An object-oriented protocol-based acquisition task scheduling management method

Abstract

A data acquisition task scheduling and management method based on an object-oriented protocol is proposed. This method, grounded in machine learning theory, prioritizes the communication capabilities of electricity meters by collecting factors influencing meter reading. During meter reading, data acquisition tasks are assigned to the highest-priority meter according to their communication capabilities, with the meter serving as the smallest scheduling unit, and data is read sequentially according to task priority. After a single meter reading task is completed, subsequent meter reading tasks are completed sequentially according to the meter's communication capabilities. This method addresses the shortcomings of traditional data acquisition task scheduling and management methods, ensuring efficient task scheduling and significantly improving the data acquisition success rate.

Description

Technical Field

[0001] This invention relates to the field of electricity consumption information collection, and in particular to a method for scheduling and managing collection tasks based on an object-oriented protocol. Background Technology

[0002] After more than a decade of smart grid construction and application, the State Grid Corporation of China had connected over 40 million data acquisition devices and more than 500 million smart meters by the end of 2021. Initially, the State Grid Corporation of China's AMI application layer communication protocol was designed using a process-oriented, flat approach to business data items, which resulted in poor scalability and low efficiency. With the continuous emergence of new business requirements such as power outage reporting, full-event data acquisition from electricity meters, multi-meter integration, batch electricity price issuance, and high-frequency data acquisition, this protocol could not meet the needs of business applications. Therefore, in 2016, the State Grid Corporation of China, drawing on the advantages of the IEC 62056 protocol framework and summarizing past application experience, innovatively designed an application layer communication protocol suitable for China's AMI—DL / T698.45 "Electric Energy Information Acquisition and Management System Part 4-5: Communication Protocol—Object-Oriented Data Exchange Protocol". This technical standard unifies the communication protocol for local meter reading and remote upload to the master station, and has technical characteristics such as flexible expansion, high-efficiency data acquisition, data traceability, and security and reliability, effectively supporting the rapid development of the State Grid Corporation of China's smart grid business. The collection tasks, collection schemes, and collection files defined in this specification have overturned the traditional fixed copying and reading mode of data collection, maximized the flexibility of data collection, and met new requirements such as high-frequency collection, full collection, and personalized collection, becoming one of the biggest features of object-oriented protocols.

[0003] Because of this characteristic, current electricity data acquisition terminals (hereinafter referred to as terminals) based on object-oriented protocols all have management functions related to data acquisition task scheduling. This function basically revolves around three elements: acquisition tasks, acquisition plans, and acquisition files, mainly including acquisition task switching management and electricity meter data acquisition management. The basic idea of ​​traditional design is to combine the acquisition tasks and acquisition plans issued by the main data acquisition station, and execute the electricity meter acquisition tasks sequentially from highest to lowest priority according to the priority of different tasks. The acquired meters are usually accessed sequentially according to the file sequence number. This process aligns with the object-oriented collaborative data acquisition approach, but it also has shortcomings. First, reading meter data sequentially according to file number can lead to problems if the transformer area environment is unstable, with some meters experiencing communication difficulties. This can consume excessive time reading these meters, preventing later-ordered meters from being read, thus impacting overall acquisition efficiency and success rate. Second, task scheduling relies entirely on priority. If high-priority tasks cannot complete due to missing meters, they consume significant time and channel resources, severely impacting low-priority tasks and preventing other tasks from being read even when meters are available. Furthermore, with the increasing demand for personalized electricity usage, many sites require higher standards for data acquisition in terms of real-time performance, completeness, accuracy, diversity, and comprehensiveness. This has resulted in increasingly complex and detailed task configurations, coupled with a continuously expanding transformer area file size, further exacerbating the shortcomings of the traditional scheduling strategy. Therefore, it is necessary to explore an effective method to improve task scheduling efficiency and increase the success rate of task data acquisition. Summary of the Invention

[0004] The purpose of this invention is to address the problem of how to improve task scheduling efficiency by proposing a data acquisition task scheduling and management method based on an object-oriented protocol.

[0005] The technical solution of this invention is:

[0006] This invention provides a data acquisition task scheduling and management method based on an object-oriented protocol. This method is based on machine learning theory, which collects factors affecting meter reading and sorts the communication capabilities of electricity meters. During the electricity meter reading process, the data acquisition tasks are associated with the highest-level electricity meter according to the communication capabilities of the electricity meters, and the electricity meters are read sequentially according to the task priority, with the electricity meter as the smallest scheduling unit. After a single meter reading task is completed, the subsequent electricity meter data acquisition tasks are completed sequentially according to the communication capabilities of the electricity meters.

[0007] Furthermore, the method specifically includes the following steps:

[0008] Step 1: Establish a set of factors affecting the communication capability of electricity meters and a ranking table of communication capabilities;

[0009] Step 2: Load the data acquisition task and periodically monitor its execution status. If the task is activated, associate the activated task with the corresponding electricity meter according to its priority and proceed to Step 3. If the task is not activated, do nothing.

[0010] Step 3: According to the communication capability ranking table, select the highest priority energy meter for each communication channel to perform the data collection task; read the data according to the priority of the tasks associated with the energy meter, and after completing all the data collection tasks for the energy meter, continue to execute the data collection task for the next energy meter according to the communication capability ranking table, until the data collection tasks for all energy meters in the communication capability ranking table under the communication channel are completed.

[0011] During the execution of the electricity meter data collection task, data on factors affecting the communication capability of the electricity meter are obtained and uploaded to the electricity meter sorting module. The electricity meter sorting module updates the communication capability sorting table periodically.

[0012] Step 4: Repeat steps 2 to 3 until all tasks are completed.

[0013] Furthermore, the communication channel includes a carrier channel and a 485 channel.

[0014] Furthermore, when the electricity meter is initially loaded with its data file, its communication capability is set to the highest level.

[0015] Furthermore, if communication with the electricity meter fails during the data collection process, the data collection task for the next electricity meter will continue according to the communication capability sorting table.

[0016] Furthermore, the electricity meters in the communication capability ranking table are divided into several levels, and each level includes several electricity meters. During the execution of the electricity meter data collection task, the data collection task is performed according to the electricity meter level until all electricity meters and all tasks in the current level have been collected, and then the electricity meters in the next level are accessed.

[0017] Furthermore, the set of factors affecting the communication capability of the aforementioned electricity meter is U = (u1, u2, u3, ..., u...). i ), where i represents the number of influencing factors, u i The factor numbered i represents the influencing factor, which includes: average single-frame communication transmission time, the number of most recent consecutive reading failures on the same day, the number of data collection tasks involved in the electricity meter, and one or more of the electricity meter carrier network layers.

[0018] Furthermore, the electricity meter sorting module periodically updates the communication capability sorting table, specifically including:

[0019] Data on factors affecting communication capabilities obtained during meter reading;

[0020] The proportion of communication capability factor data for each electricity meter in all factors is calculated using the interval method. DATA=(data1,data2,…,data i ), where data i The factor with number i represents the proportion of the data; the formulas for calculating the proportions of the factors such as average single-frame communication transmission time, the number of most recent consecutive reading failures on the current day, and the energy meter carrier network level are: (1-data_in i / max i The formula for calculating the percentage of factors in the number of data collection tasks involved in the electricity meter is: data_in i / max i ;data_in i Data collected for influencing factor number i; max i It is the maximum value of the influencing factor numbered i. The maximum value of the average single frame communication transmission time, the maximum value of the most recent consecutive reading failures on the same day, and the maximum value of the energy meter carrier network level are all preset values. The maximum value of the number of data collection tasks involved in the energy meter is the total number of current tasks.

[0021] Obtain the coefficients A = (a1, a2, ... a) for each factor. i This is obtained based on experience or the double-rate comparison method. The double-rate comparison method involves randomly ranking the factors, comparing each factor in order, and determining the multiple relationship of importance between the factors, i.e., the comparison ratio. Then, all comparison ratios are uniformly converted into a baseline value: BASE = (base1, base2, ..., base...). i Finally, normalization is performed to obtain the coefficient percentage of the corresponding factor among all factors. A = (a1, a2, ... a i );

[0022] Calculate the communication capability coefficient F of the electricity meter: F = DATA * A T *100;

[0023] The electricity meters are classified into grades based on their communication capability coefficients: 100-80 is grade A, 80-60 is grade B, and below 60 is grade C.

[0024] Furthermore, among the influencing factors mentioned:

[0025] The average single-frame communication transmission time is the average time x taken from sending a message to receiving a message each time it is sent to the energy meter. Where a is the time taken from sending a message to receiving a message, n is the number of recent communications with the electricity meter, and n is preferably 15; the maximum average single-frame communication transmission time is 90s;

[0026] The number of most recent consecutive reading failures on the current day refers to the statistical data of the electricity meter for that day. The number is reset to 0 after the day has passed or after the electricity meter has successfully collected data. The maximum number of most recent consecutive reading failures on the current day is 30.

[0027] The number of data collection tasks involved in the electricity meter refers to the number of data collection tasks involved in the current terminal data collection system for that electricity meter.

[0028] The energy meter carrier network level is obtained by periodically collecting HPLC topology information to acquire the corresponding energy meter network level data; if the master station is not configured to periodically collect HPLC topology information, or if it is an RS485 energy meter, the default level is 0; the maximum value of the energy meter carrier network level is 15.

[0029] The beneficial effects of this invention are:

[0030] This invention's data acquisition task scheduling and management method, based on machine learning theory, collects and organizes objective factors affecting meter reading, establishing a mechanism to evaluate the communication capabilities of electricity meters. Each meter requiring data acquisition is scored for its communication capabilities, and then ranked according to this comprehensive evaluation. During meter reading, meters are selected sequentially based on their communication capabilities, ensuring that meters with strong communication capabilities are accessed and read faster, while those with weaker capabilities are accessed later. In task switching and scheduling, the traditional task-based scheduling model is broken, replaced by a meter-based model. Activated tasks are associated with each meter according to priority, relying on the meter's communication capabilities to allow meters with strong communication capabilities to continue accessing lower-priority tasks, unaffected by interference from meters with weaker communication capabilities.

[0031] The method of this invention solves the shortcomings of traditional data acquisition task scheduling and management methods, ensuring that data acquisition task scheduling is in a highly efficient working state and greatly improving the data acquisition success rate.

[0032] Other features and advantages of the present invention will be described in detail in the following detailed description section. Attached Figure Description

[0033] 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.

[0034] Figure 1 This diagram illustrates a comparison between the efficiency of traditional data acquisition task scheduling and the efficiency of the data acquisition scheduling strategy of this invention.

[0035] Figure 2 This is a schematic diagram illustrating the task status update and the association of activated tasks with relevant meters in this invention.

[0036] Figure 3 This is a schematic diagram of the data acquisition process of the electricity meter in this invention.

[0037] Figure 4 This is a schematic diagram of the communication capability level switching process of the energy meter under the channel in this invention.

[0038] Figure 5 This is a diagram illustrating the relationship between task priorities in traditional data acquisition.

[0039] Figure 6 This is a schematic diagram illustrating the association between the electricity meter and the task in this invention. Detailed Implementation

[0040] 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.

[0041] A data acquisition task scheduling and management method based on an object-oriented protocol is proposed. This method, grounded in machine learning theory, prioritizes the communication capabilities of electricity meters by collecting factors influencing meter reading. During meter reading, data acquisition tasks are assigned to the highest-priority meter according to their communication capabilities, with the meter as the smallest scheduling unit, and data is read sequentially according to task priority. After a single meter reading task is completed, subsequent meter reading tasks are completed sequentially according to the meter's communication capabilities. The method specifically includes the following steps:

[0042] Step 1: Establish a set of factors affecting the communication capability of the electricity meter and a ranking table of communication capabilities; the set of factors affecting the communication capability of the electricity meter is U = (u1, u2, u3, ..., u...). i ), where i represents the number of influencing factors, u i The influencing factor numbered i is indicated. The influencing factors include: average single-frame communication transmission time, the number of most recent consecutive reading failures on the day, the number of data collection tasks involved in the energy meter, and one or more of the energy meter carrier network layers.

[0043] Step 2: Load the data acquisition task and periodically monitor its execution status. If the task is activated, associate the activated task with the corresponding electricity meter according to its priority and proceed to Step 3. If the task is not activated, do nothing.

[0044] Step 3: According to the communication capability ranking table, select the highest priority energy meter for each communication channel to perform the data collection task; read the data according to the priority of the tasks associated with that energy meter, and after completing all the data collection tasks for that energy meter, continue to execute the data collection task for the next energy meter according to the communication capability ranking table, until the data collection tasks for all energy meters in the communication capability ranking table under that communication channel are completed; if the communication of an energy meter fails during the execution of the energy meter data collection task, continue to execute the data collection task for the next energy meter according to the communication capability ranking table;

[0045] During the execution of the electricity meter data collection task, data on factors affecting the communication capability of the electricity meters are acquired and uploaded to the electricity meter ranking module. The electricity meter ranking module periodically updates the communication capability ranking table, and the update process is as follows:

[0046] Step 31: Data on factors affecting communication capabilities obtained during meter reading;

[0047] Step 32: Calculate the proportion of communication capability factor data for each electricity meter among all factors using the interval method. DATA = (data1, data2, ..., data...) i ), where data i The factor with number i represents the proportion of the data; the formulas for calculating the proportions of the factors such as average single-frame communication transmission time, the number of most recent consecutive reading failures on the current day, and the energy meter carrier network level are: (1-data_in i / max i The formula for calculating the percentage of factors in the number of data collection tasks involved in the electricity meter is: data_in i / max i ;data_in i Data collected for influencing factor number i; max i It is the maximum value of the influencing factor numbered i. The maximum value of the average single frame communication transmission time, the maximum value of the most recent consecutive reading failures on the same day, and the maximum value of the energy meter carrier network level are all preset values. The maximum value of the number of data collection tasks involved in the energy meter is the total number of current tasks.

[0048] Step 33: Obtain the coefficients A = (a1, a2, ... a2) for each factor. i This is obtained based on experience or the double-rate comparison method. The double-rate comparison method involves randomly ranking the factors, comparing each factor in order, and determining the multiple relationship of importance between the factors, i.e., the comparison ratio. Then, all comparison ratios are uniformly converted into a baseline value: BASE = (base1, base2, ..., base...). i Finally, normalization is performed to obtain the coefficient percentage of the corresponding factor among all factors. A = (a1, a2, ... a iFor example: ratio normalization based on factor i. For example, if there are four factors A, B, C, and D, and the importance of C is 0.55 times that of D, then the value is 0.55 * 1 = 0.55; B is twice that of C, so the value is 0.55 * 2 = 1.1; and so on.

[0049] Step 34: Calculate the communication capability coefficient F of the electricity meter: F = DATA * A T *100;

[0050] Step 35: Classify the electricity meter according to its communication capability coefficient: 100-80 is Grade A, 80-60 is Grade B, and below 60 is Grade C.

[0051] Step 4: Repeat steps 2 to 3 until all tasks are completed.

[0052] The communication channels include a carrier channel and a 485 channel; when the energy meter is initially loaded with its data file, its communication capability is set to the highest level.

[0053] In this embodiment, the electricity meters in the communication capability ranking table are divided into three levels. The levels are classified according to the communication capability coefficient of the electricity meters: 100-80 is level A, 80-60 is level B, and below 60 is level C. Each level includes a number of electricity meters. During the execution of the electricity meter data collection task, the data collection task is performed according to the electricity meter level until all electricity meters in the current level and all tasks are completed, and then the electricity meters in the next level are accessed.

[0054] Among the influencing factors described in this embodiment:

[0055] The average single-frame communication transmission time is the average time x taken from sending a message to receiving a message each time it is sent to the energy meter. Where a is the time taken from sending a message to receiving a message, n is the number of recent communications with the electricity meter, and n is preferably 15; the maximum average single-frame communication transmission time is 90s;

[0056] The number of most recent consecutive reading failures on the current day refers to the statistical data of the electricity meter for that day. The number is reset to 0 after the day has passed or after the electricity meter has successfully collected data. The maximum number of most recent consecutive reading failures on the current day is 30.

[0057] The number of data collection tasks involved in the electricity meter refers to the number of data collection tasks involved in the current terminal data collection system for that electricity meter.

[0058] The energy meter carrier network level is obtained by periodically collecting HPLC topology information to acquire the corresponding energy meter network level data; if the master station is not configured to periodically collect HPLC topology information, or if it is an RS485 energy meter, the default level is 0; the maximum value of the energy meter carrier network level is 15.

[0059] In practice:

[0060] This embodiment requires meter files issued by the object-oriented protocol master station. The task scheme must include at least daily frozen meter reading tasks, monthly frozen meter reading tasks, curve reading tasks, and event acquisition tasks. The meter files must include those from both the HPLC port and the RS485 port. To simulate the complex meter reading environment in the field, this embodiment requires 10 electricity meters connected to the RS485 channel and 100 electricity meters connected to the HPLC channel. It needs to simulate three scenarios: successful communication, successful communication but very slow speed, and unsuccessful communication, configured according to a 7:2:1 ratio of the total number of meters.

[0061] In this embodiment, data collection tasks need to be issued through the object-oriented protocol master station. The daily frozen task has a collection cycle of 1 day and a priority of 0; the monthly frozen task has a collection cycle of 1 month and a priority of 1; the curve meter reading task 1 has a collection cycle of 15 minutes and a priority of 1; the curve meter reading task 2 has a collection cycle of 15 minutes and a priority of 1; and the event collection task has a collection cycle of 1 day and a priority of 2.

[0062] In this embodiment, a data acquisition scheme needs to be issued by the main station using an object-oriented protocol. The daily freeze task collects data from all electricity meters, including forward active energy readings and reverse active energy readings. The monthly freeze task collects data from all electricity meters, including forward active energy readings and reverse active energy readings. Curve meter reading task 1 collects data from all electricity meters, including total forward active energy readings, total reverse active energy readings, voltage, current, active power, reactive power, and power factor. Curve meter reading task 2 collects data from designated meter numbers, collecting data from four meters: two meters via the HPLC port channel and two meters via the 485 port channel, collecting active power readings. The event acquisition task collects data from all electricity meters, including meter cover opening events and meter power failure records.

[0063] This embodiment provides an electricity consumption data acquisition terminal, which is connected to the electricity meter to be acquired. The electricity consumption data acquisition terminal uses the above-mentioned data acquisition task scheduling and management method to read the electricity meter according to the configured files, data acquisition tasks, and data acquisition scheme.

[0064] The aforementioned electricity data acquisition terminal implements a data acquisition task scheduling and management method. This terminal includes: a meter scoring management module, a meter data acquisition module, and a task management module. The meter scoring management module, based on machine learning theory, collects and organizes objective factors affecting meter reading to establish a scoring mechanism for the communication capabilities of electricity meters. It scores the communication capabilities of each meter requiring data acquisition and ranks them according to a comprehensive evaluation of their communication capabilities. This involves establishing a meter communication capability scoring mechanism and gradually improving the scores of each meter during the meter reading process. It also requires monitoring meters that have been without communication for extended periods (2 consecutive hours) to prevent score distortion, necessitating periodic forced readings to restore normal scores and ensure data acquisition completeness. Other factors affecting communication capabilities include: average single-frame communication transmission time u1 (range 0–90 seconds), the number of most recent consecutive reading failures u2 (range 0–30), the number of data acquisition tasks involving the meter u3 (range 1 to the current total number of tasks), and the meter carrier network level u4 (range 0–15). Finally, the weighting method uses a doubling-rate ratio, with the factor ratios being: 0.6u1 = u2, u2 = 3u3, u3 = u4. The meter data acquisition module uses meter communication capability scoring and grading to select meters for reading according to their communication capabilities within the channel. Data items to be read are retrieved from the task list associated with each meter. Associated tasks are sorted by priority, and after each meter is accessed once, the module switches to the next meter in the same grade, until all meters and associated tasks in that grade have been collected. This ensures that meters with strong communication capabilities are accessed and read faster, while those with weaker capabilities are accessed later. The task management module is responsible for managing task status updates and associating activated tasks with relevant meters.

[0065] In summary, the above points address the shortcomings of existing traditional data acquisition task scheduling and management methods, ensuring that data acquisition task scheduling is in a highly efficient working state and greatly improving the data acquisition success rate.

[0066] 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. A data acquisition task scheduling and management method based on an object-oriented protocol, characterized in that, This method is based on machine learning theory and ranks the communication capabilities of electricity meters by collecting factors that affect meter reading. During the electricity meter reading process, the data collection tasks are associated with the highest-level electricity meter according to the communication capability of the electricity meter. The electricity meter is used as the smallest scheduling unit and the data is read sequentially according to the task priority. After a single meter reading task is completed, the subsequent electricity meter data collection tasks are completed sequentially according to the communication capability of the electricity meter. Specifically, the following steps are included: Step 1: Establish a set of factors affecting the communication capability of electricity meters and a ranking table of communication capabilities; Step 2: Load the data acquisition task and periodically monitor its execution status. If the task is activated, associate the activated task with the corresponding electricity meter according to its priority and proceed to Step 3. If the task is not activated, do nothing. Step 3: According to the communication capability ranking table, select the highest priority energy meter for each communication channel to perform the data collection task; read the data according to the priority of the tasks associated with the energy meter, and after completing all the data collection tasks for the energy meter, continue to execute the data collection task for the next energy meter according to the communication capability ranking table, until the data collection tasks for all energy meters in the communication capability ranking table under the communication channel are completed. During the execution of the electricity meter data collection task, data on factors affecting the communication capability of the electricity meter are obtained and uploaded to the electricity meter sorting module. The electricity meter sorting module updates the communication capability sorting table periodically. Step 4: Repeat steps 2 to 3 until all tasks are completed.

2. The data acquisition task scheduling and management method based on object-oriented protocol according to claim 1, characterized in that, The communication channels include a carrier channel and a 485 channel.

3. The data acquisition task scheduling and management method based on object-oriented protocol according to claim 1, characterized in that, When the energy meter is initially loaded with its data file, its communication capability is set to the highest level.

4. The data acquisition task scheduling and management method based on object-oriented protocol according to claim 1, characterized in that, If communication with an electricity meter fails during the data collection process, the data collection task will continue to the next electricity meter according to the communication capability sorting table.

5. The data acquisition task scheduling and management method based on object-oriented protocol according to claim 1, characterized in that, The communication capability ranking table divides the electricity meters into several levels, and each level includes several electricity meters. During the execution of the electricity meter data collection task, the data collection task is performed according to the electricity meter level until all electricity meters in the current level and all tasks are completed, and then the electricity meters in the next level are accessed.

6. The data acquisition task scheduling and management method based on object-oriented protocol according to claim 1, characterized in that, The set of factors affecting the communication capability of the electricity meter is as follows: , Indicates the number of influencing factors. Indicates the number is The influencing factors include: average single-frame communication transmission time, the number of most recent consecutive reading failures on the same day, the number of data collection tasks involved in the electricity meter, and one or more of the electricity meter carrier network layers.

7. The data acquisition task scheduling and management method based on object-oriented protocol according to claim 6, characterized in that, The electricity meter sorting module periodically updates the communication capability sorting table, specifically including: Data on factors affecting communication capabilities are obtained during meter reading. The proportion of communication capability factor data for each electricity meter among all factors was calculated using the interval method. ,in, For the number The factors contributing to the proportion of data are as follows: average single-frame communication transmission time, the number of most recent consecutive reading failures on the current day, and the proportion of data contributing to the electricity meter carrier network level. The formula for calculating the percentage of factors involved in the number of data collection tasks related to the electricity meter is as follows: ; For the number The data collected on the influencing factors; It is numbered The maximum values ​​of the influencing factors, the maximum average single-frame communication transmission time, the maximum number of recent consecutive reading failures on the same day, and the maximum value of the energy meter carrier network level are all preset values. The maximum number of data collection tasks involved in the energy meter is the total number of current tasks. Obtain the coefficients of each factor This can be obtained based on experience or the double-rate comparison method. The double-rate comparison method involves randomly ranking the factors, comparing each factor in order, and determining the multiple relationship of importance between the factors, i.e., the comparison ratio. Then, all comparison ratios are standardized and converted into a benchmark value. Finally, normalization is performed to obtain the coefficient percentage of the corresponding factor among all factors. , ; Calculate the communication capability coefficient of the electricity meter : ; The electricity meters are classified into different levels based on their communication capability coefficients: 100-80 is level A, 80-60 is level B, and below 60 is level C.

8. The data acquisition task scheduling and management method based on object-oriented protocol according to claim 7, characterized in that, Among the influencing factors mentioned: The average single-frame communication transmission time is the average time taken from sending a message to the electricity meter to receiving the message. , Where b is the time taken from sending a message to receiving a message, and n is the number of recent communications with the electricity meter; the maximum average single-frame communication transmission time is 90s. The number of most recent consecutive reading failures on the current day refers to the statistical data of the electricity meter for that day. The number is reset to 0 after the day has passed or after the electricity meter has successfully collected data. The maximum number of most recent consecutive reading failures on the current day is 30. The number of data collection tasks involved in the electricity meter refers to the number of data collection tasks involved in the current terminal data collection system for that electricity meter. The energy meter carrier network level is obtained by periodically collecting HPLC topology information to acquire the corresponding energy meter network level data; if the master station is not configured to periodically collect HPLC topology information, or if it is an RS485 energy meter, the default level is 0; the maximum value of the energy meter carrier network level is 15.

9. The data acquisition task scheduling and management method based on object-oriented protocol according to claim 8, characterized in that, n is 15.

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