KR102597248B1 - Meter data unification management system - Google Patents

Abstract

An integrated weighing data management system is launched. The integrated metering data management system of the present invention includes a collection unit that collects metering data from AMI HES (Automatic Metering Infrastructure Head-End System) according to collection items or collection scale set according to AMI standards for each meter reading type; and a processing unit that independently parallel processes the metering data collected by the collection unit or distributes them for each AMI device according to the AMI standard, and the collection items or collection structure of the metering data can be arbitrarily set according to the AMI standard of the region. It is characterized by

Description

Metering data integrated management system {METER DATA UNIFICATION MANAGEMENT SYSTEM}

The present invention relates to an integrated metering data management system, and more specifically, to an integrated metering data management system that integrates metering data by changing the AMI (Automatic Metering Infrastructure) standard.

Meter Data Management System (MDMS) collects and processes AMI metering data from AMI (Automatic Metering Infrastructure) instruments and manages them in an integrated manner.

The metering data management system uses MDMS data for sales/distribution tasks such as billing through usage aggregation, placebo and challenge detection, transformer load management, loss management, analysis of new and renewable energy generation, and instrument outage management. It also provides smart smart services to customers. It provides services such as real-time power usage and rate information, consumption patterns, and electricity saving alarms through a phone app.

The metering data management system is a big data platform that distributes/parallel processes multiple metering data, enables real-time processing, and includes power company and customer services, making it competitive in integrated meter reading and overseas export business in terms of functionality and performance. It is equipped with

However, the conventional metering data management system was developed exclusively for the AMI standards of the relevant power company in collecting and processing metering data, so when reading integrated gas/heat/water, etc. or when the AMI standards of overseas power companies are different, many changes and customization work are required. This is a necessary situation.

In other words, it is inefficient to build AMI specifications and MDMS required functions that are different for each energy agency and power company, so there is a need to be able to flexibly respond to various AMI standards.

The background technology of the present invention is disclosed in 'Intelligent power grid-based meter data management system' in Korean Patent Publication No. 10-1174254 (2012.08.08).

The present invention was created to improve the above-mentioned problems, and the purpose of one aspect of the present invention is to provide an integrated metering data management system that integrates metering data by changing the AMI (Automatic Metering Infrastructure) standard.

The integrated metering data management system according to one aspect of the present invention includes a collection unit that collects metering data from the AMI HES (Automatic Metering Infrastructure Head-End System) according to the collection items or collection scale set according to the AMI standard for each meter reading type; And a processing unit that independently parallel processes the metering data collected by the collection unit or distributes them for each AMI device according to the AMI standard, and the collection items or collection structure of the metering data are optional according to the AMI standard of the corresponding region. It is characterized by being configurable.

The collection items of the quantitative data of the present invention are characterized in that addition or order can be changed.

The collection structure of the quantitative data of the present invention is characterized by being able to add, exclude, or change the order.

The collection unit of the present invention is characterized in that the metering data is manually input, and upon manual input of the metering data, the metering data is verified and displayed to the user.

The collection unit of the present invention is characterized in that it receives and manages the device installation information and customer mapping information of the AMI device in connection with the user terminal.

The user terminal of the present invention, when a photo of an AMI instrument installed in a field is taken, recognizes the instrument number in the photographed instrument photo, acquires the installation location of the instrument where the user terminal is installed, and then collects the instrument from the collection unit. It is characterized by receiving and displaying a list of customers near the installation location, and receiving the mapping information of the instrument and customer mapped by the user to the collection unit.

The processing unit of the present invention is characterized in that during parallel processing, the collected status code for each parallel processing module for the parallel processing is stored in a NoSQL DB to share the status of all processes and to remove dependencies between the parallel processing modules.

During parallel processing, the processing unit of the present invention updates the parallel processing status using the processing status code for each parallel processing module, and when collecting the processing status code for the parallel processing, it assigns a collection status code for each parallel processing module. It is characterized by maximizing the amount of data that can be processed for each parallel processing module.

The processing unit of the present invention is characterized by uniformly distributing and distributed processing using the meter number of the AMI meter.

The present invention is characterized by further including an aggregation unit that allows the manager to set and register a rate plan to be applied to actual customers after testing and verifying the time-based rate plan through a simulator.

The present invention is characterized in that it further includes a simulator that generates virtual meter reading data and performs instrument control tests.

The simulator of the present invention executes the scenario by uploading the template if the scenario is registered and the template is registered. If the template is not registered, the simulator creates the scenario and then executes the scenario. After executing the scenario, it generates a meter reading file through the Agent. It is characterized by:

The meter reading data of the present invention includes at least one of electricity data, gas data, and water data, and the type of electric data includes at least one of load profile, regular meter reading, electricity quality and event, conquest, and transformer monitoring data. Included, the meter reading file is generated according to the instrument quantity, period, and region of the scenario, and is distributed at a distribution interval and distribution location set in the scenario.

The present invention further includes a GIS facility mapping conversion unit that links power facility GIS and metering data for AMI facility operation and status management, electricity quality, transformer load management, loss and conduction management, customer and power outage management, etc. do.

The present invention is characterized by further including a multilingual setting unit that allows the user to set terms and preferred languages for each country on the screen.

The present invention performs distributed parallel processing with a plurality of servers for processing the metering data, and shares at least one hardware resource of CPU (central processing unit), memory, and disk between the servers for processing the metering data, thereby providing large capacity. It is characterized in that it further includes a distributed parallel data processing platform that enables data operations.

The distributed parallel data processing platform of the present invention is characterized by creating a plurality of instances by configuring a cloud, and managing and setting a network to manage communication between the instances.

The integrated metering data management system according to one aspect of the present invention allows flexibly responding and easily expanding in a setting manner according to AMI standards for each country/power company, thereby reducing MDMS construction costs and shortening the construction period. , productivity of customizing work can be improved through real-time data processing.

The integrated weighing data management system according to another aspect of the present invention can improve weighing data verification and estimation processing speed performance through VEE (Validation Estimation Editing) processing status management and independent parallel processing and distributed processing for each instrument according to settings. .

The integrated metering data management system according to another aspect of the present invention applies high-level API, Catalyst Optimizer, and Encoder when processing batch jobs such as transformer load/electricity quality/verification aggregation, to reduce cache size and shorten processing time. You can do it.

The integrated weighing data management system according to another aspect of the present invention improves the accuracy of master (instrument-customer-location) data by minimizing discrepant information between the field and the server by providing an on-site instrument installation mapping mobile app, and provides aggregation/statistics. The success rate of meter reading can be improved by improving city accuracy.

The integrated metering data management system according to another aspect of the present invention provides a simulator capable of generating virtual meter reading data and controlling instruments, enabling testing, exhibition, and promotion of MDMS functions and performance regardless of location.

The integrated metering data management system according to another aspect of the present invention provides distributed/parallel servers as a virtualization platform to facilitate server expansion according to the scale of AMI construction, reducing hardware introduction costs in small-scale overseas business and enabling flexible expansion of servers. Let's do it.

Figure 1 is a block diagram of an integrated metering data management system according to an embodiment of the present invention.
Figure 2 is a block diagram of a distributed parallel data processing platform module according to an embodiment of the present invention.
Figure 3 is a block diagram of an AMI deployment scale scalable data platform virtual machine according to an embodiment of the present invention.
Figure 4 is a diagram showing the types and collection cycles of AMI collected data according to an embodiment of the present invention.
Figure 5 is a diagram showing an example of a metering data collection structure according to an embodiment of the present invention.
Figure 6 is a screen of a metering data collection structure setting system according to an embodiment of the present invention.
Figure 7 is a block diagram of a mobile system for field installation of instruments according to an embodiment of the present invention.
Figure 8 is a screen of a device field installation mobile app according to an embodiment of the present invention.
Figure 9 is a diagram showing a comparative example of MDMS data processing methods.
Figure 10 is a diagram showing parallel processing and types of status codes collected by module according to an embodiment of the present invention.
Figure 11 is a country-specific rate plan setting screen according to an embodiment of the present invention.
Figure 12 is a diagram showing a real-time usage fee calculation customer service process according to an embodiment of the present invention.
Figure 13 is a diagram showing the meter reading data generation process of the simulator according to an embodiment of the present invention.
Figure 14 is a dashboard screen of a simulator according to an embodiment of the present invention.
Figure 15 is an instrument control screen of a simulator according to an embodiment of the present invention.
Figure 16 is a diagram showing a mapping tool between items of a power facility GIS object according to an embodiment of the present invention.
Figure 17 is a diagram showing an example of a terminology and multilingual support property setting structure according to an embodiment of the present invention.

Hereinafter, the integrated metering data management system according to an embodiment of the present invention will be described in detail with reference to the attached drawings. In this process, the thickness of lines or sizes of components shown in the drawings may be exaggerated for clarity and convenience of explanation. In addition, the terms described below are terms defined in consideration of functions in the present invention, and may vary depending on the intention or custom of the user or operator. Therefore, definitions of these terms should be made based on the content throughout this specification.

Figure 1 is a block diagram of an integrated metering data management system according to an embodiment of the present invention.

Referring to Figure 1, the metering data integrated management system according to an embodiment of the present invention includes an AMI (Automatic Metering Infrastructure) meter 100, an AMI HES (Automatic Metering Infrastructure Head-End System) 200, and an MDMS (Meter). Data Management System) (300).

The AMI meter 100 detects meter reading data for at least one of electricity, water, and gas. The type of detection data is not limited to the above-described embodiments. Since the AMI instrument 100 is self-evident to those skilled in the art, its detailed description is omitted here.

AMI HES (200) receives weighing data from the AMI instrument (100) and transmits it to MDMS (300).

MDMS (300) can set different collection items and structures of metering data for each region or country, and manages the processing status to enable independent parallel processing and distributed processing by instrument according to these settings.

MDMS (300) provides a mobile application for on-site instrument installation information, accurate mapping, and master data management, and also allows manual input in case remote meter reading is not possible.

MDMS (300) sets different rate plans for each power company, improves usage aggregation logic, and performs real-time rate calculation.

MDMS (300) provides a virtualization platform for distributed parallel servers to facilitate server expansion according to the AMI deployment scale, and enables multilingual settings, power facility mapping and conversion, and meter reading data generation and instrument control through the simulator (350).

To be more specific, the MDMS 300 includes a distributed parallel data processing platform module 310, a collection unit 320, a processing unit 330, an aggregation unit 340, a simulator 350, and a GIS facility mapping conversion unit ( 360), and a multilingual setting unit 370.

The distributed parallel data processing platform module 310 is mounted on a cloud virtual machine to reduce hardware construction costs and use hardware resources efficiently depending on the AMI deployment scale.

The distributed parallel data processing platform module 310 is an architecture that processes large-capacity weighing data in real time. The distributed parallel data processing platform module 310 has a horizontal scalability structure that enables distributed and parallel processing with low-cost servers. The distributed parallel data processing platform module 310 ensures stability by providing failover and HA (High Availability) functions through at least triple processing, and performs integrated monitoring of all hardware resources. do.

Figure 2 is a block diagram of a distributed parallel data processing platform module according to an embodiment of the present invention.

Referring to Figure 2, the distributed parallel data processing platform module 310 includes a number of processes, such as a data collector, Hadoop file processor, NoSQL DB processor, real-time data processor, scheduler, and resource manager. Here, the data collector, Hadoop file processor, NoSQL DB processor, real-time data processor, scheduler, and resource manager operate simultaneously.

The distributed parallel data processing platform module 310 enables large-capacity data operations by sharing resources (CPU, memory, disk) between servers for big data processing, and distributes/parallel processing history and status to manage multiple systems. Allows you to check.

The distributed parallel data processing platform module 310 is advantageous for big data processing by enabling scale-out (horizontal expansion) for low-cost expansion of the system, but may require server configuration for processing many simultaneous processes. In other words, in the case of small-scale AMI construction projects, costs may be high due to the introduction of a relatively large number of servers.

Accordingly, the distributed parallel data processing platform module 310 may have hardware virtualized by applying cloud technology. The distributed parallel data processing platform module 310 can be built as a cloud-based system for efficient use of hardware resources. It configures the cloud for small-scale systems to create multiple instances and supports communication between multiple instances. It supports network management and configuration functions for management, and the system can be expanded as processing data increases.

Figure 3 is a block diagram of an AMI deployment scale scalable data platform virtual machine according to an embodiment of the present invention.

Referring to FIG. 3, an example of configuring the distributed parallel data processing platform module 310 as a virtual machine using four servers is shown.

Here, the metering data collector cluster that requires parallel processing, distributed parallel processing cluster (verification/estimation, aggregation, etc. processing), NoSQL DB node, and Hadoop File System (HDFS) node virtualize 3 servers and adjust the required quantity according to the size of the AMI deployment. of processes has been assigned.

WAS (Web Application Server), relational DBMS, and virtual machine manager that do not require parallel processing were installed and configured on one server.

The collection unit 320 collects metering data from the AMI HES 200 (Automatic Metering Infrastructure Head-End System) according to the collection items or collection scale set according to the AMI standard for each meter reading type.

The collection unit 320 receives metering data by meter reading type from the AMI HES (200) (Head-End System), loads it into HDFS (Hadoop File System) and database by data type, and processes and collects large-capacity data in a timely manner. do.

The collection unit 320 is capable of adding and changing the order of collection items, that is, flexible setting of collection items.

Figure 4 is a diagram showing the types and collection cycles of AMI collected data according to an embodiment of the present invention.

Referring to FIG. 4, the data collected in the HES is transmitted to the metering data collection adapter module of the MDMS 300 with a meter reading code and collection cycle.

The collected data processing and database are distributed and parallel processed based on CDH (Cloudera Distribution including Apache Hadoop) and NoSQL, and the original files are stored in HDFS.

Here, the PBS is a controllable electrical plug. PBS is a Piggy-Back Switch and can be replaced with a Smart Plug.

The collection unit 320 collects standards for integrated meter reading of gas and water as well as electricity in regional or national technical standards. For example, Southeast Asia and Africa are regions with many challenges, and prepaid instruments are widely distributed, and technology to collect and process these data is required, so the collection unit 320 can define collection standards for this.

Figure 5 is a diagram showing an example of a metering data collection structure according to an embodiment of the present invention.

This is an example of the metering data collection structure for meter reading code LI (Load profile - Import).

The OBIS (object identification system) code is a code number assigned by DLMS (Device Language Message Specification), an IEC standard for instrument specifications. Mandatory is an item that must be collected, and the rest are items that can be collected optionally. Here, when data is transmitted directly from the instrument to the HES without a DCU (Data Collecting Unit), the DCU ID can be processed as the instrument number, and the DCU meter reading time can be processed as the instrument meter reading time.

Figure 6 is a screen of a metering data collection structure setting system according to an embodiment of the present invention.

Referring to FIG. 6, a setting system screen is shown where the defined quantitative data collection structure can be added or excluded in an actual expanded form, or the order can be changed.

Since the collection protocol may vary by country or HES, it is necessary to set it systematically and adaptively in order to process it flexibly.

Therefore, the collection unit 320 allows setting whether or not to use collection items, changing the collection order, collection folder, collection cycle, setting date, etc. for each type of collected quantitative data. Additionally, the collection unit 320 allows you to change the order of each item and selectively add/exclude items other than essential. Additionally, the collection unit 320 can manage metadata such as collection data file item order, code, and item name.

The collection unit 320 can manage the history of setting information for each type of collected quantitative data by setting application date, and can reflect collection file mapping setting information based on the setting application date.

The collection unit 320 manages the mapping of metering data with instrument/contract history information based on the collection date, searches the list of files in the folder for each type of collected metering data, and downloads the files.

Meanwhile, there may be cases where automatic meter reading fails. Accordingly, the collection unit 320 allows the user to download the form file, write the meter reading data acquired from the instrument or DCU in the form file, and then upload the file to manually input the meter reading value. When uploading a manual weighing data file, the collection unit 320 verifies item values (Null, Type, Length, etc.) and displays abnormal data so that the user can recognize it. At this time, the user can correct manual weighing data error items.

The collection unit 320 does not reprocess the weighing data if it already exists among manual or normal weighing data input values. The collection unit 320 backs up and stores normal/manual weighing data files, and processes the processing status (request → approval/reject → Processing completed) so that you can check the status.

The collection unit 320 allows building master information such as accurate device installation location, device number, and customer number mapping at the time of initial device installation.

Previously, many errors were discovered because instrument installers wrote the instrument number, address, and customer number by hand on paper after installing the instrument. If there is an error in the master information, processing such as verification, estimation, and aggregation is not possible in the MDMS 300, so the master information is very important information and must be managed accurately from the time of installation.

Accordingly, a mobile application is installed on a user terminal, such as a smartphone, to generate accurate master information by mapping the devices installed at the time of installation, location, and customer information on site. The collection unit 320 works in conjunction with these mobile applications to establish master information such as accurate instrument installation location, instrument number, and customer number mapping.

Figure 7 is a block diagram of a mobile system for field installation of instruments according to an embodiment of the present invention.

Referring to FIG. 7, the mobile application first sets an address to communicate with the MDMS 300 in the environment settings. The mobile application automatically recognizes the instrument number of the AMI instrument when a photo is taken after installing the AMI instrument in the field, acquires GPS information from the smartphone, and transmits the instrument installation location information, instrument photo, and instrument number to MDMS (300). .

Next, the mobile application receives a customer list in an area near the current GPS coordinates from the MDMS 300 and provides it to the user so that the user can map the exact customer number of the currently installed device. The mobile application transmits the AMI device 100 mapped by the user and the customer's mapping information to the MDMS 300.

The collection unit 320 of the MDMS 300 manages master mapping information about customers and devices in connection with the customer information system and the power facility management system. When field installation information is input from a mobile system, that is, a mobile application, the collection unit 320 can manage accurate instrument installation information consistent with the field by confirming this mapping information.

Figure 8 is a screen of a device field installation mobile app according to an embodiment of the present invention.

Figure 8 is an app screen of an actual mobile system.

The left side of Figure 8 is a screen that recognizes the instrument number after taking a photo of the instrument in the field. If there is an error in the recognized instrument number, the user can correct it. At this time, the GPS coordinate information of the photo taken is also stored internally so that the actual instrument installation location can be confirmed by linking with the geographic information system (GIS) in the MDMS (300).

In Figure 8, the screen on the right is a screen for a function that selects the radius of the current location to search for customers and then selects one customer in order to map the customer number of the corresponding instrument number location. At this time, customers can be selected by increasing the radius, and specific customers can also be selected on the GIS screen linked to MDMS (300).

If mobile communication is not possible, you can temporarily store it on your smartphone and then when you go to a location where communication is possible, you can transmit the temporarily stored instrument installation information and customer mapping information to the MDMS (300) at once.

The processing unit 330 independently parallel processes the metering data collected by the collection unit 320 according to AMI standards or distributes it for each AMI instrument 100.

Figure 9 is a diagram showing a comparative example of MDMS data processing methods.

Referring to FIG. 9, the AMI metering data processing flow in the MDMS 300 performs the processes of data collection, profiling, validation/estimation/editing (VEE: Validation Estimation Editing), and aggregation.

In the existing MDMS data processing flow, as shown in FIG. 9 above, meter reading files are collected from the AMI HES (200) for each region and processed sequentially. The unit of data processing is based on the meter reading file, so if an error occurs in the meter reading file, it may not be processed in the next step, and the load between process modules may become concentrated.

Accordingly, when the processing unit 330 receives a meter reading file from the AMI HES 200, the processing unit 330 stores the parallel processing and collected status codes for each module in the NoSQL DB to share the status of all processes.

In addition, the processing unit 330 removes dependencies between parallel processing modules to increase support utilization compared to sequential processing through independent processing for each device, and allows setting the number of processing cases between processes to maximize resource efficiency.

The existing MDMS processing method, which is a file-level processing method, moves to the next step only when the entire data is successful.

On the other hand, the processing unit 330 is capable of processing without delay regardless of whether other devices or files (a bundle of multiple devices) are processed using each device data processing method.

Meanwhile, independent parallel processing requires management of parallel processing and collection status for each module.

To this end, the processing unit 330 updates the processing status using the processing status code for each parallel processing module and assigns a status code when collecting codes for parallel processing to maximize the amount of data that can be processed by each parallel processing module. You can.

The types of parallel processing and collected status codes for each module are shown in Figure 10.

Figure 10 is a diagram showing parallel processing and types of status codes collected by module according to an embodiment of the present invention.

The existing MDMS data distribution processing method has the disadvantage of distributing data to regions, resulting in an imbalance in data distribution depending on the size of the region. Servers in regions with a large number of customers are always under load, while servers in regions with a smaller number of customers have less load, which can lead to issues with unbalanced overall system resource usage. As a result, issues may arise that require hardware design and distribution according to the size of the region.

Accordingly, the processing unit 330 performs uniform distribution processing using the meter number. Through this, the distribution quantity can be set according to the size of the site, and dynamic distribution is possible, which has the advantage of efficiently operating hardware resources. Additionally, the processing unit 330 can distribute system resources evenly by performing distributed processing using instrument numbers with even distribution.

Existing MDMS meter reading data storage management has space inefficiency due to storing source data to which master data is mapped. In addition, there is a disadvantage of wasting space due to redundant storage of NoSQL DB and relational DB for aggregated data by instrument and group, and wasting space and cost by redundantly storing source data in relational DB, which has expensive storage costs.

Accordingly, the aggregation unit 340 can improve storage space efficiency by separately storing and managing pure source data and master mapping source data for verification and aggregation.

The aggregation unit 340 saves space and cost by storing source data and aggregated data by instrument in a NoSQL DB, and storing aggregates by group with a relatively small amount of data in a relational DB.

When processing existing aggregate batch jobs, only Spark's RDD (Resilient Distributed Datasets) are used to process data, and in the case of object serialization, only the serialization method built into Java is used, which may result in a relative loss in space and speed.

Accordingly, the aggregation unit 340 applies Spark SQL and Dataset using high-level API when processing batch jobs such as transformer load/electricity quality/verification aggregation. The basic internal configuration of the Dataset is RDD, but the Catalyst Optimizer provides improved performance by optimizing code at the time of execution, and when using the Dataset, serialization and Encoder are used to reduce cache size (1/4 size compared to RDD) and improve processing time performance. It can be improved.

The existing power usage aggregation logic calculated the hourly/day/monthly usage as the sum of the section usage of the data for which the usage had been verified. Due to the nature of the logic, this method requires all data within the aggregation section to accurately calculate the usage of the section, and based on a 15-minute meter reading cycle, all four meter reading data must be collected and verified within the aggregation section time to accurately calculate the usage between sections. There is a downside to this.

Accordingly, the aggregation unit 340 calculates the usage by hour/day/month using the cumulative guideline value of the data whose usage has been verified, and when only the start and end values of the data within the aggregation section are received, it calculates the accurate usage of the section. do. In other words, if the aggregation unit 340 collects and verifies only two meter reading data, 0 minute data of the time immediately before the aggregation section time and 0 minute data of the time of the section time based on the 15-minute meter reading cycle, the previous 0 minute from the 0 minute cumulative guideline value is collected and verified. You can calculate section time usage more accurately and quickly by subtracting the cumulative guideline value.

In addition, the aggregation unit 340 can flexibly expand the power usage rate plan and rate calculation to be applied to customers according to the rate plan for each country.

The tally unit 340 tests the rate plan through the time-of-use (TOU) simulation function, goes through a verification step, and allows the administrator to set and register the rate plan to be applied to actual customers directly in the MDMS service. Through this, you can compare with the unit price information of the rate plan already in effect, and have the registered rate plan automatically reflected in customer service.

Figure 11 is a country-specific rate plan setting screen according to an embodiment of the present invention, and Figure 12 is a diagram showing a real-time usage rate calculation customer service process according to an embodiment of the present invention.

The aggregation unit 340 uses the information set as described above to allow the customer to change the usage directly through the simulator (usage movement simulation) 350 and check the fee that changes accordingly.

The aggregation unit 340 allows the user to compare the rate after the change with the rate before the change and easily check the rate change due to changes in usage.

The aggregation unit 340 uses the simulator (rate plan change simulation) 350 to check rate information when selecting a different rate plan based on the same usage, and enables comparative analysis with existing rates to determine which rate plan is right for the user. Make sure to check if it is advantageous.

The simulator 350 allows generating virtual meter reading data and performing instrument control tests.

Figure 13 is a diagram showing the meter reading data generation process of the simulator according to an embodiment of the present invention.

As shown in FIG. 13, meter reading data generation is divided into a scenario-based generation method and a template (exception and placebo data user input) upload method.

If the scenario is registered and the template is registered, the simulator 350 uploads the template and executes the scenario. If the template is not registered, the simulator 350 creates the scenario and then executes the scenario. The simulator 350 may directly execute a scenario after directly entering it, even if the scenario is not registered. Next, the simulator 350 creates a meter reading file through the Agent and distributes the generated meter reading file. Executed scenarios can be checked through the scenario status UI (User Interface).

Meter reading data that can be generated include electricity, gas, and water data. Electrical data types can include load profile, regular meter reading, electricity quality and event, conquest, and transformer monitoring data.

The simulator 350 generates a meter reading file according to the meter quantity, period, and region of the scenario created by the user, and distributes the generated meter reading file at the distribution interval and distribution location set in the scenario.

The simulator 350 performs instrument control in accordance with the IEC 61968-9 Common Information Model (CIM) message standard, outputs received control commands and transmitted messages to the user interface, and displays animation effects according to each instrument command on the screen. It can be expressed.

Figure 14 is a dashboard screen of a simulator according to an embodiment of the present invention.

Referring to FIG. 14, when a control command is given to a specific device on the Simulator Dashboard, the Agent generates a response and result (CIM message). CIM messages can be checked in the Dashboard console area.

Figure 15 is an instrument control screen of a simulator according to an embodiment of the present invention.

Referring to Figure 15, when you click the ‘Meter Control Command’ button on the Simulator Dashboard, a meter control pop-up window (yellow box) and a meter event (red box) pop-up window appear, as shown in Figure 15. You can run instrument control simulation on instruments registered in the instrument event window.

Types of meter control commands may include on-demand meter reading, meter status check, meter power shutoff and return (current limit and release), etc.

Meanwhile, most electric power companies need to efficiently operate and maintain power facilities installed on site.

Accordingly, the GIS facility mapping conversion unit 360 operates the power facility geographic information system (GIS) to manage power for AMI facility operation and condition management, electricity quality, transformer load management, loss and conduction management, customer and power outage management, etc. We provide a service that links facility geographic information system (GIS) and measurement data.

Typically, the power facility GIS coordinate system and management items may be different for each power company in each country.

Accordingly, the GIS facility mapping conversion unit 360 easily converts GIS information through GIS conversion and mapping tools and establishes a GIS environment to enable linked services with quantitative data.

The GIS facility mapping conversion unit 360 acquires the shape file (SHP) used as a standard GIS distribution format in the power facility GIS of each country's power company and inputs it into the GIS conversion and mapping tool to parse each shape file ( Parsing) to read the meta information of the spatial data and express the column information of the GIS object as shown in the left table of Figure 16.

Figure 16 is a diagram showing a mapping tool between items of a power facility GIS object according to an embodiment of the present invention.

The right side of FIG. 16 provides standard power facility objects used in the MDMS 300, so when one of the power facility objects is selected, a table in which detailed columns can be viewed is displayed.

When the manager connects the local power company items on the left to the standard items on the right, a mapping rule between items is created. If there are items that cannot be mapped to standard items, they can be added, and if there are items that do not need management, they can be deleted.

Standard power facility spatial objects within the MDMS (300) are expressed to the user according to a predefined style and scale, and a layer that defines the relationship between objects and the order of expression. These spatial object expression methods can be changed depending on settings.

The multilingual setting unit 370 supports multilingual settings that are displayed on the MDMS user screen with the following terms and preferred languages for each country.

Figure 17 is a diagram showing an example of a terminology and multilingual support property setting structure according to an embodiment of the present invention.

The multilingual setting unit 370 can set language property files for each country as shown in Figure 17, and can provide services in the language of the country through simple language setting changes.

The multilingual setting unit 370 can add language files to be registered in the Property Files tree.

The multilingual setting unit 370 allows easy language setting and change by modifying the English and Korean example file contents into the corresponding language and then registering the file name on the web server so that the entire MDMS (300) screen is displayed in the corresponding language. Make it possible.

In this way, the integrated metering data management system according to an embodiment of the present invention flexibly responds and can be easily expanded in a setting method according to AMI standards for each country/electric power company, thereby reducing MDMS construction costs and shortening the construction period. It is possible to improve the productivity of customizing work through real-time data processing.

The integrated weighing data management system according to an embodiment of the present invention can improve weighing data verification and estimation processing speed performance through VEE (Validation Estimation Editing) processing status management, independent parallel processing according to settings, and distributed processing for each instrument. there is.

The integrated metering data management system according to an embodiment of the present invention applies high-level API, Catalyst Optimizer, and Encoder when processing batch jobs such as transformer load/electricity quality/verification aggregation to reduce cache size and shorten processing time. You can do it.

The integrated weighing data management system according to an embodiment of the present invention improves the accuracy of master (instrument-customer-location) data by minimizing discrepant information between the field and the server by providing an on-site instrument installation mapping mobile app, and provides aggregation/statistics. The success rate of meter reading can be improved by improving city accuracy.

The integrated metering data management system according to an embodiment of the present invention provides a simulator capable of generating virtual meter reading data and controlling instruments, enabling testing, exhibition, and promotion of MDMS functions and performance regardless of location.

The integrated metering data management system according to an embodiment of the present invention provides distributed/parallel servers as a virtualization platform to facilitate server expansion according to the AMI construction scale, reducing hardware introduction costs in small-scale overseas business and enabling flexible expansion of servers. Let's do it.

Implementations described herein may be implemented, for example, as a method or process, device, software program, data stream, or signal. Although discussed only in the context of a single form of implementation (eg, only as a method), implementations of the features discussed may also be implemented in other forms (eg, devices or programs). The device may be implemented with appropriate hardware, software, firmware, etc. The method may be implemented in a device such as a processor, which generally refers to a processing device that includes a computer, microprocessor, integrated circuit, or programmable logic device. Processors also include communication devices such as computers, cell phones, portable/personal digital assistants (“PDAs”) and other devices that facilitate communication of information between end-users.

The present invention has been described with reference to the embodiments shown in the drawings, but these are merely illustrative, and those skilled in the art will recognize that various modifications and other equivalent embodiments can be made therefrom. You will understand. Therefore, the true technical protection scope of the present invention should be determined by the scope of the patent claims below.

100: AMI instrument 200: AMI HES
300: MDMS 310: Distributed parallel data processing platform module
320: collection unit 330: processing unit
340: tally unit 350: simulator
360: GIS facility mapping conversion unit 370: Multilingual setting unit

Claims (17)

A collection unit that collects metering data from the AMI HES (Automatic Metering Infrastructure Head-End System) according to collection items or collection structures set according to AMI standards for each meter reading type; and
It includes a processing unit that independently parallel processes the metering data collected by the collection unit according to the AMI standard or distributes it for each AMI instrument,
The collection items or collection structure of the above metering data can be arbitrarily set according to the AMI standards of the relevant region.
During parallel processing, the processing unit updates the parallel processing status using the processing status code for each parallel processing module, and when collecting the processing status code for the parallel processing, it assigns the collection status code for each parallel processing module to the parallel processing module. An integrated weighing data management system characterized by maximizing the amount of data that can be processed individually. The integrated management system for weighing data according to claim 1, wherein collection items of the weighing data can be added or their order changed. The integrated management system for weighing data according to claim 1, wherein the collection structure of the weighing data can be added, excluded, or changed in order. The integrated metering data management system according to claim 1, wherein the collection unit manually inputs the metering data, performs verification of the metering data upon manual input of the metering data, and displays the metering data to the user. The integrated metering data management system according to claim 1, wherein the collection unit receives and manages device installation information and customer mapping information of AMI devices in connection with a user terminal. A collection unit that collects metering data from the AMI HES (Automatic Metering Infrastructure Head-End System) according to collection items or collection structures set according to AMI standards for each meter reading type; and
It includes a processing unit that independently parallel processes the metering data collected by the collection unit according to the AMI standard or distributes it for each AMI instrument,
The collection items or collection structure of the above metering data can be arbitrarily set according to the AMI standards of the relevant region.
The collection unit receives and manages the device installation information and customer mapping information of the AMI device in connection with the user terminal,
When a photo of an AMI instrument installed in a field is taken, the user terminal recognizes the instrument number in the photographed instrument photo, acquires the installation location of the instrument where the user terminal is installed, and then obtains the location of the instrument installation location from the collection unit. An integrated metering data management system characterized by receiving and displaying a list of nearby customers, and receiving mapping information of instruments and customers mapped by the user to the collection unit. The method of claim 1, wherein the processing unit
During parallel processing, the collected status code for each parallel processing module for the parallel processing is stored in a NoSQL DB to share the status of all processes and eliminate dependencies between the parallel processing modules. delete A collection unit that collects metering data from the AMI HES (Automatic Metering Infrastructure Head-End System) according to collection items or collection structures set according to AMI standards for each meter reading type; and
It includes a processing unit that independently parallel processes the metering data collected by the collection unit according to the AMI standard or distributes it for each AMI instrument,
The collection items or collection structure of the above metering data can be arbitrarily set according to the AMI standards of the relevant region.
The processing unit is an integrated metering data management system characterized by uniformly distributing and decentralized processing using the meter number of the AMI meter. The integrated metering data management system according to claim 1, further comprising an aggregation unit that allows the manager to set and register a rate plan to be applied to actual customers after testing and verifying the time-based rate plan through a simulator. The integrated metering data management system according to claim 1, further comprising a simulator for generating virtual meter reading data and performing instrument control tests. The method of claim 11, wherein the simulator
If the scenario is registered and the template is registered, the scenario is executed by uploading the template. If the template is not registered, the scenario is executed after creating the scenario. After executing the scenario, a metering file is created through the Agent. Integrated data management system. The method of claim 12, wherein the virtual meter reading data includes at least one of electric data, gas data, and water data, and the types of electric data include load profile, regular meter reading, electricity quality and event, conquest, and transformer monitoring data. An integrated metering data management system comprising at least one of the following, wherein the meter reading file is generated according to the instrument quantity, period, and region of the scenario, and is distributed at a distribution interval and distribution location set in the scenario. The method of claim 1, further comprising a GIS facility mapping conversion unit that links power facility GIS and metering data for AMI facility operation and status management, electricity quality, transformer load management, loss and conduction management, customer and power outage management, etc. Features an integrated weighing data management system. The integrated quantitative data management system according to claim 1, further comprising a multilingual setting unit that allows the user to set terms and preferred languages for each country on the screen. The method of claim 1, wherein distributed parallel processing is performed on a plurality of servers for processing the metering data, and at least one hardware resource of CPU (central processing unit), memory, and disk is shared between the servers for processing the metering data. An integrated weighing data management system characterized in that it further includes a distributed parallel data processing platform module to enable large-capacity data operations. The integrated management system for metering data according to claim 16, wherein the distributed parallel data processing platform module configures a cloud to create a plurality of instances, and manages and sets a network to manage communication between the instances.

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