Method and device for analyzing and early warning electricity consumption behavior of intelligent electric energy meter

Abstract

The invention discloses an intelligent electric energy meter power consumption behavior analysis early warning method and device, and the method comprises the steps: generating a daily power consumption sequence of a user in the first seven days at a time interval of 0.5 h, and further generating a power consumption summary sequence in the first seven days; generating a comprehensive electricity consumption valley interval and a comprehensive electricity consumption peak interval according to the electricity consumption summary sequence of the first seven days, and calculating the average electricity consumption in the comprehensive electricity consumption valley interval and the average electricity consumption in the comprehensive electricity consumption peak interval; generating a real-time electricity consumption sequence of the ith day of the user by taking 0.5 hour as a time interval; delimiting a real-time electricity consumption trough interval and a real-time electricity consumption peak interval for a real-time electricity consumption sequence according to the comprehensive electricity consumption trough interval and the comprehensive electricity consumption peak interval; and according to the delimited real-time electricity consumption valley interval and the delimited real-time electricity consumption peak interval, judging an electricity consumption abnormal condition, and sending early warning information to the intelligent terminal of the user. According to the method, the electricity consumption behavior is subjected to statistical analysis, electricity consumption abnormity early warning is achieved, and the space-time complexity is small.

Description

Technical Field

[0001] The present invention relates to the technical field of data processing, and in particular to a method and device for analyzing and warning the electricity consumption behavior of a smart electric energy meter. Background Art

[0002] Smart meters are one of the basic devices for smart grid data collection, and are responsible for the collection, measurement and transmission of electric energy data. In addition to the basic electricity consumption measurement function of traditional electricity meters, smart meters also have intelligent functions such as user-side control function, two-way data communication function in multiple data transmission modes, and anti-electricity theft function. Smart electricity meters can intelligently monitor users' electricity consumption, and as part of smart homes, they challenge the existence of traditional electricity meters.

[0003] However, it is difficult for existing smart electricity meters to conduct targeted electricity consumption behavior analysis for different users, and it is difficult to realize the function of early warning of abnormal electricity consumption. At the same time, the embedded computing unit of the smart electricity meter cannot support a large amount of computing, which makes it impossible to transplant complex behavior analysis algorithm models into the smart electricity meter. Summary of the invention

[0004] In view of the above-mentioned technical deficiencies, the purpose of the present invention is to provide a method and device for analyzing and warning the electricity consumption behavior of a smart electricity meter, aiming to solve the problems that it is difficult for existing smart electricity meters to conduct targeted electricity consumption behavior analysis on different users, and it is difficult to realize the function of warning of abnormal electricity consumption, and the embedded computing unit of the smart electricity meter cannot support a large amount of calculation, resulting in the inability to transplant complex behavior analysis algorithm models into the smart electricity meter.

[0005] In view of the above problems, the present application provides a method and device for analyzing and warning the electricity consumption behavior of a smart electric energy meter.

[0006] The first aspect disclosed in the present application provides a method for analyzing and warning the electricity consumption behavior of a smart electric energy meter, the method comprising the following steps: Step 1: Count the user's daily electricity consumption in the previous seven days at 0.5 hour intervals, and generate the user's daily electricity consumption sequence X i-7 ={x1 i-7 , x2 i-7 , ..., x j i-7 , ..., x 48 i-7}, X i-6 ={x1 i-6 , x2 i-6 ,..., x j i-6 , ..., x 48i-6}, ..., X i-k ={x1 i-k , x2 i-k , ..., x j i-k , ..., x 48 i-k}, ..., X i-1 ={x1 i-1 , x2 i-1 , ..., x j i-1 , ..., x 48 i-1}, and further generate the summary sequence S of electricity consumption in the previous seven days i-7:i-1 , where i represents the i-th day, X i-k represents the daily electricity consumption sequence of the user in the previous k days, x j i-k represents the power consumption of the user in the jth time interval of the previous k days, k=1,2,...,7, j=1,2,...,48; Step 2: Summarize the power consumption of the previous seven days according to the sequence S i-7:i-1 , generate comprehensive electricity consumption valley interval and comprehensive electricity consumption peak interval, calculate the average electricity consumption in the comprehensive electricity consumption valley interval and the average electricity consumption in the comprehensive electricity consumption peak interval; Step 3: Collect the user's electricity consumption in real time at 0.5 hour intervals to generate the user's real-time electricity consumption sequence X on the i-th day i ={x1 i , x2 i , ......}; Step 4: According to the comprehensive electricity consumption valley interval and comprehensive electricity consumption peak interval, the real-time electricity consumption sequence X i ={x1 i , x2 i , ...}delineate the real-time electricity consumption low-peak interval and the real-time electricity consumption peak interval; Step 5: According to the real-time electricity consumption valley interval and the real-time electricity consumption peak interval defined for the real-time electricity consumption sequence, determine the abnormal electricity consumption and send an early warning message to the user's smart terminal.

[0007] Preferably, the step 1 specifically includes: Step 1.1: Count the user's daily electricity consumption in the previous seven days at 0.5 hour intervals, and generate the user's daily electricity consumption sequence X i-7 ={x1 i-7 , x2 i-7 , ..., x j i-7, ..., x 48 i-7}, X i-6 ={x1 i-6 , x2 i-6 ,..., x j i-6 , ..., x 48 i-6}, ..., X i-k ={x1 i-k , x2 i-k , ..., x j i-k , ..., x 48 i-k}, ..., X i-1 ={x1 i-1 , x2 i-1 , ..., x j i-1 , ..., x 48 i-1}; Step 1.2: Calculate the user's daily electricity consumption sequence X for the previous seven days i-7 ={x1 i-7 , x2 i-7 , ..., x j i-7 , ...,x 48 i-7}, X i-6 ={x1 i-6 , x2 i-6 , ..., x j i-6 , ..., x 48 i-6}, ..., X i-k ={x1 i-k , x2 i-k , ..., x j i -k , ..., x 48 i-k}, ..., X i-1 ={x1 i-1 , x2 i-1 , ..., x j i-1 , ..., x 48 i-1 The average power consumption of each time interval in the data is arranged in the original order to form a new sequence, which is the power consumption summary sequence S for the previous seven days. i-7:i-1If the user has used the smart energy meter for less than seven days, the built-in reference power consumption sequence of the same power consumption type is obtained from the smart energy meter memory as the power consumption summary sequence S for the previous seven days. i-7:i-1 .

[0008] Preferably, the step 2 specifically includes: Step 2.1: Based on the electricity consumption of the previous seven days, the sequence S i-7:i-1 , get the maximum value MAX S and minimum value MIN S , perform H S =(MAX S +MIN S )÷2, and get the intermediate value H S ; Step 2.2: Count the electricity consumption in the previous seven days i-7:i-1 The power consumption is less than the middle value H S The time interval is taken as the comprehensive electricity consumption low interval, and the electricity consumption of the previous seven days is summarized into a sequence S i-7:i-1 Among them, it is less than the middle value H S The average power consumption is calculated as the average power consumption in the comprehensive power consumption valley interval; Step 2.3: Count the electricity consumption in the previous seven days and summarize the sequence S i-7:i-1 The power consumption is greater than or equal to the middle value H S The time interval is taken as the comprehensive power consumption peak interval, and the power consumption of the previous seven days is summarized into a sequence S i-7:i-1 Greater than or equal to the middle value H S The average power consumption is calculated as the average power consumption in the comprehensive peak power consumption period.

[0009] Preferably, the step 5 specifically includes: Step 5.1: If the power consumption of the real-time power consumption sequence in four consecutive time intervals in the power consumption valley interval is greater than twice the average power consumption in the comprehensive power consumption valley interval, it is determined that there is an abnormal excessive power consumption, and an abnormal excessive power consumption warning message is sent to the user's smart terminal; Step 5.2: If the power consumption of the real-time power consumption sequence in two consecutive time intervals within the peak power consumption interval is greater than 2.5 times the average power consumption in the comprehensive peak power consumption interval, it is determined that there is an abnormal excessive power consumption, and an abnormal excessive power consumption warning message is sent to the user's smart terminal; Step 5.3: If the power consumption of the real-time power consumption sequence in the power consumption valley interval or the power consumption peak interval for four consecutive time intervals is greater than 2.5 times the average power consumption in the comprehensive power consumption peak interval, it is determined that there is an abnormal excessive power consumption, and an abnormal excessive power consumption warning message is sent to the user's smart terminal; Step 5.4: If the power consumption of the real-time power consumption sequence in all time intervals within the power consumption valley interval is greater than twice the average power consumption in the comprehensive power consumption valley interval, and the power consumption in all time intervals within the power consumption peak interval is greater than 2.5 times the average power consumption in the comprehensive power consumption peak interval, it is determined that there is a suspected power theft anomaly, and a suspected power theft anomaly warning information is sent to the user's smart terminal.

[0010] The second aspect disclosed in the present application provides a smart electric energy meter electricity consumption behavior analysis and early warning device, the device comprising: The statistical module is used to count the daily power consumption of the user in the previous seven days at a time interval of 0.5 hours, and generate a sequence X of the daily power consumption of the user in the previous seven days. i-7 ={x1 i-7 , x2 i-7 , ..., x j i-7 , ..., x 48 i-7},X i-6 ={x1 i-6 , x2 i-6 , ..., x j i-6 , ..., x 48 i-6}, ..., X i-k ={x1 i-k , x2 i-k , ..., x j i-k , ...,x 48 i-k}, ..., X i-1 ={x1 i-1 , x2 i-1 , ..., x j i-1 , ..., x 48 i-1}, and further generate the summary sequence S of electricity consumption in the previous seven days i-7:i-1 , where i represents the i-th day, X i-k represents the daily electricity consumption sequence of the user in the previous k days, x j i-k represents the power consumption of the user in the jth time interval of the previous k days, k=1,2,...,7, j=1,2,...,48; A calculation module is used to summarize the sequence S of the power consumption in the previous seven days. i-7:i-1 , generate comprehensive electricity consumption valley interval and comprehensive electricity consumption peak interval, calculate the average electricity consumption in the comprehensive electricity consumption valley interval and the average electricity consumption in the comprehensive electricity consumption peak interval; The acquisition module is used to collect the user's power consumption in real time at intervals of 0.5 hours to generate the user's real-time power consumption sequence X on the i-th day. i ={x1 i , x2 i , ......}; A division module is used to divide the real-time power consumption sequence X according to the comprehensive power consumption valley interval and the comprehensive power consumption peak interval. i ={x1 i , x2 i , ...}delineate the real-time electricity consumption low-peak interval and the real-time electricity consumption peak interval; The determination module is used to determine abnormal power consumption according to the real-time power consumption valley interval and the real-time power consumption peak interval defined for the real-time power consumption sequence, and send early warning information to the user's smart terminal.

[0011] The beneficial effects of the present invention are: (1) By statistically analyzing the short-term historical electricity consumption behavior of users, we can obtain the behavioral characterization of their electricity consumption behavior, thus solving the problem that existing smart energy meters are difficult to conduct targeted electricity consumption behavior analysis on different users.

[0012] (2) The system can judge abnormal electricity usage behavior according to different actual situations and realize the function of early warning of abnormal electricity usage.

[0013] (3) The method is simple and the calculation process is concise. It does not require excessive calculation amount and memory space requirements and has small time complexity and space complexity. Only the embedded computing unit of the smart electric energy meter is required to support the calculation amount of the method and device proposed in the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0014] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0015] Figure 1 The figure is a schematic diagram of the overall process of the smart energy meter electricity consumption behavior analysis and early warning method.

[0016] Figure 2 This is a schematic diagram of the overall structure of the smart electricity meter electricity consumption behavior analysis and early warning device. DETAILED DESCRIPTION

[0017] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0018] like Figure 1 As shown, the embodiment of the present application provides a method for analyzing and warning the electricity consumption behavior of a smart electric energy meter, the method comprising the following steps: Step 1: Count the user's daily electricity consumption in the previous seven days at 0.5 hour intervals, and generate the user's daily electricity consumption sequence X i-7 ={x1 i-7 , x2 i-7 , ..., x j i-7 , ..., x 48 i-7}, X i-6 ={x1 i-6 , x2 i-6 ,..., x j i-6 , ..., x 48 i-6}, ..., X i-k ={x1 i-k , x2 i-k , ..., x j i-k , ..., x 48 i-k}, ..., X i-1 ={x1 i-1 , x2 i-1 , ..., x j i-1 , ..., x 48 i-1}, and further generate the summary sequence S of electricity consumption in the previous seven days i-7:i-1 , where i represents the i-th day, X i-k represents the daily electricity consumption sequence of the user in the previous k days, x j i-k represents the power consumption of the user in the jth time interval of the previous k days, k=1,2,...,7, j=1,2,...,48; Step 2: Summarize the power consumption of the previous seven days according to the sequence S i-7:i-1 , generate comprehensive electricity consumption valley interval and comprehensive electricity consumption peak interval, calculate the average electricity consumption in the comprehensive electricity consumption valley interval and the average electricity consumption in the comprehensive electricity consumption peak interval; Step 3: Collect the user's electricity consumption in real time at 0.5 hour intervals to generate the user's real-time electricity consumption sequence X on the i-th day i ={x1 i , x2 i , ......}; Step 4: According to the comprehensive electricity consumption valley interval and comprehensive electricity consumption peak interval, the real-time electricity consumption sequence X i ={x1 i , x2 i , ...}delineate the real-time electricity consumption low-peak interval and the real-time electricity consumption peak interval; Step 5: According to the real-time electricity consumption valley interval and the real-time electricity consumption peak interval defined for the real-time electricity consumption sequence, determine the abnormal electricity consumption and send an early warning message to the user's smart terminal.

[0019] Furthermore, the step 1 specifically includes: Step 1.1: Count the user's daily electricity consumption in the previous seven days at 0.5 hour intervals, and generate the user's daily electricity consumption sequence X i-7 ={x1 i-7 , x2 i-7 , ..., x j i-7 , ..., x 48 i-7}, X i-6 ={x1 i-6 , x2 i-6 ,..., x j i-6 , ..., x 48 i-6}, ..., X i-k ={x1 i-k , x2 i-k , ..., x j i-k , ..., x 48 i-k}, ..., X i-1 ={x1 i-1 , x2 i-1 , ..., x j i-1 , ..., x 48 i-1}; Step 1.2: Calculate the user's daily electricity consumption sequence X for the previous seven days i-7 ={x1 i-7 , x2 i-7 , ..., x ji-7 , ...,x 48 i-7}, X i-6 ={x1 i-6 , x2 i-6 , ..., x j i-6 , ..., x 48 i-6}, ..., X i-k ={x1 i-k , x2 i-k , ..., x j i -k , ..., x 48 i-k}, ..., X i-1 ={x1 i-1 , x2 i-1 , ..., x j i-1 , ..., x 48 i-1 The average power consumption of each time interval in the data is arranged in the original order to form a new sequence, which is the power consumption summary sequence S for the previous seven days. i-7:i-1 If the user has used the smart energy meter for less than seven days, the built-in reference power consumption sequence of the same power consumption type is obtained from the smart energy meter memory as the power consumption summary sequence S for the previous seven days. i-7:i-1 .

[0020] Furthermore, the step 2 specifically includes: Step 2.1: Based on the electricity consumption of the previous seven days, the sequence S i-7:i-1 , get the maximum value MAX S and minimum value MIN S , perform H S =(MAX S +MIN S )÷2, and get the intermediate value H S ; Step 2.2: Count the electricity consumption in the previous seven days i-7:i-1 The power consumption is less than the middle value H S The time interval is taken as the comprehensive electricity consumption low interval, and the electricity consumption of the previous seven days is summarized into a sequence S i-7:i-1 Among them, it is less than the middle value H S The average power consumption is calculated as the average power consumption in the comprehensive power consumption valley interval; Step 2.3: Count the electricity consumption in the previous seven days and summarize the sequence S i-7:i-1The power consumption is greater than or equal to the middle value H S The time interval is taken as the comprehensive power consumption peak interval, and the power consumption of the previous seven days is summarized into a sequence S i-7:i-1 Greater than or equal to the middle value H S The average power consumption is calculated as the average power consumption in the comprehensive peak power consumption period.

[0021] Furthermore, the step 5 specifically includes: Step 5.1: If the power consumption of the real-time power consumption sequence in four consecutive time intervals in the power consumption valley interval is greater than twice the average power consumption in the comprehensive power consumption valley interval, it is determined that there is an abnormal excessive power consumption, and an abnormal excessive power consumption warning message is sent to the user's smart terminal; Step 5.2: If the power consumption of the real-time power consumption sequence in two consecutive time intervals within the peak power consumption interval is greater than 2.5 times the average power consumption in the comprehensive peak power consumption interval, it is determined that there is an abnormal excessive power consumption, and an abnormal excessive power consumption warning message is sent to the user's smart terminal; Step 5.3: If the power consumption of the real-time power consumption sequence in the power consumption valley interval or the power consumption peak interval for four consecutive time intervals is greater than 2.5 times the average power consumption in the comprehensive power consumption peak interval, it is determined that there is an abnormal excessive power consumption, and an abnormal excessive power consumption warning message is sent to the user's smart terminal; Step 5.4: If the power consumption of the real-time power consumption sequence in all time intervals within the power consumption valley interval is greater than twice the average power consumption in the comprehensive power consumption valley interval, and the power consumption in all time intervals within the power consumption peak interval is greater than 2.5 times the average power consumption in the comprehensive power consumption peak interval, it is determined that there is a suspected power theft anomaly, and a suspected power theft anomaly warning information is sent to the user's smart terminal.

[0022] For example, the daily electricity consumption of a certain user in the previous seven days is counted, and the daily electricity consumption sequence of the user in the previous seven days is generated as X i-7 ,X i-6 ,X i-5 ,X i-4 ,X i-3 ,X i-2 ,X i-1 Calculate the daily electricity consumption sequence of the first seven days as X i-7 ,X i-6 ,X i-5 ,X i -4 ,X i-3 ,X i-2 ,X i-1 The average power consumption of each time interval is arranged in the original order to form a new sequence, which is the power consumption summary sequence S for the previous seven days. i-7:i-1, which can be expressed as S i-7:i-1 =X i-7 +X i-6 +X i-5 +X i-4 +X i-3 +X i-2 +X i-1 ={0.2, 0.2, 0.2, 0.12, 0.12, 0.11, 0.11, 0.11, 0.1, 0.1, 0.1, 0.1, 0.7, 0.7, 0.7, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.12, 0.9,1, 0.9, 1, 1, 1, 0.9, 0.9, 0.9, 0.8, 0.8}; According to the previous seven days power consumption summary sequence S i-7:i-1 , take the maximum value MAX S =1 and minimum value MIN S = 0.1, H S =(MAX S +MIN S )÷2, and get the intermediate value H S =0.55, generate the comprehensive electricity consumption valley interval [1,14], [17,36] (i.e. 0:00:00 to 6:59:59, 8:00:00 to 17:59:59) and the comprehensive electricity consumption peak interval [15,16], [37,48] (i.e. 7:00:00 to 7:59:59, 18:00:00 to 23:59:59), calculate the average electricity consumption in the comprehensive electricity consumption valley interval to be 0.12 and the average electricity consumption in the comprehensive electricity consumption peak interval to be 0.89; The user's electricity consumption is collected in real time. As of 7 a.m. today, the user's real-time electricity consumption sequence is X={0.2, 0.2, 0.2, 0.12, 0.12, 0.11, 0.11, 0.11, 0.1, 0.1, 0.3, 0.3, 0.3, 0.3}, and referring to the comprehensive electricity consumption valley interval and the comprehensive electricity consumption peak interval, the real-time electricity consumption valley interval is [1,14], [17,36] (i.e. 0:00:00 to 6:59:59, 8:00:00 to 17:59:59), and the real-time electricity consumption peak interval is [15,16], [37,48] (i.e. 7:00:00 to 7:59:59, 18:00:00 to 23:59:59). If the electricity consumption in four consecutive time intervals in the electricity consumption valley interval is greater than twice the average electricity consumption in the comprehensive electricity consumption valley interval, it is determined that there is an abnormal excessive electricity consumption, and an abnormal excessive electricity consumption warning message is sent to the user's smart terminal.

[0023] In summary, the smart energy meter electricity consumption behavior analysis and early warning method provided in the embodiment of the present application has the following technical effects: (1) By statistically analyzing the short-term historical electricity consumption behavior of users, we can obtain the behavioral characterization of their electricity consumption behavior, thus solving the problem that existing smart energy meters are difficult to conduct targeted electricity consumption behavior analysis on different users.

[0024] (2) The system can judge abnormal electricity usage behavior according to different actual situations and realize the function of early warning of abnormal electricity usage.

[0025] (3) The method is simple and the calculation process is concise. It does not require excessive calculation amount and memory space requirements and has small time complexity and space complexity. Only the embedded computing unit of the smart electric energy meter is required to support the calculation amount of the method and device proposed in the present invention.

[0026] Based on the same inventive concept as the smart energy meter electricity consumption behavior analysis and early warning method in the aforementioned embodiment, Figure 2 As shown, the present application provides a smart electric energy meter electricity consumption behavior analysis and early warning device, the device comprising: The statistical module is used to count the daily power consumption of the user in the previous seven days at a time interval of 0.5 hours, and generate a sequence X of the daily power consumption of the user in the previous seven days. i-7 ={x1 i-7 , x2 i-7 , ..., x j i-7 , ..., x 48 i-7},X i-6 ={x1 i-6 , x2 i-6 , ..., xj i-6 , ..., x 48 i-6}, ..., X i-k ={x1 i-k , x2 i-k , ..., x j i-k , ...,x 48 i-k}, ..., X i-1 ={x1 i-1 , x2 i-1 , ..., x j i-1 , ..., x 48 i-1}, and further generate the summary sequence S of electricity consumption in the previous seven days i-7:i-1 , where i represents the i-th day, X i-k represents the daily electricity consumption sequence of the user in the previous k days, x j i-k represents the power consumption of the user in the jth time interval of the previous k days, k=1,2,...,7, j=1,2,...,48; A calculation module is used to summarize the sequence S of the power consumption in the previous seven days. i-7:i-1 , generate comprehensive electricity consumption valley interval and comprehensive electricity consumption peak interval, calculate the average electricity consumption in the comprehensive electricity consumption valley interval and the average electricity consumption in the comprehensive electricity consumption peak interval; The acquisition module is used to collect the user's power consumption in real time at intervals of 0.5 hours to generate the user's real-time power consumption sequence X on the i-th day. i ={x1 i , x2 i , ......}; A division module is used to divide the real-time power consumption sequence X according to the comprehensive power consumption valley interval and the comprehensive power consumption peak interval. i ={x1 i , x2 i , ...}delineate the real-time electricity consumption low-peak interval and the real-time electricity consumption peak interval; The determination module is used to determine abnormal power consumption according to the real-time power consumption valley interval and the real-time power consumption peak interval defined for the real-time power consumption sequence, and send early warning information to the user's smart terminal.

[0027] Through the above-mentioned detailed description of the smart power meter electricity consumption behavior analysis and early warning method, those skilled in the art can clearly understand the smart power meter electricity consumption behavior analysis and early warning device in this embodiment. Since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant parts can be referred to the method part description.

[0028] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0029] The above description of the disclosed embodiments enables those skilled in the art to implement or use the present application. Various modifications to these embodiments will be apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the present application. Therefore, the present application will not be limited to the embodiments shown herein, but will conform to the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for analyzing and warning the electricity consumption behavior of a smart energy meter, characterized in that: The method comprises the following steps: Step 1: Count the user's daily electricity consumption in the previous seven days at 0.5 hour intervals, and generate the user's daily electricity consumption sequence X i-7 ={x1 i-7 , x2 i-7 , ..., x j i-7 , ..., x 48 i-7 }, X i-6 ={x1 i-6 , x2 i-6 , ...,x j i-6 , ..., x 48 i-6 }, ..., X i-k ={x1 i-k , x2 i-k , ..., x j i-k , ..., x 48 i-k }, ..., X i-1 ={x1 i-1 ,x2 i-1 , ..., x j i-1 , ..., x 48 i-1 }, and further generate the summary sequence S of electricity consumption in the previous seven days i-7:i-1 , where i represents the i-th day, X i-k represents the user's daily electricity consumption sequence in the previous k days, x j i-k represents the power consumption of the user in the jth time interval of the previous k days, k=1,2,...,7, j=1,2,...,48; Step 2: Summarize the power consumption of the previous seven days according to the sequence S i-7:i-1 , generate comprehensive electricity consumption valley intervals and comprehensive electricity consumption peak intervals, and calculate the average electricity consumption in the comprehensive electricity consumption valley interval and the average electricity consumption in the comprehensive electricity consumption peak interval; Step 3: Collect the user's electricity consumption in real time at 0.5 hour intervals to generate the user's real-time electricity consumption sequence X on the i-th day i ={x1 i , x2 i , ......}; Step 4: According to the comprehensive electricity consumption valley interval and comprehensive electricity consumption peak interval, the real-time electricity consumption sequence X i ={x1 i ,x2 i , ...}delineate the real-time electricity consumption low-peak interval and the real-time electricity consumption peak interval; Step 5: According to the real-time electricity consumption valley interval and the real-time electricity consumption peak interval defined for the real-time electricity consumption sequence, determine the abnormal electricity consumption and send an early warning message to the user's smart terminal.

2. The method for analyzing and warning the electricity consumption behavior of a smart electric energy meter according to claim 1, characterized in that: The step 1 specifically includes: Step 1.1: Count the user's daily electricity consumption in the previous seven days at 0.5 hour intervals, and generate the user's daily electricity consumption sequence X i-7 ={x1 i-7 , x2 i-7 , ..., x j i-7 , ..., x 48 i-7 }, X i-6 ={x1 i-6 , x2 i-6 , ...,x j i-6 , ..., x 48 i-6 }, ..., X i-k ={x1 i-k , x2 i-k , ..., x j i-k , ..., x 48 i-k }, ..., X i-1 ={x1 i-1 ,x2 i-1 , ..., x j i-1 , ..., x 48 i-1 }; Step 1.2: Calculate the user's daily electricity consumption sequence X for the previous seven days i-7 ={x1 i-7 , x2 i-7 , ..., x j i-7 , ..., x 48 i -7 }, X i-6 ={x1 i-6 , x2 i-6 , ..., x j i-6 , ..., x 48 i-6 }, ..., X i-k ={x1 i-k , x2 i-k , ..., x j i-k ,..., x 48 i-k }, ..., X i-1 ={x1 i-1 , x2 i-1 , ..., x j i-1 , ..., x 48 i-1 The average power consumption of each time interval in the data is arranged in the original order to form a new sequence, which is the power consumption summary sequence S for the previous seven days. i-7:i-1 If the user has used the smart energy meter for less than seven days, the built-in reference power consumption sequence of the same power consumption type is obtained from the smart energy meter memory as the power consumption summary sequence S for the previous seven days. i-7:i-1 .

3. The method for analyzing and warning the electricity consumption behavior of a smart electric energy meter according to claim 1, characterized in that: The step 2 specifically includes: Step 2.1: Based on the electricity consumption of the previous seven days, the sequence S i-7:i-1 , get the maximum value MAX S and minimum value MIN S , perform H S =(MAX S +MIN S )÷2, and get the intermediate value H S ; Step 2.2: Count the electricity consumption in the previous seven days i-7:i-1 The power consumption is less than the middle value H S The time interval is taken as the comprehensive electricity consumption low interval, and the electricity consumption of the previous seven days is summarized into a sequence S i-7:i-1 Among them, it is less than the middle value H S The average power consumption is calculated as the average power consumption in the comprehensive power consumption valley interval; Step 2.3: Count the electricity consumption in the previous seven days and summarize the sequence S i-7:i-1 The power consumption is greater than or equal to the middle value H S The time interval is taken as the comprehensive power consumption peak interval, and the power consumption of the previous seven days is summarized into a sequence S i-7:i-1 Greater than or equal to the middle value H S The average power consumption is calculated as the average power consumption in the comprehensive peak power consumption period.

4. The method for analyzing and warning the electricity consumption behavior of a smart electric energy meter according to claim 1, characterized in that: The step 5 specifically includes: Step 5.1: If the power consumption of the real-time power consumption sequence in four consecutive time intervals in the power consumption valley interval is greater than twice the average power consumption in the comprehensive power consumption valley interval, it is determined that there is an abnormal excessive power consumption, and an abnormal excessive power consumption warning message is sent to the user's smart terminal; Step 5.2: If the power consumption of the real-time power consumption sequence in two consecutive time intervals within the peak power consumption interval is greater than 2.5 times the average power consumption in the comprehensive peak power consumption interval, it is determined that there is an abnormal excessive power consumption, and an abnormal excessive power consumption warning message is sent to the user's smart terminal; Step 5.3: If the power consumption of the real-time power consumption sequence in the power consumption valley interval or the power consumption peak interval for four consecutive time intervals is greater than 2.5 times the average power consumption in the comprehensive power consumption peak interval, it is determined that there is an abnormal excessive power consumption, and an abnormal excessive power consumption warning message is sent to the user's smart terminal; Step 5.4: If the power consumption of the real-time power consumption sequence in all time intervals within the power consumption valley interval is greater than twice the average power consumption in the comprehensive power consumption valley interval, and the power consumption in all time intervals within the power consumption peak interval is greater than 2.5 times the average power consumption in the comprehensive power consumption peak interval, it is determined that there is a suspected power theft anomaly, and a suspected power theft anomaly warning information is sent to the user's smart terminal.

5. The smart electric energy meter electricity consumption behavior analysis and early warning device is characterized by: The device comprises: The statistical module is used to count the daily power consumption of the user in the previous seven days at a time interval of 0.5 hours, and generate a sequence X of the daily power consumption of the user in the previous seven days. i-7 ={x1 i-7 , x2 i-7 , ..., x j i-7 , ..., x 48 i-7 }, X i-6 ={x1 i-6 , x2 i-6 , ..., x j i-6 , ..., x 48 i-6 }, ..., X i-k ={x1 i-k , x2 i-k , ..., x j i-k , ..., x 48 i -k }, ..., X i-1 ={x1 i-1 , x2 i-1 , ..., x j i-1 , ..., x 48 i-1 }, and further generate the summary sequence S of electricity consumption in the previous seven days i-7:i-1 , where i represents the i-th day, X i-k represents the user's daily electricity consumption sequence in the previous k days, x j i-k represents the power consumption of the user in the jth time interval of the previous k days, k=1,2,...,7, j=1,2,...,48; A calculation module is used to summarize the sequence S of the power consumption in the previous seven days. i-7:i-1 , generate comprehensive electricity consumption valley intervals and comprehensive electricity consumption peak intervals, and calculate the average electricity consumption in the comprehensive electricity consumption valley interval and the average electricity consumption in the comprehensive electricity consumption peak interval; The acquisition module is used to collect the user's power consumption in real time at intervals of 0.5 hours to generate the user's real-time power consumption sequence X on the i-th day. i ={x1 i , x2 i , ......}; A division module is used to divide the real-time power consumption sequence X according to the comprehensive power consumption valley interval and the comprehensive power consumption peak interval. i ={x1 i , x2 i , ...}delineate the real-time electricity consumption low-peak interval and the real-time electricity consumption peak interval; The determination module is used to determine abnormal power consumption according to the real-time power consumption valley interval and the real-time power consumption peak interval defined for the real-time power consumption sequence, and send early warning information to the user's smart terminal.

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