Method and system for pre-acquisition of curve data based on user cluster analysis

Abstract

This application discloses a method and system for pre-collecting curve data based on user clustering analysis. The method includes: first, acquiring the data collection messages sent by the concentrator; then, performing clustering analysis on the user behavior in the distribution area based on the data collection messages to generate multiple user types; next, sending a broadcast start collection command to the carrier station; and upon receiving the collection completion instruction from the carrier station, collecting the carrier station's data in a unicast concurrent manner according to the multiple user types. This application enables refined management of different users at the carrier level and achieves higher-frequency collection functions for different user types, improving the flexibility and reliability of carrier services while ensuring data integrity and synchronization.

Description

Technical Field

[0001] This application relates to the field of power line broadband carrier communication technology, and specifically to a method and system for pre-acquiring curve data based on user clustering analysis. Background Technology

[0002] Traditional carrier-based meter reading services can only meet data reading requirements within 15 minutes. When business scenarios demand higher frequency data, existing acquisition schemes and strategies are insufficient. Meanwhile, the application of HPLC-based technologies is expanding, but the timing of concentrator data reading is not strictly controllable, failing to guarantee data integrity and synchronization. Furthermore, frequent carrier-based meter reading requests for multiple data items significantly reduce channel resource utilization. Currently, the common practice is to control concentrator data reading by configuring acquisition schemes at the master station. However, in the existing operating environment of distribution areas, the data acquired by the master station in this way almost completely fails to meet data synchronization requirements. Moreover, the existing 1376.2 protocol cannot support user type identification, hindering refined management of distribution area users at the carrier level. Summary of the Invention

[0003] The purpose of this application is to provide a method and system for pre-collecting curve data based on user clustering analysis, in order to solve the problems of low efficiency in copying curve data and inability to guarantee data integrity and synchronization in the prior art.

[0004] To achieve the above objectives, the first aspect of this application provides a method for pre-acquiring curve data based on user clustering analysis, applied to a central coordinator, which communicates with a concentrator and a carrier site respectively. The method includes:

[0005] Obtain the reading messages sent by the concentrator;

[0006] Cluster analysis of user behavior in the transformer area is performed based on the copied messages to generate various user types;

[0007] Send a broadcast command to start data acquisition to the carrier station;

[0008] Upon receiving the data acquisition completion instruction from the carrier station, the data from the carrier station is read in a unicast concurrent manner according to various user types.

[0009] In this embodiment of the application, before sending the broadcast start acquisition command to the carrier station, the method further includes:

[0010] Acquire the time from the concentrator;

[0011] The real-time clock time of the central coordinator and carrier sites is corrected based on the time of the concentrator.

[0012] In this embodiment of the application, the message reading includes multiple collection schemes, with one collection scheme corresponding to each user type. The collection scheme includes the collection period, collection data items, and data types corresponding to each user type.

[0013] In this embodiment of the application, cluster analysis is performed on the user behavior of the transformer substation based on the copied messages to generate various user types, including:

[0014] Establish a user behavior chain list based on the copied messages;

[0015] Multiple user types are generated based on a user behavior chain.

[0016] In this embodiment of the application, the user behavior linked list includes a transformer area user identifier and multiple sets of elements for cluster analysis, with each transformer area user corresponding to a set of elements for cluster analysis;

[0017] The elements used for cluster analysis include at least one of the following:

[0018] The linked list index records the time and the data item to be collected.

[0019] In this embodiment of the application, generating multiple user types based on the user behavior chain includes:

[0020] The user to be copied is determined based on the user identifier of the area in the behavior chain;

[0021] Cluster analysis labels are obtained from the user's elements used for cluster analysis, which are copied as needed;

[0022] Based on cluster analysis labels, various user types are generated, along with the corresponding collection period, data items, and data types for each user type.

[0023] In this embodiment of the application, upon receiving a data acquisition completion instruction from a carrier station, the data from the carrier station is read in a unicast concurrent manner according to various user types, including:

[0024] Upon receiving the instruction that the carrier station has completed data collection, the system concurrently reads and copies real-time data from a preset number of carrier stations.

[0025] If no carrier station responds, the real-time data of any carrier station is reread a first preset number of times;

[0026] If any carrier station is reread a first preset number of times and no carrier station responds, skip the carrier station and use the real-time data of the previous cycle of the carrier station as the data for this cycle.

[0027] In this embodiment, the central coordinator includes a real-time refresh area, a data retention area, and a data backup area. The method further includes:

[0028] Real-time refresh of the refresh area;

[0029] At the first preset time before the next copying task starts, copy the data from the real-time refresh area to the data retention area;

[0030] When the concentrator has finished polling the user groups corresponding to any user type, it copies the data from the data retention area to the data backup area.

[0031] A second aspect of this application provides a method for pre-acquiring curve data based on user clustering analysis, applied to a carrier station that communicates with a central coordinator. The method includes:

[0032] Receive broadcast command to start data collection;

[0033] According to the second preset time collection interval and the second preset number of times, multiple data items are sequentially copied to obtain real-time data;

[0034] Real-time data is mapped using protocols and stored on physical media according to a unified data format and storage rules;

[0035] Send a data collection completion command to the central coordinator.

[0036] A third aspect of this application provides a central coordinator, comprising:

[0037] The memory is configured to store instructions; and

[0038] The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the aforementioned method for pre-acquiring curve data based on user clustering analysis.

[0039] A fourth aspect of this application provides a carrier station, comprising:

[0040] The memory is configured to store instructions; and

[0041] The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the aforementioned method for pre-acquiring curve data based on user clustering analysis.

[0042] The fifth aspect of this application provides a system for pre-collecting curve data based on user clustering analysis, comprising:

[0043] The concentrator is configured to send read messages to the central coordinator.

[0044] The aforementioned central coordinator communicates with the concentrator;

[0045] The aforementioned carrier stations communicate with the central coordinator.

[0046] A sixth aspect of this application provides a machine-readable storage medium storing instructions for causing a machine to perform the aforementioned method for pre-acquiring curve data based on user clustering analysis.

[0047] The above technical solution first acquires the data collection messages sent by the concentrator, then performs cluster analysis on the user behavior in the distribution area based on these messages to generate various user types. Next, a broadcast command to start data collection is sent to the carrier station. Upon receiving the data collection completion instruction from the carrier station, data from the carrier station is collected concurrently via unicast according to the various user types. This application enables refined management of different users at the carrier level and provides higher-frequency data collection for different user types, improving the flexibility and reliability of carrier services while ensuring data integrity and synchronization.

[0048] Other features and advantages of the embodiments of this application will be described in detail in the following detailed description section. Attached Figure Description

[0049] The accompanying drawings are provided to further illustrate the embodiments of this application and form part of the specification. They are used together with the following detailed description to explain the embodiments of this application, but do not constitute a limitation on the embodiments of this application. In the drawings:

[0050] Figure 1 The flowchart illustrates a method for pre-collecting curve data based on user clustering analysis according to an embodiment of this application.

[0051] Figure 2 This illustration schematically depicts a user clustering analysis and identification process according to an embodiment of this application;

[0052] Figure 3 A carrier curve reading strategy diagram according to an embodiment of this application is illustrated schematically;

[0053] Figure 4 The flowchart illustrating a method for pre-collecting curve data based on user clustering analysis according to another embodiment of this application is shown in the schematic diagram.

[0054] Figure 5 This schematic diagram illustrates a structural block diagram of a central coordinator according to an embodiment of this application;

[0055] Figure 6 A schematic diagram illustrating the structure of a carrier station according to an embodiment of this application is shown.

[0056] Figure 7 The diagram illustrates a system architecture block diagram for curve data pre-collection based on user clustering analysis according to an embodiment of this application.

[0057] Explanation of reference numerals in the attached figures

[0058] 1. Concentrator 2. Central Coordinator

[0059] 3 carrier sites Detailed Implementation

[0060] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only for illustration and explanation of the embodiments of this application and are not intended to limit the embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without creative effort are within the scope of protection of this application.

[0061] It should be noted that if the embodiments of this application involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a certain specific posture (as shown in the figure). If the specific posture changes, the directional indicators will also change accordingly.

[0062] Furthermore, if the embodiments of this application involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Therefore, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the technical solutions of various embodiments can be combined with each other, but this must be based on the ability of those skilled in the art to implement them. If the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed in this application.

[0063] Power line carrier meter reading refers to a system where the master station sends user information and data to the terminals, and the concentrator stores this information and data in the terminals via a storage module. The terminals, following parameters from the marketing system, collect data from residential meters within the community using power line carrier modules. Modules on the carrier meters receive messages from the concentrator and reply to the terminals. The terminals store the collected user data. When a marketing system instructs the terminal to query user data, it replies to the master station via a GPRS module, sending the stored data. The master station parses the received messages to determine the user's electricity consumption and ultimately calculates the user's electricity bill.

[0064] With the continuous innovation of electricity meter technology, metering data has become increasingly diversified. Among these, curve data, due to its flexibility and configurability according to user needs, more accurately reflects users' electricity consumption demands compared to other metering data. Therefore, more and more electricity meters are adopting curve data as their metering data. Currently, the scheme of configuring collection tasks through the main station only allows for curve reading at a maximum frequency of 15 minutes, which cannot meet the higher frequency collection needs of users across the entire distribution area, such as 5 minutes or 1 minute. Furthermore, the timing of data reading by the concentrator is not strictly controllable, making it impossible to guarantee data integrity and synchronization. Additionally, frequent carrier meter reading requests for multiple data items significantly reduce channel resource utilization. Based on this, this application proposes a curve data pre-collection method based on user clustering analysis. This method can achieve refined management of different users at the carrier level based on the existing distribution area operating environment and implement minute-level collection functionality for different user types. While ensuring data integrity and synchronization, it improves the flexibility and reliability of carrier services.

[0065] Figure 1 A flowchart illustrating a method for pre-collecting curve data based on user clustering analysis according to an embodiment of this application is shown. Figure 1 As shown in the embodiment of this application, a method for pre-collecting curve data based on user clustering analysis is provided and applied to a central coordinator. The central coordinator communicates with both the concentrator and the carrier site. The method may include the following steps.

[0066] Step 101: Obtain the reading message sent by the concentrator.

[0067] In this embodiment, the master station can issue various types of data collection schemes to different users. The concentrator, based on these schemes, sends a data reading message conforming to the 1376.2 standard to the Central Coordinator (CCO). This message includes the users within the distribution area and the corresponding data collection schemes for each user. The 1376.2 standard is typically used as the communication protocol between the concentrator and the local communication module interface, specifying the frame format, data encoding, and transmission rules for data transmission between the concentrator and the local communication module interface in a power user data collection system. It is suitable for local communication networking using low-voltage power line carrier, low-power wireless communication, and Ethernet transmission channels, and is applicable to data exchange between the concentrator and the local communication module.

[0068] Step 102: Perform cluster analysis on the user behavior of the station area based on the copied messages to generate multiple user types.

[0069] In this embodiment, after receiving the data transfer message sent by the concentrator, the central coordinator can perform cluster analysis on the user behavior within the distribution area based on the message. Cluster analysis refers to the analysis process of grouping a set of physical or abstract objects into multiple classes composed of similar objects. The goal of cluster analysis is to classify data based on similarity. Many clustering techniques have been developed in different application areas. These techniques are used to describe data, measure the similarity between different data sources, and classify data sources into different clusters. Applying cluster analysis to this embodiment involves classifying users within the distribution area according to different user behaviors to generate multiple user types. Specifically, the central coordinator can establish a user cluster analysis table based on the relevant information in the concentrator's downlink frames, dividing the various user types, data content to be transferred, collection cycles, and other information within the distribution area. This does not require extending the 1376.2 protocol or modifying the concentrator and master station programs. Furthermore, the central coordinator can subsequently adopt different collection strategies for different user types based on the cluster information table.

[0070] Step 103: Send a broadcast command to start the acquisition to the carrier station.

[0071] In this embodiment, network-wide time synchronization and broadcast-initiated data collection ensure that all carrier stations (STAs) collect real-time meter data simultaneously. After network-wide time synchronization is complete, the central coordinator distributes its default data collection scheme to each carrier station. Simultaneously, it sends a broadcast-initiated data collection command to each carrier station, enabling them to collect real-time meter data concurrently. When a carrier station receives the broadcast-initiated data collection command from the central coordinator, it sequentially reads multiple data items at preset time intervals; the number of reads can be set according to actual conditions. Network-wide time synchronization and broadcast-initiated data collection ensure the synchronization of data read by the carrier stations.

[0072] Step 104: Upon receiving the data acquisition completion instruction from the carrier station, the data from the carrier station is copied in a unicast concurrent manner according to various user types.

[0073] In this embodiment, when the central coordinator receives the data collection completion instruction from a carrier site, it can unicast and concurrently read the data stored in the carrier site based on the clustering analysis results. In one example, if the clustering analysis result is industrial and commercial users, the central coordinator reads the data stored in the corresponding carrier site at a 5-minute interval. In another example, if the clustering analysis result is photovoltaic users, the central coordinator reads the data stored in the corresponding carrier site at a 1-minute interval. That is, the collection cycle is determined based on the identification results. Specifically, when the central coordinator unicasts and reads the carrier site data, it uses a concurrent method, reading several carrier sites at once. If a carrier site does not respond with its data, it will be rewritten several times to ensure data integrity. If the carrier site does not respond after several rewrites, it is skipped, and the data from the previous cycle of that carrier site is used as the data for the current cycle. When the next collection cycle arrives, the central coordinator will prioritize collecting the data from that carrier site. In this way, the carrier site can package multiple data items and reply to the central coordinator at once, reducing the number of carrier interactions and improving interaction efficiency. When the concentrator reads data after a certain number of task cycles, it can directly receive a frame reply from the central coordinator without having to read the meter again via the carrier wave.

[0074] The above technical solution first acquires the data collection messages sent by the concentrator, then performs cluster analysis on the user behavior in the distribution area based on these messages to generate various user types. Next, a broadcast command to start data collection is sent to the carrier station. Upon receiving the data collection completion instruction from the carrier station, data from the carrier station is collected concurrently via unicast according to the various user types. This application enables refined management of different users at the carrier level and provides higher-frequency data collection for different user types, improving the flexibility and reliability of carrier services while ensuring data integrity and synchronization.

[0075] In this embodiment of the application, before sending the broadcast start acquisition command to the carrier station, the method may further include:

[0076] Acquire the time from the concentrator;

[0077] The real-time clock time of the central coordinator and carrier sites is corrected based on the time of the concentrator.

[0078] In this embodiment, since the curve data is stored primarily in chronological order, the data reading is also performed in chronological order. Therefore, a full network time synchronization is required before initiating data collection, i.e., before sending the broadcast start command to the carrier stations. This synchronizes the real-time clock (RTC) maintained by the central coordinator and the carrier stations with the concentrator time. This ensures data synchronization.

[0079] In this embodiment of the application, the message reading includes multiple collection schemes, with one collection scheme corresponding to each user type. The collection scheme includes the collection period, collection data items, and data types corresponding to each user type.

[0080] In this embodiment, the data transfer messages compliant with standard 1376.2 sent by the concentrator to the central coordinator include multiple data collection schemes for users within the distribution area. Cluster analysis can be used to categorize users within the distribution area into different user types based on their behavior, with each user type corresponding to a specific data collection scheme. The data collection scheme includes the collection period, data items, and data types corresponding to each user type.

[0081] Figure 2 The diagram illustrates a user clustering analysis and identification process according to an embodiment of this application. Figure 2 As shown in this embodiment, clustering analysis of user behavior in the distribution area based on the copied messages to generate various user types may include:

[0082] Establish a user behavior chain list based on the copied messages;

[0083] Multiple user types are generated based on a user behavior chain.

[0084] In this embodiment, during the first task cycle, the central coordinator can establish a user behavior linked list based on the copying messages issued by the concentrator. These messages include user behavior across the entire distribution area. The vertical elements of the linked list are the distribution area user identifiers, which can be user table addresses, asset codes, or other unique identifiers for distribution area users. The horizontal elements include the linked list index, record time, and data items to be collected, used for cluster analysis. Once all tasks in this cycle are completed, the central coordinator's user behavior linked list is established. When the second task cycle arrives, the central coordinator first traverses the vertical elements of the original user behavior linked list to find the users to be copied. Then, it obtains cluster analysis labels based on the horizontal elements of each node. Finally, the central coordinator generates different user types and corresponding collection cycles, data items, and data types for each user type based on these cluster analysis labels. It should be noted that the period for generating user types can be one task cycle, two task cycles, or more, depending on the actual situation.

[0085] In this embodiment of the application, the user behavior linked list includes a transformer area user identifier and multiple sets of elements for cluster analysis, with each transformer area user corresponding to a set of elements for cluster analysis;

[0086] The elements used for cluster analysis include at least one of the following:

[0087] The linked list index records the time and the data item to be collected.

[0088] In this embodiment, the vertical element of the linked list is the user identifier for the transformer area. The user identifier can be a user table address, an asset code, or other unique identifier for the transformer area user. The horizontal elements of the linked list include elements used for cluster analysis, such as the linked list index, record time, and data items to be collected.

[0089] In this embodiment of the application, generating multiple user types based on the user behavior chain includes:

[0090] The user to be copied is determined based on the user identifier of the area in the behavior chain;

[0091] Cluster analysis labels are obtained from the user's elements used for cluster analysis, which are copied as needed;

[0092] Based on cluster analysis labels, various user types are generated, along with the corresponding collection period, data items, and data types for each user type.

[0093] In this embodiment, the central coordinator can generate multiple user types based on the user behavior linked list. In a task cycle after the behavior linked list is established, the central coordinator first traverses the vertical elements of the original user behavior linked list, i.e., the station user identifier, to find the user to be copied. Then, based on the horizontal elements of that node used for cluster analysis, it obtains a cluster analysis label. Finally, the central coordinator generates different user types, as well as the corresponding collection cycle, data items, and data types for each user type, based on the different cluster analysis labels.

[0094] Figure 3 A schematic diagram illustrating a carrier curve reading strategy according to an embodiment of this application is shown. Figure 3 As shown in this embodiment, upon receiving the data acquisition completion instruction from the carrier station, the data from the carrier station is read in a unicast concurrent manner according to various user types, including:

[0095] Upon receiving the instruction that the carrier station has completed data collection, the system concurrently reads and copies real-time data from a preset number of carrier stations.

[0096] If no carrier station responds, the real-time data of any carrier station is reread a first preset number of times;

[0097] If any carrier station is reread a first preset number of times and no carrier station responds, skip the carrier station and use the real-time data of the previous cycle of the carrier station as the data for this cycle.

[0098] In this embodiment, when the central coordinator receives the data collection completion instruction from a carrier site, it can unicast and concurrently read the data stored in the carrier site according to the clustering analysis result, i.e., the user type. The concurrent + re-copying mechanism ensures data integrity at each stage. In one example, if the clustering analysis result is industrial and commercial users, the central coordinator reads the data stored in the corresponding carrier site at a 5-minute interval. In another example, if the clustering analysis result is photovoltaic users, the central coordinator reads the data stored in the corresponding carrier site at a 1-minute interval. That is, the collection cycle is determined based on the identification result. Specifically, when the central coordinator unicasts and reads the carrier site data, it uses a concurrent method, reading the real-time data of a preset number of carrier sites simultaneously. If any carrier site does not respond, it will re-read the data a preset number of times to ensure data integrity. If the carrier site does not respond after the preset number of re-copying attempts, the carrier site is skipped, and the data from the previous cycle of that carrier site is used as the data for the current cycle. Preferably, when the central coordinator concurrently unicasts and reads data from carrier stations, it reads data from 5 carrier stations simultaneously. If any carrier station fails to respond, the data will be re-read 3 times to ensure data integrity. If no response is received after 3 re-reads, the carrier station is skipped, and the data from the previous cycle is used as the data for the current cycle. When the next data collection cycle arrives, the central coordinator can prioritize reading the data from that carrier station. This allows carrier stations to package multiple data items and reply to the central coordinator at once, reducing the number of carrier interactions and improving efficiency. When the concentrator reads data after a certain number of task cycles, the central coordinator can directly frame and reply, eliminating the need for further meter reading via carriers.

[0099] In this embodiment, the central coordinator includes a real-time refresh area, a data retention area, and a data backup area. The method further includes:

[0100] Real-time refresh of the refresh area;

[0101] At the first preset time before the next copying task starts, copy the data from the real-time refresh area to the data retention area;

[0102] When the concentrator has finished polling the user groups corresponding to any user type, it copies the data from the data retention area to the data backup area.

[0103] In this embodiment, to further ensure the integrity and synchronization of curve data and avoid the problem of cross-cycle data reading caused by the concentrator interrupting the curve task due to other higher-priority tasks, the data in the central coordinator can be divided into three areas for different user types: a real-time refresh area, a data retention area, and a data backup area. The central coordinator unicasts and reads multiple data items within a carrier station and refreshes the content of the real-time refresh area in real time. That is, upon receiving data from a carrier station, the data in the corresponding area is refreshed immediately. Before the next reading task starts, the data in the real-time refresh area is copied to the data retention area at a first preset time. Preferably, the first preset time can be 10 seconds. Since the concentrator's reading task reads users within the station area in a polling manner, the data in the data retention area is copied to the data backup area only after the concentrator has finished polling the user group corresponding to any user type. Dividing the data in the central coordinator into three areas and using different refresh mechanisms can solve the problem of cross-cycle data reading by the concentrator through the acquisition task. The data that the concentrator reads in each task cycle is the data in the data backup area. The concentrator can read back all the data in a cycle by reading the curve data with a single message, which improves the efficiency of data reading.

[0104] Figure 4 A flowchart illustrating a method for pre-collecting curve data based on user clustering analysis according to another embodiment of this application is shown. Figure 4 As shown in the embodiment of this application, a method for pre-collecting curve data based on user clustering analysis is provided and applied to a carrier station. The carrier station communicates with a central coordinator. The method may include the following steps.

[0105] Step 401: Receive the broadcast command to start data acquisition;

[0106] Step 402: According to the second preset time collection interval and the second preset number of times, read multiple data items sequentially to obtain real-time data;

[0107] Step 403: Map the real-time data according to the protocol and store it in the physical medium according to a unified data format and storage rules;

[0108] Step 404: Send the data acquisition completion command to the central coordinator.

[0109] In this embodiment, when a carrier station receives a broadcast start collection command from the central coordinator, it sequentially reads multiple data items at a second preset time interval to obtain real-time data, with the number of reads being the second preset number. Preferably, the carrier station can sequentially read multiple data items at a 500ms collection interval, with the number of reads being 2. Since most meters in the actual distribution area are 07 protocol meters and 698 protocol meters, the carrier station needs to read back the real-time data from both protocol meters and perform protocol mapping, storing it in the physical medium according to a unified data format and storage rules. Because all carrier stations in the network are started via network-wide broadcast, it can be ensured that data collection starts at the same time. After strict collection intervals and recopying times, the data stored in the physical medium is also synchronized. After data collection is complete, the carrier station can send a collection completion command to the central coordinator. Through protocol mapping conversion and data storage structure design, it can be ensured that the central coordinator can read all the data from the carrier station in one interaction, improving the efficiency of carrier interaction.

[0110] Figure 5 A schematic block diagram of a central coordinator according to an embodiment of this application is shown. Figure 5 As shown in the figure, this application provides a central coordinator, which may include:

[0111] The memory is configured to store instructions; and

[0112] The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the aforementioned method for pre-acquiring curve data based on user clustering analysis.

[0113] Specifically, in this embodiment of the application, the processor 520 can be configured to:

[0114] Obtain the reading messages sent by the concentrator;

[0115] Cluster analysis of user behavior in the transformer area is performed based on the copied messages to generate various user types;

[0116] Send a broadcast command to start data acquisition to the carrier station;

[0117] Upon receiving the data acquisition completion instruction from the carrier station, the data from the carrier station is read in a unicast concurrent manner according to various user types.

[0118] Furthermore, the processor 520 can also be configured as follows:

[0119] Acquire the time from the concentrator;

[0120] The real-time clock time of the central coordinator and carrier sites is corrected based on the time of the concentrator.

[0121] In this embodiment of the application, the message reading includes multiple collection schemes, with one collection scheme corresponding to each user type. The collection scheme includes the collection period, collection data items, and data types corresponding to each user type.

[0122] Furthermore, the processor 520 can also be configured as follows:

[0123] Establish a user behavior chain list based on the copied messages;

[0124] Multiple user types are generated based on a user behavior chain.

[0125] In this embodiment of the application, the user behavior linked list includes a transformer area user identifier and multiple sets of elements for cluster analysis, with each transformer area user corresponding to a set of elements for cluster analysis;

[0126] The elements used for cluster analysis include at least one of the following:

[0127] The linked list index records the time and the data item to be collected.

[0128] In this embodiment of the application, generating multiple user types based on the user behavior chain includes:

[0129] The user to be copied is determined based on the user identifier of the area in the behavior chain;

[0130] Cluster analysis labels are obtained from the user's elements used for cluster analysis, which are copied as needed;

[0131] Based on cluster analysis labels, various user types are generated, along with the corresponding collection period, data items, and data types for each user type.

[0132] Furthermore, the processor 520 can also be configured as follows:

[0133] Upon receiving the instruction that the carrier station has completed data collection, the system concurrently reads and copies real-time data from a preset number of carrier stations.

[0134] If no carrier station responds, the real-time data of any carrier station is reread a first preset number of times;

[0135] If any carrier station is reread a first preset number of times and no carrier station responds, skip the carrier station and use the real-time data of the previous cycle of the carrier station as the data for this cycle.

[0136] Furthermore, the processor 520 can also be configured as follows:

[0137] Real-time refresh of the refresh area;

[0138] At the first preset time before the next copying task starts, copy the data from the real-time refresh area to the data retention area;

[0139] When the concentrator has finished polling the user groups corresponding to any user type, it copies the data from the data retention area to the data backup area.

[0140] Figure 6 A schematic block diagram of a carrier station according to an embodiment of this application is shown. Figure 6 As shown in the embodiments of this application, a carrier station is also provided, which may include:

[0141] The memory is configured to store instructions; and

[0142] The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the aforementioned method for pre-acquiring curve data based on user clustering analysis.

[0143] Specifically, in this embodiment of the application, the processor 620 can be configured to:

[0144] Receive broadcast command to start data collection;

[0145] According to the second preset time collection interval and the second preset number of times, multiple data items are sequentially copied to obtain real-time data;

[0146] Real-time data is mapped using protocols and stored on physical media according to a unified data format and storage rules;

[0147] Send a data collection completion command to the central coordinator.

[0148] The above technical solution first acquires the data collection messages sent by the concentrator, then performs cluster analysis on the user behavior in the distribution area based on these messages to generate various user types. Next, a broadcast command to start data collection is sent to the carrier station. Upon receiving the data collection completion instruction from the carrier station, data from the carrier station is collected concurrently via unicast according to the various user types. This application enables refined management of different users at the carrier level and provides higher-frequency data collection for different user types, improving the flexibility and reliability of carrier services while ensuring data integrity and synchronization.

[0149] Figure 7 The diagram schematically illustrates a system architecture block diagram for curve data pre-collection based on user clustering analysis according to an embodiment of this application. Figure 7 As shown in the embodiments of this application, a system for pre-collecting curve data based on user clustering analysis is also provided, which may include:

[0150] Concentrator 1 is configured to send read messages to central coordinator 2;

[0151] The aforementioned central coordinator 2 communicates with concentrator 1;

[0152] The aforementioned carrier station 3 communicates with the central coordinator 2.

[0153] In this embodiment, the concentrator 1 sends readout messages to the central coordinator 2 according to various types of data acquisition schemes issued by the master station. These readout messages include users within the distribution area and the different types of data acquisition schemes corresponding to those users. After receiving the readout messages from the concentrator, the central coordinator performs cluster analysis on user behavior within the distribution area based on these messages to obtain various user types and the corresponding acquisition period, data items, and data types for each user type. Finally, based on the various user types and the corresponding acquisition period, data items, and data types for each user type, it reads data from the carrier site 3 in a unicast concurrent manner.

[0154] This application also provides a machine-readable storage medium storing instructions that cause a machine to perform the above-described method for pre-collecting curve data based on user clustering analysis.

[0155] Those skilled in the art will understand that embodiments of this application can be provided as methods, systems, or computer program products. Therefore, this application can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this application can take the form of a computer program product embodied on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0156] This application is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this application. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart... Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0157] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0158] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0159] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0160] Memory may include non-persistent memory in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, like read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0161] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0162] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0163] The above are merely embodiments of this application and are not intended to limit the scope of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of the claims of this application.

Claims

1. A method for pre-collecting curve data based on user clustering analysis, characterized in that, Applied to a central coordinator that communicates with both a concentrator and a carrier site, the method includes: Obtain the copy message sent by the concentrator; Based on the copied messages, cluster analysis is performed on the user behavior in the distribution area to generate multiple user types; Send a broadcast command to start data acquisition to the carrier station; Upon receiving the data acquisition completion instruction from the carrier station, the data of the carrier station is copied in a unicast concurrent manner according to the various user types; The step of receiving the data acquisition completion instruction from the carrier station and then copying the data of the carrier station in a unicast concurrent manner according to the multiple user types includes: upon receiving the data acquisition completion instruction from the carrier station, concurrently copying the real-time data of a preset number of carrier stations; if any carrier station does not respond, re-copying the real-time data of that carrier station a first preset number of times; if any carrier station is re-copyed a first preset number of times and does not respond, skipping that carrier station and using the real-time data of the previous period of that carrier station as the data for this period.

2. The method according to claim 1, characterized in that, Before sending a broadcast start acquisition command to the carrier station, the method further includes: The time of the concentrator is obtained; The real-time clock time of the central coordinator and the carrier site is corrected according to the time of the concentrator.

3. The method according to claim 1, characterized in that, The message copying includes multiple acquisition schemes, with one acquisition scheme corresponding to each user type. The acquisition scheme includes the acquisition period, acquisition data items, and data types corresponding to each user type.

4. The method according to claim 1, characterized in that, The step of clustering user behavior in the transformer area based on the copied messages to generate multiple user types includes: Establish a user behavior linked list based on the copied messages; The various user types are generated based on the user behavior linked list.

5. The method according to claim 4, characterized in that, The user behavior linked list includes a transformer area user identifier and multiple sets of elements for cluster analysis, with each transformer area user corresponding to a set of elements for cluster analysis; The elements used for cluster analysis include at least one of the following: The linked list index records the time and the data item to be collected.

6. The method according to claim 4, characterized in that, The step of generating the multiple user types based on the user behavior linked list includes: The user to be copied is determined based on the user identifier of the area in the behavior chain; Cluster analysis labels are obtained based on the elements used for cluster analysis from the users who need to be copied. Based on the cluster analysis labels, the various user types and the corresponding collection period, collection data items, and data types for each user type are generated.

7. The method according to claim 1, characterized in that, The central coordinator includes a real-time refresh area, a data retention area, and a data backup area; the method further includes: The real-time refresh area is refreshed in real time; At the first preset time before the next copying task starts, the data in the real-time refresh area is copied to the data retention area; When the concentrator has finished polling the user groups corresponding to any user type, it copies the data from the data storage area to the data backup area.

8. A method for pre-collecting curve data based on user clustering analysis, characterized in that, Applied to a carrier site, the carrier site communicating with a central coordinator, the central coordinator communicating with a concentrator, the method includes: The system receives a broadcast start collection command, wherein the broadcast start collection command is received after the central coordinator obtains the copy message sent by the concentrator and performs cluster analysis on the user behavior of the station area based on the copy message to generate multiple user types. According to the second preset time collection interval and the second preset number of times, multiple data items are sequentially copied to obtain real-time data; The real-time data is mapped using protocols and stored on a physical medium according to a unified data format and storage rules; A data acquisition completion command is sent to the central coordinator. Upon receiving the data acquisition completion command from the carrier station, the central coordinator, based on the various user types, concurrently copies the data of the carrier station using unicast. This concurrent copying of data by the central coordinator, based on the various user types, includes: upon receiving the data acquisition completion command from the carrier station, the central coordinator concurrently copies the real-time data of a preset number of carrier stations; if any carrier station does not respond, the central coordinator re-copys the real-time data of that carrier station a first preset number of times; if the re-copying of any carrier station a first preset number of times fails to elicit a response, the central coordinator skips that carrier station and uses the real-time data of that carrier station from the previous period as the data for the current period.

9. A central coordinator, characterized in that, include: The memory is configured to store instructions; as well as A processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the method for pre-acquiring curve data based on user clustering analysis according to any one of claims 1 to 7.

10. A carrier station, characterized in that, include: The memory is configured to store instructions; as well as The processor is configured to retrieve the instructions from the memory and, when executing the instructions, to implement the method for pre-acquiring curve data based on user clustering analysis as described in claim 8.

11. A system for pre-collecting curve data based on user clustering analysis, characterized in that, include: The concentrator is configured to send read messages to the central coordinator. The central coordinator according to claim 9 communicates with the concentrator; The carrier station of claim 10 communicates with the central coordinator.

12. A machine-readable storage medium, characterized in that, The machine-readable storage medium stores instructions for causing the machine to perform a method for pre-acquiring curve data based on user clustering analysis according to any one of claims 1 to 8.

Images