A method and device for monitoring and early warning of power consumption

Abstract

The embodiment of the present invention discloses a method and device for monitoring and warning electricity consumption, including: obtaining historical electricity consumption data of a user in response to a data modeling instruction, generating an electricity consumption distribution curve based on the historical electricity consumption data, collecting real-time electricity consumption data of a target object, and comparing whether the real-time electricity consumption data exceeds the range of the electricity consumption distribution curve; calculating the value of the real-time electricity consumption data exceeding the range of the electricity consumption distribution curve, and generating a warning message when the value exceeds a set threshold. In the embodiment, since the overall historical electricity consumption of multiple users is fully considered, a certain target object is intelligently compared with the overall situation, and real-time electricity consumption data is collected, so that the electricity consumption situation in the current time period can be monitored and analyzed in a timely manner, and a warning can be issued in a timely manner when there is an abnormality.

Description

Technical Field

[0001] The present invention relates to the technical field of anti-electricity theft, and in particular to an electricity consumption monitoring and early warning method and device. Background Art

[0002] At present, the losses caused by a series of deceptive electricity use behaviors such as electricity theft and fraud by power users on the distribution side are collectively referred to as non-technical losses NTL (Non-Technical Loss). Non-technical losses will have a serious impact on the normal operation of the power grid, disrupt the normal dispatch of the regional power grid, and even cause safety accidents due to users' private line changes. The current anti-electricity theft situation still relies on the comparison and analysis of the theoretical line loss calculation value of a typical day with the actual management line loss value, and further proposes loss reduction measures; the traditional line loss management model has various drawbacks such as time asynchrony and data inaccuracy, which cannot reflect the real line loss status, and cannot propose more accurate anti-electricity theft and loss reduction measures. Usually, the same power supply area is divided into users of different electricity usage levels, and the electricity usage characteristics of different users are different. At present, the countermeasures for anti-electricity theft are often limited to adding detection hardware to the meter terminal to help determine whether the meter's operating speed has been changed by magnetic equipment, etc., but cannot use digital intelligent analysis to timely warn users of electricity theft. Summary of the invention

[0003] In view of the above-mentioned defects, an embodiment of the present invention discloses a method and device for monitoring and warning electricity consumption, which can intelligently monitor electricity consumption behavior and issue timely warnings for electricity theft behavior.

[0004] A first aspect of an embodiment of the present invention discloses a method for monitoring and early warning of power consumption, comprising:

[0005] In response to the data modeling instruction, acquiring historical power consumption data of the user, and generating a power consumption distribution curve based on the historical power consumption data, wherein the power consumption distribution curve includes a relationship between different time periods and power consumption;

[0006] Collecting real-time power consumption data of the target object, and comparing the real-time power consumption data to see whether it exceeds the power consumption distribution curve range;

[0007] Calculate the value of the real-time power consumption data that exceeds the power consumption distribution curve range, and generate an early warning message when the value exceeds a set threshold.

[0008] As an optional implementation manner, in a first aspect of an embodiment of the present invention, generating a power consumption distribution curve based on the historical power consumption data includes:

[0009] Divide 24 hours into different time periods, and divide the historical electricity consumption data of the year into historical electricity consumption data corresponding to the four seasons of spring, summer, autumn and winter according to local climate factors;

[0010] The historical electricity consumption data of different time periods of each day in the four seasons are collected and counted to obtain the maximum and minimum electricity consumption thresholds of different time periods of each day in different seasons;

[0011] The maximum power consumption thresholds in different seasons and different time periods are connected to draw a first curve, and the minimum power consumption thresholds in different seasons and different time periods are connected to draw a second curve;

[0012] A power consumption distribution curve is generated based on the first curve and the second curve.

[0013] As an optional implementation, in the first aspect of the embodiment of the present invention, the maximum power consumption thresholds in different time periods in different seasons are connected to draw a first curve, and the minimum power consumption thresholds in different time periods in different seasons are connected to draw a second curve, including:

[0014] Set a first coefficient and a second coefficient, the first coefficient is greater than 1, and the second coefficient is less than 1;

[0015] Based on the formula d max *a1 generates the upper limit of the power consumption threshold for different time periods, and based on formula d min *a2 generates the lower limit of the power consumption threshold for different time periods, where d max is the maximum power consumption threshold in different time periods, d min is the minimum power consumption threshold in different time periods, a1 is the first coefficient, and a2 is the second coefficient;

[0016] A first curve is drawn by connecting the upper limits of the power consumption thresholds in different seasons and time periods, and a second curve is drawn by connecting the lower limits of the power consumption thresholds in different seasons and time periods.

[0017] As an optional implementation manner, in a first aspect of an embodiment of the present invention, generating a power consumption distribution curve based on the historical power consumption data includes:

[0018] Divide 24 hours into different time periods, and divide the historical electricity consumption data of the year into historical electricity consumption data corresponding to the four seasons of spring, summer, autumn and winter according to local climate factors;

[0019] The historical electricity consumption data of different time periods of each day in the four seasons are collected and counted to obtain the maximum and minimum electricity consumption thresholds of different time periods of each day in different seasons;

[0020] Obtain historical electricity consumption data of other local users, and generate electricity consumption distribution ranges at different time periods of each day in four seasons based on the historical electricity consumption data of other users;

[0021] Compare and see whether the maximum power consumption threshold and the minimum power consumption threshold are within the power consumption distribution range. If so, generate a power consumption distribution curve based on the maximum power consumption threshold and the minimum power consumption threshold in different time periods; otherwise, correct the maximum power consumption threshold and the minimum power consumption threshold based on the power consumption distribution range, and generate a power consumption distribution curve based on the corrected maximum power consumption threshold and the minimum power consumption threshold.

[0022] As an optional implementation manner, in the first aspect of the embodiment of the present invention, the maximum power consumption threshold and the minimum power consumption threshold are corrected based on the power consumption distribution range, including:

[0023] Obtain the maximum and minimum distribution values ​​in the power consumption distribution range in different time periods in different seasons;

[0024] The middle value between the maximum distribution value and the maximum power consumption threshold is obtained as the corrected maximum power consumption threshold, and the middle value between the minimum distribution value and the minimum power consumption threshold is obtained as the corrected minimum power consumption threshold.

[0025] As an optional implementation manner, in the first aspect of the embodiment of the present invention, when the value exceeds a set threshold, generating warning information includes:

[0026] When the value exceeds the first set threshold value and does not exceed the second set threshold value, the historical electricity consumption information of the target object is obtained, and the credit rating of the target object is generated based on the historical electricity consumption information, and when the credit rating of the target object is lower than the set standard, an early warning message is generated, and when the credit rating of the target object is higher than the set standard, the real-time electricity consumption data of the target object in the next time period is continuously monitored to see whether it exceeds the electricity consumption distribution curve, and an early warning message is generated when the real-time electricity consumption data in the next time period exceeds the electricity consumption distribution curve;

[0027] When the value exceeds the second set threshold, a warning message is directly generated.

[0028] As an optional implementation manner, in the first aspect of the embodiment of the present invention, it also includes:

[0029] The real-time electricity consumption data of the target object is continuously monitored, and when the real-time electricity consumption data of the target object exceeds the range of the electricity consumption distribution curve in the same time period on the second day as on the current day, the target object is marked as a power theft risk object.

[0030] A second aspect of an embodiment of the present invention discloses a power consumption monitoring and early warning device, which is characterized by comprising:

[0031] A historical curve generating module: in response to a data modeling instruction, obtaining the historical power consumption data of the user, and generating a power consumption distribution curve based on the historical power consumption data, wherein the power consumption distribution curve includes the relationship between different time periods and power consumption;

[0032] Real-time power consumption collection module: collects the real-time power consumption data of the target object and compares the real-time power consumption data to see whether it exceeds the power consumption distribution curve range;

[0033] Warning information generation module: used to calculate the value of real-time power consumption data that exceeds the power consumption distribution curve range, and generate warning information when the value exceeds the set threshold.

[0034] As an optional implementation manner, in a second aspect of the embodiment of the present invention, generating a power consumption distribution curve based on the historical power consumption data includes:

[0035] Divide 24 hours into different time periods, and divide the historical electricity consumption data of the year into historical electricity consumption data corresponding to the four seasons of spring, summer, autumn and winter according to local climate factors;

[0036] The historical electricity consumption data of different time periods of each day in the four seasons are collected and counted to obtain the maximum and minimum electricity consumption thresholds of different time periods of each day in different seasons;

[0037] The maximum power consumption thresholds in different seasons and different time periods are connected to draw a first curve, and the minimum power consumption thresholds in different seasons and different time periods are connected to draw a second curve;

[0038] A power consumption distribution curve is generated based on the first curve and the second curve.

[0039] As an optional implementation, in the second aspect of the embodiment of the present invention, the maximum power consumption thresholds in different time periods in different seasons are connected to draw a first curve, and the minimum power consumption thresholds in different time periods in different seasons are connected to draw a second curve, including:

[0040] Set a first coefficient and a second coefficient, the first coefficient is greater than 1, and the second coefficient is less than 1;

[0041] Based on the formula d max *a1 generates the upper limit of the power consumption threshold for different time periods, and based on formula d min *a2 generates the lower limit of the power consumption threshold for different time periods, where d max is the maximum power consumption threshold in different time periods, d min is the minimum power consumption threshold in different time periods, a1 is the first coefficient, and a2 is the second coefficient;

[0042] A first curve is drawn by connecting the upper limits of the power consumption thresholds in different seasons and time periods, and a second curve is drawn by connecting the lower limits of the power consumption thresholds in different seasons and time periods.

[0043] As an optional implementation manner, in a second aspect of the embodiment of the present invention, generating a power consumption distribution curve based on the historical power consumption data includes:

[0044] Divide 24 hours into different time periods, and divide the historical electricity consumption data of the year into historical electricity consumption data corresponding to the four seasons of spring, summer, autumn and winter according to local climate factors;

[0045] The historical electricity consumption data of different time periods of each day in the four seasons are collected and counted to obtain the maximum and minimum electricity consumption thresholds of different time periods of each day in different seasons;

[0046] Obtain historical electricity consumption data of other local users, and generate electricity consumption distribution ranges at different time periods of each day in four seasons based on the historical electricity consumption data of other users;

[0047] Compare and see whether the maximum power consumption threshold and the minimum power consumption threshold are within the power consumption distribution range. If so, generate a power consumption distribution curve based on the maximum power consumption threshold and the minimum power consumption threshold in different time periods; otherwise, correct the maximum power consumption threshold and the minimum power consumption threshold based on the power consumption distribution range, and generate a power consumption distribution curve based on the corrected maximum power consumption threshold and the minimum power consumption threshold.

[0048] As an optional implementation manner, in the second aspect of the embodiment of the present invention, the maximum power consumption threshold and the minimum power consumption threshold are corrected based on the power consumption distribution range, including:

[0049] Obtain the maximum and minimum distribution values ​​in the power consumption distribution range in different time periods in different seasons;

[0050] The middle value between the maximum distribution value and the maximum power consumption threshold is obtained as the corrected maximum power consumption threshold, and the middle value between the minimum distribution value and the minimum power consumption threshold is obtained as the corrected minimum power consumption threshold.

[0051] As an optional implementation, in the second aspect of the embodiment of the present invention, when the value exceeds a set threshold, generating warning information includes:

[0052] When the value exceeds the first set threshold value and does not exceed the second set threshold value, the historical electricity consumption information of the target object is obtained, and the credit rating of the target object is generated based on the historical electricity consumption information, and when the credit rating of the target object is lower than the set standard, an early warning message is generated, and when the credit rating of the target object is higher than the set standard, the real-time electricity consumption data of the target object in the next time period is continuously monitored to see whether it exceeds the electricity consumption distribution curve, and an early warning message is generated when the real-time electricity consumption data in the next time period exceeds the electricity consumption distribution curve;

[0053] When the value exceeds the second set threshold, a warning message is directly generated.

[0054] As an optional implementation manner, in the second aspect of the embodiment of the present invention, it also includes:

[0055] The real-time electricity consumption data of the target object is continuously monitored, and when the real-time electricity consumption data of the target object exceeds the range of the electricity consumption distribution curve in the same time period on the second day as on the current day, the target object is marked as a power theft risk object.

[0056] The third aspect of an embodiment of the present invention discloses an electronic device, comprising: a memory storing executable program code; a processor coupled to the memory; the processor calls the executable program code stored in the memory to execute the power consumption monitoring and early warning method disclosed in the first aspect of the embodiment of the present invention.

[0057] A fourth aspect of an embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute the power consumption monitoring and early warning method disclosed in the first aspect of an embodiment of the present invention.

[0058] Compared with the prior art, the embodiments of the present invention have the following beneficial effects:

[0059] In the embodiment of the present invention, the historical electricity consumption data of the user is obtained to generate the relationship between different time periods and electricity consumption, and then the real-time electricity consumption data of the target object is compared to see whether it exceeds the electricity consumption distribution curve, and then whether to generate warning information is determined according to the size of the excess of the electricity consumption distribution curve. Since the overall historical electricity consumption of multiple users is fully considered, a certain target object is intelligently compared with the overall situation, and real-time electricity consumption data is collected, which can timely monitor and analyze the electricity consumption in the current time period, and can issue timely warnings when there are abnormalities. BRIEF DESCRIPTION OF THE DRAWINGS

[0060] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the drawings required for use in the embodiments 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.

[0061] Figure 1 It is a flow chart of a method for monitoring and early warning of power consumption disclosed in an embodiment of the present invention;

[0062] Figure 2 It is a flow chart of another power consumption monitoring and early warning method disclosed in an embodiment of the present invention;

[0063] Figure 3 It is a flow chart of another power consumption monitoring and early warning method disclosed in an embodiment of the present invention;

[0064] Figure 4 It is a structural schematic diagram of a power consumption monitoring and early warning device provided by an embodiment of the present invention;

[0065] Figure 5 It is a structural schematic diagram of an electronic device provided by an embodiment of the present invention. DETAILED DESCRIPTION

[0066] 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.

[0067] It should be noted that the terms "first", "second", "third", "fourth", etc. in the specification and claims of the present invention are used to distinguish different objects rather than to describe a specific order. The terms "including" and "having" in the embodiments of the present invention and any variations thereof are intended to cover non-exclusive inclusions. For example, a process, method, system, product or device including a series of steps or units is not necessarily limited to those steps or units clearly listed, but may include other steps or units that are not clearly listed or inherent to these processes, methods, products or devices.

[0068] The embodiments of the present invention disclose a method, device, electronic device and storage medium for monitoring and warning of electricity consumption. In the embodiments, historical electricity consumption data of users is obtained to generate the relationship between different time periods and electricity consumption, and then the real-time electricity consumption data of the target object is compared to see whether it exceeds the electricity consumption distribution curve, and then whether to generate warning information is determined according to the size of the excess of the electricity consumption distribution curve. Since the overall historical electricity consumption of multiple users is fully considered, a certain target object is intelligently compared with the overall situation, and real-time electricity consumption data is collected, so that the electricity consumption situation in the current time period can be monitored and analyzed in a timely manner, and a warning can be issued in time when there is an abnormality.

[0069] Embodiment 1

[0070] See also Figure 1 , Figure 1It is a flow chart of a method for monitoring and early warning of electricity consumption disclosed in an embodiment of the present invention. Among them, the execution subject of the method described in the embodiment of the present invention is an execution subject composed of software and / or hardware, and the execution subject can receive relevant information by wired or / and wireless means, and can send certain instructions. Of course, it can also have certain processing functions and storage functions. The execution subject can control multiple devices, such as a remote physical server or cloud server and related software, or it can be a local host or server and related software that performs related operations on a device placed somewhere. In some scenarios, multiple storage devices can also be controlled, and the storage devices can be placed in the same place or different places as the devices. For example Figure 1 As shown, the power consumption monitoring and early warning method includes the following steps:

[0071] 101. In response to a data modeling instruction, obtain historical electricity consumption data of a user, and generate an electricity consumption distribution curve based on the historical electricity consumption data, wherein the electricity consumption distribution curve includes a relationship between different time periods and electricity consumption.

[0072] In this step, the user refers to an electricity user in a certain administrative area, which may be an individual or an enterprise, etc. Obtaining the historical electricity consumption data of users usually refers to obtaining the historical electricity consumption data of multiple users in an area to expand the sample capacity and increase the accuracy of the data. The electricity consumption distribution curve expresses the electricity consumption corresponding to different time periods. For example, a year is divided into four seasons, and each 24 hours a day in each season is divided into different time periods. The maximum and minimum electricity consumption of each time period are counted respectively, and the electricity consumption distribution interval corresponding to the time period is generated according to the maximum and minimum electricity consumption. The maximum electricity consumption of each time period is connected to form a maximum electricity consumption distribution curve, and the minimum electricity consumption of each time period is connected to form a minimum electricity consumption distribution curve, and then the maximum electricity consumption distribution curve and the minimum electricity consumption distribution curve are combined to form the electricity consumption distribution curve.

[0073] 102. Collect real-time power consumption data of the target object, and compare whether the real-time power consumption data exceeds the range of the power consumption distribution curve.

[0074] In the embodiment, the target object refers to the object currently being monitored and analyzed for data in a certain administrative area, which may be one of the individual users in step 101, or one of the enterprises. In fact, many users can be monitored simultaneously, but since each user is predicted by separate data analysis, the target objects are used to distinguish them in the embodiment. Real-time electricity consumption data refers to the electricity consumption parameters of the monitored user in the current time period, including how many degrees the meter has turned. The unit of real-time electricity consumption data is the same as that of the electricity consumption distribution curve, which is convenient for comparison. In this application, since it is an early warning for electricity theft, whether the real-time electricity consumption data exceeds the range of the electricity consumption distribution curve usually corresponds to whether the real-time electricity consumption data is lower than the range of the electricity consumption distribution curve. When the electricity consumption is lower than the electricity consumption threshold, it indicates abnormal electricity consumption.

[0075] 103. Calculate the value of the real-time power consumption data that exceeds the power consumption distribution curve range, and generate a warning message when the value exceeds a set threshold.

[0076] When the target object exceeds the range of the power distribution curve, the real-time power consumption data is analyzed to see how much it exceeds the range of the power distribution curve. The excess value is then calculated. For example, if the real-time power consumption in the current time period is a, and the power distribution curve for this time period is b, the difference between a and b is calculated to be the excess value.

[0077] In another example, Figure 2 Another power consumption monitoring and early warning method is also shown. Figure 2 As shown, the power consumption monitoring and early warning method includes:

[0078] 201. Obtain historical electricity usage data of a user in response to a data modeling instruction.

[0079] 202. Divide 24 hours into different time periods, and divide the historical electricity consumption data of the year into historical electricity consumption data corresponding to the four seasons of spring, summer, autumn and winter according to local climate factors.

[0080] Since the time of the four seasons is different in different regions, the four seasons of spring, summer and autumn are divided according to the climate of different regions. The purpose of dividing the four seasons is to deal with the reasonable electricity consumption in different time periods. For example, the southern region usually consumes more electricity in summer and less in winter.

[0081] 203. Collect statistics on historical electricity consumption data in different time periods of each day in four seasons to obtain maximum electricity consumption thresholds and minimum electricity consumption thresholds in different time periods of each day in different seasons.

[0082] 204. Connect the maximum power consumption thresholds in different seasons and time periods to draw a first curve, and connect the minimum power consumption thresholds in different seasons and time periods to draw a second curve.

[0083] Specifically, a first coefficient and a second coefficient are set, the first coefficient is greater than 1, and the second coefficient is less than 1; based on formula d max *a1 generates the upper limit of the power consumption threshold for different time periods, and based on formula d min *a2 generates the lower limit of the power consumption threshold for different time periods, where d max is the maximum power consumption threshold in different time periods, d min is the minimum power consumption threshold in different time periods, a1 is the first coefficient, and a2 is the second coefficient; the first curve is drawn according to the upper limits of the power consumption threshold in different seasons and time periods, and the second curve is drawn according to the lower limits of the power consumption threshold in different seasons and time periods.

[0084] 205. Draw and generate a power consumption distribution curve based on the first curve and the second curve, where the power consumption distribution curve includes a relationship between different time periods and power consumption.

[0085] 206. Collect real-time power consumption data of the target object, and compare whether the real-time power consumption data exceeds the range of the power consumption distribution curve.

[0086] 207. Calculate a value of the real-time power consumption data that exceeds the power consumption distribution curve range, and generate a warning message when the value exceeds a set threshold.

[0087] In another example, Figure 3 Another power consumption monitoring and early warning method is also shown. Figure 3 As shown, the power consumption monitoring and early warning method includes:

[0088] 301. Obtain historical electricity usage data of a user in response to a data modeling instruction.

[0089] 302. Divide 24 hours into different time periods, and divide the historical electricity consumption data of the year into historical electricity consumption data corresponding to the four seasons of spring, summer, autumn and winter according to local climate factors.

[0090] 303. Collect statistics on historical electricity consumption data in different time periods of each day in four seasons to obtain maximum electricity consumption thresholds and minimum electricity consumption thresholds in different time periods of each day in different seasons.

[0091] 304. Obtain historical electricity consumption data of other local users, and generate electricity consumption distribution ranges in different time periods of each day in four seasons based on the historical electricity consumption data of other users.

[0092] 305. Compare the maximum power consumption threshold and the minimum power consumption threshold to see whether they are within the power consumption distribution range. If so, generate a power consumption distribution curve based on the maximum power consumption threshold and the minimum power consumption threshold in different time periods. Otherwise, correct the maximum power consumption threshold and the minimum power consumption threshold based on the power consumption distribution range, and generate a power consumption distribution curve based on the corrected maximum power consumption threshold and the minimum power consumption threshold, wherein the power consumption distribution curve includes the relationship between different time periods and power consumption.

[0093] In this step, the maximum power consumption threshold and the minimum power consumption threshold are corrected based on the power consumption distribution range, including: obtaining the maximum distribution value and the minimum distribution value in the power consumption distribution range in different time periods in different seasons; obtaining the middle value between the maximum distribution value and the maximum power consumption threshold as the corrected maximum power consumption threshold, and obtaining the middle value between the minimum distribution value and the minimum power consumption threshold as the corrected minimum power consumption threshold.

[0094] 306. Collect real-time power consumption data of the target object, and compare whether the real-time power consumption data exceeds the power consumption distribution curve range.

[0095] 307. Calculate the value of the real-time power consumption data that exceeds the power consumption distribution curve range, and generate warning information when the value exceeds a set threshold.

[0096] In this step, when the value exceeds the first set threshold and does not exceed the second set threshold, the historical electricity consumption information of the target object is obtained, and the credit level of the target object is generated based on the historical electricity consumption information, and when the credit level of the target object is lower than the set standard, an early warning message is generated, and when the credit level of the target object is higher than the set standard, the real-time electricity consumption data of the target object in the next time period is continuously monitored to see whether it exceeds the electricity consumption distribution curve, and an early warning message is generated when the real-time electricity consumption data in the next time period exceeds the electricity consumption distribution curve; when the value exceeds the second set threshold, an early warning message is directly generated.

[0097] 308. Continuously monitor the real-time electricity consumption data of the target object, and when the real-time electricity consumption data of the target object exceeds the range of the electricity consumption distribution curve in the same time period on the second day as on the current day, mark the target object as an electricity theft risk object.

[0098] Embodiment 2

[0099] See also Figure 4 , Figure 4 Schematic diagram of the structure of a power consumption monitoring and early warning device disclosed in an embodiment of the present invention. Figure 4As shown, the power consumption monitoring and early warning device may include: a historical curve generation module 401, a real-time power consumption collection module 402 and an early warning information generation module 403, wherein the historical curve generation module 401: obtains the user's historical power consumption data in response to the data modeling instruction, and generates a power consumption distribution curve based on the historical power consumption data, wherein the power consumption distribution curve includes the relationship between different time periods and power consumption; the real-time power consumption collection module 402: collects the real-time power consumption data of the target object, and compares the real-time power consumption data to see whether it exceeds the range of the power consumption distribution curve; the early warning information generation module 403: is used to calculate the value of the real-time power consumption data that exceeds the range of the power consumption distribution curve, and generates early warning information when the value exceeds the set threshold.

[0100] In the historical curve generation module 401, 24 hours are specifically divided into different time periods, and the historical electricity consumption data of a year is divided into historical electricity consumption data corresponding to the four seasons of spring, summer, autumn and winter according to local climatic factors; the historical electricity consumption data of different time periods of each day in the four seasons are statistically analyzed to obtain the maximum electricity consumption threshold and the minimum electricity consumption threshold of different time periods of each day in different seasons; the maximum electricity consumption thresholds of different time periods in different seasons are connected to draw a first curve, and the minimum electricity consumption thresholds of different time periods in different seasons are connected to draw a second curve; based on the first curve and the second curve, a power consumption distribution curve is generated.

[0101] In the above, the maximum power consumption thresholds of different seasons and different time periods are connected to draw a first curve, and the minimum power consumption thresholds of different seasons and different time periods are connected to draw a second curve, including: setting a first coefficient and a second coefficient, the first coefficient is greater than 1, and the second coefficient is less than 1; based on formula d max *a1 generates the upper limit of the power consumption threshold for different time periods, and based on formula d min *a2 generates the lower limit of the power consumption threshold for different time periods, where d max is the maximum power consumption threshold in different time periods, d min is the minimum power consumption threshold in different time periods, a1 is the first coefficient, and a2 is the second coefficient; the first curve is drawn according to the upper limits of the power consumption threshold in different seasons and time periods, and the second curve is drawn according to the lower limits of the power consumption threshold in different seasons and time periods.

[0102] In another example of the historical curve generation module 401, 24 hours can be divided into different time periods, and the historical electricity consumption data of a year can be divided into historical electricity consumption data corresponding to the four seasons of spring, summer, autumn and winter according to local climatic factors; the historical electricity consumption data of different time periods every day in the four seasons are counted respectively to obtain the maximum electricity consumption threshold and the minimum electricity consumption threshold for different time periods every day in different seasons; the historical electricity consumption data of other local users are obtained, and the electricity consumption distribution range for different time periods every day in the four seasons is generated based on the historical electricity consumption data of other users; the maximum electricity consumption threshold and the minimum electricity consumption threshold are compared to see whether they are within the electricity distribution range, and if so, the electricity distribution curve is generated based on the maximum electricity consumption threshold and the minimum electricity consumption threshold for different time periods; otherwise, the maximum electricity consumption threshold and the minimum electricity consumption threshold are corrected based on the electricity distribution range, and the electricity distribution curve is generated based on the corrected maximum electricity consumption threshold and the minimum electricity consumption threshold.

[0103] In the above, the maximum power consumption threshold and the minimum power consumption threshold are corrected based on the power consumption distribution range, including: obtaining the distribution maximum value and the distribution minimum value in the power consumption distribution range in different time periods in different seasons; obtaining the middle value between the distribution maximum value and the maximum power consumption threshold as the corrected maximum power consumption threshold, and obtaining the middle value between the distribution minimum value and the minimum power consumption threshold as the corrected minimum power consumption threshold.

[0104] In the warning information generation module 403, when the value exceeds the first set threshold and does not exceed the second set threshold, the historical electricity consumption information of the target object is obtained, and the credit level of the target object is generated based on the historical electricity consumption information, and when the credit level of the target object is lower than the set standard, a warning message is generated; when the credit level of the target object is higher than the set standard, the real-time electricity consumption data of the target object in the next time period is continuously monitored to see whether it exceeds the electricity consumption distribution curve, and a warning message is generated when the real-time electricity consumption data in the next time period exceeds the electricity consumption distribution curve; when the value exceeds the second set threshold, a warning message is directly generated.

[0105] The embodiment may further include a power theft risk marking module for continuously monitoring the real-time power consumption data of the target object, and marking the target object as a power theft risk object when the real-time power consumption data of the target object exceeds the power consumption distribution curve range in the same time period as the same day on the second day.

[0106] Embodiment 3

[0107] See also Figure 5 , Figure 5 Schematic diagram of the structure of an electronic device disclosed in an embodiment of the present invention. The electronic device may be a computer, a server, etc. Of course, in certain circumstances, it may also be a smart device such as a mobile phone, a tablet computer, a monitoring terminal, and an image acquisition device with processing functions. Figure 5As shown, the electronic device may include:

[0108] A memory 501 storing executable program codes;

[0109] a processor 502 coupled to the memory 501;

[0110] The processor 502 calls the executable program code stored in the memory 501 to execute part or all of the steps in the power consumption monitoring and early warning method in the first embodiment.

[0111] An embodiment of the present invention discloses a computer-readable storage medium storing a computer program, wherein the computer program enables a computer to execute part or all of the steps in the power consumption monitoring and early warning method in the first embodiment.

[0112] The embodiment of the present invention further discloses a computer program product, wherein when the computer program product is run on a computer, the computer is enabled to execute part or all of the steps in the power consumption monitoring and early warning method in the first embodiment.

[0113] An embodiment of the present invention further discloses an application publishing platform, wherein the application publishing platform is used to publish a computer program product, wherein when the computer program product runs on a computer, the computer executes some or all of the steps in the power consumption monitoring and early warning method in embodiment one.

[0114] In various embodiments of the present invention, it should be understood that the size of the serial numbers of the processes does not necessarily mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0115] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, i.e., they may be located in one place or distributed over multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0116] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0117] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-accessible memory. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution, can be embodied in the form of a software product, which is stored in a memory and includes several requests for a computer device (which can be a personal computer, a server or a network device, etc., specifically a processor in a computer device) to perform some or all of the steps of the method described in each embodiment of the present invention.

[0118] In the embodiments provided by the present invention, it should be understood that "B corresponding to A" means that B is associated with A, and B can be determined according to A. However, it should also be understood that determining B according to A does not mean determining B only according to A, and B can also be determined according to A and / or other information.

[0119] A person of ordinary skill in the art can understand that some or all of the steps in the various methods of the embodiments can be completed by instructing related hardware through a program, and the program can be stored in a computer-readable storage medium, and the storage medium includes a read-only memory (ROM), a random access memory (RAM), a programmable read-only memory (PROM), an erasable programmable read-only memory (EPROM), a one-time programmable read-only memory (OTPROM), an electronically erasable rewritable read-only memory (EEPROM), a compact disc (CD-ROM) or other optical disc storage, magnetic disk storage, magnetic tape storage, or any other computer-readable medium that can be used to carry or store data.

[0120] The above is a detailed introduction to the electricity consumption monitoring and early warning method, device, electronic device and storage medium disclosed in the embodiments of the present invention. Specific examples are used in this article to illustrate the principles and implementation methods of the present invention. The description of the above embodiments is only used to help understand the method of the present invention and its core idea; at the same time, for general technical personnel in this field, according to the idea of ​​the present invention, there will be changes in the specific implementation method and application scope. In summary, the content of this specification should not be understood as a limitation on the present invention.

Claims

1. A method for monitoring and early warning of power consumption, characterized in that: include: In response to the data modeling instruction, the user's historical electricity consumption data is obtained, 24 hours are divided into different time periods, and the historical electricity consumption data in a year is divided into historical electricity consumption data corresponding to the four seasons of spring, summer, autumn and winter according to local climate factors; the historical electricity consumption data of different time periods of each day in the four seasons are respectively counted, and the maximum electricity consumption threshold and the minimum electricity consumption threshold of different time periods of each day in different seasons are obtained, the maximum electricity consumption threshold of different time periods in different seasons are connected to draw a first curve, the minimum electricity consumption threshold of different time periods in different seasons are connected to draw a second curve, and a power consumption distribution curve is generated based on the first curve and the second curve; or, 24 hours are divided into different time periods, and one time period is divided into four time periods according to local climate factors. The historical electricity consumption data of the year is divided into historical electricity consumption data corresponding to the four seasons of spring, summer, autumn and winter. The historical electricity consumption data of different time periods of each day in the four seasons are counted respectively to obtain the maximum electricity consumption threshold and the minimum electricity consumption threshold in different time periods of each day in different seasons, obtain the historical electricity consumption data of other local users, generate the electricity consumption distribution range of different time periods of each day in the four seasons based on the historical electricity consumption data of other users, compare whether the maximum electricity consumption threshold and the minimum electricity consumption threshold are within the electricity consumption distribution range, if so, generate the electricity consumption distribution curve based on the maximum electricity consumption threshold and the minimum electricity consumption threshold in different time periods, otherwise, obtain the distribution maximum value and the distribution minimum value in the electricity consumption distribution range in different time periods of different seasons; The intermediate value between the maximum distribution value and the maximum power consumption threshold is obtained as the corrected maximum power consumption threshold, and the intermediate value between the minimum distribution value and the minimum power consumption threshold is obtained as the corrected minimum power consumption threshold, and a power consumption distribution curve is generated based on the corrected maximum power consumption threshold and the minimum power consumption threshold; the power consumption distribution curve includes the relationship between different time periods and power consumption; Collecting real-time power consumption data of the target object, and comparing the real-time power consumption data to see whether it exceeds the power consumption distribution curve range; Calculate the value of the real-time power consumption data that exceeds the power consumption distribution curve range, and when the value exceeds a first set threshold value and does not exceed a second set threshold value, obtain the historical power consumption information of the target object, and generate the credit rating of the target object based on the historical power consumption information, and generate warning information when the credit rating of the target object is lower than the set standard, and when the credit rating of the target object is higher than the set standard, continuously monitor whether the real-time power consumption data of the target object in the next time period exceeds the power consumption distribution curve, and generate warning information when the real-time power consumption data in the next time period exceeds the power consumption distribution curve; When the value exceeds the second set threshold, a warning message is directly generated; The real-time electricity consumption data of the target object is continuously monitored, and when the real-time electricity consumption data of the target object exceeds the range of the electricity consumption distribution curve in the same time period on the second day as on the current day, the target object is marked as a power theft risk object.

2. The power consumption monitoring and early warning method according to claim 1 is characterized in that: The maximum power consumption thresholds of different seasons and different time periods are connected to draw a first curve, and the minimum power consumption thresholds of different seasons and different time periods are connected to draw a second curve, including: Set a first coefficient and a second coefficient, the first coefficient is greater than 1, and the second coefficient is less than 1; Based on the formula d max *a1 generates the upper limit of the power consumption threshold for different time periods, and based on formula d min *a2 generates the lower limit of the power consumption threshold for different time periods, where d max is the maximum power consumption threshold in different time periods, d min is the minimum power consumption threshold in different time periods, a1 is the first coefficient, and a2 is the second coefficient; A first curve is drawn by connecting the upper limits of the power consumption thresholds in different seasons and time periods, and a second curve is drawn by connecting the lower limits of the power consumption thresholds in different seasons and time periods.

3. A power consumption monitoring and early warning device, characterized in that: include: Historical curve generation module: in response to the data modeling instruction, the user's historical electricity consumption data is obtained, 24 hours are divided into different time periods, and the historical electricity consumption data of the year is divided into historical electricity consumption data corresponding to the four seasons of spring, summer, autumn and winter according to local climate factors; the historical electricity consumption data of different time periods of each day in the four seasons are counted respectively, and the maximum electricity consumption threshold and the minimum electricity consumption threshold of different time periods of each day in different seasons are obtained, and the maximum electricity consumption threshold of different time periods in different seasons are connected to draw a first curve, and the minimum electricity consumption threshold of different time periods in different seasons are connected to draw a second curve, and a power consumption distribution curve is generated based on the first curve and the second curve; or, 24 hours are divided into different time periods, and according to the local climate, the historical electricity consumption data of different time periods of each day in the four seasons are counted respectively, and the maximum electricity consumption threshold of different time periods in different seasons is connected to draw a first curve, and the minimum electricity consumption threshold of different time periods in different seasons is connected to draw a second curve, and a power consumption distribution curve is generated based on the first curve and the second curve. The historical electricity consumption data of a year is divided into historical electricity consumption data corresponding to the four seasons of spring, summer, autumn and winter, and the historical electricity consumption data of different time periods of each day in the four seasons are counted respectively to obtain the maximum electricity consumption threshold and the minimum electricity consumption threshold in different time periods of each day in different seasons, and the historical electricity consumption data of other local users are obtained. Based on the historical electricity consumption data of other users, the electricity consumption distribution range of different time periods of each day in the four seasons is generated, and the maximum electricity consumption threshold and the minimum electricity consumption threshold are compared to see whether they are within the electricity distribution range. If so, the electricity distribution curve is generated based on the maximum electricity consumption threshold and the minimum electricity consumption threshold in different time periods. Otherwise, the maximum distribution value and the minimum distribution value in the electricity distribution range in different time periods of different seasons are obtained. The intermediate value between the maximum distribution value and the maximum power consumption threshold is obtained as the corrected maximum power consumption threshold, and the intermediate value between the minimum distribution value and the minimum power consumption threshold is obtained as the corrected minimum power consumption threshold, and a power consumption distribution curve is generated based on the corrected maximum power consumption threshold and the minimum power consumption threshold; the power consumption distribution curve includes the relationship between different time periods and power consumption; Real-time power consumption collection module: collects the real-time power consumption data of the target object and compares the real-time power consumption data to see whether it exceeds the power consumption distribution curve range; Warning information generation module: used to calculate the value of the real-time power consumption data exceeding the power consumption distribution curve range, when the value exceeds the first set threshold and does not exceed the second set threshold, obtain the historical power consumption information of the target object, and generate the credit rating of the target object based on the historical power consumption information, and generate warning information when the credit rating of the target object is lower than the set standard, when the credit rating of the target object is higher than the set standard, continuously monitor whether the real-time power consumption data of the target object in the next time period exceeds the power consumption distribution curve, and generate warning information when the real-time power consumption data in the next time period exceeds the power consumption distribution curve; when the value exceeds the second set threshold, directly generate warning information; Power theft risk marking module: used to continuously monitor the real-time power consumption data of the target object, and when the real-time power consumption data of the target object exceeds the power consumption distribution curve range in the same time period on the second day as on the current day, the target object is marked as a power theft risk object.

4. An electronic device, characterized in that: include: A memory storing executable program code; a processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the power consumption monitoring and early warning method described in any one of claims 1 to 2.

5. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores a computer program, wherein the computer program enables a computer to execute the power consumption monitoring and early warning method according to any one of claims 1 to 2.

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