Energy consumption self-adaptive management method and system of wireless image transmission module

Abstract

The invention relates to the technical field of image transmission, in particular to an energy consumption self-adaptive management method and system of a wireless image transmission module. Comprising the steps of obtaining operation data of at least one node, and analyzing a network load to adjust interaction frequency configuration; load balancing parameters are determined according to interaction frequency configuration, transmission task priorities are dynamically adjusted, node energy consumption data are collected, and resource waste points are checked to update an energy consumption distribution scheme; predicting the traffic of a transmission path according to an energy consumption distribution scheme, optimizing path allocation and reallocating resources when the traffic is greater than a set value, and updating node state shared data; and finally, the network operation state is calibrated according to the node state shared data, and energy consumption optimization configuration is obtained, so that energy consumption is reduced, and network efficiency is improved. According to the invention, the problem that the energy consumption management of the wireless image transmission network is difficult to finely regulate and control along with the load change is solved, and the self-adaptive energy conservation and stable transmission of the distributed image transmission module are realized.

Description

A method and system for adaptive power management of a wireless image transmission module Technical Field

[0001] This invention relates to the field of image transmission technology, and in particular to a method and system for adaptive energy management of a wireless image transmission module. Background Technology

[0002] With the rapid development of wireless communication technology, wireless image transmission modules are increasingly widely used in video surveillance, telemedicine, intelligent transportation, and other fields, especially in large-scale distributed wireless networks where the real-time transmission requirements for image data are becoming increasingly stringent. However, image transmission networks often face significant energy consumption challenges under the requirements of high bandwidth and low latency. Traditional wireless image transmission systems typically rely on static transmission configurations and energy efficiency management strategies, making it difficult to flexibly cope with network load fluctuations and energy efficiency demands under different transmission scenarios. This leads to problems such as high system energy consumption, low transmission efficiency, and equipment overheating, affecting the performance of wireless image transmission and the long-term operation of the equipment.

[0003] With the widespread adoption of smart devices and the Internet of Things (IoT), how to reduce energy consumption and improve network resource utilization efficiency while ensuring image transmission quality has become a key technical challenge in the design of wireless image transmission modules. Existing technologies often lack the ability to adaptively adjust to different loads and scenarios, cannot accurately optimize energy consumption based on real-time data, and cannot respond promptly to changes in network status under high load or sudden scenarios, resulting in limited energy efficiency improvements or even problems such as system congestion or degraded transmission quality.

[0004] To address the aforementioned issues, this invention proposes a method that precisely analyzes the energy consumption data, load status, traffic changes, and interaction frequency between each node to adjust the priority and transmission path of image transmission tasks in real time, optimizing bandwidth allocation and node sleep scheduling, thereby achieving system energy efficiency optimization. This invention not only improves the stability and quality of wireless image transmission but also minimizes energy consumption, extends equipment lifespan, and enhances network resource utilization efficiency. Summary of the Invention

[0005] This invention provides an adaptive energy management method and system for wireless image transmission modules, which optimizes the energy efficiency of wireless image transmission networks and improves transmission quality.

[0006] In a first aspect, the present invention provides an adaptive energy consumption management method for a wireless image transmission module, comprising: Step S1: acquiring the operating data of at least one node in a wireless distributed image transmission network, and adjusting the interaction frequency configuration by analyzing the network load; Step S2: determining load balancing parameters according to the interaction frequency configuration, and adjusting the transmission task priority; collecting node energy consumption data through the load balancing parameters, and checking and adjusting the energy consumption distribution scheme for resource waste points; Step S3: extracting bandwidth allocation rules and task priorities through the adjusted energy consumption distribution scheme, and combining real-time traffic fluctuation detection to predict path traffic, and determining whether it leads to packet loss risk. The system monitors congestion and optimizes path allocation. It obtains the latest energy consumption data through the optimized path allocation table, performs multi-dimensional analysis based on data collection frequency, reallocates resources for peak energy consumption areas, and assesses energy consumption changes. If energy consumption decreases, it updates node status sharing data and analyzes resource allocation efficiency. Step S4: Based on the node status sharing data, it obtains the collaboratively adjusted energy consumption management records, extracts the interaction strategy update cycle and sleep scheduling log, iteratively corrects resource waste points, and calibrates the network operating status. If image transmission quality requirements are met, it determines the final energy consumption optimization configuration, analyzes network stability, and adjusts the interaction strategy and sleep triggering conditions.

[0007] As a preferred embodiment of the present invention, step S1, adjusting the interaction frequency configuration by analyzing network load, includes: acquiring the current operating data of at least one node in the image transmission network; analyzing whether the interaction frequency adjustment mechanism between nodes is suitable for the current network load through real-time traffic fluctuation detection and inter-node communication delay monitoring; determining whether the interaction priority sorting logic causes the data packet loss rate to exceed a preset threshold; if it exceeds the preset threshold, temporarily adjusting the dynamic bandwidth allocation rules to obtain a preliminary optimized interaction frequency configuration; recording the interaction status between nodes through the preliminary optimized interaction frequency configuration and analyzing the dynamic changes in network load; determining the response speed of the interaction frequency adjustment in response to the dynamic changes in network load; updating the parameters of the interaction frequency configuration according to the response speed; monitoring the stability of data packet transmission through the interaction frequency configuration after parameter adjustment; determining whether further optimization of the interaction frequency configuration is needed based on the stability; acquiring communication efficiency data between nodes through the optimized interaction frequency configuration; and determining whether the preliminary optimized interaction frequency configuration meets the current network load requirements based on the communication efficiency data.

[0008] As a preferred embodiment of the present invention, step S2, adjusting the priority of transmission tasks, includes: extracting relevant data on node load balancing indicators and interaction strategy update cycles based on the initially optimized interaction frequency configuration; performing load distribution visualization analysis using node power consumption distribution maps for potential resource allocation conflicts between nodes; determining whether there are local energy consumption peaks; if there are local energy consumption peaks, adjusting the priority of transmission tasks according to energy consumption peak suppression rules to obtain load-balanced interaction frequency parameters; analyzing the resource allocation status between nodes using the load-balanced interaction frequency parameters; determining the adjustment range of load balancing parameters based on the resource allocation status; updating the priority ranking of transmission tasks based on the adjustment range; monitoring the distribution of node loads based on the priority ranking; determining whether the load balancing parameters need further adjustment based on the distribution; and obtaining load balancing effect data between nodes based on the adjusted load balancing parameters.

[0009] As a preferred technical solution of the present invention, step S2, which involves calibrating and adjusting the energy consumption distribution scheme for resource waste points, includes: obtaining the energy consumption data collection frequency of each node in the network and the resource waste log parsing records in the operation log through the interaction frequency parameters after load balancing; performing item-by-item data calibration for the parsed waste points using the operation log iterative correction logic; determining whether the calibrated energy consumption balance evaluation standard reaches a preset threshold; if it does not reach the preset threshold, triggering a node sleep scheduling mechanism to obtain the adjusted energy consumption distribution scheme; analyzing the changing trend of node energy consumption data through the energy consumption distribution scheme; determining the distribution pattern of resource waste points based on the changing trend; updating the frequency of energy consumption data collection based on the distribution pattern; monitoring the real-time status of node energy consumption based on the updated frequency; determining whether the energy consumption distribution scheme needs further optimization based on the real-time status; and obtaining energy consumption balance data between nodes based on the optimized energy consumption distribution scheme.

[0010] As a preferred embodiment of the present invention, step S3, optimizing path allocation, includes: extracting dynamic bandwidth allocation rules between nodes and the adjusted operating status of transmission task priorities based on the adjusted energy consumption distribution scheme; predicting path traffic through real-time traffic fluctuation detection for potential congestion points in the image transmission path; determining whether the prediction result will lead to an increase in the statistical data packet loss rate; if it increases, replanning the transmission path to obtain an optimized path allocation table; analyzing the traffic distribution of the transmission path through the path allocation table; determining the optimization direction of path allocation based on the traffic distribution; updating the dynamic bandwidth allocation rules through the optimization direction; monitoring the congestion status of the transmission path based on the updated rules; and determining whether the path allocation table needs further adjustment based on the congestion status.

[0011] As a preferred embodiment of the present invention, step S3, updating node state sharing data and analyzing resource allocation efficiency, includes: obtaining the latest energy consumption data in the node power consumption distribution map through the optimized path allocation table; performing multi-dimensional comparative analysis based on the energy consumption data acquisition frequency; redistributing resources for energy consumption peak areas using energy consumption balance assessment standards; determining whether the redistribution reduces overall energy consumption; if it reduces it, updating the state sharing data between nodes to obtain a collaboratively adjusted energy consumption management record; analyzing the resource allocation efficiency between nodes through the energy consumption management record; determining the adjustment range of resource redistribution based on the allocation efficiency; updating the synchronization frequency of node state sharing data based on the adjustment range; monitoring the state consistency between nodes based on the synchronization frequency; and determining whether further optimization of resource redistribution is needed based on the consistency.

[0012] As a preferred technical solution of the present invention, step S4, determining the final energy consumption optimization configuration, includes: obtaining the collaboratively adjusted energy consumption management records based on node status sharing data, extracting the operation logs of the interaction strategy update cycle and node sleep scheduling mechanism; performing final calibration using operation log iterative correction logic for the remaining resource waste points in the logs; determining whether the calibrated network operation status meets the image transmission quality requirements; if it does, then determining the final distributed network energy consumption optimization configuration.

[0013] As a preferred embodiment of the present invention, step S4, adjusting the interaction strategy and sleep triggering conditions, further includes: analyzing the stability of the network operating state through the energy consumption optimization configuration; determining the adjustment range of the interaction strategy update cycle based on the stability; updating the triggering conditions of the node sleep scheduling mechanism based on the adjustment range; monitoring the real-time changes in the network operating state based on the triggering conditions; determining whether the energy consumption optimization configuration needs further adjustment based on the real-time changes; and obtaining the overall network operating efficiency data based on the adjusted energy consumption optimization configuration.

[0014] Secondly, the present invention also provides an energy consumption adaptive management system for a wireless image transmission module, used to implement the above-mentioned method. The system includes: an interaction frequency adjustment unit, used to acquire the operating data of at least one node in a wireless distributed image transmission network and adjust the interaction frequency configuration by analyzing the network load; an energy consumption correction unit, used to determine load balancing parameters according to the interaction frequency configuration and adjust the transmission task priority; collect node energy consumption data through the load balancing parameters, and correct and adjust the energy consumption distribution scheme for resource waste points; and a path optimization unit, used to extract bandwidth allocation rules and task priorities through the adjusted energy consumption distribution scheme, combine real-time traffic fluctuation detection to predict path traffic, and determine whether it leads to data loss. The system includes a risk assessment and congestion monitoring unit to optimize path allocation; a resource allocation unit to obtain the latest energy consumption data through the optimized path allocation table, perform multi-dimensional analysis based on the data collection frequency, reallocate resources in energy consumption peak areas and evaluate energy consumption changes, and update node status sharing data and analyze resource allocation efficiency if energy consumption decreases; a status update unit to obtain collaboratively adjusted energy consumption management records based on the node status sharing data, extract the interaction strategy update cycle and sleep scheduling log, iteratively correct resource waste points, and calibrate the network operation status; and an energy consumption optimization unit to determine the final energy consumption optimization configuration while meeting image transmission quality requirements, analyze network stability, and adjust interaction strategies and sleep trigger conditions.

[0015] Thirdly, the present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the above-described method.

[0016] The beneficial effects of this invention are as follows: By acquiring the operational data of nodes in a wireless distributed image transmission network and dynamically adjusting the interaction frequency configuration based on network load analysis, this invention can respond to network load fluctuations in real time, avoid bandwidth bottlenecks and overloads caused by excessively high interaction frequencies, improve the accuracy of data exchange, reduce packet loss rate, and optimize bandwidth allocation. After adjusting the interaction frequency configuration, the priority of transmission tasks is adjusted according to real-time load balancing parameters, node energy consumption data is collected, and energy consumption distribution is calibrated and adjusted. Through precise identification and optimization of resource waste points, the energy consumption distribution scheme in the network can be dynamically adjusted, avoiding unnecessary resource waste and improving energy efficiency. In addition, based on path allocation and real-time traffic fluctuations, the network transmission path is optimized to ensure efficient transmission of image data. After optimizing the path, resource allocation is further adjusted through multi-dimensional analysis and energy consumption balance assessment to ensure balanced load among nodes and avoid overload of any single node. Through continuous real-time monitoring and dynamic adjustment, the system can guarantee stable image transmission quality while minimizing energy consumption and improving overall network performance. Through the cooperation of the above technical solutions, adaptive energy management under different load conditions is achieved, avoiding the limitations of traditional static configuration methods. This ensures that the network can continue to operate stably under high load or sudden situations, while reducing equipment energy consumption and extending equipment lifespan. In the field of wireless image transmission, especially in applications with high requirements for transmission quality and energy efficiency such as video surveillance and telemedicine, it has demonstrated significant technical advantages. Attached Figure Description

[0017] To more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be briefly introduced below. Obviously, the drawings described below are some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0018] Figure 1 is a flowchart of an adaptive energy management method for a wireless image transmission module in an embodiment; Figure 2 is a structure of an adaptive energy management system for a wireless image transmission module in an embodiment; Figure 3 is a schematic diagram of peak threshold setting in the peak suppression method in an embodiment; Figure 4 is a schematic diagram of reducing the priority of transmission tasks in an embodiment; Figure 5 is a diagram of the energy optimization effect after priority optimization in an embodiment. Detailed Implementation

[0019] This invention provides a method and system for adaptive power management of a wireless image transmission module. The terms "first," "second," "third," "fourth," etc. (if present) in the specification, claims, and accompanying drawings are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments described herein can be implemented in a sequence other than that illustrated or described herein. Furthermore, the terms "comprising" or "having" and any variations thereof are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or device that includes a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or devices.

[0020] For ease of understanding, the specific process of the present invention embodiment is described below. As shown in Figure 1, an energy consumption adaptive management method for a wireless image transmission module in the present invention embodiment includes: Step S1: Obtaining the operating data of at least one node in a wireless distributed image transmission network, analyzing the network load based on the operating data, and adjusting the interaction frequency configuration according to the analysis results; specifically including: obtaining the current operating data of at least one node in the image transmission network, analyzing whether the interaction frequency adjustment mechanism between nodes is suitable for the current network load through real-time traffic fluctuation detection and inter-node communication delay monitoring; determining whether the interaction priority sorting logic causes the data packet loss rate to exceed a preset threshold; if it exceeds the preset threshold... If the value is not specified, the dynamic bandwidth allocation rules are temporarily adjusted to obtain a preliminary optimized interaction frequency configuration. The interaction status between nodes is recorded using this preliminary optimized interaction frequency configuration to analyze the dynamic changes in network load. The response speed for adjusting the interaction frequency is determined in response to the dynamic changes in network load. The parameters of the interaction frequency configuration are updated based on the response speed. The stability of data packet transmission is monitored using the interaction frequency configuration after parameter adjustment. Based on the stability, it is determined whether further optimization of the interaction frequency configuration is needed. Communication efficiency data between nodes is obtained using the optimized interaction frequency configuration. Based on the communication efficiency data, it is determined whether the preliminary optimized interaction frequency configuration meets the current network load requirements.

[0021] Specifically, in one embodiment, the aforementioned interaction frequency refers to the frequency of data exchange between nodes. The interaction frequency configuration includes frequency control of data packets sent and received between nodes. A higher interaction frequency means that nodes communicate more per unit time, increasing network bandwidth requirements and leading to higher network load. This can potentially cause bandwidth bottlenecks and network congestion, increasing packet loss rates and preventing the network from efficiently transmitting valid data, especially when bandwidth is limited. Conversely, a lower interaction frequency can effectively reduce network load, communication conflicts, and data transmission pressure between nodes, thereby improving overall network stability. However, it increases data transmission latency between nodes, affecting transmission efficiency, especially in real-time applications requiring frequent interaction (such as video transmission). Therefore, by real-time traffic fluctuation detection and inter-node communication latency monitoring, the current operating data of at least one node is obtained. This current operating data includes the node's upload and download data rates, communication latency, node energy consumption, and load. After obtaining the aforementioned current operating data, the system analyzes the traffic fluctuations and latency between nodes to determine whether the interaction frequency adjustment mechanism is suitable for the current network load. For example, if the data traffic fluctuations between nodes exceed a preset range, or the communication latency between nodes exceeds a certain threshold, it indicates that the current interaction frequency configuration cannot effectively cope with changes in network load, and therefore needs to be adjusted. Next, by analyzing the interaction priority sorting logic, it is determined whether the resulting packet loss rate exceeds a preset threshold. The specific interaction priority sorting rule is based on the urgency of the packets, prioritizing the transmission of important packets such as keyframes in images, while giving less priority to the transmission of auxiliary data. When the loss rate exceeds the preset threshold, it indicates that the current bandwidth allocation and priority sorting cannot effectively guarantee the transmission quality of critical packets. At this time, it is necessary to temporarily adjust the dynamic bandwidth allocation rule to optimize the interaction frequency configuration. The bandwidth allocation adjustment is based on the network load ratio, prioritizing the allocation of more bandwidth to high-priority tasks to reduce congestion and ensure transmission quality.

[0022] After dynamic bandwidth allocation adjustment, the interaction status between nodes is recorded and the dynamic changes in network load are analyzed through the initial optimized interaction frequency configuration. At this time, the trend of network load change is further evaluated by recording the number of interactions between nodes and load changes. If large load fluctuations are detected, such as a sudden increase or decrease in load, the interaction frequency configuration is dynamically adjusted to adapt to these changes. Based on the dynamic changes in network load, the response speed of the interaction frequency adjustment is determined. The response speed refers to the time required from the occurrence of network load change to the completion of the interaction frequency configuration adjustment. During the response speed calculation process, the adjustment process is accelerated by real-time monitoring and increasing the monitoring frequency to ensure rapid response to sudden traffic changes.

[0023] Once the response speed is determined, the parameters of the interaction frequency configuration are updated based on the above response speed. For example, the upper and lower limits of the interaction frequency are adjusted to achieve more flexible load adaptation. The adjusted interaction frequency configuration will be used to monitor the stability of data packet transmission. Stability is measured by the continuous transmission success rate. If the transmission success rate is lower than the preset standard, it indicates that the current configuration still does not meet the network load requirements. At this time, it is necessary to further optimize the interaction frequency configuration to improve network stability and data transmission quality.

[0024] The optimized interaction frequency configuration will be verified based on communication efficiency data between nodes, including metrics such as throughput and latency, to ensure that the network can efficiently transmit image data and meet network load requirements through the optimized configuration. The above technical solution, through real-time monitoring and feedback mechanisms, ensures that the resources of each node are reasonably allocated, while avoiding network congestion, data loss, and energy waste, thereby achieving adaptive energy management and optimization of the wireless image transmission network.

[0025] Step S2: Determine the load balancing parameters based on the adjusted interaction frequency configuration and adjust the transmission task priority; collect node energy consumption data through the load balancing parameters, identify resource waste points, and calibrate and adjust the energy consumption distribution scheme based on the resource waste points; wherein, in step S2, adjusting the transmission task priority includes: extracting relevant data on node load balancing indicators and interaction strategy update cycles based on the initially optimized interaction frequency configuration; performing load distribution visualization analysis using node power consumption distribution maps for potential resource allocation conflicts between nodes; determining whether there are local energy consumption peaks; if there are local energy consumption peaks, adjusting the transmission task priority through energy consumption peak suppression rules to obtain the load-balanced interaction frequency parameters; analyzing the resource allocation status between nodes through the load-balanced interaction frequency parameters; determining the adjustment range of the load balancing parameters based on the resource allocation status; updating the priority ranking of transmission tasks based on the adjustment range; monitoring the distribution of node load based on the priority ranking; determining whether the load balancing parameters need further adjustment based on the distribution; and obtaining load balancing effect data between nodes based on the adjusted load balancing parameters.

[0026] Specifically, in one embodiment, the step of adjusting the transmission task priority of the wireless image transmission module achieves adaptive energy consumption management through multiple interrelated processes. First, based on the initially optimized interaction frequency configuration, relevant data on node load balancing metrics and interaction policy update cycles are extracted. This includes reading the current load value and interaction policy update timestamp of each node from the configuration. The load value represents the percentage of data transmission currently undertaken by the node, and the update timestamp records the time of the last adjustment to the node's interaction policy. The average load balancing metric for all nodes can be calculated by summing the load values ​​of all nodes and dividing by the total number of nodes. The system calculates the difference between adjacent update timestamps as the interaction strategy update cycle, providing the necessary time interval data for subsequent analysis. Load distribution visualization analysis is performed by generating a node power consumption distribution map. This map maps the energy consumption data of each node onto a two-dimensional coordinate system, with the energy consumption value used as a color depth representation. High-energy-consumption areas are displayed in red, and low-energy-consumption areas in blue. This visualization process helps the system intuitively identify potential resource allocation conflicts. For example, some nodes may experience high local energy consumption due to excessive task concentration, forming hotspots. This allows for the rapid identification of areas with excessive energy concentration, improving network diagnostic efficiency.

[0027] By scanning the color depth values ​​in the spectrum and setting the energy consumption threshold for local energy consumption peaks to a set multiple of the average energy consumption of the entire network, a local energy consumption peak is identified when the energy consumption value of a node exceeds the energy consumption threshold. This effectively detects energy consumption imbalances, avoids network overload, and helps improve system stability. When a local energy consumption peak is confirmed, the transmission task priority on that node is adjusted through energy consumption peak suppression rules. Specifically, the priority of non-critical or low-time-sensitivity tasks on the peak node is reduced, and these tasks are migrated to nodes with lower energy consumption. Before the task migration, the latency, bandwidth, and timeliness requirements of the migration are analyzed to ensure that the task migration will not lead to a significant decrease in system performance. After the task priority adjustment is completed, the interaction frequency parameters are recalculated. Based on the adjusted task priority, the interaction frequency of the peak node is reduced. For example, as shown in Figures 3-5, under low-load network conditions, the peak threshold is set to 1.2 times, and the priority is reduced by only 10%, resulting in a 10% decrease in energy consumption. However, under high-load scenarios, the threshold is set to 1.8 times, a 30% reduction, resulting in a 25% decrease in energy consumption. These adjustments improve transmission efficiency by distributing tasks.

[0028] By analyzing the interaction frequency parameters after load balancing, the resource allocation status between nodes is analyzed, and an allocation status score is calculated. If the allocation status score is lower than a predetermined standard, it indicates an unbalanced load distribution. The adjustment range of the load balancing parameters is then determined, and the priority ranking of transmission tasks is adjusted within this range. Specifically, the load distribution of nodes is monitored. By recording the number of tasks and load distribution of each node in real time, a load distribution log is generated. When the unbalanced load distribution between nodes exceeds a preset threshold, it is marked as requiring further adjustment, and the load balancing process is restarted. By repeating the above process, the load balancing parameters are continuously optimized to improve the resource utilization efficiency between nodes, reduce energy consumption, and ensure the stable and timely completion of transmission tasks. The adjusted load balancing parameters and node energy consumption data form an effect dataset, providing a quantitative basis for subsequent optimization and ensuring the efficient operation of the system during image transmission, while avoiding network congestion, delays, or packet loss caused by excessively concentrated loads.

[0029] The above technical solution, by combining real-time data, intelligent scheduling and dynamic adjustment mechanisms, realizes adaptive energy consumption management of the wireless image transmission module in the wireless distributed network, optimizes network resource allocation, improves transmission efficiency, and at the same time reduces energy consumption and overload risk, ensuring the long-term stable operation of the system.

[0030] Further, in step S2, the energy consumption distribution scheme is checked and adjusted for resource waste points, including: obtaining the energy consumption data collection frequency of each node in the network and the resource waste log parsing records in the operation log through the interaction frequency parameters after load balancing; for the parsed waste points, the operation log iterative correction logic is applied to check the data item by item; it is determined whether the energy consumption balance evaluation standard after the check reaches the preset threshold; if the preset threshold is not reached, the node sleep scheduling mechanism is triggered to obtain the adjusted energy consumption distribution scheme; the changing trend of node energy consumption data is analyzed through the energy consumption distribution scheme; the distribution law of resource waste points is determined according to the changing trend; the frequency of energy consumption data collection is updated according to the distribution law; the real-time status of node energy consumption is monitored according to the updated frequency; the energy consumption distribution scheme is determined according to the real-time status to see if it needs further optimization; and the energy consumption balance data between nodes is obtained according to the optimized energy consumption distribution scheme.

[0031] Specifically, by using the interaction frequency parameter after load balancing, the energy consumption data collection frequency of each node in the network and the resource waste log parsing records in its operation log are obtained. The energy consumption data collection frequency represents the number of times energy consumption data is collected per second, while the resource waste log records waste events obtained through operation log analysis, such as invalid calculations or unnecessary duplicate data packet transmissions generated by nodes under low load. Based on the above data, the resource waste points of the nodes are identified, and the operation log iterative correction logic is applied to check the data item by item. The iterative correction logic refers to a correction process based on multiple iterations. First, the type of waste point, such as computational redundancy or transmission delay, is identified. Then, the actual energy consumption is compared with the expected value item by item. The data is gradually corrected by calculating the difference. For example, the difference is calculated in the first iteration, and the correction factor is applied to reduce the difference in subsequent iterations until convergence, thereby ensuring that the corrected data truly reflects the resource waste situation in the network.

[0032] Next, it is determined whether the calibrated energy balance assessment value reaches a preset threshold. For example, by calculating the variance of energy consumption between nodes, if the variance is less than the preset threshold, it indicates that the network's energy consumption distribution has reached a balanced state. If the preset threshold is not reached, a node sleep scheduling mechanism is triggered, selecting high-energy-consuming nodes to enter a sleep state, and adjusting the sleep duration according to the energy consumption difference. Typically, the sleep duration is determined by multiplying the difference in node energy consumption by a preset coefficient. During the sleep period, the tasks of the sleep node are reallocated to nodes with lower energy consumption and lighter loads, thereby reducing overall energy consumption while maintaining network continuity and obtaining a new energy consumption distribution scheme. With the adjustment... The generation of the energy consumption distribution scheme further involves analyzing the changing trends of node energy consumption data, calculating the slope using time series data, identifying trends of energy consumption increase or decrease, determining the distribution patterns of resource waste points based on these trends, and further applying clustering algorithms such as K-means to group waste points, classifying waste points with similar trends into one category, and analyzing their spatial and temporal patterns. For example, it can identify repeated waste generated by certain node groups during high or low load periods. Based on these patterns, the frequency of energy consumption data collection is dynamically updated. If waste points frequently occur in certain areas, the energy consumption collection frequency of nodes in those areas is increased to more accurately monitor energy consumption changes.

[0033] During real-time monitoring, energy consumption information of nodes is continuously collected based on new interaction frequencies and data states, forming a continuous data stream. Real-time status is used to determine whether further optimization of the energy consumption distribution scheme is needed. When increased energy consumption fluctuations are detected, areas requiring optimization are marked, triggering further energy consumption optimization adjustments. Finally, by analyzing the optimized energy consumption distribution scheme, energy balance data between nodes is obtained, forming a precise energy management effect dataset to guide subsequent network configuration, task allocation, and energy efficiency improvement strategies. This technical solution can automatically adjust task allocation, load balancing, and energy efficiency optimization strategies in wireless image transmission networks based on the energy consumption status between nodes, effectively reducing energy waste caused by resource imbalances in the network, improving overall network energy efficiency, and ensuring the stability and high quality of image transmission.

[0034] Step S3: Predict transmission path traffic based on the energy consumption distribution scheme and optimize path allocation; reallocate resources through the optimized path allocation and update node status sharing data; wherein, in step S3, optimizing path allocation includes: extracting dynamic bandwidth allocation rules between nodes and the operating status after adjusting transmission task priorities based on the adjusted energy consumption distribution scheme; predicting path traffic for potential congestion points in the image transmission path through real-time traffic fluctuation detection; determining whether the prediction result will lead to an increase in the statistical data packet loss rate; if it increases, replanning the transmission path to obtain an optimized path allocation table; analyzing the traffic distribution of the transmission path through the path allocation table; determining the optimization direction of path allocation based on the traffic distribution; updating the dynamic bandwidth allocation rules through the optimization direction; monitoring the congestion status of the transmission path based on the updated rules; and determining whether the path allocation table needs further adjustment based on the congestion status.

[0035] Specifically, in one embodiment, to optimize the execution efficiency of transmission tasks and reduce energy waste, the bandwidth allocation parameters and priority ranking data of each node are read from the adjusted energy distribution scheme to obtain bandwidth allocation rules and transmission task priorities, which serve as the basis for path allocation adjustment. Based on the above data, potential congestion points in the image transmission path are further analyzed. Path traffic is predicted through real-time traffic fluctuation detection. A time series analysis method is used to collect current traffic data and historical fluctuation records for each potential congestion point, and an autoregressive integral moving average model is used to predict the trend of traffic data, thereby estimating the traffic peak in the near future. The training data for the above model consists of historical traffic data and historical fluctuation data corresponding to the nodes. The model also determines whether the predicted traffic peak will lead to an increase in the packet loss rate. If the predicted traffic peak exceeds the traffic threshold corresponding to the preset loss rate, it is determined that there is a risk of network congestion, thereby triggering path replanning. Specifically, the current energy consumption and latency data of all alternative paths are collected, and then the shortest path is used. Algorithms, such as Dijkstra's algorithm, calculate alternative paths from the source node to the target node. Path selection considers not only latency but also energy consumption. Specifically, the optimal path is obtained by weighted summing of the latency and energy consumption values ​​of each path, where the latency and energy consumption values ​​are normalized. The n paths with the smallest weighted sum are selected as the optimal paths, where n is a positive integer greater than or equal to 3. After obtaining the optimal paths, a path allocation table containing the path node sequence and allocation ratio is generated to ensure reasonable path allocation and avoid congestion. For example, replanning can be applied to video surveillance image transmission scenarios. When the inter-node latency is 50ms and the peak energy consumption is 10W, using Dijkstra's algorithm to plan new paths reduces the data loss rate from 1% to 0.3%, reducing data loss and improving image quality. It can also be extended to multi-path parallel transmission. For example, during peak network load periods, two backup paths can be planned with allocation ratios of 60% and 40%, supporting high-resolution image transmission and reducing overall latency by 15%.

[0036] After path allocation is determined, the traffic distribution in the path allocation table is analyzed to calculate the traffic proportion of each path and evaluate the uniformity of traffic distribution. If the traffic proportion of a certain path is found to be too high, optimization directions are determined based on the traffic distribution, such as increasing the number of optimal paths or performing load balancing to optimize network resource utilization. After determining the optimization direction, the dynamic bandwidth allocation rules are updated to adjust the bandwidth allocation ratio, such as increasing the bandwidth limit of some paths to alleviate the problem of excessive traffic concentration. As the bandwidth allocation rules are updated, the congestion status of the paths is monitored in real time. Traffic data is collected periodically and the congestion index is calculated to determine the ratio between path traffic and bandwidth limit. When the congestion index exceeds a certain threshold, the path allocation is adjusted again to ensure that network resources are fully utilized and that overload does not occur. The above technical solution can dynamically adjust the bandwidth allocation rules in the wireless image transmission network according to real-time traffic fluctuations and network load conditions, optimize path selection, reduce congestion and data loss during transmission, and effectively reduce network energy consumption. This ensures that the wireless image transmission module can achieve efficient and stable image data transmission under various loads and scenarios, improving the overall network energy efficiency and performance.

[0037] Further, in step S3, updating the node state sharing data and analyzing resource allocation efficiency includes: obtaining the latest energy consumption data in the node power consumption distribution map through the optimized path allocation table; performing multi-dimensional comparative analysis based on the energy consumption data acquisition frequency; redistributing resources for energy consumption peak areas using energy consumption balance assessment standards; determining whether the redistribution reduces overall energy consumption; if it reduces it, updating the state sharing data between nodes to obtain a collaboratively adjusted energy consumption management record; analyzing the resource allocation efficiency between nodes through the energy consumption management record; determining the adjustment range of resource redistribution based on the allocation efficiency; updating the synchronization frequency of the node state sharing data based on the adjustment range; monitoring the state consistency between nodes based on the synchronization frequency; and determining whether further optimization of resource redistribution is needed based on the consistency.

[0038] Specifically, in one embodiment, the transmission path information of each node is extracted from the optimized path allocation table, and the node power consumption distribution map is queried in combination with the above transmission path information to obtain the energy consumption data at the current moment. A multi-dimensional comparative analysis is performed based on the energy consumption data acquisition frequency. The time dimension calculates the average energy consumption change rate based on the energy consumption data; the spatial dimension compares the energy consumption differences between adjacent nodes; and the load dimension analyzes the peak energy consumption fluctuations under high-frequency acquisition. By combining these dimensions, a comprehensive comparison score is obtained, which is the weighted sum of the normalized average energy consumption change rate, peak energy consumption fluctuations, and energy consumption differences. If the comparison score is higher than a set threshold... If a value is too high, it is marked as an abnormal area, indicating a possible uneven resource allocation or low energy efficiency. This allows for timely detection and early warning of uneven energy consumption distribution, helping to improve network stability. After analyzing the peak energy consumption areas, resources are redistributed using energy balance assessment standards. The specific process includes first identifying the peak areas, i.e., the node groups whose energy consumption values ​​exceed the average; calculating the redistribution ratio using the energy balance assessment standards; calculating the energy consumption balance of nodes using variance; and triggering resource redistribution when the variance exceeds a preset threshold. The redistribution strategy, based on the calculated ratio, transfers excess resources from high-energy-consuming nodes to low-energy-consuming nodes, thereby achieving load balancing, reducing local overload, and optimizing network resource utilization. In high-load image transmission scenarios, this redistribution can significantly reduce peak energy consumption, improve transmission quality, and extend node lifespan. For example, if the variance is greater than a preset value, redistribution is triggered, moving excess resources proportionally, such as reducing transmission tasks from peak nodes by 20% to neighboring nodes. This redistribution reduces local overload and improves overall efficiency. In distributed image transmission networks, such as in high-traffic scenarios, peak areas may cause image delays. By redistributing the power consumption through this standard, peak power consumption can be reduced by 15%, thereby improving transmission quality.

[0039] After resource reallocation is completed, the system determines whether overall energy consumption has been successfully reduced. This determination is made by comparing the total energy consumption before and after reallocation. If energy consumption is reduced after reallocation, the optimization effect is confirmed to be significant. Next, the system updates the state sharing data between nodes and generates collaboratively adjusted energy management records. These records include broadcasting new energy consumption data and generating logs to ensure that each node in the network can promptly understand the latest energy distribution and adjustment strategies. Through these energy management records, the system further analyzes the resource allocation efficiency between nodes. Specifically, it extracts data from the records and calculates efficiency indicators such as resource utilization. For example, it evaluates allocation efficiency by calculating the ratio of used resources to total resources. If the efficiency is lower than a predetermined threshold, the system determines the adjustment range for resource reallocation based on the efficiency data. This may include expanding the adjustment range and triggering load adjustments for more nodes. Through this dynamic adjustment mechanism, task migration is performed when the load is too heavy, and unnecessary energy waste is reduced when the load is low.

[0040] To ensure timely and accurate adjustments, the synchronization frequency of node status sharing data is updated based on the adjustment range. When the range is large, the synchronization frequency is increased to ensure real-time data updates and rapid network response. Adjusting the synchronization frequency maintains state consistency between nodes and ensures the system maintains good energy efficiency under varying loads. By monitoring the state consistency between nodes, it is determined whether further resource reallocation optimization is needed. Specifically, the hash value of node data is calculated to determine the matching degree between nodes. When the consistency falls below a set value, an optimization loop is triggered, continuously optimizing resource allocation and energy efficiency management. Through this continuously optimized closed loop, the wireless image transmission module can operate efficiently under different loads and scenarios, while effectively reducing energy consumption and improving network stability and image transmission quality. This technical solution, by combining real-time energy consumption data, dynamic bandwidth allocation, task priority adjustment, and resource reallocation strategies, can optimize transmission tasks and resource configuration based on network load and node energy efficiency, thereby maximizing energy savings while maintaining network performance and transmission quality.

[0041] Step S4: Based on the node status sharing data, obtain the collaboratively adjusted energy consumption management records, extract the interaction strategy update cycle and sleep scheduling log, iteratively correct resource waste points, and calibrate the network operation status; if the image transmission quality requirements are met, determine the final energy consumption optimization configuration, analyze network stability, and adjust the interaction strategy and sleep trigger conditions.

[0042] In step S4, determining the final energy consumption optimization configuration includes: obtaining the collaboratively adjusted energy consumption management records based on node status sharing data, extracting the operation logs of the interaction strategy update cycle and node sleep scheduling mechanism; performing final calibration using the operation log iterative correction logic for the remaining resource waste points in the logs; determining whether the calibrated network operation status meets the image transmission quality requirements; if it does, then determining the final distributed network energy consumption optimization configuration.

[0043] Furthermore, in step S4, adjusting the interaction strategy and sleep triggering conditions further includes: analyzing the stability of the network operating status through the energy consumption optimization configuration; determining the adjustment range of the interaction strategy update cycle based on the stability; updating the triggering conditions of the node sleep scheduling mechanism based on the adjustment range; monitoring the real-time changes in the network operating status based on the triggering conditions; determining whether the energy consumption optimization configuration needs further adjustment based on the real-time changes; and obtaining the overall network operating efficiency data based on the adjusted energy consumption optimization configuration.

[0044] Specifically, to improve the energy efficiency and transmission quality of the wireless image transmission module in a distributed network, node status sharing data is used to obtain collaboratively adjusted energy management records, extracting the interaction policy update cycle and node sleep scheduling mechanism operation logs. Specifically, the update interval of the interaction policy is read from the energy management records, and the sleep activation status and corresponding sleep duration of each node are recorded, providing data support for subsequent calibration. Based on these logs, the resource usage patterns of nodes under different loads can be identified. Especially under low load, the node sleep scheduling mechanism can significantly reduce energy consumption. Furthermore, by analyzing the energy savings during sleep, guidance is provided for the final optimization scheme. For the remaining resource waste points shown in the logs, iterative correction logic is applied for calibration. This iterative correction logic is a feedback mechanism based on multiple iterations, used to gradually identify and correct waste points in the network, especially the energy waste caused by nodes maintaining high-frequency interactions under low traffic. Each iteration calculates the difference between the current waste point and the expected energy consumption, and adjusts the interaction frequency based on this difference, ensuring gradual optimization through iteration and ultimately eliminating unnecessary energy waste, thereby providing the network with a more efficient resource utilization and optimization strategy.

[0045] After the iterative correction logic calibration is completed, it is determined whether the calibrated network operation status meets the predetermined image transmission quality requirements. This is mainly based on quality indicators such as packet loss rate and transmission latency. By confirming that these indicators are within acceptable ranges, the system is ensured to meet the stability and quality requirements of image transmission. If the predetermined quality requirements are met, the final energy consumption optimization configuration is determined, and parameters such as interaction frequency and node sleep mechanism are fixed, so that the entire network maintains an optimal state under different load conditions. The stability of the network is analyzed through the above energy consumption optimization configuration, including performing variance analysis on energy consumption data, calculating the energy consumption fluctuation of nodes in the network, and evaluating the stability of the system through the variance value. If the variance is lower than a preset threshold, the network is considered to have reached a stable state. If the variance is large, further optimization is required to ensure stability. The above stability analysis process provides a reliable foundation for the long-term operation of the network and prevents equipment overheating or network instability caused by excessive energy consumption fluctuations.

[0046] Based on network stability, the adjustment range of the interaction strategy update cycle is further determined. When stability is high, the update cycle is longer to reduce the computational burden caused by frequent adjustments; conversely, when stability is low, the update cycle is shortened, making energy consumption optimization adjustments more flexible and timely. Simultaneously, the trigger conditions for the node's sleep scheduling mechanism are adjusted through optimized configuration, enabling automatic sleep mode entry when node load falls below a predetermined threshold, further saving energy. The adjustment of the sleep trigger conditions is based on real-time changes in network operating status. By continuously monitoring load data, the trigger conditions for the sleep mechanism can be adjusted in a timely manner, ensuring that network energy efficiency is optimized under different operating modes. Finally, real-time monitoring of network operating status... The system monitors changes in the network state to determine if further adjustments to the energy consumption optimization configuration are needed. If the monitored real-time changes indicate a significant increase in energy consumption (i.e., the increase exceeds the set threshold), the energy consumption optimization configuration is adjusted again to ensure that the network maintains high efficiency and stable performance under different load and operating conditions. Through the above dynamic adjustment and optimization process, the overall operating efficiency of the network can be tracked and analyzed in real time, and the configuration can be continuously optimized to achieve optimal energy efficiency and transmission quality. The above technical solution, through the combined optimization of dynamic bandwidth allocation, interactive frequency adjustment, task priority sorting, and node sleep mechanism, can effectively balance the energy efficiency and transmission quality of the wireless image transmission module, especially demonstrating excellent adaptability during load changes and peak transmission periods.

[0047] This invention also provides an adaptive energy consumption management system for a wireless image transmission module, used to implement the above-mentioned method. As shown in Figure 2, the system includes: Step S1: acquiring the operating data of at least one node in the wireless distributed image transmission network, and adjusting the interaction frequency configuration by analyzing the network load; Step S2: determining load balancing parameters according to the interaction frequency configuration, and adjusting the transmission task priority; collecting node energy consumption data through the load balancing parameters, and checking and adjusting the energy consumption distribution scheme for resource waste points; Step S3: extracting bandwidth allocation rules and task priorities through the adjusted energy consumption distribution scheme, and combining real-time traffic fluctuation detection to predict path traffic to determine whether packet loss occurs. Risk assessment and congestion monitoring; optimization of path allocation based on stability data and packet loss risk; acquisition of the latest energy consumption data through optimized path allocation table; multi-dimensional analysis combined with data collection frequency; resource reallocation for peak energy consumption areas and evaluation of energy consumption changes; if energy consumption decreases, updating node status sharing data and analyzing resource allocation efficiency; Step S4: based on the node status sharing data, obtaining collaboratively adjusted energy consumption management records, extracting interaction strategy update cycles and sleep scheduling logs, iteratively correcting resource waste points, and calibrating network operation status; if image transmission quality requirements are met, determining the final energy consumption optimization configuration, analyzing network stability, and adjusting interaction strategies and sleep triggering conditions.

[0048] The present invention also provides a computer-readable storage medium storing instructions that, when executed by a processor, implement the above-described method.

[0049] In summary, this invention acquires operational data from nodes in a wireless distributed image transmission network and dynamically adjusts the interaction frequency configuration based on network load analysis. This allows for real-time responses to network load fluctuations, preventing bandwidth bottlenecks and overload caused by excessively high interaction frequencies, improving data exchange accuracy, reducing packet loss, and optimizing bandwidth allocation. After adjusting the interaction frequency configuration, the priority of transmission tasks is adjusted based on real-time load balancing parameters. Node energy consumption data is collected, and energy consumption distribution is calibrated and adjusted. Through precise identification and optimization of resource waste points, the energy consumption distribution scheme in the network can be dynamically adjusted, avoiding unnecessary resource waste and improving energy efficiency. Furthermore, based on path allocation and real-time traffic fluctuations, the network transmission path is optimized to ensure efficient transmission of image data. Following the path, through multi-dimensional analysis and energy consumption balance assessment, resource allocation is further adjusted to ensure balanced load among nodes and avoid overload of any single node. Through continuous real-time monitoring and dynamic adjustment, the system can guarantee stable image transmission quality while minimizing energy consumption and improving overall network performance. Through the cooperation of the above technical solutions, adaptive energy management under different load conditions is achieved, avoiding the limitations of traditional static configuration methods. This ensures that the network can continue to operate stably under high load or sudden situations, while reducing equipment energy consumption and extending equipment lifespan. In the field of wireless image transmission, especially in applications with high requirements for transmission quality and energy efficiency such as video surveillance and telemedicine, it has demonstrated significant technical advantages.

[0050] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0051] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0052] The above-described embodiments are only used to illustrate the technical solutions of the present invention, and are not intended to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for adaptive energy consumption management of a wireless image transmission module, characterized in that, include: Step S1: Obtain the operating data of at least one node in the wireless distributed image transmission network, and adjust the interaction frequency configuration by analyzing the network load; Step S2: Determine the load balancing parameters according to the interaction frequency configuration, and adjust the transmission task priority; collect node energy consumption data through the load balancing parameters, and check and adjust the energy consumption distribution scheme for resource waste points; Step S3: Extract bandwidth allocation rules and task priorities through the adjusted energy consumption distribution scheme, combine real-time traffic fluctuation detection to predict path traffic, determine whether it leads to packet loss risk, monitor congestion status, and optimize path allocation; obtain the latest energy consumption data through the optimized path allocation table, perform multi-dimensional analysis in combination with data collection frequency, reallocate resources for energy consumption peak areas and evaluate energy consumption changes, if energy consumption decreases, update node status sharing data and analyze resource allocation efficiency; Step S4: Obtain the collaboratively adjusted energy consumption management records based on the node status sharing data, extract the interaction strategy update cycle and sleep scheduling log, iteratively correct resource waste points, and calibrate the network operating status; if the image transmission quality requirements are met, determine the final energy consumption optimization configuration, analyze network stability, and adjust the interaction strategy and sleep trigger conditions.

2. The method as described in claim 1, characterized in that, In step S1, the interaction frequency configuration is adjusted by analyzing the network load, including: acquiring the current operating data of at least one node in the image transmission network; analyzing whether the interaction frequency adjustment mechanism between nodes is suitable for the current network load through real-time traffic fluctuation detection and inter-node communication delay monitoring; determining whether the interaction priority sorting logic causes the packet loss rate to exceed a preset threshold; if it exceeds the preset threshold, temporarily adjusting the dynamic bandwidth allocation rules to obtain a preliminary optimized interaction frequency configuration; recording the interaction status between nodes through the preliminary optimized interaction frequency configuration and analyzing the dynamic changes in network load; determining the response speed of the interaction frequency adjustment in response to the dynamic changes in network load; updating the parameters of the interaction frequency configuration according to the response speed; monitoring the stability of packet transmission through the interaction frequency configuration after parameter adjustment; determining whether further optimization of the interaction frequency configuration is needed based on the stability; acquiring communication efficiency data between nodes through the optimized interaction frequency configuration; and determining whether the preliminary optimized interaction frequency configuration meets the current network load requirements based on the communication efficiency data.

3. The method as described in claim 1, characterized in that, In step S2, adjusting the transmission task priority includes: extracting relevant data on node load balancing metrics and interaction strategy update cycles based on the initially optimized interaction frequency configuration; performing load distribution visualization analysis using node power consumption distribution maps to address potential resource allocation conflicts between nodes; determining whether there are local energy consumption peaks; if there are local energy consumption peaks, adjusting the transmission task priority using energy consumption peak suppression rules to obtain load-balanced interaction frequency parameters; analyzing the resource allocation status between nodes using the load-balanced interaction frequency parameters; determining the adjustment range of the load balancing parameters based on the resource allocation status; updating the priority ranking of transmission tasks based on the adjustment range; monitoring the distribution of node load based on the priority ranking; determining whether the load balancing parameters need further adjustment based on the distribution; and obtaining load balancing effect data between nodes based on the adjusted load balancing parameters.

4. The method as described in claim 3, characterized in that, In step S2, the energy consumption distribution scheme is checked and adjusted for resource waste points, including: obtaining the energy consumption data collection frequency of each node in the network and the resource waste log parsing records in the operation log through the interaction frequency parameters after load balancing; for the parsed waste points, the operation log iterative correction logic is applied to check the data item by item; it is determined whether the energy consumption balance evaluation standard after the check reaches the preset threshold; if the preset threshold is not reached, the node sleep scheduling mechanism is triggered to obtain the adjusted energy consumption distribution scheme; the changing trend of node energy consumption data is analyzed through the energy consumption distribution scheme; the distribution law of resource waste points is determined according to the changing trend; the frequency of energy consumption data collection is updated according to the distribution law; the real-time status of node energy consumption is monitored according to the updated frequency; the energy consumption distribution scheme needs to be further optimized according to the real-time status; and the energy consumption balance data between nodes is obtained according to the optimized energy consumption distribution scheme.

5. The method as described in claim 1, characterized in that, In step S3, optimizing path allocation includes: extracting dynamic bandwidth allocation rules between nodes and the adjusted operating status of transmission task priorities based on the adjusted energy consumption distribution scheme; predicting path traffic for potential congestion points in the image transmission path through real-time traffic fluctuation detection; determining whether the prediction result will lead to an increase in the statistical data packet loss rate; if it does, replanning the transmission path to obtain an optimized path allocation table; analyzing the traffic distribution of the transmission path through the path allocation table; determining the optimization direction of path allocation based on the traffic distribution; updating the dynamic bandwidth allocation rules based on the optimization direction; monitoring the congestion status of the transmission path based on the updated rules; and determining whether the path allocation table needs further adjustment based on the congestion status.

6. The method as described in claim 5, characterized in that, In step S3, updating node state sharing data and analyzing resource allocation efficiency includes: obtaining the latest energy consumption data from the node power consumption distribution map through the optimized path allocation table; performing multi-dimensional comparative analysis based on the energy consumption data acquisition frequency; redistributing resources for peak energy consumption areas using energy consumption balance assessment standards; determining whether the redistribution reduces overall energy consumption; if it reduces it, updating the state sharing data between nodes to obtain a collaboratively adjusted energy management record; analyzing the resource allocation efficiency between nodes through the energy management record; determining the adjustment range of resource redistribution based on the allocation efficiency; updating the synchronization frequency of node state sharing data based on the adjustment range; monitoring the state consistency between nodes based on the synchronization frequency; and determining whether further optimization of resource redistribution is needed based on the consistency.

7. The method as described in claim 1, characterized in that, In step S4, the final energy consumption optimization configuration is determined, including: obtaining the collaboratively adjusted energy consumption management records based on node status sharing data, extracting the operation logs of the interaction strategy update cycle and node sleep scheduling mechanism; performing final calibration using the operation log iterative correction logic for the remaining resource waste points in the logs; determining whether the calibrated network operation status meets the image transmission quality requirements; if it does, then determining the final distributed network energy consumption optimization configuration.

8. The method as described in claim 7, characterized in that, In step S4, adjusting the interaction strategy and sleep triggering conditions further includes: analyzing the stability of the network operating status through the energy consumption optimization configuration; determining the adjustment range of the interaction strategy update cycle based on the stability; updating the triggering conditions of the node sleep scheduling mechanism based on the adjustment range; monitoring the real-time changes in the network operating status based on the triggering conditions; determining whether the energy consumption optimization configuration needs further adjustment based on the real-time changes; and obtaining the overall network operating efficiency data based on the adjusted energy consumption optimization configuration.

9. A power consumption adaptive management system for a wireless image transmission module, used to implement the method as described in any one of claims 1-8, characterized in that, The system includes: an interaction frequency adjustment unit, used to acquire operational data of at least one node in the wireless distributed image transmission network and adjust the interaction frequency configuration by analyzing the network load; an energy consumption correction unit, used to determine load balancing parameters based on the interaction frequency configuration and adjust transmission task priorities; collect node energy consumption data through the load balancing parameters, and correct and adjust the energy consumption distribution scheme for resource waste points; and a path optimization unit, used to extract bandwidth allocation rules and task priorities through the adjusted energy consumption distribution scheme, predict path traffic by combining real-time traffic fluctuation detection, determine whether packet loss risk occurs, monitor congestion status, and optimize path allocation; and resource... The allocation unit is used to obtain the latest energy consumption data by optimizing the path allocation table, perform multi-dimensional analysis in conjunction with the data collection frequency, reallocate resources for energy consumption peak areas and evaluate energy consumption changes. If energy consumption decreases, it updates the node status sharing data and analyzes the resource allocation efficiency. The status update unit is used to obtain the collaboratively adjusted energy consumption management records based on the node status sharing data, extract the interaction strategy update cycle and sleep scheduling log, iteratively correct resource waste points, and calibrate the network operation status. The energy consumption optimization unit is used to determine the final energy consumption optimization configuration while meeting image transmission quality requirements, analyze network stability, and adjust the interaction strategy and sleep trigger conditions.

10. A computer-readable storage medium storing instructions thereon, characterized in that, When the instructions are executed by the processor, they implement the method as described in any one of claims 1-8.

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