Network state-aware internet of things card device grouping optimization method and system

Abstract

The application provides a network state-aware Internet of Things card device grouping optimization method and system, and relates to the technical field of device grouping optimization. The method comprises the following steps: obtaining Internet of Things card devices connected to a network, defining a grouping ground state and a communication utility; performing device static grouping processing based on a space-time gravity relationship for the Internet of Things card devices, and determining device static grouping; constructing a dynamic grouping component according to the device static grouping, the grouping ground state and the communication utility, triggering the dynamic grouping component to perform grouping ground state collapse and consensus game binding decision through the acquisition of task flow data and real-time network state, and determining device task grouping; and performing task aggregation resource application and in-group optimization allocation control for the device task grouping. The technical problems of low communication efficiency and resource mismatching of the existing Internet of Things device grouping are solved.

Description

Technical Field

[0001] This invention relates to the field of device grouping optimization, and more particularly to a method and system for optimizing IoT card device grouping based on network status awareness. Background Technology

[0002] With the continuous expansion of IoT application scenarios, the number of IoT SIM card devices has exploded, and the diversity and heterogeneity of device types, business needs and network status have significantly increased.

[0003] However, traditional IoT device grouping methods rely on a single dimension such as device physical location and hardware type for static division. This fails to perceive dynamic changes in network status in real time and does not fully adapt to differentiated business needs, resulting in a serious lack of grouping flexibility. Consequently, this leads to low communication efficiency, resource mismatch, and difficulty in meeting the core requirements of IoT services for dynamic grouping adaptability and network status awareness in various scenarios.

[0004] Therefore, there is an urgent need for a network state-aware IoT SIM card device grouping optimization method to solve the rigidity problem of traditional static grouping and improve grouping adaptability and resource utilization efficiency. Summary of the Invention

[0005] This invention addresses the technical problems of low communication efficiency and resource mismatch in IoT device grouping in existing technologies by providing an IoT card device grouping optimization method and system based on network status awareness.

[0006] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: In a first aspect, the present invention provides a method for optimizing IoT card device grouping based on network state awareness, including: IoT SIM card devices that acquire network access define packet base state and communication utility; For the IoT card device, perform static grouping processing based on spatiotemporal gravitational relationship to determine the static grouping of the device; A dynamic grouping component is constructed based on the device static grouping, group base state, and communication utility. By acquiring task flow data and real-time network status, the dynamic grouping component is triggered to perform group base state collapse and consensus game binding decision to determine device task grouping. For the device task groups, perform task aggregation resource application and optimized allocation management within the group.

[0007] Secondly, the present invention provides a network state-aware IoT card device packet optimization system, comprising: The device acquisition module is used to acquire IoT card devices that are connected to the network and to define the packet base state and communication utility. The static grouping module is used to perform device static grouping processing based on spatiotemporal gravitational relationship for the IoT card device, and determine the device static grouping; The task grouping module is used to construct a dynamic grouping component based on the device static grouping, the group base state and the communication utility. By acquiring task flow data and real-time network status, the dynamic grouping component is triggered to perform group base state collapse and consensus game binding decision to determine the device task grouping. The allocation and control module is used to perform task aggregation resource application and optimized allocation and control within the group for the task groups of the device.

[0008] The beneficial effects of this invention are: Compared to existing technologies, this application first acquires information about IoT SIM card devices accessing the network, defines the packet ground state and communication utility, and establishes a quantitative reference standard for the packet ground state and communication utility that characterizes the device's demand tendencies, providing a precise data and parameter foundation for subsequent static and dynamic packetization. Secondly, for IoT SIM card devices, it performs static packetization processing based on spatiotemporal gravitational relationships to determine the static packetization, enabling IoT SIM card devices to aggregate based on high correlation, providing reliable support for subsequent dynamic packetization, and also enabling the early establishment of basic collaborative channels such as sidelinks for devices within the group. Thirdly, based on the device static packetization, packet ground state, and communication utility, a dynamic packetization component is constructed. By acquiring task flow data and real-time network status, the dynamic packetization component is triggered to perform packet ground state collapse and consensus game binding decisions to determine device task groups, forming device task groups that accurately match tasks and network status. This solves the pain points of traditional static packetization being rigid and having poor adaptability, and can meet the multi-task, highly dynamic collaborative needs of the IoT. Finally, for device task groups, resource application and optimization allocation management are carried out to aggregate tasks, providing accurate and efficient resource support for the dynamic grouping of IoT card devices and ensuring that devices within a group can collaboratively utilize resources to complete tasks.

[0009] Through the above technical solution, this application solves the problem of rigid grouping in traditional IoT card devices, achieves accurate adaptation to dynamic network conditions and differentiated service requirements, improves communication efficiency and resource utilization efficiency, and adapts to the dynamic needs of various types of IoT services. Attached Figure Description

[0010] Figure 1 A flowchart illustrating the IoT card device grouping optimization method based on network state awareness provided by the present invention; Figure 2 This is a schematic diagram of the structure of the IoT card device grouping optimization system based on network state awareness provided by the present invention.

[0011] In the attached diagram, the components represented by each number are as follows: Device acquisition module 11, static grouping module 12, task grouping module 13, allocation and control module 14. Detailed Implementation

[0012] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0013] In the description of this invention, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified.

[0014] In the description of this invention, the term "for example" is used to mean "used as an example, illustration, or description." Any embodiment described as "for example" in this invention is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is provided to enable any person skilled in the art to make and use the invention. Details are set forth in the following description for purposes of explanation. It should be understood that those skilled in the art will recognize that the invention can be made without using these specific details. In other instances, well-known structures and processes will not be described in detail to avoid obscuring the description of the invention with unnecessary detail. Therefore, the invention is not intended to be limited to the embodiments shown, but is consistent with the broadest scope of the principles and features disclosed herein.

[0015] Example 1, as Figure 1 As shown, this embodiment of the invention provides a network state-aware IoT card device packet optimization method, including: S10: Obtain network access from IoT card devices and define packet ground state and communication utility.

[0016] The IoT devices in IoT scenarios are numerous and highly heterogeneous, with significant differences in latency tolerance, power consumption requirements, bandwidth consumption, and communication capabilities. However, traditional IoT device grouping methods often rely on static classification based on a single dimension such as geographical location and device type. This approach fails to adequately consider the differentiated needs of devices or to quantitatively assess their communication capabilities. Consequently, the grouping results are difficult to accurately match with the inherent attributes of devices, cannot meet their personalized needs, and are ill-suited to dynamically changing business scenarios. This can easily lead to problems such as resource mismatch and low communication efficiency.

[0017] To address the aforementioned issues, this application obtains IoT SIM card devices with network access and defines their packet base state and communication utility. For example, relying on IoT network access protocols (such as NB-IoT, 5G NR), IoT SIM card unique identifiers (such as IMSI, IMEI), and real-time access logs from the network management platform, the current network environment can be scanned to filter out IoT SIM card devices that have successfully accessed the network, are online, and are functioning normally, while excluding devices that are not connected, offline, or faulty, thus forming a pool of IoT SIM card devices to be grouped. For instance, in a smart park scenario, the park's IoT management platform can identify all currently online smart access control sensors, air conditioning control terminals, parking space monitoring equipment, and other network-accessible IoT SIM card devices.

[0018] Furthermore, step S10 of the method also includes: Define a grouping dimension, wherein the grouping dimension includes at least orthogonal delay-sensitive ground states, power-sensitive ground states, and bandwidth-greedy ground states; For each IoT SIM card device, a probability distribution vector based on the group dimension is defined as the group ground state, wherein the IoT SIM card device corresponds one-to-one with the group ground state; Define utility factors, wherein the utility factors include at least communication quality and behavior vectors; For each IoT card device, a factor characteristic based on the aforementioned utility factor is defined as the communication utility, wherein there is a one-to-one correspondence between the IoT card device and the communication utility.

[0019] In this embodiment, a grouping dimension is first defined, which includes at least orthogonal time-delay-sensitive ground states, power-sensitive ground states, and bandwidth-greedy ground states. The orthogonality of the grouping dimensions means that multiple grouping dimensions exist independently, do not overlap, and do not interfere with each other, allowing for the characterization of the device's composite attributes from different perspectives.

[0020] For example, the grouping dimension includes at least three orthogonal ground states: a latency-sensitive ground state, a power-sensitive ground state, and a bandwidth-greedy ground state. The latency-sensitive ground state reflects the device's inherent tolerance limit for communication or response latency, directly determined by the device's hardware configuration and core functional requirements. The power-sensitive ground state reflects the device's inherent concern for energy consumption, determined by the power supply method and battery life design goals of different devices. The bandwidth-greedy ground state reflects the inherent bandwidth consumption intensity of the device due to functional requirements, determined by the device's data acquisition capabilities and the characteristics of the transmitted content. This design, using the device's own attributes as the grouping dimension, is because the same device may switch between different scenarios, but the three dimensions of latency, power consumption, and bandwidth remain stable. Subsequent static and dynamic grouping does not need to passively adapt to the scenario; it only needs to match the task's requirements for latency, power consumption, and bandwidth based on these inherent attributes of the device to achieve more flexible and accurate grouping, avoiding the grouping failure problem caused by scenario changes.

[0021] Secondly, based on the grouping dimension, a probability distribution vector based on the grouping dimension is defined for each IoT SIM card device as the grouping ground state, where each IoT SIM card device corresponds one-to-one with the grouping ground state. The probability distribution vector is a quantitative description of the demand tendency of IoT SIM card devices in different grouping dimensions, specifically representing the demand proportion of IoT SIM card devices in each dimension, with the sum of these proportions being 1.

[0022] For example, if the grouping dimension includes orthogonal latency-sensitive, power-sensitive, and bandwidth-greedy ground states, then each IoT SIM card device will generate a probability distribution vector based on the grouping dimension as the grouping ground state. For instance, the probability distribution vector of an industrial control sensor might be [0.8, 0.1, 0.1], representing that its demand tendency is 80% low latency, 10% power consumption, and 10% bandwidth. Similarly, the probability distribution vector of an outdoor environment sensor might be [0.1, 0.8, 0.1], representing that its demand tendency is 80% low power consumption, 10% latency, and 10% bandwidth. In this way, a one-to-one correspondence can be established between IoT SIM card devices and grouping ground states, giving each IoT SIM card device a unique quantitative label for its demand tendency, providing a precise basis for subsequent matching of business needs.

[0023] Secondly, utility factors are defined based on the communication capabilities of the IoT SIM card device. These utility factors should include at least communication quality and behavioral vectors: Communication quality measures the stability and reliability of the IoT SIM card device's communication. For example, metrics such as packet loss rate, signal-to-noise ratio, and communication latency jitter can be used as indicators of communication quality. Behavioral vectors measure the temporal and frequency patterns of the IoT SIM card device's communication. For example, metrics such as daily transmission periods, transmission frequency, and data volume fluctuations can be used as behavioral vectors.

[0024] Finally, for each IoT SIM card device, a utility-based feature is defined as the communication utility, with a one-to-one correspondence between the IoT SIM card device and its communication utility. For example, based on utility factors including communication quality and behavioral vectors, the communication quality of the target IoT SIM card device is obtained as follows: packet loss rate 0.5%, signal-to-noise ratio 25dB, communication latency jitter 3ms; and the behavioral vector is: real-time transmission throughout the day, transmission frequency 10 times / second, and data volume fluctuation ≤3%. Integrating the communication quality and behavioral vector features, the communication utility of the target IoT SIM card device is obtained. This communication utility quantifies the communication capabilities of the target IoT SIM card device, demonstrating its high stability and high real-time communication capabilities. The communication utility of each IoT SIM card device can be obtained using the same method, establishing a one-to-one correspondence. This transforms the communication capabilities of the IoT SIM card devices into measurable and evaluable quantitative characteristics, providing a clear basis for subsequent collaborative decision-making.

[0025] In summary, compared to existing technologies, this application defines the packet base state and communication utility for IoT card devices that obtain network access. This clarifies the scope of IoT card devices capable of packet operations, establishes a quantitative reference standard for the packet base state and communication utility that characterizes device demand tendencies, and provides a precise data and parameter foundation for subsequent static and dynamic packet operations.

[0026] S20: For the IoT card device, perform device static grouping processing based on spatiotemporal gravitational relationship to determine the device static group.

[0027] Due to the large scale, strong heterogeneity, and complex correlation of IoT SIM card devices in IoT scenarios, traditional device grouping methods often rely on single static dimensions such as geographical location and device type for classification, without fully considering the dynamic business interaction frequency between IoT SIM card devices. This makes it easy for the grouping results to ignore the real business correlation between IoT SIM card devices, resulting in problems such as low business interaction between IoT SIM card devices in the same group. Consequently, it is impossible to provide a stable and efficient basic cluster for network resource allocation and business collaboration.

[0028] To address the aforementioned issues, this application performs static grouping processing based on spatiotemporal gravitational relationships on the IoT card device to determine the static grouping of the device.

[0029] Specifically, step S20 in the method includes: Based on the spatiotemporal gravitational relationship, the IoT card devices are grouped to determine the static grouping of the devices; The spatiotemporal gravitational relationship is characterized as follows: Spatiotemporal gravitational magnitude = (service interaction frequency × mobile trajectory overlap) / communication delay 2 .

[0030] In this embodiment, IoT card devices are grouped according to spatiotemporal gravitational relationships to determine static device groups. The spatiotemporal gravitational relationship is characterized as: Spatiotemporal gravitational magnitude = (service interaction frequency × mobile trajectory overlap) / communication latency. 2 The frequency of business interaction, the degree of overlap of movement trajectories, and the communication latency have achieved precise quantification of the degree of connection between IoT card devices from three dimensions: business interaction, spatial correlation, and communication efficiency. The frequency of business interaction can reflect the frequency of collaboration between IoT card devices due to business needs. The higher the frequency of business interaction, the better.

[0031] For example, if two IoT SIM card devices exchange data every 3 minutes, it indicates a higher degree of business correlation between the two devices, implying a need for continuous collaboration and thus a greater spatiotemporal attraction. The degree of overlap in movement trajectories reflects the spatial correlation between IoT SIM card devices. A higher degree of overlap indicates a higher degree of spatial coordination between the two devices, meaning they are not only physically closer but also have the potential to synchronously respond to services within the area, thus a greater spatiotemporal attraction. Communication latency reflects the collaboration efficiency between IoT SIM card devices; higher communication latency indicates a greater degree of collaboration.

[0032] For example, the increased latency of cross-regional IoT SIM card devices due to long signal transmission distances indicates that the lower the real-time collaboration capability of the two IoT SIM card devices, the worse the timeliness of data interaction, and therefore the smaller the spatiotemporal attraction. The square of the communication latency is to amplify the negative impact of communication latency and prevent IoT SIM card devices with excessively high latency from being mistakenly grouped into the same group due to accidental high-frequency interactions.

[0033] In this way, by using the formula of spatiotemporal gravitational relationship, the abstract correlation between IoT card devices in terms of whether their business is close, whether their space is coordinated, and whether their communication is efficient is transformed into specific values ​​that can be directly calculated, ensuring that IoT card devices in static groups have the basic advantages of high business correlation, close spatial coordination, and low communication latency.

[0034] Furthermore, the phrase "grouping the IoT card devices and determining static device groups" includes: The first IoT SIM card device is identified as the center of gravity. The magnitude of the spatiotemporal gravitational force between the remaining IoT SIM card devices and the center of gravity is calculated and used as the first spatiotemporal gravitational field. The IoT card devices are traversed, and each is used as a gravitational center to calculate the magnitude of the spatiotemporal gravity until the Nth spatiotemporal gravitational field is determined, where N is the total number of IoT card devices; Using a preset gravity threshold as the grouping condition, the device is grouped by fusing the first spatiotemporal gravitational field to the Nth spatiotemporal gravitational field to determine the static grouping.

[0035] In this embodiment of the application, an IoT card device is first randomly selected from the IoT card devices connected to the network as the first IoT card device. The spatiotemporal gravitational force between the other IoT card devices and the gravitational center is calculated using the first IoT card device as the center of gravity, and is used as the first spatiotemporal gravitational field.

[0036] For example, IoT SIM card device A is randomly selected as the first IoT SIM card device, and IoT SIM card device A is used as the center of gravity. Based on the spacetime gravity formula, the magnitude of the spacetime gravity between other IoT SIM card devices and the center of gravity is calculated. For example, the magnitudes of the spacetime gravity between IoT SIM card device B, IoT SIM card device C, IoT SIM card device D and IoT SIM card device A are calculated to be 0.8, 0.4 and 0.1 respectively, forming a first spacetime gravity field with IoT SIM card device A as the core.

[0037] Secondly, following the same method, the IoT SIM card devices are traversed, and each IoT SIM card device is treated as a gravitational center to calculate the magnitude of its spatiotemporal gravity until the Nth spatiotemporal gravitational field is determined, where N is the total number of IoT SIM card devices. This covers the correlation perspective of all IoT SIM card devices in the network, avoiding correlation omissions caused by a single gravitational center.

[0038] Finally, using a preset gravity threshold as the grouping condition, devices are grouped by fusing the first spatiotemporal gravitational field to the Nth spatiotemporal gravitational field to determine static groups. Sidelinks are established between devices within each static group. The preset gravity threshold is a quantitative criterion for determining whether IoT SIM card devices are closely related and can be grouped into the same static group. This threshold can be dynamically set based on the scale and type of IoT SIM card devices in the actual application scenario. For example, in a smart park scenario with dense IoT SIM card devices and frequent interactions, the preset gravity threshold can be set to 0.6-0.7 to ensure high collaboration among devices within a static group. Conversely, in a wide-area agricultural monitoring scenario with dispersed IoT SIM card devices and low interaction frequency, the preset gravity threshold can be appropriately lowered to 0.3-0.4 to avoid fragmented grouping due to excessive stringency.

[0039] For example, if the preset gravity threshold is 0.6, in a spacetime gravitational field with IoT SIM card device A as the center of gravity, the spacetime gravity between IoT SIM card device A and IoT SIM card device B is 0.8. Because IoT SIM card device A and IoT SIM card device B meet the preset gravity threshold, they are classified into the same static group. A side link is established between the devices based on the direct communication channel within the static group for information exchange within the group. Simultaneously, the same IoT SIM card device can belong to multiple static groups, and the number of IoT SIM card devices within a static group is not fixed, including 2, 3, or even more IoT SIM card devices.

[0040] In summary, compared to existing technologies, this application performs static grouping processing based on spatiotemporal gravitational relationships for the aforementioned IoT card devices to determine static device groups. This allows IoT card devices to aggregate based on high correlation, providing reliable support for subsequent dynamic grouping and enabling the early establishment of basic collaborative channels such as sidelinks for devices within the group.

[0041] S30: Based on the device static grouping, the group base state and the communication utility, a dynamic grouping component is constructed. By acquiring task flow data and real-time network status, the dynamic grouping component is triggered to perform group base state collapse and consensus game binding decision to determine the device task grouping.

[0042] Traditional device grouping often relies on fixed dimensions such as device location and type to construct static groups. However, since the groups remain unchanged for a long time, they cannot adapt to the dynamic and ever-changing characteristics of tasks in IoT scenarios, making it difficult to match differentiated needs. They also fail to consider real-time network status fluctuations, resulting in grouping results that often lead to a disconnect between needs and tasks, and a mismatch between capabilities and the network, thereby affecting task execution efficiency and resource utilization.

[0043] To address the aforementioned issues, this application constructs a dynamic grouping component based on the device static grouping, group base state, and communication utility. By acquiring task flow data and real-time network status, the dynamic grouping component is triggered to perform group base state collapse and consensus game binding decision-making to determine device task grouping.

[0044] Specifically, the construction of the dynamic grouping component in step S30 of the method includes: Based on the static grouping of the devices, construct the device grouping space; Based on the device grouping space, dynamic grouping logic is formed by binding group ground state collapse and consensus game, and dynamic grouping components are trained under supervision. The dynamic grouping components are then embedded in the network control center.

[0045] In this embodiment, a device grouping space is first constructed based on the static grouping of devices. Specifically, based on the static grouping of devices, and combined with the physical distribution topology between IoT SIM card devices (such as actual installation location and spatial distance), the static grouping of devices is coherently calibrated. At the same time, the grouping base state and communication utility of IoT SIM card devices are associated to construct a device grouping space that includes the static association, physical location, and device attributes of IoT SIM card devices.

[0046] For example, in a smart park scenario, static device group 1 includes IoT SIM card device A (access control sensor in the lobby of Building 1) and IoT SIM card device B (elevator controller in the lobby of Building 1), while static device group 2 includes IoT SIM card device B and IoT SIM card device C (temperature and humidity sensor in the elevator lobby of Building 1). Based on the physical distribution topology, the spatial relationships between IoT SIM card devices A, B, and C are precisely defined. For example, IoT SIM card device A is located at the lobby entrance, IoT SIM card device B is located 5 meters from the elevator entrance, and IoT SIM card device C is located inside the elevator lobby. Then, the group base state and communication utility of each IoT SIM card device are associated. For example, IoT SIM card device A is assigned the group base state: [0.8, 0.1, 0.1], and communication quality: packet loss rate 0.5%, signal-to-noise ratio 25dB, communication latency jitter 3ms, daily transmission period is real-time transmission throughout the day, transmission frequency is 10 times / second, and data volume fluctuation ≤3%. The group base state and communication utility can intuitively reflect the communication stability and business patterns of IoT SIM card device A. Similarly, by associating the base state and communication utility of IoT SIM card devices B and C, a device group space is ultimately formed that includes the static association, physical location, and device attributes of the IoT SIM card devices.

[0047] Secondly, based on the device grouping space, and using group ground state collapse and consensus game theory as the dynamic grouping logic, a dynamic grouping component is trained under supervision and embedded in the network control center. Specifically: Among them, group ground state collapse refers to focusing and shrinking the original multi-dimensional (such as latency-sensitive ground state, power-sensitive ground state, and bandwidth-greedy ground state) group ground state of IoT card devices to the core dimension most relevant to the business characteristics, while filtering out the interference of secondary dimensions. This is used to quickly select business-related devices that meet the requirements of the task in the device group space, narrowing the scope for subsequent collaboration and negotiation, and avoiding irrelevant devices occupying decision-making resources.

[0048] Among them, consensus game binding refers to the process of determining resource allocation and data interaction rules through distributed negotiation within the range of devices selected by the group ground state collapse, relying on the side links pre-built by the static group, with the goal of completing the current task and the constraint of communication utility. This is used to clarify the collaboration logic between devices, transform the demand-adapted devices into task groups that can collaborate efficiently, and ensure that the devices within the group improve task execution efficiency through resource complementarity.

[0049] For example, by binding group ground state collapse and consensus game as dynamic grouping logic, supervised training of the dynamic grouping component can be achieved through the following technical path: 1. Training data preparation: Extract historical business data (such as business characteristics and execution results of tasks such as "elevator optimization in Building 1" and "campus environmental monitoring"), device attribute data (such as group base state and communication utility records of each device), and network status data (such as bandwidth and latency fluctuations) from the device group space to construct a labeled dataset containing business characteristics, device screening results, collaboration rules, and task effects.

[0050] 2. Grouping Ground State Collapse Module Training: The labeled dataset is classified according to task type, such as high frequency and low latency, low frequency and high reliability, etc. The dynamic grouping component is trained to focus on the dimensions of different business features. For example, for the high frequency and low latency features of elevator optimization, the dynamic grouping component is trained to automatically adjust the latency-sensitive ground state to 0.8, the bandwidth-greedy ground state to 0.2, and the power consumption-sensitive ground state to 0. The allocation rules of the grouping ground state are iteratively optimized by comparing whether the selected devices can meet the task requirements.

[0051] 3. Consensus Game Binding Module Training: With device set, communication utility constraints, and cooperation rules as training objectives, this module simulates negotiation scenarios for different device combinations. For example, for the selected combination of IoT SIM card device A (high bandwidth, low reputation) and IoT SIM card device B (low bandwidth, high reputation), a dynamic grouping component is trained with constraints of packet loss rate ≤0.5% and reputation score ≥85. This guides the two devices to negotiate a game condition where IoT SIM card device A leases 20% of its bandwidth to IoT SIM card device B, and IoT SIM card device B provides data verification for IoT SIM card device A. The validity of the rules is verified by whether the task completion latency is ≤50ms, thus optimizing the negotiation strategy.

[0052] 4. Joint Module Debugging and Optimization: The group ground state collapse module and the consensus game binding module are connected in series. The complete task process (business feature input → group ground state collapse screening → consensus game binding group → task effect output) is used as the training unit. By continuously inputting new task data, the dynamic grouping component can autonomously adjust parameters such as ground state screening threshold and game constraint strength until the grouping scheme output by the dynamic grouping component reaches the preset standard in terms of device adaptability and task completion efficiency. Finally, the supervised training of the dynamic grouping component is completed.

[0053] Finally, the dynamic packet component is embedded in the network control center to eliminate data transmission barriers and command conversion losses between the dynamic packet component and the control center. This allows the entire process of dynamic packet decision-making, from receiving data to outputting commands, to be controlled within milliseconds, meeting the real-time requirements of IoT tasks.

[0054] Further, in step S30 of the method, triggering the dynamic grouping component to perform group ground state collapse includes: Receive business flow data and extract business characteristics; Perceive real-time network status and extract network features; Based on the service characteristics and the network characteristics, locate the service-related devices in the device group space, perform group ground state collapse processing based on the service characteristics and the network characteristics, and initialize the task group space.

[0055] In this embodiment, business flow data is first received, and business features are extracted. For example, if the business flow data is "Elevator waiting time optimization for Building 1, elevator passenger count data needs to be uploaded every second, data transmission latency ≤ 50ms", the business feature extracted from "equipment needs to upload passenger count data every second" is high frequency, and the business feature extracted from "data transmission latency ≤ 50ms" is low latency.

[0056] Secondly, the real-time network status is perceived, and network characteristics are extracted. For example, the real-time network status can be obtained through network sensors or a central control center, and the current network characteristics can be extracted from it. For example, the current real-time network status of Building 1 is: available bandwidth 15Mbps (sufficient), average communication latency 20ms (meets low latency requirements), and packet loss rate 0.1% (stable). The network characteristics obtained are high bandwidth, low latency, and high stability.

[0057] Finally, based on business and network characteristics, business-related devices are located in the device grouping space. Grouping ground state collapse processing is then performed based on these characteristics to initialize the task grouping space. For example, business-related devices with latency-sensitive ground state ≥ 0.8 (matching low latency requirements), data transmission frequency support of 1 time / second (matching high frequency requirements), and acceptable communication quality (e.g., packet loss rate ≤ 0.5%) are selected from the device grouping space, such as IoT SIM card device A and IoT SIM card device B. Then, grouping ground state collapse is used to collapse the grouping ground state of the selected business-related devices from the grouping dimension, focusing on the core task dimension. For example, the power-sensitive ground state (0.1) and bandwidth-greedy ground state (0.1) of IoT SIM card device A are ignored, retaining only the latency-sensitive ground state (0.8) to avoid secondary requirements interfering with decision-making. For example, a task grouping space is constructed that only contains task-related devices and their core attributes after grouping ground state collapse processing, providing simplified operational objects for subsequent consensus game theory.

[0058] Furthermore, in step S30 of the method, the consensus game-based decision-making process for determining device task groups includes: Based on the initialized task grouping space, and according to the side link and task orientation, the group-wide distributed game consensus negotiation is carried out with the communication utility as a constraint to determine the game conditions. Among them, the devices in the group based on the static grouping of the devices have established a side link. Based on the game conditions, the task grouping space is dynamically grouped to determine the device task grouping.

[0059] In this embodiment, firstly, based on the initialized task grouping space, and according to the sidelink and task orientation, distributed game consensus negotiation within the group is conducted with communication utility as a constraint to determine the game conditions. Sidelinks are established between devices within the group based on static device grouping. Exemplarily, the distributed game consensus negotiation process is a dynamic process in which devices autonomously initiate, adjust, and reach cooperation rules based on the sidelinks.

[0060] For example, taking IoT SIM card device A and IoT SIM card device B as an example, IoT SIM card device A has sufficient bandwidth but weak data processing capabilities. It first proposes to IoT SIM card device B via a sidelink, "I will lend you 3Mbps bandwidth in exchange for you to help me with data verification to ensure a transmission success rate of ≥99.9%." Although IoT SIM card device B needs bandwidth, it only lacks 2Mbps and is worried about the processing pressure. It responds, "I can accept cooperation, but I only need 2Mbps, and you need to compress the data to within 100KB, otherwise I cannot guarantee that the verification will be completed within 50ms." After assessing its own capabilities, IoT SIM card device A agrees to the adjustment. Finally, the two parties reach a consensus on "leasing 2Mbps bandwidth + data compression to 80KB + verification latency ≤40ms". This mutually agreed rule is the game condition, which not only meets the high-frequency and low-latency requirements of the task, but also does not exceed the communication utility range of either device.

[0061] Secondly, based on the game conditions, the task grouping space is dynamically grouped to determine the device task groups. For example, after the game conditions are determined, within the task grouping space, all IoT card devices that are recognized and meet the same game conditions are grouped into the same device task group, while devices that do not meet the conditions are excluded, thus obtaining the device task groups.

[0062] For example, in an elevator optimization task, both IoT SIM card device A and IoT SIM card device B acknowledge and satisfy the same game-theoretic conditions, forming a device task group together. This device task group creates a functional cluster with clear collaboration rules and complementary capabilities. For instance, the bandwidth resources of IoT SIM card device A combined with the processing power of IoT SIM card device B can stably achieve the task objective of "transmitting data every 1 second with a latency of ≤40ms," avoiding resource waste or task failure caused by ruleless collaboration.

[0063] In this way, by replacing the central mandatory allocation with the autonomous negotiation of IoT SIM card devices, the cognitive advantages of IoT SIM card devices in terms of their own capabilities are leveraged, making the collaboration rules more in line with reality. At the same time, relying on sidelinks and communication utility constraints, the efficiency and reliability of the negotiation are ensured. The resulting device task grouping is not only the result of demand adaptation, but also the product of capability collaboration, which can maximize the adaptation to the heterogeneous characteristics of devices and dynamic tasks in IoT scenarios.

[0064] In summary, compared to existing technologies, this application constructs a dynamic grouping component based on the static device grouping, the group base state, and the communication utility. By acquiring task flow data and real-time network status, the dynamic grouping component is triggered to perform group base state collapse and consensus game binding decision-making to determine device task groups. This results in device task groups that accurately match tasks and network status, solving the pain points of rigidity and poor adaptability of traditional static grouping, and meeting the multi-tasking and highly dynamic collaboration needs of the Internet of Things.

[0065] S40: For the device task grouping, perform task aggregation resource application and group optimization allocation control.

[0066] If IoT SIM card devices apply for network resources piecemeal on an individual basis, it not only easily leads to resource fragmentation but also fails to adapt to the collaborative characteristics of device task groups, easily resulting in resource misallocation. At the same time, real-time network status fluctuates dynamically. Without unified resource planning and internal scheduling for groups, either service interruptions will occur due to resource allocation not matching task requirements, or idle resources will result from not being disassembled as needed.

[0067] To address the aforementioned issues, this application addresses the task grouping of the aforementioned devices by implementing task aggregation resource application and optimized allocation management within the group.

[0068] Specifically, step S40 in the method includes: Using the aforementioned device task group as a temporary convention, a task group request is submitted to the network; The network receives the packet request and allocates task aggregation resources to the device task group; For the aggregated resources of the tasks, the IoT SIM card device performs optimized allocation within the group based on the task grouping of the device, and manages the execution of services.

[0069] In this embodiment, the device task group is first used as a temporary convention to request a task group from the network. Specifically, the device task group integrates structured data such as group identity (e.g., the elevator optimization task group in Building 1, which includes IoT card device A and IoT card device), resource requirements (e.g., bandwidth ≥ 5Mbps, latency ≤ 50ms), and cooperation relationship (e.g., the game conditions between IoT card device A and IoT card device). This structured data is used as a temporary convention to request a task group, enabling the network to quickly and accurately identify "who needs resources and what resources they need".

[0070] Secondly, the network receives packet requests and allocates task aggregation resources to the device task groups. Specifically, after receiving a packet request, the network control center first assesses the match between real-time network load and demand: for example, if Building 1 currently has a total bandwidth of 20Mbps, with 12Mbps already used, there is still 8Mbps available, which can meet the group's demand; and ensures that the allocated resources cover the quality requirements in the temporary agreement. This aggregation resource has a temporary exclusive characteristic, belonging only to the group during the current task execution period. Other devices or groups cannot occupy it, and it will be automatically reclaimed after the task ends, linked to the subsequent restoration of static space for device groups, thus avoiding long-term resource idleness.

[0071] Finally, for aggregated task resources, the IoT SIM card devices perform intra-group optimization allocation based on device task grouping, and manage business execution. Specifically, after obtaining aggregated resources, the IoT SIM card devices need to break down the resource pool into individual devices through intra-group optimization allocation, and simultaneously manage business execution. For example, the task role and communication utility of the devices can be combined. For instance, IoT SIM card device A is the data acquisition core, requiring to upload personnel data every second, and is allocated 5Mbps bandwidth; IoT SIM card device B is responsible for data processing and elevator scheduling, and has lower bandwidth requirements, so it is allocated 3Mbps bandwidth.

[0072] Furthermore, for business flow data, as the task ends, the device task grouping is deactivated, and the device grouping space is restored to the static device grouping.

[0073] In this embodiment, the device task groups constructed for specific business flow data are essentially temporary collaborative clusters formed to complete the current task. Their internal bandwidth leasing rules, data interaction agreements, and dedicated sidelink configurations are all customized settings adapted to single-task requirements and lack long-term reusability. Therefore, as the task objective is achieved or the cycle ends, the device task groups must first be released, allowing the devices to detach from task binding and return to their initial usable state. Then, the temporary task group space constructed during the task should be cleared, ensuring that the device relationships, group base state, and communication utility all return to their original records, eliminating interference from temporary configurations on subsequent decisions. This avoids the long-term waste of temporary resources and provides unified and reliable static basic data support for the dynamic grouping of the next new business flow task.

[0074] In summary, compared to existing technologies, this application addresses the task grouping of the aforementioned devices by performing task aggregation resource application and optimized allocation management within the group. This solves the resource fragmentation problem caused by scattered applications from individual devices, providing precise and efficient resource support for the dynamic grouping of IoT card devices and ensuring that devices within a group can collaboratively utilize resources to complete tasks.

[0075] In summary, the embodiments of this application have at least the following technical effects: Compared to existing technologies, this application first obtains information about IoT SIM card devices with network access and defines their packet base state and communication utility. This clarifies the scope of IoT SIM card devices capable of packet operations and establishes a quantitative reference standard for the packet base state and communication utility that characterizes device demand tendencies, providing a precise data and parameter foundation for subsequent static and dynamic packet operations.

[0076] Secondly, this application performs static grouping processing based on spatiotemporal gravitational relationships on the aforementioned IoT card devices to determine static device groups. This allows IoT card devices to aggregate based on high correlation, providing reliable support for subsequent dynamic grouping and enabling the early establishment of basic collaborative channels such as sidelinks for devices within the group.

[0077] Furthermore, this application constructs a dynamic grouping component based on the aforementioned static device grouping, group base state, and communication utility. By acquiring task flow data and real-time network status, the dynamic grouping component is triggered to perform group base state collapse and consensus game binding decision-making to determine device task groups. In this way, device task groups that accurately match tasks and network status are formed, solving the pain points of rigidity and poor adaptability of traditional static grouping, and meeting the multi-tasking and highly dynamic collaboration needs of the Internet of Things.

[0078] Finally, this application addresses the task grouping of the aforementioned devices by performing task aggregation resource application and optimized allocation management within the group. This resolves the resource fragmentation problem caused by scattered applications from individual devices, providing precise and efficient resource support for the dynamic grouping of IoT SIM card devices, and ensuring that devices within a group can collaboratively utilize resources to complete tasks.

[0079] Through the above technical solution, this application fully considers the impact of chronic inflammation in patients with renal anemia on the monitoring results. By adjusting the monitoring influence coefficient, the preset iron metabolism monitoring cycle is dynamically optimized to obtain an optimized monitoring cycle. This improves the accuracy of iron metabolism monitoring in renal anemia.

[0080] Example 2, as Figure 2 As shown, based on the same inventive concept as the network state-aware IoT SIM card device packet optimization method provided in Embodiment 1, this embodiment of the invention also provides a network state-aware IoT SIM card device packet optimization system, including: Device acquisition module 11 is used to acquire IoT card devices with network access and define packet base state and communication utility; Static grouping module 12 is used to perform device static grouping processing based on spatiotemporal gravitational relationship for the IoT card device, and determine the device static grouping; Task grouping module 13 is used to construct a dynamic grouping component based on the device static grouping, group base state and the communication utility. By acquiring task flow data and real-time network status, the dynamic grouping component is triggered to perform group base state collapse and consensus game binding decision to determine device task grouping. The allocation and control module 14 is used to perform task aggregation resource application and intra-group optimization allocation and control for the task groups of the device.

[0081] The device acquisition module 11 is specifically used for: Define a grouping dimension, wherein the grouping dimension includes at least orthogonal delay-sensitive ground states, power-sensitive ground states, and bandwidth-greedy ground states; For each IoT SIM card device, a probability distribution vector based on the group dimension is defined as the group ground state, wherein the IoT SIM card device corresponds one-to-one with the group ground state; Define utility factors, wherein the utility factors include at least communication quality and behavior vectors; For each IoT card device, a factor characteristic based on the aforementioned utility factor is defined as the communication utility, wherein there is a one-to-one correspondence between the IoT card device and the communication utility.

[0082] The static grouping module 12 is specifically used for: Based on the spatiotemporal gravitational relationship, the IoT card devices are grouped to determine the static grouping of the devices; The spatiotemporal gravitational relationship is characterized as follows: Spatiotemporal gravitational magnitude = (service interaction frequency × mobile trajectory overlap) / communication delay 2 .

[0083] Specifically, the phrase "grouping the IoT card devices and determining static device groups" includes: The first IoT SIM card device is identified as the center of gravity. The magnitude of the spatiotemporal gravitational force between the remaining IoT SIM card devices and the center of gravity is calculated and used as the first spatiotemporal gravitational field. The IoT card devices are traversed, and each is used as a gravitational center to calculate the magnitude of the spatiotemporal gravity until the Nth spatiotemporal gravitational field is determined, where N is the total number of IoT card devices; Using a preset gravity threshold as the grouping condition, the device is grouped by fusing the first spatiotemporal gravitational field to the Nth spatiotemporal gravitational field to determine the static grouping.

[0084] Specifically, the task grouping module 13 is used for: Based on the static grouping of the devices, construct the device grouping space; Based on the device grouping space, dynamic grouping logic is formed by binding group ground state collapse and consensus game, and dynamic grouping components are trained under supervision. The dynamic grouping components are then embedded in the network control center.

[0085] Specifically, the "triggering the dynamic grouping component to perform group ground state collapse" includes: Receive business flow data and extract business characteristics; Perceive real-time network status and extract network features; Based on the service characteristics and the network characteristics, locate the service-related devices in the device group space, perform group ground state collapse processing based on the service characteristics and the network characteristics, and initialize the task group space.

[0086] Specifically, the "consensus game-based decision-making to determine device task groups" includes: Based on the initialized task grouping space, and according to the side link and task orientation, the group-wide distributed game consensus negotiation is carried out with the communication utility as a constraint to determine the game conditions. Among them, the devices in the group based on the static grouping of the devices have established a side link. Based on the game conditions, the task grouping space is dynamically grouped to determine the device task grouping.

[0087] The allocation and control module 14 is specifically used for: Using the aforementioned device task group as a temporary convention, a task group request is submitted to the network; The network receives the packet request and allocates task aggregation resources to the device task group; For the aggregated resources of the tasks, the IoT SIM card device performs optimized allocation within the group based on the task grouping of the device, and manages the execution of services.

[0088] Furthermore, for the business flow data, as the task ends, the device task group is degrouped, and the device group space is restored to the device static group.

[0089] In summary, the embodiments of this application have at least the following technical effects: Compared to existing technologies, this application first acquires IoT SIM card devices for network access through a device acquisition module, defines the packet ground state and communication utility, and establishes a quantitative reference standard for the packet ground state and communication utility that characterizes the device's demand tendency, providing a precise data and parameter foundation for subsequent static and dynamic grouping. Secondly, through a static grouping module, it performs device static grouping processing based on spatiotemporal gravitational relationships for IoT SIM card devices, determining the static grouping of devices. This allows IoT SIM card devices to aggregate based on high correlation, providing reliable support for subsequent dynamic grouping and enabling the early establishment of basic collaborative channels such as sidelinks for devices within the group. Thirdly, through a task grouping module, it constructs dynamic grouping components based on device static grouping, packet ground state, and communication utility. By acquiring task flow data and real-time network status, it triggers the dynamic grouping components to perform packet ground state collapse and consensus game binding decisions, determining device task groups and forming device task groups that accurately match tasks and network status. This solves the pain points of traditional static grouping's rigidity and poor adaptability, and can meet the multi-task, highly dynamic collaboration needs of the IoT. Finally, through the allocation and control module, task aggregation resource requests and optimized allocation within groups are performed for device task groups. This provides precise and efficient resource support for the dynamic grouping of IoT SIM card devices, ensuring that devices within a group can collaboratively utilize resources to complete tasks. In this way, the rigid grouping of traditional IoT SIM card devices is addressed, achieving precise adaptation to dynamic network conditions and differentiated service requirements, improving communication efficiency and resource utilization efficiency, and adapting to the dynamic needs of various types of IoT services.

[0090] It should be noted that the descriptions of each embodiment in the above embodiments have different focuses. For parts that are not described in detail in a certain embodiment, please refer to the relevant descriptions in other embodiments.

[0091] Those skilled in the art will understand that embodiments of the present invention can be provided as methods, systems, or computer program products. Therefore, the present invention can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the present invention can take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0092] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded computer, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0093] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0094] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0095] Although preferred embodiments of the invention have been described, those skilled in the art, once they have learned the basic inventive concept, can make other changes and modifications to these embodiments.

[0096] Obviously, those skilled in the art can make various modifications and variations to this invention without departing from its spirit and scope. Therefore, if these modifications and variations fall within the scope of this invention and its equivalents, this invention also intends to include these modifications and variations.

Claims

1. A network state-aware IoT SIM card device grouping optimization method, characterized in that, The method includes: IoT SIM card devices that acquire network access define packet base state and communication utility; For the IoT card device, perform static grouping processing based on spatiotemporal gravitational relationship to determine the static grouping of the device; A dynamic grouping component is constructed based on the device static grouping, group base state, and communication utility. By acquiring task flow data and real-time network status, the dynamic grouping component is triggered to perform group base state collapse and consensus game binding decision to determine device task grouping. For the aforementioned device task groups, perform task aggregation resource application and optimized allocation management within the group; The static grouping process for devices based on spatiotemporal gravitational relationships includes: Based on the spatiotemporal gravitational relationship, the IoT card devices are grouped to determine the static grouping of the devices; The spatiotemporal gravitational relationship is characterized as follows: Spatiotemporal gravitational magnitude = (service interaction frequency × mobile trajectory overlap) / communication delay 2 ; The process of triggering the dynamic grouping component to perform group ground state collapse includes: Receive business flow data and extract business characteristics; Perceive real-time network status and extract network features; Based on the business characteristics and network characteristics, business-related devices are located in the device group space. Based on the business characteristics and network characteristics, group base state collapse processing is performed to initialize the task group space. Here, group base state collapse refers to focusing and shrinking the original multi-dimensional group base state of the IoT card device to the core dimension most relevant to the business characteristics for specific business requirements, while filtering out the interference of secondary dimensions, and selecting business-related devices that meet the requirements of the task in the device group space. Among them, consensus game theory is used to bind decision-making and determine device task groups, including: Based on the initialized task grouping space, and according to the side link and task orientation, the group-wide distributed game consensus negotiation is carried out with the communication utility as a constraint to determine the game conditions. Among them, the devices in the group based on the static grouping of the devices have established a side link. Based on the game conditions, the task grouping space is dynamically grouped to determine the device task grouping.

2. The IoT SIM card device grouping optimization method based on network state awareness as described in claim 1, characterized in that, Define the packet ground state and communication utility, including: Define a grouping dimension, wherein the grouping dimension includes at least orthogonal delay-sensitive ground states, power-sensitive ground states, and bandwidth-greedy ground states; For each IoT SIM card device, a probability distribution vector based on the grouping dimension is defined as the grouping ground state. There is a one-to-one correspondence between the IoT SIM card device and the grouping ground state. The probability distribution vector is a quantitative description of the demand tendency of the IoT SIM card device in different grouping dimensions, which is expressed as the demand ratio of the IoT SIM card device in each dimension. Define utility factors, wherein the utility factors include at least communication quality and behavior vector, and the behavior vector measures the time and frequency patterns of communication of IoT card devices; For each IoT card device, a factor characteristic based on the aforementioned utility factor is defined as the communication utility, wherein there is a one-to-one correspondence between the IoT card device and the communication utility.

3. The IoT SIM card device packet optimization method based on network state awareness as described in claim 1, characterized in that, The IoT card devices are grouped to determine static device groups, including: The first IoT SIM card device is identified as the center of gravity. The magnitude of the spatiotemporal gravitational force between the remaining IoT SIM card devices and the center of gravity is calculated and used as the first spatiotemporal gravitational field. The IoT card devices are traversed, and each is used as a gravitational center to calculate the magnitude of the spatiotemporal gravity until the Nth spatiotemporal gravitational field is determined, where N is the total number of IoT card devices; Using a preset gravity threshold as the grouping condition, the device is grouped by fusing the first spatiotemporal gravitational field to the Nth spatiotemporal gravitational field to determine the static grouping.

4. The IoT SIM card device grouping optimization method based on network state awareness as described in claim 1, characterized in that, Before triggering the dynamic grouping component, the construction of the dynamic grouping component includes: Based on the static grouping of the devices, construct the device grouping space; Based on the device grouping space, dynamic grouping logic is formed by binding group ground state collapse and consensus game, and dynamic grouping components are trained under supervision. The dynamic grouping components are then embedded in the network control center.

5. The IoT SIM card device grouping optimization method based on network state awareness as described in claim 1, characterized in that, Perform task aggregation resource application and group optimization allocation control, including: Using the aforementioned device task group as a temporary convention, a task group request is submitted to the network; The network receives the packet request and allocates task aggregation resources to the device task group; For the aggregated resources of the tasks, the IoT SIM card device performs optimized allocation within the group based on the task grouping of the device, and manages the execution of services.

6. The IoT SIM card device grouping optimization method based on network state awareness as described in claim 1, characterized in that, For the aforementioned business flow data, as the task ends, the device task group is degrouped, and the device group space is restored to the device static group.

7. A network-state-aware IoT SIM card device packet optimization system, characterized in that, For performing the method according to any one of claims 1-6, comprising: The device acquisition module is used to acquire IoT card devices that are connected to the network and to define the packet base state and communication utility. The static grouping module is used to perform device static grouping processing based on spatiotemporal gravitational relationship for the IoT card device, and determine the device static grouping; The task grouping module is used to construct a dynamic grouping component based on the device static grouping, the group base state and the communication utility. By acquiring task flow data and real-time network status, the dynamic grouping component is triggered to perform group base state collapse and consensus game binding decision to determine the device task grouping. The allocation and control module is used to perform task aggregation resource application and optimized allocation and control within the group for the task groups of the device.

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