A data acquisition and dynamic aggregation system, method, device and medium

Abstract

This application relates to a data acquisition and dynamic aggregation system, method, device, and medium, belonging to the field of data transmission and network communication technology. The system includes a state-aware quantization module, a weight and threshold calculation module, an adaptive hierarchical module, an adaptive aggregation module, a feedback iterative optimization module, and a communication interaction module. Using this system, after data acquisition, an adaptive hierarchical structure and dynamic aggregation strategy can be constructed by fusing multi-dimensional dynamic states such as node energy consumption, signal strength, and data priority. This fundamentally solves the contradiction between real-time performance and stability in data transmission under complex environments, achieving coordinated optimization of data transmission latency, node energy consumption balance, and data reliability.

Description

Technical Field

[0001] This application relates to the field of data transmission and network communication technology, and in particular to a data acquisition and dynamic aggregation system, method, device and medium. Background Technology

[0002] In complex communication scenarios such as the Internet of Things, industrial control, and emergency rescue, network nodes are distributed in a dispersed manner and the operating environment is variable (such as signal interference, node movement, and energy consumption fluctuations). Data transmission needs to meet the dual requirements of real-time performance and stability. Real-time performance requires data to be quickly aggregated to the core node to reduce transmission delay. Stability requires resistance to interference such as node failure and signal attenuation to avoid data loss.

[0003] Furthermore, existing data acquisition, transmission, and hierarchical aggregation technologies often employ fixed hierarchical structures or static parameter configurations, making it difficult to adapt to dynamic changes in node states. This leads to an irreconcilable conflict between real-time performance and stability. Moreover, some existing hierarchical aggregation methods adjust routes based solely on single node parameters (such as signal strength or energy consumption), ignoring the impact of data priority differences and multi-parameter coupling. For example, selecting aggregation nodes solely based on signal strength can easily cause high-priority data to be delayed due to path congestion, or low-energy-consumption nodes to prematurely fail due to over-occupancy. Other technologies adjust the structure by setting preset hierarchical thresholds, but these thresholds are fixed and do not dynamically update with the environment. In complex environments (such as sudden interference or a sudden drop in energy consumption of a batch of nodes), they cannot quickly respond to state changes, either sacrificing data transmission reliability for excessive real-time performance or causing excessive latency due to overemphasis on stability.

[0004] Furthermore, existing technologies largely rely on improvements to traditional routing protocols (such as LEACH and PEGASIS), failing to break through the framework of "fixed hierarchical structure, static parameter weights, and single aggregation strategy." This makes it impossible to achieve deep collaborative adaptation between node dynamic states, data requirements, and the network environment, and thus cannot fundamentally resolve the contradiction between real-time performance and stability in data transmission under complex environments. Summary of the Invention

[0005] Therefore, it is necessary to provide a data acquisition and dynamic aggregation system, method, device, and medium to address the aforementioned technical problems. This system can construct an adaptive hierarchical structure and dynamic aggregation strategy by integrating multi-dimensional dynamic states such as node energy consumption, signal strength, and data priority after data acquisition. This will fundamentally solve the contradiction between real-time performance and stability in data transmission under complex environments and achieve coordinated optimization of data transmission latency, node energy consumption balance, and data reliability.

[0006] A data acquisition and dynamic aggregation system, the system comprising:

[0007] The state-aware quantization module is used to collect the three-dimensional state parameters of all nodes in the communication network and perform normalization quantization processing to obtain the three-dimensional state quantization values ​​of each node, including the quantization values ​​of energy consumption state, signal strength state and data priority state. The weight and threshold calculation module is used to calculate the hierarchical weight of each node in real time based on the three-dimensional state quantization value, and update the hierarchical threshold of the network in real time based on the average hierarchical weight of the network; wherein, the hierarchical threshold divides each node into convergence layer nodes, relay layer nodes and perception layer nodes. The adaptive layering module is used to determine the current layer of each node based on the layering weight and layering threshold. At the same time, it uses the interaction between the status and layering information of the nodes to construct a local node topology graph. By monitoring the changes in layering weight and the layer migration changes of neighboring nodes in the local node topology graph, it automatically triggers node re-layering and data migration. The adaptive aggregation module is used to automatically perform data acquisition, transmission and aggregation from the perception layer nodes, relay layer nodes to the aggregation layer nodes according to the current level and 3D state quantization value of each node. The feedback and iterative optimization module is used to collect data transmission performance indicators in real time in the aggregation layer nodes and determine whether the standard requirements are met. If yes, the data aggregation result is output; if no, the layer weight calculation coefficient and layer threshold update coefficient are fed back and iteratively optimized until the standard is met. The communication and interaction module is used to support high-speed transmission of status, hierarchy, and data information between nodes and modules.

[0008] In one embodiment, the state-aware quantization module is deployed on all nodes, including a state-aware unit and a quantization processing unit; The state awareness unit includes an energy consumption sensor, a signal detector, and a data priority identification unit, which are used to collect the energy consumption status, signal strength status, and data priority status of each node in real time, forming three-dimensional state parameters. Among them, the energy consumption status includes the node's remaining power and energy consumption rate; the signal strength status includes the node's received / transmitted signal power and signal interference; and the data priority status includes the urgency of the data to be transmitted and the data type weight. The quantization processing unit is used to normalize and quantize the collected three-dimensional state parameters to obtain three-dimensional state quantization values, all within the range of [0,1]. Among them, the larger the energy consumption state quantization value, the more sufficient the remaining power of the node and the lower the energy consumption pressure; the larger the signal strength state quantization value, the better the node signal quality and the less interference; the larger the data priority state quantization value, the higher the urgency of the data to be transmitted and the higher the transmission priority. Among them, the energy consumption status quantification value Represented as: ; In the formula, The remaining power of the node. For energy consumption rate, This represents the maximum power of the node. The dynamic attenuation coefficient, It is adaptively updated based on the node's historical energy consumption data; Among them, the signal strength state quantization value Represented as: ; In the formula, This represents the actual signal power of the node. For signal interference, The maximum signal power of the node. As an interference compensation factor, Adjust in real time according to the intensity of environmental disturbances.

[0009] In one embodiment, the weight and threshold calculation module is deployed on all nodes, including a multi-objective collaborative optimization unit, a reinforcement learning unit, and a threshold update unit; The multi-objective collaborative optimization unit is used to construct a dynamic hierarchical weight model based on the three-dimensional state quantization values. The dynamic hierarchical weight model adopts a multi-objective collaborative optimization algorithm to calculate the hierarchical weight of each node in real time. , represented as: ; in, , , These are the energy consumption status quantification values. Signal strength state quantization value Data priority status quantization value The dynamic weighting coefficients, and satisfying ; The reinforcement learning unit is used to adaptively update the three types of dynamic weight coefficients using a reinforcement learning algorithm. The core logic is: when the proportion of high-priority data in the network is ≥60%, increase the weight coefficient. ,reduce and Prioritize real-time performance; when the average energy consumption state quantization value of nodes in the network is ≤0.3, increase... Prioritize ensuring node battery life and network stability; when the signal interference in the network is ≥0.5, increase... Prioritize selecting nodes with superior signal quality to construct transmission paths; The threshold update unit is used to statistically analyze the hierarchical weights of all nodes in the network and calculate the average hierarchical weight of the network. Combined state fluctuation coefficient Real-time updates of network layer thresholds, including the aggregation layer threshold. Relay layer threshold and perception layer threshold , respectively represented as: ; ; ; Where, the hierarchical weight ≥ The nodes are classified as aggregation layer nodes, responsible for data aggregation and uploading; the layer weight ∈ [ , The nodes in the [] layer are relay layer nodes, responsible for data forwarding and state relay; the layer weights ∈ [] , The nodes in this layer are perception layer nodes, responsible for data collection and initial uploading; state fluctuation coefficient It reflects the overall dispersion of node states and is calculated from the standard deviation of the layer weights of all nodes. This ensures that the layer threshold can adapt to the dynamic changes in network states and avoids the rigidity of the layer structure.

[0010] In one embodiment, the adaptive hierarchical module is deployed on all nodes, including a hierarchical evaluation unit, a neighbor interaction unit, a hierarchical migration unit, and a load balancing unit; The hierarchical evaluation unit is used to determine the current hierarchical level of each node based on the hierarchical weights calculated in real time and the hierarchical thresholds updated in real time. The neighbor interaction unit is used to construct a local node topology graph by utilizing the interaction of state and hierarchical information between nodes. The hierarchical migration unit is used to monitor the hierarchical weight changes of each node and the hierarchical migration changes of neighboring nodes in the local node topology graph where each node is located. When the hierarchical weight change of a certain node is ≥0.2, or when more than 30% of the neighboring nodes in the local node topology graph undergo hierarchical migration, the current node is automatically triggered to re-hierarchize and migrate data. The data migration process adopts a seamless switching strategy, that is, before the new hierarchical role is activated, the data transmission link of the original hierarchical level is retained to avoid data loss or delay. The load balancing unit is used to set up redundancy backup mechanisms and dynamic load balancing strategies for aggregation layer nodes and relay layer nodes, respectively, to ensure the stability of data transmission and aggregation. Specifically, for aggregation layer nodes, a redundancy backup mechanism is set up. When the energy consumption status quantization value of the main aggregation layer node is ≤0.2 or the signal strength status quantization value is ≤0.3, the redundant aggregation layer node is automatically switched over. The switching priority is determined by the hierarchical weight sorting, and the switching delay is ≤10ms, ensuring the stability of the aggregation layer. For relay layer nodes, a dynamic load balancing strategy is adopted. When the data forwarding volume of a relay layer node is greater than or equal to 80% of its maximum load, some low-priority data is automatically diverted to surrounding relay layer nodes with similar hierarchical weights to avoid delays caused by relay layer node congestion.

[0011] In one embodiment, the adaptive aggregation module includes a data uploading unit, a path optimization unit, a data caching unit, a data sorting and processing unit, and an emergency control unit, which are used to implement a differentiated hierarchical data aggregation strategy based on the dynamic status of nodes at different levels. The data upload unit is deployed on the perception layer nodes to achieve dynamic selection of upload nodes. The logic is as follows: the perception layer node selects the relay layer node with the highest layer weight as the upload node based on its own data priority and the status of the surrounding relay layer nodes. When the data priority status quantization value of the perception layer node is ≥0.7, a direct connection priority strategy is adopted to directly connect to the aggregation layer node. If there are no relay layer nodes that meet the conditions in the vicinity, a temporary cross-layer connection to the aggregation layer node is also adopted to ensure real-time performance. When the data priority status quantization value of the perception layer node is <0.3, an energy-optimal strategy is adopted to select the relay layer node with an energy consumption status quantization value ≥0.6 as the upload node to reduce transmission energy consumption. The path optimization unit and data caching unit are deployed at the relay layer nodes to implement forwarding path optimization and interrupt fault tolerance. The logic is as follows: the relay layer node dynamically selects the forwarding path based on its own three-dimensional state quantization value: for high-priority data, it selects the path with a signal strength state quantization value ≥ 0.7 and uses the shortest delay algorithm to optimize the forwarding link; for low-priority data, it selects the energy-balanced path to avoid excessive consumption by a single node; at the same time, the relay layer node uses the data caching unit to cache forwarded data in real time. When the receiving node signal is interrupted, it automatically switches to the backup path. The data caching retention time is determined by the data priority. The data sorting and processing unit and the emergency control unit are deployed at the aggregation layer node to implement data priority processing and emergency response to sudden failures. The data priority processing logic is as follows: the aggregation layer node receives data uploaded by relay layer nodes and perception layer nodes, employing a dynamic priority sorting strategy, i.e., high-priority data is decoded and uploaded first, with a processing latency of ≤5ms; low-priority data is processed in batches to balance processing efficiency and system load. Simultaneously, the aggregation layer node broadcasts the overall network status, including average energy consumption, interference intensity, and hierarchical thresholds, to all nodes in real time, providing a basis for status adjustments and hierarchical decisions for each node. The emergency response logic for sudden failures is as follows: when a sudden network failure occurs, an emergency aggregation mode is triggered, automatically reducing the transmission rate of low-priority data and prioritizing the transmission of high-priority data; simultaneously, the hierarchical weight of nodes with sufficient remaining power and good signal quality is temporarily increased, quickly reconstructing the hierarchical structure to ensure uninterrupted core network transmission functions.

[0012] In one embodiment, the feedback iterative optimization module is deployed on the aggregation layer node and includes a performance statistics unit, a feedback comparison unit, and a parameter optimization unit. The performance statistics unit is used to collect real-time data on transmission performance metrics in the aggregation layer nodes, including transmission latency, data loss rate, and node energy consumption balance. The feedback comparison unit is used to compare various data transmission performance indicators with corresponding indicator thresholds and generate feedback signals; The parameter optimization unit is used to iteratively optimize the hierarchical weight calculation coefficient and the hierarchical threshold update coefficient using the gradient descent algorithm based on the feedback signal. The optimization direction is as follows: if the transmission delay exceeds the standard, the dynamic weight coefficient of the data priority state quantization value and the signal strength state quantization value is increased; if the data loss rate exceeds the standard, the dynamic weight coefficient of the signal strength state quantization value and the energy consumption state quantization value is increased; if the node energy consumption balance does not meet the standard, the dynamic weight coefficient of the energy consumption state quantization value is increased.

[0013] In one embodiment, the communication interaction module is deployed on all nodes. While supporting high-speed transmission of status, hierarchy and data information between nodes and modules, it adopts anti-interference coding technology to reduce data loss caused by signal interference; and supports rapid signal switching in emergency mode to ensure real-time transmission of feedback signals and emergency control signals.

[0014] A data acquisition and dynamic aggregation method, the method comprising: Step 1: Collect the three-dimensional state parameters of all nodes in the communication network and perform normalization and quantization processing to obtain the three-dimensional state quantization values ​​of each node, including the quantization values ​​of energy consumption state, signal strength state and data priority state. Step 2: Calculate the layer weight of each node in real time based on the three-dimensional state quantization value, and update the layer threshold of the network in real time based on the average layer weight of the network; wherein, the layer threshold divides each node into aggregation layer nodes, relay layer nodes and perception layer nodes. Step 3: Determine the current level of each node based on the layer weight and layer threshold. At the same time, construct a local node topology graph by utilizing the interaction between node status and layer information. Automatically trigger node re-layering and data migration by monitoring changes in layer weight and changes in the layer migration of neighboring nodes in the local node topology graph. Step 4: Based on the current level and 3D state quantization value of each node, automatically execute data acquisition, transmission and aggregation from the perception layer node, relay layer node to the aggregation layer node; Step 5: In the aggregation layer node, statistical data transmission performance indicators are collected in real time and it is determined whether the standard requirements are met. If yes, the data aggregation result is output; if no, feedback is provided and the layer weight calculation coefficient and layer threshold update coefficient are iteratively optimized until the standard is met.

[0015] A computer device includes a memory and a processor, the memory storing a computer program, and the processor executing the computer program performing the following steps: Step 1: Collect the three-dimensional state parameters of all nodes in the communication network and perform normalization and quantization processing to obtain the three-dimensional state quantization values ​​of each node, including the quantization values ​​of energy consumption state, signal strength state and data priority state. Step 2: Calculate the layer weight of each node in real time based on the three-dimensional state quantization value, and update the layer threshold of the network in real time based on the average layer weight of the network; wherein, the layer threshold divides each node into aggregation layer nodes, relay layer nodes and perception layer nodes. Step 3: Determine the current level of each node based on the layer weight and layer threshold. At the same time, construct a local node topology graph by utilizing the interaction between node status and layer information. Automatically trigger node re-layering and data migration by monitoring changes in layer weight and changes in the layer migration of neighboring nodes in the local node topology graph. Step 4: Based on the current level and 3D state quantization value of each node, automatically execute data acquisition, transmission and aggregation from the perception layer node, relay layer node to the aggregation layer node; Step 5: In the aggregation layer node, statistical data transmission performance indicators are collected in real time and it is determined whether the standard requirements are met. If yes, the data aggregation result is output; if no, feedback is provided and the layer weight calculation coefficient and layer threshold update coefficient are iteratively optimized until the standard is met.

[0016] A computer-readable storage medium having a computer program stored thereon, the computer program performing the following steps when executed by a processor: Step 1: Collect the three-dimensional state parameters of all nodes in the communication network and perform normalization and quantization processing to obtain the three-dimensional state quantization values ​​of each node, including the quantization values ​​of energy consumption state, signal strength state and data priority state. Step 2: Calculate the layer weight of each node in real time based on the three-dimensional state quantization value, and update the layer threshold of the network in real time based on the average layer weight of the network; wherein, the layer threshold divides each node into aggregation layer nodes, relay layer nodes and perception layer nodes. Step 3: Determine the current level of each node based on the layer weight and layer threshold. At the same time, construct a local node topology graph by utilizing the interaction between node status and layer information. Automatically trigger node re-layering and data migration by monitoring changes in layer weight and changes in the layer migration of neighboring nodes in the local node topology graph. Step 4: Based on the current level and 3D state quantization value of each node, automatically execute data acquisition, transmission and aggregation from the perception layer node, relay layer node to the aggregation layer node; Step 5: In the aggregation layer node, statistical data transmission performance indicators are collected in real time and it is determined whether the standard requirements are met. If yes, the data aggregation result is output; if no, feedback is provided and the layer weight calculation coefficient and layer threshold update coefficient are iteratively optimized until the standard is met.

[0017] Compared with existing technologies, the above-mentioned data acquisition and dynamic aggregation system, method, equipment, and medium have the following advantages: 1. Breaking through the limitations of traditional single-parameter decision-making and static hierarchical structure, it innovatively integrates three-dimensional dynamic state parameters of node energy consumption, signal strength, and data priority to calculate node hierarchical weights, achieving deep adaptation between node state and hierarchical decision-making, and fundamentally solving the contradiction between real-time performance and stability in complex environments—ensuring real-time transmission of high-urgency data through a data priority differentiation strategy, and ensuring overall network stability through collaborative decision-making on energy consumption and signal strength.

[0018] 2. A real-time update mechanism for hierarchical thresholds and a dynamic migration strategy for hierarchical levels are proposed. Without manual intervention, it can quickly respond to node state fluctuations and network environment changes to make adaptive adjustments to the node's hierarchical level, avoiding performance degradation caused by rigid hierarchical structure.

[0019] 3. By monitoring data transmission performance indicators and dynamic feedback in real time, the correlation coefficient between layer weights and layer thresholds can be continuously optimized to ensure that the data transmission aggregation process always meets the standard requirements, avoid network fluctuations caused by parameter mutations, ensure the continuous adaptation of the layered aggregation strategy to the dynamic state of nodes and the network environment, and significantly improve the stability and reliability of data transmission aggregation. Attached Figure Description

[0020] Figure 1 This is a schematic diagram of the structure of a data acquisition and dynamic aggregation system in one embodiment; Figure 2 This is a flowchart illustrating a data acquisition and dynamic aggregation method in one embodiment; Figure 3 This is an internal structural diagram of a computer device in one embodiment. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the following detailed description is provided in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative and not intended to limit the scope of this application.

[0022] In one embodiment, such as Figure 1 As shown, a data acquisition and dynamic aggregation system is provided, including: a state-aware quantization module, a weight and threshold calculation module, an adaptive hierarchical module, an adaptive aggregation module, a feedback iterative optimization module, and a communication interaction module.

[0023] 1. State Awareness Quantization Module: Used to collect the three-dimensional state parameters of all nodes in the communication network and perform normalization and quantization processing to obtain the three-dimensional state quantization value of each node, including the quantization value of energy consumption state, signal strength state and data priority state.

[0024] Specifically, the state-aware quantization module is deployed on all nodes, including state-aware units and quantization processing units.

[0025] The state awareness unit includes an energy consumption sensor, a signal detector, and a data priority identification unit, which are used to collect the energy consumption status, signal strength status, and data priority status of each node in real time, forming three-dimensional state parameters. Among them, the energy consumption status includes the node's remaining power and energy consumption rate; the signal strength status includes the node's received / transmitted signal power and signal interference; and the data priority status includes the urgency of the data to be transmitted and the data type weight.

[0026] The quantization processing unit is used to normalize and quantize the collected three-dimensional state parameters to obtain three-dimensional state quantization values, all within the range of [0,1]. Among them, the larger the energy consumption state quantization value, the more sufficient the remaining power of the node and the lower the energy consumption pressure; the larger the signal strength state quantization value, the better the node signal quality and the less interference; the larger the data priority state quantization value, the higher the urgency of the data to be transmitted and the higher the transmission priority.

[0027] Among them, the energy consumption status quantification value A dynamic attenuation coefficient correction is adopted to avoid misjudgment caused by short-term energy consumption fluctuations at nodes. The correction formula is as follows: ; In the formula, The remaining power of the node. For energy consumption rate, This represents the maximum power of the node. The dynamic attenuation coefficient, It is adaptively updated based on the node's historical energy consumption data (value range [0.1, 0.5]).

[0028] Among them, the signal strength state quantization value An interference compensation factor is introduced, and the correction formula is as follows: ; In the formula, This represents the actual signal power of the node. For signal interference, The maximum signal power of the node. This is the interference compensation factor (value range [0.2, 0.8]). Adjust in real time according to the intensity of environmental disturbances.

[0029] It should be understood that by introducing a dynamic attenuation coefficient and an interference compensation factor, the problem of misjudging short-term fluctuations in traditional quantification methods can be solved, and the accuracy of node state perception can be improved.

[0030] 2. Weight and threshold calculation module: It is used to calculate the layer weight of each node in real time based on the three-dimensional state quantization value, and update the layer threshold of the network in real time based on the average layer weight of the network; among which, the layer threshold divides each node into convergence layer nodes, relay layer nodes and perception layer nodes.

[0031] Specifically, the weight and threshold calculation module is deployed on all nodes, including a multi-objective collaborative optimization unit, a reinforcement learning unit, and a threshold update unit.

[0032] The multi-objective collaborative optimization unit is used to construct a dynamic hierarchical weight model based on the three-dimensional state quantization values. The dynamic hierarchical weight model adopts a multi-objective collaborative optimization algorithm to calculate the hierarchical weight of each node in real time. , represented as: ; in, , , These are the energy consumption status quantification values. Signal strength state quantization value Data priority status quantization value The dynamic weighting coefficients, and satisfying .

[0033] The reinforcement learning unit is used to adaptively update the three types of dynamic weight coefficients using a reinforcement learning algorithm. The core logic is: when the proportion of high-priority data in the network is ≥60%, increase the weight coefficient. (Value range [0.4, 0.6]), reduce , (Values ​​range [0.2, 0.3]), prioritizing real-time performance; when the average energy consumption state quantization value of nodes in the network is ≤0.3, increase... (Value range [0.4, 0.6]), prioritizing node endurance and network stability; when the signal interference in the network is ≥0.5, increase... (Value range [0.4, 0.6]), prioritize nodes with better signal quality to construct transmission paths.

[0034] The threshold update unit is used to statistically analyze the hierarchical weights of all nodes in the network and calculate the average hierarchical weight of the network. Combined state fluctuation coefficient Real-time updates of network layer thresholds, including the aggregation layer threshold. Relay layer threshold and perception layer threshold , respectively represented as: ; ; ; Where, the hierarchical weight ≥ The nodes are classified as aggregation layer nodes, responsible for data aggregation and uploading; the layer weight ∈ [ , The nodes in the [] layer are relay layer nodes, responsible for data forwarding and state relay; the layer weights ∈ [] , The nodes in this layer are perception layer nodes, responsible for data collection and initial uploading; state fluctuation coefficient It reflects the overall dispersion of node states and is calculated from the standard deviation of the layer weights of all nodes. This ensures that the layer threshold can adapt to the dynamic changes in network states and avoids the rigidity of the layer structure.

[0035] It should be understood that integrating three-dimensional dynamic state parameters—node energy consumption, signal strength, and data priority—for node hierarchical weight calculation enables deep adaptation between node state and hierarchical decisions, fundamentally resolving the contradiction between real-time performance and stability in complex environments. This involves ensuring real-time transmission of highly urgent data through a data priority differentiation strategy, while simultaneously guaranteeing overall network stability through collaborative decision-making based on energy consumption and signal strength. The real-time update mechanism for hierarchical thresholds and the dynamic migration strategy for hierarchical levels require no manual intervention and can quickly respond to fluctuations in node state and changes in the network environment to adaptively adjust the node's hierarchical level, avoiding data transmission performance degradation caused by a rigid hierarchical structure.

[0036] 3. Adaptive Hierarchical Module: This module determines the current hierarchy of each node based on the hierarchical weight and threshold. It also constructs a local node topology graph by utilizing the interaction between node states and hierarchical information. Furthermore, it automatically triggers node re-hierarchy and data migration by monitoring changes in hierarchical weights and hierarchical migration of neighboring nodes in the local node topology graph.

[0037] Specifically, the adaptive layering module is deployed on all nodes, including the layer evaluation unit, the neighbor interaction unit, the layer migration unit, and the load balancing unit.

[0038] The hierarchical evaluation unit is used to determine the current hierarchical level of each node based on the hierarchical weights calculated in real time and the hierarchical thresholds updated in real time.

[0039] The neighbor interaction unit is used to construct a local node topology graph by utilizing the interaction of state and hierarchical information between nodes.

[0040] Hierarchical migration units are used to monitor the changes in the hierarchical weights of each node and the hierarchical migration changes of its neighboring nodes in the local node topology graph. A node is considered to have a hierarchical weight change ≥ 0.2 (i.e., ...). When a node is automatically re-layered and its data is migrated, or when more than 30% of its neighboring nodes in a local node topology graph undergo hierarchical migration, the current node will automatically be re-layered and its data will be migrated. The data migration process adopts a seamless switching strategy, that is, the data transmission link of the original layer is retained before the new layer role is activated, so as to avoid data loss or delay.

[0041] The load balancing unit is used to set up redundancy backup mechanisms and dynamic load balancing strategies for aggregation layer nodes and relay layer nodes, respectively, to ensure the stability of data transmission and aggregation. Specifically, for aggregation layer nodes, a redundancy backup mechanism is set up. When the energy consumption status quantization value of the main aggregation layer node is ≤0.2 or the signal strength status quantization value is ≤0.3, the redundant aggregation layer node is automatically switched over. The switching priority is determined by the hierarchical weight sorting, and the switching delay is ≤10ms, ensuring the stability of the aggregation layer. For relay layer nodes, a dynamic load balancing strategy is adopted. When the data forwarding volume of a relay layer node is greater than or equal to 80% of its maximum load, some low-priority data is automatically diverted to surrounding relay layer nodes with similar hierarchical weights to avoid delays caused by relay layer node congestion.

[0042] 4. Adaptive aggregation module: It is used to automatically perform data acquisition, transmission and aggregation from the perception layer node, relay layer node to the aggregation layer node according to the current level and 3D state quantization value of each node.

[0043] Specifically, the adaptive aggregation module includes a data uploading unit, a path optimization unit, a data caching unit, a data sorting and processing unit, and an emergency control unit, which are used to implement a differentiated hierarchical data aggregation strategy based on the dynamic status of nodes at different levels.

[0044] The data upload unit is deployed on the perception layer nodes to achieve dynamic selection of upload nodes. The logic is as follows: the perception layer node selects the relay layer node with the highest layer weight as the upload node based on its own data priority and the status of the surrounding relay layer nodes. When the data priority status quantization value of the perception layer node is ≥0.7, a direct connection priority strategy is adopted to directly connect to the aggregation layer node. If there is no relay layer node that meets the conditions in the surrounding area, a temporary cross-layer connection to the aggregation layer node is also adopted to ensure real-time performance. When the data priority status quantization value of the perception layer node is <0.3, an energy-optimal strategy is adopted to select the relay layer node with an energy consumption status quantization value ≥0.6 as the upload node to reduce transmission energy consumption.

[0045] The path optimization unit and data caching unit are deployed on the relay layer nodes to implement forwarding path optimization and interrupt fault tolerance. The logic is as follows: the relay layer node dynamically selects the forwarding path based on its own three-dimensional state quantization value: for high-priority data, the path with a signal strength state quantization value ≥ 0.7 is selected, and the shortest delay algorithm is used to optimize the forwarding link; for low-priority data, the path with balanced energy consumption is selected to avoid excessive consumption by a single node; at the same time, the relay layer node uses the data caching unit to cache forwarded data in real time. When the receiving node signal is interrupted, it automatically switches to the backup path. The data retention time is determined by the data priority (e.g., high-priority data is retained for 30 seconds, and low-priority data is retained for 10 seconds).

[0046] The data sorting and processing unit and the emergency control unit are deployed at the aggregation layer node to implement data priority processing and emergency response to sudden failures. The data priority processing logic is as follows: the aggregation layer node receives data uploaded by relay layer nodes and perception layer nodes, employing a dynamic priority sorting strategy, i.e., high-priority data is decoded and uploaded first, with a processing latency of ≤5ms; low-priority data is processed in batches to balance processing efficiency and system load. Simultaneously, the aggregation layer node broadcasts the overall network status, including average energy consumption, interference intensity, and hierarchical thresholds, to all nodes in real time, providing a basis for status adjustments and hierarchical decisions for each node. The emergency response logic for sudden failures is as follows: when a sudden network failure occurs, an emergency aggregation mode is triggered, automatically reducing the transmission rate of low-priority data and prioritizing the transmission of high-priority data; simultaneously, the hierarchical weight of nodes with sufficient remaining power and good signal quality is temporarily increased, quickly reconstructing the hierarchical structure to ensure uninterrupted core network transmission functions.

[0047] It should be understood that by utilizing the emergency aggregation mode in the adaptive aggregation module and the redundant backup mechanism in the adaptive hierarchical module, network resilience can be improved while ensuring the real-time transmission of high-priority data in response to sudden failure scenarios through strategies such as data diversion, hierarchical reconstruction, and backup node switching; and by using relay layer load balancing and data caching strategies, transmission latency and data loss rate can be further reduced, achieving comprehensive performance optimization.

[0048] 5. Feedback Iterative Optimization Module: This module is used to collect and analyze data transmission performance indicators in real time at the aggregation layer nodes and determine whether the standard requirements are met. If so, it outputs the data aggregation results; otherwise, it provides feedback and iteratively optimizes the layer weight calculation coefficients and layer threshold update coefficients until the standards are met.

[0049] Specifically, the feedback iterative optimization module is deployed on the aggregation layer node and includes a performance statistics unit, a feedback comparison unit, and a parameter optimization unit.

[0050] The performance statistics unit is used to collect real-time data on transmission performance metrics in the aggregation layer nodes, including transmission latency, data loss rate, and node energy consumption balance.

[0051] The feedback comparison unit is used to compare various data transmission performance indicators with corresponding indicator thresholds (such as transmission delay threshold ≤ 50ms, data loss rate threshold ≤ 1%, node energy consumption balance ≥ 0.7) and generate feedback signals.

[0052] The parameter optimization unit uses a gradient descent algorithm to iteratively optimize the hierarchical weight calculation coefficients and hierarchical threshold update coefficients based on feedback signals. The optimization direction is as follows: if transmission delay exceeds the standard, increase the dynamic weight coefficients of the data priority state quantization value and the signal strength state quantization value; if data loss rate exceeds the standard, increase the dynamic weight coefficients of the signal strength state quantization value and the energy consumption state quantization value; if node energy consumption balance is not up to standard, increase the dynamic weight coefficient of the energy consumption state quantization value. Specifically, the iteration cycle is 10 seconds, and the update magnitude of each iteration is controlled within 0.1 to avoid network fluctuations caused by parameter mutations, ensuring continuous adaptation of the hierarchical aggregation strategy to the dynamic state of nodes and the network environment.

[0053] 6. Communication and Interaction Module: Used to support high-speed transmission of status, hierarchy, and data information between nodes and modules.

[0054] Specifically, the communication interaction module is deployed on all nodes. While supporting high-speed transmission of status, hierarchy and data information between nodes and modules, it adopts anti-interference coding technology (such as LDPC (low-density parity check code) coding) to reduce data loss caused by signal interference; and supports rapid signal switching in emergency mode to ensure real-time transmission of feedback signals and emergency control signals.

[0055] In summary, the data acquisition and dynamic aggregation system provided in this application can construct an adaptive hierarchical structure and dynamic aggregation strategy by integrating the multi-dimensional dynamic states of node energy consumption, signal strength, and data priority after data acquisition. This fundamentally solves the contradiction between real-time performance and stability of data transmission in complex environments, and achieves coordinated optimization of data transmission latency, node energy consumption balance, and data reliability.

[0056] In one embodiment, the data acquisition and dynamic aggregation system provided in this application is deployed in an industrial internet workshop monitoring scenario, including multiple network nodes (30 fixed nodes and 5 inspection robot nodes), each node deploying corresponding functional modules, as detailed below: State-aware quantization module: Each node has a built-in energy consumption sensor (collecting the node's remaining power and energy consumption rate), a signal detector (collecting the node's received / transmitted signal power and signal interference), and a data priority identification unit (identifying data types and assigning priorities); the quantization processing unit is integrated into the node processor to realize the normalization quantization and dynamic correction of three-dimensional state parameters, and output... , , .

[0057] Weight and threshold calculation module: Deployed on all nodes, the multi-objective collaborative optimization unit calculates the hierarchical weights of each node, and the reinforcement learning unit adjusts priority ratios and energy consumption status based on network data, adaptively updating... , , Threshold update unit calculates in real time and Update the hierarchical threshold and broadcast it to all nodes.

[0058] Adaptive Hierarchical Module: This module is deployed on all nodes. The hierarchical evaluation unit determines the hierarchical level of a node based on its hierarchical weight and the real-time updated hierarchical threshold. The neighbor interaction unit exchanges node status and hierarchical information through wireless communication (LoRa-WAN protocol) to construct a local node topology map. The hierarchical migration unit triggers seamless node hierarchical migration, and the load balancing unit realizes redundant backup of aggregation layer nodes and load distribution of relay layer nodes.

[0059] Adaptive aggregation module: At the perception layer node: deploy a data upload selection unit to select the upload object based on data priority and relay layer node status; at the relay layer node: deploy a path optimization unit (using Dijkstra's shortest latency algorithm) and a data caching unit (100MB cache capacity) to achieve path optimization and interruption fault tolerance; at the aggregation layer node: deploy a data sorting and processing unit (prioritizing high-priority data) and an emergency control unit (triggering emergency aggregation mode).

[0060] Feedback Iterative Optimization Module: Deployed on the main aggregation layer node, the performance statistics unit provides real-time statistics on transmission latency, data loss rate, and node energy consumption balance; the feedback comparison unit compares with preset thresholds and generates feedback signals; the parameter optimization unit uses the gradient descent algorithm to iteratively optimize the weight coefficients and threshold update parameters.

[0061] Communication Interaction Module: This module is deployed on all nodes. It adopts the LoRa-WAN wireless communication protocol, supports anti-interference coding (LDPC coding), and has a transmission rate of 100kbps. In emergency mode, the transmission rate is increased to 200kbps to ensure stable signal transmission.

[0062] It is important to note that the aforementioned state-aware quantization module needs to be calibrated periodically (e.g., monthly) to ensure the accuracy of parameters such as energy consumption and signal strength, and to avoid quantization errors affecting decision-making. The initial values ​​of the dynamic weighting coefficients and hierarchical thresholds can be adjusted according to specific application scenarios, such as the initial values ​​in emergency communication scenarios. Prioritize the transmission of high-priority data; the triggering conditions for emergency aggregation mode can be customized according to scenario requirements, such as the number of node failures ≥20% and signal interference ≥0.8, to improve system adaptability.

[0063] After the system in this embodiment is running, it can ensure stable data transmission delay for equipment fault warning, reduce data loss rate, and improve the average endurance of nodes; by effectively adapting to the dynamic changes of the complex workshop environment, it resolves the contradiction between real-time performance and stability in layered transmission.

[0064] In one embodiment, such as Figure 2 As shown, a data acquisition and dynamic aggregation method is provided, including: Step 1: Collect the three-dimensional state parameters of all nodes in the communication network and perform normalization and quantization processing to obtain the three-dimensional state quantization values ​​of each node, including the quantization values ​​of energy consumption state, signal strength state and data priority state.

[0065] Step 2: Calculate the layer weight of each node in real time based on the three-dimensional state quantization value, and update the layer threshold of the network in real time based on the average layer weight of the network; wherein, the layer threshold divides each node into aggregation layer nodes, relay layer nodes and perception layer nodes.

[0066] Step 3: Determine the current level of each node based on the layer weight and layer threshold. At the same time, construct a local node topology graph by utilizing the interaction between node states and layer information. Automatically trigger node re-layering and data migration by monitoring changes in layer weight and changes in the layer migration of neighboring nodes in the local node topology graph.

[0067] Step 4: Based on the current level and 3D state quantization value of each node, automatically execute data acquisition, transmission and aggregation from the perception layer node, relay layer node to the aggregation layer node.

[0068] Step 5: In the aggregation layer node, statistical data transmission performance indicators are collected in real time and it is determined whether the standard requirements are met. If yes, the data aggregation result is output; if no, feedback is provided and the layer weight calculation coefficient and layer threshold update coefficient are iteratively optimized until the standard is met.

[0069] This method is based on the aforementioned data acquisition and dynamic aggregation system. The entire process is designed adaptively, requiring no preset fixed parameters. It is adaptable to various complex and heterogeneous scenarios such as the Internet of Things and the Industrial Internet, and has strong versatility.

[0070] In one embodiment, the data acquisition and dynamic aggregation method provided in this application is applied to an industrial internet workshop monitoring scenario. In this scenario, nodes are distributed across various equipment in the workshop, and the operating environment presents complex conditions such as mechanical interference, node movement (e.g., inspection robot nodes), and energy consumption fluctuations (some nodes are battery-powered). The data includes equipment fault warning data (high priority), equipment operating parameter data (medium priority), and environmental monitoring data (low priority). The fault warning data transmission latency is required to be ≤30ms, and the data loss rate ≤0.5%, while ensuring stable network node operation. The method in this embodiment specifically includes the following steps: S1: Node dynamic state perception and quantification: Each workshop monitoring node (including fixed nodes and inspection robot nodes) has a built-in state perception and quantification module that collects three-dimensional state parameters in real time, including: Energy consumption status: Remaining power at fixed nodes ( ), energy consumption rate ( ), the remaining battery power of the inspection robot nodes, and the rate of energy consumption during movement; Signal strength status: Receive / transmit signal power ( Signal interference caused by workshop mechanical interference ( ); Data priority status: Equipment fault warning data ( ), equipment operating parameter data ( ), environmental monitoring data ( ); The quantification process is performed using the modified formula designed in this application: Energy status quantification: ,in Adaptively updated based on historical energy consumption data of nodes, fixed nodes Inspection robot node (Mobile energy consumption fluctuates more) Signal strength quantization: ,in It adapts to the dynamic changes in mechanical interference in the workshop; After quantization, all parameters take values ​​of [0,1]. For example, if a fixed node has 80% remaining power and a low energy consumption rate, after quantization... Signal power meets standards and interference is minimal. Current transmission equipment operating parameter data, .

[0071] S2: Dynamic hierarchical weight calculation and adaptive update of hierarchical threshold: Using formula Calculate the hierarchical weights, and update the dynamic weight coefficients through reinforcement learning; When there are no fault warnings in the workshop (high-priority data percentage <10%), , , Prioritize ensuring node battery life and signal stability; when a device fault warning is detected (high-priority data percentage ≥ 60%), , , Prioritize the real-time transmission of fault early warning data; Calculate the average layer weight of the network With state fluctuation coefficient Update the stratification threshold; For example, the average layer weight of the network at a certain moment State fluctuation coefficient ,but , , ; Nodes with a layer weight ≥ 0.65 are aggregation layer nodes (deployed near the workshop control center, with good signal and sufficient power consumption), nodes with a layer weight ∈ [0.55, 0.65) are relay layer nodes (distributed in the middle of the workshop, responsible for data forwarding), and nodes with a layer weight ∈ [0, 0.55) are perception layer nodes (deployed near the equipment, responsible for data acquisition).

[0072] Step S3: Adaptive hierarchical structure dynamic adjustment: Each node determines its level based on its own hierarchical weight and hierarchical threshold, obtains information about surrounding nodes through neighbor interaction units, and constructs a local node topology graph; When the inspection robot node moves to a weak signal area, the signal strength quantization value drops from 0.8 to 0.3, and the layer weight drops from 0.7 to 0.45 (the change in layer weight is ≥0.2), triggering a layer migration from the aggregation layer to the perception layer. During the migration, the original forwarding link is preserved to avoid data loss. When the forwarding volume of a relay layer node reaches 85% of its maximum load, it automatically diverts some environmental monitoring data (low priority) to surrounding relay layer nodes with a layer weight of 0.6 to achieve load balancing. Two redundant nodes are configured in the aggregation layer. When the primary aggregation node... When energy consumption is insufficient, the system automatically switches to a redundant node with a switching delay of 8ms to ensure the stability of the aggregation function.

[0073] Step S4: Execution of adaptive convergence strategy based on dynamic state: Perception layer node: Equipment fault early warning data ( The direct connection priority strategy is adopted, directly connecting to the aggregation layer node with a transmission latency of 25ms; environmental monitoring data ( )choose Upload data from relay layer nodes to reduce energy consumption; Relay layer node: When forwarding fault warning data, select The shortest path has a forwarding delay of 5ms; when forwarding environmental monitoring data, an energy-balanced path is selected to avoid excessive consumption by a single node; when the signal of a relay node is interrupted, the data is buffered for 20 seconds and the system switches to the backup path. Aggregation layer nodes: Prioritize the processing of fault warning data, with a decoding and upload latency of 3ms; batch process environmental monitoring data; broadcast network status (average energy consumption, interference intensity) in real time for each node to adjust its status; Sudden failure scenario: When three perception layer nodes fail simultaneously, the emergency mode is triggered, reducing the environmental monitoring data transmission rate to 50%, increasing the layer weight of the remaining nodes, and quickly reconstructing the layer structure. During the failure, the failure warning data loss rate is 0.3%, which meets the requirements.

[0074] Step S5: State Feedback and Policy Iteration Optimization Real-time performance metrics for aggregation layer nodes: transmission latency, data loss rate, and energy balance. If the data transmission delay for fault warnings reaches 35ms (exceeding the limit) at a certain moment, then increase... Up to 0.6 Reduced to 0.3 After iterative optimization to 0.1, the latency was reduced to 28ms. If the node energy balance is 0.65 (not up to standard), then increase it. The optimized energy consumption balance was improved to 0.75, reaching 0.5. The iteration cycle is 10 seconds, and the parameter update increment is 0.05 each time to ensure network stability.

[0075] In one embodiment, a computer device is provided, which may be a terminal, and its internal structure diagram may be as follows: Figure 3As shown, the computer device includes a processor, memory, network interface, display screen, and input devices connected via a system bus. The processor provides computing and control capabilities. The memory includes non-volatile storage media and internal memory. The non-volatile storage media stores the operating system and computer programs. The internal memory provides an environment for the operation of the operating system and computer programs stored in the non-volatile storage media. The network interface is used to communicate with external terminals via a network connection. When the computer program is executed by the processor, it implements a data acquisition and dynamic aggregation method. The display screen can be an LCD screen or an e-ink display screen. The input devices can be a touch layer covering the display screen, buttons, a trackball, or a touchpad mounted on the computer device casing, or an external keyboard, touchpad, or mouse.

[0076] Those skilled in the art will understand that Figure 3 The structure shown is merely a block diagram of a portion of the structure related to the present application and does not constitute a limitation on the computer device to which the present application is applied. Specific computer devices may include more or fewer components than those shown in the figure, or combine certain components, or have different component arrangements.

[0077] In one embodiment, a computer device is provided, including a memory and a processor, the memory storing a computer program, the processor executing the computer program to perform the following steps: Step 1: Collect the three-dimensional state parameters of all nodes in the communication network and perform normalization and quantization processing to obtain the three-dimensional state quantization values ​​of each node, including the quantization values ​​of energy consumption state, signal strength state and data priority state. Step 2: Calculate the layer weight of each node in real time based on the three-dimensional state quantization value, and update the layer threshold of the network in real time based on the average layer weight of the network; wherein, the layer threshold divides each node into aggregation layer nodes, relay layer nodes and perception layer nodes. Step 3: Determine the current level of each node based on the layer weight and layer threshold. At the same time, construct a local node topology graph by utilizing the interaction between node status and layer information. Automatically trigger node re-layering and data migration by monitoring changes in layer weight and changes in the layer migration of neighboring nodes in the local node topology graph. Step 4: Based on the current level and 3D state quantization value of each node, automatically execute data acquisition, transmission and aggregation from the perception layer node, relay layer node to the aggregation layer node; Step 5: In the aggregation layer node, statistical data transmission performance indicators are collected in real time and it is determined whether the standard requirements are met. If yes, the data aggregation result is output; if no, feedback is provided and the layer weight calculation coefficient and layer threshold update coefficient are iteratively optimized until the standard is met.

[0078] In one embodiment, a computer-readable storage medium is provided having a computer program stored thereon, the computer program performing the following steps when executed by a processor: Step 1: Collect the three-dimensional state parameters of all nodes in the communication network and perform normalization and quantization processing to obtain the three-dimensional state quantization values ​​of each node, including the quantization values ​​of energy consumption state, signal strength state and data priority state. Step 2: Calculate the layer weight of each node in real time based on the three-dimensional state quantization value, and update the layer threshold of the network in real time based on the average layer weight of the network; wherein, the layer threshold divides each node into aggregation layer nodes, relay layer nodes and perception layer nodes. Step 3: Determine the current level of each node based on the layer weight and layer threshold. At the same time, construct a local node topology graph by utilizing the interaction between node status and layer information. Automatically trigger node re-layering and data migration by monitoring changes in layer weight and changes in the layer migration of neighboring nodes in the local node topology graph. Step 4: Based on the current level and 3D state quantization value of each node, automatically execute data acquisition, transmission and aggregation from the perception layer node, relay layer node to the aggregation layer node; Step 5: In the aggregation layer node, statistical data transmission performance indicators are collected in real time and it is determined whether the standard requirements are met. If yes, the data aggregation result is output; if no, feedback is provided and the layer weight calculation coefficient and layer threshold update coefficient are iteratively optimized until the standard is met.

[0079] Those skilled in the art will understand that all or part of the processes in the methods of the above embodiments can be implemented by a computer program instructing related hardware. The computer program can be stored in a non-volatile computer-readable storage medium. When executed, the computer program can include the processes of the embodiments of the above methods. Any references to memory, storage, databases, or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. Volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in a variety of forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), dual data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), RAMbus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM), etc.

[0080] The technical features of the above embodiments can be combined in any way. For the sake of brevity, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0081] The above embodiments are merely illustrative of several implementation methods of this application, and their descriptions are relatively specific and detailed, but they should not be construed as limiting the scope of this application. It should be noted that those skilled in the art can make several modifications and improvements without departing from the concept of this application, and these all fall within the protection scope of this application.

Claims

1. A data acquisition and dynamic aggregation system, characterized in that, The system includes: The state-aware quantization module is used to collect the three-dimensional state parameters of all nodes in the communication network and perform normalization quantization processing to obtain the three-dimensional state quantization values ​​of each node, including the quantization values ​​of energy consumption state, signal strength state and data priority state. The weight and threshold calculation module is used to calculate the hierarchical weight of each node in real time based on the three-dimensional state quantization value, and update the hierarchical threshold of the network in real time based on the average hierarchical weight of the network; wherein, the hierarchical threshold divides each node into convergence layer nodes, relay layer nodes and perception layer nodes. The adaptive layering module is used to determine the current layer of each node based on the layering weight and layering threshold. At the same time, it uses the interaction between the status and layering information of the nodes to construct a local node topology graph. By monitoring the changes in layering weight and the layer migration changes of neighboring nodes in the local node topology graph, it automatically triggers node re-layering and data migration. The adaptive aggregation module is used to automatically perform data acquisition, transmission and aggregation from the perception layer nodes, relay layer nodes to the aggregation layer nodes according to the current level and 3D state quantization value of each node. The feedback and iterative optimization module is used to collect data transmission performance indicators in real time in the aggregation layer nodes and determine whether the standard requirements are met. If yes, the data aggregation result is output; if no, the layer weight calculation coefficient and layer threshold update coefficient are fed back and iteratively optimized until the standard is met. The communication and interaction module is used to support high-speed transmission of status, hierarchy, and data information between nodes and modules.

2. The data acquisition and dynamic aggregation system according to claim 1, characterized in that, The state-aware quantization module is deployed on all nodes, including a state-aware unit and a quantization processing unit; The state perception unit includes an energy consumption sensor, a signal detector, and a data priority identification unit, which are used to collect the energy consumption status, signal strength status, and data priority status of each node in real time to form three-dimensional state parameters. The energy consumption status includes the node's remaining power and energy consumption rate; the signal strength status includes the node's received / transmitted signal power and signal interference level; and the data priority status includes the urgency of the data to be transmitted and the data type weight. The quantization processing unit is used to normalize and quantize the collected three-dimensional state parameters to obtain three-dimensional state quantization values, all within the range of [0,1]. Among them, the larger the energy consumption state quantization value, the more sufficient the remaining power of the node and the lower the energy consumption pressure; the larger the signal strength state quantization value, the better the node signal quality and the less interference; the larger the data priority state quantization value, the higher the urgency of the data to be transmitted and the higher the transmission priority. Among them, the energy consumption status quantification value Represented as: ； In the formula, The remaining power of the node. For energy consumption rate, This represents the maximum power of the node. The dynamic attenuation coefficient, It is adaptively updated based on the node's historical energy consumption data; Among them, the signal strength state quantization value Represented as: ； In the formula, This represents the actual signal power of the node. For signal interference, The maximum signal power of the node. As an interference compensation factor, Adjust in real time according to the intensity of environmental disturbances.

3. The data acquisition and dynamic aggregation system according to claim 1, characterized in that, The weight and threshold calculation module is deployed on all nodes, including a multi-objective collaborative optimization unit, a reinforcement learning unit, and a threshold update unit; The multi-objective collaborative optimization unit is used to construct a dynamic hierarchical weight model based on the three-dimensional state quantization values. The dynamic hierarchical weight model adopts a multi-objective collaborative optimization algorithm to calculate the hierarchical weight of each node in real time. , represented as: ； in, , , These are the energy consumption status quantification values. Signal strength state quantization value Data priority status quantization value The dynamic weighting coefficients, and satisfying ; The reinforcement learning unit is used to adaptively update the three types of dynamic weight coefficients using a reinforcement learning algorithm. The core logic is: when the proportion of high-priority data in the network is ≥60%, increase the weight coefficient. ,reduce and Prioritize real-time performance; when the average energy consumption state quantization value of nodes in the network is ≤0.3, increase... Prioritize ensuring node battery life and network stability; when the signal interference in the network is ≥0.5, increase... Prioritize selecting nodes with superior signal quality to construct transmission paths; The threshold update unit is used to statistically analyze the hierarchical weights of all nodes in the network and calculate the average hierarchical weight of the network. Combined state fluctuation coefficient Real-time updates of network layer thresholds, including the aggregation layer threshold. Relay layer threshold and perception layer threshold , respectively represented as: ； ； ； Where, the hierarchical weight ≥ The nodes are classified as aggregation layer nodes, responsible for data aggregation and uploading; the layer weight ∈ [ , The nodes in the [] layer are relay layer nodes, responsible for data forwarding and state relay; the layer weights ∈ [] , The nodes in this layer are perception layer nodes, responsible for data collection and initial uploading; state fluctuation coefficient It reflects the overall dispersion of node states and is calculated from the standard deviation of the layer weights of all nodes. This ensures that the layer threshold can adapt to the dynamic changes in network states and avoids the rigidity of the layer structure.

4. The data acquisition and dynamic aggregation system according to claim 1, characterized in that, The adaptive hierarchical module is deployed on all nodes and includes a hierarchical evaluation unit, a neighbor interaction unit, a hierarchical migration unit, and a load balancing unit. The hierarchical evaluation unit is used to determine the current hierarchical level of each node based on the hierarchical weights calculated in real time and the hierarchical thresholds updated in real time. The neighbor interaction unit is used to construct a local node topology graph by utilizing the interaction of state and hierarchical information between nodes. The hierarchical migration unit is used to monitor the hierarchical weight changes of each node and the hierarchical migration changes of neighboring nodes in the local node topology graph where each node is located. When the hierarchical weight change of a certain node is ≥0.2, or when more than 30% of the neighboring nodes in the local node topology graph undergo hierarchical migration, the current node is automatically triggered to re-hierarchize and migrate data. The data migration process adopts a seamless switching strategy, that is, before the new hierarchical role is activated, the data transmission link of the original hierarchical level is retained to avoid data loss or delay. The load balancing unit is used to set up redundancy backup mechanisms and dynamic load balancing strategies for aggregation layer nodes and relay layer nodes, respectively, to ensure the stability of data transmission and aggregation. Specifically, for aggregation layer nodes, a redundancy backup mechanism is set up. When the energy consumption status quantization value of the main aggregation layer node is ≤0.2 or the signal strength status quantization value is ≤0.3, the redundant aggregation layer node is automatically switched. The switching priority is determined by the hierarchical weight sorting, and the switching delay is ≤10ms to ensure the stability of the aggregation layer. For relay layer nodes, a dynamic load balancing strategy is adopted. When the data forwarding volume of a relay layer node is greater than or equal to 80% of its maximum load, some low-priority data is automatically diverted to surrounding relay layer nodes with similar hierarchical weights to avoid delays caused by relay layer node congestion.

5. The data acquisition and dynamic aggregation system according to claim 1, characterized in that, The adaptive aggregation module includes a data upload unit, a path optimization unit, a data caching unit, a data sorting and processing unit, and an emergency control unit, which are used to implement a differentiated hierarchical data aggregation strategy based on the dynamic status of nodes at different levels. The data upload unit is deployed on the perception layer node to achieve dynamic selection of the upload node. The logic is as follows: the perception layer node selects the relay layer node with the highest layer weight as the upload node based on its own data priority and the status of the surrounding relay layer nodes. When the data priority status quantization value of the perception layer node is ≥0.7, a direct connection priority strategy is adopted to directly connect to the aggregation layer node. If there is no relay layer node that meets the conditions in the surrounding area, a temporary cross-layer connection to the aggregation layer node is also adopted to ensure real-time performance. When the data priority status quantization value of the perception layer node is <0.3, an energy-optimal strategy is adopted to select the relay layer node with an energy consumption status quantization value ≥0.6 as the upload node to reduce transmission energy consumption. The path optimization unit and the data caching unit are deployed at the relay layer nodes to achieve forwarding path optimization and interruption fault tolerance. The logic is as follows: the relay layer node dynamically selects the forwarding path based on its own three-dimensional state quantization value: for high-priority data, it selects the path with a signal strength state quantization value ≥ 0.7 and uses the shortest delay algorithm to optimize the forwarding link; for low-priority data, it selects the energy-balanced path to avoid excessive consumption by a single node; at the same time, the relay layer node uses the data caching unit to cache forwarded data in real time. When the receiving node signal is interrupted, it automatically switches to the backup path. The data caching retention time is determined by the data priority. The data sorting and processing unit and the emergency control unit are deployed at the aggregation layer node to implement data priority processing and emergency response to sudden faults. The data priority processing logic is as follows: the aggregation layer node receives data uploaded by relay layer nodes and perception layer nodes, employing a dynamic priority sorting strategy, i.e., high-priority data is decoded and uploaded first, with a processing latency of ≤5ms; low-priority data is processed in batches to balance processing efficiency and system load. Simultaneously, the aggregation layer node broadcasts the overall network status, including average energy consumption, interference intensity, and hierarchical thresholds, to all nodes in real time, providing a basis for status adjustment and hierarchical decision-making for each node. The emergency response logic for sudden faults is as follows: when a sudden network fault occurs, an emergency aggregation mode is triggered, automatically reducing the transmission rate of low-priority data and prioritizing the transmission of high-priority data; simultaneously, the hierarchical weight of nodes with sufficient remaining power and good signal quality is temporarily increased, quickly reconstructing the hierarchical structure to ensure uninterrupted core network transmission functions.

6. The data acquisition and dynamic aggregation system according to claim 1, characterized in that, The feedback iterative optimization module is deployed on the aggregation layer node and includes a performance statistics unit, a feedback comparison unit, and a parameter optimization unit. The performance statistics unit is used to collect data on transmission performance indicators in real time in the aggregation layer nodes, including transmission latency, data loss rate, and node energy consumption balance. The feedback comparison unit is used to compare various data transmission performance indicators with corresponding indicator thresholds and generate feedback signals. The parameter optimization unit is used to iteratively optimize the layer weight calculation coefficient and the layer threshold update coefficient using the gradient descent algorithm based on the feedback signal. The optimization direction is as follows: if the transmission delay exceeds the standard, the dynamic weight coefficient of the data priority state quantization value and the signal strength state quantization value is increased; if the data loss rate exceeds the standard, the dynamic weight coefficient of the signal strength state quantization value and the energy consumption state quantization value is increased; if the node energy consumption balance does not meet the standard, the dynamic weight coefficient of the energy consumption state quantization value is increased.

7. The data acquisition and dynamic aggregation system according to claim 1, characterized in that, The communication interaction module is deployed on all nodes. While supporting high-speed transmission of status, hierarchy and data information between nodes and modules, it adopts anti-interference coding technology to reduce data loss caused by signal interference. It also supports rapid signal switching in emergency mode to ensure real-time transmission of feedback signals and emergency control signals.

8. A data acquisition and dynamic aggregation method, characterized in that, The method includes: Step 1: Collect the three-dimensional state parameters of all nodes in the communication network and perform normalization and quantization processing to obtain the three-dimensional state quantization values ​​of each node, including the quantization values ​​of energy consumption state, signal strength state and data priority state. Step 2: Calculate the layer weight of each node in real time based on the three-dimensional state quantization value, and update the layer threshold of the network in real time based on the average layer weight of the network; wherein, the layer threshold divides each node into aggregation layer nodes, relay layer nodes and perception layer nodes. Step 3: Determine the current level of each node based on the layer weight and layer threshold. At the same time, construct a local node topology graph by utilizing the interaction between node status and layer information. Automatically trigger node re-layering and data migration by monitoring changes in layer weight and changes in the layer migration of neighboring nodes in the local node topology graph. Step 4: Based on the current level and 3D state quantization value of each node, automatically execute data acquisition, transmission and aggregation from the perception layer node, relay layer node to the aggregation layer node; Step 5: In the aggregation layer node, statistical data transmission performance indicators are collected in real time and it is determined whether the standard requirements are met. If yes, the data aggregation result is output; if no, feedback is provided and the layer weight calculation coefficient and layer threshold update coefficient are iteratively optimized until the standard is met.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, characterized in that, When the processor executes the computer program, it implements the steps of the method of claim 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When the computer program is executed by a processor, it implements the steps of the method of claim 8.

Images