Remote monitoring system for metering of electric energy meter

Abstract

The invention discloses a remote monitoring system for an electric energy meter, relates to the field of remote monitoring systems, and mainly solves the problems that the system is poor in capability of processing common problems of the electric energy meter, poor in expandability and flexibility and low in system data and user privacy. Comprising a data collection module, a data processing module, a communication network module, a remote verification module, a system detection module and a user interface module, data during system operation are optimized through an intelligent calculation optimization algorithm, and the stability of system operation and the data processing efficiency are improved; state data of the electric energy meter are transmitted through the wireless communication module, so that the accuracy of remote verification is improved; and through self-detection and correction of the field monitoring module, the working efficiency is improved, and the labor loss is reduced.

Description

Technical Field

[0001] The present invention relates to the field of remote monitoring systems, and more specifically to a remote monitoring system for electric energy meter measurement. Background Art

[0002] The remote monitoring system for electric energy meter measurement mainly relies on the integration of data communication technology, the Internet, remote control, database and user interface technology. With the continuous growth of global energy consumption and the continuous advancement of Internet communication technology, remote monitoring systems will rely more on automation mode to reduce costs and energy consumption, and determine the status of electric energy meters more quickly and accurately. The system will realize unattended and autonomous operation mode to meet user needs. The system can be widely used in industrial manufacturing, commercial buildings, agricultural fields and residential households to help users save costs, improve energy efficiency and assist in scientific decision-making. With the continuous development of Suizhe communication technology and Internet technology, and the perfection and maturity of remote control technology, there is still a lot of room for development of a remote monitoring system for electric energy meter measurement.

[0003] The data processing technology of traditional remote monitoring systems is not mature enough, resulting in untimely information feedback and the inability to independently handle common problems that arise in electricity meters; traditional remote monitoring systems lack scalability and flexibility, resulting in an inability to adapt to changing environments and user needs; traditional remote monitoring models lack protection for data security and privacy, resulting in data theft. Summary of the invention

[0004] In view of the deficiencies of the above technologies, the present invention discloses a remote monitoring system for an electric energy meter, which optimizes the data during system operation through the intelligent computing optimization algorithm, thereby improving the stability of system operation and data processing efficiency; transmits the status data of the electric energy meter through the wireless communication module, thereby improving the accuracy of remote verification; and through the self-detection and correction of the on-site monitoring module, improves work efficiency and reduces labor loss.

[0005] In order to achieve the above technical effects, the present invention adopts the following technical solutions:

[0006] A remote monitoring system for electric energy meter measurement, including a data collection module, a data processing module, a communication network module, a remote verification module, a system detection module and a user interface module.

[0007] The data collection module is used to monitor and collect data from the electric energy meter in real time;

[0008] The data processing module is used to process and analyze the collected data information;

[0009] The communication network module is used to realize the communication between the various modules of the remote monitoring system;

[0010] The remote verification module is used to verify and modify the parameter data of the remote monitoring system; the remote verification module includes a field detection device unit, a wireless network unit and a background management center unit; the field detection device unit includes a feature data recognition module, an information classification module and a fault diagnosis module; the feature data recognition module extracts feature data from the real-time status data of the electric energy meter through an information optimization algorithm; the information classification module divides the status information of the electric energy meter into two categories of normal and abnormal through cluster analysis; the fault diagnosis module determines the status of the electric energy meter through the online detection function of the positive and negative reactive errors of the electric energy meter and the CT secondary circuit status monitoring; the output end of the feature data recognition module is connected to the input end of the information classification module, and the output end of the information classification module is connected to the input end of the fault diagnosis module; the wireless network unit transmits the status data of the electric energy meter to the user end through WiFi communication; the background management center unit integrates the status information of the electric energy meter through an intelligent computing optimization algorithm and timely feedbacks it, the output end of the field detection unit is connected to the input end of the communication network unit, and the output end of the communication network is connected to the input end of the background management center unit;

[0011] The system detection module is used to monitor the operation status of each module in real time and send reminders and repair requests to the user end in a timely manner;

[0012] The user interface module is used for users to interact with the remote monitoring system and observe the data and operating status of the energy meter;

[0013] The output end of the data collection module is connected to the input end of the data processing module, the output end of the data processing module is connected to the input end of the communication network module, the output end of the communication network module is connected to the input end of the remote verification module, the output end of the remote verification module is connected to the input end of the system monitoring module, and the output end of the system monitoring module is connected to the input end of the user interface module.

[0014] As a further description of the above scheme, the data collection module includes a data source connection unit, a data acquisition unit and a data storage unit. The data source connection unit is connected to the remote monitoring system data source via an RS-485 bus, and the data acquisition unit uses an SQL database query statement to extract data information in the system; the data storage unit uses a relational database to store the acquired data, and the output end of the data source connection unit is connected to the input end of the data acquisition unit, and the output end of the data acquisition unit is connected to the input end of the data storage unit.

[0015] As a further description of the above scheme, the data processing module includes a data screening unit, a data analysis unit and a data integration unit. The data screening unit filters the characteristic information of the electric energy meter from the system data through the Redis database; the data analysis unit performs statistical analysis on the information of the electric energy meter through regression analysis and Tableau tools; the data integration unit manages and integrates the data using ADO database connection, the output end of the data screening unit is connected to the input end of the data analysis unit, and the output end of the data analysis unit is connected to the input end of the data integration unit.

[0016] As a further description of the above scheme, the communication network module includes a data conversion unit, a communication unit and a data transmission unit; the data conversion unit uses an analog-to-digital converter ADC to convert the collected electrical signal into a digital signal; the communication unit transmits data between different systems through W7100; the data transmission unit converts serial port data into IP data and transmits it through a wireless communication network, the output end of the data conversion unit is connected to the input end of the wireless communication unit, and the output end of the wireless communication unit is connected to the input end of the data transmission unit.

[0017] As a further description of the above scheme, the implementation method of the information optimization algorithm is:

[0018] 1) Determine user needs;

[0019] Clarify the purpose of extracting characteristic information of electric energy meters and determine data parameters and thresholds;

[0020] 2) Data collection and preprocessing;

[0021] Collect real-time data from the electric energy meter and remove data that exceeds certain parameters and thresholds;

[0022] 3) Feature data extraction;

[0023] The characteristic data of the electric energy meter is determined according to the feature extraction algorithm to analyze data screening and data statistics. The expression of the feature extraction algorithm is:

[0024]

[0025] In formula (1), C represents the feature extraction algorithm, k represents the number of data, and X k represents the current operation data, δ represents the threshold, α represents the feature parameter, X i Represents standard characteristic data, Indicates the feature data screened from real-time data, tan -1 (X i -X k ) 2Indicates the statistical range of feature data;

[0026] 4) Feature optimization;

[0027] Optimize and upgrade feature parameters and system performance based on feature data extracted by feature extraction function;

[0028] 5) Verification and adjustment;

[0029] Adjust parameter settings based on experimental verification results and tests on system performance.

[0030] As a further description of the above scheme, the implementation method of the intelligent computing optimization algorithm is:

[0031] 1) Requirements analysis;

[0032] Determine the user's need to monitor the status of the electric energy meter in real time, determine the format of the input data and the expected format of the output data

[0033] 2) Data collection and processing;

[0034] Obtain the status information of the electric energy meter, remove noise and irrelevant data, process missing values, and scale the data values ​​to [0, 1];

[0035] 3) Data model analysis;

[0036] The information obtained is analyzed according to the data model function to identify the root of the performance problem, including status information, anomaly detection and correlation analysis. The formula expression of the data model function is:

[0037]

[0038] In formula (2), S i represents the model function, μ represents the function analysis factor ω ij represents the weight of state data, i and j represent the number and order of state information, n represents the number of calculations, and x j Indicates the current state information data value, a j Indicates the state data value at the previous moment, b j represents the state data value at the next moment, L represents the detection threshold, represents function anomaly detection, represents the correlation coefficient factor, represents correlation analysis;

[0039] 4) Improve and optimize the analysis model;

[0040] Based on the model analysis results, formulate corresponding improvement plans and develop upgrade plans for hardware, software, load and cache;

[0041] 5) Implement optimization plans;

[0042] Use scripts and update system configurations to apply to system optimization solutions;

[0043] 6) Test the optimization model;

[0044] The optimization scheme is verified according to the test function, and the performance data before and after are compared. The formula expression of the test function is:

[0045]

[0046] In formula (3), M represents the test function, n represents the number of samples, i represents the order of verification calculation, and Y i represents the actual value, β i represents the value to be verified, η represents the function verification coefficient,

[0047] Indicates data testing, Indicates data comparison;

[0048] 7) Result analysis optimization

[0049] Analyze the output results of the model and adjust the model structure and modify parameters in a timely manner according to the data fed back by the remote monitoring system.

[0050] As a further description of the above scheme, the system detection module includes a real-time data acquisition unit, a remote monitoring unit and a remote control unit; the real-time data acquisition unit continuously collects various indicator data through the SQL database and displays the data to the user in real time to monitor the operating status of the electric energy meter; the remote monitoring unit records the information of abnormal events based on the B / S system and generates corresponding alarm or prompt information; the remote control unit realizes call control, user management, equipment management, and data storage and publishing tasks based on the central management server CMS; the output end of the data acquisition module unit is connected to the input end of the remote monitoring unit, and the output end of the remote monitoring unit is connected to the input end of the remote control unit.

[0051] As a further description of the above scheme, the user interface module includes a data visualization unit, a real-time monitoring unit, and an alarm and notification unit. The data visualization uses an application programming interface API to interact with the remote verification module and the system detection module at the back end to update data in real time. The real-time monitoring continuously tracks the operating status and key performance indicators of the system through the monitoring tool Nagios. The alarm and notification are processed and fed back through interactive data. The output end of the data visualization unit is connected to the input end of the real-time monitoring unit, and the output end of the real-time monitoring unit is connected to the input end of the alarm and notification unit.

[0052] The beneficial technical effects of the present invention compared with the prior art are: optimizing the data during system operation through the intelligent computing optimization algorithm, thereby improving the stability of system operation and data processing efficiency; transmitting the status data of the electric energy meter through the wireless communication module, thereby improving the accuracy of remote verification; and improving work efficiency and reducing labor loss through self-detection and correction of the on-site monitoring module. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to understand and grasp the technical solution more intuitively and clearly, when describing the embodiments of the present invention or the prior art, drawings are often used to supplement and illustrate. It should be noted that the drawings are only one way of expressing the embodiments of the present invention or the prior art. In fact, the technical solution can also have other implementation methods and changes, which all fall within the protection scope of the present invention. Therefore, technicians can design other drawings as needed to implement the technical solution of the present invention, among which,

[0054] Figure 1 It is a schematic diagram of the overall structure of the present invention;

[0055] Figure 2 This is a structural diagram of a data collection module of the present invention;

[0056] Figure 3 This is a schematic diagram of the structure of the data processing module of the present invention;

[0057] Figure 4 This is a schematic diagram of the communication network module structure of the present invention;

[0058] Figure 5 This is a schematic diagram of the structure of the system monitoring module of the present invention; DETAILED DESCRIPTION

[0059] The following will clearly and completely describe the technical solutions in the embodiments of this invention through the accompanying drawings in the embodiments of this invention. Obviously, the described embodiments are only part of the embodiments of this invention, not all of the embodiments. At the same time, the description of well-known structures and technologies is omitted in the following description to avoid unnecessary confusion of the concepts of the present invention.

[0060] like Figure 1-Figure 5 As shown, a remote monitoring system for electric energy meter measurement includes a data collection module, a data processing module, a communication network module, a remote verification module, a system detection module and a user interface module;

[0061] The data collection module is used to monitor and collect data from the electric energy meter in real time;

[0062] The data processing module is used to process and analyze the collected data information;

[0063] The communication network module is used to realize the communication between the various modules of the remote monitoring system;

[0064] The remote verification module is used to verify and modify the parameter data of the remote monitoring system; the remote verification module includes a field detection device unit, a wireless network unit and a background management center unit; the field detection device unit includes a feature data recognition module, an information classification module and a fault diagnosis module; the feature data recognition module extracts feature data from the real-time status data of the electric energy meter through an information optimization algorithm; the information classification module divides the status information of the electric energy meter into two categories of normal and abnormal through cluster analysis; the fault diagnosis module determines the status of the electric energy meter through the online detection function of the positive and negative reactive errors of the electric energy meter and the CT secondary circuit status monitoring; the output end of the feature data recognition module is connected to the input end of the information classification module, and the output end of the information classification module is connected to the input end of the fault diagnosis module; the wireless network unit transmits the status data of the electric energy meter to the user end through WiFi communication; the background management center unit integrates the status information of the electric energy meter through an intelligent computing optimization algorithm and timely feedbacks it, the output end of the field detection unit is connected to the input end of the communication network unit, and the output end of the communication network is connected to the input end of the background management center unit;

[0065] The system detection module is used to monitor the operation status of each module in real time and send reminders and repair requests to the user end in a timely manner;

[0066] The user interface module is used for users to interact with the remote monitoring system and observe the data and operating status of the energy meter;

[0067] The output end of the data collection module is connected to the input end of the data processing module, the output end of the data processing module is connected to the input end of the communication network module, the output end of the communication network module is connected to the input end of the remote verification module, the output end of the remote verification module is connected to the input end of the system monitoring module, and the output end of the system monitoring module is connected to the input end of the user interface module.

[0068] In a further embodiment, the data collection module includes a data source connection unit, a data acquisition unit and a data storage unit. The data source connection unit is connected to the remote monitoring system data source via an RS-485 bus. The data acquisition unit uses an SQL database query statement to extract data information in the system. The data storage unit uses a relational database to store the acquired data. The output end of the data source connection unit is connected to the input end of the data acquisition unit, and the output end of the data acquisition unit is connected to the input end of the data storage unit.

[0069] The working principle of the data collection module is as follows: the data source connection unit establishes a stable communication connection with the remote monitoring system data source through the RS-485 bus. This connection method ensures the efficiency and accuracy of data transmission. The RS-485 bus can also achieve multi-point connection, laying a solid foundation for the subsequent establishment of a multi-point communication network; the data acquisition unit uses SQL database query statements to extract data information from the system, which can quickly and effectively retrieve specific information from a large amount of data while ensuring the integrity of the data. In this way, the security of the data can be guaranteed while acquiring the data; the data storage unit uses relational database technology to store the acquired data in an orderly and efficient manner. This database technology has good scalability and stability, and can meet the needs of large-scale data storage and processing. At the same time, a variety of optimization measures are also adopted, such as indexing, partitioning, etc., to further improve the efficiency of data storage and query.

[0070] In a further embodiment, the data processing module includes a data screening unit, a data analysis unit and a data integration unit. The data screening unit filters the characteristic information of the electric energy meter from the system data through the Redis database; the data analysis unit performs statistical analysis on the information of the electric energy meter through regression analysis and Tableau tools; the data integration unit manages and integrates the data using an ADO database connection, the output end of the data screening unit is connected to the input end of the data analysis unit, and the output end of the data analysis unit is connected to the input end of the data integration unit.

[0071] The working principle of the data processing module is as follows: the data screening unit quickly and accurately filters out specific electric energy meter feature information from the huge system data through the Redis database, realizes high-speed screening through the Redis database, and retrieves and extracts relevant data according to preset rules or parameters to prepare for subsequent analysis and processing; the data analysis unit uses regression analysis to further statistically analyze and process the screened data. The Tableau tool provides powerful data processing and data analysis capabilities to extract valuable information and insights from the data. These analyses can help users understand the power consumption pattern of the electric energy meter, predict future trends, detect abnormal situations, and make more effective decisions based on this; the data integration unit is responsible for integrating and managing the screened and analyzed electric energy meter data, which is achieved through ADO database connection technology. ADO is a Microsoft database access interface that provides a unified method to access and operate different types of databases. The data integration unit uses ADO database connection to easily extract data from the SQL Server 2005 database and merge it with other data sources. In this way, the system can provide a unified data view for user query and report generation.

[0072] In a further embodiment, the communication network module includes a data conversion unit, a communication unit and a data transmission unit; the data conversion unit uses an analog-to-digital converter ADC to convert the collected electrical signal into a digital signal; the communication unit transmits data between different systems through W7100; the data transmission unit converts serial port data into IP data and transmits it through a wireless communication network, the output end of the data conversion unit is connected to the input end of the wireless communication unit, and the output end of the wireless communication unit is connected to the input end of the data transmission unit.

[0073] The working principle of the communication network module is as follows: the data conversion unit uses an analog-to-digital converter ADC to convert the analog electronic signals collected by the electric energy meter into digital signals. The analog-to-digital converter can convert continuous analog signals into discrete digital signals represented in binary. These converted data can be processed and transmitted by subsequent communication units; the communication unit is responsible for realizing data transmission between different systems, and improves the reliability and efficiency of communication through W7100, realizing stable and efficient data transmission between the electric energy meter and the monitoring system; the data transmission unit is responsible for converting serial port data into IP data and transmitting it through a wireless communication network. In a remote monitoring system, the electric energy meter is usually connected to the communication unit through a serial port. The data transmission unit will convert serial port data into IP data packets for transmission in the network, and can also transmit the data of the electric energy meter to other modules in the monitoring system, such as the data processing module and the user interface module;

[0074] In a further embodiment, the implementation method of the information optimization algorithm is:

[0075] 1) Determine user needs;

[0076] Clarify the purpose of extracting characteristic information of electric energy meters and determine data parameters and thresholds;

[0077] 2) Data collection and preprocessing;

[0078] Collect real-time data from the electric energy meter and remove data that exceeds certain parameters and thresholds;

[0079] 3) Feature data extraction;

[0080] According to the feature extraction algorithm, the characteristic data of the electric energy meter is determined to analyze data screening and data statistics.

[0081] The expression of the feature extraction algorithm is:

[0082]

[0083] In formula (1), C represents the feature extraction algorithm, k represents the number of data, and Xk represents the current operation data, δ represents the threshold, α represents the feature parameter, X i Represents standard characteristic data, Indicates the feature data screened from real-time data, tan -1 (X i -X k ) 2 Indicates the statistical range of feature data;

[0084] 4) Feature optimization;

[0085] Optimize and upgrade feature parameters and system performance based on feature data extracted by feature extraction function;

[0086] 5) Verification and adjustment;

[0087] Adjust parameter settings based on experimental verification results and tests on system performance.

[0088] The working principle of the information optimization algorithm is as follows: the information optimization algorithm is based on data processing and analysis technology to extract key information from the original data and optimize it to facilitate subsequent data analysis and decision support. By preprocessing the real-time data of the electric energy meter including denoising and data conversion, the availability of data can be guaranteed. Selecting the most useful features from the extracted features and removing redundant or irrelevant features can help improve the efficiency and accuracy of the model. Feature optimization can ensure the comparability of features and model stability. The information optimization algorithm can predict the future state of the electric energy meter. -1 (X i -X k ) 2 Statistical feature data is used for performance evaluation.

[0089] Table 1 Differences between information optimization algorithm and ICA algorithm

[0090]

[0091] In a further embodiment, the implementation method of the intelligent computing optimization algorithm is:

[0092] 1) Requirements analysis;

[0093] Determine the user's need to monitor the status of the electric energy meter in real time, and determine the format of the input data and the expected format of the output data;

[0094] 2) Data collection and processing;

[0095] Obtain the status information of the electric energy meter, remove noise and irrelevant data, process missing values, and scale the data values ​​to [0, 1];

[0096] 3) Data model analysis;

[0097] The information obtained is analyzed according to the data model function to identify the root of the performance problem, including status information, anomaly detection and correlation analysis. The formula expression of the data model function is:

[0098]

[0099] In formula (2), S i represents the model function, μ represents the function analysis factor ω ij represents the weight of state data, i and j represent the number and order of state information, n represents the number of calculations, and x j Indicates the current state information data value, a j Indicates the state data value at the previous moment, b j represents the state data value at the next moment, L represents the detection threshold, represents function anomaly detection, represents the correlation coefficient factor, represents correlation analysis;

[0100] 4) Improve and optimize the analysis model;

[0101] Based on the model analysis results, formulate corresponding improvement plans and develop upgrade plans for hardware, software, load and cache;

[0102] 5) Implement optimization plans;

[0103] Use scripts and update system configurations to apply to system optimization solutions;

[0104] 6) Test the optimization model;

[0105] The optimization scheme is verified according to the test function, and the performance data before and after are compared. The formula expression of the test function is:

[0106]

[0107] In formula (3), M represents the test function, n represents the number of samples, i represents the order of verification calculation, and Y i represents the actual value, β i represents the value to be verified, η represents the function verification coefficient, Indicates data testing, Indicates data comparison;

[0108] 7) Result analysis optimization

[0109] Analyze the output results of the model and adjust the model structure and modify parameters in a timely manner according to the data fed back by the remote monitoring system.

[0110] The working principle of the intelligent computing optimization algorithm is as follows: the intelligent computing optimization algorithm is designed to simulate the way the human brain processes information by connecting a large number of simple units to form a complex network structure based on the working mode of human brain neurons. It can automatically learn features from data without manual feature engineering. By adjusting the network structure, loss function and optimization algorithm, the intelligent computing optimization algorithm can adapt to various problems and tasks; by formulating a suitable model for practical problems, by adjusting the connection weights ω ij The continuous summation of the difference between the threshold value L can obtain the simulation function S i , and then through and The loss function M can be obtained by summing them up. The gap between the predicted value and the actual value can be calculated by the simulation function and the loss function. The two functions can provide strong data support for subsequent optimization. The model structure and parameters can be adjusted in time according to the data fed back by the two functions to better adapt to the stability of the system.

[0111] Table 1 Differences between intelligent computing optimization algorithms and traditional machine optimization algorithms

[0112]

[0113] In summary, the intelligent computing optimization algorithm is superior to the traditional machine optimization algorithm in terms of analysis model efficiency, degree of automation and result accuracy. It can be seen that the intelligent computing optimization algorithm is the best choice for the present invention.

[0114] In a further embodiment, the system detection module includes a real-time data acquisition unit, a remote monitoring unit and a remote control unit; the real-time data acquisition unit continuously collects various indicator data through an SQL database and displays the data to a user in real time to monitor the operating status of the electric energy meter; the remote monitoring unit records information on abnormal events based on a B / S system and generates corresponding alarm or prompt information; the remote control unit implements call control, user management, equipment management, and data storage and publishing tasks based on a central management server CMS; the output end of the data acquisition module unit is connected to the input end of the remote monitoring unit, and the output end of the remote monitoring unit is connected to the input end of the remote control unit.

[0115] The working principle of the system detection module is as follows: the real-time data acquisition unit is the basis of the system detection module, and is responsible for continuously collecting the operation data of the energy meter, such as current, voltage, power factor, active power, and reactive power, through the SQL database. These data are converted from analog signals to digital signals through the analog-to-digital converter ADC, and then processed by the data conversion unit and displayed to the user in real time. The user can monitor the operation status of the energy meter in real time through the user interface and find potential problems in time; the remote monitoring unit is based on the B / S system architecture, which realizes remote monitoring of the operation data of the energy meter. The B / S architecture can access the application on the server through the browser. The remote monitoring unit records the information of abnormal events, such as energy meter failure, data abnormality, etc., and generates corresponding alarm or prompt information. These alarm information can be sent to the user via SMS, email or system notification, so that the user can take measures in time; the remote control unit realizes remote control of the energy meter based on the central management server CMS. The central management server can process and store a large amount of energy meter data, remotely modify the parameter settings of the energy meter, and store and publish the operation data of the energy meter, providing users with comprehensive data analysis and reports, such as the communication parameters and alarm thresholds of the energy meter. The remote control unit can perform tasks such as call control, user management, device management, and data storage and publishing, basically realizing automation and improving work efficiency.

[0116] In a further embodiment, the user interface module includes a data visualization unit, a real-time monitoring unit, and an alarm and notification unit. The data visualization uses an application programming interface API to interact with a remote verification module and a system detection module at the back end to update data in real time. The real-time monitoring continuously tracks the operating status and key performance indicators of the system through the monitoring tool Nagios. The alarm and notification are processed and fed back through interactive data. The output end of the data visualization unit is connected to the input end of the real-time monitoring unit, and the output end of the real-time monitoring unit is connected to the input end of the alarm and notification unit.

[0117] The working principle of the user interface module is as follows: the data visualization unit interacts with the remote verification module and the system detection module of the back end through the application programming interface API to update the data in real time. The data obtained through the API may include the real-time status, historical data trends, and performance indicators of the electric energy meter. The data visualization unit can present these data to the user in an intuitive way using charts, graphs, and dashboards to help users better understand the patterns and trends of electric energy consumption; the real-time monitoring unit continuously tracks the operating status and key performance indicators of the system through the monitoring tool Nagios. Nagios can monitor network services, host status, and application performance. The real-time monitoring unit can set monitoring tasks, automatically detect problems in the system, such as service failures, network interruptions, and notify users in a timely manner, thereby increasing the service life of the electric energy meter and reducing losses; the alarm and notification unit is responsible for processing and feedback of interactive data. When the system detects an abnormality or problem, the alarm and notification unit will generate an alarm message and notify the user in a variety of ways, such as email, SMS, phone, system notification, etc. The user can take corresponding measures based on the alarm information, such as adjusting the settings of the electric energy meter and contacting maintenance personnel. Dual protection of users and systems is achieved.

[0118] Although the present invention has been described by the above specific embodiments, it should be understood by the technician that these embodiments are provided only as examples and do not limit the scope and application of the present invention. Without departing from the principles and essence of the present invention, the technician can make various omissions, substitutions and changes to the present invention to achieve results with substantially similar functions, and these embodiments also belong to the scope of the present invention. Therefore, the scope of the present invention is limited only by the appended claims.

Claims

1. A remote monitoring system for electric energy meter measurement, comprising a data collection module, a data processing module, a communication network module, a remote verification module, a system detection module and a user interface module, characterized in that: The data collection module is used to monitor and collect data from the electric energy meter in real time; The data processing module is used to process and analyze the collected data information; The communication network module is used to realize the communication between the various modules of the remote monitoring system; The remote verification module is used to verify and modify the parameter data of the remote monitoring system; the remote verification module includes a field detection device unit, a wireless network unit and a background management center unit; the field detection device unit includes a feature data recognition module, an information classification module and a fault diagnosis module; the feature data recognition module extracts feature data from the real-time status data of the electric energy meter through an information optimization algorithm; the information classification module divides the status information of the electric energy meter into two categories of normal and abnormal through cluster analysis; the fault diagnosis module determines the status of the electric energy meter through the online detection function of the positive and negative reactive errors of the electric energy meter and the CT secondary circuit status monitoring; the output end of the feature data recognition module is connected to the input end of the information classification module, and the output end of the information classification module is connected to the input end of the fault diagnosis module; the wireless network unit transmits the status data of the electric energy meter to the user end through WiFi communication; the background management center unit integrates the status information of the electric energy meter through an intelligent computing optimization algorithm and timely feedbacks it, the output end of the field detection unit is connected to the input end of the communication network unit, and the output end of the communication network is connected to the input end of the background management center unit; The system detection module is used to monitor the operation status of each module in real time and send reminders and repair requests to the user end in a timely manner; The user interface module is used for users to interact with the remote monitoring system and observe the data and operating status of the energy meter; The output end of the data collection module is connected to the input end of the data processing module, the output end of the data processing module is connected to the input end of the communication network module, the output end of the communication network module is connected to the input end of the remote verification module, the output end of the remote verification module is connected to the input end of the system monitoring module, and the output end of the system monitoring module is connected to the input end of the user interface module.

2. The remote monitoring system for electric energy meter measurement according to claim 1 is characterized in that: The data collection module includes a data source connection unit, a data acquisition unit and a data storage unit. The data source connection unit is connected to the remote monitoring system data source via an RS-485 bus. The data acquisition unit uses an SQL database query statement to extract data information in the system. The data storage unit uses a relational database to store the acquired data. The output end of the data source connection unit is connected to the input end of the data acquisition unit, and the output end of the data acquisition unit is connected to the input end of the data storage unit.

3. The remote monitoring system for electric energy metering according to claim 1 is characterized in that: The data processing module includes a data screening unit, a data analysis unit and a data integration unit, wherein the data screening unit filters the electric energy meter characteristic information from the system data through the Redis database; The data analysis unit performs statistical analysis on the information of the electric energy meter through regression analysis and Tableau tools; the data integration unit manages and integrates the data using ADO database connection, the output end of the data screening unit is connected to the input end of the data analysis unit, and the output end of the data analysis unit is connected to the input end of the data integration unit.

4. The remote monitoring system for electric energy metering according to claim 1 is characterized in that: The communication network module includes a data conversion unit, a communication unit and a data transmission unit; the data conversion unit uses an analog-to-digital converter ADC to convert the collected electrical signals into digital signals; the communication unit transmits data between different systems through W7100; the data transmission unit converts serial port data into IP data and transmits it through a wireless communication network, the output end of the data conversion unit is connected to the input end of the wireless communication unit, and the output end of the wireless communication unit is connected to the input end of the data transmission unit.

5. The remote monitoring system for electric energy meter measurement according to claim 1 is characterized in that: The implementation method of the information optimization algorithm is: 1) Determine user needs; Clarify the purpose of extracting characteristic information of electric energy meters and determine data parameters and thresholds; 2) Data collection and preprocessing; Collect real-time data from the electric energy meter and remove data that exceeds certain parameters and thresholds; 3) Feature data extraction; The characteristic data of the electric energy meter is determined according to the feature extraction algorithm to analyze data screening and data statistics. The expression of the feature extraction algorithm is: In formula (1), C represents the feature extraction algorithm, k represents the number of data, and X k represents the current operation data, δ represents the threshold, α represents the feature parameter, X i Represents standard characteristic data, Indicates the feature data screened from real-time data, tan -1 (X i -X k ) 2 Indicates the statistical range of feature data; 4) Feature optimization; Optimize and upgrade feature parameters and system performance based on feature data extracted by feature extraction function; 5) Verification and adjustment; Adjust parameter settings based on experimental verification results and tests on system performance.

6. The remote monitoring system for electric energy meter measurement according to claim 1 is characterized in that: The implementation method of the intelligent computing optimization algorithm is: 1) Requirements analysis; Determine the user's need to monitor the status of the electric energy meter in real time, determine the format of the input data and the expected format of the output data 2) Data collection and processing; Obtain the status information of the electric energy meter, remove noise and irrelevant data, process missing values, and scale the data values ​​to [0, 1]; 3) Data model analysis; The information obtained is analyzed according to the data model function to identify the root of the performance problem, including status information, anomaly detection and correlation analysis. The formula expression of the data model function is: In formula (2), S i represents the model function, μ represents the function analysis factor ω ij represents the weight of state data, i and j represent the number and order of state information, n represents the number of calculations, and x j Indicates the current state information data value, a j Indicates the state data value at the previous moment, b j represents the state data value at the next moment, L represents the detection threshold, represents function anomaly detection, represents the correlation coefficient factor, represents correlation analysis; 4) Improve and optimize the analysis model; Based on the model analysis results, formulate corresponding improvement plans and develop upgrade plans for hardware, software, load and cache; 5) Implement optimization plans; Use scripts and update system configurations to apply to system optimization solutions; 6) Test the optimization model; The optimization scheme is verified according to the test function, and the performance data before and after are compared. The formula expression of the test function is: In formula (3), M represents the test function, n represents the number of samples, i represents the order of verification calculation, and Y i represents the actual value, β i represents the value to be verified, η represents the function verification coefficient, Indicates data testing, Indicates data comparison; 7) Result analysis optimization Analyze the output results of the model and adjust the model structure and modify parameters in a timely manner according to the data fed back by the remote monitoring system.

7. The remote monitoring system for electric energy meter measurement according to claim 1 is characterized in that: The system detection module includes a real-time data acquisition unit, a remote monitoring unit and a remote control unit; the real-time data acquisition unit continuously collects various indicator data through an SQL database and displays the data to the user in real time to monitor the operating status of the electric energy meter; the remote monitoring unit records information on abnormal events based on the B / S system and generates corresponding alarm or prompt information; the remote control unit implements call control, user management, equipment management, and data storage and publishing tasks based on the central management server CMS; the output end of the data acquisition module unit is connected to the input end of the remote monitoring unit, and the output end of the remote monitoring unit is connected to the input end of the remote control unit.

8. The remote monitoring system for electric energy meter measurement according to claim 1 is characterized in that: The user interface module includes a data visualization unit, a real-time monitoring unit, and an alarm and notification unit. The data visualization uses an application programming interface API to interact with a remote verification module and a system detection module at the back end to update data in real time. The real-time monitoring continuously tracks the operating status and key performance indicators of the system through the monitoring tool Nagios. The alarms and notifications are processed and fed back through interactive data. The output end of the data visualization unit is connected to the input end of the real-time monitoring unit, and the output end of the real-time monitoring unit is connected to the input end of the alarm and notification unit.

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