Electric energy metering missort automatic detection system based on deep learning

Abstract

The invention discloses an electric energy metering missort automatic detection system based on deep learning, and relates to the technical field of power utilization detection. The system comprises a monitoring system, a processing system and an early warning system. The monitoring system comprises an electric quantity detection module, a time period detection module, a unit determination module and a first processing module. The first processing module is electrically connected with the electric quantity detection module, the time period detection module and the unit determination module through conduction wires. The processing system comprises a comparison module, an extraction module, a marking module, a second storage module and a second processing module. The second processing module is electrically connected with the comparison module, the marking module and the second storage module through conduction wires. The power consumption of each user is monitored in real time through the monitoring system, and the processing system can compare the monitored power consumption before and after, so that early warning can be given out in time when the power consumption is abnormal, and an electric power company and the user can find and process the abnormal power consumption in time.

Description

Technical Field

[0001] The present invention belongs to the technical field of electricity consumption detection, and in particular to an automatic detection system for electricity metering inter-household connection based on deep learning. Background Art

[0002] Electricity cross-connection refers to the situation where multiple users who share the same input power line use electricity. Due to certain deficiencies in the power distribution facilities, there is a large deviation between the power consumption detected by the users and the actual power consumption, which indirectly causes the loss of user assets.

[0003] As the installation of power distribution facilities is gradually standardized, the possibility of inter-household connection due to wiring errors is approaching zero. The existing inter-household connection mainly occurs due to faults caused by long-term power distribution implementation (such as wiring). The inter-household connection caused by this situation is sudden and difficult to detect; therefore, it is necessary to improve the existing technology to solve the above technical problems. Summary of the invention

[0004] The purpose of the present invention is to provide an automatic detection system for electricity metering inter-user connection based on deep learning. The monitoring system can monitor the electricity consumption of each user in real time, and the processing system can compare the monitored electricity consumption before and after, so that an early warning can be issued in time when the electricity consumption is abnormal, so that the power company and the user can discover and deal with it in time, which solves the problem that the existing electricity inter-user connection is mainly caused by failures caused by long-term power distribution implementation and use, and the inter-user connection caused by this situation is sudden and difficult to detect.

[0005] In order to solve the above technical problems, the present invention is achieved through the following technical solutions:

[0006] The present invention is an automatic detection system for inter-household electricity metering based on deep learning, including a monitoring system, a processing system and an early warning system;

[0007] The monitoring system includes an electric quantity detection module, a time period detection module, a unit determination module and a first processing module, and the first processing module is electrically connected to the electric quantity detection module, the time period detection module and the unit determination module through conductive wires respectively;

[0008] The processing system includes a comparison module, an extraction module, a marking module, a second storage module and a second processing module, the second processing module is electrically connected to the comparison module, the marking module and the second storage module through conductive lines, and the extraction module is electrically connected to the comparison module and the second storage module through conductive lines;

[0009] The early warning system includes a third processing module and an early warning module, and the third processing module and the early warning module are electrically connected via a conductive line.

[0010] Furthermore, the time period detection module is used to detect the time period of electricity consumption.

[0011] Furthermore, the unit determination module is used to determine users who share the same input line.

[0012] Furthermore, the monitoring system also includes a first transceiver module and a first storage module, and the first transceiver module and the first storage module are both electrically connected to the first processing module through a conductive line.

[0013] Furthermore, the comparison module is used to compare the power consumption of the same user in the same time period before and after, and the power consumption in the same time period before is the average value.

[0014] Furthermore, the marking module is used to bundle the power consumption and the power consumption time period.

[0015] Furthermore, the processing system also includes a second transceiver module, a first display module and a first input module, and the second transceiver module, the first display module and the first input module are all electrically connected to the second processing module through conductive lines.

[0016] Furthermore, the early warning system also includes a third transceiver module, a second display module, a third storage module and a second input module, and the third processing module is electrically connected to the third transceiver module, the second display module, the third storage module and the second input module through conductive lines.

[0017] Furthermore, the processing system interacts with the monitoring system and the early warning system in real time.

[0018] The present invention has the following beneficial effects:

[0019] When the present invention is in use, the power detection module can monitor the power consumption of each user and transmit the monitored power data to the processing system. When the processing system receives the data, it will compare the data with the data of the user previously monitored and stored in the processing system. When data abnormalities occur, the processing system will promptly remind the power company and the user, and the early warning module in the early warning system will promptly issue an early warning to the user to attract the user's attention and facilitate the user to handle it in time. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the accompanying drawings required for describing the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other accompanying drawings can be obtained based on these accompanying drawings without paying creative work.

[0021] Figure 1This is a block diagram of an automatic detection system for electricity metering inter-connection based on deep learning;

[0022] Figure 2 This is the principle block diagram of the monitoring system;

[0023] Figure 3 It is the principle block diagram of the processing system;

[0024] Figure 4 This is the principle block diagram of the early warning system. DETAILED DESCRIPTION

[0025] The technical solutions in the embodiments of the present invention will be described clearly and completely below in conjunction with the accompanying drawings in the embodiments of the present invention.

[0026] See also Figures 1 to 4 As shown, the present invention is an automatic detection system for electricity metering inter-household connection based on deep learning, including a monitoring system, a processing system and an early warning system. The processing system interacts with the monitoring system and the early warning system in real time, and the early warning system can be installed on hardware facilities such as mobile phones.

[0027] The monitoring system includes a power detection module, a time period detection module, a unit determination module and a first processing module, the first processing module is electrically connected to the power detection module, the time period detection module and the unit determination module through conductive lines, the monitoring system also includes a first transceiver module and a first storage module, the first transceiver module and the first storage module are electrically connected to the first processing module through conductive lines;

[0028] The power detection module is used to monitor the power consumption of the user in real time. In actual use, the power detection module is installed on the input bus; the time period detection module is used to detect the time period of power consumption; the unit determination module is used to determine the users who share the same input line; the first processing module is used to control the electrical components in the monitoring system; the first transceiver module is used to exchange data with the second transceiver module; the first storage module is used to store information in the monitoring system;

[0029] When the above arrangement is used, the time period detection module can divide a whole day into multiple time periods, the power detection module transmits the data detected in each time period in real time, and the unit determination module can determine the user of the detected data.

[0030] The processing system includes a comparison module, an extraction module, a marking module, a second storage module and a second processing module, the second processing module is electrically connected to the comparison module, the marking module and the second storage module through conductive lines, the extraction module is electrically connected to the comparison module and the second storage module through conductive lines, the processing system also includes a second transceiver module, a first display module and a first input module, the second transceiver module, the first display module and the first input module are all electrically connected to the second processing module through conductive lines;

[0031] The comparison module is used to compare the power consumption of the same user in the same time period before and after, and the power consumption in the same time period before is the average value; the marking module is used to bundle the power consumption and the power consumption time period; the extraction module is used to extract the data stored in the second storage module for comparison; the second storage module is used to store the data sent back by the monitoring system; the second processing module is used to control each electrical component in the processing system; the second transceiver module is used to exchange real-time data with the first transceiver module and the third transceiver module respectively; the first display module is used to display the data received and generated in the processing system; the first input module is convenient for the staff to input information;

[0032] When the above arrangement is in use, after the processing system receives the information transmitted by the monitoring system, the extraction module extracts the electricity consumption information of the household previously stored in the second storage module according to the household information and time period of the user, and compares the data with the received data. When the comparison percentage exceeds a range, it indicates that the electricity consumption of the user is abnormal at this time. Of course, in the case of inter-household, when the electricity consumption of a certain user is abnormal, other users of the same input line will also have certain problems. At this time, the comparison module will check the data of other users of the same input line to determine the specific situation of the inter-household;

[0033] It should be noted that when the unit determination module does not detect anomalies in the data of other users or the other users do not have a power usage status, it means that the detected abnormal data is not abnormal, but the power usage of the user has increased. At this time, the detected data is normal data and can be stored in the second storage module for subsequent reference;

[0034] In addition, after the collected data is stored as normal data in the second storage module, when it is used for comparison later, the average value of multiple sets of data from the same user and the same period in the second storage module should be taken for comparison, so as to eliminate the data deviation caused by the service life of the equipment.

[0035] The early warning system includes a third processing module and an early warning module, the third processing module and the early warning module are electrically connected through a conductive line, the early warning system also includes a third transceiver module, a second display module, a third storage module and a second input module, the third processing module is electrically connected to the third transceiver module, the second display module, the third storage module and the second input module through conductive lines respectively;

[0036] The third storage module is used to store data generated by the early warning system; the third processing module is used to control the electrical components in the early warning system; the early warning system is used to issue an early warning when an abnormality occurs; the third transceiver module is used to exchange data with the second transceiver module; the second display module is used to display information in the early warning system; the second input module is used to input information into the early warning system;

[0037] When the above arrangement is in use, when abnormal data appears, the processing system will transmit the information to the early warning system, the third processing module will issue an early warning through the early warning module, and the user can make a processing request to the processing system through the second input module.

[0038] The above are only preferred embodiments of the present invention and do not limit the present invention. Any modification to the technical solutions recorded in the aforementioned embodiments, any equivalent replacement of some of the technical features therein, and any modification, equivalent replacement, and improvement made are all within the protection scope of the present invention.

Claims

1. An automatic detection system for inter-household electricity metering based on deep learning, characterized in that: Including monitoring system, processing system and early warning system; The monitoring system includes an electric quantity detection module, a time period detection module, a unit determination module and a first processing module, wherein the first processing module is electrically connected to the electric quantity detection module, the time period detection module and the unit determination module respectively through conductive lines; The processing system includes a comparison module, an extraction module, a marking module, a second storage module and a second processing module, the second processing module is electrically connected to the comparison module, the marking module and the second storage module through conductive lines, and the extraction module is electrically connected to the comparison module and the second storage module through conductive lines; The early warning system includes a third processing module and an early warning module, and the third processing module and the early warning module are electrically connected through a conductive line.

2. The deep learning-based automatic detection system for electric energy metering interconnection as claimed in claim 1, characterized in that: The time period detection module is used to detect the time period of electricity consumption.

3. The deep learning-based automatic detection system for electric energy metering inter-household connection according to claim 1 or 2, characterized in that: The unit determination module is used to determine users who share the same input line.

4. The deep learning-based automatic detection system for electric energy metering interconnection as claimed in claim 3, characterized in that: The monitoring system further includes a first transceiver module and a first storage module, and the first transceiver module and the first storage module are both electrically connected to the first processing module through a conductive line.

5. The deep learning-based automatic detection system for electric energy metering interconnection of households as claimed in claim 1 or 4, characterized in that: The comparison module is used to compare the power consumption of the same user in the same time period before and after, and the power consumption in the same time period before is the average value.

6. The deep learning-based automatic detection system for electric energy metering interconnection as claimed in claim 5, characterized in that: The marking module is used to bundle the power consumption and the power consumption time period.

7. The deep learning-based automatic detection system for electric energy metering interconnection of households according to claim 1, 4 or 6, characterized in that: The processing system further includes a second transceiver module, a first display module and a first input module. The second transceiver module, the first display module and the first input module are all electrically connected to the second processing module through conductive lines.

8. The deep learning-based automatic detection system for electric energy metering interconnection as claimed in claim 7, characterized in that: The early warning system also includes a third transceiver module, a second display module, a third storage module and a second input module. The third processing module is electrically connected to the third transceiver module, the second display module, the third storage module and the second input module through conductive lines.

9. The deep learning-based automatic detection system for electric energy metering interconnection of households as described in claim 1, 4, 6 or 8, characterized in that: The processing system interacts with the monitoring system and the early warning system in real time.

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