Smart Meter Disconnect Timing Analysis System

Abstract

The present invention relates to a smart meter disconnection timing analysis system, comprising: an induction prediction device for intelligently predicting the predicted value of the induction current of the target meter at the next moment after the current moment using an AI prediction body based on the respective induction currents corresponding to each past moment of the target meter before the current moment, the configuration data of the target meter user's current load usage, multiple reference information of the target meter, and the number of loads currently used by the target meter user; and a timing judgment device for judging whether the next moment after the current moment is a high-risk disconnection moment for the target meter based on the predicted value. Through the present invention, a customized AI prediction body can be used to intelligently predict the predicted value of the induction current of the target meter at the next moment after the current moment, and then judge whether the next moment after the current moment is a high-risk disconnection moment for the target meter, thereby completing the intelligent prejudgment of whether each meter's future moment will be a high-risk opportunity.

Description

[0001] This invention is a divisional application of the patent with application number 202411358746X, application date September 27, 2024, and invention name “Smart Meter Disconnection Timing Analysis System”. Technical Field

[0002] The present invention relates to the field of smart electric meters, and in particular to a disconnection timing analysis system for smart electric meters. Background Art

[0003] Smart meters are important devices for measuring electric energy, but the timing of their disconnection is somewhat accidental. For example, when the coil and core of a smart meter are affected by an electromagnetic field to generate magnetic flux, an induced current is generated. When the load is too large, the induced current exceeds the rated current of the smart meter, causing the smart meter to disconnect. However, since the rated power of each load is different and the operating current of each load fluctuates, and the operating currents of multiple loads fluctuate to varying degrees, it is difficult to predict the induced current generated by the smart meter in the future. As a result, it is difficult to predict the disconnection timing of the smart meter, which hinders the prevention and investigation of high-risk accidents.

[0004] For an electricity meter, if its induced current is greater than its rated current at a certain moment, this moment is a high-risk disconnection moment for the meter. Otherwise, this moment is a low-risk disconnection moment for the meter. However, the existing technology can only determine whether the current moment or a past moment is a high-risk disconnection moment or a low-risk disconnection moment, but cannot determine whether the future moment will be a high-risk disconnection moment or a low-risk disconnection moment. The key is that there is no effective prediction mechanism for the induced current of the electricity meter at future moments. Summary of the Invention

[0005] In order to solve the technical problems in the prior art, the present invention provides a smart meter disconnection timing analysis system, which can design a customized AI prediction body for the intelligent prediction of the induced current of the target meter at the next moment after the current moment. Specifically, the AI ​​prediction body is a radial basis function neural network after each training, and the number of training times is positively correlated with the number of coil turns of the target meter, so that different AI prediction bodies are designed for different target meters, and then a plurality of basic data are targetedly screened for the intelligent prediction of the induced current of the target meter at the next moment after the current moment to ensure the reliability and stability of the intelligent prediction results. The plurality of basic data include the induced currents corresponding to the past moments before the current moment, the configuration data of the load currently used by the target meter user, and a plurality of reference information of the target meter. The configuration data of the load currently used by the target meter user includes the rated power of each share, the upper limit value of each working current and the lower limit value of each working current corresponding to each load currently used by the target meter user, as well as multiple reference information of the target meter, including the number of coil turns, coil cross-sectional diameter, coil resistance, meter core weight and meter core volume of the target meter. Finally, a timing judgment device is introduced for intelligent prediction. When the predicted value of the induced current of the target meter at the next moment after the current moment is greater than the rated current of the target meter, the next moment after the current moment is used as the high-risk disconnection moment of the target meter. Otherwise, the next moment after the current moment is used as the low-risk disconnection moment of the target meter, thereby completing the intelligent prediction of the high-risk timing of each meter and providing key reference information for avoiding and troubleshooting the high-risk timing of the meter.

[0006] According to the present invention, a smart meter disconnection timing analysis system is provided, the system comprising:

[0007] a data capture mechanism for acquiring configuration data of a load currently used by a target meter user, wherein the configuration data of the load currently used by the target meter user includes the rated power of each share, the upper limit value of each share of working current, and the lower limit value of each share of working current corresponding to each load currently used by the target meter user, and the target meter is a smart meter;

[0008] an information collection mechanism for obtaining the number of coil turns, coil cross-sectional diameter, coil resistance, meter core weight, and meter core volume of the target electric meter, and outputting the number of coil turns, coil cross-sectional diameter, coil resistance, meter core weight, and meter core volume of the target electric meter as a plurality of reference information of the target electric meter;

[0009] a network reconstruction mechanism for performing a preset number of trainings on the radial basis function neural network to obtain a radial basis function neural network after each training, and outputting the radial basis function neural network after each training as an AI prediction body, wherein the value of the preset number is positively correlated with the number of coil turns of the target electric meter;

[0010] an induction prediction device, connected to the data capture mechanism, the information collection mechanism, and the network reconstruction mechanism, respectively, for using an AI prediction body to intelligently predict the predicted value of the induction current of the target meter at the next moment after the current moment based on the respective induction currents corresponding to the target meter at various past moments before the current moment, the configuration data of the load currently used by the user of the target meter, multiple reference information of the target meter, and the number of loads currently used by the user of the target meter;

[0011] a timing judgment device connected to the induction prediction device, for setting the next moment after the current moment as the high-risk disconnection moment of the target meter when the predicted value of the induction current of the target meter after the current moment is greater than the rated current of the target meter;

[0012] The timing judgment device is further configured to use the next moment after the current moment as the low-risk disconnection moment of the target meter when the received predicted value of the induced current of the target meter after the current moment is less than or equal to the rated current of the target meter;

[0013] Among them, the AI ​​prediction body is used to intelligently predict the induced current of the target meter at the next moment after the current moment based on the respective induced currents corresponding to the target meter at each past moment before the current moment, the configuration data of the load currently used by the target meter user, multiple reference information of the target meter, and the number of loads currently used by the target meter user. The predicted value of the induced current of the target meter at the next moment after the current moment includes: each past moment before the current moment includes the current moment, and each past moment before the current moment and the next moment after the current moment are evenly spaced on the time axis;

[0014] Among them, the AI ​​prediction body is used to intelligently predict the induced current of the target meter at the next moment of the current moment based on the respective induced currents corresponding to the target meter at each past moment before the current moment, the configuration data of the load currently used by the target meter user, multiple reference information of the target meter and the number of loads currently used by the target meter user. The predicted value of the induced current of the target meter at the next moment also includes: the number of moments of each past moment before the current moment is proportional to the number of loads currently used by the target meter user.

[0015] Therefore, the present invention has at least the following three important invention points:

[0016] Invention A: A customized AI prediction body is designed for intelligently predicting the induced current of a target meter at the next moment after the current moment. Specifically, the AI ​​prediction body is a radial basis function neural network that has completed various training cycles, and the number of training cycles is positively correlated with the number of coil turns of the target meter, thereby designing different AI prediction bodies for different target meters.

[0017] Invention point B: Targeted screening of multiple basic data for intelligent prediction of the induced current of a target meter at the next moment after the current moment to ensure the reliability and stability of the intelligent prediction result. The multiple basic data include the induced currents corresponding to the respective past moments before the current moment, the configuration data of the load currently used by the target meter user, and multiple reference information of the target meter. The configuration data of the load currently used by the target meter user includes the rated power shares, the upper limit values ​​of the working current, and the lower limit values ​​of the working current corresponding to the respective loads currently used by the target meter user. The multiple reference information of the target meter includes the number of coil turns, coil cross-sectional diameter, coil resistance, meter core weight, and meter core volume of the target meter.

[0018] Invention point C: A timing judgment device is introduced to intelligently predict the target meter's induced current. When the predicted value of the target meter's induced current at the next moment after the current moment is greater than the rated current of the target meter, the next moment after the current moment is used as the high-risk disconnection moment of the target meter. Otherwise, the next moment after the current moment is used as the low-risk disconnection moment of the target meter, thereby completing the intelligent prediction of the high-risk timing of each meter and providing key reference information for avoiding and troubleshooting the high-risk timing of the meter. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] The embodiments of the present invention will be described below with reference to the accompanying drawings, in which:

[0020] Figure 1 Schematic diagram of the internal structure of a smart meter disconnection timing analysis system according to embodiment A of the present invention.

[0021] Figure 2 Schematic diagram of the internal structure of a smart meter disconnection timing analysis system according to embodiment B of the present invention.

[0022] Figure 3 Schematic diagram of the internal structure of a smart meter disconnection timing analysis system according to embodiment C of the present invention. DETAILED DESCRIPTION

[0023] The following is a detailed description of an embodiment of the smart meter disconnection timing analysis system of the present invention with reference to the accompanying drawings.

[0024] Figure 1 Schematic diagram of the internal structure of a smart meter disconnection timing analysis system according to embodiment A of the present invention, the system comprising:

[0025] a data capture mechanism for acquiring configuration data of a load currently used by a target meter user, wherein the configuration data of the load currently used by the target meter user includes the rated power of each share, the upper limit value of each share of working current, and the lower limit value of each share of working current corresponding to each load currently used by the target meter user, and the target meter is a smart meter;

[0026] For example, a data capture mechanism is used to obtain configuration data of a load currently used by a target meter user, wherein the configuration data of the load currently used by the target meter user includes each share of rated power, each share of operating current upper limit value, and each share of operating current lower limit value corresponding to each load currently used by the target meter user, and the target meter is a smart meter including: the data capture mechanism has multiple built-in parameter capture components, which are used to respectively obtain each share of rated power, each share of operating current upper limit value, and each share of operating current lower limit value corresponding to each load currently used by the target meter user;

[0027] an information collection mechanism for obtaining the number of coil turns, coil cross-sectional diameter, coil resistance, meter core weight, and meter core volume of the target electric meter, and outputting the number of coil turns, coil cross-sectional diameter, coil resistance, meter core weight, and meter core volume of the target electric meter as a plurality of reference information of the target electric meter;

[0028] a network reconstruction mechanism for performing a preset number of trainings on the radial basis function neural network to obtain a radial basis function neural network after each training, and outputting the radial basis function neural network after each training as an AI prediction body, wherein the value of the preset number is positively correlated with the number of coil turns of the target electric meter;

[0029] an induction prediction device, connected to the data capture mechanism, the information collection mechanism, and the network reconstruction mechanism, respectively, for using an AI prediction body to intelligently predict the predicted value of the induction current of the target meter at the next moment after the current moment based on the respective induction currents corresponding to the target meter at various past moments before the current moment, the configuration data of the load currently used by the user of the target meter, multiple reference information of the target meter, and the number of loads currently used by the user of the target meter;

[0030] a timing judgment device connected to the induction prediction device, for setting the next moment after the current moment as the high-risk disconnection moment of the target meter when the predicted value of the induction current of the target meter after the current moment is greater than the rated current of the target meter;

[0031] The timing judgment device is further configured to use the next moment after the current moment as the low-risk disconnection moment of the target meter when the received predicted value of the induced current of the target meter after the current moment is less than or equal to the rated current of the target meter;

[0032] Among them, the AI ​​prediction body is used to intelligently predict the induced current of the target meter at the next moment after the current moment based on the respective induced currents corresponding to the target meter at each past moment before the current moment, the configuration data of the load currently used by the target meter user, multiple reference information of the target meter, and the number of loads currently used by the target meter user. The predicted value of the induced current of the target meter at the next moment after the current moment includes: each past moment before the current moment includes the current moment, and each past moment before the current moment and the next moment after the current moment are evenly spaced on the time axis;

[0033] Among them, the AI ​​prediction body is used to intelligently predict the induced current of the target meter at the next moment after the current moment based on the respective induced currents corresponding to each past moment of the target meter before the current moment, the configuration data of the load currently used by the target meter user, multiple reference information of the target meter, and the number of loads currently used by the target meter user. The predicted value of the induced current of the target meter at the next moment after the current moment also includes: the number of moments of each past moment before the current moment is proportional to the number of loads currently used by the target meter user;

[0034] The configuration data of the load currently used by the target meter user is obtained, and the configuration data of the load currently used by the target meter user includes the rated power of each share, the upper limit value of each working current, and the lower limit value of each working current corresponding to each load currently used by the target meter user, including: for each load, the specific value of its working current is between its corresponding upper limit value and its corresponding lower limit value;

[0035] Among them, a preset number of trainings are performed on the radial basis function neural network to obtain the radial basis function neural network after each training, and the radial basis function neural network after each training is output as an AI prediction body, and the value of the preset number is positively correlated with the number of coil turns of the target electricity meter, including: the more coil turns the target electricity meter has, the larger the value of the preset number.

[0036] Figure 2 Schematic diagram of the internal structure of a smart meter disconnection timing analysis system according to embodiment B of the present invention.

[0037] Compared to embodiment A, the smart meter disconnection timing analysis system shown in embodiment B may further include the following components:

[0038] An ASIC control chip is connected to the sensing prediction device, the data capture mechanism, the information collection mechanism, and the network reconstruction mechanism, respectively, and is used to provide configuration operations of working parameters for the sensing prediction device, the data capture mechanism, the information collection mechanism, and the network reconstruction mechanism in a time-sharing manner;

[0039] The ASIC control chip is respectively connected to the sensing prediction device, the data capture mechanism, the information collection mechanism, and the network reconstruction mechanism, and is used to provide the configuration operation of working parameters for the sensing prediction device, the data capture mechanism, the information collection mechanism, and the network reconstruction mechanism in a time-sharing manner. The configuration operation includes: the sensing prediction device, the data capture mechanism, the information collection mechanism, and the network reconstruction mechanism share the same working parameter configuration interface;

[0040] Wherein, the sensing prediction device, the data capture mechanism, the information collection mechanism and the network reconstruction mechanism use different configuration address data;

[0041] Wherein, a parallel connection of a communication data link is established between each of the sensing prediction device, the data capture mechanism, the information collection mechanism and the network reconstruction mechanism via a parallel data bus;

[0042] Among them, the parallel connection of the communication data link established between each of the sensing prediction device, the data capture mechanism, the information collection mechanism and the network reconstruction mechanism through a parallel data bus includes: the parallel data bus is one of an 8-bit parallel data bus, a 16-bit parallel data bus and a 32-bit parallel data bus.

[0043] Figure 3 Schematic diagram of the internal structure of a smart meter disconnection timing analysis system according to embodiment C of the present invention.

[0044] Compared to embodiment A, the smart meter disconnection timing analysis system shown in embodiment C may further include the following components:

[0045] A power support device is connected to the sensing prediction device, the data capture mechanism, the information collection mechanism, and the network reconstruction mechanism, respectively, and is used to provide power distribution support of different working voltages to the sensing prediction device, the data capture mechanism, the information collection mechanism, and the network reconstruction mechanism in a time-sharing manner;

[0046] The power support device is respectively connected to the sensing prediction device, the data capture mechanism, the information collection mechanism, and the network reconstruction mechanism, and is used to provide power distribution support with different working voltages for the sensing prediction device, the data capture mechanism, the information collection mechanism, and the network reconstruction mechanism in a time-sharing manner, including: two or more devices with the same working voltage requirements in the sensing prediction device, the data capture mechanism, the information collection mechanism, and the network reconstruction mechanism use the same power supply line;

[0047] Wherein, the power support device is respectively connected to the sensing prediction device, the data capture mechanism, the information collection mechanism and the network reconstruction mechanism, and is used to provide power distribution support of different working voltages for the sensing prediction device, the data capture mechanism, the information collection mechanism and the network reconstruction mechanism in a time-sharing manner, and further includes: the power support device is an uninterruptible power supply device;

[0048] Among them, the power support device is respectively connected to the sensing prediction device, the data capture mechanism, the information collection mechanism and the network reconstruction mechanism, and is used to provide power distribution support with different working voltages for the sensing prediction device, the data capture mechanism, the information collection mechanism and the network reconstruction mechanism in a time-sharing manner, and further includes: providing different working voltages for the sensing prediction device, the data capture mechanism, the information collection mechanism and the network reconstruction mechanism in a time-sharing manner, including a 3.3V working voltage;

[0049] And wherein, the power support device is respectively connected to the sensing prediction device, the data capture mechanism, the information collection mechanism and the network reconstruction mechanism, and is used to provide power distribution support with different working voltages for the sensing prediction device, the data capture mechanism, the information collection mechanism and the network reconstruction mechanism in a time-sharing manner, and also includes: providing different working voltages including 5V working voltage for the sensing prediction device, the data capture mechanism, the information collection mechanism and the network reconstruction mechanism in a time-sharing manner.

[0050] In addition, in the smart meter disconnection timing analysis system, the method of using an AI predictor to intelligently predict the predicted value of the induced current of the target meter at the next moment after the current moment based on the respective induced currents corresponding to the target meter at each past moment before the current moment, the configuration data of the load currently used by the target meter user, multiple reference information of the target meter, and the number of loads currently used by the target meter user further includes: inputting the respective induced currents corresponding to the target meter at each past moment before the current moment, the configuration data of the load currently used by the target meter user, multiple reference information of the target meter, and the number of loads currently used by the target meter user into the AI ​​predictor in parallel;

[0051] And wherein, using an AI prediction body to intelligently predict the predicted value of the induced current of the target meter at the next moment after the current moment based on the respective induced currents corresponding to the target meter at each past moment before the current moment, the configuration data of the load currently used by the target meter user, multiple reference information of the target meter and the number of loads currently used by the target meter user also includes: executing the AI ​​prediction body to obtain the predicted value of the induced current of the target meter at the next moment after the current moment output by the AI ​​prediction body.

[0052] The smart meter disconnection timing analysis system of the present invention solves the technical problem in the prior art that it is difficult to predict in advance whether the future moment of the meter will be a high-risk disconnection moment. By using a customized AI prediction body, the predicted value of the induced current of the target meter at the next moment after the current moment is predicted, and then it is judged whether the next moment after the current moment is the high-risk disconnection moment of the target meter, thereby completing the intelligent prediction of whether the future moment of each meter will be a high-risk moment.

[0053] Although the present invention has been comprehensively described by way of example, it should be understood that various changes and modifications will be apparent to those skilled in the art. Therefore, unless otherwise indicated, changes and modifications depart from the scope of the present invention, and such changes and modifications should be considered to be included within the scope of the present invention.

Claims

1. A smart meter disconnection timing analysis system, characterized in that: The system comprises: a data capture mechanism for acquiring configuration data of a load currently used by a target meter user, wherein the configuration data of the load currently used by the target meter user includes the rated power of each share, the upper limit value of each share of working current, and the lower limit value of each share of working current corresponding to each load currently used by the target meter user, and the target meter is a smart meter; an information collection mechanism for obtaining the number of coil turns, coil cross-sectional diameter, coil resistance, meter core weight, and meter core volume of the target electric meter, and outputting the number of coil turns, coil cross-sectional diameter, coil resistance, meter core weight, and meter core volume of the target electric meter as a plurality of reference information of the target electric meter; a network reconstruction mechanism for performing a preset number of trainings on the radial basis function neural network to obtain a radial basis function neural network after each training, and outputting the radial basis function neural network after each training as an AI prediction body, wherein the value of the preset number is positively correlated with the number of coil turns of the target electric meter; an induction prediction device, connected to the data capture mechanism, the information collection mechanism, and the network reconstruction mechanism, respectively, for using an AI prediction body to intelligently predict the predicted value of the induction current of the target meter at the next moment after the current moment based on the respective induction currents corresponding to the target meter at various past moments before the current moment, the configuration data of the load currently used by the user of the target meter, multiple reference information of the target meter, and the number of loads currently used by the user of the target meter; a timing judgment device connected to the induction prediction device, for setting the next moment after the current moment as the high-risk disconnection moment of the target meter when the predicted value of the induction current of the target meter after the current moment is greater than the rated current of the target meter; An ASIC control chip is connected to the sensing prediction device, the data capture mechanism, the information collection mechanism, and the network reconstruction mechanism, respectively, and is used to provide configuration operations of working parameters for the sensing prediction device, the data capture mechanism, the information collection mechanism, and the network reconstruction mechanism in a time-sharing manner, including: the sensing prediction device, the data capture mechanism, the information collection mechanism, and the network reconstruction mechanism share the same working parameter configuration interface; a power support device, connected to the sensing prediction device, the data capture mechanism, the information collection mechanism, and the network reconstruction mechanism, respectively, for providing power distribution support of different working voltages to the sensing prediction device, the data capture mechanism, the information collection mechanism, and the network reconstruction mechanism in a time-sharing manner, including: two or more devices in the sensing prediction device, the data capture mechanism, the information collection mechanism, and the network reconstruction mechanism that have the same working voltage requirement use the same power supply line; The timing judgment device is further configured to use the next moment after the current moment as the low-risk disconnection moment of the target meter when the received predicted value of the induced current of the target meter after the current moment is less than or equal to the rated current of the target meter; Among them, an AI prediction body is used to intelligently predict the induced current of the target meter at the next moment of the current moment based on each induced current corresponding to each past moment of the target meter before the current moment, the configuration data of the load currently used by the target meter user, multiple reference information of the target meter and the number of loads currently used by the target meter user. The predicted value of the induced current includes: each past moment before the current moment includes the current moment, and each past moment before the current moment and the next moment of the current moment are evenly spaced on the time axis; and the number of moments of each past moment before the current moment is proportional to the number of loads currently used by the target meter user.

2. The smart meter disconnection timing analysis system according to claim 1, wherein: Obtaining configuration data of a load currently used by a target electricity meter user, the configuration data of the load currently used by the target electricity meter user including each share of rated power, each share of operating current upper limit value, and each share of operating current lower limit value corresponding to each load currently used by the target electricity meter user, including: for each load, a specific value of its operating current is between its corresponding operating current upper limit value and its corresponding operating current lower limit value; Among them, a preset number of trainings are performed on the radial basis function neural network to obtain the radial basis function neural network after each training, and the radial basis function neural network after each training is output as an AI prediction body, and the value of the preset number is positively correlated with the number of coil turns of the target electricity meter, including: the more coil turns the target electricity meter has, the larger the value of the preset number.

3. The smart meter disconnection timing analysis system according to claim 2, wherein: The sensing prediction device, the data capture mechanism, the information collection mechanism and the network reconstruction mechanism use different configuration address data.

4. The smart meter disconnection timing analysis system according to claim 3, wherein: Establishing parallel connections of communication data links between the sensing prediction device, the data capture mechanism, the information collection mechanism, and the network reconstruction mechanism through parallel data buses; Among them, the parallel connection of the communication data link established between each of the sensing prediction device, the data capture mechanism, the information collection mechanism and the network reconstruction mechanism through a parallel data bus includes: the parallel data bus is one of an 8-bit parallel data bus, a 16-bit parallel data bus and a 32-bit parallel data bus.

5. The smart meter disconnection timing analysis system according to claim 2, wherein: The power support device is respectively connected to the sensing prediction device, the data capture mechanism, the information collection mechanism and the network reconstruction mechanism, and is used to provide power distribution support with different working voltages for the sensing prediction device, the data capture mechanism, the information collection mechanism and the network reconstruction mechanism in a time-sharing manner. The power support device also includes an uninterruptible power supply device.

6. The smart meter disconnection timing analysis system according to claim 5, characterized in that: The power support device is respectively connected to the sensing prediction device, the data capture mechanism, the information collection mechanism and the network reconstruction mechanism, and is used to provide power distribution support with different working voltages for the sensing prediction device, the data capture mechanism, the information collection mechanism and the network reconstruction mechanism in a time-sharing manner. It also includes: providing different working voltages including 3.3V working voltage for the sensing prediction device, the data capture mechanism, the information collection mechanism and the network reconstruction mechanism in a time-sharing manner.

7. The smart meter disconnection timing analysis system according to claim 6, wherein: The power support device is respectively connected to the sensing prediction device, the data capture mechanism, the information collection mechanism and the network reconstruction mechanism, and is used to provide power distribution support with different working voltages for the sensing prediction device, the data capture mechanism, the information collection mechanism and the network reconstruction mechanism in a time-sharing manner. It also includes: providing different working voltages including 5V working voltage for the sensing prediction device, the data capture mechanism, the information collection mechanism and the network reconstruction mechanism in a time-sharing manner.

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