CN116699501A - Intelligent electric energy meter operation error monitoring and reporting system - Google Patents
Intelligent electric energy meter operation error monitoring and reporting system Download PDFInfo
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Abstract
The invention relates to an intelligent ammeter operation error monitoring reporting system, which comprises: the model application mechanism is arranged at the big data server end, and predicts the electric energy reading error of the target intelligent electric energy meter at the next moment by adopting an AI intelligent model according to each piece of configuration information of the target intelligent electric energy meter, each electric energy reading error, each piece of voltage data, each piece of current data and each piece of phase data, which correspond to each moment before the next moment; the signal triggering mechanism is connected with the model application mechanism, and marks the target intelligent electric energy meter as a replacement electric energy meter type when the electric energy reading error of the target intelligent electric energy meter at the next moment exceeds a set error threshold value. According to the invention, a customized AI intelligent model can be built for each intelligent electric energy meter to predict the electric energy reading error of the intelligent electric energy meter at the future time, so that the problem of large error is completed, the intelligent electric energy meter is replaced in advance, and the influence of the error reading on the robustness of the whole electricity management system is avoided.
Description
Technical Field
The invention relates to the field of intelligent electric energy meters, in particular to an intelligent electric energy meter operation error monitoring and reporting system.
Background
The intelligent electric energy meter is one of basic equipment for intelligent power grid data acquisition, bears the tasks of original electric energy data acquisition, metering and transmission, and is a foundation for realizing information integration, analysis optimization and information display. Besides the metering function of the basic electricity consumption of the traditional electric energy meter, the intelligent electric energy meter has the intelligent functions of bidirectional multi-rate metering function, user side control function, bidirectional data communication function of various data transmission modes, electricity larceny prevention function and the like in order to adapt to the use of the intelligent electric energy meter and new energy.
For power management enterprises, the number of intelligent electric energy meters in service which are managed simultaneously is huge, after the intelligent electric energy meters reach the time limit of the set annual expiration verification, if the intelligent electric energy meters in service which are out of service are replaced by inputting massive funds according to the original expiration rotation management mode, and meanwhile, the huge number of intelligent electric energy meters in service are subjected to expiration verification problems in the next several years each year. Therefore, the construction misalignment replacement operation error monitoring module is urgently needed for carrying out remote diagnosis on the operation error of the intelligent electric energy meter, only the electric energy meter with unqualified metering is replaced, a large number of qualified intelligent electric energy meters are prevented from being disassembled, a large number of manpower and material resource costs are saved, the pollution of electronic products to the environment is reduced, and meanwhile, the fine operation and maintenance management level of the electric energy meter can be greatly improved.
By way of example, the system and the method for processing running error data statistics of the intelligent electric energy meter are provided in the Chinese patent publication CN 112346000A, based on the requirement of error statistics, an error statistics end closely related to other parts of the system is designed, a sampling error statistics result is provided by connecting a feedback receiving end with a system decision monitoring end, an on-line monitoring and data acquisition system and method based on a distributed acquisition system, platform region configuration information and homotype reference information are designed, meanwhile, errors of the intelligent electric energy meter are considered to be possibly from aspects such as measurement errors, creep performance influence light load errors, sampling errors of a sampling circuit, attribution errors and the like, and error statistics of UI (user interface) display and system management are realized through normalization configuration of ternary verification information, and convenient system monitoring is realized based on multi-dimensional bottom monitoring parameters.
For example, an artificial intelligence-based electric energy meter operation error monitoring data fitting method and system are provided in chinese patent publication CN112684399 a, firstly, electric energy meter operation data and an error monitoring data set corresponding to the electric energy meter operation data are obtained, secondly, current time monitoring data are determined to perform error analysis to obtain power consumption loss error data of power consumption load node data, and then error fitting is performed on the current time monitoring data corresponding to the electric energy meter operation data based on the power consumption loss error data to obtain current error fitting data and the current error fitting data are added into the corresponding error monitoring data set. And further obtaining an operation error identification result of the electric energy meter to be monitored when the error monitoring data set meets the set condition. Therefore, by analyzing, fitting and iterating the operation errors of the different electric energy meter operation data of the electric energy meter to be monitored, the overall and continuous operation error identification result of the electric energy meter to be monitored can be obtained, and an accurate and reliable correction basis is provided for realizing the error correction of the electric energy meter to be monitored.
However, the technical scheme of intelligent ammeter operation error monitoring in the prior art cannot acquire the operation error value of each monitored intelligent ammeter at the future time, so that the reading of the intelligent ammeter with larger error can be written into the electricity management system, the robustness and reliability of the whole electricity management system are reduced, and meanwhile, the intelligent ammeter with larger error cannot be replaced in advance before the larger error occurs due to the difficulty in predicting the operation error value at the future time, so that the countermeasure of operation error monitoring is lagged.
Disclosure of Invention
In order to solve the technical defects in the related art, the invention provides an intelligent electric energy meter operation error monitoring and reporting system, which can establish a customized AI intelligent model for each intelligent electric energy meter for monitoring operation errors, and predict the electric energy reading errors of the intelligent electric energy meter at the next moment according to each piece of configuration information of the intelligent electric energy meter, each piece of electric energy reading error corresponding to each moment before the next moment, each piece of voltage data, each piece of current data and each piece of phase data, so as to provide reliable information for the advanced replacement of the intelligent electric energy meter with the problem of larger error and avoid the writing of error reading into an electricity management system.
According to a first aspect of the present invention, there is provided an intelligent ammeter operation error monitoring and reporting system, the system comprising:
the data measuring device is arranged at the big data server end and is used for acquiring each electric energy reading error, each voltage data, each current data and each phase data which correspond to each moment before the next moment of the target intelligent electric energy meter through the wireless communication network;
the configuration analysis device is arranged at the big data server end and is used for acquiring various configuration information of the target intelligent electric energy meter, wherein the various configuration information of the target intelligent electric energy meter comprises the power supply frequency, the working environment temperature, the working environment humidity, the precision grade and the number of load equipment of the target intelligent electric energy meter;
the model construction device is used for establishing an AI intelligent model for the target intelligent electric energy meter, wherein the AI intelligent model is a convolutional neural network after a set number of repeated learning operations are completed;
the model application mechanism is arranged at the big data server end, is respectively connected with the data measurement device, the configuration analysis device and the model construction device, and is used for predicting the electric energy reading error of the target intelligent electric energy meter at the next moment according to each piece of configuration information of the target intelligent electric energy meter, each electric energy reading error, each piece of voltage data, each piece of current data and each piece of phase data which correspond to each moment before the next moment by adopting the AI intelligent model;
The signal triggering mechanism is connected with the model application mechanism and is used for marking the target intelligent electric energy meter as a replacement electric energy meter type and sending a replacement request signal comprising an electric energy meter identifier corresponding to the target intelligent electric energy meter when the received electric energy reading error of the target intelligent electric energy meter at the next moment exceeds a set error threshold value;
the method for acquiring the electric energy reading errors, the voltage data, the current data and the phase data of the target intelligent electric energy meter, which correspond to the next moment before the next moment, through the wireless communication network comprises the following steps: the number of times of each time before the next time is monotonically and positively correlated with the number of load devices of the target intelligent ammeter.
According to a second aspect of the present invention there is provided an intelligent ammeter operation error monitoring reporting system, the system comprising a memory and one or more processors, the memory storing a computer program configured to be executed by the one or more processors to perform the steps of:
acquiring each electric energy reading error, each voltage data, each current data and each phase data which correspond to each moment of the target intelligent electric energy meter before the next moment through a wireless communication network at a big data server;
Acquiring various configuration information of the target intelligent electric energy meter at a big data server, wherein the various configuration information of the target intelligent electric energy meter comprises the power supply frequency, the working environment temperature, the working environment humidity, the precision grade and the number of load equipment of the target intelligent electric energy meter;
an AI intelligent model is established for the target intelligent electric energy meter, wherein the AI intelligent model is a convolutional neural network after a set number of repeated learning operations are completed;
the AI intelligent model is adopted at a big data server side to predict the electric energy reading error of the target intelligent electric energy meter at the next moment according to each piece of configuration information of the target intelligent electric energy meter, each electric energy reading error, each piece of voltage data, each piece of current data and each piece of phase data, wherein each electric energy reading error corresponds to each moment before the next moment;
when the received electric energy reading error of the target intelligent electric energy meter at the next moment exceeds a set error threshold, marking the target intelligent electric energy meter as a replacement electric energy meter type and sending a replacement request signal comprising an electric energy meter identifier corresponding to the target intelligent electric energy meter;
the method for acquiring the electric energy reading errors, the voltage data, the current data and the phase data of the target intelligent electric energy meter, which correspond to the next moment before the next moment, through the wireless communication network comprises the following steps: the number of times of each time before the next time is monotonically and positively correlated with the number of load devices of the target intelligent ammeter.
Compared with the prior art, the invention has at least the following three key invention points:
firstly, an AI intelligent model customized for a target intelligent electric energy meter is adopted, and the electric energy reading error of the target intelligent electric energy meter at the next moment is predicted according to each piece of configuration information of the target intelligent electric energy meter, each piece of electric energy reading error, each piece of voltage data, each piece of current data and each piece of phase data which correspond to each moment before the next moment, so that valuable reference information is provided for a replacement management strategy of the target intelligent electric energy meter at the next moment;
secondly, when the electric energy reading error of the target intelligent electric energy meter at the next moment exceeds a set error threshold value, the replacement processing of the target intelligent electric energy meter is finished in advance so as to avoid the wrong reading from reading into the system, and the healthy operation of each intelligent electric energy meter managed by the big data server is ensured;
the AI intelligent model customized for the target intelligent electric energy meter is customized, wherein the AI intelligent model is a convolutional neural network after a set number of repeated learning operations are completed, and the smaller the number of the load devices of the target intelligent electric energy meter is, the fewer the number of the selected learning operations is, so that the reliability and the stability of a prediction result of the AI intelligent model are ensured.
Drawings
Embodiments of the present invention will be described below with reference to the accompanying drawings, in which:
fig. 1 is a technical flowchart of an intelligent ammeter operation error monitoring and reporting system according to the present invention.
Fig. 2 is a schematic structural diagram of an intelligent ammeter operation error monitoring and reporting system according to embodiment 1 of the present invention.
Fig. 3 is a schematic structural diagram of an intelligent ammeter operation error monitoring and reporting system according to embodiment 2 of the present invention.
Fig. 4 is a schematic structural diagram of an intelligent ammeter operation error monitoring and reporting system according to embodiment 3 of the present invention.
Fig. 5 is a schematic structural diagram of an intelligent ammeter operation error monitoring and reporting system according to embodiment 4 of the present invention.
Fig. 6 is a schematic structural diagram of an intelligent ammeter operation error monitoring and reporting system according to embodiment 5 of the present invention.
Fig. 7 is a schematic structural diagram of an intelligent ammeter operation error monitoring and reporting system according to embodiment 6 of the present invention.
Fig. 8 is a schematic structural diagram of an intelligent ammeter operation error monitoring and reporting system according to embodiment 6 of the present invention.
Fig. 9 is a flowchart of steps of an intelligent ammeter operation error monitoring and reporting method according to embodiment 8 of the present invention.
Detailed Description
As shown in fig. 1, a technical flowchart of an intelligent ammeter operation error monitoring and reporting system according to the present invention is provided.
As shown in fig. 1, the specific technical process of the present invention is as follows:
the method comprises the steps that firstly, each intelligent electric energy meter under the management of a big data server is used as a target intelligent electric energy meter, an AI intelligent model for predicting electric energy reading errors of the target intelligent electric energy meter at future moments is customized, the AI intelligent model is a convolutional neural network after a set number of repeated learning operations are completed, and the customization is that the smaller the numerical value of the number of load equipment of the target intelligent electric energy meter is, the smaller the number of selected learning operations is;
as shown in fig. 1, the big data server sequentially realizes remote management of each intelligent electric energy meter through a wireless network and a firewall, in fig. 1, N intelligent electric energy meters are provided, namely an intelligent electric energy meter 1 and an intelligent electric energy meter 2.
Secondly, predicting the electric energy reading error of the target intelligent electric energy meter at future time based on the electric energy reading error, voltage data, current data and phase data of each time of the history of the target intelligent electric energy meter by adopting an AI intelligent model;
As shown in fig. 1, N intelligent electric energy meters respectively correspond to N AI intelligent models, which are model 1 and model 2.
Thirdly, when the predicted electric energy reading error of the target intelligent electric energy meter is large in future time, marking the target intelligent electric energy meter as a replacement electric energy meter type, and sending a replacement request signal comprising an electric energy meter identifier corresponding to the target intelligent electric energy meter to a meter body replacement server so as to trigger the meter body replacement server to execute the replacement action of the target intelligent electric energy meter;
as shown in fig. 1, a communication mechanism is established between a meter body replacement server and a big data server to realize the sending operation of sending a replacement request signal comprising an electric energy meter identifier corresponding to the target intelligent electric energy meter to the meter body replacement server;
in the customized AI intelligent model, the time quantity of each time of the history of the model is monotonically and positively correlated with the load equipment quantity of the target intelligent electric energy meter, so that the reliability and the stability of a model prediction result are ensured.
The key points of the invention are as follows: the customization of the AI intelligent model of each intelligent electric energy meter comprises the selection of the time quantity and the selection of the learning times of each time of history and the selection of each specific input data of the AI intelligent model, wherein each specific input data comprises the electric energy reading errors, voltage data, current data and phase data of each time of history of the target intelligent electric energy meter and each configuration information of the target intelligent electric energy meter, so that the validity of the predicted value of the electric energy reading errors at the future time is ensured.
The following will specifically describe an operation error monitoring and reporting system of an intelligent electric energy meter by way of example.
Example 1
Fig. 2 is a schematic structural diagram of an intelligent ammeter operation error monitoring and reporting system according to embodiment 1 of the present invention.
As shown in fig. 2, the intelligent ammeter operation error monitoring and reporting system comprises the following components:
the data measuring device is arranged at the big data server end and is used for acquiring each electric energy reading error, each voltage data, each current data and each phase data which correspond to each moment before the next moment of the target intelligent electric energy meter through the wireless communication network;
the wireless communication network may be, for example, a time division duplex communication network, a frequency division duplex communication network, or a 5G communication network;
the configuration analysis device is arranged at the big data server end and is used for acquiring various configuration information of the target intelligent electric energy meter, wherein the various configuration information of the target intelligent electric energy meter comprises the power supply frequency, the working environment temperature, the working environment humidity, the precision grade and the number of load equipment of the target intelligent electric energy meter;
the model construction device is used for establishing an AI intelligent model for the target intelligent electric energy meter, wherein the AI intelligent model is a convolutional neural network after a set number of repeated learning operations are completed;
For example, a MATLAB kit may be used to implement simulation processing of the AI intelligent model established for the target intelligent ammeter;
the model application mechanism is arranged at the big data server end, is respectively connected with the data measurement device, the configuration analysis device and the model construction device, and is used for predicting the electric energy reading error of the target intelligent electric energy meter at the next moment according to each piece of configuration information of the target intelligent electric energy meter, each electric energy reading error, each piece of voltage data, each piece of current data and each piece of phase data which correspond to each moment before the next moment by adopting the AI intelligent model;
for example, the model application mechanism may be implemented with an ASIC chip or an SOC chip;
the signal triggering mechanism is connected with the model application mechanism and is used for marking the target intelligent electric energy meter as a replacement electric energy meter type and sending a replacement request signal comprising an electric energy meter identifier corresponding to the target intelligent electric energy meter when the received electric energy reading error of the target intelligent electric energy meter at the next moment exceeds a set error threshold value;
the method for acquiring the electric energy reading errors, the voltage data, the current data and the phase data of the target intelligent electric energy meter, which correspond to the next moment before the next moment, through the wireless communication network comprises the following steps: the quantity of the moments before the next moment is monotonically and positively correlated with the quantity of the load devices of the target intelligent electric energy meter;
The method for establishing the AI intelligent model for the target intelligent electric energy meter comprises the following steps of: the smaller the number of the load devices of the target intelligent electric energy meter is, the fewer the number of the selected learning operations is;
the following are illustrated: the smaller the number of the load devices of the target intelligent electric energy meter, the fewer the number of the selected learning operations includes: the number of the load devices of the target intelligent electric energy meter is 10, the number of the selected learning operations is 50, the number of the load devices of the target intelligent electric energy meter is 12, the number of the selected learning operations is 80, the number of the load devices of the target intelligent electric energy meter is 15, the number of the selected learning operations is 120, the number of the load devices of the target intelligent electric energy meter is 18, and the number of the selected learning operations is 200;
the method for establishing the AI intelligent model for the target intelligent electric energy meter comprises the following steps of: in each learning operation of the convolutional neural network, known electric energy reading errors at the past time of the target intelligent electric energy meter are used as single output content of the convolutional neural network, each item of configuration information of the target intelligent electric energy meter, each electric energy reading error, each voltage data, each current data and each phase data which correspond to each time before the past time of the target intelligent electric energy meter are used as each item of input content of the convolutional neural network, and the learning operation is executed.
Example 2
Fig. 3 is a schematic structural diagram of an intelligent ammeter operation error monitoring and reporting system according to embodiment 2 of the present invention.
As shown in fig. 3, unlike the embodiment in fig. 2, the intelligent ammeter operation error monitoring and reporting system further includes the following components:
the meter body replacement server is connected with the big data server through a wireless communication link and is used for receiving a replacement request signal comprising the electric energy meter identification corresponding to the target intelligent electric energy meter and assigning the target intelligent electric energy meter to currently-idle meter body replacement service personnel;
for example, the table body replacement server may be implemented by a cloud service network element or a blockchain service network element.
Example 3
Fig. 4 is a schematic structural diagram of an intelligent ammeter operation error monitoring and reporting system according to embodiment 3 of the present invention.
As shown in fig. 4, unlike the embodiment in fig. 2, the intelligent ammeter operation error monitoring and reporting system further includes the following components:
the threshold value storage mechanism is connected with the signal triggering mechanism and is used for storing the set error threshold value and providing the set error threshold value for the threshold value storage mechanism;
the threshold storage mechanism is one of an MMC storage device, an SD storage device, a TF storage device, or a FLASH storage device, for example.
Example 4
Fig. 5 is a schematic structural diagram of an intelligent ammeter operation error monitoring and reporting system according to embodiment 4 of the present invention.
As shown in fig. 5, unlike the embodiment in fig. 2, the intelligent ammeter operation error monitoring and reporting system further includes the following components:
the parameter storage mechanism is connected with the model building device and used for storing the AI intelligent model;
the parameter storage mechanism is used for completing the storage of the AI intelligent model by storing various model parameters of the AI intelligent model;
the parameter storage means, for example, employs different physical storage addresses for the respective storage of the model parameters of the AI intelligent model.
Example 5
Fig. 6 is a schematic structural diagram of an intelligent ammeter operation error monitoring and reporting system according to embodiment 5 of the present invention.
As shown in fig. 6, unlike the embodiment in fig. 2, the intelligent ammeter operation error monitoring and reporting system further includes the following components:
the log recording mechanism is arranged at the big data server end and connected with the model application mechanism and is used for recording the electric energy reading error of the target intelligent electric energy meter at the next moment, the electric energy meter identification corresponding to the target intelligent electric energy meter at the next moment and the electric energy meter identification corresponding to the target intelligent electric energy meter as a single error log when the electric energy reading error of the target intelligent electric energy meter at the next moment exceeds a set error threshold;
Illustratively, the logging mechanism includes a record management unit and a log storage unit connected to the record management unit.
Example 6
Fig. 7 is a schematic structural diagram of an intelligent ammeter operation error monitoring and reporting system according to embodiment 6 of the present invention.
As shown in fig. 7, unlike the embodiment in fig. 6, the intelligent ammeter operation error monitoring and reporting system further includes the following components:
the overflow detection mechanism is connected with the log recording mechanism and is used for sending out a log overflow instruction when the number of the logs currently recorded by the log recording mechanism exceeds or is equal to a preset number limit;
for example, the overflow detection mechanism may be implemented using a CPLD chip, while the design and simulation of the CPLD chip is performed using VHDL language.
Next, detailed descriptions of various embodiments of the present invention will be continued.
In the intelligent ammeter operation error monitoring and reporting system according to any embodiment of the invention:
the signal triggering mechanism is further used for marking the target intelligent electric energy meter as a reliable electric energy meter type and sending out a replacement request signal comprising an electric energy meter identifier corresponding to the target intelligent electric energy meter temporarily when the received electric energy reading error of the target intelligent electric energy meter at the next moment does not exceed a set error threshold value;
For example, binary coded data may be used to mark each intelligent electric energy meter in different types, where the number of coding bits of the binary coded data used is a fixed number of bits, for example, binary coded data with a length of 8 bits may be selected to mark each intelligent electric energy meter in different types;
when the received electric energy reading error of the target intelligent electric energy meter at the next moment exceeds a set error threshold, marking the target intelligent electric energy meter as a replacement electric energy meter type and sending a replacement request signal comprising an electric energy meter identifier corresponding to the target intelligent electric energy meter comprises the following steps: when the received electric energy reading error of the target intelligent electric energy meter at the next moment exceeds a set error threshold, marking the target intelligent electric energy meter as a replacement electric energy meter type and sending a replacement request signal comprising an electric energy meter identifier corresponding to the target intelligent electric energy meter through a wireless communication link;
when the received electric energy reading error of the target intelligent electric energy meter at the next moment exceeds a set error threshold, marking the target intelligent electric energy meter as a replacement electric energy meter type and sending a replacement request signal comprising an electric energy meter identifier corresponding to the target intelligent electric energy meter through a wireless communication link comprises the following steps: adopting an IP data packet to carry a replacement request signal comprising an electric energy meter identifier corresponding to the target intelligent electric energy meter so as to send out the replacement request signal through a wireless communication link;
Illustratively, the IP data packet includes a header, payload data, and checksum data, the payload data being disposed at a central location of the IP data packet;
when the received electric energy reading error of the target intelligent electric energy meter at the next moment exceeds a set error threshold, marking the target intelligent electric energy meter as a replacement electric energy meter type and sending a replacement request signal comprising an electric energy meter identifier corresponding to the target intelligent electric energy meter through a wireless communication link comprises the following steps: and sending a replacement request signal comprising the electric energy meter identifier corresponding to the target intelligent electric energy meter to a meter body replacement server through a wireless communication link.
In the intelligent ammeter operation error monitoring and reporting system according to any embodiment of the invention:
the obtaining, through the wireless communication network, each electric energy reading error, each voltage data, each current data and each phase data of the target intelligent electric energy meter, which correspond to each time before the next time, respectively includes: the target intelligent electric energy meter is one of a plurality of intelligent electric energy meters managed by the big data server;
the method for acquiring the electric energy reading errors, the voltage data, the current data and the phase data of the target intelligent electric energy meter, which correspond to the next moment before the next moment, through the wireless communication network comprises the following steps: the next moment and each moment before the next moment are uniformly arranged at intervals on a time axis;
For example, the interval duration between every two times is fixed in each time of the next time and before the next time, and may be one of 15 minutes, 30 minutes and 60 minutes;
wherein, each time before the next time and the next time is evenly spaced on the time axis and set up includes: each time before the next time comprises the current time nearest to the next time;
each item of configuration information of the target intelligent electric energy meter comprises power frequency, voltage data, working environment temperature, working environment humidity, precision grade and load equipment quantity of the target intelligent electric energy meter, wherein the number of the load equipment comprises: and the voltage data of the target intelligent electric energy meter is 220V or 380V.
Example 7
Fig. 8 is a block diagram illustrating a structure of an intelligent ammeter operation error monitoring and reporting system according to embodiment 7 of the present invention.
As shown in fig. 8, the intelligent ammeter operation error monitoring reporting system includes a memory and one or more processors, the memory storing a computer program configured to be executed by the one or more processors to perform the steps of:
acquiring each electric energy reading error, each voltage data, each current data and each phase data which correspond to each moment of the target intelligent electric energy meter before the next moment through a wireless communication network at a big data server;
The wireless communication network may be, for example, a time division duplex communication network, a frequency division duplex communication network, or a 5G communication network;
acquiring various configuration information of the target intelligent electric energy meter at a big data server, wherein the various configuration information of the target intelligent electric energy meter comprises the power supply frequency, the working environment temperature, the working environment humidity, the precision grade and the number of load equipment of the target intelligent electric energy meter;
an AI intelligent model is established for the target intelligent electric energy meter, wherein the AI intelligent model is a convolutional neural network after a set number of repeated learning operations are completed;
for example, a MATLAB kit may be used to implement simulation processing of the AI intelligent model established for the target intelligent ammeter;
the AI intelligent model is adopted at a big data server side to predict the electric energy reading error of the target intelligent electric energy meter at the next moment according to each piece of configuration information of the target intelligent electric energy meter, each electric energy reading error, each piece of voltage data, each piece of current data and each piece of phase data, wherein each electric energy reading error corresponds to each moment before the next moment;
when the received electric energy reading error of the target intelligent electric energy meter at the next moment exceeds a set error threshold, marking the target intelligent electric energy meter as a replacement electric energy meter type and sending a replacement request signal comprising an electric energy meter identifier corresponding to the target intelligent electric energy meter;
The method for acquiring the electric energy reading errors, the voltage data, the current data and the phase data of the target intelligent electric energy meter, which correspond to the next moment before the next moment, through the wireless communication network comprises the following steps: the quantity of the moments before the next moment is monotonically and positively correlated with the quantity of the load devices of the target intelligent electric energy meter;
the method for establishing the AI intelligent model for the target intelligent electric energy meter comprises the following steps of: the smaller the number of the load devices of the target intelligent electric energy meter is, the fewer the number of the selected learning operations is;
the following are illustrated: the smaller the number of the load devices of the target intelligent electric energy meter, the fewer the number of the selected learning operations includes: the number of the load devices of the target intelligent electric energy meter is 10, the number of the selected learning operations is 50, the number of the load devices of the target intelligent electric energy meter is 12, the number of the selected learning operations is 80, the number of the load devices of the target intelligent electric energy meter is 15, the number of the selected learning operations is 120, the number of the load devices of the target intelligent electric energy meter is 18, and the number of the selected learning operations is 200;
The method for establishing the AI intelligent model for the target intelligent electric energy meter comprises the following steps of: in each learning operation of the convolutional neural network, known electric energy reading errors at the past time of the target intelligent electric energy meter are used as single output content of the convolutional neural network, each item of configuration information of the target intelligent electric energy meter, each electric energy reading error, each voltage data, each current data and each phase data which correspond to each time before the past time of the target intelligent electric energy meter are used as each item of input content of the convolutional neural network, and the learning operation is executed.
As shown in fig. 8, exemplarily, M processors are given, where M is a natural number of 1 or more.
Example 8
Fig. 9 is a flowchart of steps of an intelligent ammeter operation error monitoring and reporting method according to embodiment 8 of the present invention.
As shown in fig. 9, the method for reporting the operation error of the intelligent ammeter in embodiment 8 of the present invention specifically includes the following steps:
step S901: acquiring each electric energy reading error, each voltage data, each current data and each phase data which correspond to each moment of the target intelligent electric energy meter before the next moment through a wireless communication network at a big data server;
The wireless communication network may be, for example, a time division duplex communication network, a frequency division duplex communication network, or a 5G communication network;
step S902: acquiring various configuration information of the target intelligent electric energy meter at a big data server, wherein the various configuration information of the target intelligent electric energy meter comprises the power supply frequency, the working environment temperature, the working environment humidity, the precision grade and the number of load equipment of the target intelligent electric energy meter;
step S903: an AI intelligent model is established for the target intelligent electric energy meter, wherein the AI intelligent model is a convolutional neural network after a set number of repeated learning operations are completed;
for example, a MATLAB kit may be used to implement simulation processing of the AI intelligent model established for the target intelligent ammeter;
step S904: the AI intelligent model is adopted at a big data server side to predict the electric energy reading error of the target intelligent electric energy meter at the next moment according to each piece of configuration information of the target intelligent electric energy meter, each electric energy reading error, each piece of voltage data, each piece of current data and each piece of phase data, wherein each electric energy reading error corresponds to each moment before the next moment;
step S905: when the received electric energy reading error of the target intelligent electric energy meter at the next moment exceeds a set error threshold, marking the target intelligent electric energy meter as a replacement electric energy meter type and sending a replacement request signal comprising an electric energy meter identifier corresponding to the target intelligent electric energy meter;
The method for acquiring the electric energy reading errors, the voltage data, the current data and the phase data of the target intelligent electric energy meter, which correspond to the next moment before the next moment, through the wireless communication network comprises the following steps: the quantity of the moments before the next moment is monotonically and positively correlated with the quantity of the load devices of the target intelligent electric energy meter;
the method for establishing the AI intelligent model for the target intelligent electric energy meter comprises the following steps of: the smaller the number of the load devices of the target intelligent electric energy meter is, the fewer the number of the selected learning operations is;
the following are illustrated: the smaller the number of the load devices of the target intelligent electric energy meter, the fewer the number of the selected learning operations includes: the number of the load devices of the target intelligent electric energy meter is 10, the number of the selected learning operations is 50, the number of the load devices of the target intelligent electric energy meter is 12, the number of the selected learning operations is 80, the number of the load devices of the target intelligent electric energy meter is 15, the number of the selected learning operations is 120, the number of the load devices of the target intelligent electric energy meter is 18, and the number of the selected learning operations is 200;
The method for establishing the AI intelligent model for the target intelligent electric energy meter comprises the following steps of: in each learning operation of the convolutional neural network, known electric energy reading errors at the past time of the target intelligent electric energy meter are used as single output content of the convolutional neural network, each item of configuration information of the target intelligent electric energy meter, each electric energy reading error, each voltage data, each current data and each phase data which correspond to each time before the past time of the target intelligent electric energy meter are used as each item of input content of the convolutional neural network, and the learning operation is executed.
In addition, the present invention may also cite the following technical matters to highlight the significant technical progress of the present invention:
the AI intelligent model is adopted to predict the electric energy reading error of the target intelligent electric energy meter at the next moment according to each piece of configuration information of the target intelligent electric energy meter, each electric energy reading error, each piece of voltage data, each piece of current data and each piece of phase data, wherein each electric energy reading error corresponds to each moment before the next moment, and the electric energy reading error comprises the following steps: and taking each item of configuration information of the target intelligent electric energy meter, each electric energy reading error, each voltage data, each current data and each phase data which correspond to each moment before the next moment respectively as each item of input content of the AI intelligent model, and operating the AI intelligent model to obtain the electric energy reading error of the target intelligent electric energy meter output by the AI intelligent model at the next moment.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the disclosure. Thus, the present invention is not intended to be limited to the embodiments shown herein, but is to be accorded the full scope consistent with the claims, wherein reference to an element in the singular is not intended to mean "one and only one" (unless specifically so stated), but rather "one or more". All structural and functional equivalents to the elements of the various embodiments described throughout this disclosure (either known to those skilled in the art or later come to be known) are expressly incorporated herein by reference and are intended to be encompassed by the claims.
Claims (10)
1. An intelligent ammeter operation error monitoring reporting system, which is characterized in that the system comprises:
the data measuring device is arranged at the big data server end and is used for acquiring each electric energy reading error, each voltage data, each current data and each phase data which correspond to each moment before the next moment of the target intelligent electric energy meter through the wireless communication network;
The configuration analysis device is arranged at the big data server end and is used for acquiring various configuration information of the target intelligent electric energy meter, wherein the various configuration information of the target intelligent electric energy meter comprises the power supply frequency, the working environment temperature, the working environment humidity, the precision grade and the number of load equipment of the target intelligent electric energy meter;
the model construction device is used for establishing an AI intelligent model for the target intelligent electric energy meter, wherein the AI intelligent model is a convolutional neural network after a set number of repeated learning operations are completed;
the model application mechanism is arranged at the big data server end, is respectively connected with the data measurement device, the configuration analysis device and the model construction device, and is used for predicting the electric energy reading error of the target intelligent electric energy meter at the next moment according to each piece of configuration information of the target intelligent electric energy meter, each electric energy reading error, each piece of voltage data, each piece of current data and each piece of phase data which correspond to each moment before the next moment by adopting the AI intelligent model;
the signal triggering mechanism is connected with the model application mechanism and is used for marking the target intelligent electric energy meter as a replacement electric energy meter type and sending a replacement request signal comprising an electric energy meter identifier corresponding to the target intelligent electric energy meter when the received electric energy reading error of the target intelligent electric energy meter at the next moment exceeds a set error threshold value;
The method for acquiring the electric energy reading errors, the voltage data, the current data and the phase data of the target intelligent electric energy meter, which correspond to the next moment before the next moment, through the wireless communication network comprises the following steps: the number of times of each time before the next time is monotonically and positively correlated with the number of load devices of the target intelligent ammeter.
2. The intelligent ammeter operation error monitoring reporting system of claim 1, wherein:
establishing an AI intelligent model for the target intelligent electric energy meter, wherein the AI intelligent model comprises the following components of a convolutional neural network after a set number of repeated learning operations are completed: the smaller the number of the load devices of the target intelligent electric energy meter is, the fewer the number of the selected learning operations is;
the method for establishing the AI intelligent model for the target intelligent electric energy meter comprises the following steps of: in each learning operation of the convolutional neural network, known electric energy reading errors at the past time of the target intelligent electric energy meter are used as single output content of the convolutional neural network, each item of configuration information of the target intelligent electric energy meter, each electric energy reading error, each voltage data, each current data and each phase data which correspond to each time before the past time of the target intelligent electric energy meter are used as each item of input content of the convolutional neural network, and the learning operation is executed.
3. The intelligent ammeter operation error monitoring reporting system of claim 2, wherein the system comprises:
the meter body replacement server is connected with the big data server through a wireless communication link and is used for receiving a replacement request signal comprising the electric energy meter identification corresponding to the target intelligent electric energy meter and assigning the target intelligent electric energy meter to a currently-idle meter body replacement service personnel.
4. The intelligent ammeter operation error monitoring reporting system of claim 2, wherein the system comprises:
and the threshold value storage mechanism is connected with the signal triggering mechanism and is used for storing the set error threshold value and providing the set error threshold value for the threshold value storage mechanism.
5. The intelligent ammeter operation error monitoring reporting system of claim 2, wherein the system comprises:
the parameter storage mechanism is connected with the model building device and used for storing the AI intelligent model;
the parameter storage mechanism is used for completing the storage of the AI intelligent model by storing various model parameters of the AI intelligent model.
6. The intelligent ammeter operation error monitoring reporting system of claim 2, wherein the system comprises:
The log recording mechanism is arranged at the big data server end and connected with the model application mechanism and is used for recording the electric energy reading error of the target intelligent electric energy meter at the next moment, the next moment and the electric energy meter identification corresponding to the target intelligent electric energy meter as a single error log when the electric energy reading error of the target intelligent electric energy meter at the next moment exceeds a set error threshold.
7. The intelligent ammeter operation error monitoring reporting system of claim 6, wherein the system comprises:
and the overflow detection mechanism is connected with the log recording mechanism and is used for sending out a log overflow instruction when the number of the logs currently recorded by the log recording mechanism exceeds or is equal to a preset number limit.
8. The intelligent ammeter operation error monitoring reporting system of any one of claims 2-7, wherein:
the signal triggering mechanism is further used for marking the target intelligent electric energy meter as a reliable electric energy meter type and sending out a replacement request signal comprising an electric energy meter identifier corresponding to the target intelligent electric energy meter temporarily when the received electric energy reading error of the target intelligent electric energy meter at the next moment does not exceed a set error threshold value;
When the received electric energy reading error of the target intelligent electric energy meter at the next moment exceeds a set error threshold, marking the target intelligent electric energy meter as a replacement electric energy meter type and sending a replacement request signal comprising an electric energy meter identifier corresponding to the target intelligent electric energy meter comprises the following steps: when the received electric energy reading error of the target intelligent electric energy meter at the next moment exceeds a set error threshold, marking the target intelligent electric energy meter as a replacement electric energy meter type and sending a replacement request signal comprising an electric energy meter identifier corresponding to the target intelligent electric energy meter through a wireless communication link;
when the received electric energy reading error of the target intelligent electric energy meter at the next moment exceeds a set error threshold, marking the target intelligent electric energy meter as a replacement electric energy meter type and sending a replacement request signal comprising an electric energy meter identifier corresponding to the target intelligent electric energy meter through a wireless communication link comprises the following steps: adopting an IP data packet to carry a replacement request signal comprising an electric energy meter identifier corresponding to the target intelligent electric energy meter so as to send out the replacement request signal through a wireless communication link;
when the received electric energy reading error of the target intelligent electric energy meter at the next moment exceeds a set error threshold, marking the target intelligent electric energy meter as a replacement electric energy meter type and sending a replacement request signal comprising an electric energy meter identifier corresponding to the target intelligent electric energy meter through a wireless communication link comprises the following steps: and sending a replacement request signal comprising the electric energy meter identifier corresponding to the target intelligent electric energy meter to a meter body replacement server through a wireless communication link.
9. The intelligent ammeter operation error monitoring reporting system of any one of claims 2-7, wherein:
the obtaining, through the wireless communication network, each electric energy reading error, each voltage data, each current data and each phase data of the target intelligent electric energy meter, which correspond to each time before the next time, respectively includes: the target intelligent electric energy meter is one of a plurality of intelligent electric energy meters managed by the big data server;
the method for acquiring the electric energy reading errors, the voltage data, the current data and the phase data of the target intelligent electric energy meter, which correspond to the next moment before the next moment, through the wireless communication network comprises the following steps: the next moment and each moment before the next moment are uniformly arranged at intervals on a time axis;
wherein, each time before the next time and the next time is evenly spaced on the time axis and set up includes: each time before the next time comprises the current time nearest to the next time;
each item of configuration information of the target intelligent electric energy meter comprises power frequency, voltage data, working environment temperature, working environment humidity, precision grade and load equipment quantity of the target intelligent electric energy meter, wherein the number of the load equipment comprises: and the voltage data of the target intelligent electric energy meter is 220V or 380V.
10. An intelligent ammeter operation error monitoring reporting system, the system comprising a memory and one or more processors, the memory storing a computer program configured to be executed by the one or more processors to perform the steps of:
acquiring each electric energy reading error, each voltage data, each current data and each phase data which correspond to each moment of the target intelligent electric energy meter before the next moment through a wireless communication network at a big data server;
acquiring various configuration information of the target intelligent electric energy meter at a big data server, wherein the various configuration information of the target intelligent electric energy meter comprises the power supply frequency, the working environment temperature, the working environment humidity, the precision grade and the number of load equipment of the target intelligent electric energy meter;
an AI intelligent model is established for the target intelligent electric energy meter, wherein the AI intelligent model is a convolutional neural network after a set number of repeated learning operations are completed;
the AI intelligent model is adopted at a big data server side to predict the electric energy reading error of the target intelligent electric energy meter at the next moment according to each piece of configuration information of the target intelligent electric energy meter, each electric energy reading error, each piece of voltage data, each piece of current data and each piece of phase data, wherein each electric energy reading error corresponds to each moment before the next moment;
When the received electric energy reading error of the target intelligent electric energy meter at the next moment exceeds a set error threshold, marking the target intelligent electric energy meter as a replacement electric energy meter type and sending a replacement request signal comprising an electric energy meter identifier corresponding to the target intelligent electric energy meter;
the method for acquiring the electric energy reading errors, the voltage data, the current data and the phase data of the target intelligent electric energy meter, which correspond to the next moment before the next moment, through the wireless communication network comprises the following steps: the number of times of each time before the next time is monotonically and positively correlated with the number of load devices of the target intelligent ammeter.
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