A meter state periodic automatic sampling scheduling method for intelligent converged terminals

Abstract

The present application belongs to the technical field of electric power sampling scheduling, and provides a meter state cycle automatic sampling scheduling method of intelligent fusion terminal, which comprises: through in-depth analysis of data complete return records of the meter in a historical data collection cycle, distinguishing normal meters and stable semi-failed meters, judging the stability of historical single collection time consumption, and predicting the single collection time consumption of normal meters and stable semi-failed meters accordingly, through meter coincidence analysis of each sampling time point, determining the coincidence sampling node, and calculating the total channel demand duration corresponding to the meter at the coincidence sampling node, through in-depth analysis of the relationship between the data packet loss rate and the demand capacity ratio, combining the total channel demand duration to quantify the channel congestion and packet loss risk, and when the risk is high, according to the related analysis results of the stable semi-failed meters, reasonably scheduling the data sampling, effectively reducing the channel occupation fluctuation and the data packet loss risk.

Description

Technical Field

[0001] This invention belongs to the field of power sampling and scheduling technology, specifically a method for automatic sampling and scheduling of meter status cycles in an intelligent fusion terminal. Background Technology

[0002] In power systems, the accurate and efficient collection of meter data by smart converged terminals is crucial, directly impacting the stable operation and precise management of the power system. However, current meter data collection faces numerous challenges.

[0003] On the one hand, the states of electricity meters are complex and diverse. Over long-term operation, performance differences gradually emerge among different meters. Some meters can operate stably and normally, while others exhibit a semi-failure state, requiring multiple retries to obtain complete data, with varying frequencies and stability of these retries. However, current technology lacks effective means to accurately distinguish between normal meters and stable semi-failure meters, making it impossible to develop differentiated strategies for meters in different states during data collection, thus affecting collection efficiency and accuracy.

[0004] On the other hand, channel resources are limited and unevenly used. During the data acquisition process, multiple meters may trigger sampling at the same time, resulting in tidal congestion of the channel. Existing methods struggle to accurately predict the total channel demand duration of meters at overlapping sampling nodes, and cannot scientifically assess the risk of data loss due to channel congestion caused by synchronization cycles. This makes data acquisition failures and packet loss more likely when channel resources are scarce, severely impacting the integrity and reliability of power data.

[0005] Furthermore, there is a lack of optimization strategies for data sampling scheduling of stable half-failure meters. Existing scheduling methods do not fully consider factors such as the duration and stability of data acquisition by stable half-failure meters, resulting in unreasonable allocation of channel resources, inability to effectively reduce the risk of data loss, and difficulty in ensuring the stable and efficient operation of power data acquisition under complex conditions. Therefore, there is an urgent need for an automatic sampling scheduling method for meter status cycles in intelligent fusion terminals to solve the above problems.

[0006] Therefore, the present invention provides an automatic sampling and scheduling method for the state cycle of electricity meters in a smart fusion terminal. Summary of the Invention

[0007] In order to overcome the shortcomings of the prior art, at least one technical problem raised in the background art is solved.

[0008] The technical solution adopted by this invention to solve its technical problem is: an automatic sampling and scheduling method for the state cycle of an intelligent fusion terminal for electricity meters, comprising the following steps:

[0009] Step S10: By recording the complete data return of the electricity meter within the historical data collection period, the retry return of the electricity meter data collection is identified, and the frequency and stability of the electricity meter data retry return identification are analyzed to distinguish between normal electricity meters and stable semi-failed electricity meters.

[0010] Step S20: Perform historical single-collection time stability analysis on both normal electricity meters and stable semi-failed electricity meters, and predict the single-collection time of normal electricity meters and stable semi-failed electricity meters based on the analysis results.

[0011] Step S30: Perform meter overlap analysis at each sampling time point to determine the overlapping sampling nodes, and calculate the total channel demand duration at the overlapping sampling nodes by combining the single acquisition time of normal meters and stable semi-failed meters.

[0012] Step S40: By analyzing the relationship between data packet loss rate and demand capacity ratio, the target demand capacity ratio is determined. Combined with the total channel demand duration and the single time slot capacity of the base station of the overlapping sampling node, the risk of data packet loss caused by channel tidal congestion due to synchronization period is judged.

[0013] In step S50, if the risk is high, the scheduling optimization of data sampling for the stable half-failure meter is carried out by combining the historical single-collection time stability analysis results of the stable half-failure meter.

[0014] A further technical solution of the present invention is as follows: the retry return identification method for collecting electricity meter data is as follows:

[0015] Meter data acquisition with one or more retries is marked as a retry return identification.

[0016] As a further technical solution of the present invention, the process of distinguishing between normal electricity meters and stable semi-failed electricity meters is as follows:

[0017] Based on any electricity meter, the number of times the meter retry return identification occurred within the historical data collection period is counted, and the ratio is calculated with the total number of electricity meter data collections to obtain the retry return frequency value;

[0018] Obtain the time interval between every two consecutive retry return identifications, integrate them into a retry return interval sequence according to the time sequence, calculate the coefficient of variation of the retry return interval sequence, and obtain the stable value of retry return.

[0019] The deviation between the retry return frequency value and the retry return stable value is used to obtain the meter's half-failure value.

[0020] If the half-failure value of the meter is greater than or equal to the half-failure threshold, the meter is marked as a stable half-failure meter; otherwise, it is marked as a normal meter.

[0021] As a further technical solution of the present invention, the process of performing historical single-time data acquisition stability analysis on normal electricity meters and stable semi-failed electricity meters is as follows:

[0022] Based on normal electricity meters or stable semi-failed electricity meters, calculate the coefficient of variation of each historical single data collection time. Based on the coefficient of variation, determine whether the historical single data collection time of normal electricity meters and stable semi-failed electricity meters is stable. If the coefficient of variation is less than the coefficient of variation threshold, it means that the historical single data collection time is stable. Conversely, if the coefficient of variation is greater than or equal to the coefficient of variation threshold, it means that the historical single data collection time is unstable.

[0023] Based on normal electricity meters or stable semi-failed electricity meters, if the historical single data collection time is stable, the average value of each historical single data collection time is processed to predict the single data collection time of normal electricity meters or stable semi-failed electricity meters.

[0024] Based on normal or stable semi-failed meters, if the historical single-collection time is stable, the maximum value among the historical single-collection times is selected as the predicted single-collection time for normal or stable semi-failed meters.

[0025] As a further technical solution of the present invention, the process of performing meter overlap analysis at each sampling time point to determine the overlapping sampling node is as follows:

[0026] Extract the preset sampling trigger time points of all electricity meters. Based on any sampling time point and combined with the preset sampling trigger time points of all electricity meters, calculate the proportion of electricity meters that trigger sampling at the sampling time point to obtain the sampling trigger overlap value.

[0027] If the sampling trigger overlap value meets the preset ratio requirement, the sampling time point will be marked as an overlap sampling node.

[0028] As a further technical solution of the present invention, the calculation process for the total channel demand duration at the overlapping sampling node is as follows:

[0029] The total channel requirement duration is obtained by summing the single sampling times of each meter that triggers sampling at the overlapping sampling node.

[0030] As a further technical solution of the present invention, the process of analyzing the relationship between data packet loss rate and demand capacity ratio to determine the allowable demand capacity ratio is as follows:

[0031] The total channel demand duration and base station single timeslot capacity of each overlapping sampling node within the historical data collection period are obtained and the ratio is calculated to obtain the demand capacity ratio of each overlapping sampling node. At the same time, the data packet loss rate of each overlapping sampling node is calculated, where the data packet loss rate = (number of meters that failed to collect data at overlapping sampling nodes ÷ total number of collections) × 100%;

[0032] The demand capacity ratios at different overlapping sampling nodes are integrated into a demand capacity ratio sequence, and the data packet loss rates at different overlapping sampling nodes are integrated into a packet loss rate sequence.

[0033] The coupling relationship between demand capacity ratio and packet loss rate is determined by calculating the Pearson correlation coefficient between the demand capacity ratio sequence and the packet loss rate sequence.

[0034] If a linear coupling relationship exists, the least squares method is used to linearly fit the demand capacity ratio sequence and the packet loss rate sequence to obtain the coupling model of demand capacity ratio-data packet loss rate. If a nonlinear coupling relationship exists, the demand capacity ratio sequence and the packet loss rate sequence are nonlinearly fitted, and the fitting model with the highest goodness of fit is selected as the coupling model of demand capacity ratio-data packet loss rate.

[0035] The allowable data loss rate is input into the coupled model of demand capacity ratio and data loss rate to obtain the target demand capacity ratio.

[0036] As a further technical solution of the present invention, the process of determining the risk of data packet loss caused by channel tidal congestion due to the synchronization period is as follows:

[0037] The ratio between the total channel demand duration and the base station single timeslot capacity of the current overlapping sampling node is calculated to obtain the actual demand capacity ratio.

[0038] If the actual demand capacity ratio is greater than the target demand capacity ratio, it indicates a high risk of data packet loss due to channel tidal congestion caused by the synchronization cycle; conversely, the risk is low.

[0039] A further technical solution of the present invention is as follows: the process of scheduling optimization for data sampling of stable semi-failed electricity meters is as follows:

[0040] Normal meters that trigger sampling at overlapping sampling nodes are classified as the first tier, and stable semi-failed meters that trigger sampling at overlapping sampling nodes are classified as the second tier.

[0041] In the second tier, based on any stable half-failure meter, the ratio of the predicted single-collection time of the stable half-failure meter to the total single-collection time is calculated to obtain the single-collection time ratio of the stable half-failure meter.

[0042] The coefficient of variation of each historical single data collection time of the stable semi-failed meter is calculated proportionally to the total value of the coefficient of variation to obtain the stability ratio of the single data collection time of the stable semi-failed meter.

[0043] The sum of the single-acquisition time ratio and the single-acquisition time stability ratio is used to obtain the data acquisition priority value of the stable semi-failed meter.

[0044] In the second tier, stable semi-failed meters are sorted in order of data acquisition priority from low to high. After the normal meters in the first tier have been collected, data acquisition of stable semi-failed meters in the second tier is carried out according to the order.

[0045] As a further technical solution of the present invention, the total time for a single data acquisition is the sum of the predicted single data acquisition times of all stable semi-failed meters, and the total value of the coefficient of variation is the sum of the coefficients of variation of each historical single data acquisition time of all stable semi-failed meters.

[0046] The beneficial effects of this invention are as follows: Through in-depth analysis of the complete data return records of electricity meters during historical data collection periods, it is possible to accurately distinguish between normal electricity meters and stable semi-failed electricity meters, providing a solid foundation for subsequent targeted scheduling. In the time consumption analysis and prediction stage, the stability of historical single-collection time is assessed, and based on this, the single-collection time of normal electricity meters and stable semi-failed electricity meters is predicted. When determining overlapping sampling nodes, the overlap analysis of electricity meters at each sampling time point accurately grasps the peak channel demand, providing an important basis for rationally planning channel resources. In the risk assessment stage, through in-depth analysis of the relationship between data packet loss rate and demand capacity ratio, the channel congestion and packet loss risks are quantified, providing scientific data support for scheduling optimization decisions. Finally, in the scheduling optimization process, based on the relevant analysis results of stable semi-failed electricity meters, their data sampling is rationally scheduled, effectively reducing channel occupancy fluctuations and minimizing the risk of data packet loss. Attached Figure Description

[0047] The invention will now be further described with reference to the accompanying drawings.

[0048] Figure 1 This is a flowchart illustrating the steps of an automatic sampling and scheduling method for the electricity meter status cycle of an intelligent fusion terminal according to an embodiment of the present invention.

[0049] Figure 2 This is a logic judgment diagram of an automatic sampling and scheduling method for the electricity meter status cycle of an intelligent fusion terminal according to an embodiment of the present invention. Detailed Implementation

[0050] To make the technical means, creative features, objectives and effects of this invention easier to understand, the invention will be further described below in conjunction with specific embodiments.

[0051] Example: Please refer to Figure 1 and Figure 2 As shown in the embodiment of the present invention, an automatic sampling and scheduling method for the electricity meter status cycle of a smart fusion terminal includes the following steps:

[0052] Step S10: By recording the complete data return of the electricity meter within the historical data collection period, the retry return of the electricity meter data collection is identified, and the frequency and stability of the electricity meter data retry return identification are analyzed to distinguish between normal electricity meters and stable semi-failed electricity meters.

[0053] In step S10, the historical data collection period refers to the time period from when the electricity meter was put into use to the present.

[0054] In step S10, the complete data return record includes the number of retries each time the meter performs data acquisition. The number of retries means that the meter can only acquire complete meter data after a number of retries. It also includes the time taken for each historical single acquisition of the meter.

[0055] In step S10, the method for retry return identification of meter data acquisition is as follows:

[0056] Mark the electricity meter data collection with 1 or more retries as a retry return identification;

[0057] In step S10, the process of distinguishing between normal electricity meters and stable semi-failed electricity meters is as follows:

[0058] Based on any electricity meter, the number of times the meter retry return identification occurred within the historical data collection period is counted, and the ratio is calculated with the total number of electricity meter data collections to obtain the retry return frequency value;

[0059] Obtain the time interval between every two consecutive retry return identifications, integrate them into a retry return interval sequence according to the time sequence, calculate the coefficient of variation of the retry return interval sequence, and obtain the stable value of retry return.

[0060] The deviation between the retry return frequency value and the retry return stable value is used to obtain the meter's half-failure value.

[0061] Understandably, the physical meaning of the meter's half-failure value is as follows: the meter failure value is obtained by calculating the deviation between the retry return frequency value and the retry return stability value. The retry return frequency value reflects the proportion of times the meter will recognize a retry return. The higher the proportion, the higher the degree of half-failure of the meter. The retry return stability value reflects the fluctuation of the time interval between the retry return recognition of the meter. The smaller the fluctuation, the more stable the half-failure situation of the meter. Therefore, the meter's half-failure value is used to distinguish between normal meters and stable half-failure meters.

[0062] In some embodiments, the half-failure value of the meter is compared with the half-failure threshold of the meter;

[0063] If the half-failure value of the meter is greater than or equal to the half-failure threshold of the meter, the meter will be marked as a stable half-failure meter.

[0064] If the half-failure value of the electricity meter is less than the half-failure threshold, the electricity meter will be marked as a normal electricity meter.

[0065] It should be noted that the setting of the meter half-failure threshold is usually based on performance analysis of a large number of meters over a historical data collection period. For example, by collecting retry return records of the meters, the distribution of the meter half-failure values ​​(such as mean, standard deviation, or percentiles) is calculated, and a critical point (such as the 95th percentile) is selected as the threshold. This critical point can ensure that most normal meters are correctly classified, while effectively identifying meters with degraded performance.

[0066] The role and significance of step S10 are understandable: by marking meter data collection with one or more retries as retry return identifications, then calculating the ratio of retry return identifications to the total number of data collections, and finally obtaining the meter half-failure value, the following steps are taken: By comparing the meter half-failure value with the preset meter half-failure threshold, normal meters and stable half-failure meters can be clearly distinguished. This process lays a solid foundation for subsequent scheduling work. Only by accurately identifying the meter status can subsequent scheduling strategies for different meters be meaningful, effectively improving the targeting and rationality of sampling scheduling, and ensuring more efficient power data collection.

[0067] Step S20: Perform historical single-collection time stability analysis on both normal electricity meters and stable semi-failed electricity meters, and predict the single-collection time of normal electricity meters and stable semi-failed electricity meters based on the analysis results.

[0068] In step S20, the historical single data collection time = waiting time for the smart terminal command to be sent to the first response + retry interval time × number of retries + data reception and verification time;

[0069] Example: A partially faulty electricity meter needs to be retried twice. The first time, the waiting time is 5 seconds, the retry interval is 2 seconds per attempt, and the data verification takes 2 seconds. Therefore, the total time is 5 + 2 × 2 + 2 = 11 seconds.

[0070] In step S20, the process of performing historical single-data acquisition time stability analysis on normal electricity meters and stable semi-failed electricity meters is as follows:

[0071] Based on normal electricity meters or stable semi-failed electricity meters, calculate the coefficient of variation of each historical single data collection time. Based on the coefficient of variation, determine whether the historical single data collection time of normal electricity meters and stable semi-failed electricity meters is stable. If the coefficient of variation is less than the coefficient of variation threshold, it means that the historical single data collection time is stable. Conversely, if the coefficient of variation is greater than or equal to the coefficient of variation threshold, it means that the historical single data collection time is unstable.

[0072] It should be noted that the threshold for the coefficient of variation is set by analyzing historical data collection time from a large number of electricity meters, calculating the distribution of the coefficient of variation, and selecting a value that can effectively distinguish between stable and unstable time consumption. For example, if the coefficient of variation of most normal electricity meters is concentrated between 0.05 and 0.15, the threshold may be set to 0.15;

[0073] Based on normal electricity meters or stable semi-failed electricity meters, if the historical single data collection time is stable, the average value of each historical single data collection time is processed to predict the single data collection time of normal electricity meters or stable semi-failed electricity meters.

[0074] Based on normal electricity meters or stable semi-failed electricity meters, if the historical single collection time is stable, the maximum value among the historical single collection times is selected as the predicted single collection time of normal electricity meters or stable semi-failed electricity meters.

[0075] Understandably, the purpose of selecting the maximum value among the historical single collection times as the predicted single collection time of normal meters or stable semi-failed meters is to: perform predictive analysis based on the maximum time to ensure that subsequent channel stability does not lead to the risk of data packet loss.

[0076] Understandably, the role and significance of step S20 lies in conducting a historical single-collection time stability analysis on normal meters and stable semi-failed meters, and predicting their single-collection time accordingly. In this step, the stability of the time is first determined by calculating the coefficient of variation of each historical single-collection time. If stable, the normal meters and stable semi-failed meters are averaged or the maximum value is selected to predict the single-collection time. This process is of great significance; accurately predicting the single-collection time of meters allows staff to plan sampling time more scientifically in subsequent scheduling, rationally allocate channel resources, avoid channel conflicts or resource waste caused by time prediction deviations, effectively ensure the stability of subsequent channels, and reduce the risk of data packet loss.

[0077] Step S30: Perform meter overlap analysis at each sampling time point to determine the overlapping sampling nodes, and calculate the total channel demand duration at the overlapping sampling nodes by combining the single acquisition time of normal meters and stable semi-failed meters.

[0078] In step S30, the process of performing meter overlap analysis at each sampling time point to determine the overlapping sampling nodes is as follows:

[0079] Extract the preset sampling trigger time points (e.g., 00:00, 00:15, 00:30) of all electricity meters.

[0080] Based on any sampling time point, combined with the preset sampling trigger time points of all meters, the proportion of meters (including normal meters and stable semi-failed meters) that trigger sampling at the sampling time point is statistically analyzed to obtain the sampling trigger overlap value.

[0081] It should be noted that the sampling time point refers to any preset sampling trigger time point, specifically, at the sampling time point, data sampling of at least one electricity meter needs to be performed;

[0082] If the sampling trigger overlap value meets the preset ratio requirement (50%), then the sampling time point is marked as the overlap sampling node;

[0083] If the sampling trigger overlap value does not meet the preset ratio requirement, the sampling time point will be marked as a non-overlapping sampling node.

[0084] In step S30, the process of calculating the total channel demand duration at the overlapping sampling nodes is as follows:

[0085] The total channel required duration is obtained by summing the single sampling time of each meter that triggers sampling at the overlapping sampling node.

[0086] For example, if there are 20 normal meters (2 seconds each), 5 normal meters (4 seconds each), and 3 stable semi-failed meters (8 seconds each) during the overlap period, then the total channel required duration = 20×2 + 5×4 + 3×8 = 40 + 20 + 24 = 84 seconds.

[0087] Understandably, the role and significance of step S30 is as follows: First, extract the preset sampling trigger time points for all electricity meters, and determine the overlapping sampling nodes by statistically analyzing the proportion of electricity meters triggering sampling at each sampling time point. This process can accurately pinpoint the time points when a large number of electricity meters will sample simultaneously, i.e., peak channel usage periods. Next, sum the single-collection time corresponding to each electricity meter triggering sampling at the overlapping sampling node to calculate the total channel demand duration. This value directly reflects the overall channel resource demand for all electricity meters to complete data collection at the overlapping sampling node, providing a basis for subsequently formulating reasonable scheduling strategies.

[0088] Step S40: By analyzing the relationship between data packet loss rate and demand capacity ratio, the target demand capacity ratio is determined. Combined with the obtained total channel demand duration and the single time slot capacity of the base station of the overlapping sampling node, the risk of data packet loss caused by channel tidal congestion due to synchronization period is judged.

[0089] In step S40, the demand-capacity ratio refers to the ratio of the total channel demand duration to the base station's single timeslot capacity. Specifically, it is the ratio of the channel time required by all meters during the overlap period to the channel time that the base station can actually provide, reflecting the degree of channel resource scarcity.

[0090] In step S40, the process of analyzing the relationship between the data packet loss rate and the required capacity ratio to determine the allowable required capacity ratio is as follows:

[0091] The total channel demand duration and base station single time slot capacity (obtained from the base station broadcast system) of each overlapping sampling node within the historical data collection period are obtained and the ratio is calculated to obtain the demand capacity ratio of each overlapping sampling node. At the same time, the data packet loss rate of each overlapping sampling node is calculated, where the data packet loss rate = (number of meters that failed to collect data at overlapping sampling nodes ÷ total number of collections) × 100%;

[0092] The demand capacity ratios at different overlapping sampling nodes are integrated into a demand capacity ratio sequence, and the data packet loss rates at different overlapping sampling nodes are integrated into a packet loss rate sequence.

[0093] The coupling relationship between demand capacity ratio and packet loss rate is determined by calculating the Pearson correlation coefficient between the demand capacity ratio sequence and the packet loss rate sequence.

[0094] If the Pearson correlation coefficient is greater than or equal to the Pearson correlation coefficient threshold, it indicates that there is a linear coupling relationship between the demand capacity ratio and the data packet loss rate.

[0095] If the Pearson correlation coefficient is less than the Pearson correlation coefficient threshold, it indicates that there is a non-linear coupling relationship between the demand capacity ratio and the data packet loss rate.

[0096] It should be noted that the Pearson correlation coefficient threshold is set as follows: In power communication systems, the relationship between demand capacity ratio and packet loss rate may be affected by various factors (such as channel quality and network load). Through historical data analysis, if it is found that the two show a linear trend in most cases, the threshold may be set to 0.6-0.8; if the relationship is complex, the threshold may be lower (such as 0.5) to allow for more flexible fitting.

[0097] If a linear coupling relationship exists, the least squares method is used to linearly fit the demand capacity ratio sequence and the packet loss rate sequence to obtain the coupling model of demand capacity ratio-data packet loss rate.

[0098] If a nonlinear coupling relationship exists, nonlinear fitting is performed on the demand capacity ratio sequence and the packet loss rate sequence. Nonlinear fitting includes, but is not limited to, quadratic function fitting, power function fitting, and exponential function fitting. After fitting, the fitting model with the highest goodness of fit is selected as the coupling model of demand capacity ratio-data packet loss rate.

[0099] The allowable data loss rate is input into the coupled model of demand capacity ratio and data loss rate to obtain the target demand capacity ratio;

[0100] It should be noted that the allowable data packet loss rate can be referenced from power industry regulations, such as the smart grid standard IEC61850 or domestic power industry standards (such as DL / T 645). For example, the allowable packet loss rate for industrial electricity meter sampling is ≤5%, and for residential electricity meters it is ≤10%.

[0101] In step S40, the process of determining the risk of data packet loss caused by channel tidal congestion due to synchronization period is as follows:

[0102] Calculate the ratio between the total channel demand duration and the base station single time slot capacity of the current overlapping sampling node to obtain the actual demand capacity ratio, and compare the actual demand capacity ratio with the target demand capacity ratio.

[0103] If the actual demand capacity ratio is greater than the target demand capacity ratio, it indicates a high risk of data packet loss due to channel tidal congestion caused by the synchronization cycle.

[0104] If the actual demand capacity ratio is less than the target demand capacity ratio, it means that the risk of data packet loss caused by channel tidal congestion due to the synchronization cycle is low, and no processing is performed.

[0105] The role and significance of step S40 are understandable: It analyzes the relationship between the data packet loss rate and the demand capacity ratio. By obtaining the total channel demand duration and base station single-slot capacity of each overlapping sampling node within the historical data collection period, the demand capacity ratio and the corresponding data packet loss rate are calculated. Then, the Pearson correlation coefficient is used to determine the coupling relationship between the two, and linear or nonlinear fitting is performed accordingly to derive the coupling model of demand capacity ratio and data packet loss rate. Substituting the allowable data packet loss rate into this model, the target demand capacity ratio can be determined. By comparing the actual demand capacity ratio and the target demand capacity ratio, the risk of data packet loss caused by channel tidal congestion due to synchronization period is assessed. This step provides an important decision-making basis for whether subsequent scheduling optimization of meter data sampling is needed, ensuring that scheduling decisions are based on a scientific assessment of channel resource utilization and data packet loss risk. This helps to identify potential risks in advance and ensures that power data collection can still be carried out stably and accurately even with limited channel resources.

[0106] Step S50: If the risk is high, then combine the historical single-collection time stability analysis results of the stable semi-failed meters to optimize the data sampling schedule of the stable semi-failed meters.

[0107] In step S50, combining the stability analysis results of the meter data retry return identification and the stability analysis results of the acquisition time, the process of scheduling optimization for data sampling of stable semi-failed meters is as follows:

[0108] Normal meters that trigger sampling at overlapping sampling nodes are classified as the first tier, and stable semi-failed meters that trigger sampling at overlapping sampling nodes are classified as the second tier.

[0109] In the second tier, based on any stable half-failure meter, the ratio of the predicted single-collection time of the stable half-failure meter to the total single-collection time is calculated to obtain the single-collection time ratio of the stable half-failure meter.

[0110] The total time for a single data collection is the sum of the predicted single data collection times for all stable semi-failed meters.

[0111] The coefficient of variation of each historical single data collection time of the stable semi-failed meter is calculated proportionally to the total value of the coefficient of variation to obtain the stability ratio of the single data collection time of the stable semi-failed meter.

[0112] The total coefficient of variation is the sum of the coefficients of variation of each historical single data collection time for all stable semi-failed meters.

[0113] The sum of the single-acquisition time ratio and the single-acquisition time stability ratio is used to obtain the data acquisition priority value of the stable semi-failed meter.

[0114] It is understandable that the physical meaning of the data acquisition priority value of stable half-failure meters is as follows: the data acquisition priority value is obtained by summing the single acquisition time ratio and the single acquisition time stability ratio. The single acquisition time ratio reflects the proportion of the single acquisition time of stable half-failure meters, while the single acquisition time stability ratio reflects the stability of the single acquisition time of stable half-failure meters. In the second tier, stable half-failure meters with shorter single acquisition time and higher single acquisition time stability are prioritized for acquisition, which helps to reduce channel occupancy fluctuations and reduce the risk of data packet loss.

[0115] In the second tier, the stable semi-failed meters are sorted in order of data acquisition priority from low to high. After the normal meters in the first tier have been collected, the data acquisition of the stable semi-failed meters in the second tier is carried out according to the sorting.

[0116] Understandably, the role and significance of step S50 lies in optimizing the data sampling schedule based on the historical single-collection time stability analysis results of stable semi-failed meters. First, normal meters triggering sampling at overlapping sampling nodes are designated as the first tier, and stable semi-failed meters as the second tier. Within the second tier, the single-collection time ratio and single-collection time stability ratio of stable semi-failed meters are calculated to determine the data collection priority value. Following this priority value from smallest to largest, after the normal meters in the first tier have been collected, data is sequentially collected from the stable semi-failed meters in the second tier. This optimization strategy effectively reduces channel occupancy fluctuations, minimizes the risk of data packet loss, and makes the entire data sampling process more reasonable and efficient, strongly ensuring the stable operation of power data collection even under high-risk conditions.

[0117] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely illustrative of the principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the present invention as claimed. The scope of protection of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for automatic sampling and scheduling of electricity meter status cycles in a smart fusion terminal, characterized in that: Includes the following steps: Step S10: By recording the complete data return of the electricity meter within the historical data collection period, the retry return of the electricity meter data collection is identified, and the frequency and stability of the electricity meter data retry return identification are analyzed to distinguish between normal electricity meters and stable semi-failed electricity meters. Step S20: Perform historical single-collection time stability analysis on both normal electricity meters and stable semi-failed electricity meters, and predict the single-collection time of normal electricity meters and stable semi-failed electricity meters based on the analysis results. Step S30: Perform meter overlap analysis at each sampling time point to determine the overlapping sampling nodes, and calculate the total channel demand duration at the overlapping sampling nodes by combining the single acquisition time of normal meters and stable semi-failed meters. Step S40: By analyzing the relationship between data packet loss rate and demand capacity ratio, the target demand capacity ratio is determined. Combined with the total channel demand duration and the single time slot capacity of the base station of the overlapping sampling node, the risk of data packet loss caused by channel tidal congestion due to synchronization period is judged. In step S50, if the risk is high, the scheduling optimization of data sampling for the stable half-failure meter is carried out by combining the historical single-collection time stability analysis results of the stable half-failure meter.

2. The method for automatic sampling and scheduling of meter status cycles in a smart fusion terminal according to claim 1, characterized in that: The method for identifying retry returns during electricity meter data acquisition is as follows: Meter data acquisition with one or more retries is marked as a retry return identification.

3. The method for automatic sampling and scheduling of meter status cycles in a smart fusion terminal according to claim 2, characterized in that: The process for distinguishing between normal electricity meters and stable semi-failed electricity meters is as follows: Based on any electricity meter, the number of times the meter retry return identification occurred within the historical data collection period is counted, and the ratio is calculated with the total number of electricity meter data collections to obtain the retry return frequency value; Obtain the time interval between every two consecutive retry return identifications, integrate them into a retry return interval sequence according to the time sequence, calculate the coefficient of variation of the retry return interval sequence, and obtain the stable value of retry return. The deviation between the retry return frequency value and the retry return stable value is used to obtain the meter's half-failure value. If the half-failure value of the meter is greater than or equal to the half-failure threshold, the meter is marked as a stable half-failure meter; otherwise, it is marked as a normal meter.

4. The method for automatic sampling and scheduling of meter status cycles in a smart fusion terminal according to claim 3, characterized in that: The process of performing historical single-data acquisition time stability analysis on normal electricity meters and stable semi-failed electricity meters is as follows: Based on normal electricity meters or stable semi-failed electricity meters, calculate the coefficient of variation of each historical single data collection time. Based on the coefficient of variation, determine whether the historical single data collection time of normal electricity meters and stable semi-failed electricity meters is stable. If the coefficient of variation is less than the coefficient of variation threshold, it means that the historical single data collection time is stable. Conversely, if the coefficient of variation is greater than or equal to the coefficient of variation threshold, it means that the historical single data collection time is unstable. Based on normal electricity meters or stable semi-failed electricity meters, if the historical single data collection time is stable, the average value of each historical single data collection time is processed to predict the single data collection time of normal electricity meters or stable semi-failed electricity meters. Based on normal or stable semi-failed meters, if the historical single-collection time is stable, the maximum value among the historical single-collection times is selected as the predicted single-collection time for normal or stable semi-failed meters.

5. The method for automatic sampling and scheduling of meter status cycles in a smart fusion terminal according to claim 4, characterized in that: The process of performing meter overlap analysis at each sampling time point to determine the overlapping sampling nodes is as follows: Extract the preset sampling trigger time points of all electricity meters. Based on any sampling time point and combined with the preset sampling trigger time points of all electricity meters, calculate the proportion of electricity meters that trigger sampling at the sampling time point to obtain the sampling trigger overlap value. If the sampling trigger overlap value meets the preset ratio requirement, the sampling time point will be marked as an overlap sampling node.

6. The method for automatic sampling and scheduling of meter status cycles in a smart fusion terminal according to claim 5, characterized in that: The calculation process for the total channel demand duration at the overlapping sampling node is as follows: The total channel requirement duration is obtained by summing the single sampling times of each meter that triggers sampling at the overlapping sampling node.

7. The method for automatic sampling and scheduling of meter status cycles in a smart fusion terminal according to claim 6, characterized in that: The process of analyzing the relationship between packet loss rate and demand capacity ratio to determine the allowable demand capacity ratio is as follows: The total channel demand duration and base station single timeslot capacity of each overlapping sampling node within the historical data collection period are obtained and the ratio is calculated to obtain the demand capacity ratio of each overlapping sampling node. At the same time, the data packet loss rate of each overlapping sampling node is calculated, where the data packet loss rate = (number of meters that failed to collect data at overlapping sampling nodes ÷ total number of collections) × 100%; The demand capacity ratios at different overlapping sampling nodes are integrated into a demand capacity ratio sequence, and the data packet loss rates at different overlapping sampling nodes are integrated into a packet loss rate sequence. The coupling relationship between demand capacity ratio and packet loss rate is determined by calculating the Pearson correlation coefficient between the demand capacity ratio sequence and the packet loss rate sequence. If a linear coupling relationship exists, the least squares method is used to linearly fit the demand capacity ratio sequence and the packet loss rate sequence to obtain the coupling model of demand capacity ratio-data packet loss rate. If a nonlinear coupling relationship exists, the demand capacity ratio sequence and the packet loss rate sequence are nonlinearly fitted, and the fitting model with the highest goodness of fit is selected as the coupling model of demand capacity ratio-data packet loss rate. The allowable data loss rate is input into the coupled model of demand capacity ratio and data loss rate to obtain the target demand capacity ratio.

8. The method for automatic sampling and scheduling of meter status cycles in a smart fusion terminal according to claim 7, characterized in that: The process for determining the risk of data packet loss caused by channel tidal congestion due to the synchronization period is as follows: The ratio between the total channel demand duration and the base station single timeslot capacity of the current overlapping sampling node is calculated to obtain the actual demand capacity ratio. If the actual demand capacity ratio is greater than the target demand capacity ratio, it indicates a high risk of data packet loss due to channel tidal congestion caused by the synchronization cycle; conversely, the risk is low.

9. The method for automatic sampling and scheduling of meter status cycles in a smart fusion terminal according to claim 8, characterized in that: The scheduling optimization process for data sampling of stable semi-failed meters is as follows: Normal meters that trigger sampling at overlapping sampling nodes are classified as the first tier, and stable semi-failed meters that trigger sampling at overlapping sampling nodes are classified as the second tier. In the second tier, based on any stable half-failure meter, the ratio of the predicted single-collection time of the stable half-failure meter to the total single-collection time is calculated to obtain the single-collection time ratio of the stable half-failure meter. The coefficient of variation of each historical single data collection time of the stable semi-failed meter is calculated proportionally to the total value of the coefficient of variation to obtain the stability ratio of the single data collection time of the stable semi-failed meter. The sum of the single-acquisition time ratio and the single-acquisition time stability ratio is used to obtain the data acquisition priority value of the stable semi-failed meter. In the second tier, stable semi-failed meters are sorted in order of data acquisition priority from low to high. After the normal meters in the first tier have been collected, data acquisition of stable semi-failed meters in the second tier is carried out according to the order.

10. The method for automatic sampling and scheduling of meter status cycles in a smart fusion terminal according to claim 9, characterized in that: The total time for a single data acquisition is the sum of the predicted single data acquisition times for all stable semi-failed meters, and the total coefficient of variation is the sum of the coefficients of variation for each historical single data acquisition time of all stable semi-failed meters.

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