[0206] Among them, transmission rate subproblem Determined, as The known parameters.

[0207] Dynamic Programming State Space Design

[0208] State variable definition, taking into account The problem has multidimensional constraints, so we design a four-dimensional state space:

[0209] ,

[0210] The meaning of the state variables is as follows: A bitmask representation of the visited task set. Indicates the current task node number. Represents a discrete energy level index. This represents a discretized timestamp index.

[0211] State-space constraint set:

[0212] Access Collection Current location Battery ,time .

[0213] Discretization of power and time

[0214] Power discretization mapping:

[0215] ,

[0216] .

[0217] Time discretization mapping:

[0218] ,

[0219] .

[0220] Inverse mapping (quantization function):

[0221] ,

[0222] .

[0223] Derivation of state transition equations, basic state transitions

[0224] From state Transition to state

[0225] Transfer conditions:

[0226] ,

[0227] ,

[0228] ,

[0229] .

[0230] State transition recurrence relation:

[0231]

[0232] Transfer cost function design

[0233] Total transfer cost:

[0234]

[0235] Path distance cost

[0236] based on Encoding path cost:

[0237] ,

[0238] Where distance is defined as:

[0239] .

[0240] Constraints on the cost of violation

[0241] Power constraint violation:

[0242] .

[0243] Time window constraint violated:

[0244] .

[0245] Cost of violating comprehensive constraints:

[0246] .

[0247] Spatiotemporal correlation costs

[0248] For those belonging to the same spatiotemporal related set Task:

[0249]

[0250] Energy and time calculations for state transitions

[0251] Energy consumption model, from the task To the mission Total energy consumption:

[0252] .

[0253] Flight energy consumption: .

[0254] Communication power consumption: ,

[0255] in It is the optimal transmission rate obtained by solving P1.

[0256] Time consumption model

[0257] Flight time: ,

[0258] Service Hours: ,

[0259] Total time consumed: .

[0260] Boundary conditions and initialization

[0261] Initial state: Virtual initial state ,

[0262] in, Represents the virtual starting node number (usually set to...). ), This represents the initial battery level. The initial time is set to 0.

[0263] Boundary condition check: for each state transition Feasibility conditions need to be verified:

[0264] ,

[0265] Transfer effectiveness: .

[0266] Optimal value calculation

[0267] The status that all tasks have been accessed corresponds to ,

[0268] Global optimum: ,

[0269] Optimal path reconstruction employs a path backtracking method to reconstruct the optimal service sequence. Service sequence time segmentation divides the reconstructed path into service sequences for each time slot according to time windows.

[0270]

[0271] (2) Solving the chaotic evolution optimization algorithm

[0272] The chaotic evolutionary optimization algorithm is used to supplement the dynamic programming algorithm in optimizing the service sequence of large-scale remote sensing missions. It integrates the GeoSOT encoded information of the missions into the chaotic evolutionary optimization algorithm to represent the geospatial characteristics between missions, further improving search efficiency and spatial rationality. The chaotic evolutionary optimization algorithm and the dynamic programming algorithm optimize the exact same objective function, differing only in their solution methods.

[0273] Define the population as Among them, the individual It is a set of tasks A permutation of represents a set of candidate service ordering schemes. Each individual The fitness value is: ,in Path distance estimated by GeoSOT encoded prefix difference function This refers to the energy consumption corresponding to the service sequence. This represents penalties for violating task power constraints or time window constraints. The weighting coefficients control the relative importance of different objectives. Each individual retains its historical best value. The population simultaneously maintains the globally optimal solution. .

[0274] Based on the perturbation-driven evolution mechanism, in each iteration, the... While maintaining feasibility, each individual variable is subjected to chaotic perturbation. Triggering evolution operation:

[0275] ,

[0276] in, This represents perturbation operations (such as swapping, reversing, and insertion) in the permutation space. It is the perturbation strategy selection function. Represents the individual's historical best. This represents the best historical performance across the entire system.

[0277] The chaotic evolution optimization algorithm uses a linearly decreasing inertial weight mechanism to adjust the perturbation amplitude:

[0278] ,

[0279] in: , Indicates the current iteration number. This represents the maximum number of iterations. This strategy enables a smooth transition from large-perturbation exploration to small-perturbation fine-tuning. The search terminates and outputs upon reaching the maximum number of iterations. Alternatively, once the convergence condition is met, the chaotic evolution optimization algorithm outputs the globally optimal service order. As an approximate optimal solution to the P2 subproblem, it is provided for the scheduling system to execute.

[0280] Implementation Method Seven

[0281] This embodiment further defines embodiment one and provides an example to illustrate the optimization of the second stage, such as... Figure 2 As shown.

[0282] I. Standardized Objective Function in the Second Stage

[0283] With the goal of minimizing resource consumption:

[0284] ,

[0285] The standardized components are defined as follows:

[0286] The power consumption standard item represents the proportion of the system's total power consumption to the maximum allowable power.

[0287]

[0288] in This represents the total power consumption of all user equipment under the current scheduling scheme.

[0289] The energy consumption standard item indicates the degree of battery consumption of the drone:

[0290]

[0291] in This is the terminal's battery level after completing all tasks using the transmission rate y':

[0292] ,

[0293] Load balancing criteria indicate the uniformity of load distribution across network links:

[0294] ,

[0295] Link load rate: Average load factor: The weighting coefficient is set to (Power consumption weight) (Power consumption weight) (Load balancing weights).

[0296] II. Constraints

[0297] Utility maintenance constraints ensure that resource optimization does not significantly reduce network utility: ,in It is the dynamic adjustment of utility that reduces tolerance. Constraint satisfaction Defined as:

[0298] ,

[0299] Parameters are set to , , .

[0300] Hard constraints:

[0301]

[0302] Service order fixed constraint: ,in It is the optimal service order obtained by solving the first stage P2.

[0303] III. Link Weight Update

[0304] In the second phase, link weight updates are resource efficiency oriented:

[0305]

[0306] in =0.3 is the energy sensitivity parameter. =0.4 is the load sensitivity parameter. It is a link At any moment Standardized energy consumption, and These are the minimum and maximum link load rates in the network, respectively. This update mechanism prioritizes links with low energy consumption and light load, achieving a balance between load balancing and energy efficiency.

[0307] Implementation Method 8

[0308] This implementation method further defines implementation method one and provides an example to illustrate the two-stage collaborative mechanism, such as... Figure 3 As shown.

[0309] Design a two-stage synergistic mechanism to balance the conflict between utility preservation and resource conservation.

[0310] Resource-aware cooperative functions :

[0311]

[0312] in, It reflects the remaining power, electricity, and resource utilization of the system after scheduling, and characterizes the degree of resource pressure.

[0313] based on Introduce a dynamic tolerance mapping function :

[0314]

[0315] This function controls the maximum allowable decline in utility, which is the boundary of utility loss that the system can tolerate under resource constraints.

[0316] Inter-stage collaborative constraints: In the second-stage resource optimization model, the following collaborative constraints are added:

[0317]

[0318] That is, while optimizing resource consumption, it is still necessary to maintain a certain level of utility.

[0319] Implementation Method Nine

[0320] The above implementation methods were verified and solved using the following methods.

[0321] I. Convex Optimization Verification

[0322] Convexity analysis of the objective function: Second-stage objective function It consists of three components, including the power consumption item. It is about It is a linear function, therefore a convex function. Energy consumption term. Due to service order Fixed, flight distance Since it is a constant, this term simplifies to It is a linear function, therefore a convex function. Load balancing term It can be represented as a quadratic function , where the matrix It is positive semi-definite, therefore it is a convex function. Since... Furthermore, all three components are convex functions, and the property that a non-negative linear combination of convex functions is still a convex function can be utilized.

[0323] Convexity Verification of Constraint Functions: The constraint functions in the second-stage problem can be divided into two categories: linear constraints and special constraints. Linear constraints (such as power constraints, link capacity constraints, etc.) are clearly convex functions. The key is to verify the convexity of the utility-preserving constraints. The utility function It contains logarithmic terms and is a strictly concave function. Therefore For strictly convex functions, the entire constraint It is a convex function.

[0324] Convexity of the feasible region: The feasible region is defined as the intersection of all constraints.

[0325]

[0326] Due to all constraint functions All are convex functions, and the set defined by each constraint is a convex set. Furthermore, the intersection of convex sets is also a convex set. Therefore, the feasible region... It is a convex set.

[0327] To ensure strong duality and the necessity and sufficiency of the KKT conditions, we verify the Slater conditions. Let the optimal solution for the first stage be... ,if If all constraints are satisfied and there is slack space, then it exists. ,in This ensures that all inequality constraints are strictly satisfied. In summary, the objective function is a linear combination of non-negative weighted convex functions, which is still a convex function. All constraint functions are convex functions, the feasible region is a convex set, and the Slater condition is satisfied. This problem is a standard convex optimization problem, guaranteeing the existence and uniqueness of the global optimal solution, thus laying a theoretical foundation for efficient subsequent solutions.

[0328] II. Solution Method

[0329] (1) Gradient calculation

[0330] Gradient calculation of objective function:

[0331] .

[0332] Load balancing gradient calculation, assuming task Use link sets

[0333]

[0334] Constrained gradient:

[0335]

[0336] (2) KTT optimality condition

[0337] Construct the Lagrangian function:

[0338]

[0339] First-order optimality condition:

[0340]

[0341] in It is a task Service time set It is a task The set of links used.

[0342] Complementary relaxation conditions:

[0343]

[0344] (3) Solving using the interior point method

[0345] Barrier function construction

[0346]

[0347] in These are obstacle parameters. It is a numerical stability parameter.

[0348] Central path:

[0349] For each ,definition The unique optimal solution to the obstacle problem:

[0350]

[0351] Convergence guarantee:

[0352]

[0353] Newton direction calculation:

[0354]

[0355] Local quadratic convergence

[0356] when Sufficient hours: ,in It is the Lipschitz constant of the Hessian matrix. It is a strongly convex parameter.

Claims

1. A two-stage joint optimization and collaborative transmission method for space-based Internet of Things, characterized in that, The method is implemented based on a three-layer "space-ground-air" network framework, in which: User location information and user needs The spatiotemporal attribute information is encapsulated, transmitted, segmented, and multi-pathed back for data aggregation and forwarding; a spatiotemporal correlation factor is introduced. The spatiotemporal correlation factor Including user space association factors and data spatiotemporal correlation factors User space correlation factor Indicates user location characteristics, data spatiotemporal correlation factor To represent the spatiotemporal characteristics of data, GeoSOT encoding is introduced, combined with a scheduling strategy based on Euclidean distance, to further refine user spatial association and data spatiotemporal association, and an overlap factor is introduced. Based on overlap factor Identify overlapping data regions; these regions are transmitted only once during the transmission process. Construct a drone-assisted transmission model, a communication equipment model, and a link weight model; establish a mapping relationship between demand and user services; and establish a correspondence between aggregated demand and drone service sequence through a service mapping function. The two-stage joint optimization and collaborative transmission method adopts a two-stage collaborative mechanism, which establishes a two-stage collaborative mechanism between the first stage and the second stage. In the resource optimization of the second stage, inter-stage collaborative constraints are added to ensure that a certain level of utility is maintained while optimizing resource consumption.

2. The two-stage joint optimization and collaborative transmission method for space-based Internet of Things according to claim 1, characterized in that, In the first stage, utility-driven collaborative perception aims to maximize network utility and path efficiency. A standardized objective function is constructed, and the following constraints are set: spatiotemporal correlation constraints, path selection constraints, link selection constraints, device power constraints, UAV power constraints, and service order and distance constraints. The optimization problem in the first stage is divided into a transmission rate optimization sub-problem P1 and a service order optimization sub-problem P2. The transmission rate optimization sub-problem P1 solves for the optimal transmission rate based on a fixed service order, and the service order optimization sub-problem P2 solves for the optimal service order based on a fixed transmission rate.

3. The two-stage joint optimization and collaborative transmission method for space-based Internet of Things according to claim 2, characterized in that, In the second stage, resource optimization effectiveness is maintained synergistically: under the premise of ensuring service quality, with the goal of minimizing system resource consumption, the network resource utilization efficiency is optimized. The optimal service order obtained in the first stage is used as a fixed parameter to optimize the transmission rate allocation strategy, and the objective function is constructed with the goal of minimizing system resource consumption.

4. The two-stage joint optimization and collaborative transmission method for space-based Internet of Things according to claim 1, characterized in that, The user requirements For: regional scope +Time range At the user level, the naming mechanism in the Named Data Network (NDN) encapsulates, transmits, segments, and performs multi-path backhaul of the user's location information and its spatiotemporal attributes.

5. The two-stage joint optimization and collaborative transmission method for space-based Internet of Things according to claim 1, characterized in that, The aggregated requirements are defined as follows: for any two user requirements, if the following conditions are met: Then, any two user requirements will be aggregated to obtain the aggregated requirements: .

6. The two-stage joint optimization and collaborative transmission method for space-based Internet of Things according to claim 5, characterized in that, The service mapping function M: ， For the aggregated demand Map it to the corresponding service order .

7. The two-stage joint optimization and collaborative transmission method for space-based Internet of Things according to claim 2, characterized in that, The standardized objective function described in the first stage transforms the maximization problem into a minimization framework to unify the optimization direction. The maximization problem is as follows: ， The minimization framework is as follows: ， in, For a standardized network utility function, For the standardized path efficiency function, For standardized relaxation penalty terms, For path efficiency weights, To relax the penalty weights, The order in which drones provide data forwarding services to ground-based IoT devices. For user needs in the first phase An optimal set of transmission rates is allocated.

8. The two-stage joint optimization and collaborative transmission method for space-based Internet of Things according to claim 2, characterized in that, The method for solving the optimal transmission rate subproblem P1 based on a fixed service order is as follows: the optimal transmission rate is solved based on the Lagrange multiplier method and the KKT optimality condition; the method for solving the optimal service order subproblem P2 based on a fixed transmission rate is as follows: the optimal service order is solved by dynamic programming or chaotic evolution optimization algorithm.

9. The two-stage joint optimization and collaborative transmission method for space-based Internet of Things according to claim 3, characterized in that, The objective function described in the second stage: ， in For power consumption standard items, For energy consumption standard items, For load balancing standard items, , , These are the weighting coefficients. This represents the transmission rate that has been further optimized in the second phase.

10. The two-stage joint optimization and collaborative transmission method for space-based Internet of Things according to claim 9, characterized in that, The new inter-stage collaborative constraint added in the second-stage resource optimization is: introducing a resource-aware collaborative function. And tolerance mapping function Furthermore, the following collaborative constraints are added to the second-stage resource optimization model: ， in It is the dynamic adjustment of utility that reduces tolerance. It is the optimal solution for the first stage.

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