Online monitoring system and method for intelligent electric energy meter

Abstract

The invention relates to the field of electric energy meter monitoring, in particular to an online monitoring system and method for an intelligent electric energy meter, and the method comprises the steps: periodically determining a link transmission coefficient of each electric energy monitoring set according to a link throughput fluctuation index and a link equalization index, and determining whether each electric energy monitoring set meets a transmission optimization condition or not according to the link transmission coefficient; under a transmission optimization condition, determining a data mining strategy executed for each target monitoring device based on the data acquisition state; when time sequence analysis is carried out, whether data truncation compensation is carried out or not is determined based on the interval duration between the time sequence truncation sections, and a truncation compensation mode is determined according to the electrical load coefficient of the truncation coverage interval; under the condition that set analysis is completed, whether transmission link optimization is conducted on the target optimization set or not is determined based on the key equipment proportion and the reference equipment characteristic index, the distribution transmission strategy is determined according to the demand distribution difference value, and the data processing efficiency in the monitoring process of the intelligent electric energy meter is improved.

Description

Technical Field

[0001] The present invention relates to the field of electric energy meter monitoring, and particularly to an intelligent electric energy meter online monitoring system and method. Background Art

[0002] Monitoring the power consumption situation of the power consumption end based on an intelligent electric energy meter can save human resources and optimize the power supply plan in a timely manner. However, the optimization quality and real-time performance of the power supply plan depend on the acquisition quality and transmission quality of power consumption data. Among them, during the peak power consumption period, the amount of power consumption data to be transmitted is large, which affects the transmission process and results in low timeliness of the actual optimization result. Therefore, how to analyze the importance degree of the monitoring result for the power consumption data from different sources and adjust the data transmission process in real time to improve the timeliness of the optimization result is an urgent problem to be solved by those skilled in the art.

[0003] Chinese Patent Application Publication No. CN119165435A discloses a remote monitoring system for an electric energy meter, which mainly solves the problems of poor ability of the system to handle common problems of the electric energy meter, poor scalability and flexibility, and low privacy of system data and users. The remote monitoring system for electric energy meter 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. Through an intelligent calculation optimization algorithm, the data during the operation of the system is optimized and processed, improving the stability of the system operation and the data processing efficiency; the status data of the electric energy meter is transmitted through a wireless communication module, improving the accuracy of remote verification; through the self-detection and correction of the on-site monitoring module, the work efficiency is improved and the manual loss is reduced. However, the above solution has the following problems: It fails to perform targeted data processing and real-time optimization of the data transmission process according to the data situation obtained in the actual monitoring scenario, resulting in low effectiveness of the transmitted power consumption data, and further leading to low analysis efficiency of the subsequent power consumption data. Summary of the Invention

[0004] Therefore, the present invention provides an intelligent electric energy meter online monitoring system and method to overcome the problems in the prior art that targeted data processing and real-time optimization of the data transmission process are not performed according to the data situation obtained in the actual monitoring scenario, resulting in low effectiveness of the transmitted power consumption data, and further leading to low analysis efficiency of the subsequent power consumption data.

[0005] To achieve the above object, the present invention provides an intelligent electric energy meter online monitoring method, including:

[0006] Periodically determine the link transmission coefficients of each power monitoring set according to the link throughput fluctuation index and the link balance index, and determine whether each power monitoring set meets the transmission optimization conditions according to the link transmission coefficients;

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

[0008] When performing time series analysis, determine whether to perform data truncation compensation based on the interval duration between each time series truncation segment, and determine the truncation compensation method according to the power load coefficient of the truncation coverage interval. The truncation compensation method is to perform data truncation compensation for the target monitoring device based on power consumption behavior analysis or data fitting analysis;

[0009] Under the condition that the set analysis is completed, determine whether to perform transmission link optimization for the target optimization set based on the proportion of key devices and the reference device feature index, 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, it is determined that the power monitoring set meets the transmission optimization conditions, and the power monitoring set is recorded as the target optimization set;

[0011] The link throughput fluctuation index is determined according to the throughput change ratio and the reference throughput change ratio of the set evaluation period;

[0012] The link balance index is determined according to the link execution parameters of the associated transmission links of the relay transmission nodes of the power monitoring set;

[0013] The link transmission coefficient has a positive correlation with the link throughput fluctuation index, and the link transmission coefficient has a negative correlation with the link balance index.

[0014] Furthermore, for a single target monitoring device in 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 perform time series analysis on the power consumption data of the target monitoring device, including:

[0015] Obtain the time series truncation segment of the power consumption data of the target monitoring device in the current device monitoring period;

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

[0017] Further, for a single target monitoring device in a first-class 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, perform data truncation compensation for the electricity consumption data of the target monitoring device, and determine the truncation compensation method according to the electricity load factor and the time-series truncation ratio;

[0018] If the electricity load factor in the truncation coverage interval is greater than the preset electricity load factor or the time-series truncation ratio is greater than the preset time-series truncation ratio, perform data truncation compensation for the target monitoring device based on electricity consumption behavior analysis;

[0019] If the electricity load factor in the truncation coverage interval is less than or equal to the preset electricity load factor and the time-series truncation ratio is less than or equal to the preset time-series truncation ratio, perform data truncation compensation for the target monitoring device based on data fitting analysis, and determine the fitting extraction coefficient at each data extraction moment according to the change reference index and the extraction interval duration.

[0020] Further, when performing data truncation compensation for the target monitoring device based on electricity consumption behavior analysis, obtain the monitoring interval to be analyzed corresponding to each time-series truncated paragraph, and determine the matching analysis interval according to the load factor difference value and the trend similarity coefficient;

[0021] Determine the electricity consumption data corresponding to the time-series truncated paragraph based on the data change parameter and the change reference degree at each data extraction moment within the matching analysis interval.

[0022] Further, for a single target monitoring device in the target optimization set, 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 misalignment index is greater than the preset parameter misalignment index, perform relevant clustering analysis on the electricity consumption data of the target monitoring device, including:

[0023] Extract the key analysis paragraphs for each abnormal misalignment of the target monitoring device in the current device monitoring cycle, and perform the extraction of key analysis paragraphs based on data combination analysis.

[0024] Further, under the condition that the set analysis is completed, determine whether to perform transmission link optimization for the target optimization set based on the key device ratio of the target optimization set and the reference device feature index;

[0025] The proportion of the key devices is the proportion of the number of key monitoring devices in the target optimization set to the number of target monitoring devices in the target optimization set;

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

[0027] The condition for completing the set analysis is that the abnormal feature mining of each target monitoring device in the target optimization set is completed.

[0028] Further, for a single target optimization set, if the target optimization set is in the first preset transmission state where the proportion of key devices is greater than the preset proportion of key devices or the reference device characteristic index is greater than the preset reference device characteristic index, transmission link optimization is performed on the target optimization set, including:

[0029] Perform transmission compensation on the power consumption data of the key monitoring devices, and determine the allocation transmission strategy according to the demand allocation difference value;

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

[0031] Further, for a single target optimization set, determine the setting method of the transmission demand coefficient according to the data acquisition status of each key monitoring device. The device method is to determine the transmission demand coefficient according to the time series truncation ratio and the duration of the truncation coverage interval, or to determine the transmission demand coefficient according to the parameter misalignment index and the duration of the key analysis paragraph.

[0032] The present invention also provides a system applying the intelligent electric energy meter online monitoring method described above, including:

[0033] A set monitoring module, used to respond to the link throughput fluctuation index and the link balance index of the set determination condition to determine whether each electric energy monitoring set meets the transmission optimization condition;

[0034] A device evaluation module, connected to the set monitoring module, used to respond to the data integrity index and the parameter misalignment index of the device evaluation condition to determine the data acquisition status of each target monitoring device in the target optimization set;

[0035] A mining processing module, connected to the device evaluation module, used to respond to the mining matching condition to determine the feature mining strategy 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] A 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 proportion of key devices in the transmission analysis condition and the reference device feature index;

[0037] A link optimization module, which is connected to the link analysis module, is used to determine whether to optimize the transmission link for the target optimization set in response to the link transmission status of the target optimization set under the optimization condition, and determine the allocation transmission strategy according to the demand allocation difference value.

[0038] Compared with the prior art, the beneficial effects of the present invention are as follows. Under the transmission optimization condition, the present invention determines the data acquisition status of the target monitoring device according to the data integrity index and the parameter misalignment index, and determines the data mining strategy of each target monitoring device according to 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, and further improve the data processing efficiency of the data analysis end.

[0039] Further, in the present invention, the data acquisition status of the target monitoring device is determined according to the data integrity index and the parameter misalignment index, which is used to preliminarily characterize the abnormal situation of the power consumption data acquired by the target monitoring device, and preliminarily divide the abnormal categories of the power consumption data, so that the determined data mining strategy is more in line with the actual working scenario, while ensuring the integrity and effectiveness of the transmitted data, improving the data analysis efficiency.

[0040] Further, in the present invention, for the target monitoring device in a first type of data acquisition status, the distribution of the timing truncation segments is determined by analyzing the interval duration between the timing truncation segments of the power consumption data within the transmission optimization period, so as to determine the dominant missing factor of the power consumption data, making the subsequent processing process of the power consumption data more in line with the actual situation, ensuring the effectiveness of the truncation compensation and the abnormal feature mining, and the present invention improves the integrity and effectiveness of the acquired transmission data.

[0041] Further, in the present 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 the power consumption data for the key monitoring device, and in addition, a targeted setting method of the transmission demand coefficient is set according to the data acquisition status of different key monitoring devices, ensuring the accuracy of the determined transmission demand coefficient, and further improving the reliability of the transmission compensation for the power consumption data of the key monitoring device. Description of the Drawings

[0042] Figure 1Schematic diagram of the online monitoring method for the intelligent electricity meter of the present invention;

[0043] Figure 2 Flowchart of the feature mining strategy executed for each target monitoring device based on the data acquisition status in the present invention;

[0044] Figure 3 Flowchart of determining the truncation compensation method according to the electricity load factor in the truncation coverage interval in the present invention;

[0045] Figure 4 Module connection diagram of the online monitoring system for the intelligent electricity meter of the present invention. Detailed implementation manners

[0046] In order to make the objectives and advantages of the present invention clearer and more understandable, the present invention will be further described below in conjunction with embodiments; it should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0047] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and do not limit the protection scope of the present invention.

[0048] It should be noted that in the description of the present invention, the terms indicating directions or positional relationships such as "upper", "lower", "left", "right", "inner", "outer", etc. are based on the directions or positional relationships shown in the drawings. This is only for convenience of description and does not indicate or imply that the device or element must have a specific orientation, be constructed and operated in a specific orientation, and therefore cannot be understood as a limitation to the present invention.

[0049] In addition, it should be noted that in the description of the present invention, unless otherwise clearly specified and limited, the terms "installation", "connection", and "connection" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be directly connected or indirectly connected through an intermediate medium, and it can be the communication inside two elements. For those skilled in the art, the specific meanings of the above terms in the present invention can be understood according to specific situations.

[0050] Please refer to Figures 1 to 3 As shown, the present invention provides an online monitoring method for an intelligent electricity meter, including:

[0051] Periodically determine the link transmission coefficient of each power monitoring set according to the link throughput fluctuation index and the link balance index, and determine whether each power monitoring set meets the transmission optimization condition according to the link transmission coefficient;

[0052] Under the condition of transmission optimization, determine the data acquisition status of each target monitoring device in the target optimization set according to the data integrity index and the parameter dislocation index, and determine the data mining strategy executed for each target monitoring device based on the data acquisition status. The data mining strategy is to perform time series analysis or correlation clustering analysis on the power consumption data of the target monitoring device;

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

[0054] Under the condition that the set analysis is completed, determine whether to optimize the transmission link for the target optimization set based on the proportion of key devices and the reference device characteristic index, and determine the allocation transmission strategy according to the demand allocation difference value.

[0055] Among them, the present invention is applied to the monitoring process of intelligent electricity meters to ensure the integrity and effectiveness of the power consumption data completed in transmission. The intelligent electricity meters to be monitored are denoted as target monitoring devices, and the target monitoring devices that use the same relay transmission node to upload power consumption data are denoted as a power monitoring set. The categories of power consumption data include but are not limited to voltage, current, and active power;

[0056] In the present invention, a device monitoring cycle is applied. The user can set the duration of the device monitoring cycle according to the actual working scenario. The higher the user's requirement for the data processing efficiency in the monitoring process, the shorter the duration of the device monitoring cycle. A duration of the device monitoring cycle is provided, and the duration of the device monitoring cycle is 10 min. At the end of each device monitoring cycle, determine whether each power monitoring set meets the transmission optimization condition according to the link transmission coefficient. In the present invention, a data acquisition cycle is applied. The user can set the duration of the data acquisition cycle according to the actual working scenario. A duration of the data acquisition cycle is provided, and the duration of the data acquisition cycle is 1 s. At the end of each data acquisition cycle, acquire the power consumption data of the target monitoring device;

[0057] In the present invention, there are several device monitoring records. Any one of the device monitoring records records at least one of the link transmission coefficient, truncation duration, data integrity index, truncation interval duration, truncation interval stability coefficient, data change parameter, truncation concentration ratio, power consumption load factor, interval matching coefficient, reference change parameter, parameter dislocation index, reference device characteristic index, proportion of key devices, and demand allocation difference value during the analysis of the target incineration component. And each device monitoring record corresponds to a qualified mark, and the qualified mark records whether the data processing efficiency in 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, it is determined that the power monitoring set meets the transmission optimization condition, and the power monitoring set is recorded as the target optimization set;

[0059] The link throughput fluctuation index is determined according to the throughput change ratio and the reference throughput change ratio of the set evaluation period;

[0060] The link balance index is determined according to the link execution parameters of the associated transmission links of the relay transmission nodes of the power monitoring set;

[0061] The link transmission coefficient has a positive correlation with the link throughput fluctuation index, and the link transmission coefficient has a negative correlation with the link balance index.

[0062] Among them, 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 of the current device monitoring period and the reference throughput change ratio of the set evaluation period, the throughput change ratio of the current device monitoring period = the absolute value of the difference between the data throughput of the relay transmission node of the power monitoring set in the current device monitoring period and the previous device monitoring period / the data throughput of the relay transmission node of the power monitoring set in the previous device monitoring period, the reference throughput change ratio is the average value of the throughput change ratios of each device monitoring period in the set evaluation period, and the duration of the set evaluation period can be determined by the user according to the actual working scenario. A duration of the set evaluation period is provided, and the duration of the set evaluation period is 10 times that of the device monitoring period. The link balance index n is the number of associated transmission links of the relay transmission node of the power monitoring set, li is the link execution parameter of the i-th associated transmission link of the relay transmission node of the power monitoring set, l0 is the average value of the link execution parameters of each associated transmission link of the relay transmission node of the power monitoring set. For a single associated transmission link, the link execution parameter is the absolute value of the difference between the bandwidth idle degree of the associated transmission link in the current device monitoring period and the reference bandwidth idle degree, and the reference bandwidth idle degree is the minimum value of the bandwidth idle degrees of the associated transmission link before the current device monitoring period. The bandwidth idle degree = 1 - (the data throughput of the associated transmission link / the bandwidth capacity of the associated transmission link). How to determine the data throughput and bandwidth capacity of the associated transmission link is easily understood by 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 record. The higher the user's requirement for the data processing efficiency during the monitoring process, the smaller the value of the preset link transmission coefficient. A method for obtaining the value of the preset link transmission coefficient is provided. The minimum value of the link transmission coefficients of each target optimization set in the device monitoring record that meets the user's requirement for the data processing efficiency during the monitoring process is recorded as the preset link transmission coefficient.

[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 the preset data integrity index, 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 device monitoring cycle;

[0066] Based on the interval duration between each time series truncated segment, determine the truncation interval stability coefficient and the truncation concentration ratio of the target monitoring device within the current device monitoring cycle, and determine whether to perform data truncation compensation on the power consumption data according to the truncation interval stability coefficient and the truncation concentration ratio.

[0067] Among them, in the present invention, time series records are completed each time the power consumption data of the target monitoring device is obtained, that is, the order and time corresponding to each acquisition of the power consumption data are recorded. For a single target monitoring device, if the interval duration between the times of two adjacent acquisitions of the power consumption data is greater than the preset truncation duration, the time interval between the times of the above two acquisitions of the power consumption data is determined as the time series truncated segment, and the interval duration between the times of the above two acquisitions of the power consumption data is recorded as the truncation duration. The value of the preset truncation duration 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 the data processing efficiency during the monitoring process, the smaller the value of the preset truncation duration. A method for obtaining the value of the preset truncation duration is provided. The average value of the truncation durations corresponding to each time series truncated segment in the device monitoring record that meets the user's requirement for the data processing efficiency during the monitoring process is recorded as the preset truncation duration;

[0068] The data integrity index = 1 / the number of time - series truncated paragraphs within the current device monitoring period. The value of the preset data integrity index 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 requirement for the data processing efficiency during the monitoring process, the smaller the value of the preset data integrity index. A method for obtaining the value of the preset data integrity index is provided. The device monitoring record obtained by performing time - series analysis on the power consumption data of the target monitoring device is denoted as the analysis reference record. The maximum value of the data integrity index of the target monitoring device in the analysis reference record that meets the user's requirement for data processing efficiency during the monitoring process is denoted as the preset data integrity index;

[0069] For a single target monitoring device, the truncation interval stability coefficient Let \(m\) be the number of time - series truncated paragraphs of the target monitoring device within the current device monitoring period, \(d_j\) be the duration of the \(j\) - th truncation interval within the current device monitoring period, and \(d_0\) be the average value of the durations of all truncation intervals within the current device monitoring period. For any two adjacent time - series truncated paragraphs, the interval duration between the end time of the previous time - series truncated paragraph and the start time of the next time - series truncated paragraph is denoted as the truncation interval duration of the two time - series truncated paragraphs. The truncation - concentrated set is determined according to the truncation interval duration. For any truncation - concentrated set, the truncation interval duration between any two adjacent time - series truncated paragraphs is less than the preset truncation interval duration. The truncation - concentrated proportion = the sum of the number of time - series truncated paragraphs in each truncation - concentrated set / the number of time - series truncated paragraphs within the current device monitoring period. The value of the preset truncation interval duration 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 requirement for the data processing efficiency during the monitoring process, the smaller the value of the preset truncation interval duration. A method for obtaining the value of the preset truncation interval duration is provided. The average value of the truncation interval durations within the truncation - concentrated set in the device monitoring record that meets the user's requirement for data processing efficiency during the monitoring process is denoted as the preset truncation interval duration.

[0070] Specifically, for a single target monitoring device in a certain data acquisition state, if the truncation interval stability coefficient is greater than the preset truncation interval stability coefficient or the truncation - concentrated proportion is greater than the preset truncation - concentrated proportion, 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 consumption load factor and the time - series truncation proportion;

[0071] If the power consumption load factor in the truncation coverage interval is greater than the preset power consumption load factor or the time - series truncation proportion is greater than the preset time - series truncation proportion, then data truncation compensation is performed on the target monitoring device based on the analysis of power consumption behavior;

[0072] If the electricity consumption load factor of the truncated coverage interval is less than or equal to the preset electricity consumption load factor and the time series truncation ratio is less than or equal to the preset time series truncation ratio, then data truncation compensation is performed for the target monitoring device based on data fitting analysis, and the fitting extraction coefficients at each data extraction moment are determined according to the change reference index and the extraction interval duration.

[0073] Among them, 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 according to the device monitoring records. A method for obtaining the value of the preset truncation interval stability coefficient is provided. The device monitoring records for data truncation compensation of the electricity consumption data of the target monitoring device are recorded as compensation reference records. The minimum value of the truncation interval stability coefficient in the compensation reference records that meet the user's requirements for data processing efficiency during the monitoring process is recorded as the preset truncation interval stability coefficient. A method for obtaining the value of the preset truncation concentration ratio is provided. The minimum value of the truncation concentration ratio in the compensation reference records that meet the user's requirements for data processing efficiency during 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 device monitoring period is recorded as the truncated coverage interval. The electricity consumption load factor = change evaluation difference value / evaluation reference electricity consumption value. The evaluation reference electricity consumption value is the average value of the electricity consumption data obtained each time within the truncated coverage interval. The change evaluation difference value is the maximum value of the data change difference values within the truncated coverage interval. For any two adjacent acquisitions of electricity consumption data, the data change difference value is the absolute value of the difference between the values of the above two acquisitions of electricity consumption data. The time series truncation ratio = the sum of the durations of each time series truncation segment within the current device monitoring period / the duration of the device monitoring period. The values of the preset electricity consumption load factor and the preset time series truncation ratio can be determined by the user according to the actual working scenario. For example, the user can set according to the device monitoring records. A method for obtaining the value of the preset electricity consumption load factor is provided. The device monitoring records for data truncation compensation of the target monitoring device based on electricity consumption behavior analysis are recorded as a type of compensation record. The minimum value of the electricity consumption load factor in the type of compensation records that meet the user's requirements for data processing efficiency during the monitoring process is recorded as the preset electricity consumption load factor. A method for obtaining the value of the preset time series truncation ratio is provided. The minimum value of the time series truncation ratio in the type of compensation records that meet the user's requirements for data processing efficiency during the monitoring process is recorded as the preset time series truncation ratio;

[0075] When performing data truncation compensation for a target monitoring device based on data fitting analysis, for a single time-series truncation segment, the electricity consumption data obtained at the data extraction moment when the fitting extraction coefficient is greater than the preset fitting extraction coefficient is recorded as fitting compensation data, and the moment of obtaining the electricity consumption data each time is recorded as the data extraction moment. For a single data extraction moment, the fitting extraction coefficient is the product of the reference feature index and the extraction weight coefficient corresponding to this data extraction moment. The extraction weight coefficient has a negative correlation with the extraction interval duration of this data extraction moment. The extraction interval duration is the interval duration between this data extraction moment and the middle moment of this time-series truncation segment. The reference feature index = 1 / the absolute value of the difference between the data change parameter between this data extraction moment and the previous data extraction moment. The data change parameter of a single data extraction moment = the absolute value of the difference between the electricity consumption data obtained at this data extraction moment and the previous data extraction moment / the electricity consumption data obtained at the previous data extraction moment. Based on the fitting compensation data of this time-series truncation segment, determining the value of the electricity consumption data corresponding to this time-series truncation segment, and how to predict the electricity consumption data corresponding to each data extraction moment within the time-series truncation segment according to the determined fitting compensation data is easily understood by those skilled in the art and will not be elaborated here.

[0076] Specifically, when performing data truncation compensation for a target monitoring device based on electricity consumption behavior analysis, obtain the monitoring intervals to be analyzed corresponding to each time-series truncation segment, and determine the matching analysis interval according to the load coefficient difference value and the trend similarity coefficient;

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

[0078] Among them, when performing data truncation compensation for a target monitoring device based on power consumption behavior analysis, for a single time-series truncation segment, the middle moment of the monitoring interval to be analyzed is the middle moment 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 requirement for the data processing efficiency during the monitoring process, the longer the duration of the monitoring interval to be analyzed. A method is provided where the duration of the monitoring interval to be analyzed is 8 times the duration of the time-series truncation segment. Based on the load factor difference value and the trend similarity coefficient, an extraction of the matching analysis interval is performed for the device monitoring record of the target monitoring device. For a time interval with the same duration as the monitoring interval to be analyzed, the interval matching coefficient of this time interval is determined according to the load factor difference value and the 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 this time interval is greater than the preset interval matching coefficient, then this 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 power consumption load factor corresponding to this time interval and the monitoring interval to be analyzed. The trend similarity coefficient = 1 / the absolute value of the difference between the trend change coefficients corresponding to this time interval and the monitoring interval to be analyzed. For a time interval, the trend change coefficient = the interval change parameter / the interval reference value. The interval change parameter is the difference obtained by subtracting the minimum value from the maximum value of the power consumption data acquired within this time interval. The interval reference value is the average value of the numerical values of the power consumption data acquired within this 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 the data processing efficiency during the monitoring process, the larger the preset interval matching coefficient. A method for obtaining the value of the preset interval matching coefficient is provided. 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 the data processing efficiency during the monitoring process is recorded as the preset interval matching coefficient;

[0080] For a single monitoring interval to be analyzed for which the matching analysis interval has been determined, the data change parameters at the data extraction moments of the same order in each matching analysis interval are detected. For a single matching analysis interval, the degree of change reference is the number of reference extraction moments. If the absolute value of the difference between the data change parameters at the data extraction moments of any same order between this matching analysis interval and the monitoring interval to be analyzed is less than the preset reference change parameter, then the data extraction moment of this order is recorded as the reference extraction moment. The value of the preset reference change parameter can be set by the user according to the actual working scenario. A method for obtaining the value of the preset reference change parameter is provided. The value of the reference change parameter is 0.02;

[0081] For any data extraction moment within the time-series truncated paragraph in the monitoring interval to be analyzed, obtain the data change parameters of the data extraction moments in each matching analysis interval at this order, and the change fluctuation coefficient Let \(t\) be the number of matching analysis intervals in the monitoring interval to be analyzed, \(bs\) be the data change parameter of the data extraction moment corresponding to the \(s\)-th matching analysis interval at this order, and \(d0\) be the data change parameter of the data extraction moment corresponding to the \(s\)-th matching analysis interval at this order. If the change fluctuation coefficient is less than the preset change fluctuation coefficient, then determine the reference change parameter at this data extraction moment within the time-series truncated paragraph 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 parameters of the data extraction moments corresponding to each matching analysis interval and the corresponding reference coefficients of each matching analysis interval. The reference coefficient has a positive correlation 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, then do not set the reference change parameter at this data extraction moment within the time-series truncated paragraph;

[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 the data processing efficiency during the monitoring process, the smaller the value of the preset reference change parameter. Provide a method for determining the value of the preset reference change parameter, and record the maximum value of the reference change parameter in the device monitoring record that determines the reference change parameter according to the data change parameter and the change reference degree 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 the second-type data acquisition state where the data integrity index is greater than the preset data integrity index and the parameter misalignment index is greater than the preset parameter misalignment index, then perform relevant clustering analysis on the power consumption data of the target monitoring device, including:

[0084] Extract the key analysis paragraphs for each abnormal misalignment of the target monitoring device during the current device monitoring cycle, and extract the key analysis paragraphs based on data combination analysis.

[0085] Among them, the parameter misalignment index = the number of abnormal misalignments during the current device monitoring cycle / the number of times of obtaining power consumption data during the current device 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 misalignment once, and it is determined that this abnormal misalignment is an abnormal mutation amplitude. If there are consecutive data change parameters of several data extraction moments that are all greater than the second preset change parameter, it is determined that there is an abnormal misalignment once, and it is determined that this abnormal misalignment is 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 device monitoring records. A method for obtaining the value of the first preset change parameter is provided. The average value of the change parameters of the data with abnormal mutation amplitude in the device monitoring records that meet the user's requirements for data processing efficiency during the monitoring process is recorded as the first preset change parameter. A method for obtaining the value of the preset parameter dislocation index is provided. The average value of the change parameters of each piece of data with abnormal change fluctuation in the device monitoring records that meet the user's requirements for data processing efficiency during the monitoring process is recorded as the second preset change parameter;

[0087] The value of the preset parameter dislocation index 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 requirement for data processing efficiency during the monitoring process, the smaller the value of the preset parameter dislocation index. A method for obtaining the value of the preset parameter dislocation index is provided. The device monitoring records obtained by performing relevant clustering analysis on the power consumption data of the target monitoring device are recorded as the second analysis reference records. The minimum value of the parameter dislocation index of the target monitoring device in the second analysis reference records that meet the user's requirements for data processing efficiency during the monitoring process is recorded as the preset parameter dislocation index;

[0088] For a single abnormal dislocation, the correlation analysis paragraph for this abnormal dislocation is extracted according to the abnormal correlation coefficient in its dislocation radiation interval. The duration of the correlation analysis paragraph is positively correlated with the abnormal correlation coefficient. The abnormal correlation coefficient is the sum of the products of the dislocation interference coefficients of each associated abnormal dislocation in the dislocation radiation interval and the corresponding abnormal movement association interval duration. The category of the power consumption data corresponding to this abnormal dislocation is recorded as the source power consumption data, and the power consumption data of other categories in the power consumption data is recorded as the associated power consumption data. The abnormal dislocations existing in the source power consumption data and the associated power consumption data within the dislocation radiation interval are recorded as the associated abnormal dislocations. If the category of an associated abnormal dislocation is abnormal mutation amplitude, the dislocation interference coefficient is positively correlated with the data change parameter. If the category of an associated abnormal dislocation is abnormal change fluctuation, the dislocation interference coefficient is positively correlated with the number of data extraction times included. For any two abnormal dislocations, the abnormal movement association interval duration is the shortest interval duration between the data extraction times included in the above two abnormal dislocations.

[0089] Specifically, under the condition that the set analysis is completed, it is determined whether to optimize the transmission link for the target optimization set based on the proportion of key devices in the target optimization set and the reference device characteristic index;

[0090] The proportion of key devices is the proportion of the number of key monitoring devices in the target optimization set to the number of target monitoring devices in the target optimization set;

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

[0092] The condition for completing the set analysis is that the abnormal characteristic mining of each target monitoring device in the target optimization set is completed.

[0093] Among them, for a single target optimization set, the proportion of key devices = the number of key monitoring devices in the target optimization set / the number of target monitoring devices in the target optimization set. The key monitoring devices are the target monitoring devices in the first-class data acquisition state and the second-class data acquisition state. The reference device characteristic index is the average value of the abnormal characteristic indexes of each key monitoring device. For a single key monitoring device, the abnormal characteristic index = ln(parameter misalignment index / data integrity index);

[0094] The values of the preset proportion of key devices and the preset reference device characteristic index can be determined by the user according to the actual working scenario. For example, the user can set according to the device monitoring records. A method for obtaining the value of the preset proportion of key devices is provided. The device monitoring records for optimizing the transmission link based on the data shunt matching method are recorded as optimization reference records. The average value of the proportion of key devices in the optimization reference records that meet the user's requirements for data processing efficiency during monitoring is recorded as the preset proportion of key devices. A method for obtaining the value of the preset reference device characteristic index is provided. The average value of the reference device characteristic index in the optimization reference records that meet the user's requirements for data processing efficiency during monitoring is recorded as the preset reference device characteristic index.

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

[0096] Perform transmission compensation on the power consumption data of the key monitoring devices, and determine the allocation transmission strategy according to the demand allocation difference value;

[0097] If the demand allocation difference value is greater than the preset demand allocation difference value, allocate and transmit the power consumption data of the key monitoring devices based on the transmission demand coefficient and the reference link execution parameters.

[0098] Among them, the link transmission status includes a first preset transmission status and a first preset transmission status. If the proportion of key devices in the target optimization set is less than or equal to the preset key device proportion and the reference device characteristic index is less than or equal to the preset reference device characteristic index, it is determined that the target optimization set is in the second preset transmission status. For a single target optimization set, the demand allocation difference value = the number of differential transmission links / the number of associated transmission links. The differential transmission link is a transmission link 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 of this associated transmission link and the reference transmission demand coefficient. The cumulative transmission demand coefficient is the sum of the transmission demand coefficients of each key monitoring device responsible for transmission by this associated transmission link. 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 value 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 requirement for the data processing efficiency during the monitoring process, the smaller the value of the preset demand allocation difference value. A method for obtaining the value of the preset demand allocation difference value is provided. The device monitoring record that distributes and transmits the power consumption data of key monitoring devices based on the transmission demand coefficient and the reference link execution parameter is recorded as the distribution reference record. The minimum value of the demand allocation difference value in the distribution reference records that meet the user's requirement for data processing efficiency during the monitoring process is recorded as the preset demand allocation difference value;

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

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

[0102] If the key monitoring device is in a first-class data acquisition state, the transmission demand coefficient is determined according to the time-series truncation ratio and the duration of the truncation coverage interval;

[0103] If the key monitoring device is in the second - type data acquisition state, the transmission demand coefficient is determined according to the parameter misalignment index and the duration of the key analysis paragraph.

[0104] Among them, if the key monitoring device is in the first - type data acquisition state, the transmission demand coefficient is the product of the timing truncation ratio and the duration of the truncation coverage interval; if the key monitoring device is in the second - type data acquisition state, the transmission demand coefficient is the product of the parameter misalignment index and the duration of the key analysis paragraph.

[0105] Please refer to Figure 4 As shown, it is the module connection diagram of the intelligent electricity meter online monitoring system of the present invention. The present invention provides an intelligent electricity meter online monitoring system, including:

[0106] An aggregation monitoring module, which is used to respond to the link throughput fluctuation index and the link balance index of the aggregation determination condition to determine whether each electricity monitoring aggregation meets the transmission optimization condition;

[0107] A device evaluation module, which is connected to the aggregation monitoring module and is used to respond to the data integrity index and the parameter misalignment index of the device evaluation condition to determine the data acquisition state of each target monitoring device in the target optimization aggregation;

[0108] A mining and processing module, which is connected to the device evaluation module and is used to respond to the mining matching condition to determine the feature mining strategy 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] A link analysis module, which is connected to the aggregation monitoring module and the mining and processing module and is used to respond to the key device ratio and the reference device feature index of the transmission analysis condition to determine the link transmission state of the target optimization aggregation;

[0110] A link optimization module, which is connected to the link analysis module and is used to respond to the link transmission state of the target optimization aggregation of the optimization condition to determine whether to perform transmission link optimization for the target optimization aggregation and determine the allocation transmission strategy according to the demand allocation difference value.

[0111] So far, the technical solution of the present invention has been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it is easy for those skilled in the art to understand that the protection scope of the present invention is obviously not limited to these specific embodiments. Without departing from the principle 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 fall within the protection scope of the present invention.

[0112] The above are only the preferred embodiments of the present invention and are not intended to limit the present invention; for those skilled in the art, various modifications and variations can be made to the present invention. Any modification, equivalent replacement, improvement, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A method for online monitoring of a smart electric energy meter, characterized in that: include: Periodically determine the link transmission coefficient of each power monitoring set according to the link throughput fluctuation index and the link balance index, and determine whether each power monitoring set meets the transmission optimization condition according to the link transmission coefficient; Under the transmission optimization condition, the data acquisition status of each target monitoring device in the target optimization set is determined according to the data integrity index and the parameter shift index, and the data mining strategy executed for each target monitoring device is determined based on the data acquisition status, and the data mining strategy is to perform time series analysis or correlation cluster analysis on the power consumption data of the target monitoring device; When performing time series analysis, determine whether to perform data truncation compensation based on the interval length between each time series truncation segment, and determine the truncation compensation method based on the power load factor of the truncation coverage interval. 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; When the set analysis is completed, determine whether to optimize the transmission link for the target optimization set based on the proportion of key equipment and the characteristic index of the reference equipment, and determine the allocation transmission strategy based on the demand allocation difference value.

2. The method for online monitoring of a smart electric energy meter 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, the power monitoring set is determined to meet the transmission optimization condition, and the power monitoring set is recorded as the target optimization set; The link throughput fluctuation index is determined according to the throughput change ratio and the reference throughput change ratio of the set evaluation period; The link balancing index is determined according to the link execution parameter of the associated transmission link of the relay transmission node of the power monitoring set; 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.

3. The method for online monitoring of a smart electric energy meter according to claim 2, characterized in that: For a single target monitoring device in 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, a time series analysis is performed on the power consumption data of the target monitoring device, including: Obtain the time series truncation segment of the power consumption data of the target monitoring device in the current device monitoring cycle; Based on the interval duration between each time series truncation segment, the truncation interval stability coefficient and truncation concentration ratio of the target monitoring device in the current device monitoring cycle are determined, and according to the truncation interval stability coefficient and truncation concentration ratio, it is determined whether data truncation compensation is performed for the power consumption data.

4. The method for online monitoring of a smart electric energy meter according to claim 3, characterized in that: For a single target monitoring device in a type I 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, 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 factor and the time series truncation ratio; If the power load factor of the truncation coverage interval is greater than the preset power load factor or the time series truncation ratio is greater than the preset time series truncation ratio, data truncation compensation is performed for the target monitoring device based on power consumption behavior analysis; If the power load factor of the truncation coverage interval is less than or equal to the preset power load factor and the time series truncation ratio is less than or equal to the preset time series truncation ratio, data truncation compensation is performed for the target monitoring equipment based on data fitting analysis, and the fitting extraction coefficient of each data extraction moment is determined according to the changed reference index and the extraction interval duration.

5. The method for online monitoring of a smart electric energy meter according to claim 4, characterized in that: When data truncation compensation is performed for the target monitoring device based on the power 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 power consumption data corresponding to the time series truncation section is determined based on the data change parameters at each data extraction time within the matching analysis interval and the change reference degree.

6. The method for online monitoring of a smart electric energy meter according to claim 5, characterized in that: For a single target monitoring device in the target optimization set, if the target monitoring device is in the second data acquisition state where the data integrity index is greater than the preset data integrity index and the parameter displacement index is greater than the preset parameter displacement index, then the power consumption data of the target monitoring device is subjected to relevant cluster analysis, including: Key analysis sections are extracted for each abnormal movement of the target monitoring device within the current device monitoring cycle, and key analysis sections are extracted based on data combination analysis.

7. The method for online monitoring of a smart electric energy meter according to claim 6, characterized in that: When the set analysis is completed, determine whether to optimize the transmission link for the target optimization set based on the proportion of key devices in the target optimization set and the characteristic index of the reference device; The key device ratio is the ratio of the number of key monitoring devices in the target optimization set to the number of target monitoring devices in the target optimization set; The reference equipment characteristic index is the average value of the abnormal characteristic indexes of the key monitoring equipment in the target optimization set; The set analysis completion condition is that each target monitoring device in the target optimization set completes abnormal feature mining.

8. The method for online monitoring of a smart electric energy meter 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 key device ratio is greater than the preset key device ratio or the reference device characteristic index is greater than the preset reference device characteristic index, then the transmission link optimization is performed for the target optimization set, including: Transmit compensation for the power consumption data of key monitoring equipment, and determine the allocation transmission strategy based on the demand allocation difference value; If the demand allocation difference value is greater than the preset demand allocation difference value, the power consumption data of the key monitoring equipment is allocated and transmitted based on the transmission demand coefficient and the reference link execution parameters.

9. The method for online monitoring of a smart electric energy meter according to claim 8, characterized in that: For a single target optimization set, the setting method of the transmission demand coefficient is determined according to the data acquisition status of each key monitoring device. The device method is to determine the transmission demand coefficient according to the time series truncation ratio and the duration of the truncation coverage interval, or to determine the transmission demand coefficient according to the parameter deviation index and the duration of the key analysis section.

10. A monitoring system using the online monitoring method for smart electric energy meters according to any one of claims 1 to 9, characterized in that: include: A collection monitoring module, used to respond to the link throughput fluctuation index and link balance index of the collection determination condition to determine whether each power monitoring collection meets the transmission optimization condition; An equipment evaluation module, which is connected to the set monitoring module and is used to respond to the data integrity index and parameter deviation index of the equipment evaluation condition to determine the data acquisition status of each target monitoring device in the target optimization set; A mining processing module, which is connected to the device evaluation module, and is used to respond to the mining matching conditions to determine the feature mining strategy executed for each target monitoring device, wherein the feature mining strategy is to perform time series analysis or correlation cluster analysis on the power consumption data of the target monitoring device; A link analysis module, which is connected to the set monitoring module and the mining processing module, and is used to respond to the key device ratio and reference device characteristic index of the transmission analysis condition to determine the link transmission status of the target optimization set; The link optimization module is connected to the link analysis module to respond to the link transmission status of the target optimization set of the optimization condition to determine whether to perform transmission link optimization for the target optimization set and determine the allocation transmission strategy according to the demand allocation difference value.

Images