A method for remotely collecting electric energy meter data based on a smart terminal

Abstract

The present application relates to the field of data processing, especially to a kind of electric energy meter data remote acquisition method based on intelligent terminal, comprising: constructing to be sent queue, triggering once every preset time the freezing of electric energy meter data and forming data packet and putting into the tail of to-be-sent queue;In a single reporting period, when the current data packet fails to send, the retransmission number of the current data packet is obtained, and the retransmission upper limit of the current reporting period is calculated;When the retransmission number of the current data packet is less than or equal to the retransmission upper limit, the current data packet is retransmitted;When the retransmission number of the current data packet is greater than the retransmission upper limit, the current data packet is put into buffer, the next data packet is processed, until the to-be-sent queue is empty, and the data packet in buffer is retransmitted in the next reporting period.The present application can automatically adjust the retransmission strategy according to the current communication condition, ensure the data transmission efficiency and reliability, and can retransmit data packet, avoid key data loss.

Description

Technical Field

[0001] This invention relates to the field of data processing, and more particularly to a method for remotely acquiring electricity meter data based on a smart terminal. Background Technology

[0002] With the deepening of smart grid construction and the acceleration of energy digital transformation, electricity meters, as the most basic metering terminal in the power system, directly affect key operations such as grid dispatching, load forecasting, line loss analysis, and user-side management through the real-time, complete, and accurate data they provide. Remote data acquisition technology based on smart terminals can not only replace traditional manual meter reading, significantly reducing operation and maintenance costs, but also achieve high-frequency, high-precision automatic reporting of electricity consumption information, providing data support for power companies' refined operations and user-side energy efficiency services.

[0003] Current mainstream remote energy meter data acquisition methods mostly rely on a concentrator-meter architecture, employing standard communication protocols such as DL / T645 and 698, and combining them with fixed retransmission mechanisms (such as a fixed number of retransmissions or simple timeout retries) to handle communication failures. Some systems introduce the Binary Exponential Backoff (BEB) algorithm to alleviate channel collisions, but its retransmission limit is usually a preset constant, lacking the ability to perceive the dynamic state of the channel. This static strategy can work in light-load scenarios, but in high-concurrency reporting (such as hourly batch freezing) or environments with severe channel interference, it is very easy to lose critical data due to premature abandonment of retransmission. At the same time, existing methods fail to distinguish between occasional interference and systemic congestion, and cannot adaptively adjust strategies based on historical failure modes, resulting in decreased reliability in high-congestion scenarios where data integrity must be guaranteed.

[0004] Therefore, there is a need in this field for a remote data acquisition method for electricity meters based on smart terminals to solve the problem that the above-mentioned acquisition methods use fixed preset constants to set the retransmission limit, which leads to the easy loss of critical data. Summary of the Invention

[0005] To address the technical problem that the above-mentioned acquisition methods use fixed preset constants to set the retransmission limit, which leads to the easy loss of critical data, the present invention proposes the following solution.

[0006] A method for remotely acquiring electricity meter data based on a smart terminal, comprising:

[0007] Construct a waiting queue, and freeze the electricity meter data once every preset time and put the data packet into the tail of the waiting queue. Each freeze triggers a reporting cycle.

[0008] Within a single reporting cycle, the terminal sends data packets sequentially according to the order of the queue to be sent. When the current data packet fails to be sent, the failure time of the current data packet is recorded, the number of retransmissions of the current data packet and the total number of retransmissions in the current reporting cycle are obtained, and the retransmission limit of the current reporting cycle is calculated.

[0009] When the number of retransmissions of the current data packet is less than or equal to the retransmission limit, the current data packet is retransmitted and its retransmission count is updated; when the number of retransmissions of the current data packet is greater than the retransmission limit, the current data packet is placed in the buffer and the next data packet is transmitted until the queue to be sent is empty. In the next reporting cycle, the data packets in the buffer are retransmitted.

[0010] The calculation method for the retransmission limit in the current reporting period includes: calculating the retransmission dilemma index based on the total number of retransmissions in the current reporting period and a preset number of total transmission failures in the historical reporting periods; calculating the regularity index based on the data packets that have experienced retransmission failures and their failure times in the current reporting period; calculating the high-order retransmission cost index based on the total number of retransmissions in the current reporting period and the retransmission count of each data packet that has experienced retransmission failures; integrating the retransmission dilemma index, regularity index, and high-order retransmission cost index to obtain the comprehensive congestion index; and setting a preset maximum number of retransmissions, and calculating the retransmission limit based on the comprehensive congestion index and the maximum number of retransmissions.

[0011] Preferably, for data packets in the buffer, when the next reporting cycle begins, it is determined whether the data packet meets the first condition. If it does, the data packet is placed into the waiting queue in the order of collection time. If it does not meet the condition, the data packet is cleared.

[0012] Preferably, the first condition is that the number of cycles in which the data packet survives is less than a preset number.

[0013] Preferably, the calculation method of the retransmission predicament index includes: setting a weight coefficient, calculating the product of the total number of retransmissions in the current reporting period and the weight coefficient, calculating the difference between 1 and the weight coefficient and the product of the difference and the total number of transmission failures in a preset number of historical reporting periods adjacent to the current reporting period, and adding the two products to obtain the retransmission predicament index.

[0014] Preferably, the method for calculating the regularity index includes: for each data packet that has experienced a retransmission failure in the current reporting period, calculating the time difference between two adjacent failure times of the data packet to form a time interval sequence, calculating the standard deviation and average value of the time interval sequence, and using the ratio of the calculated standard deviation to the average value as the regularity component of the data packet; calculating the average value of the regularity components of all data packets that have experienced retransmission failure in the current reporting period to obtain the regularity index.

[0015] Preferably, the calculation method for the high retransmission cost index includes: for each data packet that has experienced a retransmission failure in the current reporting period, calculating the sum of squares of the number of retransmissions of each data packet; calculating the ratio of the sum of squares of the number of retransmissions of each data packet to the total number of retransmissions in the current reporting period to obtain the high retransmission cost index.

[0016] Preferably, the method for calculating the comprehensive congestion index includes: performing positive correlation normalization on the retransmission predicament index and the high-order retransmission cost index, performing negative correlation normalization on the regularity index, and multiplying the normalized retransmission predicament index, the high-order retransmission cost index and the regularity index together to obtain the comprehensive congestion index.

[0017] Preferably, the step of calculating the retransmission upper limit based on the comprehensive congestion index and the maximum retransmission count includes: setting a maximum retransmission count, calculating the product of the comprehensive congestion index and the maximum retransmission count, and rounding down the product to obtain the candidate retransmission count; selecting the maximum value between 1 and the candidate retransmission count as the retransmission upper limit.

[0018] The present invention has the following effects:

[0019] 1. This invention introduces a dynamic evaluation index with three dimensions: retransmission dilemma, regularity, and cost of high-order retransmission. It compresses three-dimensional information such as how congested the channel is now and in the past, whether the failure is regular, and how high the retransmission cost is into a "comprehensive congestion index". This index determines the upper limit of retransmission in real time and realizes a strategy of automatically adjusting the upper limit of retransmission according to the current communication conditions without manual intervention. It actively increases the chance of attempts when the channel conditions deteriorate and quickly completes the transmission when the conditions are good, thus balancing efficiency and reliability.

[0020] 2. This invention provides a buffering and retransmission mechanism for data packets that fail to be sent in time, thus preventing historical data from being easily discarded and ensuring data integrity. Attached Figure Description

[0021] Figure 1 This is a flowchart of steps S1-S3 in a remote data acquisition method for electricity meters based on a smart terminal according to an embodiment of the present invention.

[0022] Figure 2 This is a flowchart of the calculation of the retransmission upper limit method in steps S20-S23 of a remote data acquisition method for electricity meters based on a smart terminal according to an embodiment of the present invention. Detailed Implementation

[0023] The technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are some embodiments of the present invention, but not all embodiments.

[0024] The specific embodiments of the present invention will now be described in detail with reference to the accompanying drawings.

[0025] Reference Figure 1 A method for remotely acquiring electricity meter data based on a smart terminal includes steps S1-S3, as detailed below:

[0026] S1: Construct a waiting queue. Every preset time, the electricity meter data is frozen and a data packet is placed at the end of the waiting queue. Each freeze triggers a reporting cycle.

[0027] The smart meter terminal has a built-in high-precision real-time clock module that automatically triggers a data freeze operation at preset data reporting times (such as the hour or half-hour each day). This freeze operation is used to lock the current energy and related electrical parameters, ensuring the time consistency and business validity of the collected data.

[0028] The terminal takes the electrical energy and related electrical parameters measured at the moment of freezing, encapsulates them in a structured manner according to standard communication protocols, and generates a data frame, i.e., a data packet, that conforms to the specifications. This data frame contains necessary fields such as frame header, data, checksum, and frame trailer to ensure protocol compatibility with the concentrator or master station system.

[0029] The encapsulated data packets are placed at the end of the constructed waiting queue, awaiting transmission. Each freeze triggers a reporting cycle. The waiting queue for the current reporting cycle may contain data packets that were not successfully sent in the previous reporting cycle. Therefore, the encapsulated data packets are placed at the end of the waiting queue and sent in order of data packet collection time (i.e., freeze time).

[0030] S2: Within a single reporting period, the terminal sends data packets sequentially according to the order of the queue to be sent. When the current data packet fails to be sent, the failure time of the current data packet is recorded, the retransmission count of the current data packet and the total number of retransmissions in the current reporting period are obtained, and the retransmission limit of the current reporting period is calculated.

[0031] At the start of each reporting period, initialize the retransmission count for each data packet in the pending queue for the current reporting period. Reset the counter for the total number of retransmissions in the current reporting period, so that the total number of retransmissions in the current reporting period is... .

[0032] The terminal initiates data transmission to the concentrator or master station via a shared communication line (e.g., wireless network, low-power wireless, power line communication, or wired bus). Within a single reporting cycle, the terminal transmits data packets sequentially according to the order in the pending queue. During transmission, the terminal starts an acknowledgment timeout timer, waiting to receive an acknowledgment frame from the concentrator or master station. If the terminal successfully receives a correctly formatted and correctly verified acknowledgment frame within the preset timeout period, the data transmission is considered successful, and the next data packet is then transmitted. If no acknowledgment is received within the timeout period, or if an abnormal response such as a negative acknowledgment frame (NACK) or a checksum error frame is received, the transmission is considered a failure, indicating potential channel collisions, interference, or congestion.

[0033] When a data packet fails to be sent, the failure time and the cycle number at the time of the first failure are recorded (if it is a new data packet formed during the freeze time corresponding to this reporting cycle, then the cycle number of the current reporting cycle is used). The number of retransmissions of the current data packet within the current reporting cycle and the total number of retransmissions in the current reporting cycle are obtained, and the retransmission limit for the current reporting cycle is calculated. Whenever a data packet fails to be sent, the retransmission limit for the current reporting cycle is recalculated, thus achieving dynamic adjustment of the retransmission limit.

[0034] Reference Figure 2 The calculation method for the retransmission limit of the current reporting period includes steps S20-S23, as follows:

[0035] S20: Calculate the retransmission dilemma index based on the total number of retransmissions in the current reporting period and the preset number of transmission failures in the historical reporting periods.

[0036] The calculation method for the retransmission predicament index includes: setting a weighting coefficient; calculating the product of the total number of retransmissions in the current reporting period and the weighting coefficient; calculating the difference between 1 and the weighting coefficient and the product of this difference and the total number of transmission failures in the historical reporting periods of a preset number (e.g., three) adjacent to the current reporting period; and adding the two products together to obtain the retransmission predicament index. The specific formula is as follows:

[0037]

[0038] In the formula, This indicates the retransmission difficulty index for the current reporting period; This represents the preset weighting coefficient. This is used to balance two different types of information. In order to focus more on the retransmission process of the current reporting cycle, in one embodiment, 0.7 can be taken; This indicates the total number of retransmissions in the current reporting period, reflecting the real-time load pressure of the channel during this reporting period. The larger the value, the more retransmission attempts the terminal has made in the current reporting period, and the lower the availability of the channel for the terminal. This represents the environmental stress index, which is the total number of transmission failures in the three historical reporting cycles adjacent to the current reporting cycle.

[0039] The total number of retransmissions that have occurred at the terminal during the current reporting period directly represents the key criterion for determining whether the channel is in a high-contention state during this reporting period. Regardless of historical load, a higher... The values ​​all indicate that the channel is occupied by a large number of retransmission attempts during the current reporting period, and new transmission attempts face a higher risk of collision.

[0040] The overall failure frequency of the terminal within the previous three complete historical reporting cycles is a key indicator for assessing the recent overall channel load level. Regardless of the current reporting cycle status, a higher frequency... The values ​​all indicate that the local channel environment where the terminal is located has been under high collision pressure recently, and the overall channel availability has been significantly reduced.

[0041] For retransmission difficulty index In other words, calculation This is to comprehensively assess the overall transmission difficulty of current data packets under the current communication environment; the higher the value, the more difficult the transmission. The real-time retransmission pressure of the current reporting period is weighted and fused. Compared with recent historical congestion levels It achieves joint perception of periodic channel status, effectively distinguishing between different scenarios such as "occasional failures", "sudden congestion within a period" and "long-term high load", providing a reliable basis for dynamically adjusting retransmission strategies and helping to solve the problem of prematurely discarding critical data under high concurrency in traditional fixed retransmission mechanisms.

[0042] S21: Calculate the regularity index based on the data packets that have failed to be retransmitted and the failure time within the current reporting period.

[0043] The calculation method for the regularity index includes: for each data packet that has experienced a retransmission failure within the current reporting period, calculating the time difference between two adjacent failure times to form a time interval sequence; calculating the standard deviation and mean of the time interval sequence; and using the ratio of the standard deviation to the mean as the regularity component of the data packet. Finally, calculating the average of the regularity components of all data packets that have experienced retransmission failures within the current reporting period yields the regularity index. The specific formula is as follows:

[0044]

[0045] In the formula, Indicates a regularity index; This indicates the total number of data packets that failed to retransmit during the current reporting period; This indicates the number of retransmission failures that occurred within the current reporting period. The standard deviation of the time interval sequence of data packets; This indicates the number of retransmission failures that occurred within the current reporting period. The average value of the time interval sequence of data packets.

[0046] Regularity Index Directly characterizing the distribution regularity of failure events over time is the core basis for determining whether channel conflicts are random bursts or systemic saturation. Regardless of the failure frequency, a lower regularity index... All of these indicate that the time intervals between adjacent failure events are highly consistent, reflecting that the channel has entered a queuing saturation state, and the retransmission behavior of each terminal tends to be synchronized macroscopically; while the high regularity index This indicates that the failure events are distributed haphazardly, the conflicts are mainly caused by occasional interference or asynchronous competition, and the channel has not yet entered a state of global congestion.

[0047] Regularity indices accurately identify the inherent behavioral patterns of channel congestion, thus complementing indicators reflecting failure frequency (i.e., retransmission predicament indices). By jointly assessing "whether failures are frequent" and "whether failures are regular," the system can effectively distinguish between two fundamentally different high-congestion scenarios: "random collisions under high load" and "systematic saturation queuing." This provides a crucial decision-making basis for dynamically increasing the retransmission limit under truly dangerous saturation conditions, solving the problems of misjudgment and critical data loss caused by the inability of traditional retransmission mechanisms to identify congestion patterns.

[0048] S22: Calculate the high-order retransmission cost index based on the total number of retransmissions in the current reporting period and the number of retransmissions of each data packet that has failed to retransmit.

[0049] The calculation method for the high-order retransmission cost index includes: for each data packet that has experienced a retransmission failure within the current reporting period, calculating the sum of the squares of the number of retransmissions for each data packet; and calculating the ratio of the sum of the squares of the number of retransmissions for each data packet to the total number of retransmissions within the current reporting period to obtain the high-order retransmission cost index. The specific formula is as follows:

[0050]

[0051] In the formula, This represents the cost index for high-order retransmissions; This indicates the total number of retransmissions within the current reporting period; This indicates the total number of data packets that failed to retransmit during the current reporting period; This indicates the number of retransmission failures that occurred within the current reporting period. The number of times each data packet is retransmitted.

[0052] High retransmission cost index The high retransmission cost index directly represents the accumulated systemic resource consumption cost due to high retransmission failures and is a key basis for judging whether channel conflicts have triggered high-cost retransmission behavior. Regardless of the failure frequency or conflict mode, a high high retransmission cost index indicates that the terminal has experienced multiple deep retransmissions in the current reporting period, reflecting that the channel is not only congested, but has also caused significant energy consumption and bandwidth burden on the terminal, and the system is trending towards inefficiency and chaos.

[0053] The high-retransmission cost exponent implements a non-linear penalty for high-cost failures. For example, if a data packet fails to retransmit after 5 attempts ( The cost contribution of deep retransmission is far greater than the failure of each of the five data packets to be retransmitted once, even though the total number of failures is the same. This design accurately captures the entropy increase effect caused by deep retransmission in the system, namely the vicious cycle of increased resource waste and decreased communication efficiency.

[0054] S23: The retransmission difficulty index, regularity index and high-order retransmission cost index are combined to obtain the comprehensive congestion index. The maximum number of retransmissions is preset, and the retransmission limit is calculated based on the comprehensive congestion index and the maximum number of retransmissions.

[0055] To eliminate the differences in the dimensions, value range, and mapping direction of the retransmission dilemma index, regularity index, and high-order retransmission cost index to the congestion level, the terminal applies a nonlinear transformation to each index, uniformly mapping them to the [0,1] interval, where a value closer to 1 indicates more severe channel congestion. The specific normalization method is as follows:

[0056] Using the Sigmoid function to adjust the retransmission dilemma index Normalization yields the index ;

[0057] Using the exponential decay function to analyze the regularity index Normalization yields the index ;

[0058] Using the Sigmoid function to evaluate the cost exponent of high-order retransmissions Normalization yields the index .

[0059] The retransmission difficulty index after positive correlation normalization Regularity index after negative correlation normalization The high retransmission cost index after positive correlation normalization Multiplying these results yields the overall congestion index. The specific formula is as follows:

[0060]

[0061] In the formula, This represents the overall congestion index; This represents the retransmission difficulty index after positive correlation normalization. This represents the regularity index after negative correlation normalization. This represents the high retransmission cost index after positive correlation normalization.

[0062] Composite Congestion Index The overall congestion index directly characterizes the systemic risk level of channel congestion and is the final quantitative result of comprehensively assessing the interaction effect of conflict severity, pattern regularity, and retransmission cost. Regardless of the performance of any single index, a high overall congestion index indicates that multiple adverse factors are acting concurrently. Not only is the failure frequency high, but the failure modes tend to be highly synchronized, and the retransmission cost increases significantly, reflecting that the channel has entered a high-risk state of nonlinear deterioration.

[0063] A nonlinear interactive fusion model is constructed using a multiplicative structure, which will... As a base congestion intensity As a pattern saturation penalty factor As a high-cost penalty factor, if and only if At the same time, when it is at a high level, the comprehensive congestion index Only then will it significantly improve; if any dimension performs well (such as irregular failures or low retransmission costs), the overall congestion index will improve. It is effectively suppressed. This mechanism truly reflects the inherent deterioration logic of complex congestion and avoids misjudgments caused by abnormalities in a single indicator.

[0064] Composite Congestion Index The three-dimensional heterogeneous indicators are transformed into a unified, continuous, and operable congestion score, providing direct input for dynamic retransmission strategies. Through this index, the system can accurately identify extreme congestion scenarios characterized by "high density + high regularity + high cost," and trigger a stronger data protection mechanism when truly needed, fundamentally solving the problem of traditional linear fusion models being either slow to respond to or overly sensitive to complex congestion states.

[0065] The calculation method for the retransmission limit includes: setting a preset maximum number of retransmissions, calculating the product of the overall congestion index and the maximum number of retransmissions, and rounding the product down to obtain the candidate retransmission count; selecting the maximum value between 1 and the candidate retransmission count as the retransmission limit. The specific formula is as follows:

[0066]

[0067] In the formula, Indicates the maximum number of retransmissions; This indicates the maximum number of retransmissions for a single data packet, representing the highest number of retransmission attempts that the terminal can tolerate under extreme congestion. This represents the overall congestion index; This means rounding down the product of the overall congestion index and the maximum number of retransmissions. To ensure that at least one retransmission opportunity is reserved even when the channel is excellent, and to avoid data loss due to occasional interference, a smooth transition from "light load, low retransmission" to "heavy load, high retransmission" is achieved by linearly scaling the comprehensive congestion index.

[0068] The retransmission limit directly determines the maximum number of retransmissions a terminal can attempt under the current channel conditions, and is the final execution parameter of the adaptive retransmission strategy. Regardless of the channel's congestion state, the retransmission limit is generated by directly mapping the comprehensive congestion index to the preset maximum number of retransmissions, ensuring that retransmission opportunities are strictly matched with the channel risk level: the more severe the congestion (the larger the comprehensive congestion index), the larger the retransmission limit, and the more retransmission opportunities the data packet receives.

[0069] It should be noted that if there are no previous retransmission records in the current reporting period, and the current data packet fails to be sent for the first time (i.e., the number of retransmissions is less than 2), it is impossible to form a sequence of adjacent failure time intervals. Therefore, in the regularity exponent normalization stage, let .

[0070] This ensures the computability of the comprehensive congestion index and the robustness of the system, reflecting the conservative strategy of "there is not enough evidence to show that there is regular congestion in the channel". The retransmission limit under the initial failure scenario is mainly determined by the retransmission dilemma index and the high-order retransmission cost index, which is in line with the conservative retransmission logic under the initial failure scenario.

[0071] S3: When the number of retransmissions of the current data packet is less than or equal to the retransmission limit, retransmit the current data packet and update its retransmission count; when the number of retransmissions of the current data packet is greater than the retransmission limit, put the current data packet into the buffer and transmit the next data packet until the queue to be sent is empty.

[0072] Based on the retransmission limit calculated in step S2, determine whether the number of retransmissions of the current data packet is greater than the retransmission limit. If the number of retransmissions of the current data packet is greater than the retransmission limit, it means that the current data packet has reached the dynamic retransmission limit. The terminal determines that the retransmission behavior is terminated and moves the current data packet, its collection time (i.e., freeze time), and the cycle number when the first transmission failed into the buffer. The control flow jumps to step S2 to transmit the next data packet. If the number of retransmissions of the current data packet is less than or equal to the retransmission limit, it means that the current data packet has not exhausted the maximum retransmission opportunity allocated by the system in the current channel state. The terminal determines that the retransmission behavior is permitted, updates the number of retransmissions of the current data packet and the total number of retransmissions in the current reporting cycle, and the control flow jumps back to step S2 to continue transmitting the current data packet until the queue to be sent in the current reporting cycle is empty.

[0073] At the start of the next reporting cycle, the system first determines whether the number of lifespan cycles of the data packet is greater than or equal to a preset number (e.g., 3 times) based on the cycle number when the first transmission of the data packet in the buffer fails. If the number of lifespan cycles of the data packet is greater than or equal to the preset number, the terminal cleans up the data packet. If the number of lifespan cycles of the data packet is less than the preset number, the data packet is placed in the queue to be sent according to the order of collection time, and is sent with priority over newly frozen data packets.

[0074] This invention proposes an adaptive retransmission strategy that integrates three-dimensional conflict characteristic indicators with a nonlinear interactive fusion mechanism. While retaining the framework of the standard binary exponential backoff algorithm, this method innovatively introduces dynamic evaluation indicators based on three dimensions: retransmission dilemma, regularity, and high-order retransmission cost. A comprehensive congestion index is generated through nonlinear normalization and multiplicative fusion, and a retransmission upper limit is dynamically generated based on this index. When the channel falls into a complex congestion state characterized by high density, high regularity, and high cost, the system automatically increases the retransmission limit, extending the survival window of critical data packets. Simultaneously, a buffer storage and subsequent retransmission mechanism is used for failed data packets to avoid direct discarding. This method effectively solves the core defects of traditional fixed retransmission mechanisms in complex communication environments, such as failing to retransmit when necessary and blindly retransmitting when unnecessary. It significantly improves the data integrity rate and system robustness of remote data acquisition in high-concurrency and high-interference scenarios.

[0075] It should be noted that those skilled in the art can make various modifications and improvements without departing from the inventive concept, and these all fall within the scope of protection of this invention. Therefore, the scope of protection of this patent should be determined by the appended claims.

Claims

1. A method for remotely acquiring electricity meter data based on a smart terminal, characterized in that, include: Construct a waiting queue, and freeze the electricity meter data once every preset time and put the data packet into the tail of the waiting queue. Each freeze triggers a reporting cycle. Within a single reporting cycle, the terminal sends data packets sequentially according to the order of the queue to be sent. When the current data packet fails to be sent, the failure time of the current data packet is recorded, the number of retransmissions of the current data packet and the total number of retransmissions in the current reporting cycle are obtained, and the retransmission limit of the current reporting cycle is calculated. When the number of retransmissions of the current data packet is less than or equal to the retransmission limit, retransmit the current data packet and update its retransmission count. When the number of retransmissions of the current data packet exceeds the retransmission limit, the current data packet is placed in the buffer and the next data packet is transmitted until the queue is empty. In the next reporting cycle, the data packets in the buffer are retransmitted. The calculation method for the retransmission limit in the current reporting period includes: calculating the retransmission dilemma index based on the total number of retransmissions in the current reporting period and a preset number of total transmission failures in the historical reporting periods; calculating the regularity index based on the data packets that have experienced retransmission failures and their failure times in the current reporting period; calculating the high-order retransmission cost index based on the total number of retransmissions in the current reporting period and the retransmission count of each data packet that has experienced retransmission failures; integrating the retransmission dilemma index, regularity index, and high-order retransmission cost index to obtain the comprehensive congestion index; and setting a preset maximum number of retransmissions, and calculating the retransmission limit based on the comprehensive congestion index and the maximum number of retransmissions.

2. The method for remote data acquisition from an energy meter based on a smart terminal according to claim 1, characterized in that, For data packets in the buffer, during the next reporting cycle, it is determined whether the data packet meets the first condition. If it does, the data packet is placed into the waiting queue in the order of collection time. If it does not meet the condition, the data packet is cleared.

3. The method for remote data acquisition from an energy meter based on a smart terminal according to claim 2, characterized in that, The first condition is that the number of cycles the data packet survives is less than a preset number.

4. The method for remote data acquisition from an energy meter based on a smart terminal according to claim 1, characterized in that, The calculation method of the retransmission predicament index includes: setting a weight coefficient, calculating the product of the total number of retransmissions in the current reporting period and the weight coefficient, calculating the difference between 1 and the weight coefficient and the product of the difference and the total number of transmission failures in a preset number of historical reporting periods adjacent to the current reporting period, and adding the two products to obtain the retransmission predicament index.

5. The method for remote data acquisition from an energy meter based on a smart terminal according to claim 1, characterized in that, The calculation method of the regularity index includes: for each data packet that has failed to retransmit within the current reporting period, the time difference between two adjacent failure times of the data packet is calculated to form a time interval sequence. The standard deviation and average value of the time interval sequence are calculated. The ratio of the calculated standard deviation to the average value is used as the regularity component of the data packet. The average value of the regularity components of all data packets that have failed to retransmit within the current reporting period is calculated to obtain the regularity index.

6. The method for remote data acquisition from an energy meter based on a smart terminal according to claim 1, characterized in that, The calculation method for the high retransmission cost index includes: for each data packet that has experienced a retransmission failure in the current reporting period, calculate the sum of squares of the number of retransmissions of each data packet; calculate the ratio of the sum of squares of the number of retransmissions of each data packet to the total number of retransmissions in the current reporting period to obtain the high retransmission cost index.

7. The method for remote data acquisition from an energy meter based on a smart terminal according to claim 1, characterized in that, The calculation method of the comprehensive congestion index includes: performing positive correlation normalization on the retransmission dilemma index and the high-order retransmission cost index, performing negative correlation normalization on the regularity index, and multiplying the normalized retransmission dilemma index, high-order retransmission cost index and regularity index together to obtain the comprehensive congestion index.

8. The method for remote data acquisition from an energy meter based on a smart terminal according to claim 1, characterized in that, The calculation of the retransmission upper limit based on the comprehensive congestion index and the maximum retransmission count includes: setting a maximum retransmission count, calculating the product of the comprehensive congestion index and the maximum retransmission count, and rounding the product down to obtain the candidate retransmission count; selecting the maximum value between 1 and the candidate retransmission count as the retransmission upper limit.

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