A smart electric energy meter online monitoring system and method

Abstract

This invention relates to the field of electricity meter monitoring, and more particularly to an online monitoring system and method for smart electricity meters. The system includes: periodically determining the link transmission coefficient of each electricity monitoring set based on the link throughput fluctuation index and the link balance index; determining whether each electricity monitoring set meets the transmission optimization conditions based on the link transmission coefficient; under the transmission optimization conditions, determining the data mining strategy to be executed for each target monitoring device based on the data acquisition status; when performing time series analysis, determining whether to perform data truncation compensation based on the interval between each time series truncation segment, and determining the truncation compensation method based on the electricity load coefficient of the truncation coverage area; under the condition that the set analysis is completed, determining whether to perform transmission link optimization for the target optimized set based on the proportion of key equipment and the reference equipment characteristic index, and determining the allocation transmission strategy based on the demand allocation difference value. This invention improves the data processing efficiency in the monitoring process of smart electricity meters.

Description

Technical Field

[0001] This invention relates to the field of electricity meter monitoring, and more particularly to an online monitoring system and method for smart electricity meters. Background Technology

[0002] Monitoring electricity consumption at the consumer end using smart meters can save manpower and optimize power supply planning in a timely manner. However, the quality and real-time performance of power supply planning optimization depend on the quality of electricity data acquisition and transmission. During peak electricity consumption periods, the amount of electricity data that needs to be transmitted is large, which affects the transmission process and leads to low timeliness of actual optimization results. Therefore, how to determine the importance of electricity data from different sources to the analysis of monitoring results and how to adjust the data transmission process in real time to improve the timeliness of optimization results is a problem that urgently needs to be solved by those skilled in the art.

[0003] Chinese patent application publication number CN119165435A discloses a remote monitoring system for electricity meters, mainly addressing the problems of poor system capabilities in handling common electricity meter issues, poor scalability and flexibility, and low system data and user privacy. The remote monitoring system for electricity metering includes a data collection module, a data processing module, a communication network module, a remote verification module, a system detection module, and a user interface module. It optimizes data processing during system operation through intelligent computing optimization algorithms, improving system stability and data processing efficiency; it transmits electricity meter status data through a wireless communication module, improving the accuracy of remote verification; and it improves work efficiency and reduces manual labor through self-detection and correction by the on-site monitoring module. However, the above solution has the following problems: it fails to perform targeted data processing based on the data acquired in the actual monitoring scenario and fails to optimize the data transmission process in real time, resulting in low effectiveness of the transmitted electricity consumption data and consequently low efficiency in subsequent electricity data analysis. Summary of the Invention

[0004] To address this issue, the present invention provides an online monitoring system and method for smart energy meters, which overcomes the problem in the prior art that fails to perform targeted data processing and real-time optimization of the data transmission process based on the data obtained in the actual monitoring scenario, resulting in low effectiveness of the transmitted electricity consumption data and consequently low efficiency in subsequent analysis of the electricity consumption data.

[0005] To achieve the above objectives, the present invention provides an online monitoring method for smart energy meters, comprising:

[0006] The link transmission coefficient of each power monitoring set is determined periodically based on the link throughput fluctuation index and the link balance index, and the link transmission coefficient is used to determine whether each power monitoring set meets the transmission optimization conditions.

[0007] Under transmission optimization conditions, the data acquisition status of each target monitoring device in the target optimization set is determined based on the data integrity index and parameter misalignment index, and the data mining strategy to be executed for each target monitoring device is determined based on the data acquisition status. The data mining strategy is to perform time series analysis or related cluster analysis on the power consumption data of the target monitoring device.

[0008] When performing time series analysis, the decision on whether to perform data truncation compensation is based on the interval between each time series truncation segment, and the truncation compensation method is determined according to the power load factor of the truncation coverage area. The truncation compensation method is to perform data truncation compensation for the target monitoring equipment based on power consumption behavior analysis or data fitting analysis.

[0009] Once the set analysis is complete, determine whether to optimize the transmission link for the target set based on the proportion of key equipment and the characteristic index of reference equipment, and determine the allocation transmission strategy according to the demand allocation difference value.

[0010] Furthermore, for a single power monitoring set, if the link transmission coefficient is greater than the preset link transmission coefficient, then the power monitoring set is determined to meet the transmission optimization condition, and the power monitoring set is recorded as the target optimization set;

[0011] The link throughput fluctuation index is determined based on the throughput change ratio and the reference throughput change ratio during the collection evaluation period;

[0012] The link balancing index is determined based on the link execution parameters of the associated transmission links of the relay transmission nodes in the power monitoring set.

[0013] The link transmission coefficient is positively correlated with the link throughput fluctuation index, and the link transmission coefficient is negatively correlated with the link balance index.

[0014] Furthermore, for a single target monitoring device within the target optimization set, if the target monitoring device is in a data acquisition state where the data integrity index is less than or equal to a preset data integrity index, then time series analysis is performed on the power consumption data of the target monitoring device, including:

[0015] Obtain the time-series truncated segment of the power consumption data of the target monitoring device within the current monitoring cycle;

[0016] Based on the interval duration between each time-series truncation segment, determine the truncation interval stability coefficient and truncation concentration ratio of the target monitoring device in the current device monitoring cycle, and determine whether to perform data truncation compensation for electricity consumption data based on the truncation interval stability coefficient and truncation concentration ratio.

[0017] Furthermore, for a single target monitoring device in a data acquisition state, if the truncation interval stability coefficient is greater than the preset truncation interval stability coefficient or the truncation concentration ratio is greater than the preset truncation concentration ratio, then data truncation compensation is performed on the power consumption data of the target monitoring device, and the truncation compensation method is determined according to the power load coefficient and the time-series truncation ratio.

[0018] If the power load factor of the cut-off coverage area is greater than the preset power load factor or the time-series cut-off ratio is greater than the preset time-series cut-off ratio, then data cut-off compensation will be performed on the target monitoring equipment based on power consumption behavior analysis.

[0019] If the power load factor of the cut-off coverage area is less than or equal to the preset power load factor and the time-series cut-off ratio is less than or equal to the preset time-series cut-off ratio, then data cut-off compensation is performed on the target monitoring equipment based on data fitting analysis, and the fitting extraction coefficient for each data extraction time is determined according to the changed reference index and the extraction interval duration.

[0020] Furthermore, when performing data truncation compensation for target monitoring equipment based on electricity consumption behavior analysis, the monitoring interval to be analyzed corresponding to each time-series truncation segment is obtained, and the matching analysis interval is determined according to the load factor difference value and the trend similarity coefficient.

[0021] The electricity consumption data corresponding to the time-series truncation segment is determined based on the data change parameters and the degree of change reference at each data extraction time within the matching analysis interval.

[0022] Furthermore, for a single target monitoring device within the target optimization set, if the target monitoring device is in a second-class data acquisition state where both the data integrity index and parameter misalignment index are greater than the preset data integrity index and the parameter misalignment index are greater than the preset parameter misalignment index, then relevant cluster analysis is performed on the power consumption data of the target monitoring device, including:

[0023] For each abnormal movement of the target monitoring device within the current monitoring cycle, key analysis segments are extracted, and the extraction of key analysis segments is based on data combination analysis.

[0024] Furthermore, given the completion of the set analysis, the determination of whether to optimize the transmission link for the target optimization set is based on the proportion of key equipment in the target optimization set and the reference equipment characteristic index.

[0025] The key equipment ratio refers to the proportion of the number of key monitoring devices in the target optimization set to the total number of target monitoring devices in the target optimization set.

[0026] The reference device characteristic index is the average value of the abnormal characteristic indices of key monitoring devices within the target optimization set;

[0027] The condition for completing the set analysis is that all target monitoring devices within the target optimization set have completed the anomaly feature mining.

[0028] Furthermore, for a single target optimization set, if the target optimization set is in a first preset transmission state where the proportion of critical equipment is greater than a preset proportion of critical equipment or the reference equipment characteristic index is greater than a preset reference equipment characteristic index, then transmission link optimization is performed for the target optimization set, including:

[0029] Compensate for the power consumption data of critical monitoring equipment during transmission, and determine the allocation transmission strategy based on the difference value allocated according to demand;

[0030] If the demand allocation difference value is greater than the preset demand allocation difference value, the power consumption data of the key monitoring equipment will be allocated and transmitted based on the transmission demand coefficient and the reference link execution parameters.

[0031] Furthermore, for a single target optimization set, the transmission demand coefficient is set according to the data acquisition status of each key monitoring device. The device method is to determine the transmission demand coefficient based on the proportion of time truncation and the duration of the truncation coverage area, or based on the parameter error index and the duration of the key analysis segment.

[0032] The present invention also provides a system for applying the online monitoring method for smart energy meters, comprising:

[0033] The collection monitoring module is used to respond to the link throughput fluctuation index and link balance index of the collection judgment conditions to determine whether each power monitoring collection meets the transmission optimization conditions.

[0034] The equipment evaluation module, which is connected to the set monitoring module, is used to respond to the data integrity index and parameter misalignment index of the equipment evaluation conditions in order to determine the data acquisition status of each target monitoring device in the target optimization set;

[0035] The mining processing module, which is connected to the device evaluation module, is used to respond to mining matching conditions to determine the feature mining strategy to be executed for each target monitoring device. The feature mining strategy is to perform time series analysis or correlation clustering analysis on the power consumption data of the target monitoring device.

[0036] The link analysis module, which is connected to the set monitoring module and the mining processing module, is used to determine the link transmission status of the target optimization set in response to the key device proportion and reference device characteristic index of the transmission analysis conditions.

[0037] The link optimization module, which is connected to the link analysis module, is used to respond to the link transmission status of the target optimization set in response to the optimization conditions, to determine whether to perform transmission link optimization for the target optimization set, and to determine the allocation transmission strategy based on the required allocation difference value.

[0038] Compared with the prior art, the beneficial effects of the present invention are that, under the condition of transmission optimization, the present invention determines the data acquisition status of the target monitoring device based on the data integrity index and the parameter error index, and determines the data mining strategy of each target monitoring device based on the data acquisition status, so as to perform truncation compensation and abnormal feature mining on the acquired power consumption data, and complete the real-time optimization of the transmission process, so as to improve the integrity and effectiveness of the transmission data acquired by the data analysis end, thereby improving the data processing efficiency of the data analysis end.

[0039] Furthermore, in this invention, the data acquisition status of the target monitoring device is determined based on the data integrity index and the parameter misalignment index, which is used to initially characterize the abnormal situation of the electricity consumption data acquired by the target monitoring device. The abnormal categories of the electricity consumption data are initially classified, making the determined data mining strategy more in line with the actual working scenario. While ensuring the integrity and effectiveness of the transmitted data, the efficiency of data analysis is improved.

[0040] Furthermore, in this invention, for target monitoring devices in a data acquisition state, the distribution of time-series truncated segments is determined by analyzing the interval duration between time-series truncated segments of power consumption data within the transmission optimization cycle. This helps to identify the dominant missing factors in the power consumption data, making the subsequent processing of the power consumption data more consistent with the actual situation and ensuring the effectiveness of truncation compensation and abnormal feature mining. This invention improves the integrity and effectiveness of the acquired transmission data.

[0041] Furthermore, in this invention, for the target optimization set in the first preset transmission state, the allocation transmission strategy is determined according to the demand allocation difference value, making the allocation process of the associated transmission link more in line with the actual scenario, ensuring the transmission efficiency of power consumption data compensation for key monitoring equipment. In addition, a targeted transmission demand coefficient setting method is set according to the data acquisition status of different key monitoring equipment to ensure the accuracy of the determined transmission demand coefficient, thereby improving the reliability of power consumption data compensation for key monitoring equipment. Attached Figure Description

[0042] Figure 1This is a schematic diagram of the online monitoring method for smart energy meters according to the present invention;

[0043] Figure 2 This is a flowchart illustrating the feature mining strategy for each target monitoring device determined based on the data acquisition status according to the present invention.

[0044] Figure 3 This is a flowchart illustrating how the present invention determines the cutoff compensation method based on the power load factor of the cutoff coverage area;

[0045] Figure 4 This is a module connection diagram of the online monitoring system for smart energy meters of the present invention. Detailed Implementation

[0046] To make the objectives and advantages of the present invention clearer, the present invention will be further described below with reference to embodiments; it should be understood that the specific embodiments described herein are merely for explaining the present invention and are not intended to limit the present invention.

[0047] Preferred embodiments of the present invention will now be described with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are merely illustrative of the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0048] It should be noted that in the description of this invention, the terms "upper", "lower", "left", "right", "inner", "outer", etc., which indicate directions or positional relationships, are based on the directions or positional relationships shown in the accompanying drawings. This is only for the convenience of description and is not intended to indicate or imply that the device or element must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, it should not be construed as a limitation of this invention.

[0049] Furthermore, it should be noted that, in the description of this invention, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium; and they can refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention according to the specific circumstances.

[0050] Please see Figures 1 to 3 As shown, the present invention provides an online monitoring method for smart energy meters, comprising:

[0051] The link transmission coefficient of each power monitoring set is determined periodically based on the link throughput fluctuation index and the link balance index, and the link transmission coefficient is used to determine whether each power monitoring set meets the transmission optimization conditions.

[0052] Under transmission optimization conditions, the data acquisition status of each target monitoring device in the target optimization set is determined based on the data integrity index and parameter misalignment index, and the data mining strategy to be executed for each target monitoring device is determined based on the data acquisition status. The data mining strategy is to perform time series analysis or related cluster analysis on the power consumption data of the target monitoring device.

[0053] When performing time series analysis, the decision on whether to perform data truncation compensation is based on the interval between each time series truncation segment, and the truncation compensation method is determined according to the power load factor of the truncation coverage area. The truncation compensation method is to perform data truncation compensation for the target monitoring equipment based on power consumption behavior analysis or data fitting analysis.

[0054] Once the set analysis is complete, determine whether to optimize the transmission link for the target set based on the proportion of key equipment and the characteristic index of reference equipment, and determine the allocation transmission strategy according to the demand allocation difference value.

[0055] In this invention, the monitoring process of smart energy meters is applied to ensure the integrity and validity of the transmitted electricity consumption data. The smart energy meters being monitored are recorded as target monitoring devices, and the target monitoring devices that use the same relay transmission node to upload electricity consumption data are recorded as an energy monitoring set. The categories of electricity consumption data include, but are not limited to, voltage, current and active power.

[0056] This invention employs a device monitoring cycle, which users can set according to their actual work scenarios. The higher the user's requirements for data processing efficiency during monitoring, the shorter the device monitoring cycle. One device monitoring cycle duration is 10 minutes. At the end of each device monitoring cycle, the link transmission coefficient is used to determine whether each power monitoring set meets the transmission optimization conditions. This invention also employs a data acquisition cycle, which users can set according to their actual work scenarios. One data acquisition cycle duration is 1 second. At the end of each data acquisition cycle, the power consumption data of the target monitoring device is acquired.

[0057] This invention utilizes several equipment monitoring records. Each equipment monitoring record contains at least one analysis of the target incineration components, including the link transmission coefficient, truncation duration, data integrity index, truncation interval duration, truncation interval stability coefficient, data change parameters, truncation concentration ratio, power load coefficient, interval matching coefficient, reference change parameters, parameter error index, reference equipment characteristic index, key equipment ratio, and demand allocation difference value. Each equipment monitoring record also has a corresponding qualification mark, which indicates whether the data processing efficiency during the monitoring process meets the user's requirements.

[0058] Specifically, for a single power monitoring set, if the link transmission coefficient is greater than the preset link transmission coefficient, the power monitoring set is determined to meet the transmission optimization condition, and the power monitoring set is recorded as the target optimization set.

[0059] The link throughput fluctuation index is determined based on the throughput change ratio and the reference throughput change ratio during the collection evaluation period;

[0060] The link balancing index is determined based on the link execution parameters of the associated transmission links of the relay transmission nodes in the power monitoring set.

[0061] The link transmission coefficient is positively correlated with the link throughput fluctuation index, and the link transmission coefficient is negatively correlated with the link balance index.

[0062] For a single power monitoring set, the link transmission coefficient = ln(link throughput fluctuation index / link balance index). The link throughput fluctuation index is the sum of the throughput change ratio in the current device monitoring cycle and the reference throughput change ratio in the set evaluation cycle. The throughput change ratio in the current device monitoring cycle = the absolute value of the difference between the data throughput of the relay transmission nodes of the power monitoring set in the current device monitoring cycle and the data throughput of the relay transmission nodes of the power monitoring set in the previous device monitoring cycle / the data throughput of the relay transmission nodes of the power monitoring set in the previous device monitoring cycle. The reference throughput change ratio is the average of the throughput change ratios of each device monitoring cycle in the set evaluation cycle. The duration of the set evaluation cycle can be determined by the user according to the actual working scenario. One set evaluation cycle duration is provided, which is 10 times the device monitoring cycle. The link balance index... n is the number of associated transmission links of the relay transmission node in the power monitoring set, li is the link execution parameter of the i-th associated transmission link of the relay transmission node in the power monitoring set, and l0 is the average value of the link execution parameters of each associated transmission link of the relay transmission node in the power monitoring set. For a single associated transmission link, the link execution parameter is the absolute value of the difference between the bandwidth idleness of the associated transmission link in the current device monitoring cycle and the reference bandwidth idleness. The reference bandwidth idleness is the minimum value of the bandwidth idleness of the associated transmission link before the current device monitoring cycle. The bandwidth idleness = 1 - (data throughput of the associated transmission link / bandwidth capacity of the associated transmission link). How to determine the data throughput and bandwidth capacity of the associated transmission link is a content that is easy to understand for those skilled in the art and will not be elaborated here.

[0063] The value of the preset link transmission coefficient can be determined by the user according to the actual working scenario. For example, the user can set it according to the device monitoring records. The higher the user's requirements for data processing efficiency during the monitoring process, the smaller the value of the preset link transmission coefficient. A method for determining the value of the preset link transmission coefficient is provided, which is the minimum value of the link transmission coefficient of each target optimization set in the device monitoring records that meets the user's requirements for data processing efficiency during the monitoring process.

[0064] Specifically, for a single target monitoring device within the target optimization set, if the target monitoring device is in a data acquisition state where the data integrity index is less than or equal to a preset data integrity index, then time series analysis is performed on the power consumption data of the target monitoring device, including:

[0065] Obtain the time-series truncated segment of the power consumption data of the target monitoring device within the current monitoring cycle;

[0066] Based on the interval duration between each time-series truncation segment, determine the truncation interval stability coefficient and truncation concentration ratio of the target monitoring device in the current device monitoring cycle, and determine whether to perform data truncation compensation for electricity consumption data based on the truncation interval stability coefficient and truncation concentration ratio.

[0067] In this invention, time-series recording is completed for each acquisition of power consumption data of the target monitoring device, that is, the order and time of each acquisition of power consumption data are recorded. For a single target monitoring device, if the interval between two adjacent acquisitions of power consumption data is longer than a preset truncation time, the time interval between the two acquisitions of power consumption data is recorded as a time-series truncation segment, and the interval between the two acquisitions of power consumption data is recorded as the truncation time. The value of the preset truncation time can be determined by the user according to the actual working scenario. For example, the user can set it according to the device monitoring record. The higher the user's requirements for data processing efficiency in the monitoring process, the smaller the value of the preset truncation time. A method for determining the value of the preset truncation time is provided, which records the average value of the truncation time corresponding to each time-series truncation segment in the device monitoring record that meets the user's requirements for data processing efficiency in the monitoring process as the preset truncation time.

[0068] The data integrity index is calculated as 1 / the number of time-series truncation segments within the current device monitoring cycle. The value of the preset data integrity index can be determined by the user based on the actual working scenario. For example, the user can set it based on the device monitoring records. The higher the user's requirements for data processing efficiency during the monitoring process, the smaller the value of the preset data integrity index. A method for determining the value of the preset data integrity index is provided, in which the device monitoring records for time-series analysis of the power consumption data of the target monitoring device are recorded as analysis reference records, and the maximum value of the data integrity index of the target monitoring device in the analysis reference records that meets the user's requirements for data processing efficiency during the monitoring process is recorded as the preset data integrity index.

[0069] For a single target monitoring device, the cutoff interval stability coefficient m represents the number of time-series truncation segments of the target monitoring device within the current monitoring period, dj represents the duration of the j-th truncation interval within the current monitoring period, and d0 represents the average duration of each truncation interval within the current monitoring period. For any two adjacent time-series truncation segments, the interval between the end time of the previous time-series truncation segment and the start time of the next time-series truncation segment is recorded as the truncation interval duration of the two time-series truncation segments. The truncation set is determined based on the truncation interval duration. For any truncation set, the truncation interval duration between any two adjacent time-series truncation segments is less than the preset truncation interval duration. The percentage of the truncated set is equal to the sum of the number of time-series truncated segments in each truncated set / the number of time-series truncated segments in the current device monitoring cycle. The value of the preset truncated interval can be determined by the user according to the actual working scenario. For example, the user can set it according to the device monitoring records. The higher the user's requirements for data processing efficiency during the monitoring process, the smaller the value of the preset truncated interval. A method for determining the value of the preset truncated interval is provided, which is the average value of each truncated interval in the truncated set of the device monitoring records that meets the user's requirements for data processing efficiency during the monitoring process.

[0070] Specifically, for a single target monitoring device in a data acquisition state, if the truncation interval stability coefficient is greater than the preset truncation interval stability coefficient or the truncation concentration ratio is greater than the preset truncation concentration ratio, then data truncation compensation is performed on the power consumption data of the target monitoring device, and the truncation compensation method is determined according to the power load coefficient and the time-series truncation ratio.

[0071] If the power load factor of the cut-off coverage area is greater than the preset power load factor or the time-series cut-off ratio is greater than the preset time-series cut-off ratio, then data cut-off compensation will be performed on the target monitoring equipment based on power consumption behavior analysis.

[0072] If the power load factor of the cut-off coverage area is less than or equal to the preset power load factor and the time-series cut-off ratio is less than or equal to the preset time-series cut-off ratio, then data cut-off compensation is performed on the target monitoring equipment based on data fitting analysis, and the fitting extraction coefficient for each data extraction time is determined according to the changed reference index and the extraction interval duration.

[0073] The values ​​of the preset truncation interval stability coefficient and the preset truncation concentration ratio can be determined by the user according to the actual working scenario. For example, the user can set them according to the equipment monitoring records. A method for determining the value of the preset truncation interval stability coefficient is provided, in which the equipment monitoring record for data truncation compensation for the power consumption data of the target monitoring equipment is recorded as the compensation reference record, and the minimum value of the truncation interval stability coefficient in the compensation reference record that meets the user's data processing efficiency requirements in the monitoring process is recorded as the preset truncation interval stability coefficient. A method for determining the value of the preset truncation concentration ratio is provided, in which the minimum value of the truncation concentration ratio in the compensation reference record that meets the user's data processing efficiency requirements in the monitoring process is recorded as the preset truncation concentration ratio.

[0074] The time range between the start time of the first time-series truncation segment and the end time of the last time-series truncation segment within the current equipment monitoring cycle is defined as the truncation coverage interval. The power load factor is defined as: Change Assessment Difference Value / Assessment Reference Power Consumption Value. The assessment reference power consumption value is the average value of the power consumption data acquired within the truncation coverage interval. The change assessment difference value is the maximum value of the change difference values ​​for each data point within the truncation coverage interval. For any two adjacent acquisitions of power consumption data, the data change difference value is the absolute value of the difference between the values ​​of the two acquisitions. The time-series truncation percentage is defined as: Sum of the durations of each time-series truncation segment within the current equipment monitoring cycle / Duration of the equipment monitoring cycle. The values ​​of the preset power load factor and the preset time truncation ratio can be determined by the user according to the actual working scenario. For example, the user can set them according to the equipment monitoring records. A method for determining the value of the preset power load factor is provided, in which the equipment monitoring records that perform data truncation compensation for the target monitoring equipment based on power consumption behavior analysis are recorded as a type of compensation record, and the minimum value of the power load factor in the type of compensation record that meets the user's data processing efficiency requirements in the monitoring process is recorded as the preset power load factor. A method for determining the value of the preset time truncation ratio is provided, in which the minimum value of the time truncation ratio in the type of compensation record that meets the user's data processing efficiency requirements in the monitoring process is recorded as the preset time truncation ratio.

[0075] When performing data truncation compensation for target monitoring equipment based on data fitting analysis, for a single time-series truncation segment, the electricity consumption data acquired at the data extraction time when the fitting extraction coefficient is greater than the preset fitting extraction coefficient is recorded as the fitting compensation data. The time of each acquisition of electricity consumption data is recorded as the data extraction time. For a single data extraction time, the fitting extraction coefficient is the product of the reference feature index and the extraction weight coefficient corresponding to that data extraction time. The extraction weight coefficient is negatively correlated with the extraction interval duration of that data extraction time, and the extraction interval duration is the interval duration between that data extraction time and the middle time of that time-series truncation segment. The reference feature index is calculated as 1 / the absolute value of the difference between the data change parameter at the current data extraction time and the previous data extraction time. The data change parameter at a single data extraction time is calculated as the absolute value of the difference between the electricity consumption data obtained at the current data extraction time and the previous data extraction time / the value of the electricity consumption data obtained at the previous data extraction time. The value of the electricity consumption data corresponding to the current time truncation segment is determined based on the fitting compensation data of the current time truncation segment. How to predict the electricity consumption data corresponding to each data extraction time within the current time truncation segment based on the determined fitting compensation data is a topic easily understood by those skilled in the art and will not be elaborated here.

[0076] Specifically, when performing data truncation compensation for target monitoring equipment based on electricity consumption behavior analysis, the monitoring interval to be analyzed corresponding to each time-series truncation segment is obtained, and the matching analysis interval is determined based on the load factor difference value and the trend similarity coefficient.

[0077] The electricity consumption data corresponding to the time-series truncation segment is determined based on the data change parameters and the degree of change reference at each data extraction time within the matching analysis interval.

[0078] In the data truncation compensation of a target monitoring device based on electricity consumption behavior analysis, for a single time-series truncation segment, the midpoint of the monitoring interval to be analyzed is the midpoint of the time-series truncation segment. The duration of the monitoring interval to be analyzed can be set by the user according to the actual working scenario. The higher the user's requirements for data processing efficiency in the monitoring process, the longer the duration of the monitoring interval to be analyzed. One method provides a monitoring interval to be analyzed that is 8 times the duration of the time-series truncation segment. Based on the load factor difference value and trend similarity coefficient, the device monitoring records of the target monitoring device are matched and analyzed to extract the interval. For a time interval with the same duration as the monitoring interval to be analyzed, the interval matching coefficient of the time interval is determined according to the load factor difference value and trend similarity coefficient. The interval matching coefficient is the product of the load factor difference value and the trend similarity coefficient. If the interval matching coefficient of the time interval is greater than the preset interval matching coefficient, the time interval is recorded as the matching analysis interval of the above-mentioned monitoring interval to be analyzed. The load factor difference value is the absolute value of the difference between the electricity load factor corresponding to the time interval and the monitoring interval to be analyzed. The trend similarity coefficient = 1 / the absolute value of the difference between the trend change coefficient corresponding to the time interval and the monitoring interval to be analyzed. For a time interval, the trend change coefficient = interval change parameter / interval reference value. The interval change parameter is the difference between the maximum value and the minimum value of the electricity data obtained in the time interval. The interval reference value is the average value of the electricity data obtained in the time interval.

[0079] The value of the preset interval matching coefficient can be determined by the user according to the actual working scenario. For example, the user can set it according to the device monitoring record. The higher the user's requirement for data processing efficiency in the monitoring process, the larger the preset interval matching coefficient. A method for determining the value of the preset interval matching coefficient is provided, which is the average value of the interval matching coefficients of each matching analysis interval in the device monitoring record that meets the user's requirement for data processing efficiency in the monitoring process.

[0080] For a single completed matching analysis interval determined to be a monitoring interval to be analyzed, the data change parameters of the data extraction time in the same order within each matching analysis interval are detected. For a single matching analysis interval, the change reference degree is the number of reference extraction times. If the absolute value of the difference between the data change parameters of the matching analysis interval and the monitoring interval to be analyzed in any same order is less than the preset reference change parameter, then the data extraction time in that order is recorded as the reference extraction time. The value of the preset reference change parameter can be set by the user according to the actual working scenario. One preset value of the reference change parameter is provided, and the value of the reference change parameter is 0.02.

[0081] For any data extraction time within a time-series truncation segment of the monitoring interval to be analyzed, obtain the data change parameters and fluctuation coefficients for the data extraction times in that order within each matching analysis interval. t represents the number of matching analysis intervals in the monitoring interval to be analyzed, bs represents the data change parameter of the data extraction time corresponding to the s-th matching analysis interval at the data extraction time, and d0 represents the data change parameter of the data extraction time corresponding to the data extraction time of each matching analysis interval. If the change fluctuation coefficient is less than the preset change fluctuation coefficient, the reference change parameter of the data extraction time in the time-series truncation segment is determined according to the data change parameter and the change reference degree. The reference change parameter is the sum of the products of the data change parameter of the data extraction time corresponding to each matching analysis interval and the reference coefficient corresponding to each matching analysis interval. The reference coefficient is positively correlated with the change reference degree of the matching analysis interval, and the sum of the reference coefficients corresponding to each matching analysis interval is 1. If the change fluctuation coefficient is greater than or equal to the preset change fluctuation coefficient, the reference change parameter of the data extraction time in the time-series truncation segment is not set.

[0082] The value of the preset reference change parameter can be determined by the user according to the actual working scenario. For example, the user can set it according to the device monitoring record. The higher the user's requirement for data processing efficiency during the monitoring process, the smaller the value of the preset reference change parameter. A method for determining the value of the preset reference change parameter is provided, which records the maximum value of the reference change parameter in the device monitoring record as the preset reference change parameter.

[0083] Specifically, for a single target monitoring device within the target optimization set, if the target monitoring device is in a Class II data acquisition state where both the data integrity index and parameter misalignment index are greater than the preset data integrity index and the parameter misalignment index are greater than the preset parameter misalignment index, then relevant cluster analysis is performed on the power consumption data of the target monitoring device, including:

[0084] For each abnormal movement of the target monitoring device within the current monitoring cycle, key analysis segments are extracted, and the extraction of key analysis segments is based on data combination analysis.

[0085] Wherein, the parameter error index = the number of abnormal errors in the current equipment monitoring cycle / the number of times power consumption data is acquired in the current equipment monitoring cycle. For a single data extraction moment, if the data change parameter is greater than the first preset change parameter, it is determined that there is an abnormal error, and the abnormal error is determined to be an abnormal change amplitude. If the data change parameter is greater than the second preset change parameter for several consecutive data extraction moments, it is determined that there is an abnormal error, and the abnormal error is determined to be an abnormal change fluctuation.

[0086] The values ​​of the first preset change parameter and the second preset change parameter can be determined by the user according to the actual working scenario. For example, the user can set them according to the equipment monitoring records. A method for determining the value of the first preset change parameter is provided, in which the average value of the data change parameters with abnormal change amplitude in the equipment monitoring records that meet the user's data processing efficiency requirements in the monitoring process is recorded as the first preset change parameter. A method for determining the value of the preset parameter error index is provided, in which the average value of each data change parameter with abnormal change fluctuation in the equipment monitoring records that meet the user's data processing efficiency requirements in the monitoring process is recorded as the second preset change parameter.

[0087] The value of the preset parameter misalignment index can be determined by the user based on the actual working scenario. For example, the user can set it based on the device monitoring records. The higher the user's requirements for data processing efficiency during the monitoring process, the smaller the value of the preset parameter misalignment index. A method for determining the value of the preset parameter misalignment index is provided, in which the device monitoring records for relevant cluster analysis of the power consumption data of the target monitoring device are recorded as the second analysis reference records, and the minimum value of the parameter misalignment index of the target monitoring device in the second analysis reference records that meets the user's requirements for data processing efficiency during the monitoring process is recorded as the preset parameter misalignment index.

[0088] For a single abnormal error, the correlation analysis segment of that abnormal error is extracted based on the abnormal correlation coefficient of its error radiation range. The duration of the correlation analysis segment is positively correlated with the abnormal correlation coefficient. The abnormal correlation coefficient is the sum of the products of the error interference coefficient of each associated abnormal error within the error radiation range and the corresponding abnormal correlation interval duration. The category of the electricity consumption data corresponding to the abnormal error is recorded as the original electricity consumption data, and the other categories of electricity consumption data are recorded as associated electricity consumption data. The abnormal errors existing in the original electricity consumption data and associated electricity consumption data within the error radiation range are recorded as associated abnormal errors. If the category of an associated abnormal error is a sudden change amplitude abnormality, the error interference coefficient is positively correlated with the data change parameter. If the category of an associated abnormal error is a change fluctuation abnormality, the error interference coefficient is positively correlated with the number of data extraction times included. For any two abnormal errors, the abnormal correlation interval duration is the shortest interval duration between the data extraction times included in the above two abnormal errors.

[0089] Specifically, once the set analysis is completed, the decision on whether to optimize the transmission link for the target optimization set is made based on the proportion of key equipment in the target optimization set and the reference equipment characteristic index.

[0090] The key equipment ratio refers to the proportion of the number of key monitoring devices in the target optimization set to the total number of target monitoring devices in the target optimization set.

[0091] The reference device characteristic index is the average value of the abnormal characteristic indices of key monitoring devices within the target optimization set;

[0092] The condition for completing the set analysis is that all target monitoring devices within the target optimization set have completed the anomaly feature mining.

[0093] Wherein, for a single target optimization set, the proportion of key equipment = the number of key monitoring equipment in the target optimization set / the number of target monitoring equipment in the target optimization set, the key monitoring equipment is the target monitoring equipment in the first type of data acquisition state and the second type of data acquisition state, the reference equipment characteristic index is the average value of the abnormal characteristic index of each key monitoring equipment, and for a single key monitoring equipment, the abnormal characteristic index = ln(parameter error index / data integrity index).

[0094] The values ​​of the preset key equipment ratio and the preset reference equipment characteristic index can be determined by the user according to the actual working scenario. For example, the user can set them according to the equipment monitoring records. A method for determining the value of the preset key equipment ratio is provided, in which the equipment monitoring records optimized for transmission links based on data diversion and matching are recorded as optimized reference records, and the average value of the key equipment ratio in the optimized reference records that meet the user's requirements for data processing efficiency during the monitoring process is recorded as the preset key equipment ratio. A method for determining the value of the preset reference equipment characteristic index is provided, in which the average value of the reference equipment characteristic index in the optimized reference records that meet the user's requirements for data processing efficiency during the monitoring process is recorded as the preset reference equipment characteristic index.

[0095] Specifically, for a single target optimization set, if the target optimization set is in a first preset transmission state where the proportion of critical equipment is greater than a preset proportion of critical equipment or the reference equipment characteristic index is greater than a preset reference equipment characteristic index, then transmission link optimization is performed on the target optimization set, including:

[0096] Compensate for the power consumption data of critical monitoring equipment during transmission, and determine the allocation transmission strategy based on the difference value allocated according to demand;

[0097] If the demand allocation difference value is greater than the preset demand allocation difference value, the power consumption data of the key monitoring equipment will be allocated and transmitted based on the transmission demand coefficient and the reference link execution parameters.

[0098] The link transmission status includes a first preset transmission status and a second preset transmission status. If the proportion of key equipment in the target optimization set is less than or equal to the preset proportion of key equipment and the reference equipment characteristic index is less than or equal to the preset reference equipment characteristic index, then the target optimization set is determined to be in a second preset transmission status. For a single target optimization set, the demand allocation difference value = the number of difference transmission links / the number of associated transmission links. The difference transmission link is one where the transmission demand difference value is greater than the average transmission demand difference value. For a single associated transmission link, the transmission demand difference value is the absolute value of the difference between the cumulative transmission demand coefficient and the reference transmission demand coefficient of the associated transmission link. The cumulative transmission demand coefficient is the sum of the transmission demand coefficients of each key monitoring device that the associated transmission link is responsible for transmitting. The reference transmission demand coefficient is the average value of the cumulative transmission demand coefficients of each associated transmission link. The average transmission demand difference value is the average value of the transmission demand difference values ​​of each associated transmission link.

[0099] The value of the preset demand allocation difference can be determined by the user according to the actual working scenario. For example, the user can set it according to the device monitoring record. The higher the user's requirement for data processing efficiency in the monitoring process, the smaller the value of the preset demand allocation difference. A method for determining the value of the preset demand allocation difference is provided, which records the device monitoring record for allocating and transmitting power consumption data of key monitoring devices based on the transmission demand coefficient and the reference link execution parameters as the allocation reference record, and records the minimum value of the demand allocation difference in the allocation reference record that meets the user's requirement for data processing efficiency in the monitoring process as the preset demand allocation difference value.

[0100] When compensating for the power consumption data of a single key monitoring device, if the demand allocation difference is greater than the preset demand allocation difference, the transmission matching coefficient of any associated transmission link = 1 / (the cumulative transmission demand coefficient of the associated transmission link * 0.5 + the reference link execution parameter of the associated transmission link * 0.5), where the reference link execution parameter is the average value of the link execution parameters of the associated transmission links in the monitoring cycles of each device within the set evaluation period. If the demand allocation difference is less than or equal to the preset demand allocation difference, the transmission matching coefficient of any associated transmission link = 1 / reference link execution parameter. The associated transmission link with the largest transmission matching coefficient is used for transmission compensation of the power consumption data of the key monitoring device, that is, secondary data transmission is performed.

[0101] Specifically, the method for setting the transmission demand coefficient is determined based on the data acquisition status of each key monitoring device;

[0102] If the critical monitoring equipment is in a data acquisition state, the transmission demand coefficient is determined based on the proportion of time-series truncation and the duration of the truncation coverage area.

[0103] If the critical monitoring equipment is in the second-class data acquisition state, the transmission demand coefficient is determined based on the parameter misalignment index and the duration of the critical analysis segment.

[0104] Specifically, if the critical monitoring equipment is in a Class I data acquisition state, the transmission demand coefficient is the product of the time sequence truncation ratio and the duration of the truncation coverage area; if the critical monitoring equipment is in a Class II data acquisition state, the transmission demand coefficient is the product of the parameter error index and the duration of the critical analysis segment.

[0105] Please see Figure 4 The diagram shown is a module connection diagram of the online monitoring system for smart energy meters of the present invention. The present invention provides an online monitoring system for smart energy meters, comprising:

[0106] The collection monitoring module is used to respond to the link throughput fluctuation index and link balance index of the collection judgment conditions to determine whether each power monitoring collection meets the transmission optimization conditions.

[0107] The equipment evaluation module, which is connected to the set monitoring module, is used to respond to the data integrity index and parameter misalignment index of the equipment evaluation conditions in order to determine the data acquisition status of each target monitoring device in the target optimization set;

[0108] The mining processing module, which is connected to the device evaluation module, is used to respond to mining matching conditions to determine the feature mining strategy to be executed for each target monitoring device. The feature mining strategy is to perform time series analysis or correlation clustering analysis on the power consumption data of the target monitoring device.

[0109] The link analysis module, which is connected to the set monitoring module and the mining processing module, is used to determine the link transmission status of the target optimization set in response to the key device proportion and reference device characteristic index of the transmission analysis conditions.

[0110] The link optimization module, which is connected to the link analysis module, is used to respond to the link transmission status of the target optimization set in response to the optimization conditions, to determine whether to perform transmission link optimization for the target optimization set, and to determine the allocation transmission strategy based on the required allocation difference value.

[0111] The technical solution of the present invention has been described above with reference to the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art can make equivalent changes or substitutions to the relevant technical features, and the technical solutions after these changes or substitutions will all fall within the scope of protection of the present invention.

[0112] The above description is merely a preferred embodiment of the present invention and is not intended to limit the invention. Various modifications and variations can be made to the present invention by those skilled in the art. 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.

Claims

1. A method for online monitoring of smart energy meters, characterized in that, include: The link transmission coefficient of each power monitoring set is determined periodically based on the link throughput fluctuation index and the link balance index, and the link transmission coefficient is used to determine whether each power monitoring set meets the transmission optimization conditions. Under transmission optimization conditions, the data acquisition status of each target monitoring device in the target optimization set is determined based on the data integrity index and parameter misalignment index, and the data mining strategy to be executed for each target monitoring device is determined based on the data acquisition status. The data mining strategy is to perform time series analysis or related cluster analysis on the power consumption data of the target monitoring device. For a single target monitoring device within the target optimization set, if the target monitoring device is in a data acquisition state where the data integrity index is less than or equal to the preset data integrity index, then time series analysis is performed on the power consumption data of the target monitoring device. If the target monitoring device is in a second-class data acquisition state where the data integrity index is greater than the preset data integrity index and the parameter error index is greater than the preset parameter error index, then relevant cluster analysis is performed on the power consumption data of the target monitoring device. When performing time series analysis, the decision on whether to perform data truncation compensation is based on the interval between each time series truncation segment, and the truncation compensation method is determined according to the power load factor of the truncation coverage area. The truncation compensation method is to perform data truncation compensation for the target monitoring equipment based on power consumption behavior analysis or data fitting analysis. Once the set analysis is complete, determine whether to optimize the transmission link for the target set based on the proportion of key equipment and the characteristic index of reference equipment, and determine the allocation transmission strategy according to the demand allocation difference value. The link balancing index is determined based on the link execution parameters of the associated transmission links of the relay transmission nodes in the power monitoring set. The link transmission coefficient = ln(link throughput fluctuation index / link balancing index). The link throughput fluctuation index is the sum of the throughput change ratio of the current device monitoring cycle and the reference throughput change ratio of the set evaluation cycle. The throughput change ratio of the current device monitoring cycle = the absolute value of the difference between the data throughput of the relay transmission nodes of the power monitoring set in the current device monitoring cycle and the previous device monitoring cycle / the data throughput of the relay transmission nodes of the power monitoring set in the previous device monitoring cycle. The reference throughput change ratio is the average of the throughput change ratios of each device monitoring cycle in the set evaluation cycle. The data integrity index = 1 / the number of time-series truncation segments within the current device monitoring cycle; The parameter error index = number of abnormal errors in the current equipment monitoring cycle / number of times power consumption data is acquired in the current equipment monitoring cycle.

2. The online monitoring method for smart energy meters according to claim 1, characterized in that, For a single power monitoring set, if the link transmission coefficient is greater than the preset link transmission coefficient, then the power monitoring set is determined to meet the transmission optimization conditions, and the power monitoring set is recorded as the target optimization set.

3. The online monitoring method for smart energy meters according to claim 2, characterized in that, For a single target monitoring device within the target optimization set, if the target monitoring device is in a data acquisition state where the data integrity index is less than or equal to a preset data integrity index, then time series analysis is performed on the power consumption data of the target monitoring device, including: Obtain the time-series truncated segment of the power consumption data of the target monitoring device within the current monitoring cycle; Based on the interval duration between each time-series truncation segment, determine the truncation interval stability coefficient and truncation concentration ratio of the target monitoring device in the current device monitoring cycle, and determine whether to perform data truncation compensation for electricity consumption data based on the truncation interval stability coefficient and truncation concentration ratio.

4. The online monitoring method for smart energy meters according to claim 3, characterized in that, For a single target monitoring device in a data acquisition state, if the truncation interval stability coefficient is greater than the preset truncation interval stability coefficient or the truncation concentration ratio is greater than the preset truncation concentration ratio, then data truncation compensation is performed on the power consumption data of the target monitoring device. The truncation compensation method is determined according to the power load coefficient and the time-series truncation ratio. If the power load factor of the cut-off coverage area is greater than the preset power load factor or the time-series cut-off ratio is greater than the preset time-series cut-off ratio, then data cut-off compensation will be performed on the target monitoring equipment based on power consumption behavior analysis. If the power load factor of the cut-off coverage area is less than or equal to the preset power load factor and the time-series cut-off ratio is less than or equal to the preset time-series cut-off ratio, then data cut-off compensation is performed on the target monitoring equipment based on data fitting analysis, and the fitting extraction coefficient for each data extraction time is determined according to the changed reference index and the extraction interval duration.

5. The online monitoring method for smart energy meters according to claim 4, characterized in that, When performing data truncation compensation for target monitoring equipment based on electricity consumption behavior analysis, the monitoring interval to be analyzed corresponding to each time-series truncation segment is obtained, and the matching analysis interval is determined according to the load factor difference value and the trend similarity coefficient. The electricity consumption data corresponding to the time-series truncation segment is determined based on the data change parameters and the degree of change reference at each data extraction time within the matching analysis interval.

6. The online monitoring method for smart energy meters according to claim 5, characterized in that, For a single target monitoring device within the target optimization set, if the target monitoring device is in a Class II data acquisition state where both the data integrity index and parameter misalignment index are greater than the preset data integrity index and the parameter misalignment index are greater than the preset parameter misalignment index, then relevant cluster analysis is performed on the power consumption data of the target monitoring device, including: For each abnormal movement of the target monitoring device within the current monitoring cycle, key analysis segments are extracted, and the extraction of key analysis segments is based on data combination analysis.

7. The online monitoring method for smart energy meters according to claim 6, characterized in that, Once the set analysis is complete, determine whether to perform transmission link optimization for the target optimization set based on the proportion of key equipment in the target optimization set and the reference equipment characteristic index. The key equipment ratio refers to the proportion of the number of key monitoring devices in the target optimization set to the total number of target monitoring devices in the target optimization set. The reference device characteristic index is the average value of the abnormal characteristic indices of key monitoring devices within the target optimization set; The condition for completing the set analysis is that all target monitoring devices within the target optimization set have completed anomaly feature mining. The abnormal feature index is calculated as ln(parameter error index / data integrity index).

8. The online monitoring method for smart energy meters according to claim 7, characterized in that, For a single target optimization set, if the target optimization set is in a first preset transmission state where the proportion of critical equipment is greater than the preset proportion of critical equipment or the reference equipment characteristic index is greater than the preset reference equipment characteristic index, then transmission link optimization is performed for the target optimization set, including: Compensate for the power consumption data of critical monitoring equipment during transmission, and determine the allocation transmission strategy based on the difference value allocated according to demand; If the demand allocation difference value is greater than the preset demand allocation difference value, the power consumption data of the key monitoring equipment will be allocated and transmitted based on the transmission demand coefficient and the reference link execution parameters.

9. The online monitoring method for smart energy meters according to claim 8, characterized in that, For a single target optimization set, the transmission demand coefficient is set according to the data acquisition status of each key monitoring device. The device method is to determine the transmission demand coefficient based on the proportion of time truncation and the duration of the truncation coverage area, or based on the parameter error index and the duration of the key analysis segment.

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