Concentrator PLC meter reading data automatic processing method and system based on multi-dimensional feature analysis

Abstract

The invention discloses a concentrator PLC meter reading data automatic processing method and system based on multi-dimensional feature analysis, and belongs to the technical field of electric power data acquisition and processing. Meter reading files generated by the concentrator are loaded and analyzed in batches, and starting and ending time, consumed time and interrogation results of each round of meter reading are extracted; pLC monitoring logs are synchronously loaded, network node data and routing information are extracted, and a routing topological graph is constructed; dividing into a plurality of partitions according to the actual installation position of the electricity meter, and respectively counting the electricity meter number, the relay number and the meter reading success rate of each partition; and finally, summarizing and analyzing the multi-dimensional data to generate a standardized test report. According to the method, full-automatic processing of the PLC meter reading data is realized, the data analysis efficiency and analysis accuracy are improved, and reliable data support is provided for optimization of an electric power communication network.

Description

Technical Field

[0001] This invention relates to the field of power data acquisition and processing technology, and in particular to an automated processing method for meter reading data from a concentrator PLC based on multi-dimensional feature analysis. Background Technology

[0002] With the continuous development of smart grid technology, power line communication (PLC) technology has been widely used in smart meter data acquisition systems due to its advantages such as no need for additional wiring and wide coverage. However, in the process of PLC-based meter reading, there are common problems such as low data integrity, high abnormal data rate, and high cost of manual intervention.

[0003] Currently, most common PLC meter reading data processing methods rely on single-dimensional data analysis, such as judging anomalies solely based on electricity consumption data. These methods fail to consider multi-dimensional information such as communication link status, equipment operating status, and environmental factors, resulting in low accuracy and high false positive rates in anomaly detection. Furthermore, existing methods often require manual intervention during processing, lacking automation and efficiency, making them unsuitable for the high-efficiency operation requirements of large-scale PLC meter reading systems.

[0004] Therefore, there is an urgent need for a PLC meter reading data processing method that can integrate multi-dimensional features and improve the level of automation, thereby improving the integrity and accuracy of meter reading data, increasing processing efficiency, and reducing system operation and maintenance costs. Summary of the Invention

[0005] The purpose of this invention is to address the problems of low efficiency and error susceptibility in the current manual statistical meter reading data. It proposes an automated processing method and system for concentrator PLC meter reading data based on multi-dimensional feature analysis. Combining multiple aspects such as PLC networking process, topology structure, and partition statistics, it comprehensively and deeply analyzes the entire testing process, providing comprehensive and reliable data support for test conclusions and offering an effective way to discover potential anomalies.

[0006] The technical solution of this invention is: In a first aspect, the present invention provides an automated processing method for meter reading data from a concentrator PLC based on multi-dimensional feature analysis, comprising: S1. Load the meter installation information file, concentrator meter reading file, and PLC monitoring log file under the preset path. Parse the meter installation information file to obtain the meter number, MAC address, and installation location number of each meter. Parse the concentrator meter reading file in the order of timestamps to extract the start and end time, time consumption, and meter call results for each round of meter reading, and count the number of successful readings. Parse the PLC monitoring log to extract network timing change data, network node information, and routing interaction information, and construct a routing topology map based on the routing interaction information. S2. Divide the test environment into multiple partitions according to the installation location number, and count the number of electricity meters installed, the number of relays, and the meter reading success rate for each partition. S3. Integrate the call results, routing topology diagram, network timing change data and partition statistics results to generate a standardized test report.

[0007] Furthermore, the meter installation information file loaded in S1 under the preset path includes: The meter installation information file, i.e., the meter installation deployment diagram, is parsed to obtain the meter number, MAC address, and installation location number. The installation location number follows a hierarchical coding rule, represented by a structure of area-line-meter position. The meter number and MAC address are stored in the device list according to the hierarchical order of the installation location.

[0008] Furthermore, the concentrator meter reading files loaded in S1 under the preset path include: Identify the start and end tags in the meter reading file to obtain the start and end times and time consumption for each round of meter reading; Identify the meter recall results in the meter reading file, and use 0-2 numerical codes to represent the recall status of short data and long data. When both types of data are successfully recalled, the recall is determined to be successful and the number of successes is recorded.

[0009] Furthermore, the short data is a timestamp, and the long data is a 12-hour load curve; the meter recall results are represented by a three-level code of 0, 1, and 2, where 0 indicates that neither type of data was recalled, 1 indicates that only the short data was successfully recalled, and 2 indicates that both types of data were successfully recalled.

[0010] Furthermore, the PLC monitoring log files loaded in S1 under the preset path include: Extract network timing change data from PLC monitoring logs to determine changes in the number of electricity meters and relays; Network node information and routing interaction information are extracted from PLC monitoring logs. A routing topology map is constructed based on the routing interaction information, and the number and distribution ratio of network nodes at each level are counted.

[0011] Furthermore, the construction of the routing topology map includes: parsing routing interaction information to determine the hierarchical connections between network nodes, and establishing a routing topology map based on the hierarchical connection relationships between network nodes.

[0012] Furthermore, S2 includes: Based on the installation location number of the electricity meters, the electricity meters deployed in the test environment are divided into multiple partitions. For the electricity meters in each partition, the number of electricity meters installed is obtained from the device list, the number of relays is obtained from the network timing change data, and the meter reading success rate is obtained from the electricity meter recall results. Based on the statistical results of each zone, a horizontal comparison was made of the number of installed electricity meters, the number of relays, and the meter reading success rate.

[0013] Furthermore, in S3, the standardized test report includes a test environment overview, a diagram of the meter location, network status statistics, meter reading success rate statistics, performance indicators for each zone, and detailed meter data.

[0014] Secondly, the present invention provides an automated processing system for concentrator PLC meter reading data based on multi-dimensional feature analysis, wherein the system is configured to execute the method described herein.

[0015] Thirdly, the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the method described thereon.

[0016] The beneficial effects of this invention are: This invention discloses an automated processing method and system for concentrator PLC meter reading data based on multi-dimensional feature analysis. It automatically loads and parses standardized meter installation information files to obtain hierarchical coding locations, analyzes meter reading files to accurately calculate the success rate of short and long data recall, analyzes PLC logs to construct a complete routing topology and extract node time-series changes, then automatically divides the system into independent partitions based on installation location hierarchy, integrates multi-source data to statistically analyze relay distribution and meter reading performance in each partition, and finally generates a standardized report with one click, including an environment overview, topology map, success rate chart, and partition indicators. This fully automated process significantly improves testing efficiency, reduces human error, and ensures the accuracy and traceability of PLC network performance evaluation.

[0017] Other features and advantages of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0018] The above and other objects, features and advantages of the present invention will become more apparent from the more detailed description of exemplary embodiments of the invention in conjunction with the accompanying drawings, wherein the same reference numerals generally represent the same components in the exemplary embodiments of the invention.

[0019] Figure 1 A schematic diagram illustrating a usage scenario of the present invention is shown.

[0020] Figure 2 A flowchart of the automatic processing method for concentrator PLC meter reading data based on multi-dimensional feature analysis of the present invention is shown. Detailed Implementation

[0021] Preferred embodiments of the invention will now be described in more detail with reference to the accompanying drawings. While preferred embodiments of the invention are shown in the drawings, it should be understood that the invention can be implemented in various forms and should not be limited to the embodiments set forth herein.

[0022] In a first aspect, the present invention provides an automated processing method for meter reading data from a concentrator PLC based on multi-dimensional feature analysis, comprising: S1. Load the meter installation information file, concentrator meter reading file, and PLC monitoring log file under the preset path. Parse the meter installation information file to obtain the meter number, MAC address, and installation location number of each meter. Parse the concentrator meter reading file in the order of timestamps to extract the start and end time, time consumption, and meter call results for each round of meter reading, and count the number of successful readings. Parse the PLC monitoring log to extract network timing change data, network node information, and routing interaction information, and construct a routing topology map based on the routing interaction information. Specifically, loading the meter installation information file under the preset path includes parsing the meter installation information file, i.e., the meter installation deployment diagram, to obtain the meter number, MAC address, and installation location number. Among them, the installation location number follows the hierarchical coding rules and is represented as a structure of area-line-meter position. The meter number and MAC address are stored in the device list according to the hierarchical order of the installation location.

[0023] Loading the concentrator meter reading file from the preset path includes identifying the start and end tags in the meter reading file, obtaining the start and end times and time consumption for each round of meter reading; identifying the meter recall results in the meter reading file, using 0-2 numerical codes to represent the recall status of short and long data. When both types of data are successfully recalled, the recall is considered successful and the number of successes is recorded. The short data is a timestamp, and the long data is a 12-hour load curve. The meter recall results are represented using a three-level code of 0, 1, and 2: 0 indicates that neither type of data was recalled, 1 indicates that only short data was successfully recalled, and 2 indicates that both types of data were successfully recalled.

[0024] Loading PLC monitoring log files under a preset path includes: extracting network timing change data from PLC monitoring logs to determine changes in the number of meters and relays; extracting network node information and routing interaction information from PLC monitoring logs, parsing routing interaction information to determine hierarchical connections between network nodes, establishing a routing topology based on the hierarchical connection relationship between network nodes, and statistically analyzing the number and distribution ratio of network nodes at each level.

[0025] S2. Divide the test environment into multiple partitions according to the installation location number, and count the number of electricity meters installed, the number of relays, and the meter reading success rate for each partition. Specifically, the electricity meters deployed in the test environment are divided into multiple partitions according to the installation location number of the electricity meters. For the electricity meters in each partition, the number of electricity meters installed is obtained from the device list, the number of relays is obtained from the network timing change data, and the meter reading success rate is obtained from the electricity meter recall results. Based on the statistical results of each zone, a horizontal comparison was made of the number of installed electricity meters, the number of relays, and the meter reading success rate.

[0026] S3. Integrate the recall results, routing topology diagram, network timing change data, and partition statistics results to generate a standardized test report; the standardized test report includes a test environment overview, a diagram of meter locations, network status statistics, meter reading success rate statistics, performance indicators for each partition, and detailed meter data.

[0027] Specifically, Overview represents a summary of the test environment data and test results; DUT Location shows the location of the electricity meters in the test environment; PLC Network Evolution displays PLC network status information and routing topology statistics; Cycling Overview displays the overall meter reading success rate, time consumption, and statistics for each zone; Cycling Details lists the test data of all electricity meters in tabular form; Cycling Data Map displays the core statistical data of the cyclic meter reading test.

[0028] In practice: like Figure 1 As shown, the application scenario of this invention is set as follows: After completing a round of PLC cyclic meter reading test in the test environment, the three types of core data files, namely the meter installation deployment diagram, the concentrator meter reading file, and the PLC Sniffer monitoring log, are stored in the system's designated path respectively; after starting the automated processing software, the system will automatically load the above files, and sequentially complete the data extraction, parsing, calculation, and integration processing, and finally generate a PLC cyclic meter reading test report, providing complete data support for test result analysis and communication performance evaluation.

[0029] like Figure 2As shown, basic meter information is collected by loading the meter installation deployment diagram, and a standardized electronic template is designed to describe the meter installation location. This template strictly follows the hierarchical division rules of the test environment: first, it is divided by test area, and then further subdivided by line within each area, with each line clearly marked with a unique number for each meter position. In the template, the meter number and MAC address are accurately entered according to the correspondence between the meter position number and the actual installed meter. After completion, it is saved to the storage path specified by the system. When the system starts, it automatically imports and parses this electronic template file, extracting the meter number, MAC address, and corresponding installation location number of all installed meters. Finally, it is organized according to the hierarchical order of installation location (area → line → meter position) and stored in the system device list, providing basic data support for subsequent analysis and statistical functions.

[0030] The meter reading files generated by the concentrator are stored in a designated system path. Upon system startup, the files in this path are automatically loaded, and the contents are read sequentially according to the file generation timestamps. By recognizing the preset start and end tags of a round of meter reading in the meter reading files, the system accurately extracts and records key information for each round of meter reading, including start time, end time, round number, and total meter reading time.

[0031] The data items for the meter reading task include two types: timestamps (short data) and 12-hour load curve data (long data). The recall result is identified by a numerical code from 0 to 2, specifically defined as follows: 0 indicates that neither type of data was successfully recalled; 1 indicates that only short data was successfully recalled; 2 indicates that both types of data were successfully recalled. Only when the recall result is 2 is the recall considered successful.

[0032] The system will centrally summarize the statistical data of all meter reading rounds, and the core statistical indicators generated will include: overall test start time, overall test end time, total test time, total number of meter reading rounds, average time per round, average meter reading time per meter, success rate of each meter, and average success rate of all meters.

[0033] The above statistical results are presented precisely in the test report in the form of a list, and visualized in combination with a line chart.

[0034] After loading the PLC Sniffer monitoring log file, the system performs in-depth analysis of the log data to extract three types of core information: first, time-series changes in the number of network meters and relays; second, basic information of all network nodes; and third, complete routing interaction information. Based on the parsed routing data, a routing topology map is automatically constructed, clearly showing the hierarchical relationships between network nodes. Simultaneously, the system further analyzes the number and distribution of nodes at each level of the topology, clarifying the deployment density of meters at different levels and the network coverage.

[0035] The above statistical results are presented intuitively in the test report using a combination of tables and charts.

[0036] The deployed electricity meters in the test environment were divided into 16 zones according to their actual installation locations. For each zone, three core indicators were counted: the actual number of installed meters, the number of relays formed, and the meter reading success rate. These indicators can intuitively reflect the PLC signal strength and attenuation of each zone, providing accurate data support for subsequent signal coverage optimization, relay generation strategy adjustment, and communication link reliability improvement.

[0037] The above statistical results are presented accurately in tabular form in the test report.

[0038] The various embodiments of the present invention have been described above. These descriptions are exemplary and not exhaustive, nor are they limited to the disclosed embodiments. Many modifications and variations will be apparent to those skilled in the art without departing from the scope and spirit of the described embodiments.

Claims

1. An automated processing method for meter reading data from a concentrator PLC based on multi-dimensional feature analysis, characterized in that, include: S1. Load the meter installation information file, concentrator meter reading file, and PLC monitoring log file under the preset path. Parse the meter installation information file to obtain the meter number, MAC address, and installation location number of each meter. Parse the concentrator meter reading file in the order of timestamps to extract the start and end time, time consumption, and meter call results for each round of meter reading, and count the number of successful readings. Parse the PLC monitoring log to extract network timing change data, network node information, and routing interaction information, and construct a routing topology map based on the routing interaction information. S2. Divide the test environment into multiple partitions according to the installation location number, and count the number of electricity meters installed, the number of relays, and the meter reading success rate for each partition. S3. Integrate the call results, routing topology diagram, network timing change data and partition statistics results to generate a standardized test report.

2. The method as described in claim 1, characterized in that... In S1, the meter installation information file loaded from the preset path includes: The meter installation information file, i.e., the meter installation deployment diagram, is parsed to obtain the meter number, MAC address, and installation location number. The installation location number follows a hierarchical coding rule, represented by a structure of area-line-meter position. The meter number and MAC address are stored in the device list according to the hierarchical order of the installation location.

3. The method as described in claim 1, characterized in that... In S1, the concentrator meter reading files loaded from the preset path include: Identify the start and end tags in the meter reading file to obtain the start and end times and time consumption for each round of meter reading; Identify the meter recall results in the meter reading file, and use 0-2 numerical codes to represent the recall status of short data and long data. When both types of data are successfully recalled, the recall is determined to be successful and the number of successes is recorded.

4. The method as described in claim 2, characterized in that... The short data is a timestamp, and the long data is a 12-hour load curve; the meter recall results are represented by a three-level code of 0, 1, and 2, where 0 indicates that neither type of data was recalled, 1 indicates that only the short data was successfully recalled, and 2 indicates that both types of data were successfully recalled.

5. The method as described in claim 1, characterized in that... In S1, the PLC monitoring log files loaded from the preset path include: Extract network timing change data from PLC monitoring logs to determine changes in the number of electricity meters and relays; Network node information and routing interaction information are extracted from PLC monitoring logs. A routing topology map is constructed based on the routing interaction information, and the number and distribution ratio of network nodes at each level are counted.

6. The method as described in claim 5, characterized in that, The construction of the routing topology map includes: parsing routing interaction information to determine the hierarchical connections between network nodes, and establishing a routing topology map based on the hierarchical connection relationships between network nodes.

7. The method as described in claim 1, characterized in that S2 include: Based on the installation location number of the electricity meters, the electricity meters deployed in the test environment are divided into multiple partitions. For the electricity meters in each partition, the number of electricity meters installed is obtained from the device list, the number of relays is obtained from the network timing change data, and the meter reading success rate is obtained from the electricity meter recall results. Based on the statistical results of each zone, a horizontal comparison was made of the number of installed electricity meters, the number of relays, and the meter reading success rate.

8. The method as described in claim 1, characterized in that... In S3, the standardized test report includes a test environment overview, a diagram of the meter location, network status statistics, meter reading success rate statistics, performance indicators for each zone, and detailed meter data.

9. An automated processing system for concentrator PLC meter reading data based on multi-dimensional feature analysis, characterized in that, The system is configured to perform the method as described in any one of claims 1-8.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that... When the program is executed, it implements the method as described in any one of claims 1-8.

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