Electronic tag adaptive power control method and system based on edge calculation

Abstract

The invention discloses an electronic tag self-adaptive power control method and system based on edge computing, particularly relates to the technical field of energy consumption management of Internet of Things equipment, and is used for solving the problem that an existing electronic tag network power control method is difficult to realize effective balance between guarantee of communication reliability in a service peak period and maintenance of long-term stable operation. The method comprises the following steps: comprehensively acquiring and analyzing battery state data, network topology information and periodic service load information in a management area through an edge computing node, judging whether long-period coordination needs to be started in a service peak period or not, and if so, identifying an electronic tag set with a long-term power supply risk and a key service time period; a target electronic tag is determined by analyzing a structural hole index and combining energy state ranking to perform Pareto frontier screening, a differentiated energy distribution strategy is generated based on data evaluation of the target tag, and the differentiated energy distribution strategy is converted into a specific power control instruction sequence to be issued and executed, so that prospective and refined scheduling of network energy is realized.

Description

Technical Field

[0001] This invention relates to the field of energy management technology for Internet of Things (IoT) devices, and in particular to an adaptive power control method and system for electronic tags based on edge computing. Background Technology

[0002] In merchandise management systems for retail and warehousing, electronic tags, such as electronic shelf labels (ESLs), serve as the core data presentation and interaction carriers. Their networks typically consist of numerous battery-powered wireless terminals. To ensure communication reliability and extend tag lifespan, their wireless transmission power needs to be managed. Existing technologies often employ power control methods that dynamically adjust the transmission power of individual tags based on real-time communication link quality metrics, such as signal strength or bit error rate. This aims to respond to instantaneous channel changes to achieve immediate communication success or reduce power consumption at any given moment. Deploying edge computing nodes to aggregate regional network data and centrally implement such adaptive control can improve management efficiency.

[0003] However, existing edge computing-based power control methods essentially rely on feedback from instantaneous or short-term network conditions, neglecting the deep coupling between the nonlinear characteristics of RFID battery discharge and the periodic fluctuations of upper-layer business activities. Specifically, the strategy of universally increasing power during peak business periods to maintain service quality accelerates battery energy consumption, potentially impairing the tag's continuous operational capability in subsequent critical business cycles. Conversely, focusing solely on extending the statistical duration of a single charge may lead to insufficient power reserves during periods requiring high-reliability communication. This exposes a flaw in existing technology: a lack of coordination between its energy management strategy and dynamically changing business demands. It fails to achieve an effective balance between ensuring long-term system reliability and meeting short-term business performance requirements, potentially leading to a contradiction where short-term optimization harms long-term health, thus hindering the overall performance improvement of large-scale RFID networks. Summary of the Invention

[0004] This invention addresses the technical problems existing in the prior art by providing an adaptive power control method and system for electronic tags based on edge computing.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: An edge computing-based adaptive power control method for electronic tags includes: S1. Edge computing nodes acquire battery status data and network topology information of each electronic tag within their management area, as well as periodic business load information of the management area. S2. Analyze the potential vulnerability of the network topology during peak service load periods based on network topology information and periodic service load information to determine whether long-term coordination is required. S3. When needed, analyze the network topology importance of each electronic tag and its energy status ranking within its respective topology community based on network topology information and battery status data. Identify the set of electronic tags with long-term power supply risks and the corresponding key business periods based on the difference between network topology importance and energy status ranking. S4. For the set of electronic tags, analyze the structural hole index of each electronic tag based on the network topology information, and perform Pareto front analysis in the two-dimensional decision space composed of the structural hole index and the energy state ranking to screen out the target electronic tags. S5. Based on the battery status data, key business periods and structural hole indicators of the target electronic tag, evaluate and generate differentiated energy allocation strategies. S6. Based on the differentiated energy allocation strategy, generate and issue a differentiated power control instruction sequence to the target electronic tag.

[0006] Furthermore, S1 includes: Battery status data includes the current battery level and battery health status of each electronic tag; Network topology information includes the connectivity between various electronic tags and the quality of historical communication links; Periodic business load information includes historical peak business periods and corresponding business volume data for the managed area.

[0007] Furthermore, S2 includes: Identify one or more critical communication paths that play a key role in the overall communication connectivity within the management area based on network topology information; By combining the peak business periods included in the periodic business load information, assess the communication load pressure that one or more critical communication paths may bear during peak business periods; Based on the communication load pressure that one or more critical communication paths may bear during peak business hours, assess whether it exceeds the preset load pressure threshold. If it does, determine that long-term coordination is required.

[0008] Furthermore, based on network topology information and battery status data, the network topology importance of each electronic tag and its energy status ranking within its respective topology community are analyzed, including: Based on network topology information, a graph community discovery algorithm is used to divide all electronic tags in the management area into multiple topological communities; Calculate the network topology importance index of each electronic tag in the entire network; Within each topology community, all electronic tags belonging to the topology community are ranked by their energy status based on the current battery level and battery health status contained in the battery status data.

[0009] Furthermore, based on the difference between network topology importance and energy state ranking, a set of electronic tags with long-term power supply risks and corresponding key business periods were identified, including: For each electronic tag, calculate the difference between its network topology importance index and its energy state ranking within its respective topology community; Electronic tags with a difference greater than a preset difference threshold are identified as electronic tags that pose a long-term power supply risk. The time periods corresponding to peak business load in the periodic business load information are identified as critical business periods.

[0010] Furthermore, for the electronic tag set, the structural hole index of each electronic tag is analyzed based on network topology information, including: Based on the connectivity relationships contained in the network topology information, the network constraint coefficient of each electronic tag in the electronic tag set is calculated as an indicator of the degree to which it occupies the location of the structural hole.

[0011] Furthermore, Pareto front analysis was conducted in a two-dimensional decision space comprised of structural hole indices and energy state rankings to screen out target electronic tags, including: Using the network constraint coefficient and energy state ranking within their respective topological communities as coordinates, the data points corresponding to each electronic tag are determined in a two-dimensional decision space. Among all data points, the set of data points that are not dominated by other data points in both the objective of simultaneously optimizing the reduction of network constraint coefficients and the optimization of energy state ranking values ​​constitutes the Pareto optimal frontier. The electronic tags corresponding to the data points on the Pareto optimal frontier are identified as the target electronic tags that require priority power control coordination.

[0012] Furthermore, S5 includes: Based on the battery status data contained in the target electronic tag, including the current power and battery health status, predict the available energy of the battery from the current moment until the start of the critical business period. Based on the structural hole index of the target electronic tag, determine the minimum energy guarantee ratio required to maintain network connectivity during critical business periods. By combining the predicted available energy with the minimum energy guarantee ratio, a recommended energy budget is calculated for each target electronic tag in different time periods before the critical business period, forming a differentiated energy allocation strategy.

[0013] Furthermore, S6 includes: The recommended energy budget allocated to each target electronic tag in the differentiated energy allocation strategy at different time periods is converted into the corresponding transmission power level; According to the sequence of different time periods before the critical business period, each target electronic tag is arranged with a series of power control command sequences containing a series of transmission power levels and corresponding effective time points; Before the start of critical business hours, the edge computing nodes send a sequence of power control instructions to each corresponding target electronic tag in advance.

[0014] On the other hand, the present invention provides an edge computing-based adaptive power control system for electronic tags, comprising: The information acquisition module is used by edge computing nodes to acquire battery status data and network topology information of each electronic tag within their management area, as well as periodic business load information of the management area. The coordination and judgment module is used to analyze the potential vulnerability of the network topology during peak service load periods based on network topology information and periodic service load information, and to determine whether long-term coordination is required. The time period identification module is used to analyze the network topology importance of each electronic tag and its energy status ranking within its respective topology community based on network topology information and battery status data when needed. Based on the difference between network topology importance and energy status ranking, it identifies the set of electronic tags with long-term power supply risks and the corresponding key business time periods. The tag filtering module is used to analyze the structural hole index of each electronic tag in a set of electronic tags based on network topology information, and to perform Pareto front analysis in a two-dimensional decision space composed of structural hole index and energy state ranking to filter out target electronic tags. The strategy evaluation module is used to evaluate and generate differentiated energy allocation strategies based on the battery status data of the target electronic tag, key business periods, and structural hole indicators. The strategy generation module is used to generate and issue a sequence of differentiated power control instructions to the target electronic tag based on the differentiated energy allocation strategy.

[0015] The beneficial effects of this invention are: 1. By integrating and analyzing three types of information—battery status, network topology, and periodic service load—within the region through edge computing nodes, and coordinating and planning for the long term, a fundamental shift has been achieved from passively responding to instantaneous channel changes to proactively adapting to service cycle fluctuations. Based on the prediction of potential vulnerabilities in the network topology during peak service periods and the assessment of long-term power supply risks to tags in conjunction with battery status, edge computing nodes first identify target electronic tags that require special attention in energy allocation and their corresponding critical service periods. This ensures that power control decisions are no longer isolated and guided by immediate communication success, but incorporate a comprehensive consideration of the long-term robustness of overall network connectivity and the sustainable energy health of individual nodes. Thus, at the system design level, the two often contradictory goals of ensuring short-term service performance and long-term operational reliability have been organically unified.

[0016] 2. Through a series of steps, including topology community partitioning, structural hole index quantification, and Pareto front screening, edge computing nodes can accurately locate individuals that play a critical hub role in the network and face a high risk of energy depletion from a large number of tags. Based on this, differentiated energy allocation strategies and corresponding power control command sequences are generated for the target tags, realizing spatiotemporal two-dimensional fine planning of limited battery energy. This ensures that important tags have sufficient energy reserves to maintain necessary network connectivity before the arrival of critical business periods, while energy saving is achieved through appropriate power adjustment during non-critical periods. This optimizes the overall network energy consumption distribution. Through centralized intelligent decision-making and predictive scheduling at the edge, systematic and forward-looking management of the energy of large-scale electronic tag networks is achieved, effectively improving the overall service reliability and operational lifespan of the network throughout the complete business cycle. Attached Figure Description

[0017] Figure 1 This is a flowchart of the electronic tag adaptive power control method based on edge computing of the present invention; Figure 2 This is a schematic diagram of the electronic tag adaptive power control system based on edge computing of the present invention. Detailed Implementation

[0018] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0019] Example 1: Figure 1 The present invention provides an adaptive power control method for electronic tags based on edge computing, comprising: S1. Edge computing nodes acquire battery status data and network topology information of each electronic tag within their management area, as well as periodic business load information of the management area. S2. Analyze the potential vulnerability of the network topology during peak service load periods based on network topology information and periodic service load information to determine whether long-term coordination is required. S3. When needed, analyze the network topology importance of each electronic tag and its energy status ranking within its respective topology community based on network topology information and battery status data. Identify the set of electronic tags with long-term power supply risks and the corresponding key business periods based on the difference between network topology importance and energy status ranking. S4. For the set of electronic tags, analyze the structural hole index of each electronic tag based on the network topology information, and perform Pareto front analysis in the two-dimensional decision space composed of the structural hole index and the energy state ranking to screen out the target electronic tags. S5. Based on the battery status data, key business periods and structural hole indicators of the target electronic tag, evaluate and generate differentiated energy allocation strategies. S6. Based on the differentiated energy allocation strategy, generate and issue a differentiated power control instruction sequence to the target electronic tag.

[0020] S1. Edge computing nodes acquire battery status data and network topology information of each electronic tag within their managed area, as well as periodic service load information of the managed area. Specifically, this is implemented as follows: Edge computing nodes establish periodic or event-triggered communication connections with each electronic tag within their managed area to obtain battery status data from each tag. This battery status data explicitly includes the current battery level and battery health status, monitored and reported by the electronic tag itself. The current battery level is obtained through a standard communication protocol. Specifically, the edge computing node sends a data packet containing a battery level query command to the target electronic tag. Upon receiving the command, the electronic tag's microcontroller reads the analog or digital signal output from its built-in or external battery level monitoring circuit. After calibration and conversion, the signal forms a value representing the remaining capacity, such as a percentage between 0 and 100, or an absolute unit like milliampere-hours (mAh). The electronic tag encapsulates this value in a reply data packet and sends it back to the edge computing node. Obtaining battery health status is an assessment process based on historical operational data reported by electronic tags or stored long-term by edge computing nodes. One implementation involves the edge computing node requiring electronic tags to report their cumulative number of complete charge-discharge cycles and recording the voltage-to-charge ratio curve during recent discharges. The cycle count and the stability characteristics of the voltage curve, such as the voltage plateau decay rate during each discharge, are input into a predefined assessment function to calculate a battery health status value ranging from 0% to 100%, where 100% represents a brand-new battery. Another implementation can be performed independently by the edge computing node. The edge computing node analyzes the time elapsed from a fully charged state to triggering a low-battery warning or the number of business operations performed by the electronic tag over multiple identical business cycles. If the duration or number of operations shows a significant decreasing trend, the battery health status value is set based on the percentage decrease in this trend. The edge computing node associates and stores the current charge level and battery health status of each electronic tag to form the battery status data used for subsequent analysis.

[0021] Edge computing nodes acquire network topology information describing the communication relationships between electronic tags within their managed area. This network topology information specifically includes the connectivity relationships between electronic tags and the historical communication link quality. The acquisition of connectivity relationships relies on a neighbor discovery protocol commonly used in ad hoc wireless networks. The implementation process is as follows: The edge computing node broadcasts a network command to its managed area to initiate neighbor discovery. Each electronic tag receiving the command then periodically sends broadcast beacon frames containing its own unique identifier within its wireless communication coverage area. When an electronic tag continuously receives beacon frames from another electronic tag, the electronic tag compares the measured received signal strength indication value with a preset connectivity establishment threshold. The connectivity establishment threshold is a pre-set signal strength threshold value, the value of which is based on the receiving sensitivity of the wireless communication chip used and the link quality under the network deployment scenario. The threshold for establishing a connection is determined by the minimum stability requirements, such as the minimum receiving sensitivity specified in the communication chip's specifications, combined with the connectivity test results during the initial network debugging phase. A specific signal strength value is selected as the connection establishment threshold. If the received signal strength indicator value is consistently higher than the connection establishment threshold, the electronic tag will record the electronic tag identifier that sent the beacon frame as its neighbor node. Subsequently, each electronic tag will summarize and report its own list of neighbor nodes to the edge computing node. The edge computing node will construct an undirected graph with electronic tags as vertices and mutually reported neighbor relationships as edges by summarizing all lists. This graph represents the connection relationships in the network topology information. Historical communication link quality serves as a quantitative supplement to the reliability of the aforementioned connections, with data obtained through long-term statistics. Edge computing nodes record the results of every application-layer data communication between all neighboring node pairs within each preset statistical period, such as 24 hours. For any pair of connected electronic tags a and b, historical communication link quality can be represented by the data packet reception success rate. The success rate is calculated by counting the total number of data packets sent from tag a to tag b requiring acknowledgment within a statistical period, and counting the number of data packets correctly received and correctly acknowledged by tag b. Dividing the latter by the former and multiplying by 100% yields the percentage success rate. In addition, the average received signal strength indicator and bit error rate can also be recorded as supplementary indicators of historical communication link quality. Ultimately, the edge computing nodes store the undirected graph of connections and the historical communication link quality data corresponding to each edge together as network topology information.

[0022] Edge computing nodes acquire periodic business load information for their managed areas. This periodic business load information reflects the repetitive patterns of business activities within the area over time. Specifically, the periodic business load information includes historical peak business periods and corresponding business volume data for the managed area. Identification of historical peak business periods is achieved by analyzing long-term historical business logs. Edge computing nodes continuously record the timestamps of every business interaction event, such as a product price update or inventory data query request, within multiple complete business cycles, such as 30 consecutive calendar days. Then, time series data analysis methods are used for processing. For example, a fixed time window statistical method can be used, dividing a 24-hour day into 15-minute time windows, calculating the average number of business events occurring within the same time window over the past 30 days, and then calculating the overall average number of business events across all time windows throughout the day. To identify peak business periods, a peak business period identification threshold needs to be set. The peak period identification threshold is determined by multiplying the overall mean calculated above by a fixed coefficient greater than 1. This fixed coefficient can be empirically set to values ​​such as 1.2, 1.5, or 2.0 to filter out periods with significantly higher-than-average business volume. During comparison, the average occurrence frequency of each time window is compared with the peak period identification threshold. Continuous time windows with an average occurrence frequency higher than this threshold are identified as historical peak periods. Alternatively, a clustering algorithm can be used to project the timestamps of all business events onto a daily timeline for density clustering, outputting continuous time periods with significantly higher event density than background density as historical peak periods. After identifying historical peak periods, the corresponding business volume data is also determined. Business volume data refers to the frequency or total number of business events occurring within the identified historical peak period. For example, if a historical peak period is identified as 10:00 AM to 12:00 PM daily, the corresponding business volume data can be expressed as an average of 50 business events per minute during the period, or the total number of business events during the period accounting for 40% of the total for the day. Edge computing nodes will archive all historical peak business periods and their corresponding business volume data, which together constitute periodic business load information.

[0023] S2. Based on network topology information and periodic service load information, analyze the potential vulnerability of the network topology during peak service load periods to determine whether long-term coordination is necessary. The specific implementation is as follows: Edge computing nodes, based on acquired network topology information and periodic service load information, analyze the potential vulnerabilities of the network topology during peak service load periods and determine whether to initiate long-term coordination. The analysis begins by identifying critical communication paths. Based on the connectivity relationships and historical communication link quality contained in the network topology information, the edge computing nodes abstract the entire managed area's tagged network into a weighted undirected graph. Vertices represent tagged tags, and edges represent the connections between them. The weight of each edge is set according to the packet reception success rate in the historical communication link quality data corresponding to that edge. Specifically, the percentage packet reception success rate is converted into a weight value representing the communication cost. The principle of conversion is that the higher the success rate of a link, the lower its communication cost weight value. For example, this conversion can be achieved using the reciprocal or negative logarithm of the packet transmission failure rate. If a link has a 98% packet reception success rate, then its transmission failure rate is 2%, and the weight value calculated accordingly can be proportional to the transmission failure rate. Building upon this, edge computing nodes use the shortest path algorithm from graph theory to calculate the shortest communication path between all electronic tag pairs in the weighted undirected graph. The shortest communication path is the path with the minimum sum of edge weights from the source tag to the destination tag. Subsequently, the edge computing nodes count the shortest communication paths between all electronic tag pairs, calculate the number of times each communication link is traversed by different shortest communication paths, and identify the top few communication links with the most traversals as one or more critical communication paths that play a key role in the overall communication connectivity within the management area. The specific number of these top few links can be a fixed value, such as 3 or 5.

[0024] After identifying one or more critical communication paths, edge computing nodes assess the load pressure on these paths during peak traffic periods by incorporating periodic service load information. Edge computing nodes acquire historical peak traffic periods and corresponding traffic volume data contained in the periodic service load information. The assessment process is performed for each identified critical communication path. The edge computing node first estimates the traffic that may be transmitted on the critical communication path. One estimation method, based on the assumption that service interaction events occur uniformly in the network, is that the volume of traffic a critical communication path needs to carry is proportional to the number of tagged pairs associated with all the shortest communication paths passing through the critical communication path, and also proportional to the average service event occurrence rate during peak traffic periods. The average service event occurrence rate can be directly obtained from the traffic volume data for the corresponding time period; for example, traffic volume data shows that an average of 50 service events occur per minute during the peak period from 10:00 AM to 12:00 PM. Edge computing nodes multiply the number of tagged pairs passing through critical communication paths by the average number of service events per minute during peak hours to obtain a theoretical estimate of the communication load pressure that critical communication paths may experience during peak hours. This theoretical estimate is measured in terms of the number of service events per unit time. Another estimation method further incorporates historical communication link quality, assigning a congestion risk coefficient greater than 1 to critical paths with poor link quality. Multiplying the theoretical estimate by this congestion risk coefficient yields a higher assessment of the communication load pressure.

[0025] After assessing the potential communication load pressure on one or more critical communication paths during peak business hours, edge computing nodes determine whether long-term coordination is necessary. This determination is based on comparing the assessed communication load pressure value with a preset load pressure threshold. The preset load pressure threshold is a threshold used to define whether a network path is at risk of overload; its setting depends on the network's communication processing capacity. One method is based on the theoretical physical layer data transmission rate of the critical communication path. The edge computing node first obtains the maximum packet throughput of a single link under ideal conditions under the network's communication protocol, for example, 1000 packets per second; then, according to the management strategy, it sets an acceptable maximum utilization percentage, for example, 70%; multiplying the maximum packet throughput by the maximum utilization percentage yields a reference benchmark for the load pressure threshold, expressed in packets per second. To convert the estimated number of business events into a comparable number of packets, the edge computing node needs to preset the average number of packets generated per business event based on experience, for example, two packets per business event. This converts the estimated business load pressure into a packet rate, which is then compared with the load pressure threshold. Another approach is to use the maximum actual communication load observed during historical peak business periods as a benchmark for the load pressure threshold. Edge computing nodes compare the current assessed communication load pressure value for each critical communication path with the preset load pressure threshold. If the assessed communication load pressure value for any critical communication path exceeds the load pressure threshold, the edge computing node determines that the network faces a significant risk of decreased communication reliability due to excessive load during the currently predicted peak business period, and therefore determines that a long-term coordination process is necessary. If the assessed communication load pressure values ​​for all critical communication paths do not exceed the load pressure threshold, the edge computing node determines that the current network energy and load conditions are still within acceptable limits during peak business periods, and no further complex long-term coordination process needs to be initiated.

[0026] S3. When necessary, analyze the network topology importance of each electronic tag and its energy status ranking within its respective topology community based on network topology information and battery status data. Specifically, this is implemented as follows: Once the edge computing node determines that long-term coordination is needed based on the judgment result of step S2, step S3 is executed. Step S3 analyzes the network topology importance of each electronic tag and its energy state ranking within its respective topological community based on the acquired network topology information and battery status data. Based on the connectivity relationships contained in the network topology information, the edge computing node uses a graph community discovery algorithm to divide all electronic tags within its management area into multiple topological communities. Specifically, the edge computing node abstracts each electronic tag as a node in the network graph and the connectivity relationships between electronic tags as edges, thus constructing a network topology graph. Subsequently, a community discovery algorithm based on modularity optimization is applied. The algorithm iteratively adjusts the community grouping of each node to maximize the modularity value of the entire network. Modularity is an indicator of the strength of the community structure; a higher value indicates that the connections within a community are more tightly connected than the connections between communities. Through the iterative calculation process of this algorithm, the algorithm finally outputs a stable community partitioning result, assigning electronic tag nodes in the entire network to one or more topological communities, with each topological community containing one or more electronic tags.

[0027] After completing the topology community partitioning, edge computing nodes calculate the network topology importance index for each RFID tag within the entire network. The network topology importance index quantifies the criticality of an RFID tag's position within the overall network topology. One specific calculation method uses the betweenness centrality index. To calculate the betweenness centrality of a specific RFID tag, the edge computing node first calculates the shortest paths between all other RFID tag pairs in the network based on the network topology graph. The definition of the shortest path here is consistent with the definition used in step S2 to identify critical communication paths. Then, it counts how many of these shortest paths pass through the specific RFID tag. Finally, it divides the number of shortest paths passing through the tag by the total number of shortest paths between all possible RFID tag pairs in the network to obtain the tag's betweenness centrality value. The value ranges from 0 to 1; a higher value indicates that the tag acts as a bridge for network information flow more often, i.e., its network topology importance is higher. The edge computing node performs the above calculation for each RFID tag in the network to obtain its respective network topology importance index.

[0028] Within each defined topological community, edge computing nodes rank the energy status of all tagged objects (e.g., tags) based on battery status data. The energy status ranking is based on the current battery level and battery health status contained in the battery status data. Each edge computing node calculates a comprehensive energy status score for each e.g., a combined approach that integrates the current battery percentage with the battery health percentage. For example, a weighted summation formula can be used, multiplying the current battery percentage by a battery weight coefficient and adding the battery health percentage multiplied by a health weight coefficient; the sum of these two is the comprehensive score. Both the battery weight coefficient and the health weight coefficient are positive numbers, and their sum is 1. For instance, the battery weight coefficient could be set to 0.7, and the health weight coefficient to 0.3. The specific values ​​of these coefficients can be adjusted based on the varying degrees of emphasis placed on battery immediacy and battery life in different application scenarios. After calculating the comprehensive energy state score for each electronic tag, the edge computing nodes sort all electronic tags within the topology community in ascending order of comprehensive score. The electronic tag with the lowest score is ranked 1st, indicating the worst energy state within the topology community, the next lowest score is ranked 2nd, and so on. This ranking result represents the energy state ranking of each electronic tag within its respective topology community.

[0029] Based on the network topology importance index and energy status ranking calculated above, edge computing nodes identify sets of electronic tags with long-term power supply risks and key business periods. For each electronic tag, the edge computing node calculates the difference between its network topology importance index and its energy status ranking within its topology community. Since the network topology importance index is a continuous value between 0 and 1, while the energy status ranking is a discrete positive integer ordinal number, both need to be standardized for consistent comparison. One method is to convert the energy status ranking into a relative disadvantage score between 0 and 1. The conversion formula is to divide the electronic tag's energy status ranking value by the total number of electronic tags in its topology community. After this conversion, the lower the ranking and the worse the energy status of the electronic tag, the closer its relative disadvantage score is to 1. Subsequently, the absolute difference between the electronic tag's network topology importance index and its converted relative disadvantage score is calculated. The edge computing node compares this difference with a preset difference identification threshold. The preset difference identification threshold is used to determine whether the severity of the mismatch between network topology importance and energy status has reached a risk level. The threshold for identifying differences can be set based on network management experience or statistical analysis of historical normal state data. For example, the differences mentioned above for all electronic tags in historical data can be collected, and a higher quantile value, such as the 90th percentile, can be calculated. This quantile value can then be set as the preset threshold for identifying differences. If the calculated difference value of an electronic tag is greater than the preset threshold, it indicates that the tag has high topological importance in the network, but its energy state is relatively poor, posing a long-term risk of affecting the overall network connectivity due to premature power depletion. Therefore, it is identified as an electronic tag with a long-term power supply risk. All such identified electronic tags constitute the set of electronic tags with a long-term power supply risk.

[0030] Edge computing nodes identify the periods corresponding to peak traffic periods in periodic service load information as critical service periods. They directly reference historical peak traffic periods provided by the periodic service load information, which were used and verified in step S2 (e.g., 10:00 AM to 12:00 PM daily), and define this period as the critical service period targeted by subsequent long-term coordination strategies. This critical service period, along with the set of electronic tags with long-term power supply risks, is the output of step S3. Through these steps, the edge computing nodes complete the analysis and location of energy vulnerabilities in critical nodes within the network, and identify high-risk electronic tag sets and the concentrated service periods.

[0031] S4. For the electronic tag set, analyze the structural hole index of each electronic tag based on the network topology information. The specific implementation is as follows: For the set of electronic tags identified in step S3 that pose a long-term power supply risk, the edge computing node executes step S4. Step S4 analyzes the structural hole index of each electronic tag in the set based on network topology information. Specifically, the edge computing node calculates the network constraint coefficient of each electronic tag in the set based on the connection relationships contained in the network topology information, using this as a quantitative indicator to characterize the degree to which it occupies a structural hole position. For a specific electronic tag i, the calculation of its network constraint coefficient depends on the tag's connection pattern in its local network. The edge computing node first identifies all other electronic tags that have a direct connection relationship with tag i from the network topology information; these tags constitute the neighbor set N(i). The calculation of the network constraint coefficient Ci requires examining the relationship between tag i and each of its neighbors j. During the calculation, the edge computing node calculates a contribution value for each pair of i and j relationships. The core idea of ​​the contribution value is to measure the importance of the connection between tag i and neighbor j among all connections of i, and the tightness of the connection between neighbor j and other neighbors. The specific calculation process can be described as follows: First, calculate the connection strength pij between tag i and a neighbor j. This can be based on historical communication link quality data in the network topology information, such as directly using the packet reception success rate between the two as the value of pij; if specific quality data is lacking, pij with a connection relationship can be set to 1. Then, calculate the sum of the connection strength pjk between neighbor j and all other neighbors k of tag i except j. The network constraint coefficient Ci is equal to the sum of all neighbors j, where the summation term is pij plus the product of pij and the sum of pjk mentioned above, and then the entire sum is squared. The network constraint coefficient Ci obtained by the above calculation is a real number greater than or equal to 0. The smaller its value, the more likely the electronic tag i is to occupy a structural hole position in its social network structure, that is, there are fewer direct connections between different neighbor groups it connects to, thus giving the tag information and control advantages.

[0032] After obtaining the network constraint coefficient for each RFID tag, the edge computing node performs Pareto front analysis in a two-dimensional decision space composed of the structural hole index and energy state ranking to screen for target RFID tags. The edge computing node first constructs the two-dimensional decision space. The first dimension of the space is the network constraint coefficient, which is optimized to be as small as possible. The second dimension is the energy state ranking of the RFID tag within its topological community; a smaller ranking value indicates a worse energy state assessed in step S3. Therefore, this analysis also pursues smaller values, prioritizing tags with more pressing energy states. For each member in the set of RFID tags with long-term power supply risks, the edge computing node uses its calculated network constraint coefficient as the x-axis value and its energy state ranking within its topological community, determined in step S3, as the y-axis value, thus determining a corresponding data point on the two-dimensional plane.

[0033] Edge computing nodes identify Pareto optimal fronts within a two-dimensional data point set. The identification of Pareto optimal fronts follows the non-dominated ranking principle in multi-objective optimization. The specific identification process is as follows: each edge computing node examines each data point in the set, for example, point A. For point A, the edge computing node compares point A with all other data points in the set except itself. In a single comparison, for example, with point B, the edge computing node checks two conditions simultaneously: first, whether the network constraint coefficient value of point B is less than or equal to the network constraint coefficient value of point A; second, whether the energy state ranking value of point B is less than or equal to the energy state ranking value of point A. If, for point A, another point B can be found that satisfies both conditions, and at least one of these conditions is a strict less-than relationship rather than an equal-than relationship, then point B is said to dominate point A. After performing this type of comparison with all other points, if the edge computing node fails to find any other data point that dominates point A, then point A is determined to be a non-dominated point, i.e., a Pareto optimal solution. The edge computing node traverses all points in the data point set, repeats the above comparison and judgment process, and finally filters out all data points that are judged as non-dominant points. The set of these points is the Pareto optimal frontier sought in this analysis.

[0034] Edge computing nodes identify the electronic tags corresponding to these non-dominated data points on the Pareto optimal front as target electronic tags requiring priority power control coordination. This is because electronic tags located on the Pareto optimal front represent the individuals within the current set of electronic tags facing long-term power supply risks who have achieved the best balance between the conflicting objectives of network location advantage and energy state urgency; further optimization of one objective cannot be achieved without compromising the other. Therefore, these tags are selected as the core target objects that most need to be prioritized and coordinated when formulating differentiated energy allocation strategies in subsequent step S5. Through step S4, the edge computing nodes complete a precise screening based on multi-objective optimization theory, focusing on the subset of target electronic tags with the highest coordination priority from the set of risky tags.

[0035] S5. Based on the battery status data, key business periods, and structural hole indicators of the target electronic tag, evaluate and generate a differentiated energy allocation strategy, specifically implemented as follows: Based on the target electronic tags and their related data determined in step S4, the edge computing node executes step S5 to evaluate and generate a differentiated energy allocation strategy. The generation of the differentiated energy allocation strategy begins with the prediction of the available energy of the target electronic tag's battery. The edge computing node acquires the battery status data for each target electronic tag, which includes the current battery level and battery health status. Based on the battery status data, the edge computing node predicts the available energy of the electronic tag's battery from the current moment until the start of the critical business period determined in step S3. The prediction process requires establishing an estimation model from the current battery level to the remaining battery level at a future point in time. One implementation is that the edge computing node first estimates the average energy consumption power of the tag within a unit of time based on the electronic tag's historical operating records. The average energy consumption power can be estimated based on the battery level decrease records from the end of the critical business period to the start of the next business period within multiple past identical business cycles, by dividing the total battery level decrease by the total time length; the data comes from long-term monitoring. Then, the edge computing node calculates the time interval from the current moment to the start of the critical business period. Multiplying the estimated average energy consumption by the time interval length yields a predicted total energy consumption from the current point in time until the start of the critical business period. Considering the impact of battery health status on actual available capacity, edge computing nodes need to use the battery health status percentage to adjust the total energy represented by the current charge level to obtain the effective total energy of the battery. For example, if the battery health status is 80% and the current charge level is 75% of full capacity, first multiply the current charge level (75%) by the nominal total battery capacity to obtain the current absolute energy, then multiply this absolute energy by 0.8 to obtain the effective total energy. Finally, subtracting the predicted total energy consumption from the effective total energy gives the predicted available energy that can be flexibly scheduled and used before the start of the critical business period. Available energy is expressed in units such as joules or milliampere-hours (mAh).

[0036] Edge computing nodes determine the minimum energy guarantee ratio required for tags to maintain network connectivity during critical business periods based on the structural hole index of the target electronic tags. Here, the structural hole index uses the network constraint coefficient calculated in step S4. The smaller the network constraint coefficient value, the more critical the structural hole position occupied by the electronic tag in the local network topology. To convert the network constraint coefficient into a specific minimum energy guarantee ratio, the edge computing node needs to apply a preset mapping rule. The mapping rule is established based on the setting of the relationship between network importance level and guarantee requirements. For example, the edge computing node can pre-divide the numerical range of the network constraint coefficient into several consecutive intervals and assign a corresponding minimum energy guarantee ratio to each interval. Specifically, it can be set that when the network constraint coefficient is less than or equal to 0.1, it indicates that the tag is extremely important, and its minimum energy guarantee ratio is set to 95%; when the network constraint coefficient is greater than 0.1 and less than or equal to 0.3, the minimum energy guarantee ratio is set to 85%; when the network constraint coefficient is greater than 0.3, the minimum energy guarantee ratio is set to 75%. These specific interval boundary values ​​and ratio values ​​can be configured or adjusted once during system deployment according to network scale and business reliability requirements. By querying this mapping rule, the edge computing node determines a minimum energy reserve percentage between 0 and 100% for each target electronic tag. This minimum energy reserve percentage represents the percentage of energy reserved for the tag's battery at the start of a critical business period that should not be less than its current total available energy, ensuring basic connectivity for the tag during peak business hours.

[0037] Edge computing nodes combine predicted available energy with a determined minimum energy guarantee ratio to calculate the recommended energy budget allocated to each target tagged tag in different time periods before the critical business period, thus forming the final differentiated energy allocation strategy. The edge computing nodes divide the entire preparation period before the start of the critical business period into several consecutive equal-length time periods, for example, each time period is 2 hours long. For a specific target tagged tag, the edge computing node first calculates the minimum remaining energy value that needs to be ensured at the start of the critical business period. This minimum remaining energy value equals the tag's current total effective energy multiplied by the minimum energy guarantee ratio determined for the tag. Then, the edge computing node uses a backward calculation method: working backward from the start of the critical business period, setting the battery energy at this moment to be exactly equal to the calculated minimum energy value. Next, the penultimate time period, the time period closest to the critical business period, is calculated. Assuming that the tagged tag operates at its historical average energy consumption power during this time period, the initial energy required to reach the minimum energy value at the end of this time period equals the minimum energy value plus the predicted consumption during that time period. This initial energy is also the end energy target for the penultimate time period. Edge computing nodes use the predicted energy consumption for a time period as a reference value for the recommended energy budget for that time period. Then, they use the end energy target of the penultimate time period as the new target and repeat the process: the starting energy of a time period equals its end energy target plus the predicted energy consumption for that time period, and the energy consumption is the recommended energy budget reference value for that time period. This process iterates backwards for each time period until the current moment. During this iteration, for each time period, the recommended energy budget reference value is compared with the predicted available energy share allocated to that time period in step one. To ensure total energy constraints, the edge computing nodes need to ensure that the sum of the budget reference values ​​for all time periods does not exceed the predicted total available energy. If it does, the budget for each time period needs to be reduced proportionally, or priority needs to be given to the time periods closer to critical business periods. Through this calculation and adjustment, the edge computing nodes generate a sequence for each target RFID tag, where each element corresponds to an adjusted final recommended energy budget for a time period. This chronologically ordered sequence of recommended energy budgets constitutes the differentiated energy allocation strategy for the target RFID tag. Edge computing nodes generate their own differentiated energy allocation strategies for each target electronic tag, which together form a complete differentiated energy allocation strategy that takes into account individual differences and time dimensions for subsequent power control command generation.

[0038] S6. Based on the differentiated energy allocation strategy, generate and issue a differentiated power control command sequence to the target electronic tag, specifically as follows: According to the differentiated energy allocation strategy generated in step S5, the edge computing node executes step S6 to generate and issue the final differentiated power control command sequence to the target electronic tag. Step S6 first converts the recommended energy budget allocated to each target electronic tag in different time periods in the differentiated energy allocation strategy into a specific transmission power level that the electronic tag can execute. The edge computing node obtains the recommended energy budget allocated to each target electronic tag in different time periods before the critical business period, and the recommended energy budget is expressed in energy units such as joules. In order to convert it into a transmission power level, the edge computing node needs to establish a conversion relationship based on the RF power consumption characteristics of the electronic tag. The edge computing node pre-configures a mapping relationship table between transmission power level and typical operating power consumption for each type of electronic tag managed in the network. The construction of the mapping relationship table can be based on the hardware specifications of the electronic tag, which lists the typical operating current or power consumption value of the RF circuit in continuous transmission state under different transmission power settings; or it can be established by measuring the average power consumption of the tag at each power level through actual testing in the early stage. For a specific target electronic tag within a certain time period, the edge computing node performs a conversion calculation: First, it divides the recommended energy budget value corresponding to the time period by the duration of the time period to obtain an average power limit value allowed within the time period, with the unit of measurement being power, such as watts. Then, the edge computing node queries the mapping table corresponding to the tag model, searching for all transmit power levels whose typical operating power consumption does not exceed the calculated average power limit value. Finally, from these levels that meet the power consumption constraints, the level with the highest transmit power value is selected as the final transmit power level for the time period. The purpose of selecting the highest available level is to maintain the best possible communication link quality while strictly adhering to the energy budget. For example, if the recommended energy budget for a time period is 10 joules and the time period length is 5000 seconds, the average power limit value is 2 milliwatts. After querying the mapping table, it is found that the typical power consumption of the tag model is 1.8 milliwatts at transmit power level 2 and 2.2 milliwatts at level 3. Therefore, level 2 is the highest power level that meets the power consumption constraints, and thus, transmit power level 2 is determined for the time period.

[0039] After determining the transmission power levels for all time periods, the edge computing nodes arrange a power control command sequence for each target electronic tag in chronological order. Based on the time periods defined in step S5 and the start time of the critical service period determined in step S3, the edge computing nodes calculate a specific command activation time for each time period. The activation time is set as the start time of the corresponding time period. Following the chronological order of different time periods preceding the critical service period, the edge computing nodes combine the determined transmission power level for each time period with its corresponding activation time to form an independent power control command. A complete power control command contains at least two essential information elements: the absolute or relative time offset of the command activation, and the target transmission power level to which the electronic tag is required to switch. For a target electronic tag, the edge computing nodes arrange and encapsulate the power control commands corresponding to all time periods from the current time to the start of the critical service period in strict order of activation time from earliest to latest, thus forming a unique power control command sequence for the target electronic tag. To address the potential need for a stable state at the start of critical business periods, edge computing nodes can append a special instruction to the end of the power control instruction sequence. The instruction takes effect at the start of the critical business period, and its target transmit power level is set to a preset, higher level suitable for peak business periods or a default operating level, ensuring that the network is in the expected communication performance state at the start of the critical period.

[0040] Before the start of critical business hours, edge computing nodes reliably send the programmed power control command sequence to each corresponding target tagged object. The timing of this transmission needs to be well in advance to allow sufficient time for command transmission, processing, and handling network fluctuations. Edge computing nodes set a command transmission advance time threshold based on historical network communication round-trip latency and reliability statistics; for example, this threshold could be set to 30 minutes before the start of the critical business hours. At or before the time specified by the command transmission advance time threshold, the edge computing node initiates the command transmission process. During transmission, the edge computing node establishes a point-to-point communication connection with each target tagged object, transmitting the complete programmed power control command sequence as a data transaction. To ensure reliable command delivery, the edge computing node employs a communication protocol with acknowledgment and retransmission mechanisms. For example, after sending the sequence, a timer is started to wait for an acknowledgment frame from the tagged object; if no acknowledgment is received after the timer expires, the edge computing node retransmits the command sequence, repeating this process until acknowledgment is received or the preset maximum number of retransmissions is reached. After successfully receiving and confirming its own power control command sequence, each target electronic tag stores it completely in its local non-volatile memory. The microcontroller firmware within the tag contains a command scheduler that continuously compares the current time with the effective time of each command in the stored command sequence. When the system clock reaches or exceeds the preset effective time of a command, the command scheduler immediately sends a control command to the RF module, adjusting the RF module's transmit power to the level specified by the command. In this way, all target electronic tags can autonomously and accurately execute differentiated power control plans across time dimensions, formulated by edge computing nodes, without real-time intervention, thereby achieving fine-grained management of network energy consumption and ensuring long-term operational reliability at the system level.

[0041] Example 2: Figure 2 A schematic diagram of the edge computing-based adaptive power control system for electronic tags of the present invention is provided. The edge computing-based adaptive power control system for electronic tags includes: The information acquisition module is used by edge computing nodes to acquire battery status data and network topology information of each electronic tag within their management area, as well as periodic business load information of the management area. The coordination and judgment module is used to analyze the potential vulnerability of the network topology during peak service load periods based on network topology information and periodic service load information, and to determine whether long-term coordination is required. The time period identification module is used to analyze the network topology importance of each electronic tag and its energy status ranking within its respective topology community based on network topology information and battery status data when needed. Based on the difference between network topology importance and energy status ranking, it identifies the set of electronic tags with long-term power supply risks and the corresponding key business time periods. The tag filtering module is used to analyze the structural hole index of each electronic tag in a set of electronic tags based on network topology information, and to perform Pareto front analysis in a two-dimensional decision space composed of structural hole index and energy state ranking to filter out target electronic tags. The strategy evaluation module is used to evaluate and generate differentiated energy allocation strategies based on the battery status data of the target electronic tag, key business periods, and structural hole indicators. The strategy generation module is used to generate and issue a sequence of differentiated power control instructions to the target electronic tag based on the differentiated energy allocation strategy.

[0042] All calculations involved in the embodiments are dimensionless numerical calculations, and the preset parameters and thresholds in the calculations are set by those skilled in the art according to the actual situation.

[0043] It should be noted that this invention can be deployed on the device itself to realize embedded applications, or it can run on a PC or other terminal with a user interface, thereby meeting various hardware environments and usage requirements.

[0044] The above embodiments can be implemented, in whole or in part, by software, hardware, firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. A computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions according to the embodiments of this application are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. Computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wireless or wired transmission; wired transmission methods include optical fiber, twisted pair, coaxial cable, etc.; wireless transmission includes infrared, microwave, etc. Computer-readable storage media can be any available medium that a computer can access or a data storage device such as a server or data center that contains one or more sets of available media. Available media can be magnetic media (e.g., floppy disks, hard disks, magnetic tapes), optical media (e.g., DVDs), or semiconductor media. Semiconductor media can be solid-state drives.

[0045] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, devices, and modules described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0046] In the several embodiments provided in this application, it should be understood that the disclosed systems, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple modules or components may be combined or integrated into another system, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between apparatuses or modules may be electrical, mechanical, or other forms.

[0047] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical modules; they may be located in one place or distributed across multiple network modules. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0048] In addition, the functional modules in the various embodiments of this application can be integrated into one processing module, or each module can exist physically separately, or two or more modules can be integrated into one module.

[0049] If a function is implemented as a software module 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 this application, in essence, or the part that contributes to the prior art, or a portion 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 in the various embodiments of this application. 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.

[0050] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

[0051] In conclusion, the above are merely preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. An adaptive power control method for electronic tags based on edge computing, characterized in that, include: S1. Edge computing nodes acquire battery status data and network topology information of each electronic tag within their management area, as well as periodic business load information of the management area. S2. Analyze the potential vulnerability of the network topology during peak service load periods based on network topology information and periodic service load information to determine whether long-term coordination is required. S3. When needed, analyze the network topology importance of each electronic tag and its energy status ranking within its respective topology community based on network topology information and battery status data. Identify the set of electronic tags with long-term power supply risks and the corresponding key business periods based on the difference between network topology importance and energy status ranking. S4. For the set of electronic tags, analyze the structural hole index of each electronic tag based on the network topology information, and perform Pareto front analysis in the two-dimensional decision space composed of the structural hole index and the energy state ranking to screen out the target electronic tags. S5. Based on the battery status data, key business periods and structural hole indicators of the target electronic tag, evaluate and generate differentiated energy allocation strategies. S6. Based on the differentiated energy allocation strategy, generate and issue a differentiated power control instruction sequence to the target electronic tag.

2. The adaptive power control method for electronic tags based on edge computing according to claim 1, characterized in that, S1 includes: Battery status data includes the current battery level and battery health status of each electronic tag; Network topology information includes the connectivity between various electronic tags and the quality of historical communication links; Periodic business load information includes historical peak business periods and corresponding business volume data for the managed area.

3. The edge computing-based adaptive power control method for electronic tags according to claim 2, characterized in that, S2 include: Identify one or more critical communication paths that play a key role in the overall communication connectivity within the management area based on network topology information; By combining the peak business periods included in the periodic business load information, assess the communication load pressure that one or more critical communication paths may bear during peak business periods; Based on the communication load pressure that one or more critical communication paths may bear during peak business hours, assess whether it exceeds the preset load pressure threshold. If it does, determine that long-term coordination is required.

4. The edge computing-based adaptive power control method for electronic tags according to claim 3, characterized in that, Based on network topology information and battery status data, the network topology importance of each electronic tag and its energy status ranking within its respective topology community are analyzed, including: Based on network topology information, a graph community discovery algorithm is used to divide all electronic tags in the management area into multiple topological communities; Calculate the network topology importance index of each electronic tag in the entire network; Within each topology community, all electronic tags belonging to the topology community are ranked by their energy status based on the current battery level and battery health status contained in the battery status data.

5. The edge computing-based adaptive power control method for electronic tags according to claim 4, characterized in that, Based on the difference between network topology importance and energy status ranking, a set of electronic tags with long-term power supply risks and corresponding key business periods were identified, including: For each electronic tag, calculate the difference between its network topology importance index and its energy state ranking within its respective topology community; Electronic tags with a difference greater than a preset difference threshold are identified as electronic tags that pose a long-term power supply risk. The time periods corresponding to peak business load in the periodic business load information are identified as critical business periods.

6. The edge computing-based adaptive power control method for electronic tags according to claim 5, characterized in that, For the RFID tag set, the structural hole index of each RFID tag is analyzed based on network topology information, including: Based on the connectivity relationships contained in the network topology information, the network constraint coefficient of each electronic tag in the electronic tag set is calculated as an indicator of the degree to which it occupies the location of the structural hole.

7. The edge computing-based adaptive power control method for electronic tags according to claim 6, characterized in that, Pareto front analysis was performed in a two-dimensional decision space composed of structural hole indices and energy state rankings to screen out target electronic tags, including: Using the network constraint coefficient and energy state ranking within their respective topological communities as coordinates, the data points corresponding to each electronic tag are determined in a two-dimensional decision space. Among all data points, the set of data points that are not dominated by other data points in both the objective of simultaneously optimizing the reduction of network constraint coefficient values ​​and optimizing the reduction of energy state ranking values ​​is identified, and constitutes the Pareto optimal frontier; The electronic tags corresponding to the data points on the Pareto optimal frontier are identified as the target electronic tags that require priority power control coordination.

8. The adaptive power control method for electronic tags based on edge computing according to claim 7, characterized in that, S5 include: Based on the battery status data contained in the target electronic tag, including the current power and battery health status, predict the available energy of the battery from the current moment until the start of the critical business period. Based on the structural hole index of the target electronic tag, determine the minimum energy guarantee ratio required to maintain network connectivity during critical business periods. By combining the predicted available energy with the minimum energy guarantee ratio, a recommended energy budget is calculated for each target RFID tag in different time periods before the critical business hours, forming a differentiated energy allocation strategy.

9. The adaptive power control method for electronic tags based on edge computing according to claim 8, characterized in that, S6 include: The recommended energy budget allocated to each target electronic tag in the differentiated energy allocation strategy at different time periods is converted into the corresponding transmission power level; According to the sequence of different time periods before the critical business period, each target electronic tag is arranged with a series of power control command sequences containing a series of transmission power levels and corresponding effective time points; Before the start of critical business hours, the edge computing nodes send a sequence of power control instructions to each corresponding target electronic tag in advance.

10. An edge computing-based adaptive power control system for electronic tags, used to implement the edge computing-based adaptive power control method for electronic tags as described in any one of claims 1-9, characterized in that, include: The information acquisition module is used by edge computing nodes to acquire battery status data and network topology information of each electronic tag within their management area, as well as periodic business load information of the management area. The coordination and judgment module is used to analyze the potential vulnerability of the network topology during peak service load periods based on network topology information and periodic service load information, and to determine whether long-term coordination is required. The time period identification module is used to analyze the network topology importance of each electronic tag and its energy status ranking within its respective topology community based on network topology information and battery status data when needed. Based on the difference between network topology importance and energy status ranking, it identifies the set of electronic tags with long-term power supply risks and the corresponding key business time periods. The tag filtering module is used to analyze the structural hole index of each electronic tag in a set of electronic tags based on network topology information, and to perform Pareto front analysis in a two-dimensional decision space composed of structural hole index and energy state ranking to filter out target electronic tags. The strategy evaluation module is used to evaluate and generate differentiated energy allocation strategies based on the battery status data of the target electronic tag, key business periods, and structural hole indicators. The strategy generation module is used to generate and issue a sequence of differentiated power control instructions to the target electronic tag based on the differentiated energy allocation strategy.

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