A method and system for data acquisition based on HPLC

Abstract

The application provides a kind of based on HPLC data acquisition method and system, it is related to power line carrier communication technical field, method includes the following steps: the communication demand of wisdom energy data is analyzed, collection identification data and real-time power information, identify a variety of QoS services;For the concurrent communication demand of a variety of QoS services in wisdom energy, on the basis of the collection of real-time power information, the identification data is classified, by adding and expanding the original QoS service, different acquisition strategies are designed, efficient data acquisition is realized;Carry out the QoS service guarantee mechanism based on priority and task arrangement, realize efficient data acquisition.The massive data of power information collection system can be accurately and quickly stored in the system commercial database, and based on the data transmission of multiple strategies, it can support the data transmission and synchronization of power enterprise company level and branch level.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of power line carrier communication, in particular to a data acquisition method and system based on HPLC. BACKGROUND

[0002] The load coordination control system for customer-side smart energy is an important part of smart grid construction, and the premise of load coordination control is the acquisition and analysis of massive load energy monitoring data, and the power local communication network as the communication hub between the terminal platform and the user has a very important supporting role.

[0003] A university uses an energy internet of things platform to collect real-time energy data of 30 high-energy-consuming buildings on its 10 campuses, and analyzes and optimizes management, which not only improves energy utilization efficiency, but also expands its renewable energy utilization rate. At the same time, scholars have researched the use of micro-power wireless and WIFI to realize household appliance data acquisition and communication, but micro-power wireless has inherent deficiencies such as being easily blocked by buildings and low speed, and WIFI needs to be registered for access, and the online rate is uncontrollable; while high-speed power line carrier communication (HPLC) technology has many advantages such as no need for wiring, high communication rate, strong anti-interference, etc., and the State Grid Corporation of China has carried out large-scale application construction and achieved good results.

[0004] The customer-side smart energy efficient data acquisition method of HPLC communication technology mainly realizes data acquisition through high-speed and low-power power line carrier communication, and has the advantages of high data acquisition efficiency, fast data transmission speed and high data precision. At the same time, the technology can also realize intelligent management and control through data analysis and processing, improve energy utilization efficiency, and provide technical support for realizing energy intelligent management and control.

[0005] The existing HPLC protocol architecture is designed for traditional power consumption information acquisition, and for the massive application requirements of different quality of service (QoS) level services such as access management of a large number of nodes in the customer-side smart energy system, concurrent data acquisition of multiple services, real-time response of photovoltaic new energy, etc., how to realize efficient data acquisition needs further research. SUMMARY

[0006] The present application aims to provide a data acquisition method and system based on HPLC to solve the above technical problems, to ensure that the massive data of the power consumption information acquisition system can be accurately and quickly stored into the system commercial database, and based on the data transmission of multiple strategies, to support the data transmission and synchronization of the company level and branch level of the power enterprise.

[0007] In order to solve the above technical problems, the present application provides a data acquisition method based on HPLC, comprising the following steps:

[0008] The communication demand of smart energy data is analyzed, and the identification data and real-time power consumption information are collected and recognized, and various QoS services are identified;

[0009] For the concurrent communication demand of various QoS services in smart energy, on the basis of collecting real-time power consumption information, the identification data is classified, the original QoS service is added and expanded, different collection strategies are designed, and efficient data collection is realized.

[0010] The QoS service guarantee mechanism based on priority and task arrangement is carried out, and efficient data collection is realized.

[0011] In the above scheme, starting from the concurrent data communication demand of various services in the customer-side smart energy system, for the concurrent communication demand of various different QoS services in the smart energy system, on the basis of the original HPLC-based power consumption information collection process, the collected data is classified, and different collection strategies with high reliability, high efficiency and high real-time are designed.

[0012] The above scheme guarantees that the massive data of the power consumption information collection system can be accurately and quickly stored in the system commercial database, and based on the data transmission of multiple strategies, it can support the data transmission and synchronization of the company level and branch level of the power enterprise.

[0013] Further, the collection frequency of the identification data is 15 minutes, and the collection frequency of the real-time power consumption information is 1-60 minutes.

[0014] Further, the communication demand analysis of smart energy specifically includes: communication object, data content, communication delay, communication frequency band and importance;

[0015] The communication object specifically includes: smart meter, distribution transformer monitoring terminal, outgoing switch, distributed photovoltaic, environment detector and intelligent charging pile.

[0016] Further, the newly added and expanded original QoS service includes: data active reading, data cache polling, frequency-based active reporting, event active reporting, time calibration and synchronization.

[0017] Further, the newly added and expanded original QoS service is specifically:

[0018] Active data reading is specifically: the platform master station issues data reading tasks and control commands to the CCO node through the area fusion terminal, and the process is the same as the original process, when there is data reading and command issuing, other services give up the communication bandwidth, and guarantee the real-time of active data reading;

[0019] The data cache polling is specifically: first, the platform master station configures the data item and the reporting period reported by the electric meter or the energy-using device through a configuration command; then the electric meter or the energy-using device reports data to the STA node in a timely manner, the STA node stores the data, and when the data storage reaches a certain threshold or the CCO node polling instruction is received, the STA node will execute the cache data reporting, and the data content reported by the STA node is self-timed, facilitating the master station analysis;

[0020] The active reporting by frequency band is specifically: the platform master station obtains the running curve of the key user device in the transformer area in real time, and issues a command to notify the STA node and the user device to report in turn according to the collection frequency; after each frequency reporting command is issued, the device reports in real time according to the frequency in the set time period, and when the time period expires and no new frequency reporting command is received, the frequency reporting is automatically stopped and the data cache polling is entered;

[0021] The event active reporting is specifically: after the device end generates an event, the event message is actively reported to the STA node, the STA node adopts the active reporting mode, and the event message is reported to the platform master station through the HPLC network in stages, the STA node adopts the priority competition mode to occupy the HPLC carrier channel, and the event active reporting is realized; at the same time, the transformer area fusion terminal adopts the confirmation reply and retransmission mechanism to ensure the reliable transmission of the event in the HPLC link. In this scenario, the transformer area fusion terminal needs to have a repeated event filtering mechanism to accurately report the event to the platform master station;

[0022] The time calibration and synchronization is specifically: periodically and when the STA node is connected to the network, the time is broadcasted, the time is initiated by the transformer area fusion terminal, the broadcast time command is started with reference to the Q / GDW10376.2 protocol, and the time synchronization of the energy-using device and the transformer area fusion terminal is maintained.

[0023] Further, the trigger condition of the broadcast time is:

[0024] (1) Periodic time, initiated by the fusion terminal when the timing time is reached;

[0025] (2) STA node connection, when a new STA node is connected to the network, the CCO node actively reports the information of the connected STA node, and the transformer area fusion terminal starts the timing after receiving the information, and performs a broadcast time during the timing period without adding new STA nodes.

[0026] Further, the QoS service guarantee mechanism is specifically:

[0027] The platform master station collects a task list;

[0028] The parameters of the task list are analyzed;

[0029] The collected task list is scheduled based on priority;

[0030] analyze the success rate, completion rate and time consumption of the collection task;

[0031] the collection task is completed.

[0032] A data collection system based on HPLC comprises a data collection module, a data management module and a power control module, which are electrically connected to the output end of the data collection module;

[0033] The data collection module is used for collecting real-time power information or identification data in real time or at a fixed time;

[0034] The data management module is used for managing the real-time power information or identification data collected by the data collection module;

[0035] The power control module is used for controlling and managing the power consumption process of the user based on the real-time power information or identification data obtained by the data collection module.

[0036] Further, the real-time collection is used for acquiring the actual data of the current terminal device and is suitable for fixed-point collection of power information;

[0037] The fixed-time collection is used for collecting and storing the identification data automatically by using the set collection interval.

[0038] Further, the fixed-time collection is used for pre-setting the parameters of the identification data collection period, collection point and collection start time before the collection starts, and the collection time is controlled to be more than 15 minutes.

[0039] If the system cannot automatically collect data due to network failure or communication problems, the data collection module performs manual supplementary recording and checking. BRIEF DESCRIPTION OF DRAWINGS

[0040] Figure 1 The method flowchart provided by an embodiment of the present application;

[0041] Figure 2 The overall framework diagram provided by an embodiment of the present application;

[0042] Figure 3 The communication requirement analysis diagram of the smart energy system provided by an embodiment of the present application;

[0043] Figure 4 The data cache polling reporting flowchart provided by an embodiment of the present application;

[0044] Figure 5 The data reporting framing diagram provided by an embodiment of the present application;

[0045] Figure 6A priority-based and task arrangement-based QoS service guarantee mechanism diagram provided by an embodiment of the present application;

[0046] Figure 7 A data acquisition function structure diagram provided by an embodiment of the present application;

[0047] Figure 8 A data management function structure diagram provided by an embodiment of the present application;

[0048] Figure 9 A power consumption control function structure diagram provided by an embodiment of the present application. DETAILED DESCRIPTION

[0049] The technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, rather than all the embodiments of the present application. Based on the embodiments in the present application, all other embodiments obtained by a person of ordinary skill in the art without creative work fall within the protection scope of the present application.

[0050] Referring to Figure 1 The embodiment provides a data acquisition method based on HPLC, and the method comprises the following steps:

[0051] S1: analyzing communication requirements of smart energy data, collecting and identifying data and real-time power consumption information, and identifying a plurality of QoS services;

[0052] S2: for the concurrent communication requirements of the plurality of QoS services in the smart energy, on the basis of the collection of the real-time power consumption information, the identified data are classified, the original QoS service is newly added and expanded, different collection strategies are designed, and efficient data collection is realized;

[0053] S3: performing a priority-based and task arrangement-based QoS service guarantee mechanism to realize efficient data collection.

[0054] Referring to Figure 2 The above scheme starts from the concurrent data communication requirements of a plurality of services in a smart energy system on the customer side, classifies the collected data on the basis of the original HPLC-based power consumption information collection process, designs different collection strategies such as high reliability, high efficiency and high real-time, guarantees that the massive data of the power consumption information collection system can be accurately and quickly stored into a system commercial database, and simultaneously based on the data transmission of a plurality of strategies, supports data transmission and synchronization of the company level and the branch level of the power enterprise.

[0055] Further, in step S1, the collection frequency of the identification data is 15 minutes; and the collection frequency of the real-time power consumption information is 1-60 minutes.

[0056] Further, the communication requirement analysis of the smart energy includes: communication object, data content, communication delay, communication frequency band, and importance.

[0057] The communication object specifically includes: smart meter, distribution transformer monitoring terminal, outgoing switch, distributed photovoltaic, environment detector, and smart charging pile.

[0058] In the above scheme, the smart energy equipment, such as metering household appliances, smart sockets, and communication converters, as the main body of load regulation, needs to collect energy data, such as voltage, current, power, temperature, and state, and issue control instructions, with a collection frequency of 1-60 minutes. The electric vehicle orderly charging equipment needs to configure a charging plan, upload execution results and charging state, issue charging parameters, and support online state sensing, with a data collection frequency of about 5 minutes.

[0059] Household photovoltaic equipment needs to participate in source-load interactive energy regulation, requiring 1-minute-level energy load curve data collection, and supporting adjustment instruction sending and event active reporting.

[0060] According to the delay requirement of data communication, the frequency of communication, and the importance of data, the data communication requirements are classified and summarized as shown in Table 1. Figure 3 As can be seen, the smart energy system communication has multiple different QoS requirements, and the HPLC network needs to have powerful QoS performance adjustment and monitoring capabilities.

[0061] Further, in step S2, the new and extended original QoS services include: data active reading, data cache polling, frequency-based active reporting, event active reporting, and time calibration and synchronization.

[0062] Further, the new and extended original QoS services are as follows:

[0063] Active data reading specifically refers to that the platform master station issues data reading tasks and control commands to the CCO node through the area fusion terminal, and the process is the same as the original process. When there is data reading and command issuing, other services give up the communication bandwidth to ensure the real-time performance of active data reading.

[0064] Please refer to Figure 4The data cache polling is specifically: first, the platform master station configures the data item and the reporting period reported by the electric meter or the energy-using device through a configuration command; then the electric meter or the energy-using device reports data to the STA node in a timely manner, the STA node stores the data, and when the data storage reaches a certain threshold or the STA node receives a polling instruction from the CCO node, the STA node will report the cached data, and the data reported by the STA node contains a time stamp, which facilitates the analysis of the master station;

[0065] Please refer to Figure 5 To maintain the high efficiency of data transmission, the data reported by the STA node should be organized according to the DL / T698.45 object-oriented protocol, and the data transmission layer should be framed as much as possible according to the maximum length of 480 bytes of the HPLC physical layer single frame as the framing unit for reporting, so as to maximize the use of data bandwidth, wherein L1+L2+L3+…+Ln is not greater than 480 bytes.

[0066] The active reporting by frequency band is specifically: the platform master station obtains the operation curve of the key user device in the transformer area in real time, and issues a command to notify the STA node and the user device to report in sequence according to the collection frequency; after each frequency reporting command is issued, the device reports in real time according to the frequency in the set time period, and when the time period expires and no new frequency reporting command is received, the frequency reporting is automatically stopped and the data cache polling is entered;

[0067] The event active reporting is specifically: after the device end generates an event, it will actively report the event message to the STA node, the STA node adopts the active reporting mode, and reports the event message to the platform master station through the HPLC network in stages, the STA node adopts the priority competition mode to occupy the HPLC carrier channel, and realizes the active reporting of the event; at the same time, the transformer area fusion terminal adopts the confirmation reply and retransmission mechanism to ensure the reliable transmission of the event on the HPLC link. In this scenario, the transformer area fusion terminal needs to have a repeated event filtering mechanism to accurately report the event to the platform master station;

[0068] The time calibration and synchronization is specifically: periodic and broadcast time calibration when the STA node is connected to the network, the time calibration is initiated by the transformer area fusion terminal, and the broadcast time calibration command is started according to the Q / GDW10376.2 protocol to maintain the time synchronization of the energy-using device and the transformer area fusion terminal.

[0069] Further, the trigger condition of the broadcast time calibration is:

[0070] (1) Periodic time calibration, initiated by the fusion terminal when the timing time is reached;

[0071] (2) STA node connection to the network, when a new STA node is connected to the network, the CCO node actively reports the information of the connected STA node, and the transformer area fusion terminal starts the timing after receiving the information, and performs a broadcast time calibration during the timing period without adding new STA nodes.

[0072] In the above scheme, the platform master station is the core of the smart energy system, mainly responsible for data collection, processing, analysis and management. It can communicate with each area integration terminal through cloud technology, realize real-time monitoring and control of the whole system. At the same time, the platform master station also provides data display and analysis functions, which can help users better understand their energy use and optimize energy management.

[0073] The area integration terminal is an edge device in the smart energy system, located in the area of the power supply system, responsible for collecting, controlling and managing the power load in the area. It can connect with the platform master station through the communication network, upload the collected power data to the platform master station for analysis and processing. At the same time, the area integration terminal can also adjust the power load through control switches and other means, so as to realize the optimization management of energy.

[0074] See Figure 6 In step S3, the QoS service guarantee mechanism is specifically:

[0075] The platform master station collects a number of task lists;

[0076] The parameters of the task list are analyzed;

[0077] The collected task list is scheduled based on priority;

[0078] The success rate, completion rate and time consumption of the collection task are analyzed;

[0079] The collection task is completed.

[0080] In the above scheme, the collection task parameter analysis is specifically: based on the characteristics that different collection services have different QoS requirements in different time periods, combined with historical collection parameter indicators (success rate, delay, time consumption, etc.), the communication service is analyzed for collection task parameters, the time consumption and channel occupancy rate are estimated, and the priority order is determined according to the importance of data;

[0081] Task arrangement and time optimization are specifically: according to the real-time, reliability level requirements of the collected data, reasonably arrange the collection task and optimize the time, leave out the work bandwidth of the frequency reporting, control instruction and other services, form a time slice and task allocation table. Task arrangement and time optimization can be realized through task scheduling algorithm, common task scheduling algorithms include FIFO algorithm, SJF algorithm, RR algorithm, DE algorithm, genetic algorithm, etc.; by establishing an optimization model, the task mixed arrangement and time optimization problem is converted into a mathematical optimization problem, and then a corresponding optimization algorithm is used to solve it, or evolutionary algorithm and other methods are used to realize it;

[0082] The collection task scheduling based on priority is specifically:

[0083] Firstly, the tasks to be collected are grouped according to priority and relevance to form a task chain. For example, tasks that need to be collected in real time are placed at the front, and tasks that need to be collected at a fixed time are placed at the back, so that in the case of limited resources, high-priority tasks are executed first.

[0084] Then, according to the priority in the task allocation table, the data collection sequence, business concurrency, and other scheduling are responsible for the priority mode, and the execution time of the task is set to avoid conflicts and repeated execution between tasks. For example, tasks that need to be collected in real time are set to be executed once every certain period of time, and tasks that need to be collected at a fixed time are set to be executed at a specified time point;

[0085] Finally, the carrier channel is reasonably utilized to ensure the transmission of event active reporting, data reading, control command and other business data, and data caching polling has the lowest priority. When there are other high-priority businesses, the data is cached locally to give up the channel;

[0086] The success rate and time consumption of the collection task are analyzed in real time, and the feedback is fed back to the collection task parameter analysis for optimization adjustment until the collection task is completed.

[0087] A data collection system based on HPLC includes a data collection module, a data management module and a power control module electrically connected to the output end of the data collection module;

[0088] The data collection module is used to collect real-time power information or collect identification data at a fixed time;

[0089] The data management module is used to manage the real-time power information or identification data collected by the data collection module;

[0090] The power control module is used to control and manage the user power consumption process based on the real-time power information or identification data obtained by the data collection module.

[0091] Further, real-time collection is used to obtain the actual data of the current terminal device, which is suitable for power information point collection;

[0092] The fixed-time collection uses the set collection interval to collect power information, automatically collects and stores the identification data.

[0093] Further, the fixed-time collection pre-sets the parameters of the identification data collection period, collection point and collection start time before the collection starts, and needs to control the collection time to be more than 15 minutes;

[0094] If the system cannot automatically collect data due to network failure or communication problems, the data collection module performs manual supplementary recording and checking.

[0095] Referring to Figure 7 The data acquisition module mainly has a data acquisition function, is a core function of the power utilization information acquisition system, and is related to the input of main data of the system. Therefore, the system data acquisition function must have high accuracy and real-time performance to meet the data input requirements of the power utilization information acquisition system. The data acquisition function mainly includes real-time acquisition and timing acquisition. The real-time acquisition is mainly used to acquire the actual data of the current terminal device and is suitable for power utilization information point acquisition. The timing acquisition is mainly used to acquire and store data automatically by using the power utilization information acquisition system with a set acquisition interval. However, it should be noted that the timing acquisition method needs to set the data acquisition period, acquisition point, and acquisition start time and the like before the acquisition work starts, and the acquisition time needs to be controlled to be more than 15 minutes. If the system cannot automatically acquire data due to network failure or communication problems, the data acquisition module will also support manual supplementary recording and checking;

[0096] Referring to Figure 8 The data management module mainly has a data management function and is mainly used for managing the power utilization data collected by the system. After the data acquisition function completes the data acquisition task, the data management module will acquire high-level semantic information of the data through data screening, storage, and statistical analysis and the like, and provide a reference for tracking and analyzing the data change. In the power utilization information acquisition system, the data management module is one of the main data output modules and can help the power staff to master the digital rules behind the large amount of data. The data management module mainly includes four parts, namely, data screening management, data statistical analysis, data storage management, and data table report management. The implementation of these functions is based on the real-time information collected by the customer-side wisdom energy efficient data acquisition method, and further manages the information, for example, stores the collected data in the cloud by using the cloud storage technology, and simultaneously performs data backup to ensure the safety and reliability of the data;

[0097] Referring to Figure 9The power consumption control module mainly has the power consumption control function, and mainly controls and manages the user power consumption process based on the real-time data obtained by the data acquisition function. With the power consumption control module, the real-time power supply management of the system can be realized. The power consumption control module mainly consists of four parts, namely, power limiting control management, power supply quality control management, electricity fee control management and load control management. After analyzing the power consumption information data, whether the user has power consumption failure or violates the safe power consumption rules can be found, and the power limiting management of the user is realized with the power consumption control module. At the same time, with the power supply quality control management of the power consumption control module, the adjustment and tracking of the power supply quality can be realized. The management of the electricity fee management part is based on the electricity quantity data obtained by the data acquisition function, and the electricity fee of the user is calculated based on the data. The realization of the above functions is the targeted feedback measures after the processing and analysis of the collected information.

[0098] The above is the preferred embodiment of the present application, it should be noted that for those skilled in the art, without departing from the principles of the present application, can make a number of improvements and refinements, these improvements and refinements are also considered to be within the scope of the present application.

Claims

1. A data acquisition method based on HPLC, characterized in that, Includes the following steps: Analyze the communication requirements of smart energy data, collect and identify data and real-time electricity consumption information, and identify various QoS services; To address the concurrent communication needs of various QoS services in smart energy, based on the collection of real-time electricity consumption information, the data is categorized and different collection strategies are designed by adding and expanding existing QoS services to achieve efficient data collection. The added and expanded existing QoS services include: proactive data reading, data buffering and polling, frequency-based proactive reporting, event-based proactive reporting, and time calibration and synchronization. Proactive data reading specifically involves the platform master station issuing data reading tasks and control commands to the CCO node through the substation converged terminal. The process is the same as the original process. When data reading and command issuance are in progress, other services relinquish communication bandwidth to ensure the real-time performance of proactive data reading. Data buffering and polling specifically involves the platform master station configuring the data items and reporting cycle reported by the meter or energy-consuming device using configuration commands. Then, the meter or energy-consuming device periodically reports data to the STA node, which buffers the data. When the data storage reaches a certain threshold or a polling command is received from the CCO node, the STA node will execute the buffered data reporting. The data reported by the nodes includes a timestamp for easy analysis by the main station. Frequency-based proactive reporting works as follows: the platform main station acquires the operating curves of key user equipment within the distribution area in real time and issues commands to STA nodes and user equipment to report sequentially according to the acquisition frequency. After each frequency-based reporting command is issued, the equipment reports in real time within the set time period. When the time period expires and no new frequency-based reporting command is received, frequency-based reporting automatically stops and data buffering polling begins. Event-based proactive reporting works as follows: after an event is generated at the device end, it will proactively report the event message to the STA node. The STA node adopts a proactive reporting method, reporting the event message level by level to the platform main station through the HPLC network. The STA node uses a priority contention method to preempt the HPLC carrier channel to achieve proactive event reporting. Time calibration and synchronization works as follows: time calibration is broadcast periodically and when the STA node joins the network. Time calibration is initiated by the distribution area convergence terminal, which follows the Q / GDW10376.2 protocol to start the broadcast time calibration command and maintain time synchronization between the energy-consuming equipment and the distribution area convergence terminal. Implement a QoS service guarantee mechanism based on priority and task orchestration to achieve efficient data collection.

2. The HPLC-based data acquisition method according to claim 1, characterized in that, The identification data is collected every 15 minutes; the real-time electricity consumption information is collected every 1 to 60 minutes.

3. The HPLC-based data acquisition method according to claim 1, characterized in that, The communication requirements analysis for smart energy specifically includes: communication targets, data content, communication latency, communication frequency bands, and importance; Its communication targets specifically include: smart meters, distribution transformer monitoring terminals, outgoing line switches, distributed photovoltaic systems, environmental detectors, and smart charging piles.

4. The HPLC-based data acquisition method according to claim 1, characterized in that, The trigger condition for broadcast time synchronization is: (1) Periodic time synchronization is initiated by the converged terminal after the scheduled time has elapsed; (2) STA node joins the network. When a new STA node joins the network, the CCO node actively reports the information of the joined STA node. After receiving the information, the substation convergence terminal starts a timer. If no new STA node is added during the timer period, a broadcast time synchronization is performed.

5. The HPLC-based data acquisition method according to claim 3, characterized in that, The QoS service guarantee mechanism is as follows: The platform's main site collects a list of several tasks; Analyze the parameters of the task list; The task list for collection is scheduled based on priority; Analyze the success rate, completion rate, and time consumption of data collection tasks; Data collection task completed.

6. A data acquisition system based on HPLC, characterized in that, The method is applicable to an HPLC-based data acquisition method as described in any one of claims 1 to 5, comprising a data acquisition module and a data management module and a power control module, both electrically connected to the output terminal of the data acquisition module; The data acquisition module is used to collect real-time electricity consumption information or collect and identify data at regular intervals. The data management module is used to manage the real-time electricity consumption information or identification data collected by the data acquisition module; The power control module is used to control and manage the user's power consumption process based on the real-time power consumption information or identification data obtained by the data acquisition module.

7. The HPLC-based data acquisition system according to claim 6, characterized in that, Real-time data acquisition obtains actual data from current terminal devices, suitable for fixed-point collection of electricity consumption information; The timed data acquisition method uses a set acquisition interval to collect electrical information, automatically collecting and storing the identified data.

8. The HPLC-based data acquisition system according to claim 7, characterized in that, The timed data acquisition involves pre-setting parameters such as the data acquisition cycle, acquisition points, and acquisition start time before the acquisition begins, and the acquisition time must be controlled to be more than 15 minutes. If the system is unable to automatically collect data due to network failure or communication problems, the data collection module will manually record and check the data.

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