A method and system for optimizing task load scheduling of distributed network nodes

Abstract

The present invention provides a task load scheduling optimization method and system for distributed network nodes, which determines the task transmission path between distributed network nodes based on historical task congestion data and real-time link load prediction results; adjusts the modulation and coding strategy in the task transmission process according to the channel quality parameters on the task transmission path to obtain the adjusted modulation and coding strategy; generates voltage and frequency adjustment instructions corresponding to the distributed network nodes according to the energy consumption requirements corresponding to the adjusted modulation and coding strategy, combined with the load distribution of each communication link and each node on the task transmission path; collaboratively optimizes the task transmission path, channel quality parameters and voltage and frequency adjustment instructions to generate a task load scheduling strategy; the present invention improves the transmission reliability, energy efficiency balance and system stability of the distributed network in a highly dynamic environment through the collaborative optimization of dynamic path selection, adaptive modulation and coding adjustment and precise energy consumption control.

Description

Technical Field

[0001] The present invention relates to the field of computer technology, and in particular to a method and system for optimizing task load scheduling of distributed network nodes. Background Art

[0002] With the rapid development of technologies such as edge computing clusters and IoT terminal networking, task load scheduling between distributed network nodes must meet the dual requirements of highly reliable transmission and dynamic energy efficiency optimization to cope with the challenges of channel environment interference, stringent real-time resource allocation requirements, and balancing energy efficiency and stability.

[0003] To address the above challenges, a step-by-step path and energy consumption optimization method based on fixed-period channel detection is currently widely adopted. Existing methods use periodic channel detection methods, static energy consumption configuration, and independent decision-making mechanisms, which have some defects. For example, the periodic detection of existing methods causes path switching to lag behind the actual degradation process, which can easily lead to task accumulation or transmission interruption; static energy consumption configuration ignores power consumption fluctuations caused by modulation strategy adjustments, which can easily cause insufficient power supply to high-load nodes or waste of resources in low-load nodes; the separate path and energy consumption decision modules lack cross-layer parameter linkage, making it difficult to cope with the complex scenarios of channel interference and sudden traffic increases, resulting in low global resource utilization efficiency. Summary of the Invention

[0004] The present invention provides a method and system for optimizing task load scheduling of distributed network nodes, which are used to solve the problems in the prior art such as insufficient foresight, easy occurrence of task accumulation or transmission interruption, rigid energy efficiency, lack of coordination and low global resource utilization efficiency.

[0005] In a first aspect, the present invention provides a method for optimizing task load scheduling of distributed network nodes, comprising:

[0006] Determine the task transmission path between distributed network nodes based on historical task congestion data and real-time link load prediction results;

[0007] adjusting a modulation and coding strategy during task transmission according to a channel quality parameter on the task transmission path to obtain an adjusted modulation and coding strategy, wherein the channel quality parameter includes a link quality attenuation rate;

[0008] Generate voltage and frequency adjustment instructions corresponding to the distributed network nodes based on the energy consumption requirements corresponding to the adjusted modulation and coding strategy and in combination with the load distribution of each communication link and each node on the task transmission path;

[0009] The task transmission path, the channel quality parameter, and the voltage and frequency adjustment instruction are collaboratively optimized to generate a task load scheduling strategy.

[0010] Optionally, the channel quality parameters further include external interference intensity, link quality attenuation rate, and frequency band utilization. The task transmission path, the channel quality parameters, and the voltage and frequency adjustment instructions are collaboratively optimized to generate a task load scheduling strategy, including:

[0011] Selecting a plurality of target paths from the task transmission paths, each of which has a link quality attenuation value lower than a preset threshold, wherein the link quality attenuation value is obtained by comparing the relative attenuation amplitude and time dimension attenuation trend of the channel quality parameter with the historical reference signal quality parameter;

[0012] Calculating comprehensive priority scores corresponding to multiple target paths based on the load capacity, external interference intensity, and bandwidth utilization of the target path, and selecting the target path with the highest priority from the comprehensive priority scores as the updated task transmission path;

[0013] Generate an expected total power consumption of each node based on the task distribution ratio of the updated task transmission path and the single-node power consumption increment corresponding to the adjusted modulation and coding strategy;

[0014] selecting an optimal voltage-frequency combination from the voltage-frequency adjustment instructions according to the expected total power consumption and the power supply capability of the distributed network node to obtain updated energy consumption configuration parameters;

[0015] A task load scheduling strategy is generated according to the mapping relationship between the updated task transmission path and the updated energy consumption configuration parameters.

[0016] Optionally, selecting an optimal voltage-frequency combination from the voltage-frequency adjustment instructions according to the expected total power consumption and the power supply capability of the distributed network node to obtain updated energy consumption configuration parameters includes:

[0017] Constructing a power supply capability database corresponding to each distributed network node, wherein the power supply capability database includes the rated output power, voltage adjustment range, and frequency adjustment range corresponding to each node;

[0018] Selecting a candidate voltage-frequency combination that meets preset conditions from the voltage-frequency adjustment instructions, the preset conditions including that the instantaneous power consumption is lower than the rated output power of the corresponding node and is not less than the expected total power consumption, the voltage value is within the voltage adjustment range, and the frequency value is within the frequency adjustment range;

[0019] Selecting a combination with the smallest product of voltage value and frequency value from the candidate voltage-frequency combinations as the optimal voltage-frequency combination;

[0020] The voltage value and the frequency value of the optimal voltage-frequency combination are converted into a control instruction format to generate updated energy consumption configuration parameters.

[0021] Optionally, determining a task transmission path between distributed network nodes based on historical task congestion data and real-time link load prediction results includes:

[0022] Determine candidate transmission paths between all distributed network nodes based on the topology of the distributed network, and extract historical task congestion data of each link in the candidate transmission paths to calculate a historical congestion coefficient;

[0023] The historical congestion coefficient, the remaining bandwidth in the real-time link load prediction result, and the estimated traffic growth rate are weighted and summed according to preset weights to generate an initial path score for each candidate transmission path;

[0024] Selecting a path that meets the real-time link load capacity constraint and has the highest initial path score from the candidate transmission paths as the initial task transmission path;

[0025] According to the dynamic changes of the real-time link load prediction results, the initial path score of the initial task transmission path is periodically recalculated to obtain an updated score. If there is a path whose updated score exceeds the initial path score, the path with the highest updated score is used as the final task transmission path.

[0026] Optionally, a modulation and coding strategy during task transmission is adjusted according to a channel quality parameter on the task transmission path to obtain an adjusted modulation and coding strategy, wherein the channel quality parameter includes a link quality attenuation rate, including:

[0027] Acquire channel quality parameters of each link in the task transmission path, wherein the channel quality parameters include link quality attenuation rate, signal strength deviation value, and multi-band interference component;

[0028] Classifying the initial adjustment level according to the superposition result of the short-term change trend and the long-term change trend of the link quality attenuation rate;

[0029] Dynamically compensating the initial adjustment level based on the cumulative effect of the signal strength deviation value and the dominant frequency band distribution of the multi-band interference component to obtain a dynamically compensated adjustment level and a compensation coefficient;

[0030] According to the adjustment level and compensation coefficient after dynamic compensation, the strategy parameters in the predefined modulation and coding strategy set are matched to generate an adjusted modulation and coding strategy, wherein the strategy parameters include modulation order, coding redundancy ratio and frequency band avoidance rules.

[0031] Optionally, dynamically compensating the initial adjustment level based on the cumulative effect of the signal strength deviation value and the dominant frequency band distribution of the multi-band interference component to obtain the dynamically compensated adjustment level and compensation coefficient includes:

[0032] Counting the number of times the signal strength deviation value exceeds a preset deviation threshold within a preset continuous period to generate a cumulative number of exceeding thresholds;

[0033] Extracting the interference intensity ratio of each frequency band from the multi-band interference components, marking a preset number of frequency bands sorted by the interference intensity ratio as dominant interference frequency bands, and calculating the overlap between the dominant interference frequency bands and the frequency bands specified in the predefined modulation and coding strategy;

[0034] Generate a dynamic compensation parameter according to the cumulative number of exceeding the standard and the degree of overlap;

[0035] The dynamic compensation parameter and the initial adjustment level are superimposed and calculated to generate an adjustment level and a compensation coefficient after dynamic compensation.

[0036] Optionally, generating a voltage and frequency adjustment instruction corresponding to the distributed network node according to the energy consumption requirement corresponding to the adjusted modulation and coding strategy and in combination with the load distribution of each communication link and each node on the task transmission path includes:

[0037] Calculating the expected energy consumption requirement of each node based on the RF power consumption increment of each node in the adjusted modulation and coding strategy and the load weight of each node on the task transmission path;

[0038] The energy consumption value corresponding to the expected energy consumption demand and the basic power consumption value of each node in the idle state are weighted and accumulated to generate the total energy consumption demand of each node;

[0039] According to the total energy consumption requirement and the rated output power of each node, an initial voltage-frequency combination that meets the task processing requirements is matched, and the voltage and frequency parameters of the initial voltage-frequency combination are encoded into standardized node control instructions to generate the voltage-frequency adjustment instructions.

[0040] In a second aspect, the present invention provides a task load scheduling optimization system for distributed network nodes, comprising:

[0041] The determination module is used to determine the task transmission path between distributed network nodes based on historical task congestion data and real-time link load prediction results;

[0042] an adjustment module, configured to adjust a modulation and coding strategy during task transmission according to a channel quality parameter on the task transmission path to obtain an adjusted modulation and coding strategy, wherein the channel quality parameter includes a link quality attenuation rate;

[0043] a generating module, configured to generate a voltage and frequency adjustment instruction corresponding to the distributed network node according to the energy consumption requirement corresponding to the adjusted modulation and coding strategy and in combination with the load distribution of each communication link and each node on the task transmission path;

[0044] The optimization module is used to collaboratively optimize the task transmission path, the channel quality parameters and the voltage and frequency adjustment instructions to generate a task load scheduling strategy.

[0045] In a third aspect, the present invention provides a computing device comprising a processor and a memory, wherein the memory stores a computer program, and the processor is configured to run the computer program to execute a task load scheduling optimization method for a distributed network node as described in any one of the first aspects.

[0046] In a fourth aspect, the present invention provides a computer storage medium having computer program instructions stored thereon, which, when executed by a processor, implements a task load scheduling optimization method for a distributed network node as described in any one of the first aspects.

[0047] Compared with the prior art, the technical solution adopted in the embodiment of the present invention has at least the following technical advantages:

[0048] In the present invention, the task transmission path between distributed network nodes is determined based on historical task congestion data and real-time link load prediction results;

[0049] adjusting a modulation and coding strategy during task transmission according to a channel quality parameter on the task transmission path to obtain an adjusted modulation and coding strategy, wherein the channel quality parameter includes a link quality attenuation rate; generating a voltage and frequency adjustment instruction corresponding to the distributed network node according to an energy consumption requirement corresponding to the adjusted modulation and coding strategy and in combination with a load distribution of each communication link and each node on the task transmission path;

[0050] The task transmission path, the channel quality parameter, and the voltage and frequency adjustment instruction are collaboratively optimized to generate a task load scheduling strategy.

[0051] The technical solution provided by the present invention selects high-reliability transmission paths by integrating historical congestion patterns with real-time load predictions, thereby reducing the risk of transmission delays caused by link congestion. At the same time, based on the dynamic perception of the channel quality attenuation rate, the signal modulation and coding redundancy ratio are adaptively adjusted to improve the data transmission success rate in harsh channel environments. In combination with the energy consumption characteristics of the modulation strategy and the node load distribution, energy consumption control instructions that match the task requirements are generated to avoid node overload or resource idleness. Through the real-time linkage of path replanning and energy consumption instructions, global dynamic adaptation of network resources is achieved, which is a technical logic with extremely high research value that has not been considered in existing technologies. After the implementation of the above technology, the system stability in high-load scenarios can be guaranteed. Among them, the task transmission path is dynamically optimized based on the channel quality parameters, and the updated task transmission path is generated by screening the low-attenuation path and calculating the comprehensive priority. The optimal voltage-frequency combination is matched with the expected total power consumption of the node and the power supply capacity, and finally a path-energy consumption configuration mapping relationship is established to generate a task load scheduling strategy.

[0052] These and other aspects of the present invention will become more readily apparent from the following description of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following is a brief introduction to the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0054] Figure 1 A flowchart of a method for optimizing task load scheduling of distributed network nodes provided by an embodiment of the present invention;

[0055] Figure 2 A schematic diagram of the structure of a distributed network node task load scheduling optimization system provided by an embodiment of the present invention;

[0056] Figure 3 A schematic diagram of the structure of a computing device provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0057] In order to enable those skilled in the art to better understand the solutions of the present invention, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0058] In some of the processes described in the specification and claims of the present invention and the above-mentioned figures, multiple operations that appear in a specific order are included, but it should be clearly understood that these operations may not be executed in the order in which they appear in this article or may be executed in parallel. The serial numbers of the operations, such as 101, 102, etc., are only used to distinguish between different operations, and the serial numbers themselves do not represent any execution order. In addition, these processes may include more or fewer operations, and these operations may be executed in sequence or in parallel. It should be noted that the descriptions of "first", "second", etc. in this article are used to distinguish different messages, devices, modules, etc., and do not represent the order of precedence, nor do they limit "first" and "second" to be different types.

[0059] The following will clearly and completely describe the technical solutions in the embodiments of the present invention in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without making any creative efforts shall fall within the scope of protection of the present invention.

[0060] Figure 1 The present invention provides a flowchart of a method for optimizing task load scheduling of distributed network nodes. Figure 1 As shown, the method includes:

[0061] In response to the complex problem of dynamic channel degradation and energy efficiency load imbalance in distributed network scenarios (such as continuous degradation of link quality due to mobile occlusion of edge nodes, and sudden increase in node power consumption due to burst traffic), traditional solutions use static path planning and discrete energy consumption control mechanisms, resulting in defects such as path switching lagging behind channel attenuation and disconnection between energy efficiency strategy and task load. This solution realizes dynamic path optimization by integrating historical congestion patterns with real-time load prediction, adaptively adjusts the modulation and coding strategy based on cross-cycle perception of link quality attenuation rate, and generates voltage and frequency adjustment instructions based on the energy consumption characteristics of the modulation strategy and the spatiotemporal correlation of node load distribution. Finally, through the collaborative optimization mechanism of multi-dimensional parameters (path, channel, energy consumption), it breaks through the contradiction between local optimization and global imbalance in the existing technology, and realizes the joint improvement of transmission reliability and system energy efficiency in a highly dynamic environment. Based on this, the present invention provides a task load scheduling optimization method for distributed network nodes, such as Figure 1 ,include:

[0062] Step 101: Determine the task transmission path between distributed network nodes based on historical task congestion data and real-time link load prediction results;

[0063] In this step, historical task congestion data refers to information about link congestion events recorded by distributed network nodes during past task transmissions. This information includes the time of congestion occurrence, duration, location of the congested link, and task queue length. This data is used to analyze the periodicity of link carrying capacity and the distribution of bottleneck nodes. Real-time link load prediction results refer to load status estimates for each link within a set future time window, generated by combining real-time monitoring of network traffic, node processing capacity, and task arrival rate with a prediction model. These estimates include remaining bandwidth, expected queue delay, and link availability probability.

[0064] In an embodiment of the present invention, the link failure frequency and task queuing peak value in historical congestion data are combined with the real-time predicted remaining bandwidth to construct a link reliability scoring model. Links whose historical congestion frequency exceeds a threshold or whose real-time predicted remaining bandwidth is lower than the task requirement are excluded to generate a set of candidate paths. The reliability scores of the candidate paths are calculated using this scoring model (e.g., based on a weight ratio of 60% for historical stability and 40% for real-time load), and the path with the highest comprehensive score is selected as the task transmission path. This task transmission path is initial and requires further optimization and updating.

[0065] For example, in an edge computing scenario, a node needs to transmit real-time monitoring data to a central server. By analyzing historical congestion data, the system finds that link A has a 70% probability of congestion during peak hours, while real-time link load prediction shows that link B has sufficient remaining bandwidth. Therefore, link B is selected as the task transmission path to avoid task accumulation.

[0066] Step 102: adjusting the modulation and coding strategy during the task transmission process according to the channel quality parameter on the task transmission path to obtain an adjusted modulation and coding strategy, wherein the channel quality parameter includes a link quality attenuation rate;

[0067] In this step, the channel quality parameters represent a set of indicators that represent the transmission performance of the wireless communication link, including link signal strength, frequency band utilization, external interference intensity, link quality attenuation rate, etc. In this step, the link quality attenuation rate in the channel quality parameters is specifically used for adjustment. The modulation and coding strategy refers to the combination rule of the signal modulation method and the error correction coding ratio used in task data transmission, which directly affects the transmission rate, anti-interference capability and radio frequency power consumption. The link quality attenuation rate refers to a dynamic parameter calculated by continuously monitoring the changes in link signal strength, reflecting the degradation trend of the channel quality (such as continuous signal attenuation due to environmental obstruction or device movement), and is used to trigger the modulation and coding strategy's downgrade protection or path switching.

[0068] In an embodiment of the present invention, wireless channel state perception technology is used to collect link signal strength on the task transmission path in real time to calculate the attenuation rate (the difference between the current link signal strength and the historical benchmark value is divided by the monitoring time); the modulation and coding strategy during the task transmission process is determined according to the attenuation rate. The specific process is as follows: if the attenuation rate is less than 0.05 / second, high-order modulation (such as 64QAM) and low redundancy coding (1 / 3) are adopted; if 0.05 / second≤5 and the attenuation rate is less than 0.1 / second, medium-order modulation (16QAM) and medium redundancy coding (1 / 2) are adopted; if the attenuation rate is greater than or equal to 0.1 / second, low-order modulation (QPSK) and high redundancy coding (2 / 3) are adopted; if the signal strength deviation value exceeds the threshold for three consecutive cycles, the modulation strategy is forcibly downgraded; and finally, the adjusted modulation and coding strategy is obtained.

[0069] For example, when a drone node is moving, if it detects that the link quality attenuation rate suddenly increases from 3% / second to 8% / second (due to flying into an obstructed area), the system will automatically switch the modulation mode from 64QAM to 16QAM and increase the coding redundancy ratio to 1 / 2 to ensure complete data transmission.

[0070] Step 103: generating voltage and frequency adjustment instructions corresponding to the distributed network nodes based on the energy consumption requirements corresponding to the adjusted modulation and coding strategy and the load distribution of each communication link and each node on the task transmission path;

[0071] In this step, energy consumption demand refers to the increased RF power consumption of distributed network nodes due to the adjustment of modulation and coding strategies (such as an increase of 2W for high-order modulation). Communication link refers to the physical or logical communication channel established between two nodes in a distributed network. Each link has independent bandwidth, latency and load status attributes. The load distribution of each node refers to the current task processing volume undertaken by each node in the distributed network and the traffic proportion of its associated links, including quantitative indicators such as CPU / GPU utilization, memory occupancy and the length of the queue of pending tasks. Voltage and frequency adjustment instructions refer to standardized control instructions generated according to the node energy consumption demand and load status, including target voltage value, operating frequency value and effective time window, which are used to dynamically adjust the power supply parameters of the node's computing unit to achieve energy efficiency optimization.

[0072] In an embodiment of the present invention, the load weight is first calculated, which is obtained based on the initial weight (the number of links connecting the node) × the average of the remaining bandwidth ratios of each link; then the energy consumption requirement is calculated, that is, based on the basic power consumption (the energy consumption value when the node is in an idle state) × 1.2 + load weight × RF power consumption increment, a voltage-frequency combination that meets the energy consumption requirement is selected from a predefined configuration table, encoded into a control instruction, and a voltage-frequency adjustment instruction corresponding to the distributed network node is generated.

[0073] For example, in a smart factory, distributed network nodes processing sudden control commands caused their load weight to increase from 1.5 to 2.3, and their total energy consumption increased from 15W to 22W. The system matched the voltage of 1.0V and the frequency of 2.0GHz (power consumption = 1.0 × 2.0 = 2.0W), generating and issuing voltage and frequency adjustment commands to dynamically improve power supply parameters.

[0074] Step 104: Co-optimizing the task transmission path, the channel quality parameter, and the voltage and frequency adjustment instruction to generate a task load scheduling strategy;

[0075] In this step, the task load scheduling strategy refers to a global optimization plan generated by integrating path planning results, channel status, and energy consumption control instructions. It clarifies the task distribution path, transmission parameter configuration, and node power consumption limit rules to ensure the coordinated goals of high-reliability transmission and low-energy operation.

[0076] In an embodiment of the present invention, if the degradation of channel quality parameters triggers path switching, the energy consumption configuration of the associated nodes is synchronously updated; the node load distribution of the switched path is bound to the voltage and frequency instructions according to the time window, and a task load scheduling strategy is generated to distribute scheduling instructions according to the task start timing based on the strategy, thereby ensuring the timing consistency of path switching and power consumption adjustment.

[0077] For example, in an urban traffic monitoring network, if the quality attenuation rate of the main path exceeds the threshold due to thunderstorms, the system switches to the backup path and reduces the modulation order of the new path node. At the same time, it increases its voltage to 1.1V according to the load distribution, generates a complete task load scheduling strategy, and ensures the feedback of all-weather monitoring data.

[0078] The embodiments of the present invention systematically solve technical barriers such as path switching lag, separation of energy efficiency and load, and lack of global coordination in traditional solutions through path selection that integrates historical rules and real-time predictions, dynamic modulation adjustment driven by channel attenuation rate, generation of task load scheduling strategies with strong correlation between load and energy consumption, and multi-parameter collaborative optimization mechanism. It significantly improves the transmission reliability, energy efficiency balance and system robustness of distributed networks in highly dynamic environments, and is suitable for complex scenarios such as edge computing and industrial Internet of Things.

[0079] The present invention provides a specific embodiment, step 104, collaboratively optimizing the task transmission path, the channel quality parameter, and the voltage and frequency adjustment instruction to generate a task load scheduling strategy, specifically comprising the following steps:

[0080] Step 401: Select multiple target paths from the task transmission paths, each of which has a link quality attenuation value lower than a preset threshold, wherein the link quality attenuation value is obtained by comparing the relative attenuation amplitude and time dimension attenuation trend of the channel quality parameter with the historical reference signal quality parameter;

[0081] In this step, the link quality degradation value is a dynamic indicator calculated by comparing the difference between current channel quality parameters (such as signal strength and bit error rate) and historical baseline signal quality parameters, combined with the degradation trend per unit time (such as the percentage of signal strength drop per second). It is used to quantify the degree of link quality degradation. The preset threshold is the upper limit of link quality degradation tolerance set in advance based on network reliability requirements. For example, a link quality degradation value exceeding 15% is considered a high-risk path.

[0082] In an embodiment of the present invention, the current signal quality parameters of the target link, including the received signal strength and noise power, are monitored in real time through physical layer channel status information, and the pre-stored historical benchmark signal quality parameters of the link in a stable state are retrieved; the current signal quality parameters are compared with the historical benchmark parameters to calculate the instantaneous relative attenuation amplitude, and the continuous decline rate of the signal quality in the last several transmission cycles is counted to obtain the time dimension attenuation trend; finally, the instantaneous attenuation amplitude and the time attenuation trend are fused and calculated according to a preset weight ratio to generate a link quality attenuation value for failure risk determination; links whose link quality attenuation values ​​exceed a preset threshold (such as 15%) are excluded, and the remaining paths are retained as the target path set.

[0083] Step 402: Calculate comprehensive priority scores corresponding to multiple target paths based on the load capacity, external interference intensity, and bandwidth utilization of the target paths, and select the target path with the highest priority from the comprehensive priority scores as the updated task transmission path;

[0084] In this step, load capacity refers to the maximum task traffic that a single link can carry per unit time. It is determined by the link bandwidth, node processing power, and protocol overhead, and is usually expressed in tasks / second or bit rate. External interference intensity refers to the signal interference power intensity from non-target transmission equipment (such as adjacent communication systems and electromagnetic noise). It is measured through spectrum analysis or received signal strength indication and is expressed in decibel milliwatts (dBm). Bandwidth utilization refers to the effective utilization efficiency of the frequency band occupied by the current link. The calculation formula is: (actual transmitted data volume / theoretical maximum carried data volume) × 100%, which reflects the rationality of spectrum resource utilization. The comprehensive priority score is a score generated by weighted calculation of the target path's load capacity (weight 40%), external interference intensity (weight 30%), and band utilization (weight 30%). It is used to quantify the overall transmission performance of the path.

[0085] In an embodiment of the present invention, the load capacity, external interference intensity (the inverse), and frequency band utilization of the target path are mapped to the range of 0-1 respectively; the comprehensive priority score of each path is calculated according to the preset weights (load capacity 40%, external interference intensity 30%, frequency band utilization 30%); the target paths are arranged from high to low according to the comprehensive priority score, and the path with the highest comprehensive priority score is selected as the updated task transmission path.

[0086] Step 403: generating an expected total power consumption of each node based on the task distribution ratio of the updated task transmission path and the single-node power consumption increment corresponding to the adjusted modulation and coding strategy;

[0087] In this step, the task distribution ratio refers to the proportion of task processing allocated based on the load weight of each node in the updated task transmission path (such as the number of connected links and the remaining bandwidth ratio). For example, node A will handle 30% of the total task. The single-node power consumption increment refers to the increase in power consumption of the radio frequency unit of a single node due to adjusting the modulation and coding strategy (such as switching from QPSK to 16QAM). It is obtained by matching a predefined power consumption comparison table. The expected total power consumption refers to the predicted power consumption of each node during the task processing cycle. The calculation formula is: basic power consumption (the energy consumption value of the node in the idle state) + (single-node power consumption increment × task distribution ratio × task duration coefficient).

[0088] In an embodiment of the present invention, first, based on the load capacity and priority weight of each link in the updated task transmission path, the proportion of tasks that each distributed network node needs to undertake is calculated; wherein, the load capacity is determined by measuring the available bandwidth and current traffic data of each link, and the priority weight is obtained by comprehensively calculating the node processing capability, the urgency of the task, and the number of path hops; finally, the task proportion of each node is converted into a standardized task distribution ratio through normalization processing for subsequent power consumption calculation and resource allocation; the RF power consumption increment associated with each node in the adjusted modulation and coding strategy is extracted, and the increment is obtained from a preset power consumption comparison table by matching the signal modulation mode and transmission power level corresponding to the node, and an incremental superposition calculation is performed on the multi-link transmission node; finally, the expected total power consumption of each node is calculated, and the expected total power consumption = basic power consumption (idle state) + ∑ (single node increment × task distribution ratio × task persistence coefficient), wherein ∑ represents the product result of (single node increment × task distribution ratio × task persistence coefficient) calculated for each node in the network, and then the product result of all nodes is added.

[0089] Step 404: Selecting an optimal voltage-frequency combination from the voltage-frequency adjustment instructions based on the expected total power consumption and the power supply capability of the distributed network node to obtain updated energy consumption configuration parameters;

[0090] In this step, the power supply capability of a distributed network node refers to the sustainable power supply range supported by the node hardware. For example, a node has a rated output power of 100W, an adjustable voltage range of 0.8V-1.2V, and a frequency range of 1GHz-3GHz. The optimal voltage-frequency combination refers to selecting the configuration combination with the smallest product of voltage and frequency values ​​(e.g., 1.0V×2.0GHz=2.0W) under the constraints of expected total power consumption and power supply capability to achieve optimal energy efficiency. The updated energy consumption configuration parameters refer to the set of optimized node operating voltage, frequency, and effective time window, for example, voltage 1.1V, frequency 2.5GHz, and an effective time window of 50ms before the start of the task.

[0091] In an embodiment of the present invention, a power supply capability database of distributed network nodes is first established, in which the maximum output power and voltage-frequency adjustable range of the power supply module of each node are stored; then, a matching query is performed in the power supply capability database based on the expected total power consumption, and all voltage-frequency combinations that can meet the power consumption requirements are screened out; from the qualified combinations, the optimal voltage-frequency combination with the smallest product value of voltage and frequency (such as 1.0V×2.0GHz=2.0W) is selected as the optimal operating point; finally, the voltage value and frequency value of the optimal operating point are encoded into standardized control instructions to obtain updated voltage-frequency adjustment instructions, which are used to synchronously update the energy consumption configuration parameters corresponding to each node to generate updated energy consumption configuration parameters.

[0092] Step 405: Generate a task load scheduling strategy according to the mapping relationship between the updated task transmission path and the updated energy consumption configuration parameters.

[0093] In this step, the mapping relationship refers to the binding rules between each node in the updated task transmission path and its corresponding energy consumption configuration parameters. For example, node N1 to node N3 of path X are respectively associated with voltage configurations of 1.1V / 1.0V / 1.2V.

[0094] In an embodiment of the present invention, based on the topological connection order and task distribution ratio of each node in the updated task transmission path, the network address of each node is associated and bound with its corresponding energy consumption configuration parameters to form a complete path and energy consumption mapping relationship table, which serves as the core control basis of the task load scheduling strategy.

[0095] The embodiments of the present invention systematically solve the problems of low transmission reliability, insufficient resource utilization, poor dynamic adaptability, etc. caused by the separation of path planning and energy consumption control in traditional solutions through a collaborative mechanism of dynamic link attenuation evaluation, multi-dimensional path optimization, accurate prediction of load power consumption, optimal energy efficiency configuration, and global strategy synchronization. Specifically, the channel quality attenuation trend is linked with energy consumption demand to avoid task interruption caused by path switching delay; node power consumption is adjusted based on the real-time task distribution ratio to suppress local overload or resource idleness; and end-to-end energy efficiency and stability collaborative optimization in high-interference and high-dynamic scenarios are achieved through the mapping relationship between path and energy consumption and timing synchronization control.

[0096] The present invention provides a specific embodiment, step 404, selecting an optimal voltage-frequency combination from the voltage-frequency adjustment instructions based on the expected total power consumption and the power supply capability of the distributed network node to obtain updated energy consumption configuration parameters, specifically includes the following steps:

[0097] Step 411: constructing a power supply capability database corresponding to each distributed network node, wherein the power supply capability database includes the rated output power, voltage adjustment range, and frequency adjustment range corresponding to each node;

[0098] In this step, the power supply capability database refers to a dedicated data set that stores the power supply capability parameters of each distributed network node. The rated output power refers to the maximum output power of the node power module in a continuous operating state, which is used to limit the upper limit of the power consumption of the voltage-frequency combination (for example, if the rated power of a node is 120W, the instantaneous power consumption of all candidate combinations must be ≤120W). The voltage adjustment range refers to the operating voltage adjustment range allowed by the node hardware. For example, a node supports a voltage configuration of 0.8V to 1.2V. Exceeding this range will trigger the overvoltage / undervoltage protection mechanism. The frequency adjustment range refers to the operating frequency adjustment range supported by the node processor or communication unit. For example, the frequency of a node can be dynamically adjusted between 1GHz and 3GHz. The higher the frequency, the faster the computing rate, but the higher the power consumption.

[0099] In an embodiment of the present invention, the rated output power, voltage adjustment range and frequency adjustment range are extracted from the node hardware specification document. For example, the rated power of a node is 100W, the voltage supports 0.8V-1.2V, and the frequency supports 1GHz-3GHz. The parameters are classified by node ID and stored as a structured power supply capability database, each record of which contains the node ID, rated power, voltage range and frequency range. If the node hardware is upgraded or replaced, the corresponding parameters in the power supply capability database are synchronously updated.

[0100] Step 412: Selecting a candidate voltage-frequency combination that meets preset conditions from the voltage-frequency adjustment instructions, wherein the preset conditions include that the instantaneous power consumption is lower than the rated output power of the corresponding node and is not less than the expected total power consumption, the voltage value is within the voltage adjustment range, and the frequency value is within the frequency adjustment range;

[0101] In this step, instantaneous power consumption refers to the real-time power consumption value of the voltage-frequency combination during operation, and is calculated as follows: voltage value × frequency value × load factor (the load factor is determined by the current task amount).

[0102] In an embodiment of the present invention, all possible combinations are traversed from a predefined voltage and frequency adjustment instruction set, and a combination that meets the preset conditions is selected, wherein the preset conditions are: instantaneous power consumption ≤ node rated output power (hardware safety constraint); instantaneous power consumption ≥ expected total power consumption (task requirement constraint); voltage value is within the voltage adjustment range; frequency value is within the frequency adjustment range; combinations that meet all conditions are retained to form candidate voltage and frequency combinations.

[0103] Step 413: Selecting a combination with the smallest product of voltage value and frequency value from the candidate voltage-frequency combinations as the optimal voltage-frequency combination;

[0104] In an embodiment of the present invention, the voltage value × frequency value is calculated for each candidate combination, for example, 1.0V × 2.0GHz = 2.0; the product values ​​are sorted from small to large, and the combination corresponding to the smallest value is selected; if multiple combinations have the same product, the combination with the lower voltage is preferentially selected.

[0105] Step 414: Convert the voltage value and frequency value of the optimal voltage-frequency combination into a control instruction format to generate updated energy consumption configuration parameters.

[0106] In this step, the updated energy consumption configuration parameters refer to a set of node operating parameters generated after optimization and screening, including a target voltage value, a target frequency value, a configuration effective time window, and a parameter encoding format.

[0107] In an embodiment of the present invention, first, an instruction structure is predefined, specifically, a node control instruction format including a voltage setting field, a frequency setting field and a check field is predefined, wherein the voltage setting field stores the integer and decimal parts of the voltage value, the frequency setting field stores the integer part of the frequency value, and the check field is generated by a bit-by-bit XOR operation of the binary data of the voltage and frequency fields; then, numerical conversion and encoding are performed, that is, the voltage value of the optimal voltage-frequency combination is split into integer and decimal parts according to a preset precision and filled into the voltage setting field, the frequency value is rounded off to an integer multiple and filled into the frequency setting field, and the check field is generated based on the values ​​of the two fields; finally, instruction encapsulation and binding are performed, that is, the fields are spliced ​​into a complete control instruction in a preset order, and an independent instruction is generated for each node, and the updated energy consumption configuration parameters are formed by binding the node identifier.

[0108] The embodiments of the present invention use a power supply capability database to accurately constrain hardware safety boundaries, perform multi-condition screening to ensure task requirements and hardware adaptation, minimize products to achieve optimal energy efficiency, and standardize instruction encapsulation to ensure executability. These solutions systematically address issues such as energy efficiency optimization ignoring hardware limitations and static configuration being unable to dynamically adapt to load changes in traditional solutions. These solutions significantly improve energy efficiency balance, hardware security, and the controllability of scheduling strategies in high-load scenarios, making them suitable for industrial automation, edge computing, and other fields with stringent requirements on real-time performance and reliability.

[0109] The present invention provides a specific embodiment, step 101, determining a task transmission path between distributed network nodes based on historical task congestion data and real-time link load prediction results, specifically comprising the following steps:

[0110] Step 111: Determine candidate transmission paths between all distributed network nodes based on the topology of the distributed network, extract historical task congestion data of each link in the candidate transmission paths, and calculate a historical congestion coefficient;

[0111] In this step, the distributed network topology refers to the physical or logical connections between all nodes in the distributed network, including the location distribution of nodes (such as servers and terminal devices), the connection method of links (communication channels), and the network hierarchy (such as star, mesh, and tree topologies). The historical congestion coefficient refers to an indicator of link congestion severity calculated based on historical task congestion data.

[0112] In an embodiment of the present invention, all possible candidate transmission paths (such as nodes [A, B, C] and [A, D, C]) are generated based on the connection relationships between distributed network nodes. For each link in the candidate path, the congestion frequency (such as three times a week) and the average congestion duration (such as 30 minutes each time) are extracted from the historical database to calculate the historical congestion coefficient. The calculation formula is historical congestion coefficient = congestion frequency × weight 1 + average congestion duration × weight 2.

[0113] Step 112: The historical congestion coefficient, the remaining bandwidth in the real-time link load prediction result, and the estimated traffic growth rate are weighted and summed according to preset weights to generate an initial path score for each candidate transmission path;

[0114] In this step, the remaining bandwidth refers to the available bandwidth that is not currently occupied by the link, which is determined by the difference between the maximum load bandwidth of the link and the currently used bandwidth. The estimated traffic growth rate refers to the prediction of the increase rate of link traffic within a set time window in the future based on the real-time monitored traffic change trend, for example, generated by sliding window statistics or exponential smoothing algorithm. The preset weight refers to the user-defined historical congestion coefficient, remaining bandwidth and estimated traffic growth rate in the path score.

[0115] In an embodiment of the present invention, the remaining bandwidth of each link is obtained through the traffic monitoring module (for example, link X has 60% remaining bandwidth); an estimated traffic growth rate is generated based on a time series prediction model (for example, a 10% increase in the next cycle); and an initial path score is generated for each candidate transmission path according to the calculation formula: path initial score = historical congestion coefficient × 40% + remaining bandwidth × 35% - estimated traffic growth rate × 25% (negative weights suppress high-growth paths).

[0116] Step 113: Selecting a path that satisfies the real-time link load capacity constraint and has the highest initial path score from the candidate transmission paths as the initial task transmission path;

[0117] In this step, the real-time link load capacity constraint refers to the maximum task flow that the link can carry at the current moment, which is determined by the difference between the link's maximum carrying flow and the current flow. If the task demand exceeds the load capacity, the link is judged to be unavailable.

[0118] In this embodiment of the present invention, candidate transmission paths that meet the real-time link load capacity constraint are selected from the candidate transmission paths, and candidate transmission paths whose remaining capacity does not meet the task requirements are excluded (for example, if link Y has 15 Mbps remaining and the task requires 20 Mbps, it is eliminated); the remaining transmission paths are sorted from high to low according to the initial path scores, and the path with the highest score is selected as the initial task transmission path.

[0119] Step 114: Based on the dynamic changes in the real-time link load prediction results, the initial path score of the initial task transmission path is periodically recalculated to obtain an updated score. If there is a path whose updated score exceeds the initial path score, the path with the highest updated score is used as the final task transmission path.

[0120] In this step, periodic recalculation refers to recalculating the path score at fixed time intervals (e.g., every 30 seconds) or upon event triggering (e.g., sudden traffic changes) to ensure that path selection is synchronized with the real-time network status.

[0121] In an embodiment of the present invention, based on the dynamic changes in the real-time link load prediction results, real-time load data is re-acquired at set intervals (e.g., 30 seconds), and the remaining bandwidth and estimated traffic growth rate are updated. The scores of all candidate paths are recalculated according to the rules in step 112 to generate updated scores. If there is a candidate path whose updated score exceeds the current path score and meets the load constraint, it is selected as the highest-scoring path. That is, this highest-scoring path is the task transmission path between the determined distributed network nodes.

[0122] For example, in a smart logistics park's cargo dispatch instruction transmission scenario, the system first analyzes the network topology to identify three candidate paths: [W, A, D], [W, B, D], and [W, C, D]. Analysis of historical task congestion data reveals that link WA on candidate path [W, A, D] has the highest congestion coefficient (0.8), while link WC on candidate path [W, C, D] has a moderate congestion coefficient (0.5). Combined with real-time link load prediction results, candidate path [W, C, D], with 85% remaining bandwidth and only an estimated growth rate of 8%, becomes the initial task transmission path with an initial path score of 0.65 (calculated as historical congestion coefficient × 40% + remaining bandwidth × 35% - estimated growth rate × 25%). However, 30 seconds later, monitoring revealed that the remaining bandwidth of candidate path [W, C, D] had plummeted to 40% due to traffic bursts, the estimated growth rate had increased to 20%, and the score had dropped to 0.25. Meanwhile, the remaining bandwidth of candidate path [W, A, D] had increased to 75%, the estimated growth rate had decreased to 3%, and the initial score had risen to 0.62. Based on this, the system automatically switched the task transmission path to [W, A, D], ensuring efficient and stable transmission of scheduling instructions in a dynamically changing network environment.

[0123] The embodiments of the present invention systematically solve technical barriers such as response lag, neglect of traffic growth trends, and disconnection between historical experience and real-time status in traditional static path planning through a path scoring mechanism that integrates historical congestion patterns with real-time load forecasts, periodic dynamic re-evaluation, and constraint-driven path switching strategies. This significantly improves the reliability, resource utilization efficiency, and adaptability of task transmission in highly dynamic network environments, and is suitable for scenarios such as smart logistics and the Internet of Vehicles that require real-time response to complex environmental changes.

[0124] The present invention provides a specific embodiment, in which step 102 adjusts the modulation and coding strategy during the task transmission process according to the channel quality parameter on the task transmission path to obtain an adjusted modulation and coding strategy, wherein the channel quality parameter includes a link quality attenuation rate. Specifically, the step 102 includes the following steps:

[0125] Step 201: Acquire channel quality parameters of each link in the task transmission path, wherein the channel quality parameters include link quality attenuation rate, signal strength deviation value, and multi-band interference component;

[0126] In this step, the signal strength deviation value refers to the absolute difference between the current signal strength and the historical reference signal strength. For example, if the reference strength is -80dBm and the current strength is -90dBm, the deviation value is 10dB. The multi-band interference component refers to the interference signal strength distribution data of different frequency bands, such as the interference power values ​​detected in the 2.4GHz and 5GHz bands (for example, the interference strength of the 2.4GHz band is -85dBm and the 5GHz band is -90dBm).

[0127] In an embodiment of the present invention, the channel quality parameters of each link in the task transmission path are obtained, for example, the signal strength and interference spectrum data of each link are collected in real time by a spectrum analyzer, and the reference signal strength stored in the historical database (such as the stable signal strength at the time of initial link establishment) is retrieved; the link quality attenuation rate is calculated according to the formula, (current signal strength - historical reference strength) / monitoring time; the signal strength deviation value is the absolute difference between the current signal strength and the historical reference strength; the multi-band interference component is the distribution data of the external interference intensity of different frequency bands.

[0128] Step 202: Dividing the initial adjustment level according to the superposition result of the short-term change trend and the long-term change trend of the link quality attenuation rate;

[0129] In this step, the short-term change trend of the link quality attenuation rate refers to the average change rate of the link quality attenuation rate within the most recently set time window (such as 10 seconds); the long-term change trend refers to the overall change direction of the link quality attenuation rate within a longer period (such as 10 minutes) (such as continuous deterioration or gradual recovery); the superposition result refers to the comprehensive trend evaluation value after weighted fusion of the short-term change trend and the long-term change trend according to preset weights (such as 60% for short-term and 40% for long-term).

[0130] In an embodiment of the present invention, for example, a short-term change trend is calculated based on the average value of the decay rate per second in the last 10 seconds; a long-term change trend is calculated based on the average value of the decay rate per second in the past 1 minute; the difference between the short-term change trend and the long-term change trend is calculated (e.g., the short-term trend is 5% / second, the long-term trend is 3% / second, and the difference is 2% / second) to divide the initial adjustment level. The specific division process includes: when the difference between the short-term change trend and the long-term trend exceeds a first threshold (e.g., the difference ≥ 2% / second), a high-risk level mark is generated, indicating that a significant modulation downgrade is required; when the difference is between the first threshold and the second threshold (e.g., 1% / second ≤ difference < 2% / second), a medium-risk level mark is generated, indicating that a moderate downgrade is required; when the weighted difference is lower than the second threshold (difference < 1% / second), a low-risk level mark is generated, indicating that the modulation needs to be maintained or upgraded;

[0131] Step 203: dynamically compensating the initial adjustment level based on the cumulative effect of the signal strength deviation value and the dominant frequency band distribution of the multi-band interference component to obtain a dynamically compensated adjustment level and a compensation coefficient;

[0132] In this step, the cumulative effect refers to the cumulative value of the number of times the signal strength deviation value exceeds the preset threshold in multiple consecutive monitoring cycles. For example, if the deviation value exceeds 15dB in 5 consecutive cycles, the cumulative effect value is 5. The dominant frequency band distribution refers to the set of frequency bands with the top set number (such as the top 3) of interference intensity in the multi-band interference component and their strength ratio, such as 2.4GHz (50%), 5GHz (30%), and 1.8GHz (20%). The adjustment level after dynamic compensation is a level label that represents the degree of conservatism of the modulation and coding strategy (such as high, medium, and low risk levels). The compensation coefficient refers to the redundancy ratio increase value calculated based on the overlap of the interference frequency bands or the cumulative effect (such as increasing the coding redundancy by 10%).

[0133] In an embodiment of the present invention, the specific process of dynamic compensation is as follows: if the signal strength deviation value exceeds the preset deviation threshold within a continuous set period, the risk mark level of the initial adjustment level is increased; if the dominant frequency band of the multi-band interference component and the frequency band of the current modulation and coding strategy overlap more than a preset ratio, the compensation coefficient of the redundant coding ratio is increased; and finally, the adjustment level and compensation coefficient after dynamic compensation are obtained. For example, the number of times the signal strength deviation value exceeds the threshold (such as 3dB) continuously (such as 3 times in the last 5 periods); if the number of times the threshold is exceeded is ≥3 times, the initial adjustment level is increased by one level (such as medium risk is increased to high risk); the top two frequency bands with the highest proportion in the multi-band interference component (such as 2.4GHz and 5GHz) are extracted; if the dominant frequency band overlaps with the frequency band used by the current modulation strategy by ≥50%, the compensation coefficient is increased by 0.2, and the level increase result is associated with the compensation coefficient to generate the final adjustment level, that is, the adjustment level after dynamic compensation (such as the high risk level plus the compensation coefficient of 0.2, the final adjustment level is high risk enhancement).

[0134] Step 204: According to the adjustment level and compensation coefficient after the dynamic compensation, the strategy parameters in the predefined modulation and coding strategy set are matched to generate an adjusted modulation and coding strategy, wherein the strategy parameters include the modulation order, the coding redundancy ratio and the frequency band avoidance rule.

[0135] In this step, the modulation order refers to the signal modulation method (such as QPSK, 16QAM); the coding redundancy ratio refers to the ratio of the error correction code to the total data length (such as 1 / 3, 1 / 2); the frequency band avoidance rule refers to avoiding the frequency band that overlaps with the dominant interference frequency band (such as when the interference is concentrated at 2.4 GHz, switch to the 5 GHz frequency band for transmission).

[0136] In an embodiment of the present invention, a basic strategy is selected from a predefined strategy library according to the adjustment level after dynamic compensation (such as high-risk level matching QPSK+2 / 3 coding redundancy ratio); the coding redundancy ratio of the basic strategy is adjusted by superimposing a compensation coefficient (such as the original 2 / 3 coding redundancy ratio + 0.2 compensation coefficient, converted to 5 / 6 coding redundancy ratio); if the dominant frequency band overlap is ≥50%, a rule for disabling the frequency band is added to the strategy; the modulation order, coding redundancy ratio and frequency band rules are integrated to generate an adjusted modulation and coding strategy that can be issued.

[0137] The embodiments of the present invention systematically solve the technical barriers such as rigid modulation strategy, delayed interference avoidance, and one-sided parameter adjustment in traditional solutions through multi-period perception of channel attenuation trends, cross-layer compensation mechanism of signal deviation accumulation and interference frequency band analysis, and dynamic strategy matching rules. It significantly improves data transmission reliability, spectrum utilization efficiency and system adaptability in high-interference and high-dynamic scenarios, and is suitable for complex wireless environments such as drone communications and emergency communications.

[0138] The present invention provides a specific embodiment, in which step 203 dynamically compensates the initial adjustment level based on the cumulative effect of the signal strength deviation value and the dominant frequency band distribution of the multi-band interference component to obtain the dynamically compensated adjustment level and compensation coefficient, specifically comprising the following steps:

[0139] Step 211: Counting the number of times the signal strength deviation value exceeds a preset deviation threshold within a preset continuous period to generate a cumulative number of exceeding thresholds;

[0140] In this step, the preset continuous period refers to the number of preset continuous monitoring time windows, which is used to count the continuous exceeding of the signal strength deviation value. For example, if it is set to 5 consecutive periods (each period is 1 second), it is necessary to continuously monitor whether the deviation value exceeds the standard within 5 seconds. The preset deviation threshold refers to the signal strength deviation tolerance upper limit value preset according to the network reliability requirements. For example, the signal strength deviation is allowed to not exceed 5dB. If it exceeds this value, it is judged to exceed the standard. The cumulative number of exceeding times refers to the cumulative number of times the signal strength deviation value exceeds the preset deviation threshold within the continuous monitoring period, which is used to quantify the degree of continuous degradation of the signal quality.

[0141] In an embodiment of the present invention, a preset deviation threshold is set according to the network reliability requirements (for example, a signal strength deviation ≥ 5dB is considered exceeded); a continuous monitoring period window is set (for example, the last 5 periods); the signal strength deviation value of each period in the window is traversed, and if the deviation value of a certain period is ≥ the threshold, the count is +1; and the cumulative number of exceeding the standard is output (for example, 3 times in the last 5 periods have exceeded the standard).

[0142] Step 212: extracting the interference intensity ratio of each frequency band from the multi-band interference components, marking a preset number of frequency bands sorted by the interference intensity ratio as dominant interference frequency bands, and calculating the overlap between the dominant interference frequency bands and the frequency bands specified in the predefined modulation and coding strategy;

[0143] In this step, the preset number of frequency bands refers to the top N frequency bands (such as the top two frequency bands) with the highest interference intensity ratio selected from the multi-band interference components, which are used to characterize the frequency domain distribution characteristics of the main interference sources. The interference intensity ratio refers to the ratio of the interference intensity of a single frequency band to the total interference intensity of all frequency bands. The overlap refers to the overlapping ratio between the dominant interference frequency band and the specified frequency band in the predefined modulation and coding strategy. For example, if the dominant interference frequency bands are 2.4GHz and 5GHz, and the strategy specifies the frequency band as 5GHz, the overlap is 50%. The designated frequency band refers to the frequency band range (such as 2.4GHz-2.4835GHz) explicitly used for task transmission in the predefined modulation and coding strategy, which is usually set based on spectrum licensing or anti-interference requirements.

[0144] In an embodiment of the present invention, the frequency bands are sorted from high to low according to the proportion of interference intensity (such as frequency band A accounts for 60%, frequency band B accounts for 30%, and frequency band C accounts for 10%); the first N frequency bands (such as frequency band A and frequency band B) are selected as the dominant interference frequency bands; the number of overlaps between the dominant interference frequency bands and the specified frequency bands in the modulation and coding strategy defined according to the current state is counted (such as the strategy uses specified frequency bands A and C, and the dominant frequency bands are A and B, then the number of overlaps is 1), and the overlap degree is calculated according to the formula, overlap degree = (overlap number / total number of dominant frequency bands) × 100% (for example, 1 / 2 × 100% = 50%).

[0145] Step 213: generating a dynamic compensation parameter according to the cumulative number of exceeding the standard and the overlap degree;

[0146] In an embodiment of the present invention, if the cumulative number of violations is ≥ a set threshold (such as 3 times), the initial adjustment level will be increased by one level (such as medium risk is increased to high risk); if the cumulative number of violations is ≥ a threshold higher than the set threshold (such as 5 times), an additional incremental compensation coefficient of 0.1 is added; according to the formula, the dynamic compensation parameter is calculated, and the specific calculation process is as follows: first, the basic compensation coefficient is calculated, that is, the product of the overlap and the weight (such as overlap 50% × 0.4 = 0.2); if the cumulative number of violations is ≥ 3 times and the overlap is ≥ 50%, the total compensation coefficient (dynamic compensation parameter) is calculated, that is, the cumulative value of the basic coefficient and the incremental compensation coefficient (such as 0.2 + 0.1 = 0.3).

[0147] Step 214: The dynamic compensation parameter and the initial adjustment level are superimposed and calculated to generate an adjustment level and compensation coefficient after dynamic compensation.

[0148] In an embodiment of the present invention, the initial adjustment level (such as medium risk) is combined with the enhancement level (+1 level) to generate the adjustment level after dynamic compensation (high risk); the dynamic compensation parameter (such as 0.3) is associated with the adjustment level after dynamic compensation to form a final adjustment level of high risk + compensation 0.3.

[0149] The embodiment of the present invention solves the problems of one-sided modulation strategy adjustment and delayed interference avoidance in traditional schemes through a joint compensation mechanism of cumulative effect perception of signal degradation and overlap analysis of interference frequency bands, significantly improves the strategy response speed and anti-interference capability in high-interference scenarios, and effectively avoids data packet loss and communication interruption caused by continuous signal degradation or frequency band conflicts. It is suitable for scenarios with strong interference and high reliability requirements such as smart grids and industrial Internet of Things.

[0150] The present invention provides a specific embodiment, step 301, generating a voltage and frequency adjustment instruction corresponding to the distributed network node based on the energy consumption requirement corresponding to the adjusted modulation and coding strategy and in combination with the load distribution of each communication link and each node on the task transmission path, specifically includes the following steps:

[0151] Step 311: Calculate the expected energy consumption requirement of each node based on the RF power consumption increment of each node in the adjusted modulation and coding strategy and the load weight of each node on the task transmission path;

[0152] In this step, the RF power consumption increment refers to the increase in RF unit power consumption caused by the distributed network node adjusting the modulation and coding strategy (such as increasing the modulation order or increasing the coding redundancy), which is obtained by matching the current modulation strategy with a predefined power consumption comparison table. For example, when the node switches from QPSK modulation to 16QAM modulation, the RF power consumption may increase by 2W. The load weight refers to the proportion of task processing undertaken by the node in the task transmission path. The initial load distribution weight is generated by counting the number of directly connected links of each node in the task transmission path, and then the initial load distribution weight is dynamically corrected and determined based on the real-time remaining bandwidth ratio of each link. The expected energy consumption demand refers to the dynamic power consumption prediction value of the node during the task processing cycle, which is the product of the load weight, the RF power consumption increment, and the task duration coefficient (this coefficient is dynamically generated through the historical execution records stored locally on the node and the real-time task monitoring module, without manual configuration, and can adapt to changes in energy consumption requirements in different business scenarios).

[0153] In an embodiment of the present invention, the current modulation order (e.g., 16QAM) and coding redundancy ratio (e.g., 1 / 2) are extracted from the adjusted modulation and coding strategy. A predefined power consumption comparison table is queried to match the corresponding RF power consumption increment (e.g., 16QAM corresponds to a 1.5W increment). The number of links connected to the node is counted as an initial weight (e.g., if the node is connected to three links, the initial weight = 3). Combined with the real-time remaining bandwidth ratio of each link (e.g., link A has 60% remaining and link B has 40% remaining), a revised load weight is calculated (e.g., 3×(0.6+0.4) / 2=1.5). Finally, the expected energy consumption requirement is calculated according to the formula: expected energy consumption requirement = load weight × RF power consumption increment × task duration coefficient (e.g., 1.5×1.5×1.2=2.7W).

[0154] Step 312: Perform weighted cumulative calculation on the energy consumption value corresponding to the expected energy consumption demand and the basic power consumption value of each node in the idle state to generate the total energy consumption demand of each node;

[0155] In an embodiment of the present invention, the basic power consumption value in the idle state (such as 10W) ​​is read from the node hardware manual; the total energy consumption requirement of each node is calculated based on the preset weight distribution; for example, the basic power consumption weight is set to 1.2 (a fixed value, reflecting the necessary power consumption of the basic circuit), and the expected energy consumption requirement weight is set to 1.0 (adjusted according to the dynamic task ratio), then the total energy consumption requirement = basic power consumption value × 1.2 + energy consumption value corresponding to the expected energy consumption requirement × 1.0 (for example, 10×1.2+2.7×1.0=14.7W).

[0156] In this step, the total energy consumption requirement refers to the overall power consumption requirement of the node to complete the task, including the basic power consumption and dynamic increment

[0157] Step 313: According to the total energy consumption requirement and the rated output power of each node, an initial voltage-frequency combination that meets the task processing requirement is matched, and the voltage and frequency parameters of the initial voltage-frequency combination are encoded into standardized node control instructions to generate the voltage-frequency adjustment instructions.

[0158] In the embodiment of the present invention, first, based on the total energy consumption requirement and rated output power of the node, an initial combination that meets the following conditions is screened from the predefined voltage and frequency configuration table: 1. The instantaneous power consumption of the combination is not less than the total energy consumption requirement, 2. The voltage value is within the adjustment range supported by the node, and 3. The frequency value is within the adjustment range supported by the node. The voltage and frequency parameters of the initial voltage and frequency combination that meets the conditions are then converted into a standardized node control instruction format to directly generate the initial voltage and frequency adjustment instruction.

[0159] The embodiments of the present invention systematically solve technical barriers such as rigid static policies, neglect of hardware security boundaries, and disconnection between energy efficiency and load in traditional energy consumption control through precise matching of RF increments, dynamic correction of load weights, and screening of optimal energy efficiency combinations. It significantly improves energy efficiency balance and node operation reliability in high-load scenarios, and is suitable for scenarios such as industrial automation and edge computing that require real-time response to changes in complex tasks.

[0160] Figure 2 The present invention provides a structural diagram of a distributed network node task load scheduling optimization system. Figure 2 As shown, the system includes:

[0161] Determination module 21, used to determine the task transmission path between distributed network nodes based on historical task congestion data and real-time link load prediction results;

[0162] An adjustment module 22 is configured to adjust the modulation and coding strategy during the task transmission process according to a channel quality parameter on the task transmission path to obtain an adjusted modulation and coding strategy, wherein the channel quality parameter includes a link quality attenuation rate;

[0163] A generating module 23 is configured to generate a voltage and frequency adjustment instruction corresponding to the distributed network node based on the energy consumption requirement corresponding to the adjusted modulation and coding strategy and the load distribution of each communication link and each node on the task transmission path;

[0164] The optimization module 24 is configured to collaboratively optimize the task transmission path, the channel quality parameters, and the voltage and frequency adjustment instructions to generate a task load scheduling strategy.

[0165] Figure 2 The distributed network node task load scheduling optimization system shown can be executed Figure 1 The implementation principles and technical effects of the distributed network node task load scheduling optimization method described in the illustrated embodiment are not further elaborated. The specific manner in which each module and unit performs operations in the distributed network node task load scheduling optimization system in the above embodiment has been described in detail in the embodiment of the method and will not be elaborated on here.

[0166] In one possible design, Figure 2 The distributed network node task load scheduling optimization system of the embodiment shown can be implemented as a computing device, such as Figure 3 As shown, the computing device may include a storage component 31 and a processing component 32;

[0167] The storage component 31 stores one or more computer instructions, wherein the one or more computer instructions are called and executed by the processing component 32 .

[0168] The processing component 32 is used to: determine the task transmission path between distributed network nodes based on historical task congestion data and real-time link load prediction results; adjust the modulation and coding strategy during the task transmission process according to the channel quality parameters on the task transmission path to obtain an adjusted modulation and coding strategy, and the channel quality parameters include the link quality attenuation rate; generate a voltage and frequency adjustment instruction corresponding to the distributed network node based on the energy consumption requirements corresponding to the adjusted modulation and coding strategy, combined with the load distribution of each communication link and each node on the task transmission path; and collaboratively optimize the task transmission path, the channel quality parameters, and the voltage and frequency adjustment instructions to generate a task load scheduling strategy.

[0169] The processing component 32 may include one or more processors to execute computer instructions to complete all or part of the steps in the above method. Of course, the processing component may also be implemented as one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the above method.

[0170] The storage component 31 is configured to store various types of data to support operations at the terminal. The storage component can be implemented by any type of volatile or non-volatile memory device, or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk, or optical disk.

[0171] Of course, a computing device may also include other components, such as input / output interfaces, display components, communication components, etc.

[0172] The input / output interface provides an interface between the processing component and the peripheral interface module, which can be an output device, an input device, etc.

[0173] The communication component is configured to facilitate, among other things, wired or wireless communications between the computing device and other devices.

[0174] Among them, the computing device can be a physical device or an elastic computing host provided by a cloud computing platform, etc. In this case, the computing device can refer to a cloud server, and the above-mentioned processing components, storage components, etc. can be basic server resources rented or purchased from the cloud computing platform.

[0175] The embodiment of the present invention further provides a computer storage medium storing a computer program, which can achieve the above-mentioned Figure 1 The embodiment shown is a method for optimizing task load scheduling of distributed network nodes.

[0176] Those skilled in the art will clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0177] The device embodiments described above are merely illustrative. The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, i.e., they may be located in one location or distributed across multiple network units. Some or all of the modules may be selected based on actual needs to achieve the objectives of the present embodiment. Persons of ordinary skill in the art will be able to understand and implement the present invention without inventive effort.

[0178] Through the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, or of course, by hardware. Based on this understanding, the essence of the above technical solution or the part that contributes to the existing technology can be embodied in the form of a software product. The computer software product can be stored in a computer-readable storage medium, such as ROM / RAM, a magnetic disk, an optical disk, etc., and includes a number of instructions for enabling a computer device (which can be a personal computer, a server, or a network device, etc.) to execute the methods described in each embodiment or certain parts of the embodiments.

[0179] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention.

Claims

1. A method for optimizing task load scheduling of distributed network nodes, characterized in that: include: Determine the task transmission path between distributed network nodes based on historical task congestion data and real-time link load prediction results; adjusting a modulation and coding strategy during task transmission according to a channel quality parameter on the task transmission path to obtain an adjusted modulation and coding strategy, wherein the channel quality parameter includes a link quality attenuation rate; generating voltage and frequency adjustment instructions corresponding to the distributed network nodes based on the energy consumption requirements corresponding to the adjusted modulation and coding strategy and in combination with the load distribution of each communication link and each node on the task transmission path; The task transmission path, the channel quality parameter, and the voltage and frequency adjustment instruction are collaboratively optimized to generate a task load scheduling strategy.

2. The method according to claim 1, characterized in that The channel quality parameters also include external interference intensity, link quality attenuation rate, and frequency band utilization. The task transmission path, the channel quality parameters, and the voltage and frequency adjustment instructions are collaboratively optimized to generate a task load scheduling strategy, including: Selecting a plurality of target paths from the task transmission paths, each of which has a link quality attenuation value lower than a preset threshold, wherein the link quality attenuation value is obtained by comparing the relative attenuation amplitude and time dimension attenuation trend of the channel quality parameter with the historical reference signal quality parameter; Calculating comprehensive priority scores corresponding to multiple target paths based on the load capacity, external interference intensity, and bandwidth utilization of the target path, and selecting the target path with the highest priority from the comprehensive priority scores as the updated task transmission path; Generate an expected total power consumption of each node based on the task distribution ratio of the updated task transmission path and the single-node power consumption increment corresponding to the adjusted modulation and coding strategy; selecting an optimal voltage-frequency combination from the voltage-frequency adjustment instructions according to the expected total power consumption and the power supply capability of the distributed network node to obtain updated energy consumption configuration parameters; A task load scheduling strategy is generated according to the mapping relationship between the updated task transmission path and the updated energy consumption configuration parameters.

3. The method according to claim 2, characterized in that Selecting an optimal voltage-frequency combination from the voltage-frequency adjustment instructions based on the expected total power consumption and the power supply capability of the distributed network node to obtain updated energy consumption configuration parameters includes: Constructing a power supply capability database corresponding to each distributed network node, wherein the power supply capability database includes the rated output power, voltage adjustment range, and frequency adjustment range corresponding to each node; Selecting a candidate voltage-frequency combination that meets preset conditions from the voltage-frequency adjustment instructions, the preset conditions including that the instantaneous power consumption is lower than the rated output power of the corresponding node and is not less than the expected total power consumption, the voltage value is within the voltage adjustment range, and the frequency value is within the frequency adjustment range; Selecting a combination with the smallest product of voltage value and frequency value from the candidate voltage-frequency combinations as the optimal voltage-frequency combination; The voltage value and the frequency value of the optimal voltage-frequency combination are converted into a control instruction format to generate updated energy consumption configuration parameters.

4. The method according to claim 1, wherein Determine the task transmission path between distributed network nodes based on historical task congestion data and real-time link load prediction results, including: Determine candidate transmission paths between all distributed network nodes based on the topology of the distributed network, and extract historical task congestion data of each link in the candidate transmission paths to calculate a historical congestion coefficient; The historical congestion coefficient, the remaining bandwidth in the real-time link load prediction result, and the estimated traffic growth rate are weighted and summed according to preset weights to generate an initial path score for each candidate transmission path; Selecting a path that meets the real-time link load capacity constraint and has the highest initial path score from the candidate transmission paths as the initial task transmission path; According to the dynamic changes of the real-time link load prediction results, the initial path score of the initial task transmission path is periodically recalculated to obtain an updated score. If there is a path whose updated score exceeds the initial path score, the path with the highest updated score is used as the final task transmission path.

5. The method according to claim 1, wherein Adjusting a modulation and coding strategy during task transmission according to a channel quality parameter on the task transmission path to obtain an adjusted modulation and coding strategy, wherein the channel quality parameter includes a link quality attenuation rate, includes: Acquire channel quality parameters of each link in the task transmission path, wherein the channel quality parameters include link quality attenuation rate, signal strength deviation value, and multi-band interference component; Classifying the initial adjustment level according to the superposition result of the short-term change trend and the long-term change trend of the link quality attenuation rate; Dynamically compensating the initial adjustment level based on the cumulative effect of the signal strength deviation value and the dominant frequency band distribution of the multi-band interference component to obtain a dynamically compensated adjustment level and a compensation coefficient; According to the adjustment level and compensation coefficient after dynamic compensation, the strategy parameters in the predefined modulation and coding strategy set are matched to generate an adjusted modulation and coding strategy, wherein the strategy parameters include modulation order, coding redundancy ratio and frequency band avoidance rules.

6. The method according to claim 5, characterized in that Based on the cumulative effect of the signal strength deviation value and the dominant frequency band distribution of the multi-band interference component, the initial adjustment level is dynamically compensated to obtain the adjustment level and compensation coefficient after dynamic compensation, including: Counting the number of times the signal strength deviation value exceeds a preset deviation threshold within a preset continuous period to generate a cumulative number of exceeding thresholds; Extracting the interference intensity ratio of each frequency band from the multi-band interference components, marking a preset number of frequency bands sorted by the interference intensity ratio as dominant interference frequency bands, and calculating the overlap between the dominant interference frequency bands and the frequency bands specified in the predefined modulation and coding strategy; generating a dynamic compensation parameter according to the cumulative number of exceeding the standard and the overlap degree; The dynamic compensation parameter and the initial adjustment level are superimposed and calculated to generate an adjustment level and a compensation coefficient after dynamic compensation.

7. The method according to claim 1, characterized in that Generating a voltage and frequency adjustment instruction corresponding to the distributed network node according to the energy consumption requirement corresponding to the adjusted modulation and coding strategy and in combination with the load distribution of each communication link and each node on the task transmission path, including: Calculating the expected energy consumption requirement of each node based on the RF power consumption increment of each node in the adjusted modulation and coding strategy and the load weight of each node on the task transmission path; The energy consumption value corresponding to the expected energy consumption demand and the basic power consumption value of each node in the idle state are weighted and accumulated to generate the total energy consumption demand of each node; According to the total energy consumption requirement and the rated output power of each node, an initial voltage-frequency combination that meets the task processing requirements is matched, and the voltage and frequency parameters of the initial voltage-frequency combination are encoded into standardized node control instructions to generate the voltage-frequency adjustment instructions.

8. A distributed network node task load scheduling optimization system, characterized in that: include: A determination module is used to determine the task transmission path between distributed network nodes based on historical task congestion data and real-time link load prediction results; an adjustment module, configured to adjust a modulation and coding strategy during task transmission according to a channel quality parameter on the task transmission path to obtain an adjusted modulation and coding strategy, wherein the channel quality parameter includes a link quality attenuation rate; a generating module, configured to generate a voltage and frequency adjustment instruction corresponding to the distributed network node according to the energy consumption requirement corresponding to the adjusted modulation and coding strategy and in combination with the load distribution of each communication link and each node on the task transmission path; The optimization module is used to collaboratively optimize the task transmission path, the channel quality parameters and the voltage and frequency adjustment instructions to generate a task load scheduling strategy.

9. A computing device, characterized in that It includes a processing component and a storage component; the storage component stores one or more computer instructions; the one or more computer instructions are used to be called and executed by the processing component to implement a task load scheduling optimization method for a distributed network node as described in any one of claims 1-7.

10. A computer storage medium, characterized in that A computer program is stored, and when the computer program is executed by a computer, the method for optimizing task load scheduling of a distributed network node according to any one of claims 1 to 7 is implemented.

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