Dynamic multi-source data aggregation and scheduling method, system and device based on the Internet of Things

Abstract

The present application discloses a method, system and device for dynamic multi-source data aggregation and scheduling based on the Internet of Things. The method sets multiple constraints in the process of dynamic multi-source data aggregation and scheduling based on the Internet of Things, and the multi-source data is data from different nodes of the Internet of Things; under the condition of satisfying multiple constraints, a first objective function is constructed, and the first objective function is to minimize data redundancy and maximize transmission efficiency of data transmitted in the process of dynamic multi-source data aggregation and scheduling based on the Internet of Things; according to the first objective function, a global target path set is confirmed; when the global target path set is used to aggregate and schedule the multi-source data, the ratio between the used resources of all Internet of Things nodes in the global target path set and the total resources of the nodes is calculated; according to the ratio, the resource allocation when the multi-source data is aggregated and scheduled is adjusted. The present application can improve the efficiency of task scheduling and reduce transmission delay.

Description

Technical Field

[0001] The present application relates to the technical field of data aggregation and scheduling, and in particular to a method, system and device for dynamic multi-source data aggregation and scheduling based on the Internet of Things. Background Art

[0002] With the rapid increase in the number of Internet of Things (IoT) devices, how to efficiently transmit data and schedule tasks in a dynamic, multi-node network environment has become an important technical challenge. Devices in IoT networks, such as smart home devices, sensor networks, drone systems, and autonomous vehicles, often need to work together in a widely distributed area and perform complex multi-tasking.

[0003] The traditional centralized method mainly relies on cloud servers for computing and storage. All data and tasks need to be aggregated and scheduled through the central node. For example, in a star topology, each node in the network is connected to a central node (also known as a central transfer station, generally a hub or switch) in a point-to-point manner, and the central node transmits information to the destination node. In a dynamic IoT network, the link status and network topology between nodes will continue to fluctuate as the device moves or the connection status changes. For example, when a drone cluster performs a task, the availability of the communication link between nodes is not stable due to the distance between drones and the influence of wireless signals. This brings huge challenges to data transmission path selection and data scheduling, especially when device resources are limited. How to improve data scheduling efficiency and reduce transmission delays is an urgent problem to be solved. Summary of the invention

[0004] The present application aims to propose a method, system and device for dynamic multi-source data aggregation and scheduling based on the Internet of Things, which can improve data scheduling efficiency and reduce transmission delay.

[0005] In a first aspect, an embodiment of the present application provides a dynamic multi-source data aggregation and scheduling method based on the Internet of Things, the method comprising:

[0006] Setting multiple constraints in the process of dynamic multi-source data aggregation and scheduling based on the Internet of Things, wherein the multi-source data is data from different Internet of Things nodes;

[0007] Under the condition that the plurality of constraints are met, a first objective function is constructed, wherein the first objective function is to minimize data redundancy and maximize transmission efficiency of data transmitted in a dynamic multi-source data aggregation and scheduling process based on the Internet of Things;

[0008] According to the first objective function, a global target path set is determined, wherein the global target path set includes at least two communication paths for data transmission, one communication path includes at least one link, one link includes two IoT nodes, and the IoT nodes are IoT devices;

[0009] When the multi-source data is aggregated and scheduled using the global target path set, a ratio between the used resources of all IoT nodes in the global target path set and the total resources of the nodes is calculated;

[0010] According to the ratio, resource allocation during aggregation and scheduling of the multi-source data is adjusted.

[0011] Compared with the prior art, the first aspect of the present application has the following beneficial effects:

[0012] The method sets multiple constraints in the process of dynamic multi-source data aggregation and scheduling based on the Internet of Things. The multi-source data is data from different Internet of Things nodes. Under the condition of satisfying multiple constraints, a first objective function is constructed. The first objective function is to minimize data redundancy and maximize transmission efficiency of data transmitted in the process of dynamic multi-source data aggregation and scheduling based on the Internet of Things. Under the condition of satisfying various constraints, an objective function of minimizing data redundancy and maximizing transmission efficiency can be constructed, which can improve the data transmission efficiency of the Internet of Things and reduce redundant information in the data transmission process; then, according to the first objective function, a global target path set is confirmed. When the global target path set is used to aggregate and schedule the multi-source data, the ratio between the used resources of all Internet of Things nodes in the global target path set and the total resources of the nodes is calculated. According to the ratio, the resource allocation when the multi-source data is aggregated and scheduled is adjusted, which can effectively optimize resource allocation and improve resource utilization.

[0013] In some embodiments, the plurality of constraints include:

[0014] Each aggregation task must be completed within a specified time window, the link data transmission flow is less than or equal to the total link capacity, the resource utilization is less than or equal to the first threshold, the storage capacity is less than or equal to the second threshold, the proportion of constrained redundant data is less than or equal to the third threshold, and at least two of the constraints of task flow conservation are met.

[0015] In some embodiments, the first objective function includes:

[0016] ;

[0017] ;

[0018] in, represents the total data redundancy, Indicates at time Next Participation Task of A collection of nodes, Indicates at time Next Participation Task of A collection of nodes, Representation Node and nodes The link between The link data traffic on Indicates the proportion of redundant data, represents the global target path set, Represents a collection of data nodes. represents the number of all nodes in the global target path set, represents the trade-off coefficient of data transmission efficiency, Indicates data transmission efficiency.

[0019] In some embodiments, the method further comprises:

[0020] predicting future link states between IoT nodes when aggregating and scheduling multi-source data through the global target path set;

[0021] constructing a second objective function that minimizes transmission delay according to the future link state;

[0022] According to the second objective function, a local target path set is determined, where the local target path set is a communication path set that minimizes transmission delay in the global target path set.

[0023] In some implementations, when performing multi-source data aggregation and scheduling through the global target path set, predicting future link states between IoT nodes includes:

[0024] When performing multi-source data aggregation and scheduling through the global target path set, predicting the node position of the IoT node at each moment;

[0025] Calculate the node visible window between two IoT nodes according to the node positions of the IoT nodes;

[0026] Constructing a dynamic network topology diagram according to the node visual window;

[0027] Predicting the link availability between two IoT nodes in the dynamic network topology graph;

[0028] A future link state between two IoT nodes in the dynamic network topology graph is predicted based on the link availability.

[0029] In some implementations, predicting the future link state between two IoT nodes in the dynamic network topology graph is performed in the following manner, including:

[0030] ;

[0031] in, express Node of time and nodes The future link state between and represents the regression coefficient, represents the order of the autoregressive model, Indicates Step, express The white noise term at time , Indicates The current moment of the stage link availability.

[0032] In some embodiments, the second objective function includes:

[0033] ;

[0034] in, represents the set of local target paths that minimize transmission delay, Indicates the start time of data transmission. Indicates the end time of data transmission. Indicates link Data transmission delay on represents the penalty coefficient for link state change, Indicates at time The predicted future link status.

[0035] In a second aspect, the embodiment of the present application further provides a dynamic multi-source data aggregation and scheduling system based on the Internet of Things, the system comprising:

[0036] A constraint condition setting unit, used to set multiple constraint conditions in a dynamic multi-source data aggregation and scheduling process based on the Internet of Things, wherein the multi-source data is data from different Internet of Things nodes;

[0037] An objective function construction unit, configured to construct a first objective function under the condition that the plurality of constraint conditions are satisfied, wherein the first objective function is to minimize data redundancy and maximize transmission efficiency of data transmitted in a dynamic multi-source data aggregation and scheduling process based on the Internet of Things;

[0038] a target path confirmation unit, configured to confirm a global target path set according to the first target function, wherein the global target path set includes at least two communication paths for data transmission, wherein one communication path includes at least one link, and one link includes two IoT nodes, wherein the IoT nodes are IoT devices;

[0039] A data aggregation and scheduling unit, configured to calculate a ratio between used resources of all IoT nodes in the global target path set and total node resources when aggregating and scheduling the multi-source data using the global target path set;

[0040] The resource allocation adjustment unit is used to adjust the resource allocation when the multi-source data is aggregated and scheduled according to the ratio.

[0041] In the third aspect, an embodiment of the present application also provides an electronic device, comprising at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the dynamic multi-source data aggregation and scheduling method based on the Internet of Things as described above.

[0042] In a fourth aspect, an embodiment of the present application further provides a computer-readable storage medium, wherein the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the above-mentioned dynamic multi-source data aggregation and scheduling method based on the Internet of Things.

[0043] It can be understood that the beneficial effects of the second to fourth aspects compared with the related art are the same as the beneficial effects of the first aspect compared with the related art. Please refer to the relevant description in the first aspect, and no further details will be given here. BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The above and / or additional aspects and advantages of the present application will become apparent and easily understood from the description of the embodiments in conjunction with the following drawings, in which:

[0045] Figure 1 It is a flow chart of an embodiment of a dynamic multi-source data aggregation and scheduling method based on the Internet of Things provided by the present application;

[0046] Figure 2 It is a flow chart of the best embodiment of the dynamic multi-source data aggregation and scheduling method based on the Internet of Things provided by this application;

[0047] Figure 3 It is a structural diagram of an embodiment of a dynamic multi-source data aggregation and scheduling system based on the Internet of Things provided in this application. DETAILED DESCRIPTION

[0048] The embodiments of the present application are described in detail below, and examples of the embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals throughout represent the same or similar elements or elements having the same or similar functions. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, and cannot be understood as limiting the present application.

[0049] In the description of this application, if there is a description of first, second, etc., it is only for the purpose of distinguishing technical features, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features or implicitly indicating the order of the indicated technical features.

[0050] In the description of the present application, it should be understood that the descriptions involving orientation, such as the orientation or positional relationship indicated as up, down, etc., are based on the orientation or positional relationship shown in the drawings, and are only for the convenience of describing the present application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, be constructed and operated in a specific orientation, and therefore should not be understood as a limitation on the present application.

[0051] In the description of this application, it should be noted that, unless otherwise clearly defined, terms such as setting, installing, connecting, etc. should be understood in a broad sense, and technicians in the relevant technical field can reasonably determine the specific meaning of the above terms in this application based on the specific content of the technical solution.

[0052] With the rapid increase in the number of Internet of Things (IoT) devices, how to efficiently transmit data and schedule tasks in a dynamic, multi-node network environment has become an important technical challenge. Devices in IoT networks, such as smart home devices, sensor networks, drone systems, and autonomous vehicles, often need to work together in a widely distributed area and perform complex multi-tasking.

[0053] The traditional centralized method mainly relies on cloud servers for computing and storage. All data and tasks need to be aggregated and scheduled through the central node. For example, in a star topology, each node in the network is connected to a central node (also known as a central transfer station, generally a hub or switch) in a point-to-point manner, and the central node transmits information to the destination node. In a dynamic IoT network, the link status and network topology between nodes will continue to fluctuate as the device moves or the connection status changes. For example, when a drone cluster performs a task, the availability of the communication link between nodes is not stable due to the distance between drones and the influence of wireless signals. This brings huge challenges to data transmission path selection and data scheduling, especially when device resources are limited. How to improve data scheduling efficiency and reduce transmission delays is an urgent problem to be solved.

[0054] In order to solve the above-mentioned problems of low data scheduling efficiency and high transmission delay, the present application proposes a dynamic multi-source data aggregation and scheduling method, system and device based on the Internet of Things.

[0055] Reference Figure 1 The embodiment of the present application provides a dynamic multi-source data aggregation and scheduling method based on the Internet of Things, and the method comprises the following steps:

[0056] Step S100, setting multiple constraints in the process of dynamic multi-source data aggregation and scheduling based on the Internet of Things, where the multi-source data is data from different Internet of Things nodes;

[0057] Step S200: Under the condition of satisfying multiple constraints, construct a first objective function, where the first objective function is to minimize data redundancy and maximize transmission efficiency of data transmitted in the process of dynamic multi-source data aggregation and scheduling based on the Internet of Things;

[0058] Step S300: confirm a global target path set according to the first objective function, where the global target path set includes at least two communication paths for data transmission, where one communication path includes at least one link, where one link includes two IoT nodes, and where the IoT nodes are IoT devices;

[0059] Step S400: when aggregating and scheduling multi-source data using the global target path set, calculating the ratio between the used resources of all IoT nodes in the global target path set and the total resources of the nodes;

[0060] Step S500: According to the ratio, adjust resource allocation when aggregating and scheduling multi-source data.

[0061] In this embodiment, by setting multiple constraints in the dynamic multi-source data aggregation and scheduling process based on the Internet of Things, the multi-source data is data from different Internet of Things nodes. Under the condition of satisfying multiple constraints, a first objective function is constructed. The first objective function is to minimize data redundancy and maximize transmission efficiency of data transmitted in the dynamic multi-source data aggregation and scheduling process based on the Internet of Things. Under the condition of satisfying various constraints, an objective function of minimizing data redundancy and maximizing transmission efficiency can be constructed, which can improve the data transmission efficiency of the Internet of Things and reduce redundant information in the data transmission process; then, according to the first objective function, the global target path set is confirmed. When the global target path set is used to aggregate and schedule the multi-source data, the ratio between the used resources of all Internet of Things nodes in the global target path set and the total resources of the nodes is calculated. According to the ratio, the resource allocation when the multi-source data is aggregated and scheduled is adjusted, which can effectively optimize resource allocation and improve resource utilization.

[0062] The above constraints can be used to constrain link data transmission flow, resource utilization and storage capacity in the process of dynamic multi-source data aggregation and scheduling based on the Internet of Things.

[0063] The above-mentioned communication path may be a path for transmitting data during communication.

[0064] In some embodiments, the plurality of constraints include:

[0065] Each aggregation task must be completed within a specified time window, the link data transmission flow is less than or equal to the total link capacity, the resource utilization is less than or equal to the first threshold, the storage capacity is less than or equal to the second threshold, the proportion of constrained redundant data is less than or equal to the third threshold, and at least two of the constraints of task flow conservation are met.

[0066] In this embodiment, multiple constraints include that each aggregation task must be completed within a specified time window, the link data transmission flow is less than or equal to the total link capacity, the resource utilization rate is less than or equal to the first threshold, the storage capacity is less than or equal to the second threshold, the redundant data ratio is less than or equal to the third threshold, and the task flow is conserved. These constraints can lay a good data foundation for the subsequent selection of the optimal path set.

[0067] The above data scheduling can be when a task runs, when it ends, and the dependencies between the tasks are correctly processed.

[0068] The specified time window may be from the earliest start time of the aggregated task to the latest completion time of the aggregated task.

[0069] The first threshold may be a limiting coefficient of resource utilization, or may be pre-set data, and this embodiment does not impose any specific limitation thereto.

[0070] The second threshold may be the storage capacity of each IoT node.

[0071] The third threshold may be an allowable ratio of redundant data, or may be a preset value, and this embodiment does not impose any specific limitation thereto.

[0072] The above constraint task flow conservation may be that the flow of each aggregation task must be maintained within the time window.

[0073] In some embodiments, the first objective function includes:

[0074] ;

[0075] ;

[0076] in, represents the total data redundancy, Indicates at time Next Participation Task of A collection of nodes, Indicates at time Next Participation Task of A collection of nodes, Representation Node and nodes The link between The link data traffic on Indicates the proportion of redundant data, represents the global target path set, Represents a collection of data nodes. represents the number of all nodes in the global target path set, represents the trade-off coefficient of data transmission efficiency, Indicates data transmission efficiency.

[0077] In this embodiment, by constructing a first objective function for minimizing data redundancy and maximizing transmission efficiency for data transmitted during dynamic multi-source data aggregation and scheduling based on the Internet of Things, it is possible to construct an objective function for minimizing data redundancy and maximizing transmission efficiency while satisfying various constraints, thereby improving the data transmission efficiency of the Internet of Things and reducing redundant information in the data transmission process.

[0078] In some embodiments, the method further comprises:

[0079] Predict future link states between IoT nodes when aggregating and scheduling multi-source data through a global target path set;

[0080] According to the future link status, a second objective function that minimizes transmission delay is constructed;

[0081] According to the second objective function, a local target path set is determined, where the local target path set is a communication path set that minimizes transmission delay in the global target path set.

[0082] In this embodiment, when multi-source data is aggregated and scheduled through the global target path set, the future link status between the IoT nodes is predicted, and then the second objective function that minimizes the transmission delay is constructed based on the future link status, and then the local target path set is determined based on the second objective function. In this way, by constructing the second objective function that minimizes the transmission delay based on the future link status, it is possible to adapt to the dynamically changing network topology in real time, and significantly reduce the transmission delay caused by node movement or link failure. And when multi-source data is aggregated and scheduled through the global target path set, the future link status between the IoT nodes is predicted, which can not only ensure the data transmission efficiency and reduce the redundant information in the data transmission process, but also reduce the transmission delay and reduce energy consumption.

[0083] In some implementations, when performing multi-source data aggregation and scheduling through a global target path set, predicting future link states between IoT nodes includes:

[0084] When aggregating and scheduling multi-source data through a global target path set, predict the node location of the IoT node at each moment;

[0085] According to the node positions of the IoT nodes, the node visible window between two IoT nodes is calculated;

[0086] Build a dynamic network topology diagram based on the node visible window;

[0087] Predict the link availability between two IoT nodes in a dynamic network topology graph;

[0088] Predict the future link status between two IoT nodes in a dynamic network topology graph based on link availability.

[0089] In this embodiment, when multi-source data aggregation and scheduling are performed through a global target path set, the node position of the IoT node at each moment is predicted, and the node visible window between two IoT nodes is calculated based on the node position of the IoT node. A dynamic network topology map is constructed based on the node visible window, and the link availability between the two IoT nodes in the dynamic network topology map is predicted. Based on the link availability, the future link state between the two IoT nodes in the dynamic network topology map is predicted. In this way, by analyzing the node visible window and constructing a dynamic network topology map, the communication link between nodes can be dynamically adjusted, thereby supporting the optimization of the data transmission path.

[0090] In some implementations, predicting the future link state between two IoT nodes in a dynamic network topology graph is performed in the following manner, including:

[0091] ;

[0092] in, express Node of time and nodes The future link state between and represents the regression coefficient, represents the order of the autoregressive model, Indicates Step, express The white noise term at time , Indicates The current moment of the stage link availability.

[0093] In some embodiments, the second objective function includes:

[0094] ;

[0095] in, represents the set of local target paths that minimize transmission delay, Indicates the start time of data transmission. Indicates the end time of data transmission. Indicates link Data transmission delay on represents the penalty coefficient for link state change, Indicates at time The predicted future link status.

[0096] In this embodiment, by constructing a second objective function that minimizes transmission delay to obtain a set of local target paths that minimize transmission delay, it is possible to adapt to dynamically changing network topology in real time and significantly reduce transmission delays caused by node movement or link failure.

[0097] To facilitate understanding by those skilled in the art, a set of best embodiments is provided below:

[0098] With the rapid increase in the number of Internet of Things (IoT) devices, how to efficiently transmit data and perform data scheduling tasks in a dynamic, multi-node network environment has become an important technical problem. Devices in IoT networks, such as smart home devices, sensor networks, drone systems, and autonomous vehicles, often need to work together in a widely distributed area to perform complex multi-tasking. Due to the uneven distribution and dynamic topology changes of IoT devices, data transmission and data scheduling face many challenges.

[0099] In the field of IoT, data transmission and task execution usually rely on centralized management and control systems. Devices are connected to the IoT network through wireless communication technologies (such as Wi-Fi, ZigBee, LoRa, and NB-IoT), and data is collected from various distributed IoT nodes to the central server for processing. Traditional centralized methods mainly rely on cloud servers for computing and storage, and all data and aggregation tasks need to be aggregated and scheduled through the central node.

[0100] In a dynamic IoT network, the link status and network topology between IoT nodes will fluctuate as the devices move or their connection status changes. For example, when a drone cluster performs a task, the availability of the communication link between IoT nodes is not stable due to the distance between drones and the influence of wireless signals. This poses a huge challenge to data transmission path selection and data scheduling. Especially when device resources are limited, how to improve the efficiency of aggregated task execution and reduce transmission delay is an urgent problem to be solved.

[0101] In the existing technology, many path selection and data scheduling algorithms are designed based on fixed network topology and cannot adapt to the dynamically changing IoT environment. In addition, the data redundancy problem is also very prominent in the IoT scenario. Multiple IoT nodes may collect the same data at the same time, resulting in increased redundant information in data transmission and occupying valuable bandwidth resources.

[0102] In view of the above shortcomings of the prior art, this embodiment aims to solve the problems of data transmission, data scheduling and resource management in the Internet of Things network through the following aspects. Specifically:

[0103] 1. Path optimization that adapts to dynamic network topology.

[0104] The technical solution of this embodiment can perceive the link status changes between network nodes in real time and dynamically adjust the data transmission path. By introducing macro time periods and micro time periods, this embodiment plans the data transmission path at the global level, and adjusts the path locally in real time according to the changes in the link status, ensuring the stability and efficiency of data transmission.

[0105] 2. Distributed collaborative computing and data scheduling.

[0106] This embodiment distributes task processing and data transmission among multiple IoT nodes through a distributed collaborative computing model, and no longer relies on the processing of a centralized server. This approach effectively reduces transmission bottlenecks and improves the flexibility of data scheduling. In a large-scale IoT network, this embodiment can realize the parallel processing of multiple tasks and reduce the delay in task execution.

[0107] 3. Dynamic resource management and optimization.

[0108] In view of the heterogeneous resources of IoT nodes, the technical solution of this embodiment dynamically adjusts resource allocation according to the computing power, storage resources and bandwidth usage of the nodes to ensure that tasks can be executed under the optimal resource configuration. This dynamic resource management method can avoid resource overload or waste, and improve the resource utilization rate and task execution efficiency of the system.

[0109] 4. Intelligent elimination of data redundancy.

[0110] In order to solve the data redundancy problem in the Internet of Things, this embodiment designs an intelligent data aggregation and redundancy elimination strategy. During data transmission and task processing, the system automatically identifies redundant information and performs optimization processing to reduce the transmission and storage of duplicate data. In this way, this embodiment can significantly improve the transmission efficiency of the system under limited bandwidth and storage resources.

[0111] In summary, the main purpose of this embodiment is to solve the defects of the prior art such as unstable data transmission path, unreasonable resource allocation, low data scheduling efficiency and data redundancy in the Internet of Things network by proposing a multi-scale dynamic aggregation model. By dynamically adjusting the data transmission path, optimizing the data scheduling strategy and intelligent resource management, this embodiment can significantly improve the data transmission efficiency and resource utilization in a complex and dynamic Internet of Things network environment.

[0112] This embodiment aims to solve the data transmission and data scheduling problems in dynamic IoT networks. The technical solution of this embodiment realizes path optimization, node selection, data scheduling and resource management. The core of the technical solution of this embodiment is to combine macroscopic path planning with microscopic path adjustment to effectively reduce the delay in data transmission and improve resource utilization and communication efficiency. Each function of this embodiment and its implementation method will be described in detail below. Specifically include the following contents:

[0113] 1. System architecture description.

[0114] In a dynamic IoT network, multiple nodes (the nodes in this embodiment may be IoT nodes) need to collaborate to complete multiple tasks. The system architecture includes the following parts:

[0115] 1.1 Data nodes and target nodes.

[0116] The system consists of several data nodes and several target nodes Each data node is responsible for data collection and transmits the collected data to the target node through other nodes or directly. The link availability between the data node and the target node is affected by dynamic factors such as node location parameters and link status.

[0117] 1.2 Node Visible Window.

[0118] Node Visibility Window It means in time When the node and nodes The time window during which direct communication is possible. This time window is calculated based on the location parameters of the nodes.

[0119] 1.3 Path selection and data scheduling.

[0120] Aggregation tasks in the system are distributed across multiple data nodes, and each aggregation task contains a specific start time and completion time. The goal of data scheduling is to optimize the data transmission path and intermediate node selection to improve transmission efficiency and reduce latency while meeting the aggregation task deadline.

[0121] 2. Constraint processing.

[0122] 2.1 Task time window constraints.

[0123] Each aggregation task It must be completed within its specified time window. Define binary variables , indicating the task Is it in time The constraints of the time window are:

[0124] ;

[0125] in, Indicates the earliest start time of the aggregation task, Indicates the latest completion time of the aggregated task.

[0126] The above-mentioned aggregation task is a multi-source data aggregation task (MDAT). The multi-source data aggregation task can be in the Internet of Things. For an aggregation task, it is necessary to obtain a specific type of data from multiple heterogeneous nodes and complete the data transmission and merging within a specified time window.

[0127] 2.2 Node visibility and link capacity constraints.

[0128] The availability of node communication links depends on the distance between nodes. and nodes The distance within a certain time t is less than the maximum communication range , then the link is available. Binary variable Indicates the availability of the link, and the total link capacity is given by The constraints on the total link capacity and link data transmission flow are:

[0129]

[0130] in, Indicates time The data transmission flow of the link.

[0131] 2.3 Resource utilization and storage capacity constraints.

[0132] The resource utilization of each node must be kept within a reasonable range to ensure task execution efficiency. The resource utilization constraints are:

[0133]

[0134] in, Indicates that at time t Resource utilization, Indicates that at time t The amount of resources allocated to subtask j, Indicates that at time t The resource utilization limit coefficient, Indicates that at time t In addition, the storage capacity of each node is also limited, and the storage capacity constraint of the node is:

[0135]

[0136] in, Representation Node The storage capacity at time t, Representation Node The amount of data generated for subtask j at time t, Indicates the total number of tasks.

[0137] 2.4 Redundant data ratio constraints.

[0138] To prevent redundant data from being generated during data transmission and aggregation, the redundant data ratio It must be within the allowed range. The constraints are:

[0139]

[0140] in, Indicates the allowed proportion of redundant data, Representation Node Storage capacity at time t.

[0141] 2.5 Task flow conservation constraints.

[0142] The flow of each task must be maintained within the time window. The constraints for task flow conservation are:

[0143]

[0144] in, Indicates participation in the task at time t The collection of nodes, Indicates the source point, represents the intermediate node, represents the sink point, The amount of data (i.e. traffic) required for all nodes, flow.

[0145] 3. Optimize goal setting.

[0146] The optimization goal of the system is to find the optimal path set under the premise of satisfying the above constraints. and a collection of data nodes , to minimize data redundancy and maximize data transmission efficiency The objective function (i.e. the first objective function) can be expressed as:

[0147]

[0148] in, represents the total data redundancy, Indicates at time Next Participation Task of A collection of nodes, Indicates at time Next Participation Task of A collection of nodes, Representation Node and nodes The link between The link data traffic on Indicates the proportion of redundant data, represents the global target path set, Represents a collection of data nodes. represents the number of all nodes in the global target path set, represents the trade-off coefficient of data transmission efficiency, Indicates data transmission efficiency.

[0149] 4. Detailed description of dynamic topology adaptation algorithm.

[0150] In order to cope with the problem of frequent topology changes in dynamic IoT networks, an adaptive data aggregation method is designed. This method can efficiently handle data transmission and aggregation between nodes. The dynamic topology adaptive algorithm can perceive the trajectory of nodes in real time and adjust the data transmission path accordingly, effectively managing the fluctuation of network link status. On this basis, a distributed collaborative computing model is adopted to allocate computing resources to multiple nodes and optimize data scheduling, so that data aggregation tasks can be processed collaboratively. Finally, by designing and implementing a heuristic algorithm, the problem of solving the mixed integer optimization model is solved, providing an efficient solution for data aggregation in a dynamic environment. This multi-level innovative approach works together in various dimensions to cope with the complexity in a dynamic network environment and provides strong technical support for data aggregation between data nodes.

[0151] In the data aggregation problem in dynamic IoT networks, the core challenge is to effectively select appropriate intermediate nodes and paths to transmit data distributed in different data nodes to the target node. To solve this problem, a path selection and resource management algorithm based on a multi-scale dynamic aggregation model is proposed to improve data aggregation efficiency and reduce transmission delay. The concept of node visible window (INVW) is introduced in this paper, that is, the node and The time window in which direct communication is possible at a certain time t. This time window is calculated based on the location parameters of the nodes and is used to construct a dynamic network topology graph. By analyzing the node visibility windows, the communication links between nodes can be dynamically adjusted to support the optimization of data transmission paths.

[0152] In order to more accurately analyze and optimize the data transmission path, this embodiment introduces a macro time cycle and micro time cycles The concept of macro time cycle It is a longer time interval during which the system performs global path planning and resource allocation to optimize data transmission efficiency over a long period of time. It is a short time interval during which the system dynamically adjusts paths and resource allocations to cope with rapid changes in network conditions.

[0153] In order to solve the data aggregation problem, the technical solution of this embodiment combines macro-path planning and micro-path adjustment, dynamically adjusting the data transmission path and intermediate node selection to achieve efficient data transmission and aggregation. At the macro level, the model performs global path planning over a long period of time, taking into account the overall topology and resource distribution of the dynamic IoT network, aiming to reduce transmission delays over a long period of time through global optimization. At the micro level, the model makes real-time adjustments to local paths in a short period of time to cope with dynamic changes in link status and resource usage between nodes.

[0154] To achieve the above objectives, this embodiment designs a time series analysis model for calculating and predicting the node visible window The prediction result is used to determine the optimal path set and the set of intermediate nodes , in order to minimize the delay in data transmission. The time series analysis model takes into account the following key aspects:

[0155] 4.1 Node location prediction.

[0156] First, the precise position of each node at the future moment is calculated using the node's motion parameters, including speed, direction, and position. In time Location :

[0157]

[0158] in, Representation Node In time The velocity vector, Representation Node At the initial time location.

[0159] It should be noted that the orbital dynamics of this embodiment is a prior art well known to those skilled in the art and will not be described in detail in this embodiment.

[0160] 4.2 Node visible window calculation.

[0161] In the dynamic topology adaptation algorithm, the node visible window Used to describe a period of time Internal Node For Node The visibility of is determined by the distance between nodes. Distance With Node Distance Less than or equal to the visible range for the first time When setting:

[0162] ;

[0163] When the distance between nodes exceeds the visible range again When setting:

[0164] ;

[0165] if and If all are determined, the visible window can be determined as:

[0166] ;

[0167] 4.3 Dynamic network topology diagram.

[0168] In a dynamic IoT network, the topology changes according to time t:

[0169] ;

[0170] Among them, the node set Contains all nodes involved in the communication, which can be expressed as:

[0171]

[0172] Use the previously defined visible window To determine the node and nodes Whether it is visible at time t. Dynamically build a communication link set based on the visibility of the node . Communication Link The conditions available at time t are:

[0173] ;

[0174] in, If the time In the visible window within the range.

[0175] Update the topology graph edge set at each time t:

[0176] ;

[0177] in, Indicates the visible window Effective.

[0178] 4.4 Prediction of inter-node link availability.

[0179] Based on the above orbit prediction, calculate any time node and Availability of direct communication link between By checking whether the distance between nodes is less than the maximum communication range To determine:

[0180] ;

[0181] When the link availability is 1, it means that at time node and There is a communication link between them, and the link can become part of the data transmission path. When the link availability is 0, it means that at time node and There is no communication link between them.

[0182] 4.5 Time Series Modeling.

[0183] To further optimize path selection, a time series analysis model is used to predict future state changes of links between nodes. Specifically, an autoregressive model is built using historical link availability data. Make predictions:

[0184]

[0185] in, and represents the regression coefficient, represents the white noise term, represents the order of the autoregressive model, Indicates In this way, future link availability can be predicted, so that path planning can be adjusted in advance.

[0186] 4.6 Path optimization based on link prediction.

[0187] After obtaining the predicted link availability, the time series analysis model outputs a possible link set and uses the dynamic network topology graph Perform path optimization. The path optimization problem can be described as a multi-objective optimization problem, whose goal is to minimize the transmission delay within a given time window:

[0188]

[0189] in, Indicates link Data transmission delay on represents the penalty coefficient for link state change, Indicates the start time of data transmission. Indicates the data transmission end time.

[0190] Through the above time series analysis model, the global path set can be determined at the macro scale , and adjust the path at the micro scale based on the real-time link status , to minimize delays in data transmission.

[0191] In addition, the resource management model is used to reasonably allocate the computing, storage, and bandwidth resources of each node to ensure the global path set All nodes on the IoT can efficiently complete data transmission and aggregation tasks. Used resources of IoT nodes Total resources of the node The relationship between can be expressed as:

[0192] ;

[0193] Through this measurement model, resource allocation can be effectively optimized and resource waste can be avoided. Represents the global path set The number of all nodes in .

[0194] like Figure 2 As shown, this embodiment provides a method for multi-task parallel processing and data aggregation optimization suitable for a dynamic Internet of Things environment. The specific steps are as follows:

[0195] Step 1: Obtain resource status and topology information of IoT devices: In a smart city environment with 50 sensor nodes and 10 edge computing nodes, the system collects the computing power, bandwidth usage, storage resources, and current network topology of each node in real time. By collecting this data, the system can understand the resource utilization and link status of the device in each time period.

[0196] Step 2: Calculate the link availability and transmission delay between devices: In the sensor network of the smart city described above, the system calculates the quality of the communication link between each sensor node and the edge computing node, and estimates the data transmission delay. If the distance between the two nodes is too far or the network is congested, the link availability decreases and the transmission delay increases.

[0197] Step 3: Build dynamic network topology: In a smart city environment, the system builds a real-time dynamic network topology map based on the link availability between nodes. As IoT devices (such as drones, smart vehicles, etc.) move, the topology map will be continuously updated to reflect the dynamic connection between nodes.

[0198] Step 4: Macro data scheduling and path planning: In the IoT system of smart cities, the system schedules global tasks based on the status of all sensors and edge computing nodes. First, the overall bandwidth utilization of the network, the distribution of computing resources of the nodes, etc. are evaluated, and then the optimal path is assigned to each task to minimize the delay and energy consumption of task execution.

[0199] Step 5: Dynamic resource allocation and data scheduling: In the smart city scenario, the environmental data collected by the sensor nodes will be processed in real time by the edge computing nodes. The system will prioritize high-priority tasks based on their importance and dynamically adjust the computing and storage resources of the edge computing nodes to ensure that the tasks are completed on time.

[0200] Step 6, micro-path adjustment: When some sensor nodes experience link failure due to increased distance or network congestion, the system will adjust the data transmission path in real time and reselect an alternative path with less delay to ensure the continuity of task execution and the stability of data transmission.

[0201] Step 7: Data transmission and aggregation: The environmental data collected by the sensor nodes is first aggregated by the edge computing nodes and then transmitted to the cloud for further processing. The system continuously optimizes the data transmission path and reduces redundant information in data transmission according to the network status of the device and the task execution.

[0202] Step 8: Dynamically adjust resource management: The system monitors the resource usage of each node in real time, such as the storage space and computing load of edge computing nodes. When a node is close to resource overload, the system will readjust the task allocation and transfer some tasks to nodes with more abundant resources to ensure the overall stable operation of the network.

[0203] The key innovation of this embodiment is the proposed multi-node collaborative data aggregation and transmission method, which aims to optimize resource utilization in the IoT network, reduce communication delays, and improve task execution efficiency. The specific effects include the following:

[0204] 1. The technical solution of this embodiment can adapt and adjust the topological changes of the IoT network in real time at different time scales. The model combines the two strategies of macroscopic path planning and microscopic path adjustment, performs global path planning in a longer time period, and dynamically adjusts local paths in a shorter time period to cope with the frequent changes in link status in the IoT network.

[0205] 2. In order to reduce data redundancy and improve task execution efficiency, this embodiment designs a dynamic task merging algorithm. The algorithm can dynamically merge tasks with overlapping or similar requirements according to the time window and resource requirements of the tasks, thereby reducing the number of tasks and reducing the energy consumption of data transmission.

[0206] 3. This embodiment introduces a distributed collaborative computing model, combined with a resource management model, which can dynamically adjust the allocation of computing resources and communication resources between data nodes. The model can optimize resource utilization according to the real-time needs of the task and the network status, avoid traffic overload or resource waste, and ensure the efficient execution of each task.

[0207] 4. In order to ensure that the system achieves optimal performance during the multi-task aggregation process, this embodiment constructs a mixed integer optimization model that covers multiple constraints such as task time window, link capacity, resource utilization, etc. Based on this model, this embodiment designs a heuristic algorithm to efficiently solve the optimization problem in a dynamic environment and achieve optimal path selection and resource allocation for data aggregation tasks.

[0208] In summary, compared with the existing optimal technology, this embodiment has significant advantages in data transmission and data scheduling in the Internet of Things network. First, the multi-scale dynamic aggregation model (MSDAM) combines macroscopic path planning with microscopic path adjustment, which can adapt to the dynamically changing network topology in real time and significantly reduce the transmission delay caused by node movement or link failure. Compared with the traditional path selection method based on fixed topology, this embodiment is more adaptable and flexible, especially in mobile device networks and large-scale distributed Internet of Things systems.

[0209] Secondly, this embodiment effectively solves the resource bottleneck problem under the centralized architecture through distributed collaborative computing and dynamic resource management. Dynamic resource allocation ensures that the computing and storage resources of each node can be optimally utilized, avoiding node overload or resource idleness, thereby improving the efficiency of overall task execution. In addition, the intelligent redundant data elimination strategy reduces redundant information in data transmission and improves network bandwidth utilization, especially in IoT networks with limited resources.

[0210] The advantage of the technical solution of this embodiment in the IoT scenario is that it can handle complex and dynamic network environments, especially multi-node collaboration, real-time data transmission and resource optimization. It not only improves the efficiency of task processing, but also effectively reduces data redundancy and energy consumption, and is suitable for many IoT application scenarios such as smart cities, industrial IoT, environmental monitoring, and smart transportation. For example:

[0211] 1.Multiple devices work together.

[0212] In the Internet of Things, multiple devices (such as sensors, drones, and smart home devices) may need to collaborate to perform multiple aggregation tasks. For example, in smart city applications, multiple surveillance cameras, environmental sensors, etc. need to collaborate to collect data. The technical solution of this embodiment can help coordinate multiple Internet of Things nodes to achieve parallel processing and optimized scheduling of aggregation tasks, thereby improving the overall utilization of network resources.

[0213] 2. Real-time data processing and transmission optimization.

[0214] Many applications of the Internet of Things, such as smart transportation, industrial Internet of Things (IoT), environmental monitoring, and smart agriculture, rely on the collection and transmission of real-time data. The technical solution of this embodiment can adjust the data transmission path in real time according to the dynamic changes of the network topology, ensuring efficient and reliable data transmission when the network conditions change. This is especially suitable for edge computing scenarios, where devices process data through local computing and then efficiently transmit it to the central node for further analysis.

[0215] 3. Data redundancy optimization and bandwidth efficiency improvement.

[0216] Data redundancy is a common problem in large-scale IoT systems. Multiple devices may collect similar or duplicate data, resulting in a waste of network bandwidth. This technology can significantly improve the bandwidth utilization efficiency of IoT networks by aggregating data, optimizing transmission paths, and reducing redundant information. This is especially important in resource-constrained IoT networks (such as low-power wide area networks LPWAN).

[0217] 4. Dynamic resource allocation and data scheduling.

[0218] IoT devices usually have limited resources, such as computing power, storage space, and communication bandwidth. The dynamic resource management strategy of the technical solution of this embodiment can perform intelligent resource allocation and data scheduling according to the real-time status and task requirements of IoT devices. By optimizing the communication and computing resource utilization between nodes, it is possible to avoid the phenomenon of overloading some nodes and wasting resources of other nodes. This is very critical in applications such as industrial control systems and smart grids that require high task reliability and time sensitivity.

[0219] 5. Applications in mobile device networks.

[0220] Mobile device networks in the Internet of Things, such as autonomous vehicles, drone clusters, logistics robots, etc., have characteristics similar to dynamic node networks, that is, nodes are constantly moving and communication link states change at any time. The technical solution of this embodiment can adapt to the topological changes of these mobile networks by dynamically adjusting the transmission path and task queue, ensuring the continuity of data transmission and the smooth completion of tasks.

[0221] Reference Figure 3 The embodiment of the present application also provides a dynamic multi-source data aggregation and scheduling system based on the Internet of Things. The system includes a constraint setting unit 100, an objective function construction unit 200, a target path confirmation unit 300, a data aggregation and scheduling unit 400 and a resource allocation adjustment unit 500, wherein:

[0222] The constraint condition setting unit 100 is used to set multiple constraint conditions in the process of dynamic multi-source data aggregation and scheduling based on the Internet of Things, where the multi-source data is data from different Internet of Things nodes;

[0223] An objective function construction unit 200 is used to construct a first objective function under the condition of satisfying multiple constraints, wherein the first objective function is to minimize data redundancy and maximize transmission efficiency of data transmitted in a dynamic multi-source data aggregation and scheduling process based on the Internet of Things;

[0224] The target path confirmation unit 300 is used to confirm a global target path set according to the first target function, where the global target path set includes at least two communication paths for data transmission, where one communication path includes at least one link, where one link includes two IoT nodes, and where the IoT nodes are IoT devices;

[0225] The data aggregation and scheduling unit 400 is used to calculate the ratio between the used resources of all IoT nodes in the global target path set and the total resources of the nodes when aggregating and scheduling multi-source data using the global target path set;

[0226] The resource allocation adjustment unit 500 is used to adjust the resource allocation when aggregating and scheduling multi-source data according to the ratio.

[0227] It should be noted that since the dynamic multi-source data aggregation and scheduling system based on the Internet of Things in this embodiment and the above-mentioned dynamic multi-source data aggregation and scheduling method based on the Internet of Things are based on the same inventive concept, the corresponding contents in the method embodiment are also applicable to the system embodiment and will not be described in detail here.

[0228] An embodiment of the present application also provides an electronic device, comprising: at least one control processor and a memory for communicating with the at least one control processor.

[0229] The memory, as a non-transient computer-readable storage medium, can be used to store non-transient software programs and non-transient computer executable programs. In addition, the memory may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some embodiments, the memory may optionally include a memory remotely disposed relative to the processor, and these remote memories may be connected to the processor via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0230] The non-transient software program and instructions required to implement the above-mentioned embodiment of a dynamic multi-source data aggregation and scheduling method based on the Internet of Things are stored in the memory. When executed by the processor, the above-mentioned embodiment of a dynamic multi-source data aggregation and scheduling method based on the Internet of Things is executed, for example, the above-mentioned Figure 1 The method comprises steps S100 to S500.

[0231] The system embodiments described above are merely illustrative, and the units described as separate components may or may not be physically separated, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0232] The present application also provides a computer-readable storage medium storing computer-executable instructions, which are executed by one or more control processors to enable the one or more control processors to execute a dynamic multi-source data aggregation and scheduling method based on the Internet of Things in the above method embodiment, for example, to execute the above described Figure 1 The functions of method steps S100 to S500 in the method.

[0233] It will be appreciated by those skilled in the art that all or some of the steps and systems in the methods disclosed above may be implemented as software, firmware, hardware, and appropriate combinations thereof. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or as hardware, or as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or transient medium). As known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cassettes, magnetic tapes, disk storage or other magnetic storage devices, or any other medium that can be used to store desired information and can be accessed by a computer. Furthermore, it is well known to those skilled in the art that communication media typically embodies computer readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0234] The above is a specific description of the preferred implementation of the embodiments of the present application, but the embodiments of the present application are not limited to the above-mentioned implementation methods. Technical personnel familiar with the field can also make various equivalent modifications or substitutions without violating the spirit of the embodiments of the present application. These equivalent modifications or substitutions are all included in the scope defined by the claims of the embodiments of the present application.

Claims

1. A dynamic multi-source data aggregation and scheduling method based on the Internet of Things, characterized in that: The method comprises: Setting multiple constraints in the process of dynamic multi-source data aggregation and scheduling based on the Internet of Things, wherein the multi-source data is data from different Internet of Things nodes; Under the condition that the plurality of constraints are met, a first objective function is constructed, wherein the first objective function is to minimize data redundancy and maximize transmission efficiency of data transmitted in a dynamic multi-source data aggregation and scheduling process based on the Internet of Things; According to the first objective function, a global target path set is determined, wherein the global target path set includes at least two communication paths for data transmission, one communication path includes at least one link, one link includes two IoT nodes, and the IoT nodes are IoT devices; When the multi-source data is aggregated and scheduled using the global target path set, a ratio between the used resources of all IoT nodes in the global target path set and the total resources of the nodes is calculated; According to the ratio, resource allocation during aggregation and scheduling of the multi-source data is adjusted.

2. The method for dynamic multi-source data aggregation and scheduling based on the Internet of Things according to claim 1 is characterized in that: The multiple constraints include: Each aggregation task must be completed within a specified time window, the link data transmission flow is less than or equal to the total link capacity, the resource utilization is less than or equal to the first threshold, the storage capacity is less than or equal to the second threshold, the proportion of constrained redundant data is less than or equal to the third threshold, and at least two of the constraints of task flow conservation are met.

3. The method for dynamic multi-source data aggregation and scheduling based on the Internet of Things according to claim 1 is characterized in that: The first objective function comprises: ； ； in, represents the total data redundancy, Indicates at time Next Participation Task of A collection of nodes, Indicates at time Next Participation Task of A collection of nodes, Representation Node and nodes The link between The link data traffic on Indicates the proportion of redundant data, represents the global target path set, Represents a collection of data nodes. represents the number of all nodes in the global target path set, represents the trade-off coefficient of data transmission efficiency, Indicates data transmission efficiency.

4. The method for dynamic multi-source data aggregation and scheduling based on the Internet of Things according to claim 1 is characterized in that: The method further comprises: predicting future link states between IoT nodes when aggregating and scheduling multi-source data through the global target path set; constructing a second objective function that minimizes transmission delay according to the future link state; According to the second objective function, a local target path set is determined, where the local target path set is a communication path set that minimizes transmission delay in the global target path set.

5. The method for dynamic multi-source data aggregation and scheduling based on the Internet of Things according to claim 4 is characterized in that: When aggregating and scheduling multi-source data through the global target path set, predicting future link states between IoT nodes includes: When performing multi-source data aggregation and scheduling through the global target path set, predicting the node position of the IoT node at each moment; Calculate the node visible window between two IoT nodes according to the node positions of the IoT nodes; Constructing a dynamic network topology diagram according to the node visual window; Predicting the link availability between two IoT nodes in the dynamic network topology graph; A future link state between two IoT nodes in the dynamic network topology graph is predicted based on the link availability.

6. The method for dynamic multi-source data aggregation and scheduling based on the Internet of Things according to claim 5 is characterized in that: The future link state between two IoT nodes in the dynamic network topology graph is predicted by: ； in, express Node of time and nodes The future link state between and represents the regression coefficient, represents the order of the autoregressive model, Indicates Step, express The white noise term at time , Indicates The current moment of the stage link availability.

7. The method for dynamic multi-source data aggregation and scheduling based on the Internet of Things according to claim 4 is characterized in that: The second objective function comprises: ； in, represents the set of local target paths that minimize transmission delay, Indicates the start time of data transmission. Indicates the end time of data transmission. Indicates link Data transmission delay on represents the penalty coefficient for link state change, Indicates at time The predicted future link status.

8. A dynamic multi-source data aggregation and scheduling system based on the Internet of Things, characterized in that: The system comprises: A constraint condition setting unit, used to set multiple constraint conditions in a dynamic multi-source data aggregation and scheduling process based on the Internet of Things, wherein the multi-source data is data from different Internet of Things nodes; An objective function construction unit, configured to construct a first objective function under the condition that the plurality of constraint conditions are satisfied, wherein the first objective function is to minimize data redundancy and maximize transmission efficiency of data transmitted in a dynamic multi-source data aggregation and scheduling process based on the Internet of Things; a target path confirmation unit, configured to confirm a global target path set according to the first target function, wherein the global target path set includes at least two communication paths for data transmission, wherein one communication path includes at least one link, and one link includes two IoT nodes, wherein the IoT nodes are IoT devices; A data aggregation and scheduling unit, configured to calculate a ratio between used resources of all IoT nodes in the global target path set and total node resources when aggregating and scheduling the multi-source data using the global target path set; The resource allocation adjustment unit is used to adjust the resource allocation when the multi-source data is aggregated and scheduled according to the ratio.

9. An electronic device, characterized in that: It includes at least one control processor and a memory for communicating with the at least one control processor; the memory stores instructions that can be executed by the at least one control processor, and the instructions are executed by the at least one control processor so that the at least one control processor can execute the dynamic multi-source data aggregation and scheduling method based on the Internet of Things as described in any one of claims 1 to 7.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to enable a computer to execute the dynamic multi-source data aggregation and scheduling method based on the Internet of Things as described in any one of claims 1 to 7.

Images