Method and system for adjusting frequency of reporting power data based on communication quality feedback

Abstract

This invention relates to the field of electricity metering reporting frequency adjustment technology, and discloses a method and system for adjusting electricity data reporting frequency based on communication quality feedback. The method includes acquiring signal attenuation values, retransmission counts, and data delay duration of electricity meter nodes and fusing them to obtain channel state characteristics; classifying node clusters according to the channel state characteristics, and calculating bandwidth allocation quotas based on channel resource margins and the number of concurrent nodes; constructing a frequency set by comparing the bandwidth allocation quota with a threshold, and deducing an initial reporting frequency from the channel state characteristics; if the initial reporting frequency exceeds the threshold, reducing the frequency to obtain the target reporting frequency and generating a concurrent scheduling sequence; evaluating and sorting node priorities based on communication distance parameters to obtain the network update configuration. This method enables adaptive control of the reporting frequency of electricity metering nodes, improving data acquisition efficiency under channel quality fluctuations.

Description

Technical Field

[0001] This invention relates to the field of power metering reporting frequency adjustment technology, and in particular to a method and system for power data reporting frequency adjustment based on communication quality feedback. Background Technology

[0002] Currently, in the field of power metering reporting frequency adjustment technology, dynamic adjustment of power metering sampling frequency has become a key research topic in the field as a crucial link in preventing data packet loss and retransmission and avoiding network congestion risks.

[0003] In one existing technology, a unified configuration method distributed by the main station is mainly used to ensure the execution of data collection. This method relies heavily on ideal channel assumptions or fixed reporting rhythms for evaluation. For example, a preset rule mode is used to execute the whole network meter reading task, and the data upload status of electrical sensors and energy meter nodes is judged by fixed frequency requirements. However, this approach is clearly insufficient in complex communication environments. The unified distribution mode cannot adapt to the dynamic changes in communication link quality. The single rhythm evaluation is easily affected by the distance between different nodes and the strength of sudden interference. It is also affected by the concurrent contention of multiple users and the limited channel resources, which makes it difficult to guarantee the overall acquisition efficiency during data transmission. Furthermore, it lacks the ability to perceive and differentiate the underlying communication status in real time. Especially in scenarios with many concurrent nodes and repeated retransmissions in weak signal areas, it is difficult to avoid the normal node data delay caused by the ineffective occupation of communication resources, resulting in low overall network stability.

[0004] In summary, existing technologies are insufficient to achieve adaptive control of the reporting frequency of power metering nodes, and cannot meet the core requirement of smart grid communication technology to improve data acquisition efficiency when facing channel quality fluctuations. Summary of the Invention

[0005] This invention provides a method and system for adjusting the frequency of power data reporting based on communication quality feedback, so as to realize adaptive control of the sampling frequency of power metering nodes and meet the core requirement of smart grid communication technology to improve data acquisition efficiency when facing channel quality fluctuations.

[0006] In a first aspect, to solve the above-mentioned technical problems, the present invention provides a method for adjusting the frequency of power data reporting based on communication quality feedback, comprising:

[0007] The signal attenuation value, retransmission frequency value and data delay time of the energy meter node are obtained and fused to obtain the channel state characteristics.

[0008] Based on the channel state characteristics, a classification process is performed to obtain a node cluster, and the bandwidth allocation quota is calculated based on the pre-acquired channel resource reserve and the number of concurrent nodes.

[0009] If the bandwidth allocation is less than a preset threshold, a frequency set is constructed based on the previously acquired historical communication records; if the bandwidth allocation is not less than the preset threshold, the preset base set is used as the frequency set; and the initial reporting frequency is obtained by performing deduction calculations based on the frequency set and the channel state characteristics.

[0010] If the initial reporting frequency is greater than a preset frequency determination threshold, then frequency reduction processing is performed to obtain the target reporting frequency; if the initial reporting frequency is not greater than the preset frequency determination threshold, then the initial reporting frequency is used as the target reporting frequency; a concurrent scheduling sequence is generated based on the target reporting frequency.

[0011] The concurrent scheduling sequence is weighted according to the pre-extracted communication distance parameters to obtain node priorities. The concurrent scheduling sequence is then sorted according to the node priorities to obtain the network update configuration.

[0012] Secondly, the present invention provides a power data reporting frequency adjustment system based on communication quality feedback, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the power data reporting frequency adjustment method based on communication quality feedback as described above.

[0013] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the above-described method for adjusting the frequency of power data reporting based on communication quality feedback.

[0014] Compared with the prior art, the present invention has the following beneficial effects:

[0015] (1) This invention obtains channel state features by acquiring the signal attenuation value, retransmission frequency value and data delay time of the power meter node and performing feature construction processing. Based on the channel state features, it performs classification processing to obtain node clusters and calculates the bandwidth allocation quota by combining the pre-acquired channel resource margin and the number of concurrent nodes. This invention breaks through the limitations of the traditional unified distribution or fixed reporting rhythm configuration of the master station, which cannot adapt to the dynamic changes in the quality of the communication link. It integrates multi-dimensional underlying communication indicators to analyze the real channel state of the node, accurately captures the differences in electromagnetic environment and interference strength of different nodes, and solves the problems of multiple users competing for channel resources and single rhythm evaluation being easily affected by repeated retransmission interference from weak signal nodes. It provides accurate channel evaluation basis for the differentiated allocation and adaptive adjustment of power data reporting frequency.

[0016] (2) When the bandwidth allocation is less than the threshold, the present invention constructs a frequency set based on historical communication records, and inputs the frequency set and the channel state characteristics into a preset evaluation model to perform deduction calculation to obtain the initial reporting frequency. When the initial reporting frequency is greater than the frequency threshold, the node load status and transmission delay time are extracted to construct the occupancy ratio to determine the consumption rate, and then the frequency reduction processing is performed to obtain the target reporting frequency. This invention breaks through the limitations of traditional network overall bandwidth allocation not being able to correspond to the actual carrying capacity of each node and the fixed reporting frequency setting. It dynamically deduces the reporting frequency based on communication quality feedback and bandwidth allocation status, and optimizes node communication behavior through the frequency reduction processing mechanism. This invention makes up for the defects of existing power metering systems that regard communication links as ideal channels and lack dynamic perception and fine adjustment mechanisms for the underlying communication status.

[0017] (3) The present invention reassembles the target reporting frequency to generate a concurrent scheduling sequence, and calculates the signal attenuation value by combining the pre-extracted communication distance parameters to obtain the path loss value. Then, it performs weight evaluation to obtain the node priority, and finally performs sorting processing on the concurrent scheduling sequence according to the node priority to obtain the network update configuration. It constructs a full-link power metering communication scheduling system with channel state awareness, frequency dynamic simulation, node priority evaluation, and network configuration update. It breaks through the limitations of traditional communication systems that lack multi-user feedback linkage and resource scheduling priority sorting mechanism, takes into account the rationality of reporting frequency allocation and the efficiency of underlying channel resource allocation, effectively alleviates network communication congestion and improves the overall data acquisition efficiency, and meets the core requirements of smart grid communication scenarios for the immediacy of underlying terminal status response and dynamic adaptation of channel resources. Attached Figure Description

[0018] Figure 1 This is a schematic diagram of a method for adjusting the frequency of power data reporting based on communication quality feedback provided by the present invention;

[0019] Figure 2 This is a schematic diagram of the distribution of node cluster channel state characteristics provided by the present invention;

[0020] Figure 3 This is a schematic diagram of the candidate frequency adaptation scoring provided by the present invention. Detailed Implementation

[0021] 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.

[0022] Reference Figure 1The first embodiment of the present invention provides a method for adjusting the frequency of power data reporting based on communication quality feedback, comprising the following steps:

[0023] S11: Obtain the signal attenuation value, retransmission frequency count value and data delay duration of the energy meter node and perform fusion processing to obtain the channel state characteristics;

[0024] S12, perform classification processing based on the channel state characteristics to obtain node clusters, and calculate the bandwidth allocation quota based on the pre-acquired channel resource reserve and the number of concurrent nodes;

[0025] S13, if the bandwidth allocation quota is less than the preset quota determination threshold, then a frequency set is constructed based on the previously acquired historical communication records; if the bandwidth allocation quota is not less than the preset quota determination threshold, then the preset basic set is used as the frequency set; and the initial reporting frequency is obtained by performing inference calculation based on the frequency set and the channel state characteristics.

[0026] S14, if the initial reporting frequency is greater than a preset frequency determination threshold, then frequency reduction processing is performed to obtain the target reporting frequency; if the initial reporting frequency is not greater than the preset frequency determination threshold, then the initial reporting frequency is used as the target reporting frequency; a concurrent scheduling sequence is generated according to the target reporting frequency.

[0027] S15, perform weight evaluation on the concurrent scheduling sequence based on the pre-extracted communication distance parameters to obtain node priorities, and perform sorting processing on the concurrent scheduling sequence based on the node priorities to obtain network update configuration.

[0028] In step S11, the acquisition of the signal attenuation value, retransmission count value, and data delay duration of the energy meter node, followed by fusion processing to obtain channel state characteristics, includes:

[0029] Obtain message exchange records, calculate the difference between the transmitted power and the received signal strength, and obtain the signal attenuation value;

[0030] Perform a statistical operation on the message interaction record based on the signal attenuation value to obtain the retransmission count value;

[0031] The timestamp difference is calculated based on the number of retransmissions to obtain the data delay duration;

[0032] The signal attenuation value, the number of retransmissions, and the data delay duration are normalized and mapped to obtain the channel state characteristics.

[0033] It should be noted that, firstly, message exchange records are obtained using the communication module between the energy meter node and the concentrator, extracting the transmit power field and receive signal strength indication field from the physical layer header of the communication message. The difference between the transmit power and the receive signal strength indication is the signal attenuation value, measured in dB. Subsequently, a statistical judgment benchmark for signal attenuation is established. This benchmark is determined based on the available link budget between the typical transmit power value and the receive sensitivity of the communication module. For example, with a typical transmit power value of 20dBm and a receive sensitivity of -110dBm, the available link budget is 130dB. After deducting a 30dB system margin, the critical attenuation value can be set to 100dB. When the signal attenuation value of a message exchange record exceeds this critical attenuation value, it is marked as a high attenuation exchange record. Within a preset 60-second statistical period, the number of times the same message identifier is repeated is accumulated; the accumulated result is the retransmission count value.

[0034] Next, the data delay is calculated based on the retransmission frequency. For packets determined to have been retransmitted within the statistical period, the timestamps of the initial transmission request and the final successful reception acknowledgment are extracted from the packet interaction record. The difference between the timestamp of the initial transmission request and the timestamp of the final successful reception acknowledgment is the data delay for that interaction, in milliseconds (ms). The data delay is calculated for each packet that was retransmitted within the statistical period, and these delays are aggregated. The average of these delays is taken as the representative data delay for the current 60-second window.

[0035] Subsequently, the three indicators with different physical dimensions—signal attenuation, retransmission frequency, and data delay—are fused. Before fusion, a minimum-maximum normalization process is performed to unify them into a dimensionless range between 0 and 1, eliminating the impact of dimensional differences on subsequent calculations. The normalization process involves subtracting the minimum value recorded in the historical operation from the current value, and then dividing by the range between the historical maximum and minimum values. The historical maximum and minimum values ​​are extracted from a continuously updated rolling historical database that stores communication quality indicators for all nodes over the past 7 days. The data source is the periodic communication inspection records of all nodes on the concentrator side. After normalization, the three values ​​are combined into a three-dimensional vector, which represents the channel state characteristic. This channel state characteristic quantifies and integrates complete information from three dimensions—link loss, transmission stability, and timeliness—in vector form, comprehensively characterizing the underlying communication quality of the current energy meter node.

[0036] In step S12, the process of performing classification processing based on the channel state characteristics to obtain node clusters includes:

[0037] Obtain the initial cluster centers, calculate the feature distance between the channel state features and the initial cluster centers, and obtain the initial classification result;

[0038] The sum of squared errors is calculated based on the initial classification results, and the cluster centers are iteratively updated based on the sum of squared errors to obtain the final classification set.

[0039] Based on the final classification set, the contour coefficient is calculated, and the level mapping is performed based on the contour coefficient to obtain the node cluster.

[0040] It should be noted that, firstly, the initial cluster centers are obtained. The number of clusters K is determined as follows: within the integer range of K from 2 to 6, a complete K-Means clustering is performed on all historical channel state feature records of all grid-connected energy meter nodes retrieved from the concentrator's node management database for each K value. Each record is a three-dimensional vector, corresponding to the signal attenuation value, retransmission count value, and data delay duration, respectively. For the clustering results of each K value, the average silhouette coefficient of all nodes is calculated, and the K value with the highest average silhouette coefficient is selected as the final number of clusters. If two different K values ​​have the same highest average silhouette coefficient, the larger K value is selected to ensure finer-grained node differentiation. After determining the K value, the K-Means++ initialization algorithm is used to select K initial cluster centers from the historical channel state feature records. This algorithm prioritizes selecting points that are far from the features of the already selected centers as new centers to reduce the risk of the clustering results getting trapped in local optima.

[0041] Next, the feature distances between the channel state features output in the previous step and each initial cluster center are calculated. The feature distance uses Euclidean distance as a metric. The sum of the squares of the subtractions of the three-dimensional components of the channel state features with the corresponding dimension components of the cluster centers are taken, and then the square root is taken. Each electricity meter node is assigned to the cluster of the nearest cluster center based on its minimum feature distance, thus obtaining the initial classification result.

[0042] Secondly, the sum of squared errors is calculated based on the initial classification results. The sum of squared errors is the sum of the squared Euclidean distances between all nodes and the cluster centers of their respective clusters. The clustering update process aims to minimize the sum of squared errors. In each iteration, the arithmetic mean of all nodes within each cluster is recalculated as the new cluster centers. All nodes are then re-divided according to the new centers. This process is repeated until the decrease in the sum of squared errors falls below a preset convergence threshold of 0.1%, at which point the iteration stops. The convergence threshold is set based on the stability range of the clustering error. When the relative change in the sum of squared errors is less than 0.1% for two consecutive iterations, it indicates that the cluster structure has stabilized, and further iteration yields minimal benefit. After the iteration stops, the final classification set is output.

[0043] Subsequently, silhouette coefficients are calculated based on the final classification set. Silhouette coefficients quantify the compactness and separation of the clustering results. For each node in the final classification set, its average distance to other nodes within the same cluster is calculated as intra-cluster cohesion, and its average distance to all nodes in its nearest neighboring disjoint clusters is calculated as inter-cluster separation. The difference between these two values ​​is divided by the larger of the two values ​​to obtain the silhouette coefficient for that node. The arithmetic mean of the silhouette coefficients of all nodes is the overall silhouette coefficient of the current clustering scheme. The silhouette coefficient ranges from -1 to 1; values ​​closer to 1 indicate more compact intra-cluster clusters and greater inter-cluster separation.

[0044] Then, for each cluster in the final classification set, the arithmetic mean of the signal attenuation values ​​of all nodes within the cluster is extracted to obtain the cluster average attenuation value; the arithmetic mean of the retransmission frequency values ​​is extracted to obtain the cluster average retransmission value; and the arithmetic mean of the data delay time is extracted to obtain the cluster average delay value. These three cluster-level means are then min-max normalized according to the intervals of signal attenuation (0-130dB), retransmission frequency (0-maximum observed value), and data delay (0-500ms), respectively, and added together with equal weights to obtain the cluster communication quality score, ranging from 0 to 1. Clusters with a communication quality score between 0.7 and 1.0 are classified as having excellent communication quality, those between 0.4 and 0.7 as having medium communication quality, and those below 0.4 as having poor communication quality. These three grading intervals correspond to the transition from channel conditions approaching the link budget limit to those with sufficient margin. All electricity meter nodes within the same grade constitute a node cluster. Thus, the originally isolated channel state characteristics have been transformed into a structured node cluster partition, providing a clear scheduling basis for subsequent differentiated reporting frequency adjustment.

[0045] In one implementation, the step of calculating the bandwidth allocation quota based on the pre-acquired channel resource reserve and the number of concurrent nodes includes:

[0046] The service priority and basic transmission rate of the energy meter nodes within the node cluster are obtained and weighted summation is performed to obtain the cluster-level communication demand.

[0047] Calculate the ratio of the channel resource margin to the number of concurrent nodes to obtain the resource load status;

[0048] If the resource load status is greater than a preset load determination threshold, then the channel resource reserve is attenuated to obtain the available channel resource amount; if the resource load status is not greater than the preset load determination threshold, then the channel resource reserve is taken as the available channel resource amount.

[0049] Based on the available channel resources and the number of concurrent nodes, a proportional allocation mapping calculation is performed on the cluster-level communication demand to obtain the bandwidth allocation quota.

[0050] It should be noted that, firstly, the service priority and basic transmission rate of the electricity meter nodes within the node cluster are obtained. Service priority is extracted from the concentrator's node management database, which stores the service type tags entered by each electricity meter node during registration in the distribution area. These tags are divided into three categories: critical guarantee, routine metering, and low-priority background, quantified by values ​​of 3, 2, and 1, respectively. The basic transmission rate is obtained from the node communication module's specifications, determined based on its supported modulation and coding schemes, and is measured in kbps. During weighted summation, the weighting factor for service priority is set to 0.6 based on the impact of its fluctuations on communication demand, and the weighting factor for basic transmission rate is set to 0.4. These two weighting factors are determined by performing a grid search on historical scheduling records, with a search range of 0.1 to 0.9 and a step size of 0.1, constraining the sum of the two to 1.0. The goal is to minimize the deviation between actual bandwidth usage and ideal allocation in historical scheduling. Traverse all electricity meter nodes within the node cluster, multiply the service priority quantization value by 0.6 for each node, multiply the basic transmission rate by 0.4, and add the two products to obtain the communication demand of that node. Accumulate the communication demands of all nodes, and the accumulated result is the cluster-level communication demand, in kbps.

[0051] Next, the resource load status is calculated. Channel resource availability is obtained in real-time from the concentrator's network management module, which continuously monitors the number of currently available physical resource blocks, expressed in units. The number of concurrent nodes is the total number of electricity meter nodes active and waiting to upload data within the same scheduling cycle in the current distribution area. Dividing the channel resource availability by the number of concurrent nodes yields the resource load status, expressed in units per node. This ratio reflects the average number of physical resource blocks available to each concurrent node under the assumption of uniform allocation; a smaller value indicates a heavier load.

[0052] Secondly, it is determined whether the resource load status exceeds a preset load threshold. The load threshold is calculated based on the physical layer parameters of the current communication standard and the resource requirements of a single meter reading message. Specifically, it is the sum of the number of scheduling resource units required for a single meter reading message and the reserved margin for signaling overhead and sudden retransmissions. When the resource load status exceeds this threshold, it means that the average number of scheduling resource units available per node has exceeded the minimum guarantee line, and resources are abundant. In this case, the channel resource margin is directly used as the available channel resource quantity. When the resource load status is not greater than this threshold, it means that resources are approaching or below the minimum guarantee line, and margin needs to be reserved for signaling overhead and sudden retransmissions. In this case, attenuation processing is performed on the channel resource margin by multiplying it by an attenuation coefficient of 0.8. The attenuation coefficient of 0.8 is an engineering margin set to reserve 20% of scheduling resource units for carrying signaling overhead and sudden retransmissions. The product is the available channel resource quantity, in units.

[0053] Subsequently, a proportional allocation mapping calculation is performed. The cluster-level communication demand is used as the numerator, and the sum of the cluster-level communication demands of all node clusters across the entire network is used as the denominator to calculate the demand percentage for that node cluster. The available channel resources are multiplied by this demand percentage to obtain the number of scheduling resource units allocated to that node cluster. This number of scheduling resource units is then multiplied by the standard bandwidth value for each scheduling resource unit. The standard bandwidth value is obtained from the specifications based on the subcarrier spacing of the communication modules. The product is the bandwidth allocation quota, in kHz. At this point, the node cluster obtains a bandwidth allocation quota that matches its communication demand percentage and the current available network resources, serving as the upper limit constraint for subsequent frequency extrapolation.

[0054] For example, Figure 2 This is a schematic diagram of the channel state characteristics distribution of a node cluster. The diagram shows the joint distribution of signal attenuation values, retransmission counts, and data delay times for 120 electricity meter nodes. Classification processing categorizes nodes into medium and poor communication quality levels. These two types of node clusters are clearly distinguishable in the channel state characteristic space, providing a differentiated basis for bandwidth allocation calculation.

[0055] In step S13, if the bandwidth allocation quota is less than a preset quota determination threshold, a frequency set is constructed based on pre-acquired historical communication records, including:

[0056] Extract the node identifier and collection timestamp of each node in the node cluster;

[0057] Based on the node identifier and the collection timestamp, the historical communication records obtained in advance are matched to extract the historical reporting frequency and historical bandwidth usage records.

[0058] Perform time series alignment processing on the historical reporting frequency and the historical bandwidth usage records to obtain the node behavior sequence;

[0059] A frequency distribution feature extraction operation is performed on the node behavior sequence to construct a frequency set.

[0060] It should be noted that the preset credit limit threshold is determined based on the minimum bandwidth requirement of the energy meters within the node cluster under normal metering tasks. Referring to the message length and maximum allowable delay of a complete meter reading interaction in the DL / T 645 communication protocol, a single node requires at least 12.5kHz of bandwidth to complete one data upload; therefore, the credit limit threshold is set at 12.5kHz.

[0061] When the bandwidth allocation is less than the preset threshold, it indicates that the current channel resources are insufficient and it is difficult to guarantee the normal communication of the node at the basic reporting frequency. Therefore, it is necessary to construct a frequency set based on the historical communication records obtained in advance in order to search for the currently available reporting frequency from the historical operation data.

[0062] Subsequently, the node identifier and data acquisition timestamp of each node in the node cluster are extracted. The node identifier is read directly from the concentrator's node management database; this identifier is a unique MAC address assigned to each energy meter when it is registered in the distribution area. The data acquisition timestamp is taken as the start time of the current scheduling cycle, accurate to milliseconds, and is provided by the concentrator's internal real-time clock module.

[0063] Next, the pre-acquired historical communication records are matched against the node identifier and the acquisition timestamp. These historical communication records are stored in the concentrator's non-volatile memory. Each record contains four fields: timestamp, node identifier, historical reporting frequency, and historical bandwidth usage record. The historical reporting frequency is the actual reporting frequency used by the node in each past scheduling cycle, measured in Hz. The historical bandwidth usage record is the actual bandwidth used by the node for data upload in each cycle, measured in kHz. The matching operation uses the node identifier as the first matching key and the acquisition timestamp as the second matching key. All records in the historical communication records that match the node identifier and whose timestamps are no later than the current acquisition timestamp are retrieved and sorted in ascending order of timestamp. The corresponding historical reporting frequency sequence and historical bandwidth usage sequence are then extracted.

[0064] Secondly, time-series alignment processing is performed on historically reported frequencies and historical bandwidth usage records. The alignment benchmark is the timestamp sequence of historical bandwidth usage records. Each frequency value in the historically reported frequency sequence is mapped to the timestamp grid of the historical bandwidth usage records according to the principle of proximity of timestamps, forming a two-dimensional data pair containing both historically reported frequencies and historical bandwidth usage under each timestamp. All two-dimensional data pairs corresponding to all timestamps are arranged in chronological order to form a node behavior sequence. At this point, the node behavior sequence fully preserves the correspondence between the reported frequencies and bandwidth usage of a single node in different time periods, and the sequence length is the number of historical records matched by that node.

[0065] Subsequently, frequency distribution feature extraction is performed on the node behavior sequence to construct a frequency set. The node behavior sequence is traversed, and the historical reported frequency field is extracted. Whenever a historical reported frequency is synchronously non-zero with historical bandwidth usage, that historical reported frequency is included in the frequency statistics. The synchronous non-zero determination logic is that the historical reported frequency is greater than 0Hz and the corresponding historical bandwidth usage is greater than 0kHz, thus excluding interference from idle periods when the node is offline or data is not transmitting. After counting the frequency occurrences for all valid time periods of the node, the top four historical reported frequency values ​​are selected in descending order of frequency. This selection is determined by considering the clustering characteristics of the node's historical high-frequency frequencies; the top four high-frequency values ​​typically cover more than 90% of the reported frequency occurrences in the node's valid communication records. All distinct frequency values ​​falling within this range constitute the candidate frequency subset for the node. All nodes within the node cluster are traversed, and the union of all candidate frequency subsets is taken; the result of this union is the frequency set. Each element in the frequency set is a reported frequency value with historical operational data, and the unit is Hz.

[0066] Finally, after constructing the frequency set, the frequency set is associated with and stored with the corresponding node cluster identifier, providing an initial search space for subsequent frequency extrapolation and optimal reporting frequency decision-making. This frequency set is generated based on the historical operating habits of each node and can accurately reflect the effective reporting frequency distribution range that the node cluster can stably operate in the current communication environment.

[0067] In one implementation, if the bandwidth allocation is not less than a preset threshold, then a preset base set is used as the frequency set. When the bandwidth allocation is not less than the threshold, it indicates that the current channel resources are sufficient to support normal data interaction at the base reporting frequency, and there is no need to search for available frequencies through historical data analysis. In this case, the preset base set is directly used as the frequency set. The base set consists of preset reporting frequencies, specifically 0.2Hz, 0.1Hz, 0.05Hz, and 0.033Hz, corresponding to reporting cycles of 5 minutes, 10 minutes, 15 minutes, and 30 minutes, respectively. These frequency values ​​are set based on communication bandwidth constraints and the timeliness requirements of metering data, covering the basic requirements for data update timeliness in conventional metering scenarios.

[0068] In one implementation, the step of performing inference calculations based on the frequency set and the channel state characteristics to obtain the initial reporting frequency includes:

[0069] The channel state features are parsed to extract channel fading features and channel interference intensity;

[0070] Extract the bandwidth occupancy ratio corresponding to each candidate frequency in the frequency set, and construct a fusion vector based on the bandwidth occupancy ratio, the channel fading characteristics, and the channel interference intensity;

[0071] Each of the fusion vectors is input into a preset evaluation model to obtain the adaptation score corresponding to each candidate frequency;

[0072] The candidate frequency with the highest adaptation score is selected as the initial reporting frequency.

[0073] It should be noted that the channel state features are first analyzed. These channel state features are the three-dimensional vector obtained in step S11 through min-max normalization. The first, second, and third dimensions correspond to the normalized signal attenuation value, retransmission count, and data delay, respectively. The first dimension is directly extracted from this three-dimensional vector as the channel fading feature; the arithmetic mean of the second and third dimensions is taken as the channel interference intensity.

[0074] Next, bandwidth constraint pre-screening is performed on each candidate frequency value in the frequency set. The candidate frequency value is multiplied by the single-sample data bit width of 16 bits and then by the protocol encapsulation overhead factor of 1.2 to obtain the estimated communication data rate in bits per second (bit / s). This estimated communication data rate is divided by the spectral efficiency value of the current communication standard. The spectral efficiency value is determined based on the bit-per-hertz parameter of the modulation and coding scheme given in the communication module specification, with a typical value of 4 bits per Hz. This division yields the estimated bandwidth occupancy corresponding to the candidate frequency in Hz. The estimated bandwidth occupancy is compared with the bandwidth allocation quota, which needs to be multiplied by 1000 and converted to Hz. Only candidate frequency values ​​whose estimated bandwidth occupancy does not exceed the converted bandwidth allocation quota value are retained for subsequent processing.

[0075] If the estimated bandwidth occupancy of all candidate frequency values ​​in the frequency set exceeds the bandwidth allocation quota conversion value, the candidate frequency value with the smallest estimated bandwidth occupancy is directly selected as the initial reporting frequency, skipping subsequent model derivation steps. Then, the candidate frequency values ​​retained after pre-screening are concatenated. Channel fading characteristics, channel interference intensity, and the bandwidth occupancy ratio corresponding to each retained candidate frequency are vertically concatenated to form a three-dimensional fusion vector. The first dimension of the fusion vector is the channel fading characteristics, the second dimension is the channel interference intensity, and the third dimension is the bandwidth occupancy ratio, obtained by dividing the estimated bandwidth occupancy by the bandwidth allocation quota conversion value, with a value range of (0,1) after pre-screening. When the frequency set contains multiple candidate frequency values ​​that have passed pre-screening, each candidate frequency value generates a corresponding three-dimensional fusion vector, and all fusion vectors constitute the fusion vector group to be evaluated.

[0076] Then, each fusion vector is input into a preset evaluation model to obtain the adaptation score corresponding to each candidate frequency. The evaluation model adopts a decision tree regression model, and the complete decision structure after model training is solidified into a hierarchical decision rule table and stored in the non-volatile storage area of ​​the concentrator. The training set is constructed from the historical communication records stored in the concentrator, and each training sample contains three feature dimensions and one ground truth label. The three feature dimensions are historical channel fading, historical channel interference intensity, and bandwidth occupancy ratio, respectively.

[0077] Historical channel fading is directly extracted from the first dimension of the historical channel state characteristics for the corresponding time period; historical channel interference intensity is extracted from the arithmetic mean of the second and third dimensions of the historical channel state characteristics for the corresponding time period; bandwidth occupancy ratio is calculated by dividing the estimated bandwidth occupancy corresponding to the reporting frequency actually used by the node in that time period by the bandwidth allocation quota conversion value at that time. The truth label is the adaptation score of the reporting frequency in the corresponding time period. The adaptation score is calculated according to the data packet loss rate and upload latency for that time period according to the following rules: Assuming the packet loss rate is PLR and the upload latency is TD, the formula for calculating the adaptation score is:

[0078]

[0079]

[0080] When PLR≤0.01 and TD≤500ms, When PLR≥0.10 or TD≥1000ms, the corresponding penalty term is 1; otherwise, the calculation is performed according to the above formula, and the score is restricted to the [0,1] interval.

[0081] The training set was divided into training and validation sets in a 7:3 ratio. The maximum depth of the decision tree regression model was set to 4 layers, and the splitting criterion was minimizing the mean squared error. After the model training was completed, the model parameter combination with the lowest validation mean squared error was selected as the final evaluation model for deployment.

[0082] Each 3D fusion vector is input into a trained decision tree regression model. Starting from the root node, the model compares the 3D feature values ​​of the fusion vector with the splitting criteria of each node layer by layer until a leaf node is reached. The average fit score output by this leaf node is the fit score corresponding to the candidate frequency. After summing the fit scores of all candidate frequencies, the candidate frequency with the highest fit score is selected as the initial reporting frequency. If there is a tie for the highest fit score, a weighted composite score is calculated between the candidate frequency and the bandwidth occupancy ratio. The candidate frequency value is normalized to the 0-1 range and multiplied by a weight factor of 0.4. The bandwidth margin obtained by subtracting the bandwidth occupancy ratio from 1 is multiplied by a weight factor of 0.6. The two scores are added together, and the highest weighted composite score is selected as the initial reporting frequency. The minimum and maximum normalization of the candidate frequency values ​​is mapped using a fixed frequency range [0.01Hz, 1Hz] and remains consistent throughout the evaluation process. The resulting initial reporting frequency achieves optimal matching in terms of channel conditions, historical operating habits, and current available bandwidth.

[0083] For example, Figure 3 This is a schematic diagram of candidate frequency adaptation scores, showing the changes in candidate frequency adaptation scores for different node clusters. Node clusters with medium communication quality maintain high adaptation scores over a wide range of candidate frequencies, while node clusters with poor communication quality show a significant decrease in adaptation scores as the candidate frequency increases, reflecting the differentiated results of inferring the initial reported frequency based on channel state characteristics.

[0084] In step S14, if the initial reporting frequency is greater than a preset frequency determination threshold, then frequency reduction processing is performed to obtain the target reporting frequency, including:

[0085] Extract the node load status and transmission delay time corresponding to the initial reporting frequency, and construct the occupancy ratio based on the node load status and the transmission delay time to determine the consumption rate;

[0086] Obtain the frequency attenuation amplitude corresponding to the consumption rate, calculate the frequency reduction step size, and generate the operating frequency band based on the frequency reduction step size;

[0087] The initial reporting frequency is down-clocked based on the operating frequency band to obtain the target reporting frequency.

[0088] The concurrent communication identifier is extracted based on the target reporting frequency, and the node activity is extracted based on the concurrent communication identifier to calculate the backoff time and reassemble to generate a concurrent scheduling sequence.

[0089] It should be noted that the frequency determination threshold is determined based on the nominal transmission rate of the communication module under the current communication standard and the critical delay constraint of a single data report. The critical reporting frequency that the communication module can support without continuous backlog is obtained by dividing the uplink throughput by the amount of data reported per report. This critical reporting frequency is then multiplied by a safety factor of 0.8 to obtain the frequency determination threshold. Taking a typical 470MHz wireless private network communication module as an example, the uplink throughput is approximately 9600bit / s, and the amount of data reported per report is 12 bits. Therefore, the critical reporting frequency is approximately 800Hz, which, multiplied by the safety factor of 0.8, is approximately 640Hz. The frequency determination threshold is therefore set at 640Hz. When the initial reporting frequency exceeds 640Hz, it indicates that the data throughput corresponding to that frequency is approaching the current communication link's capacity limit, and the frequency needs to be lowered to avoid channel congestion and node processing queue overflow.

[0090] First, the node load status and transmission delay time corresponding to the initial reporting frequency are extracted. The node load status is read from the concentrator's node management database at the start of the current scheduling cycle. The value is the node's average bandwidth utilization rate over the past three scheduling cycles, expressed as a percentage. The bandwidth utilization rate is the node's actual bandwidth usage divided by its allocated bandwidth quota. The transmission delay time is extracted from the message exchange records, and the value is the round-trip time of the node's most recent successful exchange, in milliseconds. The node load status and transmission delay time are respectively processed using min-max normalization to map them to the interval between 0 and 1, then multiplied by their respective weighting factors and summed. The sum is the consumption rate. The weighting factor for node load status is set to 0.55, and the weighting factor for transmission delay time is set to 0.45. These two weighting factors are determined by performing a grid search on the historical running dataset stored in the concentrator. The search range is 0.3 to 0.7 with a step size of 0.05, and the sum of the two factors is constrained to 1.0. The set of weight values ​​is determined by maximizing the Pearson correlation coefficient between the consumption rate and the change in actual bandwidth consumption. The historical operation dataset contains records of node load status, transmission latency, and corresponding actual bandwidth consumption changes for all nodes in the Taiwan region over the past 30 days, with a total sample size of no less than 5,000 records.

[0091] Next, the frequency attenuation magnitude corresponding to the consumption rate is obtained. The criterion for determining the frequency attenuation magnitude is: at this consumption rate, the minimum frequency reduction required to reduce the node's bandwidth utilization from the current value to below 60%. Based on this, three attenuation magnitudes are set: when the consumption rate is in the range of 0 to 0.3, the bandwidth utilization is usually below 60%, and only a 47Hz attenuation is needed to release sufficient bandwidth; when the consumption rate is greater than 0.3 to 0.6, the bandwidth utilization is between 60% and 85%, and a 94Hz attenuation can effectively alleviate congestion; when the consumption rate is greater than 0.6 to 1.0, the bandwidth utilization exceeds 85%, and a 141Hz attenuation is needed to quickly restore available resources. After obtaining the consumption rate, the consumption rate is compared with the three consumption ranges to determine its corresponding frequency attenuation magnitude.

[0092] Then, the frequency reduction step size is calculated based on the frequency attenuation amplitude and the consumption rate. To avoid step size divergence at small consumption rates, the frequency attenuation amplitude is multiplied by the consumption rate and divided by a cutoff factor of 0.6 to obtain a constrained frequency reduction step size in Hz. When the consumption rate is less than 0.15, the lower limit of the consumption rate is set to 0.15 for calculation to ensure that the frequency reduction step size is not too large under low load. Then, the operating frequency band is generated according to the frequency reduction step size. The lower limit of the operating frequency band is the value of the initial reported frequency minus the frequency reduction step size, and the upper limit is the initial reported frequency. When the lower limit is 50Hz lower than the fundamental frequency, the lower limit is set to 50Hz. The operating frequency band is a continuous frequency range from the lower limit to the upper limit.

[0093] Secondly, the initial reported frequency is downgraded based on the operating frequency band. Within the operating frequency band, candidate frequency values ​​are selected one by one in 25Hz increments, starting from the lower boundary of the band. A 25Hz increment is half the 50Hz fundamental frequency, ensuring that the candidate frequencies are all sampling points that are integer multiples of the power frequency cycle. For each candidate frequency value, the estimated communication data rate is calculated. The estimated communication data rate is the candidate frequency value multiplied by the single-sample data bit width of 16 bits, then multiplied by the protocol encapsulation overhead factor of 1.2, in bits per second (bit / s). This estimated communication data rate is then divided by the spectral efficiency value of the current communication standard. The spectral efficiency value is determined based on the bit-per-hertz parameter of the modulation and coding scheme given in the communication module specification, with a typical value of 4 bits per Hz. This division yields the estimated bandwidth occupancy corresponding to the candidate frequency, in Hz. The estimated bandwidth usage is compared with the bandwidth allocation quota one by one. The unit of the bandwidth allocation quota is uniformly converted to Hz before comparison. The candidate frequency value that the estimated bandwidth usage does not exceed the bandwidth allocation quota and is closest to the bandwidth allocation quota is selected as the target reporting frequency.

[0094] Subsequently, concurrent communication identifiers are extracted based on the target reporting frequency. These identifiers are extracted from the concentrator's scheduling records and represent a list of MAC addresses of other nodes sharing the same physical resource block with that node within each scheduling cycle. Node activity is then extracted based on these identifiers. Node activity is defined as the cumulative number of times each node in the concurrent communication identifier list has uploaded data in the previous 24 hours of the current scheduling cycle; this number is calculated from each node's historical communication records. The number of backoff slots is calculated. After sorting node activity in descending order, the node with the highest activity level is allocated 0 backoff slots, the second-highest node is allocated 2 backoff slots, and the number of backoff slots increases by 2 for each subsequent rank. A backoff slot is the number of slots a node actively skips in the current scheduling cycle, and the duration of each slot is equal to the complete transmission time of a single sampled data entry after encapsulation, corresponding to the target reporting frequency. All concurrent nodes are then sorted in ascending order of their backoff slot counts; the resulting order is the concurrent scheduling sequence. The concurrent scheduling sequence specifies the transmission order of nodes within the same physical resource block within the current scheduling cycle.

[0095] In one implementation, if the initial reporting frequency is not greater than the frequency determination threshold, then the initial reporting frequency is taken as the target reporting frequency; a concurrent scheduling sequence is generated based on the target reporting frequency. In this case, a concurrent scheduling sequence also needs to be generated based on the target reporting frequency. The generation method is completely consistent with the backoff duration calculation and sorting / reorganization process described above, i.e., node activity is extracted based on concurrent communication identifiers, backoff durations are allocated in ascending order of activity, and then the concurrent scheduling sequence is obtained by arranging the backoff durations in ascending order.

[0096] In step S15, the step of performing weight evaluation on the concurrent scheduling sequence based on pre-extracted communication distance parameters to obtain node priorities, and then performing sorting processing on the concurrent scheduling sequence based on the node priorities to obtain the network update configuration includes:

[0097] The communication distance parameter is obtained by calculating the spatial straight-line distance based on the pre-extracted physical coordinates;

[0098] The path loss value is obtained by calculating the signal attenuation based on the communication distance parameter; if the path loss value is greater than the preset loss judgment threshold, the path loss value is corrected according to the pre-acquired interference intensity to obtain the quality evaluation value; if the path loss value is not greater than the preset loss judgment threshold, the path loss value is used as the quality evaluation value.

[0099] The distance weighting factor is extracted based on the quality assessment value to obtain the node priority;

[0100] The concurrent scheduling sequence is sorted in descending order according to the node priority to obtain an initial scheduling queue. Time slot allocation and conflict correction are performed on the initial scheduling queue to obtain the network update configuration.

[0101] It should be noted that, firstly, the communication distance parameter is obtained by calculating the spatial straight-line distance based on the pre-extracted physical coordinates. The physical coordinates of each electricity meter node are read from the concentrator's node management database, and the coordinate format is two-dimensional planar coordinates, with units of meters (m). For any two nodes in the concurrent scheduling sequence, the physical coordinates of the two nodes are taken, and the square root of the sum of the squares of the differences in their x and y coordinates is taken. The result is the spatial straight-line distance between the two nodes. All node pairs in the concurrent scheduling sequence are traversed, and the spatial straight-line distance between each node and the concentrator is used as the communication distance parameter for that node. The physical coordinates of the concentrator are fixed at the origin of the distribution area's planar coordinates.

[0102] Next, the signal attenuation is calculated based on the communication distance parameters to obtain the path loss value. The signal attenuation is calculated using the logarithmic distance path loss model, the formula of which is: Where PL(d0) is the path loss value at a reference distance d0, where d0 is taken as 1m, and PL(d0) is calculated using the free space path loss formula, i.e. ; n is the path loss exponent, set to 3.0 based on the typical urban environment of the power distribution area; Xσ is the shadow fading margin, taken as the quantile of a normally distributed random variable with a mean of 0dB and a standard deviation of 8dB, approximately 8dB at the 90th quantile in the link budget. The communication carrier frequency f is obtained from the concentrator's communication module specifications, taken as the center frequency of the actual wireless communication band deployed in the area, in Hz, with a typical value of 470MHz. d is the communication distance parameter, in meters, and PL is in dB. When the communication distance parameter d is less than 1m, let To avoid negative logarithms.

[0103] Next, it is determined whether the path loss value exceeds a preset loss threshold. The loss threshold is determined based on the difference between the concentrator's receiving sensitivity and the node's maximum transmit power. The concentrator's receiving sensitivity is -110dBm, and the node's maximum transmit power is 20dBm; the difference is 130dB, therefore the loss threshold is set to 130dB. When the path loss value exceeds 130dB, it indicates that the signal attenuation has exceeded the allowable range of the link budget, and the path loss value needs to be corrected based on the pre-acquired interference strength. The pre-acquired interference strength is extracted from the channel state characteristics, and its value is the arithmetic mean of the normalized retransmission frequency value and the normalized data delay duration. The normalized retransmission frequency value ranges from the minimum to the maximum retransmission frequency, and the normalized data delay duration ranges from 0ms to 500ms, with 500ms corresponding to the maximum allowable round-trip delay specified by the communication protocol. This interference strength is a dimensionless value, ranging from 0 to 1. The interference intensity is multiplied by a preset interference correction factor of 15dB, and then added to the path loss value. The resulting sum is the quality assessment value, in dB. The interference correction factor of 15dB is set based on the additional equivalent attenuation caused by impulse noise and adjacent channel interference in a typical power distribution area environment. When the path loss value is not greater than 130dB, the path loss value is directly used as the quality assessment value.

[0104] Subsequently, the node priority is obtained by extracting the distance weighting factor based on the quality assessment value. The distance weighting factor is determined by the inverse normalized result of the quality assessment value. The quality assessment value is first negative, then subtracted from -130dB, and the difference is divided by the absolute value of 130dB to obtain a normalized value in the range of 0 to 1. This normalized value is the distance weighting factor. The meaning of the distance weighting factor is that the smaller the quality assessment value, the better the link condition, and the higher the distance weighting factor, the higher the node priority. The node priority is equal to the value directly mapped by the distance weighting factor. The mapping relationship is as follows: when the distance weighting factor is in the range of 0.8 to 1.0, the node priority is level 1; when it is in the range of 0.5 to 0.8, the node priority is level 2; and when it is in the range of 0 to 0.5, the node priority is level 3. The smaller the level, the higher the priority.

[0105] Then, the concurrent scheduling sequence is sorted in descending order according to node priority. Nodes in the concurrent scheduling sequence are first sorted by priority from highest to lowest. Nodes with the same priority are then sorted by their backoff duration in the original concurrent scheduling sequence from shortest to longest. The sorted result is the initial scheduling queue. Nodes that are earlier in the initial scheduling queue receive higher priority transmission time slots in subsequent time slot allocations.

[0106] Finally, time slot allocation and conflict correction are performed on the initial scheduling queue. The sampling period corresponding to the target reporting frequency is divided into several time slots of equal length, with the length of each time slot set to the time occupied by the sampling data upload bandwidth multiplied by the reciprocal of the number of nodes. Following the order of the initial scheduling queue, the first time slot is allocated to the first node, the second time slot to the second node, and so on. Conflict correction involves checking the spatial relationship of each pair of nodes one by one during the time slot allocation process. For any two nodes, their physical coordinates are used to calculate the Euclidean distance between them. The square root of the sum of the squares of the differences in the x-coordinates and y-coordinates of the two nodes is taken to obtain the distance between the nodes, expressed in meters (m). When the distance between nodes is less than 5m, the node pair is considered to have a spatial adjacency conflict. The 5m judgment distance is set based on the main influence range of near-field interference in a typical urban environment for the 470MHz communication band. For node pairs determined to have a conflict, the node allocated a later time slot is given an additional transmission offset of one backoff time slot. If a node is in conflict with multiple other nodes simultaneously, its transmission offset is taken as the last offset position among all conflicts. The final network update configuration includes: the node identifier, node priority, allocated time slot number, backoff offset after conflict correction, and target reporting frequency of each node in the initial scheduling queue. This configuration is distributed by the concentrator to the corresponding nodes to update the reporting frequency and transmission sequence of each node in the next scheduling cycle.

[0107] In summary, this invention discloses a method for adjusting the frequency of power data reporting based on communication quality feedback, which realizes adaptive control of the sampling frequency of power metering nodes and meets the core requirement of smart grid communication technology to improve data acquisition efficiency when facing channel quality fluctuations.

[0108] The second embodiment of the present invention provides a frequency adjustment system for power data reporting based on communication quality feedback, comprising: a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps of the method described above.

[0109] It should be noted that the power data reporting frequency adjustment system based on communication quality feedback provided in this embodiment of the invention is used to execute all the process steps of the power data reporting frequency adjustment method based on communication quality feedback in the above embodiment. The working principles and beneficial effects of the two are one-to-one, so they will not be described again.

[0110] The specific embodiments described above further illustrate the purpose, technical solution, and beneficial effects of the present invention. It should be understood that the above descriptions are merely specific embodiments of the present invention and are not intended to limit the scope of protection of the present invention. In particular, it should be noted that any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the scope of protection of the present invention for those skilled in the art.

Claims

1. A method for adjusting the frequency of power data reporting based on communication quality feedback, characterized in that, include: The signal attenuation value, retransmission frequency value and data delay time of the energy meter node are obtained and fused to obtain the channel state characteristics. Based on the channel state characteristics, a classification process is performed to obtain a node cluster, and the bandwidth allocation quota is calculated based on the pre-acquired channel resource reserve and the number of concurrent nodes. If the bandwidth allocation is less than a preset threshold, a frequency set is constructed based on the previously acquired historical communication records; if the bandwidth allocation is not less than the preset threshold, the preset base set is used as the frequency set; and the initial reporting frequency is obtained by performing deduction calculations based on the frequency set and the channel state characteristics. If the initial reporting frequency is greater than a preset frequency determination threshold, then frequency reduction processing is performed to obtain the target reporting frequency; if the initial reporting frequency is not greater than the preset frequency determination threshold, then the initial reporting frequency is used as the target reporting frequency; a concurrent scheduling sequence is generated based on the target reporting frequency. The concurrent scheduling sequence is weighted according to the pre-extracted communication distance parameters to obtain node priorities. The concurrent scheduling sequence is then sorted according to the node priorities to obtain the network update configuration.

2. The method for adjusting the frequency of power data reporting based on communication quality feedback according to claim 1, characterized in that, The process of acquiring the signal attenuation value, retransmission frequency value, and data delay duration of the energy meter node and performing fusion processing to obtain channel state characteristics includes: Obtain message exchange records, calculate the difference between the transmitted power and the received signal strength, and obtain the signal attenuation value; Perform a statistical operation on the message interaction record based on the signal attenuation value to obtain the retransmission count value; The timestamp difference is calculated based on the number of retransmissions to obtain the data delay duration; The signal attenuation value, the number of retransmissions, and the data delay duration are normalized and mapped to obtain the channel state characteristics.

3. The method for adjusting the frequency of power data reporting based on communication quality feedback according to claim 1, characterized in that, The step of performing classification processing based on the channel state characteristics to obtain node clusters includes: Obtain the initial cluster centers, calculate the feature distance between the channel state features and the initial cluster centers, and obtain the initial classification result; The sum of squared errors is calculated based on the initial classification results, and the cluster centers are iteratively updated based on the sum of squared errors to obtain the final classification set. Based on the final classification set, the contour coefficient is calculated, and the level mapping is performed based on the contour coefficient to obtain the node cluster.

4. The method for adjusting the frequency of power data reporting based on communication quality feedback according to claim 1, characterized in that, The calculation of bandwidth allocation based on pre-acquired channel resource reserves and the number of concurrent nodes includes: The service priority and basic transmission rate of the energy meter nodes within the node cluster are obtained and weighted summation is performed to obtain the cluster-level communication demand. Calculate the ratio of the channel resource margin to the number of concurrent nodes to obtain the resource load status; If the resource load status is greater than a preset load determination threshold, then the channel resource reserve is attenuated to obtain the available channel resource amount; if the resource load status is not greater than the preset load determination threshold, then the channel resource reserve is taken as the available channel resource amount. Based on the available channel resources and the number of concurrent nodes, a proportional allocation mapping calculation is performed on the cluster-level communication demand to obtain the bandwidth allocation quota.

5. The method for adjusting the frequency of power data reporting based on communication quality feedback according to claim 1, characterized in that, The step of constructing a frequency set based on pre-acquired historical communication records includes: Extract the node identifier and collection timestamp of each node in the node cluster; Based on the node identifier and the collection timestamp, the historical communication records obtained in advance are matched to extract the historical reporting frequency and historical bandwidth usage records. Perform time series alignment processing on the historical reporting frequency and the historical bandwidth usage records to obtain the node behavior sequence; A frequency distribution feature extraction operation is performed on the node behavior sequence to construct a frequency set.

6. The method for adjusting the frequency of power data reporting based on communication quality feedback according to claim 1, characterized in that, The step of performing inference calculations based on the frequency set and the channel state characteristics to obtain the initial reporting frequency includes: The channel state features are parsed to extract channel fading features and channel interference intensity; Extract the bandwidth occupancy ratio corresponding to each candidate frequency in the frequency set, and construct a fusion vector based on the bandwidth occupancy ratio, the channel fading characteristics, and the channel interference intensity; Each of the fusion vectors is input into a preset evaluation model to obtain the adaptation score corresponding to each candidate frequency; The candidate frequency with the highest adaptation score is selected as the initial reporting frequency.

7. The method for adjusting the frequency of power data reporting based on communication quality feedback according to claim 1, characterized in that, The process of performing frequency downsampling to obtain the target reporting frequency includes: Extract the node load status and transmission delay time corresponding to the initial reporting frequency, and construct the occupancy ratio based on the node load status and the transmission delay time to determine the consumption rate; Obtain the frequency attenuation amplitude corresponding to the consumption rate, calculate the frequency reduction step size, and generate the operating frequency band based on the frequency reduction step size; The initial reporting frequency is down-clocked based on the operating frequency band to obtain the target reporting frequency. The concurrent communication identifier is extracted based on the target reporting frequency, and the node activity is extracted based on the concurrent communication identifier to calculate the backoff time and reassemble to generate a concurrent scheduling sequence.

8. The method for adjusting the frequency of power data reporting based on communication quality feedback according to claim 1, characterized in that, The step of performing a weight evaluation on the concurrent scheduling sequence based on pre-extracted communication distance parameters to obtain node priorities, and then performing a sorting process on the concurrent scheduling sequence based on the node priorities to obtain the network update configuration includes: The communication distance parameter is obtained by calculating the spatial straight-line distance based on the pre-extracted physical coordinates; The path loss value is obtained by calculating the signal attenuation based on the communication distance parameter; if the path loss value is greater than the preset loss judgment threshold, the path loss value is corrected according to the pre-acquired interference intensity to obtain the quality evaluation value; if the path loss value is not greater than the preset loss judgment threshold, the path loss value is used as the quality evaluation value. The distance weighting factor is extracted based on the quality assessment value to obtain the node priority; The concurrent scheduling sequence is sorted in descending order according to the node priority to obtain an initial scheduling queue. Time slot allocation and conflict correction are performed on the initial scheduling queue to obtain the network update configuration.

9. A frequency adjustment system for power data reporting based on communication quality feedback, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the power data reporting frequency adjustment method based on communication quality feedback as described in any one of claims 1 to 8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that, When executed by the processor, the program implements the frequency adjustment method for power data reporting based on communication quality feedback as described in any one of claims 1 to 8.

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